FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

POLLINATOR RISK ASSESSMENT FRAMEWORK

DOCKET NUMBER: EPA-HQ-OPP-2012-0543

FIFRA SAP WEB SITE http://www.epa.gov/scipoly/sap/

OPP Docket Telephone: (703) 305-5805

UNITED STATES ENVIRONMENTAL

PROTECTION AGENCY

CONFERENCE CENTER LOBBY LEVEL

ONE POTOMAC YARD (SOUTH BUILDING)

2777 SOUTH CRYSTAL DRIVE

ARLINGTON, VIRGINIA 22202

SEPTEMBER 11TH - 14TH, 2012
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1 '5)5('-(1.,16-5 Good morning, again. I

2 want to welcome everyone to this FIFRA Scientific Advisory Panel

3 meeting. The topic of this particular meeting is Pollinator

4 Risk Assessment Framework. 

5 My name is Fred Jenkins and I am the

6 designated Federal Official for this meeting. Before we get

7 started, I would like to just take a couple of minutes to go

8 over a few administrative items. As a DFO, I serve as a liaison

9 between the panel and the Agency and I am responsible for

10 ensuring that provisions of the Federal Advisory Committee Act

11 are met. 

12 I want to extend my thanks to this entire

13 panel for agreeing to serve in this meeting, and to the public

14 as well, for coming to this meeting. The FIFRA SAP is a Federal

15 Advisory Committee that provides independent scientific peer

16 review and advice to the Agency on pesticide issues. 

17 It is important to note that the panel only

18 provides the advice and recommendations for EPA, and that all

19 regulatory decision-making and implementation authority remains

20 with the Agency. We have worked with appropriate agency

21 officials to ensure that all appropriate ethics and regulations

22 are satisfied for this meeting.

23 Panel members have been provided their

24 provisions of the Federal Conflict of Interest laws, and each

25 participant has filed a financial disclosure report. I, along

26 with our deputy ethics officer, and in consultation with the
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1 Office of General Counsel, have reviewed these reports to ensure

2 all ethics requirements are met. 

3 This meeting provides an opportunity for

4 public comment. We have several people who will be speaking

5 today during the public comment period, which will be tomorrow

6 morning.

7 Now, if you have not made prior arrangements

8 with me to make oral comments and you wish to make an oral

9 comment during the oral comment period tomorrow morning, please

10 let me know or someone else in our SAP staff, who are all

11 situation here to my right. 

12 Now, without having made prior arrangements,

13 we ask that you please limit your comments to just five minutes.

14 There is a public docket for this meeting. The docket is

15 listed on the agenda. All background materials and other

16 related documents are available in the dockets and slides of EPA

17 presentations that you will see today. It should be available

18 now. If not, they will be available very shortly. 

19 At this point, I want to introduce and extend

20 my thanks to Dr. Daniel Schlenk, to my left, who is serving as

21 Chair to this meeting. He is also the FIFRA SAP Chair.

22 DR. DANIEL SCHLENK: Thanks, Fred. Good

23 morning, everyone. Welcome to this particular panel, the

24 Pollinator Risk Assessment Framework. 

25 At this point in time, it's customary to go

26 around the room and introduce ourselves. If you wouldn't mind
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1 stating your name, where you're from, and sort of your area of

2 expertise. 

3 I'll begin. My name is Dan Schlenk. I am a

4 professor in the Department of Environmental Sciences at the

5 University of California, Riverside. My research expertise is

6 in fate and metabolism of pesticides in aquatic organisms. 

7 My job for this particular panel, though, is

8 to keep us on time and get us through all 52 questions before

9 Friday so we can get home on the weekend. 

10 So with that, I'll turn it over to Martha. 

11 DR. MARTHA SANDY: Hello. I'm Martha Sandy. 

12 I'm a toxicologist. I'm chief of the toxicology and

13 epidemiology section of the Office of Environmental Health

14 Hazard Assessment. That a department within the California

15 Environmental Protection Agency.

16 DR. STEPHEN KLAINE: Good morning. I'm Steve

17 Klaine. I'm a professor at Clemson University and I'm an

18 aquatic ecotoxicologist. 

19 DR. JAMES MCMANAMAN: Good morning. I'm Jim

20 McManaman. I'm a professor in obstetrics and gynecology at the

21 University of Colorado. I'm vice-Chair for research there. I'm

22 a reproductive biologist.

23 DR. KENNETH DELCLOS: I'm Barry Delclos -- or

24 Kenneth Delclos from the FDA's National Center of Toxicological

25 Research. I'm a toxicologist, primarily in reproductive

26 toxicology.
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1 DR. DAVID TARPY: My name is David Tarpy. I'm

2 in the Department of Entomology at North Carolina State

3 University and the Extension Apiculturist for North Carolina.

4 DR. PAUL SCHWAB: My name is Paul Schwab. I'm

5 an environmental soil chemist at Texas A&M University.

6 DR. THOMAS POTTER: Good morning. I'm Thomas

7 Potter. I'm a research chemist with the USDA Agricultural

8 Research Service, Southeast Watershed Laboratory in Tifton,

9 Georgia. And my area of expertise is pesticide environmental

10 fate and transport assessment. 

11 MR. JENS PISTORIUS: Hello. My name is Jens

12 Pistorius. I'm from Germany and working at the Julius

13 KQhn-Institut, in charge of the risk assessment for pesticides

14 in honey bees for the Investigation Center for Honey Bee Poison

15 Incident and for the governmental research for Germany for

16 pesticide abuse. 

17 DR. JEFF PETTIS: Good morning. I'm Jeff

18 Pettis. I'm an entomologist and I'm with the USDA Agricultural

19 Research Service and I head the bee research lab here, nearby,

20 in Beltsville, Maryland.

21 DR. NANCY OSTIGUY: My name is Nancy Ostiguy. 

22 I'm in the entomology department at Penn State. I do a research

23 on honey bee epidemiology. 

24 DR. ROSALIND JAMES: My name is Rosalind James

25 and my expertise is in bee diseases, a bee pathologist, and I'm

26 director of the Pollinating Insect Research Laboratory in Logan,
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1 Utah, which is part of the USDA Agricultural Research Service. 

2 DR. GREG HUNT: Good morning. My name is Greg

3 Hunt. I'm at Perdue University. And my area of research is

4 honey bee genomics and behavior.

5 DR. NINA FEFFERMAN: Hi. I'm Nina Fefferman. 

6 I'm here from Rutgers University. My area of research is

7 applied mathematical modeling of ecological and epidemiological

8 systems. 

9 DR. MAY BERENBAUM: I'm May Berenbaum,

10 professor and head of the Department of Entomology at the

11 University of Illinois at Urbana-Champaign. And my interest is

12 in insect chemical ecology and chemical mediation with

13 interactions between plants and insects.

14 DR. DANIEL SCHLENK: Thank you, everyone. 

15 At this point in time, we're going to get introduction and

16 charge from the director of the Office of Pesticide Programs,

17 and that's Dr. Steven Bradbury. 

18 DR. STEPHEN BRADBURY: Thanks, Dr. Schlenk. 

19 Welcome, to all members of the panel to join us for this next

20 week and 52 questions.

21 I think part of the reason you're seeing so

22 many questions is because of the challenge that is before us in

23 undertaking these risk assessments and advancing the science to

24 support our decision-making. I want to give a little bit of

25 background to that before we get started.

26 First, I want to thank you all for all the
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1 work you've already done in reading our White Paper and starting

2 to put together your thoughts on the charge questions, and also

3 thank you for your time this week, and then for the month or two

4 after the meeting when you pull the report together to

5 contribute to these kinds of peer reviews. 

6 Sometimes sitting on the other side of the

7 table, I know what it takes. So I greatly appreciate your

8 contribution to the Agency's efforts to advance our scientific

9 undertaking. 

10 I also want to thank Fred and members of the

11 SAP panel for all the hard work it takes to get one of these

12 meetings together.

13 The SAP, the Science Advisory Panel, which is

14 created under the FIFRA statute, is a Federal Advisory Committee

15 that provides the Agency with expert advice on the science

16 behind its ecological risk assessments and human health risk

17 assessments for pesticides. This panel is a very important part

18 of the day-to-day operation of our business at EPA, which is

19 undertaking risk assessments for pesticides and making

20 regulatory decisions. And science is the foundation of that

21 decision-making process. 

22 So this panel is an important component to

23 that effort, in addition to transparency and openness in public

24 participation, which is critical to our business and as we go

25 forward.

26 So I also want to thank members of the public
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1 who have already provided information to the docket and for the

2 comments that will be coming around tomorrow morning because all

3 that input is very important to your deliberations, as well as

4 the work that we're undertaking.

5 As you know, this week's effort is all around

6 assessing risks of pesticides to pollinators, with the primary

7 focus on honey bees at this stage of our effort. As you know,

8 the White Paper that's been prepared reflects collaboration with

9 Health Canada's Pesticide Management regulatory Authority, as

10 well as California's Department of Pesticide Regulation. 

11 And that theme of collaboration, both

12 internationally and nationally, I want to touch upon as I go

13 forward with my remarks. Because clearly, the concepts that we

14 tried to pull together in the White Paper reflect approaches

15 that have been developed, not only with the folks here, but have

16 been enhanced and built from national and international

17 interactions and collaborations with the scientific community

18 and different countries from around the globe. 

19 So clearly, what we're presenting here today

20 is a reflection of a lot of work, across a lot of organizations

21 and with a lot of colleagues. Hopefully, we've done justice to

22 the thoughts and ideas that we've put forward in the document

23 which you'll be seeing.

24 As you know, pollinator services are critical

25 to agricultural production and in human food supply and also

26 just the honey itself, and other products that come out of the
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1 hive is an important part of both our nutrition and the commerce

2 of this country. 

3 Also, pollinators have been a critical

4 component to the viability of the ecosystems with the

5 pollination that they provide in support of the habitat that we

6 all depend upon. As we know, from the National Academy of

7 Sciences report of 2007, and the yearly USDA reports and other

8 reports around the globe, pollinator decline is a significant

9 issue that we're facing, not only in the United States, but

10 globally. And the causes of pollinator decline are quite

11 complex and clearly not straightforward -everything from habitat

12 modification, habitat loss, nutritional issues, bee management

13 practices, and pesticides all being considered as part of the

14 issues that are facing the viability of honey bees and

15 pollinators in general.

16 At least, from the best we can tell, there

17 isn't a single cause, but clearly, all these stressors are

18 playing some role, to varying degrees, in the decline. Folks in

19 the EPA, as well as our colleagues in PMRA and Cal DPR are

20 taking a hard look at issue and working very diligently with

21 colleagues across the country and the globe in trying to

22 elucidate what these causes can be and how to reconcile these

23 effects. 

24 It's clearly a complex issue that's going to

25 take a multi-faceted approach to resolving the issue and working

26 across lots of different organizations and stakeholders to reach
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1 a solution to this problem and try to turn the corner on

2 pollinator populations. 

3 In EPA and the Pesticide Program, our primary

4 focus in contributing to this global effort is to try to better

5 understand the role that pesticides may play in pollinator

6 populations and pollinator viability and to advance our risk

7 assessment technique so that we can better elucidate cause and

8 effect and what the potential role of pesticides could be and

9 pollinator honey bee effects, and thereby inform well-reasoned

10 regulatory decisions on the safe use of pesticide products.

11 So our niche in this big challenge is to try

12 to see what we can do to help advance pesticides science and

13 risk assessment techniques. So consistent with this

14 international challenge, and not just the national challenge,

15 we've been working closely with state and federal partners, as

16 we've been moving forward over the years. 

17 I think the White Paper reflects an aspect of

18 that collaboration and working with Canada and California's

19 Department of Pesticide Regulation as we pull the White Paper

20 together and some of the approaches from moving forward. 

21 Clearly, the White Paper is also drawing upon

22 the deliberations and thoughts from the SETAC Pellston Workshop

23 of a couple of years ago, which brought people together from all

24 over the world to think through the test data requirements and

25 different kinds of risk assessment approaches that could brought

26 to bear. 
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1 Also, clearly working with our colleagues at

2 the European Food Safety Authority and getting some of their

3 insights and concepts. You can see that reflected in the

4 aspects of the White Paper, as well as working with our

5 colleagues in OECD, the Organization of Economic Cooperation and

6 Development, where there is an effort across all the member

7 countries of OECD to try to develop harmonized approaches and

8 integrate a variety of techniques and ideas as we go forward.

9 The risk assessment methodology that's

10 described in the White Paper is strongly founded on US EPA's

11 ecological risk assessment framework. And that framework is

12 used, and used quite heavily in the White Paper, but it's used

13 across the agency in how we formulate our risk assessment and

14 undertake the risk assessment. Part of the beauty of that

15 framework is that it helps us integrate not only issues we want

16 to deal with, in terms of pollinators, but our broader

17 components of our ecological risk assessment. So that

18 conceptual model and how it's dealing with fate and transport,

19 for example, of a pesticide, creates a foundation that's also

20 being used in other components of the ecosystems and for other

21 receptors as well. 

22 We feel, using a framework, it provides us a

23 solid foundation for what we can do today and what we can do

24 tomorrow. Our first goal by using the framework and all of the

25 various components you'll be hearing about this morning and into

26 the afternoon is that at the end we want to have a risk
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1 characterization component to that risk assessment that provides

2 a transparent, clear, reasonable, and consistent approach to how

3 to interpret the potential risk of pesticides to pollinators,

4 honey bees in particular, in this White Paper.

5 And it's with that clarity of our risk

6 description and risk estimates that we feel it's critical to

7 informing our regulatory decisions as we go forward.

8 Now, clearly, there are a lot of uncertainties

9 that remain and a lot of scientific complexities that aren't

10 resolved yet today. And our risk assessment process in our

11 White Paper hopefully captures what we feel we know something

12 about today, and documents things that we don't know the answers

13 to yet. But our approach is that we feel that we have credible

14 science that we can start moving forward today in advancing our

15 techniques for risk assessments for pollinators, realizing we'll

16 learn more as we go forward. But hopefully we have a foundation

17 upon which we can build upon, so that as new insights and new

18 techniques come to bear, we can fold them into our risk

19 assessment process. 

20 So there's always more to learn. There's

21 always more that could be done, but our approach is not to wait

22 for perfection before we can start moving forward and to start

23 to advance our capabilities for undertaking quantitative risk

24 assessments for pollinators.

25 So with that, I want to thank EPA and the PMRA

26 and the California scientists that all contributed to the White
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1 Paper. And I'm really looking forward to the next several days

2 as we go through the presentations and hear your first thoughts

3 on the charge questions. If it's okay with the Chair, I would

4 like to turn it over to Dr. Brady for a little more focused

5 introduction.

6 DR. DANIEL SCHLENK: Sure. Before you get

7 started, Dr. Brady, we have some noise coming in on the phone. 

8 So if you are on the teleconference, can you please mute your

9 phone? Thank you.

10 Dr. Brady.

11 DR. DONALD BRADY: Good morning, members of

12 the panel. I'm Don Brady. I'm the director at the

13 Environmental Fate and Effects Division, here in the Office of

14 Pesticide Programs.

15 Today you will hear presentations designed to

16 solicit your advice on a proposed tiered process for

17 quantitatively evaluating the potential risk to pollinators,

18 utilizing honey bees as surrogates and associated with systemic

19 and non-systemic pesticides. You will hear an overview of a

20 proposed tiered process for quantifying the risks of pesticides

21 to honey bees.

22 As Steve noted, the causes of pollinators are

23 complex and result from many factors. The advice we request

24 today is intended to enhance the ability of EPA, the Pest

25 Management Regulatory Authority in Canada and the California

26 Department of Pesticide Regulation to reliably screen chemicals
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1 for direct and indirect effects on honey bee colonies and on the

2 usefulness of this framework we present for characterizing

3 potential effects to other pollinators as well.

4 I want to thank the panel for the work you

5 will embark on this week. We appreciate and value the advice we

6 receive from this panel. At the outset, I would also like to

7 acknowledge the efforts of the team that prepared this White

8 Paper and presentations. They have worked long and hard to

9 prepare these materials for you. And I want to acknowledge, in

10 addition, the work of Dr. Moshameem (ph), who led the work of

11 the team.

12 Finally, I'm pleased to be here this morning

13 with my colleagues, Mary Mitchell, from Pest Management

14 Regulatory Agency of Canada, and Richard Bireley, of the

15 California Department of Pesticide Regulation, as we initiate

16 this consultation. 

17 Their presence today and their participation

18 in the development of the White Paper and the presentations is

19 indicative of the high degree of scientific collaboration among

20 the agencies during the preparation of the White Paper and the

21 presentation and of the Agency's shared commitment to bring the

22 best possible science to bear on this issue. I would like to

23 pass it to Mary if possible.

24 DR. DANIEL SCHLENK: Thanks. The panel

25 recognizes Mary Mitchell.

26 DR. MARY MITCHELL: Good morning. I'm Mary
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1 Mitchell. I'm Director General of the Environmental Director of

2 Health Canada's Pest Management Regulatory Agency, which

3 effectively makes me Don's counterpart in Canada. 

4 I'd like to, first of all, thank the EPA for

5 inviting Canada and California to be part of this event. Like

6 Don, I'd like to acknowledge the collaborative efforts of the

7 U.S. and the Canadian team. They really have worked well

8 together to pool together the proposal you're going to be

9 looking at. 

10 I think this is an excellent example of good

11 international collaboration. I strongly believe that

12 international collaboration allows access to the best science

13 available and ultimately results in more robust assessment

14 methods and risk assessments.

15 The international work and pollinator

16 collaboration is part of an ongoing series of collaborations

17 that have been designed to improve and harmonize pesticide data

18 requirements and assessment methods. It's harmonized data needs

19 and assessment methods that make it possible for the U.S.,

20 Canada and other countries to share the evaluation of scientific

21 studies, which decreases regulatory burden for both regulated

22 and regulated communities. 

23 It also allows the coordinated and efficient

24 pesticide registration decisions which results in better access,

25 faster access to modern, safer pest control products for farmers

26 in all the countries that are involved.
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1 The U.S. and Canada have made it clear that

2 we're highly committed to continuing such international

3 collaboration, whether it be under the auspices of NAFTA, the

4 OECD, or the more recent U.S., Canada Regulatory Cooperation

5 Council that was jointly announced by President Obama and Prime

6 Minister Harper last February.

7 And finally, I would like to thank you, the

8 panel members for agreeing to participate. The breadth and

9 depth of experience in this panel is really amazing. I'm not

10 going to speak to the science; there will be plenty of other

11 people doing that this week. The importance of ensuring

12 pesticide use doesn't impact honey bee health goes without

13 saying -- and I think agencies are really looking forward to

14 your advice on how to further strengthen the proposed framework

15 for characterizing potential effects of pesticides in bees. I'm

16 quite sure this is going to be an interesting week. Thank you.

17 DR. DANIEL SCHLENK: Thank you. Next on

18 our agenda is Richard -- is it Bireley? 

19 MR. RICHARD BIRELEY: Bireley, that is

20 correct. My name is Richard Bireley. I work for the California

21 Department of Pesticide Regulation as an ecotoxicologist. I,

22 too, would like to thank the U.S. EPA for inviting staff from

23 PRMA Canada and the California Department of Pesticide

24 Regulation to participate in this Scientific Advisory Panel to

25 address the potential risks of pesticides to honey bees. 

26 The White Paper before you represents a
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1 collaborative effort of OPP's Environmental Fate and Effects

2 Division; Health Canada's Environmental Assessment Directorate

3 and California DPR.

4 Just five years ago, following the review of

5 data submitted as an adverse effects disclosure, staff at Cal

6 DPR realized that the data requirements in 4D CFR were no longer

7 adequate to address the uncertainties posed by pesticides to

8 pollinators. It quickly became clear that this issue had been

9 identified by staff at OPP's Environmental Fate and Effects

10 Division and the Environmental Assessment Directorate at Health

11 Canada. 

12 As many of you are aware, California is the

13 home of the largest pollination event in the world, when more

14 than half the bee colonies in the United States are transported

15 to California to pollinate over 800,000 acres of almonds. 

16 I believe this international collaboration has

17 resulted in a solid, focused approach that should also increase

18 the confidence of the regulated community in a meaningful

19 product that will meet international data requirements. 

20 I would also like to thank each of you serving

21 on the panel for agreeing to vet the proposal and provide input

22 and strengthen it further. I know that Cal DPR staff have found

23 this collaborative effort rewarding, and we all look forward to

24 your input. Thank you.

25 DR. DANIEL SCHLENK: Thank you, Mr.

26 Bireley. Okay. Our first presentation this morning will be
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1 from Thomas Moriarty, who is in the Pesticide Reevaluation

2 Division of OPP.

3 MR. THOMAS MORIARTY: Thank you, Chairman. 

4 And thank you, the FIFRA Scientific Advisory Panel, for allowing

5 me to address you. My name is Tom Moriarty. I'm a risk manager

6 in the Office of Pesticide Programs in the Pesticide

7 Reevaluation Division. 

8 During my presentation today, I will be

9 providing an overview of pollinator declines and some of the

10 efforts being conducted here in North American and abroad to

11 refine exposure and effects assessment methods, as well as risk

12 assessment methods for determining the potential impact of

13 pesticides on pollinators and the role the pesticides play and

14 pollinator declines in general.

15 I'll provide a brief overview of the current

16 process used in North America to evaluate the potential effects

17 of pesticides to bees and provide a brief overview of the risk

18 assessment process itself, focusing on problem formulation phase

19 where the protection goals are articulated. 

20 Finally, I'll touch upon the extent to which

21 honey bees serve as a surrogate for non-Apis bees and the extent

22 to which a proposed risk assessment process is protective of

23 non-Apis bees. This presentation relates to Charge Questions 1

24 and 3 that deals with the protection goals in non-Apis bees.

25 A number of sources have reported declines in

26 certain pollinator species globally. The 2006 report by the
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1 National Academies of Science indicated decline in some North

2 American pollinators, including North America's most important

3 and productive managed pollinator, the honey bee.

4 However, at the time the NAS report was

5 published, there was insufficient information to determine the

6 cause of these declines. At roughly the same time, some of the

7 declines were being reported in Europe in a report by Biesmeijer

8 et al. in the July 2006 issue of Science. 

9 The decline of managed honey bees has been

10 measured by the U.S. Department of Agriculture's National

11 Agricultural Statistics Survey. NASS data indicate that managed

12 honey bee colonies have declined from a peak of approximately 6

13 million colonies in 1947 to roughly 2.8 million colonies in

14 2006.

15 These declines reflect a variety of factors in

16 North American society and agriculture, as well as changes in

17 the way that a NASS itself collect survey data. Despite changes

18 in any survey techniques, data indicates that the overall number

19 of managed colonies had steadily declined over the past 60

20 years.

21 Although not depicted on the graph here, the

22 most recent estimate of 2011 places the number of colonies in

23 the U.S. at roughly 2.5 million. One of the factors affecting

24 honey bee declines has been introductions, spread of Tracheal

25 and Varroa mites. Prior to the introduction of mites, average

26 colony of losses were around five to ten percent; however, since
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1 the introduction of parasitic mites in the U.S. in the 1980s,

2 typical losses of managed honey bee colonies have roughly been

3 about 15 to 21 percent.

4 According to USDA, starting around 2007,

5 average colony loss suddenly increased and ranged between 31 and

6 36 percent. Losses varied among beekeepers, and for some,

7 losses were complete.

8 Some of these losses were marked by sudden

9 loss of adult forage bees, leaving only the queen behind, a few

10 nurse bees, developing brood and ample food reserves. This

11 scenario would lead to the eventual collapse of the colony and

12 was termed Colony Collapse Disorder.

13 While large losses in 2006 and 2007 were

14 attributed to CCD, per se, the contribution of CCD to overall

15 declines in managed colonies appears to be on the decline. 

16 Reports of CCD symptoms continue, however, declines in honey bee

17 health are attributed to symptoms such as winterkill, failure to

18 thrive and queen loss. 

19 While a number of factors and agents have been

20 hypothesized as potential contributors to CCD and general

21 pollinator declines, at this time, no factor has been identified

22 as single cause. Available science suggests that pollinator

23 declines are a result of multiple factors which may be acting in

24 various combinations. 

25 USDA, the lead federal agency on investigating

26 general declines in pollinator health has hypothesized that
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1 losses of managed colonies may be caused by primary stressors

2 such as parasitic mites, poor bee management, nutrition and/or

3 pesticides that may, in turn, cause honey bees to become

4 susceptible to disease and ultimately lead to the collapse of

5 the colony. 

6 Research continues to be directed at the range

7 of individual factors, but is also being directed at identifying

8 combinations of stressors that are most strongly associated with

9 pollinator declines. As part of the federal response, a general

10 decline in pollinators, EPA's Office of Pesticide Programs has

11 taken a number of steps to improve the understanding of the role

12 of pesticides and pollinator declines. As part of those

13 efforts, EPA, along with its state and federal partners, has

14 developed a proposed pesticide risk assessment framework for

15 pollinators.

16 Over the past several years, EPA has actually

17 been actively engaged in a number of efforts, both here and

18 abroad, aimed at improving and refining the methods for

19 examining the potential adverse effects of pesticides to bees. 

20 In 2009, EPA hosted a USDA-funded meeting to

21 discuss testing protocols for acute and chronic toxicity studies

22 with honey bees. Participants identified uncertainties and

23 limitations of existing protocols and considered refinements to

24 those test protocols. Participants also discussed moving test

25 designs toward more comprehensive and standardized protocols to

26 increase consistency and reproducibility. 
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1 In 2011, the Society of Environmental

2 Toxicology and Chemistry, abbreviated as SETAC, sponsored a

3 global Pellston Workshop on pesticide risk assessment for

4 pollinators. The intent was to synthesize the best available

5 science regarding pesticide exposure and effects assessment

6 methods for Apis and non-Apis, and to further harmonize risk

7 assessment approaches among regulatory authorities.

8 The Pellston Workshop had four major themes: 

9 1. Exposure Assessment. 

10 2. Laboratory Effects Assessment

11 3. Field Effects Assessment 

12 4. Integrating Exposure in Effects Data to

13 estimate the likelihood and magnitude of

14 potential effects known as risk assessment.

15 A fifth area of focus was on determining the

16 extent to which honey bee exposure and effects estimates could

17 serve as a reasonable measures with which to evaluate risk to

18 non-Apis bees. The International Commission for Plant Bee

19 Relationships, abbreviated at ICP-BR, which is affiliated with

20 the International Union of Biological Science, draws upon

21 expertise and government industry and academia to inform

22 scientific assessment aimed at bee protection. ICP-BR has been

23 developing methods for testing the toxicity of pesticides to

24 bees and these efforts have informed testing methods supported

25 by the European and Mediterranean Organization for Plant

26 Protection, abbreviated as EPPO, and the Organization for
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1 Economic Cooperation and Development, OECD. 

2 The prevention of colony losses, abbreviated

3 as COLOSS, is a global network of scientists, beekeepers and

4 industry members from 55 countries, focused on developing

5 standards for monitoring and research on factors associated with

6 colony losses. 

7 The OECD's working group on pesticides has

8 also developed an expert subgroup on pollinators, entitled PEIP,

9 Pesticide Effects on Insect Pollinators workgroup. EPA and PMRA

10 serve as co-chairs to the PEIP workgroup and have been working

11 with other member countries on issues related to pollinator

12 protection. The focus of the PEIP workgroup developed from a

13 2010 OECD member survey on regulatory needs related to

14 pollinators that identified four themes. These themes included

15 advancing and harmonizing the science of risk assessment,

16 developing a source for information on risk management

17 approaches, developing tools to share bee kill incident

18 information, and providing an index of research relevant to

19 pollinator health and protection. 

20 Finally, in 2012, the European Food Safety

21 Authority's panel on plant protection products and their

22 residues release a scientific opinion on the development of risk

23 assessment processes for bees, including Apis as well as

24 non-Apis.

25 The opinion examined the potential role of

26 exposure, strengths and limitations of current toxicity testing
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1 methods and provided recommendations on how to improve these

2 methods. Currently, OPP relies on a qualitative process for

3 evaluating the potential hazard of pesticide to honey bees.

4 Typically, the quantified risks to non-target

5 organisms, the Agency relies on point estimates of exposure and

6 effects and the ratio of those estimates, referred to as the

7 risk quotient. Because not every organism can be tested,

8 toxicity testing is typically conducted using surrogate species.

9

10 Selection of a surrogate species is based on

11 the extent to which they are readily available and amenable to

12 testing under laboratory and field conditions. This then

13 reflects the species for which husbandry conditions are well

14 defined. For evaluating potential hazard to terrestrial

15 invertebrates, insect pollinators, toxicity tests are frequently

16 conducted, used in Western or European honey bee. 

17 The test listed on this slide will be

18 discussed in greater detail in the presentation to follow. I'll

19 only make several notes on this current tests used in North

20 America to estimate hazard to honey bees. The toxicity tests

21 are typically tiered, where the first tier is an acute toxicity

22 test with young adult bees. 

23 According to EPA test requirements, if the

24 acute contact toxicity is less than 11 micrograms per bee and

25 toxicity of residues on foliage may be required. If the

26 toxicity is less than 11 micrograms per bee and there is
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1 evidence of prolonged exposure and/or if there is evidence that

2 bee colonies may be adversely affected, then a field study may

3 be required.

4 Unlike PMRA, EPA guidelines do not currently

5 address acute oral toxicity to capture potential exposure

6 through residues in pollen or nectar that may result from

7 systemic pesticides; nor do current guidelines address the

8 potential toxicity of pesticides to larval bees. However, acute

9 oral toxicity tests are available in the OECD Guidelines, and

10 while acute toxicity tests with larval bees are continuing to

11 evolve, several methods are also currently available. 

12 As it will become apparent through the

13 presentations to follow, the closed framework makes use of all

14 the current guideline studies and recommends the addition of

15 certain tests to address areas of uncertainty.

16 The figure above represents the overall risk

17 assessment process. This process is consistent with EPA

18 guidelines and is consistent with a process used by our

19 partners, PMRA and California DPR. It consists of three phases

20 that include problem formulation, analysis, which consists of

21 exposure and effects assessment, and risk characterizations,

22 consisting of risk estimation and risk description. 

23 Throughout the assessment process, multiple

24 lines of evidence are synthesized, and although the figure

25 suggests that the process is unidirectional, it is intended to

26 be iterative where exposure and toxicity data reviewed in the
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1 analysis phase may be used to refine the risk hypothesis

2 articulated in the problem formulation phase, or where risk

3 estimates derived in the risk characterization phase may be

4 refined through additional data from the analysis phase.

5 For the remainder of the presentations,

6 detailed elements of this process will be discussed. However,

7 before doing so, I would like to draw your attention to the box

8 at the top left of the diagram, entitled, "Planning." As it

9 indicated, the risk assessment process is intended to be

10 iterative and intended to involve both risk assessor and risk

11 managers. 

12 The planning of the risk assessment is

13 intended to occur early in the process as an opportunity for

14 risk managers and risk assessors to articulate management goals

15 and whether there are complexities or issues that may require

16 particular attention. These discussions, in turn, inform

17 decisions regarding resources and scheduling needed to complete

18 the assessment. 

19 The presentations today will highlight the

20 tiered nature of the risk assessment process. The initial tier

21 serves as a screen where conservative assumptions about exposure

22 and effects are made, and for those chemicals which do not

23 indicate a minimal risk at the screen level, additional analysis

24 in the form of refinements or higher tiers may be necessary. 

25 While the decision to proceed to higher levels

26 of refinement depend on the understanding of both the risk
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1 assessor and risk manager with regard to the management needs. 

2 Frequent discussions between the risk assessor and the risk

3 manager often yields opportunities where data or information

4 needs are identified early in the process and it can be

5 communicated to the technical registrant or the appropriate

6 stakeholder. 

7 At the top of the figure, the problem

8 formulation phase is identified. The formula is intended to

9 identify the objective of the risk assessment and produce three

10 products. 

11 The assessment and measurement endpoints that

12 reflect management goals, conceptual models that describe key

13 relationships between the stressor and the assessment endpoint

14 and the analysis plan. Other presentations you'll hear today

15 will discuss a conceptual model and analysis plan aspects of the

16 problem formulation. But over the next several slides, I'll

17 discuss goals and endpoints and how they are used in the process

18 and their relationship to one another.

19 Ecological risk assessment is typically

20 developed in a risk management context to evaluate human-induced

21 changes that are considered undesirable. Changes often

22 considered undesirable are those that alter important structural

23 or functional characteristics or components of an ecosystem. An

24 evaluation of adverse affects may include consideration of type,

25 intensity and scale of the effects and well as the potential for

26 recovery. 
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1 Acceptability of certain adverse effects

2 reflects risk management. It is done so in the light of multiple

3 factors used to weigh both the risks and the benefits of a

4 pesticide. Therefore, it's important that as a risk assessment

5 is being developed, both management goals and science objectors

6 are considered and specificity is given when developing

7 assessment endpoints in conceptual models.

8 Protection goals are statements by a

9 regulatory authority on the desired condition of values of

10 concern. When they are stated at the highest levels, they may

11 be considered generic. U.S. EPA's generic protection goal is to

12 protect human health and the environment. With respect to

13 pesticides, management goals are in part defined by the laws

14 that would provide statutory authority to regulate them. 

15 The Federal Insecticide Fungicide and

16 Rodenticide Act directs EPA to regulate pesticides, such that

17 their use does not result in unreasonable adverse effects on man

18 or the environment, taking into account economic, social an

19 environmental costs and benefits from the use of any pesticide. 

20 These generic protection goals therefore reflect statutory

21 scientific and societal goals of a regulatory agency. 

22 Generic protection goals, however, do not --

23 may not provide adequate guidance at the risk assessment level. 

24 And specific protection goals may be needed which can better

25 direct and inform decisions at that level. Specific protection

26 goals speak more directly to the objectives for an ecological
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1 entity and inform the type of data that is needed; so the

2 specific protection goals therefore must be consistent and

3 support generic protection goals. 

4 Consistence with the results of the SETAC

5 Pellston Workshop of specific protection goals with this

6 proposed risk assessment framework include protection of

7 pollination services provided by bees; protection of honey

8 production and other hive products, and the protection of

9 pollinator diversity, in terms of adequate number and diversity

10 species that contribute to the health of the environment. 

11 In its recent scientific opinion on the

12 development of risk assessment of plant protection products for

13 bees, the European Food Safety Authority has also identified

14 pollination, hive products and biodiversity as relevant

15 ecosystem services and values to protect. 

16 Once protection goals are identified, specific

17 assessment and measurement endpoints can be developed that are

18 consistent with and support the stated protection goals. As

19 defined in the EPA guidelines, assessment endpoints are explicit

20 expressions of the actual environmental values that are to be

21 protected and are operationally defined as an ecological entity

22 and its attributes. 

23 The ability of assessment endpoints to support

24 risk management decisions is dictated by whether or not there

25 are measurable characteristics that adequately represent or

26 reflect management goals. EPA has identified three criteria for
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1 determining the selection of assessment endpoints and it

2 included whether they are ecologically relevant, susceptible to

3 the known or potential stressor, and what's relevant to the

4 management goals. 

5 Assessment endpoints are then specifically

6 informed by the data generated for the risk assessment. Both

7 effects data and exposure data provides specific information as

8 measurement endpoints than inform one or more of the assessment

9 endpoints.

10 When measurement endpoints are appropriately

11 linked to assessment endpoints and specific protection goals,

12 they therefore also support generic protection goals. This

13 slide is Table 1 of the White Paper and illustrates both

14 examples of and the relationship between measurement endpoints,

15 assessment endpoints and specific protection goals. 

16 The specific protection goals identified in

17 the table are then consistent with the generic protection goals

18 of protecting human health and the environment. The measurement

19 endpoints identified in the table are not intended to be

20 exhaustive, but only serve as examples. For the remaining

21 presentations, a range of measurement endpoints will be

22 discussed in the context of the pro's risk assessment framework.

23

24 In conclusion, the presentations you will hear

25 today will layout a proposed risk assessment framework that is

26 tiered, iterative and flexible, allowing for multiple lines of
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1 evidence to be considered and refinements to be made. In this

2 way, the proposed framework is sensitive with respect to the

3 resources needed to complete a risk assessment.

4 The primary focus of the proposed framework is

5 for evaluating the potential for adverse effects to honey bees

6 and also uses a honey bee as a surrogate for other non-Apis

7 bees. The extent to which any species is a reasonable surrogate

8 for other species, involves considerable uncertainty, unless

9 sufficient data are available to put differences in

10 relationships into context. 

11 Uncertainties reflect differences in life

12 biologies and histories that can impact both exposure and

13 effects. Despite the potential differences among bee species,

14 it is believed that the proposed risk assessment process with

15 honey bees provides information on non-Apis bees as well. At

16 lower tiers, for example, where effects to individual bees is

17 tested, exposure routes considered, i.e. contact and ingestion

18 are shared by both honey bees and non-Apis bees alike.

19 Until guideline studies with non-Apis bee

20 species are better developed and available for regulatory

21 purposes, the Agency is asking the panel the extent to which it

22 believes the proposed processed is informative of a potential

23 pesticide risk to non-Apis species. 

24 Finally, EPA and its partners seek the opinion

25 of the panel regarding the extent to which the proposed risk

26 assessment framework supports the identified protection goals
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1 and the extent to which biodiversity, identified as a protection

2 goal, is supported by the use of a honey bee, insofar as it is a

3 reasonable surrogate for non-Apis bees.

4 Thank you for your time. And if there are no

5 questions, I would like to allow Mr. Sappington to move into his

6 presentation on conceptual models.

7 DR. DANIEL SCHLENK: I think we do have some

8 questions. Again, this is a general overview. So if you could

9 hold your questions to more of the general framework, I believe

10 future speakers will have very specific details on the process. 

11 So if you have questions related to the overall objectives,

12 then, yeah. Dr. James.

13 DR. ROSALIND JAMES: I have a simple question.

14 You talked about management goals. The word "management" has a

15 lot of meanings to me. Are you talking about pest management

16 goals in your model framework?

17 MR. THOMAS MORIARTY: Risk management goals,

18 balancing between the risks and benefits.

19 DR. DANIEL SCHLENK: That was Mr. Moriarty.

20 Let me just remind the speakers that anytime you respond, can

21 you state your name real quick beforehand so that we can get

22 that on the written record. Thank you.

23 Any other questions? 

24 (No response.)

25 Okay. Mr. Sappington, do you want to take the

26 reins for the next presentation? We can get that loaded. 
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1 MR. KEITH SAPPINGTON: Hello. My name is

2 Keith Sappington. I'm a biologist and senior science advisor in

3 the ecological Environmental Fate and Effects Division within

4 the Office of Pesticide Programs. 

5 I appreciate this opportunity to address the

6 FIFRA Scientific Advisory Panel on this important topic, and I

7 look forward to your feedback to help us improve our process for

8 pesticide risk assessment to bees. 

9 This morning I'll be summarizing two aspects

10 of the White Paper in back-to-back presentations. In the first

11 presentation, I will describe a set of generic conceptual models

12 we are proposing for assessing pesticide risk to honey bees. 

13 Details of these generic conceptual models are

14 found in Section II of the White Paper. Immediately following

15 this presentation I will summarize our proposed decision

16 framework for assessing risks of pesticides to honey bees. And

17 this is also found in Section II of the White Paper. 

18 With respect to conceptual models, I will

19 first provide a brief overview of the use of conceptual models

20 and ecological risk assessment, and then I'll describe the

21 context and the purpose of these models as we apply them in

22 ecological risk assessment of pesticides to honey bees. 

23 Following this introduction, I'll then

24 describe the generic conceptual models that are specific to four

25 common application methods. And these include foliar spray

26 applications for both systemic and non-systemic pesticides, soil
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1 application for systemics, and seed treatment application for

2 systemic pesticides.

3 We present a fifth conceptual model in the

4 White Paper for trunk drench and tree injection, but I will not

5 be presenting that this morning.

6 The figure on the right of this slide

7 illustrates the components of problem formulation phase and it

8 depicts, visually, how the selection of assessment endpoints and

9 conceptual models are used to inform the analysis plan of the

10 risk assessment.

11 As defined in EPA's ecological risk assessment

12 guidelines, a conceptual model is a written and visual

13 description of the hypothesized relationships between the

14 ecological entities and the stressors of concern. In the

15 conceptual models I'll be presenting today, the ecological

16 entities, or honey bees, and the stressor of concern is the

17 pesticide application, and this includes any degradates that are

18 of toxicological concern. 

19 The written portion of a conceptual model

20 includes a set of risk hypotheses which are statements regarding

21 the purported relationships between the entities and the

22 stressor. The visual portion of the conceptual model -- which I

23 will be presenting shortly -- are diagrams of these purported

24 relationships.

25 In the context of ecological risk assessment,

26 conceptual models are useful tools for communicating to risk
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1 managers, risk assessors and the public of how we believe

2 exposure to pesticides and their associated effects are likely

3 to occur.

4 With that said, there is not a single standard

5 format for depicting a conceptual model diagram. They can vary

6 widely in their level of detail, from simple, highly aggregated

7 diagrams to ones that are very complex and highly disaggregated.

8

9 In addition, the graphical format can vary,

10 for example, from the more traditional box and arrow diagrams,

11 to ones that are pictorial in their representation. But

12 regardless of format, the general structure of conceptual models

13 is generally similar and, as indicated on the bottom of this

14 slide, represents a cascade of events which begins at the

15 stressor source and its relevant exposure routes, and then to

16 receptors of concern and their responses, and ultimately to

17 changes in the attributes or the assessment endpoints

18 indentified in the risk assessment.

19 The generic conceptual models I'll be

20 presenting this morning are consistent with the format typically

21 used in EPA pesticide risk assessments. Before presenting the

22 conceptual models, I first would like to make several points

23 regarding their development and intended use in risk assessment.

24

25 First, the generic conceptual models are

26 called generic because they're intended to be used as a starting
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1 point for pesticide risk assessors. And therefore, are expected

2 to be modified, where necessary, depending on the circumstances

3 of that specific risk assessment. 

4 Second, the conceptual models presented here

5 are not comprehensive of all pesticide uses. Rather, they

6 represent several major use categories that are expected to

7 result in some exposure to honey bees.

8 Lastly, in addition to being organized around

9 different pesticide use patterns, the models also consider some

10 differences in chemical characteristics, and in particular, the

11 systemicity of a pesticide, which, in turn, impacts the nature

12 of exposure to honey bees.

13 In describing the generic conceptual models, I

14 first plan to go through in detail the conceptual model for

15 foliar applications of non-systemic pesticides here and then

16 I'll highlight the main differences for the other three

17 conceptual models that we have identified. 

18 As shown in bold, on the left, these

19 conceptual models begin at the top, with the stressor of concern

20 and continue downward by illustrating the sources of exposure,

21 the related exposure media, receptors of concern and changes in

22 the attribute or assessment endpoints. 

23 In this case, the stressor of concern is a

24 non-systemic pesticide application -- and again, including any

25 of its degradates -- that is applied to a crop foliage, be it

26 ground or application methods. As shown in these boxes, the
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1 exposure sources considered in this model include deposition of

2 spray droplets directly onto bees, plants, soil, and surface

3 water, either on the treated site or via drift, to adjacent

4 sites.

5 From these exposure sources, resulting

6 pesticide residues in various environmental media are

7 considered, including residues on plant surfaces, in soil,

8 pollen and nectar, and in surface water, which can result from

9 spray drift and pesticide runoff/erosion, shown in blue.

10 You'll notice that the dashed line surrounding

11 the residues in the soil and surface water boxes. This

12 indicates that we believe these exposure pathways are less

13 important to honey bees compared to other exposure routes

14 depicted. Additional information supporting this distinction

15 will be presented later in a presentation by Ms. Christina

16 Wendel.

17 The nature of pesticide exposure is then

18 depicted by the lines connecting the pesticide residues and

19 environmental media to the receptors. Worker bees foraging for

20 food and water sources constitute the primary receptor of

21 concern outside of the hive; although direct exposure to drones

22 and the queen is also possible during mating and orientation

23 flights. 

24 Primary routes of exposure outside of the hive

25 that we have included include dermal uptake of pesticide, via

26 spray droplets and foliar residues, inhalation -- which is
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1 applicable to more volatile pesticides, primarily -- ingestion

2 of pesticides absorbed onto pollen and nectar.

3 Secondary routes of exposure of lessor concern

4 for honey bees include contact with contaminated soil and

5 surface water. Another secondary route of exposure not depicted

6 here is contaminated dew on plant surfaces. Exposure from

7 contaminated dew droplets is discussed in detail in a later

8 presentation.

9 We note, however, that exposure from pesticide

10 residues in soil is likely to be more important for some

11 non-Apis bee species. For example, ground nesting bees that

12 collect and process soil for nest construction.

13 From here, exposure to bees inside the hive is

14 possible, via direct contact, ingestion and processing of

15 contaminated pollen and nectar brought into the hive by the

16 workers. Ingestion of honey and as well as processing of wax

17 and propolis during comb production and brood raring activities.

18

19 Exposure of hive bees to pesticide is also

20 possible from contaminated water used for consumption, honey

21 production, and evaporative cooling. And this pathway is

22 further described in a later presentation.

23 Subsequently, bee brood may be exposed to

24 pesticides via ingestion of brood provisions that includes brood

25 food, honey, and processed pollen, wax and propolis. Inside the

26 hive, queens are expected to be exposed primarily through
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1 contact with comb material and consumption of royal jelly. 

2 Lastly, the relationships between the exposure

3 of different bee castes and changes in attributes or assessment

4 endpoints are shown with these arrows and text boxes. The text

5 in bold in these boxes represent either the assessment endpoints

6 or in a case to pollinator biodiversity, the protection goal. 

7 Example of measurement endpoints are shown below at each of the

8 assessment endpoints.

9 We note that these arrows collapse many

10 complex chemical, biological and toxicological processes that

11 collectively determine the responses of individual bees and the

12 entire colony to a given pesticide exposure. For example, the

13 populations size and stability of colonies may not only be

14 impacted by a direct loss in forager bees due to mortality, but

15 also by other factors, including, but not limited to reduced

16 foraging success and associated reduction in food reserves,

17 insufficient workers to maintain hive temperatures during

18 winter, and a reduction in homing success. 

19 For systemic foliar applied pesticides, the

20 generic conceptual model is identical to that described

21 previously for non-systemic pesticides, except that it includes

22 pesticide uptake and translocation to the plant tissues. And I

23 show this in red in the arrows in this figure.

24 Thus, for systemic pesticides that are applied

25 to foliage via spray method, a portion of the residues in pollen

26 and nectar may result from pesticide deposition onto these
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1 tissues in addition to a portion that may be subsequently

2 transported to these tissues via translocation. 

3 The remainder of this conceptual model from

4 the receptors downward is the same as described previously for

5 non-systemic foliarly applied pesticides. For application of

6 systemic pesticides directly to bare soil either by granular

7 spray or soil incorporation methods, the dominate route of

8 exposure we have depicted for honey bees begins with pesticides

9 uptake and translocation within the developing plant. 

10 As with the foliarly-applied systemic

11 pesticides, the foraging bees may then be exposed from ingestion

12 and contact with residues in pollen, nectar, plant exudates, and

13 honey dew. It is assumed that direct exposure of foraging bees

14 with pesticide spray droplets would not be a primary exposure

15 route of concern because soil applications would be presumed to

16 occur prior to planting. 

17 Furthermore, spray applications to bare soil

18 typically involve a coarse droplet spectrum which would not

19 likely to be subject to extensive offsite drift. However, if

20 offsite drift of spray droplets was a concern, or if there was

21 attractive foliage in the field at the time of the soil

22 application, then these exposure routes would be addressed as

23 described previously for the foliar spray conceptual model. 

24 Runoff of a pesticide into surface water is

25 another potential route of concern, but this is expected to be

26 minor relative to exposure from residues in pollen and nectar. 
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1 Again, the remainder of the conceptual model

2 from the receptors downward is the same as described previously.

3 Exposure of honey bees to pesticides via seed treatment is

4 depicted here as a result of two major sources: offsite drift of

5 abraded seed coat dust and from pesticide residues in plants

6 that result from pesticide translocation to pollen, nectar,

7 plant exudates and honeydew. 

8 Subsequent exposure of bees to pesticides in

9 these matrices follows the same pathways as depicted earlier for

10 soil application of systemic pesticides. For drift of abraded

11 seed coat dust, foraging bees may be exposed by directly

12 intercepting dust particles containing the pesticide during

13 flight and subsequent dermal or respiratory uptake. It is also

14 possible for contaminated dust to deposit on other environmental

15 media, including soils, surface water, and plant tissues. 

16 Generally speaking, these routes are likely to

17 result in lower exposures compared to direct contact with seed

18 dust or contaminated pollen and nectar from the treated crop. 

19 We note, however, that exposure to pesticides

20 from deposition of contaminated dust particles onto flowing

21 plants may be important, particularly if these plants are

22 located in close proximity to the seed planting operations. 

23 Again, the bottom half is the same as I described earlier. 

24 So to conclude, the generic conceptual models

25 for pesticide exposure and effects on honey bees presented here

26 and in the White Paper, are intended to reflect only several
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1 major pesticide use categories. They also are intended to serve

2 as a basis for consideration in a forthcoming pesticide risk

3 assessment involving honey bees, and are expected to be modified

4 when circumstances warrant.

5 As indicated in Charge Question Number 2, we

6 are seeking feedback on these models, in terms of their overall

7 accuracy and the extent to which their implementation is

8 consistent with the proposed assessment endpoints and management

9 goals. 

10 At this time, myself and the team would

11 address any clarifying question on the generic conceptual

12 models. And after that, I will present on our proposed decision

13 frameworks. Thank you very much.

14 DR. DANIEL SCHLENK: Thank you. Okay, any

15 questions of clarifications. Dr. Potter?

16 DR. THOMAS POTTER: I have a question about

17 your category of foliar-applied pesticide. Does that

18 essentially include all non-systemic broadcast applied active

19 ingredients?

20 MR. KEITH SAPPINGTON: For the first

21 conceptual model I presented, that's for non-systemic

22 foliar-applied sprays.

23 DR. THOMAS POTTER: But does it have to be

24 foliar applied is, I guess, where I'm looking for an answer on

25 that?

26 MR. KEITH SAPPINGTON: Well, we distinguish
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1 between foliar and soil applied. So the presumption is that it

2 is being applied to foliage in that conceptual model.

3 DR. THOMAS POTTER: But it seems to leave out

4 a large category of active ingredients that one presumes

5 assessments being made regarding risks, pre-emergent herbicides

6 would be one category.

7 MR. KEITH SAPPINGTON: Right. Those would, I

8 think, be covered in the soil application methods; the

9 conceptual model for soil application.

10 DR. THOMAS POTTER: I guess what I saw for

11 soil application, though, was systemics and not, you know, non -

12 again, non-systemics are what we're talking about here. Again,

13 I'm just looking for clarification on this as to where those

14 categories of chemicals that are soil applied, that are

15 non-systemic, where do they fall into these four different

16 categories?

17 MR. KEITH SAPPINGTON: For non-systemic

18 soil-applied pesticides, you would be primarily concerned about

19 the contact exposure; although, again, if they're applied

20 pre-emergence to bare soil then the exposure to bees foraging in

21 that particular field site would be presumed to be rather low. 

22 If there were foliage present then it would follow the foliar

23 conceptual model. Or, if there was a reason to expect drifts

24 offsite from that, then it would follow the foliar conceptual

25 model framework.

26 DR. THOMAS POTTER: So again, just to be
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1 clear, all non-systemic active ingredients, which would be

2 broadcasts applied, would fall into that first schematic that

3 you showed?

4 MR. KEITH SAPPINGTON: I think that would be

5 the closest one that would cover them. Now we could split those

6 out as we refine these conceptual models to make that more

7 clear. But we focused on the systemics because of the focus on

8 the uptake in translocation in the plants and exposure through

9 pollen and nectar. But we can break that out to make that

10 clearer. Thank you.

11 DR. DANIEL SCHLENK: Any others? Yes, Dr.

12 McManaman. 

13 DR. JAMES MCMANAMAN: I see that larvae left

14 out of your model here, it doesn't look like they're present in

15 there. Is that to simply the model or is that because you have

16 in mind a different kind of model for larvae?

17 MR. KEITH SAPPINGTON: The larvae would be

18 represented towards the bottom of the conceptual model as part

19 of the brood in the hive. That's where we would be expecting

20 their exposure for honey bees.

21 DR. JAMES MCMANAMAN: So you make a

22 distinction between the brood provisions and provisions for the

23 queen, in terms of royal jelly. But my understanding is that

24 oftentimes early on, the bees receive the same kind of food

25 until it's distinguished between who's going to become queen or

26 not. Is that again left out due to simplification?
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1 MR. KEITH SAPPINGTON: Let me see if I can

2 understand the question first. So the question is, is direct

3 exposure from pollen and nectar to bee brood as opposed to its

4 processed pollen and nectar and how is that represented in the

5 conceptual model. Do I understand it correctly?

6 DR. JAMES MCMANAMAN: Yes.

7 MR. KEITH SAPPINGTON: Yes. My understanding

8 is that was left out just for simplicity reasons. I mean, at

9 some point you get to have so many hours and lines directed. My

10 understanding is that -- and we've investigated and I believe

11 Ms. Kris Garber will be presenting more information about the

12 consumption of larvae later today -- but we've investigated the

13 extent to which the larvae were consuming or may consume pollen

14 directly versus the process pollen.

15 DR. JAMES MCMANAMAN: So the Agency's view is

16 that larvae are included in this model system and the details

17 may --

18 MR. KEITH SAPPINGTON: Absolutely.

19 DR. JAMES MCMANAMAN: -- vary from adults.

20 MR. KEITH SAPPINGTON: Absolutely. 

21 MR. JAMES MCMANAMAN: Okay.

22 DR. DANIEL SCHLENK: Any other questions. 

23 Clarification? Dr. James?

24 DR. ROSALIND JAMES: This is Rosalind James. 

25 I think this is the appropriate place to ask this question. 

26 When honey is made, nectar is collected and then a significant
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1 proportion of the water is evaporated off and it becomes

2 concentrated. Is that included in your analysis at all?

3 MR. KEITH SAPPINGTON: We have a separate

4 presentation this afternoon that looks at the whole issue of

5 contaminated surface water and its relative importance, compared

6 to these other aspects.

7 MS. ROSALIND JAMES: I'm actually talking

8 about the nectar itself. So the honey is made from nectar and

9 then the nectar is predominately water. But it's the nectar --

10 I'm not talking about collected water. And then to make honey,

11 the bees evaporate off the --

12 MR. KEITH SAPPINGTON: Right.

13 MS. ROSALIND JAMES: I mean, that's processing

14 of the nectar. But is the fact that you're concentrating the

15 nectar down included in your risk assessment? It could change

16 the exposure level. 

17 MR. KEITH SAPPINGTON: That's right. There

18 are basically two competing processes, potentially, as we

19 understand them. One would be degradation of the pesticide over

20 time. And then conceptually, there could be some concentration

21 as the honey is formed. I personally am not aware of data on

22 that concentration phase, but those processes would be, I think,

23 in competition with one another. 

24 DR. THOMAS STEEGER: I would like to address

25 Dr. James's question. As you will learn from the presentation

26 from Kris Garber later on today, the concentration of residues
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1 that could potentially take place as honey is being processed

2 into the colony is accounted for in our exposure modeling

3 because it's expressed on a sugar basis, as opposed to a water

4 basis. So hopefully after you hear that presentation you'll

5 have a better understanding of how we've accounted for that

6 potential mechanism of exposure.

7 DR. DANIEL SCHLENK: Thank you, Dr. Steeger. 

8 Any other questions? Dr. Fefferman. 

9 DR. NINA FEFFERMAN: My guess is that that the

10 answer to this will be addressed in a later portion. But I was

11 wondering, in the conceptual modeling where you felt that the

12 layer for how the impact might propagate throughout a colony, it

13 looks to me like everything that you've discussed is sort of

14 exposure to individual bees and how that might get to individual

15 bees. Where do you see that layer fitting?

16 MR. KEITH SAPPINGTON: I agree. As I went

17 through the conceptual model I noted that those lines would

18 connect the exposure media to the receptors and ultimately to

19 the changes in the attributes, collapse a lot of information

20 down. One aspect that I've thought out, personally, you know,

21 there's no reason that a conceptual model necessarily has to fit

22 on one page. 

23 So we could have a conceptual model, much like

24 this, that addresses the exposure pathways and then a separate

25 one that lays out the various ways in which the response is to

26 that given exposure, could then have affects on the hive. And
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1 that could be very useful for the planning phase of a risk

2 assessment. Thank you for that comment.

3 DR. NINA FEFFERMAN: Is that currently in some

4 section somewhere for consideration or is that something that

5 really might be flushed out better in the conceptual model

6 section but separately, as you proposed?

7 MR. KEITH SAPPINGTON: We discuss, texturally,

8 in the White Paper these various ways in which the colony

9 responses could be manifested, but they're not visually

10 presented, currently.

11 DR. THOMAS STEEGER: I would like to add to

12 Mr. Sappington's response by saying that as you will learn in a

13 later presentation, it's not captured in the conceptual model,

14 but we evaluate multiple lines of evidence to estimate, not just

15 risk to individual bees, but to the colony as a whole. And part

16 of that process we will be asking for SAP input on, is the use

17 of colony level modeling in addition to the advanced or more

18 refined testing that is conducted at the semi-field and full

19 field level that look at whole colony effects. 

20 DR. DANIEL SCHLENK: Okay. Any other

21 questions before we break?

22 (No response.)

23 All right. Thanks. We're going to go by this

24 clock on the wall here. Let's be back at 10:35.

25 (Brief recess.)

26 DR. DANIEL SCHLENK: Okay. Thanks. Again,
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1 let me just reiterate, if you're on the phone, please mute your

2 line at this time so we don't hear you during the presentations.

3

4 Before we go to our next presentations, I

5 believe there were some questions, some overall framework

6 questions that were not asked the last time, for you, Keith, if

7 that's okay. I believe Dr. Fefferman has questions, please.

8 DR. NINA FEFFERMAN: So forgive me, these

9 don't actually address the specific conceptual models you were

10 putting forth because I think those are really quite complete

11 and lovely, and this is more a structural, as we consider the

12 framework of risk assessment itself type of question.

13 In the types of risk assessment frameworks

14 that I'm used to considering, sort of the very first step is

15 which things are going to be useful to measure and which things

16 are going to inform choices and how do we balance those. And I

17 feel sort of as though we skipped past that to here are the

18 things we're definitely measuring and here are the conceptual

19 models and how those measurement might play out in terms of the

20 risks associated with those particular aspects.

21 Has there been a stage of evaluation about

22 some of these? I mean, I know we're not discussing necessarily

23 the benefits of some of the pesticide use, but some of the risks

24 to colonies, to bees, has there been a stage of thinking

25 relative impact, types of measurements available, when we've got

26 diminishing returns from better measurement, that kind of thing?
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1 DR. THOMAS STEEGER: I think what we're trying

2 to depict here is the generalized process that the agency -- it

3 plans on or proposes to use for honey bees and to the extent

4 that honey bees serve as surrogates for non-Apis bees as well. 

5 And this process has to rely, at least initially, as with all

6 taxa, a screening level that is relatively generic and looks at

7 very broad categories of effects to see where resources really

8 need to be expended to focus in on where risk, at this very

9 broad level, cannot be dismissed. 

10 So when you talk about the nuances about

11 colony level effects --

12 DR. NINA FEFFERMAN: Forgive me. I don't

13 actually mean nuances of particular effects; I mean actually

14 going more abstract rather than more specific. 

15 There seems to me to be layers of very

16 specific and then we measure this type of pesticide application

17 and how much gets into food supply. You know, that all seems to

18 be to me to be a very specific measurement as opposed to much

19 broader things like, well, what are the toxicological effects at

20 this layer, as opposed to we measure this flow path or something

21 like that. 

22 Has that kind of assessment of a more abstract

23 flow process for risk been accomplished already?

24 DR. THOMAS STEEGER: I think -- and correct me

25 if I'm wrong -- you're getting at this issue that the agency

26 routinely wrestles with and it's this concept of a adverse
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1 outcome pathway and how do you transition from one level of

2 biological organization to the next. 

3 I think that we're looking, as this process

4 evolves, to help develop a more standardized approach for making

5 those linkages. That's one of the difficulties that we have in

6 the use of many of the sublethal endpoints that have been

7 identified in some of the bee studies that are reported in the

8 open literature. That's a challenge. 

9 I'll say that we don't have all the answers at

10 this point. But for the frank effects that we traditionally use

11 of acute lethality and survival and reproduction. Those are the

12 affects that right now are what the assessment endpoints are

13 based on and the measurement endpoints that we plan to quantify

14 as we develop this process further. 

15 But developing those linkages that would

16 better enable us to move from one level of biological

17 organization to the next, you're absolutely right. That's what

18 the ultimate endgame is. Have they been developed yet? No. 

19 But the science is still evolving.

20 DR. NINA FEFFERMAN: Okay. Great. Thanks. 

21 So you're thinking of that as something that comes out of this

22 level as opposed to something that is done before we get down to

23 the drill down level of these discussions?

24 DR. THOMAS STEEGER: I think that the process

25 that we're using to refine risk assessment where we identify

26 chemicals that there appears to be risk that can't be
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1 discountable at the screening level, that we move to higher tier

2 testing at the whole colony level to try to detect whether under

3 actual use conditions is the colony going to be able to survive,

4 either in the short-term or in the long-term?

5 Having very bright lines at the full colony

6 level is difficult to establish at this point because these

7 nuances that I mentioned earlier, we don't really have a clear

8 understanding of how much of an impact can a colony sustain or a

9 population of bees can sustain, over long periods of time. 

10 I think that that is something that still has

11 to evolve, but our screening level process is geared towards

12 trying to do that type of refinement, but at this point, it

13 would be a qualitative assessment to determine is the colony

14 going to make it or not. 

15 DR. NINA FEFFERMAN: Thank you. I think your

16 comments directly address my question. So I guess my take home

17 from this might be that on some level, we might consider, as

18 we're talking, that some of this might be putting the cart

19 before the horse, where as if, when we get to that qualitative

20 level, we discover that some of these aspects are not having any

21 impact than the careful ability to measure the local impact, may

22 not help us build up to that level. So that's great. Thank

23 you. I just want to get that on the record. Thanks.

24 DR. THOMAS STEEEGER: I think you put that

25 very well.

26 DR. DANIEL SCHLENK: Great. Thanks a lot
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1 for getting that clarified. Okay. Our next speaker is

2 Christina Wendel. Oh, sorry. You have a second one? Oh,

3 sorry. Keith Sappington, please.

4 MR. KEITH SAPPINGTON: Thank you. In my

5 second presentation, I will describe two risk assessment

6 decision frameworks that we've proposed to use as guide for

7 assessing pesticide risk to honey bees. Here, I will only

8 summarize the proposed decision frameworks and their components

9 and detail specific to each of these components will be

10 presented in subsequent presentations.

11 In describing the proposed decision

12 frameworks, I'll first begin by summarizing some of their key

13 attributes which shape their development. I will then describe

14 the proposed decision frameworks for pesticides that are applied

15 via foliar sprays and then for pesticides applied via soil and

16 seed treatment applications.

17 Lastly, I will conclude with a comparison of

18 our proposed decision-making process with other decision

19 frameworks that we've identified in the literature. Before

20 describing the frameworks themselves, I think it's important to

21 discuss some of the attributes or factors that shape their

22 development and, as a consequence, their overall scope.

23 First of all, the agencies represented here

24 are responsible for assessing ecological risk of over 1,000

25 active ingredients for conventional pesticides. As a result,

26 one attribute that shaped the proposed risk assessment process
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1 for bees was the need to efficiently screen out pesticides that

2 posed little or no potential risk concern to bees from those

3 that have a potential risk concern. 

4 To accomplish this, we are proposing a tiered

5 process that begins with, at first, in Tier I, with a simple

6 screening level assessment. And by design, this Tier I process

7 is both conservative and quantitative in nature. And by that, I

8 mean that it relies on high-end estimates of exposure and

9 results in a derivation of a numeric risk quotient. 

10 The proposed Tier I assessment considers

11 effects measured at the individual level of biological

12 organization, under controlled laboratory conditions. Tiers II

13 and III involve increasing information requirements that are

14 typically satisfied by studies conducted in the field. And as a

15 result, Tiers II and III involve a greater environmental realism

16 in pesticide exposure compared to Tier I.

17 In some cases, pesticide exposure information

18 generated in Tiers II and III may be used to refine exposure

19 estimated in Tier I. Importantly, however, in Tiers II and III,

20 effects are evaluated at the colony level. 

21 Our proposed frameworks are also shaped by the

22 need to rely largely on toxicity studies which have established

23 regulatory guidelines; however, we and others have noted

24 significant gaps in the exiting battery of toxicity test

25 guidelines for bees. Therefore, these frameworks include

26 placeholders for several additional studies for which test
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1 methods have not been fully vetted as regulatory guidelines, but

2 protocols are available. 

3 The proposed decision frameworks also focus on

4 major exposure pathways of concern to honey bees and are also

5 shaped by the need to have sufficient information available from

6 which a quantified pesticide exposure from these pathways. 

7 For example, some exposure pathways, such as

8 ingestion of contaminated surface water are considered less

9 important routes of exposure for honey bees, relative to direct

10 contact and the consumption of pollen and nectar. 

11 Other pathways, such as consumption of plant

12 guttation fluid may contain significant concentrations, but the

13 degree to which honey bees rely on this source of water and

14 nutrients has proven difficult to quantify. 

15 And again, later this morning, Ms. Christina

16 Wendel will provide further evaluation of the selection of

17 exposure pathways considered in this risk assessment process. 

18 The proposed decision frameworks also differ depending on the

19 type of pesticide application which is consistent with the

20 conceptual models that I presented previously. 

21 And lastly, decisions on moving from one tier

22 to the next, which incorporate both risk assessment and risk

23 management considerations are based on multiple lines of

24 evidence and considerations of uncertainty.

25 In addition to these attributes, I also wish

26 to make several points regarding the potential application of
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1 these frameworks and future pesticide risk assessments involving

2 honey bees. First, I'll be describing them as one moves from

3 the top in Tier I, down to the bottom in Tiers III.

4 As we envision this top to bottom application

5 of the frameworks, may be more typical of their use and new

6 pesticide risk assessment where the information base is just

7 being developed. 

8 However, for existing pesticides, relevant

9 information may be available at multiple tiers at the onset of a

10 risk assessment, and in this case, the expectation would be that

11 the decision-making process would reflect all available

12 information available at the time of the assessment and may

13 involve starting at various tiers simultaneously in the

14 assessment.

15 The second bullet emphasizes that the decision

16 frameworks are intended to reflect an iterative process, whereby

17 information gained at higher tiers can and should be used to

18 refine information and assessments conducted at the lower tiers

19 and vice-versa.

20 Third, these decision frameworks are intended

21 to be used as a guide to risk assessors and not as a rigid and

22 inflexible recipe for decision-making.

23 And then finally, while the frameworks reflect

24 current state of the sciences as we understand it, the

25 expectation is that as information and test guidelines are

26 developed in the future, for example, for non-Apis bees,
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1 appropriate modifications will be made to the frameworks.

2 So I will first begin by stepping through the

3 proposed decision framework for assessing risk for honey bees to

4 foliar spray application of pesticides, and this would include

5 systemics as well as non-systemics. 

6 The first step in this process is to collect

7 all relevant information regarding the nature of its pesticide

8 and its use pattern, and this is a routine step in the problem

9 formulation phase that includes gathering information on the

10 pesticide's chemical, physical and toxicological properties, as

11 well as detailed information on its environmental fate. 

12 Information on the timing and location and type and

13 attractiveness of crops to which pesticides are being applied is

14 also evaluated. 

15 Shown here, the Tier I begins with assembling

16 this information and asking the question whether or not exposure

17 of adults or brood to the pesticide is likely to be a potential

18 concern. 

19 For outdoor spray applications, the answer

20 would likely be yes, but for some other applications such as

21 indoor or greenhouse uses, the answer would likely be no.

22 In cases where pesticide exposure is not

23 considered a potential concern at this stage, a presumption of

24 minimal risk would be made. If exposure is considered to be a

25 concern, then three major exposure routes would be evaluated in

26 the Tier I part of the assessment. The first of these includes
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1 direct contact of adult foraging worker bees to the pesticide

2 which intercept the spray droplets during flight. 

3 We have proposed a process to derive

4 conservative or high-end estimated environmental concentration,

5 abbreviated EEC, to the bees via contact exposure, as depicted

6 in Box 3(a) of this diagram.

7 The derivation of this EEC and other EECs used

8 in Tier I will be discussed later by Ms. Kris Garber. As shown

9 in Box 4(a), an acute risk quotient, or RQ, is then determined

10 by the ratio of the contact EEC to the acute contact LD50. 

11 The second exposure route that would be

12 evaluated is oral exposure of adult foraging bees. And again,

13 here a conservative estimate of exposure via consumption and

14 nectar is calculated as shown in Box 3(b) and an acute oral RQ

15 is determined as a ratio of this oral EEC to the acute oral LD50

16 for adult honey bees. And that's shown in Box 4(b). 

17 In addition, we propose that a measure of

18 chronic risk to adult foragers be determined in Tier I by

19 dividing the same EEC by the no-observed adverse effect

20 concentration, or NOAEC, from a chronic toxicity study on adult

21 bees. As noted with the asterisks, a regulatory guideline is

22 not yet available for this study, although various protocols

23 have been described in the literature. 

24 In the third exposure pathway proposed for

25 evaluation in Tier I is oral exposure to developing honey bee

26 brood. An oral EEC for larvae would be determined, as shown in
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1 Box 3(c) and the acute and chronic oral risk quotient values

2 would be calculated using acute oral LD50 and a chronic oral

3 NOAEC from the larval toxicity test. 

4 We note again that the regulatory guidelines

5 have yet to be developed or finalized for measuring larval

6 toxicity in the laboratory, and these would need to go through

7 the formal guideline development process.

8 Once these RQ values are determined, they will

9 be compared to the proposed level of concern, abbreviated LOC. 

10 The proposed LOC for evaluating acute risk is 0.4, and that

11 proposed for evaluating chronic risk is 1.0. The technical

12 basis of these LOC values will be described later by Dr.

13 Steeger.

14 As the LOC comparisons are used to inform the

15 decision to proceed with additional refinement or move on to

16 Tier II; however, not explicitly depicted in Box 5, is that this

17 decision would not only include a comparison to the LOC values,

18 but also other lines of evidence, including available ecological

19 incident reports, pesticide mode of action and uncertainty in

20 the available information base. 

21 After considering the LOC, other lines of

22 evidence, if no significant risk is indicated then a presumption

23 of minimal risk would be made and documented. If risk could not

24 be ruled out at this Tier I screening phase, then other

25 information could be considered for refining the exposure

26 estimate, which may include residue information derived from
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1 crop magnitude of residue studies which are routinely submitted

2 to the Agency. Additional detail on this refinement step will

3 be provided by Mr. Baris in a subsequent presentation. 

4 If the applicable and refined RQ value is

5 recalculated using this refined information and other lines of

6 evidence are considered, the decision then becomes, again,

7 whether to presume minimal risk or continue with the risk

8 assessment process. As shown in Box 8, the next step involves

9 considering the available risk mitigation options and

10 uncertainties in the assessment and other lines of evidence as

11 part of this decision to move on to Tier II.

12 The risk mitigation option, such as modifying

13 the pesticide application rates, timing of application, types of

14 crops being treated, could then be evaluated by proceeding back

15 through the Tier I process, as indicated by this dashed black

16 arrow on the right of the diagram. 

17 If after weighing this information, including

18 a dialogue with risk managers, information from Tier II studies

19 is determined to be necessary, then different types of studies

20 could then be considered. 

21 One type of study may be an exposure-based

22 field or semi-field residue study in which pesticide residues in

23 pollen and nectar are quantified using pesticide applied to a

24 treated crop. Information from this study could then be used to

25 refine the estimates of pesticide exposure from pollen and

26 nectar used earlier in Tier I.
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1 Another type of study would be considered in

2 Tier II as a semi-field effects-based study, such as a tunnel

3 enclosure or high feeding study. Semi-field studies provide

4 pesticide effects information gathered at the whole hive level. 

5 And endpoints from these studies may include, but are not

6 limited to worker and brood mortality, brood development,

7 foraging activity, behavioral observation, colony strength and

8 survival. Additional information on the Tier II semi-field

9 studies will be provided by Mr. DeCant Later this afternoon. 

10 Once this information is gathered from the

11 Tier II studies, other lines of evidence are considered as well

12 as the uncertainties and potential risk to honey bees is again

13 assessed. The outcome of this assessment may be a presumption

14 of minimal risk, or if risk is identified, available risk

15 mitigation options would be considered and evaluated, as

16 indicated in Box 11.

17 Although not shown here, evaluation of these

18 mitigation options might also involve preceding back to the

19 beginning of the process. After evaluating these risk

20 mitigation option and considering, again, all the lines of

21 evidence and uncertainties, a decision would be made to proceed

22 to Tier III. 

23 And Tier III, as we've depicted it, includes

24 full field studies that are designed to address specific

25 uncertainties raised in the earlier tiers. An example of this

26 might be the design and conduct of a field study specifically to
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1 track overwintering success of colonies following pesticide

2 exposure.

3 As shown in Box 13, the results of the Tier

4 III field studies are then evaluated and available risk

5 mitigation options are considered, and again, along with other

6 lines of evidence and uncertainties. Risk associated with these

7 mitigation options may be evaluated, as indicated by this black

8 arrow, and risk is then characterized. 

9 This risk characterization may lead to a

10 presumption of minimal risk or it may lead to a presumption of

11 risk from the Tier III studies, as well as the other earlier

12 information. Dr. Tom Steeger will be providing additional

13 information on this risk characterization step this afternoon.

14 The proposed decision framework for risk

15 assessment of soil application and seed treatment application

16 begins in a similar way that is depicted for foliar application.

17 That is, information on the pesticide and the nature of its

18 application are evaluated and questions are asked whether or not

19 exposure is a potential concern to adults or brood. 

20 If there are little or no potential exposure

21 of adults or brood, then a presumption of minimal risk is made. 

22 But if there is potential for exposure, then conservative

23 estimates of oral exposure to adult worker bees and larvae are

24 then determined, as indicated in Boxes 3(a) and 3(b) of this

25 figure.

26 As noted in Boxes 4(a) and 4(b), Tier I
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1 screening level, acute and chronic risk quotients are then

2 calculated using these EECs and the relevant toxicity endpoints

3 from the acute and chronic oral toxicity studies to adults and

4 larvae are considered.

5 As noted in the conceptual model and in the

6 White Paper for seed treatments, exposure to bees from seed coat

7 dust has been shown to be an important route of concern and the

8 focus of numerous ecological incident reports. 

9 At this time, we're not proposing a risk

10 assessment process for this exposure pathway, primarily because

11 methods for predicting a priori pesticide exposure from drift of

12 seed coat dust are not yet available for regulatory application

13 and because the Agency is currently working to mitigate this

14 exposure pathway based on modifications of seed treatment

15 methods, seed planting equipment and associated practices. 

16 In addition, if there's a potential exposure

17 for offsite drift of spray applications of soil, this would be

18 addressed, as described previously, for the foliar spray

19 applications after accounting for the fraction of pesticide that

20 is subject to drift. 

21 As shown in Box 5, the acute and chronic risk

22 quotients are then compared against the same LOC values as

23 described previously. These RQ values are then considered along

24 with other lines of evidence, such as incident reports, to

25 determine whether a presumption of minimal risk can be made or

26 if additional refinements are considered necessary in the Tier I
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1 exposure estimate.

2 The remainder of the proposed decision

3 framework is the same as I described for foliar sprays. 

4 However, the design of tests in Tiers II and III would reflect

5 the specific pesticide application methods being used. And the

6 EECs calculated in Tier I would also differ in their method.

7 Lastly, we thought it would be useful to place

8 the proposed decision framework I just presented in the context

9 of two recently published risk assessment schemes for bees. 

10 The first is presented in a scientific opinion

11 document developed by the European Food Safety Authority, or

12 EFSA, earlier this year. And the second is a decision scheme

13 that was published by the Society of an Environmental Toxicology

14 and Chemistry, SETAC, in 2011, and based on an international

15 Pellston-style Workshop. These efforts were very useful to us

16 as we developed the White Paper.

17 And as a result, we note a number of

18 similarities among the three assessment schemes. First of all,

19 as indicated in the first bullet, they all involve a tiered risk

20 assessment process that begins with assessing effects at the

21 individual level, based mostly on laboratory studies. This is

22 then followed by effects which are assessed at the colony level

23 at higher tiers. 

24 As indicated in the second bullet, they all

25 contain risk assessment schemes that are tailored to different

26 application methods; for example, sprays, soil and seed
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1 treatment.

2 Third, exposure to pesticides from contact

3 routes as well as oral exposure is prominent in all three

4 schemes, although the EFSA scheme does include a conservative

5 estimate of dust exposure from seed treatments as a fraction of

6 the overall calculated application rate. It's not clear at this

7 time which methods would then be used to refine this screening

8 level estimate.

9 Fourth, all schemes recognize the need to

10 assess both acute and chronic toxicity early in the risk

11 assessment process. I wish to point out some of the differences

12 that we've noted as well. First, one example is that the

13 triggers used to move from Tier I to higher tiers differ in

14 their form. 

15 The process proposed in the White Paper bases

16 the Tier I trigger value on the ratio of the estimated dose to

17 the corresponding toxicity value. The approach recommended in

18 the EFSA Scientific Opinion document, bases its trigger on the

19 ratio of the application rate in grams per hectare to the

20 toxicity value. 

21 As application rate is obviously related to

22 pesticide doses, the two approaches are similar in concept, but

23 they differ in the details. Another difference is that non-Apis

24 bees are explicitly included in the risk assessment schemes

25 recommended by the SETAC Pellston Workshop and EFSA. However,

26 these schemes acknowledge that additional test guideline
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1 development is needed before they can be fully implemented for

2 non-Apis bees.

3 Lastly, the proposed approach in the White

4 Paper recommends characterizing risk by considering the overall

5 weight of evidence at higher tiers, which would include results

6 from semi-field and field studies, but also would include other

7 lines of evidence provided by suitable studies from the

8 literature and incident reports. It appears to us that the risk

9 conclusions at higher tiers from these other schemes are largely

10 limited to the results from the applicable semi-field and field

11 studies.

12 I want to thank you very much for your

13 attention. At this time, myself or the team would be pleased to

14 address your questions on the overall risk assessment process,

15 noting that detailed questions related to components of this

16 process are probably best reserved for the specific presentation

17 this afternoon. Thank you.

18 DR. DANIEL SCHLENK: Any questions? Dr.

19 Berenbaum. 

20 DR. MAY BERENBAUM: This may be best reserved

21 for the afternoon, but just as a point of clarification, the

22 acute and chronic toxicity testing at Tier I is described in

23 discrete life stages. Is there any consideration of a way to

24 incorporate life cycle testing rather than just individual

25 discreet stage testing, sort of risk, kind of concept?

26 MR. KEITH SAPPINGTON: In Tier I, we've only
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1 described, as you say, the larval and the adult stages. The

2 larval toxicity test, as I understand, that we are evaluating is

3 one that could continue out through emergence, but at this time,

4 it's quite challenging to get acceptable responses in the

5 controls from this. 

6 But I would believe with additional

7 refinement, we would be able to perhaps capture that part of the

8 development cycle. For capturing the entire life cycle, I

9 believe those are reserved now in Tier II, where we would be

10 looking at the hive level. 

11 DR. DANIEL SCHLENK: Dr. Potter.

12 DR. THOMAS POTTER: Yes. Just a quick

13 question about the LOC values that you proposed. Would those be

14 bright line values in a sense that the value is fixed as .4 and

15 you have a chemical that is say, .41? You know, what's the

16 process there?

17 DR. THOMAS SEEGER: When a compound or an RQ

18 value exceeds an LOC, that information is relayed back to the

19 risk manager. The decision to transition to higher tier levels

20 or the degree to which the LOC is exceeded, that's a risk

21 management decision. 

22 So would you consider it a bright line? It is

23 a bright line in the sense that the LOC is fixed for screening

24 level assessment. Whether it triggers the need to do higher

25 tier assessments is a risk management decision.

26 DR. DANIEL SCHLENK: Dr. Bradbury, you want
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1 to add a little bit more to that?

2 DR. STEVEN BRADBURY: That LOC isn't provided

3 in isolation from other information. So your .41 versus .39,

4 what we want to know is what do we know about the mode of

5 action; what does the dose response curve look like? 

6 All that information that Keith's been

7 describing in the conceptual model would all come into play in

8 terms of making a decision about what the uncertainties are and

9 what would be the magnitude and the nature of the uncertainties

10 around that threshold.

11 But Dr. Steeger is right in that that LOC

12 value is a value we use to make sure we pause, let's think,

13 let's talk, let's look at all the information we got and make

14 some decisions about what the next most logical step would be.

15 DR. DANIEL SCHLENK: Okay. Dr. McManaman.

16 

17 DR. JAMES MCMANAMAN: Is it the Agency's view

18 that the LD50 is the most conservative method estimate of

19 toxicity for larvae or have you considered other possibilities?

20 DR. THOMAS STEEGER: The LD50 and the LC50 are

21 the traditional measures that the Agency relies on for screening

22 level because it's considered the most precise estimate from the

23 acute toxicity studies where the confidence limits around that

24 estimate are at their narrowest point.

25 DR. JAMES MCMANAMAN: But it may be the most

26 reliable, but you view it as the most conservative estimate,
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1 especially for this first tier of screening?

2 DR. THOMAS STEEGER: Whether it's the most

3 conservative value, I mean, certainly, there are more

4 conservative values that could be selected, but again, the

5 precision that's surrounds those, the confidence limits that

6 would be surrounding those estimates would be highly variable. 

7 At this point, that's the traditional measure that we, as a

8 regulatory authority, as well as other regulatory authorities

9 have used as a conservative measure of acute toxicity. 

10 UNIDENTIFIED SPEAKER: I wondered the same

11 thing, actually.

12 DR. DANIEL SCHLENK: Excuse me. Somebody

13 on the telephone, can you please mute your line again, please? 

14 Thank you.

15 MR. KEITH SAPPINGTON: I'd like to follow-up

16 on that question. Again, I agree that the endpoint selected

17 from the acute studies, such as the LC50 and the LD50 is the

18 most precise estimate, but we then apply this level of concern

19 to that. Dr. Steeger will be presenting the basis for that, so

20 I don't want to get into that in significant detail. Part of

21 the basis is the extrapolation down to a lower effect level, in

22 this case, a ten percent effect level. 

23 DR. DANIEL SCHLENK: Dr. Bradbury. I saw

24 your hand up. Did you want to --

25 DR. STEVEN BRADBURY: Keith beat me to the

26 punch. The concept is a dose response relationship factors into
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1 that LOC. So it's a different way to get at the concept, but I

2 think in the talks this afternoon it'll come out in more detail.

3 DR. DANIEL SCHLENK: Dr. McManaman. 

4 DR. JAMES MCMANAMAN: I understand the

5 concepts here, but whereas with adult bees I can see that it

6 might be the appropriate measurement, but I'm concerned that for

7 larvae, it may not be the appropriate measurements. So that was

8 the basis of my question.

9 DR. DANIEL SCHLENK: Okay. Dr. Pettis.

10 DR. JEFF PETTIS: Just a point of

11 clarification about, currently the White Paper is considered

12 honey bees as a surrogate for all pollinators. And there's

13 little consideration of the other life history for solitary

14 bees; is that correct?

15 DR. THOMAS STEEGER: You're correct that

16 the proposed framework is using the honey bee as the focus and

17 we are asking the SAP to comment on the extent to which it can

18 be applied to non-Apis bees as well. But those measures of

19 effect are based on individual bees which would presumably

20 relate to solitary native bees. 

21 And at higher tier assessments, you're looking

22 at colony level effects that would presumably speak to potential

23 effects to social and non-Apis bees and as will be pointed out,

24 and was already pointed out in preliminary presentations, but

25 will be further discussed by Kris Garber, the measures of

26 exposure are through diet and through contact which would
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1 presumably be relevant to ground nesting bees, solitary bees or

2 non-Apis bees, just as it is relevant to the honey bee.

3 DR. DANIEL SCHLENK: That was Dr. Steeger.

4 Any other questions about the framework?

5 (No response.)

6 Okay. So now I think we're ready for Ms.

7 Wendel, is that right? 

8 MS. CHRISTINA WENDEL: Hello. My name is

9 Christina Wendel and I'm a biologist in the Environmental Fate

10 and Effects Division of the Office of Pesticide Programs. And

11 thank you for the opportunity to address the panel. 

12 This morning I will be talking about the

13 exposure of honey bees to pesticides, the exposure routes that

14 were considered and their overall relative importance in terms

15 of major versus minor routes of exposure. 

16 Discussion of the potential exposure pathways

17 is presented in the White Paper as part of the conceptual model

18 in Section II, the Tier I exposure assessment in Section III,

19 and the drinking water consumption rate analysis is in Appendix

20 Number 2. This presentation relates to Charge Question Number

21 7.

22 I will be presenting the exposure routes

23 identified and considered in the conceptual model as presented

24 and described earlier by Mr. Sappington. I will also briefly

25 discuss and describe the rationale used in determining the

26 exposure routes identified for use in the Tier I exposure
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1 assessment, as well as those routes that were not included. 

2 Ms. Garber will follow this presentation with

3 a more in-depth analysis of our proposed Tier I exposure

4 assessment and the primary routes of exposure that were chosen

5 and quantified.

6 For the Tier I exposure assessments, three

7 types of exposure routes were considered. These are contact,

8 oral, and inhalation exposure. As identified previously,

9 exposure routes differ with the type of application, which may

10 include foliar sprays, soil applications, and seed treatments. 

11 In addition, exposure routes also differ based

12 on the type of bee. Exposures differ for larval and adult bees

13 and for the castes of the bee, which include workers, drones,

14 and queen bees. 

15 The next few slides provide visual

16 representations of exposure routes for bees based on both the

17 application methodology and the type of bee. Again, this slide

18 provides a visual representation of a honey bee and depicts the

19 potential exposure routes for forager bees for foliar spray

20 applications. 

21 For foliar spray applications, adult forager

22 worker bees have the greatest potential for exposure as they

23 leave the hive. Forager bees have direct contact with

24 folairly-applied pesticides via direct spray and contact with

25 pesticide residues on foliage and soil.

26 Forager bees also are potentially exposed to
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1 pesticide residues via the collection of pollen, nectar,

2 drinking water and plant resins or propolis that may be brought

3 back to the hive. Another route of exposure for forager bees

4 includes oral exposure through the diet from the consumption of

5 contaminated pollen, nectar and/or drinking water. 

6 It is important to note that it was assumed in

7 the Tier I exposure method that the major route of oral exposure

8 is through the actual consumption of food. However, there could

9 potentially be a dose of exposure for the forager bee from the

10 carrying transport of pollen, nectar, and/or drinking water back

11 to the hive. But we expect that the dose obtained from the

12 consumption of food would be greater and productive of any dose

13 obtained during the transport of pollen, nectar, and/or drinking

14 water to the hive. 

15 Therefore, the oral exposure obtained from

16 diet and not during the collecting phase was considered to be

17 the oral route of exposure. During flight, forager bees may

18 also be exposed via inhalation of spray droplets and/or the

19 gassiest phase of volatilized chemicals. 

20 This slide provides a visual representation of

21 the honey bee and depicts the potential exposure routes for hive

22 bees for foliar spray applications. Adult bees living within

23 the hive include workers, for example, nurse bees, as well as

24 drones and queen bees. 

25 The adult hive bees are exposed primarily

26 through the oral route of exposure from the consumption and
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1 processing of pollen and nectar into bee bread, honey, brood

2 food and/or royal jelly, or the consumption of drinking water. 

3 There is no longer contact exposure to direct

4 spray; however, contact exposure can occur within the hive

5 through the transfer of pollen and/or nectar from forager bees

6 to the hive worker bees, as well as direct contact with comb wax

7 and propolis.

8 Queen bees can also be exposed to direct spray

9 and inhalation spray of spray droplets during mating and

10 orientation flights; however, the dominate exposure route for

11 queen bees is based on the exposure route as she consumes royal

12 jelly throughout her life in the hive.

13 For larval bees, items must be brought back to

14 the hive and processed prior to larval exposure. Larval bees

15 are exposed to the ingestion of the processed pollen and nectar,

16 as royal jelly and/or brood food. As is the case for all adult

17 and larval bees within the hive, inhalation of the gassiest

18 phase of a volatilized chemical is also a potential exposure

19 route. 

20 This slide also provides a visual

21 representation of the honey bee and depicts the potential expose

22 routes for forager and hive bees for soil and seed treatment

23 applications. For soil and seed treatment applications, there

24 is no longer contact exposure to direct spray. Instead, for

25 seed treatment applications, there is potential contact exposure

26 resulting from seed coat dust abrasion. 
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1 In addition, for both adult and larval bees,

2 oral exposure through the diet remains a major route of exposure

3 for soil and seed treatment applications. For systemic

4 compounds, translocation and route uptake occurs, resulting in a

5 transfer of a chemical into the growing plant tissues and

6 exudates, including pollen, nectar, as well as drinking water.

7 Although the magnitude of exposure may be

8 different between forager bees and hive bees, there is the

9 potential for contaminates to be brought back to the hive and

10 transferred to the bees living within the hive, based on

11 grooming, the transfer of the collected items to the hive bees,

12 the processing of pollen and/or nectar into bee bread, honey,

13 brood food and/or royal jelly, the ingestion of the processed

14 pollen and nectar products, as well as direct contact with comb

15 wax and/or propolis. 

16 Again, for both adult and larval bees,

17 inhalation is a potential exposure route in the hive from

18 exposure to the gassiest phase of a volatilized chemical. 

19 During the Tier I exposure assessment, the

20 exposure routes presented within the conceptual models for all

21 application methodologies and chemical classes, including

22 systemic and non-systemic compounds were examined and

23 considered. 

24 Based on our review of the available

25 information, the major routes of exposure of bees to pesticides

26 are contact from direct spray for foliar spray application and
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1 oral exposure from the ingestion of the contaminated pollen and

2 nectar for all pesticide application methods. 

3 The primary routes of exposure are shown here

4 in green. These two routes of exposure are quantified in the

5 Tier I exposure assessment by generating estimated environmental

6 exposure concentrations, EECs. The process used to validate the

7 Tier I exposure method will be discussed in the next

8 presentation.

9 For contact exposure, direct spray is the most

10 conservative exposure route for forager bees and covers the

11 other potential contact exposure routes shown here in blue,

12 including contact with contaminated foliage, soil, comb wax,

13 propolis and/or pollen. 

14 For oral exposure, the ingestion of

15 contaminated pollen and/or nectar are assumed to be protective

16 of other food consumed by bees, shown here in blue, and include

17 honey, bee-bread, royal jelly, and brood food.

18 Contact exposure via dust, oral exposure

19 resulting from consumption of contaminated drinking water and

20 inhalation exposure from spray droplets and/or the gassiest

21 phase of a volatilized chemical, shown here in red, were

22 considered and evaluated separately and will be discussed in the

23 next several slides.

24 Contact exposure from abraded dust as a result

25 of seed treatment application is a potential major route of

26 exposure to bees. Numerous incidents related to abraded dust
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1 from seed treatment applications have been reported in North

2 America. As a result, the agencies are actively working with

3 registrants, growers, applicators and seed equipment

4 manufacturers to minimize this exposure pathway. Different

5 technologies, stewardship programs and a variety of different

6 mitigation measures, including label language, have been

7 developed and research in these areas continues. 

8 Mitigation measures have been identified,

9 including BMPs, new seed coat technologies, and alternative low

10 dust lubricating agents to replace high dust producing

11 lubricants like talc and graphite. In addition, best management

12 practices are being considered and developed. A review of these

13 processes will continue to occur as these technologies and

14 practices advance over time. 

15 Although dust is considered to be a major

16 exposure route, efforts are actively being made to minimize this

17 route of exposure to bees. As a result, exposure to dust was

18 not included in the Tier I screen for all pesticides. This

19 issue is associated only with seed treatment applications and as

20 a result, will be dealt with on a case-by-case basis for

21 relevant chemical and application types.

22 Water is collected for two primary reasons. 

23 First, to regulate the temperature within the hive through

24 evaporative cooling to maintain conditions for healthy brood,

25 and second, to use in preparing larval brood food by nurse bees.

26
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1 Water is not stored within the hive; rather,

2 it must be collected by foraging bees when needed by the colony.

3 To reiterate, it is assumed in the Tier I Exposure Method that

4 the major route of oral exposure is through the actual

5 consumption of food, recognizing that there could potentially be

6 a dose of exposure for the forager bee from the carrying and

7 transport of the pollen nectar and/or drinking water back to the

8 hive. But again, we expect that the dose obtained through the

9 consumption of food would be greater and protective of any dose

10 obtained through the transport of these materials. 

11 EPA completed an analysis to estimate

12 potential exposure doses bees could receive from consuming

13 contaminated drinking water. This analysis is presented in

14 detail in Appendix 2 of the White Paper. Based on estimated

15 pesticide concentrations in various sources of water potentially

16 consumed by bees and water flux rate from the brown paper wasp,

17 the results of this analysis indicate that if bees consume the

18 majority of their water from ponds, including water adjacent to

19 treated fields or puddles of water located on treated fields,

20 the exposure is relative to dietary and direct spray are

21 discountable.

22 However, if bees drink a substantial amount of

23 water from guttation fluid found in crops, or dew, the

24 condensation on the plant, with exposure from dew occurring on

25 crops folairly sprayed with pesticides, conservative exposures

26 may be similar to or even exceed pesticide exposures through the
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1 diet or direct spray. Given the potential concern associated

2 with exposure through drinking dew and guttation fluid, these

3 exposure pathways were investigated further.

4 Based on the results of EPA's analysis

5 presented in Appendix 2 of the White Paper, pesticide exposures

6 through dew and guttation fluid are not expected to be as

7 significant when compared to the diet and direct spray because

8 of two primary reasons. First, the relative importance of dew

9 and guttation fluid as a source of drinking water to bees is

10 unknown. In addition, dew and guttation fluid are expected to

11 be present only during a portion of the day, typically in the

12 morning, with a very conservative maximum of six hours overlap

13 in foraging times of bees and the presence of dew and/or

14 guttation fluid, preventing bees from drinking a substantial

15 amount of water from these sources.

16 Secondly, for many worker bees, pesticide

17 doses, through the consumption of contaminated dew and/or

18 guttation fluid, may be much smaller due to lower or

19 non-existent drinking water consumption rates when compared to

20 the percentage of water consumed from food. 

21 As shown in this slide, the estimated food

22 consumption rates and resulting percentage of water consumed as

23 part of food, including honey or nectar for adult worker and

24 forager bees, ranges from 7 percent to greater than 100 percent

25 of their daily water requirement. And again, these calculations

26 are presented in Appendix 2 of the White Paper. 
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1 Therefore, for many bees, pesticide exposure,

2 through the consumption of either dew and/or guttation fluid is

3 expected to be minimal because of higher amounts of water are

4 likely consumed through food rather than from drinking water. 

5 Therefore, based on this analysis, pesticide exposures, through

6 drinking water, was not included in the proposed Tier I exposure

7 method for bees. 

8 Inhalation is a relevant exposure pathway for

9 only highly volatile chemicals, including fumigants applied via

10 foliar or soil application, where hives are in close proximity

11 to the treated site. It is not a relevant exposure route for

12 seed treatment applications. 

13 Based on a preliminary analysis conducted

14 after the White Paper was developed, it was determined that

15 exposure to bees via the inhalation route is substantially lower

16 than the dietary exposure routes. Therefore, the inhalation of

17 exposure pathway was not included as an exposure route in the

18 proposed Tier I Exposure Method for bees.

19 In summary, the primary routes of exposure

20 that are quantified in the proposed Tier I method include

21 contact resulting from direct spray and oral exposure from the

22 dietary consumption of pollen and nectar. Although there are

23 other potential exposure routes, these were evaluated and found

24 to be minor in comparison to the direct contact and oral routes.

25

26 Therefore, these other routes of exposure were
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1 not included within the proposed Tier I Exposure Assessment. We

2 are seeking the input from the SAP on the proposed contact and

3 oral exposure routes included in the Tier I Exposure Method, as

4 well as those exposure routes that were not included in the Tier

5 I Exposure Method for Bees. Thank you for time. The team and I

6 can address any questions you may have.

7 DR. DANIEL SCHLENK: Thank you. Any

8 questions? Dr. James.

9 DR. ROSALIND JAMES: Thank you. I thought the

10 analysis you did was very good, but I have one question. You

11 don't seem to consider Layer I, discussing about doing the

12 bioassays for Tier I test, combining the oral and dermal

13 together, whereas most bees are going to get both an oral

14 exposure and the contact exposure. What's the reason for that? 

15 Or did I misunderstand that?

16 MS. KRISTINA GARBER: You didn't misunderstand

17 that. We're in the proposed process as it's depicted in the

18 White Paper. We would just treat those two routes separately. 

19 Potentially, we could add risk quotients.

20 So one approach could be to add risk quotients

21 of the two pathways and then compare them to an LOC. But we are

22 in the proposed process of treating them separately and the test

23 would be conducted separately as well.

24 DR. THOMAS SEEGER: I would like to add to

25 Chris's comment. The in vitro assay that's been proposed for

26 larval toxicity testing, those larvae are being exposed sort of
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1 as a combination of both oral and contact exposure. So in a way

2 that concern is accounted for by that assay.

3 DR. DANIEL SCHLENK: Okay. Dr. Tarpy.

4 DR. DAVID TARPY: You had two main routes of

5 water that bees would forage on, one is ponds or kind of runoff,

6 and the other would be dew. Where would things like irrigation

7 systems that are often used and bees use as an important source

8 of water that are also conduits for pesticide treatment, where

9 would that fall among those two routes of exposure?

10 MS. KRISTINA GARBER: I think we weren't

11 actually accounting for the irrigation or even direct

12 applications to irrigation canals. That really doesn't fit into

13 the concept of the pond. If it's a direct application or a

14 chemigation application, perhaps, if that's what -- okay. 

15 You're nodding "yes." 

16 I think that, you know, one way to deal with

17 that could potentially be to take the labeled application rate

18 as it appears, you know, for the chemigation application and use

19 the drinking water ingestion rates that we've got in the paper

20 and see if the assumptions that we've got presented here still

21 hold. And if so, we could proceed as usual. But this kind of

22 speaks to what Keith was talking about earlier about how our

23 conceptual diagrams that we're presenting are kind of based on a

24 suite of application methods that are pretty typical of

25 agriculture. 

26 In this case, I mean, chemigation is a typical
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1 application as well. We could modify that conceptual model to

2 account for this other scenario.

3 DR. DANIEL SCHLENK: Dr. Pistorius.

4 DR. JENS PISTORIUS: I have a question. You

5 said the effective quantitative screening tool is not currently

6 available for dust. If such methods are available, could you

7 imagine incorporating those in the risk assessment scheme? I

8 hope this is appropriate to ask, how long do you think it would

9 take?

10 The second is it is a technical issue also. 

11 So it is a question of risk assessment and risk management. If

12 such risk management measures would be possible, do you think it

13 is appropriate to take it out from the risk assessment

14 completely?

15 DR. THOMAS STEEGER: The issue of dust,

16 abraded dust from treated seed coat, as we understand it,

17 requires a number of factors. And the difficulty in quantifying

18 the level of dust that could be generated that would allow us to

19 predict it on a regular basis, we don't know if at this time

20 such a method of prediction is possible. 

21 If one were developed, then yes, I think we

22 would consider it, but at this point we're hoping that through

23 best management practices, the likelihood of abraded dust can be

24 reduced such that it won't represent major route of exposure. 

25 But clearly there are incidents that suggest -- as was evidenced

26 in Germany in 2008 -- that abraded dust can serve as a source of
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1 exposure, given an alignment of factors that influenced the

2 generation and movement of those dust from treated seed.

3 The issue of management, as Mr. Sappington

4 portrayed throughout the movement of the framework, risk

5 assessment or risk management options are considered throughout

6 the process. And if we can discount potential exposure through

7 mitigation, that would be factored into the assessment. 

8 That's why the importance of maintaining close

9 communication with the risk manager from the very start of the

10 assessment process would enable us to better account for

11 mitigation options that might be considered.

12 DR. DANIEL SCHLENK: Do you have a follow

13 up with that Mr. Pistorius?

14 MR. JENS PISTORIUS: Just a second question. 

15 With the risk mitigation measures, this would also maybe be

16 possible to imply, for instance, if there's a risk for close set

17 up of hives near a field where there is guttation very likely. 

18 Would it be possible to make a link between risk assessment and

19 risk mitigation measures there and apply with mitigation

20 measures?

21 DR. THOMAS STEEGER: The regulating pesticides

22 at a nation level to account for the position of colonies near

23 crops that may be producing guttation fluid, we still -- and we

24 welcome panel input on this -- we're still uncertain as to the

25 degree that bees would be attracted to plants when guttation

26 fluids are actually being expressed from plants.
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1 If we had a better understanding of that route

2 of exposure, I think we would be in a better position to

3 consider whether it should be included as a routine route of

4 exposure and whether mitigation could be consistently applied to

5 that as a way of resolving it. 

6 DR. DANIEL SCHLENK: Dr. Potter.

7 DR. THOMAS POTTER: I have two questions, if I

8 may. The first has to do with the -- it's a question and a

9 comment and it has to do with the drinking water exposure. 

10 In reading through Appendix 2, I was struck by

11 what appears to be a considerable uncertainty in terms of the

12 quantity of water that a bee may consume during the day. I

13 looked at your assessment; perhaps that's all the information

14 that's available. Not being a bee expert, I can't comment on

15 that one way or the other. 

16 I will note, in that context of drinking water

17 exposure that I looked at your calculations with regard to

18 concentrations of pesticides in puddles. I think there was an

19 error in units in application of the formula that you used, that

20 you need to take a careful look at. 

21 My question connected to that is in looking at

22 the possibility of pesticide contamination of infield puddles,

23 did you look back at the work that was done in establishing the

24 ECO Framework? It was about 10 years ago that ECOFRAM was on

25 the scene.

26 Buried in there, and apparently the
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1 information is still available on the website, there is a very

2 nice little model that was developed to actually look at the

3 possibility of residues accumulating in puddles infield. I

4 think that that might be something that you'd want to take a

5 look at.

6 So my question for you is, did you look at

7 that ECO Framework at all in the context of your puddle

8 assessment?

9 MS. KRISTINA GARBER: No. We didn't look at

10 the ECOFRAM report specifically for this method, but we can

11 certainly do that. Thank you.

12 DR. THOMAS POTTER: My second question is with

13 regard to dust exposure, did you consider any other dust in the

14 bees' environment that may contain pesticide residues? And this

15 could include windblown soil, dust that is generated from soil

16 or sprays that have been applied. It could indeed include a

17 dust application or formulation that contains the active

18 ingredient. 

19 My question for you is, what other forms of

20 dust in the environment were looked at other than the

21 descriptions you made about dust that may have been generated

22 from abraded seed?

23 DR. THOMAS STEEGER: The dust that was

24 considered was that that was abraded from treated seed because

25 it would be the most concentrated form of dust where the active

26 ingredient would be present. So the dust that's potentially
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1 contributed through the movement of seeding equipment,

2 disrupting soil or any type of tillage was not considered in our

3 assessment. Again, the concentration, that would be considered

4 from dust as a result of abraded seed coat would expected to be

5 higher. 

6 DR. THOMAS POTTER: I agree it would be

7 higher, but there may be some exposure there. We'll get into

8 that, I think, when we talk about some of the questions. At

9 least from my rationale, I think it might be worth taking a look

10 at.

11 DR. DANIEL SCHLENK: Any other questions?

12 (No response.)

13 Okay. According to my schedule and agenda, we

14 have a lunch break scheduled. So let's go ahead and do that and

15 be back at 12:45 and we'll resume at that time. Thanks.

16 (Whereupon, at 11:41 a.m., a 

17 luncheon recess was taken.)

18 * * * * *

19 A F T E R N O O N S E S S I O N

20 (12:50 p.m.)

21 DR. DANIEL SCHLENK: Now that our permanent

22 panel members are here, we can kind of go forward. Let's go

23 ahead and get started.

24 Before we get started, I believe you have some

25 answers to Dr. Potter's question.

26 MS. KRISTINA GARBER: Thank you. Dr. Potter,
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1 I did a little research on my lunch break and I just wanted to

2 get back to you on the puddle comments that you had made.

3 So first off, you had something about a unit

4 error. And I was just hoping that you could note that in your

5 written comments so that we can make sure we go back and take a

6 look at that. 

7 DR. THOMAS POTTER: I absolutely intend to

8 comment on that. I just wanted to point that out, you know, I

9 went back and ran the numbers and got something different. So I

10 will certainly put it in the record.

11 MS. KRISTINA GARBER: Thank you. And then the

12 other thing related to the ECOFRAM approach, it's my

13 understanding that that's kind of based on a modification of the

14 prism model. So we did actually consider that in the

15 development of the model that we presented for estimating

16 pesticide concentrations in puddles. 

17 And we chose to go with the model that we did

18 just simply because it was a more simple approach, you know,

19 using the simple equilibrium-based approach, it indicated that

20 concentrations in puddle weren't a problem. So we didn't see

21 the need to move further to a more refined approach. That's

22 basically why we chose to proceed the way that we did. 

23 DR. DANIEL SCHLENK: Thanks. Okay. I

24 guess you're up, Kristina. So go ahead and lead us on our next

25 presentation, please.

26 MS. KRISTINA GARBER: Great. Thank you, Dr.
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1 Schlenk. Thank you, members of the panel for allowing me to

2 present on the Tier I Method for Estimating Exposure of Honey

3 Bees to Pesticides. As a formal introduction, I'm senior

4 biologist in the Environmental Fate and Effects Division of the

5 Office of Pesticide Programs. 

6 Now, the material that I'm going to cover

7 today is included in Section III in Appendix 1 of the White

8 Paper. This material is relevant to Charge Questions 3, 4, 5,

9 and 6.

10 In this presentation, I'll give a brief

11 overview of the Tier I Exposure Assessment Methodology,

12 including a description of the purpose of a Tier I assessment,

13 as well as a discussion of where this method fits into the

14 decision framework that was presented earlier by Keith

15 Sappington. 

16 As Christina Wendel discussed earlier, dietary

17 exposure is a major exposure route of pesticides for honey bees.

18 So in order to estimate pesticide exposures through the diet,

19 it's necessary to understand what honey bees eat and how much. 

20 In this presentation I'll describe the

21 proposed food consumption rates for worker larvae and for

22 adults. I'll also discuss how conservative these consumption

23 rates are when compared to other honey bees, as well as other

24 species of bees.

25 I'll then move on to describe the proposed

26 method for estimating pesticide exposure from contact that
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1 results from foliar applications as well as the methods for

2 estimating pesticide concentrations in pollen and nectar,

3 following applications that are made via foliar spray, seed

4 treatment and soil applications. 

5 These concentrations in pollen and nectar are

6 used in combination with the proposed food consumption rates for

7 adults and for larval worker bees in order to estimate doses

8 that are received by these two groups of bees. When I'm saying

9 "bees" here, I mean honey bees.

10 Now I'll give a brief overview of the Tier I

11 Exposure Assessment. Now, the goal of the Tier I Method is to

12 generate reasonably conservative estimates of pesticide

13 exposures to bees. This method is intended to estimate upper

14 bound concentrations that are comparable to an upper 95th

15 percentile exposure for honey bees.

16 When evaluating whether a method generates

17 reasonably conservative estimates of exposure, estimates should

18 preferably be higher and generally be within one or two orders

19 of magnitude of available exposure data, empirical exposure

20 data. 

21 The Tier I Exposure Assessment Method is

22 designed to allow risk assessors to confidently distinguish

23 between those pesticides that do not pose a risk to honey bees

24 and those that require further consideration. In this approach,

25 we attempt to minimize Type II errors, which would involve

26 concluding that there is no risk when there actually is a
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1 potential for effects. And at the Tier I level, Type I errors

2 are more acceptable because additional refinements can always be

3 made at higher tiers of the risk assessment. 

4 Now, this figure is a subset of the decision

5 framework for foliar spray applications that was presented

6 earlier today by Keith Sappington. And the focus of this

7 particular slide is on the Tier I Exposure Assessment Method. 

8 As depicted in Boxes 1 and 2 of this figure,

9 once the use information for a pesticide product is determined,

10 the risk assessor would determine whether or not there's a

11 potential exposure for adults or for brood to the pesticide. In

12 making this determination, the risk assessor would need to

13 consider various factors, such as the potential overlap of the

14 pesticide application and the presence of honey bees on the

15 field, the attractiveness of the crop to honey bees or plants

16 that may be present in areas adjacent to the field, such as

17 dandelions.

18 If there is the potential for exposure, the

19 risk assessor moves on to boxes 3 a, b, and c, which is the

20 focus of this particular method. These involve estimation of

21 exposures to adults and to larvae. These three boxes are the

22 focus of the Tier I Exposure Assessment Methodology. For foliar

23 spray applications, exposures are assessed separately for

24 contact and for diet. 

25 As depicted in boxes 4 a, b, and c, estimated

26 exposure values should be matched to their respective toxicity
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1 endpoints from contact and oral tests for adult honey bees and

2 larvae to derive risk quotients.

3 Tier I toxicity endpoints are expressed on a

4 dose basis in units of micrograms active ingredient, or AI, per

5 bee. Therefore, all exposure values are expressed on a dose

6 basis. Boxes 4 b and c indicate that both acute and chronic

7 risk quotients are calculated in the Tier I risk assessment. 

8 Both acute and chronic exposure estimates are represented by the

9 highest single days' dose. 

10 Although a time-weighted estimated exposure

11 value may be more representative of the exposure period in a

12 chronic toxicity test, exposure occurring over a single day

13 could potentially be sufficient to elicit effects. Therefore,

14 in the Tier I approach, chronic exposure is conservatively

15 represented by the highest single day estimated exposure. 

16 This figure is a subset of the decision

17 framework for seed and soil treatments, again, with a focus on

18 the Tier I Exposure Assessment Method. I'm not going to go into

19 detail on this figure because it's very similar to the one I

20 just discussed for foliar applications. But the one notable

21 difference here is that there's no contact exposure that's

22 included in this decision tree. So the major exposure route

23 incorporated into the Tier I Exposure Assessment Method for soil

24 and seed treatments is through the diet.

25 Now I'll move on to discuss the honey bee food

26 consumption analysis. And this is covered in detail in Appendix
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1 1 of the White Paper. The basis of the honey bee diet is pollen

2 and nectar. Honey bees will consume pollen and nectar directly

3 or they'll eat bee bread and honey, which are forms of pollen

4 and nectar that are processed for storage in a hive. 

5 In addition, honey bees consume royal jelly

6 and brood food, which are composed primarily of glandular

7 secretions that are produced by nurse bees. The amount of food

8 and specific consumption of a honey bees' diet varies by caste

9 and by age. The diet of a worker bee changes over the course of

10 its life to meet the energy requirements of its task which

11 change with age.

12 For instance, nurse bees consume the most

13 pollen, compared to other worker bees, so that they can produce

14 jelly to feed larvae and the queen. Foraging bees consume the

15 most honey and nectar in order to meet the energy demands of

16 flying. 

17 Because the proposed risk assessment method

18 involves conducting separate risk assessments for brood and for

19 adults at the Tier I level, separate food consumption rates for

20 larvae and for adults are necessary to calculate the different

21 pesticide exposures through the diet. 

22 The proposed food consumption rates for the

23 Tier I Exposure Assessment are 292 milligrams of food per day

24 for adults and 120 milligrams of food per day for larvae. As I

25 indicated earlier, the Tier I Exposure Assessment is intended to

26 produce reasonably conservative estimates of exposure. 
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1 Therefore, the proposed food consumption rates

2 are based on portions of the adult and larval life stages where

3 honey bees are expected to be consuming the greatest amounts of

4 food, and would therefore be expected to receive the highest

5 pesticide exposures through the diet, when compared to honey

6 bees at different ages. 

7 So for adults, the nectar forager bees consume

8 the most food and for larvae, it's individuals that are aged

9 five days. There are several assumptions related to the

10 proposed food consumption rates. In this approach, it is

11 assumed that exposures through consumption of nectar and pollen

12 are conservative representations of potential exposures through

13 consumption of honey and bee bread. 

14 This approach is likely to be conservative

15 because it assumes that pesticides contained in honey and bee

16 bread do not degrade while these foods are stored in the hive. 

17 For honey bees that consume honey, it's

18 assumed that the estimated pesticide exposures can be related

19 back to the original concentration of the pesticide and nectar. 

20 This is accomplished by accounting for the amount of sugar

21 that's consumed by bees and the amounts of sugar that are

22 present in honey and in nectar. This is necessary since

23 pesticide concentrations are estimated in nectar of treated

24 crops. 

25 Using this approach, the pesticide dose

26 received by a honey bee consuming nectar would be a equivalent
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1 to a dose received by a honey bee consuming honey from the same

2 source of nectar. It's also assumed that pollen and nectar

3 consumption rates and resulting exposures are protective of

4 exposures of honey bees, the pesticides through consumption of

5 royal jelly and brood food. This is based on results from two

6 studies that have observed concentrations of pesticides in royal

7 jelly that were 100 times less than the food that was consumed

8 by the nurse bees that produce that jelly.

9 Finally, for the Tier I Exposure Assessment

10 Method, we're proposing to use the estimated pesticide

11 concentrations in or on crop foliage as a surrogate for

12 pesticide concentrations in nectar and in pollen. 

13 So essentially, the pesticide concentration in

14 nectar is treated in the Tier I method as being equivalent to

15 the concentration in pollen. Therefore, for the Tier I Exposure

16 Assessment Method, the proposed food consumption rates are based

17 on consumption of pollen plus nectar. So I'll express that as

18 total food consumption. 

19 In order to indentify the group of adult

20 worker bees that have the highest food consumption rates, pollen

21 and nectar consumption information were identified in the

22 scientific literature. Now, this table provides food

23 consumption rates for different adult worker bees, as identified

24 by their task. 

25 The age range associated with each task is

26 intended to be general and basically reflects the task that a
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1 worker will carry out at different ages of her life, with

2 in-hive tasks being carried out by younger bees and foraging

3 tasks that occur outside of the hive being carried out by older

4 worker bees. 

5 Pollen consumption rates of worker honey bees

6 are based on direct empirical measures published by Crailsheim,

7 et al. in 1992. Nectar consumption rates are based on a

8 commonly cited body of work published by Rortais, et al. in

9 2005. 

10 These authors estimated sugar consumption

11 rates of different types of worker honey bees based on energy

12 requirement that were associated with the bees' specific tasks. 

13 The sugar consumption rates published by Rortais, et al. are

14 converted here to nectar equivalence by accounting for the

15 average sugar content of nectar, which is 30 percent. As you

16 can see by this table, pollen consumption rates vary among bees,

17 but are at least one order of magnitude below nectar consumption

18 rates of the same bees. 

19 When we compare the total daily food

20 consumption rates among the different worker bees, the nectar

21 foraging bees had the highest food consumption rates. As you

22 can see by this table, the majority of food consumption of this

23 group of bees is represented by nectar, since the pollen

24 consumption rate is four orders of magnitude lower.

25 Since the food consumption rate has a large

26 influence on the final estimated dose received by honey bees and
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1 for the nectar forager, this food consumption rate is

2 represented by nectar consumption. We did a detailed review of

3 the analysis conducted by Rortais, et al. for the nectar forager

4 bee. 

5 According to the method published by Rortais,

6 et al., the nectar consumption rate of the nectar foraging bee,

7 as estimated, using five variables, including the sugar required

8 for flying, the amount of sugar that's required for flying. The

9 number of foraging trips that are made in one day, the duration

10 of a single foraging trip, the fraction of time spent flying

11 during a trip, and the amount of sugar present in nectar. 

12 One limitation to the approach is that there's

13 a great deal of variability in these parameters. Also, the

14 approach presented by Rortais, et al. did not account for the

15 sugar requirements of bees while at rest. 

16 Because there's so much variability within

17 each of these parameters, we conducted a distributional analysis

18 using a Monte Carlo simulation that simultaneously accounted for

19 variability in all five parameters. We also accounted for the

20 resting metabolic requirements of nectar foragers. 

21 The results of the Monte Carlo simulation are

22 depicted in this figure. The proposed food consumption rate for

23 adult worker bees in 292 milligrams of food per day. This value

24 is based on the median estimate of nectar consumption of nectar

25 foraging bees in the simulation that was conducted. 

26 The median value is selected for two primary
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1 reasons. First, the median is considered most likely to

2 represent food consumption of this group of bees. Second, use

3 of the median avoids compounding conservative assumptions in the

4 overall Tier I Exposure Method. 

5 The proposed food consumption value already

6 targets the group of bees with the highest food consumption

7 rates compared to other adult bees. As I indicated earlier, the

8 goal of the Tier I Exposure Assessment Method is to estimate

9 reasonably conservative exposures to bees. 

10 Now, the target of the reasonably conservative

11 estimate is approximately the upper 95th percentile, and also

12 I'll discuss later, the estimated pesticide concentrations in

13 pollen and nectar represent an upper bound. 

14 If an upper bound food consumption rate for

15 nectar foragers was also proposed, the final dietary dose would

16 most likely exceed the desired 95th percentile for worker bees

17 due to a combination of a upper bound estimate of residues and

18 food with an upper bound estimate of food consumption. 

19 Next, I'd like to discuss the relative

20 conservativeness of the proposed food consumption rate for adult

21 bees when compared to other worker honey bees and drones, as

22 well as other species of bees. This figure depicts the nectar

23 and pollen consumption rates of different adult worker honey

24 bees and drones. 

25 As you can see by this figure, the worker bee

26 that forages for nectar has the highest food consumption rate
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1 among adult worker bees and drones, even when considering nectar

2 and pollen consumption rates. 

3 Nectar foraging bees are obviously summer

4 bees. Winter bees that maintain the functions of the hive have

5 different energy requirements. The nectar forager food

6 consumption rate is higher than the food consumption rate of

7 winter workers, indicating that it is also conservative for

8 winter bees. 

9 These data are presented in Appendix 1 of the

10 White Paper. In some cases, ranges of food consumption rates

11 were presented in this appendix. In this figure, the midpoints

12 of those ranges are presented. This is worth noting here

13 because the high-end estimate of nectar consumption of flying

14 adult drones actually exceeds the proposed nectar consumption

15 rate of nectar foraging workers; however, the low-end of the

16 drone's nectar consumption rate is below the proposed value. 

17 Therefore, the proposed food consumption rate

18 for the nectar forager is still considered to be representative

19 for drones. One limitation to this analysis is that data were

20 not located to estimate the food consumption rates of adult

21 queens. And this represents a data gap. 

22 However, given that queens primarily consume

23 royal jelly and that available data suggests that this food will

24 contain at least 100 times lower pesticide concentrations,

25 compared to the food that's consumed by nurse bees, the food

26 consumption rate of queens would have to be at least 100 times
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1 greater than the proposed food consumption rate for the nectar

2 forager in order for the queen to receive a higher pesticide

3 dose through the diet. Although the queen likely consumes more

4 food than other honey bees, it seems unlikely that she would

5 consume two orders of magnitude more food. 

6 In summary, it appears that the proposed food

7 consumption rate is conservative for other adult worker honey

8 bees, as well as drones and queens. Before moving on, it's

9 worth noting here that this talk is focused on the food

10 consumption rates for the Tier I Exposure Assessment. In some

11 cases, the risk assessor may choose to use different food

12 consumption rates than the proposed value. 

13 For instance, if chemical-specific pesticide

14 concentration data are available for pollen and nectar, the risk

15 assessor may want to use the pollen and nectar consumption rates

16 separately. In addition, the food consumption rates that are

17 depicted here for different worker bees could also be used for

18 colony level modeling. 

19 As I noted previously, honey bees also consume

20 honey and that we're calculating food consumption rates on a

21 nectar-equivalent basis. When we consider the relative food

22 consumption rates among worker bees, when honey and pollen

23 consumption is considered, the nectar forager is still the most

24 conservative among adult bees.

25 We also compared the proposed food consumption

26 rate for adult worker honey bees to available food consumption
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1 rates for three other species of bees, including the bumblebee,

2 the European mason bee and the alfalfa leaf-cutting bee. 

3 These data suggest that the proposed food

4 consumption rate for adult honey bee workers is similar to that

5 of the adult bumblebee and is greater than that of the European

6 mason bee and the alfalfa leaf-cutting bees. 

7 This information suggests that the proposed

8 food consumption rate for the Tier I Exposure Assessment for

9 adult honey bees would be representative and somewhat protective

10 for adults of these three non-Apis species. Please note that

11 this information is discussed in Section 5.3 of the White Paper

12 if you'd like any additional details. 

13 As with adult worker bees, the intention of

14 the food consumption rate for honey bee larvae is to focus on

15 the age of larvae that consume the most food compared to other

16 ages. Over the course of the five-day unkempt period, the diet

17 of larvae changes. During the first three days they eat brood

18 food and royal jelly. During the fourth and fifth days, they

19 eat honey and pollen.

20 Worker larvae grow at an exponential rate over

21 the course of their five-day developmental period. It's assumed

22 that their food consumption rate doubles each day in order to

23 meet the metabolic requirements of growth. The food consumption

24 rates of larvae are based on information cited by Rortais, et

25 al. that indicated that larvae consumed 120 milligrams of food

26 over the course of the fourth and fifth days.
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1 The pollen consumption rates are based on

2 empirical measures of pollen contained in the guts of larvae. 

3 It is assumed that the remainder of the 120 milligrams of food

4 consumed over days four and five is honey that is diluted with

5 sugar -- with water, to 45 percent sugar. Based on the

6 assumption that the food consumption rate of larvae doubles

7 every day, the food consumption rates were calculated as

8 depicted in this table. 

9 The proposed food consumption rate of 120

10 milligrams of food per day is based on the food consumption of

11 the larvae at Day 5. This value represents consumption of 2.7

12 milligrams of pollen per day and 117 milligrams of nectar per

13 day. In this case, the amount of honey consumed is converted to

14 nectar equivalence, assuming that the sugar content of nectar is

15 30 percent and the sugar content of honey, which is diluted with

16 water, is 45 percent. 

17 Next, I'd like to discuss the relative

18 conservativeness of the proposed food consumption rate for

19 worker larvae when compared to other castes of honey bees and

20 then to other species of bees. This figure depicts the total

21 food consumption rates of worker larvae at different days of

22 this life stage. 

23 Please note that at on the X-axis, the caste

24 of the larvae is provided along with the day of development in

25 parentheses. The Y-axis represents food consumption rates where

26 food is represented by royal jelly or brood food, honey, and
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1 pollen.

2 This figure includes the food consumption

3 rates for drone larvae at days 5 and 6. The food consumption

4 rate for the five-day-old worker larvae appears to be consistent

5 with that of the drone larvae. It should be noted here that the

6 pollen consumption rates of drone larvae are unknown. In this

7 figure, it's assumed that they are equal to the pollen

8 consumption rates of workers at day 4 and 5. 

9 Given that pollen consumption rates are

10 generally much lower than honey consumption rates, this

11 assumption is unlikely to impact the conclusion that the

12 five-day worker larval consumption rate of food is consistent

13 with that of drone larvae. 

14 This figure includes the food consumption

15 rates for queen larvae at days 1 through 5. These food

16 consumption rates are calculated by assuming that queen larvae

17 consumed 2.5 times the amount of food that's consumed by worker

18 larvae at the same age. Therefore, the food consumption rate of

19 queen larvae at day 5 is higher than that of the worker larvae

20 at day 5.

21 When we convert the honey consumption rates of

22 worker and drone larvae to a nectar equivalent basis, the

23 five-day worker larvae value is still less than the five-day

24 queen larval value for consumption of royal jelly. This is a

25 concern because we don't want to have exposure values that pass

26 screens, but the colony may be impacted due to elevated
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1 pesticide doses received by the queen. This is a case where a

2 Type II error could occur.

3 Although the queen larvae consume more food

4 compared to worker larvae at day 5, the two types of larvae are

5 consuming different types of food. As I noted earlier, we have

6 data available from, two different studies, involving pesticides

7 that indicated that pesticide concentrations and royal jelly are

8 at least 100 times lower compared to the food fed to nurse bees.

9 On the next slide I will illustrate the impact of diet and

10 relative concentrations on the different foods. 

11 This figure depicts the relative doses

12 received by larvae through consumption of royal jelly or brood

13 food at a concentration of one microgram active ingredient per

14 kilogram and pollen and nectar at a concentration that's 100

15 times greater. 

16 Although the consumption rate of queen larvae

17 at day 5 is higher than worker larvae by a factor of 2.5, the

18 overall dose received by the queen larvae is less than the

19 worker larvae because the pesticide concentration in the queen

20 larvae's food is so much lower. 

21 In summary, the proposed food consumption rate

22 for the Tier I Exposure Assessment for larval honey bees appears

23 to be conservative compared to other castes of honey bees. We

24 also compared the proposed food consumption rate for larval

25 honey bees -- which is depicted in the top row of this table --

26 to available food consumption rates for larvae of other species
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1 of bees, including the bumblebee, the European mason bee and the

2 alfalfa leaf-cutting bee.

3 These data suggest that the proposed food

4 consumption rate for larval worker honey bees is representative

5 or even protective compared to food consumption rates of these

6 three species of bees. 

7 To summarize, the food consumption rate

8 analysis, the proposed method assumes that at the Tier I level,

9 food consumption can be based on nectar and pollen. This

10 assumes that honey bees are either eating these foods directly

11 or that they're eating stored forms of these foods. 

12 It is assumed that pesticide concentrations in

13 pollen and nectar are equal. This also assumes that pesticide

14 concentrations in brood food and royal jelly are less than those

15 in nectar and pollen. The proposed food consumption rate for

16 adult honey bees is 292 milligrams per day, based on the nectar

17 foraging bee. The proposed larval food consumption rate of 120

18 milligrams per day is based on five-day old larvae. 

19 Compared to other castes of honey bees and

20 three other species of bees, these values appear to be

21 conservative. With Charge Question 5, EPA, PMRA and Cal DPR are

22 seeking feedback from the SAP on the proposed food consumption

23 rates for the Tier I Assessment, including consideration of the

24 calculations behind these values, their assumptions, their

25 strengths and limitations, and their conservativeness relative

26 to other castes of honey bees. 
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1 In Charge Question 3, we are interested in

2 feedback from the SAP on the extent to which the honey bee can

3 be a surrogate for assessing risk to other species of bees. 

4 We're interested in feedback on the extent to which the

5 available food consumption rates indicate that dietary doses

6 predicted for honey bees at the Tier I assessment level will be

7 protective for other species of bees.

8 Now I'll move on to discuss the proposed Tier

9 I Exposure Assessment Methods for estimating pesticide doses

10 received by honey bees through contact or dietary exposure. The

11 proposed contact dose for foliar spray applications is based on

12 the maximum dose on forager honey bees from a study published by

13 Koch and Weisser in 1997. 

14 Dietary exposure is assessed for foliar spray,

15 seed treatments and soil applications. All three application

16 types have different methods for estimating pesticide

17 concentrations in pollen and nectar which are converted to doses

18 using the proposed food consumption rates I just discussed for

19 adult and larval honey bees. 

20 In all three methods, estimated pesticide

21 concentrations in or on plant foliage of treated crops is used

22 as a surrogate for pesticide concentrations in nectar and

23 pollen. For foliar spray applications, the proposed approach

24 involves the use of the tall grass concentration from the T-REX

25 model, as a surrogate for pesticide concentrations in nectar and

26 pollen. 
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1 For seed treatments, the proposed

2 concentration is one-milligram a.i. per kilogram in nectar and

3 pollen based on EPPO's default screening concentration. EPPO

4 stands for the European and Mediterranean Organization for Plant

5 Protection. For soil treatments, the proposed method is based

6 on a modification to the plant soil uptake model developed by

7 Briggs, et al., and published in 1982 and 1983.

8 This model is designed to estimate pesticide

9 concentrations in plant shoots. When explaining each exposure

10 method, I'll follow a similar format. I'll start out with a

11 description of the proposed approached, followed by an

12 evaluation of the method using relevant empirical data. I'll

13 then discuss the assumptions and uncertainties associated with

14 the method and then its strengths. 

15 In determining which methods to consider for

16 the Tier I Exposure Assessment, we considered approaches that

17 have been peer reviewed with an emphasis on models that have

18 been vetted in a regulatory context. As I'll discuss, various

19 methods were evaluated by comparing estimated exposures to

20 available empirical data for honey bees, nectar and pollen to

21 determine whether the estimates could be considered reasonably

22 conservative. 

23 To compare the estimated exposures to the

24 empirical measurements, all exposure values were normalized to

25 an application rate of one point a.i. per acre. Empirical data

26 were available from the scientific literature as well as
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1 unpublished registrant-submitted studies. In some cases, the

2 identity of pesticides are disguised here and in the White Paper

3 in order to maintain confidentiality of pesticides that are not

4 yet registered. 

5 The level of detail and amount of data

6 provided in each study varied from study to study. The amounts

7 of data that could be used to evaluate the methods also varied,

8 which led to differences in the evaluation of the

9 conservativeness of the methods. 

10 For example, for foliar spray applications,

11 the empirical data available to evaluate the proposed methods

12 for estimating exposures through consumption of pollen and

13 nectar were much larger than the data for estimating contact

14 exposures. 

15 For foliar spray applications, the proposed

16 method for estimating exposures to adult forager bees through

17 contact is to use the highest dose from a field study published

18 by Koch and Weisser in 1997. In this approach, the contact dose

19 is 2.7 micrograms a.i. per bee, per one pound a.i. per acre. In

20 applying this method, the actual dose is calculated by

21 multiplying the single highest application rate in pounds a.i.

22 per acre by 2.7. 

23 The study conducted by Koch and Weisser

24 involved measuring the concentration of a tracer on honey bees

25 soon after foraging in the treated area. Honey bees were

26 collected, following applications to phacelia fields and apple
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1 orchards that were located in Germany.

2 The study involved multiple field trials that

3 were conducted over the course of several years. During this

4 study, tracer residues were quantified on over 6,000 honey bees.

5 The proposed dose represents the highest value that was

6 measured during this study. We chose the highest dose rather

7 than the upper bound due to some uncertainties associated with

8 this data set, in particular, because the study only includes

9 two crops. In our application of this methods, we want to be

10 able to represent multiple crops. 

11 As indicated by comparing the doses observed

12 in the trials on the apple and phacelia fields, the distribution

13 of doses may vary from crop to crop. This figure depicts the

14 frequency distribution of residues measured on honey bees

15 foraging on phacelia fields over the course of the five trials

16 that were conducted by Koch and Weisser. 

17 As you can see from this figure, 43 percent of

18 the honey bees analyzed had residues that were in order of

19 magnitude below the proposed dose of 2.7 micrograms a.i. per

20 bee, normalized to one pound a.i. per acre. 

21 This figure depicts the frequency distribution

22 of residues that were measured on honey bees foraging on apple

23 orchards over the course of the nine trials that were conducted

24 during this study. As you can see from this figure, 71 percent

25 of all of the honey bees that were analyzed had residues that

26 were in order of magnitude below the proposed dose of 2.7.
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1 These results, along with those from the

2 phacelia trial suggest that the proposed dose is conservative

3 when considering all of the results from this study. In

4 developing the proposed method for assessing contact exposures

5 to honey bees following foliar application, we also considered

6 the utility of the upper bound arthropod exposure value that is

7 in the current version of the T-REX model. 

8 This dose is 12 microgram a.i. per bee, per

9 one pound a.i. per acre, which is a factor of five higher than

10 the proposed contact dose from the Koch and Weisser dataset. 

11 This dose, the T-REX dose is calculated by multiplying the upper

12 bound concentration of 97 milligrams active ingredient per

13 kilogram of arthropods by the average body weight of an adult

14 worker bee, which is .128 grams. 

15 The upper bound pesticide concentration for

16 arthropods used in the T-REX model represents a 95th percentile

17 concentration on arthropods, based on empirical data from the

18 scientific literature and registrant-submitted studies. These

19 studies measured pesticide residues on various arthropods,

20 including moth and beetle larvae, crickets, grasshopper, beetles

21 and unidentified arthropods that were captured in treated areas.

22 This dataset does not include residue data for honey bees.

23 The use of the arthropod concentration relies

24 upon measured residues of pesticides in and on arthropods that

25 were located on treated fields at the time that the field was

26 sprayed. It is assumed that the residues are predominately
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1 based on direct spray; however, they may also incorporate some

2 contact of arthropods with pesticide residues on treated

3 foliage, as well as consumption. 

4 We chose not to propose the use of the T-REX

5 arthropod dose because it does not include residues from honey

6 bees. Since the focus of the Tier I risk assessment method is

7 on honey bees, the proposed dose was based on the Koch and

8 Weisser data.

9 In cases where it is necessary to estimate

10 exposures to other species of bees, the T-REX approach may be

11 more representative of residues resulting from contact exposure.

12

13 A review of the scientific literature failed

14 to identify maximum doses following contact exposures of honey

15 bees to pesticides resulting from foliar spray applications. 

16 Two studies were identified that contained mean residues of

17 pesticides on bees. 

18 The figure depicted on this slide compares the

19 mean doses reported in those studies to the mean doses from the

20 Koch and Weisser study on phacelia and apple orchards, as well

21 as the T-REX arthropod dose. The mean doses all appear to be

22 similar, generally being within an order of magnitude of the

23 Koch and Weisser data. 

24 The method evaluation for the proposed contact

25 exposure dose is uncertain because there are a limited number of

26 studies available to evaluate the approach. Of the two studies
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1 that are available, only mean measured concentrations on honey

2 bees are reported. 

3 In addition, because there are only two

4 studies available for evaluation purposes, it's unclear how

5 representative these data are related to other locations, crops,

6 and field conditions.

7 It's unknown whether the data from these two

8 studies are representative of high-end exposures that other

9 honey bees may receive under different conditions. Despite

10 these limitations, the Koch and Weisser maximum dose is

11 empirically based and appears to be consistent with the data

12 from the other two studies. 

13 Also, the proposed dose appears to be

14 conservative when we consider the overall distributions of

15 residues that were detected by the authors over the course of

16 the study, namely that the majority of the contact doses

17 measured on the foraging honey bees where in order of magnitude

18 lower than the highest dose.

19 The Koch and Weisser study appears to have a

20 robust study design. The use of the tracer facilitated

21 measurements of the full distribution of concentrations of the

22 chemical on honey bees. This would not necessarily be the case

23 for a fast-acting insecticide, where honey bees with lethal

24 doses would not return to the hive and would not be measured. 

25 In addition, this dose is based on a large

26 end. Each trial quantified doses on hundreds of honey bees for
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1 a total of more than 6,000 measurements on individual bees over

2 the course of several years. In addition, the proposed contact

3 dose is consistent with other approaches, including the T-REX

4 arthropod dose as well as the Atkins Method.

5 For dietary exposure, the proposal includes

6 the use of an upper bound residue value for foliage that is used

7 in the T-REX model. T-REX is already used by the EPA for

8 ecological risk assessments of pesticides for establishing

9 screening level exposures to terrestrial vertebrates. 

10 T-REX includes pesticide concentrations on

11 different plants or plant parts, including short and tall grass,

12 broadleaf plants, fruit, pods and seeds. The upper bound and

13 mean concentrations used in T-REX are provided in this table. 

14 These concentrations are based on a review of pesticide residues

15 on plants that was conducted by Kenega and then later updated by

16 Fletcher, et al.

17 Ideally, the proposed approach would be to

18 base the upper bound concentrations on measured values from

19 pollen and nectar; however, there was insufficient amount of

20 empirical data to confidently estimate the upper bound. 

21 Therefore, it's necessary to use a surrogate. 

22 This figure depicts the method evaluation for

23 the T-REX pesticide concentrations for short grass, broadleaf

24 plants, tall grass and fruit pods and seeds. The estimated

25 concentrations are depicted in this figure with red dashed

26 lines. And you can see to the right of the dash which
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1 particular residue from T-REX is represented by the specific

2 line. 

3 The empirical data that are relevant to this

4 method are depicted by blue columns. I'll use this format in

5 other figures throughout the rest of this presentation. 

6 The empirical data that are depicted here for

7 nectar includes mean concentrations. Some maximum values are

8 also available. Most often, only mean concentrations were

9 reported in the literature.

10 As you can see by this figure, the mean

11 concentrations for short grass, broadleaf plants and tall grass

12 are all greater than the mean concentrations available for

13 nectar. The highest empirical measurement from these studies

14 was 13.6 milligram per kilogram nectar, normalized to one pound

15 a.i. per acre. This value is on the same order of magnitude as

16 the mean concentrations for tall grass, short grass, and

17 broadleaf plants. 

18 Four of the ten mean concentration of the

19 empirical dataset exceeded the mean T-REX residue value for

20 fruit, seeds, and pods. All of the empirical concentrations

21 from the maximum dataset were below the T-REX upper bound

22 concentrations for all four categories. 

23 This figure depicts the mean empirical

24 concentration data for pollen, compared to the mean T-REX

25 concentrations. The mean concentration for short grass is

26 greater than the mean concentrations available for all of the
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1 dataset for pollen. The mean concentrations for broadleaf

2 plants and tall grass exceed all but one of the nine mean

3 empirical concentrations for pollen.

4 Five mean pollen concentrations exceed the

5 mean T-REX concentration for fruit, seeds, and pods. All of the

6 maximum empirical concentrations for pollen are below the upper

7 bound concentrations for short grass, broadleaf plants and tall

8 grass. Seven of the 14 maximum concentrations exceed the upper

9 bound concentration for fruit pods and seeds. 

10 So when considering all of the empirical data

11 that are available for nectar and pollen, the T-REX

12 concentrations for short grass, broadleaf plants and tall grass

13 are consistently above the empirical concentrations. Only one

14 value out of more than 40 exceeds the tall grass and broadleaf

15 plant concentrations. 

16 The tall grass upper bound concentration is

17 proposed as a surrogate because of the three possible surrogates

18 that appear to be conservative for nectar and pollen. Tall

19 grass is the closest to the empirical data. 

20 For the Tier I Exposure Assessment, the upper

21 bound concentration of 110 milligram a.i. per kilogram,

22 normalized to one pound a.i. per acre, would then be converted

23 to a dietary dose received by adult and worker bees using pollen

24 and nectar consumption rates for these two life stages. 

25 Therefore, the dietary-based dose for larvae is 13 microgram

26 a.i. per bee, per one pound a.i. per acre, and the value for
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1 adults is 32.

2 Estimated doses can be scaled to an

3 application rate by multiplying by the maximum single

4 application rate. And in practice, the concentrations generated

5 by T-REX also account for multiple applications and dissipation

6 of the chemical in between applications. 

7 In terms of assumptions and uncertainties

8 associated with this method, I've already referenced the most

9 significant one, which involves the assumption that the upper

10 bound concentration for tall grass will be an appropriate

11 surrogate for pesticide concentrations in pollen and nectar of

12 flowers that were directly sprayed with the pesticide.

13 Comparison of empirical data to the tall grass

14 mean and upper bound concentrations suggests that this value is

15 reasonably conservative. As empirical measures of pesticide

16 concentrations on pollen and nectar become available, we may be

17 able to refine the Tier I Exposure Assessment Method using

18 pollen or nectar-specific data rather than using plant foliage

19 as a surrogate. 

20 Also, in using the T-REX concentration, it's

21 assumed that direct spray onto foliage represents a conservative

22 estimate of concentrations in pollen and nectar for both

23 systemic and non-systemic pesticides. 

24 Although systemic transport of pesticides to

25 pollen and nectar are expected, concentrations in nectar and

26 pollen that occur due to systemic transport are not expected to
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1 exceed the concentrations from direct spray. This is because

2 systemic transport takes time and not all of the amounts of the

3 pesticide that is applied to the plant will move the pollen and

4 nectar due to dissipation of the pesticide and transport to

5 other portions of the plant.

6 The proposed Tier I Exposure Method for seed

7 treatments is based on the EPPO 2010 screening concentration of

8 one milligram a.i. per kilogram and pollen and nectar of plants

9 to which seed treatments were made. EPPO screening

10 concentration of one milligram a.i. per kilogram is based on the

11 maximum value from data compiled by Alix, et al. in 2009,

12 including pesticide residues measured in different plant parts

13 following applications to soil or seed treatments. 

14 Plant parts included in this analysis were

15 described as leaves, fruit, green parts -- in parenthesis --

16 whole plants and grains. This approach may be used for all

17 pesticides that are applied via seed treatment, by assuming that

18 the upper bound concentration for pollen and nectar is one

19 milligram a.i. per kilogram with no need for adjustment, based

20 on application rate or chemical properties. 

21 This concentration can then be multiplied by

22 the food consumption rates for adult and larval worker bees to

23 determine the upper bound doses potentially received by honey

24 bees. For adults, this dose is .29 microgram a.i. per bee. And

25 for larvae, the dose is .12. 

26 EPPO screening concentration was evaluated
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1 using empirical data describing pesticide concentration in

2 pollen and nectar of crops whose seeds were treated with the

3 pesticide. The highest concentration measured in pollen was

4 .036 milligram a.i. per kilogram, and again, no adjustment was

5 made for application rate here. This value is a factor of 28

6 lower than the EPPO screening value. 

7 Considerably less empirical data are available

8 to compare the EPPO one milligram a.i. per kilogram screen to

9 nectar concentrations. The screening concentration is three

10 orders of magnitude above the highest concentration for nectar,

11 which is .003 milligram a.i. per kilogram. 

12 In the Tier I approach, it's assumed that all

13 pesticides that are applied to seeds are systemic, and therefore

14 can be transported into pollen and nectar that may be consumed

15 by honey bees. If this is not the case for a specific

16 pesticide, registrants can submit data to refute the assumption

17 of systemicity. 

18 The proposed use of the EPPO screening level

19 exposure concentration of one milligram a.i. per kilogram for

20 pollen and nectar of seed treated crops does not account for

21 differences in application rate where the mass of the pesticide

22 that's applied to a seed. 

23 There is uncertainty in not accounting for the

24 mass of the chemical applied since it's expected that the

25 magnitude of the pesticide concentration of the plant will be

26 influenced by the magnitude of the application. However, the
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1 evaluation of this screen indicates that it is protected for

2 rates that are currently being used for seed treatments which

3 are approximately one milligram of active ingredient per seed. 

4 Even if seed treatments would exceed one

5 milligram a.i. per seed, it seems unlikely that pesticide

6 concentrations and nectar and pollen would exceed the EPPO

7 screening value since it is a factor of 28 greater than the

8 highest residue that's measured in pollen and nectar. 

9 In addition, pesticide mass applied to seeds

10 is not expected to be that much greater than one milligram a.i.

11 per seed, since this value is already similar to the overall

12 weight of the seed itself.

13 Another uncertainty associated with this

14 approach is that it does not account for physical chemical

15 properties of a pesticide that may influence its potential

16 systemic transport throughout the plant and into pollen and

17 nectar. For example, a chemical with low mobility may be

18 expected to have lower concentrations and nectar when compared

19 to a chemical with higher mobility.

20 Despite these limitations, this approach

21 appears to generate reasonably conservative estimates of

22 pesticide concentrations in the pollen of seed treated crops. 

23 The one milligram a.i. per kilogram screening value is much

24 greater than the data that are available for nectar; however,

25 there are only a few empirical concentrations, and so it may not

26 be appropriate to conclude at this time that this method is
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1 unreasonably conservative for nectar.

2 For soil treatments, the proposed method

3 involves the use of the Briggs soil plant uptake model. This

4 model predicts the concentration of a chemical in the shoot of a

5 plant, using the Kow that chemical and its concentration in the

6 water that is in contact with the plant's roots.

7 This approach is based on observations from

8 studies conducted by Briggs et al. that involved uptake of 18

9 different pesticides into the shoots of barley plants. These

10 pesticides included carbamate insecticides and phenylurea

11 herbicides with log Kow that ranged from -.57 to 4.6.

12 Note that one of the major components of the

13 Briggs equation is the transpiration stream concentration factor

14 or TSCF, which is a ratio of the concentration of the chemical

15 in the xylem of the plant to the concentration of the aqueous

16 medium that's in contact with the roots. 

17 Median TSCF values are calculated in the

18 Briggs' published model, using the Kow of the chemical.

19 Suggestions to modify the Briggs approach were

20 made by Ryan, et al. in 1988. This involved adding an

21 equilibrium partitioning component based on the chemicals Koc

22 that allows for estimation of pesticide concentrations in

23 plants, using concentrations in soil rather than in aqueous

24 medium, as was used by Briggs.

25 Note that if Koc is not available or

26 appropriate for a chemical, the Kd can be used as an alternative
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1 to the Koc and Foc component of this equation. The soil

2 properties that are required by this equation can be easily

3 obtained from EFED's standard scenarios that are used for the

4 prism model.

5 The TSCF calculation provided by Briggs

6 generates a median estimate of the TSCF in resulting

7 concentration in plant shoots. As I noted at the beginning of

8 this talk, the goal of the Tier I Exposure Assessment is to

9 derive exposure estimates that represent an upper bound. To

10 accomplish this, we recalculated TSCF values to represent the

11 95th percentile values and they're depicted in this figure by

12 the red line. 

13 These TSCF values are provided in Appendix 5

14 of the White Paper and can be selected using the Kow of a

15 chemical. Empirical data for pollen were available for four

16 pesticides applied to pumpkin, squash, tomato or melon. 

17 Comparison of estimated concentrations to these measured data

18 indicates that model predictions are generally conservative

19 compared to empirical data. There are three empirical

20 concentrations that are higher than the estimated values, but

21 they are still within an order of magnitude. 

22 It should be noted here that some

23 discrepancies between the estimated and empirical data may

24 originate from several sources. For instance, none of the

25 empirical data were from barley, which is the basis for the

26 Briggs model. And the soil properties that are used in the
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1 Briggs model were not matched to the studies that were used to

2 generate the empirical data. 

3 In addition, the empirical data represents a

4 combination of parent and degradates, whereas the estimated

5 concentrations are based on parent only. When comparing

6 empirical concentrations for nectar to the estimated

7 concentrations, the results are similar to those of the pollen

8 analysis. Empirical concentrations for nectar were available

9 for five pesticides that were applied to fuchsia, nasturtium,

10 pumpkin, squash, cotton, and melon.

11 Comparison of estimated concentrations to

12 empirical data indicates that the model predictions are

13 generally conservative compared to the empirical data. There

14 are two empirical values that are higher than the estimated

15 values, but they are still within an order of magnitude. As I

16 noted previously, the discrepancies between the estimated

17 empirical values may be attributed to differences in soil

18 properties and systemic transport of the crops that are

19 different than barley.

20 In addition, in the case of imidacloprid, for

21 which the estimated value is below one of the eight empirical

22 concentrations, the estimated value is based on the parent

23 alone, whereas the empirical values represent parent plus

24 degradates. 

25 In developing the proposed method for

26 estimating pesticide concentrations in nectar and pollen of
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1 crops that receive soil treatments of pesticides, we also

2 considered using the EPPO one milligram a.i. per kilogram

3 screening concentration that's proposed for use with seed

4 treatments. When the one milligram a.i. per kilogram default is

5 compared to maximum concentrations from available studies where

6 pesticides were applied to soil, all empirical concentrations

7 for pollen are below the one milligram a.i. per kilogram value.

8 Note that this comparison did not involve

9 adjustment of the concentrations for application rate. There is

10 one concentration for nectar that exceeds the screen by a factor

11 of almost five; however this concentration was from a study

12 where the application rate of dimethoate was equivalent to 17

13 pounds a.i. per acre, which is much higher than currently

14 registered application rates for this chemical and most other

15 insecticides in North America.

16 With the exception of the dimethoate data, all

17 the measured concentration of pesticides in pollen and nectar of

18 plants receiving soil applications are two orders of magnitude

19 below the one milligram a.i. per kilogram value. Based on this

20 information, it appears that the one milligram a.i. per kilogram

21 default concentration is a reasonably conservative estimate for

22 soil applications. However, for pesticides that have high

23 applications, this method may under-predict the exposure. 

24 In addition, this approach does not account

25 for the physical chemical properties of a pesticide that may

26 influence its potential systemic transport throughout the plant
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1 and into pollen and nectar. For example, a non-ionic organic

2 chemical with low mobility may be expected to have lower

3 concentrations in nectar when compared to a chemical with higher

4 mobility. 

5 There are several notable limitations to using

6 the modified Briggs approach. As with the use of the T-REX tall

7 grass upper bound concentration for foliar spray applications,

8 this approach assumes that estimated concentrations of a plant

9 pesticide and plant foliage is an appropriate surrogate for

10 concentrations in pollen and in nectar. 

11 Comparison of empirical data to the estimated

12 concentrations suggests that the Briggs approach is reasonably

13 conservative, relative to concentrations that have been measured

14 in pollen and nectar. In the Tier I approach, it's assumed that

15 all chemicals may be systemically transported. This assumption

16 may be limited based on a chemical's log Kow, -for example, for

17 chemicals that have log Kow values above five, they may not be

18 systemically transported. 

19 But whether a chemical is transported,

20 systemically in plants, could potentially be confirmed using

21 empirical data that is submitted to EPA; for example, plant

22 metabolism studies. However, it would be up to pesticide

23 registrants to submit sufficient data to demonstrate that a

24 pesticide is not systemic. 

25 Another limitation is that this methodology is

26 based on empirical data from only one type of plant. Uptake may
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1 be different for different types of plants. However, the

2 species that was used by Briggs, which is barley, is

3 representative of a crop species that is likely to be grown on

4 soils where pesticides are applied. 

5 Also, the dataset used to establish the Briggs

6 equation is based on a limited number of chemicals that

7 represent only two classes of pesticides. Despite this

8 limitation, the pesticides that were used in this approach are

9 systemic and cover a wide range of log Kow values, leading to an

10 approach that is applicable to a wide range of pesticides with

11 the potential to be systemically transported by plants. 

12 However, the approach may have limited utility for ionic

13 chemicals whose transport may not be predicted while using Kow

14 and Koc.

15 The final limitation I'll discuss is that the

16 proposed approach is based on passive transport of chemicals

17 into the xylem. 

18 This approach does not directly estimate

19 pesticide concentrations in plants that are the result of phloem

20 transport. One possible assumption that could be made in

21 applying this model for estimating pesticide concentrations in

22 plants would be to assume that chemical transport via phloem, is

23 equivalent to that of xylem. 

24 The alternative approach would be to use

25 different models for neutral and ionic compounds such as those

26 described by Briggs, et al. 1987; Kleier 1988; and Trapp, et al.
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1 1995. Those citations are provided in the White Paper. 

2 Despite the limitations discussed here, both

3 the Briggs model with the modifications by Ryan, et al. and

4 those proposed by EPA in the White Paper, as well as the EPPO

5 one milligram a.i. per kilogram screening value, both appear to

6 be useful tools for generating reasonably conservative estimates

7 of pesticide concentration in plants after systemic transport

8 from the soil. These approaches are consistent with the

9 screening level assessment because they're both simple and

10 efficient and are empirically based on data for pesticides. 

11 The Briggs model had the advantage that it

12 relies upon some basic physical chemical properties of

13 pesticides and also accounts for the mass that's applied to the

14 soil. Therefore, the Briggs model is the one that's proposed

15 for estimating pesticide concentrations in pollen and nectar,

16 following a soil treatment. 

17 This table summarizes the proposed Tier I

18 Exposure Methods. The values provided in the right column

19 represent the resulting doses for each method, based on an

20 application of one pound a.i. per acre. For foliar

21 applications, the proposed Tier I Exposure Assessment involves

22 quantification of pesticide doses to adult honey bees through

23 contact exposure. This method is based on the maximum dose of

24 2.7 microgram a.i. per bee, per one pound a.i. per acre that was

25 measured on honey bees as recorded by Koch and Weisser. 

26 For foliar spray applications, seed treatments
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1 and soil treatments that proposed Tier I Exposure Assessment,

2 quantifies pesticide doses received by adults and larvae through

3 the diet. These methods involve translating estimated

4 concentrations in pollen and nectar into doses by multiplying

5 the concentrations by the food consumption rates of adults and

6 larvae. For adults, the proposed food consumption rate is 292

7 milligrams of food per day. And for larvae, that value is 120. 

8

9 For foliar spray applications, the proposed

10 method involves using the T-REX tall grass upper bound

11 concentration of the surrogate for pesticide concentrations in

12 pollen and nectar. The resulting doses of 32 micrograms a.i.

13 per bee and 13 micrograms a.i. per bee for adults and larvae can

14 be adjusted to the use of the pesticide by accounting for the

15 application rate of the chemical. 

16 These doses are the highest among the proposed

17 methods, indicating that if the application rate is held

18 constant, then foliar spray applications would represent the

19 highest potential exposures to honey bees at the Tier I Exposure

20 Assessment level. 

21 For seed and soil treatments of pesticides,

22 it's assumed that the pesticides are systemically transported to

23 pollen and nectar. For seed treatments, the proposed method is

24 to use the EPPO screening value of one milligram a.i. per

25 kilogram to represent pesticide concentrations in nectar and

26 pollen of treated crops. No adjustment s made for application
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1 rate or mass of chemical applied to the seed. Therefore, for

2 all uses and chemicals, the doses would be .29 and .12 microgram

3 a.i. per bee for adults and larvae respectively. 

4 For soil treatments, the proposed method for

5 estimating pesticide concentrations in nectar and pollen

6 involves the use of the modified Briggs soil plant uptake model.

7 This model relies on a pesticide's application rate, Kow and

8 Koc to estimate the concentration in plant shoots which is a

9 surrogate for pollen and nectar. 

10 As an illustration of the relative doses

11 generated by this approach, the values depicted in this table

12 are for dimethoate, which is a mobile chemical with a low Kow. 

13 In evaluating the proposed methods using empirical data, the

14 proposed methods appear to be reasonably conservative. 

15 As additional information becomes available,

16 we may reevaluate the proposed methods, for example, as targeted

17 monitoring data become available where pesticides are measured

18 in pollen and in nectar as part of Tier II studies, it may be

19 possible in the future to replace the use of pesticide

20 concentrations on plant foliage as surrogates with empirical

21 measures of pesticides and pollen and in nectar.

22 The EPA is interested in SAP feedback on the

23 proposed Tier I Exposure Assessment Method that I discussed in

24 this presentation and is presented in detail in Section 3 of the

25 White Paper. For contact exposures resulting from foliar

26 applications, which is the focus of Question 4, EPA, PMRA and
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1 Cal DPR are interested in SAP comments on the strengths and

2 limitations of the proposed approach that is based on the

3 maximum of the Koch and Weisser data. 

4 We're also interested in SAP feedback in the

5 potential utility of the T-REX arthropod dose in assessing

6 contact exposure to non-Apis bees as well as the honey bee. The

7 focus of Charge Question 6 is on the proposed methods, meaning

8 pesticide concentration in pollen and nectar, which are relevant

9 to assessing dietary exposures. 

10 We're requesting SAP feedback to the extent to

11 which the T-REX tall grass upper bound concentration the EPPO

12 one milligram a.i. per kilogram screen and the modified Briggs

13 model can represent pesticide concentrations in pollen and

14 nectar for foliar spray, seed treatment and soil applications

15 respectively.

16 We're also interested in any other methods or

17 data that may represent viable alternatives to these proposed

18 methods or may be used to improve the proposed methods, perhaps,

19 in the form of reducing assumptions or uncertainties.

20 If I have time, I can take questions. 

21 DR. DANIEL SCHLENK: Thanks. Dr.

22 Fefferman.

23 DR. NINA FEFFERMAN: Okay. Thank you. That

24 was really interesting. I'm wondering, a lot of the estimates

25 that I saw for how you were using characterizations of larval

26 and adult uptake for consumption rates were for normal colony
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1 processes. And I'm wondering if so, how you guys are dealing

2 with non-normal but lifecycle dependent colony processes. 

3 So gorging, having to leave the hive very

4 quickly or swarming times when both the energy needs of an

5 individual may be different and also the consumption rate may

6 skyrocket for non-energetic process needs.

7 MS. KRISTINA GARBER: The proposed method is

8 based on kind of the baseline functioning of the colony. It

9 doesn't account for those scenarios like swarming. 

10 DR. NINA FEFFERMAN: Can we stick something in

11 there about that? Because if they accidentally poison

12 themselves once a year that might be really bad.

13 MS. KRISTINA GARBER: That's part of why we're

14 here. So if you have any advice on how we could potentially

15 modify that and some data that you could point to or good

16 sources that could provide another way to quantify the

17 consumption rates, that would be appreciated. 

18 DR. NINA FEFFERMAN: Sure. Thanks.

19 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum.

20 DR. MAY BERENBAUM: So estimating the contact

21 exposure doses based on models that derive from empirical

22 studies that in some cases are 15 to 30 years old, is there any

23 indication or is it a consideration that detection methods have

24 changed so that the ability to detect residues is also a

25 variable in this process?

26 MS. KRISTINA GARBER: Just for clarification,
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1 are you talking about the Briggs model as being --

2 DR. MAY BERNEBAUM: That's one that was

3 referenced to Atkins '82 [sic] in there. There are a couple of

4 studies, Koch and Weisser from '97, I think. So these are older

5 technologies. Is there some consideration of how chemical and

6 analytical techniques change so that detection is improving?

7 MS. KRISTINA GARBER: I think that at this

8 point, when we were looking at these studies, I think that the

9 detection rates were sufficient to confidently estimate the

10 values that were being detected. 

11 If you have any concerns about the specific

12 detection rates, some of the studies would definitely account

13 for that. If that's an uncertainty in some of the methods, then

14 certainly we could look at newer studies that may account for

15 that uncertainty.

16 DR. DANIEL SCHLENK: Okay. Dr. James.

17 DR. ROSALIND JAMES: Your doses are really

18 dose per day, right?

19 MS. KRISTINA GARBER: Yes.

20 DR. ROSALIND JAMES: I work with solitary bees

21 and they make a pollen provision and it's really easy to

22 calculate the total dose that larvae will have over their entire

23 development period. Honey bees also only feed for five days. 

24 One thing that I was thinking about when you had the drone

25 consumption rates and they're eating less than the workers as

26 larvae and yet they're bigger, so you know they must be eating
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1 more because they have to get bigger somehow. If the

2 measurements are accurate, it must be because they eat longer,

3 they're larvae for longer, and similarly with queens.

4 So they could potentially be exposed to higher

5 amounts of pesticide as larvae. But if you're only looking at

6 dose per day you would miss that, right?

7 MS. KRISTINA GARBER: In developing this

8 model, we did go back and forth between the idea of the duration

9 of the exposure we were trying to represent. I think that what

10 you're saying is a valid point, you know, that the duration of

11 the EEC or the exposure that we're looking at, it is relevant. 

12 And part of why we settled on day was that it could be matched

13 to the acute toxicity test when we're only exposing the bees for

14 a shorter period of time.

15 DR. DANIEL SCHLENK: Dr. Tarpy.

16 DR. DAVID TARPY: I have a couple of

17 questions, actually. One is largely out of my own ignorance,

18 not being a toxicologist. But knowing that that there is

19 variation, sometimes substantial variation, among workers, not

20 to mention, huge variation with different castes, and even

21 larger variation among different pollinators of which honey bees

22 are supposed to be a proxy in this means; in the in vitro

23 studies, why is the denominator a per bee, per day basis rather

24 than say, milligram per individual?

25 MS. KRISTINA GARBER: By "denominator" you

26 mean the toxicity endpoint?
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1 DR. DAVID TARPY: Yeah. All of the

2 recommendations here on the milligrams of active ingredient per

3 bee. Why is it on a per individual basis rather than something

4 more standardized that can translate across different life forms

5 and different types of pollinators?

6 MS. KRISTINA GARBER: I think that part of

7 that is the historical basis of how honey bee toxicity tests are

8 conducted where they tend to be on a dose per individual bee. 

9 That's how the residues are quantified. 

10 DR. DAVID TARPY: Okay. So it's just based on

11 practice and historical purposes, but there is variation there

12 but it's not captured in these types of bioassays. 

13 MS. KRISTINA GARBER: Just to clarify, by

14 "variation," do you mean individual variability?

15 DR. DAVID TARPY: Yeah. Bees weigh different

16 from each other.

17 MS. KRISTINA GARBER: Right.

18 DR. DAVID TARPY: And I would presume, and

19 again, correct me if I'm wrong, but that toxicity would be a

20 function of weight, of body weight to some capacity, assuming

21 that's even linear. It might even be nonlinear effects, right. 

22

23 I just wanted to ask that question, not

24 knowing what the answer was. So you seem to have answered that.

25

26 MS. KRISTINA GARBER: Okay. Presumably, you
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1 would catch some of the variability in the confidence interval

2 that would surround your LD50. That would be generated from

3 that. So you'd probably get -- if there is a relationship

4 between weight and toxicity, then that would be translated in

5 the 95th percentile confidence around your LD50. 

6 DR. DAVID TARPY: Per individual.

7 MS. KRISTINA GARBER: Correct.

8 DR. DAVID TARPY: Okay. The second question

9 is combining the total food intake for the different adult

10 caste, behavioral caste, especially with the pollen versus

11 nectar, is there consideration in doing these bioassays for

12 lipophilic versus hydrophilic compounds in those different

13 phases that in some cases, combining those two things in total

14 food consumption per day might not be captured for particular

15 compounds that are being screened. 

16 MS. KRISTINA GARBER: So I have to confess,

17 you're asking a lot of toxicity questions that are related to a

18 talk that Joe DeCant will be giving.

19 DR. DAVID TARPY: Okay.

20 MS. KRISTINA GARBER: If I may, would you mind

21 deferring your question to that time? Maybe that will be

22 covered.

23 DR. DAVID TARPY: Mm-hmm. No problem.

24 MS. KRISTINA GARBER: Thank you.

25 DR. DANIEL SCHLENK: Dr. Potter.

26 DR. THOMAS POTTER: I had a question about any
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1 consideration that may have been made regarding physical

2 chemical properties of the chemicals and the potential

3 concentrations in nectar, given its importance, in terms of an

4 exposure pathway. Something, for example, is the aqueous

5 solubility of the chemical or its solubility in the 30 percent

6 sugar solution or something to that degree. 

7 At some point, solubility will define an upper

8 bound. Perhaps that's something that you guys thought about. 

9 Essentially, I'm asking, is there anything that went into your

10 thought processes in that regard?

11 MS. KRISTINA GARBER: We didn't directly

12 consider using the solubility. I would think that given that

13 the nectar would have more than just water, it might complicate

14 the way that we address solubility, but that's something that we

15 could consider in the future. But we didn't consider that as an

16 alternative to the Briggs model.

17 DR. THOMAS POTTER: I wasn't referring to the

18 Briggs model, per se, but in terms of contact exposure from

19 foliar sprays that are intercepted by pollen and nectar. You

20 had several studies that you pulled from the literature where

21 there were some measured data.

22 What was interesting about those studies, and

23 I'll comment on them in the question period, but they were

24 looking at -- a couple of the chemicals that were extremely

25 insoluble in water. So you wouldn't expect to be finding them

26 in the nectar. 
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1 I had some concerns about one of the

2 particular studies. Also about their analytical techniques as

3 well. But I think it's an important consideration and without

4 too much effort, I think you could put some thought into that

5 and bring that into the process. I think it would strengthen it

6 and certainly add to its credibility.

7 DR. THOMAS STEEGER: I would just like to add,

8 part of the risk assessment process that we'll be talking about

9 later on this afternoon is the risk characterization component. 

10 Part of that is the risk discussion and just risk description

11 section of risk characterization and in there the risk assessor

12 would attempt to capture solubility issues and how it relates to

13 screening level assumptions so that those types of uncertainties

14 are indeed characterized in our risk assessments. 

15 DR. THOMAS POTTER: What you're saying is

16 you're catching that downstream then?

17 DR. THOMAS STEEGER: To the best of our

18 ability.

19 DR. THOMAS POTTER: Okay.

20 DR. THOMAS STEEGER: Okay. Thanks.

21 DR. DANIEL SCHLENK: Dr. Sandy.

22 DR. MARTHA SANDY: I have a question about the

23 estimate of contact exposures for the foliar spray applications.

24 So you were basing that on a study with two crops, apples -- I

25 have to admit I don't know what that other crop was, phacelia or

26 whatever. So I'm wondering, how representative are those two
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1 crops to other crops, like corn and maybe there are others that

2 bees might come into more contact in the process of gathering

3 nectar and pollen.

4 DR. THOMAS STEEGER: The use of phacelia, it's

5 a European plant and it has come into considerable use, as of

6 late, in the semi-field studies, intent on doing screening level

7 studies is to use a crop that is particularly pollinator

8 attractive and is a good source of both pollen and nectar. 

9 So phacelia fits into that category well. It

10 has a robust bloom. So presumably, based on it being pollinator

11 attractive, it is, as I say, presumed to be relatively

12 protective and a source of information regarding residues that

13 could accumulate in pollen and nectar, either through a foliar

14 application or because of systemic transfer to pollen and

15 nectar. 

16 DR. DANIEL SCHLENK: Dr. McManaman. 

17 DR. JAMES MCMANANMAN: I have a question of

18 clarification, basically because I'm not a bee physiologist, but

19 your pesticide dose used for the larvae, you claim that the

20 pollen and nectar have pesticide concentrations, about 100 times

21 higher in pollen and nectar compared to royal jelly. Is that

22 correct?

23 DR. THOMAS STEEGER: That is correct.

24 DR. JAMES MCMANAMAN: I think that your Tier

25 I, for larvae testing, you're planning on using Day 5 larvae. 

26 What I've read from the literature over the course of the past
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1 couple of weeks is that Day 5 larvae still consume royal jelly. 

2 I mean, it certainly would be more conservative, but would that

3 be inaccurate using those levels to test, would that actually

4 give result that really wouldn't be applicable to the real

5 world?

6 MS. KRISTINA GARBER: I think that is an

7 uncertainty associated with the proposed approach that we are

8 assuming that they would be eating honey and pollen only. We

9 didn't have data available to quantify how much royal jelly they

10 may be consuming at that time. 

11 So as a conservative approach, we chose to

12 represent the entire diet as pollen and nectar. So that may

13 over-predict, but honestly, we don't have any data to tell us

14 how much. 

15 DR. JAMES MCMANAMAN: Of the bee experts in

16 the room, do they consume royal jelly or do they consume pollen

17 at Day 5 larvae?

18 UNIDENTIFIED SPEAKER: Brood food.

19 DR. JAMES MCMANAMAN: Pollen? Brood food. 

20 Okay. All right. So it's accurate then. Good.

21 DR. DANIEL SCHLENK: Dr. Hunt.

22 DR. GREG HUNT: Maybe this will be clarified

23 later, but with -- it's suggested to use .29 micrograms for seed

24 treatment products. For clothianidin, that represents about 70

25 times the LD50 for the smallest estimate of the LD50. 

26 Given that this is already being used in corn,
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1 I assume that means that it needs to go to Level II trials and

2 Level III trials. I'm wondering, what are the ramifications? 

3 How long will that take? I'll just throw that out there for

4 whoever wants to respond.

5 DR. THOMAS STEEGER: Without getting into how

6 this actually plays out in terms of data requirements, as you've

7 pointed out, for a chemical that has failed a Tier I screen,

8 first, the decision has to be made by the risk manager whether

9 to ask for additional information. And the process is

10 management-based process as opposed to a risk assessment-based

11 process.

12 So the higher tier studies, it depends on the

13 nature of the study and it depends on the results, how far you

14 move up in the refinement process. I don't want to digress too

15 much here, but depending on the risk assessment question or the

16 risk management question that's being asked at the higher tier

17 will dictate the length of the study, particularly if you want

18 to know what effect overwintering; what effect the chemical may

19 have overwintering, these can be long studies. 

20 The bottom line is that we can't rush the

21 science to derive an answer that involves regulating pesticides

22 at a national level. The foundation of our risk assessments has

23 to be quality science. That being said, it can take a while for

24 quality science to be generated. I know that's frustrating, but

25 that's the reality that we have to deal with in terms of being a

26 regulatory agency that's founded on quality science.
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1 DR. DANIEL SCHLENK: Okay. Any other

2 questions or clarification on the presentation?

3 (No response.)

4 Okay. Let's go ahead with the next

5 presentation which will be by Reuben Baris. 

6 MR. REUBEN BARIS: Good afternoon. My name is

7 Reuben Baris. I'm an environmental scientist in the

8 Environmental Fate and Effects Division of the Office of

9 Pesticide Programs. Thank you very much for the opportunity to

10 address the SAP.

11 I will be talking about methods for

12 characterizing Tier I exposures to bees, as well as study design

13 and objectives for Tier II exposure studies. These topics are

14 presented in detail in Section III, and in part, Section IV of

15 the White Paper.

16 The studies that I will present in the first

17 part of this presentation are currently included in the Part 158

18 data requirements. With minor adjustments and modifications,

19 these studies have the potential to be used to refine exposure

20 estimates.

21 As discussed previously, the risk assessment

22 process is intended to be iterative. At the Tier I level,

23 exposure value is used to estimate risk quotients are intended

24 to be conservative. However, for chemicals identified to be of

25 concern at the first tier, refined estimates of exposure may be

26 needed to provide more realistic understanding of pesticide
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1 exposures to bees under actual use conditions. 

2 Moving from Tier I to Tier II is a function of

3 risk management needs and decisions. Tier II studies can be

4 solely measures of exposure as pollen and nectar or can be

5 coupled with measures of exposure and effects. Higher tiered

6 studies on the effects of pesticides on honey bees will be

7 presented by Mr. DeCant, following this presentation. 

8 I would like to return your focus to the

9 proposed decision tree, specifically, seen here in Box 6,

10 Refining the Tier I Exposure Assessment. I will touch on a few

11 studies that are currently available, where with modern

12 modifications to the protocols could be used to refine Tier I

13 exposure estimates and recalculate risk quotients. The effects

14 data will not change, but with these studies, we are able to

15 refine estimates of dietary exposure. 

16 For pesticides, they are applied as a foliar

17 spray and exceed levels of concern for dietary exposure to bees.

18 It may be possible to refine dietary-based estimates of

19 exposure. 

20 This refinement could be accomplished using

21 pesticide-specific measurements of treated foliage taken within

22 24 hours of application. Registrant-submitted magnitude of

23 residue studies used by the Health Effects Division may be used

24 for this type of refinement. 

25 The empirical studies presented on the next

26 slide may contain information on the transport of a compound in
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1 the plant. That is, systemic or non-systemic transport;

2 therefore, will influence the need for higher tiered studies. 

3 For example, if the chemical in soil is applied and not detected

4 in plant tissue, fruit, or flower, it is not likely systemic. 

5 Therefore, exposure is unlikely and higher tiered studies would

6 not be required.

7 The table displayed here outlines empirical

8 data where identified to be potentially useful for refining Tier

9 I exposure estimates. These data are routinely submitted as

10 part of registration or could be submitted if conditionally

11 required. The Xs represent which study could be used to refine

12 Tier I estimates of exposure, based on the application type.

13 None of the studies listed are specifically

14 designed to measure high-end pesticide concentrations in pollen

15 and nectar at times when bees may be exposed. With modification

16 to the study design, these studies may be used to generate

17 chemical-specific measures of pesticides in pollen and nectar

18 that could be used for chemicals that do not pass the initial

19 Tier I screen. Some of these studies may be used as refinements

20 for seed treatments as well.

21 With some limitations recognized, the studies

22 listed here are currently Part 158 data requirements for

23 registering pesticides and may be used, with minor

24 modifications, to study protocols to obtain Day 0 data, or at

25 the very least, could be used as confirmatory data for

26 determining the systemic nature of the compound in plants.
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1 Again, I would like to bring your attention

2 back to the proposed decision tree. For the second portion of

3 my talk, I will be referring to Box 9(a), Conducting Tier II

4 Exposure Studies. Box 9(a) is intended for chemical-specific

5 residue studies as presented earlier by Mr. Sappington. 

6 The purpose of Box 9(a) and the intent of the

7 Tier II Exposure Assessment is to obtain pesticide-specific,

8 empirically-based exposure data that represent concentrations

9 encountered by bees. Like the Tier I exposure method, potential

10 exposures should be assessed through quantification of doses

11 through direct spray or diet. 

12 Specifically, studies should be designed to

13 quantify pesticide residues in pollen and nectar on bees

14 foraging in the treated field. The dotted line depicted in this

15 figure shows the process where the Tier II exposure values will

16 be used to refine Tier I exposure estimates for adults and brood

17 and then recalculate risk quotients. 

18 Tier II exposure studies with bees are not

19 typically conducted in the laboratory, but rather focus on

20 relatively controlled field studies where the movement of bees

21 to and from the colony is restricted through the use of

22 enclosures containing the treated crop. These studies may be

23 designed to measure residues on parent only or parent and

24 potential degradates of concern. 

25 The next few slides describes the basic

26 objectives and design elements of the two types of field studies
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1 that can be used to quantify pesticide exposure and matrices

2 that are relevant to bees, such as targeted field studies and

3 semi-field tunnel studies. 

4 The higher tiered studies are designed to be

5 conducted under vulnerable targeted conditions to provide

6 quantifiable, refined estimates of pesticide concentrations in

7 bee food sources.

8 The strength of basing the Tier II exposure

9 approach on empirical data from field studies is that some of

10 the uncertainties associated with the Tier I exposure method are

11 reduced or eliminated. For instance, the assumption

12 incorporated into the Tier I Exposure Assessment for foliar

13 applications that pesticide concentrations are in grass are

14 equivalent to nectar and pollen, is not necessary if pesticide

15 concentrations are quantified directly in pollen and nectar. 

16 Also, the Tier I use of upper bound exposure

17 values is based on a large set of chemicals that is not

18 necessary for pesticide-specific residues, can be estimated

19 under field conditions that are representative of the

20 pesticide's intended use pattern. 

21 As part of the tiered testing process for

22 evaluating potential exposures and effects on bees, higher

23 tiered refinements shift the focus from exposure and effects on

24 individual bees to that of the intact colony. Typical designs

25 of semi-field studies include a crop that is grown outdoors in

26 an enclosed system with controlled or confined exposure. 
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1 The test crop is grown under good agricultural

2 practices, receiving the maximum-labeled application rate, with

3 a minimum interval between applications. The test crop provides

4 the source of pollen and nectar. The design could be structured

5 to reflect a desired exposure system or foraging environment

6 such as the mixture of crops and weeds, or flowering margins,

7 although, such designs can introduce additional variables which

8 may be difficult to control across replicates and treatments.

9 As is evident, there are a range of different

10 designs and the actual test structure and study design will

11 depend on the specific questions asked in the risk assessment in

12 coordination with risk management needs. Ultimately, the Tier

13 II exposure studies must be conducted in a manageable way so

14 that data is collected and used to inform the risk assessment. 

15 The field and tunnel residue studies can

16 provide important information regarding the levels of

17 contamination potentially encountered by bees in the field;

18 however, there are a number of uncertainties associated with

19 these types of studies. One major uncertainty is that the level

20 of residues inside the hive may be different than those first

21 encountered by foraging bees in the field. 

22 In other words, pesticide concentrations in

23 freshly collected nectar and pollen could differ from residues

24 and honey and bee bread. Bee bread and honey, which form the

25 basis of the hive diet, undergo processing by bees. 

26 Consequently, measures of residues in pollen and nectar
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1 collected directly from the flower or from returning bees may be

2 different from the exposure to individuals within the hive.

3 Tier I exposure estimates are conservative for

4 unprocessed food sources. The objective of the higher tiered

5 studies for exposure are dependent upon the Tier I assessment

6 and where we have the ability to refine those estimates.

7 Refinements are chemical specific

8 concentrations in pollen and nectar that are used to refine the

9 quantitative risk quotient. Another notable limitation for the

10 Tier II Exposure Assessment Method is that it will require a

11 substantial amount of time and resources to complete.

12 In the future, it may be possible that the

13 data required from targeted monitoring studies may be used to

14 establish patterns of exposure based on application methods,

15 crops and location. These patterns could then be used to

16 develop a refined method where the data could be extrapolated to

17 other chemicals.

18 In addition, some field and tunnel residue

19 studies that are currently being conducted also involve the

20 collection of data on residues and leaves and flowers. These

21 data potentially provide a more complete dataset from which to

22 evaluate the ability of flowers or leaf residues to provide a

23 conservative measure of residues in pollen and nectar.

24 The first type of Tier II study presented is a

25 targeted field study or field residue trial, where pesticide

26 residues are quantified in pollen and nectar, collected directly
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1 from the treated crop. This study could be specifically

2 designed with the sole intent of quantifying pesticide residues

3 and matrices relevant to honey bees, or could be incorporated

4 into the study design of an existing protocol required for

5 pesticide registration, such as a cropped terrestrial field

6 dissipation study. 

7 In the design of these studies, it is

8 important to consider the type of application, that is, foliar,

9 soil, or seed treatment and relate the design to the desired

10 outcome. 

11 The second study is referred to a tunnel study

12 because the treated crop is kept under an enclosure, along with

13 a nucleus hive of bees, which are small colonies with roughly

14 5,000 bees. Study design guidance is currently provided by

15 EPPO, OACD, and also more recently by EFSA. 

16 The selection and use of this study, compared

17 to the targeted field study, must be considered, along with the

18 desired outcomes of the Tier II study. For example, if the

19 desired outcome is to collect pollen, nectar, and bee residue

20 data, that is, pollen sacs, pollen traps, or regurgitated

21 nectar, then the semi-field study would meet those objectives. 

22 If the goal is to obtain targeted field data,

23 that is, measured residues in pollen and nectar directly from

24 plants, the targeted field study would meet those objectives. 

25 The difference in the goals of the Tier II studies is the

26 measured residues of relevant bee matrices, where the tunnel
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1 study design is set up to measure bees and hive products. 

2 Additional detail for the Tier II study design will be

3 presented, following this presentation. 

4 There are several sources of variation related

5 to the anticipated use of the pesticide that should be

6 considered when designing the studies for Tier II Exposure

7 Assessment, including application methods, props, and location. 

8 Application methods employed for a chemical can vary. Because

9 different application methods may result in different levels and

10 types of exposure application practices that are expected to

11 generate high-end exposure concentrations for pesticides should

12 be selected. 

13 In determining which crops to include in field

14 studies, a focus should be placed on registered uses for crops

15 that are highly attractive to bees and that have the highest

16 application rates. For systemic pesticides in particular,

17 different plant species may have differences in uptake,

18 distribution, and metabolism of the test substance.

19 In addition, different plants have different

20 matrices that represent food sources for bees. For example,

21 corn only produces pollen that bees will collect as a food

22 source, whereas, canola produces both nectar and pollen. Some

23 plants, such as cotton, also produce extra floral nectaries

24 which may be present when flowers are not.

25 The sampling scheme is an important

26 consideration in the design of the Tier II studies. Sampling
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1 should target the maximum concentrations found in pollen and

2 nectar, therefore, these studies should sample pollen and nectar

3 at different time points during bloom to evaluate the presence

4 and decline of the compound over time.

5 Sample timing should be selected based on the

6 consideration of the dissipation of the chemical though

7 degradation and transport, away from the plant, as well as

8 systemic transport through the plant to pollen and nectar. For

9 foliar applications, studies should be designed such that the

10 pesticide concentrations are quantified on the day of

11 application and during subsequent days that are selected to

12 represent the concentrations on bees and relevant plant samples.

13 For soil and seed treatment applications,

14 sampling should account for the delayed transport from roots to

15 pollen and nectar. Different field locations have different

16 climates and soils, and therefore, growing conditions at a

17 spacial scale.

18 The locations of the studies should be

19 selected so that they represent anticipated use sites of the

20 chemical. If possible, honey bee specific studies should be

21 conducted in multiple regions that are identified to be of

22 concern. 

23 As a result of these considerations, it may be

24 necessary to conduct targeted studies, using multiple locations,

25 methods, crops, and locations in order to quantify potential

26 pesticide exposures to bees under conditions that are
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1 represented of the anticipated use of the chemical.

2 Because field residue data may exhibit extreme

3 spacial and temporal variability, it is necessary to collect a

4 sufficient amount of data to allow the risk assessor to have an

5 understanding of the central tendency and upper bound exposure

6 that represents the potential environmental exposures. Thank

7 you very much for your time. I would like to open the floor to

8 any clarifying questions the panel may have.

9 DR. DANIEL SCHLENK: Any questions

10 regarding exposure. Again, we're going to be talking about

11 toxicity in the next presentation. Dr. James?

12 DR. ROSALIND JAMES: Did you take into account

13 the formulation at all? Is it at this point? Do you ever take

14 into account formulation?

15 DR. THOMAS STEEGER: At a screening level, we

16 have available to us formulation toxicity data that are

17 submitted for all different types of formulations. And from

18 that, we get a sense for the extent to which different

19 formulations may be toxic - more toxic than the technical-grade

20 active ingredient.

21 To be candid, those data are on mammals. If

22 we have information for existing chemicals that particular

23 formulations may be more toxic than a technical-grade active

24 ingredient based on open literature data or an incident data, we

25 have the purview to require studies looking specifically at

26 those particular formulations.
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1 DR. ROSALIND JAMES: There's a toxicity

2 question, but then also it might affect exposure, like one of

3 the famous cases of microencapsulation where the bees were going

4 after the microcapsules if they were pollen, so they were

5 actually having a higher exposure. So to me, it seems like Tier

6 II might be a place where that came into play, but I wasn't sure

7 if you had considered that.

8 DR. THOMAS STEEGER: The microencapsulated

9 products, particularly like Penncap-M that had particular affect

10 on bees, a lot of that information became available to us

11 through incident data. As we'll talk about it in my

12 presentation, incident data serves as a means for the Agency to

13 sort of ground truth its risk assessments with an understanding

14 of how that chemical is performing under actual use conditions. 

15

16 So it's a critical need of ours to have access

17 to that information as well as open literature information that

18 would also inform us. But you're correct; there are

19 formulations that you would be able to better glean through a

20 higher tier testing. 

21 DR. DANIEL SCHLENK: Dr. Potter.

22 DR. THOMAS POTTER: I have a quick question. 

23 Have you had any studies of this type, the Tier II type or the

24 Tier III type, submitted for your review?

25 DR. THOMAS STEEGER: Yes, we have.

26 DR. THOMAS POTTER: How did it go?
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1 DR. THOMAS STEEGER: I think that the

2 higher tier studies, to date, have been difficult to interpret

3 because they tend to ask or look at very broad questions. One

4 of the things that we're proposing in the White Paper is that as

5 you transition through the various tiers of refinement, the

6 questions that are being asked are intended to address specific

7 uncertainties and that we get away from shotgun approaches to

8 doing field studies to those that are answering very, very

9 refined question. I'm hoping that through that process it'll be

10 much easier to interpret the results of those studies.

11 DR. DANIEL SCHLENK: Something to add?

12 MR. KEITH SAPPINGTON: I would like to just

13 add that based on my experience in looking at a number of field

14 studies, first of all, in those studies, the typical end-use

15 product was used so we did have a sense of the different

16 formulations.

17 But secondly, and this is, I believe, part of

18 the charge questions, is understanding biological significance,

19 visa-vis, statistical significance because we have difficulties

20 with statistical power. It is important, even more, I think, to

21 have a good sense of what is biologically significant from

22 different endpoints.

23 DR. DANIEL SCHLENK: Any other -- Dr.

24 Tarpy.

25 DR. DAVID TARPY: Based on Figures 2 and 3 in

26 the White Paper, if a particular compound is shown in Tier I not
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1 to have any concern, then it does not go on to Tier II or III by

2 default, correct?

3 DR. THOMAS STEEGER: As Steve Bradbury

4 pointed out earlier, the risk quotients that are calculated at a

5 screening level are not viewed in and of themselves. We take

6 into consideration other lines of evidence that might be

7 available, and those would include open literature studies that

8 might've been conducted; field studies that the registrant may

9 have conducted as part of their testing process, and again,

10 incident data. 

11 It's those other lines of evidence that if we

12 feel are in accordance with our understanding of the lack of

13 risk at a screening level, then that would be correct. But if

14 there is information to suggest otherwise, then it would be up

15 to the risk manager to determine whether higher tier levels of

16 refinement would be needed.

17 DR. DAVID TARPY: Have there been examples of

18 compounds at Tier I have shown no concern, but tested that Tier

19 II and actually do have effects at that level?

20 DR. THOMAS STEEGER: I don't want to cite a

21 particular chemical, but I think that some of the current

22 limitations in the testing process where we don't do -- we

23 haven't done larval toxicity testing as a matter of routine, and

24 compounds that can be completely innocuous to adult bees, but

25 quite detrimental, such as insect growth regulators to the

26 larval stage, might make it through that screen but now we are
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1 proposing a means to address both life stages.

2 DR. DAVID TARPY: Thank you.

3 DR. DANIEL SCHLENK: Okay. Any other

4 questions before we break? Let's try to be back about five 'til

5 3:00. 2:55.

6 (Brief recess.)

7 DR. DANIEL SCHLENK: Okay. Our first

8 presentation is by Joseph DeCant.

9 MR. JOSEPH DECANT: Good afternoon. My name

10 is Joseph DeCant, and I'm an ecologist in the Environmental Fate

11 and Effects Division. I'll be talking about the various methods

12 that are available for evaluating the toxicity of pesticides to

13 bees, as outlined in the White Paper. 

14 Throughout this talk, I intend to present our

15 studies. The first step, though, will be to provide an overview

16 of how these toxicity studies relate to each other and the risk

17 assessment process, and the overall tiered approached to

18 toxicity evaluation. 

19 This talk will also encompass the linkages

20 between the different tiers and introduce the triggers that

21 provide the foundation for moving from one tier to the next

22 within the toxicity-testing framework. Finally, I would like to

23 call your attention to Charge Questions 8 through 12, which this

24 presentation will touch on and to which it will provide context.

25

26 In order to understand the importance of the
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1 toxicity studies themselves and their relation to the risk

2 assessment process, I want to recall Table 1 from the White

3 Paper, regarding the various protection goals. Note, that for

4 each protection goal, there are associated assessment endpoints

5 that will be evaluated in the risk assessment. 

6 In addition, there are also associated

7 measurement endpoints that will be evaluated through certain

8 toxicity studies. Therefore, keep in mind that each of the

9 toxicity studies available for the risk assessment should

10 provide one or more measurement endpoints with a clear linkage

11 to an assessment endpoint of concern.

12 As we have already heard in the previous

13 presentations, we have explored methods relating to evaluating

14 exposure, both at the screening level and more refined methods

15 that provide information and actual residues in the crops of

16 interest.

17 The goal of these refinements has been to

18 capture more realistic measures of exposure, albeit at greater

19 expense in terms of the cost of conducting and reviewing such

20 studies. The approach is similar with toxicity studies and

21 their use within the risk assessment process. 

22 Consequently, the tiered approach to toxicity

23 testing is meant to provide a means of evaluating the

24 measurement endpoints described in the previous slide through an

25 iterative evaluation process that reflects a progressively more

26 realistic environment and refines the earlier tier evaluations.
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1 For example, at the screening level, toxicity

2 studies are based on the individual in a laboratory; however, as

3 we move through Tiers II and III, we move to studies that

4 evaluate the entire colony and eventually in the field under

5 actual application scenarios for specific labeled uses.

6 For the first year, the U.S. EPA 40 CFR Part

7 158 data requirements currently require an acute contact

8 toxicity test with young adults identified as guideline

9 850.3020. In addition to this test, Canada also requires an

10 acute oral toxicity test with young adult bees.

11 A recent EFED Guidance document, the Interim

12 Guidance on Honey Bee Data Requirements, provided and overview

13 of various studies for use of the screening level and included

14 both the acute contact and oral studies with young adult bees

15 and in an in vitro study for the larval stage of honey bees.

16 Similarly, the proposed approach presented in

17 the White Paper expands upon the current U.S. EPA requirement of

18 the acute contact study to include these two additional oral

19 exposure studies. This expansion is consistent with the

20 USDA-led workshop, as well as two recent publications; one from

21 a 2011 SETAC Pellston Workshop, as well as one from the European

22 Food Safety Authority in 2012.

23 Through the expanded Tier I battery of

24 toxicity tests, we can evaluate the toxicity of a pesticide for

25 both contact and dietary routes of exposure. I also want to

26 mention at this time that these Tier I toxicity tests will be
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1 used to evaluate the stressors of concern to honey bees. 

2 Therefore, in some cases, degradates may be identified to be of

3 concern as well. In these cases, toxicity tests will evaluate

4 the parent compound as well as any potentially toxic degradates.

5

6 A U.S. EPA and OECD Guideline evaluate acute

7 contact toxicity to young adult honey bees. The study produces

8 a 48-hour LD50 or a lethal dose at which 50 percent of the test

9 bees dies. The study can be expanded to 96 hours if it is

10 appropriate, as in the case of chemicals that showed delayed

11 toxicity.

12 The most sensitive LD50 values are intended to

13 be used in combination with the appropriate estimated exposure

14 value to derive risk quotients for the Tier I risk assessment. 

15 In cases where a pesticide is not applied by foliar spray, the

16 acute contact toxicity data may not be necessary for assessing

17 the risks of a chemical because for soil applications, seed

18 treatments and tree trunk applications, dietary exposure is

19 expected to be the predominant route.

20 Depending on the outcome of these toxicity

21 tests, pesticides are classified as practically non-toxic,

22 moderately toxic, or highly toxic to bees on an acute exposure

23 basis. If the acute contact LD50 is less than 11 micrograms of

24 active ingredient per bee, additional testing may be required in

25 the form of a foliar residue study to determine the duration

26 over which field weathered foliar residues remain toxic to honey
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1 bees. 

2 This guideline study, 850.3030, provides an

3 RT25 or the time at which a chemical produces 25 percent or less

4 mortality. The RT25 will not be used to quantitatively evaluate

5 risk, but rather the study will provide another line of evidence

6 related to the contact toxicity of residues on foliages over

7 time.

8 Although not currently required by EPA, Canada

9 and the European Union routinely require acute oral toxicity

10 data for adult worker bees. The acute oral toxicity study is a

11 laboratory test method designed to assess a mortality of young

12 adult worker bees, following a single oral dose of a pesticide. 

13

14 A standard OECD Guideline exists for this

15 study. Similar to the acute contact toxicity test required by

16 EPA, the study provides a 48-hour LD50 value and can be extended

17 to include a 96-hour LD50 if mortality is demonstrated to

18 increase between 24 and 48 hours.

19 The proposed Tier I Risk Assessment Methods

20 involves using the 48-hour or 96-hour LD50 generated from this

21 study to derive risk quotients representing risk to adult bees

22 exposed to pesticides through the diet, following foliar spray

23 application, soil application, seed treatments, and tree trunk

24 treatments of pesticides. 

25 The previous two slides show the studies that

26 will provide measures of toxicity to adult honey bees. Unlike
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1 with the acute contact in oral toxicity studies for adult worker

2 bees, established regulatory test guidelines for assessing

3 larval toxicity do not exist. Given that larvae may be exposed

4 to pesticides through the diet, the lack of understanding of the

5 toxicity of a chemical on larvae is considered to be a

6 significant data gap.

7 Therefore, efforts are underway to identify

8 critical design elements of an acute oral larval toxicity study

9 that may be used to understand the toxicity of a chemical to

10 this lifestage of worker bees. One option is a field-feeding

11 design that looks at impacts to the brood at the whole hive

12 level. This study is considered a semi-field design and will be

13 discussed later in the presentation.

14 Another option is an in vitro assay based on

15 the method, developed by Aupinel et al., with larval honey bees,

16 which will provide a screening evaluation of toxicity to

17 immature stages. 

18 In this method, first instar bee larvae are

19 transferred into 48-well plates and fed a synthetic royal jelly

20 sucrose solution mixture containing known quantities of the test

21 chemical over the 48-hour period. 

22 This study can provide an LD50 but it can also

23 potentially provide a no effect concentration, hereafter

24 referred to as NOAEC, if there is sufficient replication. 

25 The methodology has been ring tested and a

26 review indicated that additional adjustment in the method may be
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1 needed to adjust potential sources of variability, including

2 colony origin of the brood, season and larval heterogenedia

3 grafting. It is also important to note that the study design

4 has a number of uncertainties associated with it, including

5 laboratory conditions in which the larvae must survive outside

6 of the hive environment. 

7 In addition, the larvae are directly fed an

8 artificial mixture of royal jelly and sucrose solution, which is

9 different from what the larvae would be fed, after processing,

10 by adult nurse bees in the hive.

11 The studies that I have discussed so far are

12 meant to evaluate acute toxicity. Chronic laboratory-based

13 toxicity testing has not been routinely required on individual

14 bees in the U.S.; rather information on the potential chronic

15 toxicity of pesticides to bees has historically been discerned

16 from whole colony studies conducted under the semi-field or full

17 field conditions.

18 The proposed Tier I risk assessment method for

19 bees includes quantification of the toxicity of larvae and adult

20 worker bees following chronic exposures to pesticides; however,

21 no formal guidelines have been developed, to date, for

22 conducting chronic toxicity tests with either adult or larval

23 bees.

24 Efforts are underway to identify design

25 elements of chronic larval and adult toxicity studies that may

26 be used to understand the toxicity of a chemical to worker bees
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1 and test protocols already exist for these types of studies. A

2 10-day oral chronic toxicity test with young adults would be

3 used to quantitatively evaluate risk to adults.

4 The USDA technical working group report noted

5 that this type of chronic toxicity test with individual adult

6 bees has already been conducted to support registrations in the

7 EU. These tests consist of collecting newly emerged juvenile

8 bees from the comb and placing 50 of these bees per cage. 

9 A sufficient number of replicates are run,

10 along with suitable negative and reference controls to conduct

11 hypothesis testing to establish a NOAEC and LOAEC. The bees are

12 fed a sucrose solution; however, no protein source, such as

13 pollen, is provided. 

14 Exposure is through spikes made to the sucrose

15 solution and cages of bees are provided 10 milliliters of test

16 solution every other day. Bees are observed for a total of 10

17 days, and mortality is a primary measurement endpoint. The in

18 vitro chronic larval toxicity study is similar in design to the

19 previously mentioned acute larval toxicity study, though with a

20 longer duration of feeding and monitoring. 

21 It should also deliver in NOAEC as well when

22 monitoring of toxic effects is extended to seven days; however,

23 at this point, the chronic adult and larval study methods

24 require further evaluation prior to use in a regulatory setting.

25 Another approach is to derive a no effect

26 concentration from an acute toxicity study; however, because it
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1 is a regression-based study targeted to ascertain the LD50, the

2 dosing regime is not intended to assess the no effect level. 

3 Consequently, there is uncertainty as to the actual

4 concentration where no effects occur. 

5 Hypothesis-based testing in chronic studies is

6 more appropriate as the dose election targets the low

7 concentrations around the no effect level. Additional data are

8 often available that may be useful in characterizing the effects

9 of a pesticide in bees. 

10 The first major bullet notes scientific

11 literature which often includes toxicity data involving bees. 

12 Acute toxicity data for honey bees may be available in

13 scientifically valid studies from the open literature, for

14 example, peer-reviewed journal articles. But these studies from

15 the open literature must be sufficiently robust, based on EFED

16 open literature review guides. 

17 In some cases, these values may be used to

18 derive risk quotients when these endpoints are lower than those

19 found in registrant-submitted studies. 

20 The second major bullet point relates to data

21 on non-target arthropods, other than bees. These data are not

22 used to derive risk quotients, but are considered in the effects

23 characterization. For example, data are often available from

24 registrant-submitted acute toxicity studies involving other

25 non-target arthropods, such as the green lacewing and parasitoid

26 wasps. 
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1 The final major bullet point addresses reports

2 of honey bee mortality events -- that is, bee kill incident

3 reports -- following pesticide exposures. These incident

4 reports may be useful in characterizing the available risk

5 estimates from laboratory toxicity data.

6 Although the proposed measurement endpoints

7 for acute and chronic exposures are based on mortality, there

8 are an increasing number of sublethal endpoints reported in Apis

9 and non-Apis toxicity studies, which may be conceptually

10 relevant to the proposed assessment endpoints.

11 Sublethal effects therefore may also provide

12 additional lines of evidence in the risk assessment. These

13 effects are those that do not directly cause the death of an

14 individual organism and are listed in the slide. 

15 The acute and chronic studies may provide

16 information on sublethal effects, but they were designed to

17 evaluate mortality and not address concentrations that would

18 elicit sublethal effects. This is the reason that the EFSA

19 Scientific Opinion of pollinator risk assessment concluded that

20 the current battery of toxicity studies are not adequate at

21 addressing sublethal effects. It recommended that sublethal

22 effects are important indicators of bee health and vitality and

23 must be accounted for in the risk assessment process.

24 These endpoints also represent a challenge in

25 that in most cases, they do not provide a direct connection to

26 our assessment endpoints. Before using these endpoints to
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1 derive risk quotients, the relevancy of these sublethal

2 measurement endpoints for evaluating assessment endpoints needs

3 to be established through quantitative links to colony strength

4 and survival.

5 As the last bullet notes, although such

6 endpoints may not be used quantitatively to estimate potential

7 effects on assessment endpoints, studies on sublethal effects

8 could potentially be used qualitatively to describe effects

9 which may not have clearly established relationships to colony

10 level effects or for explaining mechanisms by which colony level

11 effects might occur. 

12 Consequently, these sublethal effects will be

13 used to qualitatively describe risk in the assessment until

14 further research has been conducted that establishes a link

15 between these effects and assessment endpoints. 

16 This slide provides a representation as to how

17 the data will be used in the risk assessment. The endpoints for

18 quantitative use, which are depicted in the green box, will be

19 combined with either model estimates of exposure or measured

20 residue levels in order to develop risk quotients.

21 The information contained in the sources in

22 the red box will be used qualitatively to further describe the

23 risk that the risk assessor is quantified. This risk analysis

24 will then be related to the assessment endpoints of concern,

25 highlighted in the blue box, and will be used to inform the risk

26 manager, regarding the screening level of results of the use of
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1 a particular pesticide. 

2 At the Tier I screening level, effects data

3 may primarily be based on laboratory studies, while exposure

4 estimates may be based on models and/or conservative default

5 values, as well as more refined measures of exposure from field

6 monitoring studies. 

7 These studies are evaluated using relatively

8 conservative assumptions to provide high-end estimates of

9 potential risk such that if a chemical passes a screen, the

10 presumption of low risk to non-target organisms is considered,

11 in most cases, protected. 

12 As part of the tier process, there's a

13 progression from the left box to the right box in that higher

14 tiered studies are intended to reflect increasing levels of

15 realism, albeit from a less controlled testing environment, in

16 terms of how organisms may be exposed and the nature of the

17 effects which results from such exposure. 

18 Also, as part of the tier testing process,

19 higher tiered refinements shift the focus from exposure and

20 effects on individual bees to that of the intact colony. The

21 social nature of honey bees requires extensive interaction and

22 communication among various castes of bees for successful

23 development and propagation of colonies. 

24 As a result, numerous linkages exist between

25 pesticide exposure and effects on individual bees and their

26 collective impact on honey bee colony health, which are not
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1 readily assessed in the laboratory. 

2 Additionally, the Tier II semi-field studies

3 provide a means of assessing both exposure and effects that are

4 less conservative and more realistic than the methods used in

5 laboratory testing. It is important to note that Tier II

6 studies address the limitation of the Tier I screening level

7 related to sublethal effects, chronic exposure and potential

8 degradate toxicity by integrating these factors into the colony

9 level measurement endpoints. 

10 Thus, Tier II studies may be conducted if

11 concerns remain from Tier I levels of the risk assessment and

12 further information is needed for a more refined estimate of

13 potential risk to honey bees. Ultimately, the transition from

14 Tier I to Tier II is based on more than just the relationship

15 between exposure and toxicity.

16 In the case of a chemical that either passes

17 or fails a screen, the risk manager has a number of different

18 types of information available, including the previously

19 described toxicity and exposure data, other lines of evidence,

20 such as incidents, uncertainties related to the characteristics

21 of the chemical itself and whether the Tier I analysis has

22 substantial gaps and knowledge, potential mitigation options,

23 and finally, the option to off-label specific uses. 

24 Consequently, Tier II studies are based on the

25 needs of the risk manager and are based on information of the

26 impact of the pesticide, as identified, in lower tier studies. 
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1 Unlike the screening level studies, Tier II

2 studies with bees are not typically conducted in the laboratory,

3 but rather focused on relatively controlled field conditions,

4 referred to as semi-field. In these studies, the movement of

5 the bees to and from the colony is restricted through the use of

6 enclosures containing crops treated with the pesticide, or

7 feeding bees a pesticide-spiked diet. 

8 Therefore, a Tier II toxicity study is meant

9 to refine the Tier I assessment by providing a means of

10 evaluating the impacts to the assessment endpoints at the whole

11 hive level. Compared to laboratory studies where exposure is to

12 an individual organism and typically through a single route,

13 such as contact or oral exposure, semi-field studies can provide

14 clear lines of evidence for linking multiple routes of exposure

15 to adverse ecological effects at the colony level. 

16 Semi-field studies are also useful for

17 determining the extent to which effects on invidivudal bees

18 identified in Tier I laboratory studies are expressed at the

19 colony level. The decision to move to higher tier testing is

20 also supported by current U.S. EPA 40 CFR Part 158 data

21 requirements for the testing of pesticides. 

22 The conditions for the transition to higher

23 tier studies are based on the residual toxicity data that

24 indicate there's the potential for effects other than mortality

25 on the colony, and data that indicate potential chronic,

26 reproductive, or behavioral effects.
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1 Although the current EPA data requirements for

2 pesticides identified in the 40 CFR Part 158 do not specifically

3 include semi-field studies, the Guideline 850.3040 on field

4 testing for pollinators is general and would accommodate such

5 studies. 

6 The design of the Tier II semi-field study is

7 based upon the specific issues raised at the initial tiers of

8 the risk assessment. The specific design is flexible and,

9 consequently, can be tailored to suit the needs of the risk

10 assessment. 

11 In general, there are two basic designs of the

12 semi-field studies. One employs an enclosure, often called a

13 tunnel, as seen in the top picture of the slide, and can find

14 small nucleus hives to ensure that the bees forage on a treated

15 crop. Smaller hives are used in this type of study in order to

16 prevent the colony from overwhelming the forage base within the

17 enclosures. 

18 The other design allows bees to freely move,

19 but provides artificial food sources, as seen by the lower

20 picture with pollen cake, spiked with a pesticide of interest. 

21 Although the feeding study designs do not involve enclosures and

22 bees are allowed to forage freely, the studies are considered

23 semi-field since test organisms are provided pesticide-spiked

24 food, which does not occur in full field studies. Both designs

25 offer relatively controlled conditions in comparison to Tier III

26 full field studies, yet enable whole hive evaluations to be on
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1 Tier I screening laboratory studies.

2 As is evident, there are a range of different

3 designs and the actual test structure will depend on the

4 specific questions asked in the risk assessment. There are a

5 number of already established guidelines for the semi-field

6 tunnel study. 

7 The typical designs of semi-field studies

8 include a highly pollinator attractive crop or surrogate species

9 such as phacelia that is grown outdoors in an enclosed system

10 with confined exposure. 

11 The test crop is grown under good

12 agricultural practices using the maximum application rate with

13 the minimum application interval. The test crop provides the

14 source of pollen and nectar. The test design employs a tunnel

15 in which small nucleus hives containing about 5,000 worker bees,

16 brood in all stages, and stores of pollen and nectar are placed.

17 A test crop is used at or near full bloom that

18 is representative of high-end exposure condition for the given

19 type of application method and rate. For foliar sprays, the

20 hives are introduced in the tunnel two to three days before

21 pesticide application and hive condition is assessed while the

22 bees acclimate to the enclosure.

23 Pesticides can then be applied at peak bee

24 foraging activity to maximize exposure to foraging bees. Hives

25 are kept in the tunnel for a period of seven to ten days, after

26 the first pesticide application and allowed to forage on the
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1 treated crop. The hives are then removed from the tunnel and

2 moved to an area of minimal pesticide use to forage on untreated

3 areas with continued monitoring, which is usually up to a total

4 study period of 28 days but sometimes longer.

5 A reference chemical is also frequently tested

6 to demonstrate the application method used that actually results

7 in exposure to bees, as well as the ability of the study to

8 detect and quantify effects. Reference chemical selection is

9 dependent on the expected mode of action of the pesticide under

10 consideration, which would be a chemical with known toxicity to

11 the hive.

12 For pesticides that are expected to directly

13 impact brood development, an insect growth regulator, such as

14 fenoxycarb, is typically used. For pesticides that are expected

15 to elicit effects directly on adults and indirectly on brood,

16 through rapid knockdown, a reference chemical that acts

17 similarly, such as dimethoate, is often selected. 

18 In addition to the monitoring of toxic

19 effects, residues in pollen and nectar on the flowering crop can

20 also be provided in order to assess the levels of exposures to

21 the honey bee colonies. 

22 As the last bullet highlights, it is important

23 to note that this design can be used to evaluate each of the

24 application methods, depending on when the tunnels are in place.

25 Moving to the feeding designs, one design

26 typically addresses brood toxicity. The study is based on a
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1 method by Oomen, et al. in 1992, which uses hives that can

2 freely forage in order to test for the possibility of adverse

3 effects on the brood within a honey bee hive. The basic design

4 uses one liter of a sucrose solution that is placed in a feeder

5 near a full-sized honey bee hive. The sucrose solution is

6 either a control without contamination or a solution spiked with

7 a known concentration of a pesticide. 

8 The solution is provided as a food source and

9 once the hive has collected all of the solution, weekly

10 measurements are taken on brood development within the hives. 

11 In addition to the data on brood development, data on adults and

12 brood mortality estimates can be obtained through counts of bees

13 collected in dead bee traps, placed at the entrance of the

14 colony.

15 The test chemical concentrations are not

16 necessarily meant to be reflective of an environmentally

17 relevant concentrations, but rather may be exaggerated in order

18 to screen chemicals for possible impacts to the brood when in a

19 whole hive situation. 

20 The EPA has also explored the modification of

21 the feeding design to expand it to adults as well as brood

22 toxicity, to incorporate multiple dietary pathways of exposure

23 and to increase the duration of the study if necessary. It

24 should be noted that these refinements have not been well

25 vetted, but a number of field feeding designs are increasingly

26 found in the open literature, reflecting the potential for this
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1 type of experiment to address uncertainties related to dietary

2 exposure. 

3 For this type of study, spiked food, as in

4 pollen patties, sucrose solution or both, can be fed ad libitum

5 to full size honey bee hives of at least 10,000 bees over a

6 specified duration of time and during a specified time of the

7 year. Different concentrations of the chemical and food are

8 used with the intent of identifying a NOAEC and LOAEC. 

9 The treatment levels are either selected from

10 concentrations of concern that have been identified in the body

11 of literature or by environmentally relevant concentrations

12 found in targeted monitoring studies for residues in pollen and

13 nectar. Therefore, the design should be based on

14 environmentally relevant concentrations in an attempt to

15 ascertain where the no effect level is found, relative to these

16 concentrations.

17 The design does not employ tunnels, so the

18 bees are allowed to forage freely. As the second bullet notes,

19 while the potential exists for bees to forage in areas where

20 other pesticides may be in use, test site selection is intended

21 to minimize the potential for such exposure. Consequently, the

22 test hives are placed in an area of low pesticide use in order

23 to minimize any exposure to other pesticides. 

24 Related to the third bullet, during a field

25 feeding study, exposure can be controlled to a greater extent

26 than a full field study through the use of pollen traps, to
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1 limit incoming pollen and encourage feeding on the treated food

2 source. As previously mentioned, the enclosures used in the

3 typical semi-field design introduced stress to the colony and

4 effective brood performance on the colonies in as little as

5 seven to ten days.

6 Moving to the fourth bullet, the open field

7 feeding design allows a colony to freely forage, thereby

8 eliminating the stress of the enclosure. Furthermore, as the

9 fifth bullet states, the study hives can be large enough to

10 survive through overwintering, unlike the smaller nucleus hives

11 typically used in the tunnel studies. 

12 The use of larger colonies enables a study to

13 proceed over a longer duration than other semi-field studies in

14 an enclosure. With this flexibility, the full field feeding

15 design can be used to evaluate a colony's response to either

16 short-term or a long-term exposure condition to evaluate

17 overwintering performance of the hives.

18 Consequently, this study can address the

19 uncertainties identified at the lower tier levels or even at the

20 initial stages of the Tier II toxicity assessment with other

21 semi-field studies, specifically when oral exposures is the

22 pathway of concern. 

23 Finally, as the last bullet states, the

24 measurement endpoints can be taken at specified time periods to

25 evaluate the hive at the most relevant time points, relative to

26 the nature and concern of the chemical. 
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1 The results of this study are intended to

2 reflect to the extent to which specific effects may occur in

3 response to various pesticide levels in pollen and nectar when

4 compared to measured residues from targeted monitoring studies.

5 Assuming that targeted and monitoring studies

6 are available to design the field feeding study or are available

7 following the completion of the feeding study, these data would

8 provide a measure of the level of residues in pollen and nectar

9 from various crops. This feeding study could then provide a

10 context for these residue data at the whole hive level. 

11 Given the assumption that exposure to

12 concentrations in the pollen and nectar are equivalent to

13 exposure from concentrations in the sucrose solution and/or

14 pollen patties, the study would reveal to what extent the risk

15 assessor could anticipate effects at the residue levels in the

16 different crops by comparing the concentrations that produce an

17 effect and measured concentrations in pollen and nectar.

18 The comparisons between the no effect level

19 and the residue levels are not quantitative comparisons to be

20 used as triggers, but rather a means of understanding the

21 potential impacts on the colony, given realistic residue levels

22 in pollen and/or nectar from various crops.

23 The study results could also further describe

24 the nature of these impacts to the risk manager to consider with

25 other lines of evidence. The measurement endpoints are

26 determined by the protection goals and remaining uncertainties
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1 from the lower tiers in coordination with the risk manager.

2 Measurement endpoints include adult bee

3 mortality, brood condition, hive strength, queen health, and

4 hive resources which are measured at various points over the

5 duration of the study.

6 The endpoints should also be specific to the

7 uncertainties identified earlier in the tiered process as they

8 relate to the assessment endpoints, so as to minimize the number

9 of endpoints to be collected because continued opening of the

10 hives can add stress to the colony.

11 Another major strength of these semi-field

12 studies is that they integrate effects from toxic degradates,

13 chronic exposure and sublethal effects. Two prominent

14 uncertainties at the Tier I screening level are that the

15 laboratory studies either are inadequate to screen for various

16 types of sublethal effects and there are no currently

17 established protocols to evaluate chronic toxicity.

18 The endpoints measured in the semi-field

19 studies integrate each of the previously mentioned factors and

20 that have toxic degradates, chronic exposure of sublethal

21 effects impact these measurement endpoints which are also tied

22 to assessment endpoints of concern and the study reflects the

23 sum of both these lethal and sublethal effects.

24 In addition to the strength of the semi-field

25 studies, it is also important to note that there are limitations

26 to these study designs as well. Since the studies are conducted
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1 outside of a laboratory environment, feeding and tunnel designs

2 are subject to the changes of weather, as noted in the OECD

3 Guidance for Semi-Field Studies, the test cannot be performed

4 under adverse weather conditions. In addition, there is a high

5 degree of variability in the measurement endpoints, not only

6 between hives, but within the same hive over time as well. 

7 Related specifically to the tunnel design

8 because bees are not allowed to forage freely over large areas,

9 the quantity and quality of food they are able to gather may be

10 compromised, compared to open field conditions. Experience with

11 these studies indicates that confinement stress on foraging

12 bees, among other factors, often leads to adverse effects on

13 egg, larval, and pupil abundance, with increasing duration of

14 confinement in the tunnels. 

15 As a result, hives can only be kept in tunnels

16 for a relatively short period of time that is seven to ten days,

17 to ensure adequate condition of controls for comparison. Even

18 during this time declines in control hive condition are commonly

19 encountered.

20 Furthermore, effects at other critical time

21 periods of hive development, such as overwintering, are not

22 addressed. Although such effects could be assessed if the post

23 tunnel observation period were extended and colonies were of

24 sufficient strength to support overwintering.

25 For the feeding studies, the bees are allowed

26 to freely forage, so consumption of the spiked food provided to
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1 the colony depends on a range of factors that may not be

2 constant for all experimental conditions. For example, if

3 alternative forage is ample, then the bees may use less spiked

4 sucrose solution or spiked pollen then when outside forage

5 sources are scarce. Thus, this type of study, which does not

6 use an enclosure, is subject to numerous sources of variability

7 at the field level, including weather, disease, parasites and

8 exposure to other pesticides present in the environment. 

9 On the second bullet, the feeding aspect is

10 artificial and may not accurately mimic typical foraging

11 behavior by honey bees, or the stability of the test material in

12 such matrices under natural conditions. 

13 The third bullet, the foragers only need to

14 move minimal distances to the food source, therefore, use of an

15 internal spiked food source can confound efforts to integrate

16 impacts on foraging behavior when foragers feed on a treated

17 crop.

18 The fourth bullet, the performance of the

19 colonies has not been evaluated in relation to long-term

20 continual use of the artificial food source. And the final

21 bullet notes that as a study increases in duration, it also

22 increases in complexity and potentially confounding factors,

23 such as the interventions required to enable a colony to

24 overwinter.

25 It is essential to highlight the importance of

26 establishing the statistical significance necessary to
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1 adequately evaluate a desired level of protection based on

2 biological significance. The issue of statistical and

3 biological significance relates to both semi-field and full

4 field studies; the latter of which will be explored later in the

5 presentation. 

6 Both of these types of studies seek to

7 evaluate the potential for an effect, relative to the control

8 group at a specified application rate or concentration within a

9 supplied food source. In some cases, we are able to evaluate

10 time trends and hive performance, though these are not routinely

11 reported. Yet there is a high level of variability typically

12 associated with the measurement endpoints in both types of

13 studies, where variability may be so high that it is difficult

14 to isolate any effect.

15 While 10 percent mortality has been

16 established as a level of protection at the screening level, a

17 level of biological significance has not yet been established

18 that links measurement endpoints at either the semi-field or the

19 full field level to the assessment endpoints. Furthermore,

20 because of compensatory mechanisms within the colony, apparent

21 effects may only be transitory. 

22 Well designed semi-field studies, despite

23 their uncertainties, should be able to inform the risk manager

24 by providing clear lines of evidence linking multiple routes of

25 exposure to adverse ecological effects and provide for a more

26 robust characterization of the risk related to the assessment
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1 endpoints. 

2 Semi-field studies assess both exposure and

3 effects at the colony level that are less conservative and more

4 realistic than laboratory assays on the individual bees. These

5 studies also integrate effects from toxic degradates, sublethal

6 effects and chronic exposure. 

7 Furthermore, the Tier II feeding study for

8 adults and brood should provide a no effect concentration that

9 can then be compared to residue levels in various crops

10 identified in targeted residue studies. 

11 Unlike the Tier I toxicity studies that are

12 used to calculate risk quotients, the use of the Tier II studies

13 will be used to qualitatively evaluate risk and to help

14 characterize the quantitative risk estimates derived from

15 laboratory studies on the individual at the first tier. 

16 Consequently, semi-field studies will help

17 inform the risk manager regarding any need for mitigation

18 options or options for further refinement. When uncertainties

19 remain from the Tier II assessment or risk cannot be mitigated,

20 the risk manager may decide that additional data are necessary

21 in the form of Tier III full field studies. 

22 The transition to Tier III is based on

23 addressing remaining uncertainties at the previous tiers. This

24 slide highlights some of the major uncertainties inherent to the

25 semi-field study designs and how the Tier III full field study

26 may address those design limitations. In the case of the tunnel
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1 study, a full field study does not employ tunnels and therefore

2 eliminates the stress of the tunnel on the various aspects of

3 colony functioning, as well as limitations in the size of the

4 colonies and the duration of the study. 

5 Related to the feeding designs, a Tier III

6 full field study allows hives to naturally forage and it also

7 allows a study to integrate any impacts on the foragers that

8 visit the treated crop or consequences to the hive based on

9 foraging on the treated crop.

10 In summary, the full field study overcomes the

11 major limitations of the stress placed on the colonies from the

12 enclosure in the tunnel design and the artificial exposure

13 pathway in the feeding designs. 

14 A Tier III study should further refine any

15 uncertainties identified by the Tier II assessment, related to

16 the assessment endpoints. The Tier III study is a full field

17 design that provides the highest level of refinement of the

18 tunnel and feeding study designs. 

19 Consequently, Tier III effects assessments are

20 intended to represent highly refined studies that address

21 specific uncertainties and/or risks identified in lower tiered

22 studies.

23 Similar to the transition from Tier I to Tier

24 II, the risk manager will consider the availability of multiple

25 sources of information. The need to transition to Tier III is

26 therefore not an automatic requirement, but rather, based on
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1 multiple lines of evidence. 

2 A field study serves as a means of addressing

3 uncertainties raised in lower tier studies conducted either in

4 the laboratory or under semi-field conditions and can be useful

5 in examining the effects of pesticides with extended residual

6 toxicity. 

7 However, a full field study is intended to

8 represent realistic application conditions, whereby the test

9 substance is applied to a specific crop in which bees are

10 foraging freely, without the use of an enclosure, and whereby

11 test colonies are primarily exposed through residues carry back

12 to the hive by the forager bees.

13 Consequently, a full field study is also the

14 most resource-intensive, relative to studies used for the Tier I

15 and II assessments, to conduct in terms of both time and money. 

16 These studies can also be the most difficult to interpret given

17 the level of associated variability. Therefore, they can

18 represent a significant investment to regulatory agencies in

19 terms of the resources that are required to review these

20 studies, but these studies may also provide contacts relative to

21 other lines of evidence. 

22 By serving as a study to refine the

23 uncertainties and effects identified in the previous tiers, the

24 field study is also meant to test for the potential of an effect

25 and not the mechanistic cause of an effect. Furthermore, the

26 Tier III field study should have a focused risk hypothesis, that
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1 is, a risk hypothesis informed by and built upon the previous

2 tiers. 

3 There are a number of guidance documents

4 related to the conduct of a field study. Currently, the EPA has

5 guidance for conducting the pollinator field study. 

6 Similarly, the EPPO Guidance also addresses

7 pollinator field study design. Regarding the first major

8 bullet, the EPA field pollinator guidance does not provide much

9 detail, but rather, provides flexibility for designing a study

10 based on the needs of the risk assessment. 

11 The second major bullet mentions Guideline

12 850.2500. Although intended as guidance for conducting field

13 studies with mammals and birds, this EPA Guidance titled, "Field

14 Testing for Terrestrial Wildlife" provides very useful

15 information on study design elements to consider for field

16 testing. 

17 This guidance addresses the basic purpose of a

18 field study, geographic area and selection of study sites,

19 sample size, and the test conditions of the study. EPPO

20 Guidance provides more specific detail that can be used to

21 design a field pollinator study with honey bees in terms of

22 experimental conditions, such as selection of the crop,

23 placement of the colonies, relative to the treated field, size

24 of the colonies and plot size. 

25 Also, application of treatments, such as use

26 of controls, timing of application and rates; and finally, mode
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1 of assessment, such as timing and frequency of assessments. 

2 Similar to Tier II studies, full field studies

3 are flexible in terms of their specific design, as they are

4 based on uncertainties highlighted in the previous tiers, as

5 well as the nature of the pesticide. Consequently, registrants

6 should submit protocols for the field study prior to study

7 initiation in order to identify the key design elements based on

8 specific risk management concerns. In general, though, there

9 are design elements common to most field studies. 

10 The second bullet notes that a field study for

11 honey bees, in general, should be conducted under actual use

12 conditions in which a typical end-use product is used at its

13 maximum application rate, frequency and method, as described in

14 the label for a specific crop. The design should reflect a

15 realistic foraging environment and exposure system in the field

16 where the pesticide is to be applied.

17 The third bullet notes that the study is

18 conducted in a crop grown outdoors without enclosures. 

19 Consequently, the primary exposure route is to foragers that are

20 allowed to freely fly and forage. 

21 The fourth bullet notes that the crop is

22 subject to good agricultural practices. And the fifth bullet

23 mentions the size of the hives, each with a minimum of

24 approximately 10,000 bees which are placed within or at the edge

25 of treated or controlled flowering plots and monitored for a

26 specified period of time, depending on the concerns of the
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1 chemical, as in the case of a systemic compound that is

2 persistent in high food stores.

3 Finally, the last bullet deals with the

4 duration of the study. The evaluation of the hive should

5 ideally proceed for a minimum of 50 days, based on the lifecycle

6 of the honey bee within the colony. Because hives may differ in

7 size, colonies should be distributed as equitably as possible

8 between treatments. The colonies should be in position

9 approximately two to three days before the trial in order to

10 acclimate to the study site. 

11 If the chemical is a foliar spray, treatments

12 should be applied when the test crop is in full flower during

13 the daytime when bees are demonstrated to be actively foraging

14 on the test crop. Hives should be queen ripe; that is to say

15 have a single functional queen. Use sister queens that is a

16 common maternal source and be of similar strength at the start

17 of the study. 

18 Equipment should be free of other chemicals

19 and the hives should have low incidences of disease and

20 parasites, though it is nearly impossible to use hives complete

21 free of disease and pests. Unlike Tier II studies, there are

22 currently no pesticides identified as an adequate positive

23 control at the full field Tier III level. 

24 The range of endpoints that may be measured,

25 as well as the selection of endpoints in the Tier III full field

26 study are similar to those in the Tier II semi-field study. As
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1 mentioned in the Tier II section, the endpoints should also be

2 specific to the uncertainties identified earlier in the tier

3 process as they relate to the assessment endpoint so as to

4 minimize the number of endpoints to be collected and the

5 continued opening of the hives.

6 Also similar to the Tier II study designs,

7 Tier III studies integrate toxic degradates, chronic exposure

8 and sublethal effects into the measurement endpoints. A major

9 advantage of the Tier III field study is that the duration of

10 the study with possible overwintering and the consequent timing

11 of measurements are flexible and could be suited to the

12 properties of the chemical under actual use conditions.

13 In order to adequately interpret a field

14 study, it would be helpful for the study to properly evaluate

15 exposure. It can be difficult to evaluate the effects of the

16 test chemical within a full field study because the design

17 allows for bees to forage freely and to obtain food both onsite

18 and off the treated site. 

19 In studies with limited bloom density on the

20 treated crop and extensive alternative forage in the vicinity,

21 bees may actually forage very little in the test plot and this

22 may lead to an underestimation of potential effects on the hive

23 due to reduced exposure or alternatively to an inability to

24 detect treatment effects due to controlled contamination 

25 There are a number of ways of measuring the

26 degree to which the colony is exposed to the test substance. 
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1 First, pollen traps are fixed at the entrances of the hives,

2 force pollen off of the legs of a percentage of foragers when

3 they return to the hive. This pollen can then be analyzed for

4 residues.

5 Second, freshly stored nectar and pollen or

6 stored bee bread and honey can be measured in the hives to

7 determine the level of residues that the bees are storing for

8 consumption or processing prior to feeding of the queen and

9 brood.

10 Measures of residues in the blossoms would

11 provide a comparison of stored concentrations with those

12 collected from the blossoms. When pollen is collected from the

13 pollen trap, the study may analyze floral origin and percent

14 composition of the pollen. Some pollen from the flowers may

15 also be collected by the bees and mixed with the nectar when it

16 is stored. So in addition to sampling of residues and freshly

17 stored nectar, pollen could potentially be obtained from within

18 this collected nectar to determine the floral origin of the

19 nectar as well. 

20 Finally, qualitative measures of foraging

21 activity, potential quantitative measures of the return of

22 forager bees and quantitative measure of bloom density can

23 provide information on the potential attractiveness of the crop

24 of interest. 

25 The Tier III full field study is complex and

26 difficult to carry out. So there are a number of design
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1 challenges that we face and need to address. Consequently, we

2 are requesting feedback on the design challenges of the field

3 studies and our suggestions for improving these designs that

4 will be covered later in the presentation. 

5 Concerning the design challenges, honey bees

6 have an extensive foraging range. Given an alternative to

7 forage, honey bees may minimally forage on the test fields as

8 there is no means of controlling where honey bees forage. In

9 addition, uncontaminated food reserves collected prior to study

10 initiation may result in delayed exposure of the colonies to the

11 test substance, as these previously collected food reserves may

12 be used first.

13 The weather can also affect the ability of

14 bees to forage, where similar to the Tier II studies, adverse

15 weather may prevent honey bees from foraging on a test crop. 

16 Finally, to date, typical plot sizes in field studies are

17 minimal, relative to both the representative acreage of some

18 crops in North America and the potential foraging radius of the

19 honey bee. 

20 This slide represents the range of plot sizes

21 for typical field studies ranging in sizes from two and a half

22 to 13 acres, which equates to .004 to .02 square miles. 

23 Relative to the maximum foraging area of the honey bee colony,

24 based on a five-mile foraging radius, this represents .0051 to

25 .025 percent of the potential maximum foraging area. The

26 question then remains: what size a plot would satisfy the needs
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1 of the colony to ensure that it forages to the maximum extent on

2 the treated field; especially considering that replication of

3 the colonies at the within plot level is typically three to four

4 full size hives that can each be up to 50,000 to 60,000 bees.

5 The colony size is another factor to consider.

6 The EFSA Scientific Opinion and pollinator risk assessment also

7 notes several points related to the advantages of different

8 sizes of the hives for the field study. Larger populations may

9 be more efficient honey producers and produce more honey on a

10 per bee basis while using less per bee over the winter. 

11 Thus, a larger colony with more foragers and

12 in nest bees could provide a better observation of the effects

13 of the pesticide because of the higher number of bees involved. 

14 However, the EFSA Scientific Opinion also notes that smaller

15 colonies may be more sensitive due to reduced resilience to

16 replace foragers with nurse bees in a smaller brood area.

17 The second major bullet notes that hives at

18 the full field level cannot be completely protected from the use

19 of other pesticides, either on the test field itself or adjacent

20 to the test fields. These products may be applied to the same

21 crop as part of grower's standard practice or they may be

22 applied on neighboring fields on which honey bees may forage or

23 from which the pesticides may drift into the test or control

24 plots of a full field study.

25 In the case of persistent pesticides, these

26 chemicals may remain on the test field or in the adjacent field
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1 well after the application. These products may then be

2 collected by honey bees and brought back to the hive, thereby

3 serving as a source of exposure to other chemicals or even the

4 chemical under study.

5 As the third major bullet highlights, the

6 treatment and control fields should also be adequately

7 separated, though logistical constraints associated with

8 replication of sites may make this difficult.

9 For control in treatment plots with inadequate

10 separation where honey bees can forage freely, bees form control

11 plot may forage on treated plots, thereby bring contaminated

12 food into the control hives. Conversely, treatment plot hive

13 bees may forage in control plots as well, thereby decreasing the

14 extent of exposure. This movement of control and treatment plot

15 bees between plots can confound the interpretation of the field

16 study. 

17 Parasites and certain diseases can also affect

18 honey bee colonies, as the fourth major bullet states. Some of

19 these can impact the same measurement endpoints as the pesticide

20 of interest. 

21 The next major bullet highlights the duration

22 of the study that also presents a challenge, especially when the

23 study is carried through overwintering and attempts to ascertain

24 the effect of a pesticide while minimizing any potential

25 confounding effects of the interventions that are required to

26 enable a honey bee colony to survive overwintering. 
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1 The final bullet addresses replication and

2 variability. As previously noted in the section on Tier II

3 studies related to variability and biological significance, the

4 level of variability in the measurement endpoints is relatively

5 high, due to all of the potential confounding factors at the

6 full field level. 

7 As a result of these design challenges, we

8 have identified a number of modifications that may enable more

9 robust field designs. The size of the plots should increase to

10 be more reflective of the size of representative crop acreage in

11 North America. The size of the areas should also adequately

12 support the nutritional requirements of the hive for the

13 duration of the study with the specific purpose of maximizing

14 the foraging of the hives on the test crop to provide an upper

15 bound exposure scenario for that crop.

16 It is recognized, however, that the bloom

17 period of the test crop may be short, relative to the overall

18 study period. But the study design must consider that the

19 nutritional requirements of the study colonies are met. 

20 A field study should also provide for adequate

21 separation of treated and control plots, however, climatic and

22 landscape variations may preclude greater distances between

23 plots, depending on these variations over varying geographic

24 scales.

25 According to the EFSA Scientific Opinion, the

26 field test sites should use areas with similar environmental
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1 conditions, where possible, with at least four to six kilometers

2 that is 2.5 to 3.7 miles between treated and control plots.

3 Statistical analysis should be conducted for

4 all field studies; therefore, field studies should also

5 determine the power of the test to adequately assess the

6 endpoints relative to the appropriate level of biological

7 significance. An appropriate power analysis should accompany

8 each protocol proposal in order to determine the number of hives

9 necessary for the conduct of the study.

10 In addition, the final report should also

11 contain an appropriate power analysis to determine if the level

12 of statistical power has been achieved. Measures of exposure

13 are essential to the interpretation of the field study. 

14 Consequently, it would be helpful for the study to measure

15 residues in incoming pollen through the use of pollen traps

16 and/or nectar, as in sampling of the honey bee stomach; measure

17 residues and store pollen and nectar pre-exposure and at various

18 times, post-exposure; measure bloom intensity and duration of

19 that intensity and identify sources and percent composition of

20 pollen and nectar. 

21 The studies should also limit disease and

22 pests using new hive equipment and obtain bees from sources with

23 low pest occurrence. This slide presents other modifications

24 that could help to improve the quality of the field studies that

25 regulatory agencies receive. The duration of the study should

26 match the concern of the chemical. According to the EFSA
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1 Scientific Opinion, the colonies that are used in the experiment

2 should be monitored for a period of time, covering the entire

3 flowering period and last at least two brood cycles. When

4 residues are persistent, monitoring should continue through

5 winter to after the overwintering period. 

6 Other recommendations proposed by the EFSA

7 Scientific Opinion, related to field study designs, include the

8 use of a highly attractive crop, quantitative assessment of

9 brood condition, residue analysis of in hive bees and evaluation

10 of the storage of food and their associated residue levels, and

11 in some cases, the removal of food stocks from the hive at study

12 initiation to maximize the exposure to the test substance. 

13 Given the previously mentioned modifications

14 to the design of the field study, it is important to note that

15 some of them notably increasing the plot sizes and ensuring

16 adequate replication would likely be difficult to implement,

17 given the logistics of a field study.

18 The purpose of these studies is that they

19 provide important information relative to the risk assessment. 

20 Note, that as we move up the tiers to Tier III, we not only

21 increase the realism, but we also increase the variability due

22 to the number of potentially confounding factors as well as the

23 cost, in terms of time, money, and resources.

24 In fact, full field studies have cost a

25 considerable amount of resources, both to conduct and review,

26 without being classified as acceptable, and only providing
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1 minimal gains, in terms of the risk assessment. 

2 In these cases, field studies have not been

3 particularly refined, but rather, measured numerous endpoints,

4 whether quantitatively related to the assessment endpoints

5 outlined earlier or not.

6 Furthermore, there is uncertainty in the

7 results, due to factors, such as minimal distance between

8 treated and control plots that allows the forager bees of the

9 two treatment groups to cross forage; the exposure of test

10 colonies to numerous other chemicals that are used as standard

11 grower practice and the lack of adequate replication given high

12 levels of variability and the measurement endpoints.

13 To date, for the studies that we've seen

14 related to the exposure studies, the refined exposure studies,

15 there appears to be less variability in those studies relative

16 to the ones that assess effects to the bees at the whole hive

17 level. 

18 The previously mentioned modifications to the

19 Tier III field study should enable a stronger, more robust

20 design. A properly design and implanted field study should

21 therefore provide essential information on actual uses of the

22 pesticide of interest that can inform the risk manager in any

23 additional mitigation options, if available.

24 The field study should also provide a more

25 clear linkage between the measurement endpoints and the

26 assessment endpoints from the actual use of the chemical, as a
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1 field study can be screening or definitive in nature to either

2 determine the presence or absence of an effect or define the

3 magnitude and duration of an effect. 

4 The ultimate design of the study will be

5 dictated by the specific uncertainties it is intended to address

6 in order to qualitatively describe the risk, effects and

7 uncertainties identified in the lower tiers. While these

8 studies are conducted in response to concerns identified in

9 lower tier studies, the full field study is ultimately intended

10 to address whether risks estimated using lower tier exposure and

11 effect data occur under actual use conditions. 

12 It is also important to note that the field

13 study will not be interpreted in isolation of the other tiers

14 and other information. Rather, we will evaluate and interpret

15 the field study and their risk assessment using all of the data

16 available to the risk assessor, including data from laboratory

17 toxicity studies at the first year, open literature, non-target

18 arthropod studies, sublethal effects, incident reports, other

19 semi-field or full field studies and the residue levels in

20 various crops, based on exposure data. This weight of evidence

21 approach will be further explained in the next presentation. 

22 Thank you for your time. We can now take any

23 questions that the panel may have.

24 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum.

25 DR. MAY BERENBAUM: I may have missed this,

26 but in transitioning from Tier I to Tier II to Tier III, is
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1 there a task that assumption that colony genetics will be

2 controlled for or minimally subspecies? Is there a

3 consideration of consistency in terms of genetic identity from

4 one tier to the next?

5 DR. THOMAS STEEGER: At this time, we're

6 not proposing the use of doing genetic analyses to ensure that

7 the bee populations that are typically used in these studies are

8 drawn from healthy hives and presumably reflect a relatively

9 broad genetic base. 

10 DR. DANIEL SCHLENK: Dr. Hunt.

11 DR. GREG HUNT: I'm wondering, have there been

12 any full field studies that have been classified as acceptable

13 by the EPA?

14 DR. THOMAS STEEGER: We have classified

15 studies as acceptable as having met the standard or the

16 guideline requirement for conducting a full field study, but as

17 was pointed out by Joe DeCant, the 850.3040 guideline is

18 relatively broad and does not specify specific expectations that

19 would have to be met or specific criteria that would have to be

20 met to downgrade a study unless things that have happened, such

21 as contamination, have reached a point that the ability of the

22 study to differentiate a treatment level effect has been

23 completely compromised. 

24 If the study is scientifically unsound it will

25 be downgraded, but we have accepted field studies; but primarily

26 because there aren't strong criteria for potentially downgrading
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1 those, except best professional judgment that the scientific

2 integrity has been compromised.

3 DR. GREG HUNT: Thank you. That answers my

4 question. I was just concerned about the level of cost and time

5 in doing these studies. 

6 DR. DANIEL SCHLENK: Mr. Pistorius. 

7 MR. JENS PISTORIUS: Would it be possible to

8 skip Tier II and go to Tier III directly?

9 DR. THOMAS STEEGER: Again, the transition

10 from screening level assessments to higher tier assessments are

11 a risk management decision. It is entirely possible to

12 transition from a screening level to a full field study;

13 however, the intent, as you move up in refinement, is to refine

14 the uncertainties; and I'm not sure that you would have been

15 able to do that by transitioning quickly to a full field study

16 from what could be gleaned from laboratory studies, unless you

17 had available to you open literature studies or incident data

18 that would enable you to do those types of refinements so that

19 you can better fine tune the risk hypothesis that's being tested

20 at the full field level.

21 MR. JENS PISTORIUS: Another one. What would

22 be the basis for election of which Tier II test has to be

23 conducted, taking into account the different limitations of the

24 described methods?

25 When would you say use this test, use this

26 test, free feeding, Oomen, et al. and all these different kind
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1 of --

2 DR. THOMAS STEEGER: I think that you're

3 asking some very excellent questions and we're hoping that the

4 SAP can provide insight on that. Again, the planning dialogue

5 that takes place as part of the risk assessment process -- the

6 risk assessor will inform the risk manager of what kind of

7 uncertainties there are and we attempt to work with the

8 regulated community in terms of how best to address those

9 uncertainties. 

10 So whether you use a semi-field tunnel study

11 or whether you use a feeding study or you use the Oomen method,

12 those are all on the table for discussion in terms of what

13 direction the Agency will actually require the registrant to

14 take. 

15 DR. DANIEL SCHLENK: Okay. Dr. Fefferman.

16 DR. NINA FEFFERMAN: Forgive me if this is a

17 really silly question, it relies on two things I don't know of

18 honey bee physiology and basic toxicology.

19 The discussions I've been hearing about

20 toxicity levels, again, I know you're mainly focusing on the

21 acute side of things, but on the chronic side, the flip side of

22 exposure, of course, is elimination. 

23 I was wondering whether or not these are

24 things such that once they're ingested the stay in the bee

25 permanently or if there is a rate of elimination kind of

26 question that we should be asking. And if so, if there's any
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1 room in the kind of toxicity studies that you're running to look

2 at the durational dose response curves and dose maintenance

3 curves in a bee over its individual life and then possibly life

4 of a colony. 

5 I'm thinking specifically of things that might

6 be pulse treatment types of pesticides and whether or not that

7 will actually be a constant daily level or whether or not as

8 long as they survive the main pulse and we don't pulse them

9 again until that's out of their system that might be okay. That

10 kind of thing.

11 DR. THOMAS STEEGER: I think you raised

12 some very interesting questions. The screening level assessment

13 is obviously testing or focused on acute toxicity and the

14 exposure estimates that we're using are based on the presumption

15 that none of the compound is degrading, so that's why they're

16 very conservative assessment estimates. But as you move up in

17 higher levels of refinement and the duration of these studies is

18 more protracted, presumably you are able to incorporate how the

19 chemical is moving through the colony, through the animal and

20 looking at its potential to rebound from the effects that

21 might've been observed at lower level studies. 

22 So actually attempting to isolate out the

23 formation and decline curves of compounds within the bee can be

24 a problem because you're going to have to be manipulating the

25 colony quite a bit. And as Joe DeCant had indicated, the more

26 that you manipulate the colony the more variability it's likely
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1 that you're going to introduce into it. 

2 So our focus at this time is trying to collect

3 data points that provide us a sense for the colony level

4 response, as opposed to the individual bee response and under as

5 natural conditions as possible without over manipulating the

6 colonies to try to get at those answers.

7 DR. DANIEL SCHLENK: Do you have something

8 to add?

9 MR. KEITH SAPPINGTON: I just wanted to add

10 that some of the characteristics of the compound -- while I

11 agree in the Tier II and Tier III level you would have a

12 long-term exposure -- but some of the characteristics of the

13 compound, particularly its persistence in bioaccumulative

14 abilities or characteristics would be sort of indicators, up

15 front, that you might consider longer term accumulation within

16 the organism. We held a separate SAP about four years ago on

17 this whole process of how you do risk assessment for persistent

18 bioaccumulative and toxic chemicals.

19 With that said, we have a process for aquatic

20 organisms that relies on bioaccumulation modeling as well as

21 measured information in bioaccumulation tests. And there are

22 some models that we use as a kind of a screen, up front, in

23 problem formulation for terrestrial organisms. But to my

24 knowledge, I have not seen a model yet that looks specifically

25 at the toxicokinetics and accumulation within the bee; but at

26 least we might get an indication early on.
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1 DR. DANIEL SCHLENK: I think Dr. Potter

2 had a --

3 DR. THOMAS STEEGER: This is Tom Steeger. 

4 I'm sorry. 

5 DR. DANIEL SCHLENK: Go ahead. One more.

6 DR. THOMAS STEEGER: I'd just like to add

7 onto a question that had been asked earlier by Dr. Potter

8 regarding the utility of the higher tier field studies. My

9 interpretation of your question was relative to full hive

10 studies. 

11 If your question was relative to exposure,

12 higher tier exposure studies, those studies have proven to be

13 very effective means of documenting the level of exposure that

14 colonies or individual bees are exposed to. 

15 We've been working collaboratively with the

16 California Department of Pesticide Regulation and with Health

17 Canada Pest Management Regulatory Agency to collect those

18 studies from field-based information and they are very useful

19 for documenting exposure.

20 DR. DANIEL SCHLENK: Dr. Potter.

21 DR. THOMAS POTTER: I guess as a follow-up,

22 are information from those studies imbedded in this risk

23 assessment process that we're looking at here today?

24 DR. THOMAS STEEGER: We are proposing that

25 was depicted in the flow chart that Keith Sappington discussed

26 and as Chris Garber discussed as well in her presentation that
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1 -- it's not up on the board -- but Box 6(a) is where you're

2 evaluating potential exposure through RQ values, through

3 field-based studies and those estimates of exposure that are

4 actually measured values feed back into the calculation of RQ

5 values that would hopefully, potentially reduce the RQ value

6 below the level of concern. 

7 So what is inherent in this risk assessment

8 framework that we are proposing is that field level measurement,

9 whether they're collected either from the semi-field environment

10 or from a full field study where you're actually focusing on

11 exposure, you're measuring residues in pollen and nectar, would

12 then be used at the numerator in the risk quotient that we'll be

13 talking about in the next section that would be able to

14 potentially -- at a screening level, where you're still

15 quantifying risk -- to get risk quotients below LOCs and be able

16 to come to a presumption of minimal risk.

17 DR. THOMAS POTTER: Can I follow up on that?

18 DR. DANIEL SCHLENK: Sure.

19 DR. THOMAS POTTER: In looking through the

20 White Paper, I was struck by what I perceived to be a lack of

21 information or good quality data regarding concentrations of

22 active ingredients in pollen and nectar so that to a large

23 degree, we're dependent on a limited number of studies to

24 compare the proposed exposure levels to. 

25 If there is additional information that's been

26 submitted as part of the registration process, it certainly
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1 would seem to me to be very helpful, in terms of supporting this

2 entire process to somehow get that data out to the light of day.

3 DR. THOMAS STEEGER: I think that Dr.

4 Berenbaum raised that same issue in terms of what appears to be

5 relatively dated information. Those are published studies, but

6 the Agency does have registrant-submitted studies that have been

7 conducted either on the initiative of the regulated community or

8 in response to data call-ins, such as that of California's, that

9 are providing that very information and we deem it to be high

10 quality information that would enable us to test the

11 assumptions. 

12 In fact, we've compared some of these measured

13 residues against the model estimates, the Chemical X or Chemical

14 1, the undisclosed chemical. That information is still

15 considered confidential business information, but those are

16 actually measured values that some of these model estimates and

17 default values are being compared to.

18 MS. KRISTINA GARBER: Just to add a little bit

19 more, we did actually look at all the registrant-submitted data

20 that we had for pollen and nectar and we incorporated everything

21 we could in the White Paper.

22 DR. THOMAS POTTER: I will note that if you

23 look at the executive summary of the Pellston conference -- I

24 believe it's in the European Risk Assessment document as well --

25 that identified the need for data that can be used to support

26 the risk assessment process as a critical need. So I'll leave
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1 it at there. We can go around in circles with that, but thank

2 you.

3 DR. DANIEL SCHLENK: Any other questions

4 related to the presentation?

5 (No response.)

6 Okay. Dr. Steeger, do you want to move us

7 ahead?

8 DR. THOMAS STEEGER: Thank you for this

9 opportunity to address the Scientific Advisory Panel. Again, my

10 name is Tom Steeger. I'm a biologist and I'm a senior science

11 advisor in the Environmental Fate and Effects Division.

12 In this presentation, I will discuss the risk

13 characterization component of the risk assessment process and

14 how risk characterization integrates exposure and effect

15 estimates with other lines of evidence to provide risk managers

16 with an understanding of potential risk from the proposed use of

17 the chemical.

18 I'll also discuss how the risk

19 characterization articulates the assumptions, limitations, and

20 uncertainties associated with the available data. 

21 The risk characterization is intended to

22 provide risk managers with an overall understanding of potential

23 risks to the environment. In doing so, the assessment must be

24 transparent, that is, it must be explicit with respect to the

25 process that was followed. The risk assessment must be clear

26 and easy to understand and consistent with Agency guidance and
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1 reasonable such that the risk manager and any stakeholder can

2 understand how conclusions were arrived at, such that the risk

3 assessment could be repeated and reach a relatively similar

4 conclusion given the same process, underlying data and

5 assumptions. 

6 As was discussed in the preceding

7 presentations by Tom Moriarty and Keith Sappington this morning,

8 the risk assessment process consists of three phases or steps: 

9 the problem formulation phase, the analysis phase, where effects

10 are characterized and exposure is characterized, and that's

11 followed by the risk characterization phase. 

12 Although the flow chart on the left is

13 depicted as unidirectional, as Keith had pointed out earlier,

14 the various phases in the risk assessment are intended to be

15 iterative and as more information becomes available through the

16 analysis phase, the problem formation may evolve. 

17 Communication with risk manager is an

18 essential component of the risk assessment process since

19 potential mitigation measures or other additional data can be

20 evaluated in the context of how exposure estimates may be

21 affected. 

22 Once the analysis phase is concluded, the risk

23 assessment process proceeds to the risk characterization phase. 

24 This phase consists of two components: the risk estimation and

25 the risk description. 

26 During risk estimation step of risk
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1 characterization, risk estimates are calculated. Risk estimates

2 are based on a deterministic approach using point estimates or

3 providing that there are sufficient data, estimates can be based

4 on a distributional approach. 

5 Although not discussed extensively in the

6 White Paper, distribution-based approaches, as opposed to point

7 estimate-based approaches, can be use to characterize both

8 exposure and effects and joint probability curves constructed to

9 then estimate the likelihood and magnitude of an adverse effect.

10 However, in today's presentation, we are

11 focused on point estimate-based approach, which compares risk

12 estimates to levels of concern and the potential for risk as

13 articulated in terms of whether the level of concern is exceeded

14 or not.

15 The proposed framework for estimating risk at

16 a screening level is consistent with the process currently used

17 by OPP to estimate risk to other taxa, and is based on the

18 concept of risk being a function of both exposure and toxicity. 

19 Point estimate of environmental concentrations, such as those

20 described earlier by Kris Garber, are compared to point

21 estimates of toxicity endpoints discussed by Joe DeCant. 

22 The quotient of the ratio of exposure to

23 effects is the risk quotient. As discussed by Kris Garber, at

24 the screening level, point estimates of exposure are initially

25 based on relatively conservative models or default values.

26 However the risk assessment process is
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1 intended to be iterative, and as discussed by Reuben Baris,

2 exposure values used to generate RQs can be further refined

3 through the use of measured values obtained through other

4 studies, typically submitted to EPA, such as magnitude of

5 residue studies or through targeted monitoring studies of

6 residues and foliage or in pollen and nectar. 

7 Table 22 of the White Paper provides a summary

8 of the proposed exposure and effect point estimates used to

9 calculate risk quotients and the screening level risk assessment

10 for pesticides applied folairly, as seed treatments, soil

11 treatments, and tree trunk injections. 

12 This is an excerpt from the table and it's

13 taken from Table 22, for pesticides applied to seed treatments,

14 the dietary-based RQ values for adults is based on the products

15 of the Briggs model estimated environmental concentration,

16 multiplied by .29 grams per day, serving as a numerator, divided

17 by the acute oral LD50 value for individual adult bees. 

18 RQ values estimated in the risk estimation

19 phase of the risk characterization are compared to the Agency's

20 level of concern. Levels of concern represent the Agency's

21 interpretive policy and are used to analyze potential risk to

22 non-target organisms and the need to consider regulatory action.

23

24 These criteria are used to indicate when a

25 pesticide is used as directed on the label, has the potential to

26 cause adverse effects on the non-target organisms. 
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1 Historically, the Environmental Fate and Effects Division has

2 used the default probit dose response slope of 4.5 to estimate

3 acute effects to organisms; however, the level of concern for

4 estimating acute risk to honey bees is based on an analysis of

5 honey bee acute contact and acute oral toxicity studies that had

6 been submitted to EPA.

7 This analysis resulted in a mean probit dose

8 response slope for acute contact toxicity at 3.93 and a mean

9 probit dose response slope for acute oral toxicity studies of

10 3.4. The Environmental Fate and Effects Division has relied on

11 the Individual Effect Chance model, which is abbreviated the

12 IEC, to estimate the likelihood of an effect, for example, acute

13 mortality. 

14 This model uses the calculated risk quotient

15 and the probit dose response slope that integrates those values

16 to derive the probability of an individual mortality. However,

17 the model can be used in reverse to estimate the risk quotient

18 value that would be needed to result in a particular magnitude

19 of an effect, given a specific dose response slope. 

20 Since data are available for both acute oral

21 and acute contact lethality studies, both sets of data were

22 combined. Rather than using the mean dose response values,

23 though, the median dose response slope across all acute contact

24 and oral toxicity studies was used as a measure of central

25 tendency and this median dose response slope is 3.2. 

26 Using the individual effect calculated model,
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1 the level of concern was then back calculated, which was a

2 result in 10 percent mortality based on the median dose response

3 slope of 3.2. This slide depicts Table 24 from the White Paper,

4 summarizing that the median slope of 3.2 and the likelihood of

5 10 percent mortality that is in effect, the LOC of 0.4 is

6 necessary. 

7 The 10 percent level of effect, that is the

8 acute mortality, is based on the maximum level of mortality

9 permitted in controls. That is, untreated studies of acute oral

10 and contact toxicity based on current guideline recommendations.

11 Although the chronic toxicity study guidelines

12 are not currently available for honey bees the proposed chronic

13 risk level of concern is 1) since the RQ value for estimating

14 chronic risk is the ratio of the maximum estimated exposure

15 concentration to the no-observed adverse effect concentration;

16 that is, the NOAEC, an LOC of one is consistent with that used

17 for evaluating chronic risk to other taxa.

18 As discussed by Mr. Sappington and as depicted

19 in Figures 2 and 3 of the White Paper showing the proposed risk

20 assessment framework for honey bees, the process is intended to

21 be iterative. The slide depicts the risk estimation portion of

22 the framework where risk quotients are calculated in Boxes 4(a)

23 and 4(b), based on conservative exposure estimates derived in a

24 screening level assessment.

25 Charge Question 13 asks the SAP to provide

26 comment on the exposure and effect data used to estimate risk
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1 and the acute and chronic risk LOCs that are in turn used to

2 evaluate the risk quotients. Question 13 also asks the SAP to

3 comment on how other line of evidence are integrated into the

4 risk assessment during the second phase of risk

5 characterization, that is, the risk description phase, which I

6 will now discuss.

7 The second component of risk characterization

8 is risk description where RQ values are calculated in the risk

9 estimation phases are considered in conjunction with other lines

10 of evidence which may either support or refute these estimates,

11 based on assessment endpoints. In this part of the risk

12 characterization, the relevance of other lines of evidence are

13 considered. 

14 The risk description builds on the RQ values

15 calculated during the risk estimation phase of the risk

16 characterization process. The risk description includes a

17 discussion of possible refinements and exposure values --

18 discussed by both Chris Garber and Reuben Baris -- that lead to

19 refined RQ values, using toxicity estimates for individual and

20 adult bees.

21 The quantitative information is then

22 considered in the context of available studies that were

23 discussed by Joe DeCant of the whole colony that may have been

24 conducted under relatively controlled conditions, that is,

25 semi-field studies, or under conditions that are intended to be

26 more reflective of actual use conditions, that is, the full
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1 field Tier III studies.

2 While adverse effects may be measured on

3 individual bees and these effects quantified through risk

4 quotients, the higher tier refinements are intended to answer

5 whether adverse effects are observed at the whole colony level. 

6 The intent is for the risk assessor to determine whether or not

7 there is a concordance of information to indicate whether there

8 is a potential for adverse effects that could limit the extent

9 to which the protection goals may be met. 

10 Thus, while RQ values may indicate that

11 individual bees may be at risk from acute exposures to a

12 pesticide, semi-field studies may indicate that no adverse

13 effects occur at the whole colony level when the pesticide is

14 applied to pollinator attractive crop at the maximum proposed

15 application rate.

16 Conversely, risk concerns, based on individual

17 bees, may be supported by semi-field or full field studies,

18 showing colony level effects. Other scientifically relevant

19 information would be considered as well. 

20 The risk description integrates multiple lines

21 of evidence in the context of assessment endpoints discussed by

22 Tom Moriarty in the opening presentation. As such, the risk

23 description is not intended to be simply a comparison of risk

24 quotients to the levels of concern, rather, the quantitative

25 estimates of risk, that is, the RQ values are integrated with

26 other lines of evidence that may be available through higher
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1 tier guideline toxicity testing, non-guideline studies reported

2 in open literature, and incident data. 

3 Other lines of evidence, such as incident

4 data, available through the Office of Pesticides Program

5 Incident Data System, the IDS, and the Ecological Incident

6 Information System, the EIIS, provide a means for understanding

7 the effects of chemicals under actual use conditions.

8 As these additional lines of evidence are

9 considered, the risk hypothesis and the conceptual model are

10 refined. As discussed in the preceding presentations, the

11 intent of the tiered risk assessment process is to provide

12 increasingly realistic understanding of potential risk that

13 reflects how the chemical will actually be used. 

14 For new chemicals, available data may be

15 confined to guideline laboratory and field testing; however, for

16 older chemicals, a wider array of data may be available and are

17 considered, provided those data meet the standard established by

18 EPA for inclusion in risk assessments. EPA has developed

19 guidance for evaluating multiple lines of evidence, and an

20 example of such guidance is discussed in the White Paper where

21 elements of the endocrine disrupting screening program, weight

22 of evidence document are articulated. 

23 Also, the Agency's risk characterization

24 handbook developed by the EPA Office of Research and

25 Development, also provides guidance on the factors that should

26 be considered in risk characterization and which are discussed
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1 in the following slides.

2 In considering the additional lines of

3 evidence that may be available, the evaluation considers 1) the

4 adequacy and quality of the data; 2) the degree and type of

5 variability and uncertainty; and 3) the relationship of the

6 evidence to risk assessment endpoints.

7 With respect to the adequacy and the quality

8 of the data, risk assessors consider whether the data quality

9 objectives were followed. OPP has published formal guidance on

10 the conduct and evaluation of guideline toxicity studies and has

11 developed guidance for staff in evaluating the utility of open

12 literature studies that may not adhere to testing guidelines.

13 Just as the White Paper reflects and is

14 consistent with the Agency's commitment to transparency, each

15 risk description articulates what assumptions are made, where

16 there are data gaps and what type of limitations may be present

17 when non-guideline or supplemental data are used. As such, the

18 experimental designs have been considered when evaluating the

19 utility of the data for characterizing risk. 

20 As was discussed in the presentation by

21 Christina Wendel, the Agency is clear as to what is considered

22 in the risk assessment process and what is not considered and

23 why. Also, when evaluating lines of evidence, degree and type

24 of uncertainty have to be articulated. 

25 With respect to characterizing the degree and

26 type of uncertainty and variability, variability refers to the
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1 differences in characteristics of individuals or exposure,

2 reflecting inherent variety in test organisms or exposure

3 scenarios that can be quantified. 

4 With respect to characterizing uncertainty,

5 the risk characterization should include a discussion of what is

6 not known, that is, of the uncertainties that may be associated

7 with the available data. Also in evaluating lines of evidence,

8 the relationship of the evidence to risk assessment has to be

9 articulated.

10 Relationship of available evidence to risk

11 assessment can draw from previous assessments on the pesticide

12 or similarly structured pesticides and/or risk assessments

13 conducted by other organizations, such as Pest Management

14 Regulatory Authority -- or Agency, the Australian APVMA, and the

15 European Food Safety Authority. Plus, the risk characterization

16 must reflect current agency policy regarding the use and

17 interpretation of data.

18 As discussed earlier by Tom Moriarty, the risk

19 assessment framework discussed in the White Paper is intended to

20 apply to honey bees; however, given that laboratory studies are

21 based on individual bees and that higher tier studies, discussed

22 by Joe DeCant examine both individual bees and colony level

23 effects, the assessment may provide insight on potential

24 exposure to and effects of the pesticide to solitary and social

25 non-Apis bees as well.

26 In addition to measurement endpoints that are
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1 directly related to assessment endpoints of a impaired growth,

2 survival and reproduction, the risk description would also

3 provide a discussion of sublethal effects that may have been

4 measured in the various studies that have been evaluated, and

5 the uncertainties associated with how these measurement

6 endpoints may be related to the assessment endpoints. 

7 Finally, the risk description should, to the

8 extent possible, discuss the potential adversity of risks in

9 terms of the nature and intensity of the effects, where there is

10 evidence to suggest temporal or spacial trends, and whether the

11 data indicate that recovery may be possible. 

12 In the following presentation by Reuben Baris,

13 the relative conservatism of the proposed risk assessment

14 process will be briefly discussed. Also, as multiple lines of

15 evidence are intended to be covered in the risk description, the

16 white paper discusses the potential utility of colony level

17 models to estimate potential effects of pesticides. Therefore,

18 Keith Sappington will provide a presentation on how these models

19 can inform the risk assessment process. Any questions? 

20 DR. DANIEL SCHLENK: Wow. Thanks, Dr.

21 Steeger. Any questions for Dr. Steeger? Dr. Pistorius? 

22 MR. JENS PISTORIUS: I have one. Has there

23 been an exercise done with a new proposed risk assessment

24 process? How many substances would fail or pass compared to the

25 old risk assessment? Is it many more or few more or all? 

26 DR. THOMAS STEEGER: This is Tom Steeger. 
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1 That is a most excellent question and you're going to get an

2 answer to it in the next presentation. 

3 DR. DANIEL SCHLENK: Any other questions? 

4 Okay. We're running a bit behind. I want to take a poll of the

5 panel here. Who would rather take about a five minute break or

6 keep going? We have two more presentations left to go. So, in

7 favor of keep going, hands; and five minute break? Okay. Looks

8 like we're going to keep going. Okay. With that, Reuben Baris,

9 I guess you're up next. 

10 MR. REUBEN BARIS: Thank you very much. My

11 name is Reuben Baris. The information presented in this

12 presentation was not included in the White Paper, but is

13 intended to provide contextual information about the efficiency

14 of the Tier I screen as presented in earlier presentations. As

15 presented earlier, the purpose of the Tier I screen is to

16 efficiently identify those pesticides that do not pose a risk to

17 honey bees, so that researches can be focused on pesticides that

18 potentially have risks. 

19 Available acute oral and contact toxicity for

20 adult worker bees from unpublished registrant studies were

21 compiled for this analysis. This is a partial examination and

22 is based only on available toxicity data for adult worker bees. 

23

24 Individual chemical risk quotients were

25 calculated and compared to the proposed level of concern that

26 was presented just now by Dr. Steeger. This effort was
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1 completed to provide some context on the efficiency of the

2 initial exposure screen using the most conservative assumptions

3 of exposure. The Tier I screen for foliar applications were run

4 for each chemical using the maximum foliar application permitted

5 on the label. The Briggs model was not included in this

6 analysis because there is limited oral toxicity information for

7 adult worker bees for chemicals where soil applications are

8 permitted on the label. Therefore, this analysis applies to

9 foliar applications only. 

10 A subset of adult worker acute contact

11 toxicity data that cover a breadth of chemical categories,

12 chemical groups and modes of action were used in this analysis. 

13 Shown here, the number of chemicals in each category is similar,

14 and a few examples of the types of chemical groups are shown in

15 the parentheses. 

16 An attempt was made within each category to

17 use chemicals from different groups and modes of action in order

18 to remove potential bias towards a specific chemical activity. 

19 All available data from unpublished registrants submitted acute

20 oral toxicity studies for adult worker bees were used in the

21 acute oral analysis. Many of the chemical groups and modes of

22 action represented in the acute contact analysis were also

23 represented in this acute oral analysis. 

24 This table and the following slide represent a

25 snapshot of the Tier I screen based on available acute contact

26 toxicity data. As was presented by Kris Garber this afternoon,
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1 the Koch and Weisser approach with honey bee specific data is

2 the agency's proposed approach for the Tier I contact exposure

3 screen. 

4 Shown here, fewer insecticides make it through

5 the screen and would likely require additional refinement. 

6 However, herbicides and fungicides generally pass the screen

7 with approximately 32 and 4 percent respectively requiring

8 additional evaluation or refinement. 

9 Fewer insecticides pass the initial Tier I

10 screen compared to herbicides and fungicides. This is expected

11 because insecticides are more likely to directly affect insects

12 such as honey bees. It is clear from the table shown there that

13 while greater than 85 percent of the insecticides evaluated did

14 not pass the screen, the evaluation was based on acute toxicity

15 studies with young adult bees. 

16 Larval toxicity testing may result in RQ

17 values that exceed the level of concern of 0.4. However, even

18 for some insecticides, it is possible that bees are not

19 particularly sensitive, and these chemical will likely have RQ

20 values that are below the LOC, just as not all plants are

21 sensitive to herbicides. 

22 Since fungicides and herbicides tend to target

23 fungi or plants, it is less likely that they will directly

24 affect insects. This analysis was also completed with the T-REX

25 contact approach, and similar results were observed. 

26 Again, as was expected in this acute oral
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1 analysis for adult worker bees and shown previously on the

2 previous slide by the contact exposure analysis, fewer

3 insecticides pass the initial Tier I screen compared to

4 herbicides and fungicides. There are a limited number of

5 fungicides that are included in this analysis because of limited

6 oral toxicity data that are available for adult worker bees. 

7 But again, as shown by these preliminary results, the Tier I

8 screen is functioning well as a screen by identifying the

9 chemicals that need additional refinement and screening out the

10 majority of non-insecticides that are not likely to be a risk

11 concern.

12 This analysis was an exercise to illustrate

13 the decision point of Box 5(a) and (b) of the proposed decision

14 tree. That is, does the contact or oral RQ exceed the level of

15 concern? It provides context for the efficiency of the Tier I

16 screen. The analysis included a variety of chemical groups and

17 modes of action for contact and oral toxicity, identifying those

18 chemicals that require additional refinements. These

19 refinements, Box 6 of the decision tree would include working

20 with risk managers to refine the Tier I exposure assessment. 

21 Therefore, the conclusion is that the Tier I

22 approach is functioning as expected as an initial screen. It is

23 important to keep in mind that these preliminary results are

24 intended to give a very rough example that the screen in not

25 serving as a wall for all classes of pesticides, but rather, it

26 is identifying those chemicals that are most likely to need
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1 additional review. In doing so, only model-estimated exposure

2 values have been evaluated. 

3 It is possible that measured exposure values

4 could result in a reduction in risk estimates such that further

5 refinements -- that is higher tiered testing -- may not be

6 required. However, as mentioned by Dr. Steeger in the previous

7 presentation, multiple lines of evidence are used in determining

8 the risk to honey bees.

9 The next presentation given by Mr. Sappington

10 will discuss colony level models and how they can be used in the

11 risk characterization process. Thank you for your time.

12 DR. DANIEL SCHLENK: Questions? Dr.

13 Berenbaum? 

14 DR. MAY BERENBAUM: While fungicides might

15 themselves not be considered to present as greater risk, there

16 is increasing evidence that in combination fungicides can

17 exacerbate risks. Were any studies conducted on combinations or

18 synergistic interactions? 

19 DR. THOMAS STEEGER: This is Tom Steeger. 

20 That as Reuben pointed out towards the end of his presentation,

21 the risk quotient isn't viewed in isolation and other lines of

22 evidence would be considered. And you're absolutely right,

23 there is open literature pointing to potential interactive

24 effects and we have required studies to address those

25 uncertainties, even for chemicals that had initially RQ values

26 that would have been below the LOC. 
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1 DR. DANIEL SCHLENK: Okay. Any other

2 questions? Yeah, Dr. Hunt? 

3 DR. GREG HUNT: Well, along those same lines,

4 some formulations come as a package, such as seed treatments. 

5 Is there any consideration that that might be required of

6 registrants to - I know you can't test every possible

7 interaction, but there are some common formulations that could

8 be tested.

9 DR. THOMAS STEEGER: There is a terminology

10 difference that we deal with. When we talk about formulations,

11 oftentimes what the agency is meaning is chemicals that are

12 co-formulated into a single product as opposed to what you might

13 see happen out at the field level where are tank mixes, and

14 these aren't really formulations. These are brews, for lack of

15 a better term, that are used. 

16 Where we have an indication that a particular

17 formulation could potentially be more toxic to a taxa, whether

18 it be honey bee or any taxa that we're evaluating, we may call

19 in - again it's a risk management call - data to address that

20 uncertainty. 

21 DR. DANIEL SCHLENK: Okay. No further

22 questions? Our last presentation of the day, no pressure there

23 Keith. 

24 MR. KEITH SAPPINGTON: Thanks for bringing up

25 brew. This will make me go faster. 

26 Good afternoon. Again, my name is Keith
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1 Sappington with the Environmental Fate and Effects Division. I

2 will be changing gears a little bit here in the context of our

3 previous discussions and talking about the potential use of

4 mathematical models in the context of pesticide risk assessment

5 for honey bees. 

6 For convenience, we've termed these as colony

7 level models, which mean that the models focus on describing the

8 dynamics of a single colony, not a population of multiple

9 colonies. This information is found in section 5.4 of the White

10 Paper. I would also like to acknowledge the work of Kris Garber

11 who helped greatly in the review, providing additional support

12 for this topic in the White Paper. 

13 So, first, I will briefly describe some of the

14 reasons why we may be interested in colony-level models in the

15 context of pesticide risk assessment with honey bees and then I

16 will discuss their current state of the science. Finally, I

17 will conclude with a brief description of selected models that

18 we identified in the literature. 

19 There are a number of reasons why conceptually

20 we would be interested in these models, and I'm sure they are

21 obvious to the panel at this point. First of all, these models

22 enable the dynamics of a honey bee population to be measured. 

23 These dynamics is shaped numerous interdependent processes,

24 which can be partially informed and managed through biological

25 feedback loops. 

26 For example, excessive loss of foragers may be
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1 partially compensated for by an earlier recruitment of hive bees

2 into the forager workforce. Such a change may compensate for

3 the loss of foragers in the short term, but may in the longer

4 term lead to adverse effects on the colony through a reduction

5 in brood rearing efficiency as well as a reduction in worker

6 longevity. 

7 While empirical studies may capture the net

8 effect of these interdependent processes on overall colony

9 strength, a properly constructed colony-level model could

10 provide a means for understanding these relationships, and

11 therefore enable one to extrapolate observed effects over

12 perhaps longer period of time and possibly under different

13 environmental conditions.

14 Second, colony level models could help in

15 interpreting sublethal effects, which we have discussed on

16 repeated occasions in earlier presentations. And in particular,

17 for linking sublethal effects quantitatively on individual bees

18 to effects at the whole colony level.

19 Third, colony level models could also help in

20 a design of empirical studies such as the Tier II and Tier III

21 field studies described by Joe DeCant. For example, they could

22 be used in addressing study design elements such as when to

23 conduct a study, at what spacial scale, and even help with

24 deciding on the degree of replication necessary. They also can

25 help in identifying the duration that may be needed for a

26 particular semi-field or field study.
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1 Fourth, such models could also assist in

2 interpreting the results from the Tier II and Tier III studies,

3 such as accounting for the seasonal effects on measurement

4 endpoints or other factors like the impact of the weather on

5 model results, or on observed results. 

6 In the context of the proposed risk assessment

7 scheme, colony-level models could enable one to interpret

8 results of toxicity studies and integrate these results from

9 different risk assessment tiers, all the way from Tier I at the

10 individual level through Tier II and Tier III. 

11 Finally, such models could potentially

12 incorporate not only the effects of a single stressor, but those

13 of multiple stressors, which may be either chemical or

14 nonchemical in nature. 

15 The overall state of the science of

16 colony-level models for honey bees was recently reviewed and

17 published as part of an executive summary of the SETAC Pellston

18 workshop. We've included the conclusions of this review here

19 because it was conducted specifically in the context of their

20 potential application in pesticide regulatory risk assessment. 

21 In this review, it was concluded that none of

22 the available honey bee models seemed suitable for regulatory

23 risk assessment for several reasons listed here. Collectively,

24 these include a lack of explicit linkage of foraging bees to

25 their surrounding landscape, insufficient testing of model

26 results against empirical data, lack of a comprehensive
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1 sensitivity analysis in order to understand the impact of model

2 parameters and assumptions, exclusion of multiple stressor

3 impacts on model results and insufficiency documentation of

4 model assumptions and/or algorithms in some cases.

5 At this time, only the SETAC executive summary

6 is available from the Pellston workshop. So, the detailed

7 review specific to each model was not available to us. 

8 Because the details were not available at the

9 time we were drafting the White Paper, we decided to conduct a

10 very brief review of only selected models that we identified in

11 the literature. As we point out in the White Paper, this review

12 is not intended to be comprehensive or exhaustive. Instead, it

13 reflects a sampling of more recent models that had been

14 published for honey bees as a way for us to explore their

15 potential utility in pesticide risk assessment. 

16 In this and the subsequent table, we summarize

17 some of those attributes of the models we reviewed. The models

18 are listed here from top to bottom, alphabetically by the lead

19 author of the publication in which they appeared, and various

20 attributes are listed at the top of this table. We note that

21 inclusion of a model in this list does not constitute our

22 endorsement nor does exclusion imply lack of endorsement. 

23 So, in looking at the first two columns, one

24 can see that all but one of the models we considered address

25 effects at the colony level with output including overall colony

26 strength in composition. The model published by Makela et al.
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1 in 1993 is a true population model for honey bee in that it

2 considers multiple honey bee colonies since it was designed to

3 predict the impact and distribution of Africanized bees on the

4 European honey bee. 

5 In the third column of this table, I've

6 included some key model input parameters and model processes. 

7 I've also provided some of the unique features of each model in

8 bold. For example, the model by Becher et al. in 2010 shown in

9 the first row focuses on the distribution of comb temperature

10 and how this in turn affects queen egg laying rate, brood

11 development and overall colony strength. These authors

12 acknowledge that the their model is, in effect, a partial model

13 and it's not intended to address overall colony dynamics. 

14 Among other features, the BeePoP model

15 published by DeGrandi-Hoffman shown in the second row includes

16 an explicit representation of weather patterns in addition to

17 modeling queen fertility as a function of queen age. 

18 The model by Khory et al. shown here in the

19 third row was specifically designed to evaluate age-related

20 division of labor among hive bees that is related to the onset

21 of foraging. This model was designed to investigate

22 specifically how the early onset of foraging could impact the

23 survival of foragers as well as brood rearing efficiency. 

24 The inter-colony model, I just mentioned by

25 Makela et al. in the fourth row contains some unique attributes

26 such as bee swarming, inter-colony migration of bees and even
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1 changes in genotype. 

2 Perhaps the most comprehensive colony model in

3 this collection in this table is the HoPoMo model published by

4 Schmickl and Crailsheim in 2007. This model listed in the fifth

5 row includes, among many other processes, foraging activity,

6 food availability and even larval cannibalism and their impact

7 on colony strength and composition. 

8 Lastly, the model in the last row by Thompson

9 published in 2005 focuses on pesticide impacts on honey bee

10 processes, including the queen egg laying rate, the onset of

11 foraging activity, brood rearing and worker longevity. 

12 Other features of these models that we

13 summarize include whether or not it is able to accommodate

14 spatially explicit information, whether a formal sensitivity

15 analysis was performed, the extent to which it has been verified

16 using empirical data and its application to pesticide risk

17 assessment. 

18 Several models we looked at were able to

19 include some spatially explicit or geographic based information,

20 although the BeePoP and HoPoMo models, this inclusion is limited

21 to climate data, which can be made region or site-specific. 

22 Most models did not include a formal sensitivity analysis except

23 for the BeePoP model, although some limited analysis of selected

24 model input parameters was conducted in some other studies. 

25 In terms of verifying model projections with

26 empirical data, only some of the models included a comparison of
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1 model results or model components to observe data. For example,

2 Becher et al. compared their predicted timing of peak hive

3 strength and minimum number of bees necessary for successful

4 overwintering of hives to observe values. 

5 Khoury compared a predicted onset of foraging

6 and the forager age to literature values. For the HoPoMo model,

7 Schmickl and Crailsheim compared several model subcomponents as

8 well as the entire model output. That is a number of bees to

9 observed data. 

10 Lastly, only two of the models that we

11 identified in this list included an explicit application to

12 pesticide risk assessment. 

13 In this slide, we thought it would be useful

14 to articulate some of the main attributes that we believe are

15 desirable in order to apply a colony-level model in pesticide

16 risk assessment. As indicated in the first bullet, it's is

17 clearly important to account for the major processes that govern

18 the dynamics of honey bee colonies, initially at the colony

19 level and eventually at the population of inter-colony level.

20 Second, the model should quantitatively link

21 the various measurement endpoints from pesticide toxicity

22 studies to the proposed assessment endpoints. For example,

23 colony strength and survival, production of hive products. 

24 As indicated in the third bullet, such models

25 should be able to be readily parameterized using existing

26 biological and pesticide-specific exposure and effects data. 
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1 Another desirable feature shown in the fourth

2 bullet is the ability to tailor model results to a specific

3 region of the country in order to account for differences in

4 pesticide use patterns, crops, climate and even landscape

5 features. 

6 Lastly, but no less important, the model must

7 be scientifically defensible, highly transparent, well

8 documented and publically available in keeping with the Agency's

9 efforts to promote transparency with clear, concise and

10 reasonable risk characterizations and tools used therein. 

11 We note in the footnote that EPA has published

12 guidelines for the development of environmental models in

13 general that are intended for use in regulatory activities. In

14 addition, the European commission is developing several model

15 guidelines specifically for mechanistically-based ecological

16 effects models. 

17 Among other factors, a colony-level model

18 should include a formal sensitivity analysis and uncertainty

19 analysis in addition to verification of the model results to

20 observe empirical data. 

21 So to conclude, the SETAC Pellston workshop

22 participants indicate the currently colony level simulation

23 models are not ready for regulatory application. This

24 conclusion seems consistent with our cursory review of a limited

25 subset of models. However, once these models are fully

26 developed and evaluated, we think the colony-level models could
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1 provide an important line of evidence in the risk

2 characterization of pesticide effects on honey bees, and

3 potentially help to identify adverse outcome pathways across

4 multiple levels of biological organization. 

5 As we pointed out, they could be extremely

6 valuable in the problem formulation phase in an assessment in

7 the design and interpretation of field studies and in

8 integrating pesticide effects and exposure data generated at

9 multiple risk assessment tiers and at various levels of

10 biological organization. 

11 Therefore as indicated in the final charge

12 question number 14, we are interested in SAP feedback on the

13 concept of using colony-level models in pesticide ecological

14 risk assessment. In other words, is this concept worth pursuing

15 by the pesticide regulatory agencies represented? 

16 In addition, we would like SAP feedback on the

17 current state of the science of honey bee models in relation to

18 their potential use and application in pesticide risk

19 assessment. 

20 And finally, we would appreciate your input on

21 the most important model design elements that we should consider

22 as part of any review of a colony level model for application in

23 pesticide risk assessment. Thank you very much. 

24 DR. DANIEL SCHLENK: Any questions? Dr.

25 Fefferman? 

26 DR. NINA FEFFERMAN: Just as a very high-level
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1 cursory question in shifting from the White Paper discussion to

2 your presentation today, there was a shift from a discussion of

3 general models for colony-level use to simulation models. I was

4 wondering if that was purposeful and if you were interested in

5 analytic mathematical models and formulations as well as

6 simulation-based methods? 

7 MR. KEITH SAPPINGTON: Could I ask you to

8 clarify general models as compared to mathematical models? 

9 DR. NINA FEFFERMAN: Sorry, so in my mind sort

10 of mathematical analytic models live on one side and simulation

11 models live on another. So, simulation models are ones where

12 there are algorithms for action, and from those you get

13 numerical solutions, whereas mathematical models are ones where

14 by understanding the interactions of systems of equations, you

15 do things like generate threshold behavior or equilibrium

16 states. Things that don't involve, for example, running Monte

17 Carlo analyses in the same way as outcome measurements. 

18 So, simulation models produce data. Analytic

19 models produce sort of dynamic insight, and I was wondering if

20 you are specifically restricting yourself to simply the

21 simulation methods or if you're also interested in methods that

22 provide close form equation type understanding.

23 MR. KEITH SAPPINGTON: Keith Sappington. 

24 There was not an intentional restriction to the simulation

25 models, although they were attractive because of the analytical

26 components and the type of measuring endpoints included and the
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1 outcome aligning very closely to our assessment endpoints. With

2 that said, if the analytical versions of the models could

3 provide us insights, for example, the thresholds that need to be

4 maintained for long-term hive success, we would welcome input on

5 that. 

6 DR. NINA FEFFERMAN: Thank you. I'm sure I'll

7 talk about this more with you when we get to the specific

8 modeling section of the discussion, but thanks. That's great. 

9 DR. DANIEL SCHLENK: Dr. Schwab? 

10 DR. PAUL SCHWAB: Given the fact that the

11 number of times we have talked about chronic effects, one of the

12 models, I think it was the last one in your table, was talking

13 about the impact of pesticides in sort of a chronic fashion

14 rather than in terms of mortality, on laying of eggs and so on. 

15 What was the basis of that model if there is really a lack of

16 data to put that together? 

17 MR. KEITH SAPPINGTON: Are you referring to

18 the Thompson model? 

19 DR. PAUL SCHWAB: Yes I am. 

20 MR. KEITH SAPPINGTON: That was built on - in

21 that paper, the authors actually coupled semi-field experiments

22 on colonies and used that information as input into their -- it

23 was actually a colony model or, excuse me, a model developed for

24 Varroa mites, actually predicting Varroa mite infestations. 

25 They adapted that model, which was published earlier, and then

26 included information from their colony-level tests as well as -
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1 I think, I'll have to check - individual level information from

2 various pesticides, and then looked at the results from that,

3 both in terms of the empirical studies as well as the

4 simulation.

5 DR. PAUL SCHWAB: So, the effect of the

6 pesticide was a negative impact on the bees from the pesticides

7 themselves, or somehow the pesticides interacting to take care

8 of the mites that they were originally looking at? 

9 MR. KEITH SAPPINGTON: Keith Sappington. They

10 did not include mites in this particular simulation. The model

11 itself was derived from a model built to investigate mite

12 infestations, but they adapted that and then coupled measured

13 data on colony level effects, and basically tried to compare the

14 two. 

15 DR. DANIEL SCHLENK: Okay. Dr. McManaman? 

16 DR. JAMES MCMANAMAN: Yeah, I wonder if we

17 could bring up Mr. Moriarity's presentation. I have a question

18 that's related to models, but it's more related to population of

19 bees. Is that possible?

20 DR. DANIEL SCHLENK: They are working on it. 

21 Is there another question that we can do while they're bringing

22 that up? Does somebody have something else that we can do? Oh,

23 he's got it. 

24 DR. JAMES MCMANAMAN: It's number four. So, I

25 was struck by this, and I didn't know exactly - it didn't seem

26 like it was an appropriate time to ask during the presentation. 
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1

2 This is kind of an unusual curve in that it

3 looks like there is a steady decline from about 1950 to about

4 1970 in the pollinator population. Then, there's this plateau,

5 kind of up and down plateau for about 20 years, and then when

6 the mites hit, there is a sharp decline. Then, from you know,

7 1995 to about 2006, it looks like there that there is another

8 plateau here. 

9 So, this looks like there is a multivariable

10 interaction going on with the pollinator population in general. 

11 Has there been any kind of analysis to try to attempt to look at

12 potential -- kind of like an epidemiological analysis -- to look

13 at what was going on? Because in order to understand what could

14 contribute to pollinator decline would really be nice to know

15 what environmental changes have happened, what other kinds of

16 factors could have gone into the declines that we have seen

17 historically.

18 And part of this question is that this

19 plateau, the last plateau, it looks like there is a bit of a

20 decline at about 2005/2006 - do you have more recent data on

21 that the know does that continue to go down or is that just kind

22 of a blip and it really steadies off? 

23 DR. THOMAS STEEGER: This is Tom Steeger. A

24 couple of comments about this graph. I think that you need to

25 be careful in terms of interpreting it as it speaks to

26 pollinator declines per se. It's certainly informative about
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1 the issue of pollinator declines. This is a graph of the number

2 of commercial colonies that are used in honey production in the

3 United States. 

4 What it is probably more reflective of is a

5 changing demographic; that after the war there wasn't the need

6 for people to produce sweetener because sugar was in short

7 supply. And so, more and more people got out of the business of

8 honey production and the other part of this, it's hard to say

9 how much of an impact it had, was on the way that NASS actually

10 collected data, and their exclusion of sort of back yard

11 beekeepers that are producing honey to more commercial honey

12 production is an artifact that is part of this graph. So, it

13 doesn't really present what might be represented -- the

14 contribution from actual pollinator declines. So you have to be

15 careful with how you interpret this graph. 

16 As was pointed out in the presentation, it's

17 not - this graph goes out to 2006. The peak was about roughly 6

18 million colonies. It was actually 5.5 million in the late

19 1940s. The most current estimate, the 2011 estimate, places the

20 number of managed honey bee colonies for honey production at

21 2.49 million. So, you still see this decline taking place. As

22 was discussed in Tom Moriarity's presentation, these losses that

23 are being reported by beekeepers of 30 to 34 percent and the

24 high 20s this year because of a mild winter, these are real

25 losses. It's not just changing demographics. 

26 There is something happening that is really
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1 difficult to understand in terms of what combination of factors

2 are actually causing it. But getting to your question about

3 epidemiological approaches, it's interesting you should bring

4 that up because Rosalind James certainly has proposed that to

5 potentially pull in super-computing capabilities to get at that

6 issue. It's certainly an issue that USDA has been tasked with

7 over the past five years, congressionally mandated to determine

8 causes, and the week after next, there is actually a conference

9 to look at causal analysis on what is causing bee decline. 

10 So, there is a lot of interest and energy

11 being put at addressing this. It certainly was an issue that

12 was flagged by NAS in their publication on pollinator declines

13 in North America, but relative to this graph in terms of

14 interpreting plateaus and the peaks, it can be difficult to

15 interpret. It looks like Dr. Berenbaum has something to add.

16 DR. MAY BERENBAUM: Yeah, this was a subject

17 of discussion of the NRC, a study on the status of pollinators

18 in North America. Just to amplify what Dr. Steeger said, this

19 is only, it's not pollinator decline. It's honey producer

20 decline. These data don't reflect the changing nature of the

21 apiculture industry with a greater emphasis on provision of

22 pollinator services. It's just honey production up through

23 2006. That steep drop in the mid 1980s also, although it's easy

24 to attribute that drop to the introduction of mites, it

25 coincides with the change in methodology. So, there is a

26 confounding factor there.
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1 As you said, the sideliners, hobbyists, anyone

2 with five or fewer hives is not included. So, it's not suitable

3 for epidemiological analysis. It's really reflective more of

4 the industry than it is of the pollinator workforce of the

5 United States. You know, it's hard to over interpret because it

6 was not initially set up for that purpose. It was more for

7 honey production.

8 DR. JAMES MCMANAMAN: This is Jim McManaman

9 again. So, what is the state and in terms of assessing the role

10 of pesticides and their contributions to decline, whether this

11 is through honey production, but in terms of overall pollinator

12 decline, it would be helpful to really have an accurate idea of

13 what the real nature of the decline has been because it sounds

14 like this is maybe an artificial - it may not be reflecting

15 reality. So that's the basis of my question. Is it still going

16 down or has it plateaued off? 

17 DR. MAY BERENBAUM: Maybe Jeff, Dr. Pettis

18 could address the Apiary Inspector of America Effort to obtain

19 better data. 

20 DR. DANIEL SCHLENK: Go ahead, Jeff. Thanks.

21 DR. JEFF PETTIS: Very briefly, Jeff Pettis,

22 USDA. As May pointed out, this is just managed honey bee, and

23 actually there has been an analysis of the slope before that

24 blip and afterwards that it's the same - it's mostly driven by

25 economics, honey prices in fact. 

26 As far as getting at the loss rate of let's
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1 say honey bee colonies in the U.S., NASS was encouraged to take

2 over and do a very large study and they had no funding to do

3 that. There has been an effort for six years now, but we've

4 done a survey of losses in the U.S. and we asked a number of

5 questions. 

6 We're looking at ways to probe that data and

7 try to get at, you know, Varroa mites versus pesticide exposure,

8 et cetera. It's not the best dataset, but it's the best dataset

9 we have. We are looking at various ways to use that dataset to

10 try to get at some of these multifactorial issues, but a lot of

11 it is based on questionnaires where we have, depending on the

12 beekeeper to give us truthful answers. 

13 So, we have some other better datasets, but

14 only for specific colonies, two to three hundred colonies, not a

15 huge dataset. 

16 DR. DANIEL SCHLENK: Okay. Any questions for

17 the Agency? Thanks. Any questions? Dr. McManaman? 

18 DR. JAMES MCMANAMAN: This is Jim McManaman

19 again. So, I'm going to push this point just a little bit more.

20 So, in terms of understanding the risk and developing risk

21 assessment, it really would be good to have numbers about if

22 there are any inaccuracies and what the actual decline is, it

23 really would in terms of setting policy and going forward on

24 this, it doesn't sound like we really have the numbers in hand

25 to really assess the real risk to the pollinators. Maybe I'm

26 over stating that. 
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1 DR. THOMAS STEEGER: This is Tom Steeger. I

2 think that if there's any uncertainty in the room that

3 pollinators, particularly the honey bees, where there is a

4 question as to whether they are experiencing significant

5 declines, as someone who is responsible for monitoring the

6 incident database, I have received calls and talked to

7 individual beekeepers that have lost within a period of two

8 weeks 21,000 colonies. As a biological event, if you figure

9 that there are 50,000 bees per colony, the loss of 21,000

10 colonies in that short of a period of time is a staggering

11 event. 

12 We are unclear as the role that pesticides are

13 playing in pollinator declines. There have been multiple

14 factors as was discussed in Tom Moriarity's presentation that

15 USDA has identified as contributing to pollinator declines. Do

16 we have a firm understanding of what declines are actually out

17 there? I don't think so, because it's difficult to get a handle

18 on this data, but I think that as an agency that is responsible

19 for regulating pesticides, we have a responsibility to document

20 to the best of our extent, the extent that pesticides are

21 playing a role and to mitigate those effects to the extent

22 possible. 

23 But it has been my experience based on

24 information that I get through incident reports and through my

25 interactions with the beekeeping community in the United States

26 that these losses are significant, and people who have been in
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1 the business of beekeeping who have an understanding of how to

2 keep bees are experiencing staggering losses. This is not a

3 contrivance. It is a real problem. The ability of the country

4 to meet pollination service requirements is becoming a question,

5 given the increased need for pollination services for

6 pollinator-dependent crops. 

7 DR. JAMES MCMANAMAN: This is Jim McManaman

8 again. I certainly didn't want to imply that there was a

9 contrivance, but you know, as a matter of it's just difficult to

10 assess what the contributions of pesticides might be to the

11 loss. There may be multiple factors leading to it. So, without

12 hard numbers, it's just really, you know, we might be shooting

13 at the wrong target is all I'm saying. 

14 DR. DANIEL SCHLENK: Yeah, Dr. Fefferman? 

15 DR. NINA FEFFERMAN: Something occurred to me

16 very briefly. For the transparency and clarity aspects of the

17 demands from the modeling side, transparent and clear to whom? 

18 To practitioners? To biologists? To beekeepers? To modelers? 

19 Where does that benchmark get set? 

20 DR. THOMAS STEEGER: Presumably to the risk

21 manager as the primary client of our risk assessment, but it's

22 to the stakeholders as well who are affected by our decisions. 

23 DR. NINA FEFFERMAN: So does that mean that

24 the methodology by which it was arrived must also be clear

25 because I think that might eliminate almost all modeling from

26 being sufficiently transparent. 
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1 DR. THOMAS STEEGER: As Keith Sappington

2 pointed out, the Agency has specific guidance on the development

3 of models and the transparency has to do with the coding that

4 goes into the algorithms that are used to support those models,

5 and by the testing that goes on subsequent to determine how well

6 those model estimates reflect actual exposure or effects that

7 occur at the colony level. 

8 MR. KEITH SAPPINGTON: Keith Sappington. I

9 would add in addition to just having the equations in the Paper,

10 having the basis for parameters that are used for those

11 equations, and then again, having that information. So, then it

12 could be evaluated by anybody in terms of a scientific

13 defensibility. 

14 DR. NINA FEFFERMAN: So, thank you. I mean I

15 guess part of why I'm asking is that again, moving towards the

16 more analytic side of models. There are occasionally very

17 valuable insights that can be provided in the absence of data or

18 validation in the sense of ground truthing outcomes, as long as

19 the process itself is understood to be reflective of a realistic

20 process. So, in some sense, there are no parameters that come

21 in, there are simply functional relationships. And in that

22 sense, then the equations can still provide quite a bit of

23 insight into things like the existence of a meaningful threshold

24 within, for example, this thing can't go negative, and therefore

25 there exists no satisfiable threshold, because otherwise we've

26 created bees out of thin air kind of validity. 
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1 On that level, sometimes there can be English

2 descriptions, but there isn't so much to grab on to. So, I'm

3 wondering are there guidelines that apply equally well to models

4 like that? Are they necessarily then off the table? What

5 happens? 

6 DR. STEPHEN BRADBURY: Maybe there is another

7 document we could try to get into the record, which may get a

8 bit at what you're asking. There is an OECD document, and it's

9 for quantitative structure activity relationships, which is a

10 different specific topic, but it talks about model - it calls it

11 model validation; but it is more around what kind of thing

12 should you kind of describe about your model. 

13 It doesn't come at that a model is right or

14 wrong, it's about describing what the model does and various

15 attributes of the model and explaining it in such a way that a

16 user of the model can be informed as to whether or not it's

17 appropriate to use a given model for the given purpose that

18 somebody maybe - or question somebody may be trying to answer. 

19 So, I don't know if it will match exactly what

20 you're getting at, but I think it might provide some insights

21 into where we're coming from. It isn't the model is good, bad

22 or indifferent, it's how do you try to describe attributes of

23 the model so that you can understand how to use it or not use it

24 or use it with care for a given question you may need to answer.

25

26 Getting back to your transparency and
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1 understandability, certainly it's in the risk assessment

2 framework that the risk assessor and the risk manager hopefully

3 understand each other so they're not passing in the night and

4 burning resources in time, misunderstanding each other; but also

5 at the end of the day it's to ensure that the public understands

6 what it is we're thinking and how we formulated out position. 

7 Clearly, sometimes these documents are huge. So, we have a

8 range of audiences from scientific experts that know the very

9 excruciating detail of a certain component of the risk

10 assessment and somewhere in that risk assessment document, being

11 able to explain it to that audience. But ultimately being able

12 to go through risk communication and analysis and writing so

13 that public can understand the rationale to the decision making.

14 So it gets challenging, but that's what we're trying to strive

15 for. 

16 DR. NINA FEFFERMAN: Thank you, that sounds as

17 though the extra document you were thinking of would get to

18 exactly the sort of thing that I'm thinking, and it does also

19 set a useful benchmark for me in considering. Thank you. 

20 DR. DANIEL SCHLENK: Dr. Pettis? 

21 DR. JEFF PETTIS: More of a comment, but it

22 does tie into the modeling, and it gets back to your question a

23 bit. 

24 As you go from Tier I to Tier II or Tier III,

25 you go from control studies with individual bees, which we can

26 control. We can control the genetics. We can control
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1 everything. Then you get an interpretation of some result of

2 these individual bees. As soon as you take it Tier II or Tier

3 III, where you have colony level, you bring in all this

4 uncertainty. I think that's what we grapple with. 

5 I mean, so maybe a modeling approach,

6 especially at the colony level, it can be very useful and maybe

7 applying a certain degree of loss or something attributable to

8 one thing and then following that over time. But as soon as you

9 go to the colony level, it's impossible to control all these

10 other factors, and that's what we grapple with.

11 DR. DANIEL SCHLENK: That would be a great

12 discussion for tomorrow and the next day. Looking for any other

13 questions for the Agency while we got them here. Yes, Dr.

14 Potter? 

15 DR. THOMAS POTTER: This is Tom Potter here. 

16 We've been looking at question 14 because I was identified as an

17 associate discussant on there. So, I have been trying to get my

18 head around it. I think it's question 14 and it has to do with

19 a colony-level modeling. I though the answer was easy. Yes,

20 it's a great thing. Boy, I wish we had the simulation model

21 where we could assess these effects, but apparently it doesn't

22 exist and they exist in various, what appear to be crude form. 

23 So, ultimately my question comes around to you

24 folks is you know, what kind of response are you looking for? 

25 Is the answer yes, models are a great idea? I think that one's

26 easy. And the second part of it is who is going to develop
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1 those models and support them? Obviously, that's a

2 fundamentally important question with regard to any simulation

3 model that we might use. 

4 MR. KEITH SAPPINGTON: Keith Sappington. I

5 think the first thing we wanted was the simple question, perhaps

6 yes, was that is this concept worth dedicating some amount of

7 resources to, because it is a rather large effort even to take

8 an existing model and understand the transparency, understand

9 all of the information that went into it, develop it and modify

10 it and then bring it to a panel like this for final review. 

11 So, before doing that and in the context of

12 pollinator declines and multiple stressors, we wanted to just

13 check in before considering this option further. 

14 The second thing is getting sort of a check in

15 on this state of the science. Do you agree that with the

16 current state of the science, what we were able to do just

17 briefly was to summarize a few of the models. 

18 And then thirdly, sort of helping us evaluate

19 or even perhaps dedicate efforts to developing some models,

20 understanding what are the most important factors to include in

21 a model? 

22 The HoPoMo model has 66 differential equations

23 within it, and all models are wrong and some models are useful. 

24 So, I'm not sure that we need a model with 66 differential

25 equations. So, the advice that would be very helpful to us

26 would be to say, well here's your top tier of things that you
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1 must include in a model to have any chance of getting a

2 realistic result, and then here are some other sort of secondary

3 tiers that gee, if you have the data, this will be useful and it

4 will be useful for this purpose.

5 DR. DANIEL SCHLENK: Dr. Berenbaum? 

6 DR. MAY BERENBAUM: Not to prolong this, but

7 just to follow up and reflect my profound ignorance of how

8 Washington, DC, works. So, we are likely to identify data gaps,

9 but isn't EPA primarily regulatory and whose responsibility is

10 it then to fill those data gaps? Is it kind of who will help me

11 bake the bread scenario. Yeah, how does our response to the

12 charges figure into meeting the EPA's need for certain types of

13 information? 

14 DR. DANIEL SCHLENK: Dr. Bradbury, I think

15 would answer that one, yeah? 

16 DR. STEPHEN BRADBURY: I will just be careful

17 on my editorial comment about Washington. So, it gets back to

18 some of the earlier questions in terms of will it go quick. But

19 the big picture, what's the cause of pollinator decline. That's

20 an activity, USDA is leading it and multiple federal agencies

21 are involved in academia. So, as we said at the beginning of

22 today's discussion, we're focusing on the pesticides first in

23 terms of under FIFRA we do take a look at effects of pesticides

24 on nontarget organisms, pollinators are an important component

25 in the ecosystems. They have benefits for agricultural

26 production and the products of the hive, with everything else
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1 that's going on, making sure we're advancing our capability of

2 doing that.

3 Some of our discussion that was in isolation

4 of interactive effects and we talked a little bit about it today

5 and you may have some insights as we go through the charge

6 questions, that perhaps while not directly related to what we do

7 in the pesticide program, your report combined with the SETAC

8 report any other reports, EFSA report, may help spawn research

9 in different organizations in the federal government or

10 internationally.

11 With regard to specific uncertainties that may

12 relate to doing pesticide risk assessment, there is a variety of

13 scenarios that could play out. The Part 158 test guidelines

14 that some of the speakers discussed already has an opening -

15 more than an opening, it has the ability to do different kind of

16 field studies. But those guidelines are written, I think

17 appropriately so, open and not very prescriptive because they

18 are designed to answer specific questions. 

19 Some of the feedback potentially from the

20 charge questions could give us insights as to how to tailor, how

21 to better define what the attributes of the semi-field studies

22 or full-field studies, or maybe variations on the laboratory

23 experiments could be to help focus, because our guidelines

24 already give us some flexibility. That could be a way through

25 chemical, by chemical or risk assessment scenario by scenario to

26 get information, but you would be giving us some guidance on how
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1 to be more efficient or effective in focusing the chemical risk

2 assessment specific issues. 

3 There could be some feedback that you provide

4 that with the work we're doing with other countries and OECD or

5 in other bilateral multilateral arrangements, there may be

6 advice along with the SETAC Pellston document that may help

7 advance some test guidelines, say at the laboratory level, that

8 we all could use and then work with the regulating committee to

9 go forward. 

10 So, I think there is a variety of ways that

11 this could play out. There may be some really pure research

12 questions that we suggest and there are other parts of the

13 federal family, state, international groups that will read your

14 report and use it as well. 

15 DR. DANIEL SCHLENK: Okay. Are we done? Any

16 other questions? What we will do just to again summarize,

17 mentioned this, this morning in our early morning, I think the

18 Agency will be here tomorrow after our public commenting period.

19 Before we start questions, you'll have another opportunity to

20 ask any questions that come out tonight in your discussions and

21 deliberations, and just mulling over what has been gone over

22 today that you ask one last time again before we actually get

23 into the charge questions. 

24 Okay. All right, before I turn it over to

25 Fred, I just want to acknowledge thanks for the Agency's

26 presentations today, and also for the panel if you would please
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1 meet in the conference room after this, after you can do a

2 restroom break, we need to meet and hash some things out before

3 you head back to your rooms. 

4 DR. FRED JENKINS: Okay. I just have a few

5 things to say that are directed towards the public commenters

6 for tomorrow. If you are giving a public comment that has a

7 power point presentation, please see me as soon as possible,

8 either now or early in the morning before the meeting,

9 preferably no later than 8:30 tomorrow morning so I can load

10 your power point presentation onto the computer behind me. 

11 Also, to all the public commenters, please

12 arrive to the meeting on time tomorrow at 9 a.m. We don't want

13 you to miss your opportunity to provide your public comment. 

14 Once the public comment period closes, there will be no more

15 opportunity to provide oral public comments at the meeting. 

16 As a reminder, if you have not made prior

17 arrangements with me to provide oral public comments, please see

18 me as soon as possible so we can get you signed up as a public

19 commenter and I ask also as a reminder, please limit your

20 comment to five minutes. 

21 And the last point I want to make, I ask that

22 all public commenters please keep your oral comment times to the

23 agreed upon time frame. If you've asked for five minutes,

24 please only speak for five minutes, 10 minutes, please only

25 speak for 10 minutes, an hour, please only speak for an hour. 

26 We have a whole lot of ground to cover this
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1 week, and we ask all of our public commenters to please help us

2 in covering and accomplishing what we need to do for this

3 meeting. Please stay within your specified timeframe. And

4 that's all I have to say. This meeting is adjourned for today

5 and we look forward to seeing everyone in the morning. Thank

6 you for coming. 

7 (WHEREUPON the meeting was adjourned for the

8 day.)

9 DR. DANIEL SCHLENK: Okay. So, let's go

10 ahead and just do a real brief introduction around the panel

11 again. Just state your name and affiliations before we get

12 started so everybody can identify themselves. My name is Dan

13 Schlenk. I am professor in the Department of Environmental

14 Sciences, University of California Riverside and I Chair of the

15 SAP. 

16 DR. STEPHEN KLAINE: My name is Steve Klaine. 

17 Good Morning. I am with Clemson University and I am an aquatic

18 ecotoxicologist.

19 DR. JAMES MCMANAMAN: Good morning, I am Jim

20 McManaman. I am at the University of Colorado in the Department

21 of Obstetrics and Gynecology.

22 DR. KENNETH DELCLOS: Barry Delclos from the

23 Food and Drug Administration's National Center for Toxicological

24 Research.

25 DR. DAVID TARPY: Good morning, I'm David

26 Tarpy. I'm at North Carolina State University in the Department
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1 of Entomology. I am a honey bee biologist and the extension

2 apiculturist for North Carolina. 

3 DR. PAUL SCHWAB: I am Paul Schwab from Texas

4 A&M University. I am an environmental soil chemist.

5 DR. THOMAS POTTER: Tom Potter, USDA

6 Agriculture Research Service, Southeast Watershed Laboratory in

7 Tifton, Georgia. I am a pesticide chemist.

8 MR. JENS PISTORIUS: Jens Pistorius, Germany,

9 Julius Kuehn Institute. I work with risk assessment for

10 pesticides in honey bees, investigation of bee poisoning

11 incidents and research pesticides in honey bees. 

12 DR. JEFF PETTIS: Good morning, I am Jeff

13 Pettis, I am with USDA Agricultural Research Service, Bee

14 Research Laboratory here in Beltsville, Maryland, and I am an

15 entomologist. 

16 DR. NANCY OSTIGUY: Nancy Ostiguy, from

17 Entomology Department at Penn State.

18 DR. ROSALIND JAMES: Rosalind James, I am with

19 the USDA Agricultural Research Service. I am director of the

20 Pollinating and Sex Research Laboratory in Logan, Utah.

21 DR. GREG HUNT: Good morning, I am Greg Hunt. 

22 I am at Purdue University and I study honey bee genomics,

23 behavior and health issues. 

24 DR. NINA FEFFERMAN: Good morning, I am Nina

25 Fefferman from Rutgers University. I am an applied mathematical

26 modeler of ecology and epidemiological systems. 
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1 DR. MAY BERENBAUM: I am May Berenbaum, the

2 Profession and Head of Entomology at the University of Illinois

3 at Urbana-Champagne, and I am an insect chemical ecologist. 

4 DR. DANIEL SCHLENK: Thank you everyone. 

5 Before we get started, Fred has some comments he needs to make

6 to our public commenters. 

7 DR. FRED JENKINS: Good morning everyone and

8 welcome to the second day of our FIFRA SAP on Pollinator Risk

9 Assessment Framework. My name is Fred Jenkins, and I am the

10 Designated Federal Official for the FIFRA SAP. So, I just

11 wanted to briefly say to the public commenters - we have quite a

12 few public commenters this morning. If you have a power point

13 presentation, please see someone on our staff so that we get

14 your presentation loaded onto the computer behind us. Also, if

15 you have any handouts of your comments that you wanted

16 distributed to the Panel, please bring those now to our staff so

17 that we can have those distributed as well. With that said, I

18 am going to turn the floor over to Dr. Daniel Schlenk. 

19 DR. DANIEL SCHLENK: Thanks Fred. Couple

20 comments -- this morning just speaking of materials - there were

21 a couple papers that were placed in the chairs of some of the

22 panel members. If you have materials that need to be presented

23 to the panel they need to go through the EPA staff. We can't

24 have materials distributed without a formal distribution pattern

25 through the EPA. So again, if you have materials that you need

26 to get to the Panel or you would like the Panel to see, please
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1 do so through the EPA staff here. Okay. With that, we turn it

2 over to the EPA and any sort of follow-up issues you need to

3 present today before we start with the public comments.

4 DR. DONALD BRADY: No, thanks very much Dr.

5 Schlenk, I think we are ready to go right to public comments.

6 DR. DANIEL SCHLENK: Great, what I would like

7 to do, if you don't mind, is following the public comment period

8 if you guys could come back up, I will ask the panel if they

9 have any last minute questions related to the materials. Great,

10 thanks. 

11 Okay. Our first public commenter is CropLife

12 America, and I believe that is going to be Dr. McAllister making

13 that presentation. Again, as Fred had mentioned, please hold to

14 the time that you have been allotted. We have an hour allocated

15 for you guys, for CropLife, if you can please keep to that time

16 that would be great.

17 DR. RAYMOND MCALLISTER: Thank you very much. 

18 My name is Ray McAllister. I am the Senior Director of

19 Regulatory Policy for CropLife America. CLA is the National

20 Trade Association representing the crop protectin industry in

21 the United States. We would like to express our sincere

22 gratitude to the bee technical workgroup, which has worked very

23 hard over the past month or so to prepare the written comments,

24 which have been distributed to you as well as the oral summary

25 that you will be hearing this morning. I will just making a few

26 introductory remarks, and then we will hear from David Fischer
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1 of Bayer and Jay Overmyer of Syngenta for the bulk of the

2 remarks today.

3 We all recognize that pollinator health is a

4 vital issue for American agriculture. CLA in particular

5 recognizes that crop protection technology, as well as the

6 pollination services provided by both managed honey bee colonies

7 as well as native pollinators are necessary for successful

8 agriculture in feeding a hungry planet. 

9 CropLife America commends the efforts of EPA,

10 PMRA and Cal DPR in improving the science of pollinator risk

11 assessment. Our member companies appreciate this opportunity to

12 provide input into a very important process. We believe that

13 improving the quality and rigor of risk assessments will ensure

14 that the crop protection tools, which are critical for

15 sustainable agriculture production, can be used in a manner

16 compatible with pollinator health. 

17 Pollinator health can be adversely affected by

18 a combination of stress factors, which include a variety of

19 diseases, some very devastating parasites, problem with

20 nutrition, loss of habitat, chemical stress and bee management

21 practices. 

22 There are some who have asserted that

23 pesticides are the root cause of colony collapse disorder in

24 honey bees, but this is not supported by the weight of the

25 evidence. We believe that establishment of a scientifically

26 credible risk assessment approach for pollinators will help
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1 promote sustainable agricultural practices and these are of

2 benefit to all involved, from farmers, beekeepers and the

3 American Consumer. 

4 The establishment of sound and scientifically

5 valid test methods and risk assessment processes should aid all

6 interested researchers in optimizing study designs to ensure

7 that the results are reproducible and that they can be

8 interpreted clearly. Lack of such guidance has led to

9 publication of some recent studies, which are not optimized for

10 contribution to risk assessment and have led to some erroneous

11 conclusions about the relative importance of pesticides in

12 pollinator health in practical circumstances.

13 The improved risk assessments will be

14 important to support the appropriate labeling of pesticide

15 products and accompanying best management practices for

16 safeguarding pollinator health. 

17 CLA believes that the scientifically sound

18 risk assessment procedures are critical not only to ensure that

19 the EPA's and the nation's environmental protection goals are

20 met, but to instill confidence in the public in the regulatory

21 approval process. In the hopes of improving these risk

22 assessment processes under review, we are offering the following

23 comments, and I will turn the time over to Dave Fischer.

24 DR. DAVID FISCHER: Thanks Ray. I am David

25 Fischer. I am an ecotoxicologist with Bayer CropScience, and on

26 really the comments that I am presenting and Jay Overmyer from
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1 Syngenta will be presenting is the work of - technical workgroup

2 we have among the companies and CropLife America. 

3 First, some general comments on the White

4 Paper Risk Assessment Process. We support the harmonized

5 approach among the three regulatory agencies. This is something

6 that is of great benefit to our industry that we have a

7 consistent approach stake in various jurisdictions. Not only in

8 North America, but we would like to see harmonization around the

9 world as well. 

10 The tiered risk assessment approach we

11 support. This is similar to what we do with other taxa. We try

12 and screen out the chemicals that you can eliminate concern

13 about, focus the resources where there is the main

14 uncertainties; and we also support expanding the Tier I data

15 requirements as the Pellston Workshop recommended and as the

16 White Paper recommends to fill in some of the gaps we currently

17 have with just the limited tests that are in the current

18 guidelines. 

19 We also support a flexible approach to doing

20 higher tier testing, Tier II and Tier III. It would be nice if

21 there was a prescription for doing this, but every chemical in a

22 use pattern really will have its own differences that need to be

23 taken into account. The key thing is to make sure that when we

24 go to higher tiers, we are focusing on what the uncertainties

25 are and addressing those with the studies. 

26 We do have some concerns. We think the Tier I
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1 assessment, especially the way the dietary exposure is being

2 estimated, is overly conservative and will lead a lot of

3 chemicals that are reasonably safe or really should not cause a

4 concern progressing to higher tiers. I will say more details

5 about that, why we think that might be true; and also how we

6 think it might be remedied. 

7 We are also concerned if we get into a system

8 where a lot of chemicals have to go to higher tier testing that

9 we have clear acceptability criteria in place for those higher

10 tier tests and how they are going to be conducted, how they the

11 data is going to be interpreted. Right now, there is a lot of

12 uncertainty there. 

13 So, we will go through sort of the parts of

14 the White Paper with respect to problem formulation. We support

15 the protection goals, which largely were the same as we

16 developed at the Pellston Workshop, which our industry

17 participated in along with the EPA scientists and academic

18 scientists. 

19 One thing that is appreciated in the White

20 Paper that we have not always seen in other assessment documents

21 is a real clear explanation for how levels of concern are

22 established. We appreciate the clarity and the transparency of

23 how they said this is what we are trying to do and this is the

24 RQ value that seems to meet that criteria; and its science

25 based, so we support that. 

26 I mentioned flexible testing beyond Tier I. I
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1 won't say anything more about that. Use of conceptual model -

2 we thought that this White Paper has a very good illustration of

3 how to do a conceptual model and how to use it to focus the

4 assessment and the development of higher tier data to address

5 the main uncertainties. It is very difficult to assess

6 everything in an assessment, and I think this does a good job of

7 focusing on that major routes of exposure. 

8 Also, we support using honey bees as a

9 practical surrogate for pollinators in general. This was

10 discussed at length at the Pellston Workshop and the report will

11 have some more information on non-Apis bees, but for practical

12 purposes and certainly with the effects testing, the honey bee

13 should be a reasonable surrogate. 

14 Okay. Characterization of Contact Exposure -

15 we basically support what is proposed in the White Paper using

16 the relationship from the Koch and Weisser Database. It seems

17 to be the best available data. It's consistent with what was

18 agreed to at the Pellston Workshop. As the White Paper analysis

19 shows, it seems to be an improvement on previous approaches. 

20 Now into some areas where we see some room for

21 improvement. Characterization of Dietary Exposure - first of

22 all, the soil applications, the Briggs' model, we actually think

23 is reasonably good Tier I starting point. You know, if you have

24 to come up with an estimate of systemic uptake into plants, into

25 field crops, this is a reasonable starting point. It doesn't

26 necessarily tell you what is going to be expressed in the
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1 flowers of a plant. It is not developed to do that, but it

2 should be, if anything, an overestimate of that. 

3 Again, as more data are developed, this will

4 be a theme on a lot of our slides. Some of these Tier I

5 estimates may need to be reevaluated.

6 With respect to seed treatments, the default

7 value of PPM or mg/kg, I think was originally proposed for all

8 systemic risk assessments. So, whether it was a seed treatment

9 or a soil treatment, that seemed to be an upper-bound for seed

10 treatments. However, the field data are quite a bit less. If

11 you look at the field measurements in table 17, the highest

12 value is 36 parts per billion and the Tier I estimate is more

13 than an order of magnitude; it's like 30 times higher than that.

14 It looks to us like a default value of 0.1 mg/kg would be more

15 appropriate and adequately conservative just based upon the data

16 that we have. 

17 For soil treatments, the numbers can be

18 higher, but when they are the Briggs' model does an adequate job

19 of reflecting that. 

20 Again, I think maybe it's implied in the White

21 Paper, but it doesn't specifically state, so we want to state

22 it. There is no point in concluding there is a risk of use of

23 seed treatments that aren't systemically active. There are a

24 lot of them; especially a lot of the fungicides won't

25 necessarily be expressed in the pollen and nectar to any extent.

26 We should be able to screen those out rather than just assume
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1 that there is a concentration there. 

2 Tree trunk applications - here is an area

3 where we are in need of a lot more data, a lot of research. So,

4 what the Agency has come up with is a back-of-the-envelope

5 calculation that basically tries to say what if all the mass of

6 compound applied is evenly distributed in the biomass of the

7 tree, the leaves and flowers. There is a lot of uncertainty

8 whether this is an accurate prediction. We think it is probably

9 very conservative because flower concentrations are often orders

10 of magnitude less than leaf concentrations, but it is a starting

11 point. 

12 So, you know, we think it is going to be very

13 conservative. We do think that there may be some data out there

14 from the USDA studies on Asian longhorn beetle control programs,

15 Emerald Ash borer, maybe the Hemlock Woolly Adelgid. I think -

16 and we don't have access to all these data, but there may be

17 some data that USDA can pull in here that EPA hasn't considered

18 yet. 

19 Okay. So, our main concern is the foliar use,

20 the dietary estimate that is being proposed is basically to use

21 the tall grass value from the T-REX model, which really comes

22 from the Hoeger and Kenaga nomogram as modified by Fletcher et

23 al. For just looking at the numbers versus what these estimates

24 are, the pollen numbers appear to be reasonably conservative. 

25 They're high. They're about 2X higher than the highest value

26 that is in the dataset, but the dataset is small, so that
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1 doesn't seem to be unreasonable as a starting point. 

2 But for nectar, it's much, much higher. It

3 appears to be overly conservative with the highest field

4 measurements more than an order of two magnitude below what the

5 T-REX model value is. 

6 So, since nectar consumption, nectar

7 concentrations really are what drive this risk assessment -- if

8 you crunch the numbers 97 percent of the exposure comes from

9 nectar and 3 percent from pollen according to the calculations

10 -- it's critical that you not grossly overestimate the nectar

11 concentrations. 

12 So, just looking at the data, and I think I

13 will transition to the next slide, looking at the data in table

14 5, it looked to us that there is about a 3X factor between

15 pollen and nectar, particularly when you look at the studies

16 that are reporting average field measurements. We put more

17 weight on those than the maximum ones because those can be just

18 more variable. 

19 So, we are proposing that you take the value

20 for pollen and multiply it by 0.33 to get the estimate for

21 nectar. 

22 Then the question - how conservation should

23 Tier I exposure estimates be? There is a statement that the

24 Tier I method is intended to generate reasonably conservative

25 estimates. We agree with that; but meaning that the estimates

26 should generally be within one or two orders of magnitude higher
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1 than the true environmental exposure. That seems to us to be

2 too high. One to two orders of magnitude bias in your estimate

3 can greatly change the number of the compounds that you fail to

4 screen out and have to conduct higher tests with.

5 If you look at what Fletcher did in developing

6 the nomogram values that are used in T-REX, they look at field

7 residue measures and just took the 95th percentile. Really, if

8 you look at EPA policy in the framework documents, they refer to

9 high end estimates usually being in the 90th-95th percentile

10 range; that is traditionally what is done. Now here we do not

11 have a lot of data, so we may not have enough to just say use a

12 95th percentile, but taking the highest value that you have and

13 going 10X or 100X more than that seems to us to be too

14 conservative. 

15 We point out that the distributions and

16 residue measurements tend to be log-normal, which means they are

17 skewed to the low side with a fewer high-end values. Those

18 high-end values can be statistical outliers and probably are

19 less reliable. Sometimes, they may even be errors due to

20 contamination when the sample is collected or analytic

21 methodology. 

22 Just what is the consequence if you over

23 estimate exposure by 10 to 100 times? Just as an example, a lot

24 of us probably had our morning coffee. If you look at caffeine,

25 I don't know if you know this, but caffeine is actually pretty

26 toxic. 
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1 The LD50 is in the range of 150-200 mg/kg body

2 weight, which is about 80 cups of coffee with a typical strength

3 of caffeine. So, that's about a little over 1 percent of an

4 LD50 in each cup of coffee. 

5 If you over estimate the caffeine

6 concentration in coffee by 10X, you conclude that each cup has

7 more than 10 percent of an LD50 dose, which in most risk

8 assessment schemes would at least cause some concern. If you

9 overestimate it by 100X, you can conclude that a cup of coffee

10 is lethal to a lot of people. So clearly that would -- you know

11 just as an analogy -- I think we would want to screen out very

12 reasonably safe products like we would screen out having to do

13 high risk assessments for caffeine in coffee.

14 The other thing is the food intake rate, which

15 I think there is a lot of uncertainty, or at least we are

16 wondering if that needs to be looked at - and I really

17 appreciate what the Agency did because they pulled off a higher

18 tier methodology approach by using a Monte Carlo procedure to

19 try and take the data that Rortais et all had and come up with

20 an estimate of how much food a bee ingests in a day based upon

21 energetics and other things. We did a lot of the similar things

22 in ECOFRAM a while back. Having done some of that modeling,

23 some of the pitfalls were made aware to me because I made some

24 of the same mistakes. 

25 First of all, log-normal distribution was

26 assumed for the sugar concentration in nectar. That might be
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1 fine, but just setting the mean at 30 percent means that most of

2 those trips out there, the assumption is that they're coming

3 back with nectar with less than 30 percent because the

4 log-normal is going to be skewed to the low side. 

5 Of course, why is that significant? It's

6 because you are basically totaling up the amount of nectar the

7 bees are ingesting in order to get a certain amount of sugar and

8 summing the mass of pesticide in that quantity of nectar. So,

9 there could be some bias there. At least it is something that

10 you might want to look at. 

11 There is also an assumption that bees are

12 foraging randomly. They don't show any preference for picking

13 out food sources that have higher sugar content. I wonder if

14 that is true. I would be surprised if it was. I think if you

15 put a bunch of feeders out there, and there are probably people

16 on the panel that know the answer to this, you would find that

17 they would select the higher sugar source. You know, it would

18 just be -- optimal foraging theory would predict they would. 

19 There are three variables in this Monte Carlo

20 Simulation that use uniform distribution. This is where some of

21 my personal experience comes in. I did this myself. I put in

22 uniform distributions in some of the ECOFRAM models and when

23 Dwayne Moore reviewed them he told me, don't ever put in a

24 uniform distribution. It puts too much weight on the tails,

25 come up with some sort of beta distribution that uses expert

26 judgement. 
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1 So, I don't know if this changes - you know, I

2 would want to see a sensitivity analysis to see if changing the

3 distribution would change the result of the simulation. 

4 Then finally, number of foraging flights per

5 day - I think and I could be wrong, but there was nothing that

6 indicated to me when I read it that they weren't just assuming

7 this was an independent variable from the distance traveled or

8 the duration of the flight. It makes sense to me that wouldn't

9 you expect this to be highly correlated that if you were making

10 more flights per day, they were shorter flights. So, you

11 wouldn't need as much energy, you wouldn't need as much sugar to

12 fuel those flights. 

13 So, if you have correlated variables - and

14 there are ways of doing that - you want to specify that in your

15 modeling. I'm not sure if that was done. All of these factors

16 may result in a nectar consumption being over estimated. 

17 So, I want to support the concept of using

18 this sort of modeling technique. I just think it might need to

19 be vetted. 

20 Dose from food intake - another thing is and

21 this is common to assessments we do for birds and mammals as

22 well - we do back-of-the-envelope, you know how many LD50s per

23 day does an animal get? That's kind of what we are doing. We

24 are calculating the cumulative dose over entire day of foraging

25 per bee, but we are not taking into account metabolism and

26 detoxification that is going on as this food is being ingested. 
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1 That can be a significant factor. 

2 Going back to the caffeine example with

3 coffee, you get a different dose if you have one cup of coffee

4 an hour versus if you have 10 at once. You know, the caffeine

5 is metabolized and detoxified, excreted so the doses don't

6 necessarily add up, aren't completely additive. So, that is

7 another source of conservatism in the assessment. 

8 Then there are a lot of other factors. We

9 support the option of addressing uncertainties in the risk

10 assessment factors with whole colony exposure studies, which

11 really is, I think, where we need to get better data and really

12 find out what the colony is being exposed to. 

13 Then there is in these other bullets, all

14 these other factors that need to be taken into account as far as

15 the use patterns and just how likely is exposure to occur. We

16 would hate to see us wasting time doing higher tier assessments

17 on exposure scenarios that should be screened out just on the

18 basis that the way you are using a product as a bait or applying

19 to a crop that bees aren't going to visit like leaf lettuce or

20 applying post bloom. All of these factors I think EPA currently

21 takes into account. We just want to emphasize that maybe the

22 White Paper did not state that as clearly as needs to be stated.

23 So there will be other uses that will be screened out besides

24 just based upon risk quotients. 

25 Okay. I will turn the next piece on effects

26 and risk assessment over the Jay Overmyer. 
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1 DR. JAY OVERMYER: Thanks Dave. Again, my

2 name is Jay Overmyer. I am an ecotoxicologist with Syngenta

3 Crop Protection. I am going to go over a couple of the slides

4 here related to effects testing. 

5 As Dave mentioned earlier, CLA supports the

6 use of tier testing and the approach in the risk assessment. 

7 Obviously moving from Tier I to Tier III, if looking at the

8 amount of ability to control the situation or control the test,

9 there is a reduction in the amount of control from Tier I to

10 Tier III. Also, moving from Tier I to Tier III, we also have an

11 increase in environmental realism within those studies.

12 So the Tier I studies are highly controlled

13 laboratory tests designed to predict an LD50 or NOAEC, often

14 subjecting the organisms to high doses to achieve those

15 endpoints. Basically to come up with these endpoints, we are

16 able to determine what the relative toxicities of these

17 compounds are. 

18 Moving to Tier II, again a little bit less

19 controlled, semi-field type studies. Exposure is closer to real

20 world, but yet we are still looking at forced exposures with the

21 bees inside of a tunnel or an enclosure, which inhibits them

22 from naturally foraging that they might do. The benefits of the

23 Tier II we're also increasing environmental realism, therefore

24 we're getting up to a hive-level effect as opposed to

25 individual-level effect. 

26 Then with Tier III, obviously again, we're
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1 losing a little bit more control. The bees are allowed to

2 forage naturally, often looking at natural application rates and

3 exposures, and also being able to obtain information related to

4 the hive, which is important. 

5 For the Tier I testing, CLA does support that

6 the use of the Tier I test, the adult contact and oral acute

7 toxicity test as well as the seven day larval test when the

8 methods are finally validated or for use of that. We also

9 support development protocols for chronic toxicity testing of

10 both adult bees and the larvae; and the industries interested in

11 participating in this process if it is possible. 

12 We also support the White Paper position that

13 studies need to produce measured endpoints that can be clearly

14 tied to assessment endpoints, such as colony strength and

15 survival and the protection goals. We also feel that the

16 internationally-common data requirements should have harmonized

17 test guidelines so that we can use our studies for multiple

18 countries and multiple regulatory agencies. 

19 For Tier II, as Dave mentioned also that we

20 like to see flexible study designs to be able to be produced to

21 address uncertainties in the lower tier assessments; and we also

22 realize that it is critical to communicate with the agencies on

23 the study design such that we design studies that help answer

24 questions that are apparent or has been useful for the risk

25 assessment purpose. 

26 I would also like to point out that within the
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1 semi-field studies, again we are subjecting the bees to a

2 somewhat unrealistic exposure scenario, again force feeding on a

3 plot of a crop where insecticide has been applied or pesticide

4 has been applied. And that in these studies they can produce

5 false negatives or fail to show effects that occur at real field

6 sites; but often produce false positives that show effects that

7 don't occur at real field sites. 

8 So again, this relates to the fact that we

9 need to go to Tier III studies to help answer, again, some of

10 these questions related to the uncertainty of Tier II. 

11 So again, as with the Tier II studies, we also

12 feel that it is necessary to consult with the agencies to

13 develop appropriate protocols and site designs to help answer

14 the questions that are necessary for risk assessment purposes. 

15 Studies should be designed and endpoints selected to provide the

16 most appropriate information to assess protection goals. 

17 Again, the study should be focused and help to

18 answer specific questions rather than addressing all potential

19 scenarios. Adding additional scenarios to study design creates

20 more confusion as to how to interpret the results. So, keeping

21 them focused would definitely be of benefit.

22 The Tier III studies incorporate potential

23 effects of the plant protection products. It is multiple levels

24 of organizations, so we would incorporate information from lower

25 levels of biological organization up through the population

26 level. So, we feel like this is, you know, important that we
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1 focus on these higher level assessment endpoints that are able

2 to relate to protection goals, such as protecting pollination

3 services. 

4 I would also like to point out in field

5 studies, control plots are really reference plots because in

6 most situations we have to apply chemicals to the control plots

7 in order to grow a viable crop for appropriate comparisons. And

8 also I would like to indicate that real-world biological systems

9 are inherently variable and the variability should be properly

10 interpreted within these studies. 

11 The next two slides are a couple of graphs

12 illustrating some data we obtained from some Tier III field

13 studies. Again, these graphs are in the White Paper on page

14 170. 

15 The first graph here is illustrating effects

16 of individual mortality, and that would be dead worker bees,

17 pupae and larvae over the course of the study. As we see here,

18 at specific days throughout the course of the study, we see

19 differences in mortality between treatments and controls. 

20 I would also like to point out that the

21 highest level of mortality was observed prior to initiation of

22 the treatment where the hives were brought into the actual study

23 site. 

24 Throughout the course of the study we do see

25 again, variability within individual mortality. If we move to

26 or look more closely at higher level assessment endpoint such as
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1 colony strength -- and again this is estimated number of bees

2 per hive -- we see that the controls and treatment track fairly

3 well.

4 So again, at this higher level endpoint, it is

5 incorporating all of the lower level assessment endpoints, the

6 lower levels of biological organization into this higher level

7 of biological organization endpoint. We feel that this colony

8 level type of assessment is where we need to be as far as

9 determining effects of potential compounds on the health of

10 honey bees. 

11 In reality, these assessment endpoints will

12 then help looking toward protection goals. So, we are working

13 towards determining endpoints that help to assess the protection

14 goals as stated in the White Paper. With that, I will hand it

15 back to Dave. 

16 DR. DAVE FISCHER: Okay. So, in terms of risk

17 assessment, again we support a tiered approach for all the

18 reasons we have said. It is consistent with the standard

19 methodology the EPA uses for other taxa. 

20 Science-based approach to develop the proposed

21 levels of concern, I mentioned that before; use of honey bees as

22 surrogate for non-Apis bees; use of whole colony studies when

23 triggered to put the results of laboratory testing with

24 individual adult or larval bees into real-world context. This

25 is an opportunity that we have with bees that we don't frankly

26 have with some of the other species we do testing and risk
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1 assessment for. It is hard to do a population level test with

2 birds, fish and mammals. 

3 Bees - that is what usually we want to protect

4 is that level. With bees, we can test colonies and even acres. 

5 It is just an opportunity that we can test at that level that we

6 don't always have. 

7 Communication of the results of pollinator

8 risk assessments should convey the uncertainties and inherent

9 conservatism of the assessments methods. That is something we

10 think is key with these Tier I assessments that are going to -

11 there are compounds that are going to not be screened out and

12 the concern is that that is going to be communicated as there is

13 a risk. It doesn't necessarily mean there is any risk. It

14 means that with the conservatism in the assessment, you couldn't

15 say there wasn't one. 

16 Population modeling - this is an area that a

17 number of companies are investing in. We support the

18 development of population modeling to address uncertainties in

19 lower tier risk assessments. We are interested in participating

20 in model development, and as I mentioned, we are investing some

21 resources in that. Models should be transparent, have options

22 for refinement and be well vetted before use by regulatory

23 agencies. 

24 So, to summarize sort of our technical review

25 of this, there are a number of points of agreement. We think

26 the new methodology represents an important advancement that
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1 should improve the scientific credibility of pollinator

2 assessments and increase the transparency and predictability of

3 regulatory decisions. That's a good thing. We support the

4 efforts of EPA, PMRA and CDPR to harmonize their pollinator risk

5 assessments processes. Pollinator assessments should be based

6 on scientifically sound methods and we think the proposal is

7 consistent with that goal. Risk assessment should be

8 interpreted within the risk-benefit context as required by

9 FIFRA. That is particularly well explained in the White Paper. 

10

11 There are some areas where we would suggest

12 there are opportunities for improvement. Proposed screening

13 criteria appear in some cases to be overly conservative and will

14 produce many false positives. Many chemicals that pose no

15 real-work risk to pollinators will fail the Tier I screen. 

16 There was some information presented on that, but I just thought

17 I would throw up here that if you apply the Tier I dietary risk,

18 it's basically - there is a fixed assumed dose per 1 pound per

19 acre applied that is something like 32 micrograms that the bee

20 ingests.

21 So, how high does your - I'm sorry I have to

22 go back to my spread sheet - but basically to pass the

23 assessment, you need an LD50 - if you have 1 pound per acre, you

24 need to have an LD50 of 80 if you are applying 5 pounds per

25 acre, the LD50 has to be above 400. We only test up to 100, so

26 any chemical that is slightly above 1 pound per acre is not
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1 going to pass this assessment, whether it is toxic or not unless

2 we test higher. We think that the limit test should be enough. 

3 If you are down at 1/10 of a pound a.i. per acre, you need to

4 have an LD50 higher than 8, which again that is a very low rate

5 and that is not a particularly low LD50 number. 

6 Just comparing it to the Hazard Quotient

7 method in Europe - in Europe there is sort of a

8 back-of-the-envelope calculation, which the application rate is

9 compared to the LD50 and you compare that result, that Hazard

10 Quotient to a trigger value of 50 and to pass that to get a

11 Hazard Quotient that is less than 50 for 5 pound-per-acre

12 application rate, the numbers are up there - 112 micrograms per

13 bee is what the LD50 has to be for 1 pound per acre, 22, all of

14 these are 3.6X different. 

15 So this North American approach is 3.6X more

16 conservative than the EU Hazard Quotient approach; and it's all

17 because of the assumed dose that these are getting from nectar

18 and the fact that you are estimating the nectar concentrations

19 to be as high as they are. 

20 And by the way I think everybody in Europe is

21 pretty comfortable that Hazard Quotient is very conservative. 

22 We are going even more conservative with this one. 

23 I mentioned that if you have a chemical that

24 is nontoxic at the limit dose in the acute oral test then these

25 chemicals should just be considered safe and you should not

26 proceed to higher tiers. 
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1 It is important the Tier I assessments are

2 used only to identify chemicals requiring further testing and

3 not for concluding or implying that such chemicals pose a

4 real-world risk, I mentioned that. 

5 Since higher tier testing will be triggered

6 for many chemicals, there must be clear guidance and achievable

7 acceptability criteria for higher tier studies. I mentioned

8 that at the outset. I will mention it again. That is a

9 concern. We have a long history of doing higher tier tests on

10 other taxa, for which there is a high rejection rate of the

11 studies when they are reviewed by the Agency. So, this is an

12 area where we see the need for a lot of discussion, maybe

13 workgroup, research, whatever and we are interested in

14 participating in the process. 

15 Just as a last slide, industry continues to be

16 proactive in advancing the science related to bee health issues

17 such as conducting research programs, developing best management

18 practices, formulation improvements, stewardship programs,

19 pollinator habitat initiatives, and this is an area that we are

20 active in and we want to continue to collaborate with the

21 agencies and with the research community to continue to advance

22 the science. Thank you for your attention. 

23 DR. DANIEL SCHLENK: Thank you for your

24 comments. Questions from the panel? Dr. Hunt? Dr. Hunt is

25 going to go first. 

26 DR. GREG HUNT: I wonder if given the problems
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1 with Tier III testing and getting an acceptable study that EPA

2 would accept, would CropLife be open to the idea if EPA only

3 required Tier II testing? 

4 DR. DAVE FISCHER: Jay might want to answer

5 this as well, but I think we definitely want to have the option

6 for field testing. At Tier II, we exaggerate the exposure when

7 we do bee tunnel tests. We know of cases where there are

8 affects in those tests that are basically false positives with

9 respect to what happens in the field because the exposure is

10 exaggerated. There is also the problem - I think the Agency

11 gave a good summary of all the deficiencies or short comings of

12 tunnel studies in that if you stress the bees you have limited

13 amount of time that you can keep them. 

14 So, I think we want to definitely have the

15 option of doing full field testing and the challenge is to

16 develop protocols where the results are clearly interpretable

17 and everybody agrees on how to interpret the results. 

18 DR. GREG HUNT: On the other hand, full field

19 testings may lead to false negatives. I just wanted to make

20 that comment. 

21 DR. DANIEL SCHLENK: Thanks. Dr. Overmyer,

22 did you want to add anything to that? 

23 DR. JAY OVERMYER: No. I agree with what Dave

24 had mentioned.

25 DR. DANIEL SCHLENK: Dr. James, you said you

26 had a - no? Okay. Anyone else? Dr. Potter? 
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1 DR. THOMAS POTTER: With regard to Tier II and

2 Tier III, where does evaluation of mitigation practices fit into

3 the framework and the approach? 

4 DR. DAVE FISCHER: We see mitigation as being

5 an option at every stage of when we develop a product. Before

6 we even submit it for a registration, we are determining what

7 the hazards are to various taxa and developing a label with

8 potential mitigation in mind; so some products, people like

9 myself and Jay are asked by the company, do we need an at bloom

10 restriction? Do we need to off label this crop or that crop

11 because of particular attractiveness to pollinators. 

12 So, that is part of preparing the label and as

13 you work through the system there should be an option for -

14 instead of conducting another million dollars of work,

15 mitigating the problem with a label restriction. 

16 DR. DANIEL SCHLENK: Yes, Dr. Pettis? 

17 DR. JEFF PETTIS: Yeah, Jeff Pettis. With

18 regard to systemics and trying to predict concentration in

19 nectar and pollen, do you feel that what is found in stems or in

20 the plant tissue can accurately predict that? And also could

21 you comment on just the ratio which we find in the few studies

22 that we have about the concentrations in pollen versus nectar? 

23 DR. JAY OVERMYER: From the data that we

24 generated, we typically see higher levels in the actual plant

25 leaves and stems compared to what we see in nectar and pollen,

26 but we do see variability within different plants. So, I think
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1 it is important that we indicate that in generalization you

2 cannot take one example and then extrapolate it to all different

3 examples. As far as nectar and pollen goes, we typically do see

4 more residues of pollen than nectar. Again, the ratios there

5 vary between different plants. 

6 DR. DAVE FISCHER: This is Dave Fischer. I

7 will just add to that that it may differ between the chemistry,

8 the compound involved. There are definitely a lot of chemicals.

9 Most of the systemic chemicals are largely xylem-limited in

10 their transport in the plant. They will move with the water

11 transport system, but not with the sugar transport system, but

12 then there are some other chemicals that have the right chemical

13 properties that will also be systemic in the phloem. 

14 So, just looking at data and saying what is

15 the concentration of stem versus the flowers, you really

16 probably need to look a little bit more carefully about, you

17 know how does that compare based upon the chemical properties

18 that would affect their ability to move in these different

19 transport systems. But as a general rule, the stem and leaf

20 concentrations are an order of magnitude higher than what is in

21 the flowers - what gets in the pollen and nectar. 

22 DR. DANIEL SCHLENK: Okay. Any other

23 questions? Okay. Thank you very much. Our next public

24 commenter is CropLife Canada on my list. Presenter is Pierre

25 Petelle.

26 MR. PIERRE PETELLE: Good morning. So, yeah,
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1 my name is Pierre Petelle. I am the Vice President of Crop

2 Protection Chemistry at CropLife Canada. I just have a few

3 slides to do through, and you will see probably a lot of

4 repetition from what we just talked about because we do

5 collaborate quite a bit with our colleagues to the south here. 

6 Our technical working group in Canada, we

7 actually share some members with the technical team here at CLA.

8 We collaborate on this file in particular because it is a very

9 important file for the industry. CropLife Canada and our

10 members support the CLA position and the comments you just heard

11 on the White Paper. 

12 As indicated previously, pollinators and

13 pesticides are complementary components of the sustainable

14 agricultural system. Many of the crops grown in Canada, like

15 here in the U.S., depend on both crop protection and pollination

16 services. 

17 So, a sound scientific approach to

18 understanding impact of pesticides on pollinators is going to be

19 absolutely critical here. 

20 As Ray pointed out earlier, bee health is a

21 complex issue. Our industry is committed to the protection and

22 promotion of pollinator health, and we are very supportive of

23 efforts to advance the science. So, as we move forward in this

24 process, we want to reiterate the need for scientifically sound

25 risk assessment procedures and the use of data from

26 well-designed, valid scientific studies. 
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1 Canada and the U.S. enjoy a long history of

2 cooperation and collaboration in the pesticide regulatory area. 

3 PMRA and EPA in particular have been world leaders in the area

4 of regulatory harmonization, pesticide reviews, undertaking

5 joint reviews, work sharing projects and just improving the

6 scientific processes. I think this has helped both growers and

7 regulators and our industry on both sides of the border in terms

8 of access to newer safer products, in terms of using the most

9 modern science and in terms of efficient regulatory review. 

10 This process has been developing over the past 15 years at

11 least, and you know, I think as we move forward with this

12 particular issue, we are highly supportive of harmonized data

13 requirements, test guidelines and risk assessment processes. We

14 are glad to see the role that PMRA has played in this whole

15 process with the White Paper and here at this Panel. 

16 So just lastly, this is a reiteration of some

17 of the comments that CLA made a few minutes ago, but in order to

18 ensure the protection goals are met and instilling public

19 confidence, we circle back to this need for a scientifically

20 sound approach to risk assessment. We feel that the proposed

21 approach is broadly consistent with this goal looking forward.

22 In particular, we want to stress the

23 importance of the tiered risk assessment approach that was just

24 discussed in detail, and the use of honey bees as a good

25 surrogate for non-Apis. 

26 That's all I wanted to say today. Thanks very
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1 much, and we again appreciate the role of PMRA in this process

2 and stress the importance of scientifically rigorous process as

3 we move forward.

4 DR. DANIEL SCHLENK: Any questions from the

5 panel? Dr. Berenbaum? 

6 DR. MAY BERENBAUM: Given the differences that

7 exist in apicultural practices in Canada and the U.S. and

8 variance on reconstraints, do you think standards are completely

9 harmonizable, or do you think that there still may be means for

10 recognition of differences? 

11 MR. PIERRE PETELLE: Yeah, I mean I think in

12 terms of the science and the risk assessment process, I think

13 there is an ability to harmonize those processes. Are there

14 Canadian specific or U.S. specific practices that need to play

15 into that? I mean that is going to have to be a question as we

16 move forward, but from our experience up to this date,

17 harmonization of those processes should be fine. 

18 DR. DANIEL SCHLENK: Thank you very much.

19 MR. PIERRE PETELLE: Thank you.

20 DR. DANIEL SCHLENK: Our next presenter is the

21 Center for Regulatory Effectiveness, and that is Mr. William

22 Kelly. 

23 MR. WILLIAM KELLY: I do not have any slides

24 or handouts. My name is Bill Kelly. I am with the Center for

25 Regulatory Effectiveness. We are an independent organization

26 that evaluates regulatory issues that are of widespread concern
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1 and usually have quite a bit of controversy. We are also

2 placing great emphasis on what we call information quality and

3 the Information Quality Act and its guidelines. 

4 We have already submitted some detailed

5 written comments, lots of footnotes and quotations, so I won't

6 bother you with that. 

7 Dr. Steeger yesterday at the end of the

8 meeting emphasized that the high quality of EPA studies and the

9 studies that are conducted or required to be submitted under

10 their guidelines, and we recognize that. I think that the

11 problem we have is with a lot of journal articles. 

12 The Information Quality Act Guidelines

13 recognize that peer review of journal articles is of very uneven

14 quality. A lot of articles are published simply because they

15 are, let's say, thought provoking and not really contributory to

16 the knowledge base. Those need to be examined carefully for

17 compliance with the standards and guidelines under the IQA. 

18 Near the end of yesterday's meeting, the Panel

19 asked a good question that relates to the Information Quality,

20 and that is what to do with data gaps. 

21 Data gaps are not really a problem with Tier I

22 or Tier II, but when you get to the final risk assessment and

23 you have gone through Tier III, the way the data gaps have to be

24 handled is a matter of law under the IQA and its guidelines is

25 for the scientists -- that are the risk assessors as opposed to

26 the risk managers -- to simply describe the data gap and its
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1 significance, and then leave it to the risk managers to decided

2 what to do with that. The data gap should not be filled with

3 assumptions or default values or whatever. Those are usually

4 policy-driven values that should not be used in the risk

5 assessment process. 

6 Another good example that came up very late

7 yesterday after unfortunately a lot of people had left the

8 meeting was the issue of what we are really dealing with here. 

9 The extent and I would say also the distribution of the

10 pollinator decline, which is often equated with colony collapse

11 disorder unfortunately. A chart was presented, which showed the

12 decline was presented by Mr. Steiger or Mr. Carrington. Then we

13 found out that that chart was based on honey production. So,

14 that would not pass muster under the Information Quality Act

15 Guidelines for example. 

16 I mean we really do need good data on the

17 extent of this problem and its distribution. I think that that

18 is important, especially for Tier I, especially the

19 distributional aspect of it. I mean if you look at distribution

20 of the problem and the extent of the problem in various areas,

21 you might find out that there are say, crop areas. We're not

22 talking about states necessarily, but crop areas or particular

23 crops where there is a high level of decline, but a particular

24 pesticide is not used on those crops or in that area. That

25 could be a basis for eliminating that pesticide from Tier I. 

26 On the other hand, you could have an area in
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1 which a particular pesticide is used very intensively, and yet

2 there is no significant increase in pollinator decline beyond

3 what you would expect from, you know, say ordinary winter kill. 

4 So again, that could factor into Tier I determinations. 

5 This issue of the extent of pollinator decline

6 and the distribution of the problem is really missing from the

7 framework at this point. There could be an interagency

8 coordination issue here because it appears that USDA has taken

9 the lead on this issue.

10 Back in January, the Inspector General of the

11 Department of Agriculture issued a report that was on progress

12 the USDA had made in evaluating this issue. The quote from the

13 report says, "The true extent and impact of CCD -- which they

14 equated with general decline -- in the United States has not

15 been adequately assessed despite USDA's use of significant

16 resources for honey bee research to address CCD." 

17 The EPA is part of that steering committee

18 that is working on this issue. And as I understand it, part of

19 the problem is money either from Congress or with redirecting

20 resources within the agencies. 

21 So, I think in looking at the matter of

22 problem formulation, this issue is really missing from the

23 framework at this point and needs to be added. The SAP needs to

24 address it. We need to do some sort of pattern analysis on

25 where this problem is occurring, the extent of it and whether it

26 is really associated with pesticide use in those ares. 
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1 And finally, I think if we are going to do

2 this since it is missing from the framework at this point, we

3 need some further public comment on this particular aspect of

4 the issue. Okay. That is all I have at this point. Are there

5 any questions? 

6 DR. DANIEL SCHLENK: Thanks. Dr. Berenbaum? 

7 DR. MAY BERENBAUM: Actually, I am a little

8 confused. In view of the fact that bees are livestock and

9 there's at least some legal precedence that bees are owed the

10 duty of common humanity if they trespass on agricultural land,

11 why is documenting decline relevant to risk assessment of

12 pesticide use on agricultural land? 

13 MR. WILLIAM KELLY: Thank you. Well,

14 presumably this framework and this risk assessment could lead to

15 FIFRA Regulatory Action, particularly on registration. FIFRA,

16 as a matter of law, requires a balancing of risks and benefits. 

17 In order to do that, you are going to need to quantify the risks

18 together with the benefits, and you can't do that with some

19 accurate appreciation of the extent of the impact of what's

20 occurring. Does that.

21 DR. DANIEL SCHLENK: Dr. Berenbaum? 

22 DR. MAY BERENBAUM: Well as you pointed out,

23 bee decline is not equal to bee mortality. So, bee death per se

24 isn't colony collapse disorder, but still risk assessment for

25 pesticide use involves determining bee susceptibility, bee

26 mortality. So, I am still not sure where the linkage to
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1 documenting decline, at least for honey bees. 

2 MR. WILLIAM KELLY: I think what we are

3 interested in is decline. I don't know how we would measure

4 decline in health short of colony loss and bee mortality. At

5 least that's how the problem has been posed to date in terms of

6 colony loss and mortality. If we want to start looking for sick

7 bees, I guess that takes us to another level.

8 DR. DANIEL SCHLENK: Okay. Any other

9 questions or comments? Okay. Thank you very much. Our next

10 presenter is Scott Schertz with the National Agricultural

11 Aviation Association. 

12 MR. SCOTT SCHERTZ: Hello. I do appreciate

13 the opportunity to be in front of the SAP on the pollinator

14 protection issues, and we do support this initiative.

15 Basically, well my name is Scott Schertz. I

16 do own and operate Schertz Aerial Service, an aerial application

17 operation in central Illinois in corn-soy-bean country. I am a

18 past president of the NAAA and the comments have to do with

19 basically aerial application of crop protection products. 

20 The NAAA does represent a majority of the

21 aerial application industry in the U.S. There are approximately

22 1700 members in 46 states, and the majority of the members of

23 the NAAA are operations such as mine or small or medium-sized

24 businesses that provide these services to many crops, everywhere

25 from the major crops to many of the specialty crops. 

26 As the White Paper reflects that mode of
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1 application is one of the areas of concern, you know I would

2 like to give a little introduction to the aerial application

3 industry as far as our scope and qualifications. 

4 Aerial application does account for

5 approximately 25 percent of the application of crop protection

6 products in the U.S. and commercial bases, and in many areas, it

7 is the preferred ways of applying due to its speed, able to get

8 over tall crops or in conditions that make ground application

9 impractical. 

10 In addition, that means that aerial

11 application does have, you know, a unique opportunity on

12 pollination protection in that the airplanes and helicopters can

13 apply a lot of product very rapidly and in a narrow time window.

14 When the hives - the areas of the prime bee activity are known

15 as many states required, this does allow a tiered approach,

16 basically applying at time of low pollinator activity.

17 I do want to emphasize though, that that

18 doesn't mean that it is practical for all applications to be

19 done then; and that knowledge of where they are is very, very

20 important as I will emphasize many states require.

21 When you start looking at the crop protection

22 needs of, you know, many of the major field crops that I am very

23 familiar with, there is a need for viable products with

24 reasonable label requirements, even during pollination time. I

25 mean, that is when the majority of the insects actually affect

26 plants, and obviously there is a conflict here in times. 
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1 And then also our members do serve many of the

2 specialty crops that may be intensively utilizing managed

3 pollinators, and in those cases, I mean these types of things of

4 spraying at night are common. But it doesn't, I mean, it isn't

5 a matter of just getting more airplanes. I mean, this is a very

6 specialized industry. There are a finite number of airplanes. 

7 The airplanes are very sophisticated. There is a lot of

8 technology in them that I will go through briefly in a couple

9 minutes.

10 Then also the night operation is a very

11 specialized subpart of it. Yet, some areas it is common;

12 typically it is in more of the specialty crop areas. In my

13 area, yes I do some night work, but there are also many fields

14 that it is impractical and unsafe to do at night. When you

15 start talking about dealing with obstacles such as met towers,

16 winter events, large power lines, the judgement at night, the

17 depth perception is a real issue. So, you can't just say well

18 that is going to magically fix all of these issues. 

19 The industry is very active on refining the

20 safety programs and the deposition of the airplanes. Our main

21 program is something called PASS (phonetic) that has been in

22 effect for about 14 years. It is something that is an active

23 training session that goes around the country and is available

24 to the entire industry. Basically, the subjects have to do with

25 drift mitigation, aeronautical decision making, security and

26 pollinator issues have been something that has been addressed in
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1 the last several years. 

2 Another unique thing about the aerial

3 application industry is what is called our safe program. 

4 Obviously, these details are in the paper submitted, and that is

5 a program that is also unique to aerial application industry on

6 an infield way of measuring the deposition from the airplanes

7 and refining that to be appropriate for the products to be used.

8

9 So, I did want to give you a bit of

10 introduction and how that, you know, aerial application does

11 have some unique opportunities on this, but you will also need

12 to be realistic that it isn't a silver bullet of just doing

13 everything at night. 

14 There is further detail in the paper that was

15 submitted that you should have a copy of. I do appreciate that

16 opportunity to be in front of you. Any questions? 

17 DR. DANIEL SCHLENK: Any questions from the

18 Panel? Okay. Thank you Mr. Schertz. 

19 MR. SCOTT SCHERTZ: Thank you.

20 DR. DANIEL SCHLENK: Our next presenter is

21 James Doan, who is an owner of a bee keeping operation. 

22 UNIDENTIFIED SPEAKER: Mr. Doan is running

23 late.

24 DR. DANIEL SCHLENK: Okay. Is Mr. Jenkins

25 available, for Center for Food Safety? 

26 MR. PETER JENKINS: Greetings. I am Peter
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1 Jenkins, Attorney and Consultant working with the Center for

2 Food Safety. You should have copies of the oral comments that I

3 am going to give you now.

4 We are in contact with Mr. Doan, and he is

5 hung up on the Metro somewhere. Hopefully he will be here

6 before the end of the comment period. Dr. Jenkins if you could

7 be a bit lenient on that, and we will let you know when he shows

8 up. He is a beekeeper who came from New York. 

9 Let's see, I am just going to basically read

10 my comments. We have written comments that are in the record

11 and I will be glad to answer any questions at the end about

12 either the oral or written comments. 

13 I am pleased to make these comments on the

14 White Paper that was submitted in support of the risk assessment

15 process or the proposed RA process for short. 

16 My organization, the Center for Food Safety,

17 has got comments in your packet. Please give those your close

18 attention because our comments as well as the comments of Beyond

19 Pesticides and some of the other NGOs involved here -- and

20 beekeepers -- have submitted some detailed scientific studies

21 that are omitted from the proposed RA process document that we

22 think very much relate to this process.

23 Let me stress the critical role you are

24 playing at ensuring a state-of-the art risk assessment framework

25 to protect honey bees and native bees from excessive and perhaps

26 unsustainable mortality from pesticides. It appears,
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1 unfortunately, that EPA lacks inhouse PhD levels entomological

2 expertise, at least very much of it that we can see. So, you

3 can help to provide that. 

4 The process you are a part of has involved

5 real world cost and impacts already. Years of delay and

6 thousands of staff hours have gone into creating the proposed

7 framework, but it still has major areas of uncertainty and

8 omission. Further, EPA has refused to follow through on the

9 prior condition that had it required years ago when it

10 registered two neonicotinoid insecticides, Clothianidin and

11 Thiamethoxam, for which providing a valid bee impact field

12 study, a pollinator field study or Tier III sort of test as we

13 have been hearing about, was made a condition to continue in the

14 registration of those pesticides by EPA. That condition has

15 still not been met nine years after it was first imposed in the

16 case of Clothianidin. 

17 An excuse that EPA now uses is its condition

18 currently is being "held in reserve" pending the outcome of this

19 FIFRA SAP process. So you, as SAP Panelists, have become part

20 of EPA's non-precautionary approach to this issue of pollinator

21 field test design for these pending conditional registrations.

22 So, now you can at least help make sure that

23 the cross bar on the shifted goal post is high enough to be

24 really comprehensively protective. It has taken so long to get

25 to this point. 

26 A written comment highlighted the inadequacy
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1 of the proposed RA framework as far as taking sublethal effects

2 into account. Our comment also urged modeling for both the

3 contaminated dust and the guttation fluid exposure routes for

4 systemic neonicotinoid insecticides, and to have a clear call

5 out in the RA framework for more Tier II and Tier III testing,

6 if necessary, of those roots before approving such insecticides

7 in the future.

8 On the neonicotinoid contaminated dust route,

9 it was very inappropriate for the RA framework on page 99 to

10 include evasive statements from the agency that in effect say,

11 the SAP can just rely on EPA to mitigate the risks of this route

12 through discussions with seed treatment registrants and the

13 manufacturers of seed planting equipment. 

14 That sort of vague, hopeful, self-serving

15 assertion restated at least twice by EPA officials yesterday has

16 no place in a risk assessment framework. 

17 EPA has no real power to enforce such a

18 speculative, undefined requirement for modifying tens of

19 thousands of planting machines across the entire country, and

20 even if EPA had such power, the practical obstacles to EPA

21 enforcing that sort of requirement in any reasonable timeframe

22 far exceed the Agency capabilities.

23 This contaminated dust exposure route and its

24 impact on honey bees has led to Germany, Italy, Slovenia, to

25 some extent France, prohibiting various neonicotinoid seed

26 treatments in the last four years. So relying in EPA's attempts
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1 to downplay this route in the U.S. would be irresponsible. 

2 The SAP must demand a more rigorous treatment

3 of this route or it won't be doing its job. Please take careful

4 account on this issue of the written comments by Dr. Christian

5 Krupke of Purdue University that in the packets - I hope they

6 are in your packets - which reinforce what I have just said. 

7 Proposed RA framework document makes

8 occasional mention of EPA's ecological incident information

9 system or EIIS. I would remind the SAP members that it is well

10 accepted across numerous stake holders, especially beekeepers,

11 that the EIIS does a very poor job of collecting national bee

12 kill incident data. 

13 In contrast, Canada's PRMA reporting system is

14 well regarded. Here is an illustration. In EPA's July 27, 2012

15 response letter to a petition filed by my organization, the

16 Center for Food Safety, 25 beekeepers and other NGOs to suspend

17 the registration of Clothianidin, the Agency stated with respect

18 with EIIS, "We are however aware of 14 additional incidents

19 occurring in the U.S. in 2012 that are not yet present in the

20 database and approximately 120 additional incidents reported in

21 Canada." 

22 Extensive media and non-EIIS reports make

23 clear that both Canadian and U.S. beekeepers suffered vast

24 numbers of spring 2012 bee kills due to the contaminated dust

25 exposure route associated with corn planting just this past

26 spring. It is not plausible at all that 120 documented bee kill
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1 incidents associated with neonicotinoid seed treatments

2 involving several thousand bee colonies occurred in Canada's

3 relatively small corn planting area -- mostly in southwestern

4 Ontario and Quebec as we understand it -- but only 14 additional

5 bee kill incidents occurred in the entire U.S. across its orders

6 of magnitude larger corn areas.

7 The seed treatment products, machinery used

8 and environmental conditions do not change dramatically when you

9 cross the border. What changes is the reliability of the bee

10 kill reporting systems. My point here is that a revised RA

11 framework should not rely on the EPA, EIIS unless it is

12 dramatically improved. 

13 On the issue of drinking water - our written

14 comments focused on the guttation liquid route and those are

15 important comments, but I urge your attention to a separate

16 exposure route that our written comments did not really address.

17 A key paper by Starner and Goh 2012, is Detections of the

18 Neonicotinoid Insecticide, Imidacloprid, in Surface Waters of

19 Three Agricultural Regions in California. It documents that a

20 significant portion of sampled surface waters were contaminated

21 above EPA allowed levels for chronic invertebrate exposure

22 across diverse agricultural landscapes. 

23 These waters are wide-spread sources of

24 drinking water for all bees, but the proposed RA framework does

25 not mention this California DPR paper, nor does it quantify the

26 risk to bees from the real world levels that were measured in
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1 that study. Instead, it follows a very questionable academic

2 exercise in appendix 2 in the RA framework to discount this

3 drinking water route all together. 

4 The fact this environmental contamination by a

5 major neonicotinoid exists now in California and elsewhere is

6 indicative of Agency failure to present undue consequences and

7 it has passed inadequate risk assessments. The SAP can now

8 correct this failing going forward. 

9 On the portion of the document addressing the

10 risks to non-Apis bees, I think any fair appraisal of the

11 framework document would conclude that this is its weakest

12 section. After years of delay, merely suggesting that there

13 might be future "modifications" of the basic RA framework to

14 address the unique circumstances of thousands of native North

15 American bee species is utterly inadequate to the risks that you

16 are facing here. 

17 Given that many native species have small

18 localized ranges, the RA process must at least explicitly assess

19 the need to restrict or limit the use of pesticides in these

20 limited locations, which is a common sense way to reduce risk to

21 these localized species. Several of these non-robust species

22 already are disappearing and lack any margin for Agency error

23 moving forward and ensuring their survival.

24 To the extent that the proposed RA framework

25 has overlooked, down played or minimize various exposure routes,

26 the classes of sublethal effects, the existing U.S. National Bee
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1 Kill Baseline and the other issues such as cumulative and

2 synergistic impacts that I can't go into, the document

3 constitutes a best case scenario from the perspective of the

4 companies that you have heard from seeking to approve

5 pesticides. But it is a worst-case scenario from the

6 perspective of bee conservation and preservation and preserving

7 beekeeper livelihoods.

8 You hold the intellectual key to finding the

9 right medium between these best and worst case scenarios. If

10 you fumble an important element of the risk framework, you could

11 put the long-term sustainability of these species, particularly

12 the thousands of relatively poorly studied native bee species in

13 jeopardy. 

14 I want to hit one last point because it just

15 came up in a previous talk by CropLife. I have been looking at

16 the RA framework on page 170. There are two figures, figures 12

17 and 13; and some generalizable point is made about those

18 figures. It is in our written comment. We said what are the

19 labels, what is the citation, what is going on in these figures

20 12 and 13, because they are basically cleansed of any

21 information except to try to make a generalized point. 

22 Well, it turns out that those figures are the

23 same ones that the gentleman from Syngenta showed in his slides.

24 He actually had labels on his slides indicating what was going

25 on there. 

26 So, it appears that EPA has just taken
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1 Syngenta's figures, put them into the RA framework at page 170

2 without the labels and tried to make a generalizable point about

3 it, but the point doesn't flow and it doesn't make sense. So, I

4 just bring that to your attention. It's a bit concerning that

5 EPA has used an unattributed bit of information from Syngenta or

6 CropLife, whoever it was that put it together and just stuck it

7 into the document. 

8 So, with that, again I am an attorney. I

9 cannot answer scientific questions, but I am glad to answer any

10 questions that I can. Thank you. 

11 DR. DANIEL SCHLENK: Thank you for your

12 comments. Any questions from the Panel? Okay. So let's go

13 ahead and take a break right now - 15 minutes - be back at

14 10:45. We will continue with our public comments at that time. 

15

16 (Brief recess.)

17 Okay. If I can have everybody take their

18 seats please. Okay. Is James Doan here? Yes. Okay. Mr.

19 Doan, if you don't mind, we are going to have you come second

20 after Beyond Pesticides comes forward if that's okay. Sorry

21 about that. 

22 So, can I have Nichelle Harriott, is she in

23 the room? As Nichelle is coming forward, just to point out, I

24 believe Dr. Fefferman you had asked for the OECD Guidance

25 Document on the Validation of Structure Activity Relationship

26 Models? That is on the docket. So, for anybody else who would
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1 like to do that, just go onto the website and you can down load

2 a copy of that. Okay. Ms. Harriott? 

3 MS. NICHELLE HARRIOTT: Hi, good morning. 

4 Thank you for allowing me to present these comments. My name is

5 Nichelle Harriott. I am representing Beyond Pesticides. Beyond

6 Pesticides is a not-for-profit organization that works at the

7 grassroots level to protect public and environmental health,

8 identifying the hazards of chemical-intensive land, building and

9 community management practices, and promoting healthy,

10 sustainable and organic systems. 

11 Beyond Pesticides is very concerned about the

12 status of our nation's pollinators, especially the honey bee. 

13 Each year since 2006, commercial beekeepers have reported

14 unprecedented bee losses. Normal losses occur around 17.6

15 percent, but over the 2008 and 2009 period an estimated 29

16 percent of all U.S. bee colonies died. 

17 Since the honey bee is the most economically

18 valuable pollinator worldwide, responsible for the pollination

19 of high value crops such as almonds and broccoli, it is

20 important that these pollinators are protected from the

21 potential harmful effects of pesticides under EPA's

22 jurisdiction. 

23 One class of pesticides in particular has

24 emerged as a major suspect in recent bee decline. Several

25 studies have demonstrate that neonicotinoid insecticides like

26 Imidacloprid, Clothianidin and Thiamethoxam have sublethal
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1 effects in honey bees, which include disruptions in mobility,

2 navigation and feeding behavior. Lethal and sublethal exposures

3 have been shown to decrease foraging activity along with

4 olfactory learning performance and decreased hive activity. 

5 When exposed to sublethal doses of

6 neonicotinoids at levels present in the environment, honey bees

7 were less likely to return to the hive after foraging. Bees

8 exposed to these chemicals also exhibit convulsions,

9 uncoordinated movements and tremors that are typical of

10 neurotoxic insecticide exposure. One study analysis of dead or

11 dying bees by Krupke et al. found residues present at the range

12 of 3.8 to 13.3 parts per billion. 

13 A study by Schneider et al. shows that under

14 field-like conditions, as little as 0.05 to 2 nanogram per bee,

15 Clothianidin can significantly reduce foraging activity. In

16 context, Reetz et al. found that corn seeds treated with

17 Clothianidin result in translocated concentrations up to 8000

18 nanograms per milliliter. For the honey bee, toxicity has been

19 observed at an LD50 of 22 nanograms per bee for Clothianidin, 13

20 nanograms per bee for Thiamethoxam; levels lower than those

21 noted in EPA's Clothianidin registration documents, which report

22 an LD50 more than 43.9 nanograms per bee. 

23 In fact Clothianidin and Thiamethoxam are only

24 second in toxicity to Imidacloprid with a LD50 17.9 nanograms

25 per bee. 

26 Studies have also reported that bees exposed
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1 to sublethal doses of pesticides are highly susceptible to

2 pathogens that lead to their decline. A 2012 study by USDA

3 researchers discovered that newly emerged bees exposed to

4 sublethal levels of Imidacloprid during larval development and

5 indirectly from brood food from nurse bees had higher levels of

6 the gut parasite Nosema, which is known to adversely affect

7 colony health. 

8 Systemic uses on corn seeds means that

9 pesticide residues are translocated throughout the plant to

10 pollen and nectar. Pesticide-intensive corn cultivation, which

11 accounts for 80 million acres of land planted in the U.S. 

12 According to Krupke et al., the application

13 rates for Clothianidin on corn can range from 0.25 to 1.25 mg

14 per kernel. A single kernel therefore contains several orders

15 of magnitude of active ingredient more than the published LD50

16 values for honey bees. The translocation of residues throughout

17 the plant means that pollen and nectar are highly contaminated. 

18

19 Similarly, Clothianidin has been found in or

20 on nearby plants like dandelions resulting from translocation of

21 residues from the soil to the flower, from surface contamination

22 of the flowers from airborne particles, or from a combination of

23 both factors. 

24 The panel, when making its recommendations to

25 the EPA, must always keep in mind that bees are exposed to

26 chemical residues through multiple routes of exposures. Bees
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1 are exposed to pesticides via foliar and systemic treatments

2 when they are pollinating flowering crops, and pesticide drift

3 from surrounding areas. 

4 They are also exposed to residues from wild

5 plants contaminated through cross-pollination with corn

6 systemically treated with pesticide. Bees are also exposed to

7 residues in plant exhaust material produced during the planting

8 of treated seed. Guttated water of seed-treated plants, which

9 provides a source of water for bees, is also a source of

10 contamination and exposure. 

11 Similarly, bees can be exposed to multiple

12 pesticides at any one time while foraging. It is now accepted

13 and supported by the scientific literature, that chemical

14 mixtures can pose greater health hazards in combination. In

15 fact, research by scientists at the University of Florida linked

16 bee decline to larval exposure to a cocktail of pesticides. 

17 Unfortunately, EPA's risk assessment process does not

18 accommodate chemical mixtures, which means that synergistic

19 effects continue to go unevaluated. 

20 Pollinator field studies must therefore

21 reflect real world events. Study designs should be based on the

22 planting practices of the crop treated with pesticide, including

23 evaluating the impacts of farm equipment that may result in

24 airborne residues.

25 Field sites must be large enough to

26 accommodate the distances travelled by foraging bees, and hive
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1 entrances should be placed in a location to ensure bees will

2 sample the field site. Life-cycle studies should also be a

3 requirement. It is also important to note that non-Apis bees,

4 in addition to pollen and nectar, are exposed to soil and tree

5 pesticide residues. 

6 EPA states that the protection goals for honey

7 bees include protecting pollination services, honey production

8 and bee diversity, with the endpoints being acute lethality,

9 survival, growth and the reproduction of the colony. 

10 For years, the science has shown that exposure

11 to pesticides is detrimental to bees, and that levels not

12 immediately lethal to worker adults may cause significant hidden

13 damage to colonies. We hope the panel places an emphasis on a

14 reliance on broadly representative, multiyear, controlled field

15 tests, to provide realistic data points from which to judge a

16 pesticide's impacts on honey bees.

17 It is imperative that the EPA has available

18 all the relevant data before any pesticide product can be

19 registered and introduced into the environment. It is

20 unacceptable that EPA allows pesticides to be sold and

21 distributed, or expands uses of existing registrations without

22 knowing how these pesticides would impact bees, other

23 pollinators and essential nontarget organisms. 

24 In closing, we wish to impress upon the panel

25 the importance for finalizing a uniform, comprehensive protocol

26 that aligns with the pollinator protection goals stated by the
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1 Agency. 

2 Again, thank you for this opportunity to

3 comment and Beyond Pesticides has already submitted written

4 comments to the docket with a list of relevant scientific

5 studies for the Panel's consideration. Thank you.

6 DR. DANIEL SCHLENK: Thanks for your comments.

7 Any questions from the panel? Okay. Thank you very much. 

8 Okay. Mr. Doan, we will get you next here. 

9 MR. JAMES DOAN: Good morning. My name is

10 James Doan, and I live in Hamlin, New York. I own and operate

11 Doan Family Farms, an apiary where we keep 2000 hives of honey

12 bees. Our apiary places 1600 hives in fruit orchards in the

13 spring and we place another 1000 hives in vegetable crops in the

14 summer. Our hives travel south to Fort Meade, Florida for the

15 winter, and we place our bees in orange groves in the spring for

16 orange honey production. 

17 Beekeeping has been in my family for 57 years.

18 Myself - I have kept bees for 44 years. My son, Ben, has also

19 recently joined the family business and will be a fourth

20 generation beekeeper. I come here today to talk about a problem

21 that we have seen this year in our industry and that I feel must

22 be fixed.

23 In May of this year 2012, I saw our apiary's

24 first heavy bee kill since 1987. Our honey bees that were hit

25 then were with a chemical called methyl parathion. The

26 Department of Environmental Conservation that year documented
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1 and did tests on the hives showing the very high loss at that

2 point due to methyl parathion. What we saw this year was very

3 much like methyl parathion. We haven't seen a bill kill like

4 this since that point 25 years ago, and I really don't want to

5 see damage like this again. 

6 We had placed bees in an apple pollination

7 location in Red Jacket, Geneva in March of this year. We were

8 called to remove them, and when we got there this spring, the

9 bees were dead, crawling on the ground in front of the hives. 

10 These are huge hives of bees, single stories, nine frames a

11 brood, two boxes of honey on them. 

12 We removed them, took them back to my farm. I

13 called the DC, which is our Department of Conservation that next

14 morning. They refused to come out. The lady at Region 8

15 Department of Environmental Conservation told me they had no

16 budgetary money to come and look at any bee kills. I will side

17 note here - if I shot a bear, they would have been out in about

18 15 minutes. Bees are not a priority apparently, in New York, to

19 be tested. 

20 So I called our New York State Inspection

21 Service. The New York State Inspection Service said, yeah we

22 will come out, at least we can check for mites, we can check for

23 nosema, and we will pull samples, because we knew at this point

24 we had to do something to follow through. We had a good live

25 bee kill. The bees were still coming out the next morning just

26 piling out of these hives. 
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1 When he got there, the bee inspector, Bob

2 Duncan, opened the hives up. They had two boxes of honey capped

3 off on them. There was eight to nine frames a brood in a

4 single-story colony bees there were about two to three frames of

5 bees left at that point. 

6 He collected up wiggling bees on the fronts of

7 the hives and in the grass in front of the hives, put them into

8 bags, froze them, and he took them to his place and held them. 

9 At that point, we didn't know quite where to

10 send them. So, I called Maryann Frazier down to Penn State. 

11 She said that they had a program where they would pay half the

12 cost of getting the samples run. We sent them to her. She sent

13 them on to the USDA bee lab down in Gastonia. 

14 They ran them through as quickly as possible

15 because, as I said, we've never seen anything like this at the

16 same time -- and you will see, I have an illustration, there is

17 a picture here on the forth page. That is what the bees looked

18 like the day we got them back to our place out front. That is

19 after already they have been at least one or two days into bee

20 kill on that. 

21 I called the farmer the next morning also. I

22 wanted to find out what the hell he had done to my bees. He

23 said he had not sprayed yet. They were waiting for us to get

24 the bees out. The only thing he told us was the guys around him

25 were planting corn at that point. 

26 So, we took that information, you know, along
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1 with pulling the samples and that. What we came back with is as

2 you can see on my next page is they checked for mites of course.

3 Our mite levels range from 9 to 3 to 18. They weren't

4 extremely high, high enough where we probably had been in

5 treating, but we wouldn't have to worry about that. 

6 Nosema samples - the first sample they pulled

7 came back negative, so there wasn't nosema on that one. The

8 second one came back with 3,600,000 spores, which is still, in

9 the bee industry today, is not considered a high level of nosema

10 anymore. I know men that are running 50 to 75 million spore

11 counts and still not suffering huge losses. 

12 We got back our results and on the page

13 following the nosema report, you will see what things were found

14 in the pollen. The first page is the pollen. You know, and

15 certainly some of the things we found, Clothianidin was one of

16 them, Captan in the pollen at extremely high levels, but in the

17 bees themselves, Clothianidin was the thing that showed up.

18 I mean, there are a few other things here, but

19 nothing that really strikes you like Clothianidin with all the

20 bees sitting there in those orchards with corn being planted

21 around them. 

22 So, you can take your assumptions for what

23 they are as far as how the bees got into Clothianidin. It was

24 an apple orchard with dandelions. They shouldn't have been out

25 working in any corn fields at that point, and I don't know if

26 they had been flying through dust. But that brings us to the
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1 next problem.

2 We lost all those hives, all 148. 

3 Consequently, we tried to put splits and nukes back into those. 

4 Those all died also. So, it was useless for us to waste our

5 time in doing that. We might as well burn the equipment and

6 that. That is an economic huge loss to me, 148 hives out of

7 2000 is a lot of money. 

8 The next thing I want to show you is in June

9 on the last page, you will see a big color photo, and those are

10 mostly all drones. I have about 47 bee arches that had an inch

11 thick pile of drones a foot in front of the hives. I haven't

12 gotten the test results back, but I can tell you that is not

13 nosema and its not rural mites. 

14 So, I don't know what chemical it is, but I

15 have never seen it. I can tell you that I've talked to guys

16 across the country, nobody has ever seen this. The genitalia

17 are out on these bees, some of these drones are only days old. 

18 This is not normal. It's something that you need to be aware

19 of. There are chemicals out here that are doing really bad

20 things in bee arches. 

21 Consequently what happened after this was we

22 had no matings on our queens, so all the hives that swarmed -

23 drone layers or queen-less hives. My loss this summer is

24 running over 20 percent and that is after we put in 1000 queens

25 on a 2000 hive operation over the summer. This is pretty

26 dramatic stuff. 
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1 You know, I didn't put it in here, but we are

2 probably re-queening 100 percent of our hives over the course of

3 a year, and I'm not alone in what is going on. It's everyone

4 replacing huge amounts of queens in their operations just to

5 maintain the hives they have right now. And we are still losing

6 bees.

7 I mean, don't get me wrong, there are other

8 problems out there, but when I see bees laying in front of hives

9 like this, that is not those problems. 

10 You know, I know there is a big problem - I

11 want to go back to some of the other problems I see are the LD50

12 of Clothianidin. I don't know who runs the test exactly beside

13 the applicant, but in the tests that they do on LD50, do they

14 specify the age of the bees they are working with? You know,

15 contact versus oral - we've got so many multitudes of variables

16 with these things. I am not sure the LD50 - I think it needs to

17 be re-looked at. 

18 Then we talk about synergy, stacking products.

19 You know, I talked to one of my corn friends, he has seven

20 different products that go on his corn seed when they go in the

21 ground. What does that do? I mean, when guys are spraying

22 apple orchards, they don't just put in one product. They are

23 mixing two or three at the same time. What's that doing and

24 what do those things together? Is that being looked at? 

25 You know, another issue is the tolerance

26 level. I don't know how many of you are aware of it, but there
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1 are only three products that have a tolerance level at honey,

2 fluvalinate, coumaphos and fenpromexite. That's it. 

3 All this other stuff that we find in bee

4 hives, it isn't supposed to be there; and I can tell you today

5 the push is in the United States, they want pollen and honey in

6 those containers of honey. They want beeswax. We know what's

7 in beeswax guys and ladies. We know what's in pollen. I mean,

8 this isn't rocket science. 

9 I mean, one of these days, there's gonna be a

10 big issue, who's is going to pay for that cleanup and mess? 

11 What I am going to do with barrels of honey that I can't do

12 anything with? I can't sell them to anybody and I have to take

13 them to the HAZMAT? No. We need tolerance levels for all of

14 these chemicals that are out there today in honey. Captan, you

15 know, all of them. We just have to have them. You have them in

16 every other food out there. We have to have them. 

17 You know, EPA's job is to protect the citizens

18 of this country from unregulated pollution in our environment. 

19 As a citizen, I don't feel I'm protected right now. I have

20 honey bees that are being killed by products that are not

21 supposed to kill bees. I have pesticides that cause my cost to

22 go up the tree. And because the results are increased disease

23 levels, they threaten my livelihood. War has been started over

24 less and I feel as though I'm in one; a war to save the bee and

25 me. 

26 Please look at the way data I gathered on
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1 incidents. Please look at LD50s, please work on getting

2 tolerance levels in honey for chemicals, and please help us save

3 the bees. Thank you. 

4 DR. DANIEL SCHLENK: Thanks for your comments.

5 Any questions for Mr. Doan? Okay. Thanks a lot. Appreciate

6 it. Oh you have one, Dr. Pistorius? 

7 MR. JENS PISTORIUS: Yes, Pistorius. I was

8 wondering, maybe I missed it during the talk. You said that one

9 incident where you provided pictures that a lot of drones were

10 found dead. 

11 MR. JAMES DOAN: Right.

12 MR. JENS PISTORIOUS: Was that the colony that

13 the relation of female bees to drones were changed and do you

14 think that a lot of drones were affected only? 

15 MR. JAMES DOAN: It seemed like only drones. 

16 In that picture, there are some workers, but predominantly it

17 was drones -- all the drones in the hives and every hive it was

18 like that.

19 MR. JENS PISTORIOUS: Okay, thank you. 

20 DR. DANIEL SCHLENK: Dr. Fefferman? 

21 DR. NINA FEFFERMAN: Sorry, this is just

22 scientific curiosity, do you have any impression from that? 

23 Were the drones trying to get back in and being forced out, or

24 were they just flying out? 

25 MR. JAMES DOAN: No, we were in a major honey

26 flow at that point. Those drones were not kicked out. They
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1 were crawling in the grass in front of the hives dying. We are

2 still waiting on the results back from that. I have 50 bags of

3 samples to run. We have lots of samples collected. It is a

4 matter of who can pay for all these to be run. Right now,

5 beekeepers are paying the cost of running all these things. Yes

6 sir? 

7 DR. GREG HUNT: So, in those same hives

8 earlier in the year, you had a bee kill.

9 MR. JAMES DOAN: Right.

10 DR. GREG HUNT: And now this is June. 

11 MR. JAMES DOAN: Right. 

12 DR. GREG HUNT: Okay. I just wanted to get

13 the background.

14 MR. JAMES DOAN: Now those 148 hives

15 completing were died after the second time. We just push that

16 stuff off to the side and dump all the frames out and burned it

17 up. I couldn't - I was - I couldn't deal with them. There was

18 nothing there - I wasn't going to put anymore bees into it.

19 DR. GREG HUNT: So these drones were in other

20 hives? 

21 MR. JAMES DOAN: Yes sir, in every yard, every

22 hive in every yard.

23 DR. GREG HUNT: In every yard. The crops

24 around were similar? 

25 MR. JAMES DOAN: Well, our area is

26 predominantly corn, soybeans, cabbage, apples, it has dairy,
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1 it's a mixture of everything. 

2 DR. GREG HUNT: Okay. Thanks. 

3 DR. DANIEL SCHLENK: Dr. Berenbaum? 

4 DR. MAY BERENBAUM: Did you have to pay for

5 the Gastonia analysis? 

6 MR. JAMES DOAN: Yes.

7 DR. DANIEL SCHLENK: Dr. Pettis? 

8 DR. JEFF PETTIS: Back to the incident

9 reporting, I realize that state by state, there's a lot of

10 variation in that. The EPA, I believe, has set up a way of

11 directly reporting, have you tried that at all? 

12 MR. JAMES DOAN: I did do that once I found

13 out about it, but there were a lot of beekeepers in New York

14 state that didn't know about it at the time and a lot of

15 beekeepers didn't have samples pulled. I've talked to people

16 from all across the country that had very difficult time trying

17 to get somebody from their state to come up and pull samples. I

18 have talked to the Canadian beekeepers and they're telling me

19 very similar stories to what I'm telling you, other than they

20 got a lot of help out of their ministry to pull samples. Thank

21 you very much.

22 DR. DANIEL SCHLENK: Any other questions? 

23 Okay. Our next presenter is Dr. Mike Beevers from California

24 Agriculture Research. 

25 DR. MIKE BEEVERS: Can you hear me? Yeah, my

26 name is Mike Beevers. I'm a research director for California
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1 Agricultural Research, and we're a contract research

2 organization sometimes called a private lab. We've done

3 regulatory honey bee studies for about 26 years. I'm just here

4 to share a little bit of perspective of the people who are doing

5 some of the work right now and may be doing a lot of the

6 research in the future, just to let you know kind of our

7 understanding of this. 

8 The subject here is Potential Changes in

9 Regulatory Research Requirements for Pollinators. Regulatory

10 Research is the bailiwick of the contract research organization.

11 We perform scientific studies for submittal to EPA or similar

12 government entities by manufacturers of agricultural products. 

13 Our goal is to provide independence, objectivity and reliability

14 in the studies. 

15 Our studies produce data to answer very

16 specific risk assessment concerns regarding pesticides held by

17 government regulatory bodies and our data is confidential. I am

18 trying to just describe this as regulatory research. 

19 Exploratory research is something entirely different and seeks

20 to answer more broad ranging hypothetical questions. Most of

21 this work is peer reviewed and is not under GLP and it is done

22 at the universities. Obviously, this work is very important

23 going forward with bees, the university work especially. 

24 Contract research organizations assure the

25 public of their independence by following good laboratory

26 practices as established by the EPA. 
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1 I was going to ask the Panel, are you familiar

2 with the Good Laboratory Practice Provisions? Just to let you

3 know, we do function under these. These standards are the

4 cornerstone of most contract research organizations, and there

5 are specific standards regarding the data, the recording of

6 data, raw data, protocols, study director, legal

7 responsibilities, quality assurance, in-life audits, data

8 auditing of final reports, archiving, standard operating

9 procedures, short-notice inspections by the enforcement branch

10 of the EPA out of Denver. 

11 The purpose of these guidelines is to assure

12 that the agencies, when they get data from our labs, are getting

13 something they can rely on that they can go back years later and

14 produced to defend their decisions. 

15 A very simple example of GLP Practice would be

16 placing a sample of nectar or pollen in a freezer. That freezer

17 has to have a maintenance log. The sample must be logged into

18 the freezer. The freezer must have 24/7 temperature monitoring.

19 It has to have alarms, probably a generator backup for power

20 failures. When the sample is removed, it must be logged out,

21 chain of custody, so forth and so on. I am just trying to

22 indicate that there is a lot of work that goes into these

23 studies to make them valid. 

24 Noncompliance with these laws can result in

25 fines and imprisonment. There are people on contract labs who

26 have actually gone to prison. I probably don't need to explain
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1 that if working under these circumstances, people take the data

2 very, very seriously. I just would like to communicate that to

3 you guys, and I appreciate you listening on this.

4 The lab's interest is in alignment with the

5 Agency's interest in terms of data quality and objectivity. The

6 people in the labs are interested in learning the newest methods

7 and learning to implement them. 

8 I would just like to say that, you know, the

9 people in these labs, and I'm speaking for myself, would like to

10 be a resource in the future as part of this and we are operating

11 under a very strict guideline. 

12 Finally, if anyone knows of any problems with

13 labs -- some of these labs have been criticized in the past -- I

14 would be interested in talking with them at some point. That is

15 all I have to say, thank you. 

16 DR. DANIEL SCHLENK: Thank you. Any questions

17 for Dr. Beevers? Dr. Pettis? 

18 DR. JEFF PETTIS: There has been a lot of work

19 in Tier I testing on Apis mellifera. Have you done any testing

20 with other species of pollinators at all? 

21 DR. MIKE BEEVERS: Not successfully, no. The

22 non-Apis issue is very tricky for, as I understand it, for

23 getting predictable, replicable tests. The honey bee is just a

24 wonderful organism to work with in comparison with those.

25 DR. JEFF PETTIS: I wouldn't argue about the

26 honey bee, but my concern is that if we were to move to other
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1 species testing, what would be reliable? 

2 DR. MIKE BEEVERS: We would be - I have worked

3 with leaf cutter bees. We have tried starting with bumble bees.

4 Yes, that can be done and we are interested in doing it, but to

5 be very honest with you, I haven't had a lot of demand for that.

6 But that is an area we are interested in, and we would love to

7 try. 

8 DR. DANIEL SCHLENK: Okay. Any other

9 questions? Yep, Dr. Tarpy? 

10 DR. DAVID TARPY: This is Dave Tarpy, NC State

11 University. Is there an analogous kind of GLP, but not for the

12 L but for kind of like ecological setting that would hold up to

13 that same stringency as the GLPs that you work under? 

14 DR. MIKE BEEVERS: That's an excellent

15 question, and not to my knowledge. The studies in ecological

16 settings are extremely complicated. We have applied the good

17 laboratory practice to the data. There're a lot of things that

18 would transfer there, but in terms of actually addressing some

19 of the possible issues that you may have in mind, we are dealing

20 with a good laboratory practice for that purpose right now. 

21 DR. DAVID TARPY: Thank you.

22 DR. DANIEL SCHLENK: Any other questions? Dr.

23 Pistorius? 

24 MR. JENS PISTORIUS: Assuming you have a lot

25 of contact with other GLP laboratories in the U.S. too, my

26 question is have you established in vitro test method yet as a
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1 routine method and with acceptable control mortality, and are

2 you able to do to tests longer than day 7? 

3 DR. MIKE BEEVERS: The answer to that is

4 essentially no. The in vitro test has been tested -- no from me

5 personally. Some of the other labs may have developed that, but

6 I'm not aware of. We have ring tested one in vitro test, but my

7 understanding is it's still in development. 

8 DR. DANIEL SCHLENK: Any other questions? 

9 Okay. Thank you, Dr. Beevers.

10 DR. MIKE BEEVERS: Thank you.

11 DR. DANIEL SCHLENK: Our next presenter is Rod

12 Snyder from the National Corn Growers Association. 

13 MR. ROD SNYDER: Good morning. My name is Rod

14 Snyder, Director of Public Policy for The National Corn Growers.

15 We appreciate the opportunity to just have a few brief remarks

16 this morning. I will be providing comments on behalf of NCGA,

17 which represents more than 37,000 members in 48 states and more

18 than 300,000 corn farmers who contribute to state checkoff

19 programs across the country. 

20 I want to focus just these very brief

21 statements on the benefits of seed treatments since that is

22 where we've been getting a lot of questions recently regarding

23 the studies for their potential impacts on pollinators. 

24 Seed treatments are critically important to

25 growers because they protect the seeds from disease and pests

26 from planting through germination. Unpredictable weather
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1 patterns during the planting season could lead to the seed

2 remaining in the ground for an extended period of time and

3 without these products, yields would be reduced and entire

4 fields may need to be replanted. 

5 Seed treatments lead to a higher yield corn

6 crop by providing a healthier root system, particularly by

7 maximizing early season plant stands. Growers also consider

8 these seed treatments an important investment as they seek to

9 protect and maximized high-value seed. 

10 Field tests for the products have demonstrated

11 a 6 bushel per acre average yield increase in corn. For

12 example, Clothianidin is an important neonicotinoid seed

13 treatment for corn in the U.S. The product is used in

14 approximately 60 percent of corn planted in this county. It is

15 a valuable product as growers seek to control wireworms, black

16 cutworms, white grubs and other early-season pests that attack

17 corn seeds and seedlings at a period when they are most

18 vulnerable. 

19 It is also worth noting that in no-till

20 practices, no-till operations, because the soil health is

21 improved, it also means additional pest pressures. So, all of

22 the benefits from no-till operations could be lost if we have to

23 start tilling again because of pressure on the seeds. 

24 The use of seed treatments reduces the

25 potential human health effects for corn growers because it

26 reduces the need for soil-applied sprays or granules. 
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1 Environmental exposures are also reduced due to the fact that

2 the seed is incorporated beneath the soil surface at planting

3 time. This leads to a lower probability of exposure to birds or

4 runoff to water bodies when compared to soil-applied products. 

5 Seed treatments actually have the effect of reducing the amount

6 of active ingredient needing for effective pest control.

7 Eliminating foliar applications also removes

8 the concern with making extra trips across the field during a

9 time of the year when rainfall and muddy soils can create major

10 time constraints during planting season. Fewer trips across the

11 field also leads to fewer greenhouse gas emissions from fossil

12 fuels used in tractors and other farm equipment.

13 While corn growers remained concerned about

14 the recent declined in bee populations in the U.S. and the

15 potential impact on the larger agricultural economy, studies

16 suggest that there are multiple and complex causes of colony

17 collapse disorder that cannot be explained by a single class of

18 chemicals or agricultural practices.

19 There are no demonstrated long-term effects on

20 bee colonies from neonicotinoid pesticides. Furthermore, corn

21 growers work diligently to minimize dust from planters and often

22 establish relationships with nearby beekeepers to minimize

23 exposure to pollinators. We appreciate the opportunity to

24 comment today, and we urge this Panel to maintain a

25 scientifically sound risk assessment process when reviewing the

26 impact of seed treatments on pollinators. I am certainly happy
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1 to follow up with other information that you need. Thank you. 

2 DR. DANIEL SCHLENK: Any questions? Yes, Dr.

3 Hunt? 

4 DR. GREG HUNT: Yeah, I've heard that

5 undoubtedly seed treatments are good for corn yields. I've

6 heard the 6-bushel an acre data before. Is that in a published

7 study, and has there been a study that looked at the benefit of

8 just the neonicotinoids without the other fungicides, et cetera?

9

10 MR. ROD SNYDER: Sure, I'd have to go back and

11 figure as we were piecing this together where that citation came

12 from, but we can easily follow up on that. Yeah, absolutely. 

13 DR. DANIEL SCHLENK: Dr. Potter and Dr.

14 Berenbaum? 

15 DR. THOMAS POTTER: Yeah, I just feel

16 compelled to make a comment here because regarding your point

17 that you made about increased pest pressure with no-till

18 practices associated with improved soil health. I'm not sure

19 what you mean by that, but I think there're probably other

20 issues other than improved soil health that may cause increased

21 problems with pests, probably just the fact that the soils are

22 colder and wetter. 

23 I don't mean to make too big a point about

24 that, but I think improved soil health is a really good thing. 

25 It is something that I've connected my career mostly to, so you

26 know, I think there are other issues than soil health that may
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1 contribute to pest pressure in no-till systems, which certainly

2 are important conservation system set up throughout the country.

3 MR. ROD SNYDER: Yes, by all means. Improved

4 soil health is a big part of the reason why farmers shift to

5 no-till, and I'm sorry if I've left any confusion there. There

6 are many, many benefits to no-till systems. Our growers are

7 moving in that direction rapidly in the last 10 or 20 years. 

8 But you are right. Whether it is the temperature of the soil of

9 what have you, there can be additional pest pressures in those

10 settings. So, seed treatments allow that adaptation to no-till.

11 That's just, sorry, the basic point I was trying to make. 

12 DR. MAY BERENBAUM: Wireworms and white grubs

13 and black cutworms are not uniformly distributed across the

14 corn-growing region and differ in their importance as economics

15 pests. Do you know if there is - that 60 percent of corn

16 growers who use pretreated seed - is that predominantly in

17 regions where there is history of pest problems? 

18 MR. ROD SNYDER: As far as the distribution of

19 where the product is used, I'd have to look that up and get back

20 to you. I mean certainly it's a common product in the corn-belt

21 in the midwest and upper midwest, but I can look that up for

22 sure. 

23 DR. DANIEL SCHLENK: Any other questions? Dr.

24 Pistorius? 

25 MR. JENS PISTORIUS: Short one. How high do

26 you think is the percentage of non-tillage systems for maze
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1 cropping? 

2 MR. ROD SYNDER: I'm sorry, the question is

3 how -

4 MR. JENS PISTORIUS: How big is the portions

5 of fields where there is no tillage for sowing? 

6 MR. ROD SNYDER: That is a little bit region -

7 there are regional differences. I can say with certainty in the

8 mid Atlantic region where there's been a lot of emphasis on

9 water quality in trying to prevent runoff because of the

10 Chesapeake Bay situation, recent studies have shown that close

11 to 90 percent of acreage in this region has moved to either

12 no-till or conservation tillage. It is a little lower in the

13 midwest, but I mean, it's a very high percentage.

14 DR. DANIEL SCHLENK: Dr. Hunt? 

15 Dr. GREG HUNT: Just real quick, I was a

16 little surprised by you saying that only 60 percent of the

17 growers are using treated seed.

18 MR. ROD SNYDER: That was Clothianidin

19 specifically. Other products would drive that number much

20 higher. Clothianidin has a lot - there has been a lot of talk

21 about that product in particular. 

22 DR. GREG HUNT: Perhaps up to 98 percent.

23 MR. ROD SNYDER: Right. 

24 DR. DANIEL SCHLENK: Okay. Any other

25 questions? Okay.

26 MR. ROD SNYDER: All right, thanks.
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1 DR. DANIEL SCHLENK: Thank you. Our last

2 public commenter today is Stephen McFadden from Independent

3 Scientific Research Associates. And Mr. McFadden, if I could

4 have you to about a 5 to 10 minute range, is that appropriate? 

5 Thanks. 

6 MR. STEPHEN MCFADDEN: Hello. I'm from

7 Midland, Texas and after all the academic discussion, I want to

8 point out where the rubber hits to road. We recently had a

9 larger urban aerial spraying in Dallas, Texas. The DFW

10 Metroplex is 6.5 million people. It's the largest metropolitan

11 area in the south. The city of Dallas is 300 square miles. In

12 2003, we beat the West Nile virus using an IPM program put

13 together by former EPA'er, Bill Curry. 

14 The West Nile virus virulence has a feedback

15 term where if you have a bad year, the next year a lot of birds

16 are immune. And so, you have a good year the next year. But

17 after nine years, then the birds aren't immune. 

18 So, after nobody was watching the store for

19 2010 and 2011, in 2012 we had the worst outbreak probably in the

20 U.S. ever. They aerially sprayed 1150 square miles twice,

21 mostly two nights in a row, homes of maybe 2 million people. 

22 They used Clarke Duet with Sumithrin and Prallethrin from

23 twin-engine planes at 300 feet at night. From 300 feet, the

24 spray probably takes between 20 minutes and 2 hours to hit the

25 ground, I'm guessing. So there is probably a lot of drift. 

26 A few organic farmers were upset. Some
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1 beekeepers were upset. I would image that a number of bees were

2 upset. 

3 There were presumably a bunch of governmental

4 affairs, three issues. One of our issues is like on the City of

5 Dallas website it says that aerial spraying is safe and

6 effective. EPA does not allow the term safe to be used to apply

7 to pesticides, but this came from the top state health office in

8 Texas who was using the term safe including on a CDC

9 teleconference, and nobody confronted him on it. 

10 The remedy is to sticker the label of public

11 health pesticides with the statement that it is a violation of

12 EPA regulations and of the label to state that the pesticide is

13 safe. 

14 Secondly, one resident ran an adverse effects

15 hotline on the reregistration docket for Prallethrin. They

16 submitted 14 cases of adverse effects. Given the number of

17 instances of vomiting, they wonder that if Prallethrin, which is

18 supposed to cause benign agitation of mosquitos and rapid knock

19 down, if that may have some human effects such as vomiting. 

20 Thirdly, they hit 1150 square miles twice. 

21 Some people think this is the new norm. We wonder if maybe they

22 should file an Environmental Impact Statement because in the EIS

23 process, maybe the bees would get some representation. Thank

24 you.

25 DR. DANIEL SCHLENK: Any questions for Mr.

26 McFadden before he leaves. Would you like to answer any
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1 questions? You don't have to. Dr. James? 

2 DR. ROSALIND JAMES: Could you tell me what

3 your organization is again. 

4 MR. STEPHEN MCFADDEN: Independent Scientific

5 Research Advocates.

6 DR. ROSALIND JAMES: Okay, who is that? 

7 MR. STEPHEN MCFADDEN: Well, it's me, myself

8 and I, and I also run the website dalcap.org. And I work with

9 several people in the Dallas Fort Worth area. In 2002 and 2003,

10 I was living in Dallas, and we got on the city council to try to

11 put together IPM program for the West Nile virus. So, in 2002

12 and 2003, I was living there on a daily basis. 

13 This year, I cancelled my trip to Dallas when

14 I saw aerial spraying listed, but I followed basically every

15 news article on the internet and I work closely with the

16 activists there. 

17 It's just like all of a sudden, government

18 screwed up, nobody is watching the store and all of the sudden,

19 they tell the public we are going to be spraying by air in a

20 couple of days and it's going to hit 1000 square miles. 

21 The bees don't like it, we don't like it. 

22 They claim its safe in the all the press and we know that that

23 isn't supported that the claim of safety fails to consider

24 individual factors such as genetics, health history, et cetera. 

25 So the term safe is conclusive when you can't make a conclusory

26 term about 2 million people. Thank you.
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1 DR. ROSALIND JAMES: Thank you. 

2 DR. DANIEL SCHLENK: Okay. At this point in

3 time, our time of public commenting is closed. I believe so. 

4 So, before lunch if we could, if we can have the Agency come up

5 again, and if the Panel has any questions related to the

6 presentations that were made yesterday, you've had the evening

7 to sort of mull over those presentations and your question

8 groups discuss a few of those. If you have any particular

9 questions to any particular representatives here, now is the

10 time to do it before we get into our panel question

11 deliberations. So, Dr. Berenbaum? 

12 DR. MAY BERENBAUM: I just have a question,

13 don't even know to whom it should be directed, but it is for

14 general information and I apologize if everyone else knows this,

15 but I don't. Incident reporting - who is, how is that - we've

16 heard there is state level, federal level, what agency, who has

17 oversight, how is it regulated? 

18 DR. THOMAS STEEGER: The only entity that's

19 required to report incidents to the Agency is the Regulated

20 Community Registrants themselves under FIFRA 6A2. The Agency

21 provides funding to states who are the - we refer to them as the

22 State Lead Agencies. They have primacy for enforcing labels and

23 they are responsible for investigating incidents that are

24 reported to them. 

25 The Agency is aware that many beekeepers feel

26 that for a variety of reasons incident reports are not making it
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1 back to the Agency. The Agency makes clear in its assessments

2 that the absence if incident reports cannot be construed as the

3 absence of incidents. 

4 So, understanding that there have been

5 breakdowns in communication and that the number of incidents is

6 potentially being under reported, there is reluctance by many

7 beekeepers to report incidents because of fear of offending

8 growers that they depend on for pollination services and for

9 honey production. And, there is actually fear of retribution

10 that bee colonies will be destroyed. 

11 That is very unfortunate. But understanding

12 that there are impediments to reporting incidents, the Agency

13 has established other means for individuals to contact the

14 Agency directly through the National Pesticide Information

15 Center, and they can contact us through beekill@epa.gov. I

16 monitor the portal for the beekill@epa.gov. 

17 We did have the opportunity to record Jim

18 Doan's bee kill incidents, and we were in contact with the state

19 of New York through our regional offices to encourage them to

20 investigate those incidents. 

21 DR. DANIEL SCHLENK: Any other questions? Dr.

22 Potter? 

23 DR. THOMAS POTTER: Yes, I have a question

24 about the data that was summarized in the document describing

25 pesticide concentrations in pollen and nectar. And my question

26 is does that represent the entire set of information that you
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1 utilized in the process of establishing a dietary exposure

2 estimate using T-REX? 

3 MS. KRIS GARBER: This is Kris Garber. We

4 used all the available information from the open literature and

5 unpublished registrant submitted studies that we are aware of. 

6 We did some literature searches, and we searched our database of

7 submitted studies. 

8 We did exclude some studies from the open

9 literature that didn't provide application rate information

10 because it was really important for us to be able to link the

11 mass of the pesticide that was put on the field so that, you

12 know mass that was observed in the different samples, especially

13 for the T-REX and for the Briggs' analysis. So, there are some

14 studies in the literature that we excluded based on application

15 rate. 

16 DR. THOMAS POTTER: I got the impression, and

17 maybe again, that is why I'm following up on this, that there

18 were registrant submitted studies that contained information on

19 active ingredient concentrations in pollen and nectar that were

20 utilized. So, what you're telling me is that is not the case

21 other than what you describe in the White Paper? 

22 MS. KRIS GARBER: What I'm saying is that

23 there were registrant studies that had concentrations in pollen

24 and nectar, and those are included in the White Paper and they

25 are summarized in appendices 3 and 4. 

26 DR. THOMAS POTTER: Right, and again, I will
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1 just make a point here that I think there are to me a

2 surprisingly small number of measurements that are available to

3 use in making assessments about the dietary exposure here. You

4 know, I think that is critically important. We will get into

5 that this afternoon. Maybe I'm jumping the gun here a bit, but

6 I wanted to confirm what data you had utilized in your

7 assessments. 

8 DR. DANIEL SCHLENK: Dr. Pettis? 

9 DR. JEFF PETTIS: Jeff Pettis. Kind of

10 following up on that, in looking at the consumption rate of

11 nectar versus pollen, I just had a feeling that the pollen

12 consumption rate seemed abnormally low. Can you comment on the

13 calculations of the average bee consuming nectar versus pollen? 

14

15 MS. KRIS GARBER: Well, we - both of the

16 consumption rates for pollen and nectar were empirically based. 

17 The pollen consumption rates are based on direct counts of

18 pollen grains in the guts of bees, including adults and larvae. 

19 So, I think those are some pretty confident measures because

20 they were actually able to, you know, count how many pollen

21 grains were in each bee. 

22 Now the nectar consumption rates are not based

23 on direct measures of how much a bee eats, either nectar or

24 honey. So we had to calculate it based on the energetic

25 requirements of the bees in terms of how much sugar they may

26 need based on their different tasks. So the nature of the
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1 estimate is different. 

2 Perhaps that could lead to some differences in

3 the magnitude of those two. But for a nectar forager that

4 spends a lot of its time flying, it makes sense that it would

5 need a lot of sugar to, you know, fulfil its task. 

6 DR. DANIEL SCHLENK: Dr. James? 

7 DR. ROSALIND JAMES: This is Rosalind James. 

8 I have a follow-up question to that. Later today we will

9 probably talk some more about this difference between the pollen

10 and nectar in the EPA assessment for Tier I. In some ways, it

11 wouldn't matter because you are essentially saying that the

12 concentration of pesticide is the same in both, and yet some of

13 the commenters today were saying pesticide concentration in

14 pollen is quite a bit higher than in nectar. So, this is an

15 important question.

16 MS. KRIS GARBER: When we were coming up with

17 the Tier I method for foliar spray applications, that's based on

18 the tall grass residue of the surrogate for pollen and nectar,

19 we did actually consider applying some kind of adjustment

20 factor, which was something that was brought up in the public

21 comments; but there were some studies where - and I think that

22 one of them is Choudhary et al., which is summarized --

23 Choudhary and Sharma I think are the two authors. There were

24 actually some cases where the concentrations in the pollen and

25 the nectar were equivalent. 

26 So, we thought that you couldn't necessarily
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1 assume that the concentrations are always going to be less in

2 nectar, although it is true that there are several studies where

3 that is the case. So, that's why we decided to assume that

4 nectar and pollen were equivalent and that, you know, like Dr.

5 Potter had pointed out, there isn't a lot of data available for

6 pollen and nectar. So, we didn't think that we had enough

7 information to necessarily apply an appropriate adjustment

8 factor. 

9 DR. DANIEL SCHLENK: Go ahead.

10 MR. KEITH SAPPINGTON: Keith Sappington, EFED.

11 I just want to follow up with one point on those data that they

12 represent day zero values. So values, you know, measured

13 immediately after application or within 24 hours, so that is

14 something to keep in mind with the foliar applications. 

15 The other thing is we notice some differences

16 across plant species. You might think that the availability of

17 the nectaree to receive the spray droplets would be a factor, so

18 different plants and different physiologies associated with the

19 nectarees could come into play. So we looked at this, but we

20 felt that the range of, basically the variability around the

21 pollen to nectar ratio was too large to proposed as a method. 

22 DR. DANIEL SCHLENK: Dr. Fefferman? 

23 DR. NINA FEFFERMAN: Hi thanks. Along the

24 same lines, I was actually - I like very much the sort of

25 energetic analysis of the need for the workers for nectar

26 consumption and the absence of their being a measurement for
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1 that. I was wondering of the similar logic couldn't be applied

2 to the protein needs of egg-laying queens in order to establish

3 a rate separate for consumption there. 

4 MS. KRIS GARBER: I think that's an excellent

5 point and we could definitely consider that. 

6 DR. DANIEL SCHLENK: Dr. Pistorius? 

7 MR. JENS PISTORIUS: I have question. Would

8 the EPA be in charge and in power to implicate risk mitigation

9 measures of beyond the action of application of pesticides and

10 especially, for instance, concerning seed treatment facilities,

11 prescribing a certain seed quality level? And in addition to

12 that, for instance like that for non-tilling systems, that there

13 has to be a herbicide application or removal of flower plants

14 before application - is that possible? And machine technique as

15 well, so you could prescribe use of deflectors? 

16 DR. THOMAS STEEGER: The EPA has been working

17 with the seed treatment organizations and with the seed

18 equipment manufacturers as well as the registrants to develop

19 that very type of mitigation. The label though, is specific to

20 how the product is applied as opposed to how it might be

21 distributed in plantars. 

22 So, it is very difficult from my understanding

23 and out my realm for speaking to this issue, but we feel that

24 the best means of mitigating those potential effects is through

25 best management practices that would be imposed by industry. 

26 DR. DANIEL SCHLENK: Dr. Hunt? 
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1 DR. GREG HUNT: I want to get back to - this

2 kind of relates to the previous question in a way. I want to

3 get back to the pollen, the amount of pesticide in pollen,

4 specifically Clothianidin; the White Paper cites our Krupke et

5 al. paper as 3.9 parts per billion in the corn pollen that we

6 analyzed. But we also took pollen from pollen traps on hives

7 and found up to 88 parts per billion of Clothianidin. And this

8 was at a time when corn was not being planted.

9 So, my hypothesis is that this is from talc

10 dust that had remained on flowers for a while because that kind

11 of level, we just don't find it in corn pollen that has been

12 treated, although the bees were bringing in up to 80 percent

13 corn pollen in the sampling. 

14 DR. DANIEL SCHLENK: So, is this a question

15 for the Agency or a comment? 

16 DR. GREG HUNT: I guess this is a comment. 

17 DR. DANIEL SCHLENK: Yeah, let's hold the

18 comments until later. Okay? 

19 DR. GREG HUNT: All right, sorry.

20 DR. DANIEL SCHLENK: These are questions the

21 Agency that will help you in your comments later on. Okay? 

22 That's what this time is for. Any other questions? Okay. 

23 Thanks a lot. Let's break for lunch. We will come back at

24 12:50, and read in the questions at that time. Thanks. 

25 (WHEREUPON lunch was taken)

26 DR. DANIEL SCHLENK: Question one - and just
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1 to save a little bit of time, who's going to be reading the

2 question in? Who is planning to read the question in? Okay. 

3 You just want to read the letter (a) instead of the whole thing.

4 I think that part will be sufficient instead of reading the -

5 so the sub-letters basically throughout I think will be good. 

6 Great, thanks. 

7 MR. THOMAS MORIARTY: This is Tom Moriarty. I

8 also understand that you want us to pause after each

9 subquestion? 

10 DR. DANIEL SCHLENK: Correct. We will go

11 subquestion by subquestion. 

12 MR. THOMAS MORIARTY: Just enough to take a

13 breath so I can move onto the next one.

14 DR. DANIEL SCHLENK: Okay, go ahead.

15 MR. THOMAS MORIARTY: Tom Moriarty, U.S. EPA. 

16 Charge question 1, sub (a) - please comment on whether the

17 assessment endpoints, population size and stability of managed

18 bees, quantity and quality of hive products, and species

19 richness and abundance, identified in Table 1, Section 2.2.1 of

20 the White Paper, are consistent with the Agencies protection

21 goals. Please include a discussion of any additional assessment

22 endpoints that may be necessary to meet those protection goals. 

23

24 DR. DANIEL SCHLENK: Okay. Our lead

25 discussant for this particular question is Dr. James.

26 DR. ROSALIND JAMES: This is Rosalind James. 
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1 Since I am answering the first question, I have a couple of

2 general comments. They won't be long. To the group, I wanted

3 to commend EPA on the excellent job in writing the White Paper. 

4 It's very thorough and well thought out. It's clear that a lot

5 of care went into writing this. This proposal is definitely a

6 step in the right direction. It is very heartening to see EPA

7 taking on this task with bees.

8 And I have another short comment to the group,

9 the Panel here. This is a small plea on the use of language,

10 and in particular, I would like to ask you not to use the words

11 bees and honey bees synonymously because remember, we are

12 talking about diversity and we will be talking bees other than

13 honey bees. So, please say honey bees when you mean honey bees

14 and when you say bees, mean more than just honey bees. It will

15 be helpful for me, I know, and probably for EPA as well. 

16 With regard to question 1(a), essentially this

17 question has to do with Table 1 on page 44. So, I am going to

18 work off of the Table in my answer here.

19 First we are looking at the protection goals

20 that have been set by EPA and the question is whether or not the

21 assessment endpoints address the protection goals. So, here I

22 would like to reiterate about the bees and in question 1 for

23 provisions of pollination and services. The assessment endpoint

24 says managed bees. So, we wanted to make sure that EPA is clear

25 that this means commercial managed bees, which means honey bees,

26 bumble bees, alfalfa-leaf cutting bees and mason bees and any
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1 other bees that may become managed in the future. 

2 So when it says population size, we weren't

3 really sure if this meant colony population size as for honey

4 bees or actually truly population size, and that really it

5 should probably be populations in terms of numbers of colonies. 

6

7 Then for production of hive products - the

8 assessment endpoint is quantity and quality of hive products. 

9 Quantity and quality of hive products is fine endpoint, but

10 probably quantity would be sufficient, unless quality really

11 means pesticide-free or low pesticide residues. In this case

12 quality is an important measure we will see in the next

13 subquestion.

14 Onto number 3 -- contribution to pollinator

15 biodiversity. The question really is asking about the

16 assessment endpoints and whether or not these endpoints address

17 a protection goal. However, we feel that the protection goal is

18 very broad. It is sort of like saying we're going to cure

19 cancer, or we are going to save the world - we are going to stop

20 global warming. It's just a very, very large goal, and

21 likewise, the assessment endpoint is very, very broad to species

22 richness and abundance. Furthermore throughout the White Paper,

23 there really are no measures for species richness and abundance

24 taken. 

25 So we recommend that the protection goal be

26 rewritten to read contribution to bee biodiversity. If you are
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1 talking about pollinators, this would mean all pollinators,

2 insect pollinators and mammals and other vertebrates like

3 humming birds and so on. It just seems beyond the scope of this

4 White Paper. 

5 The honey bees - the entire genus Apis - they

6 are not native to the Americas, and there is approximately 4000

7 species of bees that are native to the Americas. New species

8 are discovered every year. Just in our laboratory alone, we

9 probably discover two every year. This year, there was a new

10 species discovered in the middle of New York City. So, that's

11 how poorly they're known. I mean, there are 4000 species that

12 are known, but - so, we just wanted you to keep that in mind as

13 you think about this goal. I think that is the end of what I

14 have for (a). It is hard for me to separate out (a), (b) and

15 (c), but we will get to (b) and (c). 

16 DR. DANIEL SCHLENK: Okay. Our first

17 associate discussant is Dr. Berenbaum. 

18 DR. MAY BERENBAUM: I concur. 

19 DR. DANIEL SCHLENK: And Dr. Pettis.

20 DR. JEFF PETTIS: I concur as well.

21 DR. DANIEL SCHLENK: Dr. Pistorius? 

22 MR. JENS PISTORIUS: I concur as well.

23 DR. DANIEL SCHLENK: Dr. Ostiguy.

24 DR. NANCY OSTIGUY: I concur also.

25 DR. DANIEL SCHLENK: All right, so let's open

26 it up to other Panel members for any other comments for Question
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1 1(a). Anything else to add? Okay. Let's - who's going to be -

2 any questions for clarification for the Agency for that? We're

3 good? All right, thanks. All right, you want to read in 1(b),

4 Mr. Moriarty. 

5 MR. THOMAS MORIARTY: Please comment on

6 whether the measurement endpoints at the level of the colony,

7 for examples, colony strength and survival, contamination of

8 pollen and nectar, and species richness and abundance identified

9 in Table 1 are consistent with the assessment endpoints

10 identified in the Table and any additional assessment endpoints

11 discussed in Part (a) of this question. Please include a

12 discussion of any additional measurement endpoints that may be

13 necessary to represent those assessment endpoints.

14 DR. DANIEL SCHLENK: And again, Dr. James, you

15 are our lead discussant for that question.

16 DR. ROSALIND JAMES: All right, this is

17 Rosalind James. So, again we are still working off of Table 1

18 and now we are on the third column measurement of endpoints and

19 how these meet the protection goals. So, back again to bee

20 diversity, 98 percent, probably closer to 99 percent of bee

21 species are solitary, and thus they do not have colonies. And,

22 so a measurement of the endpoint colony strength would not

23 really be measuring these bees directly. We want to make sure

24 that that's clear. However, if honey bees serve as a surrogate

25 for all bees, then this endpoint should be fine, but EPA needs

26 to keep in mind that this is honey bee serving as a surrogate,
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1 so it is addressing honey bees and other bees. We also wanted

2 to add to this colony strength and survival is listed, but we

3 also wanted to add colony development, which means the growth of

4 the colony during growth season.

5 For the second item, production of hive and

6 hive products, again, we recommend removing the word quality and

7 also we wanted to add other hive products to this list. In

8 particular, pollen, propolis and royal jelly; so that this

9 element would now read, "quantity of hive products; and residues

10 levels in honey, pollen, wax, propolis and royal jelly." 

11 And this was partly keeping in mind for honey

12 bees and then for the native bees as well. Some of these plant

13 resins and so on are very important to native bees in their

14 natural habitat. If they are contaminated with pesticides,

15 especially the larvae would be exposed. 

16 So then onto number 3 - contribution to bee

17 biodiversity. What's currently the measurement endpoints are

18 colony, strength and survival, and the second element is species

19 richness and abundance. Colony survival is repeated here from

20 up above and that's fine, but all my earlier comments about

21 colony strength and survival apply here as well, and especially

22 now we are talking about not honey bees, but other bees. So,

23 those comments are doubly important here. 

24 Then species richness and abundance - again,

25 this is a noble goal, but it's not really included anywhere in

26 the assessments that occur in the White Paper. There's no way
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1 that you can assess species diversity using only one species,

2 the honey bee. Again, this is a species in addition,

3 essentially a domestic animal that's not native to any of the

4 Americas. So, species richness and abundance is - it would be a

5 good measurement endpoint, but you haven't addressed it at all. 

6 All right, that is the end of my comment.

7 DR. DANIEL SCHLENK: Thank you. Dr.

8 Berenbaum.

9 DR. MAY BERENBAUM: I just want to clarify or

10 emphasize that taking quality out of the measurement endpoint

11 for hive products does not affect the validity of residue

12 levels. So, quality independent of residue levels is not really

13 the issue, but residue levels certainly do affect quality. 

14 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 

15 DR. JEFF PETTIS: I concur with what's been

16 said. 

17 DR. DANIEL SCHLENK: And Dr. Pistorius? 

18 MR. JENS PISTORIUS: Same here. 

19 DR. DANIEL SCHLENK: Dr. Ostiguy? 

20 DR. NANCY OSTIGUY: I just wanted to add that

21 in looking at the using of honey bees to measure biodiversity,

22 one of the things that we discussed are the studies that have

23 been published on the negative impact of the presence of honey

24 bees on native species. So, it's a significant consideration

25 that we not use honey bees as a measurement of biodiversity.

26 DR. DANIEL SCHLENK: Okay. Yes, Dr. James? 
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1 DR. ROSALIND JAMES: This is Rosalind James. 

2 I want to add to that. At the foot note of this Table, and I

3 believe it was said elsewhere in the White Paper also, the very

4 last sentence says, in addition, protection of the honey bee

5 would contribute to pollinator diversity indirectly by

6 preserving the pollination and propagation of many plant species

7 pollinated by honey bees, which also serve as food sources for

8 other pollinating insects. Although, we have not been asked to

9 edit this document, I would recommend deleting that because of

10 the issue that Nancy brought up that many people - there's

11 considerable body of research investigating how honey bees

12 actually compete with native bees. So honey bees in themselves

13 are not a good measure of bee diversity. 

14 DR. DANIEL SCHLENK: Dr. Fefferman? 

15 DR. NINA FEFFERMAN: Hi, I'm not a discussant

16 on this question, so I hope this is still okay. I wanted to

17 mention that maybe instead of looking just at measurements of

18 biodiversity also to consider possibly as a measurement, the

19 genetic variation seen within a species; because long-term

20 conservation, especially population genetics models tell us that

21 when we look for effective population sizes, those can be quite

22 important. So, if we are looking at end measures of quality in

23 the long term instead of the short term, along the same lines of

24 biodiversity, genetic variation might be useful. 

25 DR. DANIEL SCHLENK: Thank you. Any other

26 comments from panel members? Okay. Dr. James, I am sure that
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1 you are going to get Dr. Fefferman's comments into that? Okay. 

2 Great. All right. Let's go to the Agency. Any questions of

3 clarification? Okay. Great. Moving right along. Okay. Mr.

4 Moriarty, you want to read in 1(c) please? 

5 MR. THOMAS MORIARTY: Thank you. Question 1,

6 subpart (c) - please comment on whether the measurement

7 endpoints at the level of the individual bee, individual adult

8 and larval survival, queen fecundity, brood emergence success

9 and worker longevity, identified in Table 1 are consistent with

10 the assessment endpoint identified in the Table and any

11 additional assessment endpoints discussed in part (a) of this

12 question. Please include a discussion of any additional

13 measurement endpoints that may be necessary to represent those

14 assessment endpoints. 

15 DR. DANIEL SCHLENK: And Dr. James, lead

16 discussant. 

17 DR. ROSALIND JAMES: All right, so we are

18 still on Table 1 and now looking at the fourth column

19 measurement endpoints at the individual level and how this meets

20 the protection goals. The items listed as measurement endpoints

21 are very similar among the three different goals, and therefore

22 I am going to discuss them all together. 

23 Individual worker survival is a very good

24 measure. Larval survival should also be included. It's not

25 listed here. Larval bioassays, I think, are going to be a very,

26 very important contribution to understanding the possible
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1 effects of pesticides on bees. 

2 We suggest here using the word larvae and not

3 brood, so that it's clearly inclusive of more than just honey

4 bees. Brood is a word that is used basically for different

5 larval stages and pupae of honey bees, even though the test will

6 be for honey bees. 

7 We also recommend adding larval development

8 time as one of your measurement endpoints; in particular, the

9 time from egg lay to when a cell is capped. The negative

10 effects of some pesticides such as insect growth and regulators

11 may be able to be best detected using this sublethal measure. 

12 Also, we were not clear what is meant by brood

13 success, which is on the products of the hive, so it's on the

14 second box. We recommend changing this to larval survival and

15 delayed development instead of brood success, which is

16 essentially a repeat of what I just said above for the previous

17 box. 

18 Brood size is a good measure, and there are

19 fairly standard ways for measuring this in a hive. However,

20 some people might think that this means individual larval size. 

21 So we recommend changing the wording to brood nest size and

22 define it as the area of the sealed brood in a colony. 

23 Queen fecundity is a very useful endpoint, but

24 at this time, it's difficult to measure. Perhaps other people

25 on the Panel would want to make some comments on fecundity and

26 the utility of using this and how it might be measured. That's
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1 the end of my comments. 

2 DR. DANIEL SCHLENK: Dr. Berenbaum? 

3 DR. MAY BERENBAUM: One of the parameters that

4 are difficult to quantify is worker bee longevity because it

5 tends to vary over the life cycle of the bee. The assays that

6 exist remove the bee from its social context, so their meaning

7 of the results is difficult to ascertain. So, in terms of

8 reliable endpoints, worker bee longevity may present some

9 experimental challenges. 

10 DR. DANIEL SCHLENK: Okay. Dr. Pettis you're

11 next.

12 DR. JEFF PETTIS: Just a couple of comments. 

13 One is for protection goal three and the measurement endpoints. 

14 Since it is a biodiversity or bee biodiversity, we are

15 recommending that it be bee biodiversity protection goal. Then

16 brood success for solitary pollinators would be a very excellent

17 measure if you were testing solitary bees; the female's brood

18 production would be an excellent measure and endpoint. 

19 Then last, the queen fecundity issue, I see

20 that as a research gap, that we don't have adequate ways to

21 measure queen fecundity at this time. 

22 DR. DANIEL SCHLENK: Thank you. Dr.

23 Pistorius? 

24 MR. JENS PISTORIUS: Adding to the queen

25 fecundity, I think we have a possibility of an indirect

26 measurement by the assessment of brood and colony development. 
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1 That is the only method available. 

2 DR. DANIEL SCHLENK: Okay. And Dr. Ostiguy? 

3 DR. NANCY OSTIGUY: I concur with the

4 comments. 

5 DR. DANIEL SCHLENK: Okay. Any other panel

6 members want to chime in on this one? Yes, Dr. Hunt.

7 DR. GREG HUNT: I am just responding to what

8 May Berenbaum said. Worker longevity does present some

9 problems, but if you could measure it as kind of an average

10 worker longevity if you, for example, mark the bees and then

11 take photographs of the comb, so that could be a possibility. 

12 DR. DANIEL SCHLENK: Dr. Berenbaum? 

13 DR. MAY BERENBAUM: Yeah, just to stay it will

14 present experimental challenges. That value is still going to

15 vary depending on season and timing and all kinds of other

16 factors. I'm not saying not to use it, but it can be hard to

17 standardize. 

18 DR. DANIEL SCHLENK: Yes, Dr. Tarpy? 

19 DR. DAVID TARPY: This is Dave Tarpy. I would

20 probably feel a little more comfortable rather than using kind

21 of a more restrictive term of queen fecundity in this context to

22 really mean something more like queen reproductive potential,

23 which incorporates other things. Fecundity, at least to me - I

24 could be a minority of one here - but fecundity is really kind

25 of egg laying rate. I think there are other factors dealing

26 with queen reproductive success and reproductive potential that
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1 could be important and captured here. 

2 DR. DANIEL SCHLENK: I see a lot of heads

3 nodding, so I assume that's a consensus sort of statement there.

4 Okay. Any other comments on 1 (c)?

5 Okay. Hearing none let me go back to the

6 Agency. Do you have what you need for that particular question?

7 Fabulous. Let me just say thanks to Dr. James and that group

8 for getting together and sort of expediting things. I really

9 appreciate that. Okay. Move on to charge question 2. Again,

10 if you wouldn't mind reading the subletters for 2, that would be

11 great. 

12 MR. KEITH SAPPINGTON: Keith Sappington, EPA. 

13 Charge question 2, subpart (a) - please comment on whether the

14 conceptual models depicted in Figures 4 through 8 are consistent

15 with the protection goals and assessment endpoints identified in

16 Table 1 and as discussed in question 1. 

17 DR. DANIEL SCHLENK: Okay. And lead

18 discussant for this particular question is Dr. Ostiguy. 

19 DR. NANCY OSTIGUY: First comment - the

20 conceptual models are very good. Lots of detail. We have

21 comments that will help, I hope, fine tune things. I am going

22 to start with some general comments, and then I have specific

23 comments on specific figures. 

24 At least according to our reading at this

25 point, none of the models include wax contamination, most

26 specifically for larvae. They are sitting in contact with wax
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1 during the larval stage. Wax is included, sort of after the

2 hive worker, the nurse bee, but it needs to also be looked at as

3 an exposure media for the immatures. 

4 One of the things that came up yesterday was

5 the comment about adding the RQs for oral and contact for the

6 larva. And because of, you know, the specific lifestyle of the

7 larva, that probably would be a good idea to combine that

8 information rather than working just from the oral contact. 

9 One of the other comments, again dealing with

10 the larval stage, most of our comments - a lot of the comments

11 are on larva because we are still, of course, developing the

12 proper protocols for evaluating them -- is to actually look at

13 the total consumption of food during the larval stage rather

14 than a per day consumption. It would then give you a subacute

15 test, but a more realistic test since the quantities of food

16 consumed over time change rather significantly and the types of

17 food change. So, one value might actually be more useful. 

18 This is probably going to come up in a number

19 of locations. Since I'm talking about larva, what will have to

20 be chosen is a particular - well, it probably would be the day

21 that capping occurs, is that you would actually use the weight

22 rather than a larval measurement. So, a lot of the things that

23 we have talked about are done as a quantity of exposure per bee.

24

25 While this has been the way that the

26 apiculture research community has reported exposures in the
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1 past, it's not really appropriate when we are going to be using

2 honey bees as a surrogate because the other non-honey bees are

3 certainly different in sizes. 

4 A more general comment, the feeling is that

5 the assorted dotted lines should be changed to be solid lines. 

6 They should not be removed at this point as less important

7 exposures or the receptors, the receiving end. 

8 One of the reasons for wanting to include more

9 exposure media, more receptors, is that we are using honey bees

10 as a surrogate and there are going to be exposures that are

11 potentially important to non-honey bees that otherwise would not

12 be captured. So for instance, leaf cutter bees - please correct

13 me if I get this wrong - leaf cutter bees used for alfalfa

14 pollination, at this point, we probably bring in as many or

15 nearly as many leaf cutting bees to pollinate alfalfa, some

16 coming from as far away as Canada. 

17 These bees are in contact with soil. So

18 excluding soil when we are doing the evaluation is not a good

19 idea. In addition, since we are going to be using honey bees as

20 a surrogate for, let's say tube nesting, solitary bees, they use

21 mud for their partitions. So, we should really include that

22 soil exposure when we are looking at the honey bees. Generally,

23 the group did agree that, you know, the matrices that you

24 created are appropriate for both honey bees and other assorted

25 native bees.

26 Two specific comments - figure 6 - there
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1 should be a line going from drift abraded seed coating as a

2 source to the exposure media residues in or on pollen, nectar,

3 exudates and honey dew. Then figure 7 needs to include dust as

4 an exposure media. Then, residues - it should read for the

5 exposure media of pollen, nectar and exudates, it should be

6 residues in or on since we are looking at soil as a possible, or

7 dust as a media. Those are the comments that I have at this

8 point. 

9 DR. DANIEL SCHLENK: Thank you. Dr. Hunt, you

10 are our first associated discussant. 

11 DR. GREG HUNT: I concur.

12 DR. DANIEL SCHLENK: Thanks. Dr. Pistorius? 

13 MR. JENS PISTORIUS: I concur, except one

14 comment for figure 6. As you have said, there's one line from

15 drift abraded seed coating to residues in pollen and nectar, we

16 are talking about residues on planted seeds, but what we mean is

17 on off crop lands.

18 DR. DANIEL SCHLENK: Okay.

19 MR. JENS PISTORIUS: Or in crop lands if there

20 is no tillage.

21 DR. DANIEL SCHLENK: Okay. Yes, Dr. Ostiguy? 

22

23 DR. NANCY OSTIGUY: Nancy Ostiguy. I did not

24 understand and didn't hear what you wanted in there.

25 MR. JENS PISTORIUS: Okay. With this line

26 drift of abraded seed coating, as you said, we want the line
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1 drift to residues in pollen, nectar and exudates and honey dew;

2 but the box above is talking about residues on planted seeds. 

3 But what we mean is not drift on the planted seeds nor the

4 secreted seeds that we bring into the soil, but we want to look

5 at residues in pollen and nectar off crop or if there are

6 flowering weeds in the area which is sown. Does that clarify? 

7 DR. DANIEL SCHLENK: Do you have that

8 information? Perhaps just a written component of your comments,

9 if you could send that, that would probably be appropriate as

10 well just to confirm. Okay. Dr. Potter? 

11 DR. THOMAS POTTER: I concur with everything. 

12 I just wanted to add one small detail, and this is in regard to

13 the soil exposure. I believe that one thing we may want to

14 consider is the treated soil as a source of dust via wind

15 erosion and eroded particles from soil via wind can be strongly

16 enriched if pesticides and active ingredients are implied to

17 them. So, that might be an added twist in terms of the exposure

18 issues that we may be addressing in the case of pesticides and

19 soil. 

20 DR. DANIEL SCHLENK: Okay. Thanks. And Dr.

21 Schwab? 

22 DR. PAUL SCHWAB: Yes, I only have one comment

23 and that's on figure 6. With the seed based translocation, the

24 box from the residue on planted seeds, this is -- just that term

25 seed based translocations is a bit confusing to me in itself. 

26 I'm not really sure what that means, and it could be - I don't
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1 know if this means that the seed itself is somehow assimilating

2 the pesticide directly from the dust or the coating I mean, but

3 I think it would help to clarify if we could consider the seed

4 coating as becoming part of the soil as well, at least some

5 fraction of that, and then being subject to uptake trough the

6 roots. Maybe that's what is implied already, but it wasn't

7 clear to me. But otherwise, I concur with all the other

8 statements. 

9 DR. DANIEL SCHLENK: Okay. Other Panel

10 members? Yes, Dr. Berenbaum.

11 DR. MAY BERENBAUM: Did the group discuss

12 whether there is a significant possibility of exposure via

13 contamination of resins used for propolis, among other things,

14 populus as a biofuel crop tree and is a common source for bees

15 to harvest propolis materials? 

16 DR. DANIEL SCHLENK: It would appear not. 

17 Okay. Dr. James and then Dr. Hunt. 

18 DR. ROSALIND JAMES: I've a few different

19 comments - it's a little bit hard to separate out the different

20 questions. I will be addressing some of this in the next

21 question. I am probably the proponent here for the non-Apis

22 bees. These diagrams are a good place where the non-Apis bees

23 don't show up. Dr. Ostiguy already talked a little bit. I just

24 wanted to clarify some of the comments she made. 

25 So, the leaf-cutter bees will be using leaf

26 pieces and the larvae will be growing up in an environment with
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1 leaf pieces. So residues that are on these leaf pieces would

2 have a different experience for alfalfa-leaf cutting bees than

3 for honey bees. Honey bees would be that foraging adults would

4 land on them as opposed to larvae being raised inside them. The

5 majority of solitary bees are actually ground-nesting bees. 

6 So, we heard today with public comments and in

7 some of the public comments, and again it shows up over and over

8 in the diagrams that bees do not come into contact with soil. 

9 Well, the majority of the wild bees grow up in the soil. 

10 And there are lots of models, some of which

11 are presented here about tracking what the soil residues might

12 be and those could be applied, perhaps, to include in-contact

13 toxicity test with larvae. So again, in-contact toxicity tests

14 on larvae are often excluded because honey bee larvae would not

15 be exposed to the soil, but many of the solitary bee larvae

16 would. 

17 Also, I would like to reiterate a comment that

18 was made yesterday in one of the questioning that what about

19 nonsystemic soil applied pesticide. For the same reason the

20 bees would actually be exposed to the soil, so even though it is

21 not a systemic pesticide, it could have some affect on the

22 non-Apis bees. 

23 Throughout the discussions yesterday, I was

24 kind of struck by a very crop-centric view, but we are actually

25 talking about an environmental impact and we have flowering

26 plants in the crops that are not part of the crop. You can have
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1 overspray and so on, and the bees would be visiting some of

2 these. 

3 I am not exactly sure where to put it into

4 these diagrams, but certainly flowering weeds, or in my

5 laboratory - I work for the Agricultural Research Service, and I

6 know some of the other folks here are really trying to encourage

7 farmers to intentionally plant flowering plants to increase the

8 bloom period and the blood resources for bees. How do we

9 include that in a pesticide assessment if there is overspray of

10 pesticide? 

11 Pesticides are a very big problem for us in

12 trying to introduce non-Apis bees into farming crop systems. 

13 These are some of the areas. That's it. 

14 DR. DANIEL SCHLENK: Thanks. Dr. Hunt? 

15 DR. GREG HUNT: I don't know if -- this

16 comment may not be as important, but in talking about what May

17 Berenbaum was saying about propolis, this is collected from

18 typically trees that are not directly exposed to pesticide, but

19 propolis in the hive could absorb pesticides and residues could

20 be there.

21 DR. DANIEL SCHLENK: Great. Dr. Ostiguy, I

22 see you typing this up, so I'm assuming you are putting all this

23 in. Great. Any other Panel members? Dr. Tarpy? 

24 DR. DAVID TARPY: This is Dave Tarpy. I've

25 noticed on these flowcharts, which I agree are very good and

26 they also are very kind of Apis centric and don't quite capture
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1 some of the subtleties of the solitary bees. But with that

2 said, the main receptors in these flowcharts as they ought to be

3 are the foraging bees. In doing so, the arrows that point to

4 them really, it's implying that the vast majority of the

5 ingestion is with the foraging bees. 

6 I know that the presentations really focus on

7 that with nectar and is true, but really they are transporting,

8 but not always ingesting, especially when it comes to pollen. 

9 So, there are some subtleties where hive bees, for example --

10 the nurses are kind of the direct consumers, the ones that

11 ingest pollen, for example, rather than the nurse bees. So, I

12 don't know if that really affects the overall concept of these,

13 but there is a division of labor when it comes to ingestion. It

14 doesn't all go through the foragers per se. 

15 DR. DANIEL SCHLENK: Okay. Thank you. Any

16 other comments from the Panel members? Okay. Let me go to the

17 Agency - any questions or clarification? 

18 MR. KEITH SAPPINGTON: Keith Sappington. I

19 just have clarifying question on the figures. Do you want us to

20 move the wax and propolis, which are found in the figures to a

21 different location? 

22 DR. DANIEL SCHLENK: Who wants to address

23 that? Dr. Ostiguy, do you want to try to address that? 

24 DR. NANCY OSTIGUY: Yes, I will try - this is

25 Nancy Ostiguy. Yes, because it is an exposure media for larvae

26 whereas where it is right now is a receptor, at least the way
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1 that I'm reading it, wax seems to be incidental?

2 MR. KEITH SAPPINGTON: Keith Sappington. Yes,

3 in terms, I think I understand the comment now that along the

4 boxes that are listed as receptors, we have receptors, but we

5 also have essentially exposure media that are relevant to those

6 receptors, and wax and propolis is listed as an exposure media

7 for the brood, for the larvae. But that is not necessarily

8 tracking with the bold - is that the question that that's not

9 tracking with where the exposure media are listed above it? 

10 DR. DANIEL SCHLENK: Dr. James? 

11 DR. ROSALIND JAMES: Yeah, I would like to

12 address that. I agree with what you suggested actually that

13 moving the honey wax and propolis as an exposure media instead

14 of being an attribute change - I mean, it may also be an

15 attribute change, but that's more with human. Here? I think he

16 meant here. Oh, we don't know what you mean. Is it here or

17 here? At receptors or attribute change? You have it in two

18 places.

19 DR. NANCY OSTIGUY: This is Nancy Ostiguy. I

20 think that we were talking about putting wax and propolis as an

21 exposure media separate from and maybe in addition to where you

22 have it right now in the receptors. 

23 MR. KEITH SAPPINGTON: Okay. I can

24 understand. Thank you.

25 DR. DANIEL SCHLENK: Okay. Everybody clear on

26 that? Oh, Dr. James? 
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1 DR. ROSALIND JAMES: I'm the slow one, in the

2 head here, I guess. To me, you don't really have it as a

3 receptor. You have it on top of the arrow, what does that mean?

4

5 MR. KEITH SAPPINGTON: Yes, on top of the

6 arrow means a mechanism by which, in this case it would be the

7 bee brood and the queen would be exposed, so they would be

8 exposed via brood provisions, contact with wax, propolis and

9 royal jelly. 

10 So, it was just to rather than have a blank

11 arrow going to them from the hive bees, nurse, worker, drones,

12 which all participate in processing that material, it was just

13 to illustrate how they would be exposed. But it could easily be

14 put above under exposure media and avoid that confusion. 

15 DR. ROSALIND JAMES: This is Rosalind James. 

16 It could be in both. I mean, because it really is then an

17 exposure media even by your explanation and by putting it on the

18 arrow, it just sort of explains how it is an exposure media. 

19 DR. DANIEL SCHLENK: Dr. Ostiguy? 

20 DR. NANCY OSTIGUY: This is Nancy Ostiguy. 

21 One of the reasons in addition to placing wax and propolis as an

22 exposure media is that those particular materials are recycled

23 by the workers. So, they're going to then repeatedly be taking

24 it up and placing it back down again. 

25 DR. DANIEL SCHLENK: Dr. Hunt? 

26 DR. GREG HUNT: I hate to complicate things,
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1 but in the exposure media could also have an arrow down to the

2 hive bees, all the adults, because they are exposed by the wax. 

3 Their exoskeleton dissolves liquids.

4 DR. DANIEL SCHLENK: Yes, Dr. Ostiguy? And

5 then Dr. Berenbaum? 

6 DR. MAY BERENBAUM: And to complicate things

7 further; the royal jelly arrow goes from hive bees to queens,

8 but in fact larvae eat royal jelly too. So, all three of those,

9 brood provisions, wax propolis, royal jelly, should go to that

10 joint line because they go to both queens and to regular worker

11 grubs. 

12 DR. DANIEL SCHLENK: Dr. Ostiguy? 

13 DR. NANCY OSTIGUY: This is Nancy Ostiguy. I

14 think we've talked a little bit about that yesterday where there

15 was sort of an - because the conceptual model just would get

16 incredibly messy if we had lines all over the place, which might

17 be the argue for having provisions, wax, propolis, royal jelly

18 up in the exposure media maybe to clarify that everything is

19 moving. 

20 DR. DANIEL SCHLENK: Okay. Dr. James? 

21 DR. ROSALIND JAMES: I'm hoping to simplify,

22 not complicate here. So, maybe your bee brood and your queen

23 boxes could be one box called larvae, queen and worker, drone

24 larvae, all of them.

25 DR. DANIEL SCHLENK: Okay. Let me go back to

26 Keith. It sounds like you have a few options here. 
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1 MR. KEITH SAPPINGTON: I think we understand

2 some of the confusion. Should the Panel have time or feel it

3 useful to actually suggest the modifications in the report or

4 have a revised figure in the report that might also clarify

5 things. And we could even provide the electronic versions if

6 necessary because it takes time to.

7 DR. NANCY OSTIGUY: This is Nancy Ostiguy. 

8 That would be wonderful, and then we can make sure that we are

9 clear on what we are trying to tell you.

10 DR. DANIEL SCHLENK: If you would email that

11 to Fred that would be great and then he can distribute that. 

12 Other than that, everything is clear? Clear as honey? Sorry. 

13 I was going to say mud, but - all right. 

14 We are ready to move onto question three. Who

15 is going to be reading that in? Okay. Mr. Moriarty? Again,

16 please read (a) if you wouldn't mind.

17 MR. THOMAS MORIARTY: Question 3, subpart (a)

18 - please comment on the extent to which the assessment of risk

19 to the honey bee may serve to meet the protection goals

20 identified in the White Paper, that is protection of pollination

21 services, protection of honey and hive product production, and

22 protection of pollinator diversity. 

23 DR. DANIEL SCHLENK: Okay. Dr. James, you are

24 the discussant.

25 DR. ROSALIND JAMES: This is Rosalind James. 

26 I want to make a request. I want to address (a) and (b)
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1 together. They are hard for me to separate. Can I do that? 

2 DR. DANIEL SCHLENK: Sure. 

3 DR. ROSALIND JAMES: Do you want to read (b)

4 also? 

5 Dr. DANIEL SCHLENK: Do you want to read (b)

6 in also then if we are going to do that? 

7 MR. THOMAS MORIARTY: 3(b) - until guidelines

8 are developed for testing non-Apis species of bees, please

9 comment on the extent to which the honey bee may or may not

10 serve as a reasonable surrogate for non-Apis bees. Given the

11 differences in lift history, strategies, and potential different

12 sensitivities to pesticide toxicants, please include a

13 discussion of which types of non-Apis bees may be particularly

14 well represented by either the individual level or the colony

15 level endpoints identified in Table 1 of the White Paper, as

16 well as which types of non-Apis bees may not be well represented

17 and therefore may be the focus of potential research areas in

18 the future.

19 DR. DANIEL SCHLENK: Okay. Dr. James? 

20 DR. ROSALIND JAMES: This is Dr. James. To me

21 these two questions kind of go together. Due to the large

22 diversity of non-Apis bees, I understand the EPA cannot test a

23 significant number of them and furthermore, most of them are

24 wild and they are not in production in any way and this makes it

25 difficult to do Tier I types of testing. 

26 So, I think that honey bees probably could
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1 make a good surrogate species to help represent the

2 susceptibility of other bees, but I would implore EPA to

3 recognize that it is a surrogate species that represents other

4 bees. This then affects how you - this part addresses (a), in

5 which how you do the risk assessment. So, when you come up with

6 your flow diagrams and when you come up with your measures of

7 exposure and so on, you have to keep these other bees in mind as

8 well as a honey bee. 

9 The biology of these other bees is diverse and

10 great and it's hard to know about all of the possibilities, but

11 I think there are some basic principles that you could learn. 

12 We discussed some of them already, exposure to leaf pieces of

13 the larvae and nesting in the ground. Another one is the

14 seasons in which they fly are going to be very different than

15 for honey bees. 

16 But there are some things that also have to be

17 kept in mind where honey bees may not be very good surrogates

18 for representing these other bees. So, as a stop gap measure,

19 maybe honey bees are adequate, but there has been discussion

20 within the group of whether really you ought to include some

21 other solitary bee to help keep in mind some of the particulars

22 that occur with solitary bees. 

23 In particular, we discussed that honey bees,

24 because of their colony behavior and the fact that the colony

25 lives for multiple years, they store food and they have large

26 number of workers and they are able to change status of the
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1 workers from being nurse bees to foragers and so on, to

2 compensate for various environmental factors. So, they can ride

3 through essentially bad times or they can withstand a certain

4 amount of mortality among the workers.

5 But with solitary bees or something like most

6 of the social non-Apis bees that have very small colonies or

7 their colonies are annual, they don't have the ability to

8 withstand these kinds of stresses. An example would be with

9 pollen storage.

10 With your solitary bees and with bumble bees

11 too, the bees go out and collect pollen - I will back up. With

12 solitary bees, the female provisions each one of her larvae. So

13 the female is going to go out and she is going to have one whole

14 provision made for that larva at a particular time. There may

15 be a pesticide exposure out of that time. The entire life of

16 that larva is going to be exposed to that pesticide. A later

17 larva may not have any exposure to pesticide if it's a

18 short-lived pesticide out in the field. 

19 Whereas with honey bees, maybe they could

20 avoid the pesticide and live on reserves within the colony if

21 there is an avoidance mechanism - so their exposure would be not

22 as forced as it is with the solitary bees. 

23 We also had a considerable amount of

24 discussion about the pollen and how much pollen the honey bees

25 consume. I think over lunch time, we resolved that. Your

26 numbers are probably not that inaccurate, they are probably a
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1 good estimate. What threw us off was your use of nectar instead

2 of honey. If we take your nectar numbers and divide them by

3 three, then we come up with a more reasonable estimate of honey

4 consumption. 

5 With your solitary bees and your bumble bees,

6 to some extent they don't consume as much honey. They don't

7 consume any honey - bumble bees make some honey. It's not as

8 processed as honey bee honey. Your solitary bees generally

9 don't have honey. They just collect nectar and the primary food

10 source is really going to be pollen. So, probably more pollen

11 based. 

12 When I look at numbers from my laboratory, I

13 have numbers from this summer and I know what size my pollen

14 provisions are. I divide them by 14 days, that's how long the

15 larvae take to live. I come up with pollen consumption rates

16 that are similar to what you're finding for honey bees. These

17 bees are much smaller, but they are eating about the same amount

18 of pollen per day. 

19 So, another bee such as the Osmia lignaria,

20 the blue orchard bee, which has a size more comparable to honey

21 bees, are probably going to eat more pollen than honey bees. 

22 So, there is probably more pollen consumption in the non-Apis

23 bees; and certainly the ratio of pollen to nectar is going to

24 probably be higher than it is with honey bees. 

25 Again - I don't want to reiterate too much,

26 but I'm kind of bent out of shape a little bit about not doing
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1 contact toxicity to solar soil-applied pesticides - I think that

2 should be included. 

3 For the honey bee test, even though honey bees

4 will never be exposed probably to soil pesticides, other bees

5 are likely to be exposed to soil pesticides. So, there should

6 be some contact toxicity done for those. 

7 The other big difference is some of your

8 solitary bees are going to be more specialists than honey bees. 

9 The honey bees are what we call generalist bee. They are

10 probably the extreme of generalist bees. Bumble bees also are

11 fairly general. They will forage on most any kind of flowering

12 plant. Not any kind of flowering plant, but they have a very

13 broad host range. Your solitary bees tend to have a narrower

14 host range. 

15 So with the mason bees, the Osmia lignaria,

16 they tend to forage mainly on fruit trees or flowering trees. 

17 They are not going after only one plant, but there's a much

18 smaller group of plants that they go after. This will be true

19 for many of your native bees. So, they are going to be very

20 specialized. Somehow, this should be taken into consideration

21 in the risk - what do you call -- the risk management aspect I

22 think, if I understand the difference between risk assessment

23 and risk management. 

24 So when you are depending on the pesticide and

25 where it's being applied, this may affect some bees more than

26 others. So, perhaps the honey bee could be the surrogate in
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1 your Tier I bioassay, but in your risk assessment you need to

2 include what specialist bees might be exposed under a particular

3 circumstance. Especially - I mean not all applications are

4 agricultural. So, we spray for mosquitos, we spray for forest

5 pests and so on. 

6 Again, if you were to include one other bee, I

7 thought about this question. Probably the easiest bee to

8 include in addition to honey bee, and I don't think it's just a

9 personal bias on my part, but would be Megachile rotundata,

10 which is the alfalfa-leaf cutting bee. You can buy it by the

11 gallon.

12 Doing bioassays with them really is no more

13 difficult than honey bees and probably easier, but they aren't

14 honey bees, so you can't treat them exactly the same as honey

15 bees. Certainly adult bioassays would be easy because you can

16 buy the bees by the gallon, incubate them and emerge them as

17 adults. 

18 Doing larval tests will be more difficult

19 because like honey bee, you will have to collect larvae out in

20 the field and that can only be done during the time that the

21 bees are pollinating alfalfa. So, that's more difficult, but

22 you could include adult alfalfa-leaf cutting bee or even blue

23 orchard bee. You can buy those, but they are a little more

24 expensive and they are not by the gallon, they are by the bee. 

25 So, they are about 50 cents a bee. 

26 At least - you know one thing, keep that in
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1 mind I guess as you go through the assessment. Something to

2 force you to keep in mind, not the particularly of only honey

3 bees if you want to use honey bees as a surrogate. 

4 DR. DANIEL SCHLENK: Okay. Thanks Dr. James. 

5 Dr. Berenbaum? 

6 DR. MAY BERENBAUM: I concur.

7 DR. DANIEL SCHLENK: And Dr. Pettis? 

8 DR. JEFF PETTIS: I just have to add a bit to

9 the using honey bees as a surrogate. It is somewhere along the

10 lines of Dr. James's comment. Just that, I think two genera,

11 Osmia and Megachile, both would make excellent things for Tier I

12 testing. They can be used; you can do direct toxicity testing

13 on adults and get some idea about the degree to which honey bees

14 can serve because protection goal 3 is about bee biodiversity. 

15 So really to get at that, some testing using at least one or

16 both of those species might be valid. 

17 DR. DANIEL SCHLENK: And Mr. Pistorius.

18 MR. JENS PISTORIUS: And I would like to add

19 those are probably also the easiest species to go to a higher

20 tier tests, you want semi-field test, you can also use them. 

21 DR. DANIEL SCHLENK: Okay. Other Panel

22 members? Dr. Ostiguy? 

23 DR. NANCY OSTIGUY: This is Nancy Ostiguy. I

24 would like to add that we are sort of tossing out non-honey bee

25 species that are useful for testing. Bumble bees are actually

26 quite good for Tier II, because you can actually confine them
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1 and they forage normally. 

2 DR. DANIEL SCHLENK: Mr. Pistorius? 

3 MR. JENS PISTORIUS: Except one exception, the

4 brood development is very difficult to assess because there are

5 multiple layers of brood and you have no way of assessing them. 

6 That is why it is easier with Megachile or Osmia. 

7 DR. DANIEL SCHLENK: Yes, Dr. James? 

8 DR. ROSALIND JAMES: Why would brood

9 assessment be so difficult? I mean you can put them in small

10 colonies that you can open up and look at them.

11 MR. JENS PISTORIUS: Because you cannot

12 estimate the exact number of larvae and you cannot watch the

13 individual development of larvae. When they make all those

14 piles, one across the other, you don't know what is happening. 

15 DR. DANIEL SCHLENK: That was Mr. Pistorius. 

16 Okay. Any other Panel input here? Okay. Let me go to the

17 Agency in terms of questions or clarification. Got a lot of

18 comments here. Okay. So that wraps up charge question 3. 

19 Moving right along. Let's go to charge

20 question 4. If you could read letter (a) there would be great.

21 MS. KRIS GARBER: This is Kris Garber. Charge

22 question 4, subpart (a) - please comment on the strengths and

23 limitations of the proposed approach for assessing contact

24 exposures to honey bees in Tier I exposure assessments, i.e. 2.7

25 micrograms a.i. per bee per 1 pound a.i. per acre, which is

26 based on the honey bee specific maximum concentration reported
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1 by Koch and Weisser, 1997. 

2 DR. DANIEL SCHLENK: Okay. Our lead

3 discussant for that is Dr. Schwab. 

4 DR. PAUL SCHWAB: Okay. Thank you. This is

5 Paul Schwab. This is a very important consideration because

6 foliar sprays can result in some of the very highest levels of

7 residues other than the foliar spray diet, and thus the highest

8 level of exposure to the foraging bees. 

9 Well, there is a lack of data for bee contact

10 and general exposure to key pesticides, so some sort of

11 surrogate is necessary. That was the motivation, I'm assuming,

12 behind this study in the first place. 

13 The approach that's being used has a lot of

14 merit for a lot of reasons, but it also some limitations. 

15 Clearly in the absence of any data at all, and we will get to

16 this in part (b), then a model would be necessary, and we will

17 talk about that in a moment. 

18 Some of the strengths of this approach would

19 be that the surrogate tracer was not being used anywhere else in

20 the region, so there would be - there were a lot of concerns in

21 other studies about bees going outside the region of control and

22 getting into pesticides that weren't part of the study. 

23 That wouldn't be the case with this particular

24 tracer because it's unique. This would not be applied to crops

25 under any circumstances. And so, these compounding factors are

26 eliminated. The concentration reported was the highest value
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1 that was normalized and that should be fairly protective. 

2 Some of the weaknesses would be that it is a

3 bit of an old study - a 15-year-old study. It doesn't mean that

4 it has no value, but it's possible that some updating could be

5 in order.

6 The compound that was used was a sodium

7 fluorescein. It's a fluorescent dye. It's not really one of

8 the pesticides we look at obviously. It may not even have many

9 of the same properties that these pesticides would have, but

10 nevertheless, it probably is a reasonable start for a surrogate

11 and under these circumstances. 

12 Bees were actually foraging during the

13 application and subject to direct spray, so this could result in

14 some high estimates. This was not what was being studied, so

15 any bees that were intercepting some of this spray would be

16 subject to having anomalous concentrations, anomalously high.

17 And then finally, the normalization -- and

18 this is not just in this particular study, but elsewhere as well

19 - but the normalization assumes the linear relationship between

20 the rate of spraying and the rate of accumulation by bees, which

21 is not necessarily valid. I will talk about this later in case

22 there are some questions about this. Anyway, those are my

23 comments. I know that Tom - you also have something? 

24 DR. DANIEL SCHLENK: Thanks. Dr. Potter? 

25 DR. THOMAS POTTER: Okay. I certainly concur

26 with Dr. Schwab. I believe that this study was reasonable and
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1 certainly an elegant piece of work, and gets at some very

2 important questions using a very novel approach. 

3 My primary concern about the study is in the

4 manner of which the dye was applied to the leaves. It certainly

5 does not necessarily - and its certainly unlikely to mimic how

6 pesticides are applied or co-applied with a number of different

7 adjuvants that are blended into the material, including

8 spreaders and stickers and all sorts of other things.

9 So, that makes this type of study, I guess

10 somewhat questionable in terms of acting as a firm surrogate for

11 pesticides and then until such time more work is done, I think

12 there is considerable uncertainty there in terms of whether this

13 accurately represents potential exposures or not. 

14 DR. DANIEL SCHLENK: Does anybody else want to

15 chime in on this particular question? Okay. Dr. James? 

16 DR. ROSALIND JAMES: I don't know, Dave, we

17 talked a little bit about by weight. I mean, and this is

18 consumption, which the next question is exposure, but again as

19 we move away from - as we move towards thinking of honey bees as

20 a surrogate and not just as honey bees, bees come in different

21 sizes and should the consumption rate be by weight or you have

22 different -- drones are larger than workers and queens. Do you

23 have anything you want to add to that? 

24 DR. DAVID TARPY: This is Dave Tarpy, NC

25 State. Yeah, that was a comment and a question that I had for

26 the EPA members yesterday about standardizing it to kind of per
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1 milligram of body weight of an individual rather than on a per

2 bee basis. 

3 To kind of get to your point and to your

4 question, question number 3 about using honey bees as a good

5 surrogate, that would and I think - maybe I'm wrong here, I

6 would like other comments on this -- but it seems to me that

7 that would translate a lot more effectively to other life, to

8 other castes and to other types of bees to do it on a per

9 milligram basis. I was going to raise those points in question

10 number 8 later on. But I think it's applicable here too. 

11 DR. DANIEL SCHLENK: I see a lot of heads

12 nodding on that in terms of normalizing per mass it sounds like,

13 in terms of per bee. Yeah, Dr. Pettis? 

14 DR. JEFF PETTIS: I agree with what Dr. Tarpy

15 said. The one exception in honey bee colony would be the queen

16 in which she, even by her body mass, she's actually processing a

17 great deal more food than any other individual in the colony. 

18 She is kind of this egg laying machine, thus her nutritional

19 requirements are extremely high relative to anybody else. So,

20 body weight wouldn't account for that. 

21 DR. DANIEL SCHLENK: Dr. Berenbaum? 

22 DR. MAY BERENBAUM: Am I correct in assuming

23 that Koch and Weisser's study involved only one race of bee, and

24 again, there is race variation and size and pursuitness and

25 other attributes. So, another argument for weight rather than

26 per bee. 
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1 DR. DANIEL SCHLENK: So again, let me just

2 encourage you to get your written comments to Dr. Schwab at

3 least for this particular question - oh sorry Dr. James, didn't

4 see you over there.

5 DR. ROSALIND JAMES: Sometimes it is hard to

6 know where to put the comments, I guess; but also the

7 consumption rate had been by day. The bee larvae have such a

8 short life span, that it's often easier to measure it across

9 their whole life span and with the honey bee, they are only

10 eating for five days and mostly consumption is done in two days.

11 There are some early. 

12 Some other possibility would not to be looking

13 at the consumption per day, but over the lifecycle, I believe

14 for the larvae. This also would be very applicable to the

15 solitary bees where they have given them mass provision and then

16 that is what they eat is that mass provision. Again as I said,

17 their exposure - whatever day that was collected, that's the

18 exposure level that they are stuck with unless, of course, you

19 have degradation over time, but it's a relatively short period

20 of time that they are feeding on. 

21 So, I did want to suggest not looking at

22 consumption rate per day for larvae, but at total consumption

23 for larvae and then the question again about the pollen versus

24 the nectar, and if there is a difference in pesticide

25 concentration between pollen and nectar, that ratio could be

26 very important. Different life stages have different ratios. 
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1 Your solitary bees are going to be more dependent on pollen than

2 your worker honey bees as adults. Thank you.

3 DR. DANIEL SCHLENK: Sure. Dr. Fefferman? 

4 DR. NINA FEFFERMAN: So, just to quickly

5 follow up on that idea, I'm strongly in favor of the consumption

6 per body weight idea. I also very much like, but I'm not sure

7 how to integrate with that per body weight idea the idea of

8 whole mass over your stage feeding, because your weight changes

9 substantially over the course of being a larva at different

10 days. So, there are good ways to do it, but one should be

11 chosen and it should probably be discussed quite carefully how

12 to do that. 

13 DR. DANIEL SCHLENK: Yes, Dr. Ostiguy? 

14 DR. NANCY OSTIGUY: This is Nancy Ostiguy. 

15 This question actually is looking at contact exposure. So, we

16 may want to look at instead of a milligram per kilogram, a

17 surface area measurement if we're looking at contact. 

18 DR. DANIEL SCHLENK: Okay. Jumping the gun a

19 little bit I guess. The following questions, I'm thinking. 

20 Yes. In terms of diet, yes. Dr. Tarpy? 

21 DR. DAVID TARPY: To answer Dr. Fefferman's

22 point, to reconcile those things, one is larval and the other is

23 adult. So, I think it'd be pretty much impossible, I think

24 logistically to measure the weight of larvae effectively,

25 especially as it changes over time. So, it's not a constant.

26 But for adults, they don't grow, so that
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1 standardizing by the adult exposure by weight would be possible,

2 but then the total consumption over the feeding lifetime of the

3 larvae would be the way I think would do it. Again, I think

4 this will come up probably again in question number 8. 

5 DR. DANIEL SCHLENK: You guys have any other

6 comments from the Panel before I turn it over to the agency? 

7 Okay. Any questions or clarification? Ms. Garber? 

8 MS. KRIS GARBER: Yes, Kris Garber. Dr.

9 Schwab, you mentioned that you had some concerns about the

10 assumption of linear scaling for application rate. I'm curious

11 if you have an alternate suggestion to the linear assumption? 

12 DR. PAUL SCHWAB: No, not really. I can't say

13 that I have something that is superior to that, but that is

14 definitely an inherent assumption that it is all linear and it

15 may or may not be. 

16 DR. DANIEL SCHLENK: Okay. So, we are all

17 good with (a)? Oh, sorry, Mr. Pistorius has another comment

18 here. 

19 MR. JENS PISTORIUS: Sorry, I was a little bit

20 late. I just wanted to add, we calculated on this approach and

21 it is actually in line with the HQ data and also the incident

22 data from exposures, so I think this approach, as it was said

23 before, it quite conservative.

24 DR. DANIEL SCHLENK: Okay. This is for -- Dr.

25 Schwab, did you get his comments? 

26 DR. PAUL SCHWAB: Not thoroughly, can you
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1 write that up for me? 

2 DR. DANIEL SCHLENK: All right. Let's go to

3 (b) if we can, and then we will take a break.

4 MS. KRIS GARBER: Kris Garber. Question 4,

5 part (b) - please comment on the potential utility of the T-REX

6 upper-bound residue value, i.e. 12 microgram a.i. per bee per 1

7 pound a.i. per acre for a broader number of arthropod species to

8 represent contact exposures to honey bees and to other insect

9 pollinators that are directly sprayed with pesticides.

10 DR. DANIEL SCHLENK: Okay. Dr. Schwab? 

11 DR. PAUL SCHWAB: This approach is sort of a

12 modeling approach that's being used when there is an absence of

13 hard data. Obviously, the direct studies of any sort of bees or

14 pollinators would be preferred, but in the absence of data, this

15 would be a method that could be used. One of the strengths of

16 this that the data used in generating T-REX represents actual

17 measurements on arthropods and are expected to actually reflect

18 what you might see in honey bees and non-Apis bees. 

19 Some of the weaknesses would include that,

20 although it was based on 14 studies, it was only done on a few

21 different kinds - actually 2 different categories of pesticides,

22 the carbamates and the organophosphates. Some of the others

23 like the neonicotinoids - gee, I probably couldn't pronounce my

24 own name at the moment - with totally different chemistry

25 perhaps may not be very well reflected in this model.

26 Let's see, doses and residues are based on
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1 normalization. Again, this gets back to the same thing. I

2 don't have a better way to go, but it does assume that

3 linearity. That was the sum of my comments at this point. 

4 DR. DANIEL SCHLENK: Dr. Potter? 

5 DR. THOMAS POTTER: I will say I agree with

6 Dr. Schwab in regards to the points and the applicability of the

7 study. Again to repeat, it would certainly be appropriate to

8 have measurements on the target organism rather than have to

9 extrapolate to others. With that said, I'm not sure that the

10 arthropod data is any more uncertain that the traits for

11 estimate. They are both, I think, highly uncertain. I think it

12 is clearly a data gap that could be filled relatively easy

13 without substantial cost. So, I certainly would want to

14 emphasize that that be done. 

15 I will add perhaps there is another way of

16 thinking about this in terms of estimating exposure, and that is

17 simply to take the assumed spray rate, which is 1 pound per acre

18 or 1 kilogram per hectare, if you do the arithmetic on that,

19 that becomes 11 micrograms per square centimeter. Hopefully my

20 arithmetic is working today, but I believe that is correct. So,

21 that's the application rate for square centimeter. If we look

22 in the literature, and I did some digging on this and I actually

23 found this notation. I was pleased, which provided the surface

24 area of a honey bee. 

25 Its these guys, Steve Roberts and John

26 Harrison, maybe you know them whether they have been working in
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1 the field for a number of years, but this was an article

2 published in the Journal of Experimental Biology in 1999. They

3 pulled some wings off the bees. That sounded kind of gruesome,

4 but anyway, the pulled the wings off and measured the surface

5 area. They came up with 2.4 square centimeters. So, if you

6 take your 11 micrograms per square centimeter and divide it by

7 the 2.4, you get essentially a potential contact rate. That is,

8 in essence, a 45 percent interception rate, if you will, of the

9 chemical. So, that in some ways, I think it provides, you know,

10 perhaps another back check in terms of what might be the

11 reasonableness of this number, this 12 microgram per bee number.

12 Perhaps there are some other folks who are much more familiar

13 with bees in the room than I am can comment about the surface

14 area as an issue, but I think that that at least provides a test

15 of reasonableness to the value. 

16 DR. DANIEL SCHLENK: Yes, we will go with Dr.

17 Fefferman first and then Dr. James. 

18 DR. NINA FEFFERMAN: Sorry, this may be way

19 too detailed, but in terms of surface area of bees, there are

20 some regions of bees that they (inaudible) much more frequently

21 than others, so I don't know if you want to default the entire

22 surface of your bee unless it's actually sort of an absorption

23 through cuticle kind of thing. I don't know if bees do that. 

24 DR. DANIEL SCHLENK: Dr. James? 

25 DR. ROSALIND JAMES: This is Rosalind James. 

26 Maybe that would be like your upper limit of what they could
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1 possibly be exposed to. Unless you are spraying soil, a field

2 is actually three-dimensional, so that pesticide rate is being

3 spread over plant leaves and mostly on the upper and not so much

4 on the lower. And in addition to that, bees are designed to

5 collect things, and they are full of hairs, and they may

6 actually collect more liquid than their surface area. So,

7 contrary to what you would say, I would say the opposite that

8 they have the surface tension and are made to gather things. 

9 So, that would be a very rough estimate I would say. 

10 DR. THOMAS POTTER: I will say that, but

11 again, it's in line with the upper-bound estimate that is being

12 proposed in the White Paper. So, that's why I put it forward as

13 to say there is maybe one other way to think about this and that

14 is indeed looking at surface area and potential for

15 interception. I agree with all the things you said and

16 certainly not being a honey bee physiologist, or bee

17 physiologist at all, I have to default to that. But, you know,

18 perhaps there is some more thought that can go into this

19 generalized area, which might help us get some appreciation for

20 interception rate, essentially is what we are talking about

21 here. 

22 DR. DANIEL SCHLENK: Dr. Ostiguy? 

23 DR. NANCY OSTIGUY: This has come up a little

24 bit before, but it shows up here very specifically. We are

25 looking at exposures to honey bees and to other insect

26 pollinators. As Dr. Berenbaum said yesterday, there are other
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1 pollinators than bees. We need to be clear what we are talking

2 about. So are we talking about honey bees or are we talking

3 about honey bees and other bee pollinators or are we including

4 flies, or who are we including? 

5 DR. DANIEL SCHLENK: Okay. Any other comments

6 from Panel members? It sounds like there's a proposed check

7 that's out there with some caveats obviously. 

8 DR. THOMAS POTTER: I get carried away here. 

9 Tom Potter here. I'm sorry about that. Yes, you know, it's

10 perhaps another check. It's a factor of two greater than the

11 proposed value that's been suggested in the White Paper, which

12 you know - and essentially we're talking about a 100 percent

13 interception here. So, in that sense, I would think it puts the

14 value that we are talking about in this case is probably

15 reasonably conservative. 

16 DR. DANIEL SCHLENK: Any other comments from

17 the Panel? Okay. Let me go to the Agency. Any questions or

18 clarification? 

19 MS. KRIS GARBER: This is Kris Garber. I just

20 want to just summarize what I heard back and make sure that I

21 heard correctly. This is going to pertain to the whole

22 question. So, it sounds like the proposed value of 2.7

23 microgram a.i. per bee normalized to 1 pound a.i. per acre is a

24 reasonable approach and that T-REX wouldn't necessarily provide

25 an alternative or a better alternative, and that this surface

26 area approach could be another way to just kind of like another
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1 line of evidence to support the use of the 2.7? But you're not

2 necessarily proposing to use the 2.7 to calculate - I mean,

3 you're not necessarily proposing to use the surface area

4 approach to calculate risk quotients.

5 DR. THOMAS POTTER: Not at this moment, no. 

6 This is Tom Potter here. Maybe Arthur had initial response

7 here. I just wanted to leap in on the surface here. I simply

8 put that as, you know, as another way of thinking about the

9 problem and helping to define what the maximum exposure. I feel

10 that whether or not the T-REX 2.7 value or the arthropod 12

11 value, which one is better - I don't think we have the available

12 information at this point to say. I would strongly emphasize

13 that studies need to be done, collecting bees in sprayed fields

14 and making residue measurements. I believe that that is the

15 most appropriate path forward here. 

16 DR. DANIEL SCHLENK: Okay. Are we good? All

17 right, let's go ahead and break for 15 minutes. Let's try to be

18 back at 2:30. 

19 (WHEREUPON, a recess was taken)

20 Go ahead and get going? We can? All right,

21 Kris are you going to read 5 in? 

22 MS. KRIS GARBER: Yes. This is Kris Garber

23 again. Charge question 5, part A - although bee larvae

24 typically consume processed foods in the form of royal jelly and

25 brood food throughout much of their development, they also

26 consume honey and pollen during the last two days of the
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1 uncapped period. Please comment on the proposed use of nectar

2 and pollen consumption rate of larvae during the last day of the

3 larval developmental stage. Please include a discussion of the

4 conservatism, strengths and limitations of this approach as well

5 as a discussion of how this value may or may not correspond to

6 data generated from larval toxicity endpoints.

7 DR. DANIEL SCHLENK: Okay. Our lead

8 discussant on that is Dr. Pettis.

9 DR. JEFF PETTIS: Yes, Jeff Pettis. I'll lead

10 with a question that we have for EPA about the data presented

11 where royal jelly concentrations were graded 100 times lower

12 than that in food consumed. I just like - in a few minutes when

13 EPA has a chance to respond - to talk about those studies

14 whether they've been published, whether they're a single study

15 or multiple data points. I would like some clarification

16 because they affect our response. 

17 But in general, we agree that unprocessed

18 nectar and pollen would represent a worst case scenario. If we

19 accept that, then they should be a conservative estimate by

20 using them over brood food or royal jelly. 

21 As far as relevance to the larval toxicity

22 endpoints, if you are doing larval assays, most of those are

23 done with dilute royal jelly and so, the use of nectar and

24 pollen again would be a conservative estimate and may not lead

25 directly to larval endpoints. Most of those larval endpoints

26 are done by spiking the royal jelly or the brood food. So they
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1 provide a valid assay tool for looking at larval toxicity

2 issues. 

3 We see no issue with the use of dilute royal

4 jelly in these larval assays. Some of the larval assay data

5 that I've seen generated shows that when using brood food or

6 dilute royal jelly, the adults produced in these assays lived as

7 long and foraged normally as would workers reared in whole

8 hives. So, those larval assays are valid, and again back to the

9 use of nectar and pollen, I think they've produced a

10 conservative estimate. So, that's just for 5A. 

11 DR. DANIEL SCHLENK: Dr. Ostiguy? 

12 DR. NANCY OSTIGUY: I concur. 

13 DR. DANIEL SCHLENK: Okay. Other panel

14 members? Yes, Dr. Tarpy? 

15 DR. DAVID TARPY: This is Dave Tarpy. Jeff, I

16 know that your lab has experience with these larval bioassays,

17 but there is a lot of other labs that have slightly different

18 approaches and in some cases, quite different approaches. So,

19 how those translate to each other is, I think, something of

20 consideration. So, is there an inherent assumption here of

21 always having a control group in these bioassays to make sure

22 that these things are relativized to different concentrations of

23 the compound. 

24 DR. JEFF PETTIS: Jeff Pettis. I guess - I

25 think there is a need for standardization within these larval

26 assays. I know there has been some ring testing in the EU to
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1 try to standardize and make these larval assays valid. I don't

2 think we are quite there yet with most of the labs in the U.S.

3 DR. DANIEL SCHLENK: Dr. James? 

4 DR. ROSALIND JAMES: But at the same time,

5 larval assays are exceedingly important. I mean, so we have to

6 find a way to do them.

7 DR. DANIEL SCHLENK: Mr. Pistorius? 

8 MR. JENS PISTORIUS: I would like to add to

9 the comment of Jeff, that it is the same in the EU. I think

10 most labs are not currently there yet. I have a question, or

11 maybe a concern or I'm not sure what it is - maybe we will also

12 discuss it later in the Aupinel section of question 9, but now

13 when I read this here, I read that the concentration in royal

14 jelly is in fact 100 lower. I think that somewhere it should be

15 discussed if we do such a larva toxicity test, but maybe this is

16 too early to ask. Are we actually using the lower

17 concentrations, 100 times lower? If you do a risk assessment

18 refinement, you have residue data in nectar and pollen, do you

19 then say we go for concentration 100 times lower? Then again, I

20 apologize if it's not appropriate to ask this at this time.

21 DR. DANIEL SCHLENK: Well, I think that's up

22 to Dr. Pettis if he thinks it appropriate to answer that

23 particular question with that sort of component. Would you

24 rather do that now or wait, I guess? 

25 DR. JEFF PETTIS: I think I would like to

26 address it - Jeff Pettis. I'm hoping that the idea would be not
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1 to use 100 times lower value, but to use more of a worst case

2 scenario in a larval assay. 

3 MR. JENS PISTORIUS: Agreed.

4 DR. DANIEL SCHLENK: okay. Sounds good. Any

5 other comments from the Panel for A? Okay. We go to the

6 Agency. Are you clear with that response? 

7 MS. KRIS GARBER: Yes, I actually do have one

8 clarifying question though. I can also address Dr. Pettis's

9 question about the 100 times factor. So, Dr. James, you have

10 mentioned earlier that we should consider accounting for dose

11 over the entire larval life stage. So, would that entail

12 expanding the consumption rate out to the fourth and fifth days

13 or all five days? 

14 DR. ROSALIND JAMES: If I remember right, your

15 data was actually on the entire food consumed, and then you had

16 to extrapolate out to a daily rate - right? 

17 MS. KRIS GARBER: This is Kris Garber. So to

18 clarify, it was broken out by the first three days and then food

19 consumption over the fourth and fifth days.

20 DR. ROSALIND JAMES: I would say the whole

21 life stage. The royal jelly for the first three days and then

22 the pollen and nectar consumed during the fourth and fifth or

23 out to the seventh, depending on whether you are talking about

24 workers or drones. I might as well say this here now - you sort

25 of ruled out royal jelly because it had lower pesticide

26 concentration in it, but if I remember right, that was based on
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1 one study. Is that right? 

2 MS. KRIS GARBER: So, this gets to Dr.

3 Pettis's question also. It was based on two studies. The first

4 one is actually a published study involving dimethoate and

5 carbofuran. I believe the authors were Davis and Shuel, and

6 it's cited on appendix 1, so you can confirm that. Actually,

7 they observed concentrations that were more than 100 times

8 different. I think it was a factor of 1000? And then the

9 second study - those results are unpublished. Part of the

10 results - the analysis was conducted by EPA, and that involved

11 Imidacloprid. That was actually the basis for the 100X factor. 

12 So, nurse bees were fed concentrations of food that were 100

13 times more than what showed up in the royal jelly that they were

14 producing.

15 DR. DANIEL SCHLENK: Just hold on a second. 

16 So, Dr. Pettis does that answer your question? 

17 DR. JEFF PETTIS: That does answer the

18 question. 

19 DR. DANIEL SCHLENK: Okay. Great. And then

20 Dr. Berenbaum, you have another comment? 

21 DR. MAY BERENBAUM: Just to say that this does

22 come up in question 10, that the royal jelly estimates are based

23 on one, but also there are some issues about the relative

24 nutritional value and contribution, even if discounting the

25 consumption of royal jelly ignores the relative impact on

26 growth, but that may be more relative for question 10 right now.
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1

2 DR. DANIEL SCHLENK: Let's wait for question

3 10, yeah, if we can. Any other 5A questions? Any other 5A

4 comments? Okay. Let's move to the second letter of the

5 alphabet. Somebody asleep out there.

6 MS. KRIS GARBER: This is Kris Garber. Charge

7 question 5, part B - please comment on the strengths and

8 limitation of basing the Tier I screen for adult honey bees on

9 food consumption rates of nectar foraging bees, including a

10 discussion of the conservatism of this approach and how it

11 relates to other types of worker bees and castes. 

12 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 

13 DR. JEFF PETTIS: In general, I think the use

14 of nectar consumption is generally appropriate, but may not be

15 reflective of the age of the bees, which are often tested in

16 Tier I. Tier I testing could use either randomly aged bees or

17 young bees, and especially if they are young bees or newly

18 emerged bees, then their diet normally would contain high

19 amounts of pollen or pollen intake. So the use of just nectar

20 consumption information may not be as appropriate as we would

21 like. 

22 As far as relative to worker bees and other

23 castes, as was brought up earlier by Dr. Tarpy, using weight

24 might be a better estimate accounting for caste differences with

25 the exception of the queen as I have already pointed out. But I

26 do think that in general, worker bees - obviously they are
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1 easier to manipulate, they're available year round, so their use

2 in Tier 1 screening is appropriate. With the exception of the

3 pollen intake for young bees, it may not be reflective of the

4 nature progression of the worker bee development. So, if you

5 don't include some protein intake into the Tier I testing, which

6 I don't think is normally the case, then I think we are missing

7 an important component of their lifespan. That's all I have for

8 that.

9 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

10 DR. NANCY OSTIGUY: I concur. 

11 DR. DANIEL SCHLENK: Okay. Other Panel

12 members? Dr. James? 

13 DR. ROSALIND JAMES: Just to reiterate about

14 using honey bees as a surrogate for other bees, the non-Apis

15 bees, all of the bees are going to probably eat pollen in

16 addition to nectar. You can think of perhaps, every bee as a

17 queen in the solitary bee. So, just one more argument for

18 including pollen in the diet. It really maybe only matters if

19 you have a difference in pesticide exposure between pollen and

20 nectar. 

21 DR. DANIEL SCHLENK: Anyone else. Yes, Dr.

22 Hunt? 

23 DR. GREG HUNT: I would just like to say I

24 agree that including pollen is important because if you don't,

25 it's an artificially high stress on the bee. 

26 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 
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1 DR. JEFF PETTIS: And just one other thought

2 that occurred to me during the discussion was that with some of

3 the systemics, the residue levels in pollen are generally higher

4 than they are nectar. I think that has already been taken into

5 account, but again, pollen consumption is normal for young bees.

6

7 DR. DANIEL SCHLENK: Anyone else? Okay. Ms.

8 Garber is that - any questions or clarification? 

9 MS. KRIS GARBER: I do have one point of

10 clarification. I know that it's often difficult to separate out

11 the exposure and effects components of the process, but I just

12 want to make sure that I understand that the responses are

13 specific to the exposure side so the consumption - just make

14 sure that we're not talking about the toxicity tests or the

15 conduct of the toxicity test. Is that correct? 

16 DR. DANIEL SCHLENK: Dr. Pettis? 

17 DR. JEFF PETTIS: Could you repeat the -

18 because I'm not - go ahead.

19 MS. KRIS GARBER: Okay. Thank you. Kris

20 Garber again. So, the focus of this question is on the

21 consumptions rates that we would use to estimate exposure in the

22 Tier I analysis, and I know there is some overlap with how it

23 may be related to the toxicity side of the risk quotient

24 equation. I just want to make sure that the comments aren't

25 focused on giving us advice on how to conduct the toxicity tests

26 themselves. 
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1 DR. JEFF PETTIS: Your point is well taken. I

2 think there was some overlap in that, so yeah, you're right. If

3 you're trying to look at just exposure, then we have different -

4 some of the comments about pollen consumption and all were maybe

5 not as appropriate as they might have been. 

6 DR. DANIEL SCHLENK: Okay, Dr. James? 

7 DR. ROSALIND JAMES: Along those lines, it

8 mainly matters if there is a difference in past site

9 concentration in the pollen versus the nectar. And there's a

10 question that still seems to be hanging in the air. So, until

11 there is more hard data on that, you do sort of need to keep the

12 pollen and nectar ratio in mind when you are doing the

13 calculations I think.

14 DR. DANIEL SCHLENK: Dr. Ostiguy? 

15 DR. NANCY OSTIGUY: That actually brings up

16 something that I have been thinking about and that is an area

17 where we have a data gap. Half lives of our pesticides are

18 calculated usually soil, water, et cetera, we don't necessarily

19 know what the half life would be in the different matrices

20 within the hive. That to me is a data gap. For us to be able

21 to actually then extrapolate with our models and say yes, that's

22 an appropriate exposure. 

23 DR. DANIEL SCHLENK: Dr. Berenbaum? 

24 DR. MAY BERENBAUM: And in terms of exposure

25 too, one of the issues of using limited amount of data to

26 estimate pesticide concentrations particularly when they are
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1 older studies is that today's chemistries are very different

2 from old chemistries, so we really don't know whether they're

3 comparable. So, that is why it's an issue of whether it would

4 be another data gap in terms of estimating the degree of

5 exposure through foods like royal jelly. We just don't have

6 those data. 

7 DR. DANIEL SCHLENK: Okay. Any other comments

8 for 5B? Are you good with 5B? Yeah? Okay. The Agency is

9 nodding their heads. All right, let's read 5C into the record

10 then. 

11 MS. KRIS GARBER: Kris Garber. Charge

12 question 5C - please comment on the assumption that exposures

13 through consumption of nectar and pollen are conservatively

14 representations of potential exposures through consumption of

15 honey, bee bread, brood food and royal jelly, all of which

16 represent processed foods.

17 DR. DANIEL SCHLENK: Dr. Pettis? 

18 DR. JEFF PETTIS: Our general assumption is

19 that we agree with this that in fact, nectar and pollen would

20 represent a worst case scenario or more so than processed food. 

21 As example, you bring out the royal jelly - it's a processed

22 food - goes through the honey bee and there are processed

23 intraglandular secretions, so even though bee bread and other

24 stored products would undergo some degradation, so yes, we agree

25 that it's the worst case scenario. 

26 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 
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1 DR. NANCY OSTIGUY: This is actually one

2 situation where our extrapolation to native bees might not be as

3 far because they're actually provisioned as larva with pollen

4 and potentially a little bit of nectar. So, they're not getting

5 brood food or royal jelly. 

6 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum? 

7 DR. MAY BERENBAUM: Does anyone actually know

8 the degree to which processing effects pesticide concentrations?

9 I know of only one case in the literature, and it's degradation

10 of phytochemicals in nectar or honey as it cures, but I don't

11 know of any studies that have shown degradation of pesticides. 

12 DR. DANIEL SCHLENK: Sounds like a data gap. 

13 Okay. I guess we want to put that in the response as a data

14 gap. Yeah, Dr. James? 

15 DR. ROSALIND JAMES: So, this is Rosalind

16 James. May, would you say it's a reasonable assumption that it

17 the concentration would not increase with processing? 

18 DR. MAY BERENBAUM: Well, it increases in

19 nectar. It increases when you go from nectar to honey because

20 it's concentration. It goes from, you know 80 percent water to

21 18 percent water, so that is an increase in terms of what

22 happens as it is stored in the hive. I don't think anybody

23 knows. It is not going to increase, presumably, although there

24 are some pesticides like organophosphates that can be

25 bio-activated. Whether or not they are in honey or bee bread, I

26 don't know if anybody would know. I'm not saying it's a good or
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1 bad - it's not an adequate proxy, I just don't know.

2 DR. DANIEL SCHLENK: Dr. Sandy? 

3 DR. MARTHA SANDY: I think another data gap is

4 we don't know what the degradation or metabolic products,

5 whether they might be more toxic than the parent compound to the

6 bees. 

7 DR. DANIEL SCHLENK: Yes, Mr. Pistorius? 

8 MR. JENS PISTORIUS: I don't think that

9 although in the first moments on logic, when you go from nectar

10 to honey that you would have a concentration of the residues,

11 but I think data that we have and that were generated in EU has

12 shown that it's actually vice versa, that you have a lower

13 concentration in honey than nectar. And does data, for

14 instance, also opinion which undermines it? 

15 DR. DANIEL SCHLENK: Dr. Berenbaum? 

16 DR. MAY BERENBAUM: Is that published? 

17 MR. JENS PISTORIUS: Yes, those are published

18 data, for instance, in the Scientific Opinion 2012. There is a

19 list of data that was used.

20 DR. DANIEL SCHLENK: Mr. Pistorius. Dr.

21 Fefferman? 

22 DR. NINA FEFFERMAN: Sorry, just for my own

23 clarification, is that given the assumed half life of the active

24 agents in the pesticide over that duration of time that it takes

25 to reduce to the honey irrespective of honey reduction, or is it

26 the action of reducing the nectar to the honey that you think is
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1 doing that? 

2 MR. JENS PISTORIUS: I'm not quite sure what

3 is the reason for it? There has been data from (inaudible) who

4 have shown that actually when the bees incorporate nectar and

5 then they have it in the honey they still make themselves that

6 actually after a few hours, the concentration is lower. So, the

7 bee seems to absorb a little bit, but that's only one process. 

8 There seems to be another process that over time, and here we

9 only have data - we have measurements of nectar directly when

10 the foragers come back home, and then nectar in the hive and

11 then this nectar to honey again. You can see that the

12 concentrations get lower and lower, but I'm not quite sure what

13 the exact process is behind it. 

14 DR. DANIEL SCHLENK: So, that would support

15 conservation in terms of consumption of nectar and pollen,

16 right? Yes.

17 DR. MAY BERENBAUM: May Berenbaum. Can we

18 include that in our - can we include that reference in our.

19 DR. DANIEL SCHLENK: Definitely. I would

20 think that would support exactly what Dr. Pettis initially said,

21 yeah. Any other comments? Okay. Dr. Pettis? 

22 DR. JEFF PETTIS: I just would like to ask the

23 group if they kind of agree with the things that are processed,

24 the royal jelly or brood food, if there is general consensus

25 that those values would be lower. It sounds like with honey, it

26 could also be lower, but with pollen, there is a lot of level of
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1 uncertainty as far as stored pollen versus bee bread.

2 DR. DANIEL SCHLENK: Okay. Everyone shake

3 their heads. It looks good. Oh, question mark? No? Okay. 

4 Yeah, a lot of uncertainty there, it sounds like. Any other

5 comments for 5C? Okay. Let me go Kristina - do you have any

6 questions or clarification? 

7 MS. KRIS GARBER: No, we're good. 

8 DR. DANIEL SCHLENK: Okay. Great. All right,

9 let's move on to charge question 6. Going to read letter A into

10 the record? 

11 MS. KRIS GARBER: Kris Garber. Charge

12 question 6, part A, foliar spray - please comment on the

13 analysis presented in Section 3.1.1.1, with a focus on the

14 extent to which the T-REX tall grass upper-bound residue may

15 serve as an adequate surrogate to represent upper-bound

16 pesticide concentrations in pollen and nectar of flowers that

17 are directly sprayed with pesticides.

18 DR. DANIEL SCHLENK: Okay. Our lead

19 discussant on that is Dr. Schwab.

20 DR. PAUL SCHWAB: Okay. Paul Schwab. The use

21 of the T-REX model is necessitated due to the lack of data

22 directly available on this subject. So, clearly data would be

23 better, but when we look at the strengths of this, both in terms

24 of the nectar and the pollen from the point of view of the

25 nectar, T-REX is used currently by EPA. This is something that

26 has been done for a while. It was based on actual experimental
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1 data as we spoke on the last question. The model actually uses

2 very few input parameters, which helps in terms of its

3 simplicity of use. It was developed with a large quantity of

4 field data. The upper limit values are the upper-bound on

5 residues form the actual measurements. 

6 The empirical data for nectar concentrations

7 were compiled to evaluate the upper boundaries of T-REX and then

8 specifically there were seven studies that were used. And, tall

9 grass was chosen because it is three times higher than the

10 upper-bound value in the empirical data. So, they were choosing

11 from different species that were available and felt that the

12 tall grass would be the best to sort of match the conservative

13 approach that was being sought, and yet have as few as possible,

14 if any, values of the empirical data exceed the tall grass

15 value.

16 From a pollen point of view, 11 studies for

17 direct spray were used in developing for T-REX. The values that

18 were compared to the empirical value show that the T-REX for

19 tall grass is two times higher than the highest measure, so it

20 is conservative. Twenty three actual data point were used in

21 the development of this set, and the 110 milligrams per kilogram

22 per pound of active ingredients per acre should be protected

23 then. 

24 Some of the weaknesses would include that

25 clearly no actual nectar or pollen data were used, so this is a

26 very strong weakness, if that makes sense. Normalize to 1 pound
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1 per acre assumes a linear change, again sorry. The model does

2 not contain actual values for pollen nectar from flowers that

3 were directly sprayed. The highest empirical values correspond

4 to a mean measurement of pesticides in nectar not a maximum. 

5 Therefore, the comparisons were made to T-REX as not an

6 upper-bound really - this is just a matter of the way it was

7 developed, really doesn't have to do with whether or not it's

8 conservative. And, crop specific factors really would be

9 difficult to account for under these circumstances.

10 DR. DANIEL SCHLENK: Dr. Pettis? 

11 DR. JEFF PETTIS: I just have one additional

12 comment or weakness. The T-REX model may in fact be

13 conservative, but only if active ingredients are tested. They

14 may not be predictive of the actual residues of the formulated

15 product. So, if the formulated product was used, it would

16 change the movement, penetration and longevity of the active

17 ingredient in the field. So, that's a weakness of just using

18 the a.i., the active ingredient. 

19 DR. DANIEL SCHLENK: Dr. Potter? 

20 DR. THOMAS POTTER: You know, I generally

21 concur with both Dr. Schwab and Dr. Pettis. I think tall grass

22 appears to be conservative, but I am not convinced that we have

23 the data to determine whether the value is conservative or not. 

24

25 There is an appearance of conservatism, but

26 the reality is -- and I've made this comment several times
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1 during the meeting in the form of questions - we have apparently

2 a paucity of data, actual residue measurements on nectar and

3 pollen with which to compare to this estimate. 

4 I wanted to make one comment about one of the

5 papers, peer review papers that was used in the data

6 compilation. That was the paper by Choudhary and Sharma. I

7 took a quick read through it, and I think this is one of ones

8 where maybe a little more peer review would have helped. 

9 I had some serious questions after I read

10 through that paper. I still am puzzled as to how they came up

11 with the detection limits that they reported. With regard to

12 study design, the spray was applied when 50 percent of the

13 flowers were open. There is no accounting for flowers opening

14 after the spray was applied. The pollen and nectar were

15 collected from bees that in theory were foraging in their crops,

16 that they had treated, but there is no way of knowing that. 

17 So, we have a data quality problem in terms of

18 study design. With that said, I think our subset of data that

19 we have that it is reliable with which to compare to this T-REX

20 value was even smaller unfortunately. So, I just wanted to make

21 the comment about that and again, beat on this idea that we need

22 a data call-in, need to get that information compiled, and

23 that's going to, I think, give his whole risk assessment process

24 in terms of dietary exposure a whole lot more credibility.

25 One final point about nectar, because it's a

26 liquid, was that it's roughly a 30 percent sugar solution. 
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1 Pesticides do dissolve in it. Therefore, the solubility of

2 pesticide active ingredients in the nectar ought to be

3 considered in terms of assessing potential exposures. I think

4 if we go through that process, we might gain some further

5 insight into how conservative out estimates are. Again, I

6 strongly emphasize we need to call in data, we need more

7 information in order to be able to determine whether T-REX is an

8 appropriate exposure estimate or not. Secondly, we perhaps

9 ought to - in the case of nectar, which appears to be the

10 primary exposure pathway, we need to take into account the

11 physical and chemical properties of the active ingredients in

12 terms of ultimately deriving our exposure estimates.

13 DR. DANIEL SCHLENK: Any other panel members

14 want to weigh in on this one? Dr. Hunt? 

15 DR. GREG HUNT: Yeah, I don't know if this

16 data is relevant to Tom's question, but the managed pollinator,

17 coordinate agricultural project has sentinel apiaries and in six

18 different states, I believe, throughout the country and

19 collecting all kinds of pesticide residue data on bee-collected

20 pollen and wax and bees, and there are some other published

21 studies that are looking at extant samples, so that may help to

22 identify maximum levels that are seen. 

23 DR. DANIEL SCHLENK: Would you provide that

24 information to Dr. Schwab, I guess, for his comments so that he

25 can get that in there? Yes, Mr. Pistorius? 

26 MR. JENS PISTORIUS: Again, from the EFSA
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1 opinion or the EFSA opinion site that there are a few more

2 studies that undermine some of the residue data that are

3 mentioned here in Table 3 of the White Paper, so I completely

4 agree and offer to send those data to compile them.

5 DR. DANIEL SCHLENK: Thanks. Anyone else to

6 comment? Okay. Go to Ms. Garber, any questions or

7 clarification? No? Okay. Read in the letter that follows A. 

8 MS. KRIS GARBER: Kris Garber. Charge

9 question 6, part B, soil application - please comment on the

10 analysis presented in Section 3.1.2, with a focus on the extent

11 to which the Briggs' model may generate estimates of pesticide

12 exposure in plant stems that can represent upper-bound pesticide

13 concentrations in pollen and nectar of flowers.

14 DR. DANIEL SCHLENK: Dr. Schwab.

15 DR. PAUL SCHWAB: Okay. Looking at this from

16 the point of view of the strengths. This has been used in a

17 couple of different approaches. It's used in the PRZM model. 

18 It has also been selected by United Kingdom Environmental Agency

19 as coming closest to estimating empirical data. All the

20 pesticides that were used in this model were nonionic. That's

21 not necessarily a strength, but it does relate to an awful lot

22 of the pesticides in question. 

23 The range in the Log Kow values that were used

24 in this pretty well encompasses the range of Kows of various

25 pesticides that we discussed thus far. The original model was

26 based only on the Log Kow and regression analysis of
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1 experimental data, which made it fairly simple. Ryan et al.

2 modified this and added three soil parameters to make the model

3 more soil specific. They added the soil bulk density, so a

4 water content, fraction of organic content in the soil. This it

5 makes it then possible because the way that the model was

6 developed was out of solution. This makes it possible then to

7 do it out of the soil. It does require having these parameters,

8 however. 

9 It was tested against experimental data that

10 is by Briggs' on five chemicals. Modeling was conservative to

11 maximize the concentrations in solution. Now the predicted data

12 are within an order of magnitude to be experimental, but in the

13 White Paper, a development was used to get the 95th percentile

14 upper-bound estimates for the transportation stream

15 concentration factors. This was discussed yesterday.

16 Some of the weaknesses are that it is indeed a

17 model. It does only examine nonionic compounds, so actually the

18 mechanism of uptake could be completely different for ionizing

19 species. The insecticides that were examined were carbamates

20 and phenylureas, and based on only one kind of plant. This is

21 definitely a drawback. 

22 The soil parameters that were used in the Ryan

23 modification may or may not be known. Most particularly, the

24 soil moisture content will be a huge variable. Bulk density

25 needs to be known, but along with the fraction of the organic

26 carbamate is very much spacially dependent both in terms of the
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1 horizontal and vertical directions. So, this can actually add

2 some uncertainty to the whole process. I mean, it's a necessary

3 step, but it doesn't really tighten up the model very much. 

4 Now the model is used for stem concentrations

5 in the transportation stream and not for pollen or for nectar. 

6 So, it sort of gets back to the same problem we had with T-REX,

7 in that we are examining one thing and extrapolating to another

8 in terms of plant tissues. 

9 In testing the model against five chemicals

10 with experimental data, various assumptions had to be made

11 because this was not really using - well apparently these were

12 unknown. So, there are multiple other assumptions. So, this

13 would get into some uncertainty again. 

14 Not all the values predicted by Briggs' model

15 were greater than the experimental values. This is why they

16 went to the 95th percentile method, but even when this was done

17 to try to gather the upper bound, still not all the data were

18 above the empirical data, so it's not really truly conservative.

19

20 I would like to add, some discussion has been

21 forward both within the White Paper itself as well as other

22 comments that this particular relationship is somewhat dated,

23 somewhat limited. But, I would like to throw in that it was

24 modified, reexamined and other data were put together by

25 Burghardt and Schonherr in 1998 added another 10 or 15 years or

26 so to updating this. These were a much broader array of
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1 chemicals used. Also a different plant species was used. So,

2 this could really add to the dataset and help strengthen the

3 model. That reference will be included in my comments. 

4 And finally, we need to put an upward bound on

5 the water concentration of the pesticide determined by

6 solubility of the compound in question. It kind of gets back to

7 sort of the nectar question that Tom was talking about just a

8 moment ago, in that we can't assume that we can get to just any

9 water soluble or any water concentration, but that we must

10 identify and upper bound and make sure that it's not exceeded

11 because of the physical properties of these compounds. 

12 DR. DANIEL SCHLENK: Thank you. Dr. Pettis? 

13 DR. JEFF PETTIS: I agree with the comments of

14 Dr. Schwab. I just want to elaborate a bit on one more aspect,

15 and that is the use of stem data to extrapolate to nectar and

16 pollen. I would agree in principle that it's a conservative

17 estimate, and there are a few published studies out there, which

18 examine stem, pollen and nectar, and almost all of those show

19 that there are higher concentrations in the stem or leaf. So, I

20 would agree that in general, that's a valid assumption. One

21 caveat is that plant by plant could be quite different. So,

22 trying to extrapolate to all plants from hardwood or few crop

23 plants may not be valid. So it could be a crop-by-crop

24 difference there. 

25 DR. DANIEL SCHLENK: Okay. Thanks. Dr.

26 Potter? 
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1 DR. THOMAS POTTER: I concur. I think that

2 both Dr. Schwab and Dr. Pettis have, I think, hit on the points.

3 I generally agree that Briggs is a good first cut. I want to

4 emphasize that there are substantial uncertainties when that

5 model is applied to charge species and that needs to be taken

6 into account. Indeed, there are many active ingredients,

7 particularly in the herbicide realm these days, that are

8 charged, in fact I think increasingly, we will see more of that.

9 So, that's a major data gap that needs to be filled, not only

10 for this risk assessment, but for the use of this same equation

11 and many other pesticide fate and transport models. It has been

12 propagated through the system, shall we say. So, there is

13 substantial effort needed to update that model, particular for

14 charge species. 

15 DR. DANIEL SCHLENK: Any other comments? 

16 Okay. Ms. Garber, any questions or clarification? 

17 MS. KRIS GARBER: No, we're good thank you.

18 DR. DANIEL SCHLENK: Great. 

19 DR. PAUL SCHWAB: Dr. Schlenk, before we move

20 on, may I request that we reverse the order of discussion on the

21 next two? 

22 DR. DANIEL SCHLENK: Sure.

23 DR. PAUL SCHWAB: I think that it would be a

24 more natural discussion to talk about the 1 milligram per

25 kilogram value before we compare that the Briggs' model. Would

26 that be okay? 
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1 DR. DANIEL SCHLENK: So you'd like to go with

2 D first before C? 

3 DR. PAUL SCHWAB: Yes.

4 DR. DANIEL SCHLENK: Okay. Sure. Go ahead

5 and read D in, great.

6 MS. KRIS GARBER: Kris Garber. Charge

7 question 6, part D, seed treatments - please comment on the

8 analysis presented in Section 3.1.3, including a discussion of

9 the strengths and limitations of the use of 1 milligram a.i. per

10 kilogram value as an upper-bound concentration for pollen and

11 nectar of seed-treated crops. 

12 DR. DANIEL SCHLENK: Thanks. Dr. Schwab? 

13 DR. PAUL SCHWAB: Okay. This particular

14 approach is addressing section 3.2.2 of the white paper, and

15 quoting from that, it says that if pesticide specific residues

16 in pollen and nectar are not available, a screening value of 1

17 milligram a.i. per kilogram plant matrix is the assumed

18 exposure. This is from EPPO 2010. This is based on the upper

19 limit value from empirical data of pesticide concentrations and

20 different plant parts. The strengths of this would be that it

21 is stated that the 1 mg per kg value tends to be higher than the

22 reported concentrations. Most pesticides tend to be about two

23 orders of magnitude below this particular value. 

24 Obviously, a weakness of this is that because

25 there are no data that this is just simply - it's not exactly

26 random, but it is selecting a value rather than depending on
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1 data. We sort of visited this concept before and this is a real

2 obvious problem here. 

3 The value may not be completely protective. 

4 The value really takes into account no real aspects of the real

5 situations that are going on in the field, but that's why

6 there's no data. 

7 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 

8 DR. JEFF PETTIS: I concur. 

9 DR. DANIEL SCHLENK: And Dr. Potter? 

10 DR. THOMAS POTTER: I concur. 

11 DR. DANIEL SCHLENK: Wow. Any other comments?

12 Okay. Ms. Garber? Are you good with that one? 

13 MS. KRIS GARBER: Good. Thank you. 

14 DR. DANIEL SCHLENK: So, then, you want to

15 read in C next, is that correct? Okay. 

16 MS. KRIS GARBER: Charge question 6C, soil

17 application - please discuss the relative strengths and

18 limitations of the 1 milligram a.i. per kilogram value and the

19 soil uptake model (the Briggs' model) proposed in the white

20 paper as Tier I screens, including consideration of the extent

21 to which this method may generate conservative Tier I estimates

22 of dietary exposures to bees. Does the Panel conclude that the

23 one approach may be better suited to specific types of

24 assessment scenarios? If so, please elaborate. Alternatively,

25 if both approaches are equally suited for a Tier I screen,

26 please provide guidance on how to best capture variability and
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1 uncertainty in the exposure estimates using the two approaches.

2 DR. DANIEL SCHLENK: And Dr. Schwab? 

3 DR. PAUL SCHWAB: Okay. There were three

4 parts to this. One is to compare the two and the other is to

5 try to decide if one is better suited to specific scenarios than

6 the other, and finally then to provide guidance on capturing

7 variability and uncertainty. 

8 We just reviewed the Briggs' model as well as

9 the 1 milligram per kilogram level. That won't be repeated

10 here, but EPPO approach is that if all positions - assuming that

11 nothing is known about the soil, the plants or the pesticides -

12 and this approach assumes that any attempt to estimate pesticide

13 residues offers no more reliability or even unreliability than

14 simply assigning a value. The Briggs' model makes an attempt to

15 try to integrate information about the soils and the chemistry

16 of the chemical in the plants, to try to make a projection of

17 the concentrations. 

18 So, in this way, the Briggs' model might be a

19 more suitable way to go. Unfortunately, it also has its

20 uncertainties because of limitations of the number of chemicals

21 that were involved and development of the model as well as the

22 limited number of plant species. So this is clearly a problem. 

23

24 In terms of one approach being better than the

25 other for a given scenario, I would assume then that the Briggs'

26 model would be best used for those plant species that are
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1 closest to the - as modified also by Burghardt and Schonherr -

2 best suited to those plant species that were used in the

3 development of the model. We would have to investigate more,

4 more information to see where this model broke down. So, just

5 assigning a single value would be best. 

6 Then finally in terms of providing guidance on

7 how to capture variability and uncertainty, there is so much

8 uncertainty and it's really hard to pin this down. 

9 DR. DANIEL SCHLENK: Dr. Pettis? 

10 DR. JEFF PETTIS: I concur with the comments

11 of Dr. Schwab. 

12 DR. DANIEL SCHLENK: Dr. Potter? 

13 DR. THOMAS POTTER: I concur. 

14 DR. DANIEL SCHLENK: Okay. Anyone else on the

15 Panel? Ms. Garber, you clear with that one? 

16 MS. KRIS GARBER: Clear, thank you.

17 DR. DANIEL SCHLENK: Great. Okay. So, last -

18 let's get back to letter E, read that into the record please.

19 MS. KRIS GARBER: Kris Garber, charge question

20 6E - please comment on other approaches or data that should be

21 considered for estimating upper-bound estimates of pesticide

22 residues in pollen and nectar as a Tier I screening-level

23 assessment for pesticides applied via foliar spray, soil

24 application or seed treatment. 

25 DR. DANIEL SCHLENK: And Dr. Schwab? 

26 DR. PAUL SCHWAB: Well, just sort of quickly
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1 reviewing the methods that have been described in terms of

2 trying to get a handle on these values, we have direct

3 measurements of pesticides and questions on the very specific

4 plant tissues of interest, whether they are nectar or pollen or

5 whatever. Secondly, there would be direct measurement of the

6 pesticide in question and other plant issues such as stems and

7 leaves and so on.

8 Third, direct measurement of surrogate

9 pesticides in various plant tissues, then we have modeling

10 approaches after foliar spray, such as the T-REX modeling,

11 pesticide uptake from the soil, the Briggs'/Ryan/Burghardt

12 approach to this. And then finally the sixth one that we looked

13 at, assigning a default value with no other method is available.

14

15 This is sort of an exhaustive list. There

16 might be some other methods. There might be some other modeling

17 methods, some purely mathematical kinds of approaches that might

18 be taken, but the way I - at least in my own limited perspective

19 on this would see this as just sort of tweaking what we already

20 have. Other approaches might not be all that truly different. 

21 Some may disagree.

22 DR. DANIEL SCHLENK: Dr. Pettis? 

23 DR. JEFF PETTIS: I agree. I guess I'd just

24 like to emphasize that, yeah, we need direct measurements and

25 real data, and maybe some of this data may be held by the

26 registrants and could be useful in the evaluation process. 
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1 DR. DANIEL SCHLENK: And Dr. Potter? 

2 DR. THOMAS POTTER: I will throw this out

3 again that I think that perhaps an upper-bound on nectar has to

4 do with the solubility of the chemical in the nectar solutions. 

5 I'm not aware of any systematic study looking at solubility of

6 organic chemicals in nectar, but that's something that can be

7 relatively easily rectified, and perhaps would be a useful data

8 gap to fill.

9 DR. DANIEL SCHLENK: Okay. Any other Panel

10 members? Okay. Ms. Garber, are you good with question 6? 

11 MS. KRIS GARBER: Yes we are, thank you. 

12 DR. DANIEL SCHLENK: Okay. So, we are

13 actually doing really well in terms of our schedule. Would I

14 would like to do is go ahead and finish up the exposure side

15 today. Maybe we'll take break after that and start the effects

16 side tomorrow. So, the last exposure question really is 7. I

17 know you guys haven't met together, but apparently, everybody

18 has an answer, so we'll go the traditional route. You guys can

19 go through that. You got it? Okay. So, if we can go ahead and

20 read 7A into the record. It's way down there. Yeah, I think

21 it's the next page perhaps - there it is. 

22 MS. CHRISTINA WENDEL: Christina Wendel, 7A -

23 please comment on the strengths and limitations of basing the

24 Tier I exposure method on contact and diet. Does the Panel

25 agree that for the majority of pesticide applications, the

26 primary exposure routes for bees will be represented by contact
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1 and diet?

2 DR. DANIEL SCHLENK: And Dr. Potter, you are

3 lead on this one.

4 DR. THOMAS POTTER: Okay. And the way this

5 question seems to broken out, I think we are going to have to

6 have a short answer to the beginning and then we'll address the

7 individual exposure pathways down in the subsequent questions. 

8 DR. DANIEL SCHLENK: Okay. 

9 DR. THOMAS POTTER: First and foremost, I

10 would simply say that diet and direct contact are likely

11 dominant exposure pathways. However, other exposure pathways

12 have the potential to be quantitatively significant and should

13 therefore be considered. 

14 DR. DANIEL SCHLENK: Okay. 

15 DR. THOMAS POTTER: And we've covered some of

16 that earlier when we talked about the conceptual model, and we

17 will get to some of the other issues on that further.

18 DR. DANIEL SCHLENK: Okay. Dr. Hunt, I have

19 you as your firs associate. 

20 DR. GREG HUNT: I don't have anything to add. 

21

22 DR. DANIEL SCHLENK: And Dr. Pettis? 

23 DR JEFF PETTIS: Just briefly, I agree that

24 contact and diet may be the primary routes, and that may vary

25 contact over diet or diet over contact by chemical type. Then,

26 I don't think we should discount these other routes of exposure
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1 yet. We will get to those. 

2 DR. DANIEL SCHLENK: Other Panel members? 

3 Okay. I will go to the Agency. Are we clear with the answers

4 for 7A? Okay. So let's go to the letter that precedes C. 

5 MS. CHRISTINA WENDEL: Question 7, subpart

6 (b), dust - if the panel believes that this exposure route

7 should be quantitatively included in the Tier I exposure method,

8 excuse me, Christina Wendel -- for the relevant application

9 types i.e., seed treatment, please discuss the data that may be

10 needed to address the exposure route quantitatively.

11 DR. DANIEL SCHLENK: And Dr. Potter? 

12 DR. THOMAS POTTER: Okay. Well first, I would

13 like to propose a slight modification to the question here,

14 which is we don't restrict it to seed treatments. Is that

15 acceptable? 

16 DR. DANIEL SCHLENK: I think as long as you

17 state that's how you answer the question, then you can put that

18 in there.

19 DR. THOMAS POTTER: Okay. And I say that

20 because I think that there are basically two sources of

21 pesticide laden dust that may be present in the environment of

22 the bees. Certainly, emissions from seed treatments have been

23 widely discussed. I believe Jens Pistorius is going to say

24 something about that, so I won't get into detail on that. I

25 think the data strongly indicates that it should be considered. 

26
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1 Ideally, we should have information that

2 quantifies those admissions. You know, we've heard they exist,

3 but not being a person who has conducted research in this area,

4 I'm hard pressed to know what the emission rates are from

5 planters. 

6 We've also heard that there are a number of

7 mitigation measures that can be applied in modified planters and

8 reducing emissions. Certainly, it's important and relevant that

9 we get quantitative information about how effective those

10 practices may or may not be. So, with regard to the seed

11 coating, I think we again, the theme is we need more data and we

12 are greedy for data here.

13 The second part of the dust issue, which I'd

14 like to raise, which hasn't come up in the discussion and didn't

15 surface in the White Paper is the potential for dust to be

16 generated by wind from treated fields. This is a remarkably

17 little researched area, although I think it's potentially

18 important. The limited data that are available in the published

19 literature, I'm not aware of any unpublished registrant studies,

20 indicate that in certain fine fractions of dust generated from

21 fields, in particular something we define as PM10. Probably

22 most of us in the room are familiar with that. 

23 You can have very strong enrichment of

24 pesticide active ingredients. So dust, i.e. generated from

25 wind-blown materials from fields, can have relatively high

26 pesticide concentrations. Therefore, I think we perhaps need to
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1 step back and contemplate that a little bit and see if this may

2 be an exposure pathway for bees, which would include a direct

3 deposition of the dust on hives that are actually remote from

4 the field as well as body contact from dust that accumulated on

5 various surfaces, including plant surfaces. 

6 I strongly feel that there are two parts of

7 the dust question that need to be examined and relative to

8 potential exposures in this case. 

9 DR. DANIEL SCHLENK: Dr. Hunt: 

10 DR. GREG HUNT: Well, I'll just speak to the

11 seed treatment. Neonicotinoid seed treatments in particular, in

12 the conceptual model, this is modeled - the EPA White Paper is

13 only looking primarily at systemic movement plant parts. But

14 clearly, we're seeing a problem with dust, particularly with

15 corn planting and in regards to the soil. We see at least twice

16 the concentrations of Clothianidin that we find in corn pollen. 

17 We're seeing a lot of reports, many of which apparently aren't

18 getting transmitted to the EPA, and I think there is a lag in

19 that also because, for example, in Indiana, the office of the

20 state chemist has looked at 14 incident reports and they all

21 came up positive for Clothianidin. 

22 In Ohio, there was something like 50 reports,

23 incident reports, which again have not gotten their way to the

24 EPA. In Ontario and Quebec, there are a lot of positive reports

25 - over 130 of them, I understand - just from this year. Last

26 year in the midwest, there were no incident reports. The
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1 difference is that now the beekeepers know about it, so they're

2 making reports. 

3 I think dust through talc and abrasion of seed

4 is a mode of exposure that needs to be addressed. We need more

5 data on residues and wax comb, which we have virtually nothing

6 from yet, but we're working on that, and more pollen samples. 

7 So, I think I'll leave it at that.

8 DR. DANIEL SCHLENK: Okay. And Dr. Pettis? 

9 DR. JEFF PETTIS: Just a few comments to add

10 relative to dust, I agree that it's generated a lot of interest

11 and public opinion of things this year. I don't think in

12 general - there probably are two ways that dust could become an

13 issue. One is the seed treatment and the other is just general

14 dust from sprayed crops. 

15 I do feel though, however, as far as looking

16 at exposure levels that even with corn and seed treatment that

17 the dust exposure is going to be a temporary effect, temporary

18 and short-term effect whereas the translocation throughout the

19 plant and expression in say, corn pollen, would be more of a

20 chronic effect. So, I don't want to sight of that in focusing

21 on the dust. I do think dust is a real issue that should maybe

22 have a solid box rather than a dotted box. In terms of relative

23 to dietary exposure, I think we just need to keep in mind that

24 the expression of systemics in pollen and nectar are probably of

25 more concern. 

26 DR. DANIEL SCHLENK: Okay. Mr. Pistorius? 
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1 MR. JENS PISTORIUS: Thank you. I think I

2 have to have a longer comment on this. Looking at the question,

3 I think there are two questions in which data should be a useful

4 Tier I exposure method. The second part of the question is

5 basically what data may be needed to address the exposure route

6 quantitatively. 

7 From the research that we've done at the

8 Governmental Institution in the last year, I think we can say,

9 I've had the honor to make a presentation to EPA on the 27th of

10 July last year. We presented a tip of the iceberg of all these

11 data. We can say that dust is a relevant route, which has to be

12 integrated into the risk assessment. Nevertheless, I think on

13 Tier I, so that's the answer for the first part of the question,

14 we don't need any more data. Although there are scientific

15 question still open, for instance, that we have the indication

16 that compared in gram a.i. per hectare for instance or the oral

17 and contact toxicity compared with dust to spray. There is a

18 higher problem, a higher toxicity of dust compared to sprays,

19 also when we compare the effects of the sprays and dust, between

20 a spray application and a dust application, looking at the same

21 gram a.i. per hectare with higher effects for dust. 

22 Nevertheless, I think we have the option to

23 say for Tier I screening exposure method, we can say that it is

24 enough. If we look at the acute oral and contact data that we

25 get with feeding bee nectar. So, we get an indication that, you

26 know, we have highly toxic substances for instance. Then the
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1 question is what is out exposure? We have done a lot - and I

2 think I must apologize, we haven't published all the data yet,

3 we are still working on the science. We've done a lot on

4 generating data of seed treatment quality, what does it mean,

5 how can you improve it, then machinery, what does it mean, how

6 does the culmination of the two, considering that you can use

7 deflectors with different efficacy, translate into potential

8 exposure off crop. 

9 With this off-crop exposure, we think is it

10 really important issue that has to be considered. There would

11 be data available that actually allow integration into the risk

12 management process. What I believe is that the management

13 issues that have been considered by EPA are an effective way of

14 mitigating the risks if you have a quality assurance of the seed

15 treatment quality and also the planting practices. 

16 But in general for this answer, I can only

17 give a limited answer for the second part of the question. The

18 data that may be needed to address the exposure route

19 quantitatively, I think that for highly toxic substances, you

20 may have to go for higher tier tests.

21 DR. DANIEL SCHLENK: So, it sounds like a

22 little bit of disagreement there. Some would say yes dust and

23 Mr. Pistorius says no dust, I guess, in terms of the Tier I, is

24 that what I hear? 

25 MR. JENS PISTORIUS: Jens Pistorius. I think

26 what you can do in the oral and contact laboratory tests, you
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1 get an indication of how toxic is such a substance. If you have

2 highly toxic substance, those are a matter of concern. We can

3 clearly state from our findings that no toxic substances are not

4 a matter of concern. Regarding the question - do we need an

5 extra test to assess the toxicity of dust compared to liquid

6 formulations in a laboratory level? No, we can extrapolate from

7 the dust toxicity from the feeding toxicity and normal toxicity.

8 DR. DANIEL SCHLENK: I think this is just

9 looking at exposure though, not necessarily toxicity. So, I

10 guess the question - I could be wrong here, and other Panel

11 members, but in terms of the Tier I exposure side is should dust

12 be added into the other two variables. I guess that's how I see

13 the question being asked. 

14 DR. THOMAS POTTER: I say yes. 

15 DR. DANIEL SCHLENK: That's what I thought. 

16 Yes? 

17 DR. JEFF PETTIS: If I understood you

18 correctly that the difference between the dust application and

19 oral or dermal, you could gain the same information by. 

20 DR. DANIEL SCHLENK: So the consensus is yes,

21 or dust should be included. 

22 DR. THOMAS POTTER: Can I get in here, because

23 now I'm confused. This is Tom Potter here. When we're talking

24 about dust, are we talking about dust that was generated from

25 treated seed? Are we talking about dust as a formulation of the

26 pesticide? Or are we talking about the dust associated with
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1 wind-blown material from treated fields? So, I think we need to

2 be clear in terms of what type of dust we are talking about in

3 terms of exposure here. I'm a bit confused on that.

4 MR. JENS PISTORIUS: Jens Pistorius. Thank

5 you for this because I forgot to mention that it's not only seed

6 treatments, which may generate such problematic dust. Also

7 granules may. What we did is we did investigations with abraded

8 dust. So basically, it's the formulation. 

9 DR. DANIEL SCHLENK: Yeah, Dr. Hunt? 

10 DR. GREG HUNT: I think there is some

11 consensus. I think we're talking about two different things in

12 a way, but I think there is consensus among the group that

13 abraded dust from treated seed should be considered on

14 off-target plants as another method of exposure and that we need

15 to - and I think that's clear. Just looking at the

16 concentrations that we see in bee collected pollen, I think that

17 as my colleague, Christian Krupke mentioned, that this is an

18 area where clearly there could be some mitigation, planters

19 could be modified. We could, as Tom Potter mentioned, estimate

20 how much dust is coming out of the planter. There could be

21 changes in formulation. There could be other regulatory actions

22 or reanalysis of the regulation of this using so much

23 neonicotinoids over such a large acreage, especially since it

24 has such a long half life than high water solubility and such

25 high toxicity. 

26 DR. DANIEL SCHLENK: Okay. Any other
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1 comments? Dr. Pettis? 

2 DR. JEFF PETTIS: Well, I guess I would just

3 like to hear maybe from the group or from EPA about EPA was

4 proposing that mitigation measures might be the primary means of

5 dealing with this dust issue. I don't know how the Panel feels

6 about that, well other than - in other words, are mitigation

7 measures at the time of planting sufficient for this issue? 

8 DR. DANIEL SCHLENK: Okay. 

9 DR. THOMAS POTTER: This is Tom Potter. Can I

10 just comment then because I brought that up because honestly, I

11 have no idea how effective the mitigation measures might be. I

12 went and googled around it, but I didn't come up with anything. 

13 So, perhaps there's something marvelously effective? 

14 DR. DANIEL SCHLENK: Okay. Just, let's do one

15 thing at a time. Is that something the Agency wishes to answer

16 or has an answer for? 

17 DR. STEVEN BRADBURY: It's a challenging

18 combination of entities that come to bear on reducing dust. The

19 equipment manufacturers, the seed planters, and other

20 manufacturers, the pneumatics - I'm not an engineer, I don't

21 pretend to be one, but you all get that part. Then there's the

22 pesticide itself and the product in the sticking agent that the

23 registrant, but also the seed company are in play there. Then

24 there's talc or graphite or other components that are mixed in

25 with the seeds so they don't in the planter. The seed treatment

26 is a little sticky because it wants to hold the fungicide and
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1 insecticide on. 

2 So without getting too much into statutory

3 stuff, the FIFRA label only goes so far in terms of what we can

4 or can't do in terms of asking for data. Then there are things

5 we do that aren't on the label, but part of the stakeholders

6 getting together and trying to work through some solutions. 

7 There was a public meeting mid July, end of July at Iowa State

8 and the State of Iowa convened in sort of an open meeting to

9 sort of brain storm on this very topic. Some companies were

10 describing with how they're coming up with some alternatives to

11 talc, and they're working with the seed planting companies and

12 they're working with registrants and they're working with

13 formulators. In fact, there is equipment that can be used to

14 measure how much dust is coming off the benchtop in the

15 laboratory, sort of reasonable surrogates to get a sense of

16 different combinations of material can change the amount of

17 abrasion that occurs, and you can measure the amount of the

18 pesticide in that dust.

19 So, as far as data, it appears that there are

20 people working on the kinds of experiments where you could start

21 to get some insight, which then could give us some ways of

22 between what you could potentially do. I'm not trying to

23 project too far into the future, but theoretically some things

24 you might be able to do on a FIFRA label versus things that play

25 out in best management practices, things USDA and others could

26 be doing in terms of improving DMPs, which may be related to
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1 dust management in general. There is probably going to be a

2 combination of data we can get and some experiments to get

3 insights into how products are used. 

4 DR. DANIEL SCHLENK: Okay. Does that answer

5 your question Tom? 

6 DR. THOMAS POTTER: Yes, I think very well. 

7 Obviously it is in our complex set of characters involved. If I

8 could just offer one more comment on this, I think that by

9 including some estimate of exposure associated with dust

10 unmitigated, it may in some sense motivate, force, compel other

11 parties to you know, increase their effort in this mitigation

12 area. 

13 DR. STEVEN BRADBURY: I would like to say one

14 thing that information that was shared at this meeting at Iowa

15 State wasn't the worst bias to the credit of the seed planting,

16 the John Deere and the other companies and folks working on

17 formulations and some registrants. They were working on it on

18 their own. So, I think people are working hard and trying to

19 figure out the engineering and the chemistry and the efficacy of

20 the material for plant protection. And there were beekeepers at

21 this meeting. It was quite a good meeting actually, people

22 trying to come up with solutions. 

23 DR. DANIEL SCHLENK: That was Dr. Bradbury. I

24 think we had Dr. James first? You put your hand back down.

25 DR. ROSALIND JAMES: I just wanted to

26 reiterate, I guess, about the dust from pesticide applications
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1 that are not soil treatments also, and make sure that doesn't

2 get lost in this discussion.

3 DR. DANIEL SCHLENK: And Dr. Berenbaum? 

4 DR. MAY BERENBAUM: I just was not clear on

5 whether EPA has regulatory enforcement authority over dust? I

6 mean, is that within bailiwick or.

7 DR. STEVEN BRADBURY: Not under FIFRA. If

8 there is a formulation that is a dust, then that is covered. If

9 it gets a little less - if it's dust blowing off of soil from

10 soil on a field that has been in corn for two to three to four

11 years, and there is probably something in there along with the

12 nitrogen and phosphorus. That blowing around - FIFRA is not

13 capturing that. It could be aspects of the Clean Air Act that

14 are capturing that. You have probably heard of the PM10 rule

15 and there is a PM10 rule now in the agricultural setting that is

16 working through. So, without projecting too far, there is

17 probably some air mottling that Office of Air did try to

18 understand PM10 movement. There may be a way to tap into some

19 of that at the bounding estimates, but you start to get the

20 shade of gray in terms of where one statute drops off and

21 another one starts.

22 DR. DANIEL SCHLENK: That was Steve Bradbury. 

23 Dr. Hunt? 

24 DR. GREG HUNT: I guess I'm a little out of my

25 element here because I'm not a toxicologist. I'm just a bee

26 researcher, but I guess we're just looking at the risk
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1 assessment side. We're not looking at the benefit side. To me,

2 that is an unknown because we don't have access to these yield

3 studies. I don't know if they've been done just with

4 neonicotinoids or with the entire seed treatment mixture, but it

5 seems like this already registered product is being used on a

6 wide scale. It's kinda of an experiment ongoing. I've talked

7 to the chemical companies. They are working on litigation and

8 they've shown me data, how different seed treatments come off

9 the seeds more than others. Corn is the worst in that regard. 

10 I guess I'm wondering what is the EPA thinking in the big

11 picture with neonicotinoid seed treatments. I don't know about

12 regulatory laws very much. Is there any consideration.

13 DR. DANIEL SCHLENK: Dr. Hunt, that's getting

14 a little bit into policy and sort of away from the question that

15 we're being asked at this particular point. Let me try to bring

16 us back to that question, which is basically should dust be

17 included in the Tier I assessment and what other ways do we

18 quantify that. From what I understand is that we are in

19 consensus that dust should be used. Apparently, there have been

20 several recommendations of different methods to do that. Is

21 there anything else that we need to add on that? Yes, Mr.

22 Pistorius? 

23 MR. JENS PISTORIUS: Jens Pistorius. I think

24 dust is not only an issue for maze, maybe also for (inaudible),

25 but there is large number of other crops where dust is not an

26 issue at all because of the seed treatment quality, which is
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1 very different for different seeds.

2 DR. DANIEL SCHLENK: If you can, again, give

3 those comments to Dr. Potter to simulate that information, that

4 would be great. Dr. Hunt? 

5 DR. GREG HUNT: I'm sorry, but the question

6 actually was should dust be considered as a route of exposure,

7 and I think we addressed that. 

8 DR. DANIEL SCHLENK: Yes, that's what I said,

9 yep. Okay. Any other comments on letter B? Okay. Let me go

10 to Ms. Wendel. Are you clear with Panel's answer? Question or

11 clarification? 

12 MR. KEITH SAPPINGTON: Clarifying question -

13 Keith Sappington. If we would be interested in the methods of

14 how you would incorporate a dust estimate of exposure a priority

15 in the Tier I assessment.

16 DR. DANIEL SCHLENK: Okay. I think we are

17 clear on that. Okay. Let's move onto C, and read that into the

18 record please.

19 MS. CHRISTINA WENDEL: Christina Wendel,

20 question number 7, subpart (c), drinking water -- please comment

21 on the analysis, discussions provided in Appendix 2 of the White

22 Paper and the conclusion that pesticide exposure to bees through

23 drinking contaminated water is not expected to be a major route

24 of exposure when compared to contact following foliar spray

25 applications and diet. If the Panel believes that this exposure

26 route should be quantitatively included in the Tier I exposure
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1 method, for the relevant application types, i.e. foliar spray,

2 soil treatment, seed treatment, or trunk spray, please discuss

3 why and what data may be needed to address the exposure route

4 quantitatively.

5 DR. DANIEL SCHLENK: And Dr. Potter.

6 DR. THOMAS POTTER: Okay. Well, I think it

7 was said earlier that we felt like it was important for this

8 route of exposure to be included. I believe that Tier I, we

9 haven't had a discussion among the group here. So, we may

10 disagree, but I don't think we will. So, the short answer is

11 yes, it should be included. 

12 Okay. Now the second part of the answer,

13 we're going to need to look at two different issues that

14 ultimately translate to exposure. One is potential

15 concentrations of pesticides in water that bees may be exposed

16 to. That was addressed to some degree in the appendix, and

17 we'll get to that, but I just wanted to mention the second part,

18 and that is the question of just how much water do bees and/or

19 other pollinating organisms consume on a daily basis. Clearly,

20 we need to multiply the two amount times concentration to come

21 up with exposure. So, we will break out the answer into those

22 two parts in terms of the comment. 

23 First with regard to potential concentrations

24 of active ingredients in water sources that bees may use, I

25 believe and I think there will be consensus on that, that the

26 concentration levels that we generally find in surface waters in
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1 aquacultural and in urban areas across the United States are

2 generally very low. Typical datasets that are available were in

3 the parts per billion or subparts per billion level for most

4 active ingredients. So, these are exceedingly low relative to

5 potential exposures given estimated volumes of water consumed

6 during the day. I would say, generally data that is out there

7 supports the conclusion that water should not be concluded. So,

8 there, I've backed up and gone in the opposite direction here,

9 but we'll get there. 

10 The second part of this is that the Agency in

11 their appendix went through and did a couple of assessment of

12 potential exposures. One of them, they used the model GENEEC,

13 which is the standard model to use, screening model to be used

14 to estimate pesticide exposures in drinking water and surface

15 water. It's a first tier model. I believe that model is used

16 correctly. Basically, it shows that even in a worst case

17 scenario, if you are looking at a farm pond, which has a lot of

18 water in it, obviously - I mean, I'm not saying that

19 facetiously. So, there is a potential for substantial dilution.

20 The concentrations again are relatively low. So, that's not

21 likely, again, to be a significant exposure pathway. 

22 Finally, the third area that we may want to

23 consider in this, is the idea of puddles that are left in the

24 field, either from irrigation water or rain. Those can be

25 directly sprayed as noted in the appendix. In their

26 deliberations, Agency staff went through and did some
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1 calculations using a simple equilibrium model that I think was

2 extracted from a rice paddy assessment that was done regarding

3 pesticides the use in rice paddies. I went in and I looked at

4 that. I think that there was an error in the calculations. I

5 will provide this in written comment. That error resulted in

6 the estimated concentrations in puddles being essentially 1000

7 times lower than what they would likely be using the same set of

8 assumptions. So, anyway, I think that ends up indicating that

9 puddles that are in fields that are sprayed may have relatively

10 high concentrations of pesticides. We're talking about in the

11 parts per million level. If we use those values and then go

12 into looking at the volume of water that bees may consume on a

13 daily basis. 

14 First, if we use the low value, which was

15 recommended, I think, from the paper wasp study -- is that

16 correct? Forty-five microliters a day, if we multiply that

17 times these estimated high-end estimated concentrations in

18 pesticide contaminated puddles, the drinking water exposure

19 could be somewhere in the order of 10 percent to 20 percent of

20 the dietary exposure. So, I think that's a potentially

21 significant exposure pathway and should be included. 

22 Further, if we go to the second part of the

23 exposure equation, which is that the volume of water consumed, I

24 certainly would like to hear from some of the other panel

25 members who are more knowledgeable about bees than I am. It

26 struck me that there was a substantial uncertainty with regard
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1 to the volumes that may be consumed on a daily basis. The

2 assessments that were made indicated they varied from somewhere

3 in the order of 45 microliters a day to 6 milliliters a day,

4 which is a factor of about 100. 

5 So, if we do some bounding estimates here

6 using those high-end concentrations that we could estimate by

7 doing our equilibrium model for pesticide-contaminated puddles. 

8 Drinking water exposure could indeed exceed dietary exposure. 

9 So therefore, I strongly feel that it needs to be captured in a

10 Tier I assessment, and perhaps some effort could also be made in

11 terms of further refining potential environmental

12 concentrations. I made this comment yesterday in the form of a

13 question. But in ECOFRAM, there is a module that actually

14 looked at runoff in fields and accumulation in puddles using

15 some subcomponents of PRZM, relatively simple model to use and

16 run. Perhaps that should be an alternate approach in terms of

17 estimating pesticide contamination in field puddles that might

18 provide some insight into this exposure issue.

19 DR. DANIEL SCHLENK: Thank you, Dr. Potter. 

20 Dr. Hunt? 

21 DR. GREG HUNT: I'm not sure, but I believe

22 that bees get most of the dietary water through nectar. I would

23 need to look in the literature to confirm this. They will

24 collect water in the fields or in puddles primarily to cool the

25 hive, the deposit in the comb, to cool the hive and also to

26 dilute the brood food, but they will also use nectar for that. 
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1 That's my understanding. 

2 DR. DANIEL SCHLENK: So, I guess you would say

3 the water should not be included - is that.

4 DR. GREG HUNT: I'm saying I don't know. 

5 DR. DANIEL SCHLENK: Okay. 

6 DR. THOMAS POTTER: Can I ask a followup on

7 that? 

8 DR. DANIEL SCHLENK: Sure, yeah.

9 DR. THOMAS POTTER: This is Tom Potter here. 

10 I think what we're talking about is around the lunch break, and

11 I was asking how do the bees get the water back to the hive? 

12 What my understanding was that they were essentially ingesting

13 it and then regurgitating it. 

14 DR. GREG HUNT: Yes, that's right. They

15 ingest it into the honey stomach, which is like a waterproof bag

16 more or less, but they have the pyloric valve from the honey

17 stomach to the midgut and some water could pass through that. 

18 And I suppose pesticides could be absorbed through the walls of

19 the stomach. 

20 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 

21 DR. JEFF PETTIS: I'll just a bit to what Dr.

22 Hunt said about water used by bees. Primarily for two reasons,

23 as you said. One is to dilute honey to then use as food and

24 also to dilut brood food somewhat. The big issue - so the most

25 volume is in cooling the hive. There again, certain bees go

26 out, they collect the water and bring it back in. They can
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1 actually sit in the hive and hold his water in their body for

2 extended periods of time, act like little water tanks held

3 within. Then, they move it around the hive and evaporate it so

4 it can be put into cells and evaporate in the cells. So, at

5 certain times, and in climatic conditions where you have hot

6 weather, the use of water can be extensive. 

7 I would probably agree with you Greg, that

8 that in general under non-hot conditions that there is a lot of

9 - the water requirements are probably met by consuming honey

10 and/or nectar. But when you have hot conditions, certainly the

11 increase for cooling needs is great. So, I think from my

12 perspective, I would think that it can't be discounted as a

13 source of exposure. How will you incorporate it into the Tier I

14 - I'm uncertain at this time.

15 DR. DANIEL SCHLENK: Mr. Pistorius and then

16 Dr. Fefferman? 

17 MR. JENS PISTORIUS: I have a proposal how to

18 integrate it in Tier I risk assessment, and it will be published

19 in about one or two weeks along with the run of data on

20 guttation issues published in the proceedings of the ICPBR. 

21 Basically, what we've done in Germany again, is a lot of

22 different crops. We looked at the residue that get into the

23 guttation droplets and then also, we did semi-field and field

24 trials with worst case and realistic exposure. What you can say

25 - okay in semi-field, you get high mortality if you don't give

26 any other water to the bees, but if you give another water
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1 source, it is quite lower. 

2 Now to the field test and monitoring that we

3 have done, we have had, not only by us but agricultural estates

4 and institutes - about 12 different monitoring with a lot of bee

5 hives and worst case exposure directly at maze fields. 

6 In one of the trials, we had on two days

7 increased mortality. So, as a general answer to the question

8 that is asked here - what was it? Drinking guttation water is,

9 in my opinion, not expected to be a major route of exposure when

10 compared to contact and diet is what I agree upon. I'm not

11 quite sure what it is like here for all the desert situations. 

12 That is maybe different. I can only say from my findings. What

13 we can do is have a little bit of distance, so it is a

14 mitigation measure between highest and the crop, whoever has to

15 keep this distance. 

16 DR. DANIEL SCHLENK: Dr. Fefferman? 

17 DR. NINA FEFFERMAN: So, while I don't know to

18 speak to the drinking water per se, I agree that most of the

19 water collected is not being used for drinking. I think that

20 same intent - although maybe this comes under other exposure

21 routes and I don't know, its still water. By virtue of the fact

22 that that water is being collected for cooling, I think plays

23 directly into what we were talking about with the answer to the

24 early question about wax exposure. 

25 As it's circulated around the wax and

26 evaporated, I don't know how much pesticide evaporates with that
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1 water and how much is left residually on the wax. I think that

2 actually might be, especially at the hottest points in the

3 summer, a substantial introduction of pesticide into the wax

4 comb itself and that may be a temporal aspect of all of this

5 that might be important to look into. 

6 DR. DANIEL SCHLENK: It sounds like a seasonal

7 component for sure. Any other comments? Yep, Dr. Tarpy? 

8 DR. DAVID TARPY: I just wanted to reiterate

9 that point too, is that if using mostly for cooling, the three

10 studies that are cited in Appendix 2, at least one of them is

11 really done in upstate New York, right? Where it doesn't get

12 above 70 degrees in July, right? So, in other places, Central

13 Valley of California, with 120 degrees and the bees keeping the

14 brood nests at 95 degrees, it takes a substantial amount of

15 water for that cooling to occur. So, I would say that those

16 estimates in worst case scenarios could be incredibly low, but

17 there seems to be a data gap there obviously and very high water

18 needs, not drinking water, but high-cooling water needs. 

19 DR. DANIEL SCHLENK: Dr. Berenbaum? 

20 DR. MAY BERENBAUM: Shame on me for

21 introducing anecdotal unscientific observations here. I'm

22 asking my bee keeping colony - I'm not a bee biologist, but I'm

23 the one who gets the calls from people who want to save the bees

24 and help the bees. I can't tell you how many calls I've gotten

25 from people who have had bees that drown in bird water baths -

26 that's the word. So, my impression is that a tiny bit
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1 unscientific, I can't site literature here, but bees do visit

2 water and get immersed in water. So, that's totally anecdotal,

3 but that may be something to consider, particularly because

4 there can be puddles that vary in their pesticide content. 

5 That's not unexpected. I don't know how that fits into

6 calculations of exposure.

7 DR. DANIEL SCHLENK: Yes, Dr. Pettis? 

8 DR. JEFF PETTIS: We would agree with you that

9 bees do visit water and they need water. We were asked to

10 comment on the exposure method relevant to the application

11 types, foliar spray, soil treatments, seed treatments, trunk

12 spray. There might be an important component here for the type

13 of chemical involved, whether it is water soluble or insoluble

14 in water. I would think that maybe the agency could take into

15 account - if it's insoluble in water, then only foliar

16 applications may be an issue were it collects in puddles or

17 temporary things, whereas water soluble compounds, soil

18 treatments, seed treatments, et cetera, could move through the

19 soil and accumulate in water. So, there could be, based on the

20 chemical type, we may consider that. Some of the studies

21 looking at residues in standing water or temporary water,

22 they've looked at Imidacloprid, which is water soluble. 

23 DR. DANIEL SCHLENK: Yes, Mr. Pistorius? 

24 MR. JENS PISTORIUS: I completely agree. It's

25 no problem for a nonsystemic and only for highly toxic - it may

26 only be a potential problem for highly toxic substances. 
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1 DR. DANIEL SCHLENK: Okay. Anyone else? 

2 Sounds like you got a range of answers there. Sounds like

3 definitely maybe on that one. So, we will leave that to Dr.

4 Potter to put all of those answers together in some coherent

5 fashion there. You've heard several answers here. Do you have

6 any questions or clarification? 

7 MR. KEITH SAPPINGTON: Keith Sappington. 

8 Yeah, just methods for quantifying it, but yes, drinking maybe. 

9

10 DR. DANIEL SCHLENK: Okay. Is that clear to

11 you? 

12 DR. THOMAS POTTER: Tom Potter here. I just

13 want to say, you know, and thanks for all the discussion here, I

14 think this has been very informative, certainly to me, but it's

15 not just drinking water. Obviously, it is water. And so, we

16 ought to refine our terminology here to say water used.

17 DR. DANIEL SCHLENK: Sure. Good point. Okay.

18 Let's move onto - I think this might be our last one of the day

19 here, perhaps, letter D.

20 MS. CHRISTINA WENDEL: Christina Wendel. 

21 Question 7, subpart (d), other routes - please identify and

22 discuss additional exposure routes, if there are any besides

23 contact with dust and consumption of drinking water, that would

24 contribute significantly to pesticide exposure of bees and

25 explain how and why such exposures could be considered

26 quantitatively in establishing the Tier I exposure value. 
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1 DR. DANIEL SCHLENK: Dr. Potter? 

2 DR. THOMAS POTTER: Well, I think I'm going to

3 send this over the Rosalind - just teasing, but hopefully you'll

4 comment on this. I feel that the route of exposure that would

5 need to be considered here would be direct soil contact. I

6 think Rosalind described earlier that there are many bees who

7 come in directly in contact with soil using the material for

8 nesting, nest building and et cetera. So certainly, there is

9 potential for contact. Now the question of how we would

10 evaluate that quantitatively, I'm hoping somebody here is going

11 to help me with that. 

12 DR. DANIEL SCHLENK: Dr. Hunt, any help for

13 Dr. Potter there? 

14 DR. GREG HUNT: Not really. One other obvious

15 route of exposure that we haven't talked a lot about is the wax

16 comb itself with the brood swimming in royal jelly, which is

17 liquifilic and the wax comb, which is liquifilic, but that would

18 come under food, I mean ingestion or absorption through the

19 cuticle. But, I don't think that needs to be, in my opinion,

20 considered under Tier I exposure, but it's just another route of

21 exposure. 

22 DR. DANIEL SCHLENK: So, not a significant

23 route of exposure, not to be included in that first tier

24 approach. Okay. And Dr. Pettis? 

25 DR. JEFF PETTIS: Really, not much to add that

26 hasn't been already said, other than the fact that bees are
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1 great samplers of the environment, and they have these hairs

2 that will pick up all kinds of dust and particles and whatever,

3 but it's not really - it's more of a general issue with bees,

4 not so much a regulatory issue.

5 DR. DANIEL SCHLENK: Well, what do you say,

6 Dr. James - are you going to help Dr. Potter out there? 

7 DR. ROSALIND JAMES: I'm probably just

8 reiterated what I have said earlier today in that if you are

9 going to use honey bees as a surrogate for other bees, you need

10 to think about where other bees go besides just where honey bees

11 go, and you should add contact toxicity for soil pesticides. 

12 So, you already have some estimates and some methods for

13 estimating what concentration of pesticides should be in the

14 soil, and I recommend adding larval contact toxicity test for

15 pesticides that are in the soil, and the reasoning being you

16 have your soil nesting bees and you also have mason bees that

17 are using mud for agricultural fields. 

18 DR. DANIEL SCHLENK: Okay. Anyone else? Oh

19 sorry, Dr. Hunt? 

20 DR. GREG HUNT: Just a question for Rosalind. 

21 So, that would be kind of like the brood food. I mean, if you

22 spike the brood food, then there are contacts with that, right? 

23 Would that cover it?

24 DR. ROSALIND JAMES: But this has to do with

25 exposures and your estimating exposure levels from soil, I guess

26 is the difference. What is a contact exposure level from soil? 
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1

2 DR. DANIEL SCHLENK: Okay. Any other comments

3 for question 7? Going once, going twice. Okay. Let me go to

4 the Agency. Are you clear or have any questions or

5 clarification in terms of the Panel's response? We're good to

6 go. Okay. Well, let's call it here for a break until tomorrow.

7 We've made great headway. Congratulations to the Panel and

8 appreciated for your hard work and meeting together in getting

9 these things efficiently done without redundancy. We are about

10 one-third of the way done, a bit over a third of the way in

11 terms of number of questions, although there are 14 total

12 questions - there's seven, but there are a lot of other subparts

13 that we have to go through yet. So with that, I will turn it

14 over to Fred for some administrative comments. 

15 DR. FRED JENKINS: I don't have a whole lot

16 extra to add, but I just want to thank the panel for a good day

17 of deliberations and also the public commenters. I think we

18 accomplished a lot today. So, I just wanted to say to the

19 panel, remind them to please return your thumb drives to us with

20 the presentation, so we can have those recycled. And with that

21 said, we will see everyone in the morning at 9 a.m. Have a

22 great evening. 

23 (WHEREUPON the meeting was adjourned for the

24 day.) 

25 DR. DANIEL SCHLENK: Good morning everyone. I

26 think in lieu of time, we are going to sort of skip our
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1 introductions since, I think everybody know everyone by now, I

2 hope. This morning, the Agency is going to hit us with some

3 questions of clarification, I believe, on question 7, is that

4 correct? Yep, anybody who is going to be doing that. Okay.

5 MR. KEITH SAPPINGTON: Thank you, Dr. Schlenk.

6 I would like to offer, on behalf of the Agency, just a little

7 question of clarification on charge question 7, but first, we

8 want to thank again the Panel for all their work over in

9 preparing for this meeting and the deliberations of the last

10 couple of days. We've already gained quite a lot of insight on

11 how we can improve the proposed process. So, we really

12 appreciate that.

13 With regards to question 7, and I know the

14 Panel has all ready considered this, so this is perhaps more of

15 an emphasis and that is in considering the alternate pathways in

16 Tier I exposure, it would be helpful to us in understanding the

17 conditions in which considering these additional pathways, like

18 drinking water and dust in associated oral exposure from dust

19 onto other plants, conditions in which that might change the

20 outcome of the Tier I assessment from say passing the assessment

21 to not passing the assessment since our goal is to minimize type

22 2 errors. 

23 So, in thinking about that and then in

24 thinking about the different context, so perhaps for example,

25 for the seed treatment where we have a default value of 1 mg per

26 kilogram. Is there is enough margin and safety such that not
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1 including drinking water or some of these other sources, would

2 that likely really push that from a no risk or no LOC exceedance

3 to a yes exceedance. Thank you very much.

4 DR. DANIEL SCHLENK: I believe that Dr. Potter

5 is the lead on question 7, so do you have any questions related

6 to how you want to present that? 

7 DR. THOMAS POTTER: Well, I have a comment. I

8 probably have to come up with a few questions too here. Anyway,

9 good morning, Tom Potter here. Are you specifically asking

10 about the seed treatment scenario only or all of the scenarios

11 that we were looking at? 

12 MR. KEITH SAPPINGTON: Keith Sappington. All

13 the scenarios and considering the alternate routes. 

14 DR. THOMAS POTTER: Right. You know, one of

15 the things that, you know, in terms of, let's just break them

16 out here. I'll allow for a few thoughts on it. I saw Dr. Hunt

17 also had a comment that he wanted to make, but you know one of

18 the things about the dust is that hopefully there will be some

19 effort to look at the potential exposures. Perhaps you've done

20 that all ready. So, if indeed you could come to some well

21 established conclusion that that is not an issue, it certainly

22 may not belong in Tier I. This is my opinion. 

23 So there needs to be some assessment,

24 particularly in regard to wind-blow soil dust. That's kind of

25 my issues. We will let Jens talk about the abraded material

26 from seed coat in a few minutes perhaps.
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1 With regard to drinking water, I would offer

2 the same line of thought or reasoning. I think that, you know,

3 a reassessment to determine if there are potentially significant

4 exposures. I think we noted yesterday that there was

5 considerable uncertainty in the amount of water that bees drink

6 and come in contact with on a daily basis, varying on a factor

7 of a range of 100. So, certainly that could have a major impact

8 on potential exposures. 

9 And then there was the question of assessing

10 potential concentrations, in again a worst case scenario and

11 overspray on a field that had standing water in it, using the

12 type of exposure scenario to come up and say well, you know is

13 this 1 percent of the dietary contact exposure or is it 50

14 percent or is it greater than the dietary - I mean if you use

15 the higher numbers for the water exposure in high end numbers

16 for field puddles, you can come up with a pretty significant

17 load. No, I don't necessarily think that that's reasonable or

18 realistic thing to do, but I think it's something that you

19 should spend some more time working on in the process of

20 refining the White Paper.

21 DR. DANIEL SCHLENK: Dr. Hunt? 

22 DR. GREGORY HUNT: Well, in considering, for

23 example, the abraded seed coat dust, I don't think that

24 considering that route of exposure would affect your Tier I, you

25 know, because 1 mg per kilogram is very conservative. It might

26 affect the downstream, for example, and I look at the figures on
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1 the tiered assessment. Downstream if it exceeds the LOC, then

2 it says use available crop residue studies, recalculate our

3 cues. So, in that blight, the crop residue studies may be

4 should include like incoming pollen that the bees are bringing

5 in. What are the maximal values you are seeing in those? 

6 DR. DANIEL SCHLENK: Dr. Pettis, you're also

7 on this one too.

8 DR. JEFF PETTIS: I have no immediate comments

9 at this time. 

10 DR. DANIEL SCHLENK: Dr. Fefferman, and then.

11 DR. NINA FEFFERMAN: Thanks. I think one

12 major thing I took away from the conversation yesterday from 7

13 is maybe even the concept of the phrase drinking water might be

14 an inappropriate way to frame that, and that just water usage

15 might be a way to frame that. While I don't know, again,

16 whether or not on testing this will turn out to be significant. 

17 It does lead, in my mind, to the suggested additional outcome

18 that maybe wax sampling concentrations should be measured as

19 part of a Tier I assessment, because that could tell us a

20 gradual accumulation over time as opposed to a dose response

21 from an initial carrying of what exposed larvae are facing or

22 what recycling of wax within the hive is doing. 

23 So, if we're worried about deposition

24 gradually over time from evaporation leaving not so much

25 drinking water but evaporated water for cooling leaving residues

26 of pesticides within the wax. Then that suggests to me not only
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1 a potential for it to trigger a positive case where maybe it

2 should be. Not only false but true, but also a new mechanism to

3 measure, which is the concentration in the wax. 

4 DR. DANIEL SCHLENK: Okay. Mr. Pistorius? 

5 MR. JENS PISTORIUS: I want to thank EPA for

6 this excellent question. No, it will not change the outcome of

7 the risk assessment and the Tier I seems to be conservatively

8 enough that the substances of concern are identified. So, it is

9 an issue of higher tier testing to address if there are concerns

10 under realistic conditions, but it will not change. Also the

11 route of dust and guttation, and I think also for the risk of

12 water, a lot changed the outcome of the risk assessment. You

13 will be able to identify the substances of concern in Tier I. 

14 DR. DANIEL SCHLENK: Okay. And Dr. James, did

15 you have your.

16 DR. ROSALIND JAMES: Wouldn't it change a risk

17 assessment for nonsystemic pesticides applied to the soil with

18 the dust because the dust would provide another route of

19 possible exposure that you didn't account for with a

20 nonsystemic? You talk a lot about nonsystemic soil-applied

21 pesticides. 

22 DR. DANIEL SCHLENK: Mr. Pistorius? 

23 MR. JENS PISTORIUS: I think it is both for

24 the systemic and nonsystemic that the Tier I level will be able

25 to identify those substances of concern where you find a high

26 toxicity and you have a classification as highly toxic
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1 substance, which will detect in the laboratory, oral and contact

2 toxicity tests. You will be able to identify substances with

3 potential concern. For nonsystemic substances, guttation is not

4 an issue at all, but I agree dust is for highly toxic

5 substances. 

6 DR. ROSALIND JAMES: Rosalind James. But I'm

7 out for dust, not guttation. Right. 

8 MR. JENS PISTORIUS: Yes, as I said for dust,

9 highly toxic substances are a concern no matter if systemic or

10 nonsystemic. 

11 DR. DANIEL SCHLENK: Anyone else want to weigh

12 in on this one? Okay. Mr. Sappington, does that answer

13 questions? Are you guys good with that? 

14 MR. KEITH SAPPINGTON: Thank you very much. 

15 It helps a lot. 

16 DR. DANIEL SCHLENK: Okay. Great. Okay. At

17 this point in time, I guess we are ready to move onto question

18 8, and if you could read in letter A for question 8? 

19 MR. JOSEPH DECANT: Good morning. This is Joe

20 Decant of EFED. So, I'll start us off on the toxicity side. 

21 Question 8, subpart (a) - please comment on the extent to which

22 currently available bee toxicity tests, which focus primarily on

23 mortality or survival, serve as an effective Tier I screen.

24 DR. DANIEL SCHLENK: And our lead discussant

25 is David Tarpy.

26 DR. DAVID TARPY: Thank you, Mr. Chairman. 
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1 Good morning everybody. This is David Tarpy with North Carolina

2 State University. I just want to thank the EPA and those who

3 crafted the White Paper are really doing an excellent job. It's

4 been incredibly informative to me. It's been obviously very

5 thorough, very well articulated, and I think very helpful to

6 have this discussion. I very much welcome it and I'm thrilled

7 we're all doing it. 

8 I foresee our conversations today looking at

9 the effects on honey bees as a proxy for all bees and

10 pollinators as being very important because the use of a social

11 system to study solitary systems in general, it adds many

12 complexities of biological organization that need to be taken

13 into account, particularly at Tier I types of bioassays. So, I

14 think we have a lot of thoughts concerning that that we'll be

15 discussing today. 

16 A couple of points that the discussants for

17 this particular question wanted to bring up before we get into

18 other details and later sub-questions and later question, one is

19 to reiterate the issue of using the individual bee as the basal

20 determinator rather than standardizing for body weight of the

21 individual, especially if honey bees are going to be used as a

22 proxy for other types of bees that can vary quite dramatically

23 in their size. Seemingly to standardize on a milligram per bee

24 is going to be much more translatable to those other symptoms

25 than just doing it on a per bee basis. 

26 Also, because honey bees are obligatorily
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1 social as the White Paper very well articulates, they're a super

2 organism, and that introduces a lot of important biological

3 phenomenon to measuring them in vitro and in vivo. Sometimes

4 those can be tested - sometimes honey bees can be tested in a

5 vacuum as an individual, but often times, it's not very

6 biologically meaningful. So, including the social context in

7 Tier I screenings I think is going to become very important.

8 Another aspect, and this derives from the

9 social complexity of honey bees as social insects is that a lot

10 the Tier I screening techniques that's commonly used for

11 solitary insects don't really capture a temporal component to

12 the potential effects of screening for these compounds. Cutting

13 it off at that 48 hours or extending that to 72 or even 96 hours

14 doesn't really capture a lot of the social effects over time. 

15 So, adding that additional dimension to these bioassays we think

16 would be very informative, and we'll be discussing those in a

17 little more detail. 

18 Specifically to address the need for measuring

19 outcomes of individual bees in a social context, luckily there

20 are bioassays that are routinely used in apicultural and honey

21 bee biology research that can do this in vitro very high

22 throughput and very readily, very reliable. For example, Evans

23 et al. 2009 published a paper that just articulates the use of

24 what they call bee cups, which are placing dozens, usually

25 around 50 or so newly emerged adult honey bee workers into

26 plastic cups, clear plastic cups where they are fed at lib with
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1 food and can be subject to various treatments, and then in an

2 incubator followed over a period of time. 

3 So, it seems logical to the Panel who weighed

4 in on this that there can be two bioassays whereby that type of

5 system could capture these types of phenomenon. One is - not to

6 use acute versus chronic here, but rather kind of a one-time

7 application of a compound in question and the following a marked

8 cohort of individuals within that social group, within that very

9 small social unit over time, and then looking at their

10 longevity. So, measuring longevity curves within that group of

11 a one-time application of a particular compound. 

12 Another analogous means of doing that would be

13 to feed the entire group consistently with different compounds,

14 so that the entire group is constantly exposed to that and the

15 following the same longevity over time to see how long the focal

16 workers live. We can provide additional detail about the

17 logistics of these bioassays, but I think the endpoint here is

18 that studying them in a social context when it deals with honey

19 bees I think is very important compared to isolating them and

20 testing them in a vacuum, which has a lot less biological

21 meaning to honey bees as a social system than doing it in a

22 social context. 

23 One last point to point out again to deal with

24 his particular question about just looking at the currently

25 available toxicity test with looking at mortality and survival

26 as endpoints is also another temporal component and that is the
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1 seasonality of honey bees themselves and how honey bees can be

2 physiologically different over different times of the year. 

3 I'll let Dr. Ostiguy comment on this further if she wants, but

4 bees in the summer are very physiologically different from bees

5 in the winter. So, incorporating the types of bees that would

6 actually be used in these bioassays is biologically meaningful

7 and nontrivial and out to taken into consideration on this. 

8 DR. DANIEL SCHLENK: Dr. Berenbaum? 

9 DR. MAY BERENBAUM: Fine. The only other

10 thing - no I just see Ostiguy before Berenbaum. The only other

11 - I concur, would just add to that there be useful to be at

12 least cognisant of the genetic differences among populations and

13 races, which is a well-known biologic attribute of bees among

14 bee keepers. Dog breeds differ in the susceptibility to drugs

15 or any issues with races and population. 

16 DR. DANIEL SCHLENK: My apologies. Didn't

17 mean to jump the gun there. My version is different than yours

18 I think. Dr. Ostiguy? 

19 DR. NANCY OSTIGUY: I concur with what David

20 said. One thing about the summer/winter bees that we should

21 also be taken into consideration when designing Tier I studies

22 is that the particular exposures that you might see are

23 different. So for instance, exposure to honey dew is going to

24 be a fall phenomenon. Those will only be winter bees then. So,

25 you can actually very much target where you think an exposure

26 might be.
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1 DR. DANIEL SCHLENK: Okay. Mr. Pistorius? 

2 MR. JENS PISTORIUS: For the available bee

3 toxicity tests as OECD 213 to 214, I think it is important to

4 take into account behavioral abnormalities that are observed in

5 the laboratory tests, especially if they occur at several doses

6 that are clearly below mortality in a larger number of bees. 

7 DR. DANIEL SCHLENK: Anyone else on the Panel

8 want to get in on this one? Okay. Let me go to the Agency. 

9 Mr. DeCant, any questions of clarification? Okay. Let's move

10 onto the second section then. 

11 MR. JOSEPH DECANT: Joe DeCant, EFED. 

12 Question 8, section B -- please comment on additional

13 measurement endpoints such as growth, which should be considered

14 in future modifications of Tier I test protocols, and which are

15 appropriately linked to the proposed assessment endpoints. Given

16 that the queen is the reproductive unit of the colony, please

17 comment on methods to evaluate effects on individual queens,

18 considering practical limitations of testing with queens.

19 DR. DANIEL SCHLENK: Dr. Tarpy? 

20 DR. DAVID TARPY: This is Dave Tarpy again, NC

21 State. So this is where some of those other effects that Mr.

22 Pistorius was just addressing, which are kind of the endpoints

23 that we didn't articulate in part A. There are many - I think

24 because of the social complexity of doing these bioassays in

25 groups, it actually adds a lot of different phenomenon that can

26 be measured and easily quantified and adds some robustness to
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1 these types of bioassays. One case in point including the

2 additional temporal component rather than just looking at 48

3 hour mortality of 72 or whatever, just having kind of binary

4 data like that, by looking at these social groups, measuring the

5 number of individual focal bees that have been treated or not

6 treated, and as they die over time, that facilitates the ability

7 to look at survivorship and analysis with survival statistics. 

8 So in essence, that's adding that temporal

9 component to these types of bioassays. So rather than just

10 taking a cross section in time, you're looking over the course

11 of time and being able to make important inferences based on

12 that. So, if you were to just look at 48 hours or really any

13 one time point, that would be a unit dimensional means of

14 assessing that. But there are multiple ways that one can arrive

15 at those same kinds of mortality percentages at that time point.

16 So looking at the curves and the shapes of the curves can be

17 incredibly informative. That's what is captured by survival

18 statistics. 

19 So, that's one outcome that I think would be

20 facilitated with really not much additional work in these

21 screening bioassays to measure the entire lifespans of

22 individuals and these small in vitro groups, but then

23 quantifying the slopes of mortality over time would give very,

24 very informative information, we believe. Again, this has been

25 done in many studies in this same capacity. So, this is fairly

26 well documented in the literature. 
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1 Because the question also addresses

2 specifically the effects on queens, and I totally agree that

3 this is incredibly important. Queens, of course are the sole

4 reproductive females within colonies. They're kind o the

5 carrier of all the genetic material in the colony and therefore

6 a real focal point. Moreover, just on a side note, in the

7 apiculture industry, there are a lot of reports about problems

8 with queens, although those problems are quite nebulous. It's

9 very hard to define them and to pin them down, but there seems

10 to be consistent issues in the management practices when it

11 comes to queens. Given their importance to the social

12 super-organism of honey bee colonies, it seems to be pretty

13 paramount to try and quantify that phenotype and effects on the

14 queen phenotype. 

15 There are ways of doing that, of looking at

16 quantifying queen reproductive potential, not just queen

17 fecundity that is egg-laying rate, but other aspects of queens

18 ability to keep the colony healthy, productive and well

19 populated. I won't go over all of those different measures that

20 can be done, but let's just suffice it to say that there are

21 more logical measures and then reproductive measures that could

22 be done looking at things like the stored sperm count of the

23 queens, the viability of sperm, ovarial number, egg-laying

24 rates, all of those types of things. 

25 How those many different characters translate

26 to the ultimate colony phenotype however, it not entirely known
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1 and may be difficult to make extrapolations on measuring those

2 characters in an individual queen to the ultimate colony

3 phenotype. So, in that sense, at a Tier I level, it may be very

4 difficult to be able to quantify effects of particular compounds

5 on queens and reproductives, the drone as well. So, it might

6 really necessitate Tier II levels in order to be able to truly

7 elucidate effects on queens and drones. That's something that

8 we will get to in later questions as well.

9 Some other potential measurements and

10 endpoints that the discussants brought up on these in vitro

11 bioassays is quantifying immunocompetence, effects of compounds

12 on abilities of individuals to withstand disease or other

13 challenges. While this is being studies quite heavily in the

14 literature, it was pretty well agreed that we still don't have a

15 very good cause effect understanding of a lot of these

16 measurements such as phenoloxidase activity, encapsulation

17 response, expression of antimicrobial peptides, those types of

18 things. They can be done, but we don't understand the full

19 relationship to the end phenotype. So, while it might be

20 interesting to keep those in mind, it be premature to include

21 them in a high throughput endpoint at this point.

22 As was mentioned earlier just a few moments

23 ago, other endpoints that could and probably should be

24 considered are changes in behavior, which can be again, very

25 difficult to quantify and the present their own challenges. But

26 in a social organism such as honey bees, they are paramount. 
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1 They are everything. So, looking at the super-organism cannot,

2 almost by definition, ignore effects on behavior. 

3 So, measuring simple behavioral bioassays such

4 at motility, social interactions, triple axis, other behavioral

5 changes can be and have been, in the literature, documented. We

6 also feel that in order to assist this type of data collection

7 in a repeatable high throughput measure that there is a real

8 need for technologies that could capture and automate that type

9 of data collection. And, we know of several initiatives that

10 are trying to take video recordings of social insect colonies

11 and trying to automate the complexities of a lot of the social

12 insect interactions that are going on. So, there ought to be a

13 real premium on developing those types of technologies in order

14 to incorporate them into here, but again, at this point in time,

15 it might be premature. 

16 One behavioral bioassay that is well

17 articulated in the study for decades now is and also discussed

18 in the White Paper, so I won't go too much into it, but that's

19 the PER and learning conditioning bioassays. This is proboscis

20 extension reflex, which incorporates the natural instinct of

21 honey bee worker to stick out her tongue in response to her

22 antenna being touched by a small droplet of sucrose solution. 

23 That reflex can then be conditioned and pared with the stimulus

24 such as an odor or tactile response so that over a few trials of

25 that association tested individuals will then immediately stick

26 out their tongues if they are exposed to that same odor. That
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1 is a means by which researchers can quantify learning in their

2 ability to make this association. 

3 There is a very rich literature on using PER

4 for learning and conditioning. These are also potential

5 endpoints that can be incorporated and harnessed as part of a

6 Tier I assessment.

7 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum? 

8 DR. MAY BERENBAUM: Just two additional

9 comments. One is drones represent a significant data gap. 

10 They're sort of literally the forgotten men here. Even though,

11 they may seem nonessential most of the time, they do serve as a

12 vital and irreplaceable function in colony life. There's not

13 much known about their toxicology. 

14 The other comment is that just to point out

15 that the proboscis extension reflex represents a particular type

16 of learning, repetitive learning, and that's only a component of

17 the incredibly rich behavioral repertoire of honey bees. So,

18 it's a proxy maybe. So we have a lot of links to make. First

19 per has to be linked to in general to colony phenotype and in

20 addition, its place within the role of very complex behavioral

21 repertoire of honey bees and needs to be considered. 

22 DR. DANIEL SCHLENK: Thank you. Dr. Ostiguy? 

23

24 DR. NANCY OSTIGUY: I concur with Dr. Tarpy

25 and Dr. Berenbaum. 

26 DR. DANIEL SCHLENK: Okay. Dr. Fefferman? 
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1 DR. NINA FEFFERMAN: Hi, sorry, I feel like

2 I'm talking a lot. The one thing about queens that I don't see

3 discussed ant it could be completely irrelevant and this is a

4 toxicology question - is either her pheromone production

5 directly or the interpretation of pheromones by workers with

6 higher toxicity at a sublethal effect? There's been a lot of

7 debate in the literature about who is controlling what happens

8 because of queen pheromone, but it doesn't really matter if that

9 gets disrupted. Then that's a huge disruption in colony health

10 and colony reproduction. So, I have no idea if there're

11 appropriate assays for this, but my learned colleagues certainly

12 know if this is an appropriate question to add and if so, how to

13 measure it if it can be measured.

14 DR. DANIEL SCHLENK: Yes, Dr. Pettis? 

15 DR. JEFF PETTIS: There is some recent

16 literature - recent papers in the literature about the effect of

17 disease on pheromone production, and probably you might expect

18 the same thing with some pesticide exposure. Bioassays might

19 include things like new behavior, the number of bees attending

20 the queen, things like that, so there are bioassays that could

21 be looked at. 

22 If I may, I'd just like to comment a bit on

23 what Dr. Tarpy said about how to look at queen - or how to

24 incorporate queens into Tier I. I think it's difficult because

25 of the reports that we are getting of queen failures. I think

26 it behooves us to try to look at ways to incorporate it. As I
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1 pointed out earlier, the queen, we believe and we don't have

2 good numbers on it, but in effect, they process much more food

3 than any other worker or drone in the colony because of their

4 high egg laying rate. So, there could be some biomagnification

5 of whatever residue is present in the colony. 

6 I can think of a couple of ways. With your

7 group dynamics, it would be possible to just simply introduce

8 queens into that environment. I don't know if it's practical to

9 watch behavior, but certainly you could introduce queens and

10 then see compared to control queens if there was any direct

11 effect in a Tier I test. So, just adding queens the workers

12 that are in those Tier Is, it's a possibility. 

13 Dave pointed out some of the things we look at

14 in queen health, such as sperm viability. The queen stores the

15 sperm, so we can actually measure after a certain period of time

16 of exposure of the viability of sperm within the queen. I

17 believe there are issues with queens become drone layers. So,

18 they either run out of sperm or the sperm is dying. That seems

19 to be a common theme of late. So, we have bioassays for that as

20 far as ways of looking at queens. 

21 Lastly without sacrificing the queen, you can

22 actually take a fecal sample from the queen and that might be

23 telling. You take and analyze the fecal material. But again,

24 usually at the end of a bioassay, you just sacrifice the queen

25 and look at a number of parameters. I will say that while we're

26 doing a lot of this, the methods and the standardization, I
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1 don't this is there at this point. 

2 DR. DANIEL SCHLENK: Yes, Dr. Hunt? 

3 DR. GREG HUNT: Hi. I'm just willing to bring

4 up another potential behavioral endpoint, which is homing

5 ability. There have been a number of recent studies looking at

6 pesticide effects on bees' ability to find their way home. You

7 can train them to a feeding station and then take them a

8 distance to the side to make it a little bit more challenging to

9 see who gets home. And, this has some - it's very important for

10 the bees to be able to find their way home, obviously. 

11 Otherwise, they're toast. And it has some advantages over the

12 PER assay because with the learning assays, it depends on the

13 bees' sort of motivation, their inherent response, threshold to

14 sucrose and things of that sort. 

15 DR. DANIEL SCHLENK: Yes, Dr. Tarpy? 

16 DR. DAVID TARPY: This is Dave Tarpy again. 

17 So, Greg, I totally agree with that of course. It makes a lot

18 of sense at Tier II and Tier III, but these being questions

19 about Tier I, would you think that there would be a good proxy

20 at the Tier I level that would be able to kind of quantify

21 homing behavior, kind of, you know, in an in vitro setting? 

22 DR. GREG HUNT: No, I guess I was getting

23 ahead of the game there, but I'm glad you pointed that out.

24 DR. DAVID TARPY: Well, then bring it up again

25 with the Tier II. I think that will be - because that is

26 obviously important. 
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1 DR. DANIEL SCHLENK: Yes, Dr. McManaman? 

2 DR. JAMES MCMANAMAN: So, this is going to

3 come up in the next session, but I just want to ask the panel. 

4 There's a lot of evidence that there is pretty definitive

5 genetic changes that occur in queens when they become queens. 

6 They occur in the brain. So, I'm just wondering if genetic

7 markers could be used as a way as an alternative method of

8 evaluating queen health and the effects of pesticide on her

9 reproductive ability. 

10 DR. DAVID TARPY: This is Dave Tarpy again. 

11 Actually, there's no genetic - genome of workers and queens are

12 exactly the same. It's just what genes are getting turned on

13 and off. That's actually a function. There have been genomic

14 studies that have looked at caste determination and everything

15 like that, but unless other panel members would like to correct

16 me on this, I don't know if any of them would be good genetic

17 markers that would be reliable to really quantify in a

18 continuous way of some sort of measure for queen quality and

19 queen reproductive potential, but I could be wrong on that.

20 DR. DANIEL SCHLENK: Dr. James? 

21 DR. ROSALIND JAMES: Two comments. One in

22 response to Jim. I agree with Dr. Tarpy. We're just not there

23 yet, I think, in understanding. We're starting to do work with

24 transcript looking at gene expression, but expression levels of

25 transcriptome isn't necessarily related to what is actually

26 happening physiologically and it's complex. We're just
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1 beginning to look at that. I don't think it's anywhere near

2 being ready as some sort of a screening tool. 

3 And I had another comment. I was waiting for

4 some of the queen discussion to die down because I want to

5 change the subject. You can go back to queens if you want now

6 that I have the mic. You asked for other endpoint measurements

7 and we'll probably talk about this a little bit more later when

8 we get in - you sort of re-asked the same questions in different

9 ways. So we'll get to this more later, but in part A, we talked

10 a little bit about using longevity, using these bioassays that

11 are run for a longer time and one advantage to doing that. And

12 I'm speaking now about larval bioassays instead of adults as you

13 can look at development time. 

14 So, I think later we'll talk a little bit more

15 about alternative bioassays, but one of the things you can do

16 are these larval bioassays and then run them out to some

17 biological endpoint such as cell capping or pupation, and then

18 measure how long it takes to get to that stage for each

19 individual. This would give you another endpoint measurement in

20 addition to mortality. 

21 DR. DANIEL SCHLENK: Okay. Any other

22 comments? I didn't hear growth mentioned at all. Is that an

23 endpoint? Because that's mentioned in the question. Mr.

24 Pistorius? 

25 MR. JENS PISTORIUS: One comment that is also

26 related to charge question 9, I apologize if I may be a bit
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1 early in this. I think growth could be included in the future

2 coming Aupinel test method or OECD guidelines, which is

3 developed and regarded in the Tier I, although it needs to be in

4 short, additional handling of the larvae does not affect

5 mortality, which is a bit critical in this test. I think this

6 is the only Tier I test method which has been or is under

7 validation that would actually cover growth on a Tier I. 

8 DR. DANIEL SCHLENK: Dr. James? 

9 DR. ROSALIND JAMES: Hopefully this isn't just

10 reiterating the same thing, but the larvae are extremely

11 delicate and you can't really pick them out and weigh them

12 regularly. At the end, perhaps you could weight them, but

13 growth is difficult to measure for the larvae. And then the

14 adults, you are not going to have growth occurring. So, this is

15 one reason way we use development time rather than growth. 

16 DR. DANIEL SCHLENK: Yes, Dr. Fefferman? 

17 DR. NINA FEFFERMAN: Although it is - well,

18 I'm sure everyone meant individual growth, the rate of change of

19 the size of the colony after mating or once the queen starts egg

20 laying, not just a measurement of fecundity but a successful

21 rate of change - that ramp up, the differential there might

22 actually be a meaningful colony health measure. 

23 DR. DANIEL SCHLENK: Yes, Dr. Ostiguy? 

24 DR. NANCY OSTIGUY: One difficulty with that

25 is there are tremendous strain differences. So, if you're going

26 to ask whether or not or how fast a colony builds, you'd better
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1 standardize for which type of honey bee you're using. 

2 DR. DANIEL SCHLENK: Mr. Pistorius? 

3 MR. JENS PISTORIUS: Especially regarding

4 growth, we would like to discuss this also in the Tier II and

5 Tier III tests because they are very reliable and very

6 well-suited methods available for measuring growth. 

7 DR. DANIEL SCHLENK: Okay. Any other comments

8 on this section? Dr. Delclos? 

9 DR. KENNETH DELCLOS: Question - now I think

10 that in the first part A, I think we are talking about using a

11 single dose. How critical is the timing of the dose for these

12 various - I mean, I don't know anything about the development of

13 metabolic capacity. I don't hear much about that toxicokinetics

14 discussed in here, but also in the response if you're looking at

15 a specific response, how tight a window - is there an apriority

16 way of choosing one that should be administered? 

17 DR. DAVID TARPY: This is David Tarpy. I

18 don't think there is an apriority way, but often times in these

19 bioassays, they're done by newly closed workers, newly emerged

20 workers as they're placed into these experimental arenas, but

21 it's true. There may be differences in the time of application

22 on their effects. 

23 DR. DANIEL SCHLENK: Okay. Any other comments

24 for question 8? Okay. I'm going to go to the Agency -

25 questions of clarification? Mr. DeCant? 

26 MR. JOSEPH DECANT: Yeah, this is Joe DeCant
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1 from EFED. I just have a question. It sounds like - thank you

2 to the Panel. We got some good feedback in terms of the number

3 of different endpoints. I think some of the endpoints like Dr.

4 Tarpy, you mentioned about immunocompetence. It sounds like

5 some of the endpoints are quite there and aren't quite there and

6 aren't quite developed yet to be able to include into some of

7 our studies. I also want to ask a clarification question - some

8 of the endpoints that were mentioned like the PER Assay - I know

9 that we are trying to link these endpoints with assessment

10 endpoints. So survival, mortality and to what extent do you

11 think some of these measurement endpoints are actually linked to

12 our assessment endpoints? 

13 DR. DANIEL SCHLENK: Dr. Tarpy? 

14 DR. DAVID TARPY: This is Dave Tarpy again. I

15 would argue all of them to greater or lesser degrees. Again,

16 adding that extra layer of biological complexity, that is the

17 colony, inherently brings in all of these different aspects. I

18 think logistically in measuring them in a repeatable,

19 quantifiable way is really a difficulty. And as you're correct

20 at that immunocompetence measures may not be as well elucidated

21 to be able to make those connections to the social level yet,

22 but it is definitely something to keep in mind. Some of the

23 other bioassays like the PER are more robust and standardized.

24 So, we bring all of them up as a means of

25 keeping them in mind as to complexity of dealing with the social

26 organism rather than a solitary one, but that there is an
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1 understanding that just based on feasibility that not all of

2 them are going to be incorporated or at least right away.

3 DR. DANIEL SCHLENK: Dr. Berenbaum? 

4 DR. MAY BERENBAUM: With that said, I think

5 it's important to - at least one component of your question is

6 the linkage to colony phenotype. And, I don't even think for

7 proboscis extension reflex that there is a bulletproof linkage. 

8 So, there is another data gap. 

9 DR. DANIEL SCHLENK: Yes, Mr. Pistorius? 

10 MR. JENS PISTORIUS: I still think it is very

11 difficult to actually link the findings of the PER Test to the

12 realistic situation that bee colonies are encountered with. I

13 would like to point out that there is an area of major need for

14 research to make this link to actually establish a link and only

15 very limited number of studies that have looked into it, but

16 with those for instance, we could find some effect in PER, we

17 could not find any relation as to foraging behavior or to honey

18 flow or honey yield of the colonies. But again, there is so

19 much limited data available on how to link those two that I

20 think we're a little bit unsure about this. 

21 DR. DANIEL SCHLENK: Dr. Tarpy? 

22 DR. DAVID TARPY: This is Dave Tarpy again. I

23 failed to mention last time, and I agree with all of the points

24 that were raised there is because of these difficulties and

25 linkages to full colony phenotypes, it places a real premium and

26 a priority on Tier II and III bioassays, and that it is almost
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1 inherent to study a super-organism at the level of that

2 super-organism, which is the colony. So, it's incredibly

3 difficult to use Tier I in vitro tests to truly adequately

4 capture the entire biology at the colony level. So that's an

5 inherent difficulty seen here, which emphasized Tier II and Tier

6 III bioassays for these screenings. 

7 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

8 DR. NANCY OSTIGUY: I'm going to disagree a

9 little bit with some of the comments. I actually think the PER

10 does provide some very useful information that can be related

11 then to the full colony in the same way that we evaluate muscle

12 rechemical exposure, et cetera, on human learning ability that

13 we then apply to a population. So, if you adversely affect

14 individuals within the group, which is what we're looking at

15 with honey bees, you are going to at some point impact the whole

16 group. So to me, there's the connection, you know. The

17 individuals do make up the whole. And if we interfere with the

18 ability of individual bees to learn, then we interfere with the

19 whole. So, what we might need to do with the PER is set a

20 particular standard for the degree of learning inhibition rather

21 than saying we can't use the PER as a Tier I screen to try to

22 get at some of that some of that social insect interaction. 

23 DR. DANIEL SCHLENK: Okay. Dr. Fefferman? 

24 DR. NINA FEFFERMAN: We'll talk more about

25 this at the end of everything, but I think that kind of question

26 is exactly the sort of thing where modeling can help. 
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1 DR. DANIEL SCHLENK: Dr. Pettis? 

2 DR. JEFF PETTIS: Just in regard to the PER

3 and other tests that we might run, I agree that the PER has been

4 vetted well as far as demonstrating effects, but I think I would

5 have more confidence in something like Dr. Hunt suggested in

6 these returning forager assays, but it may not be exactly a Tier

7 I. It may be closer to a Tier II test where you actually dose

8 foragers at realistic exposure levels and then ask them to

9 return to the hive. I think there have been several papers on

10 that of late. They seem very convincing that realistic field

11 exposure can disrupt returning forager behavior. You can see

12 that that can have direct effects on the colony. 

13 DR. DANIEL SCHLENK: Okay. Dr. Klaine? Dr.

14 Ostiguy? 

15 DR. NANCY OSTIGUY: I actually agree, but I

16 think Tier I -- using PER as a Tier I would then trigger us to

17 do that field returning ability. So, the PER could be set

18 sufficiently conservative so that if we need to, we can then

19 check to see if it really has that effect on the whole group.

20 DR. STEPHEN KLAINE: This is Steve Klaine with

21 Clemson. Thinking about this on a larger scale in terms of

22 trying to move from a single organism to a more complex

23 super-organism, it seems to me that any one behavior may not get

24 us all the way, but that a bank of these behaviors may give us a

25 lot of insight into the whole hive health by using single

26 organisms. So, I think it ultimately - and I agree with the
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1 discussions last night - we're not there yet and what you said

2 today, but I think that's where we need to be if we're going to

3 start making predictions on hive health. 

4 DR. DANIEL SCHLENK: Anyone else to weigh in

5 on this one? No? I hear some discussions back there. Do you

6 guys need some more clarification or are we.

7 MR. JOSEPH DECANT: This is Joe DeCant from

8 EFED. Could you elaborate a little bit on the bank of behaviors

9 that you mentioned about and what type of behaviors will be

10 helpful? I know Dr. Pistorius, you mentioned about some of the

11 behavioral effects that are observed in the OECD Guidelines, and

12 so if you could clarify or elucidate on those behaviors. 

13 MR. JENS PISTORIUS: Well there are a number

14 of described categories of behavioral abnormalities, and it is

15 quite difficult to now put really into a frame when this would

16 actually trigger a real additional concern. But for instance,

17 during my work, I was encountered with one substance that had

18 doses clearly below the lethal doses at a number of dosings,

19 also in very low dosings, in different laboratories, repeatable

20 effects, severe effects on behavior of very agitated, very

21 nervous, trembling, shaking, all this. So, I would just like to

22 point out is that it is important that the assessed behavioral

23 abnormalities are reported by the contractors and that the risk

24 assessor also regardless of any trigger values still has the

25 flexibility to say, on the basis of these concerns, we have to

26 go to a higher tier testing and do additional testing. Does
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1 that answer your question? 

2 DR. DANIEL SCHLENK: Any other questions, Mr.

3 DeCant? Oh, I'm sorry, Dr. Klaine has something to add here. 

4 DR. STEPHEN KLAINE: Yeah, I was just - you

5 talked about the bank - I mentioned a bank of behaviors. I

6 think there are a lot of them that maybe aren't standardized but

7 at least in my discussions with my bee colleagues, have really

8 been intriguing. The discussion we had last night on the

9 nursing, the in vitro nursing assay, where you could - that's

10 another behavior that you can quantify, because in my mind, at

11 least, it's not one behavior that describes the health of the

12 hive, but its culmination of all these behaviors that make these

13 hives work. So, if you can capture, quantify three or four of

14 them, you might get a lot better insight into bee health or hive

15 health. 

16 DR. DANIEL SCHLENK: Okay. Anyone else? Let

17 me go back to Mr. DeCant. Do you have any other questions of

18 clarification. 

19 DR. JOSEPH DECANT: No, I think we're good. 

20 DR. DANIEL SCHLENK: Great. Okay. Let's go

21 ahead and move onto question 9. Want to read letter A into the

22 record for us? 

23 MR. JOSEPH DECANT: Joe DeCant, EFED. 

24 Question 9, subpart (a) - please comment on the extent to which

25 the Aupinel et al. in vitro method serves as an appropriately

26 conservative estimate of Tier I acute oral exposure of honey bee
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1 larvae to pesticides, given differences in this test design from

2 actual in-hive exposure conditions, for example during the first

3 three days of the larval development stage, larvae consume royal

4 jelly and brood food, and the uncertainty regarding the extent

5 to which larvae rely exclusively on pollen or nectar as opposed

6 to royal jelly or brood food.

7 DR. DANIEL SCHLENK: Our lead discussant for

8 this question is Dr. Berenbaum. 

9 DR. MAY BERENBAUM: Determining toxicity of

10 pesticides to larvae using in vitro testing is complicated by

11 several factors including possible genetic differences among

12 colonies and difficulties synchronizing larval age, both of

13 which were mentioned by Aupinel and also included in the White

14 Paper. In addition as yet uncharacterized attributes of royal

15 jelly, brood food, honey and been bread may be relevant to

16 pesticide tolerance in larvae. 

17 Royal jelly, for example, contains substances

18 that inhibit DNA methylation and histone deacetylase activity,

19 also promote gene expression and similarly honey and bee bread

20 constituents contain, including phytochemicals that can regulate

21 detoxification pathways in worker bees at least. These factors

22 have the potential to alter responses to pesticides, but without

23 more complete understanding of how the composition of food

24 affects toxicity or bioavailability despite compound, it's

25 difficult to say conclusively that the method is conservative,

26 although available information points in that direction. 
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1 One specific concern is that the proposed

2 method for testing larvae is based on pesticide levels in pollen

3 and nectar administered to day-5 larvae. Day-5 larvae destined

4 to be queens eat royal jelly. If royal jelly has 1/100th the

5 level of pesticides contaminants the nectar and pollen, test

6 conducted in Tier I using maximum concentrations of pesticides

7 based on worker exposure will be much higher than those that are

8 experienced by queen larvae. 

9 So, in terms of toxicity, this approach is

10 reasonable, but if caste determination and hive viability are

11 concerns, then these pesticide amounts might not be appropriate.

12 As noted earlier too, the assumption that royal jelly

13 concentrations of pesticides are 1/100th those in other bee

14 foods, those estimates are based on one published and one

15 unpublished study. So, that underlines that assumption. 

16 So, the impact of larval diet on pesticide

17 detoxification represents a significant data gap. I forgot to

18 introduce my comments by saying I am extremely grateful that the

19 EPA is undertaking this effort and also to point out that the

20 first indication that pesticides used for agricultural purposes

21 have nontarget impacts on bees goes back to 1895, and the fact

22 that we are now a century later talking about major data gaps, I

23 think is unsettling at the least, and that's one reason I'm very

24 grateful to the EPA for convening this Panel. 

25 Okay. So, data gap - the artificial diet used

26 for bioassay contains glucose and fructose as proxies for honey,
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1 but honey is substantially more than just carbohydrates. 

2 Phytochemicals of honey may well influence pesticide toxicity

3 and metabolism as they do in other herbivorous insects. There

4 is a rich literature in the effect of phytochemicals and

5 detoxification in other herbivores. But in larval bees, it's

6 essentially nonexistent. 

7 More generally, the concept of an acute dose

8 for a larva may be biologically difficult to interpret. Unlike

9 workers who may experience one-time encounters through pesticide

10 drift outside the hive, larvae mature continuously provide food

11 that has been already processed by workers seem less likely to

12 have acute exposures more over because larvae are confined

13 during their development, they're subject to continuous contact

14 with wax, which if contaminated is likely to be a source of

15 toxicity that is not directly assessed by the proposed methods. 

16

17 Contact bioassays would be very useful in this

18 context, particularly using pupae just for operational and

19 logistic reasons and also there are precedents for pupal

20 bioassays of microbial toxicity and the like. I guess those are

21 my comments.

22 DR. DANIEL SCHLENK: Okay. Thanks. Dr.

23 McManaman? 

24 DR. JAMES MCMANAMAN: My comments were

25 incorporated into Dr. Berenbaum's. 

26 DR. DANIEL SCHLENK: Dr. Ostiguy as well? 
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1 DR. NANCY OSTIGUY: I concur, my comments were

2 included by Dr. Berenbaum. 

3 DR. DANIEL SCHLENK: Okay. Open to other

4 Panel members to weigh in on this one. Yep, Dr. James? 

5 DR. ROSALIND JAMES: I would have to weigh in

6 on the larval bioassays. Because I do so many of them myself,

7 although not with honey bees, but in line with what Dr.

8 Berenbaum said about the one time acute exposure, one of the

9 things that we talk about is contaminating all of the food that

10 you feed to the larvae. So, during the full five-day feeding

11 period, the bees would be continually fed with pesticide

12 contaminated food stuff instead of a one-time dose. This would

13 also get at the question about which age is most sensitive in

14 the developing larvae, which size increase is quite amazing, and

15 we don't really know about sensitivity of the different ages. 

16 But I think it would be more realistic to have - they would

17 continually be fed pesticides. 

18 DR. DANIEL SCHLENK: Any other comments? Just

19 curious on my own side here is would you propose then a

20 standardized diet that would have the same reference material in

21 terms of phytochemical input that could be a baseline

22 standardized diet that would used? Is that something that you

23 would propose? 

24 DR. MAY BERENBAUM: This is May Berenbaum. It

25 would be ideal. It would be preferable. What constituents

26 would go into that diet would be probably subject to
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1 considerable discussion. 

2 DR. DANIEL SCHLENK: Okay. Anything else on

3 A? Oh, sorry. 

4 DR. JAMES MCMANAMAN: I have a question for

5 the Panel. This is just out of ignorance - is royal jelly royal

6 jelly? I mean, are the constituents of royal jelly independent

7 of strain of kind of bee? 

8 DR. ROSALIND JAMES: This is Rosalind James. 

9 I'm going to let one of the honey bee folks answer that

10 question, but I have to add that you do want to make sure that

11 when you buy the royal jelly it's not all ready contaminated

12 with pesticides. 

13 DR. DANIEL SCHLENK: Dr. Pettis? 

14 DR. JEFF PETTIS: Jeff Pettis. I was going to

15 say a solitary person commenting on royal jelly, come on Rose. 

16 That was going to be my point was that royal jelly is royal

17 jelly by and large, but you really have to consider the source

18 and we've had major issues in doing lab studies and even trying

19 to do any kind of larval rearing in that we get contamination in

20 the royal jelly, so that the batch of royal jelly has to be

21 tested and be standardized and known to be clean before it's

22 used. Otherwise, you get confounding results. 

23 DR. DANIEL SCHLENK: Dr. Fefferman and Dr.

24 Hunt? 

25 DR. GREG HUNT: I was just about to say that

26 that was an excellent question. I don't really know what the
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1 variability is in royal jelly, but ideally, we would have an

2 artificial diet. It seems that just doesn't work very well.

3 DR. DANIEL SCHLENK: That was Dr. Hunt. Dr.

4 McManaman? Hold on, let me get Dr. McManaman back.

5 DR. JAMES MCMANAMAN: Yeah, so my question was

6 - you bring up some important points about contamination, but my

7 question was related to the charge question is that if we're

8 trying to understand the effects of pesticides and the food can

9 interfere or affect their responses and royal jelly is a

10 component of that, especially for queens in early development,

11 then I think it's important to know whether it really is the

12 same thing because there are factors in royal jelly that can

13 influence strain differences.

14 DR. DANIEL SCHLENK: Okay. Dr. Fefferman? 

15 DR. NINA FEFFERMAN: Sorry, thanks. I still

16 have the same question I had before, which is when you say

17 contamination in the royal jelly that you're buying, is it

18 pesticide contamination? Because from listening to the

19 conversation just a bit ago, I was under the impression that

20 royal jelly pesticide contamination was really sort of the

21 lowest level of concern in terms of what they were eating, and

22 that sounds scary.

23 DR. DANIEL SCHLENK: Dr. Pettis? 

24 DR. JEFF PETTIS: Probably one of the major

25 contaminants is antibiotics. You can get antibiotic

26 contamination. Beekeepers use antibiotics in the colony to
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1 control bacterial diseases. I think 80 percent or 90 percent of

2 the world's production of royal jelly comes from China, and so

3 you can have issues there. You need to really get your royal

4 jelly from colonies that are managed almost totally organic. 

5 You also can get some miticide compounds that are used by

6 beekeepers to control mites that get incorporated in the royal

7 jelly. 

8 DR. DANIEL SCHLENK: Dr. Ostiguy and then Dr.

9 Hunt? 

10 DR. NANCY OSTIGUY: We also need to be

11 concerned about the possible pathogens that are in that royal

12 jelly. 

13 DR. DANIEL SCHLENK: Dr. Hunt? 

14 DR. GREG HUNT: I was just going to comment

15 that it might be best to collect your royal jelly fresh and then

16 just make sure that the controls and the treated get the same

17 stuff. 

18 DR. DANIEL SCHLENK: Okay. Any other comments

19 on 9A? Mr. DeCant, do you have all you need for 9A there? 

20 MR. JOSEPH DECANT: I do. 

21 DR. DANIEL SCHLENK: Okay. All right, let's

22 move on to the second question of 9. 

23 MR. JOSEPH DECANT: Joe DeCant, EFED, question

24 9, subpart (b) please comment on the extent to which pesticides

25 would be more or less bioavailable using the synthetic matrix

26 relied on for feeding developing bees in this in vitro method.
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1 DR. DANIEL SCHLENK: Dr. Berenbaum? 

2 DR. MAY BERENBAUM: Bioavailability will be

3 influenced by the nature of the compound, larval metabolism,

4 possibly food composition. Without accurate information about

5 how these factors influence bioavailability within honey bee

6 larvae, it's not really possible to really answer this question.

7 Aupinel et al. 2007 used dimethoate, which is an

8 organophosphate the highly water soluble. It's not clear how

9 pesticides with different structures and properties will perform

10 in this particular synthetic matrix. 

11 DR. DANIEL SCHLENK: Okay. Dr. McManaman? 

12 DR. JAMES MCMANAMAN: I have nothing to add.

13 DR. DANIEL SCHLENK: And Dr. Ostiguy? 

14 DR. NANCY OSTIGUY: I concur with what Dr.

15 Berenbaum has said all ready. 

16 DR. DANIEL SCHLENK: Anyone else on this

17 section? Okay. Seems pretty clear to the point. Any

18 clarification question there Mr. DeCant? 

19 MR. JOSEPH DECANT: I think we're good. 

20 DR. DANIEL SCHLENK: I'm hoping so. Okay. 

21 We're going to move on to C please. 

22 MR. JOSEPH DECANT: Joe DeCant, EFED, question

23 9, subpart (c) - please comment on the extent to which the

24 absence of trophallaxis that is the transfer of food or fluids

25 between colony members, may render larvae more or less

26 vulnerable to pesticides.
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1 DR. DANIEL SCHLENK: And Dr. Berenbaum? 

2 DR. MAY BERENBAUM: You thought B was straight

3 forward. In response to the question, contribution of

4 trophallaxis to pesticide resistance or susceptibility is to our

5 knowledge completely and utterly unknown. This represents a

6 data chasm.

7 DR. DANIEL SCHLENK: Okay. Dr. McManaman? 

8 DR. JAMES MCMANAMAN: A very deep chasm, I

9 totally concur. 

10 DR. DANIEL SCHLENK: And Dr. Ostiguy? 

11 DR. NANCY OSTIGUY: I have to commend the EPA

12 for even asking the question, because we truly have no idea. 

13 DR. DANIEL SCHLENK: Yes, Dr. Berenbaum? 

14 DR. MAY BERENBAUM: Let me add that it's

15 important. So, it is important to find out. 

16 DR. DANIEL SCHLENK: Yes, Dr. Tarpy? 

17 DR. DAVID TARPY: And not to beat the dead

18 horse, but I don't want to move on too quickly. And that is I

19 think this again underscores the complexities of all of the

20 social interactions when we're dealing with a super-organism as

21 opposed to just individual larvae or individual adults. 

22 Everything is interconnected in these types of social systems. 

23 So, these are important questions to ask, but it just

24 underscores our data gap and the complexity of the social

25 organism. 

26 DR. DANIEL SCHLENK: Dr. James, did you have
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1 something? 

2 DR. ROSALIND JAMES: Well, the only thing I

3 would add is, of course, increases of spread of pesticides that

4 is collected and then it may end up getting fed to many more

5 individuals than originally come into contact with it. In that

6 way, it would perhaps dilute the pesticide, but spread it out

7 among more individuals. 

8 DR. DANIEL SCHLENK: Okay. Any other comments

9 on C? Mr. DeCant, did you get what you needed on that one?

10 MR. JOSEPH DECANT: Yeah, I think we're good. 

11 Thanks. 

12 DR. DANIEL SCHLENK: All right. Then read

13 letter D into the record please. 

14 MR. JOSEPH DECANT: Joe DeCant, question 9,

15 subpart (d) - please comment on alternative methods for testing

16 individual larvae that may be appropriate for quantitative use

17 in a Tier I screening-level assessment.

18 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum? 

19 DR. MAY BERENBAUM: Literature reports of

20 alternative methods for testing larvae quantitatively are

21 vanishingly rare. Atkins and Kellum in 1986 treated larvae in a

22 colony by micropipetting pesticides directly into the cells, but

23 that might quantify more as a Tier II rather than Tier I. Pupal

24 bioassays have not been widely used for pesticides toxicity

25 testing, but they have been used in other contexts, and it seems

26 that they may be productively employed in this context,
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1 particularly given the confounding problems of social context. 

2 I mean, well it said people live alone and die alone - the same

3 is true for bees, but they also pupate alone. So, there is one

4 aspect of their life that is independent, more or less

5 independent of a social context. So, that might be particularly

6 suitable for Tier I. 

7 Let's see - following up on Dr. Tarpy's

8 suggestions about other endpoints, larval transcriptomes of

9 honey bees have been described. The existence of such data

10 provides quantitative measures of effects of toxins on larval

11 development that might be useful as alternative indicators. 

12 Genetic resources could allow the use of gene expression,

13 detoxification, antioxidant enzyme activities, senescence traits

14 as biomarkers, but in fact, the Johnson et al. 2009 suggested

15 the use of ribosomal RNA fragments as a biomarker, but such

16 methods would require vetting and validation before they could

17 be used in Tier I screening. 

18 DR. DANIEL SCHLENK: Okay. Dr. McManaman? 

19

20 DR. JAMES MCMANAMAN: My comments were

21 incorporated in Dr. Berenbaum's response.

22 DR. DANIEL SCHLENK: And Dr. Ostiguy? 

23 DR. NANCY OSTIGUY: I concur with what's been

24 said. 

25 DR. DANIEL SCHLENK: Okay. Other Panel

26 members? Dr. James? 
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1 DR. ROSALIND JAMES: I'm going to call on Dr.

2 Tarpy because last night he mentioned a cage study where you put

3 the brood with a few workers. We're getting to that? All

4 right.

5 Dr. DANIEL SCHLENK: I think that's for

6 question 10 right? I think they're going to get to that later. 

7 Anyone else for D? New alternatives? Okay. Mr. DeCant, any

8 questions of clarification? 

9 MR. JOSEPH DECANT: Joe DeCant, EFED. Maybe

10 one question, Dr. Berenbaum, you mentioned about pupal

11 bioassays. Do you have references for these protocols? Have

12 they been around for a while? 

13 DR. MAY BERENBAUM: I also defer to Dr. Tarpy.

14 DR. DAVID TARPY: This is Dave Tarpy. I don't

15 have them off the top of my head. All I know is that there are

16 - well, we're writing a paper right now looking at that. I know

17 that many of those bioassays are also being utilized in the

18 Beltsville Lab. So, we can dig those up for you for sure, yeah.

19

20 DR. DANIEL SCHLENK: Yeah, it sounds like if

21 you could provide references for some of those, it would be

22 good. Dr. James? 

23 DR. ROSALIND JAMES: But this could only be a

24 contact toxicity bioassay. 

25 DR. DANIEL SCHLENK: Okay. All right, let's

26 go ahead and move onto letter E.
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1 MR. JOSEPH DECANT: Joe DeCant, EFED, question

2 9, subpart (e) - typically acute toxicity tests are concluded

3 between 48 and 96 hours. Please comment on the appropriate

4 duration of toxicity tests for assessing acute toxicity to

5 individual larval and adult bees. 

6 DR. DANIEL SCHLENK: And Dr. Berenbaum.

7 DR. MAY BERENBAUM: The problems interpreting

8 larval survival over a two to four day period is a metric of

9 toxicity. The four day endpoint represents 80 percent of the

10 period of active feeding. So, distinguishing between acute and

11 chronic becomes difficult. Some agents induce toxicity due to

12 the larval development, which would be over looked in the

13 proposed approach. In general, biological transitions such as

14 pupation provide a more reliable consistent and interpretable

15 endpoint. Time to pupation, pupation rate and pupal weights are

16 more biologically meaningful. Queens and drones can be assessed

17 separately. 

18 Such tests however, have not yet been vetted

19 thoroughly. Assessing acute toxicity over seven days for the

20 present time will allow the EPA to harmonize in the national

21 efforts at the OECD level until a satisfactory standard pupation

22 assay has been developed, but developing such a test should be a

23 priority. Among other things, these assays will be more

24 sensitive to pesticide impacts on growth rate and development,

25 such as IGRs.

26 DR. DANIEL SCHLENK: Thank you. Dr.
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1 McManaman? 

2 DR. JAMES MCMANAMAN: I concur. 

3 DR. DANIEL SCHLENK: Dr. Ostiguy? 

4 DR. NANCY OSTIGUY: I concur. 

5 DR. DANIEL SCHLENK: Other Panel members? 

6 Nothing further to add? Wow, that's a shocker. Okay. Oh

7 sorry. Kiss of death on there. Mr. Pistorius? 

8 MR. JENS PISTORIUS: Thank you. One thing to

9 add regarding the in vitro test, I think it is important that

10 also additional standards are testing. Now in the reference, it

11 has mainly mentioned dimethoate. Also fenoxycarb would be

12 necessary, so that should be principally considered in the

13 guidelines.

14 DR. DANIEL SCHLENK: Okay. Anyone else? 

15 Okay. Mr. DeCant, any questions of clarification? 

16 MR. JOSEPH DECANT: No more questions. 

17 DR. DANIEL SCHLENK: Oh, hold on. Ms. Garber

18 has something.

19 MS. KRIS GARBER: Kris Garber. Dr. Berenbaum,

20 you indicated that it be more relevant to use the seven-day

21 duration? And that's what we've been calling the chronic

22 toxicity test? So does that mean that you're suggesting the we

23 kind of eliminate the ideas of acute and chronic for larvae and

24 just have one, kind of one risk quotient, one toxicity test? 

25 DR. MAY BERENBAUM: This is May Berenbaum. 

26 This is the conclusion we reached yesterday. It's just
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1 biologically hard to interpret and acute exposure of a larva. 

2 So, given the relative return on investment in terms of, you

3 know, information that's relevant to deciding about risk and on

4 whether to proceed to Tier II. The general consensus, at least

5 among the discussants last night is that maybe just time to

6 pupation, that interval is more biologically informative. I

7 don't know if anyone else wants to weigh in here. 

8 DR. DANIEL SCHLENK: Mr. Pistorius? 

9 MR. JENS PISTORIUS: To now, the in vitro

10 method is described especially for the period of egg or young

11 larvae until the phase of pupation. But by doing this and

12 ending there, you may get a toxicity for the larvae when they

13 are white, but we know for some substances like fenoxycarb,

14 which has been shown to be critical under practical conditions

15 that those affect after pupation before emergence. So, by just

16 looking at the first seven days, you will miss this effect and

17 this has been an effect, which has been critical in practices. 

18 This is not developed yet in the in vitro method. It has not

19 been validated. So, this is a major issue that should be pushed

20 further immediately, that this test can be extended. Instead,

21 there are several endpoints in this test. One would be the

22 seven-day exposure and the larval mortality. Then the second

23 one is the time until hatch as the endpoint, the second one. 

24 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

25 DR. NANCY OSTIGUY: Basically, at least my

26 understanding of the conversation was that you would get the
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1 information about acute toxicity without actually having to run

2 a separate test. So, you then would be able to, right now,

3 what's being proposed and certainly is acceptable because it's

4 been tested and is reliable, is measurements to the seven days. 

5 Then, as soon as we get information that allows us to take the

6 larva to pupation and then to emergence, those would be good

7 endpoints to strive for. So, method development is needed for

8 the latter two pieces. 

9 DR. DANIEL SCHLENK: Okay. Dr. James? Yep. 

10

11 DR. ROSALIND JAMES: I'll just add, just in

12 case it's not clear. If you're going to do development time,

13 you'll be measuring the larvae every single day anyway. So,

14 you'll still have your 48 and 96 hour mortality measures in

15 there because you need to do that for your survival time

16 analysis. 

17 DR. DANIEL SCHLENK: Any other questions from

18 the Agency? We're good? Okay. Let's go ahead and take a break

19 right now. We will convene at 20 till.

20 (Brief recess.)

21 Mr. DeCant, you want to read in question

22 10(a)? 

23 MR. JOSEPH DECANT: Joe DeCant, EFED, question

24 10, subpart (a) - please comment on the conclusion that adequate

25 procedures have not been sufficiently developed and validated

26 for assessing chronic toxicity to individual bees in a risk
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1 assessment context.

2 DR. DANIEL SCHLENK: Dr. Berenbaum? 

3 DR. MAY BERENBAUM: The concept of the

4 individual adult bee presents a challenge to assessing toxicity

5 in Apis mellifera because of the importance of the social

6 context, age-related polyethism over time is circumvented in the

7 absence of a social medium in foragers are hard-to-keep alive

8 individually. However, exposing emerging workers for 10 days at

9 low doses with known ages of bees has been attempted according

10 to OECD 213 with some success. 

11 Cage brood tests and broodless bee cup tests

12 may also be employed productively in this context. So, it's

13 important to emphasize that, although there may not be at the

14 moment entirely reliable and interpretable assays assessing

15 chronic toxicity is nonetheless remains very important.

16 DR. DANIEL SCHLENK: Okay. Thanks. Our next

17 discussants, Dr. - where am I - Dr. James, yes? 

18 DR. ROSALIND JAMES: That's me, Rosalind

19 James. I concur. I would just add that we had some discussion

20 about this on the previous question about possible chronic tests

21 that could be done in lieu of doing actually the acute tests.

22 DR. DANIEL SCHLENK: Dr. Pettis? 

23 DR. JEFF PETTIS: I concur with what's been

24 said. 

25 DR. DANIEL SCHLENK: Dr. Ostiguy? 

26 DR. NANCY OSTIGUY: I concur.
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1 Dr. DANIEL SCHLENK: Other panel members want

2 to weigh in on this one? Okay. Mr. DeCant, do you have what

3 you need on that one? Okay. Let's move onto 10B. Want to read

4 that in? I have to admit, that was Keith Sappington's

5 recommendations. 

6 MR. JOSEPH DECANT: Joe DeCant, EFED -- please

7 comment on the potential use of the 10 day adult worker and

8 seven-day in vitro larval toxicity tests discussed in the White

9 Paper for assessing chronic toxicity once these methods are

10 fully vetted.

11 DR. DANIEL SCHLENK: Dr. Berenbaum? 

12 DR. MAY BERENBAUM: The optimal design, we

13 feel, for a larval in vitro test would be one that encompasses

14 the entire active feeding period through pupation with time to

15 pupation, pupal weight, percent pupation, as quantifiable and

16 interpretable measurements of pesticide impact. Such a test can

17 provide data on both acute and delayed responses. It goes

18 without saying, but I will nonetheless say, that such testing

19 requires vetting of course. 

20 With respect to adults, the use of a 10-day

21 viability test for adult bees, although it does present some

22 interpretation challenges, nonetheless has a virtue of allowing

23 for harmonization with ongoing efforts in Europe. 

24 DR. DANIEL SCHLENK: Dr. James? 

25 DR. ROSALIND JAMES: Yes please do them. I

26 don't know how else to emphasize that the 48 hour, 96 hour tests
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1 are a great frustration to anybody working with bees and

2 toxicity is how come EPA looks at such a short time period? I

3 believe perhaps Dr. Pistorius can comment on - there are some

4 standard OECD tests already available for the seven-day test.

5 MR. PISTORIUS: Thank you. If I may answer

6 directly, there is no real OECD test guideline available for the

7 10 day test, but what is done and what is asked for in special

8 cases with the concern of especially insecticides is that the

9 OECD 213 is used and the protocol is used accordingly to OECD

10 213, which means basically that all the test conditions, like

11 how many bees per cage in all this is used accordingly, but the

12 test is extended to 10 days. The only difference is that

13 usually freshly emerged bees are taken, so this test could

14 easily be done accordingly. 

15 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 

16 DR. JEFF PETTIS: Just a bit of followup of

17 when Jens said that in doing these cage studies, you often see

18 that they use random age bees and for a longer test, it would

19 not be appropriate. You would want to start the test with newly

20 emerged bees. 

21 DR. DANIEL SCHLENK: And Dr. Ostiguy? 

22 DR. NANCY OSTIGUY: I concur with what's been

23 said. 

24 DR. DANIEL SCHLENK: Okay. Any other Panel

25 members? Okay. Mr. DeCant, any questions of clarification? 

26 Okay. Let's move on to C please and read that into the record.
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1 MR. JOSEPH DECANT: Joe DeCant, EFED, question

2 10, part C - although a 10-day adult and seven-day larval

3 toxicity tests have been proposed, please comment on whether

4 alternative durations of pesticide exposure may be more

5 appropriate for determining chronic toxicity for adult and

6 larval bees at a Tier I screen.

7 DR. DANIEL SCHLENK: And Dr. Berenbaum? 

8 DR. MAY BERENBAUM: A 10-day test of adult

9 survival can be informative, but interpreting the biological

10 meaning outside the social context remains challenging. Rather

11 than an alternative duration of exposure, an alternative design,

12 which we've all ready discussed, might be considered. Survival

13 could be assessed in social context with the use of these cage

14 brood or broodless bee cup tests, which have been used with some

15 success in other contexts.

16 As for the larval toxicity tests as we stated

17 earlier, estimating the test duration through pupation rather

18 than terminating it at an arbitrating interval would be most

19 appropriate. 

20 DR. DANIEL SCHLENK: Dr. James? 

21 DR. ROSALIND JAMES: I concur with what was

22 said. Just to clarify because she said it quickly, I mean there

23 is an alternative way of doing brood tests. The standard tests

24 are putting brood inside a container like a 48 well played and

25 feeding them by hand and cleaning them, but you can also cage

26 adult bees with some brood and allow the adult bees to care for
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1 the larvae, and you can still monitor the mortality of the

2 larvae.

3 DR. DANIEL SCHLENK: Dr. Pettis? 

4 DR. JEFF PETTIS: I concur with what's been

5 said. 

6 DR. DANIEL SCHLENK: And Dr. Ostiguy? 

7 DR. NANCY OSTIGUY: I concur.

8 DR. DANIEL SCHLENK: Any other comments? Mr.

9 Pistorius?

10 MR. JENS PISTORIUS: Thank you. Maybe chronic

11 toxicity is a question of definition for the larva bees. It is

12 for the larval test always the question of dosing. Is it acute

13 dosing at one day of feeding or dosing on every day of feeding? 

14 We discussed this in the group and we were of the opinion that

15 everyday should be - the pesticide should be added to food every

16 day. 

17 DR. DANIEL SCHLENK: Other Panel members? 

18 Yeah, Dr. James? 

19 DR. ROSALIND JAMES: I'll just add to that a

20 little bit. Essentially the larval feeding period is so short. 

21 A difference between acute and chronic, you know, it's hard. We

22 have difficulty defining it. It's such a short period. It

23 should be treated, perhaps like chronic, but it is still not a

24 very long test. 

25 DR. DANIEL SCHLENK: Dr. Tarpy? 

26 DR. DAVID TARPY: This is Dave Tarpy. I
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1 think, to Dr. James's point, that that's why we avoided in

2 question 8 beginning this morning, we're seeing a one-time

3 application versus a continuous application type of language

4 rather than acute and chronic, just to avoid that distinction of

5 previous usage of that.

6 DR. DANIEL SCHLENK: Anyone else? Dr. Hunt? 

7 

8 DR. GREG HUNT: Well, I'll just reiterate what

9 has been said before. The reason behind this is that it's more

10 biologically relevant for the larvae because they're expected to

11 be exposed to chronic doses. 

12 DR. DANIEL SCHLENK: Okay. Dr. Klaine? 

13 DR. STEPHEN KLAINE: Yeah, but I don't want to

14 lose something that Dr. Delclos mentioned earlier, and that is

15 timing of pesticide application. Really, the results are not

16 really known. So, while for this particular test and a

17 standardized test, it might be worthwhile to have pesticide dose

18 to each day. Somebody needs to look at what happens when they

19 only get in on, say day three or on day five, in terms of the

20 development and ultimately the affect on things like pupation,

21 et cetera. 

22 DR. DANIEL SCHLENK: Dr. James? 

23 DR. ROSALIND JAMES: But within the colony,

24 you're going to have all different life stages going on at the

25 same time, so within a colony - my name is Rosalind James -

26 within a colony, you're going to have all the different life
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1 stages going on at once, and you'll also have all this

2 trophallaxis and storage. It's just not - it's really rare that

3 they're just going to one day get a dose in a colony and then

4 it's all gone. 

5 DR. STEPHEN KLAINE: Steve Klaine - I agree

6 with you about the different life stages going on at the same

7 time, but in terms of from an exposure situation, it's not

8 unrealistic to think that they would have a pulsed exposure

9 where they might only see the pesticide one day or two days,

10 depending on how that exposure occurs. 

11 DR. DANIEL SCHLENK: Dr. Berenbaum? 

12 DR. MAY BERENBAUM: Physiologically, it would

13 be very useful and informative to know when pesticides exert

14 their effects throughout the development, of course, their

15 development of larvae. But in terms of the ecology of exposure,

16 it's difficult to imagine how larvae would be subjected to acute

17 exposures that weren't also at the same time horrifically

18 catastrophic. So, you can imagine some kind of direct spray to

19 the entire colony. But, larvae don't go anywhere and everything

20 they eat is handed to them and it's already processed food. So,

21 it's already been through several levels of - well it's been

22 through several layers of bees actually.

23 So, my bee biology colleagues, correct me if

24 I'm wrong, but it's hard to conceive of a scenario of acute

25 exposures that recur frequently at different developmental

26 stages. 
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1 DR. DANIEL SCHLENK: Dr. Ostiguy? 

2 DR. NANCY OSTIGUY: I think we might be

3 talking about two different things here. Dr. Klaine, I think

4 you're correct that it's a data gap that we don't know at what

5 point we might have physiological changes that trigger in terms

6 of Tier I or Tier II. I don't know that we need that kind of

7 test done by an applicant. So, it's a data gap in terms of

8 understanding some of the information that we might get from a

9 Tier I or Tier II test, but it's not probably necessary to do

10 during a Tier I or Tier II test. 

11 DR. STEPHEN KLAINE: This is Steve Klaine. 

12 You interpreted me correctly. Thanks. 

13 DR. DANIEL SCHLENK: Dr. Pettis? 

14 DR. JEFF PETTIS: I agree with Dr. Berenbaum

15 that the majority of the food is processed and goes into larval

16 development, but there is some direct pollen feeding and direct

17 honey that is fed in late larval stage. So, there could be

18 scenarios where you might see things coming into the hive that

19 are then used directly. 

20 DR. DANIEL SCHLENK: It seems to me that

21 you're kind of interested in windows of susceptibility during

22 that developmental stage. So, when are those sort of critical

23 periods of susceptibility that, you know, changes maybe an

24 outcome that happens at that day, but wouldn't happen in another

25 exposure period. I think that's - yeah. Any other comments or

26 discussion? Okay. Let's move onto letter D. Sorry, I didn't
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1 ask for clarification questions, I'm sorry. Mr. DeCant? 

2 MR. JOSEPH DECANT: Yeah, Joe DeCant, EFED.

3 Earlier in the discussion, Dr. James, I think you brought up

4 the caged bee test, and that came up earlier in some of the

5 Panels' comments. Is it a semi-field study that you are talking

6 about or is this a laboratory based? 

7 DR. DANIEL SCHLENK: Yes, Dr. Tarpy? 

8 DR. DAVID TARPY: This is Dave Tarpy, NC

9 State. These are in vitro studies, so they're all done in an

10 incubator under controlled conditions, so these would be Tier I.

11 However, I think what was just mentioned is by combining adults

12 and larvae in vitro starts to blur Tier I and Tier II, but there

13 are Tier I group bioassays that have been used successfully. 

14 That's what mostly we've been referring to up until now. 

15 DR. DANIEL SCHLENK: Yes, Dr. James?

16 DR. ROSALIND JAMES: We had some discussions

17 about how you would do the dosing. I mean, perhaps you could

18 dose the larvae directly by hand and then have the adult bees

19 take care of them so the exposure of the adult bees was minimal

20 and you could make it a larval test. But, you're using a little

21 more natural method for caring for the brood. When you do it

22 yourself by hand, you maybe feed the bees every four hours or

23 how often you feed them, and you have to clean out the wells. 

24 It's very labor intensive to do honey bee larval bioassays. 

25 It's a lot easier to let the nurse bees do this for you and they

26 feed about every half hour, so the brood gets better care, and
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1 it would just be a little natural setting and this was one idea.

2 So, it would be easier to conduct, I think, and the brood would

3 get better care, so you would have lower control mortality.

4 MR. JOSEPH DECANT: Joe DeCant, EFED. So,

5 maybe just a clarification that this cage of brood study would

6 be a replacement for the larval toxicity study to be able to

7 take it through pupation? Or is it an alternative design that

8 could be used, but the larval toxicity study as we have it in

9 the Tier I screen would be an appropriate replacement given that

10 we can take it through pupation. Once it goes through pupation,

11 it would be sufficient in lieu of the cage brood study. 

12 DR. DANIEL SCHLENK: Dr. James? 

13 DR. ROSALIND JAMES: Sorry, Dr. Tarpy, are

14 there standard methods published on this method? I am familiar

15 with the method, but I'm not familiar with the literature on it.

16

17 DR. DAVID TARPY: This is Dave Tarpy. I don't

18 think there are standard methods on a caged brood study, but it

19 is certainly, I think, feasible. But I don't think there is a

20 standard think like the Aupinel paper on the hand rearing of

21 larvae. 

22 DR. DANIEL SCHLENK: Yes, Dr. James? 

23 DR. ROSALIND JAMES: So, in lieu of that

24 answer, my answer to you would be it would be an alternative

25 method that could be used. 

26 DR. DANIEL SCHLENK: Dr. Ostiguy, did you have
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1 something to add? No. Dr. Berenbaum? 

2 DR. MAY BERENBAUM: And Aupinel offers the

3 benefit of harmonization with European ongoing efforts. There

4 are advantages to that, at least until method alternatives are

5 developed. 

6 DR. DANIEL SCHLENK: Okay. Any other

7 questions Mr. DeCant? Okay. Let's move onto D.

8 MR. JOSEPH DECANT: Joe Decant, EFED, question

9 10, subpart (d) - the White Paper identifies NOAEC as the

10 chronic toxicity measurement endpoint. Please comment on the

11 possible use of ECx values as a measure of chronic toxicity for

12 use in RQ calculations. 

13 DR. DANIEL SCHLENK: Dr. Berenbaum? 

14 DR. MAY BERENBAUM: I must confess, there was

15 a considerable amount of confusion about this question. If I'm

16 not answer the question you're asking, I'm hoping that associate

17 discussants will address it better. 

18 But the gist of our conversation generally is

19 that the proposed regression based method for calculating ECx

20 levels seems feasible if a sufficient number of concentrations

21 can be tested to allow for confident estimates given the

22 competence levels do drop at the high and low ends of dose

23 response curves. 

24 Some discussion ensued on including a temporal

25 component of chronic effects to provide a quantitative estimate,

26 sort of a lethal time estimate like quantitative analysis of
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1 survivorship curves using Kaplan-Meier type survivorship as a

2 way of estimating chronic impacts. I will leave it to the

3 associate discussants to elaborate.

4 DR. DANIEL SCHLENK: Dr. James? 

5 DR. ROSALIND JAMES: I like the use of

6 something like a survivorship analysis that gives you a

7 different kind of endpoint, but it doesn't give you and

8 effective concentration because it gives you a time to effect

9 for a given dose, and then has to be repeated at different

10 doses. So, the calculation of the RQ wouldn't fit very well in

11 the survivorship type of analysis. I don't have a better answer

12 for you than that. 

13 DR. DANIEL SCHLENK: Dr. Ostiguy? 

14 DR. NANCY OSTIGUY: I concur with what's been

15 said.

16 DR. DANIEL SCHLENK: Dr. Pettis? 

17 DR. JEFF PETTIS: I have nothing to add.

18 DR. DANIEL SCHLENK: Okay. Other Panel

19 members? Yes, Dr. Fefferman? 

20 DR. NINA FEFFERMAN: So, I don't know if any

21 of them would wind up appropriate, but there are a diversity of

22 different measures for combining dose response and survival

23 curves from the epidemiological human literature. So, that

24 might be somewhere to look for methods that might fit with your

25 requirements somewhat well, specifically in both cancer research

26 for carcinogens, but also more specifically even in cancer for
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1 teratogens. The teratogen studies and time-tell spontaneous

2 abortion or miscarriage is a place where I've seen a diversity

3 of measures that might work really well for you.

4 DR. DANIEL SCHLENK: Any other input? Okay. 

5 Oh, sorry, Mr. Pistorius?

6 MR. JENS PISTORIUS: Maybe just a comment - in

7 the coming OECD Guidance document, there is an attempt to

8 integrate this chronic toxicity measurement endpoints and using

9 ECx values and the use for risk assessment in trigger values. 

10 DR. DANIEL SCHLENK: Okay. If we could maybe

11 reference that in the minutes, that would be good. Okay. Any

12 other comments from the Panel on D? Okay. Mr. DeCant, any

13 questions of clarification or Ms. Garber? Yeah? 

14 MS. KRIS GARBER: Yes please, Kris Garber. I

15 think it might be a little helpful just to clarify the question

16 a little bit, but I don't think we're quite getting what we're

17 looking for, would that be all right? 

18 DR. DANIEL SCHLENK: Sure, yeah.

19 MS. KRIS GARBER: Okay. So, just to put it in

20 my own words, you know the endpoint that we traditionally rely

21 upon to measure a chronic duration of exposure would be the no

22 observed adverse effects level and you know, the design of that

23 study. The study is designed in a particular way to derive that

24 NOAEC concentration and, you know, there's been a lot of

25 discussion in the scientific community about perhaps using an

26 ECx value, and X would be whatever we decided to be biologically
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1 relevant. You see 10, you see 20 or EC50, of course, is

2 relevant as well, and that would have, you know, a different

3 study design as well. 

4 So, you know the specific endpoint that we

5 would be going after for a chronic study is really relevant for

6 considering the design of the chronic study. So, we're just

7 curious on some thoughts for, you know, looking at effects to

8 bees and the endpoints that you have all been discussion of the

9 strengths and limitations of the NOAEC versus the EC whatever X

10 is. I hope that helps. 

11 DR. DANIEL SCHLENK: Dr. Berenbaum? 

12 DR. MAY BERENBAUM: We, or at least I was a

13 little confused about this question and how it relates to the

14 next question, which seems to be setting the EC level, question

15 C. So, maybe we can -- what percent effect - should I go on to

16 C? E, right, sorry. I am myopic. I just wondered if we could

17 meld these two, because what you're asking is where should EC be

18 set and should we use EC? No. 

19 DR. DANIEL SCHLENK: No, she is not asking. I

20 believe she's asking would you rather use a NOAEC or an ECx

21 value. And which one, what are the benefits and you know, cost

22 associated with those different measures essentially? Because

23 one is a more deterministic sort of endpoint and the other one

24 is much more based on the probabilistic curve. You set a value,

25 which could be 5 percentile, 10 percentile, 20 percentile. I

26 don't think she is asking what X should be, I think she's asking
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1 which method do you want to use. Would it be better to use, you

2 know, a single point that's statistically significant above the

3 note in terms of a single point value that you have in your data

4 set or a derived value that you get from a probabilistic curve. 

5 That's what it sounds like. Yes, Mr. Pistorius? 

6 MR. JENS PISTORIUS: I hope this is slightly

7 an answer or maybe helps. I assume now that for the chronic

8 toxicity testing to determine the NOAEC, you would use the

9 10-day tests. And then, it is the question how do you actually

10 come closer to the dosing that you had testing. Do you go from

11 an acute oral LD50 test identifying doses where you have no

12 LD50? Then it will be a matter that you have to test quite a

13 large number of doses to get a reliable level. Also, usually we

14 know that with no-observed effect of concentrations and a low

15 observed, that's why for other tests always the LD50 is used. 

16 The LD50 gives a lot higher certainty. So, that may be an

17 argument to rather use the ECx values. 

18 DR. DANIEL SCHLENK: Yes, Dr. James? 

19 DR. ROSALIND JAMES: My brain is hurting on

20 this question. But, the no-effect level will be more

21 conservative, and maybe with some of these complex behavioral

22 things, we want to trigger up higher tiered level. So, that

23 would be an argument for the no-effect level. But, I don't know

24 that we really know enough about the statistical variability

25 that we're going to get out of these bioassays to really answer

26 that question in terms what you're saying, unless you have
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1 experience with that. 

2 I really don't know how much variability

3 you're going to have. Because sometimes like, you know, an EC10

4 or an EC20 level may have a high amount of variability

5 associated with it. It might be easier to draw a line at the

6 no-effect level. You know, we are not always looking for the

7 easy way out, but in some ways, triggering the Tier II for the

8 behavioral effects might be a good thing to do if some effect is

9 seen. 

10 DR. DANIEL SCHLENK: Dr. Berenbaum? 

11 DR. MAY BERENBAUM: The one advantage of EC

12 versus no-adverse effect level is that it's always an open

13 question just to why no effects have been observed. So, an EC

14 measurement at least is quantifying a known response to the

15 degree that that's reassuring and repeatable that could be an

16 advantage for using EC rather than no observable effect. 

17 DR. DANIEL SCHLENK: Dr. Klaine? 

18 DR. STEPHEN KLAINE: Yeah, let me weight on

19 this. As you indicated before, using the NOAEC has a problem in

20 that fact that it's heavily dependent on the on experimental

21 design. So, I'm not sure that the issue with bees has any

22 relevance to which one you use. I think that the better choice

23 would be the EC number because its derived in a more realistic

24 or more probabilistic manner. But, I'm really pleased that you

25 brought up the issue of variability because, Dr. James, because

26 I think that's the real driver here. We all use EC50s because
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1 it's the most accurate. However, if you have the ability to use

2 a higher end, you then drive the uncertainty regarding an EC5. 

3 You decrease that uncertainty. So, it may make it more

4 realistic with this organism to actually generate EC5s than with

5 classic toxicological organisms where it's impossible to get the

6 N above the 3 or 4 or 5. So.

7 DR. DANIEL SCHLENK: Mr. Pistorius? 

8 MR. JENS PISTORIUS: I think that in any case,

9 it is an old problem that both the ECx values and the NOAEC

10 levels are reported from those studies. So, I just question

11 which one to use later on, but you can always, from those

12 studies, get both values. 

13 DR. DANIEL SCHLENK: Dr. Ostiguy? 

14 DR. NANCY OSTIGUY: If there is a question

15 about a low sample size, then my preference is the NOAEC. But

16 for a large sample size, setting a low EC value, that would

17 absolutely be preferable. So, the larger sample size would

18 certainly produce better data. 

19 DR. DANIEL SCHLENK: Any other comments? Ms.

20 Garber, does that answer your question? Oh, Mr. Sappington? 

21 MR. KEITH SAPPINGTON: Keith Sappington. I

22 think, yeah, this question is difficult. The NOAEC versus the

23 hypothesis testing, i.e. NOAEC versus point estimation ECx issue

24 has been around for decades. So, that issue is generic. I

25 think it would be helpful - and there are advantages and

26 disadvantages to each one, and there have been a number of
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1 papers published on that. 

2 In my mind, it would be helpful to know if

3 given the biology of the honey bee or non-Apis bees, but given

4 the social nature of at least honey bees, might there be an

5 Apis-specific reason for favoring one over the other. So, that

6 is kind of perhaps where we're coming from, because this is an

7 issue for all tox tests. 

8 DR. DANIEL SCHLENK: Anybody want to comment

9 on that? Yes, Dr. Hunt? 

10 DR. GREG HUNT: I don't know much about

11 testing pesticides, but you can expect with honey bees to have

12 with this kinds of tests, kind of small sample size and hive

13 variability. So, that should factor into your thinking. 

14 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum,

15 since you're kind of the lead discussant, do you need any more

16 input in terms of how to answer that? Or are you good?

17 DR. MAY BERENBAUM: I don't think so. 

18 DR. DANIEL SCHLENK: Okay. Dr. Klaine? 

19 DR. STEPHEN KLAINE: Not to beat this, but Dr.

20 Hunt, why do we have to have small sample size? 

21 DR. GREG HUNT: Well, you don't have to have

22 small sample size, of course, given enough resources, but it's

23 difficult to get a lot of incubators and a lot of cages and to

24 the practicality of finding emerging brood, which you need to

25 emerge in an incubator and collect the bees, get it all going at

26 the same time before, you know, they start dying from lack of
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1 food and there are practical constraints on it. And we see in

2 cage studies hive variability without treatment in terms of

3 mortality. 

4 DR. DANIEL SCHLENK: Mr. Pistorius? 

5 MR. JENS PISTORIUS: One remark, I just saw

6 that in the OECD Guidance document, which would be out for

7 public commenting next week, I think, there is an approach

8 which, and also a small discussion about which could be used and

9 why. So, just as a recommendation maybe. 

10 DR. DANIEL SCHLENK: We will include that

11 reference as a document to come, I guess, as you meant.

12 MR. JENS PISTORIUS: It will be out for public

13 consultation, so everybody can vote on this by the end of next

14 week as a visual document. 

15 DR. DANIEL SCHLENK: Great. Okay. You have

16 what you need now? Let's move on into letter E.

17 MR. JOSEPH DECANT: Joe DeCant, question 10,

18 subpart (e) - please provide comments on what percent effect

19 would be considered a relevant measure of chronic toxicity for

20 individual bees given the potential compensatory effects which

21 honey bee colonies may exhibit relative to the effects of

22 pesticides. 

23 DR. DANIEL SCHLENK: And Dr. Berenbaum? 

24 DR. MAY BERENBAUM: Compensatory responses are

25 essentially a society-level phenomenon, so, inherently or maybe

26 better suited for Tier II assessment. Recruitment of precocious
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1 foragers provides a short-term solution to colonies but may

2 exert long-term costs on colony health. There is one study

3 cited in the White Paper, Khoury et al. 2011, that estimates a

4 reduction of worker longevity of 2.8 days corresponds to a loss

5 of 0.35 bees per day. 

6 With that said, then aiming for an EC level

7 that takes that number into account may be an approach for

8 addressing the compensatory behavior. How that translates,

9 we're not sure, but the recommendation is to be conservative. 

10 So, if it's not 35 percent, because its 0.35 bees per day, but

11 in general, based on discussions yesterday, it seemed that a

12 level of EC10 or EC20 might be conservative and attainable under

13 these experimental conditions. 

14 What we think the question is at what point

15 will worker losses send the colonies into the irretrievable

16 death spiral. And again, there was a general subjective

17 impression that 35 percent is not sustainable documenting that

18 with literature was difficult. So, there is basically one

19 study, possibly two studies. That was the extent of our

20 discussion. 

21 DR. DANIEL SCHLENK: Dr. James? 

22 DR. ROSALIND JAMES: Maybe some of the

23 modeling that we'll discuss later today or tomorrow will help

24 answer this question? 

25 DR. DANIEL SCHLENK: Dr. Ostiguy? 

26 DR. NANCY OSTIGUY: I concur with the
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1 comments.

2 DR. DANIEL SCHLENK: And Dr. Pettis?

3 DR. JEFF PETTIS: Just kind of reiterating

4 what Dr. Berenbaum said that there was a general feeling that

5 somewhere around 30 percent loss of foragers might be a tipping

6 point, if you will, on the colony level and then maybe something

7 slightly more conservative than the 20 percent realm might be

8 reasonable, but we don't have good data to back that up. And

9 yes, maybe a modeling approach would help.

10 DR. DANIEL SCHLENK: Okay. Any other - yes,

11 Dr. Hunt? 

12 DR. GREG HUNT: Just to clarify, and I'm not

13 sure, but I think that we're talking about like a 30 percent

14 ongoing loss. Correct me if I'm wrong, because a one-time loss,

15 they could recover from that.

16 DR. DANIEL SCHLENK: So, an ongoing loss of 30

17 percent sounds like the estimate, right? Dr. Tarpy? 

18 DR. DAVID TARPY: This is David Tarpy. I

19 should also note that in a full colony, we're still in Tier I

20 here, but in a full colony situation that that's very

21 environmentally contextual, so that different times of year,

22 colonies are more or less susceptible. So in the fall when

23 they're populations are naturally declining, a smaller

24 proportion of bee loss would probably have a much larger impact

25 versus in the spring when their colonies are rapidly increasing,

26 presumably more likely to be able to absorb that type of loss. 
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1 But again, modeling approaches I think would be very informative

2 here. 

3 DR. DANIEL SCHLENK: Dr. Fefferman? 

4 DR. NINA FEFFERMAN: So, just quickly - my

5 reason for our needs on that if that the ramp up is actually

6 supporting the continued ramp up, so that if you decrease too

7 much during that increased growth, what you've done has lost the

8 capacity to support increased queen fecundity. So, you're

9 right, I want to model it. 

10 DR. DANIEL SCHLENK: Mr. Pistorius? 

11 MR. JENS PISTORIUS: I would just like to

12 point out that in my opinion it is not appropriate to state 30

13 percent mortality no matter at what time of the season is

14 acceptable in this discussion here, and also later on I think,

15 30 percent mortality even of foragers could be compensable by

16 the colony, but this may be my perspective as a risk assessor is

17 not acceptable. 

18 DR. DANIEL SCHLENK: Yes, Dr. Ostiguy? 

19 DR. NANCY OSTIGUY: Actually, I think what our

20 discussion was that if it hit 30 percent, the colony was at the

21 point of no recovery. So, the level of acceptable loss level

22 has to be set less than that. The problem is that we don't have

23 data to say where that should be. Our sense was somewhere

24 around 20 percent. 

25 DR. DANIEL SCHLENK: Yes, Mr. Pistorius? 

26 MR. JENS PISTORIUS: I think that even in some
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1 cases, colony an recover if the damage is more than 30 percent

2 because we've had some incidents where larger percentages were

3 damaged. But on the other hand, I'm not quite sure if this is

4 the issue of the questions that we are currently discussion. I

5 don't know if we're supposed to intend to extend this discussion

6 at this moment.

7 DR. DANIEL SCHLENK: Dr. Ostiguy? 

8 DR. NANCY OSTIGUY: I think what we were

9 talking about was a continuous 30 percent loss, not a one-time

10 loss, because certainly colonies can recovery from a significant

11 loss depending on environmental conditions. 

12 MR. JENS PISTORIUS: I agree, and I think the

13 difficulty was that we have just been mingling laboratory issues

14 and realistic world conditions, basically in the discussions. 

15 But I agree to what Nancy Ostiguy has said. 

16 DR. DANIEL SCHLENK: Okay. Any other

17 comments? Okay. Mr. DeCant, do you have what you need for that

18 question? Okay. All right, let's move on to the next

19 subsection and read that into the record. 

20 MR. JOSEPH DECANT: Joe DeCant, EFED, question

21 10, subpart (f) - although the White Paper identifies some

22 measurement endpoints for assessing chronic toxicity to

23 individual bees such as survival, please comment on other

24 endpoints to consider in chronic toxicity studies which the

25 Panel believes are important and the associated study design

26 elements.
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1 DR. DANIEL SCHLENK: And Dr. Berenbaum?

2 DR. MAY BERENBAUM: As Dr. Tarpy mentioned in

3 the response to question 8, other endpoints to consider in

4 chronic toxicity tests may include behavioral impacts, learning

5 assays, automated recording of motility and behavioral

6 interactions and the like, although these haven't been vetted

7 extensively in the context of chronic toxicity. 

8 Future endpoints could include gene expression

9 relative to detoxification and/or antioxidant status,

10 immunocompetence senescence traits and the like. Such surrogate

11 biomarkers are widely used in human risk assessment. They

12 haven't been validated for bees, but in many ways, they are more

13 appropriate given the social nature of the organism. With that

14 said, we don't have any - at least yesterday, discuss any

15 guidelines known for identifying biomarkers or validating them. 

16

17 DR. DANIEL SCHLENK: Okay. Dr. James? 

18 DR. ROSALIND JAMES: I think we answered this

19 in 8B when we talked, the similar question was proposed for

20 acute toxicity. Since we were recommending just one test, we

21 really just answered the question already in 8B, time to

22 pupation and larval development time, success of pupation, that

23 sort of thing. 

24 DR. DANIEL SCHLENK: And Dr. Ostiguy? 

25 DR. NANCY OSTIGUY: One thing that we might,

26 that would be a future possibility, there has been work done
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1 looking at colony level ability to respond to health threats,

2 would be one way to describe it. And these are responses to

3 specifically, pathogens is where the work has been done that

4 occur at a colony level but not at an individual level. And,

5 that would then be potentially a future - well hopefully a

6 future test, but there's a lot of data that needs to be

7 collected to make that at all viable. 

8 DR. DANIEL SCHLENK: Sounds like the colony

9 level effects will be discussed later on, yeah? Okay. And Dr.

10 Pettis? 

11 DR. JEFF PETTIS: I concur with what has been

12 said. 

13 DR. DANIEL SCHLENK: Dr. Fefferman? 

14 DR. NINA FEFFERMAN: As one potential

15 mechanism for sublethal kinds of effects like this one that we

16 haven't discussed so much is response to temporal task

17 switching. So, most bees have an age-based polyethism of what

18 they'll do primarily, but in terms of things like health, not

19 just health threats, but things like if there's a point source

20 of heat, the recognition of individual bees to go and act as

21 their coolant batteries themselves, heat shielding, response to

22 heat shielding, response to solicitation from nurse bees and the

23 lack of nursing bees from older bees, switching back to nursing

24 with that stimulus. Those kinds of task switching behaviors

25 could be critical over chronic toxicity. 

26 So, all of the these questions sort of boil
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1 down into my mind into how stoned does the average New Yorker

2 have to be for the busses to stop running? And that's really

3 hard to measure locally. So, but I haven't heard anyone talk so

4 much about the communication and solicitation response aspects

5 of colony organization. I want to put that out there as one

6 thing to try and measure as we look at this.

7 DR. DANIEL SCHLENK: Mr. Pistorius? 

8 MR. JENS PISTORIUS: In addition to what Dr.

9 James has said, on the in vitro studies, I just want to refer

10 the comment I made before for the acute contact and oral

11 toxicity studies that in this case. Also the behavioral aspects

12 should be noted and this might, even if not really, a trigger

13 can be recommended on what portion of bees we see those effects

14 that the risk assessments needs to have the flexibility to

15 trigger other tests on this information of sublethal behavioral

16 abnormalities, especially because when you think that a chronic

17 toxicity test is aiming at doses going below the LD50 and its

18 behavioral abnormalities persist, then it might trigger further

19 concern. The risk assessment needs an option to go to a higher

20 tier test or to other tests as needed. 

21 DR. DANIEL SCHLENK: Okay. Any other input? 

22 Yes, Dr. Potter? 

23 DR. THOMAS POTTER: Yes, this is Tom Potter. 

24 We had a few side bar discussions here at the last break,

25 discussing the possibility of using respiration measurements as

26 index of stress. And, I would be interested to hear some
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1 comments from the rest of the Panel on whether or not that would

2 be a useful approach. 

3 DR. DANIEL SCHLENK: Anybody want to weigh in

4 on that one? Dr. James? 

5 DR. ROSALIND JAMES: This is a little remote,

6 but my first thought is that there really aren't any

7 standardized sort of methods for relating that to anything,

8 except there is a fair amount of work done with respiration

9 rates and diapause in the dormant solitary bees. The honey bee.

10 DR. DANIEL SCHLENK: Can you - do you want to

11 - can you get that on record please? 

12 DR. NINA FEFFERMAN: Sorry, Nina Fefferman. 

13 Just that there's a lot on colony level respiration needs and

14 stress and success of colonies in ants, but I don't know of

15 anything in bees.

16 DR. DANIEL SCHLENK: Yes, Dr. James? 

17 DR. ROSALIND JAMES: At the colony level, so a

18 Tier II type of test? 

19 DR. NINA FEFFERMAN: Actually both individual,

20 but those tend to be challenged type individual studies where

21 they take an ant and they either an antimate pheromone signal

22 that signals help, there's a bird or something like that, and

23 then there's sort of what you would call psychological stress

24 for individual ants, but then yeah, most of the stuff is really

25 quite good for regulating stress and colony impact at the Tier

26 II level, yeah.
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1 DR. DANIEL SCHLENK: Yes, Dr. Hunt? 

2 DR. GREG HUNT: Well, there are studies in

3 looking at the flight metabolic rate of honey bees, which have

4 the highest mass flight metabolic rate of anything, even humming

5 birds. So, I mean that could be investigated, and it being a

6 physiological test, it could be a good stress test. 

7 DR. DANIEL SCHLENK: Yes, Dr. Pettis? 

8 DR. JEFF PETTIS: I was going to follow up on

9 what Greg said that we have these treadmills that you can

10 actually just glue the bee to and force it to fly and look

11 energetic, so it might be - it hasn't been fully developed, but

12 it might be a way to go about it. 

13 DR. DANIEL SCHLENK: So, a flight tunnel,

14 right? Okay. So, it sounds like there are some other endpoints

15 that are still in development so to speak that could be, you

16 know, proposed - physiological biomarkers perhaps, if you want

17 to put it into a category I guess. Okay. Any other comments on

18 other endpoints? Okay. I think we got barely enough brain left

19 for one more question before lunch here if we can squeeze in

20 this last letter here. So, Mr. DeCant, do you want to read that

21 into.

22 MR. JOSEPH DECANT: Joe DeCant, question 10,

23 subpart (g), section 4.1.3 of the White Paper discusses the

24 uncertainties associated with developing risk assessments based

25 on studies of sublethal effects when sufficient linkages have

26 not been developed to understand how the sublethal endpoints may
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1 be quantitatively related to typical assessment endpoints, that

2 is growth, impaired survival, and reproduction, at the whole

3 colony level. Please comment on the proposal to use data on

4 sublethal endpoints to qualitatively, that is, no Risk Quotients

5 are derived to characterize effects and risk until sufficient

6 linkages between these endpoints and the defined assessment

7 endpoints have been developed as in the Adverse Outcome

8 Pathways.

9 DR. DANIEL SCHLENK: Dr. Berenbaum? 

10 DR. MAY BERENBAUM: The social environment

11 complicates the process of identifying reliable, reproducible

12 and relevant sublethal endpoints. But given the complexities,

13 sublethal endpoint use is conservative and is very important for

14 providing insight into super-organism responses and they are

15 critical triggers for signaling a need for moving onto Tier II

16 or higher tiers. So, we would be considered generally advisable

17 not to discount or dismiss sublethal endpoints. 

18 An important priority for future research

19 though, would be developing assessments for neurotoxicity

20 symptoms. Although direct links to colony health may not yet be

21 fully understood, they're indicative of biological activity of

22 chemicals and therefore appropriate candidates for further

23 investigation at the colony level. 

24 So, the results of these types of studies can

25 demonstrate a need for additional quantitative tests at Tier II.

26 Eventually, links have to be made, but given the state of
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1 knowledge at the present time, sublethal endpoints are really

2 critical indicators for biological activity that may be

3 difficult to assess using traditional toxicological measures

4 designed for organisms that are not so dependent on a social

5 environment. 

6 DR. DANIEL SCHLENK: Okay. Dr. James? 

7 DR. ROSALIND JAMES: I concur, but I will add

8 in defense of the nonsocial bees, they have complex behaviors

9 also in nest building and so on. It would be applicable to the

10 non-Apis bees as well. 

11 DR. DANIEL SCHLENK: Dr. Ostiguy? 

12 DR. NANCY OSTIGUY: Just in case this matters,

13 there is not a 4.1.3, so I think it's a 4.1.2.1. So, we think

14 we're answering the correct one, but let us know if we're not. 

15 DR. DANIEL SCHLENK: Okay.

16 DR. MAY BERENBAUM: This is May Berenbaum. 

17 4.1.2.1.2 is the section. 

18 DR. DANIEL SCHLENK: Yeah. I got that one. 

19 Okay. Dr. Pettis? 

20 DR. JEFF PETTIS: I concur. 

21 DR. DANIEL SCHLENK: That we're on the right

22 number? 

23 DR. JEFF PETTIS: We actually spent quite a

24 bit of time trying to find the 4.3. Are we missing something,

25 but no we haven't. 

26 DR. DANIEL SCHLENK: Just a challenge, it was
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1 a challenge. Yes? Dr. Fefferman? 

2 DR. NINA FEFFERMAN: Sorry, in all

3 seriousness, this is yet another one that I think we'll end up

4 discussing the context for later. But really anytime that these

5 questions relate to how to scale unknown relationships linking

6 individual level sublethal effects, or lethal effects frankly,

7 to the colony level compromise, that's a place where modeling

8 actually may be able to help. Even working backwards toward a

9 Tier I type study to recommend what kinds of sublethal behaviors

10 could be used as proximate measures in individuals that would

11 then scale up to call any compromise, but we'll talk about that

12 more later.

13 DR. DANIEL SCHLENK: Any other comments? So,

14 it sounds like at least for adverse outcome pathways,

15 neurotoxicity would be at least one sort of pathway to pursue

16 it. I'm interested in the Panel's response to the beekeeper

17 that had the eviscerated males that were lying around. Would

18 that indicate maybe and endocrine pathway would be another sort

19 of target that would be something to explore. Yes, Dr. Hunt? 

20 DR. GREG HUNT: I don't really know why drones

21 fall dead with their genitalia everted, but I think it has

22 something to do with general stress. I'll leave it there.

23 DR. DANIEL SCHLENK: Yes, Dr. Tarpy? 

24 DR. DAVID TARPY: This is David Tarpy. This

25 is pure speculation, but because the kind of default situation

26 for drones is to turn themselves inside out to evert their
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1 genitalia to mate, but it's some of the lower ganglion that

2 inhibit that, that it actually suggest a potential neural

3 pathway for that particular report. But, that's again, just

4 speculation. 

5 DR. DANIEL SCHLENK: All right. Any other

6 comments? Mr. DeCant, do you have questions of clarification? 

7 MR. JOSEPH DECANT: Joe DeCant, EFED. I just

8 want to sort of make sure that I understood the panel correctly,

9 that the sublethal endpoints, some of them are still being

10 developed. The assays are either still in development or we're

11 still waiting on the quantitative linkage between the sublethal

12 endpoints and some of the colony level effects. Given that

13 though, these sublethal endpoints are important and that while

14 we may not use them quantitatively, the panel would agree that

15 we should use them qualitatively and perhaps use them to also

16 trigger higher tier studies. 

17 DR. DANIEL SCHLENK: I see most heads nodding

18 on that one. So, it seems like a general agreement. Okay. All

19 right, I think we're sufficiently toasted. So, let's take a

20 lunch break and come back at 12:45. 

21 (WHEREUPON lunch was taken)

22 All right, if you would please take your

23 seats. Okay. Let me just go to the Agency. Do you have any

24 other clarifying questions that you have before we start with

25 question 11 on any of the previous questions? We're good? 

26 Great. Okay. Let's go ahead and read 11A into the record if
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1 it's up there. 

2 MR. JOSEPH DECANT: Joe DeCant, EFED, question

3 11, subpart (a) - although colonies are typically confined to

4 enclosures for Tier II studies and these enclosures can limit

5 the environmental realism of the study conditions, tunnel

6 studies provide an opportunity to collect colony-level effects

7 and potentially exposure information. Please comment on the

8 relative strengths, that is foraging activity by adult worker

9 bees is limited to treated crop; trophyllaxis enabled and

10 limitations such as limited study duration, smaller colony sizes

11 and reduced forage area of these methods.

12 DR. DANIEL SCHLENK: Lead discussant for

13 that's Dr. Tarpy. 

14 DR. DAVID TARPY: This is David Tarpy, NC

15 State, good afternoon everybody. I want to preface the comments

16 in talking about Tier II and other studies by kind of briefly

17 reiterating the point of linking this to our discussion this

18 morning about Tier I bioassays, and how when we're looking at

19 social insects such as honey bees that it starts to blur the

20 lines very much. So, that simple LD50 mortality measurements of

21 individual adult worker honey bees doesn't actually really

22 capture the myriad of different exposure pathways and other

23 effects of social insects. So, this to me is really the

24 fundamental question that faces the panel and is driving a lot

25 of our discussions for this White Paper. 

26 So, one means of addressing that obviously is
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1 to incorporate a lot of these other bioassays that we discussed

2 in question 8 and others to help bridge this gap, but it still

3 doesn't really fully serve as a proxy for colony-level

4 phenotype. And again, this is because honey bee colonies are

5 like super organisms. So, in that way, biologically the basal

6 unit is that entire colony. It's very hard to test these

7 systems in a reductionistic approach. 

8 The kind of analogous or an analogy to this is

9 to study individual cell cultures of say a rat and looking at

10 LD50s of individual cells of that cell culture and then trying

11 to extrapolate that to the entire rat. So, the way that it's

12 actually done is to study individual rats and looking at LD50s

13 of different compounds of whole rats. This is the same that is

14 true for entire colonies. 

15 So, in some ways, it's inescapable to have to

16 test entire colonies to find full phenotypic effects at that

17 level. Not to say that Tier I screens of individual bees or

18 brood are not informative, it's just that they are not perfect

19 proxies or surrogates for higher levels at the colony level. 

20 So with all that said and with all that in

21 mind, the question asked to identify some strengths and

22 weaknesses of Tier II studies, and obviously some to strengths

23 or because it's measuring at in vivo in this super organism

24 context, it does better enable and capture a lot of those

25 complex social interactions that are going on that result in

26 full fledge colony phenotype. 
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1 The limitations, many of which are kind of

2 logistical in that there is difficulty in getting large sample

3 size when you're dealing with entire colonies. It's just a

4 realistic problem with trying to get sufficient number of

5 colonies, especially because they're so highly variable social

6 insects. One of their keys to their ecological success is their

7 huge variability and their ability to adapt different

8 environments. So, there's a lot of variation across colonies. 

9 So, trying to account for that requires higher sample sizes and

10 that's obviously a real logistical constraint. Again,

11 automation of data collection and means to be able to streamline

12 that would be incredibly helpful, but I don't think the

13 technology is quite there yet.

14 In doing so, it's always helpful - or the

15 Panels feel that the type Tier II studies, it's very helpful to

16 contextualize a lot of these colony level phenotypes by always

17 having adequate control, either positive and/or negative control

18 groups to relativize it. That helps be able to place the

19 findings into the proper context. It's hard to have kind of

20 universal measurements across these different studies because

21 they're done different times of the year under very different

22 conditions, environmental conditions. So, doing to contextually

23 is very helpful.

24 We can discuss this in part B and in other

25 places, but we do also want to throw out the notion that the two

26 types of type II studies that were discussed in the White Paper,
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1 one was enclosed foraging arenas in cages and then others where

2 they were on a treated target crop and other where they were

3 free flying, but they were being actively fed through

4 supplemental feed these different compounds in question that I

5 think that the former approach might be more amenable to other

6 social insects, particularly bumble bees. 

7 Honey bees are notoriously difficult to keep

8 in small enclosed areas. Their foragers always want to be

9 trying to go outwards, further outwards, and that's why honey

10 bees are really not used in greenhouse pollination, for example.

11 They're just not as amenable to that type of condition. Bumble

12 bees, however, which are primitively used social and not highly

13 used social, but they still live in colonies. They tend to be

14 much more amenable to those types of systems. 

15 For the free-flying type of approach however,

16 honey bee colonies do have some strengths and would probably be

17 more amenable to that type of Tier II approach. 

18 DR. DANIEL SCHLENK: Okay. Thanks. Dr.

19 Ostiguy? 

20 DR. NANCY OSTIGUY: Our concern about Tier I

21 studies that Dr. Tarpy was mentioning because of honey bees

22 being a social insect may be partially addressed when we talk

23 about question 14 and I'm going to probably use the wrong word,

24 Nina, but meta-models. So, the higher level of modeling so we

25 can select which of the Tier I studies might actually provide us

26 with the best information about what's going to happen then at
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1 the colony level. 

2 So, there may be a way for us to address that

3 social insect issue that in some ways is better studied

4 empirically or with hard data with Tier II and Tier III, Tier I

5 still may provide us with some useful information once we have a

6 better idea of which of those Tier I pieces of data that we

7 might collect, actually would trigger colony-level problems. 

8 DR. DANIEL SCHLENK: Thanks. And Dr. Pettis? 

9

10 DR. JEFF PETTIS: Just a bit to add. There's

11 a question about colony size in some of these Tier II and Tier

12 III studies and how small colonies might not be representative

13 of large colonies. I think if small colonies are properly set

14 up, even if their nucleus colonies, the can be somewhat

15 representative of whole colonies. You actually might - using

16 small colonies, you actually might see an effect, which you

17 wouldn't see in larger colonies. So, I just wanted to get that

18 point in. I concur with the other comments. 

19 DR. DANIEL SCHLENK: Other Panelists? Yes,

20 Dr. Berenbaum? 

21 DR. MAY BERENBAUM: Just one caveat about

22 supplemental feeding, there are among these types of studies

23 posed in the literature, there is supplemental feeding and

24 there's force feeding, and it's important to emphasize that

25 honey bee behavior is incredibly sophisticated, particularly

26 with respect to food selection and food evaluation. So, suffice
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1 to say that not all supplemental feeding studies may be

2 equivalently useful. 

3 DR. DANIEL SCHLENK: Okay. Mr. Pistorius?

4 MR. JENS PISTORIUS: I would like to go into

5 detail on the usefulness of different semi-field studies

6 described here, those feeding studies later on. Let me say this

7 all ready, I think it is absolutely -- it has to be refused to

8 use such feeding studies. They are - for me personally, they

9 are intermediate between Tier I and Tier II, but they're not

10 really a Tier II test. I would give the justification for this

11 rather on the point D when we look at different studies. 

12 So, now for me, I'm talking about the OECD 75

13 and EPPO 170, which I think are very appropriate to with the

14 worst case enclosure to assess the acute effects on bees. Of

15 course, there are some limitations we know in the confinement. 

16 For instance, there may be a natural reduction of the brood

17 because the bees don't want to breed as much under natural

18 conditions, that in effect what you can eradicate by appropriate

19 replications. But I think that, especially the EPPO 170 and

20 OECD 75 are the ones to go for as a Tier II test. 

21 You have acute and you've got oral and contact

22 exposure combined. What has come out of the comments that we

23 basically want to have a look at all the different exposure

24 routes and effect on the bee colony. And small colonies, I

25 agree with Jeff, may be used to interpret the effects on

26 colonies to some extent. In some cases, it might be a necessary
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1 step to go to Tier III, but it gives you a clear indication,

2 especially the OECD 75 method, I think, is very reliable. For

3 one, it builds on the EPPO 170, but specifies the brood

4 assessment. 

5 This in general allows to have really worst

6 case exposure of bees, of different castes of bees. You have

7 some certain limitations that you have to supply some food, but

8 that is always the case with every method except one feeding

9 method where you could reduce the amount to almost zero, but you

10 have to apply a little bit of food. Of course, there is a

11 little uncertainty, but I think in general, they are very

12 appropriate studies, but only the OECD 75 and the EPPO 170, the

13 semi-field studies - I will elaborate on that later on - the

14 semi-field feeding studies that have been mentioned here before

15 in my opinion are not really appropriate as Tier II. 

16 DR. DANIEL SCHLENK: Dr. Hunt? 

17 DR. GREG HUNT: I'm not sure I understand you,

18 Jens. You're saying that in Tier II, you don't think

19 supplementary feeding studies have any place? 

20 MR. JENS PISTORIUS: Thank you, I will

21 elaborate on this. I think three different types of studies

22 have been mentioned. Please correct me if I'm wrong. First,

23 the Oomen method. The second one is free-flying colony study,

24 so it's a field, but still with supplementary feeding. Then you

25 can do sugar feeding or pollen package feeding. I think this is

26 not appropriate for Tier II tests. 
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1 I think the most appropriate is to have a crop

2 that offers nectar and pollen to over spray bees. During

3 flowering, you get contact and oral exposure. With the colony

4 that you feed either sugar syrup or pollen, you'll definitely

5 have bees that go somewhere else and you will not be able to

6 quantify the exposure. 

7 You will have a portion of bees even if you

8 can say, okay the sugar water has been consumed and pollen has

9 been eaten to this extent would never be able to quantify what

10 exact amount has been. And it's not a worst case exposure

11 anymore, which is the aim of a Tier II test in my opinion. 

12 So, with those feeding methods, I'm not happy

13 at all. Once exception - the Oomen test can be used, but it's

14 regarded as intermediate between Tier I and Tier II. The big

15 advantage, why I still like it a little bit, is that you can

16 actually do such a dose response. The test method, as it is

17 described in the literature in 1992 is not appropriate. It

18 needs modifications. For instance, there is a dosing of one

19 liter sugar solution, a unique dosing. And this is not

20 appropriate. It should be dosing over a longer time with syrup,

21 at least, whatever at least nine or ten days that they get

22 everyday sugar that you actually - and the big advantage is you

23 can find some dose response via this. 

24 With the other ones, I think you are really

25 neglecting something. If you do a feeding method, you cannot

26 quantify exposure and there is a lot of worst case exposure. 
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1 DR. DANIEL SCHLENK: Dr. Hunt? 

2 DR. GREG HUNT: I guess that clarifies it

3 because you're not talking about tunnel experiments. In a

4 tunnel experiment, you could quantify exposure. We just set one

5 up last week. If you think that the exposure will be through

6 pollen, then you establish colonies with depleted pollen and you

7 feed them paddies and you measure what they eat. 

8 DR. DANIEL SCHLENK: Okay. Mr. Pistorius? 

9 MR. JENS PISTORIUS: When I get a study, I'm

10 happy when I get a study that gives me a lot of information and

11 not only partial information. What I would suggest to use EPPO

12 170 and OECD 75 instead of such feeding studies. The reason for

13 this is that you have a combined exposure. You will not be able

14 to get a contact exposure of the bees. 

15 With a Tier I, we look at what is the toxicity

16 for oral exposure, what is the oral toxicity, contact toxicity,

17 but by making a spray application on the flowering crop where

18 the bees are actively foraging. This is the only possibility

19 where you have the option to get both effects of oral and of

20 contact exposure in one goal. With this feeding study, if you

21 identified a risk only for oral exposure, okay. But trying to

22 get that we want to have the worst case situation, which I

23 believe should be still the Tier II test, then I think those

24 methods of feeding are not really giving us the worst case. 

25 DR. DANIEL SCHLENK: Dr. Tarpy? 

26 DR. DAVID TARPY: This is Dave Tarpy. So
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1 then, from what I'm hearing then is that that's what you're

2 arguing that a full-field rather than a semi-field assessment is

3 best, which I'm not disagreeing with, but what we're talking

4 about here are Tier II semi-field tests. So, they're not

5 supposed to recapitulate all of nature, you're supposed to be an

6 intermediate right? So, while I agree that by forces feeding or

7 by supplemental feeding to kinda of impose exposure may not

8 always mimic the proper exposure routes. I think the question

9 here is do they still have some utility. So, I don't think the

10 question is are they mimicking realistic means of exposure, but

11 do they have some sort of utility in effecting final colony

12 outcome or final colony phenotype. I would probably say that

13 they do, but I agree that they do not perfectly mimic how they

14 would be exposed in the full field, right? But this is not full

15 field, this is a semi-field Tier II level. 

16 DR. DANIEL SCHLENK: Mr. Pistorius? 

17 MR. JENS PISTORIUS: I just want you to

18 clarify for me a Tier II is always a semi-field intolerance, not

19 free flying. Free flying in nature, whatever, that for me is a

20 Tier III test.

21 DR. DAVID TARPY: This is David Tarpy again. 

22 While in the White Paper, they articulate and discuss the two

23 types of Tier II studies. One type is in tunnels, and that's

24 correct. The other is not confined spatially, right? So,

25 that's what I was referring to in that. 

26 MR. JENS PISTORIUS: You are right, and this
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1 is exactly what I propose to be changed or what I personally

2 feel is not appropriate to consider free-flying colonies as Tier

3 II tests.

4 DR. DANIEL SCHLENK: Dr. Pettis? 

5 DR. JEFF PETTIS: Along this line then, if and

6 again I think in the White Paper, they do talk about a possible

7 Tier II with feeding meter, either protein or syrup. If we move

8 that to a Tier III study, do you still think it's not valid to

9 at least have some level of exposure where you reduce the stores

10 in the colony prior to the starting of the test and you start to

11 provide supplemental syrup and/or protein over time? They are

12 free flying and I agree -- I know one of the comments is going

13 to be reduction of incoming pollen is not going to be absolute. 

14 We get dearth periods here in the mid Atlantic of two or three

15 months where there is virtually no incoming nectar. So, we do

16 get a period when we can do these tests and not have much

17 incoming outside resources. So, we are providing the resources,

18 we've limited the amount at that start, and we do feel that

19 we're getting exposure. I just think that by controlling the

20 dose a bit, we are in some cases doing maybe a worse case, I

21 don't know. 

22 DR. DANIEL SCHLENK: Mr. Pistorius? 

23 MR. JENS PISTORIUS: I think it is actually

24 what you mentioned, you are right. I think for some cases it

25 can actually be used in the field conditions. For instance, now

26 let's assume that we have a very highly toxic substance and it's
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1 even systemic and you have very much concerns and you want to

2 have this long-term duration really checked. 

3 I think as an additional method after you have

4 had an exposure to the real crop - I mean for instance, let's

5 have a look at systemic and you have whatever we have in

6 Germany, a winter oilseed rape crop, which is attractive and

7 offering nectar and pollen. Then you say, okay maybe the test

8 crop didn't flower long enough and in real life, you maybe have

9 different flowering crops and this is the situation which is

10 very hard to cover when you conduct a field test. To time the

11 flowering of different crops, I mean that is maybe reality, but

12 if you want to design the study, that is not possible. 

13 So then, I would agree that you can say, okay

14 at field realistic concentrations, you can add pollen paddies

15 and you can also do sugar feeding to extend exposure. To have,

16 you know, a long-term test and to mimic that the exposure is not

17 like winter oilseed flowering taking place four weeks, you can

18 mimic that the exposure would be continuous over five weeks to

19 get a worst case experiment. So I think, this case, additional

20 pollen and nectar feeding is appropriate, but I think it's

21 different study design from how I understood -- maybe I

22 understood it wrong from how I understood what was described.

23 DR. DANIEL SCHLENK: Okay. Dr. Hunt? 

24 DR. GREG HUNT: I think I better understand

25 what Dr. Pistorius was taking about. You consider Tier II

26 tunnel experiments, and I agree that is probably the most
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1 practical way to go. You have the most control. It's going to

2 depend on the crop and what you think is the route of exposure. 

3 Some crops, the bees might not forage on in the tunnel, and in

4 some cases it may be appropriate to do a feeding experiment.

5 DR. DANIEL SCHLENK: Okay. Mr. Pistorius, we

6 need to get on with this. Go ahead.

7 MR. JENS PISTORIUS: Just for crops which are

8 not attractive to bees, it is a question of systemic seed

9 treatments for instance. I mean, if it's not attractive to

10 bees, then the question is would you actually have exposure via

11 nectar and pollen, which would then, as I understood, would not

12 be the case. Then if you look at other issues, for instance if

13 you have some guttation issue or whatever, you can do that. 

14 Maybe then, you can do some additional feeding. That's okay,

15 maybe. But if you have a target crop where you want your

16 application on, at least what we do and this target crop is not

17 attractive to bees and we want to test the impact of a spray

18 application, we use a surrogate flowering crop, which offers

19 nectar and pollen because we want to know what the pesticide

20 does. If it is intended not to be used on or if it's intended

21 to be used on a not flowering crop, which offers no nectar and

22 pollen, then this is basically a risk mitigation measure. 

23 DR. DANIEL SCHLENK: Okay. Any other comments

24 on this? It sounds like that tunnels everybody is good with,

25 but there may be some issues relate to the semi-field. Is that

26 what I'm hearing? Yes? Mr. Pistorius? 
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1 MR. JENS PISTORIUS: May I? Sorry. It's not

2 tunnel or semi-field for me. For me, a semi-field is a tunnel

3 study. It is the question, are they free flying and is

4 additional syrup fit as a main exposure.

5 DR. DANIEL SCHLENK: So, let's just be sure we

6 document that very well in the written comments. Dr. Tarpy,

7 you're responsible for that, I guess. Any other comments on

8 this particular issue? Okay. Let me go to the agency then. 

9 Any specific questions of clarification? I will give you a

10 moment or two. 

11 MR. JOSEPH DECANT: This is Joe DeCant, EFED. 

12 I think that - so question A refers to the tunnel design and

13 specifically the tunnel design in question. B is going to touch

14 on the feeding design that Mr. Pistorius was talking about. I

15 think that maybe for clarification - so what I heard were the

16 strengths and the weaknesses of the tunnel design sort of at the

17 first part of the conversation and the discussion. I think what

18 would be helpful is if the question is about where the feeding

19 design fits into the tiered approach, if the Tier II is

20 semi-field and we're talking about tunnel studies, then is the

21 feeding design, does it sit in between Tier I and Tier II or

22 does it sit in between the tunnel and the Tier III full field? 

23 Maybe just some clarification on where that might sit and

24 getting to the utility of it in subpart (d). 

25 DR. DANIEL SCHLENK: Do you want and answer

26 for that now? Yes? Can we answer that now? Dr. Tarpy? 
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1 DR. DAVID TARPY: This is David Tarpy. Since

2 I'm lead, I guess I'm the one in the crosshairs on that. I

3 don't know if there is a clear answer to that. I think in some

4 ways, it depends on the experimental design. A lot of these

5 boundaries are blurred because of the social organism that we're

6 using here as a model for it. In some cases, I would say that

7 it lies between the Tier I and the food house design. In

8 others, I would say it would not be. So, I don't know if there

9 is a definitive answer to that question, if I'm understanding it

10 correctly. 

11 DR. DANIEL SCHLENK: Yes, Mr. Sappington? 

12 MR. KEITH SAPPINGTON: Keith Sappington. With

13 regards to subpart (a) and as you draft the report, it would be

14 very helpful to the agencies for any insights that you can

15 provide on the likelihood of false negatives with tunnel

16 studies, semi-field tunnel studies as well as false positives,

17 especially if this may end up being sort of the only study being

18 considered in Tier II. Thank you.

19 DR. DANIEL SCHLENK: Okay. Yes, Dr. James? 

20 DR. ROSALIND JAMES: The tunnel studies I've

21 done have been with solitary bees, and I assume these problems

22 are exacerbated with honey bees in that say with bumble bees,

23 you have to feed them inside a greenhouse even when they're used

24 for greenhouse pollination. So, the idea of being able to treat

25 the plant in the tunnel and then expect normal foraging, you're

26 not really going to get normal foraging because you have to have
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1 a really big tunnel. Bees need a lot of plants. There is not

2 very much nectar and pollen produced per flower. So, they

3 really need many, many plants and they quickly deplete out a

4 greenhouse. So your dose may be reduced in a tunnel test. 

5 DR. DANIEL SCHLENK: Okay. Mr. Pistorius? 

6 MR. JENS PISTORIUS: I agree, maybe a

7 different issue with bumble bee colonies, although we have also

8 done some studies with bumble bee colonies, for instance oilseed

9 rape or Phacelia tanacetifolia, which also offer good nectar. 

10 At least for the time of flowering, you have good exposure and

11 you were able to keep the colonies maintain - I mean of course,

12 you have to have a certain amount of food inside a bee colony,

13 just at least a few hundred grams of honey and a little bit of

14 pollen. So if there is a weather change for a few days, they

15 don't starve to death because that will of course, kill your

16 study. 

17 On the other hand, I think that this is still

18 appropriate. You don't have normal foraging behavior. I agree

19 because the tunnels may be of limited or they are always of

20 limited size, but you can keep them and you can document that by

21 the income of nectar and pollen by when you do the brood

22 assessment that actually pollen came in. When you do the

23 assessments for instance on bee brood, you can actually see at

24 least they were able to, they were consuming the pollen that

25 they had before, then they stored new pollen and they were able

26 to take care of the brood for a while. 
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1 Then to clarify this again, I think this is a

2 good way of exposing colonies, but then if you want an extended

3 exposure over the duration of the flowering crop, you can do

4 additional feeding. That's what I thought. And what you said

5 with greenhouse application, that may be true that you have for

6 instance for tomato pollination, that you will have bumble bees

7 starving a little bit and that you have to feed something

8 additionally. Maybe it's a different system. I don't know. 

9 What we do is we register the substance. We register a product

10 and we have to test that this product is safe, not just special

11 application. So, what we would go for is we make an application

12 on a flowering crop that is attractive to bumble bees, like

13 Phacelia and would use these results to interpret the potential

14 effects of the product on bumble bee colonies. Then what you

15 can do to extend this again, you do some feeding. 

16 DR. DANIEL SCHLENK: Dr. James? 

17 DR. ROSALIND JAMES: So, it would help if you

18 used something like the Celia that is a very high nectar and

19 pollen producing plant instead of the crop. But still, if you

20 have to start feeding at the end, you're effectively diluting

21 your dose. Well, you're reducing the dose if you're adding - if

22 you have to feed supplemental nectar and pollen, they're not

23 collecting it from a crop that was treated. 

24 MR. JENS PISTORIUS: This is a very good

25 point, thank you. When I mean additional feeding is possible

26 for extending the exposure, of course I mean that you have to
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1 use spiked food with realistic field concentrations. Because I

2 agree if you then feed uncontaminated sugar or pollen paddies,

3 that is again a reduction of the exposure. This is basically

4 reducing the certainty of the test. Then you get uncertainty

5 and we don't know how to make the interpretation. 

6 DR. DANIEL SCHLENK: Okay. Mr. Sappington? 

7 MR. KEITH SAPPINGTON: Just one clarification.

8 Mr. Pistorius, when you talk about potentially extending the

9 study with feeding, is that being considered in the context

10 within a tunnel or a separate study like a feeding study where

11 they have free-flying bees.

12 MR. JENS PISTORIUS: It would be considered

13 for after the exposure to the flowering crop, so it's not an

14 extra study. It is with the same colony. After the exposure

15 period or after the forage in the tunnel is finished, the

16 flowers are all gone basically, then what you could do is you

17 add, have to think about opening up that tents so they are free

18 flying or maybe you can close, but then you add basically sugar

19 solution, spiked sugar solution and spiked pollen to extend this

20 period over the blooming period if you have such a concern that

21 it is in real life maybe more covering the realistic situation. 

22

23 DR. DANIEL SCHLENK: Okay. Any other

24 questions of clarification from the Agency? Okay. Dr. Tarpy,

25 you want to - oh sorry, first we need to read in the next

26 question please.
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1 MR. JOSEPH DECANT: Joe Decant, question 11,

2 subpart (b) - please comment on any other types of colony-level

3 studies that should be considered as part of Tier II.

4 DR. DANIEL SCHLENK: Dr. Tarpy? 

5 DR. DAVID TARPY: This is David Tarpy. So,

6 the discussants talked about three kind of general alternatives

7 that may be used, not really, well in Tier II as far as inside

8 hoop houses or in cage studies. One is to utilize the power of

9 observation hives, which are glass-walled colonies, glass-walled

10 hives that enable observations of individual nest mate

11 behaviors. So, comparable to what we were discussing in

12 question number 8 this morning about how nest mate interactions

13 and individual behaviors has relevance to colony level

14 phenotype. 

15 Recapitulating that in a type II or a Tier II

16 environment that could possibly augment those same types of

17 things done in Tier I level may very well be helpful. Again, I

18 think that the exact behaviors and the manner by which those

19 behaviors are recorded and analyzed is a data gap, but I think

20 that it is something that should be considered in these colony

21 level studies, part of Tier II.

22 Secondly as was eluded to in previous

23 questions about the blurring of Tier I and Tier II when it comes

24 to the colony reproductives. So, queens and drones are both

25 very, very important. Unlike workers, which may be more

26 amenable to small groups in vitro, it's very, very difficult to
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1 do that with queens and drones. So, Tier II studies ought to

2 consider and incorporate means by which phenotypic effects on

3 individual queens and drones can be measured, because those are

4 in some ways a consequence of colony level phenotype. So

5 measuring the reproductive potential of queens, the reproductive

6 potential of drones is something that could also be folded into

7 this level of assessment. 

8 Then finally, there was discussion earlier

9 this morning about possible Tier II type studies that are done

10 in vitro, right? So, having small colonies with functional

11 queens, workers of all ages and brood that they're actively

12 raising, but done in incubators and not actually in hoop houses

13 or done outside, but in more controlled environments. These

14 could probably be done for a period of days, may be weeks

15 depending on the size and other factors.

16 So these are, what I would consider Tier II

17 studies because they're involving whole-level colony phenotype,

18 but they're actually done in vitro. So again, it's this

19 blurring of these different tiers when it comes to looking at

20 super organisms.

21 DR. DANIEL SCHLENK: Okay. Thank you. Dr.

22 Ostiguy? 

23 DR. NANCY OSTIGUY: I concur.

24 DR. DANIEL SCHLENK: And Dr. Pettis? 

25 DR. JEFF PETTIS: I concur with the comments

26 of Dave. I will just add a bit about some of these potential in
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1 vitro kind of cage trials, it would more mimic a whole colony. 

2 We actually did some of those where we took a small - 100

3 workers and a queen and introduced a known number of eggs to the

4 colony and asked them to rear them and looked at the success

5 rate of that small unit to rear that brood. It was pretty

6 telling. I mean, you can get results with a fairly simple set

7 up. They're not refined. They're not, in all cases - but I

8 think that is something that moves between straight Tier I and

9 Tier II where we begin to capture the super organism.

10 DR. DANIEL SCHLENK: Any other panel members? 

11 Yes, Dr. Hunt? 

12 DR. GREG HUNT: Obviously, we're throwing out

13 a lot of ideas on possible assays, and only a few of these will

14 probably end up being the ones considered until there is further

15 evidence of their effectiveness. But one possibility for

16 another one is to - since there is a general feeling among

17 beekeepers and bee researchers that perhaps we're seeing more

18 problems with queens lately. There are indications that certain

19 compounds like the miticides that beekeepers use in their hives,

20 the queens are more sensitive to. 

21 So, one study that can be done in two weeks'

22 time is to rear queens in a somewhat contaminated hive. This

23 can be done very rapidly. The question is what is the best

24 method to contaminate the colony, and I see Dr. Pistorius is

25 raising his hand, so he has an idea on this. 

26 DR. DANIEL SCHLENK: Mr. Pistorius? 
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1 MR. JENS PISTORIUS: I do have an idea. What

2 you could do is make a shook swarm, which is basic for the

3 non-beekeeper that you put the bees without the queen in a cage,

4 in a box and then you put this box into a tent, and you take

5 very, very low food reserves or if it's good weather, you can

6 even do no food reserves, wait one or two days and then you put

7 the queen frame in. Before this or one day before you put the

8 queen frame in, you make your application so you ensure that the

9 bees have collected nectar and pollen already. 

10 So, you know that that pollen and nectar

11 inside is already contaminated and that they will be forced

12 during the rearing of the queen to forage on this contaminated

13 crop. Then, that is actually quite a short process until the

14 queen cell is capped. You will definitely have the flowering

15 period still, and then you can take them out and you can observe

16 them, because then the queen is not fed anymore and then you can

17 do additional experiments to the queen if you would like, but

18 that's actually quite short and quite efficient test I would

19 say. I don't know. 

20 DR. DANIEL SCHLENK: Dr. Hunt? 

21 DR. GREG HUNT: I agree. 

22 DR. DANIEL SCHLENK: Dr. Pettis? 

23 DR. JEFF PETTIS: Jeff Pettis. And I all

24 ready want to modify the method. No. You don't like feeding

25 nectar and spiked pollen, but I think this might be a perfect -

26 rather than have them forage on the crop, if you know something
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1 about the residue in the nectar and pollen, why not just

2 simulate that in a shook swarm, and just keep them completely

3 artificial in little cages by themselves and introduce the

4 queen. I agree, it's a great idea that asking a group of

5 queenless bees to rear queen cells should be a great bioassay. 

6 DR. DANIEL SCHLENK: Mr. Pistorius? 

7 MR. JENS PISTORIUS: And I agree with Dr.

8 Pettis. In this case, I would actually think if you know the

9 concentrations that you have to spike nectar and pollen, that's

10 a great idea then in a confined tunnel condition.

11 DR. DANIEL SCHLENK: Dr. James? 

12 DR. ROSALIND JAMES: I don't know if this

13 would really be considered a Tier II test. Throughout this,

14 I've been wondering could you require at the Tier II level that

15 a field experiment be conducted without bees in which then you

16 measure actual residues in the pollen and nectar from different

17 types of applications. That will give you some of these unknown

18 answers, well how much then do we feed to bee. 

19 DR. DANIEL SCHLENK: Okay. Anyone else? 

20 Okay. Let me go to the agency. Any questions of clarification?

21 We're good? Okay. Let's move onto letter C.

22 MR. JOSEPH DECANT: Joe DeCant, question 11,

23 subpart (c) - please comment on the most important endpoints

24 that should be measured in the Tier II studies, such as adult

25 forage bee mortality, brood development, queen fecundity,

26 overall colony strength, that are linked to assessment endpoints
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1 and their associated protection goals.

2 DR. DANIEL SCHLENK: Dr. Tarpy? 

3 DR. DAVID TARPY: This is Dave Tarpy. We

4 discussed this briefly and largely agreed that those that were

5 outlined in the White Paper were very adequate at looking at

6 amount of brood, the overall colony population and strength, all

7 of those measures of colony phenotype are very appropriate, and

8 are a good way to quantify colony level effects. I should note 

9 I should note that by and large, all of those

10 different measurements are highly correlated in and of

11 themselves. So, quantifying a composite colony phenotype in

12 these ways can be done statistically, principle component

13 analyses, using different loading variables into that is a way

14 to - is a means of comparison and is quite effective in doing

15 that. 

16 To touch on the points that were just raised

17 about looking at queen and don't forget drones - right, drones

18 as well as queens - queen and drone viability and their

19 individual phenotypes is another outcome and endpoint that

20 should be measured in these types of Tier II studies as well. 

21 A third and final major endpoint is to sample

22 and process returning foragers and to actually see what the

23 foraging behavior is, quantifying their exposure rates,

24 quantifying what their foraging on, and looking at the returning

25 foraging force in these Tier II studies to really quantify their

26 potential exposure routes. 
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1 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

2 DR. NANCY OSTIGUY: I think this is also a

3 point where some in-hive samples could be taken to potentially

4 supplement. We had information on the contamination at the

5 pollen and nectar to then look at the corresponding

6 concentrations of the pesticides within honey and bee bread. 

7 DR. DANIEL SCHLENK: And Dr. Pettis? 

8 DR. JEFF PETTIS: To extend what Nancy said

9 just a bit further, at the end of the exposure period within a

10 tunnel in addition to looking at bee bread an pollen, you can

11 take newly emerged adult bees that have been fed and processed

12 during that exposure period and assay them directly, or then put

13 them in cages and do longevity studies. A fairly easy thing to

14 do, at the end of the study, doesn't add much to the cost. 

15 DR. DANIEL SCHLENK: Dr. James? 

16 DR. ROSALIND JAMES: Question for Dr. Tarpy. 

17 So, you said sample then process returning foragers to see what

18 they're foraging on, but in a tunnel study, we pretty much know

19 what they're foraging on, no? 

20 DR. DAVID TARPY: Sorry, this is Dave Tarpy. 

21 One can quantify the nectar loads of individual bees by

22 capturing them at entrances, anesthetizing them with carbon

23 dioxide, then squeezing their crop contents into capillary

24 tubes, then very accurately measuring the nectar loads of each

25 forager and then sampling many foragers to get an idea of the

26 variation in how many are foraging on nectar versus pollen, what
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1 the average nectar loads are. You can even look at sugar

2 content and other things. This is again, very well documented

3 in the literature. That's what I meant is actually to sample

4 the incoming foragers to see kind of more quantitatively of what

5 their exposure rates are. 

6 DR. DANIEL SCHLENK: Okay. Any other Panel

7 members? Mr. Pistorius? 

8 MR. JENS PISTORIUS: Real short - for me, this

9 list is missing behavior at the hive entrance and behavior of

10 foraging bees on the crop.

11 DR. DANIEL SCHLENK: Okay. Make sure that we

12 get that on the list. Okay. It seems pretty straight forward,

13 but I'll ask if you guys have any questions of clarification? 

14 Yeah, Mr. Sappington? 

15 MR. KEITH SAPPINGTON: Keith Sappington. 

16 Quick question regarding the nectar loads in measuring those. 

17 Would that include measurement of residues in the nectar as well

18 from the tunnel studies? 

19 DR. DAVID TARPY: This is David Tarpy. I

20 assume that that could be done as part of that, yeah. 

21 DR. DANIEL SCHLENK: Okay. Let's go ahead and

22 move onto letter D.

23 MR. JOSEPH DECANT: Joe DeCant, question 11,

24 subpart (d) - section 4.2.2 of the White Paper discusses a

25 full-field feeding design. This methodology is discussed under

26 Tier II assessments since the colony is relatively confined to
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1 foraging on either spiked sucrose solutions or spikes pollen. 

2 The intent of this methodology is to ensure that colonies are

3 exposed to known residue levels over longer durations than the

4 semi-field tunnel study designs. A limitation to the study is

5 that bees may simply store spiked food rather than consume it

6 and that the reliance on a single source of food may introduce

7 confounding effects, such as nutritional deficits into the

8 study. Please comment on the environmental realism and utility

9 of full-field feeding studies as a line of evidence in

10 characterizing risk to honey bee colonies.

11 DR. DANIEL SCHLENK: Okay. Dr. Tarpy? 

12 DR. DAVID TARPY: So, I feel like we've all

13 ready kind of discussed this quite a bit. So, I don't know if

14 my comments here are really going to be all the relevant, but I

15 guess there is some disagreement on the panel here as to whether

16 is kind of zero utility, some utility or a lot of utility. I

17 guess what it comes down to is that a well-designed full-field

18 Tier II study, I think, would provide information that would not

19 be gleaned at a Tier I level. Again, it may not realistically

20 mimic a full ecological type of realism, but I believe it would

21 have some - this type of design would have some utility. 

22 Again, honey bee colonies, I would think,

23 compared to tunnel studies where there are some limitations just

24 on their behavior being amenable to being in small enclosures

25 that they would be more amenable to these full-field types of

26 designs. I think that by being in enclosures, one of the
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1 limitations that we discussed earlier in this question is the

2 shortened duration where as a full-field design would facilitate

3 longer measurement durations of colonies and for chronic levels

4 of exposure that could be very beneficial, especially looking at

5 colony population and turnover where brood raised under a

6 particular exposure could then be measured as adults. 

7 So, extending it beyond 28 days, which I

8 believe the White Paper was mentioning, spending it beyond that

9 to try and quantify some of those phenotypes of individual bees

10 that were raised under those particular environmental

11 conditions.

12 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

13 DR. NANCY OSTIGUY: One possible way to

14 address the issue about whether or not the bees store the

15 contaminated material and not use it to raise current brood

16 would be to actually sample the colony to require some

17 indication of what was in the colony in the first place in terms

18 of brood food, honey, et cetera, and then have a measurement

19 that told you how much of what was used would possibly have been

20 from the contaminated material. 

21 DR. DANIEL SCHLENK: And, Dr. Pettis? 

22 DR. JEFF PETTIS: Recognizing the concerns of

23 my esteemed colleague, Mr. Pistorius, I actually do like these

24 studies. If we recognize it, they may not represent the contact

25 element. I think given that we reduce the stores at the

26 beginning of the study and then spike them with relative rates
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1 in nectar and pollen that they do have some value. Again,

2 running them at a time of year when there is less incoming

3 resources, so you have some control over the consumption rate. 

4 We have a distinct spring and fall pollen and nectar flow. So,

5 there are times in the year when they can be done.

6 DR. DANIEL SCHLENK: Dr. Berenbaum? 

7 DR. MAY BERENBAUM: I think just as integral

8 to the inclination of bees to forage broadly geographically is

9 their tendency to glean. One missing worry here - there is a

10 concern about nutritional deficits and storage of compounds, is

11 that force feeding eliminates that behavior. It's an incredibly

12 sophisticated system of communication, decision making, force

13 feeding does not capture that flexibility in bee behavior. So,

14 yes, these force feeding studies can be informative if and only

15 if there is an understanding of the natural disposition of bees

16 to learn a verse of stimuli and avoid them in the future and to

17 sample and detect and possibly chose not to feed. My experience

18 is mostly with caterpillars and even caterpillars, when given a

19 choice, can avoid toxic food. It's much more difficult to

20 interpret a no choice test than a choice test in terms of its

21 ecological reality. 

22 DR. DANIEL SCHLENK: Thanks. Dr. Pettis? 

23 DR. JEFF PETTIS: Good point, May. I guess in

24 some of the studies that I've observed or been a part of, you

25 look at consumption rate by the colonies and often they are

26 equal, so you can at least - you know, again, they're not given
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1 a choice. You have a spiked syrup and non-spiked. At least

2 there is no deterrent to feeding or obvious deterrent. 

3 DR. DANIEL SCHLENK: Mr. Pistorius, you have

4 something to add on this one? 

5 MR. JENS PISTORIUS: I will add a little bit. 

6 Just from feeding studies, I think we have less of, for

7 instance, repellency effect with feeding sugar solutions. So,

8 you can actually - and maybe this is, you know, for some

9 specific questions, but it should not be the only one for your

10 lines of evidence if you have a good OECD and EPPO study. You

11 can have such studies as additional information, but for

12 instance, we know from all the laboratory toxicity tests that

13 the bees will feed on this. Even with pyrethroids, which have

14 repellency effect when you spray them on flowering crops, if you

15 give a sugar solution to the colony, they will just consume it. 

16 They'll take whatever is there. 

17 DR. DANIEL SCHLENK: I hear Mary Poppins in

18 there somewhere right? Any other comments on section D here? 

19 Yes, Dr. Hunt? 

20 DR. GREG HUNT: Well, I think that when you do

21 the experiment this way in the field and feeding, you don't know

22 what the dose is. You don't really know what the dose is, but

23 possibly it could be useful for, as Dr. Tarpy said, for looking

24 at that cohort of bees that was raised during the time you're

25 feeding and they're receiving some unknown dose, field relevant

26 dose, and see if there're effects. 
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1 DR. DANIEL SCHLENK: Okay. Any other comments

2 on letter D. Okay. Let me go to the Agency. Mr. DeCant, do

3 you have any questions of clarification? 

4 MR. JOSEPH DECANT: Joe DeCant, EFED. I have

5 one question of clarification. If we're talking about this

6 field feeding design and we're talking about the cohort that was

7 raised under those conditions and we talked about the ability of

8 the bees to store pollen and nectar, to what extent does the

9 Panel agree about just that cohort of bees or the actual

10 duration of the study? I guess there's a question of the

11 duration of the study when bees can store the food and consume

12 it later.

13 DR. DANIEL SCHLENK: Dr. Tarpy, you want to

14 lead off on that? 

15 DR. DAVID TARPY: Is the question about what

16 the duration should be? Or which cohort you're asking? 

17 MR. JOSEPH DECANT: Joe DeCant. I think it

18 would be about which cohort to look at, which also relates to

19 the duration of the study. And I say that because, you know,

20 we're talking about measurement endpoints and I think - so the

21 White Paper discusses about minimizing the number of times that

22 we have to open up the hive to collect our measurement

23 endpoints. So, I think if the Panel has any insight about what

24 cohort to target, maybe a timing of the study if we're talking

25 about the dearth period, if that's in the middle of the summer

26 time and there is a honey flow in the spring and the fall, you
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1 know the duration of the study - and I guess ultimately this

2 gets back to maybe a clarification about under what conditions

3 would this type of study be most useful. If we're talking about

4 longer duration, the measurement endpoints, when to collect

5 those measurement endpoints and the cohort to target as you

6 mentioned about, Dr. Tarpy.

7 DR. DAVID TARPY: Okay. This is David Tarpy. 

8 Others feel free to chime in here, but I think that there are

9 many cohorts that would be informative to this type of design. 

10 One would be those active foragers and nurse bees. So, say

11 30-day-old bees and 10-day-old bees, during the time of

12 exposure, during the time of feeding, and then several weeks

13 later, emerging, marking and following of the cohort of bees

14 that were larvae during the time of that exposure as well. 

15 So, minimizing the number of times going into

16 the colony, I don't see that as a limitation or a problem. I

17 just see that all of the different cohorts would be very

18 informative in their different ways. So, spending the period of

19 time - and plus you could go on for a very long period of time

20 looking at then subsequent cohorts as well. 

21 So, there really is no definitive endpoint to

22 this, it just comes down to tracking these things over time. I

23 don't know if there is one particular time period that makes

24 more sense than others. Certainly, just looking at one period

25 of time is going to be limited in scope. 

26 DR. DANIEL SCHLENK: Dr. Pettis? 
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1 DR. JEFF PETTIS: Kind of echo what David

2 said. If you have a 20-day flowering period and you have

3 colonies even in a tunnel study, you want to look at adults

4 right during the flowering period, their behavior and affect on

5 adult mortality. But this brood you want to target maybe the

6 midpoint of the flowering period, 10 days in, as a start point. 

7 You need those nurse bees to process that food for about 10 days

8 before they actually, before their glands are fully developed

9 and they're feeding larvae. 

10 So, then you just keep moving the clock. 

11 We've been going out about 30 days beyond the midpoint of the

12 flowering period to capture that. And of course, if you have

13 opportunity to follow multiple cohorts, it's better. You may

14 see during the peak of incoming nectar and pollen, you may see

15 an effect. Then later, you may see that effect disappear. So,

16 following multiple cohorts - I noticed in the White Paper that

17 there was a concern about disruption to the colony. I think

18 given very good beekeeping practices, that's not such an issue. 

19 You will see sometimes a spike in adult mortality if the person

20 managing the hives is not careful, but I think the need to get

21 the data probably overweighs and just hiring a good beekeeper is

22 a way to avoid that. 

23 DR. DANIEL SCHLENK: Anybody else want to

24 chime in on this one? Dr. Hunt? 

25 DR. GREG HUNT: Well, I think that some of

26 this information can be gotten out of tunnel studies. I think
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1 there is some kind of consensus that tunnel studies are good,

2 more controlled and should be the main focus of Tier II. If

3 anybody disagrees, speak up now. 

4 DR. DANIEL SCHLENK: Yeah, in terms of writing

5 this up, it sounds like it would be nice to present just exactly

6 what you said. It seems like some people are for them, limited

7 and the caveats as well as those, the reasons why they're not

8 useful, I guess that would probably be a good way to put that in

9 the minutes. Would that be helpful? Yeah? Dr. Pettis? 

10 DR. JEFF PETTIS: I guess along that line, do

11 we feel as a group that the free-flying feeding studies are not

12 - like you feel that they're not Tier II at all, they're really

13 Tier III studies. Is there is any general consensus among the

14 group about whether free-flying colony level studies with spiked

15 stuff, is that more of a Tier III or more Tier II? Is there a

16 feeling? 

17 DR. DANIEL SCHLENK: How many say it's a Tier

18 III? Okay how many IIs? Look like we're split. So, sounds

19 like it's a split. Looks pretty even actually. Pardon? Yes,

20 you can clarify, Mr. Pistorius.

21 MR. JENS PISTORIUS: With respect to what is

22 the ultimate possibility for decision? So I guess it would

23 actually be rather a Tier II. That was not my point. I think

24 the study types are not the only ones that you can rely on and

25 it was basically the study design that I was criticizing, but

26 now in the discussions, I think we have elaborated some very few
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1 possibilities where actually a feeding study can also be useful

2 or in addition to a really good OECD, EPPO, you can use this

3 feeding to enhance. I hope that clarifies. 

4 DR. DANIEL SCHLENK: I'm sure we'll look

5 forward to the written comments on this one. Dr. Hunt? 

6 DR. GREG HUNT: I want to change my vote. 

7 Yes, it's more of a Tier II, and clearly the experiment with

8 raising queens would be Tier II because they're not free flying.

9 DR. DANIEL SCHLENK: Okay. Noted. I guess

10 that I think there is - are we II as well? 

11 DR. JEFF PETTIS: Last comment - I've all

12 ready cast my vote. Last comment along these lines, I do feel

13 that the EPPO guidelines and the OECD - there was a lot of

14 thought and effort that goes into them. They are very valid

15 things to follow. 

16 DR. DANIEL SCHLENK: Yeah, we can set this up

17 in the minutes in terms of kind of a split decision and people

18 can weigh in before it actually comes out at the end to get a

19 final vote, whatever that is, whatever that means for you guys. 

20 Okay. Any other comments on this? Mr. DeCant, are you fine

21 with what we've given you so far on that at this point? Okay. 

22 You want to read in letter E then? 

23 MR. JOSEPH DECANT: Joe DeCant, question 11,

24 subpart (e) - as discussed in section 4.3.4 of the White Paper,

25 it is important to consider the biological significance of a

26 measured effect in addition to its statistical significance. 
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1 Please comment on the nature and magnitude of effects that would

2 be sufficient to conclude biologically significant effects on

3 the colony and/or the need to transition to Tier III

4 assessments.

5 DR. DANIEL SCHLENK: Dr. Tarpy? 

6 DR. DAVID TARPY: This is David Tarpy. I

7 think we agree 100 percent that reconciling statistical and

8 biological significance can be a challenge. I would probably

9 just want to leave it at that. This is an incredibly difficult

10 question to answer with any sort of certainty. I mean, this is

11 something that we all wrestle with of course, and it's even

12 further blurred and confused by this kind of social nature of

13 honey bees, and it just makes it even more difficult of a

14 challenge because what can be very, very small, seemingly

15 insignificant effects at the individual level can have profound

16 ramifications at the colony level.

17 Conversely, very large, seemingly striking

18 effects between individuals can be balanced out and can be

19 compensated for at the colony level. So, it is really, really

20 difficult to try to reconcile this. So, I don't know if we're

21 going to really be able to do justice to this particular

22 question, but all of that said, we believe that power analyses

23 that was asked for in the White Paper in multiple occasions at

24 these different tiers feel like that's incredibly informative

25 and should definitely be included in all of the results. 

26 This is especially true with the small sample
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1 sizes, small colony sample sizes because of the inherent

2 variability across colonies in different contexts, different

3 environments with larger variability, higher sample size is

4 needed really to be able to elucidate statistically significant

5 effects, which hopefully will provide inference to biologically

6 meaningful effects. So, larger sample size is obviously a

7 logistical limitation, but it's one that needs to be maximized

8 with a sense of urgency, you know, at all possibility. 

9 There were some comments earlier talking about

10 the Tier II studies and different measurements that would be

11 taken. One that touches on this particular question about kind

12 of teasing apart statistical versus biological relevant effects

13 is doing ground truthing and looking at actual foraging behavior

14 in the target crop or whatever the bees are actually foraging

15 on. Those type of data, I think, would really help augment the

16 interpretation of trying to tease apart statistical versus

17 biological effects. 

18 Some other points here that I think we've

19 already raised, so I won't go over those again. I think one

20 last approach is trying to merge data sets or trying to

21 consolidate data. One difficulty of having low sample sizes and

22 lower power, if different data sets could be combined and have a

23 meta analysis approach, that is a way to also reconcile this

24 point that there may be small, even statistically nonsignificant

25 effects that aren't detected within a single study, but by

26 bolstering sample size by doing this similar study in different
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1 environments and different contexts, meta analyses can often

2 pull out biologically relevant effects where each individual

3 study might not have statistically meaningful effects.

4 So, that again might be beyond the scope of

5 this, but that is another avenue to consider in consolidating

6 data.

7 DR. DANIEL SCHLENK: Thanks. Dr. Ostiguy? 

8 DR. NANCY OSTIGUY: I'm going to make another

9 plug for the analytical models because you can get two things

10 from them. One data that may not show statistical significance

11 or variables that may not show statistical significance may turn

12 out to be incredibly biologically important. You may have

13 somethings that the analytical model would tell you that you

14 have statistical significance on that variable, but it doesn't

15 really matter for the colony. 

16 DR. DANIEL SCHLENK: And Dr. Pettis? 

17 DR. JEFF PETTIS: Just very briefly, again we

18 all feel better when we have high statistical power and we have

19 good P values that lead to better publications and stuff like

20 that. But, I don't think we can discount some of the biological

21 effects that we see and I'll give you an example. 

22 So, you have a Tier I study, you're measuring

23 mortality and other things, but you notice that the bees are

24 excessively grooming, which could mean a response to pesticides.

25 Then you notice the same response in a Tier II or a Tier III

26 study. So, you see an effect, but it's statistically not valid.
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1 I think these biological effects that we see, we have to take

2 them into consideration and try to figure out what they mean for

3 the colony level. But we recognize that for many of these

4 colony level studies, the ability to do adequate replication is

5 hard to get there - economically it's hard to design a study

6 well enough and have a enough replication to strong statistical

7 power.

8 DR. DANIEL SCHLENK: Okay. Any other panel

9 members want to weigh in on this one? Dr. Hunt? 

10 DR. GREG HUNT: Just a comment. You're

11 dealing with a bunch of academics here. So modeling and meta

12 analyses are maybe good for discovery and for planning, but to

13 echo what Jeff said, you know, I guess what we're really talking

14 about is testing for risk analysis. So, there're two different

15 things going on here. 

16 DR. DANIEL SCHLENK: Dr. Fefferman? 

17 DR. NINA FEFFERMAN: While modeling - so, I'm

18 not going to speak so much to the meta analysis for statistics

19 because honestly, I'm not a biostatistician, but while modeling

20 has a huge role in discovery, it can also lead to

21 recommendations for which things to test as measurements, and

22 what those thresholds for the tests should be. So, I think it

23 would be a mistake to dismiss as a purely academic concern that

24 doesn't directly lead to applied recommendations.

25 Dr. DANIEL SCHLENK: Any other comments? 

26 Okay. Go to the Agency, any questions of clarification? Keith?
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1

2 MR. KEITH SAPPINGTON: Keith Sappington. 

3 Yeah, this is a difficult question and it again, cuts across

4 taxa, the biological and statistical significance and it would

5 obviously differ depending on endpoints. 

6 Speaking from my own experience in reviewing

7 half a dozen or so semi-field studies, I understand there is

8 going to be a lot of gray area where best professional judgement

9 is needed, but if there are bounds if we could put on, if

10 there're areas where you say, look this is an obvious place

11 where its biologically significant because we're dealing with

12 endpoints like brood compensation index and brood termination

13 rates as well as percent comb areas and various stages of brood.

14 So to help us with that interpretation, I'm just speaking to

15 the point of biological significance, that would be extremely

16 helpful.

17 DR. DANIEL SCHLENK: So if I may, it sounds

18 like which biological endpoints have a more biological relevance

19 than say a statistical relevance out of the ones that you're

20 sort of measuring. Is that.

21 MR. KEITH SAPPINGTON: Keith Sappington. 

22 Partly, yes, which endpoints maybe carry a little more

23 biological weight, but even within an endpoint. For example,

24 with the OECD studies 75, there are endpoints including brood

25 termination rate and so forth, but there isn't, to my knowledge,

26 a criterion of what's acceptable for that in the controls. So,
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1 there's interpretation around that. And then just understanding

2 the biological significance, so if you do have a two-fold change

3 regardless of what the stats can say, is that meaningful? And

4 again, obviously that can get into the modeling, which we will

5 be talking about, but any insights based on your experience with

6 conducting these studies and knowledge of bee biology would be

7 helpful. Thank you.

8 DR. DANIEL SCHLENK: Dr. Tarpy, you want to

9 come up with some guesses there? 

10 DR. DAVID TARPY: So would it be helpful to

11 the Agency - sorry this is Dave Tarpy again - would it be

12 helpful to perhaps rank or somehow qualify the list of different

13 colony phenotypes on page 132 and 133 in the White Paper to say

14 as biologically critical versus more environmentally

15 temperamental or something like that where that would give you

16 an idea of the gravity of the particular measurement, is that

17 kind of what you're looking at as far as which are more

18 biologically relevant versus less? 

19 MR. KEITH SAPPINGTON: Keith Sappington. Yes,

20 if that can be done with some confidence, then that would be

21 helpful, sort of a first tier versus maybe a second tier in

22 terms of biological significance. But again, even within an

23 endpoint, understanding daily mortality rates while I have a

24 six-fold increase for three days, but then things return back to

25 normal. I'm not asking for a specific answer on just that, but

26 understanding even within an endpoint if there are bounds and
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1 very clear bounds on the magnitude that says yes, this is a

2 problem. Like we had discussed earlier that perhaps a sustained

3 mortality of 30 percent based on experience, it looks pretty

4 major. So, if there are some bounds that we can put around the

5 zone of uncertainty, that would be helpful, thank you.

6 DR. DANIEL SCHLENK: Okay. We'll go with Dr.

7 Berenbaum first and then Dr. Fefferman.

8 DR. MAY BERENBAUM: Completely shooting from

9 the hip, in as much as I have never done any studies. But just

10 in terms of statistics and biological relevance, it would seem

11 those parameters that relate to the queen who is not replicated

12 in any colony should be given some latitude in terms of

13 biological reality. Also, given the incredibly central role of

14 the queen, you have on page 132 there are some measurements

15 relating the presence of the same queen. I mean, within a

16 single colony, you don't have that same statistical power that

17 you do with even a small colony with 5 or 10,000 workers. So, I

18 would say you should have some sort of - if something goes wrong

19 with the queen and that is biologically relevant, let the bee

20 biologists address that one. 

21 DR. DANIEL SCHLENK: Dr. Fefferman? 

22 DR. NINA FEFFERMAN: So I think we're

23 conflating two different aspects into the same conversation, and

24 one is - we're conflating two different elements. One is how to

25 get from the statistical significance to the biological

26 significance and the other is the criticality of the biological
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1 significance. 

2 I think Dr. Berenbaum's statements speak

3 directly to how to get from the statistical to the biological,

4 but it would be a mistake to try and intuit which aspects are

5 critical biologically relevant aspects as opposed to transient

6 biologically relevant aspects. Within the category of being

7 biologically relevant just isn't off the cuff because we've got

8 example after example from the last 50 to 100 years of research,

9 of experts in the field with very good reasons and very sound

10 science behind them going, I really think this is the important

11 thing, later testing that in either modeling or as greater

12 sensitivity of empirical techniques become available, realizing

13 that the intuition is in fact really off. 

14 So, while we can do that, I think we have to

15 separate the discussion from the statistical to the biologically

16 relevant and then be very careful within the biological

17 relevance of estimating relative criticality. 

18 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

19 DR. NANCY OSTIGUY: I'd like to add that in

20 addition to queens, if something goes wrong with your drone

21 population, you're in trouble. 

22 DR. DANIEL SCHLENK: Anybody want to add the

23 other ones too? Dr. Pettis? 

24 DR. JEFF PETTIS: I'm glad to hear that female

25 side speaking up for drones. That's encouraging. Not Greg, are

26 we looking at here.
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1 DR. DANIEL SCHLENK: Except for Greg here. 

2 Greg's kind of.

3 DR. JEFF PETTIS: Back to biological

4 significance, even with the comment of Dr. Fefferman, I think

5 one thing that I look at and Dave, I think we could take some

6 stab at trying to prioritize to some degree, maybe in two

7 categories. The one that jumps out at me always is just the

8 level of brood production and brood survival. The reason that

9 is so important is it incorporates so many other aspects of the

10 hive activity. Incoming pollen and nectar, nurse bee activity,

11 it really is a great measure.

12 When we look - if we're measuring colony

13 strength, we can measure adult population, which is harder to

14 measure, but we can measure sealed brood production. It's real

15 easy to measure very accurately as a very robust measure. So,

16 if I had to look to one measure, it would be something about

17 brood survival or brood production. But again, I agree that we

18 can't over look everything that is out there. We have to

19 consider that it is perhaps important. 

20 DR. DANIEL SCHLENK: Okay. Anybody else got

21 another favorite measure? Yes, Mr. Pistorius? 

22 MR. JENS PISTORIUS: I was keeping back,

23 because it is such a hard question and we ask ourselves a lot

24 too. It depends on the level of protection that you want to

25 achieve for your protection goals. If your major protection

26 goal is the colony size, okay then you might have forager
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1 mortality event, which is not that relevant for colony survival

2 and colony development over the long term.

3 On the other hand, we are for instance,

4 incident driven. We don't want incidents because that is part

5 of my job, investigating the incidents. Don't need too much

6 work. What we don't want, for instance, acute mortality events.

7 So for us, it is a concern if we have clearly increased - it is

8 again the question what level. But if we clearly increase

9 mortality, there was a saying from a bird person from the

10 Netherlands who said in the Netherlands, they don't want clearly

11 visible dead birds. We want exactly the same for bees. We

12 don't want clearly visible dead bees. That's why with the

13 methodology of the bee trap we are a lot more protective

14 compared to what the beekeeper actually realizes.

15 If we see a clear increase in mortality, we

16 even say okay, this is for us biologically relevant, even if the

17 brood production is not yet affected. So for me personally, the

18 most critical, in my personal top list, is acute mortality. 

19 Then I agree, it is not really below it on the same very

20 important level is the brood production. Because depending on

21 the substance, if you have an IGR, of course you would not get a

22 clearly visible incident, but you will have no more brood. So,

23 they are both basically on the same level. They are the most

24 important ones in my opinion. 

25 Foraging behavior, okay this is also very

26 important. For instance, if you would have a substance where
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1 you have a clear impact on and a long lasting impact on the wish

2 of the bee to forage on that crop, that would of course affect

3 one of your protection goals too, the quantity of hive products.

4 So, I think this is also very important. We have to take them

5 all into account. Ranking will be difficult, but those are I

6 think the most relevant ones.

7 DR. DANIEL SCHLENK: Dr. Berenbaum? 

8 DR. MAY BERENBAUM: I think maybe splitting

9 hairs to say what's most or least relevant. But there's one

10 profound difference in assessing nontarget impacts on bees than

11 for birds or most other nontarget arthropods is that most

12 people, even though the nontarget organisms can be beneficial. 

13 Most people don't make a living out of those beneficial

14 organisms. 

15 So, in the case of nontarget impacts on bees,

16 it's important economically not only that they are not dead, but

17 that they are also functional and capable of both delivering the

18 economic services for which they are being raised. So, it's a

19 different context. I used the analogy earlier in a conversation

20 in some ways and army, a dead soldier - no disrespect to the

21 military - but a dead soldier at least is easier to deal with

22 than one that's suffering mental of physical problems. 

23 So, in a way, we have these troops out there

24 that are delivering pollination services. So, it's not just

25 that they're numbers are reduced. If their behaviors are

26 profoundly altered so that they are not delivering the
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1 ecological and economic services, for which they are being

2 maintained. That's important in the context of risk assessment.

3

4 DR. DANIEL SCHLENK: Dr. James? 

5 DR. ROSALIND JAMES: A point taken with the

6 answer, you could go back to your protection goal and say what

7 are your three protection goals and weigh them. So, what are

8 they provision of pollination services, so what is going to be

9 important for provision of pollination service. I will be hive

10 activity but also be colony size and colony survival. That goes

11 back to what you were saying about protecting the beekeeper

12 also, and then similar for production of hive products will be

13 the same factors, generally colony size and colony survival. 

14 DR. DANIEL SCHLENK: Dr. Pettis? 

15 DR. JEFF PETTIS: You beat me to it, Rosalind.

16 But going back to those two protection goals, the first two

17 protection goals, your point about loss of forager force in a

18 pollination contract could be critical. So, a short impact on

19 pollination or the pollinating force would be critical at that

20 point, even though it may not have colony level effects, which

21 would affect whatever protection goal number two. 

22 DR. DANIEL SCHLENK: Dr. James? 

23 DR. ROSALIND JAMES: But then I still have to

24 add - the fecundity still keeps coming back. We had a case with

25 a growth regulator and alfalfa leaf cutting bees. So a new

26 growth regulator was approved for alfalfa, applications to
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1 alfalfa seed and after it was used in fields the first year we

2 had the beekeepers as alfalfa leaf cutting beekeepers come to us

3 and say we got very poor bee return, that is the number of bees

4 that came back out of the field, from those fields that were

5 treated with this product and be return was normal in the fields

6 where the product wasn't used. The product had very low acute

7 toxicity to adult bees. 

8 So then, some of the scientists in our group

9 investigated it and well, if it's a growth regulator, it's not

10 going to really affect adults, but maybe it'll affect fecundity.

11 It turned out a closer investigation that the queens were

12 laying inviable eggs, either that or the larvae exposed to the

13 pollen provision was contaminated. We don't know which, but the

14 eggs were not hatching or when they hatched, they died

15 immediately. So, fecundity kind of comes into play always also.

16

17 DR. DANIEL SCHLENK: Dr. Fefferman? 

18 DR. NINA FEFFERMAN: So, in some sense, I love

19 all of these comments and I'm now almost stealing my own

20 thunder, because you asked me to put examples into question 14. 

21 This was the example that I put in. We've been talking about

22 rates of mortality as an issue and now we're talking about which

23 things are most critical. It's a mistake to separate those two.

24 Mathematically, if you had an analytic model for this, it is

25 the ratio of worker death to worker recruitment. Whether that's

26 from eclosion or egg laying, whatever is driving that, that
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1 ratio has to be greater than one in order for the colony to be

2 successfully stable as a population demographic. And either

3 side of that, either because the eclosion rates drop or because

4 mortality goes up can drive that ratio less than one. 

5 That is why I'm really sort of cringing about

6 identify these set of things as being critical because neither

7 of those in isolation is necessarily critical to colony

8 survival. It is critical - I like very much Dr. Pettis's idea

9 if it is critical to the pollination services over a duration,

10 but in terms of colony survival, neither one in isolation is

11 itself critical. The ratio of the two is incredibly critical,

12 and that is the sort of thing where just shooting from the hip

13 on what seems to be critical gives you sort of the wrong insight

14 into that. 

15 DR. DANIEL SCHLENK: So, let me just remind

16 the Panel again that anything that you say, you need to get

17 written comments to Dr. Tarpy on this particular issue, because

18 he needs to put these together into some coherency. So, just a

19 reminder there. Any other comments? Dr. Hunt? 

20 DR. GREG HUNT: Maybe I'll get the last word. 

21 We can't really rank these things as has been said, but anything

22 the affects the reproductives would probably be at the top. 

23 Like if you get deformed queens, that's bad. 

24 DR. DANIEL SCHLENK: Okay. I think you're

25 going to get a list, Keith, on a bunch of stuff. 

26 MR. KEITH SAPPINGTON: Thank you very much. 
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1 DR. DANIEL SCHLENK: Any other questions,

2 clarification questions from the Agency? Alright, let's take a

3 break. Let's come back at a quarter to three.

4 (WHEREUPON a recess was taken)

5 We're ready to lead off with question 12,

6 letter A.

7 MR. JOSEPH DECANT: Joe DeCant, EFED, question

8 12, subpart (a) - please comment on the strengths and

9 limitations of full-field studies described in the white paper.

10 DR. DANIEL SCHLENK: All right, Dr. Pettis,

11 you're our lead discussant. 

12 DR. JEFF PETTIS: Thank you -- Dr. Pettis. 

13 Tier III studies are meant to be ultimate tests of real world

14 exposure and potential effects. However, these studies are

15 fraught with potential pitfalls with regard to the ability to

16 carry out controlled experiments in the open environment and

17 with adequate controls. 

18 When properly executed, Tier III studies are

19 the best means to assess the risk and impacts of pesticides to

20 Apis mellifera colonies and other managed colonies such as

21 Bombus or Osmia. In the context of the regulatory framework,

22 Tier III studies are considered highly refined from the White

23 Paper. Studies meant to address specific concerns raised in

24 lower tier studies, in that said, each study design should vary

25 with regard to the endpoints that we measured to address these

26 specific concerns that may be raised in Tier I or Tier II
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1 testing. 

2 So, I will point out some strengths and some

3 weaknesses that the group felt were relative. So for strengths,

4 the natural exposure that can be achieved by allowing bees to

5 forage freely on the treated crop and then measuring endpoints

6 allows for the testing of effects at the colony level that are

7 unattainable at Tier I and Tier II level testing. The exposure

8 achieved can accurately simulate the actual use of the

9 pesticide, another strength. 

10 Tier III tests allow for colony-level testing

11 of protection goals. So, it's the only aspect. Maybe a little

12 bit in Tier II, but you can do colony-level test. If

13 significant effects are noted at the colony level, the results

14 can be interpreted with a high level of confidence as the colony

15 has many buffering mechanisms such as reserve worker force,

16 ability to compensate for lost or dead workers, things like

17 that. An effect seen at the colony level is probably realistic.

18

19 The use of surrogate crops such as Phacelia is

20 possible the help increase foraging on the crop, or maybe the

21 crop of interest, and thus insuring exposure. So those are some

22 of the strengths. 

23 Some of weaknesses - study site selection is

24 probably the single most critical factor in designing a decent

25 study, competing vegetation can result in low or no exposure to

26 the target crop and plot size can often be limited when
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1 designing these Tier III studies. 

2 Again, now plot size will vary with the

3 attractiveness of the crop in question. So, if you have an

4 unattractive crop, you may have to have a slightly larger plot

5 size to ensure adequate exposure. 

6 As far as competition, bees will forage on the

7 most attractive crop within the flight range, and this again can

8 lead to inadequate exposure to the target crop. So, a knowledge

9 of the surrounding area is critical and attempts to minimize

10 competing vegetation is important. 

11 The need to treat the control plots to keep

12 them healthy to some degree may confound the effects that you

13 may be looking for. So, treating the control plots can be a

14 major issue, but the lack of treating the control plot may make

15 it non-comparable because of the health of the plants themselves

16 may be compromised without treatment. 

17 Then lastly, positive control plots are

18 normally not possible in an environment like this where you

19 spray a highly toxic compound on a third set of plots to

20 demonstrate an effect. Those are the things that I listed, the

21 strengths and weaknesses. 

22 DR. DANIEL SCHLENK: Thanks. Dr. Ostiguy? 

23 DR. NANCY OSTIGUY: I concur.

24 DR. DANIEL SCHLENK: Dr. Potter? 

25 DR. THOMAS POTTER: I have one comment. We

26 may want to make this in several places, is the possible and
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1 potential confounding effect of the need to use other

2 agrochemicals to have agronomic success of your crop may indeed

3 make it difficult to interpret or evaluate the impacts of the

4 active ingredient that's the focus of the investigation. 

5 DR. DANIEL SCHLENK: Okay. Dr. Tarpy? 

6 DR. DAVID TARPY: I have nothing further to

7 add. 

8 DR. DANIEL SCHLENK: Other panel members? Dr.

9 Ostiguy? 

10 DR. NANCY OSTIGUY: What I remember as talking

11 about that, maybe it was with you Jens that we talked about how

12 if you then applied the material that is applied to the control

13 crop, you apply it to your treatment crop to attempt at least to

14 remove that effect from the control crop.

15 DR. THOMAS POTTER: You know, absolutely. You

16 know, in terms of treating your control and hopefully keeping

17 your materials limited, but even still those other products are

18 stressors and may ultimately obscure what you're really try to

19 measure is the effect of the ingredient you're working with.

20 DR. DANIEL SCHLENK: That was Dr. Potter. Dr.

21 James? 

22 DR. ROSALIND JAMES: Along the lines of those,

23 sometimes if you don't control pests in your control plot, you

24 may not have very good flowering and you could have poor control

25 mortality. Poor growth of your colony and the control

26 essentially is what your concerned about right, besides the
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1 possible interactive effects that you're adding a pest control

2 measure. 

3 The other thing I wanted to add to Dr.

4 Pettis's list. I hope I didn't miss it. It was the whole study

5 had to be perhaps repeated in different locations and different

6 places. Because if you do it one time in a same crop, I mean,

7 it would be better if it could be repeated in multiple years or

8 multiple sites and locations. The more times you could repeat

9 it the better. You didn't really have that, right? Or did I

10 miss it? 

11 DR. JEFF PETTIS: That's covered later on. In

12 trying to say how robust the test would be, it's better to do it

13 in several geographic areas. We haven't gotten to that. 

14 DR. ROSALIND JAMES: I apologize. You're

15 right. That doesn't really address this question.

16 DR. DANIEL SCHLENK: Okay. Yes, Dr. Pettis? 

17 Mr. Pistorius? 

18 MR. JENS PISTORIUS: Just a last comment on

19 this. I think the uncertainties can be, to a large extent,

20 avoided. Of course, it should be avoided that if you have a

21 flowering crop where you do your tests that you apply no other

22 insecticides. Also with fungicides because then you might run

23 if that is not the purpose of the study into testing synergisms,

24 but I think that actually should not be a problem. 

25 DR. DANIEL SCHLENK: Yes, Dr. Pettis? 

26 DR. JEFF PETTIS: Just again, back to both
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1 those comments. I think the need to have a crop in the control

2 that's viable, that's producing and flowers and stuff is

3 important, but you have to balance that. Maybe the best is to

4 not use a target crop and use something like Phacelia that

5 wouldn't need those treatments. But again, the best is to

6 actually apply the formulated material to the crop of interest,

7 but you have to take measures to make sure the control plots are

8 not confounding but in good health. 

9 DR. THOMAS POTTER: Exactly, and I think

10 those are all things that come into experimental design and I

11 think are intended to, I guess, provide support for that process

12 that was described in the White Paper. This is Tom Potter here.

13 DR. DANIEL SCHLENK: That was Tom Potter. Any

14 other strengths or limitations out there? Okay. Let's go ahead

15 and move onto the next section, question 12.

16 MR. JOSEPH DECANT: Joe DeCant, question 12,

17 subpart (b) - please comment on the proposed modifications to

18 the field study design elements presented in section 4.3.2 of

19 the White Paper.

20 DR. DANIEL SCHLENK: Dr. Pettis? 

21 DR. JEFF PETTIS: There will be some

22 repetition throughout these things, but - so testing the

23 formulation at the highest label weight is valid. I think

24 that's a good idea. We agreed that plot location is a critical

25 step in competing vegetation is an important consideration to

26 ensure exposure to the target crop. The exposure achieved can
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1 accurately simulate the actual use of the pesticide, I think

2 that's already been stated. 

3 The proposed studies are meant to stimulate

4 worst case scenarios. This is true to the extent that the

5 pesticide is applied when the highest label rate on the blooming

6 crop. However, limited plot size may result in a less than

7 worst case scenario in that these in fact are not going to be

8 foraging solely on the target plot. 

9 Until the pesticide is in actual use and used

10 on large acreage is you may not see the full extent of the

11 exposure. So, that's kind of a limitation. 

12 I've already pointed out that plot size may

13 vary depending on the attractiveness of the crop and that less

14 attractive crops may necessitate having large field sizes. This

15 is between controlled and treated plots. The exact distance

16 should may be a minimum of three to six kilometers, but that

17 distance has to be balanced to get some need to keep those crops

18 in the same geographic and microclimatic conditions. So, there

19 is a balance there between the need to keeping them in the same

20 area and yet keep the distance such that you are not overlapping

21 the foraging. 

22 We agree that balancing or equalizing of bee

23 colonies is an important step to be taken in advance of placing

24 the hives in the study site. Otherwise, colony variation

25 between colonies is too great to see differences. So, colony

26 equalization prior to placing the colonies into things is a very
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1 good step. 

2 We agreed that power analysis is a useful tool

3 to determine the number of colonies needed to produce a

4 statistically significant result. However, in practice often,

5 plot size and replication are not such that we can use the power

6 analysis and those studies aren't often done with high

7 statistical power. We think the use of a power analysis to at

8 least guide those studies should be used. 

9 The potential endpoints listed are useful to

10 look at potential adverse effects. I have a couple of specifics

11 that you list the returning of forager bees. I think you should

12 be listing something more like the returning of bees over some

13 time period. 

14 I would add that maybe we should add a

15 consideration of the brood pattern, so you can analyze the

16 consistency of the brood pattern by measuring 100 cell areas,

17 and that's not listed on your list. So, there's a way to

18 measure the consistency or solidness of the brood pattern. We

19 have shown in other studies that that can be an accurate

20 predictor of overall colony health. 

21 We concur that the duration of the field trial

22 is going to be variable, and we dictate it by the endpoints of

23 interest and also by the crop the products are sprayed on. 

24 We also concur that opening the hives can

25 result in increased disturbance from possible introduction of

26 variation within the results, increased queen loss, things like
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1 that increase spikes in adult bee mortality, but the hive

2 manipulation shouldn't be minimized, but undue concerns about

3 introducing these artificial effects. So, as long as the

4 colonies in both plots are treated the same, I think you could

5 minimize that. I don't think the disturbance factor is so

6 great. So, hire a good beekeeper basically. 

7 And the last comment - most of these field

8 studies are designed with use of honey bee colonies. So, you

9 design a large field study and you put honey bee colonies in

10 there. Certainly, you already have the field design there and

11 the big problems with these are size of the plot and separation

12 between control plots and treated plots. The use of things like

13 Bombus or Osmia added to those plots would not incur a great

14 deal of cost. Both of those genera fly shorter distances, so

15 they would be more likely to stay within the field.

16 So most of these Tier III studies are designed

17 with honey bees in mind with the addition of Bombus or Osmia to

18 those field studies could be telling in that both they fly

19 shorter distances, they stay within the treated field and

20 secondly, they have different life histories. So, you might see

21 different effects by using two additional species. And that's

22 all I have. 

23 DR. DANIEL SCHLENK: Thank you. Dr. Ostiguy? 

24

25 DR. NANCY OSTIGUY: I concur. 

26 DR. DANIEL SCHLENK: Dr. Potter? 
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1 DR. THOMAS POTTER: I concur with everything

2 that was said. I just wanted to add that I think there is some

3 merit in value to possibly incorporating these studies with

4 terrestrial field dissipation studies or plant residue decay

5 studies. The more bang you can get for the buck obviously would

6 be great. So, certainly there is potential for that type of

7 integration here and certainly something to be contemplated and

8 encouraged. 

9 DR. DANIEL SCHLENK: Dr. Tarpy? 

10 DR. DAVID TARPY: This is David Tarpy. I

11 agree with Jeff's excellent real assessment there and really

12 have nothing else to add except underscore the utility of the

13 power analysis and that that's a very useful tool. 

14 DR. DANIEL SCHLENK: Other Panel members? Dr.

15 James? 

16 DR. ROSALIND JAMES: I have a few things to

17 add to the list. In terms of the honey bees, in terms of all

18 bees, bees are like the rest of us and they are somewhat lazy

19 and they will only fly as far as they have to generally. So, if

20 you can keep the - if there is plenty of bee forage available,

21 they are less likely to fly long distances and leave the

22 treatment area. So, placing the bees in the middle of the field

23 and making sure that there's lots of bloom and the design of the

24 experiment will help. Like a lettuce field, you know, is not

25 going to have a lot bloom in it. 

26 Then, going back to table 1, which I was in
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1 charge of table 1, so I'm going to go back to it. One of the

2 things that we talked about was that the brood nest size would

3 be an important measure. So for your list of measures, I would

4 add brood nest size. That's something relatively easy to

5 measure. 

6 Then, Dr. Pettis brings up a good point about

7 other bees, and this is a good place now where we can move away

8 from the honey bee being entirely a surrogate. Since you're

9 doing a field trial, there are two things to consider. One, you

10 could have a sample - there's a bee in front of me right now -

11 you could just go out and do net sampling or pan traps, do some

12 sort of sampling for what bees are already in the field and

13 compare between control and treated or before and after. 

14 There, you can get your bee diversity measure

15 in there and hit your third production goal, but also as risk

16 managers, what is the crop being registered for. If it's being

17 - the pesticide excuse me - if the pesticide is being registered

18 for a crop in which something other than honey bees as a primary

19 pollinator, then maybe the field trial ought to be conducted

20 with that pollinator instead of honey bees. So, if it's being

21 registered for alfalfa seed, you ought to be looking at alfalfa

22 leaf cutting bee. If it's being registered for a tree crop, you

23 could include something like one of the Osmia bees. So, those

24 are my additions, thank you.

25 DR. DANIEL SCHLENK: Any other input? Yes,

26 Dr. Hunt? 
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1 DR. GREG HUNT: I don't know if this was

2 mentioned, but it's also important to do scouting to see what

3 the bees are foraging on, to see if they're on nontarget crops

4 and look at the pollen they're bringing in. 

5 DR. DANIEL SCHLENK: Okay. Anyone else? 

6 Okay. Apologies - I forgot to ask you guys on the last

7 subquestion if you had any comments. If you have any comments

8 on the last one and this one, any questions of clarification? 

9 Yes, Mr. DeCant? 

10 MR. JOSEPH DECANT: Joe DeCant, EFED. Maybe a

11 clarifying question about the plot sizes - Dr. Pettis, you

12 mentioned about the different crops given different

13 attractiveness may require different size of the plots to be

14 adequate in terms of the field study. Does the panel have any

15 recommendations or thoughts on minimum plot sizes to be able to

16 be able bound what we are looking at in terms of the field study

17 and plot sizes, or a way to calculate how we're going to come up

18 with the plot sizes or the field study related to the crop? 

19 DR. JEFF PETTIS: Jeff Pettis - on plot size,

20 if you calculate the foraging range of an average colon, even

21 conservative, and you even to a 1-hectare plot or a 5-hectare

22 plot, the proportion of the total forage area that they could

23 visit is really small. So, it's terrible to say, but you know,

24 larger plots are better and more attractive and reducing

25 competing vegetation. I would - this is maybe personal and

26 maybe you can speak to this a bit more. Somewhere, 1 hectare is
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1 small, 5 hectares is starting to get, for me, is seemed like you

2 would be getting in the realm of a reasonable plot size. What,

3 are you thinking larger? I've seen some of these field studies

4 where you can get replication. You don't end up having large

5 fields, I don't know. 

6 DR. DANIEL SCHLENK: Okay. Who would like to

7 go first? Dr. James? 

8 DR. ROSALIND JAMES: I was going to suggest 40

9 acres for your plot size. I don't think that is ridiculous. 

10 And for seed - so the rules for certified seed and not getting

11 cross pollination to keep your seed certified, I think, is 1

12 mile. So, you know, that would be 1 mile between plots. 

13 DR. DANIEL SCHLENK: Mr. Pistorius? 

14 MR. JENS PISTORIUS: Well, I think this is one

15 point where the EPPO needs some modification. Larger field

16 sizes should be used and described in the old EPPO version, but

17 I'm quite sure in the next version, there will be an update on

18 this. 

19 I think we have to consider the different

20 crops. For instance, if we compare maze and winter oilseed rape

21 - for maze, I would personally go for larger field sizes then

22 would be necessary for winter oilseed rape. It depends on the

23 landscape where you do such studies. 

24 My personal experience, in former times when I

25 was young, I conducted such studies a lot. We have a region

26 where I come from and where I work where you could actually do
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1 peer trials, which I think give you the information that you

2 need with winter oilseed rape of size of one hectare even. 

3 I don't know if that is appropriate and I'm

4 not quite sure the agricultural structure is the same, but we

5 had a discussion on this too, and it basically depends on the

6 interpretation of effects and demonstration of exposure. I

7 think, for instance, if you look at the number of foragers. 

8 Nowadays, we will say okay, maybe this one hectare field size

9 used to be small for winter oilseed rape in former times. 

10 Nowadays, we say okay, yeah we want to go a little bit larger

11 because you know in spring, it might be hard to track all the

12 bees even if you really ensure that in a two or three kilometer

13 circle, you have no relevant major flowering crops around.

14 It may be different, for instance, if you do

15 trials with Phacelia because the foraging density is a

16 completely different one. You get, for instance in our

17 condition, 5-OH foragers per square meter in winter oilseed rape

18 and you get about 25 or 30 for Phacelia. So, I think in terms

19 of these numbers, you may have a certain difference. I think

20 with one or two hectares, you can start a very bee-attractive

21 crop and ensuring that there is no cross foraging possible and

22 landscape which does not provide alternative forage basically. 

23 You will not be able to invite completely, but no major nectar

24 flow and no major pollen flow. Then you could start to get very

25 good results with this. 

26 When you interpret it as a risk assessor,
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1 basically the effects that you get with care, that is a crucial

2 point because we talked about if you have a controlled level of

3 mortality of whatever 50. If you get 50 more dead bees, so 100

4 dead bees after the application, let's assume that it's a

5 pyrethroid. If you then careful as a risk assessor and say

6 okay, I've got a concern. Then, it doesn't matter. You know,

7 if you have five hectares or 50 hectares, the amount of bees

8 that you will find from your colony is also limited. Because if

9 you have the exposure maximized and if you basically achieve

10 that the very large portion of your foragers visit this field

11 and brings back this contamination - I know this is a crucial

12 point - then you ensure that those have been well exposed. 

13 The number of foragers is limited to some

14 extent, and you will not get completely different results if you

15 have good exposure in smaller fields compared to when you have

16 fields with 50 hectare. It has been the work of Helen Thompson,

17 the colleague from the U.K. where they have actually addressed

18 these concerns in studies with the pyrethroid in the U.K. where

19 they had a field study of I think, one-half or two hectares of

20 winter oilseed rape. Then they compared it to a landscape

21 application with 200 hectares. The outcome was not different

22 from the individual colonies. So, I think, you know, we all

23 want that the bees are well exposed but maybe depending on the

24 circumstances, but I think one or two hectares for flowering

25 crops you're well off. Maybe for pollen and maze, you should

26 need a little bit larger one. 
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1 DR. DANIEL SCHLENK: Okay. First Dr. Pettis

2 and Dr. Ostiguy.

3 DR. JEFF PETTIS: Just to follow up, I would

4 agree that one hectare maybe on the low end, but for crops such

5 as Phacelia where you have high visitation rates and in areas

6 where you don't have competition, they could be adequate. I

7 would feel more comfortable with five hectare plots for general

8 crops. 

9 You have to in all cases, trap incoming pollen

10 and analyze that pollen to guarantee that their visiting the

11 crop of interest, there is not too much competition, or if there

12 is competition, it's equal between the control and the treated

13 plot. So, there are things you can do to guarantee that you're

14 getting some visitation or reasonable visitation to the plot

15 size. It's just in practicality that large plots would be

16 great, but they're just not usually feasible in most settings. 

17 So, five hectares may be - and then maybe down even as low as

18 one hectare on very attractive crops. 

19 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

20 DR. NANCY OSTIGUY: This is actually where

21 Tier III studies are of concern to me because I think that you

22 get a fair amount of type II error. It helps knowing that the

23 bees have actually foraged on what you want them to forage on,

24 but our sizes of plots are too small. I think part of the

25 difference between what Dr. James said and Dr. Pettis has to do

26 with differences between the eastern U.S. and the western U.S. 
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1 Your plot sizes in the west are going to be much larger

2 generally. 

3 I was originally a westerner. That's sort of

4 my vision also is a bigger area. I recognize though that here

5 in the east, the size of each plot is smaller, but it does

6 present a concern to me about type II errors. 

7 DR. DANIEL SCHLENK: Dr. James. 

8 DR. ROSALIND JAMES: This is Rosalind James. 

9 This occurred to me too the difference between I think even the

10 midwest probably, although I have less experience with the

11 midwest and the west. We are talking about, you know,

12 three-quarters of the United States. If we throw the midwest

13 and the west in there together, and especially something like

14 California agriculture. California is wall-to-wall agriculture,

15 and there is not a lot of margin places for the bees to hide out

16 in. 

17 So if you have pesticide use, it's going to be

18 solid and you're talking about cornfields in the midwest, it's

19 going to be solid. There's not going to be other places for the

20 bees to gain forage from. Typical irrigation unit in the west

21 is 40 acres. If you're doing center pivot irrigation, I think

22 it's 120 acres. So that's one farmer's field that is going to

23 be very large. Now, so that's one reason why I say 40 acres,

24 which is what about 12 hectares maybe. It's going to more

25 because it's three to one. So about 12 hectares would be what

26 we're talking about. 
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1 But you could put more than one colony of bees

2 in the middle of that. You could put four or so there or more. 

3 So, you could put more than one colony in the middle of that and

4 it's somewhat pseudo-replication. So, your experimental design

5 is going to be affected by that. 

6 DR. DANIEL SCHLENK: Dr. Hunt? 

7 DR. GREG HUNT: This is just a side note. 

8 Regardless of plot size, it may be a good standard practice or a

9 practice to use sometime to just collect the incoming foragers

10 and analyze them for pesticides to at least document that they

11 were exposed. I don't know how good an idea it will give you of

12 the total level of exposure, but some qualitative documentation.

13 DR. DANIEL SCHLENK: Yeah, Dr. Pettis? 

14 DR. JEFF PETTIS: I think it's a good idea,

15 Greg. I guess in the studies I've seen that trapping incoming

16 pollen gives you a really nice measure of the level of exposure,

17 you know, because you can trace it to the specific crop that

18 you're growing. I was going to ask you, Dr. James, a question

19 about - because let's say you can only have one plot of each. I

20 mean, ideally, you have three or four plots of each type. There

21 I think I would argue for small plot size. Take your 40 acres

22 and divide it four ways into four 10 acre plots and get true

23 plot replication because otherwise, we end up with just putting

24 10 colonies on this one plot and using the colony as the source

25 of replication. Ideally, you'd like to use the plot as the

26 source of replication, but that's not always possible. So, I
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1 think that's some of the trade offs. 

2 DR. DANIEL SCHLENK: Okay. Yeah, Mr.

3 Pistorius? 

4 MR. JENS PISTORIUS: I agree, but I think that

5 actually comes in a later question. It would be desirable to

6 have those in different environments, then you can actually

7 cover part E of the question a little bit and do the

8 representativeness. We do that even in our very tiny Germany. 

9 We ask for northern, eastern and southern or something like that

10 when we test different environments.

11 DR. DANIEL SCHLENK: Okay let's - you still

12 have another comment? Yeah? Okay. Dr. James? 

13 DR. ROSALIND JAMES: Watching the clock, he

14 wants to go home today. But maybe in the west we just have more

15 land, and a 40 acre plot doesn't seem outrageous to me. I'm not

16 back down. I think one hectare is way too small. Five - I

17 don't know, your bees fly a long way. 

18 DR. DANIEL SCHLENK: So, let me make a

19 recommendation instead of bantering back and forth. What's in

20 the written documentation, you can agree to disagree and say

21 that the positives for the small term plots and then you can

22 document your justifications for the larger term plots. Yep,

23 Dr. James? 

24 DR. ROSALIND JAMES: With my suggestion that

25 you could sample for bee diversity within the plot, if your plot

26 is too small, I don't think you can - sampling for bee diversity
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1 is not going to work as well because you're going to have the

2 potential for influx from outside bees into your plot also. 

3 Unless you have immediate acute kill, you may not be able to

4 tell that there's been an effect on bee diversity. So, if the

5 plot's too small, that measure may not be effective for you.

6 DR. DANIEL SCHLENK: Okay, which one? Dr.

7 Pettis? 

8 DR. JEFF PETTIS: Okay. Back to the -- not

9 dwelling on the larger plot size, but I think the point brought

10 up by Dr. Pistorius was that if we have a chance of replication

11 at the plot level, it's probably better across geographic

12 regions. Then, you gather more information than doing it within

13 one region. So, testing it in Kansas and Utah and Georgia would

14 be preferable I think.

15 DR. ROSALIND JAMES: The alfalfa seed isn't

16 grown over all those areas.

17 DR. DANIEL SCHLENK: Yes, Mr. Pistorius? 

18 MR. JENS PISTORIUS: Question to Dr. James. 

19 Did you say sampled bee diversity? Because I think that is a

20 very complicated measure, because the bee diversity on such a

21 crop would not be depending on this treatment basically, but how

22 the population of different bee species was established before. 

23 If you cannot standardize this, then you cannot make basically

24 an assumption later on how your treatment affected this bee

25 diversity. 

26 DR. DANIEL SCHLENK: So, the question, I
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1 believe, for clarification was size, right? Is that basically

2 what the clarification was? I mean, do you want more

3 information with regard to other endpoints as well? 

4 MR. JOSEPH DECANT: This is Joe DeCant, EFED. 

5 The main question was about plot size, so I think the panel

6 touched also on the number of hives per plot. So, we're talking

7 about, you know, this brings in loading rate as well in terms of

8 the number of hives per plot. I think that, you know, usually

9 we're talking about three to four full-size hives at each plot. 

10 So, we're talking about hives of about 50 to 60,000 bees. 

11 Taking that into consideration in terms of the loading rates of

12 the Panel, and the Panel's recommendations about plot size, if

13 you also have a recommended loading rate, relating plot size to

14 the number of hives per plot as well, that would be helpful.

15 DR. DANIEL SCHLENK: Okay. That's not

16 addressed further on in any of the questions? Okay. You might

17 want to make a statement if you can at the present time so we

18 can get it on the record. 

19 DR. JEFF PETTIS: Well, certainly with a small

20 plot size, you could, by putting large colonies on the small

21 plots, you could overwhelm the plot and there wouldn't be - but

22 you still may get some representation, foraging rate and each

23 colony would get some level of exposure. There is probably a

24 formula out there that would, depending on the attractiveness of

25 the crop, you could predict exactly how many colonies. It was

26 kind of like a pollination, like how many colony spray do you
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1 need for almonds, almond crop, things like that. 

2 So, there probably are formulas out there. 

3 They're not at the top of my head right now. So, we can address

4 that a bit in balancing the plot size versus the number of

5 colonies. Even with and extremely small plot size, you're still

6 - four colonies per plot would be a minimum because three is the

7 minimum for any kind of replication. 

8 With honey bee colonies, one is going to die

9 on you automatically. So, you have to start with four. I

10 prefer to go up from that. But you will recognize that there

11 could be an overloading effect and try to address that.

12 DR. DANIEL SCHLENK: Okay. Yeah, Dr. Klaine? 

13

14 DR. STEPHEN KLAINE: If you put multiple hives

15 on a single plot, isn't that pseudo-replication? 

16 DR. JEFF PETTIS: It is. 

17 DR. ROSALIND JAMES: This is Rosalind James. 

18 Or you could consider like a subsample. I mean, you want to put

19 multiple hives on there because you might lose one for various

20 reasons.

21 DR. STEPHEN KLAINE: Then your replicate is

22 your plot, not your hive. 

23 DR. DANIEL SCHLENK: Dr. Pettis? 

24 DR. JEFF PETTIS: But there is some discussion

25 about that, because really you want to expose the bees and the

26 unit that you're measuring is the colony. You're measuring a
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1 colony level response. So, in some sense, the colony is the

2 replicate. But in reality, we would like to have even that

3 colony replication exposed in multiple plots. That's a better

4 design. 

5 DR. DANIEL SCHLENK: So, again, just letter E

6 I think we talk about study design elements. So, if we can -

7 yeah. Okay.

8 DR. ROSALIND JAMES: I want to answer the

9 question about how many bees per acre because I think I could

10 give you a rough guideline of kind of a magic number, 3000 bees

11 per acre. 

12 DR. DANIEL SCHLENK: That would be Dr. James.

13 DR. ROSALIND JAMES: This is based on what we

14 do with alfalfa leaf cutting bees, which are slightly smaller

15 than honey bees and generally the growers put out really about

16 5000 bees per acre. I'm sorry, 30,000 bees per acre. I will

17 correct that. From, I think, 30,000 bees per acre is probably a

18 good rule of thumb. That would be one colony per acre.

19 DR. DANIEL SCHLENK: Just to remind the Panel,

20 we're not here to try to get out quickly, we're just trying to

21 focus the discussion to the questions that are being asked. So,

22 that's what my job is, is to try to focus us. It's not to get

23 us out early and get us home early, just to let you guys know

24 that. We're just trying to focus the discussion to the question

25 that's being asked. So, again, Mr. Pistorius you have another

26 comment? 
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1 MR. JENS PISTORIUS: Just a question for

2 clarification basically, how much is 3000 bees per acre? 

3 DR. ROSALIND JAMES: 30,000 per acre, so that

4 would be 2.5 per acre. 

5 MR. JENS PISTORIUS: There are 2.5 acres in a

6 hectare? Okay, thank you. Therefore there is also some

7 recommendation on the minimal number of bees per square meter

8 that should be there, and I think for Phacelia before you do

9 your spray application, for Phacelia it's at least eight and I

10 will have to look up the number. I think they said two to three

11 for winter oilseed rape and other crops. 

12 DR. DANIEL SCHLENK: Okay. So, it sounds like

13 several recommendations, which will be placed in the minutes. 

14 Dr. Pettis again, we are relying on you to put those in there

15 and each panel member to provide their justifications for

16 whatever numerical value they provide. It sounds like there is

17 a little bit of disagreement. So, just make sure whatever

18 number you come across, whether it's hectare size or bee number

19 that there's a justification for the rest of the panel to weigh

20 in on. Any other questions of clarification from the Agency for

21 letter B? Okay. So, let's move onto letter C please.

22 MR. JOSEPH DECANT: Joe DeCant, EFED, question

23 12, subpart (c) - please comment on factors that should be

24 considered in evaluating the biological significance of effects

25 measured in full-field studies in relation to the proposed

26 assessment endpoints and related protection goals. 
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1 DR. DANIEL SCHLENK: Dr. Pettis? 

2 DR. JEFF PETTIS: Colony growth patterns is

3 measured by brood production or adult bee population or robust

4 measures of colony health and should be directly related to the

5 protection goals or many of the protection goals. Similarly,

6 things like colony mortality would be obviously biologically

7 relevant, but you're unlikely to see these adverse direct

8 effects. The effect on queen survival and queen replacement

9 would be of biological significance as it has implications for

10 long-term colony survival and may not be accounted for if test

11 duration is only of a few weeks or months. 

12 Over winning success may be the real ultimate

13 test for colony survival, but it's unlikely to be realized

14 unless the exposure period is closer to the fall and you can

15 control for factors outside the test period when colonies can

16 suffer from parasitic mites and things like that. 

17 Adult bee mortality is a good biological

18 indicator as are foraging rates in adult bee longevity. Each of

19 these could be good indicators of adverse effects, but effects

20 could be transitory in nature, and thus extrapolation to the

21 colony level impacts is not necessarily direct. So, you may

22 measure those effects, but how they affect total colony survival

23 is not always one to one.

24 Taken together any effects noted in the above

25 endpoints could be used in the risk assessment decision making

26 even if each endpoint alone was not significant, that is taking
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1 the total things measured and looking at those even if they

2 weren't significant, the weight of impacts could be taken into

3 consideration as well as Tier I and Tier II testing. 

4 Colonies are complex social organisms, and

5 thus several endpoints measured may be more appropriate than

6 relying on a single endpoint alone.

7 Then finally, disease in pest levels and

8 worker longevity, foraging rates and other endpoints that are

9 measured are less robust, but they should be considered and they

10 should be actually equalized as far as we can at the beginning

11 of each study. 

12 Any adverse effects noted in things such as

13 foraging rates or adult bee longevity during the study should be

14 noted and taken into consideration with any evidence from Tier I

15 or II testing. So, again, just what effects seen in Tier III

16 testing should be looked at in context of anything gathered in

17 Tier I or Tier II. Those were the comments that I had.

18 DR. DANIEL SCHLENK: Okay. Dr. Ostiguy? 

19 DR. NANCY OSTIGUY: I concur. 

20 DR. DANIEL SCHLENK: Dr. Potter? 

21 DR. THOMAS POTTER: I concur.

22 DR. DANIEL SCHLENK: Dr. Tarpy? 

23 DR. DAVID TARPY: I agree as well. 

24 DR. DANIEL SCHLENK: Other Panel members? 

25 Okay. Any questions of clarification? All right. We're going

26 to move on to letter E then. Oh D, sorry. 
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1 MR. JOSEPH DECANT: Joe DeCant, EFED, question

2 12, subpart (d) - please comment on factors and methods that

3 should be considered when extrapolating observed effects at the

4 colony level in semi-field and field studies to those expected

5 to occur in the environment, for example spatial and temporal

6 scale of exposure, hive management practices, presence of

7 multiple chemical and nonchemical stressors, et cetera. 

8 DR. DANIEL SCHLENK: Dr. Pettis? 

9 DR. JEFF PETTIS: In Tier II and Tier III

10 studies, they cannot possible incorporate all seasonal and

11 colony management practices that may occur, or changes in

12 response to the colony due to seasonality changes in colony

13 growth. But if testing is conducted in the normal growing

14 season, a certain level of confidences can be had and the

15 results generated. 

16 So, testing whatever the crop of interest is

17 would normally be grown and then using colonies during that test

18 period would be ideal. If you want to do a test and you do it

19 outside, what would normally be expected as far as seasonality,

20 then you could have less confidence in the results.

21 As has been stated earlier, using colonies in

22 either field or semi-field, they should be uniform in strength

23 and as disease free as possible at the start of the study to

24 avoid confounding effects. This is just a personal note - I

25 would tend to skew towards smaller colonies, although there is

26 some discussion in the White Paper about larger colonies being
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1 preferred. I think well-balanced, small colonies would give you

2 reliable results and are meaningful biologically. 

3 The seasonal nature of colony growth and the

4 importance of colony preparation for winter, extra stores and

5 honey and things that gathered, should be taken into

6 consideration if we're going to try to do a study where we're

7 going to look at overwintering. So, the exposure period, if

8 you're going to do an overwintering study, exposure period

9 should be in the summer or early fall in advance of that to

10 minimize the amount of time that those colonies are followed

11 where we have less control of the variables. 

12 In Tier III studies, the longer the study

13 continues or the more variables that will come into play, even

14 if you've done a good job of equalizing the colonies at the

15 beginning of the study relative to pest and disease, you're

16 going to have other outside influences, such as other crops that

17 they may visit in the interim, additional pesticide exposure,

18 things that can influence the results of trying to follow these

19 Tier III studies for longer periods of time. 

20 One way to approach this is to take colonies

21 from the treated plots and the control plots and move them into

22 a common area that is away from urban or agricultural settings

23 and try to put them in an as clean and pristine environment as

24 possible to avoid some of these confounding effectives. 

25 Lastly, there could be some consideration of

26 whether a crop to be tested is a minor or major crop. If it is
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1 a major crop, then even the test scenarios under Tier III may

2 not adequately reflect the ultimate use of the product in such

3 that these may be exposed more widely. I'm thinking of crops

4 like soy beans. So, if you test small plot size and you draw a

5 conclusion and then the products in use are widespread, the

6 number of acres in things like soy beans may make in reality,

7 the exposure levels that bees receive in the real world even

8 greater, and that would be during the test period. So, those

9 are some of the ideas that we have. 

10 DR. DANIEL SCHLENK: Dr. Ostiguy? 

11 DR. NANCY OSTIGUY: I concur, but I'd also

12 like to add that one of the issues that I have with Tier III

13 studies is that our goal is to try to approach what are the

14 ecological conditions that will occur during use of the

15 material, yet we are at the same time trying to eliminate

16 confounders. The confounders are part of the reality of the

17 ecological conditions for how we use honey bees. So, our type

18 II errors, we're in many ways by our design, increasing the

19 chance that we're going to find the type II error. 

20 On the other hand, we have to control for the

21 confounders, otherwise you don't have repeatability in your

22 studies. So, this probably goes in at an evaluation level above

23 the type II study itself, but there is a very severe limitation

24 to how good the date actually are for the ecological conditions

25 we see. 

26 DR. DANIEL SCHLENK: Thanks. Dr. Potter? 
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1 DR. THOMAS POTTER: I concur with what Jeff

2 and Nancy had stated. I have one thing to add. It comes in the

3 form of a question, and also from long years of experience doing

4 other types of studies, field studies, and that is the question

5 of multiple year or studies that have to be conducted over

6 multiple years to derive some sense of how climatic variables

7 and other may strongly influence results. So, I do think that's

8 been addressed here very effectively. Probably an appropriate

9 topic for discussion. 

10 DR. DANIEL SCHLENK: Okay. Dr. Tarpy? 

11 DR. DAVID TARPY: My comments have all ready

12 been made by the others. 

13 DR. DANIEL SCHLENK: Okay. Dr. Pettis? 

14 DR. JEFF PETTIS: To the question raised by

15 Dr. Potter, I think it's listed here in the last set of

16 recommendations. The idea of testing in several geographic

17 locations may get at that a bit. It certainly doesn't account

18 for all weather and possibilities, but if you do a test or a

19 series of tests in several geographic locations, you have a bit

20 more confidence that the results represented the real world. 

21 DR. THOMAS POTTER: I agree, but I don't with

22 that provisionally. I don't think that simply looking at

23 different geographic regions addresses the issue of local

24 climate variability and the potential confounding effect on

25 results. 

26 DR. DANIEL SCHLENK: That was Dr. Potter. Mr.
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1 Pistorius? 

2 MR. JENS PISTORIUS: Dr. Potter also mentioned

3 the studies conducted over several years and the longer you

4 conduct such a study, the more you get influence by the

5 beekeeper and less by the pesticide. What you can do is for the

6 environmental conditions, what is done in Europe for instance,

7 if there were no concerns that stopped the registration that we

8 ask for monitoring in the real world. So, you follow some bee

9 colonies over a while. Nevertheless, the longer you do it with

10 exactly the same colonies, treating every colony alike, the more

11 get an influence of the beekeeping and of individual factors. 

12 Okay sorry. The longer you do it basically with the same

13 colonies, the more you get an influence of other factors than

14 pesticides I think. 

15 DR. DANIEL SCHLENK: Dr. Ostiguy? 

16 DR. NANCY OSTIGUY: I agree. This is one of

17 the reasons that I would do a replicate study versus following

18 the same colonies over years. Certainly, our experience with

19 the large CAP study, keeping colonies beyond even a year can be

20 a challenge. 

21 DR. DANIEL SCHLENK: Dr. Pettis? 

22 DR. JEFF PETTIS: I will just do a followup. 

23 I think your suggestion of using colonies that are

24 representative of the real world is a good one. It kind of gets

25 to Dr. Berenbaum's thing, that in reality, there are sick bees

26 out there. They're not perfectly healthy. I guess I would
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1 argue for reasonably healthy colonies that are uniform in

2 nature, as disease free as possible, but that's why I would

3 argue for the small 10 to 20,000 bee colonies that you might be

4 more likely to see an effect. And you also don't have this

5 loading issue of putting 60,000 adult bee colonies in there. 

6 So, I would opt for smaller, well balanced colonies that are in

7 reasonable health. 

8 DR. DANIEL SCHLENK: Any other comments? 

9 Okay. Let me go to the Agency. Any questions of clarification

10 for letter D? Good. Okay. Let's move onto the last

11 sub-question there, letter E. 

12 MR. JOSEPH DECANT: Joe DeCant, EFED, question

13 12, subpart (e) - a number of study design elements are

14 discussed in section 4.3.5 of the White Paper. However, even in

15 the best designed studies, there can be confounding effects

16 which can limit the utility of these studies in risk assessment.

17 Please comment on factors that should be considered in

18 determining the utility of field studies for pesticide risk

19 assessment, including a discussion of the representativeness of

20 a study for a National Level assessment, that is the pesticide

21 may be used anywhere in the United States and its territories.

22 DR. DANIEL SCHLENK: Dr. Pettis? 

23 DR. JEFF PETTIS: Again, kind of reiterating

24 some of the things that were brought our earlier. If the

25 pesticide is to be used on a widely planted crop common across

26 the U.S., then testing should be representative and should be
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1 done in several environments across the U.S. for it to

2 representative of a valid study. 

3 The ideal test would be using the formulated

4 product on the crop of interest. In other words, definitely

5 using it on the exact crop, not on a crop such as Phacelia, and

6 with the highest level of attraction. So if you had multiple

7 crops, maybe using it on oilseed rape, whatever the most

8 attractive crop would be appropriate. 

9 If visitation to the crop is confirmed by

10 analyzing pollen and you still see no adverse effects, then I

11 think you could have some level of confidence that maybe you're

12 not producing a type II error that you have good visitation,

13 crop is sprayed at a high rate, then I think you can have some

14 level of confidence that the study actually did give you a

15 reasonable result. 

16 The design element, the plot size and plot

17 separation, we've already gone over. I think they're critical

18 in designing it. In an appropriate study, the identification of

19 incoming pollen during the exposure period is probably the

20 single best measure to give you some level of confidence that in

21 fact, the exposure has occurred and additional residue analysis

22 of that pollen can confirm that. 

23 I don't know how well that addresses all of

24 the aspects in E, but maybe they can come out in the discussion.

25 So, that's what I had. 

26 DR. DANIEL SCHLENK: Dr. Ostiguy? 
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1 DR. NANCY OSTIGUY: I concur. 

2 DR. DANIEL SCHLENK: Dr. Potter? 

3 DR. THOMAS POTTER: I concur. What this

4 sounds like is an opportunity to do some field work in Hawaii. 

5 So, that sounds good. I do want to add a couple of points here,

6 and I think that again, it comes back to somewhat of what I said

7 as a response in the prior question is that we need to very

8 carefully look at climate and make sure that we're taking

9 climate into account in terms of its impact on bee populations,

10 and I'm talking about local climate, you know, in terms of equal

11 regions within the country as well as variations in climate and

12 certainly among other things that's year to year, and also of

13 course issue surrounding climate change. 

14 So, you know, in designing these studies, you

15 know I guess we need to put our thinking caps on and kind of

16 work our way through some of these issues related to climate as

17 well as geography, et cetera, but you know I generally agree

18 with Jeff and unfortunately don't have the easy answer. 

19 DR. DANIEL SCHLENK: Dr. Tarpy? 

20 DR. DAVID TARPY: I agree with the comments

21 that have been said all ready as well, and underscore the

22 utility of ground truthing and ensuring that the bees are

23 actually exposed and are foraging on the target crop for a Tier

24 III study to come back with negative results that does not

25 demonstrate that the bees were, in fact, foraging on that crop

26 is not very useful, and that introduces that increased risk of
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1 type II errors. So, that's one way to really kind of

2 standardize across these that the bees are actually doing what

3 they're thought to be doing. 

4 DR. DANIEL SCHLENK: Other Panel members? Dr.

5 James? 

6 DR. ROSALIND JAMES: I would just add, make

7 sure you have irrigated and non-irrigated crops because humidity

8 probably could play a big role and still would call for some

9 measure of bee biodiversity in the field. 

10 DR. DANIEL SCHLENK: Dr. Berenbaum? 

11 DR. MAY BERENBAUM: This may be unanswerable,

12 but hearing the requirements for plot sizes, you know, they're

13 sufficiently large that are free of pesticide contamination that

14 are in reasonably undisturbed areas that are replicated year to

15 year to accommodate climatic variation. I live in central

16 Illinois. I don't know that there are any places to put ideal

17 experimental plot that isn't one way or another compromised. 

18 So, I don't know how to factor this in, but is this a

19 limitation? I don't know.

20 DR. DANIEL SCHLENK: Dr. Pettis? 

21 DR. JEFF PETTIS: You raise a good point, Dr.

22 Berenbaum. I think in hearing the German situation, they have

23 valleys in which there are heavily forested areas. They go in

24 and plant specific plots up and down those different valleys. I

25 think in large parts of the U.S. that's impossible and you have

26 almost at any time the year, you have some competition. One
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1 thing I think you can do is look at early spring or summer where

2 maybe there is less - there's other crops that are maybe there,

3 but they're not attractive. Your planting them and hopefully if

4 you're plots are not too far apart, you still got whatever the

5 other crops of interest - or not crops of interest, the other

6 competing crops are equal across that. Again, trapping incoming

7 pollen is a way to measure the amount of visitation to your

8 target crop, things like that. But there are huge obstacles in

9 setting up decent plot size and then reducing competing

10 vegetation. 

11 Just one other comment while I've got the mic

12 - I think it's really important if we're looking at a product

13 that is going to be used, I will say for a major use versus a

14 minor crop. If we make an error on a major use and we're going

15 to license something for use on a major crop, I think there is

16 more responsibility there than if we make a mistake on a minor

17 crop use, where the bees may or may not be attractive to the

18 minor crop but it's not in widespread plantings. 

19 Major crops where things are being used, you

20 know, in like you say the Midwest is a good example. Mistakes

21 there where we don't adequately test and field test them, then

22 those should be of concern. 

23 DR. DANIEL SCHLENK: Mr. Pistorius? 

24 MR. JENS PISTORIUS: I have to add I don't

25 live anymore in the hilly areas. It is totally flat where I

26 live now. Even there, some contract labs are situated and you
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1 can still do good tests also in flat landscapes, but we are in

2 the same situation. It's just a matter of distance between

3 fields. You may have to choose a little bit larger distances

4 when it's hilly, but the main thing is the plot isolation. 

5 DR. DANIEL SCHLENK: Dr. James? 

6 DR ROSALIND JAMES: I think in the west this

7 is less of a problem where the majority is agriculture. 

8 Two-thirds of American agricultural products come out of the

9 state of California. And I have done large scale field

10 experiments with farmers and you can get replicates in one year.

11 Also, a lot of its desert, so you only have crop growing where

12 there is irrigation and you don't have the much competing forage

13 and forest and things like that, depending on where you do it. 

14 So, maybe a large number of these tests just need to be done in

15 the west. 

16 DR. DANIEL SCHLENK: Any other comments? 

17 Okay. We go to the Agency. Mr. DeCant, you have any questions

18 of clarification? 

19 MR. JOSEPH DECANT: Joe DeCant, EFED. I have

20 one question of clarification. So, the Panel mentioned about

21 the field studies and the design of the field studies and making

22 sure that we have adequate plot sizes and looking at the

23 chemicals under good agricultural practices, but at the same

24 time, the Panel is recommending looking at foraging. So, part

25 of this speaks to the study design and even if we have adequate

26 study design, then we also need to ensure that the bees are
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1 foraging. So, my question to the panel is do you have any

2 recommendations or thought on what would be considered an

3 adequate level of foraging, either in the case of if we identify

4 a difference between the control and the treatment in terms of

5 where they're foraging or just an overall foraging and what we

6 would consider as adequate. 

7 DR. DANIEL SCHLENK: Dr. Pettis? 

8 DR. JEFF PETTIS: I'll take a stab at that. 

9 Knowing that you can't control - I mean, you've got a wide array

10 of options out there to forage on, just as a ballpark figure, if

11 50 percent of the pollen was coming back from the target crop, I

12 would think that would be good exposure. Anything above that is

13 even better. And then, are you interested in foraging rate? 

14 Are you interested in something about foraging rate - I think

15 just determining that they are foraging on the crop of interest

16 and using the incoming pollen and the indentifying of that

17 pollen such that you have some assurance that a reasonable

18 amount of the exposure has been undertaken. For lack of a

19 better - off the top of my head, 50 percent of the pollen, you

20 know you're getting 50 percent of their diet from that target

21 crop. With Dr. James's 48 acres, that would be easy to do,

22 right? Acres, yeah. I don't think you want approach 100

23 percent. You're just not going to get there, but anything 50

24 percent or greater of the incoming pollen would indicate a

25 reasonable amount of foraging on the target crop. 

26 DR. DANIEL SCHLENK: Let me just go through
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1 their main ones. Dr. Ostiguy, do you have a number, percentage?

2

3 DR. NANCY OSTIGUY: No, I don't think there

4 really - I don't know of any data that actually gives us that

5 information. I'm just still back to my problem with type II

6 errors. 

7 DR. DANIEL SCHLENK: Okay. Dr. Potter? 

8 DR. THOMAS POTTER: Getting back to what Dr.

9 Berenbaum said about Illinois, certainly that's the case in some

10 other parts of the country where you have the landscape

11 literally heavily disturbed, but also covered with vast wasps of

12 crops of interest. So, in the context of these studies, I'm

13 trying to get my head around how you do pollen counts or pollen

14 typing and are assured that that's indeed from the pesticide

15 treated crop. So, I guess I'm asking a question here and maybe

16 Dr. Pettis has some thoughts on that. 

17 DR. DANIEL SCHLENK: Dr. Pettis? 

18 DR. JEFF PETTIS: Obviously, you couldn't if

19 you were treating canola. You couldn't have competing canola

20 fields because they would be confounding. So, whatever your

21 target crop that you're planting would need to be unique. So,

22 you would have to be able to identify, and then you do analyze

23 that 50 percent of the incoming pollen is from canola, and you

24 analyze the pesticide level of that versus control to see if

25 there's been crossing between the plots. It's fairly straight

26 forward, and I agree that Midwest, it would be very difficult. 
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1 I'm thinking desert, Southwest and these nice big circles. When

2 you fly out west and you see these nice big circles, those are

3 idea places of to do that. 

4 DR. THOMAS POTTER: Yeah, I brought that up

5 because I don't - that is what I wanted to capture in the sense,

6 in our notes here. This is Tom Potter. 

7 DR. DANIEL SCHLENK: Thanks. Dr. Tarpy? 

8 DR. DAVID TARPY: This is Dave Tarpy. You

9 want a percentage from me or just to follow up on that?

10 DR. DANIEL SCHLENK: Well, I think the EPA is

11 wanting a measure of foraging success. 

12 DR. DAVID TARPY: So, to Dr. Pettis's point

13 then on that, I think 50 percent is probably a good target. I

14 mean in some cases, that might be high. I don't know if there

15 is a magic number as far as percentage of returning pollen

16 foragers with target crop pollen on their corbiculae, right? 

17 Because there are a lot of target crops where they are not

18 pollen bearing at all and they are not going to be collecting

19 any pollen, right? So, that may be difficult. 

20 What I was referring to as far as ground

21 truthing was actually doing things like transect walks or what

22 Dr. James was saying of the pan traps and actually collecting

23 bees in those traps to verify that bees are actually in the

24 field of interest. Actually, doing transects up and down the

25 rows of that particular crop and visibly verifying that honey

26 bees and other are foraging on those flowers. Therefore,
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1 putatively coming into contact with and being exposed with the

2 chemical in question. 

3 There are many ways to get to that, and it's

4 probably crop dependent as to the best measure of verifying that

5 honey bees are actually in the target crop. 

6 DR. DANIEL SCHLENK: Okay. Other panel

7 members? Dr. Hunt? 

8 DR. GREG HUNT: Yeah, these studies are so

9 hard to set up. I don't know the answer to the question, other

10 than to say that you ground truth, you measure the pollen,

11 proportion, whatever it is and report it, and what was the other

12 thing I was going to say - oh yeah, foraging rate, it depends on

13 if what you're applying is expected to be like a foliar

14 application and it's expected to be a burst of time point. Then

15 you would want to take the foraging rate that day. 

16 DR. DANIEL SCHLENK: Okay. Any other

17 comments? Oh sorry, Mr. Pistorius? 

18 MR. JENS PISTORIUS: Well, depending on the

19 crop, it's very different. For instance, Phacelia, you can do

20 very easily an optical differentiation because it's a purple

21 pollen. Almost no other crop has a purple pollen. You can

22 easily count by the numbers of pollen grain for that color. 

23 That is Phacelia pollen, if you have ensured that there are no

24 other Phacelia crops around, which I assume no. It may be more

25 difficult with, for instance, canola, because at that time of

26 blooming probably the same year, you get a few other flowers or
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1 some other flowers in the surrounding, which also have a

2 yellowish color. So, you have to do pollen analysis. 

3 Then what I still think is a very good measure

4 is foraging, the number of bees foraging on that specific crop. 

5 For us for our conditions, we have by experience, we have

6 minimum numbers. For instance, for winter oilseed rape, if we

7 know that if it's below two or three foragers per square meter,

8 it is not valid because also depends on the bee size. For

9 Phacelia, you can say that normal is at least 12 to 25. I think

10 those numbers may also - I mean we would have to look at other

11 crops to find certain numbers. I mean for a tree, it is

12 probably more difficult to estimate, but our possible estimates

13 to ensure that the bees have actively been foraging on the crop.

14

15 DR. DANIEL SCHLENK: Yes, Dr. Fefferman? 

16 DR. NINA FEFFERMAN: Sorry, I just have a

17 question and listening to all this. Hopefully, this question is

18 really stupid, but it gets back to something Dr. Berenbaum

19 mentioned about choice. I'm wondering if we separated the

20 exposure route from actual ingestion versus contact if we should

21 worry at all about landing rates on crops that are then rejected

22 as forage, if that's a contact exposure without the concomitant

23 ingestion exposure. 

24 DR. DANIEL SCHLENK: Again, I think we're kind

25 of trying to answer a design question here with this. Okay. 

26 Anybody want to chime in? Yeah, Dr. Hunt? 
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1 DR. GREG HUNT: Well, if I understand the

2 question, you're talking about bees visiting a flower and then

3 rejecting it. Once they reject that flower, they've all ready

4 landed on it. I think you'll get this from the scouting. You

5 know, if they continue to reject, they will continue visiting

6 flowers of the same kind. But pretty soon, they're going to

7 look for something better. 

8 DR. NINA FEFFERMAN: This is Nina Fefferman

9 again. This is basically my thought in response to Dr. Tarpy's

10 idea of watching who's flying where and having that mismatch

11 with what they're doing. So yeah, I agree over time if they're

12 going back and communicating that it wasn't a good source and

13 they have to go elsewhere, yeah they just won't keep visit the

14 area. But what you're locally doing is walking a transect and

15 just looking, let's say early in the morning for where scout

16 foraging is going. That might be a real disconnect in what's

17 happening. 

18 DR. DANIEL SCHLENK: Okay. Does that answer

19 your question in regards to foraging? Any other questions of

20 clarification? Okay. Let's go ahead and move on to 13. 

21 DR. THOMAS STEEGER: This is Tom Steeger,

22 question 13A please comment on the use of data on individual

23 bees to transition to higher tier studies given that the Tier I

24 studies focus on survival as the primary measurement endpoint

25 although additional endpoints may be forthcoming as test designs

26 continue to develop. 
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1 DR. DANIEL SCHLENK: Our lead discussant is

2 Dr. Tarpy. 

3 DR. DAVID TARPY: This is Dave Tarpy, NC

4 State. So, many of these points or at least for this

5 sub-question, I think have been articulated all ready. For the

6 sake of time and just to not have to recapitulate them all over

7 again, we'll just refer to those previous questions. So, this

8 is starting to funnel up a lot of our discussions from yesterday

9 and today on this and starting to distill them. 

10 In general, much of those discussions, it

11 seems pretty clear that there isn't this perfect proxy of Tier I

12 and lower tiers that really capture a lot of the complexity of

13 the upper tiers. This is especially true in social systems like

14 in honey bees. So, additional care needs to be taken in trying

15 to equivocate these different tiers. 

16 So, because of that, I think a lot of what

17 we've been talking about in question 8, question 11 in

18 particular about blurring the lines between the Tier I, II and

19 III in using groups of individuals rather than individual bees,

20 not solitary bees. But to incorporate bioassays that test the

21 social unit will go a real long way to addressing some of this. 

22 Certainly not completely, but it really does help to capture the

23 biological reality of looking at colony and group phenotype

24 rather than just individual phenotype. 

25 With of course the exception of queens and

26 drones, where looking at individual phenotypes on their
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1 reproductive potential is something that would be obviously

2 worthwhile. We also agree as the discussants that mathematical

3 modeling will be able to help hopefully tie a lot of these types

4 of aspects together, particularly if the same phenotypes are

5 measured at these different levels of reductionistic to larger

6 scale types of experimental designs. So if the same phenotypes

7 can be measured at those different increasing levels being able

8 to kind of verify them across those different levels of

9 biological organization would be very powerful means of

10 verifying that and using mathematical modeling to have

11 predictions from one level to another. 

12 We also, and Dr. Berenbaum can extrapolate on

13 this if she wants, but that data on the individual pupae can be

14 very informative at lower levels to higher levels. That doing

15 Tier I bioassays on contact exposure on individual pupae, that

16 is likely to have very strong penetrants to colony-level

17 phenotype compared to some of the other more nebulous ones like

18 we've been talking about of subtle behavioral differences, which

19 again can have very important biological relevance, but much

20 more difficult to capture and to translate from one level to

21 another. 

22 DR. DANIEL SCHLENK: Dr. Berenbaum? 

23 DR. MAY BERENBAUM: What I was going to say

24 was said, so I concur.

25 DR. DANIEL SCHLENK: Great. Dr. Pettis? 

26 DR. JEFF PETTIS: I concur. 
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1 DR. DANIEL SCHLENK: Other Panel members? 

2 Must be getting late in the day. Oh, sorry, Mr. Pistorius?

3 MR. JENS PISTORIUS: Well, I just want to make

4 a comment, for instance, on the IGRs. I may have made this

5 comment all ready. I think for IGRs for instance, that the

6 current Tier I studies as they are designed, also taking into

7 account that there's a seven-day Aupinel method available soon,

8 we hope. Unless the Aupinel method is soon also extended to the

9 pupation and the hatching phase, that just looking at the

10 individual mortality data of adult bees as measured by acute

11 oral or contact toxicity, we would miss the effects of IGRs. 

12 In principle, I agree that this data on the

13 individual bees, the transition to higher tier tests is a

14 reasonable approach given some limitations, but I still think

15 that there must be some flexibility for the risk assessor to

16 identify, for instance, like the IGRs, the need for other

17 information like the mode of action go to higher tier tests

18 immediately or in addition. So, saying that for the risk

19 assessor, there is a need that we don't have a calculation

20 somewhere. This is limiting the option to go into higher tiers

21 because currently there would be, or if you say okay, no trigger

22 bridge by what we know. Some things could be missed. 

23 DR. DANIEL SCHLENK: So, it sounds like you

24 would like at qualitative component to that sort of decision

25 process there. Any other comments on letter A? Okay. Let's

26 move forward to letter B.
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1 DR. THOMAS STEEGER: This is Tom Steeger,

2 question 13B - please comment on the derivation of the Level of

3 Concern, that is the LOC of 0.4 and the extent to which is

4 serves as an appropriate screen to transition to higher tiers of

5 testing and refinement.

6 DR. DANIEL SCHLENK: Dr. Tarpy? 

7 DR. DAVID TARPY: So this is Dave Tarpy. The

8 discussants on this particular sub-question would also like to

9 hear from many of the toxicologists on the panel to provide

10 their insights since none of us really are toxicologists, but it

11 seems to be that our take on this in distilling a lot of the

12 other discussions, that level of concern as derived at kind of

13 lower tier more abductionistic levels are a good empirical way

14 to arrive at those kind of baseline levels that need to be

15 vetted and verified at upper levels. Those are obviously a very

16 good starting point. 

17 But because of the transient property from one

18 level to another is not 100 percent obviously, that I think that

19 that would provide a good starting point, but it's not

20 necessarily going to be exactly the same as one increases in

21 biological complexity and in environmental variability. So, it

22 seems an appropriate starting point, but that it should be

23 modulated depending on the different tiers in which they're

24 being tested. 

25 DR. DANIEL SCHLENK: Okay. Dr. Berenbaum? 

26 DR. MAY BERENBAUM: I just like to repeat the
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1 call for input from our toxicologist colleagues who have

2 examined these kinds of estimates in much broader context than

3 we have. 

4 DR. DANIEL SCHLENK: Dr. Pettis, let me guess

5 what you're going to say. 

6 DR. JEFF PETTIS: I'm gonna say that our level

7 of concern among the three of us was raised that we were looking

8 for help basically. 

9 DR. DANIEL SCHLENK: Okay. With that, anybody

10 want to jump in? Dr. Klaine, not saying any names. 

11 DR. STEPHEN KLAINE: You know, this thing is

12 really confounded by the fact that as you move from one level to

13 another. You've got this issue of single organisms moving to

14 more complex systems. And I'm really not sure if we're able to

15 quantify the level of concern that would be the most useful as

16 you move into these higher tiers. 

17 DR. THOMAS STEEGER: Could I make a point of

18 clarification? 

19 DR. DANIEL SCHLENK: Sure, Dr. Steeger. 

20 DR. THOMAS STEEGER: The level of concern is

21 applied to the Tier I laboratory-based studies only. The higher

22 tiers studies that are at the colony level do not have a level

23 of concern that's applied to them. 

24 DR. DANIEL SCHLENK: Thanks for that

25 clarification. This is just for the Tier I. 

26 DR. STEPHEN KLAINE: All right. Okay. I
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1 misread the question. 

2 DR. DANIEL SCHLENK: Are you done? No? Are

3 you thinking?

4 DR. STEPHEN KLAINE: For the moment. 

5 DR. DANIEL SCHLENK: Okay. Dr. Hunt? 

6 DR. GREG HUNT: Well, my understanding is this

7 LOC 0.4 corresponds to about 10 percent mortality. Given that a

8 lot of the suggestions are to follow the larvae to pupation and

9 to have longer observation of adults in survival analysis, I

10 don't see how this particular number applies. But probably some

11 number could be developed or some qualitative, you know, in

12 terms of development maybe, there could be a number developed

13 there as well. But, anything that is enough standard deviations

14 from the norms. 

15 DR. DANIEL SCHLENK: Mr. Pistorius? 

16 MR. JENS PISTORIUS: With limitations of not

17 knowing what exactly which substances, which products exactly

18 were behind the data, that table that Dr. Steeger showed the

19 other day on how many substances would actually pass the first

20 trigger. Given that there is a need in my opinion for tiered

21 approach in risk assessment and also keeping into account that

22 we might miss a very small number, I think really a very small

23 number of active with such a tiered approach, I have the

24 impression that this level of concern is a conservative measure

25 and an appropriate screen considering the comparison with the HQ

26 data, which has been for a long time used in Europe and very
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1 well information we have on incidents in different countries. 

2 The clearly improved conservatism compared to this approach, I

3 don't remember which presentation it was but, in one

4 presentation it was said that this approach is about a factor

5 3.6 more conservative than the approach that we had. So that

6 might be a German interpersonal perspective. 

7 DR. DANIEL SCHLENK: Thanks. Dr. Klaine? 

8 DR. STEPHEN KLAINE: Yeah, I agree. It's very

9 conservative. My hesitation before really comes from trying to

10 get my head around the fact that when we do this with other

11 species, when you have an acute number, you get some reflection

12 about the impact on the population. In this case, we did it

13 with single organisms, and we really don't know how that

14 reflects that actual health of the hive or the ultimate

15 population of bees. So, I agree it's conservative. I'm

16 actually bordering on the fact that it might be too

17 conservative. I'm not sure how to quantify that. 

18 DR. DANIEL SCHLENK: Anyone else want to weigh

19 in on that? For what it's worth, I think it's conservative in

20 terms of you know, what it's trying to do and the function that

21 it's trying to serve in a Tier I screen that seems to have very

22 limited error with regard to false negatives, which is what

23 you're trying to do, right? Yes? 

24 DR. STEPHEN KLAINE: Yeah, and where I

25 hesitate is what is the right number. 

26 DR. DANIEL SCHLENK: Isn't it 42? Yes? Dr.
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1 Pettis? 

2 DR. JEFF PETTIS: I guess just a question to

3 EPA and Dr. Hunt touched on this. This value roughly relates to

4 10 percent mortality, and that would be above whatever the

5 control mortality is. So, if you had control mortality of 10

6 percent, then you would have to see 20 percent mortality in

7 another group. Is that correct? 

8 DR. THOMAS SEEGER: This is Tom Seeger. 

9 Essentially it's taking that LD50 value. The slope that is

10 calculated off of all of the acute toxicity data that we have

11 for oral and contact toxicity and estimating what would the

12 value have to be to back it down to control mortality. So,

13 you're essentially bringing it down to a NOAEC that would be -

14 well you're not able to distinguish between the treated and the

15 control value. So, the chronic LOC is set to 1, because the

16 NOAEC is compared to the exposure value and the acute value

17 because we're backing that LD50 back down to what we think is

18 background mortality, acute mortality is set to 10 percent. 

19 DR. DANIEL SCHLENK: Dr. Berenbaum? 

20 DR. MAY BERENBAUM: Point of clarification

21 that you say all available acute mortality data, toxicity data,

22 are those from forager or workers, right? Are there any larval

23 data that is factored in? 

24 DR. THOMAS STEEGER: These data were based on

25 adult - young worker data, that's correct. 

26 DR. DANIEL SCHLENK: So, do we have a
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1 consensus that 0.4 is valid? That's what the question is

2 asking, right? Yes? If you don't then you need to come up with

3 another number, I guess. That's the question. Yeah, Dr.

4 Klaine? 

5 DR. STEPHEN KLAINE: Steve Klaine. You know,

6 the goal is to be conservative and to not make errors. So, you

7 know if we're gonna go in that direction, then this is in fact

8 conservative. I think that as more data become available in

9 terms of the relationship between individuals and hive health, I

10 think that you may want to hold out the opportunity to change

11 that. 

12 DR. DANIEL SCHLENK: Yeah, what I would say

13 too is particularly if you're going to change the assay to the

14 other assays that have been discussed, that number may actually

15 change as well if we go with some of the alternative assays that

16 have been proposed, at least durations. Yeah, Dr. Fefferman? 

17 DR. NINA FEFFERMAN: So, this is actually a

18 strange context for me to propose this, but this is actually one

19 of the context in which I would propose a sensitivity analysis

20 from the end, not of the risk assessment, but for the risk

21 manager. Because the only danger I see in being too

22 conservative is that if you accidentally make it so conservative

23 that almost every viable benefit already puts you over the

24 threshold, then it makes it a yes or a no, do we want the

25 benefit at all question. 

26 So, if there is a way to do a sensitivity on
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1 the relative benefits from the management perspective in

2 interpreting the outcome of the risk assessment based on the

3 sensitivity of the cutoff for how conservative that should be,

4 there is a utility of not being so conservative that the answer

5 is anything you do is equally bad, so maximize your gains if

6 you're going to do anything because that can push - am I being

7 clear enough? Okay. Cool. 

8 DR. DANIEL SCHLENK: Did you get that Dr.

9 Tarpy? Any questions of clarification? Yeah, we're good? 

10 Okay. We're going to go forward to letter C then. 

11 DR. THOMAS STEEGER: This is Tom Steeger,

12 question 13C - please comment of the quantitative aspect of the

13 screening-level Tier I assessment and the use of Tier II and

14 Tier III whole-hive studies to qualitatively characterize risk.

15 DR. DANIEL SCHLENK: Dr. Tarpy? 

16 Dr. DAVID TARPY: This is Dave Tarpy. I think

17 our comment on the answer to this one is because it is very

18 similar to the others about extrapolating from one level to the

19 others is a little more difficult in light of the social life

20 history of honey bees. So, this pretty much the same caveat is

21 given as to the previous question.

22 DR. DANIEL SCHLENK: Dr. Berenbaum? 

23 DR. MAY BERENBAUM: I concur. 

24 DR. DANIEL SCHLENK: Dr. Pettis? 

25 DR. JEFF PETTIS: I concur. 

26 DR. DANIEL SCHLENK: Any other panel members? 
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1 So what it sounds like is this has been addressed in earlier

2 questions. Are you guys okay with that? Yeah? Okay. Letter

3 D.

4 DR. THOMAS STEEGER: This is Tom Steeger,

5 question 13D - please comment on the assumption that the

6 effects on individual bees measured in laboratory studies must

7 be considered in the context of whole colony studies conducted

8 under semi-field and full-field conditions. 

9 DR. DANIEL SCHLENK: Dr. Tarpy. 

10 DR. DAVID TARPY: This is Dave Tarpy. Again,

11 as has been covered in many of the questions. I think it has

12 been pretty well flushed out, but the assumption that

13 individuals serve as a valuable proxy for the whole group is

14 pretty much nullified and doesn't exist, which is why we spend

15 so much time emphasizing alternative means of testing groups in

16 vitro and in vivo to try to more realistically approximate in

17 vivo colony dynamics. 

18 So, there should be real degrees of care to

19 make broad-sweeping inferences of data on at the individual

20 honey bee level to full-field colony phenotypes. So, by being

21 able to assess at Tier I with social groups and kind of more

22 biologically relevant bioassays that will go a long way to

23 alleviate this particular question.

24 DR. DANIEL SCHLENK: Dr. Berenbaum? 

25 DR. MAY BERENBAUM: I concur. 

26 DR. DANIEL SCHLENK: Dr. Pettis? 
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1 DR. JEFF PETTIS: I have just a bit to add. 

2 Just in looking at effects that may be realized in Tier I, they

3 may help guide you and then what you might look for in full

4 colony or semi-field or field studies, but there is a caution. 

5 I think Dave has already touched on it that an effect that you

6 might see in a cage with individual bees, there are so many

7 compensation mechanism of grooming and prophylaxis and things

8 that happy at the colony level. 

9 That effect may totally disappear and you may

10 be looking for other effects that may happen at the colony

11 level. Food transfer may magnify affects towards the queen,

12 things like that. So, I think it's important to look at the

13 effect that Tier I and move forward. But the social dynamics

14 and colony interactions can really - it's really hard to predict

15 how those individual measures might be realized in the colony. 

16 So again, kind of reiterating what is being said. 

17 DR. DANIEL SCHLENK: Other panel members? 

18 Okay. We go back to the Agency. Any questions of

19 clarification? 

20 DR. THOMAS STEEGER: Point of clarification. 

21 In the absence of those other socially proper studies, the

22 current guidelines that exist serving as the basis for Tier I

23 studies and using them in a screen, does the Panel concur or can

24 you comment on the use of the currently available guidelines. 

25 DR. DANIEL SCHLENK: Dr. Tarpy? 

26 DR. DAVID TARPY: So, this is Dave Tarpy. I
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1 guess that they are informative, but not as informative as they

2 could be because of those complexities. So I think, and the

3 Panel can agree or disagree, but that's why we've spent so much

4 time urging the development of more biologically relevant in

5 vitro bioassays that would kind of harness that a bit better. I

6 mean obviously, some informed information is better than no

7 information, but I think that because of this disconnect of the

8 individual versus group level that those data derive at the

9 individual level ought to be interpreted with much greater

10 caution. 

11 DR. DANIEL SCHLENK: Dr. Berenbaum? 

12 DR. MAY BERENBAUM: One additional benefit of

13 using the existing studies as approaches is that it would allow

14 harmonization of internation efforts and an acquisition of a

15 greater data base to use in trying to make that connection. But

16 I just want to echo the call for reexamining assay design to be

17 more reflective of biological reality.

18 DR. DANIEL SCHLENK: Dr. Ostiguy? 

19 DR. NANCY OSTIGUY: I want to actually

20 strongly support the use of Tier I data this point, because we

21 don't have anything else to work with. It is much better than

22 nothing. Actually, so much better than nothing that it's almost

23 no comparison, which doesn't negate the fact that there might be

24 better ways to assess things. I do think that they will provide

25 us with a sufficiently sensitive set of criteria for making

26 decisions that we should not commit too many type II errors. 
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1 DR. DANIEL SCHLENK: Okay. Mr. Pistorius? 

2 MR. PISTORIUS: Yes, I think that the current

3 guidelines are very suitable. We know that there are a few

4 guidelines or there is especially one guideline, which has to be

5 further developed or established at all at the Tier I level. 

6 This is yet to be done. I would say as a general remark that

7 some of the existing guidelines, also the OECD 75 and EPPO 170

8 need a little bit of updating. But I think those were the

9 points that were discussed in our discussions here already, and

10 they are minor points, which do not criticize the whole approach

11 that is mentioned in the guidelines. 

12 DR. DANIEL SCHLENK: Dr. James, did you have

13 you hand up earlier? No? Yes? 

14 DR. ROSALIND JAMES: I don't like the current

15 guidelines. I think they're - we're having problems with bees. 

16 They are not a very good assessment of what our effects are

17 going to be. So, I guess I don't know that they are much better

18 than nothing. We're talking about the 48 hour contact toxicity

19 on adult tests, right? When you say current guidelines? At

20 Tier I. They're recommended in the White Paper or the current

21 guidelines, I'm not sure what you're asking I guess.

22 DR. THOMAS STEEGER: This is Tom Steeger,

23 point of clarification. Two points of clarification. We are

24 talking about now actually quantifying risk using a formal

25 process, which has not been done before. We are also talking

26 about modifications to the testing process that will give us a
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1 much better understanding of not just adult toxicity, but larval

2 toxicity and a path forward to where there are potential risks

3 that are identified using these screening level tests to

4 transitioning the higher tiered tests so would better enable us

5 to understand whole colony effects.

6 DR. ROSALIND JAMES: This is Rosalind. And

7 when you said current guidelines, that's what you're referring

8 to? 

9 DR. THOMAS STEEGER: This is tom Steeger. I'm

10 discussing what has been laid out in the White Paper. 

11 DR. ROSALIND JAMES: Okay. 

12 DR. DANIEL SCHLENK: Okay. Dr. Fefferman? 

13 DR. NINA FEFFERMAN: So I think the Tier I

14 studies for individual level are really good for the individual

15 effects. Where I find the disconnect for the context of the

16 question of scaling those up is actually propagates back down,

17 and I don't remember seeing quantitative ways to characterize

18 the sublethal effects that sufficiently trigger Tier I response

19 to go to Tier II. And I don't know if that's because I'm just

20 not remembering it or because it's not there. I know there was

21 discussion of how to try to measure some sublethal effects, but

22 I don't remember seeing how that gets interpreted in the

23 conceptual flow model for then going from okay, we've finished

24 our Tier I study, we saw the sublethal effects. That means we

25 now really do need to go to Tier II. That's the part where I

26 start not knowing how to get my mind around if this working
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1 well. 

2 DR. DANIEL SCHLENK: Any other comments? Dr.

3 Hunt? 

4 DR. GREG HUNT: I think the guidelines

5 proposed in the White Paper are a good advance - a good

6 improvement. But I don't think there was proposed the use of

7 sublethal effects. In Tier I, it was proposed as something that

8 might be useful in the future. I think that what the panel is

9 saying is that there could be improvements in sublethal effects

10 included for example, following the growth of the larva to

11 pupation and looking at biological endpoints and things of that

12 sort. So I think that what's proposed, if I'm understanding

13 correctly is good, but we're suggesting there could be some easy

14 improvements. Maybe not so easy. 

15 DR. DANIEL SCHLENK: Any other comments? Dr.

16 Klaine? 

17 DR. STEPHEN KLAINE: Yeah, I just want to

18 weigh in on this. From the panel discussions and all the

19 discussions we've had, it just at that level, it appears that

20 mortality is just the wrong endpoint and it's difficult to

21 interpret to move forward with that. I think that's why

22 everyone is calling for sublethal endpoints. So that the issue

23 with behavioral endpoint or other endpoints as they are

24 developed would better able this movement from single organisms

25 in Tier I up to the semi-field and field studies. 

26 DR. THOMAS STEEGER: Point of clarification? 
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1 DR. DANIEL SCHLENK: Sure. 

2 DR. THOMAS STEEGER: For the - in the White

3 Paper, it describes that the sublethal effects at this time

4 because of the absence of appropriate linkages to assessment

5 endpoints, while they would not be used quantitatively, they

6 would be used qualitatively and that they would be considered

7 with the other lines of evidence available to determined whether

8 higher tier testing would be required. So, the fact that the

9 current battery of Tier I studies tend to focus on mortality as

10 the primary effect. There is the opportunity that additional

11 endpoints may be reported, but we're not confined to only

12 considering the mortality endpoint. We do leave open the option

13 to qualitatively consider whether the sublethal effects are

14 sufficiently compelling to warrant with other lines of evidence

15 transitioning to higher tier testing. 

16 DR. DANIEL SCHLENK: I believe that is going

17 to be the next sub-question, right? Yes. Dr. Bradbury, did you

18 have something you want to say? No, he just got - okay. Great.

19 Well with that, are you okay with the answer that we received

20 for this sub-question? So, let's call it quits for now. I'm

21 sensing a little bit of brain drain here this afternoon. So,

22 we'll start in the morning with this. There are three more

23 subsections yet, and we'll start in the morning. And to get

24 better conversation, we'll start in the morning on sub-question

25 E and start with the weight of evidence at that point. Okay. 

26 Everybody will come back fresh in the morning. Okay. So with
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1 that, do you need to say anything Fred. Okay. We're adjourned

2 for today. We will meet a nine o'clock tomorrow morning. 

3 (WHEREUPON the meeting was adjourned for the

4 day) 

5 DR. DANIEL SCHLENK: As with yesterday, I

6 think we will skip the introductions and get into our charge

7 questions.

8 Before we move forward I believe the Agency

9 had a couple of comments they wanted to make before we move into

10 the next letter for the charge questions.

11 DR. THOMAS STEEGER: Thank you very much Dr.

12 Schlenk. This is Tom Steeger with Environmental Fate and

13 Effects Division. 

14 Just as a point of clarification in the

15 discussions as we are talking about higher tier studies and how

16 they relate to the laboratory-based studies. The intent of

17 those higher tier studies as described in the White Paper is to

18 focus in on particular uncertainties that have been identified

19 in either the lower tier studies or incident reports that we

20 might have received or open literature studies, so that you are

21 addressing very specific risk hypotheses and not generalized

22 questions.

23 Our experience in the agency with other taxa

24 and certainly our experience with some of the pollinator studies

25 that have been submitted in the past, is that they are

26 attempting to answer too many questions and in the process you
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1 do not end up with any answers. So please keep in mind that the

2 intent of higher tier studies is to focus in on particular

3 hypotheses.

4 DR. DANIEL SCHLENK: Dr. Potter, I think you

5 had a paper you found for your comments yesterday?

6 DR. THOMAS POTTER: Yes, I did. This was in

7 regards to Question 7, and there was a question from the EPA

8 staff about examining dust emissions and what might be a

9 reasonable approach to that. 

10 I did some digging last night and I found an

11 article that was published in Environmental Science and

12 Technology in January of this year, by an Italian research

13 group. I'll just enter that into the record.

14 It provides some emission factors for some of

15 the seed coating insecticide, and using the emission factors and

16 some of the subsequent deposition data that they were able to

17 obtain we should be able to come up with an exposure estimate

18 and see how that compares to the value that has already been

19 used in the White Paper. I'll make sure we get that into the

20 record.

21 DR. DANIEL SCHLENK: Mr. Pistorius.

22 MR. JENS PISTORIUS: Thank you. May I add on

23 this that is also the report of the opponent, whether reported

24 on a wider scale on issues on the dust and this paper is one of

25 the issues that is also in this report. 

26 And all of this and even more data is also
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1 mentioned in this report, which we include in the document. 

2 DR. DANIEL SCHLENK: Dr. Berenbaum.

3 DR. MAY BERENBAUM: Is this the point at which

4 I can introduce.? No, okay.

5 DR. DANIEL SCHLENK: Okay, we left off last

6 time at Charge Question 13d, so who wants to read in Charge

7 Question 13e? 

8 MR. KEITH SAPPINGTON: Please comment on the

9 proposed use of a weight-of-evidence approach based on

10 information obtained from multiple tiers of risk assessment for

11 characterizing pesticide risks to honey bees. 

12 DR. DANIEL SCHLENK: The lead on that is Dr.

13 Tarpy.

14 DR. DAVID TARPY: Good morning everybody. The

15 discussants on this particular point just again want to

16 reiterate that it is always difficult to move from one level of

17 biological organization to others but we definitely concur and

18 agree with the weightof-evidence approach by trying to

19 synthesize the information from these multiple tiers.

20 And so we also thought that high throughput

21 methods that are reliable and repeatable are obviously going to

22 be the most valuable and hopefully this discussion from this

23 week will be able to generate ideas of how to do those types of

24 screening techniques that account for the biological complexity

25 that is added by social organisms such as honey bees.

26 But then we also believe that because the
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1 basil unit of selection for a social insect like honey bees is

2 the colony level, we feel that there should be a centralized

3 intendancy of the weight-of-evidence towards the Tier II level

4 that is going to be the most informative. This is also going to

5 be discussed in the next sub-question for Charge Question 13. 

6 DR. MAY BERENBAUM: Just two additional

7 comments that we discussed. One is that certain parameters may

8 be very readily quantifiable and statistically analyzed but they

9 may not necessarily be as informative as less easily measured

10 criteria, which is an argument for weight-of-evidence.

11 And in addition the consideration that the

12 impact that is being assessed is on a workforce rather than

13 simply an organism, it is not just an up or down, alive or dead

14 criterion, and it is quality as well.

15 So quality issues are not as readily

16 quantifiable so weight-of-evidence of multiple indicators would

17 be particularly appropriate for assessing honey bee pesticide

18 sensitivity.

19 DR. JEFF PETTIS: Just one additional comment

20 along the same lines. 

21 We talked about biological effects and

22 statistically significant effects and this weight-of-evidence

23 approach will allow us to consider those biological effects

24 that, because of sample size in Tier II and Tier III may be

25 reduced and we cannot reach statistical significance, we should

26 keep in mind there is biological effect and I think this
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1 weight-of-evidence approach allows us to do that.

2 DR. DANIEL SCHLENK: Other panel members.

3 Really, wow. Great.

4 Let me go back to Mr. Sappington, do you have

5 any questions or clarifications? Okay, great. Let's go ahead

6 and read in Charge Question 13f.

7 MR. KEITH SAPPINGTON: Charge Question 13f;

8 please comment on how best to characterize overall uncertainty

9 or weigh different areas of uncertainty in risk

10 characterization.

11 DR. DAVID TARPY: So this again is what I

12 alluded to in sub-question e that Tier III studies we feel are

13 much more prone to type II errors and false negatives and

14 therefore, Tier II studies seem to be much more robust in trying

15 to reduce uncertainty in this type of risk characterization. 

16 And that is because type II studies are more prone to type I

17 errors but therefore are much more conservative.

18 Tier I studies, of course, are very robust and

19 repeatable but at the individual level do not translate as well

20 to whole colony systems, and so the weight-of-evidence at that

21 screening level, there ought to be caution in interpretation of

22 that.

23 So again, it places the real emphasis of

24 testing and screening at the Tier II level at whole colony in

25 vivo type settings.

26 DR. MAY BERENBAUM: I concur.
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1 DR. JEFF PETTIS: I concur.

2 DR. DANIEL SCHLENK: Other panel members. Dr.

3 Fefferman.

4 DR. NINA FEFFERMAN: Good morning. Just a

5 brief comment on it; I concur with everything that has been

6 said. I want to try and disambiguate. In risk assessment,

7 especially in modeling right there are. I'm sorry.

8 UNKNOWN SPEAKER: Disambiguate?

9 DR. NINA FEFFERMAN: Yes, to choose a part. I

10 do not actually mean a clarification of something that has

11 already been said but I mean it using a part of the dual

12 meanings of the word uncertainty. Did I invent a word? 

13 Uncertainty itself has two very meaningful and

14 very very very functionally different sources. One is

15 biological stochasticity leading to different ranges of

16 measurements that are accurate in their measurement and the

17 other is not knowing what those values would be.

18 And so treating those as one thing can lead

19 itself to some confusion. I just wanted to make sure that that

20 was somewhere in the conversation.

21 DR. DANIEL SCHLENK: Other comments?

22 MR. JENS PISTORIUS: When we address

23 uncertainty, for instance with the investigation of incidence,

24 that may be used to describe the risk that has occurred under

25 practical conditions but nevertheless it only has a limited use

26 for actually the risk assessment.
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1 If you cannot prove that because you have the

2 proper use and you have the improper use. I guess it is the

3 same, yes like in Europe to assess as a risk assesses the proper

4 use of the substance. This is basically one uncertainty that

5 you have to take into account when considering incidents.

6 I think basically you have on all different

7 tiers uncertainties starting with lower uncertainties from Tier

8 I; they increase a little bit, Tier II. I think Tier II studies

9 with worst case exposure have quite a low uncertainty.

10 Depending on the number and the design of the

11 studies you may have a small uncertainty or medium uncertainty

12 also in the tier test, if the study design is bad you may have a

13 very high uncertainty for sure. But it depends on also the

14 number of studies that are actually handed in. 

15 So I think you have to take those into account

16 but nevertheless even when you have a certain number of

17 incidents does not have to change the outcome of the risk

18 assessment necessarily, if it is not proven or if you cannot

19 link it to a proper use.

20 For instance, we get a small number of

21 poisoning incidents with a few insecticidal substances, spray

22 application, every year. But then again in most cases where we

23 could trace it down - we cannot trace it down in all cases - we

24 actually found out that it was improper uses.

25 So too high amount applied, wrong crop applied

26 mitigation measures not used. But on the other hand, especially
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1 the incident may be used to give a feedback on how good is

2 actually the risk assessment to be incorporated in the real life

3 situation.

4 This is a part which also may give a feedback

5 on the uncertainty that you create in your risk assessment

6 process. And then in some cases may even be valid enough to

7 say, Okay this application is again a risk management issue not

8 practical for the farmers to achieve.

9 DR. DANIEL SCHLENK: Any other comments? Yes,

10 Dr. Ostiguy.

11 DR. NANCY OSTIGUY: I'm not sure if I

12 understood Jens' last part well enough. And I do not know if

13 EPA can address improper use, but when you have an overwhelming

14 - and this of course is a judgment call - a large number of

15 improper uses, I do think that that has to weigh into whether or

16 not we are going to use a particular substance.

17 DR. DANIEL SCHLENK: Other comments? Mr.

18 Pistorius.

19 MR. JENS PISTORIUS: I think that's basically

20 then the part of the risk management to see how good it is

21 actually possible to incorporate this behavior of normal people

22 into this and it is not enforcement action but again looking

23 from at all states we have got different responsibilities.

24 And so it is not a risk assessment; it is a

25 risk management issue and in all cases it is not the federal but

26 then it is a state issue of ensuring that those enforcements are
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1 done; for clarification of my statement.

2 DR. DANIEL SCHLENK: Any other comments for

3 this particular question? Mr. Sappington, any questions or

4 clarifications? Dr. Steeger.

5 DR. THOMAS STEEGER: Just as a point of

6 clarification on the incident data. We do make an effort to

7 distinguish where chemicals have been used properly or misused,

8 and actually a trend in the misuse of a chemical can inform risk

9 management decisions as well; that can be a problem in and of

10 itself. Thank you.

11 DR. DANIEL SCHLENK: All right, let's go ahead

12 and move on to our last sub-question for 13. 

13 MR. KEITH SAPPINGTON: Charge Question 13g,

14 please comment on how to focus or prioritize uncertainties when

15 designing and interpreting Tier II and Tier III studies.

16 DR. DANIEL SCHLENK: Dr. Tarpy.

17 DR. DAVID TARPY: So most of the

18 recommendations and issues that have been brought up that weigh

19 in on this particular question, we feel have been discussed

20 previously so we do not really want to recapitulate them here.

21 But things like ensuring adequate and

22 sufficient sample size to increase statically power and then

23 weighing that evidence to focus uncertainties is good. 

24 Replication of study obviously, and focusing at Tier II levels,

25 at the colony level for testing phenotypic effects, are all

26 means of really focusing the designs of those types of studies
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1 that seems to be a priority.

2 Also, I understand the real need of wanting to

3 focus on one or two critical measures and hypotheses, but we

4 have been discussing all week the complexities of different

5 routes of exposure and different modes of action and different

6 ill effects, sometimes very subtle on social insect colonies

7 that can all result in ill health.

8 It is hard to always identify kind of with one

9 broad stroke that this is the one thing that needs to be

10 measured and if that is okay then everything else is going to be

11 okay with honey bee colonies. So I think including those

12 complexities in these knowing that one size fits all is not

13 going to be the case in all of these Tier II and Tier III

14 studies is definitely something to keep in mind.

15 But hopefully, as we will discuss in Charge

16 Question 14 that there may be ways to try and narrow those down

17 and identify some of the real key aspects that can be focused

18 on.

19 DR. MAY BERENBAUM: I just want to add one

20 other factor to address uncertainties in addition to the

21 geographic replication, season replication. I like us to keep

22 in mind genetic variability within managed populations of Apis

23 mellifera.

24 DR. JEFF PETTIS: I concur with what have been

25 said.

26 DR. DANIEL SCHLENK: Other panel members? Dr.
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1 Pistorius.

2 MR. JENS PISTORIUS: When we focus on how to

3 prioritize uncertainties when designing and later on

4 interpreting tier studies, when designing I think that you have

5 to have a look at the concern that you get from the lower tiers.

6 You have to identify the relevant root of

7 exposure that is of concern to you. For instance, you get

8 different toxicity for certain insecticide; you may get

9 substances with a high or/and low contact toxicity and vice

10 versa, or substances which have both high levels.

11 Such information from the lower tier basically

12 is needed and should be written down what the concern is and

13 then used to be designing and focusing on the design for the

14 Tier II and Tier III studies. For instance, also what I

15 mentioned with the sub-lethal behavioral effect, if you have

16 such a study where you have severe sub-lethal behavioral effect

17 then you might have to change the design accordingly and maybe

18 focus differently on the different aspects that you can test

19 with your Tier II and Tier III studies.

20 What we have not mentioned in this round is

21 basically, "What are the different options that you can test in

22 such a Tier III study."

23 We just talked about tier studies and this is

24 basically standard tier study, but there are additional methods

25 available and we cannot elaborate on them all in this short

26 time, which would actually be able to address specific concerns.
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1 We have shortly mentioned, for instance, the

2 impact on homing behavior. There are tests available which may

3 be used to address such questions.

4 In a standard field test, according to EPPO,

5 for instance, you do a brood assessment according to the leave

6 or failure method, you look at all the brood and the number of

7 brood and this is basic in estimation. But you can additionally

8 combine it with the OECD 75 method where you actually do mapping

9 of the brood and follow the individual brood.

10 So I think this can just be a general comment,

11 but I think the level of the concern that you get from the tier

12 I study is basically the most important one which help you for

13 designing this.

14 DR. DANIEL SCHLENK: Dr. James.

15 DR. ROSALIND JAMES: Jens that was an

16 excellent answer I think. My point seems minor in comparison.

17 I would add to your answer, I think yesterday

18 some comments were made to me by the EPA folks that when we had

19 discussions about the Tier I experimental design, that we were

20 throwing out sort of too many measures that could be taken. And

21 that is one way that they could focus down to which of these

22 measures are the ones that are most important and base it on the

23 results of the Tier I and the Tier II effects that were seen.

24 The other thing I was going to add about

25 helping with designing an uncertainty is proper replication and

26 controls. Honey bees are highly variable systems and then when
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1 you go to a colony level you really need to make sure that the

2 experiments are replicated enough and that the controls are good

3 controls.

4 DR. JEFF PETTIS: Just building on those

5 comments; another good example is if you had a concern raised in

6 Tier I that maybe there was chronic exposure that was going to

7 shorten the longevity of adult worker bees and one ramification

8 for that would be at the colony level maybe overwintering

9 success.

10 So designing a study to get at where the

11 colonies could adequately overwinter after exposure, that kind

12 of thing. And those get longer in duration, they are harder to

13 control, but they are possible.

14 DR. GREG HUNT: Along those same lines, if it

15 is going to affect individual longevity in Tier II and Tier III,

16 it is pretty simple to emerge some bees in an incubator, put

17 those newly emerged bees that are paint-marked in the colony and

18 then census them over time to get an average longevity.

19 MR. JENS PISTORIUS: To add to what Jeff

20 Pettis has said, I think it's completely right.

21 One idea that might help you in designing, or

22 looking at when do I have to do an overwintering or long-term

23 study. This, of course, is basically self explaining, but use

24 for instance the substance properties; as the persistence and

25 the degradation and also the mode of application if it is an

26 acute spray treatment. Also taking into account the information
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1 that is available on other organisms or on the efficacy side,

2 the mode of action that is proposed.

3 DR. MARTHA SANDY: I have a question for the

4 folks here that have the expertise. You are saying the honey bee

5 colonies are very variable; I wonder how would you design a

6 study to take that into account? Would you have sister queens

7 in the different colonies or what would be the ways to do that?

8 DR. NANCY OSTIGUY: There are some things you

9 can do to reduce that issue. You would pick the same line, so

10 it is all Italians or all carniolans. You would pick sister

11 queens.

12 As much as possible you would have the

13 colonies in a similar micro-environment. I'm sure others have

14 suggestions.

15 DR. GREG HUNT: What she said, but you would

16 have sister queens all mated in the same mating yard. Of

17 course, they are mating to about a dozen different males so you

18 have a lot of genetic diversity in the colony but hopefully that

19 all averages out and then you have of course more replication.

20 DR. NINA FEFFERMAN: But that all said, you

21 need the replication of those control samples in order to make

22 sure that you are not looking just within one line of effect.

23 DR. STEPHEN KLAINE: I just wanted to ask, do

24 we even know enough about the variation in order to do a power

25 analysis; so we even have an idea of how many replicates we need

26 in these designs?
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1 DR. GREG HUNT: The answer is probably not

2 because it depends on which study you are doing and some studies

3 are going to have so much variability due to environmental and

4 colony dynamics.

5 I mean, in theory it is possible, but I do not

6 know.

7 DR. STEPHEN KLAINE: Real quickly, just go to

8 probably the easiest one or the one that you guys like, the

9 tunnel study. Do you have enough information about variability

10 in the tunnel study? You know, how many hives to put in?

11 DR. NANCY OSTIGUY: I am going to disagree

12 with Dr. Hunt a little bit. 

13 For particular outcome measurements I think we

14 can do the power analysis just fine. It gets very difficult

15 with some of the other measures or something that we really have

16 not been talking a lot about. The things that we have suggested

17 as possible future ways to analyze what is going on in honey bee

18 colony, those we do not necessarily have enough information to

19 do a proper power analysis. But colony survivorship, fecundity,

20 those sorts of things. There is a tremendous amount of

21 variability in the data but we can still do a power analysis. 

22 DR. JEFF PETTIS: There is a paper out there

23 Rose et al., I do not remember the year; that was looking at

24 effects of Bt corn pollen owned bees. And they did a power

25 analysis, I think at the individual bee level, to look at sample

26 size but also at the colony level, so there is a starting point.
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1

2 And then my other comment was going to be

3 about the way to standardize colonies. We often will begin a

4 group of colonies, just a group of 10,000 bees and a queen. We

5 call it package bees. So they all start with the same starting

6 population and presumably same disease levels and grow them for

7 about a two month period. 

8 It is always advisable, if you need 40

9 colonies for the study, start about 50 colonies; because as you

10 began the study you can go through those and just take the ones

11 that are of most equal strength. So there are ways to do that. 

12

13 DR. GREG HUNT: Unfortunately other sources of

14 variability will be disease and honey bees have a lot of viruses

15 with commonly asymptomatic viruses. If I went and tested my 150

16 colonies now, they all have deformed wing virus. And the ones

17 that have higher mite levels have bees that show deformed wings.

18 If I get rid of the mites, they will still have deformed wing

19 virus but they will look fine.

20 One way to deal with that is to control your

21 mites and to quantitatively measure your virus titers. I mean

22 they could have three different viruses in them at one time so

23 that is a source of variability.

24 MR. JENS PISTORIUS: In addition, I think all

25 has been said, but not by me. That's a saying we have. 

26 You made a very good point, Nancy, in saying
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1 that for most of the variables there are very good statistical

2 tests possible with a high certainty. 

3 But on the other hand, for instance when you

4 look at the number of colonies, when you are just looking at the

5 certainty that you want to achieve, just look at the statistical

6 numbers you would need, you end up with ridiculous numbers of

7 colonies that are feasible to be tested. It is not feasible to

8 test for every test, hundreds of colonies, basically just to get

9 a statistical significance, but in my opinion this is not

10 necessary at all to detect biological significance. 

11 So I think it has to be looked at with

12 caution. Where you need really this statistical yes or no and I

13 think we all agree that we cannot look at colonies just by

14 evaluating statistics. 

15 So I think it is not appropriate to say, okay

16 for power analysis reasons we need whatever, 40 colonies

17 preferred because in other circles I know that such numbers have

18 come up from statisticians who had no experience with bee

19 trials. 

20 So I would like to warn that this may be a

21 problem when communication the results. And I think that with a

22 lot lower numbers preferred you already are able to do a good

23 test. And probably this is also the same with semi-field test. 

24 You can do statistics, for instance, on mortality, but the most

25 important thing is that you know a bit of bee biology, that you

26 have expert judgement and you consider the biological
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1 significance.

2 DR. DAVID TARPY: I just want to agree with

3 all of those things but just to kind of step back to the

4 question a little bit and say how we can focus and prioritize

5 these uncertainties.

6 I agree that we are never going to reduce them

7 to real acceptable level without a ridiculously high sample

8 size; you know all those kinds of things. And so I think what

9 we need to emphasis from the answers from the previous question

10 is that we are just going to have to rely on the interpretation

11 of the given power of a given study and the inherent variability

12 that is part of that study, that we can try to control by using

13 sister queens and other means of standardizing, but we are not

14 going to be able to get rid of all of them. We are not going to

15 be able to get rid of all of the diseases. 

16 And so focusing on statistical power by

17 increase sample size, replication of study, and trying to

18 minimize the factors that we can, but looking at the important

19 biological phenomenon at Tier IIs is really the issue.

20 So I mean we can go on and on about all of

21 these different things that need to be control, but in a

22 practical way cannot always. So it is the accumulation of

23 evidence that is important.

24 DR. NINA FEFFERMAN: I normally do not suggest

25 this especially for practical use because it is not a practical

26 science, but everything that we have discussed in terms of
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1 significance seems to be boiling down to things like power

2 analysis and p-values, which is a frequent, just statistical

3 method. 

4 There are actually Bayesian methods for

5 incorporating lack of understanding of variation in the system

6 that give you, no longer a threshold test for "Yes this is

7 meaningful; this is not", but an understanding of how meaningful

8 given the amount of measurement and the amount of the inherent

9 variation you expect for that measurement, how meaningful the

10 interpretation of your measure have been. 

11 I do not want to go on record as suggesting

12 that that become part of the standard expectation for these test

13 but I would like to pipe up and say, "It is not the fact that

14 statisticians are living in a black box and because they've

15 never seen a bee, their work doesn't apply."

16 There are methods for this - they are very

17 good, the times that they have been used. They usually get

18 used, honestly, at this point in practice only in things like

19 human medicine or international politics that has nothing to do

20 with science, kind of sociological studies to lend credence to

21 some others position.

22 But when these things have been applied,

23 especially in the areas of risk assessment for then separate

24 managerial decision making, they have proven to be very

25 informative. So that may be something to keep in mind maybe in

26 generations out - it would be a mistake in this conversation to
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1 assume that there do not exists techniques to handle this.

2 DR. DANIEL SCHLENK: Just to follow up on that

3 point. I do know having to attend SAP meetings recently that

4 that is actually being proposed in risk assessment paradigm,

5 Bayesian techniques actually. That is a huge discussion point.

6 DR. GREG HUNT: Yes, Bayesian methods are used

7 in many areas of biology. But getting back to the power

8 analysis thing and agreeing with Jeff and Dave that with honey

9 bee colonies, in Tier III studies, we cannot expect at the

10 colony level statistical significance. But at Tier II and Tier

11 III, if you can identify things that are important at the

12 individual level, you can use statistics at the individual level

13 to predict.

14 DR. ROSALIND JAMES: Do you have a good

15 general reference on how Bayesian might be used in a risk

16 analysis, or analyzing this kind of data?

17 DR. DANIEL SCHLENK: There will be a report

18 coming out - well, the draft is being circulated right now, so I

19 guess in another month, maybe?

20 DR. ROSALIND JAMES: Can we put a reference to

21 that in our notes?

22 DR. DANIEL SCHLENK: You could, yeah. Yes.

23 DR. STEPHEN KLAINE: There are a number of

24 publications using Bayesian in terms of uncertainty analysis. 

25 Dwayne Moore and several others have published on this.

26 DR. ROSALIND JAMES: That would be relevant
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1 for this type of experiment.

2 DR. STEPHEN KLAINE: I'll get the references. 

3

4 DR. MAY BERENBAUM: It is a lit bit alarming

5 to hear statements that you cannot do statistics on. I just

6 want to make the observation that there appears to be a

7 correlation, which is likely statistically between the amount of

8 media interest in a particular subject and the number of poorly

9 designed studies that lacks statistical analysis, which gets a

10 disproportional amount of media attention.

11 So I just want to emphasis that the absence of

12 a statistical analysis or design that allows statistical

13 analysis profoundly reduces the utility of any study, as it

14 should. And this particular field is really prone to

15 high-profile statistically - for want of a better word -

16 worthless studies.

17 DR. JEFF PETTIS: Just an additional comment

18 about all of this. We convene Vets to talk about bee diseases

19 and transmission of other animal diseases, and bees always come

20 out as the exception. If you are dealing with dairy cow,

21 chickens, or whatever, they are just so much easier to deal with

22 because you do not have this individual to colony level effect,

23 so this is nothing new, and in disease transmission around the

24 globe and stuff. 

25 If you start dealing with any organism, a

26 flying bird or a land animal, the things are all the same until
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1 you get to bees and then all the rules changes, so it is not

2 unusual.

3 DR. DANIEL SCHLENK: Okay, Dr. Tarpy a lot to

4 integrate into that answer for you so I will leave that to your

5 best judgment. And again let me just reiterate that if you are

6 making comments, please get those written comments to Dr. Tarpy

7 so that he can integrate that into the minutes. 

8 Any other panel comments? Okay, let me go to

9 the agency, Mr. Sappington do you have any questions or

10 clarifications.

11 MR. KEITH SAPPINGTON: No Mr. Chairman we are

12 good.

13 DR. DANIEL SCHLENK: Great. Okay, let's move

14 on to our last question, 14. You want to read that into the

15 record. 

16 MR. KEITH SAPPINGTON: Charge Question 14a;

17 please comment on the concept of using colony-level ecological

18 models to inform the proposed risk assessment process for honey

19 bees, as indicated above.

20 DR. DANIEL SCHLENK: And our lead on this

21 question is Dr. Fefferman.

22 DR. NINA FEFFERMAN: So I'm kind of worry that

23 everyone is going to hate me on this question. My basic answer

24 to all three parts of this question is, each of these questions

25 is important but I think you are starting with the wrong

26 questions. So if you do not mind I am going to start with a
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1 sort of general explanation of what I mean by that and then move

2 into answering the three particulars. Is that okay with the

3 Chair? Okay.

4 So to start with an idea of methodology and

5 model choice that we have talked about a little bit earlier this

6 week of analytic versus simulation models. And it seems that

7 most of these questions have focus on the stimulation modeling

8 level. 

9 And in my opinion this misses really where the

10 greatest utility of modeling is going to be for these types of

11 questions for risk assessment that are being asked throughout

12 the rest of the White Paper.

13 And that you guys have done an amazing job of

14 picking out where the simulation type of questions have been

15 addressed in the literature and where they might be able to help

16 in the future but that that misses an entire analytic level. 

17 Especially for Tier I studies, I actually think that the need

18 for simulation modeling might be obviated by the fact that - and

19 this we will get to later in a sub-question -- by the time you

20 have measured carefully enough all of the parameters that you

21 would need and all of the outputs that you would then validate

22 the model against, you may not need the model to answer the

23 question anymore. And that for simulation modeling may for Tier

24 I studies no longer then help you, although for Tier II and Tier

25 III type equivalents would be very helpful.

26 So to explain analytic models, they are kind
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1 of easy examples certainly from physics and I can detail some of

2 those in the notes, although I probably will not go through them

3 here. But the example that we discussed yesterday of the ratio

4 instead of making the direct link from worker mortality for

5 colony success to the ratio of worker mortality to worker

6 eclosion rate was a very informative discussion from my end in

7 terms of what an analytic model can be. 

8 That is an analytic model right there saying,

9 "We need to look at that ratio." Now, of course that ratio is

10 not all we need to discuss colony health; so for example notice

11 that one worker dying over one worker eclosing for an entire

12 colony would keep that ratio fairly healthy looking and that

13 would be terrible.

14 So we need to have minimum constraints on

15 things like population sizes; and in some cases we need to have

16 maximum constraint on population sizes to do things like if we

17 want to prevent swarming or if we want to worry on a population

18 level about carrying capacity type issues.

19 But incorporating just those ideas of that

20 ratio and that constraint without knowing any measurements

21 already gives us sort of a universally applicable metric of

22 colony health knowing if the colony is thriving or dying. And

23 that is the hallmark of an analytically valid model, one that we

24 can apply to gain insight regardless of the system. 

25 And actually Dr. James and I was just talking

26 a little bit in the hallway afterward and I can put the details
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1 of that conversation in the notes, about how this could be

2 tailored with the inclusion of one other variable to represent

3 solitary bees as well and their success as a population instead

4 of as an individual. 

5 And that means that we can get from this

6 somewhat abstract. We are just talking about sort of workforce

7 and its ability to provide for itself as a sustainable

8 population. We can get very interesting insights and it is

9 abstract enough to tell us where the detailed measurements need

10 to go to inform that.

11 So just off the top of my head right, if we

12 say, okay there needs to be a minimum colony size -- that is

13 something we would need to go measure. Is there a minimum size

14 of the colony under which - and we do not actually need to

15 measure it, we know that, but you guys see what I mean.

16 So there are ways using these abstractions and

17 equation-based things that give us thresholds. If that ratio of

18 worker death to worker eclosion is less than one, the colony is

19 going to die. There just is not replacement value for colonies.

20 That is a strict threshold, that number is one. Right, if that

21 ratio is less than one. That does not change regardless of what

22 the worker eclosion rate is; so before we measure anything we

23 now know something.

24 So these are a way to get at some of the

25 conversation we were having yesterday about how we identify the

26 important things to go measure, where do we go look and the more
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1 generally applicability across these things. Of course then you

2 sort of standardly validate them against expertise to ensure

3 that you captured the relationships appropriately and they do

4 still make simulation level predictions. 

5 From each analytic model you can then simulate

6 an outcome. You probably can use an Excel program that captures

7 that ratio and goes, "Okay, for a colony of this size should it

8 do okay?" "How long should it take before it is not okay?" 

9 That is a testable hypothesis prediction that leads to more

10 immediate approximate metrics for how to test, even if those

11 metrics are not the goal endpoint.

12 So it is actually one of the greatest

13 utilities of this type of model in addition to providing those

14 theoretical insights that you then generate incidental things to

15 measure along the way that do not lead to the outcome of is the

16 colony dying, but are still testable and measureable.

17 And say, "Okay, if the colony is going to

18 succeed there should be this other thing that has to happen as a

19 logical necessity of that successful process. That is not an

20 outcome we would discuss even normally a biologically relevant

21 thing, but is a logical equivalent necessity of a good process

22 happening. Where do we go measure those?

23 This is not to say that this is the equivalent

24 of a data gap. My saying that the questions and the White Paper

25 do not address this type of modeling, that is not to say that

26 there is a data gap here. 
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1 One of the benefits of this analysis is that

2 for non-risk assessment, non-pesticide and toxicity purposes, we

3 have analytic models of healthy honey bee colony processes. 

4 I've written a couple of them and can provide references for

5 that if that will be helpful, and I will just stick some in when

6 I write up these notes.

7 Each of those has mechanisms of impact from

8 different pathways and it would be very easy in a modular sense

9 to sort of unplug the pathway that is studied in that setting. 

10 Sometimes it is foraging richness and in fields there is

11 uncertainty there. Sometimes it has been competition, sometimes

12 it has been predators, any of these sets of things. 

13 We can modularly then plug in models that say,

14 "Well, if the empirical measurement of toxicity says here is the

15 impact on longevity, here is the impact on colony level

16 fecundity, not necessary queen fecundity, but colony support of

17 the eggs the queen lay.

18 If we plug those in modularly to the numerator

19 and the denominator, here is our outcome for is this colony

20 thriving. And so there is not necessarily not at all forget

21 necessarily. There is not a gap in the availability of these

22 tools; they simply have not been applied directly yet to this

23 question. So they have not been analyzed to say, "Well then if

24 I just look at this model, the clear threshold would be blah." 

25 But they are easily calculable as soon as

26 someone turns their mind to it. And as soon as someone has some
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1 of the individual and group level measurement that you guys are

2 discussing getting already, without that necessarily being the

3 end point. Where we could use those as proximate ways to get at

4 the ultimate question of what is the success of a colony and

5 what is the success of a population at providing pollinator

6 services, at being sustainable itself and having population

7 level success.

8 Lastly, in the general sense - sorry I realize

9 this is a very long answer -- in the general sense modeling does

10 create one set of complications that nothing else we have

11 discussed creates for the levels of the Tiers. It is important

12 to point out that the tiers themselves are somewhat problematic

13 in the sense that the kind of analytic modeling that I am

14 suggesting would be useful.

15 Because colony level models should ideally be

16 used backwards to inform which Tier I types of studies should

17 happen to be most useful as a pass; by which if you pass Tier I

18 and you do not set off any alarms to trigger further study,

19 those were your important metrics. Those need -- not semi-field

20 and field, in that sense I do not mean tier, but because of the

21 way that these study works we have been discussing Tier II and

22 III models as being the ones that leads to colony level and

23 population level type questions, because those are hard to get

24 to in a lab.

25 So that equivalence of which tiers mean which

26 level of scale, no longer applies within the modeling. And a
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1 Tier I study should possibly be, even a population of colonies

2 level model in order to backwards inform what the individual

3 honey bee or solitary bee metrics should be that are most

4 critical to indicting health, and I mean health in a

5 sustainability kind of sense.

6 Very briefly then to address the specific

7 question of (a) which was the concept of colony-level ecological

8 models, yes it is great. The short answer is we should do it. 

9 We should do it in intelligent ways. So exactly the types of

10 questions that we have been saying are very hard to get to

11 experimentally of how do individuals scale to colony, scale to

12 populations, and how does all of that go as things fluctuates

13 over time both in terms of natural stochastic fluctuations and

14 in terms of bees changes.

15 The year changes, bees changes. The need

16 changes; bees have different behaviors if a bear comes by, that

17 kind of stuff; although agricultural bears are probably not a

18 big problem, but still. 

19 So these kinds of models are simulation and

20 analytic but especially simulation, the kinds of models you

21 proposed directly in the paper, are going to be quite useful for

22 doing that. All this being true, there is a huge pitfall to be

23 avoided in doing this. It would be a mistake to just suppose

24 that the best modeling practice for colony-level ecological

25 models to inform risk assessment would be an all-inclusive, it

26 has every aspect of colony function and every risk model. That
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1 is a bad way to model this even in simulation.

2 And that has to do both with being able to

3 interpret the outcomes and knowing which factors were driving

4 them but also as we just addressed in some of question 13, there

5 is a confounding uncertainty from each modular plug-in. And

6 that is both the uncertainty of the measurement and the

7 uncertainty of the stochasticity. 

8 And there are really intricate ways in which

9 those interact and there are some tools from statistical

10 mechanics to try and correct for that, that we do not want to

11 use for this. 

12 And so a much better way of approaching an

13 all-inclusive type of model is to have littler models as some of

14 are in the White Paper discussed, and then have a risk assessor

15 as a person with expertise decide how the outcomes from those

16 really might interact. Sticking them all into one model where

17 the math comes out as one number is a great way to confuse

18 ourselves. 

19 That is basically my answer to a.

20 DR. ROSALIND JAMES: So my role on this team

21 was to help Nina give examples where things were not clear. So

22 I am going to ask you a couple of questions that I had.

23 When you are talking about the tier test you

24 said it slightly backward. I wanted to make sure you understand

25 in the tier testing things move to higher tiers when they fail

26 the lower tier, right?
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1 DR. NINA FEFFERMAN: Sorry, I misspoke. What

2 I mean is if there is a trigger that says I failed Tier I.

3 DR. ROSALIND JAMES: So then a higher risk,

4 right, not less risk?

5 DR. NINA FEFFERMAN: Yes, right.

6 DR. ROSALIND JAMES: Okay, I wanted to make

7 sure that you understood that. 

8 And so I was not exactly sure what you were

9 saying regarding the tier-level and maybe to help clarify I will

10 put it in another question. Could you go back and look at

11 current colony-level data that we already have for pesticides

12 that are in the literature to go back and help determine what

13 Tier I factors we should note?

14 DR. NINA FEFFERMAN: Thank you Dr. James so

15 much. Yes, that is exactly what I meant we should be doing. 

16 That we should take the colony-level understanding that we have

17 and any future colony-level understanding from whichever either

18 Tier II or Tier III studies are proposed, if things triggers

19 higher concerns from Tier I; to go back to suggest further

20 refinements in which Tier I should trigger further concern. 

21 Does that help clarify?

22 DR. ROSALIND JAMES: I think so, but could you

23 use an example?

24 DR. NINA FEFFERMAN: For example, and this is

25 a silly example because it will wind up seeming obvious but

26 there are others. Actually, can I depart from honey bees
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1 comfortably for an example?

2 Okay, awesome. So we have been talking about

3 some desert tortoise conservation issues and doing some of the

4 desert tortoise conservation work - I was helping model some of

5 this -- we went initially to a bunch of desert tortoise experts

6 and said what do you think are the most salient features of the

7 endangered population of desert tortoises we are working with. 

8 And they gave us a list of here are the ones we think are really

9 important and then here are the ones that we think are sort of

10 ancillary but tortoises depend upon these things. 

11 By looking at the population level

12 determinants of these factors we were able actually to move some

13 things out of the categories that the experts expected were

14 going to be very important. Saying, okay, yes that is clearly

15 killing tortoises; that is not the thing driving population

16 decline. That is some of the things you are seeing when you go

17 find a dead tortoise; that is what you are seeing the dead

18 tortoise do. But it turns out that the value of that age-class

19 to the demographic process of population growth, it is not

20 valid. 

21 On the other hand, there were some things that

22 were unexpectedly incredibly important. Water reservoir

23 balance, if you have a respirator infection as a tortoise it is

24 apparently really important for conservation. And water balance

25 was known to be important. Respiratory infections were not

26 necessarily. And the fact that you get a runny nose turns out
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1 to drastically change your water balance. 

2 Fugitive dust was assumed to be a very

3 important thing. Fugitive dust, you have an ATV kick up dust in

4 the desert and then it doesn't settle for a while, turns out

5 basically unimportant for our population.

6 So is that a kind of concrete example where,

7 from going from the population level in modeling, what is the

8 demographic growth process of that population.

9 We were then able to go back to these

10 individual measurable things either about the environment or

11 about the individual tortoise. And say okay, these are the

12 things you should measure. If you want to be concerned about

13 your population or if you want to be confident your population

14 is okay.

15 Does that clarify?

16 DR. ROSALIND JAMES: So here is the case where

17 you could go back. Now the next endangered species. What did

18 you learn form that that you can say now for Tier I test we want

19 to measure? 

20 DR. NINA FEFFERMAN: So the process of that

21 modeling is the thing that ports across species. But it would

22 be true that we would reformulate the models themselves if we

23 went from - I mean, if we look at something that is still like a

24 tortoise then it would still apply. Sure, for other tortoises

25 not the desert tortoise. 

26 Then we could re-apply the same logic and use
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1 the same parameters and that does give us thresholds for which

2 measurements. So we could say, well when we stick in the value

3 for -- here is for example the non-stable age demography. It

4 turns out that many of the models -- the outcome is very

5 sensitive to how many juvenile you have versus how many

6 reproductive adults. And it turns out that the size of an adult

7 female is a really important thing for tortoises because eggs

8 have to fit under the shell.

9 But that turns out to be important in the

10 demography we had for our population. We would reapply that

11 analysis in a new population and say well how many adult

12 tortoises do you have? If you have zillions of adult female

13 tortoises, then female tortoises' mortality might not be the

14 issue; it might be egg survival. And that is putting in new

15 number to the same analysis to get out a different insight for

16 that population, or for that species. 

17 That model would not directly apply to

18 colonies of honey bees but the exact same methods would and we

19 do have colony and population-level models of honey bees that do

20 equivalent things. It is just that since I have not done them

21 in this context of toxicology I do not have as ready yet, an

22 answer for like, "this is a surprising result I expect to get

23 out" because if I expected it, it would not be surprising.

24 DR. DANIEL SCHLENK: Okay, let's go to Dr.

25 Potter.

26 DR. THOMAS POTTER: Well I certainly agree
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1 with my colleagues and concur that it is important to have a

2 multi-dimensional approach to the problem, if I am correct in

3 paraphrasing here. And certainly there are many different types

4 of models that can be used, again both analytic and simulation. 

5

6 To further try to compartmentalize this

7 problem, which obviously is exceedingly complex, we can -- again

8 as Nina mention there are modular approaches that can be used

9 and at minimum I believe we need to step back and say you know

10 this is a two-part problem. 

11 And forgive me for stating the obvious, but it

12 is exposure and response. So I would say that as a first cut we

13 divide our problem down into two parts, number one exposure and

14 response.

15 Exposure is probably more amendable to

16 simulation modeling I would hazard a guess, maybe, maybe not. 

17 It certainly has a long history in all of the other aspects that

18 the Office of Pesticide Programs addresses in terms of

19 evaluating exposure; certainly simulation models are critical

20 tool, certainly on the exposure side.

21 And so I strongly would emphasize that effort

22 be directed in that area to develop simulation models, whether

23 they be pesticide Environmental Fate model that works for a

24 colony in terms of residues that are in the colony and

25 determining what their fate may be in terms of persistence,

26 longevity, whatnot. Obviously those are critical questions that
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1 need to be answered in terms of exposure at different

2 life-stages, et cetera, overwintering success. 

3 And then the other part of that problem, and I

4 think again Dr. Fefferman identified this and I think it is

5 critically important and an exceptionally useful tool, perhaps

6 at Tier I, and that would be the use of inverse modeling to try

7 to do some ground-truthing, particularly in the side of the

8 exposure pathway.

9 Again, obviously as has been discussed over

10 the last few days, we have some uncertainty about exposures. 

11 Efforts have been made to identify where those are. One

12 possible approach to get at this problem is to take existing

13 colony data and do adverse modeling; looking at the different

14 compartments where residues are stored within the colony and

15 inverse model to see if there are insights to be gained on where

16 those residues may have come from, whether they are potentially

17 dust emission.

18 Again, there are some timing issues and

19 whatnot here et cetera, whether they came in by water or they

20 are totally dietary. I think to some degree we can gain insight

21 by going through that process.

22 I will just sum up that that is obviously a

23 big problem. We recognize that and we would benefit

24 substantially by subdividing it and proceeding forward from

25 there.

26 DR. DAVID TARPY: I agree with and very much
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1 appreciate this discussion and really have nothing more to add

2 other than to emphasize the previous comments that were made

3 that colony-level modeling is very helpful in a kind of a

4 top-down approach. I think the statement was made earlier that

5 predictive modeling from lower tiers to upper tiers is something

6 that we need to be very cautious about.

7 And so just simply taking information from

8 sub-colony phenomena and trying to model that upwards to colony

9 and population-level is froth with potential problems; rather

10 taking a colony-level top-down approach of trying to understand

11 that phenomena that informs the lower tiers is going to be much

12 more effective.

13 DR. DANIEL SCHLENK: Other panel members.

14 MR. JENS PISTORIUS: 1:02:08 I wish I had only

15 a fraction of understanding of modeling of what you guy do. I'm

16 really impressed and my brain is working a lot at the moment and

17 I might be confused so excuse me in advance if this answer may

18 sound funny and some things may have been said. My answer will

19 be short.

20 Yes, to inform and to identify potential risk,

21 which trigger maybe the need for higher tier tests; but I want

22 to point out that, no for if you have a model that tells you if

23 you identify a risk in Tier I, and then you come up with a model

24 calculation that said well you do not need a Tier II and Tier

25 III, I think this would be a funny way you would have two

26 complimentary ways in risk assessment, which somehow does not
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1 fit in a risk assessment scheme.

2 DR. NINA FEFFERMAN: Mr. Pistorius, I am

3 probably misunderstanding. I think that what you just said is

4 that you should only use modeling for what to do in Tier I if it

5 agrees with the empirical work. That cannot be what you meant.

6 MR. JENS PISTORIUS: I'm not quite sure if I

7 understood because again my brain might be overworked at the

8 moment. 

9 I just want to say, if you identify a risk in

10 the known Tier I studies and if you have this risk assessment

11 process as it is outlined now and if you trigger higher tier

12 tests. And then if you come up in parallel with the modeling

13 that says, okay but all you have done in your Tier I

14 identification of a potential risk is all not valid. And then

15 you basically have to decide okay our Tier I process identified

16 would have to go to a higher tier study. 

17 And then on the other hand you come up with a

18 model that says, well yes you have identify the risk in Tier I

19 but actually it is not necessary, you do not need to do any

20 further studies. That is something I would reject because then

21 you would have two parallel ways in risk assessment. 

22 Was that clear and understandable?

23 DR. NINA FEFFERMAN: I think I understand you

24 but disagree with you. I think that in my mind that is exactly

25 the use of half of one of the ways that this works.

26 One would be to identify Tier I things that
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1 should be measured empirically that would then trigger the need

2 for higher studies by concern; but the other would be to show

3 when we think things that need to be measured that would trigger

4 higher concern should actually not effect success.

5 And if we reject that half of the modeling

6 then I do think that we disagree. I think that is still a use

7 for modeling.

8 DR. MAY BERENBAUM: Good luck explaining that

9 to the public. That is an issue that exactly addresses your

10 point that if there is a cause for concern generated by

11 empirical data at Tier I and a decision is made based on models

12 not to proceed, at lease for the American public, that would I

13 think create a major concern.

14 MR. JENS PISTORIUS: I think it is the same

15 for the European public but also for me as a person

16 understanding. Sorry that I really have to say I disagree.

17 I want to see studies with bee colonies and

18 because we have identified that bee colonies are a lot more

19 complex than the individual and modeling may be very good and

20 may inform a little bit, but if this decision is to be made and

21 say, okay you have identified a risk with a normal risk

22 assessment Tier I process, if model comes up to say, well you do

23 not have to do higher tier studies, I would clearly reject that.

24 DR. NANCY OSTIGUY: I think that this

25 discussion points out the importance of doing the analytical

26 models first. And we really should do those first so we can
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1 then target which Tier I studies are the most important to do,

2 so we do not have contradictory conclusions that we have to

3 resolve.

4 DR. MARTHA SANDY: Just to remind everyone,

5 risk assessment is -- if you remember, the framework diagram we

6 saw - it is supposed to be an iterative process. And I think

7 that that is where we have some confusion going on.

8 I think what Nina is suggesting is, we have

9 come up with some Tier I endpoints we think are important to the

10 colony. She is suggesting we use the analytical models to see

11 if those endpoints in fact are relevant to colony health. And

12 she gave the example using a different species.

13 But to take another example from the honey

14 bee, what if we think mortality to drones is an important thing

15 but in the model it turns out that that does not seem to be a

16 driver to colony health but what does seem to be a driver is

17 mortality to larvae and length of time of development of larvae

18 for example; those are really key.

19 Then what you would do is you would take that

20 information from the model, once you felt confident that you

21 could rely on it, you might go back and revise the Tier I test

22 requirements with which things in Tier I endpoints the

23 measurements that you place and emphasis on that triggered going

24 to the Tier II or Tier III test.

25 So I think you are talking at cross-purposes

26 because you are not, right now Mr. Pistorius, ready to accept
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1 that the modeling has been validated and everyone has agreed to

2 change the current Tier I endpoints that are measured. 

3 We are not there yet; we have not even done

4 the modeling. But, there would be a period of time of

5 discussion and then different groups would decide whether they

6 still want to evaluate a certain measurement.

7 MR. JENS PISTORIUS: Well I think such

8 modeling are beautiful for the weight-of-evidence and the

9 multiple information that you need for the conclusion on the

10 risk assessment; on the other hand from my experience it is not

11 that an applicant comes with one study and then you get concern.

12 On the Tier I level, usually what we get is a package of the

13 standard requirement, which are in our case oral contact and

14 maybe in future the larvae if we are able -- or larvae will be

15 addressed as we get there.

16 So that is basically the standard package we

17 ask for to be able to make a first conclusion. And then, okay I

18 admit maybe such models may inform do you need other studies on

19 the tier level one risk assessment. But still on the first

20 three that you get, on the contact, oral and larvae, you already

21 do make a decision, you know, are we having a concern? 

22 And so from my understanding, but I may be

23 completely wrong, that is already triggering something. And I

24 am really glad if you say as a risk assessor, okay we have

25 identify risk in Tier I, we go to Tier II and we ask for

26 semi-tier study and we find no effect.
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1 And then you say okay for our

2 weight-of-evidence you take those multiple information into

3 account and then you can say with addressing the uncertainties

4 that you get in your risk assessment and say okay with this

5 weight-of-evidence we have the Tier I, Tier II and then we find

6 no effect and this is basically also what the colony said, so I

7 think you have a lot of weight.

8 That is my simple understanding of the issue.

9 DR. NINA FEFFERMAN: Thank you very much Dr.

10 Sandy. I think the iterative process is what I have in mind and

11 it sounds to me like what you are suggesting is that you would

12 trust modeling to suggest additional test but not to eliminate

13 existing ones. And I think then we just have a difference of

14 opinion on the utility of models.

15 But to address Dr. Berenbaum's comment about

16 could the public ever accept this, the answer in my mind is

17 salesmanship. We have a couple of examples where we botched it

18 entirely and a couple of examples where we have done it really

19 well.

20 We botched it entirely with vaccine safety; we

21 have also botched that with empirical studies. So just having

22 empirical studies is also not a guarantee of public acceptance

23 of the results.

24 But we have done it really really well with

25 some of the U.S. Fish and Wildlife Services Conservation

26 Endangered Species Act requirements. And when you should close
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1 roads that inconvenience people in order to left spotted

2 salamanders cross and things like that. And we have had really

3 good public acceptance and that spotted salamander work, all

4 modeling, all of it.

5 And it originally met with huge public

6 resistance and we did really good outreach. And we went in and

7 we said, okay guys this is what went into this. We did not

8 explain the equations but we explained the conceptual flow of

9 what we were modeling and we now have communities that come back

10 to us every year and go, "Should we be closing the same roads at

11 the same time this year?" 

12 And so my answer to that part is, that is a

13 matter of public outreach and effective communication. It is a

14 slightly harder sell to sell the public on modeling versus

15 empirical work, but forgive me, I have great faith in the

16 public; I do not have great faith in the average person as a

17 scientist.

18 So I think we have work to do to explain

19 empirical science to the public, also we just have a little bit

20 more work to do to explain mathematical science.

21 DR. THOMAS POTTER: I just want to make a

22 comment. I'm not sure I heard anywhere in the discussion,

23 certainly in Dr. Fefferman's answer at the outset as well as the

24 other discussants, that we as a group or anyone was recommending

25 that modeling be used to in some way subvert or eliminate or in

26 otherwise change the tier risk assessment process. 
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1 We have Tier I, if you fail Tier I one assumes

2 then you get to Tier II. And certainly, this again, there is a

3 long history of that in terms of looking at other problems that

4 the agency deals with. I appreciate your concerns that somehow

5 an ill-informed model may somehow obscure the need for further

6 testing but I do not think that that was in any way suggested.

7 DR. JAMES MCMANAMAN: So I got really

8 confused. I really appreciate your discussion Nina.

9 It appears to me that (a) is asking a

10 different kind of question than what we have really been

11 discussing. Because if we take your examples of analytical

12 modeling - and I may be way off base here -- if you take your

13 examples of analytical modeling and apply them to what are the

14 causes of loss of bees, I think that the European data would

15 suggest that pesticides are a cause of loss of bees.

16 If the question is then how should we model

17 pesticide effects on bee colonies, then it seems to me it is a

18 different kind of modeling process all together. Is that not

19 the case? Because in one case you are asking, is it a risk

20 factor, and if the answer is no, then we have eliminated the

21 entire question. So if the answer is no, but we still want to

22 know more about how pesticides affects bees, then I think it

23 seems to be more of a different type of modeling rather than

24 analytical type.

25 DR. NINA FEFFERMAN: Thank you. That is a

26 wonderful clarification question. 
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1 When I was talking before about colonies

2 thriving or dying as an outcome, I should have been more

3 explicit. I meant that restricted to the example I was using

4 from the discussion from yesterday, of what is the importance of

5 a particular threshold level in worker mortality.

6 But I do mean to suggest that analytic models

7 themselves are going to directly and validly, in risk

8 assessment, be able to point to us the most important either

9 that variables or functional relationships or thresholds for

10 where pesticides can affect success by whichever metric we

11 adopt, either pollinator services or survival of the colony or

12 sustainability of the population. 

13 At any level of those analytic models will be

14 able to suggest to us -- for it to be a good model both testable

15 predictable outcomes that should be before we get to the outcome

16 that we are interested in so that we can validate the model and

17 not necessarily on an agreed outcome of what we want but

18 proximate measures that should be easy to measure also easy to

19 see when the answer is negative. They should not be patently

20 obviously always going to be true.

21 So a way of validating, if you would, the

22 by-products of the model as a way to have confidence in the

23 entirety of the model when we are asking these difficult

24 questions; but then apply it to gain insight into which things

25 should be measured that will directly affect whether or not a

26 pesticide is posing a risk to our outcome. Thank you so much.
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1 DR. JAMES MCMANAMAN: Thanks for the

2 clarification.

3 DR. DANIEL SCHLENK: Okay, any other comments

4 right now? I'm going to try to bring us back hopefully. It

5 seems like all three of the questions have sort have been

6 discussed at the same time interactively here. So let's go back

7 to (a) and just say, please comment on the concept of using

8 colony-level ecological models to perform proposed risk

9 assessment for honey bees.

10 Okay so we have had comments, and it appears

11 that in general the panel feels, yes definitely use models. I

12 think there is some disagreement perhaps in what outcomes those

13 models could be used for, perhaps, so let's make sure we get

14 that into the minutes in terms of concerns for certain panel

15 members and others. 

16 But it would appear that everybody is

17 comfortable with the concept of using models for this particular

18 parameter. Is that what I'm hearing? Yes, okay. 

19 All right, any other comments before we ask, I

20 am sure there is going to be questions of clarification from the

21 agency, I am guessing. Any other comments before we go to the

22 agency?

23 DR. THOMAS STEEGER: I would just like to make

24 a comment and I appreciate the dialog that is going on regarding

25 how you interpret information across multiple levels of

26 biological organization.
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1 The National Research Council through the

2 National Academy of Science in their publication, Toxicity

3 Testing in the 21st Century, has sent a strong message that

4 there should be movement away from whole animal testing and

5 taking advantage of as much of the available information as you

6 can so that you can move from one level of biological

7 organization to another without as much reliance as we have in

8 the past on whole animal testing.

9 And that particularly deals with the screening

10 level approaches where that would enable of regulatory authority

11 to determine where you need to best use resources and apply

12 them.

13 And this whole idea that we touch on in the

14 paper, adverse outcome pathways, expert systems, that once those

15 relationships become clear and you can, based on the mode of

16 action of a chemical, you can have a mechanistic approach that

17 would enable you to make some decision that would be less

18 reliant on animal testing, is the direction that this agency I

19 believe is headed.

20 But with that said, where you have identified

21 risks you would be transitioning likely to higher tiered test to

22 substantiate these potential concerns. Because companies do put

23 a lot of money into the development of chemicals and it is

24 ultimately up to the risk manager to decide where a higher tier

25 testing is required but it is also the purview of the regulated

26 community to demonstrate their product is safe as it is actually
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1 planned to be used. So the higher tier testing would still have

2 a role.

3 But I applaud the efforts to introduce this

4 idea of using information at multiple levels of biological

5 organization to inform he risk management decision.

6 DR. DANIEL SCHLENK: Okay, so with that I am

7 assuming you got what you need for this particular letter. Is

8 that correct? Okay. With that let's just take a break I think

9 before we go on to (b) and (c).

10 (Brief recess.)

11 DR. DANIEL SCHLENK: Okay, just to remind the

12 panel, during breaks if you do have any interactions with the

13 audience or the agency just to make sure that we state that for

14 the record that you have had those interactions and if it has

15 changed your comments, you need to state that for the record as

16 well. Just to let you guy know we could do that.

17 DR. MAY BERENBAUM: Do you want all

18 interactions recorded or only those that have any bearing on the

19 discussion.

20 DR. DANIEL SCHLENK: Those that affect your

21 comments please.

22 DR. ROSALIND JAMES: Do you want those now?

23 DR. DANIEL SCHLENK: I will give you the

24 opportunity as soon as we get to the questions, yes. Let's go

25 ahead and read letter (b) into the record.

26 MR. KEITH SAPPINGTON: Dr. Schlenk, if I could
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1 ask one clarifying question. 

2 I think it would be helpful to the agencies in

3 responding - and I'm not sure which of these questions or all of

4 them - about the difference between an analytical model and a

5 simulation model and perhaps a concrete example, and perhaps

6 when one may be preferred over another.

7 DR. DANIEL SCHLENK: Dr. Fefferman.

8 DR. NINA FEFFERMAN: Thank you for asking that

9 question and actually we did have a useful conversation on

10 exactly that point during the break. So I will bring up all of

11 those points but this is my active disclosure on that one. Do

12 you want me to answer or just put it in the minutes?

13 DR. DANIEL SCHLENK: No, I think just the fact

14 that you can do that would be appropriate.

15 DR. NINA FEFFERMAN: Oh, okay. Yes, we can do

16 that.

17 DR. DANIEL SCHLENK: Okay, ready to go on to

18 letter (b)?

19 MR. KEITH SAPPINGTON: Question 14, letter

20 (b); please comment on the state of the science regarding

21 available honey bee models discussed in Section 5.4.2 of the

22 White Paper in relation to the potential application in a

23 regulatory risk assessment context. In particular, please

24 comment on the extent that such models have been evaluated using

25 empirical data related to honey bee population dynamics and the

26 availability of such data for their parameterization.
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1 DR. NINA FEFFERMAN: I going to rely on the

2 answer to the last question, which is going to come down to for

3 analytic models data is less important than for simulation

4 models. And with that context in mind many of the models

5 mentioned in the White Paper have only very sparse assess to

6 data thus far. And we have discussed during this panel many

7 good ways to try and fill some of those data gaps.

8 And while it is very important to collect that

9 further input data, for measurements, for parameter values, and

10 for model validation and to characterize functional

11 relationships. Many of these analytic models are useful in the

12 absence of direct measure data as long as the functional

13 relationships themselves are hypothesize correctly by the

14 experts building them.

15 And so this does not happens in a vacuum of

16 data but what happens is that there is empirical work that

17 informs relational data. So how do we get - on a very stupid

18 level - how do we get a bigger colony? We have more babies than

19 we have deaths, right? That is something that at some point

20 someone measured and went, "Oh look, demography, demography

21 happens" when there are more births than death. From that you

22 get an abstract analytic model, okay births has to outweigh

23 deaths for a population to grow; the equivalent for that has to

24 happen for a colony for worker mortality to worker eclosion. 

25 That is a valuable insight; that one is a

26 trivial insight but valuable insights come out of that
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1 relationship in the absence of, what is the worker mortality

2 today; or what is the overall colony survival with that.

3 So the conclusion of these models to provide

4 insights into how the systems work and to make specific

5 predictions about which focal metrics are important, are really

6 the utility of the analytic side for purposes of risk

7 assessment. The more general employment of models that can help

8 assess the importance of unknown levels of threat involves both

9 the analytic and the simulation levels of these things.

10 So even if very little insight is known about

11 individual sensitivity to -- for instance exposure to particular

12 chemicals what you can do is know which pathways to effect,

13 either within the analytic models that are more abstract or the

14 empirical models that are the specific. Okay, the link is

15 multiply this number by .003, get that number that is the

16 answer.

17 Within the narrow context of the empirical

18 models discussed in the White Paper, the simulation type models

19 -- in modeling, simulation models are also called empirical

20 models, which is not to be confused with empirical experiments. 

21 Sorry, I am going to try and remember not to call them that.

22 Simulation experiments, in the narrow context

23 of the simulation models being used we then need to discuss the

24 appropriate sensitivity of the data that is being used, the

25 sensitivity and robustness testing for the individual

26 parameters. So there is sensitivity which is how do the value
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1 affects the ultimate outcome and there is robustness, which is

2 what are the range of values for which this model is still

3 valid. 

4 And for a simulation model we have not

5 discussed robustness at all. There are very good local range

6 simulation models that tell you exactly as long as PH does not

7 go off the scale everything in the system behaves exactly this

8 way; but as soon as you have a PH under three, oh my God do not

9 use this model; use a different model.

10 There is a different behavior, in physics it

11 is called a phase transition, right, something else has

12 happened. You have coalescence or rain from a cloud, right. 

13 There is water, suddenly something happens and water falls out

14 of the cloud. A model for how water is suspended in the air

15 works really well up until a point, and then you need a model

16 that says okay how does rain falls. And I am not a

17 meteorologist so they might actually have a good model that does

18 both of those; I do not know.

19 So within the available data that we have

20 first we have to ask sort of what do we want out of the model;

21 because if the answer is most appropriately answered by an

22 analytic model, we may not need any of the data; what we want is

23 the insight and that will tell us which data then is going to be

24 important, and then we know whether or not we have it. But we

25 do not know whether or not we have it until we have that

26 analytic model to tell us.
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1 For a simulation model we do have to go

2 through the sensitivity of each of the values that we would need

3 and say, well what if this was everywhere from here to here. 

4 Does that change the outcome at all? If the answer that comes

5 out of the entire model of the simulation does not change with

6 regard to the quality of the data input for that parameter, you

7 do not need a better measurement of that parameter. On the

8 other hand if it is very sensitive to that parameter, you need a

9 very good set of empirical experiments to do that.

10 So unfortunately there is not an easy answer

11 to, oh for all of the ones in the White Paper here is the data

12 gap, here is what we know; we are good just use those. Go

13 measure those and at the end we will be fine. On the other hand

14 there is sort of an algorithmic approach that we can take with

15 each parameter for each model going, is it sufficient; is our

16 knowledge good enough to use that with confidence in this

17 context.

18 DR. ROSALIND JAMES: I'm debating whether just

19 to say I concur or try to elaborate more. I'll try to give a

20 little example maybe.

21 In the White Paper I think the review of

22 simulation model is excellent and fairly complete; there may be

23 some missing models but I think it is a fairly complete review

24 and a nice summary of the model.

25 An example I think of what Nina is talking

26 about is say with the Khoury et al model, we discussed this
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1 earlier and one of the conclusions was there is this critical

2 threshold on colony of .355 bees per day. If you lose a third

3 of the bees per day then the colony will collapse but that may

4 actually be partially dependent on what the reproductive rate of

5 the queen is in that colony. 

6 So that would be an example of a type of

7 relationships we are talking about.

8 DR. THOMAS POTTER: In direct response to the

9 question I'll say that I feel in the case of exposure modeling

10 and for pesticides the state of the science is not well

11 advanced.

12 DR. DAVID TARPY: I have no additional

13 comments.

14 DR. DANIEL SCHLENK: Other panel members. 

15 MR. JENS PISTORIUS: I also have this feeling

16 that they are valuable to be considered but that they are not

17 well enough advanced to really be used in the risk assessment. 

18 They may inform the risk assessment very well

19 but for the available models I think all have still some lacks

20 which really make potentially use very valuable for this moment.

21 Especially the valuation I think is missing and I think that it

22 has been said for some questions before that the honey bee

23 colonies, they are a very complex organisms and there are so

24 many different factors that I think this will always limit the

25 possibility to describe everything that happens with the bee

26 colony.
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1 Nevertheless, I think there is some use but I

2 also know there have been recent publications and also for the

3 query model in recent discussions we did identified that there

4 is one important factor missing but at the moment I cannot

5 recall which one it was exactly, but I can write that into the

6 notes too.

7 So I think at the moment the models that we

8 have are useable to inform risk assessment but again like with

9 the comment on the question before, I think the most important

10 is that we still rely on the higher tier test.

11 DR. DANIEL SCHLENK: Other comment. No one

12 else? Okay. Mr. Sappington do you have any questions of

13 clarification?

14 MR. KEITH SAPPINGTON: I think we are good,

15 thank you.

16 DR. DANIEL SCHLENK: All right, we are on our

17 last question, I think, perhaps, maybe. You want to read (c)

18 into the record please?

19 MR. KEITH SAPPINGTON: Question 14, final

20 subpart; please comment on the important elements that should be

21 considered in reviewing available honey bee colonies, ecological

22 models for potential application in risk assessments.

23 DR. NINA FEFFERMAN: The first two questions

24 that should be asked about any model are what insight does it

25 provide that accurate empirical studies could not, or if there

26 are none, how does the model spare efforts or expense to provide
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1 those insight? 

2 In the case of risk assessment there are two

3 main components, and Dr. Potter was specifically addressing this

4 earlier as well; how likely is the scenario in question to occur

5 and how severe is the likely outcome once that scenario has

6 occurred.

7 In the case of colony-level ecological models

8 the former question relates really to sort of a dose response

9 aspect of do we get doses out there and if the dose is out there

10 does it get on a bee?

11 Where models actually help with that is kind

12 of questionable because if we have the understanding about those

13 routes of exposure, we can do a model that follows those but the

14 routes are either basically there or not and its impact is

15 either there or not. And we can have some sub-position models

16 but, nah. 

17 The latter question relates to how does those

18 individual affects actually both interact together in one bee

19 and how does that scale up into a society of bees and how is

20 that society of bees scale to a population of bees and how does

21 those population of bees provide the conservation and service

22 endpoints that we have been discussing?

23 Those questions are a sweet spot for the

24 ability of models. These relate to the impact of individual

25 responses at sub-lethal effects. They can be ways to study the

26 concomitant effects of different pathways. And we were talking
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1 about I think you guys call them brews of pesticide before or

2 something like that - mixtures, unintended or un-marketed

3 mixtures of pesticide. This is a great place to try and explore

4 those things.

5 For risk assessment itself and the models here

6 presented that were described in the White Paper - and if I fail

7 to mention it I'm sorry - I agree the review of the simulation

8 models in the White Paper was wonderfully done. And as you guys

9 said not completely exhaustive but representative and well

10 described so thank you for doing that, that made my comments

11 able to back up to say and analytic instead of just, oh do not

12 do that with simulation.

13 So as those model presented were not

14 specifically designed with these types of questions in mind,

15 they do not specifically address these particular questions of

16 risk assessment directly and in useful ways; but they could be

17 very important in formulating answers within each level of

18 concern to produce a globally useful risk assessment. So this

19 is where that modularity that we were talking about earlier

20 comes into play.

21 Well how do we understand the impact of this

22 kind of toxicity to queen fecundity? Or to the dispersion of

23 queen (inaudible) in a colony or to the ability of foragers to

24 get home? Those are specific questions and specific effects

25 that could be plug modularly into an overall model.

26 I would like to reiterate again that I do not
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1 mean to suggest that we should plug in every available module

2 because then both analytic and simulation models become unwieldy

3 and the interpretation is very very difficult.

4 Again I will point to the analytic side of

5 things to both designate and integrate which of these specific

6 modules will be of most impact to the risk assessment outcomes

7 for particular questions.

8 And again by employing those more algebraic

9 analytic models it is going to be easier to assess which of the

10 more specific fine-grain models or simulations require then

11 their own empirical metrics to parameterize correctly and how to

12 validate those by as I was saying sort of incidental validation

13 portions of models.

14 I can give an example but I am going to leave

15 it to Dr. James and others to call me on it if an example feels

16 necessary for this question.

17 These sorts of models both analytic and

18 simulation can generate insight into which valid threshold of

19 outcome for risk assessment are going to be meaningful

20 measureable endpoints for compromising the goals of all of the

21 services we are talking about, both to ourselves and to bees

22 themselves.

23 DR. ROSALIND JAMES: So the question asks

24 which of the most important elements to consider in a model and

25 I will add to what was said already. My view might be a little

26 bit different than yours but for both theoretical and simulation
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1 models there needs to be some sort of empirical validation

2 before you use them.

3 And ideally I think for simulation models

4 there really needs to have been done, or you can do it, some

5 sort of sensitivity analysis, meaning that each component and

6 how sensitive is that component to the outcome of the model. 

7 And then that tells you something about how accurate the data,

8 how precise the data has to be that are the inputs.

9 And then of course hopefully the model gives

10 you the critical elements that you are looking for as an

11 endpoint, which I would think one of those would be to help

12 identify what endpoints you need to request data from the

13 registrant for.

14 DR. THOMAS POTTER: I generally agree with

15 what Dr. Fefferman and also Dr. James has stated, I just simply

16 want to add that there is perhaps another question and this is

17 in direct response to Dr. Fefferman that we would ask about a

18 model.

19 And that is, and again there are certain

20 conditions that you have to apply to this, once calibrated

21 and/or validated can it be used in the absence of additional

22 empirical data to inform decisions. And certainly that would be

23 critically important to the agency when we look particularly at

24 Tier II and Tier III studies recognizing they are expensive and

25 that there is a limited number of studies that can be done.

26 Is it possible to use data from a study that
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1 was say conducted in Illinois to inform decisions about whether

2 or not it is suitable to use the active ingredient in the same

3 way in the state of Georgia for example?

4 So certainly these are critical questions that

5 one would ask about a model in terms of what it can do and how

6 effective it is as a tool because I see it as part of a toolkit

7 that we use in risk assessment rather than maybe the other way

8 around, which is the model is used to simply evaluate risk.

9 And I will sum up by saying and again maybe

10 because I'm sitting in the exposure side of the room here I

11 strongly think that considerably more work can be done and

12 substantial progress can be made on the exposure side, which I

13 think will serve to support particularly the Tier I testing and

14 advance the process.

15 DR. DAVID TARPY: I echo the comments by the

16 previous discussants.

17 DR. DANIEL SCHLENK: Other panel members.

18 MR. JENS PISTORIUS: As the last addition I

19 concur to all what has been said. When mentioning to the model

20 of Henry (phonetic) and specifically because that has caused a

21 lot of trouble and concern and it was a comment I made to the

22 previous answer.

23 I just wanted to point out that was not my

24 work who found that this model in specific had was not validated

25 for the purpose because there was one parameter and maximum

26 daily rate of production of new work, which was not empirically
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1 based.

2 And this is basically the work of Dr. Creswell

3 (phonetic), and he made a technical letter to science, I do not

4 know if it is published yet or not but I have it; it is

5 confidential and it is his work. I want to point this out. 

6 And this leads me to my part of the answer, I

7 think considering the high variability, I am not quite sure if

8 it is the same in the U.S., but in European region for instance

9 for colonies to develop it is just important to point out that

10 all of these parameters that are measured and that come into

11 those models actually suits also the geographical location and

12 the climate conditions. 

13 For instance, colony growth is a completely

14 different issue. When you have southern Europe, you have

15 basically the colony growth is like this. There is a higher

16 population in winter and it grows slower in spring and does not

17 achieve such a growth rate basically. And then it goes to a

18 moderate amount of bees. But what they do in Germany is they

19 come from a very small number amount of bees and then there is

20 basically explosion of the colony. You can call it a really

21 massive increase of number of bees. And then in autumn there is

22 a very big decrease.

23 And when those models are suitably adapted, I

24 am very happy.

25 DR. NINA FEFFERMAN: I want to thank Mr.

26 Pistorius for providing a fantastic example of what an analytic
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1 model can show you, what kinds of colony growth trajectories you

2 could expect, what are the parameters to change without having

3 to go measure all of the parameters. Awesome, thank you.

4 DR. DANIEL SCHLENK: Any other comments?

5 DR. ROSALIND JAMES: I will add one more that

6 I forgot to say. On the review of models you have the de grande

7 (phonetic) bee pop model, but the newer varroa pop model is

8 probably better and includes climate data in it also.

9 DR. JEFF PETTIS: This was a follow up to

10 that. The only problem with using simulation models solely for

11 that is that a northern researcher would never get to work in a

12 southern climate, so that would be a drawback.

13 DR. NINA FEFFERMAN: For simulation I agree. 

14 For analytic, um.

15 DR. DANIEL SCHLENK: Any other comments from

16 the panel? Okay, let me go to the agency; any questions of

17 clarification? 

18 MR. KEITH SAPPINGTON: I think we are good,

19 thank you.

20 MR. STEVEN BRADBURY: I think it compliments

21 what you all are talking about to the extent it can get captured

22 in the report could be helpful. 

23 So in my mind the Tier II or Tier III studies

24 are physical models trying to provide insights into the real

25 world, whatever the real world is. But you can only do a Tier

26 II or Tier III study in certain places at a certain point in
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1 time with that batch of bees. And either implicitly or

2 explicitly we are running a model in our brain to project what

3 that means in many places and at many times with different races

4 of bees or whatever. And so those empirical models have

5 shortcomings. I mean in empirical like those physical models. 

6 

7 But all of those things are rolling through

8 our heads and then the simulation models or the analytic models

9 are ways to try to think about all of those things rolling

10 around in our heads and using mathematics and other kinds of

11 knowledge to try to help us think through that.

12 And none of them are right but all of them

13 give us different insight and they are all at various stages of

14 development.

15 So to the extent that question 13 and 14

16 emerge in your writing, if there is any way that you can opine

17 or provide advice on how you try to blend what we know today

18 between insight of simulation or an analytic model might provide

19 to help us be more explicit in the assumptions that we are going

20 to use when we look at those physical models to make projections

21 about what is happening in the southeast or what is happening in

22 the northwest of the United States or the parries in Canada, the

23 Sacramento watershed in California.

24 Because there is probably a blending of all of

25 these, those physical models and what we do not really get real

26 clear about what we are assuming when we look at the results of
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1 physical models and how these analytical or simulation models

2 can help inform what we should be saying about our assumptions.

3 I think that would just be helpful if

4 something between 13 and 14 could try to blend what I think I

5 heard in a lot of the comments but if you guys could bring it

6 together that would, I think, help a lot of people not just us. 

7 Thanks.

8 DR. DANIEL SCHLENK: Okay, at this point this

9 will be our final sort of commenting period so I would like to

10 just go around the panel, if you have any final comments you

11 would like to get on the record this is the time to do so. And

12 begin with Dr. Sandy.

13 DR. MARTHA SANDY: I do not have anything to

14 add.

15 DR. STEPHEN KLAINE: I for one would just like

16 to thank the EPA for the document; it was great, and my bee

17 colleagues for educating me about bees. This is a really

18 important and very fascinating subject and I really look forward

19 to see how this thing emerges. It has become obvious to me that

20 we are not dealing with single bees to protect but we are

21 dealing with colonies of bees, at least for honey bees. 

22 So I think that is going to require some sort

23 of out of the box thinking as we move forward. Concepts like

24 homeostasis might be better applied to bee colonies than to

25 bees. So anyways I just think it has been an excellent week and

26 very informative.
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1 DR. MARTHA SANDY: If I could have a go back I

2 would also like to say I really enjoyed meeting all of you and

3 enjoyed the White Paper too to the EPA staff, thank you.

4 DR. JAMES MCMANAMAN: I echo Dr. Sandy and Dr.

5 Klaine's comments and appreciation of the White Paper and all

6 the work that has gone into this and the discussions of the

7 panel.

8 I think the one thing that I came away from

9 this was that, and it emphasizes that I do not think -- I think

10 it was a clear indication that survival is not the only endpoint

11 that should be considered and that should be taken to heart by

12 the agency in terms of looking at other ways of evaluating the

13 effects of pesticides. 

14 DR. KENNETH DELCLOS: I will just say it was a

15 most interesting week but I have nothing to add here.

16 DR. DAVID TARPY: I would like to echo all of

17 those same comments. I think this has been a fantastic

18 discussion, one that has been needed and I look forward to the

19 evolution of this process. 

20 I hope everybody has gained an appreciation

21 for just how messy and dirty honey bee colonies can be. And I

22 think that that does not distract at all from their importance

23 to our commercial production agriculture and just what an

24 important issue this really is for our entire system.

25 DR. PAUL SCHWAB: This has been quite an

26 education for me. I did not have very much information about
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1 bees before I started reading this White Paper. So my learning

2 curve has been very steep but I appreciate the patience that all

3 of you have express when I have been inclined to weave my way

4 into this panel.

5 Just a little bit of bookkeeping, I did send -

6 I have talked to Christina about this and to Tom - but I did

7 send in a comment dealing with a correction to the absorption

8 equation that was being used for contamination of puddles during

9 a spraying event. 

10 I know that we were supposed to have all of

11 this into the record or it really could not officially exist so

12 that is the point of this.

13 DR. THOMAS POTTER: I would like to thank

14 first off the EPA staff that put together the White Paper,

15 obviously a huge amount of work and probably done on a

16 compressed timeframe I would expect. It was certainly

17 informative and I can say I learned a tremendous amount from

18 reviewing it and really enjoyed being part of the panel.

19 And I will say that I think that the outcome

20 of this is certainly going to have a very strong impact, I

21 think, on the whole process of ecological risk assessment. I

22 think there are many core elements of this that will transcend

23 into the whole area of looking at ecological risk.

24 MR. JENS PISTORIUS: First one technical

25 comment to the bee issues we are talking about before and then

26 on to the rest. I have been making a list with all the points,
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1 you know, where I thought during the discussions I hoped this

2 was going to be mentioned. 

3 One last comment I have for synergistic

4 effects for spray application on flowers and crops; when you

5 know that there are a certain -- and I know that it is taken

6 into consideration, I just want to point it out again -- that

7 there are synergistic actions between two classes of compounds

8 like the pyrethroids and the EBI fungicides. Please consider

9 those synergisms as well as all others really documented. We do

10 not have to test every sector, but with those two classes

11 pyrethroids and EBI fungicides.

12 Please also do a higher tier risk assessment

13 or do risk mitigation measures that are appropriate because we

14 know we had problems with it in the past. But I think this is

15 taken into consideration so I'm going to end it with a technical

16 comment.

17 But now let me say a few personal words on

18 this whole issue. Before I met the first EPA staff in 2011 at

19 the SETAC Pellston conference, I will tell you frankly that one

20 person told me what is going to happen. You are going to the

21 U.S. and they'll let you talk, let you know your experiences and

22 then they will say, thank you, and not take anything of this

23 into account. 

24 And I am deeply impressed, seriously, deeply

25 impressed and I will go to this person and I will tell him all

26 of my experiences. Really it is such a great honor to be here. 
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1 I was really impressed by all the good work. I have to thank

2 the whole EPA staff. I think it is such an incredible effort

3 that you have made. 

4 This White Paper, when I read it I thought,

5 hey this is great. Really, I think there is so much effort in

6 this and I must say really I am deeply impressed how far you

7 have brought this and how good the advancement is and we have

8 been working together a little bit on other issues since 2011; I

9 love to work with you. And it has been great fun, great honor

10 and I think this has been such a great scientific progress. 

11 Thanks a lot, I take my hat.

12 DR. JEFF PETTIS: I would just like to offer

13 thanks for the invitation to be part of the panel. I have very

14 much enjoyed it. I would like to thank EPA for outlining the

15 three protection goals, in particular, for me protection goal

16 three which was broader than just honey bees. We tend to get

17 caught up in honey bee biology and the complexities of that, but

18 pollinated declined is bigger than just honey bee issues.

19 So I thank the effort put into the White

20 Paper, in particular the protection goals I thought were spot

21 on. Thank you.

22 DR. NANCY OSTIGUY: This was probably the most

23 intense, but also the most fun four days that I have spent in a

24 long time. Everyone has been incredibly collegial. It has been

25 very informative. I have enjoyed myself thoroughly. I have

26 collected also an amazing amount of information I am going to
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1 take back to my students. 

2 I have always been a defender of EPA because I

3 think you get picked on too much but I have some very personal

4 stories now to tell my students about what a wonderful job you

5 are doing under very difficult circumstances.

6 And I also have to say something in defense of

7 those messy bees. They are no messier to think about than when

8 you think about human as a population. I feel extremely sorry

9 for the sociologists that are trying to study us. And in many

10 ways maybe we should think of ourselves as sociologists of honey

11 bees.

12 But in defense of those bees they are

13 extremely neat inside those colonies.

14 DR. ROSALIND JAMES: I almost want to say, I

15 concur with my colleagues because I guess I will say the same

16 thing. I was very glad to see EPA take on this issue. Everyone

17 has been complimentary; I do not want to be too uncomplimentary

18 but I do want to say, finally. It is a little behind times but

19 I am very glad to see EPA take it on.

20 And when I started reading the White Paper I

21 was very impressed. I mean throughout it. It was better than I

22 expected I guess is what I mean. And I appreciate being invited

23 to this panel with my beyond Apis mellifera views. Thank you.

24 DR. GREG HUNT: I concur; and I also am having

25 my students read some papers that relates to these issues and we

26 will be talking about it next week.
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1 And I thank the EPA for their efforts. I

2 think that the increase emphasis in Tier I that is put in the

3 White Paper is very good and I hope that we as a panel can

4 provide a kind of a tiered response in our writings so that we

5 can give you some, putting the simple things at the top and then

6 the more future things at the bottom.

7 DR. NINA FEFFERMAN: Thank you guys, all of

8 you, the EPA members, the panel the public commenters, you guys

9 running this, oh my God.

10 I have had an awesome time and I have learned

11 a huge amount and I have made friends and I like it. As I read

12 into the existence record I want to say that there are some

13 analytic models of honey bee colonies and I will provide some of

14 those for the notes. Thank you.

15 DR. MAY BERENBAUM: And to come crashing down

16 on an incredibly mundane technical note just to read it into the

17 record but I will follow that with some more inspirational

18 language.

19 Our discussions of prolonging the endpoint of

20 Tier I larvae assay through pupation lacked literature

21 documentation. There were some hand waving or remarks about,

22 yes papers are out there. Well one such paper is "Honey Bee

23 Risk Assessment: New Approaches for In Vitro larvae Rearing and

24 Data Analyses" by Hendriksma, et al. "Methods in Ecology and

25 Evolution" 2011. It is sort of a modification of Aupinel et al,

26 but the point is it is a non-grafting method and the endpoint is
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1 pre-pupation and it was used with some success.

2 On a higher note, as I mentioned 117 years

3 inadvertent bee mortality caused by pesticide is really -- I am

4 deliriously happy that it is now a national priority to protect

5 not just honey bees but bees in general and I do not know if

6 people will appreciate what a massive societal change that is.

7 Insect toxicology for decades involved how to

8 kill insects so this is really un-trodden ground for the most

9 part. And having said that the repeated references to data

10 gaps, I think, obscures the fact that the White Paper represent

11 there has been a tremendous explosion of extremely informative

12 and relevant data so that we now can make science-based

13 decisions on the best way to access risk and protect bees.

14 So I am glad bees are having their day here in

15 Washington D.C. and thank you for letting me be part of it.

16 DR. DANIEL SCHLENK: Let me say you are all

17 making history. As I mentioned before this was a record number

18 of questions that we have ever gotten, and you did it. You did

19 do it. I want to congratulate you. This has been one of the

20 best panels I have had the honor of being a part of.

21 I appreciate the tremendous hard work that

22 many of you did throughout this week and days leading up to it

23 of course. But also just to your collegiality and working

24 together and being efficient in terms of answering the

25 questions; that was just phenomenal. Great job, hat off to you

26 guys. 
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1 DR. JEFF PETTIS: A point about your

2 leadership on the panel. There were some issues when you called

3 it yesterday at 4:30, and we were like "why not press on?" and

4 so there was a lot of discussion afterward but you made the

5 right call there. We never would have gotten through these last

6 questions with the level of detail and discussion that was

7 needed. So you made a great call yesterday. We were pretty

8 burnt yesterday.

9 DR. DANIEL SCHLENK: Thanks, actually that was

10 Dr. Jenkins pushing me on the right side over there. I wish I

11 could take credit for that but.

12 DR. NINA FEFFERMAN: I also just quickly want

13 to acknowledge the amazing staff that has been helping us

14 throughout; they have made this run smoothly as well and I want

15 to thank them.

16 DR. DANIEL SCHLENK: Definitely, where would

17 we be without the SAP staff? Thank you very much, getting you

18 all here and out safely and soundly hopefully.

19 Again, thanks to the panel members for your

20 tremendous work. It has been a pleasure to be a part of this. 

21 Obviously the EPA for the presentation, I totally concur on the

22 White Paper and one of the things I think you hear at least

23 among the academics is, even though you never what happens in

24 terms of the policies that come out of this but in terms of

25 academia and education the things that you are doing are making

26 a difference at least in student's lives. Because I know every
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1 single academic in here uses this information to educate our

2 students. And that to me has always been a tremendous benefit

3 of being part of these things; it is just so educational not

4 just for me but for people that we also interact with.

5 So it is an exponential type of information

6 sharing that comes out of this. So just congratulates you on

7 that and again, I thought the White Paper was amazing. Not

8 being familiar with this particular topic and the detail and

9 clarity that was addressing these particular issues was just

10 fabulous, kudos for that as well.

11 I would like to thank our public commenters,

12 if they are still here, probably all gone but for their input

13 and realism in terms of what this issue really means to a lot of

14 those people.

15 EPA staff as well; I already said that. And

16 with that I will turn it over to Dr. Bradbury usually have a

17 word or two to say at the end.

18 DR. STEVEN BRADBURY: Real quick, just want to

19 thank again the panel for all the hard work. We know we had a

20 record setting number of questions and I appreciate all the work

21 to do it. And every single topic you guys worked through I

22 think gave us a lot of valuable insights. And the way you were

23 synthesizing information like a super colony in action so we

24 appreciate that too.

25 And we are committed to moving forward and I

26 appreciate the comments about incremental steps and feedback
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1 will help us move bit by bit and we will try to do better and

2 better with our colleagues in Canada and California and around

3 the world that we are all working on this.

4 I just want to thank you; you are a big

5 contributor to advancing science and our ability to evaluate

6 these situations.

7 DR. FRED JENKINS: I first want to start off

8 by thanking Dr. Schlenk; this was a very important meeting,

9 obviously a record breaking meeting and he did a great job in

10 leading this panel through these charge questions.

11 And thank you so much to this panel. You all

12 were fantastic and I appreciate you all agreeing to serve on

13 this panel. When I send my invitation, I find you, send my

14 invitations and talk to you and I really appreciate you all

15 expertise, your talents, your commitments in this effort. And

16 it has been truly a pleasure this whole week working with you

17 all.

18 To the EPA-OPP team, there is a lot of hard

19 work that goes on building up to having this meeting and OPP

20 really works very hard. I used to work in OPP so I know and I

21 used to work with EFED as a biologist, as an eco risk assessor

22 for a long time. And so I know the work ethics in that office

23 and I appreciate the opportunity to be there working with you

24 all for this meeting.

25 Special thanks to Dr. Steven Bradbury the

26 leader of the Office of Pesticide Programs. Dr. Donald Brady,
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1 EFED Director, and the Branch Chief who actually led the team

2 who did this work. Dr. Thomas Steeger, Thomas Moriarty, Mr.

3 Keith Sappington, Christina Wendel, Kristina Garber, Reuben

4 Baris, Joseph DeCant, all the speakers and scientists involved

5 in developing this very good White Paper. 

6 Thank you for the collaborative efforts of Cal

7 EPA and PMRA, Ms. Mary Mitchell and Richard Bireley from Cal EPA

8 Department of Pesticide Regulation. Thank you for your efforts

9 in this whole process.

10 Thank you to all the public commenters and to

11 all those who provided written public comments; there are a lot

12 of written public comments in regualations.gov; so thank you to

13 all those in the public for participating in this process.

14 And I would be of remiss if I personally did

15 not thank my FIFRA SAP colleagues under the leadership of Laura

16 Bailey, my colleagues Joseph Bailey, Sharlene Matten, Joyce

17 Coates, Lu-Ann Kleibacker, our team lead, and Shirley Percival. 

18

19 The meeting report will be available no later

20 than 90 days after the close of this meeting. This meeting is

21 now officially concluded. Everyone have a nice weekend and

22 thank you all.

23 (WHEREUPON the meeting was adjourned) 

24

25

26
FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

POLLINATOR RISK ASSESSMENT FRAMEWORK

DOCKET NUMBER: EPA-HQ-OPP-2012-0543

FIFRA SAP WEB SITE http://www.epa.gov/scipoly/sap/

OPP Docket Telephone: (703) 305-5805

UNITED STATES ENVIRONMENTAL

PROTECTION AGENCY

CONFERENCE CENTER LOBBY LEVEL

ONE POTOMAC YARD (SOUTH BUILDING)

2777 SOUTH CRYSTAL DRIVE

ARLINGTON, VIRGINIA 22202

SEPTEMBER 11TH - 14TH, 2012
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A

abbreviated 22:2,19,26 23:2 58:5 59:9 207:11

abductionistic 608:13

abilities 199:14 449:12

ability 13:24 29:23 52:21 130:24 136:18 146:6,22

170:7 187:13 195:21 228:2 239:3 246:15 266:8

277:18 280:13 360:7 447:6 448:18 451:2 454:5,6

455:9 461:12,18 462:17 497:1 505:1 515:7 543:7

551:4 562:15 563:16 646:7 677:24 678:23 695:5

able 52:3 67:7 109:10 116:17 141:14 151:19 176:9

178:9,23 196:15 198:18 201:13,15 226:13,18

227:25 242:11,11 244:16 258:26 266:16 267:3,20

269:1 286:7 316:2 327:10 328:20 342:11 359:26

387:20 396:7 418:24 440:13,24 441:2 447:11

449:4,6 454:10,20 459:6,21 480:2 490:6 501:26

511:8 515:11,18 520:5,9 521:13 527:24 528:11

528:24,25 548:21 549:4 573:15,16 575:23 581:3

600:22 606:3,7 609:14 612:14 615:21 620:24

623:16,17 624:23 632:26 638:22 639:14,15

644:15 651:2 653:12 654:9 662:14,17 666:8,14

678:11

abnormalities 446:4 463:14,23 506:16,18

abnormally 328:12

abortion 493:2

abraded 41:5,10 76:24,26 83:16,23,26 86:22,24

87:4 348:1,15,26 416:7,13 437:25 438:23

abrasion 74:26 412:3 418:17

abroad 18:10 21:18

absence 194:2 240:17 326:2,3 330:26 366:15

373:12,14 472:24 481:7 616:21 621:4 642:11

671:12 672:1 680:21

absolute 523:13

absolutely 45:18,20 51:17 88:7 219:22 278:19

319:12 497:17 518:7 565:15

absorb 352:19 391:7 501:26

absorbed 38:2 427:18

absorption 375:22 433:18 687:7

abstract 50:14,22 646:6,9 671:22 672:13

abstractions 646:16

abundance 176:13 333:19 335:22,23 337:8 338:19

338:24 339:4

abuse 5:16

academia 22:21 245:21 693:25

academic 256:17 294:1 322:7 551:23 694:1

academics 551:11 693:23

Academies 19:1

Academy 9:6 668:2

accept 275:2 379:19 661:26 663:16

acceptability 28:1 256:9 274:7

acceptable 67:4 91:2 192:26 195:12,15 275:1

316:1 409:15 480:3 502:14,17,21 552:26 639:7

acceptance 663:22 664:3

accepted 195:25 292:10 300:12

access 15:12,24,25 151:16 259:16 279:8 421:2

692:13

accidentally 130:11 613:22

acclimate 169:22 184:10

accommodate 168:4 226:13 300:18,26 596:15

accompany 191:7

accompanying 254:15

accomplish 54:4 121:10

accomplished 50:23 94:20 141:20 435:18

accomplishing 249:2

account 28:18 83:2 84:10,22 97:14 116:5 118:20

119:14 123:24 130:9 131:12,14 149:14 150:12,14

196:23 227:17 228:3 255:23 264:25 265:14,21

286:4 291:2 292:4 369:20 386:5 394:9 396:10

401:6 403:4 431:15 440:19 442:13 446:4 500:7

515:9 558:5 591:17 595:9 607:7 610:21 624:24

628:5,15 634:26 635:6 663:3 688:23

accounted 47:2,5 82:2 97:18,19 163:23 586:10

accounting 63:19 82:11 94:20 96:14 118:23 127:14

223:3 382:10 384:24 395:13

accounts 126:13 299:11

accumulate 137:13 431:19

accumulated 411:4

accumulating 86:3

accumulation 199:15,25 367:20 426:14 439:20

639:22

accuracy 42:7

accurate 132:2 138:20 236:12 259:8 284:19 472:4

497:1 569:19 627:16 676:25 680:7

accurately 177:10 276:20 368:13 537:24 556:15

563:8 568:1

achievable 274:6

achieve 266:14 556:25 576:9 629:8 638:5 682:17

achieved 191:12 563:4,8 567:26

acknowledge 14:7,9 15:6 65:26 221:10 225:12

247:25 693:13

acquisition 617:14

acre 107:25 108:19,22 109:20 110:9 114:15 115:22

115:26 123:13 126:20,24 272:19,23,25,26 273:3

273:13 317:11 319:6 365:25 373:7 374:17 377:23

393:22 394:1 579:22 580:15 584:9,11,16,16,17

584:18 585:2,3,4

acreage 187:17 190:10 321:11 416:23 568:10

acres 17:15 187:22 271:4 299:11 574:9 578:21,22

578:23 579:21 585:5 590:6 599:21,22

act 2:10 28:16 281:3,12 282:14 420:13 428:2

505:20 663:26
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acting 20:23 368:10

action 59:19 68:5 170:9 206:22 216:12,17,22

218:17 230:12 284:15 331:9 390:26 607:17

629:22 631:5 635:2 668:16 694:23

actions 416:21 688:7

active 42:18 43:4 44:1 53:25 86:17,25 92:4 104:13

110:12 119:3 133:2 150:20,23 157:24 201:22

258:23 274:20 287:19,22 299:15 318:6 327:19

349:16 390:23 393:22 394:13,16,18 396:2,11

401:6 410:24 423:24 424:4 477:10 482:14 544:10

565:4 610:23 670:11 681:2

actively 21:17 77:2,16 184:13 516:3 521:18 532:11

603:13

activists 324:16

activities 38:17 228:13 475:13

activity 61:7 169:24 186:21 216:18 226:5,11 241:9

245:20 286:14,16 296:25 298:3,4,15 449:16

465:18 509:21 510:2 513:8 556:10,10 559:10

acts 170:16

actual 29:20 52:3 73:8 78:4 108:20 141:1 145:10

151:14 155:15 156:5 162:3 169:3 183:11 185:12

193:21,26 194:11 209:26 211:7 234:14 237:22

240:6 269:22 276:24 373:16 392:26 393:5,20,25

394:2,14 395:2 465:2 535:16 543:9 549:13 563:8

568:1,9 603:20 611:14

acute 21:21 24:21,24 25:5,8,10 51:11 58:9,10,14

58:15 59:1,2,10 63:1,3,21 65:10 66:22 68:23 69:9

69:17 92:6,8 132:13 156:7,10,14,18 157:6,16,22

157:23 158:9,10,15 159:1,8 160:12 161:19,26

162:12,24 163:7,15 197:21 198:13 206:17 207:3

207:4,5,5,8,9,12,20,21,23 208:8,9 209:1 210:11

215:19 216:10,19,21,22,23,25 217:14,26 267:6

273:24 301:8 413:24 444:6 464:26 467:7,12

468:8 477:2,4,10,19 478:23 479:1 480:1 481:21

482:17 485:12,21 486:4 487:16,24 495:11 504:20

506:10 518:14,21 557:6,18 560:6 581:3 607:10

611:11 612:10,16,18,21 634:26

ad 172:4

adapt 515:7

adaptation 320:10

adapted 231:25 232:12 682:23

add 48:11 68:1 81:19,20,24 136:6,7 152:11,13

175:10 199:8,9 200:6 202:18 235:15 240:9 265:6

275:22 277:7 337:1 338:2,3,7 339:20 340:2

349:12 364:8,18,24 368:23 372:20 374:15 381:8

399:1,20 400:2 408:20 412:9 421:21 433:25

434:11 435:16 445:11 452:12 464:3 469:10

472:12 473:14 474:3 478:6,9 480:11 481:19

485:19 491:1 492:17 510:7 517:10 524:14 530:17

530:18 532:26 537:14 542:4,5 555:19,22 559:24

565:7 566:3 569:14,14 571:2,12,17 572:4 590:12

591:2 595:5 596:6 597:24 616:1 623:22 631:19

633:17,24 634:19 658:1 679:25 680:16 683:5

685:14 686:15

added 283:23 349:17 398:2,3 399:25 415:12

485:15 570:13 624:25

adding 120:20 268:19 342:7 343:24 346:5 434:14

443:15 447:8 453:11 459:16 529:21 566:1

addition 7:23 14:10 25:14 35:9 36:8 40:1 48:17

55:25 58:17 63:16 72:11 75:1 77:11 79:10 93:5

100:16 112:3,25 113:2 119:9 122:3,20 123:24

146:18 148:19 155:6 156:9 160:7 170:18 171:11

175:24 176:4 186:16 187:9 191:10 213:26 225:16

228:14,19 229:16 240:9 286:10 301:4 331:11

339:2 340:4 347:19 354:21 355:21 363:8 376:4

385:16 451:20 456:20 465:14 506:8 537:10 547:2

547:26 555:20 570:17 607:18 625:11 631:20

637:24 647:13 681:18

additional 26:4,23 37:14 54:26 59:15 60:2 61:8

62:12 63:26 65:26 67:6 91:2 101:12 128:15 139:9

145:7 148:2 156:18 157:24 159:26 162:7 163:12

179:20 193:23 201:25 204:19 211:8 212:2 217:5

217:8 218:9,18 219:1 221:11 268:19 292:18,20

293:4 317:21 320:9 333:21 337:10,12 341:11,12

394:11 432:22 436:17 439:17 443:15 444:16

446:12 447:2,20 451:8 457:4 463:16,26 478:10

509:25 524:3,19 525:14 526:4 529:4,25 534:17

542:11 570:21 589:17 594:21 604:25 605:14

617:12 621:10 625:6,19 632:24 642:17 663:12

675:12 680:21

additionally 166:2 529:8 633:7

additions 572:24

additive 265:6

address 16:25 17:7 18:5 25:5,7,15 33:5 42:11

46:24 49:9 52:16 61:24 66:14 71:11 81:6 135:14

140:10 152:6 154:1 163:17 166:6 172:1 173:18

179:26 180:20 187:1 194:5,10 197:8 203:9

219:24 220:19 224:24 225:13 236:18 257:4

267:21 271:18 283:16,24 293:16 294:14 334:21

335:16 353:22,23 354:12 357:26 381:26 382:8

408:6 409:10 413:5 414:18 423:3 440:9 443:18

491:17 517:2 540:14 554:20 562:23,25 566:15

583:3,11 627:22 629:13 631:20 632:26 633:3

647:25 650:6 663:15 678:15

addressed 40:22 47:10 63:18 176:22 287:26 339:5

412:4 422:7 423:16 516:22 576:17 582:16 591:8

615:1 644:15 651:4 662:15

addresses 47:24 163:1 170:26 182:6,17 190:1

359:4 448:1 591:23 594:23 656:18 660:9

addressing 163:21 179:23 181:2 222:22 235:11
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255:25 265:9 268:18 294:9 315:18 338:1 349:18

350:20 402:14 446:22 500:8 513:26 605:21

622:21 663:3 677:3 694:9

adds 442:11 446:25,26

Adelgid 259:15

adequacy 212:4,7

adequate 17:7 28:23 29:9 163:20 176:17 184:22

190:20 192:16 193:11 258:18 343:20 359:19

390:1 392:15 480:24 515:17 536:5 551:4 562:17

564:5 573:14 577:6 598:22,25 599:3,6 630:21

adequately 29:25 178:1 185:13 189:6 190:11 191:5

258:15 283:15 461:3 590:2 597:21 634:11

adhere 212:12

adjacent 37:3 78:18 91:16 188:19,26

adjourned 249:4,7 435:23 622:1,3 696:23

adjust 160:1

adjusted 127:14

adjustment 117:19 118:4 123:9 127:26 159:26

329:19 330:7

adjustments 140:18

adjuvants 368:7

administered 458:16 466:3

Administration's 249:23

administrative 2:8 435:14

admissions 410:2

admit 136:25 482:4 662:18

adopt 666:11

adult 20:9 24:22 58:1,12,16,18,20 62:23 67:1 70:5

72:12,21 73:22,25 74:16 75:1,16 79:23 92:1 94:2

95:19,23 97:23 98:7,20,23 99:1,14,20 100:7,24

100:26 101:4,5,9,13 105:16 106:19 108:16

110:13 115:23 117:22 126:22 129:26 134:9

153:24 156:10,14 157:7 158:10,12,21,26 159:1

160:10,19,22,25 161:5,23 175:2 206:17 209:20

215:20,22 216:7,10,20 217:15 218:1,6 267:6,10

270:24 341:7 363:15,22 371:23 372:1 384:8

443:25 477:5 481:4 482:7,21 484:2,5,8,26,26

489:18,19 513:8,21 535:24 537:11 545:5,19

556:13 560:7 570:1 586:3,17,18 587:13 593:5

607:10 612:25 618:19 619:1 634:7 655:6,11,12

adults 45:19 57:17 62:19,21 63:3 89:22 90:7 91:11

91:21 93:19,20,24 94:7 101:10 116:1 117:24

127:2,5,6,13 128:3 143:16 156:8 161:2,3 170:15

171:11,21 179:8 206:14 301:12 328:18 351:3

356:2 363:17 364:13 371:2,26 380:6 456:12

457:14 473:21 482:20 489:11 540:6 545:3 560:10

610:9 655:6

advance 7:8 10:6,12 12:23 77:14 247:7 274:21

278:23 568:23 589:9 620:5 658:17 681:14

advanced 48:17 675:11,17

advancement 271:26 689:7

advancing 6:23 12:14 23:15 246:1 274:16 695:5

advantage 126:11 185:9 456:11 496:11,16 520:15

520:22 668:5

advantages 188:7 454:11 491:4 497:25

adverse 17:5 21:19 27:24 28:1,17 31:5 50:26 58:19

167:15 171:2 176:4,12 178:25 187:14 205:9

206:26 208:15 210:2,5,8,12 222:4 229:3 323:14

323:16 493:22 509:7 511:14 569:10 586:7,19

587:12 594:10 657:13 668:14

adversely 25:2 253:17 299:6 461:13

adversity 214:8

advice 2:16,18 7:15 13:16,23 14:5 16:14 130:14

244:25 247:6 386:25 684:17

advisable 509:16 637:8

advisor 33:2 203:11

Advisory 2:2,10,15 7:13,14 16:24 18:4 33:6 203:9

Advocates 324:5

aerial 285:16,16,19,21 286:2,4,10 288:2,5,10 322:9

323:5 324:14

aerially 322:20

aeronautical 287:25

affairs 323:4

affect 151:2,9 187:13 189:17 217:11,24 277:18

286:25 299:6 339:11,13 351:21 362:25 379:16

438:24,26 457:4 461:13 470:9 479:15 486:20

545:4 558:2 559:21 560:10,10 586:22 634:15

666:10,25 669:20

affiliated 22:19

affiliations 249:11

Africanized 225:3

afternoon 11:26 46:4 61:9 62:13 66:17,21 70:2

136:9 140:6 154:9 216:26 220:26 328:5 513:15

621:21

afterward 645:26 693:4

age 93:9,11 95:25 101:15 103:18 225:17 227:6

307:14 384:15 465:12 468:13 483:18 655:3

aged 94:8 384:16

agencies 14:20 16:13 53:23 77:2 181:18 191:25

229:15 245:20 255:5 267:18,22 268:12 271:23

274:21 283:20 313:12 325:22 333:20 527:14

670:2

agency 2:9,16,20,20 4:15 7:15 11:13 14:14 15:2

20:25 24:5 28:21 31:21 50:2,25 60:2 63:13 68:21

139:26 151:12 197:13 200:17 202:6 203:26

212:21 213:14,16 220:11 237:17 238:18 240:2

243:13 247:18 259:4 262:17 274:11 275:10

291:10,22 292:17 294:6,22 302:1 325:4,16,19,20

325:25 326:1,1,12,14 332:15,21 337:2 341:2

345:6 353:17 365:17 372:6 377:17 382:6 388:8
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397:18 409:3 417:15 424:10,26 431:14 435:4

436:2,6 446:8 458:24 480:18 512:23 526:8

530:24 535:20 543:2 551:26 553:11 562:2 585:20

593:9 598:17 616:18 622:8,23 643:9 665:4

667:21,22 668:18 669:13 680:23 683:16 686:12

agency's 7:8 14:21 45:15 68:17 206:19,20 211:23

212:14 217:2 228:8 247:25 314:5

agenda 3:15 16:18 87:13

agent 417:22

agents 20:19 77:10 390:24 477:11

ages 94:6 96:1 101:16 468:15 481:9 532:11

age-based 505:17

age-class 653:18

age-related 225:19 481:6

aggregated 35:6

agitated 463:20

agitation 323:18

ago 10:23 17:4 85:24 199:16 279:17 290:9 303:4

400:8 449:23 470:19

agree 47:16 69:16 87:6 199:11 244:15 260:25

275:23 347:23 352:25 354:12 369:14 374:5

376:15 379:17 385:24 388:19,24 391:23 397:4

400:13,16,20 401:3 406:23 407:25 408:23 412:10

428:7 429:10,18 431:8,24 441:4 448:2 454:17

455:22 460:23 462:3,15,26 487:5 488:14 503:12

503:15 512:14 518:25 522:6,13 523:12 524:13,26

528:6,18 530:2 534:21 535:4,7 543:9 548:7

556:17 557:19 568:22 571:11 577:4 580:4,20

587:23 591:21 592:16 595:17,20 600:26 604:11

606:2 607:12 611:8,15 617:3 624:18 638:13

639:2,6 655:26 657:26 678:7 680:14 683:13

agreed 248:23 257:18 382:3 449:14 536:4 567:24

569:2 662:1 666:17

agreeing 2:13 16:8 17:21 641:8 695:12

agreement 271:25 512:18

agrees 275:17 659:5

agricultural 5:7,18 6:1 8:25 19:11 145:1 169:12

183:22 245:25 250:13,19 254:1 278:14 284:10,12

285:10 293:19,22 312:1,12 318:15,18 352:5

363:4 396:17 420:15 429:3 434:17 466:20 575:4

589:22 598:8,23 650:17

agriculture 19:16 82:25 250:6 253:4,8,15 283:11

311:24 578:14,14 598:7 686:23

Agriculture's 19:10

agrochemicals 565:2

agronomic 565:2

ahead 87:14,23 88:24 140:4 200:5 203:7 236:20

249:10 296:13 330:9 333:14 378:17,20 386:18

402:4 407:14,19 454:23 464:21 476:26 480:18

512:26 525:6 538:21 567:14 604:20 626:5 630:11

669:25

AI 92:4

aid 254:5

aim 520:11

aimed 21:18 22:22

aiming 500:6 506:17

air 240:26 324:19 387:10 420:13,17,17 673:14

airborne 299:22 300:24

airplanes 286:12 287:5,6,7,20 288:6

al 19:8 96:7,8,13 97:3,6,14 101:25 107:7 113:16

117:11 120:8,20 125:26,26 126:3 159:15 171:1

196:26 224:26 225:8,18,25 227:2 259:23 298:11

298:13,16 299:12 329:22 332:5 398:1 443:23

464:25 472:7 475:14 500:3 636:23 674:26 691:24

691:25

alarming 642:4

alarms 313:19 649:18

albeit 155:18 165:15

alfalfa 101:2,6 105:2 347:13,15 363:21 559:25,26

560:1,2 572:21,21 581:15 584:14

alfalfa-leaf 334:26 351:2 363:10,22

algebraic 679:8

algorithmic 674:14

algorithms 224:4 230:12 240:4

aligning 231:1

alignment 84:1 314:4

aligns 301:26

alike 31:18 592:10

alive 481:7 625:13

Alix 117:11

alleviate 615:23

allocated 252:14

allotted 252:14

allow 32:5 83:18 90:22 150:4 286:15 320:10 323:6

414:11 437:16 475:12 477:20 484:26 491:21

563:10 617:13 625:23

allowed 168:22 169:26 172:18 176:8,25 183:20

267:1 293:21

allowing 18:4 30:26 89:1 297:4 482:22 563:4

allows 15:12,23 120:22 168:18 173:7 180:6,7

185:17 193:8 301:20 480:5 519:5 563:6 626:1

642:12

alluded 626:12

all-inclusive 650:25 651:13

almond 583:1

almonds 17:15 297:19 583:1

alphabet 384:5

alphabetically 224:18

Alright 562:2

alter 27:22 465:22
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altered 558:26

alternate 372:11 426:16 436:15 437:13

alternative 77:9 120:26 125:24 135:16 177:3

185:20 187:6 377:25,25 455:7 456:15 474:15,20

475:11 484:4,11,11,23 490:7,24 575:22 613:15

615:15

alternatively 185:23 403:24

alternatives 129:17 418:10 476:7 491:4 531:6

amazing 16:9 468:14 644:13 689:26 693:13 694:7

amenable 24:11 516:5,11,14,17 531:26 539:24,25

amendable 656:15

America 18:16 24:20 77:2 123:15 187:18 190:11

235:13,18 236:18 252:12,19 253:9 255:2,8

American 18:10 19:2,16 253:4 254:3 273:15

294:15 598:8 660:12

Americas 336:6,7 339:4

America's 19:2

amount 78:22 79:15 93:7 94:20 97:8,11 102:13

103:17 108:5 113:19 146:11 150:4 192:25 244:6

263:6,7 266:8,9 275:13 318:5 332:3 360:4,23

361:17 387:25 418:16,17 423:20 430:14 438:5

491:15 496:4 507:8 519:9 520:10 523:18 528:12

536:6 576:7 577:22 589:10 597:7 599:18,25

628:25 636:20 640:8,8 642:7,10 682:18,19

687:15,17 689:26 691:11

amounts 80:3 94:3,21 108:6 117:2 132:5 307:4

384:19 466:11

ample 20:10 177:3

amplify 235:18

analogous 315:11 444:12 514:8

analogy 262:11 514:8 558:19

analyses 195:6 230:17 536:13 548:22 550:1 551:12

691:24

analysis 25:20 26:1,4,23 27:14,15 34:9 46:2 71:19

72:3 78:11,13,17 79:4 80:5,13 81:10 92:26 97:3

97:17 99:19 105:8 117:14 122:8 164:23 166:21

191:3,7,11 192:9 204:9,16,22 207:4,7 215:21

216:6,8,12,21,22,23 217:24 218:1,2,5,12,16

224:1 226:15,22,23 228:18,19 233:11,12 235:9

236:3,23 242:12 257:18 264:2 283:24 298:10

311:5 327:13 330:25 383:10 386:22 392:13

397:10,26 402:8 422:21 447:7 480:16 491:26

492:6,11 549:23 551:14,18 569:2,6,7 571:13

594:21 603:2 610:9 613:19 635:25 636:14,19,21

636:25 638:16 640:2 641:8,16,24 642:9,12,13

648:1 655:11,15 680:5

analytic 230:5,10,18 240:16 261:20 560:24 644:6

644:16,26 645:7,8 647:5 648:3 649:13 650:20

656:4 666:6,13 671:3,11,22 672:6,9,13 673:22,26

678:11 679:2,4,9,17 682:26 683:14 684:8,18

691:13

analytical 131:6 136:2 230:25 231:2 550:9,13

660:25 661:10 665:11,13,24 670:4 685:1

analytically 645:23

analyze 186:13 206:21 453:23 569:15 577:10

579:10 600:22,24 636:17

analyzed 109:18,25 186:3 332:6 531:19 625:8

648:23

analyzing 594:10 641:16

ancillary 653:10

and/or 21:2 25:1 73:5,10,13,18 74:2,5,16 75:12,13

75:15 76:13,15,20 78:7 79:13,17 80:2 165:4

174:13,22 180:21 191:16 213:12 224:4 423:18

428:10 504:9 515:17 523:11 548:3 680:21

anecdotal 430:21 431:2

anesthetizing 537:22

animal 198:19 264:23 339:3 642:19,26 668:4,8,18

announced 16:5

annual 360:7

anomalous 367:16

anomalously 367:16

answer 42:24 47:10 57:19,21 133:24 139:21 210:4

215:2 241:18,24 243:19,25 245:15 246:18 263:16

267:23 268:9,13,18 275:4 289:11 296:9,9 312:15

312:20 316:3 323:26 334:18 371:21 381:22

383:16,17 407:18 408:6 409:17 413:13 414:16,17

417:15,16 419:4 422:10 423:10,12,21 429:7,23

441:12 464:1 469:9 472:6 483:5 490:24,24

491:16 492:11 495:7,25 497:20 498:16 500:24

526:25,26 527:3,9 548:10 553:25 559:6 584:8

595:18 602:9 603:25 604:18 614:4,17 621:19

622:26 633:16,17 636:1 643:4,23 644:22 649:9

650:8 651:19 655:22 658:17,18 663:16 664:12,23

665:20,21 666:19 670:12 671:2 672:16 673:21

674:4,10 681:22 682:6

answered 133:24 504:18,21 657:1 673:21

answering 152:8 334:1 510:14 644:2 692:24

answers 12:12 51:9 87:25 196:3 199:6 237:12

409:3 432:2,4,5 535:18 623:1 639:9 678:17

ant 452:3 507:21

antenna 450:22

antibiotic 470:25

antibiotics 470:25,26

anticipate 174:15

anticipated 148:5 149:19 150:1

antimate 507:21

antimicrobial 449:17

antioxidant 475:13 504:9

ants 507:14,24

anybody 240:12 296:26 308:12 368:14 369:19
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389:22,26 436:4 483:1 498:8 507:3 545:23 546:3

555:22 556:20 603:26 609:9

anymore 305:10 310:18 520:11 534:16 597:25

644:23

anytime 32:20 511:4

anyway 367:22 375:4 425:8 437:8 480:13

anyways 685:25

apart 549:12,16 597:4

apiaries 396:17

apiary 236:18 302:11,12

apiary's 302:23

apicultural 280:7 443:20

apiculture 235:21 346:26 448:7

apiculturist 5:3 250:2

Apis 22:6 23:23 163:8 314:19 336:5 352:26 481:5

562:20 631:22 690:23

Apis-specific 498:5

apologies 445:16 573:6

apologize 325:14 381:20 414:2 456:26 566:14

apparent 25:12 178:20 267:24

apparently 85:26 243:21 303:18 395:1 399:11

407:17 411:17 421:19 653:24

appear 105:20 111:21 115:18 126:5 128:14 243:22

259:24 272:13 350:16 667:16

appearance 394:25

appeared 224:19

appears 20:15 51:26 66:8 82:18 85:11 100:6 103:4

104:22 112:11,13,19 119:21 123:20 193:15 202:4

260:3 283:8 289:26 295:26 394:22 396:9 418:19

620:19 642:6 665:9 667:10

appendices 327:25

appendix 71:19 78:14 79:5,26 85:10 89:7 92:26

99:9,11 121:13 294:2 383:6 422:21 423:16

424:11,25 430:10

applaud 669:3

apple 108:26 109:12,22 111:20 303:6 305:24

307:22

apples 136:24 310:26

applicability 374:6 647:1

applicable 38:1 60:4 66:10 125:10 138:4 369:10

370:14 510:9 645:21

applicant 307:13 488:7 662:11

application 33:25 34:1,1,17 36:24,26 40:5,22

41:10 43:8,9,11 50:16 55:19,26 56:4 57:4 60:13

60:13 62:15,15,16,18 63:12 64:5,26 65:6,19,21

72:9,17 75:21,26 76:2,25 77:21 80:10 82:13,14

82:17,18,24 83:1 85:19 86:17 91:14 106:15

107:25 108:21 109:9 110:5 116:3,4 117:20 118:5

118:21,26 123:9,12,14 126:20 127:15,17,26

128:7 137:14 141:22 142:12 145:2 146:14 147:8

148:7,8,9,10,16 149:11 156:5 158:23,23 169:12

169:13,19,21,26 170:6,24 178:8 181:8 182:25,26

183:13 189:1 210:15 216:4 223:20 226:16 227:11

228:23 229:18,22 267:2 273:8,12 285:16,19,21

286:1,2,4,5,8,11 288:3,5,10 299:12 327:9,14

330:13 331:9,13,14 367:13 372:10 374:21 397:9

403:17 405:24 409:8 413:20,20 415:18 423:1

431:10 444:7,11 458:21 486:3,3,15 521:17

525:16,18 529:5,11,11 534:8 576:4,21 585:9

602:14 628:22 629:7 634:25 670:22 676:22 688:4

applications 33:26 36:15 40:15,17 53:16 57:19,20

63:17,19 72:10,20,21 73:22 74:23,23,25 75:3

77:1,19 80:12 82:12 90:1,3,4 91:5,23 92:20

106:11,15,23 108:10,15,26 111:15 116:5,6

117:13 123:18,22,23 124:7 126:21,26 127:9,18

128:26 129:14 136:23 144:13 145:3 149:9,13

157:17,18 216:3,7,9 257:22 259:2 286:18 318:7

329:17 330:14 363:3 407:25 419:26 422:25

431:16 535:17 559:26

applicators 77:3

applied 6:7 36:25 39:19,24 40:5 42:18,24 43:1,2,14

43:19 44:2 53:14,15 57:13 60:23 70:18 80:9 85:4

86:16 117:3,17 118:13,22,24 119:9 121:16 122:9

123:6 125:4 126:13 128:1 141:16 142:3 157:15

169:23 181:9 183:16 184:12 188:20,22 206:10,13

210:14 250:25 259:6 268:3,4 272:19 315:16

331:1,20 351:12,19 362:25 366:24 368:4,6

395:12,14 401:5 405:23 410:7 440:17 551:24

565:12,12 568:5 609:21,23 628:25,25 640:22

648:22 685:24

applies 216:8 610:10 649:26

apply 33:21 69:18 84:19 213:20 227:15 241:3

269:6 272:17 286:13 323:6 330:7 338:21 461:13

519:10 565:13 566:21 567:6 640:15 645:24

654:24 655:17 665:13 666:24 668:11 680:20

applying 108:20 125:21 243:7 265:18,20 272:24

286:7,16 329:19 602:13

appraisal 294:10

appreciate 7:7 14:5 33:5 229:20 253:11 256:22

262:17 280:1 285:12 288:15 309:5 314:3 316:15

318:23 345:9 436:12 658:1 665:4,8 667:24 687:2

690:22 692:6,21 694:20,24,26 695:12,14,23

appreciated 130:17 256:19 435:8

appreciation 284:19 376:19 686:5,20

approach 9:25 12:2,13,21 17:17 51:4 65:17 66:3

81:20 88:12,18,19,21 90:24 92:14 94:10,14,25

97:12,14 106:23 108:18 111:10,26 113:17 117:16

118:12 119:14,20 120:7,19 123:24 124:6,8,12,14

125:8,10,12,16,18,24 128:11 129:2 138:7,11

144:9 155:20,22 156:16 161:25 194:21 205:2,4
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205:11 217:1,2,25 218:22 243:5 253:26 255:5,7

255:10,19 262:18 266:6 270:17,20 273:15,16

276:3 278:17 279:20,21,23 286:15 290:20 365:23

366:13,18 368:2 372:20,22 373:11,12 377:24,26

378:4 379:4 384:10 393:13 402:14 403:23 404:10

404:12,24 406:12 426:16 433:24 466:9 477:13

499:7 500:7 501:9 507:2 514:7 516:5,15,17

526:19 549:20,23 589:20 590:13 599:22 607:14

610:21,23 611:2,4,5 618:10 623:9 624:9,18

625:23 626:1 656:2 657:12 658:4,10 668:16

674:14

approached 107:11 154:17

approaches 8:14 10:20,25 11:7 22:7 23:17 65:22

107:16 113:3 126:8 152:7 205:6,7 235:3 257:19

380:18,18 397:17 403:25 404:1 405:20 406:10,17

406:20 502:1 617:13 656:8 668:10 691:23

approaching 651:12

appropriate 2:20,21 27:5 45:25 57:1 70:6,7 83:8

83:13 116:10 119:26 120:26 124:9 157:10,13

162:6 191:6,7,11 232:26 241:17 254:14 258:15

268:13,16 269:7 288:7 322:4 330:7 347:1,24

349:9 374:7 378:15 381:20,22 384:14,20 385:2

387:5,22 396:8 452:11,12 466:11 474:16 477:3

483:19 484:5,19 490:9 492:21 502:12 504:13

509:22 518:13,18 519:12,15,26 520:1,17,20

523:2 524:20 525:4 528:18 536:7 575:3 587:5

591:8 594:8,18 608:4,22 610:25 621:4 625:17

638:15 670:14 672:24 688:13

appropriately 30:10 246:17 446:15 464:25 647:3

673:21

approval 254:21

approve 295:4

approved 559:26

approving 291:6

approximate 615:16 647:10

approximately 19:12 98:11 119:3 183:24 184:9

217:7 285:21 286:5 292:20 317:14 336:6

apriority 458:15,18

APVMA 213:14

aquacultural 424:1

aquatic 4:6,18 199:19 249:17

aqueous 120:15,23 135:4

arbitrating 484:18

arches 306:10,20

archiving 313:8

area 4:1 5:9 6:3,6 22:15 108:25 170:2 172:22

182:18 187:23,25 188:16 259:2 271:16 274:12,19

279:2,3 282:24,26 287:13 293:3 310:25 315:6

322:11 324:9 342:22 349:6 371:17 374:24 375:5

375:14,19 376:6,14,19 377:26 378:3 387:16

410:3,17 416:18 419:12 424:22 460:13 513:11

552:8 564:9 568:20 571:22 573:22 578:4 589:22

604:14 656:22 687:23

areas 25:15 77:7 91:16 110:21 170:3 172:19 176:8

190:11,26 257:20 272:11 282:20,21,22 286:1,6

286:14 287:11,12 290:7 293:6 300:3 352:13

358:17 424:1 516:8 552:10,13 566:13 569:16

577:5 581:16 596:14,23 597:25 626:9 640:23

641:7

arenas 458:20 516:1

ares 283:26

argue 314:25 356:17 459:15 579:21 593:1,3

arguing 522:2

argument 369:25 385:17 495:17,23 625:10

arithmetic 374:18,20

army 558:20

arrangements 3:7,12 247:5 248:17

array 211:16 399:26 599:9

arrive 248:12 447:14 608:14

arrived 204:2 239:24

arrow 35:10 60:16 62:8 355:3,6,11,18 356:1,7

arrows 39:4,9,23 353:3

art 289:24

arthropod 110:6,23 111:5,21 113:4 129:5 194:18

373:7 374:10 378:10

arthropods 110:13,16,17,19,21,24 111:2 162:21

162:25 373:17 558:11

Arthur 378:6

article 324:15 375:1 623:11

articles 162:14 281:11,13,14

articulate 26:14 227:14 446:23 522:22

articulated 18:19 26:2 205:13 211:22 212:24 213:9

442:5 450:17 605:5

articulates 203:19 212:15 443:1,23

artifact 234:12

artificial 160:8 168:19 177:10,20 180:12 236:14

466:25 470:2 535:3 570:3

artificially 385:25

ascertain 162:1 172:15 189:23 343:7

Ash 259:15

Asian 259:14

asked 49:6 62:18 139:16 145:11 152:6 169:4 200:7

237:4 248:23 276:9 281:19 296:24 340:8 415:13

421:15 429:8 431:9 456:6 483:7 514:21 533:4

548:23 560:20 584:21,25 644:11 676:24

asking 31:21 48:16 57:16 70:17 134:17 135:9

197:3,26 240:15 241:8 335:15 418:4 427:11

430:22 437:9 473:12 491:16 494:17,19,20,26,26

535:4 543:16 553:25 600:15 613:2 618:21 665:9

665:19 666:23 670:8
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asks 208:25 209:2 679:23

asleep 384:5

aspect 10:17 47:20 177:9 282:19 284:3 362:21

400:14 430:4 443:8 475:4 563:11 614:12 650:26

677:9

aspects 11:4 27:15 33:9 46:6 49:20 52:20 180:2

239:16 403:4 420:13 448:17 459:17 506:4,11

554:23 555:4,5,6 556:9 594:24 606:4 631:17

632:18 656:17

assay 81:25 82:2 159:14 380:1,4 382:2 454:12

459:8 464:9 477:22 537:12 613:13 617:16 691:20

assays 179:4 343:5 379:22 380:4,6,8,26 381:1,5

452:11 454:12 462:6 477:23 481:14 504:5 512:10

533:13 613:14,15

assembling 57:15

asserted 253:22

assertion 291:15

assess 65:10 158:11 162:2 170:20 179:2 191:5

193:16 237:25 239:10 243:21 257:5 268:16

270:13 294:18 339:1 365:4 415:5 510:3 518:14

562:19 615:21 617:24 628:3 671:5 672:8 679:9

assessed 61:13 64:22 91:23 106:14 143:10 166:1

169:21 176:22 283:15 463:22 467:15 477:16

484:13 625:12

assesses 628:3

assessing 8:6 33:12,16 53:7,24 57:3 64:20 106:3

110:4 129:5,9 157:16 159:2 166:3 236:9 365:5,23

396:3 438:9 447:14 477:4,19 480:26 481:4,14

482:9 503:22 558:10 625:17

assessment 2:4 3:24 4:14 5:10,13 10:7,13,25 11:9

11:11,13,14,17 12:1,10,19 15:13,18,19 17:2,10

18:11,12,18,22 21:14 22:3,5,7,9,10,11,14,22

23:15,23 25:17,21,23 26:9,12,18,20 27:9,11,13

27:19 28:4,7,23 29:6,12,17,19,23 30:1,5,6,8,11

30:15,22,25 31:3,14,26 33:8,20,22 34:8,10,11,25

35:17,18,23 36:3,22 39:3,5,8 42:3,8 46:15 48:2

49:12,13 50:22 51:12,25 52:13 53:5,26 54:6,10

55:17,22 56:6,10,12,14 57:26 60:8,10 61:13

62:15 63:10 64:9,18,20,25 65:11,24 66:14 67:24

71:18 72:1,4 75:19 76:5 81:1 83:7,11,13 84:5,7

84:10,18 85:13 86:8 87:3 89:11,12 90:11,21 91:3

91:7,22 92:7,18,23 93:17,23,25 95:9,16 98:8

100:10 101:8 104:22 105:23 106:6,9 107:16

111:6 115:20 116:17 121:8 126:9,21 127:1,20

128:23 136:8 139:15 140:21 141:10 143:7 144:12

145:11,14 146:5,10 148:7 154:17 155:2,4,5,9,11

155:21 157:14 158:19 160:18 163:10,12,19,23,26

164:2,7,13,15,17,24 166:11 167:9,10 168:8,10

169:4 173:20 175:8,22 178:19,26 179:19 180:15

180:16 182:10 183:1 185:3 188:6 192:8,19 193:1

193:4,26 194:15 197:5 198:12,16 199:17 200:23

201:7 202:24,26 203:13,23,25 204:3,8,14,18,23

205:26 206:9 208:20,24 209:4,11 210:21 211:11

212:6,22 213:8,11,19,23 214:1,6,13,19,23,25

218:20 221:4,15 223:6,9,20,23 224:15 226:17

227:12,16,22 229:6,9,14,19,23 231:1 237:21

239:21 242:1,10,10 246:12,25 247:2 250:9 251:9

253:11,26 254:5,10,18,22 255:4,10 256:1,20

257:4,6 260:7 262:8 265:7,10,26 266:6 267:14,25

268:14 269:1,26 270:5,8,11,17 271:1,14 272:7,23

273:1 278:25 279:13,20,23 280:12 281:22 282:5

284:11,14,24 289:14,24 291:16 300:17 312:16

318:25 329:10 333:17,21 334:21,23 335:8,16,21

337:9,10,13 341:10,11,14 343:26 345:15 352:9

357:18 359:5 362:22 363:1 364:1 365:9 381:17

395:23 401:10 403:24 405:23 413:12 421:1,17

422:15 424:11 425:2 426:10 428:18 436:20,20,21

437:23 439:1,19 440:7,12,17 446:15 451:6 459:9

459:12 474:17 481:1 493:9 499:26 504:11 506:19

509:1,6 519:4 522:2 528:22 532:7 535:26 559:2

571:11 585:26 586:25 593:16,19,20 610:21

613:20 614:2,13 618:16 621:4 624:10 627:6,26

628:18 629:2,5,24 633:5 640:23 641:4 643:18

644:11 648:2 650:25 658:26 659:1,10,21 660:22

661:5 662:10,19 663:4 664:26 666:8 667:9

670:23 672:7 675:17,18 676:8 677:2 678:5,16,18

679:6,19 681:7 687:21 688:12 691:23

assessments 6:23 7:16,17,19 12:15,24 15:14 35:21

43:5 56:1,18 67:25 70:21 72:6 93:18 113:8

136:14 139:22 151:13 180:19 181:15 183:1

196:10,10 211:18 213:11,12 253:13 254:13 258:8

262:13 264:21 265:16 267:21 271:8,9,10,19

272:2,5,5 274:1 294:7 326:1 328:3,7 338:26

365:24 426:2 506:14 508:24 509:19 528:23

538:26 548:4 676:22

assessment-based 139:10

assessor 26:10 27:1,2 91:10,12,19 100:11,15

136:11 150:4 164:23 174:15 194:16 197:6 210:6

242:2 463:24 502:16 575:26 576:5 607:15,19

651:14 662:24 695:21

assessors 26:14 35:1 36:1 56:21 90:22 212:8

281:25

assigning 404:14 405:5 406:13

assimilating 350:1

assist 223:1 450:6

associate 243:17 336:17 408:19 491:16 492:3

associated 13:18 21:8 23:5 35:2 39:16 49:20 62:6

63:15 77:19 79:1 95:25 96:12 107:13 109:7 116:8

119:13 138:7 144:10 145:18 155:4,6 160:4

178:12 181:17 189:7 192:10 203:20 213:6 214:5
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283:26 292:25 293:1 319:18 330:18 348:10

415:26 419:9 436:18 494:22 496:5 503:25 508:24

536:1

Associates 322:3

association 252:20 285:11 316:12 450:25 451:2

assorted 347:5,24

assume 125:22 139:1 258:26 330:1,3 345:3 374:2

400:8 404:25 495:7 523:26 527:21 538:20 576:4

602:24 641:1

assumed 40:13 73:6 76:15 78:3 94:11,18 95:2

101:21 102:3 103:7 105:12 110:26 116:21 118:12

124:14 127:22 262:26 272:18 273:17 374:17

390:23 402:17 654:2

assumes 94:15 105:8,10,13 124:8 367:19 394:1

404:12 665:1

assuming 102:14 103:16 117:17 133:20 138:8

174:5 264:6 315:24 352:22 366:11 369:22 404:10

669:7 684:26

assumption 102:6 103:11 116:9 118:16 124:15

125:20 144:11 174:11 195:1 263:2,11 372:10,11

372:14 380:20 388:12,18 389:16 400:20 466:12

466:15 581:24 615:5,12

assumptions 26:21 82:20 94:9 98:3 105:24 107:13

116:7 129:19 136:13 165:8 202:11 203:19 204:5

212:15 216:2 224:2,4 282:3 305:22 399:10,12

425:8 684:19 685:2

assurance 313:7 414:14 599:17

assure 312:24 313:11

assured 600:14

asterisks 58:21

asymptomatic 637:15

Atkins 113:4 131:3 474:21

Atlantic 321:8 523:14

attack 317:16

attainable 500:12

attempt 90:25 136:12 172:14 197:7 216:16 233:11

404:12,14 493:7 565:13

attempted 481:9

attempting 198:22 622:26

attempts 189:23 291:26 564:9

attend 641:3

attending 452:19

attention 26:7,16 66:13 143:1 154:23 274:22

289:18 293:15 296:4 642:10

attorney 289:1 296:8

attracted 84:25

attraction 594:6

attractive 40:21 137:8,11 148:15 169:8 192:8

210:14 230:25 524:6 525:8,9,17 529:12 564:7

568:14 573:24 577:18 594:8 597:3,17

attractiveness 57:13 91:15 186:23 276:11 564:3

568:13 573:13 582:24

attributable 243:7

attribute 36:22 53:26 235:24 354:14,15,17 445:13

attributed 20:14,17 122:17

attributes 29:22 35:17 39:3 47:19 53:13,21 55:25

224:17,20 225:25 227:14 241:15,22 246:21

369:25 465:14

ATV 654:3

audience 242:11 669:13

audiences 242:8

auditing 313:8

audits 313:7

augment 531:16 549:15

Aupinel 159:15 381:12 457:2 464:25 465:13 472:7

490:20 491:2 607:7,8 691:25

auspices 16:3

Australian 213:14

author 224:19

authorities 22:7 69:8

authority 2:19 8:9 11:2 13:25 28:9,14 29:13 64:11

69:8 156:22 213:14,15 420:5 668:10

Authority's 23:21

authors 96:10 112:15 225:11 231:21 329:23 383:5

automate 450:8,11

automated 504:5

automatic 180:26

automatically 583:9

automation 515:11

autumn 682:21

availability 180:24 226:6 330:16 648:21 670:26

available 3:16,17,18 15:13 20:22 22:4 24:11 25:9

25:11 31:9,20 49:25 55:2,5 56:9,11,12 58:22

59:18,20 60:9 61:14 62:4 63:12 75:24 83:6,6

85:14 86:1 90:19 99:23 100:14,26 104:6,26 106:5

107:20,26 108:11 111:26 112:1,4 114:8,12,26

115:11 116:16 118:7 119:24 120:25 121:15 122:8

123:5 128:15,17 138:9 141:11 150:16 151:10

153:7 154:12 155:9 162:8,12,23 163:4 166:18

174:6,6 193:23 194:16 196:17 203:20 204:15

207:20 208:12 209:22 210:26 211:4,14,16 212:3

213:7,10 215:19,22 216:19,25 218:6 223:22

224:6,7,8 228:8 257:17 287:23 288:25 301:17

327:4 328:2 330:5 344:1 378:11 385:1 392:22

393:11 402:16 406:13 410:18 414:11 424:2 439:2

441:22 444:25 446:2 458:6 460:19 465:26 483:4

483:6 555:12 571:20 607:7 612:21 613:8 616:24

621:7 632:25 633:2 635:1 668:5 670:21 673:19

675:19 676:21 679:1 696:19

avenue 550:5
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average 19:25 20:5 96:15 110:13 260:16 317:11

328:13 344:9 506:1 538:1 573:20 634:18 664:16

averages 635:19

Aviation 285:11

avoid 355:14 360:20 486:4 541:16,19 545:22

588:24 589:24

avoidance 360:21

avoided 486:1 566:20,20 650:23

avoids 98:3

aware 17:12 46:21 262:23 292:18 306:18 307:26

316:6 325:25 327:5 407:5 410:19

awesome 653:2 683:3 691:10

awful 397:21

axis 450:4

A&M 5:5 250:4

a.i 107:2,25 108:19,19,21 109:19,20 110:8,9

114:15 115:21,22,26,26 117:8,10,19,24 118:4,8

118:11,19 119:5,10,23 123:2,4,7,13,19,20 126:5

126:20,24,24 127:12,13,24 128:3 129:12 273:3

365:25,25 373:6,7 377:23,23 394:18 402:9,17

403:18 413:16,21

a.m 87:16 248:12 435:21

B

b 91:19,25 92:6 218:13 336:14,15 357:26 358:3,5

366:16 373:3,5 384:7 397:9 409:6 422:9 446:12

471:24 473:2 515:24 526:13 531:2 567:17 585:21

607:26 669:9,25 670:18,20

babies 671:18

back 48:24 60:14 61:18 67:18 73:3,10 74:13 75:9

78:7 85:23 87:15 88:2,5,9 94:19 132:8 143:2

154:4 181:11 189:2 201:4 208:1 234:10 241:26

242:22 245:17 248:3 252:8 262:22 263:3 265:2

270:15 272:22 279:19 283:10 296:13 303:12

304:18 305:1,7,8,12 306:3,12 307:11 309:23

310:2 311:8 313:13 319:10 320:19 326:1 332:1,3

332:23 337:19 345:5 355:24 356:25 360:11 374:1

375:10 377:20 378:18 380:8 391:10 399:6 400:6

405:18 411:1 419:24 421:16 427:11,26 456:5

463:5 464:17 466:21 470:4 501:8 505:23 512:20

544:2 553:24 556:3,22 559:6,11,16,24 560:4

562:3 566:26 571:26 572:1 576:11 580:16,19

581:8 595:6,24 599:11 600:5,8 603:18 604:12

612:12,17 616:18 619:16 621:26 626:4 639:3

641:7 652:10,12,19 654:9,17 656:9 661:21 664:9

667:4,6 678:11 686:1 690:1

backed 424:8

background 3:15 6:25 310:13 612:18

backing 612:17

backup 313:19

backward 651:24

backwards 511:8 649:16 650:2

back-of-the-envelope 259:4 264:22 273:8

back-to-back 33:10

bacterial 471:1

bad 130:12 241:21 306:19 322:15 360:3 390:1

561:23 614:5 628:12 651:1

bag 427:15

bags 304:8 310:2

Bailey 696:16,16

bailiwick 312:10 420:6

bait 265:18

bake 245:11

balance 49:16 567:3 568:19 653:23,24 654:1

balanced 548:18 568:17 593:6

balancing 32:18 284:16 568:22 583:4

ballpark 599:10

bang 571:5

bank 462:24 463:8 464:5,5

bantering 580:19

bar 290:23 506:24

bare 40:6,17 43:20

barely 508:18

Baris 60:3 140:5,6,7 206:1 209:18 214:12 215:8,10

215:11 696:4

barley 120:9 121:25 122:19 125:2

barrels 308:11

Barry 4:23 249:22

basal 442:19 514:5

base 56:6 59:20 113:18 168:16 195:9 281:16

617:15 633:22 665:12

based 24:10 51:13 55:23 63:14 64:14,21 70:19

72:11,16 74:11 75:10,24 78:14 79:4 80:5,13

82:23 88:13 94:2 95:5,16 96:6,7,11 97:24 101:24

102:1,5,10 105:9,16,18 106:11 107:3,5 110:17

111:1,7 112:11,25 113:14 117:7,10,19 120:7,21

122:5,22 123:19 124:16,26 125:6,16 126:10,19

126:23 129:2 130:8,21 133:10 137:10 142:12

144:17 146:14 149:5 150:24 152:13,25 156:2

159:14 162:15 163:7 165:3,4 166:14,24,25

167:23 168:7 170:26 172:13 178:1 179:22 180:8

180:26 182:10 183:4,7 184:5 187:24 194:20

198:14 205:2,3,17,25 206:14 207:4 208:2,8,10,23

209:11 210:16 213:21 215:22 216:25 217:14

226:19 237:11 238:23 256:25 258:15 262:20

265:24 272:5 277:17 282:13 300:21 327:14

328:16,17,22,24,26 329:17 349:23,25 361:11

365:26 373:20,26 382:26 383:3,22 392:26 397:26

398:20 402:18 431:19 447:11 460:1 466:2,7,14

489:6 491:19 494:24 500:11 508:24 553:5 554:3

584:13 612:24 614:2 624:9 660:11 668:15 682:1
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baseline 130:8 295:1 468:21 608:14

bases 65:15,18 286:6

basic 126:12 143:25 168:11 171:3 182:17 197:18

294:13 320:11 359:11 534:2 633:7 643:23

basically 46:18 88:22 95:26 137:18 232:13 257:15

259:5,20 263:6 266:15 272:18,22 275:8 285:15

285:19 286:16 287:24 289:9 295:20 324:14

330:20 333:5 342:4 409:20 413:5 416:8 421:16

424:16 428:21 479:25 483:10 500:18 503:14

518:23 525:22 530:3,16,18 546:25 557:23 570:6

575:5,22 576:1,9 581:21,23 582:1 585:2 592:12

604:9 609:8 628:4,6 629:19 632:11,21,24 633:12

634:23 638:8 651:19 654:5 659:15 662:16 663:6

677:14 682:2,15,17,20

basil 625:1

basing 136:24 144:8 384:8 407:23

basis 42:2 47:3,4 59:12 69:19,21 70:8 77:20 83:19

92:4,6 93:1 100:21 103:22 121:25 132:23 133:3,7

145:25 157:23 188:10 196:22 231:15 236:15

240:10 265:18 282:25 324:12 369:2,9 383:11

423:19 425:13 426:1 438:6 442:25 463:25 616:22

batch 469:20 684:1

baths 430:25

batteries 505:21

battery 54:24 156:23 163:20 621:9

Bay 321:10

Bayer 253:1 254:25

Bayesian 640:4 641:5,6,15,24

beans 590:4,6

bear 10:26 12:18 14:22 303:17 417:18 650:16

bearing 601:18 669:18

bears 650:17

beat 69:25 322:12 395:21 473:17 498:19 559:15

beautiful 662:8

beauty 11:14

Becher 225:8 227:2

becoming 239:4 350:4

bee 5:14,19,23,25,25 6:4 9:12 10:9 14:1 16:12

17:14 19:3,12,24 20:2,16 21:2 22:16,18,22 23:17

24:16,24,26 25:2 31:6,13,19 32:2 38:11,23 39:3

45:3 51:7 58:25 70:16 71:2 72:12,13,17,18 73:9

73:21 74:1,21 75:12 78:6 85:12,14 92:5,25 93:1,3

93:9 94:13,15,26 95:1 97:4,6 98:25 101:2,2,4,6

101:14 105:1,2,17 106:2 108:19 109:20 110:8,14

115:26 117:24 126:24 127:13,13 128:3 129:6

132:23 133:3,7,8 137:18 138:15 144:7 145:24,24

147:19,26 149:20 156:12 157:24 159:18 163:2,2

163:22 165:26 169:23 170:21 171:3,5,13 172:5

175:2 184:6 186:6 187:19,23 188:10,10 189:18

189:26 191:16 195:7 197:18,24 198:3,23 199:4

199:25 207:5 217:1 220:18 221:22 223:22 225:1

225:2,4,26 226:9 227:18 229:17 234:20 235:9

236:22 237:1 250:1,10,13,22 252:22 253:6,20

257:12 262:20 264:25 272:19 273:13 274:16

275:7 278:20 283:16 284:23,23,23,25,25 285:4

286:14 288:21 290:11 292:11,24,26 293:2,5,9

294:15,26 295:6,12 297:12,14,16,17,24 298:14

298:18,19,20,22,25 300:16 301:8 302:24 303:16

303:25 304:1,13,19 305:9 306:10,20 308:3,24

310:8 312:3 314:23,26 318:14,20 326:10,18

328:13,21,23 335:26 337:19,20,26 338:16 339:2

340:4,13 341:7 343:4,5,6,8,14,15 346:2,23

351:14,15 355:7 356:22 357:19 358:9 359:8,21

361:8,19,20 362:3,9,26 363:6,7,8,10,19,22,23,24

363:25 364:14,24 365:25,26 366:9 369:2,13,15

369:23,26 370:7,9 373:6 374:24 375:11,22

376:16,16 377:3,23 378:23 385:4,16,17,25

388:15,22,23 389:25 391:7 392:1 413:25 416:16

420:25 429:4 430:22,22 441:22 442:19,23,25

443:21,24,25 445:14 446:2 448:12 450:21 458:1

460:12 464:7,14,26 465:19 466:13 469:7,9 472:5

481:4,11 484:14 487:23 489:4,24 498:3 499:21

501:24 507:9 508:10 514:4 516:16 517:25 518:24

528:7,8,12,23 529:14 533:17 535:18,25 537:6,10

539:10,22 541:13 553:6 554:19 556:10 557:13

558:2 560:3 568:22 570:1,8,9 571:20 572:8,10,14

572:22 580:25,26 581:4,19,20,22,24 583:8

585:18 586:3,17,18 587:13 592:8 593:3,5 595:9

596:9 603:8 615:20 625:17 631:11 635:4 636:17

636:25 638:18,25 640:15 641:9 642:18 648:3

650:3,3 660:17,18 661:14 665:17 670:21,25

675:22,25 676:21 677:10,18 683:7 685:16,24

686:21 687:25 689:17,18 691:13,22 692:3

beekeeper 237:12 289:8 295:7 302:20 511:16

545:21 557:14 559:11 570:6 592:5

beekeepers 20:6 23:3 234:11,23 238:7 239:18

254:2 289:20 292:10,16,23 297:13 310:5 311:13

311:15,18 318:22 323:1 325:25 326:7 412:1

419:20 470:26 471:6 533:17,19 560:2,2

beekeeping 238:25 239:1 302:17 545:18 592:11

beekill@epa.gov 326:15,16

BeePoP 225:14 226:20,23

bees 5:14 8:7 9:14 12:4 13:18,21 16:15,25 18:17,21

18:21,23,24 19:9 20:9,10 21:3,19,22 22:18,24

23:23 24:3,20,22 25:8,10 29:7,13 31:5,7,15,15,16

31:18,18 32:3 33:8,12,16,22 34:16 36:7,12 37:2

37:13,19 38:4,11,13,19 39:11,14 40:8,11,13 41:3

41:8,11,25 42:3 43:20 44:20,24 46:11 47:14,15

48:15 49:24 50:3,4,4 52:9 53:7 54:1,2,25 55:4,9

55:13 56:2,26 57:3 58:1,5,12,16,21 61:12 62:23
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63:6 64:9 65:24 66:2 70:5,12,14,18,19,20,23 71:1

71:1,2,13 72:12,14,16,19,22,23,26 73:3,17,22,22

73:23,24,25 74:5,6,8,11,13,14,17,22 75:1,8,8,10

75:11,16,25 76:10,16,26 77:17,25 78:2,12,16,17

78:22 79:9,13,14,16,24 80:1,7,15,18 81:5,13 82:5

82:7 84:25 86:14 89:3,17,19,23,24 90:7,8,9,9,13

90:15,23 91:14,15 92:1 93:2,5,7,8,12,13,14 94:3

94:6,7,17,21 95:4,8,20,23 96:2,4,5,11,12,16,18

96:20,21,23,26 97:15,23,25 98:2,6,7,9,16,21,21

98:22,24 99:1,3,4,4,8,25 100:4,8,17,19,22,24,26

101:1,6,9,13 102:19,20 104:8,22,23,25 105:1,4,6

105:10,16,19,20,26 106:3,6,7,10,12,19 107:20

108:16,24,25 109:4,14,18,22,25 110:5,22 111:6,7

111:10,15,17 112:2,9,17,22,23,26 113:1 115:23

117:22,24 118:15 126:22,25 127:19 129:6 131:20

131:23 132:13,21 133:15 137:2 140:12 141:1,6

141:17 142:15 143:9,13,18,20 144:2,22,24

145:17,21,25 146:1 147:3,13,14 148:1,15,20,21

149:12,26 151:3,10 153:24 154:13 156:10,14,15

157:1,7,9,22 158:1,10,12,21,26 159:2,10,15

160:10,14,15,19,20,23,26 161:6,8,8,11,15,16

162:9,11,12,21 165:20,21,22,25 166:13 167:2,5,7

167:17 168:14,18,22 169:15,22,24 170:7 171:12

172:5,18,19 176:8,12,25 177:3,11 179:4 181:9,12

182:21 183:11,24 184:13 185:17,21 186:7,15,22

187:5,7,8,14,15 188:4,12,13,16,22 189:2,10,10

189:13,15 191:22 192:9 193:8,16 200:14 206:17

207:4 208:12,20 209:20 210:3,11,17 213:20,21

213:22,25 215:17,20,22 216:7,20 217:12,15,18

218:1,6 219:8 221:5,15 222:1,17 223:16,24

224:14 225:3,20,26 227:3,8 229:2 232:6,19 238:3

238:9 239:2 240:26 242:25 243:2 250:10,11

253:24 257:8,11 263:7,11 265:19 266:21 267:1

267:10 268:1 269:16 270:1,10,21,22,24,25 271:3

271:4 275:12 279:24 284:8,9 285:1,7 289:25,25

291:24 293:24,26 294:10 298:1,6,7,11,26 299:3,5

299:16,25,26 300:6,9,11,26 301:1,3,7,11,16,22

302:12,15,18,24 303:6,9,10,18,25 304:4,5,6,17

304:22,24 305:17,20,23 306:17 307:6,8,14

308:20,21 309:3,13 310:18 312:23 315:3,3 323:1

323:23 324:21 328:18,25 332:12 333:18 334:7,11

334:11,12,13,13,13,14,14,22,24,25,25,26,26,26

335:1,4 336:5,7 337:23,24,25 338:1,1,12,12,13

338:22,22 339:21,24,25 340:7,11,12,12 342:1,4,5

342:6 343:17 344:10 347:2,2,9,11,12,13,15,17,19

347:20,22,24,25 350:14,22,22,25 351:2,3,3,5,5,8

351:9,20,22 352:1,8,12 353:1,3,5,9,11 355:11

356:2,7 358:8,10,13,16,22,26 359:2,4,7,9,15,17

359:18,19,22,23 360:1,5,6,10,10,11,12,19,22,24

361:5,5,7,8,16,17,21,21,23,24 362:3,4,8,8,9,10

362:10,13,15,19,25 363:2,13,14,15,16,21 364:3,3

364:9,13,25 365:24 366:8,21 367:12,15,20

368:19,20,20 369:4,8 370:15 371:1,2 373:8,13,18

373:18 375:3,13,19,20,23 376:4,25 377:1,2,3

378:13 383:12 384:8,9,11,15,16,17,17,18,22,26

385:3,14,14,15,15 386:5 389:2 390:6 391:4

395:15 396:20 403:22 407:26 409:22 411:2

422:22 423:15,18,24 425:12,25 426:22 427:11,22

427:25 428:26 430:13,23,24,25 431:1,9 432:24

433:6,26 434:3,9,9,10,10,16,16 438:5 439:4

442:9,9,21,22,26 443:4,9,19 444:19,21 445:1,1,4

445:4,5,13,20,24 446:6 447:5 449:26 451:17,21

452:19 454:6,10,13 461:15,18 465:21 466:21

467:5 468:7,11 471:26 475:3,9 477:5 480:26

481:9 482:21 483:1,11,13,18,20 484:6,26,26

485:11 487:22 489:18,19,22,25 494:8 496:21

498:3,4,11,25 499:20 500:5,10 503:23 504:12

505:17,20,22,23,23 506:13 507:9,15 508:3 510:8

510:10 513:9,19,21 514:17 516:6,7,10,12,21

518:14,17 519:6,6 520:2,5,7 521:14,18 525:3,8

525:10,17 527:21,22,22 528:1 529:6,12 530:11

534:3,9 535:5,15 537:11,21 538:10 539:5 540:9

540:14 541:8,15 542:13,24 543:8,9,11 544:10,11

544:11,13 545:7 548:13 549:14 550:23 557:11,12

558:10,15 559:25 560:3,7 563:4 564:6 569:11,12

570:17 571:17,18,18,22 572:7,12,18,20,23 573:3

575:12 576:3,4,7,23 577:23 578:15,20 579:1

580:17 581:2 582:10 583:25 584:9,10,14,15,16

584:16,17 585:2,7 590:7,17 592:25 595:22,25

596:2 597:17 598:26 601:23,23,26 602:5 603:4

603:13 604:2,23 605:14,19,20 607:10,13 611:15

614:20 615:6 616:6 618:15 624:11,25 625:1

633:26 634:7,16,17 636:24 637:4,5,14,17 642:19

643:1,19 646:3 650:14,15,16 652:26 655:18,19

665:14,15,22 667:9 675:2,3 677:19,20,20,21

679:21 682:18,19,21 684:1,4 685:17,20,21,21,25

687:1 689:16 690:7,11,12 692:5,5,13,14

beeswax 308:6,7

beetle 110:20 259:14

beetles 110:20

Beevers 311:23,25,26 314:17,21 315:2,14 316:3,9

316:10

bee-attractive 575:20

bee-bread 76:17

bee-collected 396:19

began 637:10

beginning 61:19 121:7 245:21 408:6 456:1 486:2

540:26 587:10 589:15

begins 35:14 40:8 54:5 57:15 62:16 64:20

behalf 316:16 436:6
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behaves 673:7

behavior 6:4 177:11,16 230:15 250:23 298:2

359:24 449:24 450:2 452:19 453:9 454:21 460:17

462:11,23 463:20 464:10,11 500:8 517:25 528:18

536:23 538:9,9 539:24 541:11,13 545:4 549:13

557:25 629:21 633:2 673:10

behavioral 61:7 134:10 167:26 446:4 450:3,4,16

451:17,20 454:4 463:11,14,22 495:21 496:8

504:4,5 506:11,15,18 606:18 620:23 632:15,16

behaviors 462:24 463:8,9,12 464:5,12 505:24

510:8 511:9 531:11,13,18,19 558:25 650:16

behooves 452:26

believe 15:11 17:16 32:9 35:1 37:12 45:10 49:5,7

67:6,9 87:24 152:17 202:24 227:14 252:12

253:12,25 296:24 311:10 325:3 340:3 349:13

367:26 370:13 374:20 378:14 383:5 396:18

409:23 414:12 423:8,25 424:15 426:21 436:3

437:4 447:24 453:1,17 483:3 494:20 521:23

539:20 540:8 548:22 582:1 621:16 622:8 624:26

656:9 668:19

believed 31:14

believes 31:22 254:17 409:6 422:25 503:25

belong 437:22

Beltsville 5:20 250:14 476:18

Ben 302:18

benchmark 239:19 242:19

benchtop 418:14

beneath 318:2

beneficial 540:4 558:12,13

benefit 254:2 255:6 268:21 319:7 421:1 491:3

613:23,25 617:12 657:23 694:2

benefits 28:3,19 32:18 49:23 245:25 266:22 284:16

284:18 316:21 317:22 320:6 494:21 614:1 648:1

benign 323:18

bent 361:26

Berenbaum 6:9,9 66:19,20 130:19,20 194:24,25

202:4 219:13,14 235:15,16 236:17 245:5,6 251:1

251:1 280:5,6 284:6,7,21,22 311:3,4 319:14

320:12 325:11,12 336:17,18 339:8,9 343:2,3

344:8,12,13 350:10,11 352:17 356:5,6 364:5,6

369:21,22 376:26 383:20,21 387:23,24 389:6,7

389:18 390:15,16 391:17,17 420:3,4 430:19,20

445:8,9,10 451:7,8,25 460:3,4 465:8,9 468:2,8,24

468:24 472:1,2,15 473:1,2,13,14 474:18,19

476:10,13 477:6,7 478:19,25,25 481:2,3 482:11

482:12 484:7,8 487:11,12 488:14 491:1,2,13,14

494:11,12 496:10,11 498:14,17 499:23,24 501:4

504:1,2 509:9,10 510:16,16 517:20,21 541:6,7

554:7,8 558:7,8 596:10,11,22 600:9 603:18

606:12,22,23 608:25,26 612:19,20 614:22,23

615:24,25 617:11,12 624:2,3 625:6 626:26

631:19 642:4 660:8 669:17 691:15

Berenbaum's 467:25 475:21 555:2 592:25 663:15

BERNEBAUM 131:2

best 9:16 14:22 15:12 22:4 66:16,20 77:11 83:23

136:17 196:1 197:8 237:8,8 238:20 254:15

257:17 274:17 295:3,9 331:24,25 342:11 393:12

403:26 404:26 405:2,5 418:25 471:15 516:26

522:3 533:23 552:8 562:19 567:3,5 593:15

594:20 602:4 626:8 643:5 650:24 668:11 692:13

692:20

beta 263:25

better 10:4,7 15:24 28:24 31:20 47:5 48:5 49:26

51:16 84:10 85:1,2 151:19 188:12 196:19 220:15

236:19 237:13 246:21 265:11 374:2 377:25

378:11 384:24 392:23 403:23 404:5,24 457:26

464:14 489:26 490:3 491:17 492:11 495:1 496:22

497:18 499:26 514:24 517:3,6 524:24 545:13

550:18,19 566:7,9,12 573:24 581:11 584:3

599:13,19 604:7 617:5,6,21,22,24 618:17 619:1,4

620:24 621:24 642:15 651:12 674:7 683:8 685:24

690:21 695:1,2

beyond 256:26 283:2 289:18 296:20 297:5,5,11

302:3 331:9 336:3 540:7,8 545:11 550:4 592:19

690:23

bias 216:18 261:2 263:9 363:9 419:15

Biesmeijer 19:7

big 10:11 245:19 306:9 307:10 308:10 319:23

320:4 321:4 352:11 362:7 421:10 427:24 520:14

520:22 528:1 570:11 596:8 601:1,2 650:18

657:23 682:22 695:4

bigger 131:26 132:1 578:4 671:18 689:18

bilateral 247:5

bill 280:24 303:3 322:13

billion 258:12 298:12 332:5,7 424:3,3

binary 447:3

bioaccumulation 199:20,21

bioaccumulative 199:13,18

bioassay 363:1 450:16 453:24 466:26 476:24 535:5

bioassays 81:12 133:12 134:11 341:25 363:12,15

380:16,21 442:13 443:15,20 444:4,17 445:6

446:24 447:1,9,21 449:11 450:3,19 452:18,20

453:19 456:10,12,15,16 458:19 459:23 460:26

461:6 467:17,20 468:6 474:24 476:11,17 489:13

489:24 495:25 513:18 514:1 605:20 606:15

615:22 617:5

bioavailability 465:24 472:2,5

bioavailable 471:25

biodiversity 29:14 32:1 39:6 335:15,26 338:17

339:21,25 340:18,24 343:14,14,15 364:14 596:9
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biofuel 350:14

biologic 445:13

biological 22:20 39:10 51:2,16 54:11 152:18 178:2

178:3,17 190:3 191:6 221:24 227:26 229:4,10

238:8 268:25 269:8 270:6,7 442:12 443:2 444:20

456:17 459:16 477:13 484:9 509:21 510:2 547:25

548:8 549:12,17 550:20 551:1 552:4,15,18,18,23

553:2,22 554:10,13,25,26 555:3,16 556:3 585:24

586:9,17 605:23 606:9,19 608:21 617:17 620:11

624:17,24 625:21,23,26 627:15 638:10,26 639:19

667:26 668:6 669:4

biologically 152:21 443:6 445:6 467:8 477:16

479:1,6 486:10 493:26 514:5 548:2 549:5 550:2

550:12 552:11 553:14,18 554:19 555:5,6,7,15

557:16 586:6 589:2 615:22 617:4 647:20

biologies 31:12

biologist 4:22 33:2 71:9 89:4 203:10 250:1 430:22

695:21

biologists 239:18 554:20

biology 359:9 375:2 443:21 461:4 487:23 498:3

553:6 638:25 641:7 689:17

biomagnification 453:4

biomarker 475:15

biomarkers 475:14 504:11,15 508:16

biomass 259:6

biostatistician 551:19

bio-activated 389:25

bird 430:25 507:22 557:9 642:26

birds 182:13 264:21 271:2 318:3 322:15,17 336:3

508:5 557:11 558:11

Bireley 14:14 16:18,19,19,20 17:26 696:7

births 671:21,22

bit 6:24 68:1 198:25 202:18 215:4 221:2 233:19

237:19 240:22 241:8 242:23 246:4 258:10 266:18

267:1 277:16 278:5 281:1 288:9 289:7 296:4,5

312:4 321:6 328:5 329:14 333:1 349:25 350:19

350:23 356:14 361:26 364:8 367:3 368:17 371:19

372:19 376:24 389:4 391:7 400:14 411:1 414:22

416:3 421:14 427:21 429:13 430:26 435:10

452:22 454:8 456:7,10,14,26 457:5 460:20 461:9

463:8 470:19 483:16 485:20 493:16 510:24

517:10 519:10 520:15 523:20 528:13 529:7

532:26 537:9 539:13 542:5 563:12 573:26 575:10

576:26 580:7 583:4 585:17 591:17,19 598:3

616:1 617:5 618:8 621:21 628:8 636:12 638:25

639:4 642:4 644:5 645:26 660:20 664:19 679:26

687:5 689:8 695:1,1

black 60:15 62:7 317:15 320:13 640:14

blah 648:24

blank 355:10

blend 684:17 685:4

blended 368:7

blending 684:24

blight 439:3

blip 233:22 236:24

blood 352:8

bloom 137:10 149:3 169:17 185:19 186:22 190:16

191:18 265:20 276:9 352:8 571:23,25

blooming 530:20 568:5 602:26

blossoms 186:10,12

blowing 420:9,12

blue 37:9 76:11,16 114:4 164:25 361:20 363:22

blur 489:12 513:19

blurred 527:5 548:12

blurring 531:23 532:19 605:18

BMPs 77:9

board 201:1

Bob 304:1

bodies 312:17 318:4

body 96:8 110:13 133:20 172:10 262:1 340:11

369:1,16,20 371:6,7 411:4 428:1 442:20

boil 505:26

boiling 640:1

bold 36:18 39:5 225:8 354:8

bolstering 549:26

Bombus 562:21 570:13,17

bookkeeping 687:5

border 279:7 293:9

bordering 611:16

borer 259:15

botched 663:17,20,21

bother 281:6

bottom 35:13 41:23 44:18 56:3,4 139:20 224:18

691:6

bound 90:14 98:13,14,17,18 109:7 110:6,12,15

113:6,12,18,20 114:21 115:7,9,16,21 116:10,14

117:18,23 121:9 124:7 127:10 129:11 135:8

144:16 150:5 190:15 399:17 400:4,10 573:16

boundaries 393:7 527:5

bounding 420:19 426:5

bounds 552:9 553:26 554:1,4

box 26:7 35:10 58:6,9,14,16 59:1,16 60:8 61:16

62:3 63:21 141:9 143:3,4,6 164:18,22,25 165:13

165:13 201:1 218:13,19 342:14,17 349:2,24

356:23 412:22,22 534:4,4 640:14 685:23

boxes 36:26 37:11 39:4,5 62:24,26 91:8,19,21,25

92:6 208:22 303:11 304:2 354:4 356:23

Boy 243:20

Bradbury 6:17,18 67:26 68:2 69:23,25 153:3

241:6 245:14,16 417:17 419:13,23 420:7,22
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621:17 683:20 694:16,18 695:25

Brady 13:4,7,10,11,12 252:4 695:26

brain 418:9 455:6 495:19 508:18 621:21 658:16

659:7 684:2

branch 313:9 696:1

bread 74:1 75:12 93:3 94:13,16 145:24,24 186:6

245:11 388:15,23 389:25 392:1 465:15,19 537:6

537:10

breadth 16:8 216:11

break 44:9 48:21 87:14 88:1 154:4 215:5,7 248:2

296:13 332:23 373:3 378:17 407:15 423:21

427:10 435:6 437:15 480:18 506:24 512:20 562:3

669:8 670:10

breakdowns 326:5

breaking 695:9

breaks 669:12

breath 333:13

breed 518:17

breeds 445:14

brew 220:25

brews 220:14 678:1

bridge 514:2 607:22

brief 18:15,17 33:19 48:25 89:10 90:10 154:6

221:17 224:10 249:10 296:16 316:15,20 480:20

627:5 669:10

briefly 71:24 214:14 221:13 236:21 239:16 244:17

251:11 287:8 408:23 513:16 536:4 550:17 650:6

Briggs 107:7 120:3,8,13,18,19,24 121:5,26 122:1

124:6,12 125:2,5,26 126:3,11,14 128:6 129:12

131:1 135:16,18 206:15 216:5 257:22 258:18

327:13 397:11 398:10 399:14 401:3,25 403:19

404:8,14,18,25 406:11

bright 52:5 67:14,22,23

bring 14:21 136:5 143:1 189:11 232:17 235:3

243:3 244:10 251:16 296:4 347:14 349:4 388:21

421:15 427:26 442:17 454:3,24 459:24 470:6

667:4 670:10 685:5

bringing 220:24 232:21 332:12 439:4 573:4 612:13

brings 305:26 387:15 459:17 572:6 576:11 582:7

broad 50:7,9 152:3 195:9,18 312:20 335:18,21

362:13 631:9

broadcast 42:18

broadcasts 44:2

broader 11:16 50:19 373:7 399:26 609:2 689:16

broadleaf 113:12,23 114:11,17 115:1,7,12,14

broadly 279:21 301:14 541:8

broad-sweeping 615:19

broccoli 297:19

broke 405:4

broken 382:18 408:5

brood 20:10 38:17,23,24,24 44:19,22 45:3 57:17

58:26 61:6,6 62:19,21 74:1,16 75:13 76:17 77:24

77:25 91:11 93:6,18 95:5 101:17 102:26 104:12

105:14 138:18,19 143:16 159:11 160:2 169:16

170:13,15,26 171:3,10,11,12,18,21 173:4 175:3

179:8 186:9 188:16 192:3,9 222:5 225:10,23

226:11 299:5 303:11 304:3 341:8 342:3,4,12,15

342:18,21,22 343:16,17,26 354:7 355:7,8 356:9

356:22 365:4,5,8 378:25 379:20,26 380:5 388:15

389:5 391:24 426:26 427:24 430:14 433:16

434:21,22 465:4,6,15 476:3 481:11 484:14,23,24

484:26 489:21,26 490:2,5,11,18 498:24 514:18

518:16 519:3 528:21,23,26 532:11 533:5 535:25

536:6 540:5,15,18 545:5 552:12,12,13,24 556:8,8

556:14,17,17 557:17,20,22 569:15,16,18 572:2,4

586:3 633:5,6,7,9,9

broodless 481:11 484:14

brought 10:23,25 38:15 73:2 74:13 75:9 189:2

269:22 329:20 340:10 384:23 417:10 449:10

489:3 496:25 581:9 593:24 601:4 630:18 689:7

brown 78:16

Bt 636:24

buck 571:5

budgetary 303:16

buffering 563:15

build 12:17 52:22

building 297:8 433:8 510:9 634:4 671:14 695:19

builds 209:14 457:26 519:3

built 8:16 182:1 231:20 232:11

bulk 253:1 398:3,24

bullet 56:15 64:19,24 162:10,20 163:1 164:5

170:22 172:18,24 173:6,9,23 177:9,13,18,21

182:8,11 183:10,17,21,22 184:3 188:17 189:5,18

189:21 190:1 227:16,24 228:2 288:12

bulletproof 460:7

bullets 265:13

bumble 315:3 334:26 360:10 361:5,7 362:10

364:25 516:6,11 527:22 528:7,8 529:6,12,14

bumblebee 101:1,5 105:1

bunch 263:15 323:3 551:11 561:25 653:5

burden 15:21

Burghardt 399:25 405:1

Buried 85:26

burn 306:5

burned 310:16

burning 242:4

burnt 693:8

burst 602:14

bushel 317:11

business 7:18,24 202:15 234:7 239:1 302:19
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businesses 285:24

busses 506:2

buy 363:10,16,23 469:11

buying 470:17

by-products 666:22

C

c 91:19,25 92:6 336:15,15 341:6 345:4 402:2

403:15 409:4 422:17,20 472:21,23 474:9 483:26

484:2 494:15,16 535:21,23 585:21,23 614:10

669:9 676:17

cabbage 310:26

caffeine 261:24,25 262:3,5,13 265:2,4

cage 161:8 476:2 481:11 483:11,17 484:13,25

490:5,11 499:2 531:8 533:1 534:3 616:6

caged 489:4 490:18

cages 161:15 498:23 516:1 535:3 537:13

cake 168:20

Cal 9:19 17:5,22 105:21 129:1 253:10 696:6,7

calculable 648:25

calculate 93:20 131:22 179:12 206:9 328:24 378:2

378:4 573:17,20

calculated 58:14 59:2 63:2 64:6 65:6 92:7 102:7

103:16 108:20 110:11 120:17 153:4 205:1 207:14

207:26 208:1,22 209:8,15 215:25 372:20 387:18

612:10

calculating 100:20 264:24 491:19

calculation 121:5 201:4 259:5 273:8 492:10 607:19

658:24

calculations 79:25 85:17 105:24 260:9 328:13

387:13 425:1,4 431:6 491:12

calibrated 680:20

California 4:5,14 12:26 13:25 14:15 15:5 16:20,23

17:3,12,15 25:19 200:16 249:14 293:19,25 294:5

311:23,26 430:13 578:14,14 598:9 684:23 695:2

California's 8:10 10:18 202:8

call 154:23 220:18,19 281:2 291:4 362:9,21 396:6

435:6 443:24 476:1 507:23 511:11 596:8 609:1

617:16 621:20 629:14 637:5 672:21 678:1 679:15

682:20 693:5,7

called 35:26 168:12 287:21 288:3 302:25 303:8,13

303:20 304:10,21 312:2 356:23 672:19 673:11

693:2

calling 478:21 620:22

calls 238:6 241:10 430:23,24

call-in 395:22

call-ins 202:8

Canada 10:18 13:25 14:14 15:3,5,20 16:1,4,23

17:11 156:9 158:8 200:17 277:24 278:2,6,9,14

279:1 280:7 292:21 347:16 684:22 695:2

Canada's 8:9 15:2 17:2 292:13 293:2

Canadian 15:7 280:14 292:23 311:18

canals 82:12

cancelled 324:13

cancer 335:19 492:25,26

candid 150:21

candidates 509:22

cannibalism 226:6

canola 148:22 600:19,19,23 602:25

CAP 592:19

capabilities 12:23 235:5 291:22

capability 246:1

capable 558:17

capacity 133:20 447:25 458:13 502:8 645:18

capillary 537:23

capped 304:2 342:9 534:14

capping 346:21 456:17

caps 595:15

Captan 305:16 308:14

capture 25:5 67:7 136:12 155:18 222:7 352:26

403:26 405:7 443:11,14 444:5 450:8 461:4

464:13 513:22 514:24 533:9 541:13 545:12 601:5

605:12,22 606:20

captured 48:13 110:21 133:12 134:14 345:1

347:12 426:9 447:17 647:3 683:21

captures 12:11 647:6

capturing 67:8 404:6 420:13,14 537:22

carbamate 120:10 398:26

carbamates 373:22 398:19

carbofuran 383:5

carbohydrates 467:1

carbon 537:22

carcinogens 492:26

care 232:7 241:24 334:5 484:26 489:19,26 490:3

528:26 576:1 605:14 615:18

career 319:25

careful 52:21 85:20 233:25 234:15 245:16 292:3

545:20 555:16 576:5

carefully 277:16 281:16 371:11 595:8 644:20

caring 489:21

Carlo 97:18,21 230:17 262:18 263:19

carniolans 635:10

Carolina 5:2,3 249:26 250:2 442:1

carried 96:2,3 189:23 377:8

carrier 448:5

Carrington 282:12

carry 96:1 181:11 186:26 552:22 562:16

carrying 73:10 78:6 439:21 645:18

cart 52:18

cascade 35:14

case 36:23 39:6 56:10 69:22 74:16 82:26 102:13
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104:1 112:22 118:15 122:20 157:10 166:16

179:26 184:1 188:25 290:16 295:3,9 327:20

330:3 335:11 349:18 355:6 366:23 367:21 377:14

379:18 382:1 385:6 388:20,25 389:9 396:9 411:8

424:16 428:24 429:5 430:16 438:10 440:1 447:1

480:12 497:8 506:11 510:12 518:14 519:6,8

520:10,26 521:22,24 523:20 524:19,19 525:12

535:8 558:15 559:24 568:4,7 599:3 600:9 611:12

628:9 631:13 654:16 662:13 665:19,19 675:9

677:2,7

cases 54:17 57:22 99:10 100:11 108:1 111:9

130:22 134:13 151:3 157:2,3,15 162:17 163:25

165:11 178:9 192:11 193:2 224:4 272:13 275:7

287:3 323:16 329:24 380:18 483:8 503:1 518:26

523:20,24 525:4 527:6 533:7 577:9 601:14

628:22,23 629:6,25 645:15

case-by-case 77:20

cast 547:12

caste 93:8 102:23 134:10,10 384:24 455:14 466:10

castes 39:3 72:13 102:19 104:23 105:19,26 132:20

165:22 369:8 384:11,23 519:6

catastrophic 487:18

catch 134:1

catching 136:16

categories 36:6 42:1 43:14,16 50:7 114:22 216:11

373:21 463:14 556:7 653:13

category 42:17 43:4,6 137:9 216:13,16 508:17

555:6

caterpillars 541:18,18

caught 689:17

causal 235:9

cause 9:17 10:7 19:6 20:22 21:3 163:13 181:25

206:26 245:19 253:23 256:3 262:8 301:12 308:21

319:20 323:18 449:15 660:10 665:15

caused 21:1 681:20 692:3

causes 9:10,22 13:22 235:8 318:16 665:14

causing 235:2,9

caution 616:4 617:10 626:21 638:12

cautious 658:6

caveat 400:21 517:21 614:20

caveats 377:7 546:7

CCD 20:14,14,16,20 283:13,16

CDC 323:8

CDPR 272:4

Celia 529:18

cell 342:9 456:17 514:9,10 534:14 569:16

cells 428:4,4 474:22 514:10 535:5

census 634:18

center 4:24 5:14 249:23 280:21,24 288:25 289:1,16

292:16 326:15 578:21

centimeter 374:19,21 375:6

centimeters 375:5

central 150:5 207:24 285:17 430:12 554:13 596:15

centralized 625:2

centric 352:26

cents 363:25

century 466:22 668:3

certain 18:26 25:15 28:1 155:7 189:17 242:9 243:7

245:12 263:7 331:11 360:3 410:20 427:25 428:5

453:15 519:7 528:12 533:18 575:19 588:14

603:11 625:7 628:16 632:8 662:6 667:14 680:19

683:26,26 688:5

certainly 69:3 86:11 88:10 131:14 136:6 138:2

201:26 233:26 235:4,6,11 239:8 242:1 257:12

305:15 318:26 320:1,20 339:13 347:3 352:4

361:23 363:15 367:25 368:1,4,5 374:7,13 376:16

409:22 410:8 425:24 428:10 432:14 433:8 437:21

438:7 452:11 453:9 480:3 490:19 497:18 503:10

544:24 570:10 571:6,7 582:19 591:17 592:18

595:12 600:9 605:22 622:24 645:1 655:26 656:3

656:17,19,20 664:23 665:2 680:22 681:4 687:16

687:20

certainty 321:7 495:16 530:4 548:10 638:2,5

certified 574:10,11

cetera 237:8 319:8 324:24 387:18 431:18 433:8

461:12 486:21 540:18 588:7 595:17 657:2,19

CFR 17:6 156:6 167:20 168:2

chain 313:21

Chair 3:21,21 13:3 249:14 644:3

Chairman 18:3 441:26 643:11

chairs 251:21

challenge 6:22 10:11,14,14 51:8 163:24 189:22

275:15 481:4 510:26 511:1 548:8,14 592:20

challenged 507:20

challenges 187:1,2,5 190:7 343:9 344:14 449:13,25

482:22

challenging 67:4 242:14 417:17 454:8 484:10

chance 207:11 245:1 379:13 581:10 590:19

change 46:15 93:11 131:6 141:14 222:2 235:25

261:3 264:3 293:8 346:16,17 354:14,15,17

359:26 394:1,16 418:16 436:19 440:6,10,16

456:5 457:18,21 528:14 547:6 553:2 595:13

613:10,13,15 628:17 632:17 646:21 654:1 662:2

664:26 674:4,5 683:2 692:6

changed 130:24 309:13 347:5 440:12 523:1 669:15

changes 19:16,17 27:21,21 35:17 36:21 39:3 47:19

93:9 101:17 176:2 226:1 233:15 264:1 293:9

312:8 371:8,25 416:21 449:24 450:5 455:5 488:5

488:23 588:11,12 643:1 650:14,15,15,16

changing 221:2 234:5,25 235:20 264:2 342:14,21
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characteristics 27:23 29:25 36:10 166:20 199:10

199:12,14 213:1

characterization 12:1 26:3 62:9,13 136:9,11

162:23 178:26 203:13,14,19,21 204:11,23 205:1

206:19 209:5,7,12,16 211:23,26 213:5,15 219:11

229:2 257:14,21 626:10,15

characterizations 25:21 129:25 228:10

characterize 179:14 205:7 509:5 614:14 619:17

626:8 671:10

characterized 62:8 136:14 204:10,10

characterizing 14:2 16:15 66:4 140:12 162:8 163:4

212:19,25 213:4 539:10 624:11

characters 419:7 448:25 449:2

charge 5:13 6:16 7:2 13:3 18:23 42:5 71:20 89:8

105:21 106:1 129:7 152:18 154:23 208:25 229:11

246:5,20 247:23 331:8 333:16 345:9,13 365:18

365:19,21 378:23 384:6 388:11 392:9,11 397:8

401:5,14 402:6 403:16 405:19 436:7 456:26

470:7 572:1 622:6,10 624:6,6 625:5 626:6,7

630:13 631:15 643:16 695:10

charged 401:8

charges 245:12

chart 200:25 204:12 282:11,13

chasm 473:6,8

check 232:1 244:13,14 303:22,22 375:10 377:6,10

462:19

checked 305:2 524:2

checkoff 316:18

chemical 6:12,12 36:10 39:10 57:10 67:15 74:18

75:5,18,21 76:21 77:21 112:22 116:6 117:20

118:24 119:14,17,19 120:4,5,14,18,26 121:15

123:14,25 124:2,3,19 125:22 126:12 127:15

128:1,12 131:5 135:2,5 139:7,18 142:3 146:7

148:8 149:6,20 150:1 151:14 153:21 157:17

158:3 159:5,9,21 160:26 165:9 166:16,21 170:5,8

170:10,16 171:15 172:7 173:26 184:1,11 185:12

185:16 189:4 191:26 193:26 198:19 202:13,13,14

203:17 211:13 215:24 216:4,11,12,14,18,21

217:19 218:16 223:13 246:25,25 247:1 251:3

253:20 255:21 272:26 273:23 277:12,17 299:26

300:13,18 302:25 306:14 375:9 396:11 404:16

407:4 408:25 421:7 431:13,20 588:7 602:2 630:8

668:16

chemicals 13:26 26:22 43:14 51:26 73:19 80:9

120:21 124:15,17 125:6,13,16 128:2 135:2,24

140:24 142:18 144:17 146:17 150:22 157:10

171:18 184:18 188:26 189:3 193:10 199:18 211:7

211:14,16 216:7,13,17 218:9,18,26 219:25

220:11 255:12 256:3,8 269:6 272:14 273:25

274:2,3,6 277:8,9,12 298:8 306:19 308:14 309:2

318:18 398:10 399:9 400:1 404:20 407:6 509:22

598:23 630:7 668:23 672:12

chemical's 124:16

chemical-intensive 297:8

chemical-specific 100:13 142:17 143:4

chemigation 82:14,18,26

chemist 5:5,7 250:4,7 411:20

chemistries 388:1,2

chemistry 22:2 64:14 277:7 278:2 373:24 404:15

419:19

Chesapeake 321:10

chickens 642:21

chief 4:12 696:1

chime 344:6 368:15 544:8 545:24 603:26

China 471:2

choice 496:22 541:19,20,20 542:1 603:19 644:5

choices 49:16

choose 100:11 598:3 627:9

choosing 393:10 458:16

chose 88:17,22 109:6 111:4 138:11 541:17

chosen 72:4 346:20 371:11 393:9

Choudhary 329:22,23 395:6

Chris 200:26 209:18

Christian 292:4 416:17

Christina 37:15 53:2 55:15 71:8,9 89:16 212:21

407:22,22 409:5,8 422:19,19 432:20,20 687:6

696:3

Chris's 81:25

chronic 21:21 58:18,20 59:1,2,11 63:1,3,21 65:10

66:22 92:6,8,12,14 160:12,14,20,22,25 161:2,5

161:18,23 162:5 163:7,15 166:7 167:25 175:13

175:17,20 179:6 185:7 197:21 208:11,12,14,17

209:1 231:11,13 267:9 293:21 412:20 444:6

477:11 478:21,23 480:26 481:15,20 482:9 484:5

485:10,21,23 486:4,11 491:10,11,25 492:2 493:8

493:21 494:5,6 495:7 499:19 503:22,24 504:4,7

505:25 506:16 540:3 612:15 634:6

circle 279:19 575:13

circles 203:1 601:1,2 638:17

circulated 429:25 641:18

circumstance 363:3

circumstances 36:2 42:4 254:12 294:14 314:1

366:25 367:11 394:9 576:24 690:5

circumvented 481:6

citation 295:19 319:11

citations 126:1

cite 153:20

cited 96:8 101:24 383:6 430:10 500:3

cites 332:4

citizen 308:19
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citizens 308:17

city 322:11 323:4 324:10 336:10

CLA 252:19 253:4 254:17 266:5 267:5 278:7,10

279:17

claim 137:19 324:22,23

clarification 43:13 45:23 66:21 70:11 130:26

137:18 140:2 337:2 341:3 353:17 365:17 372:7

377:18 379:15 386:8,10 390:23 392:6 397:7

401:16 422:11 432:6 435:5 436:3,7 446:9 458:25

459:7 463:6 464:18 472:18 476:8 478:15 483:25

489:1 490:5 493:13 512:6 526:9,15,23 530:7,24

535:20 538:13 543:3,5 544:2 551:26 562:2 573:8

582:1,2 585:2,20 587:25 593:9 598:18,20 604:20

609:18,25 612:20 614:9 616:19,20 618:23,23

620:26 622:14 627:10 630:1,6 665:26 667:2,20

676:13 683:17

clarifications 42:15 626:5 630:4 643:10

clarified 53:1 138:22

clarifies 521:2 547:3

clarify 133:13 230:8 339:9 349:6 350:3,24 356:18

357:4 382:18 463:12 484:22 493:15 501:12

522:18 529:1 546:20 652:9,21 654:15

clarifying 42:11 150:8 353:19 382:8 422:12 512:24

573:11 670:1

clarity 12:5 239:16 256:22 694:9

Clarke 322:22

class 297:23 318:17

classes 75:21 125:7 218:25 294:26 688:7,10

classic 497:5

classification 440:26

classified 157:21 192:26 195:12,14

clean 420:13 469:21 489:23 589:23

cleaning 484:25

cleansed 295:20

cleanup 308:10

clear 12:2 16:1 17:8 44:1,7 52:7 65:6 155:10

167:14 178:24 193:25 203:25 212:21 217:12

228:9 239:17,24 256:9,21 274:6 291:4 292:23

326:1 334:4,24 337:24 342:12 350:7 354:25

357:9,12,12 377:1 382:6 405:15,16 409:3 416:2

416:15 420:4 422:10,17 432:10 435:4 443:26

472:8,17 480:12 519:1 527:3 554:1 557:15 558:1

605:11 614:7 648:24 651:21 659:22 668:15

684:26 686:10

clearer 44:10

clearly 8:13,19 9:11,17,24 10:21 11:1 12:8 83:25

164:9 227:17 242:7 254:8 262:10 265:22 267:13

275:16 342:3 366:15 374:12 392:22 393:25

404:22 411:14 415:3 416:18 423:19 446:6 463:18

547:7 557:7,8,10,12,22 611:2 653:14 660:23

Clemson 4:17 249:17 462:21

client 239:21

climate 226:21 228:4 591:24 595:8,9,10,11,13,16

682:12 683:8,12

climates 149:16

climatic 190:21 428:5 591:6 596:15

clock 48:24 545:10 580:13

close 41:22 80:10 84:8,16 230:22 289:17 321:10

530:18 663:26 696:20

closed 25:13 325:3 458:19

closely 10:15 231:1 269:26 324:15

closer 266:19 337:20 462:7 495:10 560:11 586:14

closes 248:14

closest 44:5 115:19 397:19 405:1

closing 301:24 664:10

clothianidin 138:24 290:10,16 292:17 297:26

298:15,17,19,21,23 299:13,19 305:15,17,19,23

307:12 317:12 321:18,20 332:4,7 411:16,21

cloud 673:12,14

coalescence 673:12

coarse 40:18

coat 41:5,11 63:6,12 74:26 77:9 83:16 87:4 437:26

438:23

Coates 696:17

coating 348:1,15,26 350:2,4 410:11 623:15

cocktail 300:16

coding 240:3

coffee 261:24 262:2,4,6,9,13 265:3,3

cognisant 445:12

coherency 561:18

coherent 432:4

cohort 444:8 542:24 543:6,9,16,18,24 544:5,13

cohorts 544:9,17,20 545:13,16

coincides 235:25

colder 319:22

collaborate 274:20 278:5,8

collaboration 8:8,11 10:18 14:19 15:11,12,16 16:3

17:16 279:2

collaborations 8:17 15:16

collaborative 15:6 17:1,23 696:6

collaboratively 200:15

collapse 20:11,12 21:4 39:9 47:19 253:23 282:10

284:24 318:17 675:3

colleague 416:17 540:23 576:17

colleagues 8:21 9:19,21 11:1,5 14:13 278:5 452:11

464:7 487:23 609:1 656:1 685:17 690:15 695:2

696:15,16

collect 19:17 38:12 57:6 147:19 148:21 150:3

199:2 200:17 360:11 361:9 363:19 376:5,6

426:24 427:26 471:15 498:25 513:6 517:7 543:22
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544:4 579:9 671:8

collected 45:26 46:10 75:11 77:22 78:2 108:26

145:14,23 146:1,26 171:9,13 175:9 185:4 186:12

186:12,15,18 187:9,11 189:2 201:9 234:10

261:20 304:6 310:3 352:17 370:17 395:15 416:16

429:19,22 474:4 505:7 534:9 689:26

collecting 73:16 161:7 292:11 378:13 396:19

529:23 601:18,22

collection 73:1 146:20 226:3 450:6,9 515:11

collective 165:26

collectively 39:11 223:23

collects 431:16

collegial 689:24

collegiality 692:23

colon 573:20

colonies 14:1 17:14 19:12,13,13,19,22 20:2,15 21:1

25:2 39:13 49:24 62:1 84:22 147:13 165:23

170:21 173:4,12 176:23 177:19 180:4,11 181:11

182:23,24 184:7,8 187:10 188:3,15 189:18

190:19 192:1 193:10 199:6 200:14 221:9 225:2

227:18 231:22 234:2,18,20 237:1,14,14 238:8,10

253:6 271:4 293:2 297:16 301:13 318:20 326:10

335:5 337:21 360:6,7 365:10 448:4,12 450:10

460:12,18 465:12 471:4 499:21 500:1,15 501:22

501:25 503:10 507:14 513:3 514:4,14,16 515:3,5

515:8 516:13,16 517:12,13,13,14,15,16,17

518:24,26 521:6 523:2 528:7,8,11 529:2,14 531:9

532:10 539:2,10,22 540:3 541:25 545:3 549:2

562:20,20 568:23,25,26 569:3 570:4,8,9 576:22

579:24 582:20,25 583:5,6,8 586:15 587:4 588:17

588:21,25,26 589:1,10,14,20 592:9,10,13,18,19

592:23 593:1,3,5,6 631:6,11 634:11 635:5,7,13

637:3,4,9,9,16 638:4,7,8,13,16 641:9 646:19

650:1 655:18 660:17,18 665:17 666:1 675:23

676:21 682:9 685:21,24 686:21 690:13 691:13

colony 19:26 20:5,11,12 21:5 23:2,6 39:12 47:2,12

48:8,15,17,19 50:11 52:2,3,5,8,13 54:20 61:7

64:22 70:22 78:2 100:18 103:26 129:26 130:2,8

143:21 144:24 156:4 160:2,16 164:3,9,10 165:20

165:26 166:8 167:5,15,19,25 168:16 171:14

173:3,7 174:21 175:10 177:1,23 178:20 179:3

180:3 184:6 185:26 187:23 188:1,5,11 189:26

195:1 198:4,19,25,26 199:3 209:23 210:5,13,18

213:22 214:16 219:10 221:6,8 222:4,8,14,18,19

224:25,25 225:11,13 226:2,7 227:18,23 228:22

229:22 231:23 232:13 238:9 240:7 243:3,6,9

253:23 265:10,12 267:14 270:1,7,22 282:10

284:24 285:4,6 299:7 301:9 304:4 309:12 318:16

335:3 337:6,7,22 338:2,3,4,18,19,21 342:22

343:26 358:14 359:24,24 360:20 369:15,17

430:22 446:16 448:5,18,26 449:2 451:12,19

452:9,10 453:3,5 457:19,22,26 459:17 460:6,25

461:2,4,11 462:12 470:26 472:25 474:22 486:23

486:25,26 487:3,19 500:2 501:6,19,20 502:16,20

503:1 505:1,4,8 506:5 507:13,17,25 509:3,20,23

511:7 512:12 513:10 514:6,19,26 515:16 517:1

517:11 518:24 519:23 520:3 522:11,12 523:10

528:12 530:14 531:13,20,24 532:4,17 533:1,4,24

535:26 536:6,7,8,11 538:26 540:5,16,17 542:15

544:16 545:17 546:14 548:3,16,19 549:1 550:15

551:3,4 553:13 554:12,16,17 556:12,26 557:1,2

559:10,10,13,13,20 561:1,7,10 563:6,13,14,17

565:25 568:24,25 569:20 576:8 579:1,3,24

582:23,26 583:26 584:1,1,3,18 586:2,4,6,10,13

586:21,22 588:4,11,12,12 589:3,4 592:10 605:23

609:22 615:7,17,20 616:4,8,10,14,15 619:5 625:2

626:20,24 630:25 634:1,8,17 635:18 636:4,18,19

636:26 641:10 642:22 645:5,10,12,22,22 646:12

646:14,18 647:7,16,17 648:3,15,16,19 649:4,15

649:22 650:11,26 655:19 656:24,24 657:13,14

658:8 661:10,11,16 663:6 666:11 671:18,24

672:2 675:2,3,5,26 678:23 682:13,15,20 683:1

694:23

colony's 173:15

colony-level 221:14 222:9 223:7,16 227:15 228:17

228:26 229:13 230:3 231:26 243:19 513:6 514:3

517:7 531:2 563:10,12 606:16 643:17 650:7,24

652:11,16,17 658:3,10 667:8 677:7

color 306:9 602:22 603:2

Colorado 4:21 249:20

COLOSS 23:3

column 126:18 225:5 337:18 341:18

columns 114:4 224:23

comb 38:17 39:1 74:6 75:14 76:12 161:8 225:9

344:11 412:5 426:25 430:4 433:16,17 552:13

combination 82:1 90:6 98:17 122:4 157:13 219:16

235:1 253:18 299:22 300:14 417:18 419:2

combinations 20:24 21:8 219:17 418:16

combine 346:7 633:8

combined 164:19 207:22 246:7 518:22 521:13

549:22

combining 81:12 134:9,13 489:11 492:22

come 8:26 12:18 68:7 70:2 137:2,5 201:16 220:4

240:20 241:13 247:20 252:8 257:24 259:4 262:19

263:25 266:15 296:19 302:20 303:14,16,22

311:17 325:4 330:19 332:23 346:18 351:8 359:5

359:6 361:3,15 368:20 372:4 376:23 383:22

391:10 410:14 417:12,18 419:22 421:8 423:20

433:7,18 437:8,20 438:6,12,16 455:3 469:15

474:5 483:2 495:10 499:11 512:20 518:22 553:9
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560:2 562:3 567:10 573:17 574:26 585:18 589:13

594:24 595:24 598:8 613:2 621:26 623:17 638:18

642:19 657:16 658:23 659:12,17 661:9 664:9

671:2,26 682:10,19 691:15 693:24

comes 51:21 243:23 259:21 260:8 263:21 296:20

353:8,13 429:20 448:11 471:2 531:23 532:19

539:17 544:22 547:18 560:15 580:5 591:2 595:6

611:9 650:16 651:17 660:22 662:11 674:4 678:20

694:6

comfortable 273:21 344:20 577:7 667:17

comfortably 653:1

coming 2:14 8:2 13:7 241:21 249:6 263:2 296:23

303:25 329:16 347:16 397:19 416:20 418:10,14

457:2 488:18 493:7 498:6 559:24 599:11 602:1

641:18

comings 275:11

commend 334:3 473:11

commends 253:9

comment 3:4,5,9,9 48:2 70:17 81:25 85:9,14 88:8

135:23 208:26 209:3 242:21 245:17 248:6,13,14

248:20,22 252:7 275:20 276:21 284:3 289:6

290:26 291:2 295:18 302:3 318:24 319:16 328:12

332:15,16 333:16 334:8 337:5 339:6 341:6

345:13,19 346:5 347:4 348:14 349:22 351:17

352:16 354:3 357:18 358:9 364:10 365:22 368:25

372:17 373:5 375:13 379:1 381:9 383:20 384:7

388:12 392:12 394:12,26 395:4,21 397:6,9 402:7

405:20 407:23 413:2 417:10 419:8 422:20 423:22

425:5 426:12 431:10 433:4 437:7,17 441:21

445:3 446:12,17 451:14 452:22 456:3,25 464:24

471:14,24 472:23 474:15 477:3 480:24 482:7

483:3 484:3 491:10 493:6 498:8 503:23 506:10

509:3 513:7 531:2 535:23 539:8 547:11,12 548:1

551:10 556:4 562:8 564:25 566:18 567:17 570:7

580:12 584:26 585:23 588:2 593:17 597:11

604:22 607:4,5 608:2 614:12,17 615:5 616:24

624:8 625:19 626:8 627:5 630:14 633:10 637:2

642:17 643:17 663:15 664:22 667:7,24 670:20,24

676:9,11,20 681:21 687:7,25 688:3,16

commenter 248:19 252:11 277:24 322:2

commenters 248:5,11,22 249:1 251:6,11,12 329:13

435:17 691:8 694:11 696:10

commenting 247:18 325:3 469:15 499:7 685:9

comments 3:8,13 8:2 52:16 88:2,5 129:1 233:24

248:15,17 251:5,15,20 252:3,5,23 254:23,26

255:3 274:24 278:10 279:17 281:5 285:9,18

289:2,10,10,12,13,17,18,18 292:4 293:14,15,16

296:12,14 297:4 302:4,6 309:4 316:16 329:21

332:18,21 334:2 336:26 338:20,23 340:26 341:1

342:25 343:1,12 344:4 345:4,21,22,23 346:9,10

346:10 347:26 348:7 349:8 350:19,24 351:6,7

353:16 365:18 367:23 369:6 370:2,6 372:6,25

374:3 377:5,16 382:5 384:4 386:24 387:4 388:7

391:21 392:5 396:24 399:22 400:3,13 401:15

403:11 405:10 412:9 417:1 422:3,9 430:7 435:2

435:14 439:8 451:9 455:21 456:22 458:7,23

461:9 466:18 467:21,24 468:1,18 471:18 474:8

475:20 485:8 488:25 489:5 493:12 497:19 499:18

501:1 503:17 507:1 508:17 511:13 512:6 513:15

517:18 518:22 523:12 525:23 526:6,7 532:25

539:14 542:18 543:1 547:5,20 549:9 551:25

560:19 561:17,19 567:1 573:7,7 587:17 591:11

593:8 595:20 598:16 602:17 607:25 620:2,15

622:9 623:5 625:7 627:21 629:9,17 630:2 633:18

634:5 643:6,6,8 658:2 667:3,10,19,21 669:15,21

675:13 678:10 681:15 683:4,15 685:5,10 686:5

686:17 694:26 696:11,12

commerce 9:1

commercial 234:2,11 286:6 297:13 334:25 686:23

commission 22:18 228:14

commit 617:26

commitment 14:21 212:14

commitments 695:15

committed 16:2 278:21 694:25

committee 2:10,15 7:14 247:8 283:17

common 33:25 183:9 184:16 220:7 264:21 284:10

287:4,11 294:20 320:20 350:14 453:19 589:22

593:25

commonly 96:8 176:18 443:10 637:15

communicate 267:22 314:2

communicated 27:5 271:12

communicating 34:26 604:12

communication 84:9 165:22 204:17 242:12 271:7

326:5 506:4 541:12 638:21 664:13

communities 15:22 664:9

community 8:17 17:18 197:8 202:7 238:25 274:21

297:9 325:20 346:26 493:25 668:26

companies 253:11 255:2 271:17 295:4 418:9,11

419:16 421:7 668:22

company 276:9 417:23

comparable 90:14 361:20 388:3 531:11

compare 81:21 96:19 107:23 118:8 201:24 232:13

273:9 277:17 395:3,19 401:25 404:4 413:19

572:13 574:20

compared 37:13 41:17 46:5 54:16 59:9 63:22 79:7

79:19 89:23 93:13 94:5 98:7,21 99:25 100:25

101:15 102:19 104:4,8,23,24 105:5,19 114:24

119:18 121:19 122:13 123:5 124:3 137:21 147:16

167:11 174:4 176:10 179:9 202:12,17 205:20

206:19 214:24 215:25 217:10 218:3 227:2,5,7
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230:8 273:9 276:25 318:4 393:18 413:16,17,18

415:5 422:24 429:10 444:19 453:10 539:23

557:14 576:15,20 606:17 611:2 612:16

compares 111:18 205:11 623:18

comparing 107:19 109:11 122:5 174:16 273:6

comparison 53:17 59:17 80:24 116:13 121:17

122:11 123:8 124:11 168:25 176:17 186:11

210:23 226:26 314:24 536:14 610:25 617:23

633:16

comparisons 59:14 174:18,19 269:7 394:5

compartmentalize 656:6

compartments 657:14

compatible 253:16

compel 419:10

compelled 319:16

compelling 621:14

compensable 502:15

compensate 222:2 360:2 563:16

compensated 222:1 548:19

compensation 552:12 616:7

compensatory 178:20 499:20,24 500:8

compete 340:12

competence 491:22

competing 46:18 563:25 564:10 567:25 573:25

597:6,9 598:12 600:19

competition 46:23 564:6 577:6,11,12 596:26

648:11

compilation 395:6

compile 397:4

compiled 117:11 215:21 393:7 395:22

complementary 278:13

complete 20:7 26:17 31:3 49:10 146:11,21 184:20

465:23 674:22,23

completed 78:11 216:1 217:24

completely 83:14 153:24 188:18 195:23 265:6

280:8 397:3 398:18 403:3 431:24 452:3 473:5

535:2 554:8 575:16,23 576:14 605:22 634:20

662:23 678:9 682:13

completing 310:15

completion 174:7

complex 9:11,24 13:23 35:7 39:10 186:25 278:21

318:16 419:7 451:20 455:26 462:22 495:21 510:8

514:25 587:4 609:14 656:7 660:19 675:23

complexities 12:9 26:15 442:12 450:11 473:19

509:12 617:2 631:4,12 689:17

complexity 177:22 443:9 446:24 459:16,25 473:24

605:12 608:21 624:24

compliance 281:17

complicate 135:13 355:26 356:6,22

complicated 315:16 465:10 581:20

complicates 509:11

complications 649:10

complimentary 658:26 690:17

compliments 683:20

component 7:22 9:4 12:1 120:21 121:1 136:9

203:13 204:18 209:7 242:9 245:24 349:8 381:23

385:7 430:7 431:12 443:11 444:26 447:2,9

451:16 460:5 470:10 491:25 536:12 607:24 680:5

680:6

components 11:17,20,25 27:23 34:7 53:8,9 66:15

120:12 204:24 227:1 230:26 278:13 386:11

417:24 677:3

composed 93:6

composite 536:11

composition 186:14 191:19 224:26 226:7 465:23

472:4

compound 67:17 141:26 142:26 149:4 152:26

157:4 184:1 198:15 199:10,13 259:6 277:8 367:6

380:23 390:5 400:6 444:7,11 465:24 472:3

564:19

compounding 98:3 366:25

compounds 75:4,22 125:25 134:12,15 153:18,24

198:23 261:3 266:17 270:9 271:11 398:17 400:11

431:17 443:12 444:13 449:4,11 471:5 514:13

516:4 533:19 541:10 688:7

comprehensive 21:25 36:5 223:26 224:12 226:2

301:25

comprehensively 290:24

compressed 687:16

compromise 511:7,11

compromised 176:10 195:23 196:2 564:16 596:17

compromising 679:20

computer 248:10 251:14

conceive 487:24

concentrated 46:2 86:25

concentrating 46:14

concentration 46:20,22,26 58:4,20 87:3 94:19

95:13,15 100:14 104:13,14,19 106:24 107:2,3

108:24 110:12,15,17,23 114:18,24,25 115:5,9,16

115:21 116:10,20 117:7,10,18,21,26 118:1,3,9,10

118:19,25 120:4,5,13,14,15 121:7 123:3,10,11,17

123:21 124:7 126:7 127:11 128:8 129:8,11

159:23 161:26 162:4 171:7 178:8 179:8 206:15

208:15,15 259:1 262:6,26 276:18 277:15 329:12

329:13 365:26 366:26 370:25 381:13,19 382:26

387:9 389:17,20 390:10,13 391:6 398:15 400:5,9

402:10 423:20,26 434:13 440:3 492:8 493:24

concentrations 55:12 76:6 78:15 85:18 88:16,20

90:2,5,14 94:23 95:6,11,12 98:12 99:24 104:7,10

105:12,14 106:17,21,22,25 107:9 112:1,21
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113:10,13,14,18,23,25 114:7,8,11,12,16,20,22,25

114:26 115:1,3,4,6,7,8,12,13,15 116:4,11,14,16

116:22,25 117:1 118:9 119:6,18,22,25 120:22,23

121:17,20 122:5,6,7,8,11,22,26 123:5,6,9 124:3,8

124:10,12,13 125:19,21 126:15 127:4,5,11,25

128:5,20 129:13 135:3 137:20 142:14 143:8

144:6,13,15 145:22 146:8 148:11 149:1,10,12

162:7 163:17 171:15,17 172:7,10,11,14,16

174:12,13,16,17 186:11 201:21 205:19 259:9,10

260:7,11 273:18 276:22 277:20 298:17 326:25

327:19,23 329:24 330:1 367:16 379:11 380:22

381:17 383:7,12 387:26 389:8 391:12 392:16

393:6 397:13 398:11 399:4 400:19 402:19,22

404:17 410:26 411:16 416:16 423:15,23 424:20

425:6,10,17 426:6,12 438:10 439:18 466:6,13

491:20 495:14 524:14 530:1 535:9 537:6

concept 50:26 65:22 66:25 69:26 70:1 82:13

205:18 229:13,14 244:6 264:17 353:12 403:1

439:13 467:7 481:3 643:17 650:7 667:7,17

concepts 8:13 11:3 70:5 685:23

conceptual 11:18 27:12,15 28:7 32:6 33:11,13,18

33:19,24 34:3,9,12,15,19,22,26 35:5,12,19,22,25

36:4,13,14,17,19 39:20 40:3,23 41:1,24 42:11,21

43:2,9,23,24 44:6,18 45:5 47:11,17,21,23 48:5,13

49:9,18 55:20 63:5 68:7 71:17,23 75:20 82:23

83:1 211:9 257:1,3 345:14,20 356:15 408:16

411:12 619:23 664:8

conceptually 46:20 163:9 221:19

concern 28:10 34:14,16,18 35:16 36:19,21,23

37:21 38:3 40:15,20,25 54:2,3 55:4 57:18,23,25

59:9 62:19 63:7 69:18 79:1 82:2 103:25 140:25

141:17 143:24 149:22 153:1,18 155:11 157:1,3

164:24 172:10 173:22,26 175:22 191:26 201:6

205:12,13 206:20,20 207:3 208:1,13 210:24

215:25 217:17 218:11,15 255:12 256:4,21 259:19

262:8 270:21 271:12 274:9 280:26 286:1 314:26

318:8 368:3 381:11 412:25 415:2,4 440:8,13,25

441:3,9 463:16 466:1 470:21 483:8 506:19

516:20 530:20 541:10 545:17 551:23 557:7 576:6

577:21 578:6 597:22 608:3,12 609:7,15,20,23

610:24 632:5,7,12 633:11 634:5 652:20 660:2,4

660:10,13 662:11,21 678:18 681:21

concerned 43:18 70:6 196:4 256:7 297:11 318:13

471:11 565:26 654:12

concerning 187:5 296:4 331:10 442:14

concerns 131:11 136:1 166:11 183:8,26 194:8

210:16 255:26 312:16 366:20 372:9 440:9 463:25

466:11 524:1 540:22 562:23,26 570:2 576:18

592:7 632:26 652:19 665:4 667:14 668:22

concise 228:9

conclude 41:24 53:17 119:26 221:17 228:21 262:6

262:9 294:11 403:22 548:2

concluded 163:19 204:22 223:21 424:7 477:2

696:21

concluding 90:26 258:22 274:3

conclusion 30:24 103:11 204:4 218:21 228:24

422:22 424:7 437:21 478:26 480:24 590:5 662:9

662:17 672:3

conclusions 66:9 204:2 223:18 254:11 661:2 675:1

conclusive 324:25

conclusively 465:25

conclusory 324:25

concomitant 603:22 677:26

concordance 210:7

concrete 654:6 670:5

concur 336:18,20,22,24 339:15 344:3 348:11,13

349:11 350:7 364:6 367:25 380:12 385:10 394:21

401:1 403:8,10 405:10,13 445:11,19 451:24

468:1 472:14 473:9 475:23 478:2,4 481:19,23,26

483:22 484:21 485:4,7 492:14 500:26 505:11

510:7,20 517:18 532:23,25 564:23 569:21,24

570:25 571:1 587:19,21 590:11 591:1 595:1,3

606:24,26 614:23,25 615:25 616:23 624:17

626:26 627:1,5 631:24 656:1 674:19 681:19

690:15,24 693:21

condensation 78:24

condition 28:9 169:18,21 173:16 175:3 176:17,18

192:9 290:9,13,14,17 516:11 535:10 575:17

conditional 290:21

conditionally 142:10

conditioned 450:23

conditioning 450:19 451:4

conditions 24:12,13 52:3 54:12 77:24 112:6,9

141:1 144:5,19 149:16,26 151:14 160:5,17 167:3

167:22 168:25 176:4,10 177:2,12 181:4,8 182:19

182:22 183:12 185:12 191:1 194:11 199:5 209:24

209:25,26 211:7 222:13 286:8 293:8 298:14

428:5,8,10 436:17,19 440:10 465:2 479:14

483:10 489:10 500:13 503:11,14 513:5 515:22,22

518:18 523:25 540:11 543:7 544:2 568:18 590:14

590:17,24 592:6 603:5 615:8 627:25 680:20

682:12

conduct 61:26 149:24 161:10 181:15 182:4 191:9

192:25 212:10 222:23 224:9 261:4 386:15,25

490:2 524:10 592:4

conducted 18:10 24:8,16 48:18 54:14 56:18 80:13

81:23 97:3,17,25 108:23 109:3,16,23 113:15

120:8 133:8 143:19 144:5 145:13 146:19 149:21

153:8,9 160:16 161:6 164:14 166:10 167:2

175:26 181:3 183:11,18 191:3 194:8 196:23
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256:10 281:9 383:10 410:3 466:6 535:15 572:19

574:25 588:13 591:5 592:3 615:7 681:1

conducting 93:18 143:3 155:19 160:22 182:5,12

195:16 274:17 276:14 553:6

conduits 82:8

conference 202:23 235:8 248:1 688:19

confess 134:16 491:14

confidence 17:18 68:23 69:5 134:1,5 254:20

279:19 462:5 553:20 563:14 588:20 591:20

594:11,14,20 666:22 674:16

confidences 588:14

confident 328:19 491:21 654:13 661:20

confidential 202:15 312:17 682:5

confidentiality 108:3

confidently 90:22 113:20 131:9

confine 364:26

confined 144:26 169:10 211:15 467:12 513:3

522:24 535:10 538:26 621:11

confinement 176:11,14 518:15

confirm 328:6 349:10 383:6 426:23 594:22

confirmatory 142:25

confirmed 124:20 594:9

conflating 554:23,24

conflict 2:24 286:26

confound 177:15 189:15 564:12

confounded 609:12

confounders 590:16,16,21

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469:22 475:1 539:7 565:1 567:8 588:24 589:24

591:24 593:15 600:20 651:5

confronted 323:9

confuse 651:17

confused 284:8 415:23 416:3 494:13 548:12

658:17 665:8 672:20

confusing 349:25

confusion 268:20 320:5 355:14 357:2 491:15

627:19 661:7

congratulate 692:19

congratulates 694:6

Congratulations 435:7

Congress 283:19

congressionally 235:7

conjunction 209:9

connect 47:18

connected 85:21 319:25

connecting 37:18

connection 163:25 461:16 617:15

connections 459:21

consensus 345:3 391:24 415:20 416:11,12 421:19

423:25 479:4 546:1,13 613:1

consequence 53:22 261:22 532:4

consequences 180:8 294:6

consequent 185:10

consequently 145:26 155:22 162:3 164:12 166:24

168:9 172:21 173:18 179:16 180:19 181:13 183:5

183:19 187:1 191:14 306:3,21

conservation 260:22 295:6 302:26 303:13,15 320:2

321:12 340:20 391:15 653:3,4,24 663:25 677:21

conservatism 214:13 265:7 271:9,14 379:4 384:10

394:25 611:2

conservative 26:21 54:7 58:4,13 62:22 65:4 68:18

68:26 69:3,4,9 76:10 78:25 79:12 89:22 90:12,17

93:26 94:12,14 98:3,9,10 99:7 100:7,24 104:23

105:21 107:22 110:2 112:14 115:18 116:15,21

119:21 120:1 121:18 122:13 123:21 124:13 126:6

128:14 138:2,11 140:24 146:3,23 165:4,8 166:4

179:3 198:16 205:25 208:23 216:2 256:2 258:15

259:9,13,24 260:3,24 261:14 272:13 273:16,21

273:22 372:23 377:15 379:19,24 380:10 393:12

393:20 394:8,13,22,23 396:5 398:10 399:18

400:16 403:21 438:25 462:18 464:26 465:25

495:21 500:9,12 501:7 509:13 573:21 610:24

611:5,9,15,17,19 613:6,8,22,22 614:3,4 626:17

conservatively 92:14 388:13 440:7

conservativeness 98:20 102:18 105:25 108:9

consider 36:9 49:11 52:17 67:22 81:11 83:22 85:3

86:13 88:14 91:13 100:21 107:15 112:14 135:12

135:15,15 147:8 174:24 180:24 182:15 188:5

190:18 199:15 206:22 212:8 229:21 317:7 324:23

329:19 331:5 340:18 349:14 350:3 382:10 424:23

431:3,20 469:17 503:24 504:3 523:2 524:25

532:2,16 547:25 550:5 556:19 572:9 574:19

583:18 599:6 621:13 625:23 638:26 679:24 688:8

considerable 31:8 85:11 137:5 192:25 340:11

360:23 368:12 438:5 469:1 491:15

considerably 118:7 681:11

consideration 27:24 42:2 48:4 66:23 70:13 90:24

105:23 130:23 131:5 134:11 135:1 136:3 148:26

149:6 153:6 170:10 195:3 220:5 302:5 339:24

362:20 366:5 380:20 403:20 421:12 445:7,21

551:2 567:25 569:15 582:11 587:3,14 589:6,25

625:11 688:6,15

considerations 55:23,24 149:23

considered 9:13 21:23 27:21,22 28:6,11 31:1,17

37:1,7 55:8,17 57:23,24 59:25 60:6,20 61:1,11,15

62:5 63:4,23,26 68:19,22 70:11 71:14,23 72:7

73:16 75:23 76:22 77:12,15 84:5,11 86:24 87:2,3

98:1 99:18 100:23 107:16,21 110:5 123:2 147:17

148:6 151:7 159:5,12 162:22 165:10 168:22
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202:15 209:9,13,22 210:19 211:9,17,26 212:18

212:21,22 219:15,22 224:24 259:17 273:25 305:9

396:3 405:21 408:13 409:25 414:10,13 416:13

422:6 432:25 433:5,20 436:14 446:13 449:24

451:21 478:12 484:12 499:19 509:16 527:18

530:9,12 531:3,20 533:14 535:13 562:22 585:24

587:9 588:3 593:17 599:2 615:7 621:6 675:16

676:21 686:11

considering 49:14 59:21 60:9 61:20 66:4 99:1

110:3 115:10 188:2 212:2 242:19 244:13 414:6

436:15,17 437:13 438:22,24 446:18 494:6 610:25

621:12 628:5 682:7

considers 54:10 212:3 225:2

consist 161:7

Consistence 29:4

consistency 21:26 195:3 569:16,18

consistent 10:13 12:2 25:17,18 29:2,18 30:17

35:20 42:8 55:19 103:4,12 112:11 113:3 126:8

156:19 203:26 205:16 208:16 212:14 228:24

255:7 257:17 270:18 272:7 279:21 333:20 337:9

341:9 345:14 448:10 477:14

consistently 85:4 115:13 444:13

consisting 25:22

consists 25:19,20 204:8,24

consolidate 549:21

consolidating 550:5

constant 127:18 177:2 198:7 371:25

constantly 444:14

constituents 465:20 468:25 469:6

constitute 37:20 224:21

constitutes 295:3

constraint 515:10 645:16,20

constraints 189:7 318:10 499:1 645:14

constructed 205:8 222:9

construction 38:12

construed 326:2

consult 268:12

Consultant 289:1

consultation 2:26 14:16 499:13

consume 45:13 78:17 85:12 93:2,5,12,14 94:7,17

99:22 100:5,19 101:15 104:3 138:1,16,16 360:25

361:6,7 378:24,26 423:19 425:12 465:3 539:5

542:15 543:11

consumed 76:16 78:16 79:20,22 80:4 94:21 95:7

99:25 101:25 102:4,13 103:17,17 118:14 346:16

379:12 382:15,22 424:5 425:23 426:1 520:8

Consumer 254:3

consumers 353:10

consumes 74:11 100:3

consuming 45:13 78:12 94:3,26 95:1 104:5 138:10

328:13 428:9 528:24

consumption 38:20 39:1 45:12 55:10,11 58:13

71:19 73:4,8,12,26 74:2 76:19 78:5,9 79:17,19,22

80:2,22 89:21,22 90:6 92:26 93:8,19,22 94:1,10

94:11,13 95:3,4,16,17,18,20,21,23 96:5,7,10,13

96:16,17,20,21,22,24,25 97:1,2,6,22,24 98:2,5,6

98:14,18,20,23,26 99:2,6,6,10,13,14,16,17,20,26

100:1,7,10,12,15,16,20,22,23,25,26 101:4,8,14

101:22,23 102:1,6,7,9,10,11,18,21,25 103:2,3,6,8

103:9,10,12,14,16,18,21,24 104:12,16,21,24,26

105:4,5,7,9,15,17,22 106:5,18 108:12 111:3

115:24 117:22 127:5,6 129:26 130:5,17 131:25

134:14 176:26 186:8 260:6 264:16 328:10,12,16

328:17,22 330:26 331:3 346:13,14 361:4,15,22

368:18,21 370:7,10,13,22,22 371:5 372:2 379:2

382:12,19 383:25 384:9,14,20 386:5,13 387:4

388:13,14 391:15 432:23 541:3,25

consumptions 386:21

contact 24:24 31:17 38:4,14 39:1 40:12 41:17

43:19 55:10 58:1,5,10,10 65:2 70:26 72:7,23,24

74:3,4,6,24,25 75:14,26 76:9,11,12,18,24 80:21

80:24 81:2,14 82:1 89:26 91:24 92:1,21 106:10

106:11 108:13,17,18 110:4,10 111:2,11,14,24

112:16 113:2 120:6,16 126:23 128:25 129:6

130:20 135:18 136:23 137:2 156:7,14,18,25

157:7,16,23 158:6,15 159:1 167:13 207:5,8,21,23

208:10 215:19 216:10,22,25 217:2,25 218:2,14

218:17 257:14 267:6 289:4 307:15 315:25 326:13

326:15,18 345:26 346:5,8 347:17 351:8 355:8

362:1,6 365:23 366:9 371:15,17 373:8 375:7

407:24,26 408:10,24,25,25 411:4 413:17,24

414:26 422:24 429:10 432:23 433:5,7,9 434:11

434:14,26 438:6,13 441:1 467:13,17 474:5

476:24 506:10 518:21 520:3 521:14,16,20 540:24

602:1 603:20,22 606:15 607:11 612:11 618:18

632:9 662:13,20

contacts 181:20 434:22

contain 55:12 64:25 86:14 99:24 141:26 191:11

384:18 394:2 465:20

contained 94:15 102:2 111:16 164:21 327:18

container 484:24

containers 308:6

containing 41:12 143:22 159:20 167:6 169:15

contains 86:17 225:25 299:14 465:17 466:26

contaminants 466:5 470:25

contaminate 533:24

contaminated 38:4,6,7,15,20 41:14,18,20 46:5

55:8 73:5 76:1,12,15,19 78:13 79:17 189:11

291:3,8,23 292:24 293:20 299:17 300:5 338:14

422:23 425:18 467:14 468:12 469:11 533:22
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534:11,12 540:15,20 560:13

contaminates 75:9

contaminating 468:9

contamination 85:22 145:17 171:6 185:24 195:21

261:20 294:4 299:21 300:10 337:7 345:25 350:13

426:17 469:19 470:6,17,18,20,26 537:4 576:11

596:13 687:8

contemplate 411:1

contemplated 571:7

content 96:15 102:14,15 263:13 398:4,4,24 431:4

538:2

contents 537:23

context 27:20 30:22 31:10 33:21 34:25 64:8 85:16

86:7 107:18 154:24 174:10 204:20 209:22 210:21

216:1 218:15 221:2,4,15 223:6,19 244:11 270:24

272:8 298:16 343:6 344:21 436:24 443:6,19

444:18,22 467:18 474:26 475:1,5 481:1,6,12

484:10,13 504:7 511:4 514:24 515:19 530:9

558:19 559:2 562:21 587:16 600:12 609:2 613:18

613:19 615:7 619:15 655:21 670:23 671:4 672:17

672:22 674:17

contexts 474:25 484:15 549:2 550:1

contextual 215:13 501:21

contextualize 515:16

contextually 515:22

continual 177:20

continually 468:11,17

continue 20:16 36:20 60:7 67:3 77:13 192:4

233:21 274:20,21 290:13 296:14 300:19 604:5,5

604:26

continued 170:3 175:9 185:5 502:6

continues 21:6 77:7 274:15 589:13

continuing 16:2 25:10

continuous 455:18 467:13 486:3 503:9 524:18

continuously 467:10

contract 312:1,10,24 313:4,25 559:18 597:26

contractors 463:23

contradictory 661:2

contrary 376:7

contrast 292:13

contribute 7:5 29:10 233:14 316:18 320:1 340:5

432:24

contributed 12:26 87:1

contributing 10:4 238:15

contribution 7:8 20:14 234:14 254:10 335:14,26

338:16 341:26 383:24 473:3

contributions 236:10 239:10

contributor 695:5

contributors 20:20

contributory 281:15

contrivance 239:3,9

control 15:25 145:8 171:6 176:18 178:7 184:23

188:23 189:6,9,10,12,13,14 190:21 191:2 193:8

242:25,26,26,26 243:9 259:14 266:8,8,9 267:1

269:5,6 316:1 317:15 318:6 366:21 380:21

453:10 471:1,6 490:3 515:17,17 525:1 541:3

564:11,13,14,17 565:12,14,16,23,23,24,25 566:1

567:1,7 570:12 572:13 577:12 586:15 589:11,21

590:20 599:4,9 600:24 612:5,5,12,15 634:13

635:21 637:20 639:12,21

controlled 54:12 143:20 144:26 165:15 167:3

168:25 172:25 183:25 185:24 195:2 209:24

266:12,19 301:14 489:10 532:13 546:2 562:16

568:15 576:2

controlling 187:8 452:7 523:19

controls 67:5 161:10 176:17 182:26 208:9 269:19

270:2 471:16 552:26 562:17 633:26 634:2,3

controversy 281:1

convene 480:19 642:18

convened 418:8

convenience 221:6

convening 466:24

conventional 53:25

conversation 439:12 470:19 479:26 491:18 526:17

554:23 558:19 621:24 627:20 640:26 646:1,25

670:9

conversations 442:8

Conversely 189:12 210:16 548:17

convert 103:21

converted 96:14 102:13 106:17 115:22

convey 271:8

convinced 394:22

convincing 462:10

convulsions 298:8

cool 426:24,25 614:7

coolant 505:21

cooling 38:21 77:24 427:25 428:11 429:22 430:9

430:15 439:25

cooperation 11:5 16:4 23:1 279:2

coordinate 396:17

coordinated 15:23

coordination 145:12 175:1 283:8

copies 289:2

copy 288:15 297:2

corbiculae 601:16

core 687:22

corn 137:1 138:26 148:21 292:25 293:3,6 298:16

299:8,10,13 300:5 304:25 305:20,25 307:19,20

310:26 316:12,14,18 317:5,11,13,14,17,25

318:13,20 319:5 320:15 332:5,8,11,13 411:15,16
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412:16,19 420:10 421:9 636:24

corner 10:1

cornerstone 313:4

cornfields 578:18

corn-belt 320:20

corn-growing 320:14

corn-soy-bean 285:17

correct 16:20 50:24 70:14,15 133:19 134:7 137:22

137:23 151:18 153:2,13 294:8 333:10 347:12

369:22 374:20 386:15 403:15 425:16 436:4

455:15 459:19 487:23 488:4 501:14 510:14

519:22 522:24 584:17 612:7,25 651:10 656:2

669:8

correction 687:7

correctly 45:5 377:21 415:18 424:16 488:12 512:8

527:10 620:13 671:13 679:11

correlated 264:9,13 536:10

correlation 642:7

correspond 379:5 394:3

corresponding 65:17 537:5

corresponds 500:4 610:7

cost 155:19 192:23,24 196:4 290:5 304:12 308:21

310:5 374:13 494:21 537:14 570:14

costs 28:19 500:2

cotton 122:10 148:23

coumaphos 308:2

council 16:5 324:10 668:1

Counsel 3:1

count 328:20 448:22 602:22

counterpart 15:3

countries 8:18 11:7 15:20,26 23:4,11 247:4 267:18

611:1

country 9:2,21 228:3 239:3 285:17 287:23 291:19

306:16 308:18 311:16 316:19 320:2 396:18

595:11 600:10

counts 171:12 305:11 328:17 600:13

county 317:14

couple 2:7 10:23 131:3 132:16 135:24 138:1

233:24 251:19,21 266:3 269:11 287:8 324:20

334:1 343:12 397:17 424:11 436:10 442:16 453:6

569:10 595:5 622:9 648:4 651:22 663:17,18

coupled 141:5 231:21 232:12

course 93:9 101:16,21,26 109:3,15,23 112:15

113:2 137:26 197:22 263:5 269:17,18,24 305:2

307:2 346:11 370:18 371:9 447:10 448:3 454:17

474:3 482:19 487:14 494:1 498:22 518:15 519:10

528:11,15 529:26 545:12 548:11 557:21 558:2

566:20 595:13 605:25 626:18 629:14 634:23

635:17,19 645:9 647:1 680:9 692:23

cover 44:5 89:6 125:9 216:11 248:26 434:23 457:7

524:10 580:7

covered 43:8 92:26 134:22 187:4 214:15 408:15

420:8 566:11 600:11 615:11

covering 192:2 249:2 530:21

covers 76:10

cow 642:20

co-applied 368:6

co-chairs 23:10

co-formulated 220:12

crafted 442:3

Crailsheim 96:6 226:4 227:7

crashing 691:15

crawling 303:9 310:1

create 318:9 629:5 649:10 660:13

created 7:14 240:26 347:24

creates 11:19 268:19 649:11

creating 290:6

credence 640:20

credibility 136:6 272:1 395:24

credible 12:13 253:26

credit 419:15 693:11

Creswell 682:2

crickets 110:20

cringing 561:5

criteria 29:26 195:19,26 206:24 256:9,24 272:13

274:7 617:25 625:10

criterion 552:26 625:14

critical 7:24 8:24 9:3 12:6 151:16 159:8 176:20

202:26 253:14 254:18 260:10 267:22 278:19

289:23 457:5 458:11 479:14,17 488:22 505:25

509:15 510:2 553:14 555:5 557:18 559:18,19

560:23 561:6,7,8,9,11,11,13 563:24 564:9 567:24

594:17 631:3 650:4 656:19,26 675:1 680:10

681:4

criticality 554:26 555:17

critically 316:24 328:4 657:5 680:23

criticize 618:10

criticized 314:13

criticizing 546:25

crop 36:25 41:18 60:1,24 91:15 95:11 109:13,13

125:3 136:25 137:7 143:22 144:25 145:1,3 147:1

147:12 168:15 169:8,11,13,17 170:1,19 177:17

180:8,9 181:9 182:22 183:14,18,21 184:12,14

185:20 186:23 187:15 188:21 190:10,14,15,17

192:8 210:14 252:20 253:5,14 265:19 266:3

268:3 269:7 276:10,10 278:1,15 282:21,22

285:19 286:5,21 287:12 300:22 317:6 348:17,19

349:5 350:14 351:26 352:12 394:8 400:22 414:8

429:14 439:2,3 513:9 516:2 520:1 521:17 524:4,6

524:8 525:2,15,16,18,21 529:3,12,19,23 530:13
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534:13,26 537:23 538:10 549:14 558:2 563:5,20

563:21,26 564:3,4,7,8 565:2,13,13,14 566:6,21

567:1,4,6,26 568:6,13 569:23 572:16,18,22

573:18 575:21 577:11 579:17 581:21 582:25

583:1 588:16 589:26,26 590:1 593:25 594:4,5,5,8

594:9,13 595:23,25 597:8,14,15,17,18 598:11

599:11,15,21,25 600:15,21 601:16,25 602:4,5,19

602:21 603:4,13 628:25

CropLife 252:11,15,19 253:9 255:2 275:2 277:24

278:2,9 295:15 296:6

cropped 147:5

cropping 321:1

crops 57:13 60:14 78:23,25 84:23 94:24 106:21

109:9,10 112:5 118:2,20 119:22 122:18 123:1

127:26 136:24 137:1,1 145:6 146:15 148:13,14

149:25 155:15 167:6 174:9,16,22 179:9 187:18

194:20 228:4 239:6 257:25 278:14 282:23,24

285:24,25,25 286:8,22 287:2 297:19 300:2

302:13 310:23 351:26 366:24 395:15 402:11

412:14 421:25 428:22 524:9,11 525:3,7 542:14

563:19 568:14,17 573:3,12 574:20 575:13 576:25

577:4,8,18 585:11 589:16 590:3 594:7 596:7

597:2,5,5,6,19 600:12 601:17 602:24 603:11,21

688:4

CropScience 254:25

crop-by-crop 400:23

crop-centric 351:24

cross 193:9 290:23 293:9 447:10 574:11 575:21

664:2

crosshairs 527:2

crossing 600:25

cross-pollination 300:5

cross-purposes 661:25

crucial 576:1,11

crude 243:22

crunch 260:8

cues 439:3

cuff 555:7

culmination 414:6 464:12

cultivation 299:10

culture 514:10

cultures 514:9

cumulative 264:24 295:1

cup 262:4,6,9 265:3 481:11 484:14

cups 262:2 443:24,26,26

cure 335:18

cures 389:10

curiosity 309:22

curious 372:10 468:19 494:7

current 18:15 23:26 24:19 25:7,14 56:24 110:7

153:21 156:17 163:20 167:20 168:1 208:10

213:16 221:16 229:17 234:19 244:16 255:17

540:15 607:6 616:22 618:2,14,19,20 619:7 621:9

652:11 662:2

currently 24:2 25:4,11 48:3,10 63:13 70:11 83:5

119:2 123:13 140:17 141:11 142:22 146:19

147:14 156:7 158:8 175:16 182:4 184:22 205:16

208:12 228:22 255:16 265:20 290:18 338:17

381:10 392:25 441:22 444:24 503:4 607:21

616:24

Curry 322:13

cursory 228:24 230:1

curve 68:5 233:2 494:24 495:4 687:2

curves 198:2,3,23 205:8 444:10 447:16,16 491:23

492:1,23

custody 313:21

customary 3:25

cut 401:3 656:12

cuticle 375:23 433:19

cutoff 614:3

cuts 552:3

cutter 315:3 347:12,13

cutting 334:26 347:15 351:2 363:10,22 443:12

559:25 560:2 572:22 584:14

cutworms 317:16 320:13

cycle 66:24 67:8,8 343:5

cycles 192:3

D

d 402:2,5,7 432:19,21 474:13,15 476:7 488:26

491:7,9 493:12 518:11 526:24 538:22,24 542:18

543:2 587:26 588:2 593:10 615:3

daily 79:25 96:19 198:7 324:12 382:16 423:19

425:13 426:1 438:6 553:23 681:26

dairy 310:26 642:20

dalcap.org 324:8

Dallas 322:9,11 323:5 324:9,10,13

damage 301:13 303:5 503:1

damaged 503:3

Dan 4:3 249:12

dandelions 91:17 299:20 305:24

danger 613:21

Daniel 3:20,22 6:14 13:6 14:24 16:17 17:25 32:7

32:19 42:14 44:11 45:22 47:7 48:20,26 52:26

66:18 67:11,26 68:15 69:12,23 70:3,9 71:3 81:7

82:3 83:3 84:12 85:6 87:11,21 88:23 129:21

130:19 131:16 132:15 134:25 136:21 137:16

138:21 140:1 150:9 151:21 152:11,23 154:3,7

194:24 195:10 196:6 197:15 199:7 200:1,5,20

201:18 203:3 214:20 215:3 219:12 220:1,21

229:24 231:9 232:15,20 236:20 237:16 239:14
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242:20 243:11 245:5,14 247:15 249:9 251:4,18

251:19 252:6 274:23 275:21,25 276:16 277:22

280:4,18,20 284:6,21 285:8 288:17,20,24 296:11

302:6 309:4,20 311:3,7,22 314:16 315:8,22 316:8

316:11 319:2,13 320:23 321:14,24 322:1 323:25

325:2 326:21 328:8 329:6 330:9,22 331:6,26

332:14,17,20,26 333:10,14,24 336:16,19,21,23

336:25 337:14 339:7,14,17,19,26 340:14,25

341:15 343:2,10,22 344:2,5,12,18 345:2,17 348:9

348:12,18,21 349:7,20 350:9,16 352:14,21

353:15,22 354:10,25 355:19,25 356:4,12,20,25

357:10,23 358:2,5,19 364:4,7,17,21 365:2,7,15

366:2 367:24 368:14 369:11,21 370:1 371:3,13

371:18 372:5,16,24 373:2,10 374:4 375:16,24

376:22 377:5,16 378:16 379:7 380:11,13 381:3,7

381:21 382:4 383:15,19 384:2,12 385:9,11,21,26

386:7,16 387:6,14,23 388:7,17,26 389:6,12 390:2

390:7,15,20 391:14,19 392:2,8,18 394:10,19

396:13,23 397:5,14 400:12,25 401:15,18,22

402:1,4,12 403:7,9,11,14 404:2 405:9,12,14,17

405:25 406:22 407:1,9,12 408:2,8,14,18,22 409:2

409:11,16 411:9 412:8,26 414:21 415:8,15,20

416:9,26 417:8,14 419:4,23 420:3,22 421:13

422:2,8,16 423:5 426:19 427:2,5,8,20 428:15

429:16 430:6,19 431:7,23 432:1,10,17 433:1,12

433:22 434:5,18 435:2,25 437:4 438:21 439:6,10

440:4,14,22 441:11,16,24 445:8,16 446:1,7,19

451:7,22,26 452:14 454:2,15 455:1,20 456:21

457:8,16,23 458:2,7,23 459:13 460:3,9,21 461:7

461:23 462:1,13 463:4 464:2,16,20 465:7 467:22

467:26 468:3,18 469:2,13,23 470:3,14,23 471:8

471:13,18,21 472:1,11,13,16,20 473:1,7,10,13,16

473:26 474:8,12,18 475:18,22,25 476:5,20,25

477:6,26 478:3,5,14,17 479:8,24 480:9,17 481:2

481:16,22,25 482:1,11,24 483:15,21,24 484:7,20

485:3,6,8,17,25 486:6,12,22 487:11 488:1,13,20

489:7,15 490:12,22,26 491:6,13 492:4,13,16,18

493:4,10,18 494:11,19 495:18 496:10,17 497:7

497:13,19 498:8,14,18 499:4,10,15,23 500:21,25

501:2,10,16 502:3,10,18,25 503:7,16 504:1,17,24

505:8,13 506:7,21 507:3,10,16 508:1,7,13 509:9

510:6,11,15,18,21,26 511:13,23 512:5,17 513:12

516:18 517:8,19 518:3 519:16 521:1,8,25 522:16

523:4,22 524:23 525:5,23 526:5,25 527:11,19

528:5 529:16 530:6,23 531:4 532:21,24 533:10

533:26 534:20,22 535:6,11,19 536:2 537:1,7,15

538:6,11,21 539:11 540:12,21 541:6,22 542:3,17

543:1,13 544:26 545:23 546:4,17 547:4,9,16

548:5 550:7,16 551:8,16,25 552:17 553:8 554:6

554:21 555:18,22 556:1,20 558:7 559:4,14,22

560:17 561:15,24 562:1,10 564:22,24 565:5,8,20

566:16,25 567:13,20 570:23,26 571:9,14 572:25

573:5 574:6,13 577:1,19 578:7 579:6,13 580:2,11

580:18 581:6,17,26 582:15 583:12,23 584:5,12

584:19 585:12 586:1 587:18,20,22,24 588:8

590:10,26 591:10,13,26 592:15,21 593:8,22

594:26 595:2,19 596:4,10,20 597:23 598:5,16

599:7,26 600:7,17 601:7,10 602:6,16 603:15,24

604:18 605:1 606:22,25 607:1,23 608:6,25 609:4

609:9,19,24 610:2,5,15 611:7,18,26 612:19,26

613:12 614:8,15,22,24,26 615:9,24,26 616:17,25

617:11,18 618:1,12 619:12 620:2,15 621:1,16

622:5 623:4,21 624:2,5,12 626:2 627:2,21 629:9

629:17 630:2,11,16 631:26 633:14 641:2,17,22

643:3,13,20 655:24 658:13 667:3 669:6,11,20,23

670:7,13,17 675:14 676:11,16 681:17 683:4,15

685:8 692:16 693:9,16

dash 113:26

dashed 37:10 60:15 113:25

data 10:24 15:17,18 17:5,6,19 19:11,17,18 22:12

25:26 26:4 27:3 29:1 30:6,7,7 31:9 46:21 90:19

90:20 99:9,19,21,23 100:14 101:3 104:6 105:3

107:12,20,25 108:5,7,11,13 109:8 110:17,22

111:8,23 112:5,7,11 113:20 114:3,6,24 115:10,19

116:13,18 117:11 118:1,7,16 119:24 121:15,17

121:19,23,25 122:2,3,12,13 123:16 124:11,21,23

124:26 126:10 128:13,17 129:3,17 130:15 135:21

138:9,13 139:6 140:18 141:14 142:8,9,22,24,25

143:8 144:9 145:14 146:13,16,20,21 147:20,22

150:2,4,16,21,24,24 151:11,12 153:10 156:7,12

157:16 158:10 159:6 162:7,11,12,20,21,23 163:5

164:17 165:2 166:19 167:20,23,25 168:1 171:11

171:11 174:7,10 179:20 194:11,15,16,20 196:17

199:3 201:21 202:2,8,19,25 203:20 204:4,19

205:3 207:20,21 208:26 211:2,4,5,14,16,17 212:4

212:8,8,16,17,19 213:7,17 214:11 215:22 216:11

216:19,26 217:1 218:6 220:19 223:26 226:16,21

226:26 227:1,9,26 228:20 229:8 230:18 231:16

232:13 233:20 234:10 235:20 236:19 237:6

238:18 240:17 245:3,8,10 255:14 256:11 257:4

257:17 258:3,10,15 259:3,13,16,17 260:12,13

261:11 262:19 265:11 267:16 269:12 276:23

277:14 278:25 279:12 281:20,21,23,26 282:2,16

292:12 301:15,18 308:26 312:15,17 313:5,6,6,7

313:12 314:1,5 315:17 319:6 326:24 328:6 330:5

330:11 366:9,15 372:21,22 373:13,14,16 374:10

374:12 379:6,10,15 380:4 381:18 382:15 387:11

387:17,20,25 388:4,6 389:12,13 390:3,11,13,18

390:19 391:3,9 392:21,22 393:1,4,6,10,14,20,25

394:23 395:2,5,17,18,22 396:6,16,19 397:2,4,19
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398:1,9,11 399:10,17,18,24 400:15 401:9 402:19

402:25 403:1,6 405:20 406:25,25 407:7 409:9,25

410:11,12,18 412:5 413:3,5,11,14,24 414:2,4,11

414:18 418:4,19 419:2 421:8 423:3 424:6 428:19

430:17 447:4 450:6,9 451:9 460:8,19 466:17,22

466:25 473:6,24 475:9 482:17 488:4,7 495:3

497:18 501:8 502:23 505:6 509:3 515:11 517:4,6

531:19 545:21 549:15,20,21,22 550:6,10 600:4

604:22 606:13 607:10,12 610:18,26 612:10,21,21

612:23,24,25 613:8 615:19 617:8,15,20 623:16

623:26 630:6 636:21 641:16 647:24,26 652:11

657:13 660:11 665:14 670:25,26 671:3,6,7,9,12

671:16,17 672:24 673:19,22,23 674:6,11 680:7,8

680:12,22,26 683:8 691:24 692:9,12

database 238:6 257:16 292:20 327:6

dataset 110:10,22 114:19,21 115:1 125:5 146:21

237:8,8,9,15 259:26,26 400:2

datasets 237:13 424:2

date 152:2 160:21 187:16 193:13 280:16 285:5

590:24

dated 202:5 399:22

Dave 254:23 266:1,5 267:19 270:15,16 275:4,23

276:4 277:6,6 315:10 344:19 352:24 368:16,24

380:15 446:20 453:13 454:16 455:10 459:14

460:22 476:14 485:26 489:8 490:17 521:26

532:26 536:3 537:20 553:11 556:5 601:8 605:3

608:7 614:16 615:10 616:5,26 641:8

David 5:1,1 82:4 132:16 133:1,10,15,18 134:6,8,19

134:23 152:25 153:17 154:2 249:25,25 252:26

254:24,24 315:10,21 344:19 352:24 368:24

371:21 380:15 430:8 441:25,26 442:1 445:19

446:20 454:16,24 455:10 458:17,17 459:14

460:22 473:17 476:14 485:26 489:8 490:17

501:18,18 511:24,24 513:14,14 521:26 522:21,21

527:1,1 531:5,5 536:3 537:20 538:19,19 539:12

543:15 544:7,7 545:1 548:6,6 553:10 565:6

571:10,10 587:23 591:11 595:20 601:8,12 605:3

608:7 614:16 615:10 616:26 624:14 626:11

630:17 639:2 657:26 675:12 681:15 686:16

Davis 383:5

day 79:11 85:12 92:12,15 93:23,24 97:9,23 101:22

102:7,10,11,12,13,24 103:8,19,20 104:4,17

105:16,18 127:7 131:18 132:6,12,23 134:14

137:25 138:1,17 142:24 149:10 161:16 202:2

206:16 220:22 242:5 243:12 249:8 251:8 262:20

264:5,10,23,24 267:7 304:18 316:2 330:12

346:14,20 361:18 370:7,13,17,22 379:2 424:6

425:16 426:3,3 432:18 435:16,24 477:8,9 480:13

482:7 483:7 485:13,13,16 486:18,19,19 487:3,9

488:24 500:5,10 534:7 602:15 607:2 610:19

622:4 675:2,3 692:14

days 13:1 92:9 94:9 101:17,18,26 102:4,21 103:3

103:15 131:23 149:11 161:17,22 169:20,25 170:4

173:5 176:16 184:5,9 269:18 304:19 306:17

308:9 324:20 361:14 370:10,10 371:10 378:26

382:12,13,18,19,21 401:7 429:6 436:10 465:3

477:19 479:16 480:4 481:8 483:12 487:9 500:4

520:21 528:14 532:14 534:6 540:7 545:6,7,11

553:24 657:10 689:23 692:22 696:20

daytime 184:13

day-to-day 7:18

day-5 466:3,3

DC 245:8 303:13

de 683:6

deacetylase 465:18

dead 171:13 269:16 298:10 303:9 309:10 473:17

511:21 557:11,12 558:16,20,21 563:16 576:3,4

625:13 653:17,17

deal 11:16 82:16 97:13 139:25 220:10 310:17

369:17 444:23 558:21 570:14 637:20 642:21

dealing 11:18 130:1 282:8 287:15 315:19 344:25

346:9 417:5 459:25 473:20 515:3 551:11 552:11

642:20,25 685:20,21 687:7

deals 18:24 184:3 444:18 665:4 668:9

dealt 77:20

dearth 523:14 543:25

death 163:13 284:23 478:7 500:16 528:15 560:25

646:18 671:21

deaths 671:19,23

debate 452:7

debating 674:18

decades 450:17 497:24 692:7

DeCant 61:9 134:18 141:7 154:8,9,10 195:17

198:25 205:21 209:23 213:22 222:21 441:19,20

446:9,11,11 458:25,26,26 463:7,7 464:3,17,19,23

464:23 471:19,20,23,23 472:18,19,22,22 474:9

474:10,14,14 476:7,9,9 477:1,1 478:15,16 480:21

480:23,23 482:2,6,6 483:25 484:1,1 489:1,2,2

490:4,4 491:7,8,8 493:12 499:17,17 503:17,20,20

508:20,22,22 512:6,7,7 513:2,2 526:11,11 531:1

531:1 535:22,22 538:23,23 543:2,4,4,17,17

547:20,23,23 562:7,7 567:16,16 573:9,10,10

582:4,4 585:22,22 588:1,1 593:12,12 598:17,19

598:19 696:4

decay 571:4

decent 563:24 597:9

decide 179:20 404:5 651:15 659:15 662:5 668:24

decided 224:9 282:1 330:3 493:26

deciding 222:24 479:3

decision 26:25 33:15 42:12 53:6,8,11,14,18 55:3,18
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56:15,20 57:3 59:15,17 60:6,11 61:21 62:14 64:2

64:8,12 67:19,21,25 68:8 89:14 91:4 92:16,22

139:8 141:9 143:2 167:19 196:11 218:13,13,19

242:13 287:25 541:12 546:22 547:17 586:25

607:24 640:24 660:11,20 662:21 668:17 669:5

decisions 7:20 10:10 12:7 15:24 26:17 28:25 29:24

55:21 68:14 141:3 239:22 272:3 313:14 617:26

630:9 680:22 681:1 692:13

decision-making 2:19 6:24 7:21 53:18 56:11,22

decline 9:8,10,18 19:1,9 20:15 21:10 149:4 198:23

233:3,6,14,20 234:21 235:9,19,20 236:10,12,13

237:22 245:19 282:10,12,23 283:2,5,14 284:11

284:23 285:1,3,4 297:24 299:2 300:16 653:16

declined 19:12,19 318:14 689:18

declines 18:9,14,25 19:6,7,15,24 20:15,16,21,23,26

21:9,12 176:18 233:16,26 234:1,14 235:12 238:5

238:13,15,16 244:12

declining 501:23

decrease 298:3 497:3 502:6 682:22

decreased 298:4

decreases 15:21

decreasing 189:13

dedicate 244:19

dedicating 244:6

deem 202:9

deep 473:8

deeply 688:24,24 689:6

Deere 419:16

default 107:3 123:4,21 153:2 165:4 202:17 205:25

207:2 258:6,14 282:3 375:21 376:17 406:13

436:25 511:25

defend 313:14

defender 690:2

defense 510:8 690:6,12

defensibility 240:13

defensible 228:7

defer 476:13

deferring 134:21

deficiencies 275:11

deficits 539:7 541:10

define 135:7 194:2 246:21 342:22 378:9 410:21

448:9

defined 24:14 28:13 29:19,21 34:11 509:6

defining 485:22

definitely 49:18 131:12 268:21 275:5,14 277:8

331:5 334:5 372:14 391:19 398:21 432:3 459:22

520:4 534:14 548:25 594:4 624:17 631:14 667:11

693:16

definition 450:2 485:11

definitive 194:1 455:4 527:9 544:21

deflectors 331:15 414:7

deformed 561:23 637:16,17,18

degradate 166:8

degradates 34:17 36:25 122:4,24 143:24 157:2,4

175:12,20 179:5 185:7

degradation 46:19 149:7 370:19 388:24 389:9,11

390:4 634:25

degrade 94:16

degrading 198:15

DeGrandi-Hoffman 225:15

degree 14:19 55:13 67:20 84:25 135:6 176:5

185:26 201:23 212:4,23,25 222:24 243:7 364:13

388:4 389:8 423:16 461:20 496:15 556:6 564:12

657:20

degrees 9:18 430:12,13,14 459:15 615:18

delay 290:5 294:12

delayed 149:14 157:10 187:10 342:15 482:17

Delclos 4:23,23,24 249:22,22 458:8,9 486:14

686:14

deleting 340:9

deliberations 8:3 10:22 247:21 325:11 424:26

435:17 436:9

delicate 457:11

deliriously 692:4

deliver 161:21

delivering 558:17,24,26

demand 292:2 315:5

demands 93:15 239:17

demographic 234:5 561:2 653:19 654:8

demographics 234:25

demography 655:3,10 671:20,20

demonstrate 124:23 170:6 297:25 509:25 564:20

595:25 668:26

demonstrated 158:17 184:13 317:10 318:19

demonstrating 462:4

demonstration 575:6

denominator 132:23,25 648:19

density 185:19 186:22 398:3,24 575:15

Denver 313:10

depart 652:26

department 4:4,14 5:2,22 6:10 8:10 10:19 13:26

14:15 16:21,23 19:10 200:16 249:13,20,26

250:17 283:11 302:26 303:13,15 696:8

depend 9:6 26:26 145:11 169:3 278:15 326:8 525:2

653:10

dependent 130:2 146:5 170:9 201:23 371:1 398:26

496:20 510:4 602:4 675:4

depending 36:2 55:18 139:15 157:20 170:24

183:26 190:23 237:11 344:15 362:24 382:23

402:26 487:10 503:11 532:15 552:5 557:20
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568:13 576:23 581:21 582:24 598:13 602:18

608:23 628:10

depends 139:12,13 177:1 454:12 527:4 556:24

574:22 575:5 602:12 603:8 628:13 636:2

depict 50:2

depicted 19:21 37:14,18 38:5 40:8 41:4,9 58:5

59:16 61:23 62:16 81:17 91:8,25 97:22 100:17

102:8 104:25 111:18 113:25 114:4,6 121:11

128:11 143:14 164:18 200:25 204:13 208:18

345:14

depicting 35:5

depicts 34:8 72:18 73:21 74:21 98:22 102:20

104:11 109:13,21 113:22 114:23 208:3,21

deplete 528:3

depleted 521:6

deposit 41:14 426:25

deposition 37:1 39:26 41:20 287:20 288:6 411:3

439:23 623:16

depth 16:9 287:17

deputy 2:26

derivation 54:9 58:7 608:2

derive 58:3 92:2 121:9 130:21 139:21 157:14

158:21 161:25 162:18,22 164:1 207:16 493:23

591:6 617:8

derived 26:3 59:26 179:14 208:23 232:11 495:4

496:23 509:5 608:12

derives 443:8

deriving 396:12

dermal 37:25 41:13 81:12 415:19

describe 27:12 33:11,20,24 53:5,13 71:25 89:20,25

164:8,13,22 174:23 194:6 221:13 241:12,22

281:26 312:18 327:21 505:2 627:24 675:25

described 11:10 38:22 39:20 40:4,23 41:2,23 58:23

59:12 63:18,23 64:3 66:22 67:1 71:24 117:15

125:26 155:24 166:19 183:13 196:24 205:20

222:21 406:1 433:6 463:14 475:9 479:10 518:6

520:17 524:22 562:9 567:12 574:16 622:17 678:6

678:10

describes 143:25 464:11 621:3

describing 36:13 53:11,20 56:2 68:7 118:1 221:7

241:14 326:24 418:10

description 12:6 25:22 34:13 89:12 107:11 136:10

204:25 209:5,8,14,16 210:20,23 212:15 214:2,7

214:15 221:17

descriptions 86:21 241:2

desert 429:11 598:11 601:1 653:3,4,5,7 654:4,25

design 54:6 61:26 64:4 112:20 140:12 142:16

143:26 145:4,10 147:4,7,9,14 148:1,2,26 159:8

159:11,12 160:3,24 161:18 168:6,8,18 169:14

170:23,25 171:3,21 172:13,17 173:3,7,15 174:6

176:7 179:26 180:12,17 182:7,15,21 183:3,7,9,14

185:16 186:26 187:2,5 190:7,18 192:14 193:20

193:20 194:4 222:20,22 229:7,21 267:23,23

268:19 290:21 395:12,18 465:1 482:12 484:11

490:7 493:22 494:3,6 496:21 503:25 524:12,21

526:12,13,14,16,19,21 527:4,7 538:25 539:21

540:2 543:6 544:9 546:25 551:5 562:24 567:10

567:18 570:9,10 571:23 579:4 584:4,6 590:18

593:13 594:16 598:21,25,26 603:25 617:16

628:10,12 632:13,17 633:19 635:5 642:12

designate 679:5

designated 2:6 251:10

designed 13:15 15:17 61:24 90:22 107:8 142:14

143:12,23 144:4 147:2 149:9 158:11 163:16

178:22 225:2,19,21 246:18 266:13 268:15 376:4

493:23 510:4 570:8,16 593:15 607:6 642:9

678:14

designing 148:6 182:9 445:21 563:24 564:1 594:18

595:14 630:15 632:3,4,13 633:13,25 634:10,21

designs 21:25 144:24 145:7,10 168:11,21,24 169:3

169:7 170:25 171:25 175:26 176:1 179:25 180:5

180:13,18 185:6 187:3 190:9 192:7 212:18 254:6

267:20 268:13 300:21 539:4,26 604:25 606:6

630:26 635:26

desirable 227:15 228:1 580:5

desired 28:9 98:16 145:5 147:9,18,19 178:1

despite 19:17 31:13 112:9 119:20 125:7 126:2

178:22 283:15 465:24

destined 466:3

destroyed 326:10

detail 24:18 35:6 36:14 38:7 53:9 60:2 69:20 70:2

78:14 92:19,26 108:5 128:24 140:14 148:2 182:9

182:20 242:9 279:24 288:14 345:20 349:12

409:24 443:17 444:16 518:5 645:1 693:6 694:8

detailed 26:6 57:11 66:15 97:2 224:6 281:4 289:20

375:19 646:9

details 32:10 33:13 45:16 65:23 101:12 224:8

256:4 288:4 442:18 645:26

detect 52:2 130:24 170:8 185:24 441:1 541:17

638:10

detected 112:15 131:10 142:3 342:11 549:25

detection 130:23 131:6,9,12 395:11

Detections 293:17

determinants 653:12

determination 91:12 455:14 466:10

determinations 283:4

determinator 442:20

determine 19:5 39:11 52:13 63:25 91:10 107:21

117:23 153:15 157:25 186:7,18 191:5,8,11 194:2

210:6 235:7 240:5 266:16 394:23 396:7 438:3
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495:8 569:3 652:12 668:11

determined 58:9,15,18,26 59:8 60:19 62:24 80:14

91:9 174:26 400:5 621:7

determining 18:12 22:15 30:1 71:25 107:15

142:26 148:13 167:17 219:7 270:9,13 276:6

284:25 465:9 484:5 593:18 599:15 656:25

deterministic 205:2 494:23

deterrent 542:2,2

detoxification 264:26 465:21 466:17 467:5 475:13

504:9

detoxified 265:5

detrimental 153:25 301:11

devastating 253:19

develop 11:7 51:4,14 146:16 164:20 243:26 244:9

268:13 270:20 275:16 276:5 331:18 604:26

656:22 682:9

developed 8:15 21:14 23:8,12 27:20 28:5 29:17

31:20 51:18 56:7,26 59:5 64:11,16 77:7,12 80:14

83:21 86:2 107:6 159:15 160:21 211:18,24

212:11 228:26 231:23 256:16 258:1,3 316:5

358:8 393:3 394:7 398:6 457:3 459:6 477:22

479:18 480:25 491:5 508:11,26 509:7 512:10

545:8 610:11,12 618:5 620:24

developing 20:10 22:23 23:4,16,17 28:6 40:9 51:15

58:25 110:4 122:25 132:7 228:14 237:20 244:19

261:5 274:17 276:7 279:10 346:11 393:17 450:13

468:14 471:26 477:22 508:24 509:19 696:5

development 11:6 14:18 23:1,22 29:12 35:23

53:13,22 59:7 61:6 66:1 67:8 88:15 102:24

131:23 165:23 170:13 171:10,11 176:21 211:25

225:11 228:12 240:2 257:4 267:9 271:18,20

299:4 316:7 338:3 342:7,15 343:26 365:4,13

378:25 385:4 393:21 398:13 404:21 405:3 456:13

457:15 458:12 465:3 467:13 470:10 475:11

477:12,24 480:7,12 486:20 487:14,15 488:16

504:22 508:15 512:10 535:25 557:2 610:12 617:4

661:17 668:23 684:14

developmental 101:21 379:3 487:25 488:22

deviations 610:13

dew 38:6,7 40:13 78:23,24 79:2,6,8,10,13,17 80:2

82:6 348:3 349:1 445:23

DFO 2:8

DFW 322:9

diagram 26:8 35:5 58:6 60:16 661:5

diagrams 34:23 35:7,10 82:23 350:22 351:8 352:4

359:6

dialog 667:24

dialogue 60:18 197:4

diapause 507:9

dictate 139:17 569:22

dictated 29:24 194:5

die 447:6 456:4 475:2 583:8 646:19

died 297:16 306:4 310:15 560:14

dies 157:9

diet 70:26 73:4,16 75:2 79:1,7 89:18 91:24 92:24

93:1,8,9,21 94:5 100:3 101:16 104:9 127:3

138:12 143:11 145:25 158:22 159:4 167:7 366:7

371:20 384:18 385:18 407:24 408:1,10,24,25,25

422:25 429:10 466:16,25 468:20,22,26 470:2

599:20

dietary 78:20 80:16,22 89:16 98:15 106:5,10,14

113:5 115:23 129:9 141:15,17 156:25 157:18

171:22 172:1 256:1 257:21 259:20 272:17 327:1

328:3 395:24 403:22 412:23 425:20 426:8,22

438:13,14 657:20

dietary-based 115:25 141:18 206:14

differ 55:18 64:6 65:13,23 72:9,11,12 145:23 184:6

277:7 320:14 445:14 552:5

difference 65:23 92:21 147:25 220:10 329:9 362:7

362:22 370:24 385:19 387:8 400:24 412:1 415:18

434:26 483:12 485:21 558:10 575:19 577:25

578:9 599:4 663:13 670:4 693:26

differences 31:9,11,13 36:10,16 65:11 108:8

118:21 122:17 148:17 213:1 228:3 255:22 269:19

280:6,10 321:7 329:2 330:15 358:11 384:24

445:12 457:25 458:21 465:1,11 470:13 568:25

577:26 606:18

different 8:18 9:26 10:25 36:9 39:3 43:15 44:16

60:19 64:25 70:1 75:8 77:4,5 88:9 93:20 94:6

95:23 96:1,11,20 98:23 99:5 100:11,17 102:21

104:5,6,10 106:16 112:9 113:11 117:12 120:9

122:19 125:1,1,25 130:5 132:20,21 133:4,5,15

134:9,12 145:9,20 146:2 148:9,9,17,19,19 149:3

149:15,15 150:17,18 152:15,22 154:20 160:9

166:17 169:2 172:7 174:16 188:7 196:23,26

216:17 222:12 223:9 241:10 246:9,15 265:3

273:14 276:26 277:2,5,18 307:20 312:19 327:12

328:26 329:1 330:18,18 341:21 342:4 347:3

350:18,19 351:2 353:21 358:11 359:14 368:6,20

368:22 370:26,26 371:9 373:21,21,24 380:17,18

380:22 383:8 387:3,19 388:1 393:11 396:18

397:17 398:18 400:1,21 402:20 406:20 414:7

416:11 418:16 421:8,20 422:1,1 423:13 428:22

429:4,12 436:24 444:13 445:2,2,4,17,23 446:25

448:19,25 456:8 459:3,17 463:19 468:15 472:9

486:24,26 487:6,25 488:3 492:7,9,22 494:2,22

501:21 513:22 514:13 515:7,20,21,21 516:4

518:5,11,23 519:6,21 524:9,11,21 528:7 529:8

532:19 535:16 536:10,13 544:17,18 548:24 549:2

549:2,10,22,26 550:1 551:14 553:12 554:23,24
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558:19 566:5,5 570:20,21 573:12,12,13 574:19

575:14,16 576:14,21 580:6,10 581:22 591:23

596:24 602:19 605:15 606:5,7,8 608:23 611:1

626:9 627:14,15 628:6 629:23 631:4,5,5 632:8,18

632:21 635:7,17 637:22 639:21 648:8 650:16

655:15 656:3 657:1,13 661:12 662:5 665:10,18

665:23 673:9,10 675:24 677:26 679:26 682:14

684:3,13

differential 244:22,24 457:21

differentiate 195:22

differentiation 602:20

differently 632:18

difficult 52:6 55:14 145:8 152:2 178:13 181:16

185:15 186:26 189:8 192:16 235:1,14 238:17

239:9 257:5 311:16 331:22 342:24 343:4,7

358:25 363:13,18,21 365:4,9 386:10 394:9 449:1

449:4,25 452:24 457:13 460:11 461:3 463:15

465:25 467:8 477:11 487:16 497:22 498:23

500:18 510:3 516:7 531:26 541:19 548:9,13,20

552:3 558:5 565:3 600:26 601:19 602:25 603:12

606:20 614:19 620:20 624:16 636:14 666:23

679:3 690:5

difficulties 51:5 152:19 460:24 465:12

difficulty 83:17 457:24 459:19 461:5 485:22

503:13 515:2 549:21

dig 476:18

digging 374:22 623:10

digress 139:14

diligently 9:20 318:21

dilut 427:24

dilute 379:23 380:3,6 426:26 427:23 474:6

diluted 102:4,15

diluting 529:20

dilution 424:19

dimension 443:15

dimensional 447:13

dimethoate 123:12,16 128:12 170:17 383:4 472:7

478:11

diminishing 49:26

dioxide 537:23

direct 14:1 28:25 37:21 38:14 39:14 40:13 41:17

45:2 55:9 58:1 72:23,24 74:3,6,8,24 75:14,26

76:9 78:20 79:1,7 80:21,24 82:11,13 96:6 111:1

116:21 117:1 143:11 163:25 328:17,23 353:10

364:12 367:13 373:13 393:17 406:2,5,8,24

408:10 411:2 433:5 453:10 462:12 487:18 488:16

488:16 509:20 586:7,21 645:4 671:12 675:8

680:17

directed 21:6,7 45:9 206:25 248:5 325:13 656:22

direction 197:13 320:7 334:6 424:8 465:26 613:7

668:18

directions 399:1

directly 28:26 37:2 40:6 41:11 45:14 52:16 93:2

105:10 116:12 125:18 135:11 144:15 146:1,26

147:23 160:7 163:13 170:12,15 196:8 214:1

217:11,23 246:6 311:11 326:14 337:23 350:2

352:18 373:9 379:25 391:9 392:17,22 394:3

424:25 429:5,23 433:7 452:5 467:15 474:22

483:6 488:19 489:18 537:12 551:24 555:3 586:4

648:22 650:21 655:17 666:7,25 678:16

director 5:26 6:16 13:12 15:1,1 250:19 252:18

311:26 313:6 316:14 696:1

Directorate 17:2,10

directs 28:16

dirty 686:21

disadvantages 497:26

disaggregated 35:7

disagree 406:21 423:10 461:8 580:20 617:3 636:11

659:24 660:6,16

disagreeing 522:3

disagreement 414:22 539:15 585:17 667:12

disagrees 546:3

disambiguate 627:6,8

disappear 545:15 616:9

disappearing 294:22

discerned 160:15

disclosure 2:25 17:5 670:11

disconnect 604:16 617:7 619:15

discount 84:6 294:2 408:26 509:17 550:20

discountable 52:1 78:21

discounted 428:12

discounting 383:24

discover 52:20 336:9

discovered 299:3 336:8,10

discovery 551:12,20

discreet 66:25

discrepancies 121:23 122:16

discrete 66:23

discuss 21:21 27:15,17 48:7 53:21 71:25 89:22

92:25 98:12,19 102:17 106:8 107:13,18 125:15

203:12,18 209:6 214:8 219:10 221:16 325:8

341:22 350:11 381:12 403:17 409:9 423:2 432:22

458:4 500:23 504:14 515:24 522:22 631:15

645:10 647:20 672:23

discussant 243:17 333:25 336:17 337:15 340:15

341:16 345:18 348:10 357:24 366:3 379:8 392:19

441:24 465:7 498:15 513:12 562:11 605:1

discussants 442:16 449:10 479:5 481:17 491:17

492:3 531:6 606:2 608:8 624:15 664:24 681:16

discussed 21:24 24:18 26:6 30:22 38:7 47:13 58:8
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70:25 76:7,22 89:16 92:20 101:11 106:18 126:2

128:23 140:21 159:13 160:11 200:25,26 204:6

205:5,21,23 206:1 208:18 209:18,23 210:21

211:10,20,26 212:20 213:18,19,21 214:14 222:15

234:22 238:14 246:14 257:10 279:24 337:11

339:22 341:11 345:16 359:12,23 371:11 381:15

397:25 398:15 409:23 450:17 452:3 458:14 482:8

484:12 485:14 505:9,16 514:1 515:26 536:4

538:25 539:13 540:1 547:24 554:2 593:14 613:14

618:9 625:5,7 630:19 639:26 645:3 649:11

651:14 657:9 667:6 670:21 671:6 672:18 673:5

674:26

discusses 214:16 508:23 538:24 543:21

discussing 49:22 81:11 442:15 443:16 506:25

511:4 531:11 619:10 631:4 649:2,21 665:11

677:22

discussion 71:16 89:13 136:10 197:12 209:17

213:5 214:3 230:1,2 231:8 235:17 243:12 245:22

246:3 274:12 322:7 333:21 337:12 341:12 358:13

359:19 360:24 379:3,5 384:10 386:2 399:20

401:20,24 402:8 410:14 420:2 423:9 432:13

442:6 456:4 464:8 469:1 481:19 488:26 489:3

491:24 493:25 494:8 499:8 500:20 502:14,20

503:4,5 513:17 526:17 532:8 555:15 575:5

583:24 584:21,24 588:26 591:9 593:19 594:24

619:21 624:22 641:5 645:6 658:1 660:25 662:5

664:22 665:8 666:4 669:19 686:18 693:4,6

discussions 26:16 27:2 51:23 197:19 221:3 247:20

291:12 351:23 422:21 463:1,5 464:7 489:16

500:11 503:14 506:24 513:25 546:26 605:8,10

608:12 618:9 620:18,19 622:15 633:19 676:3

686:6 688:1 691:19

disease 21:4 177:7 184:19,21 191:21 308:22

316:25 449:12 452:17 587:7 588:23 589:15 593:2

637:6,14 642:23

diseases 5:25 189:17 253:19 471:1 639:15 642:18

642:19

disguised 108:2

dismiss 509:17 551:23

dismissed 50:9

disorder 20:12 253:23 282:11 284:24 318:17

dispersion 678:22

displayed 142:7

disposition 541:15

disproportional 642:10

disrespect 558:20

disrupt 462:11

disrupted 452:9

disrupting 87:2 211:21

disruption 452:9 545:17

disruptions 298:1

dissipation 116:5 117:4 147:6 149:6 571:4

dissolve 396:1

dissolves 356:3

distance 193:7 264:7 429:13,15 454:8 568:15,17

568:20 598:2

distances 177:14 190:22 300:26 570:14,19 571:21

598:3

distill 605:9

distilling 608:11

distinct 541:4

distinction 37:14 44:22 486:4

distinguish 42:26 90:22 612:14 630:7

distinguished 44:25

distinguishing 477:10

distract 686:22

distribute 357:11

distributed 184:7 251:16,17,24 252:24 259:6

301:21 320:13 331:21

distribution 109:12,14,21 112:21 148:18 225:3,9

251:24 262:25 263:20,24,25 264:3 282:9,17,19

283:6 320:18

distributional 97:17 205:4 282:19

distributions 112:14 261:15 263:22

distribution-based 205:6

disturbance 569:25 570:5

disturbed 600:11

diverse 293:22 359:9

diversity 29:9,9 301:8 334:12 337:20 339:1 340:5

340:13 357:22 358:22 492:21 493:2 572:14

580:25,26 581:4,19,20,25 635:18

divide 361:2,14 375:6 579:22 656:13

divided 206:16

dividing 58:19

division 13:13 17:2,10 18:2,7 33:3 71:10 89:4

140:8 141:23 154:11 203:11 207:1,10 221:1

225:20 353:13 622:13

DMPs 418:26

DNA 465:18

Doan 288:21,22 289:4 296:18,19 302:8,9,10,11

309:5,11,15,25 310:9,11,14,21,25 311:6,12

Doan's 326:18

docket 3:14,14 8:1 296:26 302:4 323:15

dockets 3:16

document 8:22 64:11 65:18 156:11 202:24 211:22

238:19 241:7,8 242:10,17 247:6 289:21 292:7

294:9,11 295:2 296:7,25 326:24 340:9 493:7

499:6,11,14 526:6 528:20 579:10 580:22 624:1

685:16

documentation 224:3 579:12 580:20 691:21
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documented 59:23 228:8 292:26 302:26 447:26

450:5 538:2 688:9

documenting 200:13,19 284:11 285:1 500:17

documents 3:16 12:12 182:3 242:7 256:20 261:8

293:19 298:21

Dog 445:14

doing 16:11 26:7 81:11 134:11 137:6 152:8 195:6

196:5 203:23 219:1 244:11 246:2,12 247:4

255:19,21 264:14,23 265:16 274:9 275:15 288:12

292:3 306:5,19 307:23 312:4,5 315:4 347:18

353:3 361:26 363:12,18 379:22 387:12 391:1

407:13 418:26 426:7 436:4 439:22 442:3,7,25

444:12,21 446:24 448:15 453:26 456:11 469:18

479:11 481:21 483:17 484:23 515:14,22 523:20

536:14 549:13,26 572:9 578:21 581:12 591:3

596:2,3 601:21,24 604:11,14 606:14 636:2

650:22,23 652:15 653:3 660:25 678:10 690:5

693:25

dollars 276:14

domestic 339:3

dominant 408:11

dominate 40:7 74:10

Don 13:12 15:6

Donald 13:11 252:4 695:26

Don's 15:3

dormant 507:9

dose 65:16 68:5 69:26 73:9,11,12 78:6,8,9 92:4,5,9

94:25 95:1 96:26 98:15 100:3 104:18 106:11,12

108:17,18,20 109:5,6,19,26 110:2,8,10,11,11

111:5,7,21,25 112:10,13,18,25 113:3,4 115:23,25

117:24,25 126:23 129:5 131:18,22 132:6 133:8

137:19 157:8 158:12 162:6 198:2,2 207:2,7,9,15

207:19,22,23,25 208:2 262:7 264:20,24 265:3

272:18 273:17,24 382:10 439:20 458:11,11 462:7

467:7 468:12 486:17 487:3 489:18 491:22 492:9

492:22 520:16,23 523:20 528:4 529:21,21 542:22

542:22,25,26 677:8,9

doses 65:22 78:12 79:17 90:7 104:1,11 106:5,9,17

109:11,13 111:14,19,19,21 112:16,24,26 116:2

117:23 126:19,22 127:2,4,12,16 128:2,10 130:21

131:17 143:10 265:5 266:14 298:5 299:1 373:26

446:5 463:18,18 481:9 486:11 492:10 495:11,13

506:17 677:9

dosing 162:2 485:12,13,13 489:17 495:10 520:18

520:19,20

dosings 463:18,19

dotted 143:14 347:5 412:22

doubles 101:22 102:6

doubly 338:23

downgrade 195:20

downgraded 195:25

downgrading 195:26

downplay 292:1

downstream 136:16 438:26 439:1

downward 36:20 40:4 41:2

dozen 552:7 635:17

dozens 443:24

DPR 9:19 17:3,6,22 25:19 105:21 129:1 253:10

293:25

Dr 2:1 3:20,22 4:11,16,19,23 5:1,4,6,17,21,24 6:2,5

6:9,14,17,18,18 13:4,6,7,10,11 14:10,24,26 16:17

17:25 32:7,12,13,19 42:14,15,16,23 43:3,10,26

44:11,11,13,21 45:6,15,19,22,23,24 46:24,25

47:7,7,8,9 48:3,11,20,26 49:7,8 50:1,12,24 51:20

51:24 52:15,24,26 59:12 62:12 66:18,18,20 67:11

67:11,12,17,26,26 68:2,11,15,15,17,20,25 69:2

69:12,19,23,23,25 70:3,3,4,9,9,10,15 71:3,3 81:7

81:8,9,24 82:3,3,4 83:3,3,4,15 84:12,21 85:6,6,7

86:12,23 87:6,11,21,25,26 88:7,23,26 129:21,21

129:23 130:10,18,19,19,20 131:2,16,16,17,20

132:15,15,16 133:1,10,15,18 134:6,8,19,23,25,25

134:26 135:17 136:7,15,17,19,20,21,21,22 137:4

137:16,16,17,23,24 138:15,19,21,21,22 139:5

140:1 150:9,11,12,15 151:1,8,21,21,22,25,26

152:1,11,23,23,25 153:3,17,20 154:2,3,7 194:24

194:24,25 195:5,10,10,11,14 196:3,6,9 197:2,15

197:15,16 198:11 199:7 200:1,1,3,5,6,7,20,20,21

200:24 201:17,18,19 202:3,3,22 203:3,6,8 214:20

214:20,21,21,26 215:3,26 219:6,12,12,14,19

220:1,2,3,9,21 229:24,24,26 230:9 231:6,9,9,10

231:19 232:5,15,15,16,20,24 233:23 235:15,16

235:18 236:8,17,17,20,21 237:16,17,18 238:1

239:7,14,14,15,20,23 240:1,14 241:6 242:16,20

242:20,21 243:11,13,15 245:5,5,6,14,14,16

247:15 248:4 249:9,16,19,22,25 250:3,5,12,16,18

250:21,24 251:1,4,7,18,19 252:4,4,6,12,17

254:24 266:1 270:16 274:23,24,24,26 275:4,18

275:21,21,23,25,25,26 276:1,4,16,16,17,23 277:6

277:22 280:4,5,6,18,20 281:7 284:6,6,7,21,21,22

285:8 288:17,20,24 289:6 292:4 296:11,24 302:6

309:4,6,20,20,21 310:7,10,12,19,23 311:2,3,3,4,7

311:7,8,22,23,25 314:16,17,17,18,21,25 315:2,8

315:9,10,14,21,22,22 316:3,8,9,10,11 319:2,2,4

319:13,13,13,15 320:12,23,23 321:14,14,15,22

321:24 322:1 323:25 324:1,2,6 325:1,2,11,12,18

326:21,21,23 327:16,26 328:8,8,9 329:6,6,7

330:4,9,22,22,23 331:6,6,16,26,26 332:1,14,16

332:17,19,20,26 333:10,14,24,25,26 336:16,17

336:18,19,19,20,21,21,23,23,24,25 337:14,14,16

339:7,7,9,14,14,15,17,17,19,19,20,26,26 340:1
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340:14,14,15,25,26 341:1,15,15,17 343:2,2,3,10

343:10,12,22,22 344:2,2,3,5,6,7,12,12,13,18,18

344:19 345:2,7,17,18,19 348:9,9,11,12,12,18,21

348:21,23 349:7,10,11,20,20,22 350:9,10,11,16

350:17,17,18,23 352:14,14,15,21,21,23,24

353:15,22,23,24 354:10,10,11,19,25,26 355:1,15

355:19,19,20,25,25,26 356:4,4,5,6,12,12,13,20

356:20,21,25 357:7,10,23,23,25 358:2,3,5,19,19

358:20,20 364:4,4,5,6,7,7,8,10,17,21,22,23 365:2

365:7,7,8,15 366:2,3,4 367:24,24,25,26 368:14

368:15,16,24 369:11,13,14,14,21,21,22 370:1,2,3

370:5 371:3,3,4,13,13,14,18,20,21,21 372:5,8,12

372:16,24,24,26 373:2,10,10,11 374:4,4,5,6

375:16,16,17,18,24,24,25 376:10,22,22,23,26

377:5,8,16 378:5,16 379:7,8,9 380:11,11,12,13

380:14,15,24 381:3,3,4,7,21,22,25 382:4,8,9,14

382:20 383:2,15,16,17,19,20,21 384:2,12,12,13

384:23 385:9,9,10,11,12,13,21,21,23,26,26 386:1

386:7,16,16,17 387:1,6,6,7,14,14,15,23,23,24

388:7,17,17,18,26,26 389:1,6,6,7,12,14,15,18

390:2,2,3,7,15,15,16,20,20,22 391:14,17,19,20

391:21,22 392:2,8,18,19,20 394:10,10,11,19,19

394:20,21,21 396:13,14,15,23,24 397:5,14,14,15

400:12,12,13,14,25,25 401:1,2,2,15,18,19,19,22

401:23 402:1,3,4,12,12,13 403:7,7,8,9,9,10,11,14

404:2,2,3 405:9,9,10,11,12,12,13,14,17,25,25,26

406:22,22,23 407:1,1,2,9,12 408:2,2,4,8,9,14,15

408:18,18,20,22,22,23 409:2,11,11,12,16,19

411:9,9,10 412:8,8,9,26 414:21 415:8,14,15,17

415:20,22 416:9,9,10,26 417:1,2,8,9,14,17 419:4

419:6,13,23,23,24,25 420:3,3,4,7,22,23,24

421:13,13 422:2,3,4,5,8,16 423:5,5,6 426:19,19

426:20,21 427:2,4,5,6,8,9,14,20,20,21,21 428:15

428:16 429:16,16,17 430:6,7,8,19,19,20 431:7,7

431:8,23 432:1,3,10,12,17 433:1,1,2,12,12,13,14

433:22,24,25 434:5,6,6,7,18,19,20,24 435:2,15

435:25 436:5 437:4,4,7,14,16 438:21,21,22 439:6

439:6,8,10,10,11 440:4,14,14,16,22 441:6,11,16

441:24,26 445:3,8,8,9,16,18,19 446:1,7,19,19,20

451:7,7,8,22,22,24,24,25,26,26 452:1,14,14,15

452:23 454:2,2,3,15,15,16,22,24 455:1,1,2,10,20

455:20,21,22 456:21 457:8,8,9,16,16,17,23,23,24

458:2,7,8,9,17,23 459:3,13,13,14 460:3,3,4,9,21

460:21,22 461:7,7,8,23,23,24 462:1,1,2,5,13,13

462:13,15,20 463:4,10 464:2,3,4,16,19,20 465:7

465:8,9 467:22,22,24,25,26,26 468:1,2,3,4,5,7,18

468:24 469:2,4,8,13,13,14,23,23,23,25 470:3,3,3

470:4,5,14,14,15,23,23,24 471:8,8,8,10,13,13,14

471:18,21 472:1,1,2,11,11,12,13,13,14,14,16,20

473:1,1,2,7,7,8,10,10,11,13,13,14,16,16,17,26,26

474:2,8,12,18,18,19 475:7,18,18,20,21,22,22,23

475:25,26 476:1,1,5,10,13,13,14,20,22,23,25

477:6,6,7,26,26 478:2,3,3,4,5,14,17,19,25 479:8

479:24,24,25 480:9,9,11,17 481:2,2,3,16,17,17

481:18,22,22,23,25,25,26 482:1,11,11,12,24,24

482:25 483:3,15,15,16,21,21,22,24 484:7,7,8,20

484:20,21 485:3,3,4,6,6,7,8,17,18,19,25,25,26

486:1,6,6,8,12,12,13,14,22,22,23 487:5,11,11,12

488:1,1,2,3,11,13,13,14,14,20 489:3,7,7,8,15,15

489:16 490:12,12,13,13,17,22,22,23,26,26 491:1

491:2,6,13,13,14 492:4,4,5,13,13,14,16,16,17,18

492:19,20 493:4,10,18 494:11,11,12,19 495:18

495:18,19 496:10,10,11,17,17,18,25 497:7,13,13

497:14,19 498:8,9,10,14,14,17,18,18,19,19,21

499:4,10,15,23,23,24 500:21,21,22,25,25,26

501:2,2,3,4,10,11,12,16,17,18 502:3,3,4,10,18,18

502:19,25 503:7,7,8,16 504:1,1,2,2,17,17,18,24

504:24,25 505:8,9,11,13,13,14 506:7,8,21,22,23

507:3,4,5,10,12,16,16,17,19 508:1,1,2,7,7,8,13

509:9,9,10 510:6,6,7,11,11,12,15,16,18,19,20,21

510:23,26 511:1,2,13,19,20,23,23,24 512:5,17

513:12,13,14 516:18,18,20,21 517:8,8,10,19,20

517:21 518:3 519:16,16,17 521:1,1,2,8,25,25,26

522:16,21 523:4,4,5,22 524:23,23,24,25 525:5,23

526:5,6,25,26 527:1,11,19,19,20 528:5 529:16,16

529:17 530:6,23,24 531:4,4,5 532:21,21,23,24,24

532:25 533:10,11,12,24,26 534:20,20,21,22,22

534:23 535:6,7,11,11,12,19 536:2,2,3 537:1,1,2,7

537:7,8,15,15,16,16,20 538:6,11,19,21 539:11,11

539:12 540:12,12,13,21,21,22 541:6,6,7,22,22,23

542:3,17,19,20,23 543:1,13,13,15 544:6,7,26,26

545:1,23,24,25 546:4,9,10,17 547:4,5,6,9,11,16

548:5,5,6 550:7,7,8,16,16,17 551:8,9,10,16,16,17

551:25 552:17 553:8,8,10 554:6,6,7,8,21,21,22

555:2,18,18,19,22,23,24 556:1,3,4,20 558:7,7,8

559:4,4,5,14,14,15,22,22,23 560:17,17,18 561:8

561:15,17,19,20,24 562:1,10,10,12,12 564:22,22

564:23,24,24,25 565:5,5,6,8,8,10,15,20,20,20,22

566:3,11,14,16,16,25,25,26 567:9,13,20,20,21

570:23,23,25,26,26 571:1,9,9,10,14,14,16 572:6

572:25,26 573:1,5,11,19 574:6,7,8,13 577:1,1,2,3

577:19,19,20,25,25 578:7,7,8 579:6,6,7,13,13,14

579:18 580:2,11,12,13,18,23,24 581:6,6,8,10,15

581:17,18,26 582:15,19 583:12,12,14,16,17,21

583:23,23,24 584:5,8,12,12,13,19 585:3,12,14

586:1,1,2 587:18,18,19,20,20,21,22,22,23,24

588:8,8,9 590:10,10,11,26,26 591:1,10,10,11,13

591:13,14,15,21,26,26 592:2,15,15,16,21,21,22

592:25 593:8,22,22,23 594:26,26 595:1,2,2,3,19

595:19,20 596:4,4,6,10,10,11,20,20,21,21 597:23
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598:5,5,6,16 599:7,7,8,21,26 600:1,3,7,7,8,8,16

600:17,17,18 601:4,7,7,8,10,12,12,22 602:6,7,8

602:16 603:15,15,16,18,24,26 604:1,8,9,18,21

605:1,2,3 606:12,22,22,23,25,25,26 607:1,23

608:1,6,6,7,25,25,26 609:4,4,6,9,10,11,17,19,19

609:20,24,26 610:2,4,5,5,6,15,18 611:7,7,8,18,24

611:26,26 612:2,3,8,19,19,20,24,26 613:3,5,12

613:16,17 614:8,8,11,15,15,16,22,22,23,24,24,25

614:26 615:4,9,9,10,24,24,25,26,26 616:1,17,20

616:25,25,26 617:11,11,12,18,18,19 618:1,12,12

618:14,22 619:6,9,11,12,12,13 620:2,2,4,15,15

620:17,26 621:1,2,16,17 622:5,11,11 623:4,4,6

623:21 624:2,2,3,5,12,12,14 625:6,19 626:2,11

626:26 627:1,2,2,4,9,21 629:9,10,11,17 630:2,4,5

630:11,16,16,17 631:19,24,26,26 633:14,14,15

634:4,14 635:3,8,15,20,23 636:1,7,11,12,22

637:13 639:2,24 641:2,6,14,17,20,22,23,26 642:2

642:4,17 643:3,3,6,13,20,21,22 645:25 651:20

652:1,3,5,6,14,14,22,24 654:16,20 655:24,24,26

657:4,26 658:13 659:2,23 660:8,24 661:4 663:9,9

663:15 664:21,23 665:7,25 667:1,3,23 669:6,11

669:17,20,22,23,26 670:7,7,8,13,15,17 671:1

674:18 675:8,12,14 676:11,16,23 677:3 679:15

679:23 680:14,15,15,17 681:15,17 682:2,25

683:4,5,9,13,15 685:8,12,13,15 686:1,4,4,4,14,16

686:25 687:13 689:12,22 690:14,24 691:7,15

692:16 693:1,9,10,12,16 694:16,18 695:7,8,25,26

696:2

draft 527:13 641:18

drafting 224:9

drain 621:21

dramatic 306:26

dramatically 293:8,12 442:22

drastically 654:1

draw 26:7 213:11 496:5 590:4

drawback 398:21 683:12

drawing 10:21

drawn 195:8

draws 22:20

drench 34:4

drift 37:3,9 40:19,20 41:4,10 63:11,17,20 188:23

287:25 300:2 322:25 348:1,15,26 349:1,3 467:10

drifts 43:23

drill 51:23

drink 78:22 438:5

drinking 71:19 73:2,5,10,13 74:2 75:6 76:19 78:7

78:13 79:2,9,14,19 80:4,6 82:19 85:9,16 293:13

293:24 294:3 422:20,23 424:14 425:18 426:8

429:8,18,19 430:18 432:8,15,23 436:18 437:1

438:1 439:13,25

drive 260:7 321:19 497:2 561:4

driven 236:24 557:4

driver 496:26 661:16,16

drives 435:19

driving 513:24 560:26 651:3 653:15

drone 103:3,5,6,13,22 131:24 306:23 356:23 449:5

453:3,17 536:18 555:20

drones 37:21 72:13 73:24 98:21,24 99:1,14,19

100:8 306:10,11,17 309:9,13,14,15,17,17,23,26

310:19 355:11 368:22 382:24 449:7 451:9 477:16

511:20,26 531:24 532:1,3,6 536:17,17 555:25

605:26 661:14

drone's 99:16

drop 235:23,24 491:22 561:3

droplet 40:18 450:22

droplets 37:2,26 38:7 40:14,20 58:2 73:18 74:9

76:20 330:17 428:23

drops 420:20

drown 430:25

Drug 249:23

drugs 445:14

dual 627:11

due 39:14 44:26 79:18 98:17 103:26 109:7 116:26

117:4 185:23,24 188:15 190:5 192:21 193:7

261:19 286:7 292:24 303:2 318:1 358:21 392:21

477:11 588:12 636:3

Duet 322:22

dump 310:16

Duncan 304:2

duration 97:9 132:8,10 157:25 161:20 171:23

172:6 173:13 175:5 176:13 177:21 180:4 184:4

185:9 189:21 190:13 191:18,25 194:3 198:17

222:25 264:8 390:24 477:4 478:21 484:11,17

493:21 513:10 524:2 529:3 540:2 543:10,11,16

543:19 544:1,4 561:9 569:21 586:11 634:12

durational 198:2

durations 484:4 539:3 540:3 613:16

dust 41:5,11,12,14,18,20 63:7,12 65:5 74:26 76:18

76:24,26 77:10,10,15,17 83:6,15,16,18,23,26

84:2 86:13,13,15,17,20,21,23,25,26 87:4 291:3,8

291:23 292:24 305:26 318:21 332:10 348:3,7

349:14 350:2 409:6,21 410:13,15,20,24 411:3,4,7

411:14 412:3,10,12,14,17,21,21 413:11,17,18,19

413:20,21 414:22,23 415:5,7,11,18,21,24,24,25

415:26 416:2,6,8,13,20 417:5,18 418:14,18 419:1

419:9,26 420:5,8,9 421:16,19,24,25 422:6,14

432:23 434:2 436:18,18 437:18,24 438:23 440:11

440:18,18 441:4,7,8 623:8,24 654:2,3,3 657:17

duty 284:10

Dwayne 263:23 641:25
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dwelling 581:9

dye 367:7 368:4

dying 298:11 310:1 453:18 498:26 645:11,22

647:16 666:2

dynamic 230:19

dynamics 221:8,22,23 225:13 227:18 453:7 615:17

616:13 636:4 670:25

D.C 692:15

E

e 87:19,19 405:18 476:26 477:2 494:16 499:16,18

547:22,24 580:7 584:5 587:26 593:11,13 594:24

621:25 626:12

earlier 41:9,23 52:7 60:26 61:25 62:11 64:12 71:24

82:22 89:14,16 91:6 93:25 98:7 104:5 143:5

153:4 155:26 175:7 185:2 193:5 200:7 204:13

205:20 213:18 215:14,15 222:1,16 231:25 245:18

266:5 278:20 310:8 338:20 382:10 384:23 408:16

423:7 433:6 434:8 449:22 453:1 466:12 484:17

486:14 489:3,4 532:8 540:1 549:9 554:2 558:19

588:21 593:24 615:1 618:13 644:5 658:4 675:1

677:4 678:19

early 26:13 27:4 44:24 65:10 199:26 225:22

247:17 248:8 317:7 370:11 381:16 429:24 457:1

470:10 584:23,23 589:9 597:1 604:15

early-season 317:16

easier 152:10 363:13 365:6 370:8 385:1 489:25

490:2 496:5 558:21 642:21 679:9

easiest 363:7 364:19 636:8

easily 121:2 319:12 355:13 407:7 446:26 483:14

602:20,22 625:9 648:25

east 578:5

eastern 577:26 580:9

easy 131:21 203:26 235:23 243:19,26 363:15

374:12 496:7 537:13 556:15 572:4 595:18 599:21

620:13,14 645:1 648:8 666:18,18 674:10

eat 89:19 93:3 101:17,19 132:2 356:8 361:21

370:16 385:15 466:4 487:20 521:7

eaten 520:9

eating 105:10,11 131:25,26 138:8 361:17 370:10

470:21

eats 328:23

EBI 688:8,11

EC 494:9,14,17,18 496:11,13,16,23 497:16 500:6

echo 545:1 551:13 617:16 681:15 686:4,16

eclosing 645:11

eclosion 560:26 561:3 645:6 646:18,22 671:24

eco 85:24 86:7 695:21

ECOFRAM 85:24 86:10 88:12 262:22 263:22

426:13

ecological 6:7 7:16 11:11,17 27:19 28:26 29:21

33:3,20,22 34:11,14,15,25 53:24 59:18 63:8

113:8 167:15 178:25 211:5 228:15 229:13 292:8

315:12,15 515:6 539:20 541:21 559:1 590:14,17

590:24 643:17 650:7,24 667:8 676:21 677:7

687:21,23

ecologically 30:2

ecologist 154:10 251:3

ecology 6:12 250:26 487:15 691:24

economic 11:5 23:1 28:18 306:6 558:18 559:1

economically 297:17 551:5 558:16

economics 236:25 320:14

economy 318:15

ecosystem 27:23 29:15

ecosystems 9:4 11:20 245:25

ecotoxicologist 4:18 16:21 249:18 254:25 266:2

ECx 491:11,19 493:9,26 494:20 495:17 497:9,23

EC10 496:3 500:12

EC20 496:4 500:12

EC5 497:2

EC5s 497:4

EC50 494:1

EC50s 496:26

edge 183:24

edit 340:9

editorial 245:17

educate 694:1

educating 685:17

education 686:26 693:25

educational 694:3

EEC 58:5,7,10,15,19,26 132:11

EECs 58:7 63:2 64:6 76:6

EFED 156:11 162:15 330:10 441:20 446:11 459:1

463:8 464:23 471:23 472:22 476:9 477:1 480:23

482:6 484:1 489:2 490:4 491:8 503:20 512:7

513:2 526:11 543:4 562:7 573:10 582:4 585:22

588:1 593:12 598:19 695:21 696:1

EFED's 121:3

effect 10:8 58:19 69:21,22 70:19 139:18,18 159:23

161:25 162:2,7 172:15 174:17,18 178:7,14 179:8

181:24,25 189:24 194:2,3,11 195:22 203:14

205:9 206:8 207:11,12,19,26 208:5,7,15,26 222:8

225:12 232:5 266:24,25 287:22 291:10 318:5

412:17,18,20 449:15 452:6,16 453:2,11 460:16

462:19 467:4 479:16,17 492:8 494:15 495:14

496:8,12,16 499:18 517:16 518:18,24 542:7,14

545:15,15 547:26 550:26 563:17 564:20 565:1,14

565:19 581:4 583:11 586:8 591:24 593:4 616:5,9

616:13 621:10 625:26 632:15,16 635:22 642:22

660:4 662:26 663:6 672:12

effecting 522:11
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effective 83:5 173:4 200:13 247:1 318:6 323:6

340:21 410:9 414:13 417:11,13 441:23 492:8

536:14 581:5 658:12 664:13 681:6

effectively 15:3 369:7 371:24 529:20 591:8

effectiveness 280:21,25 533:15

effectives 589:24

effects 9:23 10:9 13:13 14:1,3 16:15 17:1,5,9 18:11

18:16 21:19 22:5,10,11,12,14,16 23:9 24:6 25:21

26:22 27:25 28:1,17 30:7 31:5,13,16 33:3 35:2

41:25 48:19 50:7,11,13,19 51:10 54:11,20 61:4

64:20,22 70:22,23 71:10 89:4 91:1 92:13 133:21

140:8 141:5,6,13,23 144:22,23 153:19 154:11

161:22 162:4,8,22 163:11,13,16,18,21,22 164:7,7

164:8,10,11,12,15 165:2,17,20,25 166:3,7 167:15

167:17,24,26 170:8,15,19 171:3 174:2,15 175:12

175:13,16,21,23 176:12,20,22 178:21,25 179:3,5

179:6 180:19 181:5,23 185:8,15,22,24 188:12

189:25 193:16 194:6,18 198:20 203:11 204:9

205:8,23 206:26 207:1,3,10 210:2,3,5,8,13,18

211:7 213:23,24 214:3,9,17 219:24 221:1 222:4

222:11,15,17,18 223:3,12 224:25 227:26 228:16

229:2,8 231:11 232:13 238:21 240:6 243:21

245:23 246:4 257:12 265:25 266:4 268:5,6,23

269:15 270:9 291:1 294:26 297:21 298:1 300:19

317:25 318:19 323:14,16,19 331:24 342:1,10

386:11 389:8 407:15 413:19,21 442:9 443:12,14

446:17,21 448:2,13 449:4,7,11 450:2 454:6 455:8

458:22 462:4,12 463:11,20,20 470:8 475:10

487:14 491:25 493:22 494:7 496:8,13 499:20,21

505:9,15 506:13 508:25 509:5 511:6,6 512:12

513:6,23 514:16 518:14,25 521:19 529:14 532:2

536:8 539:7 542:26 548:1,2,15,18 549:5,6,12,17

549:25 550:2,3,21 551:1 559:20 562:14 563:6,13

564:12 566:1 569:10 570:3,21 575:6 576:1

585:24 586:8,19,19,22,24 587:12,15 588:3,24

593:15 594:10 607:11 615:6 616:2,10 618:16

619:5,15,18,21,24 620:7,9 621:3,13 622:13

625:21,22,23 630:25 631:6 633:23 636:24 665:17

677:25,26 678:24 686:13 688:4

effects-based 61:2

efficacy 414:7 419:19 635:1

efficiency 215:13 216:1 218:15 222:5 225:23

efficient 15:23 126:10 188:9 247:1 279:9 534:18

692:24

efficiently 54:1 215:16 435:9

effort 7:23 8:5,7 10:4 11:6 17:1,23 136:4 215:26

236:18 237:3 244:7 401:13 419:11 426:10 437:19

466:19 547:14 630:6 656:21 689:2,5,19 695:15

efforts 7:8 14:7 15:6 18:10 21:13,17 22:24 64:15

77:16 159:7 160:24 177:15 228:9 244:19 253:9

272:4 278:23 477:21 482:23 491:3 617:14 657:11

669:3 676:26 691:1 696:6,8

EFSA 64:12 65:4,18,25 147:15 163:18 188:6,14

190:25 191:26 192:6 246:8 396:26 397:1

egg 176:13 225:10 226:10 342:9 344:25 369:18

453:4 457:19 479:10 560:26 655:14

eggs 231:14 533:3 560:12,14 648:17 655:7

egg-laying 331:2 448:17,23

eight 122:21 304:3 585:9

EIIS 211:6 292:9,11,18 293:11

EIS 323:22

either 37:3 39:5 40:6 52:4 80:2 105:10 137:13

160:22 164:19 166:16 171:6 172:9 173:15 175:15

178:18 181:3 188:19 194:1 201:9 202:7 209:10

223:13 248:8 283:19 289:12 321:11 328:23

358:14 384:16 424:24 452:4 453:18 507:21

512:10 515:17 520:4 523:7 539:1 555:11 560:12

561:2,3 588:22 599:3 622:19 652:17 654:10

666:8,11 672:13 677:14,15 684:1

elaborate 400:14 403:24 463:8 492:3 519:13,21

632:25 674:19

elaborated 546:26

election 162:6 196:22

electronic 357:5

elegant 368:1

element 295:10 338:9,18 420:25 540:25 594:16

elements 26:6 143:26 159:8 160:25 182:15 183:7,9

211:21 222:22 229:21 503:26 554:24 567:18

584:6 593:13 676:20 679:24 680:10 687:22

elevated 103:26

elicit 92:13 163:18 170:15

eliminate 239:25 255:12 478:23 590:15 663:12

664:25

eliminated 144:11 366:26 665:20

eliminates 180:2 541:11

eliminating 173:8 282:25 318:7

elimination 197:22,25

elucidate 9:22 10:7 449:7 463:12 549:4

elucidated 459:20

eluded 531:22

email 357:10

embark 14:5

Emerald 259:15

emerge 363:16 498:25 634:16 684:16

emerged 161:7 297:24 299:3 384:18 443:25 458:19

483:13,20 537:11 634:17

emergence 67:3 341:8 479:15 480:6

emerges 685:19

emerging 481:8 498:24 544:13

emission 410:4 623:14,15 657:17
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emissions 318:11 409:22 410:8 623:8

emphasis 107:17 235:21 281:2 301:13 321:8

436:15 626:23 639:9 642:11 661:23 691:2

emphasize 265:21 286:17,20 339:10 374:14 378:12

396:6 401:4 406:24 481:13 482:26 517:24 656:21

658:2

emphasized 281:8 461:5

emphasizes 56:15 686:9

emphasizing 615:15

empirical 90:19 96:6 102:2 107:12,20,24,25

108:11 110:17 113:20 114:3,6,13,19,20,23 115:3

115:6,10,13,19 116:13,15 118:1,7 119:25 121:15

121:19,19,23,25 122:2,3,6,8,12,13,14,17,21,23

123:6 124:11,21,26 128:13,20 130:21 141:25

142:7 144:9 222:7,20 223:26 226:16,26 228:20

232:3 393:6,10,14,18 394:3 397:19 399:18

402:19 555:12 608:13 648:14 659:5 660:11

663:21,22 664:15,19 670:25 671:16 672:14,17,19

672:20 674:9 676:25 679:11 680:1,22 684:4,5

empirically 112:11 126:10 328:16 517:4 660:1

681:26

empirically-based 143:8

employ 172:17 180:1

employed 148:8 474:26 481:12

employing 679:8

employment 672:7

employs 168:12 169:14

enable 51:16 84:10 168:26 177:23 189:26 190:8

193:19 196:18 202:10 221:22 222:11 223:7

514:24 531:10 619:4 668:10,17

enabled 513:9

enables 173:12

encapsulation 449:16

enclosed 144:26 169:9 516:1,8

enclosure 61:3 147:12 168:12 169:22 173:8,14

177:6 180:12 181:10 266:21 518:14

enclosures 143:22 167:6 168:17,21 173:2 183:18

513:4,4 539:24,26

encompass 154:19

encompasses 397:24 482:13

encountered 143:9 145:17,21 176:19 460:12

463:17

encounters 467:9

encourage 173:1 326:19 352:6 370:2

encouraged 237:1 571:8

encouraging 555:25

endangered 653:7 654:17 663:26

endgame 51:18

endocrine 211:21 511:18

endorsement 224:22,22

endpoint 27:13 69:16 132:26 155:11 161:17 185:3

269:26 270:4,7 334:23 335:8,9,21 337:22,25

339:5,10 341:10 342:23 343:18 444:17 449:21

454:4 456:6,17,19,23 477:9,15 479:23 491:10

492:7 493:20 494:4,23 509:13 536:19,21 544:21

552:23 553:23,26 586:26 587:6 604:24 620:20,23

621:12 647:11 680:11 686:10 691:19,26

endpoints 27:11,17 28:7 29:17,19,23 30:1,5,8,9,10

30:11,14,15,19,21 34:8 35:17 36:22 39:4,5,7,8

42:8 51:6,12,13 61:5 63:2 92:1,3 152:22 155:4,7

155:10,24 162:18 163:6,8,10,24,26,26 164:2,2,6

164:7,15,17,24 166:9 167:10 173:24 174:25

175:2,6,8,9,18,21,22 176:5 178:12,18,19 179:1

180:16 184:24,25 185:1,4,8 189:19 190:4 191:6

193:3,4,12,25,26 205:21 209:11 210:21 212:6

213:26 214:1,6,6 223:4 227:21,22 230:26 231:1

266:15,15 267:13,14 268:15 269:1 270:5,11,13

301:8 333:17,22 334:21 335:16,16 337:6,9,10,12

337:13,18 338:17 341:7,11,13,14,19,20 342:8

343:8,13 345:15 358:15 379:6,22,25,25 444:26

446:13,15,22 449:10,23 451:5 459:3,3,5,8,9,10

459:11,12 475:8 479:21 480:7 493:8 494:8

503:22,24 504:3,8 508:14,18,26 509:1,4,6,7,12

509:17 510:1 512:9,12,13 535:23,26 543:20,23

544:4,5 552:5,12,18,22,24 562:25 563:5 569:9,22

582:3 585:26 586:25 587:5,8 604:25 620:11,22

620:23 621:5,11 661:9,11,22 662:2 677:22

679:20 680:12

ends 425:8 491:22

end-use 152:14 183:12

energetic 328:24 330:25 508:11

energetics 262:21

energy 93:10,15 96:11 99:5 130:4 235:10 264:11

enforce 291:17

enforcement 313:9 420:5 629:22

enforcements 629:26

enforcing 291:21 325:22

engaged 21:17

engineer 417:20

engineering 419:19

English 241:1

enhance 13:24 547:3

enhanced 8:16

enjoy 279:1

enjoyed 686:2,3 687:18 689:14,25

enriched 349:16

enrichment 410:23

ensued 491:24

ensure 2:21 3:1 168:14 176:17 188:1 195:6 242:5

253:13 254:6,18 279:18 301:1 534:8 539:2 564:5
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567:26 575:12 576:12 598:26 603:13 647:2

ensured 602:23

ensuring 2:10 16:11 192:15 289:24 294:23 575:21

595:22 629:26 630:21

entail 382:11

enter 623:13

entire 2:12 39:12 67:8 131:22 138:12 156:4 192:2

202:2 227:8 264:24 287:24 291:19 293:5 317:3

326:26 336:5 360:15 375:21 382:11,15 421:4

444:13,14 447:21 461:4 482:14 487:19 514:6,11

514:14,16 515:3 644:16 645:11 665:21 674:5

686:24

entirely 196:11 312:19 448:26 481:14 572:8

663:18,20

entirety 666:23

entities 34:14,16,21 312:12 417:18

entitled 23:8 26:8

entity 29:1,21 325:18

entomological 290:1

entomologist 5:18 250:15

entomology 5:2,22 6:10 250:1,17 251:2

entrance 171:13 538:9

entrances 186:1 301:1 537:22

environment 28:12,18 29:10 30:18 86:14,20 145:5

155:26 160:6 165:15 176:1 177:8 183:15 201:9

203:23 298:6 301:19 308:18 350:26 409:21 434:1

453:8 509:10 510:5 531:16 562:16 564:18 588:5

589:23 654:10

environmental 4:4,13,15 5:5,9 13:13 15:1 17:1,2,9

17:10 22:1 28:19 29:20 33:3 37:6,19 41:14 54:15

57:11 58:4 64:13 71:9 76:5 89:4 140:7,8 150:6

154:10 190:26 203:11 205:19 206:15 207:1,10

221:1 222:13 228:12 233:15 249:13 250:4 254:19

261:1 266:11,23 293:8 294:4 297:7 302:26

303:15 318:1 323:22 351:25 360:2 397:18 426:11

503:11 513:5 515:22 539:8 540:10 592:6 608:21

622:12 623:11 636:3 656:23

environmentally 171:16 172:11,14 501:21 553:14

environments 515:8 532:13 549:3 550:1 580:6,10

594:1

envision 56:4

enzyme 475:13

EPA 2:18 3:16 7:18 9:19 10:3 12:25 13:24 15:4

16:22 21:13,16,20 23:9 24:23 25:4,17 28:16

29:19,26 31:24 35:21 78:11 105:21 113:7 124:21

126:4 128:22,26 156:6,17 157:6 158:8,16 167:20

168:1 171:20 182:4,8,13 195:13 206:4 207:6

211:18,18,24 228:11 245:9 251:23,25 252:1,2

253:9 256:17 259:17 261:8 265:20 270:19 272:4

275:1,2 279:3 281:8 283:17 290:1,8,14,17 291:11

291:15,17,20,20 293:11,21 295:26 296:5 299:25

301:6,17,20 311:10 312:11,26 313:10 323:6,12

329:10 331:8,16 333:15 334:3,6,15,20,24 337:25

345:12 358:22 359:2 368:26 379:10,13 383:10

392:25 411:12,18,24 413:9 414:13 417:3,3 420:5

421:10 440:5 442:2 466:19,24 473:11 477:20

483:2 601:10 612:3 623:7 629:13 633:18 685:16

686:3 687:14 688:18 689:2,14 690:2,16,19 691:1

691:8 693:21 694:15 696:7,7

EPA'er 322:13

EPA's 11:10 21:10 28:11 34:11 79:4 245:12

254:19 290:20 291:26 292:8,14 297:21 298:21

300:17 308:17

EPA-OPP 695:18

epidemiological 6:7 233:12 235:3 236:3 250:26

492:23

epidemiology 4:13 5:23

EPPO 22:26 107:3 117:7,9,26 118:6,8,18 119:6

123:2 126:4 127:24 129:11 147:15 182:6,19

402:18 404:10 518:13,19 519:3,12 521:11 542:10

547:2,13 574:15,16 618:7 633:4

EPPO's 107:3

equal 103:7 105:13 284:23 541:26 577:12 595:10

597:6 637:11

equalization 568:26

equalized 587:10

equalizing 568:22 589:14

equally 241:3 403:25 614:5

equated 282:10 283:14

equates 187:22

equation 120:13 121:1,2 125:6 230:22 386:24

401:10 425:23 687:8

equations 230:14 240:9,11,22 244:22,25 664:8

equation-based 646:17

equilibrium 120:21 230:15 425:1 426:7

equilibrium-based 88:19

equipment 63:15 77:3 87:1 184:18 191:22 291:13

300:23 306:5 318:12 331:18 417:19 418:13

equitably 184:7

equivalence 96:14 102:14 649:25

equivalent 94:26 95:14 103:22 123:12 125:23

144:14 174:12 329:25 330:4 647:21,23 655:20

671:23

equivalently 518:2

equivalents 644:25

equivocate 605:15

eradicate 518:18

eroded 349:15

erosion 349:15

erroneous 254:10
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error 85:19 88:4 104:2 294:22 425:4,5 577:22

590:19 594:12 597:14 611:22

errors 90:25 91:1 261:19 436:22 578:6 590:18

596:1 600:6 613:6 617:26 626:13,17

especially 69:1 134:10 188:2 189:22 243:6 256:1

258:24 282:18,18 292:10 297:12 312:23 327:12

331:10 338:15,21 340:20 353:8 363:3 371:25

384:17 416:23 430:2 442:21 446:5 458:3 470:10

479:10 483:8 506:16 515:5 518:19 519:2 527:17

540:4 548:26 578:13 605:13 618:4 627:7 628:26

639:25 640:23 644:17 650:20 675:21

essence 375:8 447:8

essential 177:25 191:13 193:21 204:18 301:23

essentially 42:18 95:13 135:9 316:4 329:11 334:16

339:3 342:16 354:5 360:3 375:7 376:20 377:12

425:6 427:12 467:6 485:20 494:22 499:25 565:26

612:9,13

establish 52:6 125:5 146:14 161:11 318:22 331:2

460:14 521:6

established 54:22 159:2 164:3,9 169:5 175:17

178:16,17 211:17 256:22 312:26 315:26 326:13

437:21 581:22 618:5

establishes 164:14

establishing 85:23 113:8 177:26 327:1 432:26

establishment 253:25 254:4

estates 429:3

esteemed 540:23

estimate 19:22 22:13 24:20 48:14 58:13 59:26 64:1

65:5,8 68:18,22,24,26 69:18 78:11 89:18 90:7,13

97:24 98:8,11,17,18 99:13,20 107:8 111:9 113:20

116:22 121:6 123:21 125:18 128:8 131:9 136:23

138:25 140:23 164:6 166:12 205:9,17,19 207:2

207:12,17 208:26 214:17 234:19,19 257:24

258:12 259:20 260:20 261:2,23 262:5,20 327:2

329:1 361:1,3 365:12 374:11 376:9,11 379:19,24

380:10 384:24 386:21 387:26 395:3 396:8 400:17

404:12 416:19 419:9 422:14 424:14 426:6 464:26

491:25,26 501:17 603:12 623:17

estimated 54:19 58:4 65:16 76:5 78:14 79:21 91:25

92:10,15 94:18,23 95:10 96:10,26 97:7 98:12

106:20 107:19,23 113:24 116:2 121:17,20,23

122:4,6,11,14,16,21,22 124:8,11 127:3 144:18

157:13 194:10 206:15,18 208:14 256:2 264:16

270:1 297:15 424:5 425:6,17,17

estimates 12:6 22:16 24:5,6 26:3 54:8 60:25 62:23

69:6 90:12,17,17 92:8 93:26 107:21 119:21 121:9

126:6 129:24 140:20,25 141:13,15,18 142:9,12

143:16 144:6 146:3,6 163:5 164:19 165:4,8

171:12 179:14 198:14,16 201:3 202:13,16 203:15

204:20 205:1,1,2,3,12,21,24 206:8 208:23 209:10

209:19 210:25 219:4 240:6 258:5 259:23 260:23

260:25,25 261:9 367:14 383:22 396:5,12 397:11

398:14 403:21 404:1 405:21 420:19 426:5 430:16

434:12 466:14 491:21 500:3 603:12 609:2

estimate-based 205:7,11

estimating 88:15 89:2,26 90:2 106:9,16 108:12,13

108:16 122:26 125:21 126:15 128:5 130:20

205:15 207:4 208:13 273:18 374:16 388:4 397:19

405:21 426:17 434:13,25 484:17 492:2 555:17

612:11

estimation 25:22 91:20 120:22 204:24,26 206:18

208:21 209:9,15 497:23 633:7

et 19:8 96:7,8,13 97:3,6,14 101:24 107:7 113:16

117:11 120:8,20 125:26,26 126:3 159:15 171:1

196:26 224:26 225:8,18,25 227:2 237:8 259:22

262:19 298:11,13,16 299:12 319:8 324:24 329:22

332:4 387:18 398:1 431:18 433:8 443:23 461:12

464:25 472:7 475:14 486:21 500:3 540:18 588:7

595:17 636:23 657:2,19 674:26 691:24,25

ethics 2:21,26 3:2 695:22

EU 161:7 273:16 380:26 381:9 390:11

Europe 19:7 273:7,7,20 482:23 592:6 610:26 628:3

682:14

European 11:2 22:25 23:20 24:16 29:13 64:11

101:2,5 105:1 107:4 137:5 156:21 158:9 202:24

213:15 225:4 228:14 491:3 660:15 665:14 682:8

evaluate 18:16 22:17 27:20 48:14 108:7,11 111:26

146:22 149:3 156:4,24 157:1,3,6 158:4 160:12

161:3 163:17 170:23 173:15,16,25 175:17 178:1

178:7,9 179:13 185:14,15 194:14 209:2 225:19

244:18 393:7 433:10 446:17 461:11 565:3 662:6

681:8 695:5

evaluated 54:20 57:14,25 58:12 60:14 61:15 62:4,7

62:18 76:22 80:23 107:19 117:26 155:5,7 165:7

177:19 204:20 214:4 217:13 219:2 228:26 240:12

670:24

evaluates 280:26

evaluating 13:17 24:3,14 31:5 59:10,11 61:19 67:2

90:16 128:13 144:22 154:12 155:13,23 164:2

167:10 201:2 208:17 211:19 212:11,18,23 213:7

220:18 283:12 300:23 346:12 455:8 585:24

638:14 656:19 686:12

evaluation 15:20 27:24 49:21 55:16 58:25 61:17

107:12 108:8 111:24 112:4 113:22 119:1 154:18

155:25 159:16 161:24 184:4 192:9 212:3,10

217:8,14 276:2 347:18 406:26 517:26 590:22

evaluations 155:26 168:26

Evans 443:22

evaporate 46:11 428:3,4

evaporated 46:1 429:26 439:25
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evaporates 429:26

evaporation 439:24

evaporative 38:21 77:24

evasive 291:10

evening 325:6 435:22

evenly 259:6

event 15:5 17:13 238:8,11 557:1 687:9

events 35:14 163:2 287:16 300:21 557:6

eventual 20:11

eventually 156:4 227:19 509:26

evert 511:26

everted 511:21

everybody 249:12 273:20 275:17 296:17 354:25

407:17 436:1 442:1 499:13 513:15 525:24 621:26

624:14 667:16 686:20

everyday 485:15 520:22

evidence 25:1,1,24 31:1 48:14 55:24 59:18,22 60:6

60:10 61:11,21 62:6 63:24 66:5,7 153:6,11 158:5

163:12 166:19 167:14 174:25 178:24 181:1,21

194:20 203:15 209:3,10,12 210:21,26 211:3,8,19

211:22 212:3,6,23 213:7,8,10 214:10,15 219:7,16

219:22 229:1 253:25 378:1 455:4 533:15 539:9

542:10 587:14 621:7,14,25 630:23 639:23

evidenced 83:25

evident 145:9 169:2

eviscerated 511:17

evolution 686:19 691:25

evolve 25:11 52:11 204:16

evolves 51:4

evolving 51:19

exacerbate 219:17

exacerbated 527:22

exact 365:12 391:13 520:10 531:18 568:15 594:5

655:18

exactly 232:25 241:19 242:18 307:12 352:3 363:14

391:20 402:25 455:12 461:26 462:6 523:1 546:5

557:11 567:9 582:25 592:10 608:20 610:17,17

650:9 652:8,15 659:24 660:9 670:10 673:6,7

676:5

exaggerate 275:6

exaggerated 171:17 275:10

examination 215:21

examine 213:22 398:17 400:18

examined 23:25 75:22 281:16 398:19 411:7 609:2

examining 21:19 181:5 399:7 623:8

example 11:19 15:10 31:16 35:10 38:11 39:7,12

55:7 56:26 61:25 64:26 65:12 73:23 108:10

119:17 124:1,16,21 128:16 135:4 142:3 147:18

148:20 156:1 162:14,23 177:2 207:12 211:20

218:24 221:26 222:21 225:8 227:1,22 230:16

231:3 240:24 261:23 265:2 277:2 282:6,15

313:15 317:12 344:10 353:9,11 360:8 388:21

411:19 436:24 438:23,26 443:22 465:2,17 516:10

550:21 552:23 555:8,8 560:21 588:5 597:20

620:10 634:5 645:3,10 652:23,24,25 653:1 654:6

655:3 661:12,13,18 666:3 670:5 674:20,25 675:6

679:14,15 681:3 682:26

examples 30:14,20 153:17 216:14 277:3 337:7

560:20 645:1 651:21 663:17,18 665:11,13

exceed 78:26 98:16 115:2,4,8 117:1 119:4,6 141:17

217:17 218:14 291:22 393:14 426:8

exceedance 437:2,3

exceeded 67:20 114:19 205:13 400:10

exceedingly 381:5 424:4 656:7

exceeds 67:18 99:14 115:14 123:10 439:1

Excel 647:6

excellent 15:10 197:3 215:1 315:14 331:4 334:3

343:16,18 364:11 440:6 442:3 469:26 571:11

633:16 674:22 685:25

exception 123:16 365:3 369:15 384:25 385:2

520:13 605:25 642:20

exceptionally 657:5

excerpt 206:12

excessive 221:26 289:25

excessively 550:24

exclude 327:8

excluded 327:14 351:14

excluding 347:18

exclusion 224:2,22 234:10

exclusively 465:5

excreted 265:5

excruciating 242:9

excuse 69:12 231:23 290:17 409:8 572:17 658:17

executed 562:18

executive 202:23 223:17 224:5

exercise 214:23 218:12 294:2

exert 487:13 500:2

exhaust 300:7

exhaustive 30:20 224:12 406:15 678:9

exhibit 150:2 298:8 499:21

exist 159:3 161:1 165:24 243:22,22 280:7 343:6

410:2 615:14 616:22 687:11

existence 240:23 475:9 691:12

existing 21:23 56:8 147:4 150:22 227:25 244:8

294:26 301:21 617:13 618:7 657:12 663:13

exists 158:14 172:19 240:25 294:5 641:1

exiting 54:24

exoskeleton 356:3

expand 171:21

expanded 156:23 157:9
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expanding 255:14 382:12

expands 156:17 301:21

expansion 156:19

expect 43:23 73:11 78:8 135:25 264:9 283:3

452:17 498:11 527:25 640:9 641:9 655:22 683:2

687:16

expectation 56:10,25 640:12

expectations 195:18

expected 36:1,6 38:26 40:25 42:3 79:6,10 80:3

87:4 94:3,4 116:25,26 118:24 119:10,18 124:2

148:10 157:19 170:9,12,14 217:10,26 218:22

373:17 422:23 429:9 486:10 588:4,19 602:13,14

653:13 655:23 690:22

expecting 44:19

expediting 345:8

expended 50:8

expense 155:19 676:26

expensive 363:24 680:24

experience 16:9 152:13 176:10 238:23 263:21

280:16 351:2 380:16 467:9 496:1 541:17 552:6

553:5 554:3 574:24 578:10 591:3 592:18 603:5

622:23,24 638:18 662:10

experienced 466:8

experiences 688:21,26

experiencing 238:4 239:2

experiment 172:1 192:1 421:6 521:4 524:19 525:4

535:15 542:21 547:7 571:24 642:1

experimental 177:2 182:22 212:18 343:9 344:14

375:2 392:26 398:1,9,12 399:10,15 458:20

496:20 500:13 527:4 567:10 579:4 596:17 606:6

633:19

experimentally 650:11

experiments 231:21 246:23 418:20 419:2 521:3

524:26 534:17 562:16 598:10 634:2 672:20,22

674:9

expert 7:15 23:8 85:14 263:25 638:26 668:14

expertise 4:2,5 5:9,25 22:21 290:2 635:4 647:2

651:15 695:15

experts 138:15 242:8 555:9 653:5,13 671:14

explain 242:11 313:26 432:25 644:26 664:8,18,20

explained 194:21 272:9 318:17 664:8

explaining 107:9 164:10 241:15 634:23 660:8

explains 355:18

explanation 256:21 355:17 644:1

explicit 29:19 203:24 223:24 225:16 226:14,19

227:11 666:3 684:19

explicitly 59:16 65:24 294:18 684:2

Exploratory 312:19

explore 224:14 511:19 678:3

explored 155:13 171:20 178:4

explosion 682:20 692:11

exponential 101:20 694:5

expose 74:21 583:25

exposed 38:23,26 40:11 41:11 72:26 73:18,25 74:8

74:15 81:26 132:4 142:15 158:22 159:3 165:16

181:11 185:26 200:14 265:12 298:5,8,26 299:3

299:25 300:1,4,6,11 301:4 338:15 351:15,20

352:18 355:7,8,13 356:2 360:16 362:4,5 363:2

376:1 423:15 439:21 444:14 450:26 486:11

522:14 539:3 560:12 576:12,23 579:11 584:3

590:3 595:23 602:1

exposing 132:13 481:8 529:2

exposure 18:11 22:5,9,12,16 23:26 24:5 25:1,5,21

25:26 26:21 30:7 31:12,17 35:2,15 36:7,12,20,21

37:1,5,12,13,17,21,24 38:3,5,6,9,13,19 39:2,12

40:8,13,14,22,26 41:3,8,19,25 43:19,20 44:8,20

45:3 46:16 47:2,6,14,18,24,26 54:8,16,17,18 55:4

55:6,7,9,17 57:16,22,24,25 58:5,11,12,13,24,25

59:25 60:25 62:2,19,20,22,23 63:6,10,11,14,16

64:1 65:2,3,5 70:26 71:13,13,15,16,18,22,26,26

72:3,4,6,7,8,9,11,16,19,22 73:3,4,7,7,9,15,17,21

73:26 74:3,4,10,11,14,18,24,25 75:2,2,7,17,18,19

75:20,25 76:1,3,4,5,6,7,9,10,11,14,18,18,20,24

76:26 77:4,16,17,17 78:3,4,6,12,20,24 79:2,3

80:1,6,8,11,15,16,17,17,18,19,21,23,26 81:1,3,3

81:4,5,14,14 82:1,9 83:24 84:1,6 85:2,4,9,17

86:13 87:7 89:2,11,17,17,26 90:11,15,17,19,19

90:21 91:7,11,18,22,26 92:5,8,10,11,12,14,15,18

92:21,22,23 93:23,25,26 95:9,15 98:4,8 100:10

101:8 103:25 104:22 106:9,10,14 107:9,16,24

110:6 111:11,25 113:5 115:20 116:17 117:6

118:19 121:8,9 123:23 126:18,21,23 127:1,19

128:23 129:6 130:21 132:9,11 135:4,18 140:13

140:19,23,25 141:4,5,10,13,15,17,19 142:5,9,12

143:4,7,8,9,15,16,18 144:1,8,10,12,16,23,26

145:5,13 146:2,3,5,10,14 148:6,10,11 150:5,10

151:2,5 155:14,18 156:19,25 157:13,18,22

161:14 164:19 165:3,5,17,19,25 166:3,7,15,19

167:11,13,14 169:10,18,24 170:7 171:22 172:2

172:21,23,25 173:16 174:11,13 175:13,20 177:8

178:25 179:2,6 180:12 183:15,19 185:7,15,23

187:10 189:3,14 190:15 191:12 192:12 193:9,14

193:14 194:10,20 197:22 198:14 199:12 200:11

200:12,13,19 201:2,3,11,24 203:14 204:10,20

205:8,18,22,24 206:2,8 208:14,23,26 209:17

213:1,2,24 216:2,3 217:2 218:2,20 219:1,3

227:26 229:8 237:7 240:6 256:1 257:7,14,21

260:8,23 261:1,23 265:10,15,17 266:19 268:2

275:6,9 291:3,23 292:25 293:16,21 294:25

298:10 300:10,16 301:10 318:3,23 327:1 328:3
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346:3,23 347:9,22 348:2,4,5 349:13,17 350:12

353:25 354:5,6,9,13,21 355:14,17,18,22 356:1,18

359:7,12 360:15,17,21 365:24 366:8,10 368:18

370:17,18 371:15 372:1 374:16 378:9 385:19

386:11,13,21 387:3,22,24 388:5 395:24 396:8,10

396:12 397:12 402:18 404:1 407:14,16,24,26

408:7,11,11,26 409:6,7,10 411:2 412:4,16,17,23

413:4,5,23 414:1,8,9,18 415:9,11 416:3,14 419:9

422:6,14,22,24,25,26 423:3,8,14,21 424:21

425:18,20,21,23 426:8,8,18 428:13,24 429:5,9,20

429:24 431:6,10 432:22,24,26 433:4,15,20,21,23

434:25,26 436:16,18 438:12,13,15,24 440:19

445:23,25 452:18 453:16 461:12 462:8,11 464:26

465:2 466:7 468:8 479:1,22 484:4,11 487:7,8,10

487:15 488:25 489:19 493:21 513:7,22 518:22,23

519:6 520:3,6,10,26,26 521:4,5,13,14,16,20,21

522:7,8,10 523:9,19 524:4,15,16,18 525:2,10

526:4 528:10 529:3,26 530:3,13,14 536:23,26

537:9,12 538:5 540:4,6 544:12,14 562:14 563:4,7

563:21,25 564:5,8 567:26,26 568:11 575:6 576:9

576:15 579:12,16 582:23 586:14 588:6 589:7,8

589:17 590:7 594:19,21 599:12,18 603:20,22,23

606:15 612:16 623:17 628:9 631:5 632:7 634:6

634:11 656:12,13,15,19,20 657:1,8 672:11 675:9

677:13 681:10,12

exposures 41:17 72:12 78:25,26 79:5 80:5 89:18

90:13 91:21,23 93:21 94:5,11,12,18 95:3,4 98:9

107:19,23 108:12,14,16 110:4 111:10,14 112:8

113:9 127:19 128:25 129:9 136:23 140:12 141:1

143:10 144:22 149:26 150:6 160:20 163:3,7

170:20 173:21 210:11 266:20 267:3 298:2 299:26

318:1 346:26 347:7,10 365:24 368:13 372:22

373:8 376:25 388:12,14 396:3 403:22 411:8

424:5,12,14 432:25 434:25 437:19 438:4,8

445:22 467:12 487:17,25 657:10

exposure-based 60:21

express 95:17 252:21 687:3

expressed 47:3 84:26 92:3,5 167:18 257:26 258:25

expression 412:19,24 449:17 455:24,24 465:19

475:12 504:8

expressions 29:20

extant 396:21

extend 2:12 3:19 503:5 524:15 529:15 530:19

537:8

extended 158:16 161:22 176:23 181:5 317:2 428:2

479:20 483:12 529:2 607:8

extending 443:13 529:26 530:8 540:7

extension 5:3 250:1 450:20 451:15 460:7

extensive 40:19 165:21 185:20 187:6 292:22 428:6

extensively 205:5 504:7

extent 18:20,21 22:16 24:11 31:7,21,25 32:1 42:7

45:13 50:3 70:17 106:2,4 129:10 150:18 167:17

172:25 174:2,14 188:1 189:14 210:8 214:8

226:15 238:20,20,21 258:25 282:9,17,20 283:5

283:13,25 284:19 291:25 294:24 357:18 358:9

361:6 392:14 397:10 403:20 441:21 459:10

464:24 465:4 471:24 472:23 500:19 518:26 520:9

543:8 566:19 568:4,10 576:14 608:3 670:24

683:21 684:15

extra 148:23 242:17 318:8 415:5 435:16 459:16

530:14 589:4

extracted 425:2

extrapolate 222:11 277:2 374:9 382:16 387:21

400:15,22 415:6 514:11 606:12

extrapolated 146:16

extrapolating 399:7 588:3 614:18

extrapolation 69:21 389:2 586:20

extrapolations 449:1

extreme 150:2 362:10

extremely 135:24 229:5 305:4,16 315:16 369:19

457:10 466:18 552:15 583:5 690:8,13 692:11

exudates 40:12 41:7 75:6 348:3,5 349:1

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f 87:19 503:21

fabulous 345:7 694:10

face 187:1

faces 513:24

facetiously 424:19

facilitate 540:2

facilitated 112:20 447:20

facilitates 447:6

facilities 331:10

facing 9:9,14 294:16 439:21

fact 46:14 192:24 202:12 231:10 236:25 268:8

273:18 284:8 294:4 298:23 300:15 318:1 319:21

356:8 359:24 381:14 388:19 394:12 401:8 418:13

429:21 433:26 466:21 475:14 496:20 555:13

568:7 594:21 595:25 609:12 611:10,16 613:7

617:23 621:8 640:13 644:18 653:26 661:11

670:13 692:10

factor 20:21 104:17 110:9 118:5 119:7 120:13

123:10 188:5 235:26 260:14 265:1 283:4 329:20

330:8,17 377:10 382:9 383:8,11 426:4 438:6

498:13 563:24 570:5 596:18 611:4 631:20 665:20

676:4

factored 84:7 612:23

factors 13:23 19:15,23 20:19,23 21:7 23:5 28:3

39:15 53:21 69:26 83:17 84:1 91:13 166:8 175:19

176:12 177:1,22 190:5 192:22 193:7 211:25

223:4 228:17 233:16 235:1 238:14 239:11 243:10
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failure 20:17 294:6 633:6

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fascinating 685:18

fashion 231:13 432:5

fast 457:26

faster 15:25 220:25

fast-acting 112:23

fate 4:6 5:10 11:18 13:13 17:1,9 33:3 57:11 71:9

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622:12 656:23,25

favor 215:7 371:5

favoring 498:5

favorite 556:21

FDA's 4:24

fear 326:7,9

feasibility 460:1

feasible 490:19 491:20 577:16 638:7,7

feature 228:1

features 225:7,14 226:12 228:5 653:6

February 16:6

fecal 453:22,23

fecundity 341:8 342:23,25 343:19,21,25 344:21,23

344:24 448:17 457:20 502:8 535:25 559:24

560:10,15 636:19 648:16,16 678:22

fed 104:8 159:19 160:7,9 161:12 172:4 383:12

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feedback 33:7 42:6 105:22 106:2,4 128:22 129:4

129:10 187:2 221:25 229:12,16 246:19 247:3

322:14 459:2 629:1,4 694:26

feeder 171:4

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feeding 61:3 161:20 167:7 168:21 170:25 171:21

171:25 172:25 173:1,7,14 174:6,7,9 176:1,25

177:9 179:7 180:5,13,18 186:8 196:26 197:11

253:8 268:2 298:2 370:20 371:8 372:2 413:25

415:7 454:7 468:10 471:26 477:10 482:14 484:25

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548:24 550:18 577:7 625:2 626:12 630:19 675:9

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feeling 328:11 347:4 501:4 533:16 546:16 675:15

feels 417:5 667:11 679:15

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52:15 129:22,23 130:10,18 197:15,16 229:25,26

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250:25 296:24 309:20,21 330:22,23 340:14,15

371:3,4 375:17,18 390:21,22 428:16 429:16,17

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felt 47:11 330:20 393:11 423:7 563:3 661:20

female 309:13 360:12,13 555:24 655:7,12,13

females 448:4

female's 343:17

fenoxycarb 170:14 478:11 479:13

fenpromexite 308:2

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fewer 217:4,9 218:2 236:2 261:17 318:10,11

field 22:11 24:12 25:2 40:21 43:21 48:19 54:14

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178:4,19 179:21,25 180:1,6,10,16 181:2,7,13,24

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286:22 290:11,12,21 300:20,25 301:2,14 317:10

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188:20,22 189:6 287:13 305:25 317:4 321:5

378:13 410:16,21,25 416:1 425:9 426:14,24

429:5 434:17 560:1,4,5 574:5 576:15,16 598:3

600:20 648:10

field-based 200:18 201:3

field-feeding 159:10

field-like 298:14

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272:9 284:15,15 290:19 325:20 418:3,24 420:7

420:12 696:15

fifth 22:15 34:3 101:18,26 173:9 183:22 226:4

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92:19 97:22 98:22,25 99:11 102:20 103:2,7,14

104:11 109:13,17,21,24 111:18 113:22,25 114:10

114:23 121:11 143:15 238:8 245:12 319:11

347:26 348:3,14 349:23 357:4 419:19 551:2

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296:1 345:14,23 353:19,20 438:26

file 278:8,9 323:22

filed 2:25 292:15

fill 245:10 255:16 407:8 671:7

filled 282:2 374:12 401:9

final 96:26 98:15 125:15 163:1 177:20 190:1

191:10 229:11 244:10 281:22 313:8 395:25

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finalized 59:5

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154:22 166:23 173:23 182:26 184:3 186:20

187:16 214:7 221:16 223:11 229:20 264:4 267:8

284:1 314:12 367:17 400:4 404:6 405:6 406:12

424:22 532:8 587:7 690:18

financial 2:25

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308:3 332:11 381:6 411:16 423:26 440:25 454:6

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finding 135:25 295:8 361:16 498:24

findings 415:3 429:12 460:11 515:19

fine 196:19 263:1 280:17 335:9 337:25 338:20

345:21 410:20 445:9 547:20 636:14 637:19

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finish 407:14

finished 530:15 619:23

finite 287:6

firm 238:16 368:10

firs 408:19

first 6:26 11:24 13:2 15:4 17:26 24:21 33:10,19

35:22,25 36:14 42:20 44:2 45:2 49:14 53:12,23

54:5 56:2 57:2,6,26 64:10,18,19 65:12 69:1 77:23

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five-mile 187:24

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flagged 235:12

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flexibility 173:14 182:9 246:24 463:25 506:14

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flies 377:4

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flip 197:21

floor 150:7 251:18

floral 148:23 186:13,18

Florida 300:15 302:14

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flowcharts 352:25 353:2

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fluorescein 367:7

fluorescent 367:7

flushed 48:5 615:12

fluvalinate 308:2

flux 78:16

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257:7 412:20 630:24,26 632:13 639:16

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folairly-applied 72:24

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folded 532:6

foliage 24:25 36:25 39:25 40:21 43:2,22 72:25

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73:22 75:26 80:10 90:1,3 91:5,22 92:20 106:11

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98:6,14,18,18,20,26 99:5,6,10,17,20,23,25,25

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101:22,23,25 102:3,6,7,9,10,10,18,21,25,26,26

103:2,3,12,14,15,17,18 104:3,5,8,13,20,21,24,26

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145:5,21 169:24,24 176:11 177:10,16 180:9

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187:25 190:14 223:24 225:21,22 226:5,11 227:5

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351:3 353:3,5 366:8 367:12 384:9 395:15 460:17

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557:25 563:20 568:8,21 573:3,20 575:15,21

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force 186:2 268:2 364:2 419:10 508:10 517:24

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forces 522:6

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forgotten 451:10

form 26:24 65:14 86:25 129:19 145:24 157:25

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426:12 591:3 654:18

formal 59:7 89:3 160:21 212:9 226:14,22 228:18

251:24 618:24

format 35:5,9,12,20 107:10 114:4

formation 198:23 204:16

formed 46:21

former 322:13 516:5 574:24 575:9 677:8

forms 86:19 93:3 105:11 133:4

formula 27:8 85:19 582:24

formulas 583:2

formulate 11:13

formulated 242:6 394:14,15 567:6 594:3

formulating 678:17

formulation 18:18 25:20 26:2 27:8,16 34:7 57:9

86:17 150:13,14,16 199:23 204:9 220:17 229:6

256:14 274:18 283:22 415:25 416:8,21 420:8

567:23

formulations 150:17,19,23,26 151:19 152:16

220:4,7,10,14 230:5 415:6 419:17

formulators 418:13

Fort 302:14 324:9

forth 49:10 132:8 304:17 313:21 552:25 580:19

forthcoming 42:2 604:25

Forty-five 425:16

forward 7:25 8:13,22 10:16,20 11:8 12:7,14,16,22

13:1 16:13 17:23 33:7 87:22 237:23 247:9 249:5

278:23 279:11,21 280:3,16 294:8,23 296:20,23

312:23 376:12 378:15 399:21 473:3 538:12 547:5

600:26 607:26 614:10 616:13 619:2 620:21 622:8

657:24 685:18,23 686:18 694:25

fossil 318:11

found 17:22 33:14,17 78:23 80:23 149:1 162:19

171:26 172:12,15 221:9 276:19 282:13 298:11,16

299:19 305:13,15 309:10 311:12 332:7 353:20

374:23 623:5,10 628:24 681:24

foundation 7:20 11:19,23 12:16 139:22 154:21

founded 11:10 139:26

four 22:8 23:14 33:24 43:15 96:24 102:4 114:18,22

121:15 188:3 191:1 199:16 232:24 291:26 420:10

464:13 477:8,9 489:22 524:17 579:2,20,22,22

582:9 583:6,9 689:23

fourth 65:9 101:18,26 173:6 177:18 183:21 189:18

223:1 225:25 228:1 302:19 341:18 382:12,19,22

fraction 63:19 65:5 97:10 350:5 398:4,25 658:15

fractions 410:20

fragments 475:15

frame 248:23 439:14,15 463:15 534:7,8

frames 303:10 304:3,4 310:16

framework 2:4 3:24 11:11,11,15,22,24 14:2 16:14

21:14 25:13 29:6 30:22,25 31:2,4,26 32:9,16

33:16 43:25 49:5,12 57:3 62:14 64:3,8 70:16 71:4

84:4 85:24 86:7 89:14 91:5 92:17 154:22 201:8

205:15 208:20,22 213:19 242:2 251:9 261:8

276:3 283:7,23 284:2,14 289:24 290:7 291:1,5,9

291:16 292:7 293:11,24 294:2,11,13,24 295:10

295:16 296:1 562:21 661:5

frameworks 42:13 49:13 53:6,8,12,14,19,20 54:21

54:25 55:3,18 56:1,5,16,20,23 57:1

France 291:25

frank 51:10

frankly 270:25 511:6 688:19

fraught 562:15

Frazier 304:10

Fred 2:1,5 3:22 7:10 247:25 248:4 251:5,7,9,19

252:13 357:11 435:14,15 622:1 695:7

free 184:18,21 196:26 516:3 522:19,19 523:12

526:3 530:17 544:8 547:8 588:23 593:2 596:13

freely 168:18,22 171:2 172:18 173:7 176:8,26

181:10 183:20 185:17 189:10 563:5

freezer 313:16,16,18,18

free-flying 516:15 519:23 523:2 530:11 546:11,14

frequency 109:14,21 183:1,13

frequent 27:2 640:2

frequently 24:15 170:5 375:20 487:25

fresh 471:15 621:26

freshly 145:23 186:5,16 483:13

Friday 4:9

friends 307:19 691:11

front 199:15,22 285:13 288:16 303:9 304:7,18

306:11 307:8 310:1 572:10

fronts 304:6

froth 658:9

froze 304:8

fructose 466:26

fruit 113:12,24 114:20 115:5,9 117:15 142:4

302:12 362:16

frustrating 139:24

frustration 483:1

fuchsia 122:9

fuel 264:12

fuels 318:12

Fugitive 654:2,3

fulfil 329:5

full 48:18 52:5 61:24 112:21 160:16 168:24,26

169:17 172:5,26 173:14 178:3,19 179:21,25

180:1,6,10,16 181:7,13 183:2 184:12,23,25

185:16 186:25 188:4,18,24 190:6 192:24 194:9

194:19 195:12,16 196:12,15,20 200:9 201:10

209:26 210:17 275:15,18 376:5 449:18 460:25

461:11 468:10 501:19,20 514:16,26 522:14,14
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526:22 539:20 568:10 616:3

fully 55:1 66:1 228:25 482:10 508:11 509:21 514:3

545:8

full-field 246:22 522:2 538:25 539:9,17,25 540:2

562:9 585:25 615:8,20

full-size 582:9

full-sized 171:5

fumble 295:10

fumigants 80:9

fun 689:9,23

function 133:20 141:2 205:18 225:17 313:3 451:12

455:13 611:20 650:26

functional 27:23 184:15 240:21 532:10 558:17

666:9 671:10,12

functionally 627:14

functioning 130:8 180:3 218:8,22

functions 99:4

fundamental 513:24

fundamentally 244:2

funding 237:2 325:21

fungi 217:23

fungicide 28:15 417:26

fungicides 217:6,10,22 218:4,5 219:14,16 258:24

319:8 566:22 688:8,11

funnel 605:8

funny 658:18,25

further 16:14 17:22 22:6 38:22 51:14 55:16 70:25

79:3 88:21 90:24 161:24 164:14,22 166:12

174:23 179:18 180:14 194:21 206:2 219:4 220:21

244:13 274:2 284:3 288:14 290:8 356:7 396:4

408:17 425:22 426:11 445:3 478:6 479:20 506:18

509:22 516:9 533:14 537:9 548:12 565:6 582:16

618:5 649:18 652:19,20 656:6 659:20 665:5

671:9

furthermore 40:17 173:8 176:20 178:19 179:7

181:25 193:6 318:20 335:22 358:23

future 32:10 56:1,26 128:19 135:15 146:12 291:7

294:13 312:6 314:10 335:1 358:18 418:23 446:14

457:1 504:8,26 505:5,6 509:18 541:16 620:8

636:17 644:16 652:17 662:14 691:6

G

g 508:23

gain 396:4 415:19 578:20 645:24 657:20 666:24

gained 56:17 436:10 657:15 686:20

gains 193:1 614:5

gallon 363:11,16,24

game 454:23

ganglion 512:1

gap 99:21 159:6 281:26 282:2 343:20 359:18

374:12 387:17,20 388:4 389:12,14 390:3 401:9

407:8 430:17 451:9 460:8 466:17,25 473:24

488:4,7 514:2 531:19 647:24,26 648:21 674:12

gaps 54:24 166:22 212:16 245:8,10 255:16 281:20

281:21,23 466:22 671:7 692:10

Garber 45:11 46:26 58:8 70:25 72:2 81:16 82:10

86:9 87:26 88:11,26 130:7,13,26 131:7,19 132:7

132:25 133:6,13,17,26 134:7,16,20,24 135:11

138:6 200:26 202:18 205:20,23 209:18 216:26

221:10 327:3,3,22 328:15 329:16 331:4 365:21

365:21 372:7,8,8 373:4,4 377:19,19 378:22,22

382:7,17,17 383:2 384:6,6 386:8,9,19,20 388:11

388:11 392:7,11,11 397:6,8,8 401:16,17 402:6,6

403:12,13,16 405:15,16,19,19 407:10,11 478:17

478:19,19 493:13,14,14,19 497:20 696:3

gas 318:11

gassiest 73:19 74:17 75:18 76:20

Gastonia 304:13 311:5

gather 176:9 376:8 399:17 581:12

gathered 61:4,10 308:26 587:16 589:5

gathering 57:9 137:2

geared 52:11

gears 221:2

gee 245:3 373:23

gene 455:24 465:19 475:12 504:8

GENEEC 424:12

genera 364:10 570:14

general 3:1 9:15 15:1 18:14 20:20,26 21:9 32:8,9

35:12 95:26 168:4,11 183:8,11 228:13 230:3,8

233:10 255:3 257:9 277:19 283:10,14 325:14

334:2 345:22 347:4 362:11 366:10 379:17 384:13

384:26 388:18 391:24 400:20 412:12,13 414:16

419:1 428:8 429:7 434:3 442:11 451:19 477:13

479:4 500:11,16 501:4 511:22 512:18 519:5,11

531:6 533:16 546:13 577:7 605:10 618:6 633:10

641:15 644:1 649:8,9 667:11 672:7 692:5

generalist 362:9,10

generalizable 295:17 296:2

generalization 277:1

generalized 50:2 295:21 376:19 622:21

generally 35:13 41:16 90:18 103:10 111:22 121:18

122:13 217:6 260:26 347:22 361:8 384:14 386:3

394:20 401:3 423:26 424:2,6 467:7 491:18

509:16 559:13 571:19 578:2 584:15 595:17 647:1

680:14

generate 90:12 119:21 122:2 142:16 148:11 206:2

230:15 260:24 397:11 403:21 416:6 497:4 624:23

647:14 679:18

generated 30:6 54:18 83:18 86:15,21 116:4 128:11

134:2 139:24 158:20 229:8 276:24 379:6 380:5

390:11 410:16,20,24 412:10 415:24 588:15
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660:10

generates 90:16 121:6

generating 76:5 126:6 373:16 414:4

generation 84:2 302:20

generations 640:26

generator 313:19

generic 28:11,11,20,22 29:3 30:12,17 33:11,13,24

35:19,25,26 36:13 39:20 41:24 42:11 50:6 497:24

genes 455:12

genetic 195:3,6,9 340:19,24 445:12 448:5 455:5,6

455:11,16 465:11 475:12 631:22 635:18

genetics 195:1 242:26 324:24 340:20

Geneva 303:7

genitalia 306:16 511:21 512:1

genome 455:11

genomic 455:13

genomics 6:4 250:22

genotype 226:1

gentleman 295:23

genus 336:5

geographic 182:18 190:23 226:19 566:13 568:18

581:11 591:16,19,23 631:21

geographical 682:11

geographically 541:8

geography 595:17

Georgia 5:9 250:7 581:13 681:3

German 596:22 611:6

Germany 5:12,15 83:26 109:1 250:8 291:24

428:21 524:6 580:8 682:18

germination 316:26

getting 11:2 50:25 53:1 139:5 235:2 236:26 241:20

241:26 244:14 245:1 266:24 273:17 275:1 287:5

304:12 309:1 313:12 314:23 316:22 345:8 366:22

389:4 411:18 418:2,6 421:13 435:8 452:25

454:22 455:12 474:4 476:3 493:16 515:2 523:19

526:24 574:2,10 577:14 599:20 600:8 607:2

641:7 649:2 693:17

gist 491:18

give 6:24 89:10 90:10 138:4 218:24 237:12 246:20

246:24 286:2 288:9 289:3,17 346:14 395:23

414:17 418:21 422:2 428:25,26 447:23 456:19

462:24 492:7 518:10 526:9 535:17 542:15 550:21

553:15 575:1 579:11 584:10 589:1 594:14,20

618:26 629:1,4 640:6 646:17 651:21 655:1

669:23 674:19 679:14 684:13 691:5

given 28:6 39:12 47:26 79:1 84:1 99:22 103:9

135:3,12 138:26 159:3 169:18 174:11,21 181:16

187:6 192:13,17 193:11 204:4 207:19 213:20

219:9 231:10 239:5 241:17,17,24 274:26 280:6

294:17 323:16 358:10 370:15 390:23 404:25

424:5 446:15 448:11 465:1 475:1 479:2 490:9

491:21 492:9 498:3,3,22 499:20 504:13 509:12

509:26 512:12 540:25 541:18,26 545:18 547:21

554:12,13 573:12 604:23 607:14 610:7,20 614:21

639:11,11 640:8

gives 492:6,8 495:16 519:1 521:10 561:13 579:16

600:4 645:21 680:9

giving 134:18 246:26 248:6 386:25 521:24

glad 279:14 289:11 296:9 454:23 555:24 662:24

690:16,19 692:14

glands 545:8

glandular 93:6

glass-walled 531:9,9

glean 151:19 541:9

gleaned 196:16 539:19

global 10:4 22:3 23:3 335:20

globally 9:10 18:26 678:18

globe 8:18 9:8,21 642:24

GLP 312:21 313:15 315:11,25

GLPs 315:13

glucose 466:26

glue 508:10

go 2:7 3:25 7:24 8:12 11:8 12:7,16 13:2 36:14

48:23 49:4 59:6 87:14,22,22 88:5,17,24 92:18

132:8 139:1 140:4 151:26 153:1 196:8 200:5

203:1 215:6 220:25 233:21 236:20 240:24 242:12

242:24,25 243:9 245:18 246:5 247:9 249:9

251:23 252:5 255:24 256:8,13 266:3 268:9

272:22 274:25 287:8 295:2 296:12 297:1 300:19

307:11,20,20 308:22 313:13 319:10 330:9 333:10

333:14 341:2 345:5 353:14,16 356:9,10,25

358:21 360:11,13 362:18 364:1,19 365:16,19

373:2 374:2 375:16 376:18 377:17 378:17,20

381:19 382:5 386:18 389:19 390:9 392:5 396:4

397:6 402:1,4 404:19 407:14,18,19,19 409:3,4

414:20 422:9 425:11,22 427:25 434:10,11 435:3

435:6,13 446:8 448:19 450:18 456:5 458:24

463:26 464:17,20 468:26 476:26 480:18 487:19

494:15 495:10 505:20 506:19 508:12 512:23,26

516:9 518:4,20 519:1 520:5 525:1,6 526:8 529:11

535:20 538:21 543:2 544:19 549:19 551:26 554:6

559:6 567:14 572:1,11 574:7,21 575:10 580:14

583:10 593:9 596:23 598:17 599:26 604:13,20

605:21 607:17,20 613:7,15 614:10 615:22 616:18

619:19,25 626:4,5 630:11 634:1 636:7 637:10

639:20 640:11 643:8 645:2 646:10,13,26,26

647:22 650:12 652:10,12,19 653:16 654:9,17

655:24 659:16 661:21 662:25 664:10 667:6,21

669:9,24 670:17 673:7 674:1,12 683:3,16 685:10

686:1 688:25
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goal 11:24 28:11 32:2 39:6 90:11 98:8 121:8

147:22 155:4,17 272:7 279:21 290:23 312:13

335:17,17,20,25 336:13 338:25 343:13,15 364:14

436:21 521:20 556:26 559:6,21 572:15 590:13

613:6 647:11 689:15

goals 18:19,24 26:14 27:12,17 28:5,8,13,20,21,22

28:24,26 29:2,3,5,16,18,26 30:4,11,12,15,16,17

31:26 32:14,16,17 42:9 147:25 155:3 174:26

210:9 254:19 256:15 267:15 268:16 269:2 270:12

270:14 279:18 301:6,26 333:21,22 334:19,21

337:19 341:20,21 345:15 357:19 536:1 556:25

558:3 559:7,16,17 563:11 585:26 586:5,5 679:20

689:15,20

God 673:8 691:9

goes 16:12 234:17 240:4,5 277:3 287:23 313:22

356:7 388:22 389:20 418:3 466:21 482:17 488:15

490:10 547:14 554:18 555:20 559:10 561:4

590:22 647:7 682:17 695:19

Goh 293:17

going 6:15 9:24 15:8 16:10,16 44:25 48:23 49:15

49:16 50:14 52:3,14 81:13 89:6 92:18 150:10

151:3 198:24 199:1 215:1,6,7,8,8 233:10,13

236:15 237:19,23 243:26 246:1 251:18 252:12

256:10,11 257:26 259:12 261:13 263:4 264:26

265:2,19 266:3 271:10,11,12 273:1,22 274:25

278:18 280:15 284:1,17 287:18 289:3,9 294:8

295:19,24 296:19 307:3 308:10,11 310:18 312:23

313:1 324:19,20 330:1 333:1 334:17 335:18,19

335:19 337:1 341:1,22,25 344:14 345:21 346:18

347:1,10,19 348:1 355:11,23 357:13,15 358:6

359:14 360:13,13,16 361:10,21,23 362:8,17,19

366:21 369:9 371:1 377:21 378:20,21 385:15

389:23 392:9 395:23 403:5 408:5 409:23 412:17

419:1 423:13 433:2,10 434:6,9 435:3,3,26 436:2

436:4 442:21,24 443:7 445:23 450:12 455:2

457:14,25 458:24 460:2 461:8,15 463:2 469:9,14

469:16 471:14 472:21 476:1,6 480:12 486:24,24

486:26 487:1,3,6 494:5 495:25 496:3 498:25

506:17 508:8 514:25 516:23,26 523:12,13 525:1

526:13 527:26 539:14 544:15,25 545:11 548:21

550:8 551:15,18 552:8 555:10 559:8,16 560:10

561:25 568:7 569:22 571:25,26 572:1 573:17

574:8 578:1,17,19,19,22,24 579:5,18 581:1,1

583:8 587:25 589:6,7,8,16 590:19 597:13,14

599:23 601:18 602:12 604:6,12,16 606:23 608:20

609:5 613:13 614:6,10 618:17 619:23 621:16

624:21 625:4,4 629:16 631:10,13 633:24 634:6

634:15 636:3,11,17 637:2 639:6,10,14,14 643:23

643:26 644:10 646:19 647:17 650:21 651:22

653:14 654:7 657:21 658:11 661:7,23 666:7,20

667:4,20,24 671:1,2 672:21 673:23 674:15 679:9

679:14,19 684:19 685:22 687:20 688:2,15,20,20

689:26

gonna 308:9 609:6 613:7

good 2:1 3:22 4:16,19 5:6,17 6:2 13:11 14:26 15:10

81:10 130:15 137:8 138:20 140:6 145:1 152:21

154:9 169:11 183:22 201:21 220:26 237:21

241:21 249:17,19,25 250:12,21,24 251:7 257:2,6

257:23 272:3 275:11 277:26 279:24 281:19 282:6

282:16 297:3 302:9 303:24 312:25 313:2 315:16

315:20 316:13 319:5,24 322:16 333:5 337:3

339:5 340:13 341:23 342:18 345:20 346:7 347:18

350:22 352:25 359:1,17 361:1 364:26 369:4

371:10 372:17 378:16 382:4 388:8 389:26 392:3

392:7 401:3,17 403:12,13 407:10 419:21 432:17

435:5,16,25 437:9 441:13,19 442:1 449:15 453:2

454:19 455:16 459:2 464:19 472:19 474:10

476:22 480:6,18 493:11 496:8 498:16 501:8

507:25 508:6 512:25 513:15 525:24 528:9,10

529:2,24 534:5 535:21 536:8 541:23 542:10

545:18,21 546:1,8 547:2 550:19 551:12 555:9

565:24 567:8,24 569:1 570:6 572:6,7 575:25

576:15 579:8,11,14 584:18 586:17,19 589:14

590:24 592:24 593:10 594:12 595:5 596:21

597:20 598:1,23 599:12 601:13 603:3 604:12

608:13,16,19 614:9 618:16 619:14 620:5,5,13

624:14 627:4 629:1,20 630:23 634:2,5 637:26

638:1,22 640:17 641:14 643:12 647:21 660:8,19

664:3,6 666:14 671:7 673:5,17 674:9,12,16

676:14 683:18 689:1,7 691:3 696:5

googled 417:12

gorging 130:3

gotten 306:12 411:23 430:24 545:26 566:13 692:18

693:5

govern 227:17

government 22:21 246:9 312:12,17 324:17

governmental 5:15 323:3 413:8

grab 241:2

graded 379:11

gradual 439:20

gradually 439:24

grafting 160:3

grain 602:22

grains 117:16 328:18,21

gram 413:16,21

grams 65:19 110:14 206:16 528:13

grande 683:6

granular 40:6

granules 317:26 416:7

graph 19:21 233:24 234:1,12,15,17 235:13 269:15
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graphical 35:9

graphite 77:11 417:24

graphs 269:11,13

grapple 243:4,10

grass 106:24 113:11,23,24 114:11,11,16,16,25

115:2,7,8,12,12,14,16,19 116:10,13 124:7 127:10

129:11 144:13 259:21 304:7 310:1 329:18 392:14

393:9,12,14,19 394:21

grasshopper 110:20

grassroots 297:7

grateful 466:18,24

gratitude 252:22

gravity 553:16

gray 420:20 552:8

great 51:20 52:22,26 88:26 97:13 231:8 243:11,20

243:25 252:6,9,16 255:6 281:2 333:6 341:2,3

345:11 352:21,23 357:11 359:10 365:20 369:17

383:19 392:8 401:18 402:5 405:17 422:4 428:11

434:1 435:7,22 441:16 464:20 483:1 499:15

512:26 535:4,5,10 556:11 568:25 570:6,13 571:6

577:16 606:25 621:18 626:3,5 643:13 650:8

651:17 664:15,16 678:3 685:16 688:26 689:5,9,9

689:10 692:25 693:7 695:9

greater 24:18 54:15 73:12 78:9 79:24 100:1 101:5

104:15 114:12,26 119:7,10,24 155:18 172:25

190:22 217:13 219:15 235:21 300:14 377:10

399:15 438:14 459:15 555:11 561:1 590:8 599:24

617:9,15

greatest 72:22 94:3 644:10 647:12

greatly 7:7 221:11 261:3

greedy 410:12

green 76:4 117:15 162:25 164:18

greenhouse 57:21 318:11 516:10 527:23,24 528:4

529:5

Greetings 288:26

Greg 6:2,2 138:22 195:11 196:3 220:3 250:21,21

274:26 275:18 310:7,10,12,19,23 311:2 319:4

321:15,22 332:1,16,19 344:7 348:11 352:15

355:26 385:23 396:15 408:20 411:10 416:10

420:24 422:5 426:21 427:4,14 428:7 433:14

434:20 454:3,17,22 469:25 471:14 486:8 498:10

498:21 501:12 508:2,9 511:20 519:17 521:2

524:24 533:12 534:21 542:20 545:25 547:6

551:10 555:25 556:1 561:20 573:1 579:7,15

602:8 604:1 610:6 620:4 634:14 635:15 636:1

637:13 641:6 690:24

GREGORY 438:22

Greg's 556:2

grooming 75:11 550:24 616:7

grossly 260:10

ground 36:26 38:11 71:1 151:13 240:18 248:26

286:8 303:9 307:21 317:2 322:25 359:13 549:13

595:22 601:20 602:10 692:8

ground-nesting 351:5

ground-truthing 657:7

group 23:7 95:19 96:23 98:2,6 161:4 178:8 278:6

334:2,8 345:7 347:23 350:11 359:20 362:18

380:21 391:23 416:12 417:3 423:9 444:8,10,13

444:14 453:7 461:14,16 462:19 485:14 489:13

535:4 546:11,14 560:8 563:3 605:23 612:7

615:13 617:8 623:13 637:4,4 649:1 664:24

groups 90:8 193:9 216:12,14,17,21 218:16 247:13

325:8 446:25 447:4,22 515:18 531:26 605:19

615:15,21 662:5

groves 302:15

grow 101:20 269:7 351:9 371:26 637:6 671:23

grower 193:11

growers 77:3 279:6 316:12,14,25 317:7,15,25

318:13,21 320:6,16 321:17 326:8 584:15

grower's 188:21

growing 75:5 149:16 350:26 579:18 588:13 598:11

grown 125:3 144:25 145:1 169:9,11 183:18 278:14

581:16 588:17

grows 682:16

growth 101:23 153:25 170:13 214:1 301:9 338:3,4

342:10 383:26 446:13 456:22 457:1,7,13,14,15

457:18 458:4,6 477:24 502:7 509:2 559:25,26

560:9 565:25 586:2 588:13 589:3 620:10 653:19

654:8 682:13,15,17 683:1

grubs 317:16 320:12 356:11

gruesome 375:3

guarantee 577:10,13 663:22

guess 42:24 43:10 47:9 52:16 88:24 200:21 215:9

240:15 285:7 332:16 355:2 364:1 368:9 370:6

371:19 380:24 381:24 389:13 396:24 406:23

414:23 415:10,12 417:2 419:26 420:24,26 421:10

427:2 434:25 441:17 454:22 467:20 499:11

508:17 521:2 526:7 527:2 539:15,17 541:23

543:10 544:1 546:8,10,22 547:9 551:13 567:11

579:15 592:26 595:15 600:15 609:4 612:2 613:3

617:1 618:17,21 628:2 641:19 656:16 690:15,22

guesses 553:9

guessing 322:25 667:21

guidance 28:23 147:14 156:11,12 176:3 182:3,5,6

182:8,12,13,17,20 203:26 211:19,20,25 212:9,11

240:2 246:26 254:8 274:6 296:24 403:26 404:6

405:6 493:7 499:6

guide 53:6 56:21 569:8 616:3

guideline 25:14 31:19 58:21 59:7 65:26 156:8

157:6 158:2,14 168:3 182:11 195:16,17 208:10
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358:7 457:2 463:11 478:13 504:15 547:13 616:22

616:24 618:3,4,7,11,15,19,21 619:7 620:4

guides 162:16

gun 328:5 371:18 445:17

gut 299:6

guts 102:2 328:18

Guttated 300:8

guttation 55:12 78:23 79:2,6,9,10,14,18 80:2 84:17

84:23,25 291:3 293:14 428:20,23 429:8 440:11

441:3,7 525:13

guy 658:15 669:16

guys 130:1 135:8 252:8,15 304:24 306:15 307:21

308:7 314:3 372:5 374:25 407:17,18 441:13

463:6 538:13 547:19 573:6 584:23 615:2 636:8

644:13 646:15 649:1 664:7 678:1,8 685:5 691:7,8

692:26 694:21

gynecology 4:20 249:21

H

habitat 9:5,11,12 253:20 274:19 338:14

hairs 376:5 434:1 558:9

half 17:14 41:23 187:21 304:11 387:17,19 390:23

416:24 489:26 552:7 659:25 660:5

hallmark 645:23

hallway 645:26

Hamlin 302:10

hand 69:24 237:24 270:14 275:18 282:26 419:24

484:25 489:18,22 490:20 503:3 528:17 533:25

557:3 590:20 618:13 628:26 638:3 653:21 659:17

662:10 674:8,13 691:21

handbook 211:24

handed 487:20 628:14

handle 238:17 406:2 641:1

handled 281:24

handling 457:4

handouts 251:15 280:24

hands 215:7

hanging 387:10

happen 220:13 488:24 516:26 616:10 647:18

649:17 671:24 688:20

happened 195:20 233:15 306:21 673:12

happening 234:26 365:14 455:26 604:17 647:22

684:21,21

happens 241:5 275:9 389:22 452:7 486:18 488:24

671:15,16,21 673:13 675:25 693:23

happy 318:26 520:12 521:10 616:8 682:24 692:4

hard 7:11 9:20 14:8 234:8 236:5 239:12 252:23

271:1 336:14 344:16 350:19 358:1 359:10 370:5

373:13 387:11 405:8 410:4 419:18 435:8 448:9

479:1 485:21 487:24 506:3 514:6 515:19 517:4

524:10 551:5,5 556:23 575:11 602:9 616:14

631:8 649:23 650:10 692:21 694:19 695:18,20

harder 556:13 634:12 664:14

hardwood 400:22

hard-to-keep 481:7

harmful 297:21

harmonizable 280:9

harmonization 255:8 279:4 280:17 482:23 491:3

617:14

harmonize 15:17 22:6 272:4 280:13 477:20

harmonized 11:7 15:18 255:4 267:16 279:12

harmonizing 23:15

harness 617:5

harnessed 451:5

Harper 16:6

Harriott 296:22 297:2,3,5

Harrison 374:26

harvest 350:15

hash 248:2

hat 689:11 692:25

hatch 479:23

hatched 560:14

hatching 560:14 607:9

hate 265:16 355:26 643:23

Hawaii 595:4

hazard 4:14 24:3,14,20 273:6,9,11,16,21 656:16

hazards 276:7 297:8 300:14

HAZMAT 308:13

head 5:19 6:10 243:18 248:3 251:2 355:2 476:15

583:3 599:19 600:13 611:10 646:11

headed 668:19

heads 345:2 369:11 388:9 392:3 512:17 684:8,10

headway 435:7

health 4:13 7:16 8:9 15:2 16:12 17:2,10 20:17,26

23:19 28:12 29:10 30:18 141:23 163:22 165:26

175:3 200:16 250:23 253:3,16,17 254:12,16

270:9 274:16 278:20,22 285:4 297:7 299:7

300:14 317:20,25 319:18,20,24,26 320:4 323:7

323:11 324:24 452:9 453:14 455:8 457:22 462:25

463:3 464:11,14,15 500:2 505:1,18,19 509:20

564:15 567:8 569:20 586:4 593:7 611:14 613:9

631:7 645:10,22 650:4,4 661:11,16

healthier 317:6

healthy 77:24 195:8 297:9 448:18 564:12 592:26

593:1 645:12 648:3

hear 13:2,15,19 27:14 30:24 47:4 49:2 252:26
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heart 686:11

heartening 334:6

heat 505:20,21,22

heavily 11:12 449:13 496:20 596:23 600:11

heavy 302:24

hectare 65:19 374:18 413:16,21 573:26 575:2,8

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hectares 574:1 575:20 576:7,7,19,21,24 577:17

578:24,25

held 127:17 199:16 290:18 304:8 312:16 406:25

428:2

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helicopters 286:12

hell 304:22

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help 10:12 33:7 51:4 52:22 179:13,16 191:24

222:14,19,23,25 229:3 245:10 246:8,23 247:6

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434:6 461:26 500:23 501:9 507:22 511:8 514:2

529:17 549:15 552:14 563:20 571:24 605:22

606:3 609:8 616:3 633:12 634:21 644:15,24

651:21 652:9,12,21 672:7 677:11 680:11 684:11

684:19 685:2,6 695:1

helped 221:11 279:6 395:8

helpful 185:14 191:14 202:1 236:12 244:25 334:15

436:16 442:5 463:10 493:15 497:25 498:2 515:12

515:14,15,23 526:18 527:14 531:17 546:9 552:16

553:7,10,12,21 554:5 582:14 644:25 648:5 658:3

670:2 683:22 685:3

helping 244:18 378:9 633:25 653:4 693:13

helps 11:15 393:2 441:15 494:10 495:7 515:18

577:22

Hemlock 259:15

Hendriksma 691:24

Henry 681:20

herbicide 331:13 401:7

herbicides 43:5 120:11 217:6,10,21,22 218:4

herbivores 467:5

herbivorous 467:3

hesitate 611:25

hesitation 611:9

heterogenedia 160:2

hey 689:5

Hi 6:5 297:3 330:23 340:15 452:1 454:3

hidden 301:12

hide 578:15

high 14:19 61:3 77:10 123:22 176:4 178:11,13

184:2 190:5 193:11 202:9 234:24 259:25 261:2,9

262:13 266:14 272:21 273:19 274:10 281:8

282:23 290:23 297:19 303:1 305:4,4,9,16 320:25

321:13 367:14,16 369:19 384:18 385:25 410:25

416:24,25 425:10 428:25 430:17 438:15 440:25

443:21 449:21 450:7 453:4 491:22 496:4 529:18

550:18 563:14 569:6 577:5 594:13 601:14 624:20

628:13,25 632:9,10 638:2 639:7 682:7

higher 26:24,25 52:1 56:17 64:23 65:13 66:5,9

67:19,24 70:21 80:3 87:5,7 90:18 91:3 99:6 100:2

103:19 104:17 110:9 119:19 121:20 122:14

123:13 124:3 132:4 137:21 139:12,16 141:5

142:2,5 144:4,22 146:4 151:5,20 152:2 153:15

165:13,19 167:19,22 188:13 196:10 198:17 200:8

200:12 210:4,26 213:21 219:5 255:20,24 256:4,8

256:9 257:4 258:13,18 259:25 260:2,26 261:4

262:17 263:13,17 265:16 269:1,26 270:4,6 273:2

273:4,26 274:5,7,9 276:24 277:20 299:5 317:5

321:20 329:14 361:24 364:19 386:3 393:9,19

400:19 402:21 413:18,18,21 414:20 438:15 440:9

452:6 463:26 466:7 495:16,22 497:2 506:19

509:16 512:16 514:19 515:9 516:24 549:3 604:23

606:14 607:13,17,20 608:4 609:16,21 619:4

621:8,15 622:15,17 623:2 637:17 651:25 652:3

652:19 658:21 659:11,16 660:2,4,23 668:21,24

669:1 676:10 682:15 688:12 692:2

highest 28:10 92:9,15 94:4 95:20 96:21 98:6,26

108:17,21 109:5,6 112:18 114:13 118:3,10 119:8

127:16,19 148:15 180:17 258:11 259:25 260:3

261:12 269:21 366:6,7,26 393:19 394:3 429:14

508:4 567:23 568:5 594:6

highlight 26:19 36:16 177:25

highlighted 164:25 183:4 290:26

highlights 170:22 179:24 189:5,21

highly 16:2 35:6,7 69:6 80:9 148:15 157:22 169:8

180:20 192:8 228:7 264:9 266:12 279:12 299:1

299:17 374:11 413:26 414:19 415:2 431:25,26

440:26 441:4,9 472:8 515:5 516:12 523:26

536:10 562:22 564:19 633:26

high-cooling 430:18

high-end 54:8 58:4 99:13 112:8 142:14 148:11

165:8 169:18 261:17,18 425:17 426:6

high-level 229:26
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high-value 317:9

hilly 597:25 598:4

hip 554:9 561:12

hire 570:6

hiring 545:21

histone 465:18

historical 133:7,11

historically 160:15 207:1 233:17

histories 31:12 570:20

history 70:13 274:9 279:1 320:17 324:24 358:11

614:20 656:17 665:3 692:17

hit 233:6 295:14 302:24 322:24 323:20 324:20

401:2 436:2 502:20 572:15

hits 322:8

hive 9:1 29:8,14 37:21,24 38:13,15,19,26 39:17

44:19 47:26 61:4 67:10 72:23 73:3,11,14,21,23

73:25 74:4,6,12,14,17,22 75:8,9,10,11,17 77:23

78:1,8 93:4 94:16 96:3 99:4 112:24 130:3 145:20

145:25 146:2 147:13 148:1 159:11 160:6,10

167:11 168:26 169:21 170:11 171:3,5,9,19

173:25 174:10 175:3,4 176:6,18,21 178:10 180:8

181:12 184:4 185:22 186:3 189:2,12 190:12

191:22 192:9,11 193:16 200:9 222:1 225:20

227:2,23 231:4 245:26 267:4 270:2 298:4,7

300:26 306:25 309:17 310:22 333:18 335:7,8,9

338:5,6,7,9 339:11 342:13,19 346:2 352:19 353:9

355:11 356:2,7 357:21 387:20 389:22 391:10

426:25,25 427:11,25 428:1,3 439:22 462:9,25

463:3 464:12,14 466:10 467:10 488:18 498:12

499:2 533:22 538:9 543:22 556:10 558:3 559:9

559:12 570:1 583:22 588:6 611:14 613:9

hives 80:10 84:17 168:14,15 169:15,20,24 170:1

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180:6 183:23 184:6,14,19,20 185:5 186:1,6 188:4

188:8,17 189:12 190:14 191:8 195:8 227:4 236:2

269:22 286:14 302:11,12,13,14 303:1,9,10,26

304:2,7,7 306:2,6,11,22,23 307:2,5,8 308:4

309:17 310:1,7,14,20 332:6 380:8 411:3 429:5

464:13 531:9,10 533:19 545:20 568:24 569:24

582:6,8,9,10,14 583:14,19 636:10

hive-level 266:24

hobbyists 236:1

Hoeger 259:22

hold 32:9 82:21 252:13 295:8 315:12 332:17

383:15 417:26 428:1 470:4 478:17 613:10

holders 292:10

home 4:9 17:13 52:16 391:10 454:6,9,10 580:14

584:23 678:24

homeostasis 685:24

homes 322:21

homing 39:18 454:4,21 633:2

honest 315:5

honestly 138:13 417:10 551:19 640:18

honey 5:14,14,23 6:4 8:7,26 9:14 10:9 12:4 13:18

13:21 14:1 16:12,25 18:21 19:3,9,12,24 20:2,16

21:3,22 22:16 24:3,16,20 29:7 31:5,6,15,18 32:2

33:12,16,22 34:16 36:7,12 37:13 38:4,16,20,25

40:8,13 41:3,25 42:3 44:20 45:26 46:8,10,21 47:1

50:3,4 53:7 55:4,9,13 56:2 57:3 58:16,25 61:12

70:12,16 71:2,13 72:18 73:21 74:1,21 75:12

76:17 79:23 89:2,17,19,23 90:9,15,23 91:14,15

92:1,25 93:1,2,3,5,8,15 94:3,5,13,15,17,17,22,26

95:1,1,4 96:5,11,26 98:21,23 100:4,7,19,20,22,26

101:4,9,14,19 102:4,13,15,19,26 103:10,21

104:22,23,25 105:4,10,16,19,26 106:2,6,10,12,19

107:20 108:24,25 109:4,14,18,22,25 110:5,22

111:5,7,14 112:1,9,17,22,23,26 117:23 118:15

126:22,25 127:19 129:6 131:23 132:21 133:7

138:8 141:6 145:24,24 147:3 149:20 156:12,15

157:1,7,26 158:26 159:15 162:12 163:2 165:21

165:26 166:13 170:21 171:3,5 172:5 177:11

182:21 183:11 184:6 186:6 187:5,7,8,15,19,23

188:9,9,22 189:2,10,18,26 191:16 197:18 207:4,5

208:12,20 213:20 215:17 217:1,12 219:8 220:18

221:5,15,22 223:16,22 224:14 225:1,2,4 226:9

227:18 229:2,17 234:2,8,11,11,20,20 235:19,22

236:7,11,22,25 237:1 238:3 250:1,10,11,22 253:6

253:24 257:8,12 270:10,21 279:24 282:13 283:16

285:1 289:25 291:24 297:12,17 298:1,6,18

299:16 301:6,7,16 302:11,16,24 303:11 304:2

308:1,5,6,11,14,20 309:2,25 312:3 314:23,26

326:9 328:24 334:11,13,13,13,14,25 335:3 336:5

337:24,26 338:1,10,11,22 339:2,21,23,25 340:4,7

340:11,12 342:3,5,6 347:2,9,19,22,24 348:3

349:1 351:3,3,14 354:13 357:12,19,21 358:9,26

359:8,15,17,19,23 360:19,24 361:2,3,6,7,7,8,8,9

361:16,20,21,24 362:3,3,8,9,26 363:8,13,14,14

363:19 364:2,3,9,13 365:24,26 368:19,20 369:4

369:15 370:9 371:2 373:8,18 374:24 376:16,25

377:2,3 378:26 384:8 385:14 388:15,22 389:10

389:19,25 390:10,13,25,25,26 391:5,11,25

427:15,16,23 428:9 434:9,10 442:9,21,26 443:4,9

443:20,25 444:18,21 445:1,1,23 448:12 449:26

450:21 451:17,21 458:1 460:17,18 461:15 464:26

465:15,19 466:26 467:1,2 468:7 469:9 472:5

475:9 488:17 489:24 498:3,4,11 499:21 507:9

508:3 513:19,21 514:4 516:7,9,16,21 517:25

527:22 528:13 537:6 539:10,22 540:18 543:26

548:13 570:8,9,17 571:17 572:8,18,20 583:8
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655:18,19 661:13 667:9 670:21,25 675:22 676:21

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honeydew 41:7

honor 413:9 688:26 689:9 692:20

hoop 531:8 532:12

hope 83:8 292:5 301:13 340:16 345:21 436:2

494:10 495:6 547:3 566:4 607:8 686:20 691:3

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hopeful 291:14

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hoping 83:22 88:4 152:9 197:3 356:21 381:26

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HoPoMo 226:3,20 227:6 244:22

horizontal 399:1

horrifically 487:17

horse 52:19 473:18

host 362:13,14

hosted 21:20

hot 428:5,10

hotline 323:15

hottest 430:2

hour 248:25,25 252:14 265:4 447:3 480:14 482:26

482:26 489:26 618:18

hours 45:9 79:12 141:22 157:9 158:18 290:6

322:24 330:13 391:6 443:13,13 447:12 477:3

489:22

house 527:7

houses 531:8 532:12

HQ 372:21 610:25

huge 132:20 237:15 242:7 303:10 305:11 306:6

307:4 398:24 452:9 515:7 551:20 597:8 641:5

650:22 664:5 687:15 691:11

human 7:16 8:25 28:12 30:18 317:25 323:19

354:15 461:12 492:23 504:11 640:19 690:8

humanity 284:10

human-induced 27:20

humidity 596:7

humming 336:3 508:4

hundred 237:14 528:13

hundreds 112:26 638:8

hung 289:5

hungry 253:8

Hunt 6:2,3 138:21,22 195:10,11 196:3 220:2,3

250:21,21 274:24,24,26 275:18 310:7,10,12,19

310:23 311:2 319:3,4 321:14,15,22 331:26 332:1

332:16,19 344:6,7 348:9,11 350:17 352:14,15

355:25,26 385:22,23 396:14,15 408:18,20 411:9

411:10 416:9,10 420:23,24 421:13 422:4,5

426:20,21 427:4,14,22 433:12,14 434:19,20

437:16 438:21,22 454:2,3,22 462:5 469:24,25

470:3 471:9,13,14 486:6,8 498:9,10,20,21 501:11

501:12 508:1,2 511:19,20 519:16,17 521:1,2

524:23,24 533:11,12 534:20,21 542:19,20 545:24

545:25 547:5,6 551:9,10 561:19,20 572:26 573:1

579:6,7 602:7,8 603:26 604:1 610:5,6 612:3

620:3,4 634:14 635:15 636:1,12 637:13 641:6

690:24

hurting 495:19

husbandry 24:13

hydrophilic 134:12

hypotheses 34:20 622:21 623:3 631:3

hypothesis 26:1 161:11 181:26 182:1 196:19 211:9

332:9 497:23 647:9

Hypothesis-based 162:5

hypothesize 671:13

hypothesized 20:20,26 34:13

hypothetical 312:20

I

iceberg 413:10

ICPBR 428:20

ICP-BR 22:19,22

idea 132:8 236:12 243:25 275:2 346:7 347:19

364:13 371:5,6,7,7 381:26 395:21 417:11 424:23

452:10 473:12 490:1 517:6 527:24 533:25 534:1

535:4,10 537:25 553:16 561:8 567:24 579:11,14

591:16 601:3 604:10 634:21 635:25 644:4 668:13

669:4

ideal 468:25 588:18 594:3 596:16

ideally 113:17 184:5 410:1 470:1 579:20,25 649:15

680:3

ideas 8:22 11:8 478:23 533:13 590:9 624:23

645:19

identical 39:20

identification 594:18 659:14

identified 17:9 20:21 21:22 23:14 27:4,8 29:13,16

29:26 30:16,19 31:26 32:1 36:17 51:7 53:19

61:14 71:23,26 72:8 77:8 95:21,23 111:16 140:24

142:8 149:21 156:8 157:2 166:26 167:18 168:2

172:10 173:19 175:7 179:10 180:15,21 181:23

184:22 185:2 190:8 194:7,8 202:25 221:18

224:10 227:11 238:15 243:16 333:19 337:8,10

341:9,10 345:15 357:20 358:15 440:8 521:21
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619:3 622:18 657:4 659:15 660:18,21 668:20

676:3

identifies 491:9 503:21

identify 27:9 51:25 111:14 159:7 160:24 183:7

191:19 215:16 229:3 245:8 249:12 274:2 396:22

400:10 432:21 440:13,25 441:2 514:21 561:6

599:3 600:22 607:16 631:8,17 632:6 641:11

646:25 657:11 658:20,23 659:9,18,26 662:25

680:12

identifying 21:7 172:8 218:8,17,26 222:25 297:8

495:11 504:15 509:11

identity 108:2 195:3

IDS 211:5

IEC 207:12

ignorance 132:17 245:7 469:5

ignore 450:2

ignores 383:25

IGR 557:21

IGRs 477:25 607:4,5,11,16

II 33:14,17 54:12,15,18,19 59:16 60:11,18 61:2,8

61:11 64:4 67:9 71:18 90:25 104:2 128:18 139:1

140:13 141:2,3 143:3,7,15,18 144:8 145:13

146:10,24 147:18,25 148:2,6,26 151:6,23 153:1

153:19 156:3 166:2,5,10,14,24 167:1,8 168:6

173:20 179:7,12,19 180:15,24 181:15 183:2

184:21,26 185:1,6 187:14 190:2 194:26 196:8,22

199:11 222:20 223:2,10 242:24 243:2 255:20

266:18,23 267:19 268:10,11 275:3,6 276:1

281:22 291:5 364:26 449:6 454:18,25 458:4

460:26 461:5 462:7 474:23 479:4 488:6,9,10

489:12 496:7 499:26 507:18,26 509:15,25 513:4

513:16 514:22 515:15,26 516:17 517:4,11 518:9

518:10,20 519:15,18,26 520:11,14 521:23 522:4

522:15,18,23 523:3,7 524:25 526:19,21 527:18

531:3,7,15,15,21,23 532:1,9,16 533:9 535:13,14

535:24 536:20,25 538:26 539:18 546:2,12,15,23

547:7,8,10 549:10 550:25 562:26 563:7,12

577:22 578:6 587:3,15,17 588:9 590:18,19,23

594:12 596:1 600:5 605:18 614:13 617:26 619:19

619:25 625:3,24 626:13,14,16,24 628:8,8 630:15

630:24 631:13 632:14,19 633:23 634:15 641:10

644:24 649:21 652:18 658:24 661:24 662:25

663:5 665:2 680:24 683:23,26

III 54:13,15,18,19 56:3 61:22,23 62:4,11 64:4

71:18 89:7 139:2 140:14 151:24 153:1 156:3

168:25 179:21,22,25 180:5,14,16,19,25 181:26

184:23,25 185:7,9 186:25 192:20 193:19 194:26

196:8 199:11 210:1 222:20 223:2,10 242:24

243:3 255:20 266:7,10,10,26 268:9,22 269:12

275:1 276:2 281:23 290:12 291:5 454:18 458:5

460:26 461:6 517:4,12 519:1 522:20 523:8

526:22 546:13,15,18 548:3 550:25 562:13,18,22

563:10 564:1 570:16 577:21 587:15 588:9 589:12

589:19 590:1,12 595:24 605:19 614:14 625:24

626:12 630:15 631:13 632:14,19,22 634:15 641:9

641:11 644:25 649:22 652:18 658:25 661:24

680:24 683:23,26

IIs 546:18 639:19

ill 631:6,7

Illinois 6:11 251:2 285:17 596:16 600:9 681:1

illustrate 104:9 218:12 355:13

illustrates 30:13 34:7

illustrating 36:20 269:12,15

illustration 128:10 257:2 292:14 304:16

ill-informed 665:5

image 323:1

imagine 83:7 487:16,18

imbedded 200:22

imidacloprid 122:20 293:18 297:26 298:24 299:4

383:11 431:22

immature 159:17

immatures 346:3

immediate 439:8 581:3 647:10

immediately 33:14 301:12 330:13 450:25 479:20

560:15 607:18

immersed 431:2

immune 322:16,17

immunocompetence 449:11 459:4,20 504:10

impact 16:12 18:12 31:12 47:12 49:25 52:8,21,21

103:11 104:9 165:26 166:26 170:13 175:21

189:19 223:4 224:1 225:3,22 226:6 231:13 232:6

234:9 278:18 283:13 284:19 290:11 291:24

301:22 318:15,26 323:22 339:23 351:25 383:25

438:7 461:15 466:16 482:16 501:24 507:25

525:17 558:1,1 559:18 595:9 611:12 625:12

633:2 648:7,15,15 677:14,24 678:21 679:6

687:20

impacted 39:14 103:26

impacts 36:11 159:11 167:10 171:18 174:21,24

177:16 180:7 224:3 226:9 290:5 295:2 300:23

301:16 316:23 466:21 477:24 492:2 504:4 558:10

558:15 562:19 565:3 586:21 587:2

impaired 214:1 509:2

impediments 326:12

imperative 301:17

implanted 193:20

implement 192:16 314:7

implementation 2:19 42:7

implemented 66:1

implicate 331:8
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implications 586:9

implicitly 684:1

implied 258:20 349:16 350:6

implore 359:2

imply 84:16 224:22 239:8

implying 274:3 353:4

importance 16:11 46:5 71:14 79:8 84:8 135:3

154:26 177:25 254:11 279:23 280:2 301:25

320:14 448:11 481:5 589:4 660:25 666:4 672:8

686:22

important 2:17 7:17,22 8:3 9:1 19:2 27:22 28:4

33:6 37:13 38:10 41:21 53:20 55:9 63:7 73:6 82:7

136:3 145:16 147:8 148:25 152:20 160:3 163:22

166:5 170:22 175:25 192:14,19 194:12 218:23

227:17 228:6 229:1,21 244:2,20 245:24 253:12

254:14 267:4 268:26 271:26 274:1 277:1 278:9

282:18 286:20 293:15 295:10 297:20 301:3

312:22 316:24 317:8,12 320:2 327:10 328:4

329:15 335:12 338:13,23 340:22 341:26 345:1

347:6,11 352:16 366:5 368:2 370:26 381:5 385:7

385:24 410:8,18 414:10 423:7 430:5 431:12

442:10 443:2,7 444:19 446:3 447:11 448:3 454:9

454:26 460:5 463:22 470:6,11 473:15,15,23

478:9 481:13,15 503:25 509:13,18 512:13 517:24

531:25 535:23 547:25 550:12 555:10 556:9,19

557:20,24,26 558:4,16 559:2,9 564:10 567:3,25

568:23 572:3 573:2 597:12 606:19 616:12 633:12

633:22 638:25 639:18,23 641:11 643:25 646:26

649:11,19 653:9,14,22,24,25 654:3 655:7,9 656:1

657:5 661:1,9,14 666:8 671:3,8 672:5 673:24

676:4,9,20 678:17 679:24 680:23 682:9 685:18

686:24 695:8

Importantly 54:19

impose 522:7

imposed 290:15 331:25

impossible 184:20 243:9 371:23 497:5 596:25

impractical 286:9 287:14

impress 301:24

impressed 658:16 688:24,25 689:1,6 690:21

impression 309:22 327:16 430:26 470:19 500:17

610:24

imprisonment 313:25

improper 628:2,24 629:13,15

improve 15:17 21:11 24:1 33:7 129:18 191:24

272:1 414:5 436:11

improved 254:13 293:12 317:21 319:18,20,24

320:3 611:2

improvement 257:19,21 272:12 620:6

improvements 274:18 620:9,14

improving 21:18 131:6 187:3 253:10,13 254:21

279:5 418:26

inability 185:23

inaccuracies 237:22

inaccurate 138:3 360:26

inadequacy 290:26

inadequate 175:15 189:9 294:7,15 564:8

inadvertent 692:3

inappropriate 291:9 439:14

inaudible 375:20 391:3 421:24 678:23

inch 306:10

incidence 627:23

incidences 184:19

incident 5:15 23:17 59:19 63:8,24 66:8 150:24

151:11,12 153:10 163:2,3 194:18 196:17 211:2,3

211:5,5 238:6,24 292:8,12 309:9 311:8 325:15,26

326:2 372:21 411:20,23,26 557:4,22 622:19

629:1 630:6

incidental 354:1 647:14 679:12

incidents 76:26 83:25 166:20 250:11 292:18,20

293:1,5 309:1 325:19,23 326:3,5,7,12,18,20

503:2 557:4,5 611:1 628:5,17,21

inclination 541:8

inclined 687:3

include 25:20 27:24 29:6 33:25 37:1,25 38:4 42:18

54:25 57:4 59:17,26 61:5 65:4 66:5,6 72:10,13

73:23 76:16 80:20 86:15,16 110:22 111:5 144:25

148:13 153:7 156:18 158:17 168:3 169:8 175:2

192:7 213:5 218:19 223:24 226:13,19,22 228:18

232:10 244:20 245:1 253:18 291:10 298:1 301:7

333:21 337:11 341:12 345:25 347:8,21 348:3

351:12 352:9 358:12 359:20 363:2,6,8,22 373:19

379:3 385:5 391:18,18 393:24 411:2 439:4

449:20 452:19 459:6 499:10 504:4,8 538:17

572:23 624:1

included 23:14 30:2 37:25 45:16 46:2,15 65:24

72:1 77:18 80:6,17 81:1,3,4 85:3 89:7 92:22

117:14 120:10 140:17 156:13 215:12 216:5 218:5

218:16 223:18 225:6 226:26 227:11 230:26

231:26 236:2 327:24 338:25 341:24 346:1 362:2

400:3 409:7 415:21 421:17 422:26 423:8,11

425:21 427:3 433:23 457:1 465:13 468:2 548:25

620:10

includes 34:17,20 38:24 39:21 57:9,26 61:23 73:4

103:2,14 109:8 113:5,10 114:7 160:19 162:11

209:16 225:15 226:5 683:8

including 19:2 23:23 36:24 37:7 39:15 41:15 59:18

60:17 75:6,21 76:12 77:6,9 78:18 79:23 80:9

89:12 97:7 101:1 105:1,23 110:20 113:3,11

117:12 148:7 160:1,4 166:18 177:7 194:16

224:25 226:10 300:22 323:8 328:18 368:7 377:3
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377:4 384:9 385:18,24 402:8 403:20 411:5 419:9

437:1 443:6 447:1 465:11,20 491:24 552:24

593:19 631:11

inclusion 211:18 224:21 226:20 646:2

inclusive 342:3

income 528:21

incoming 173:1 191:15 439:4 523:13,15,17 538:4

541:2 545:14 556:10 577:9 579:9,15 594:19

597:6 599:16,24 600:23

inconvenience 664:1

incorporate 55:22 66:24 111:1 171:22 198:18

223:12 268:22,24 391:4 422:14 428:13 450:14

452:24,26 514:1 532:2 588:10 605:20 629:21

incorporated 92:23 144:12 147:3 202:20 318:2

451:5 460:2 467:25 471:6 475:21 629:2

incorporates 344:23 450:20 556:9

incorporating 83:7 270:5 445:5 571:3 640:5

645:19

incorporation 40:7

increase 17:17 21:26 158:18 171:23 190:9 192:21

192:21 266:11 272:2 283:2 317:11 352:7 389:17

389:21,23 419:11 428:11 468:14 553:24 557:8,15

563:20 570:1 628:8 630:22 639:17 682:21 691:2

increased 20:5 239:5 308:22 319:17,20 429:7

502:7,8 557:7 569:25,26 595:26

increases 177:21,22 389:18,19 474:3 608:20

increasing 54:13 163:8 165:14 176:13 192:15

219:16 266:23 501:25 590:18 606:7

increasingly 171:25 211:12 401:8

incredible 689:2

incredibly 356:16 430:16 442:4 447:17 448:3

451:17 461:2 515:12 517:25 541:11 548:9,24

550:12 554:13 561:11 653:22 689:24 691:16

incremental 694:26

incubate 363:16

incubator 444:2 489:10 498:25 634:16

incubators 498:23 532:12

incur 570:13

indentified 35:18

indentify 95:19

indentifying 599:16

independence 312:13,25

independent 2:15 264:7 280:25 322:2 324:4

339:12 469:6 475:4,5

index 23:18 506:26 552:12

Indiana 411:19

indicate 19:11 26:23 78:17 92:6 106:5 167:24,25

206:24 210:7,10,12 214:11 228:22 269:8 277:1

313:22 410:20 511:18 599:24

indicated 19:1 26:9 35:13 42:5 59:22 60:15 61:16

62:7,24 64:19,24 88:19 93:25 98:7 101:25 104:7

109:11 159:26 198:25 227:16,24 229:11 264:6

278:12 426:2 478:20 496:19 643:19

indicates 19:18 37:12 119:1 121:18 122:12 176:11

409:25

indicating 99:7 127:17 295:24 425:8

indication 130:23 199:26 220:16 413:15,25 415:1

466:20 519:1 540:17 686:10

indications 533:18

indicative 14:19 294:6 509:21

indicator 586:18

indicators 163:22 199:14 475:11 510:2 586:19

625:16

indicting 650:4

indifferent 241:22

indirect 14:1 343:25

indirectly 170:15 299:5 340:5

individual 21:7 31:16 39:11 47:14,14 48:15 54:11

64:21 66:24 70:19 113:1 130:5 132:24 133:3,8,14

134:6 144:24 156:2 160:13 161:5 163:14 165:20

165:25 167:12 179:4,15 198:3 199:4 200:14

206:17 207:11,16,26 209:19 210:3,11,16 213:21

213:22 215:24 222:17 223:10 232:1 238:7 242:25

243:2 269:16,25 270:24 324:24 341:7,7,19,23

342:20 358:14 365:13 369:1,17 408:7 442:19,21

443:5,19 446:17 447:5 449:2 456:19 457:18

461:18 473:21,21 474:16 477:5 480:26 481:4

499:20 503:23 505:4,20 507:19,20,24 511:6

513:21 514:9,10,12,17 531:10,13 532:3 536:19

537:21 540:9 548:15 550:2 576:22 592:11 604:22

605:19,24,26 606:13,15 607:10,13 615:6,19

616:6,15 617:8,9 619:14,14 626:19 633:9 634:15

636:25 641:12,12 642:22 646:4 649:1 650:2

654:10,11 660:19 672:11,25 677:18,24

individually 481:8

individuals 94:8 146:2 213:1 326:13 444:8 447:22

449:12 450:25 461:14,17 474:5,7 511:10 548:18

605:19 613:9 615:13 650:11

individual-level 266:25

indoor 57:21

induce 477:11

industries 267:10

industry 22:21 23:4 235:21 236:4 252:20 255:6

256:16 274:15 278:9,21 279:7 285:21 286:3

287:6,19,24 288:3,5 302:21 305:9 331:25 448:7

inescapable 514:15

infection 653:23

infections 653:25

inference 549:5

inferences 447:11 615:19
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infestations 231:24 232:12

infield 85:22 86:3 288:6

inflexible 56:22

influence 96:26 119:15 123:26 142:2 467:2 470:13

472:5 589:18 591:7 592:4,11,13

influenced 84:1 118:26 472:3

influences 589:16

influx 581:2

inform 10:9 22:21 26:16 28:25 29:1 30:8 34:9

49:16 59:14 145:14 151:18 164:25 178:23 179:17

193:22 197:6 214:19 630:8 643:18 646:10 649:16

650:2,25 658:20 660:20 662:18 669:5 675:18

676:8 680:22 681:1 685:2

information 8:1 19:5 23:16,18 27:3 30:7 31:15

37:14 45:11 47:19 54:13,17 55:5 56:6,9,12,17,18

56:25 57:7,9,11,12,16 59:20,25,26 60:5,17,18,24

61:4,8,10 62:12,13,17 67:18 68:3,6,13 75:25

85:13 86:1 91:9 95:21 101:7,11,24 123:20 128:15

137:12 139:9 141:26 145:16 150:22 151:10,17,17

153:14 155:15 160:14 163:16 164:21 166:12,18

166:25 180:25 182:15 186:23 192:19 193:21

194:14 199:21 200:18,22 201:21,25 202:5,9,10

202:14,15 204:15 209:21 210:7,19 211:6 215:11

215:13 216:6 221:9 226:14,19 231:22,26 232:1

238:24 240:11 244:9 245:13 246:26 257:11 267:3

268:16,24 272:16 281:2,3,12,19 282:14 292:8

295:21 296:5 304:26 319:1 325:14 326:14,26

327:4,9,18 330:7 346:8 349:8 378:12 384:20

395:22 396:7,24 404:15 405:4 410:1,9 415:19

419:14 422:3 447:24 461:10 465:26 472:4 479:3

480:1,5 488:8 506:15 513:7 516:26 517:5 521:10

521:11 537:4 539:18 542:11 545:26 575:1 581:12

582:3 600:5 607:17 611:1 617:6,7 624:10,19

632:11 634:26 636:9,18 658:7 661:20 662:9

663:2 667:25 668:5 669:4 686:26 689:26 694:1,5

694:23

informative 31:22 233:26 432:14 442:4 443:16

447:17,24 479:6 484:9 487:13 502:1 514:18

541:14 544:9,18 548:24 606:14 617:1,1 625:4,9

640:25 645:6 685:26 687:17 689:25 692:11

informed 22:24 30:6 182:1 221:24 241:16 617:6

informing 12:7

informs 658:11 671:17

ingest 353:11 427:15

ingested 197:24 264:26

ingesting 263:7 353:8 427:12

ingestion 31:17 38:1,14,16,24 40:11 55:8 74:15

75:13 76:1,14 82:19 353:5,13 433:18 603:20,23

ingests 262:20 272:20

ingredient 86:18,26 92:4 104:13 110:12 119:3

133:2 150:20,24 157:24 299:15 318:6 327:19

394:17,18 565:4,19 681:2

ingredients 42:19 43:4 44:1 53:25 201:22 349:16

393:22 394:13 396:2,11 401:6 410:24 423:24

424:4

inhalation 37:26 72:8 73:18 74:9,17 75:17 76:20

80:8,15,16

inherent 179:24 201:7 213:2 271:8 372:14 380:20

454:13 461:1,5 549:1 639:11 640:8

inherently 269:9 459:17 499:25

inhibit 465:18 512:2

inhibition 461:20

inhibits 266:21

inhouse 290:1

initial 26:20 142:18 168:7 173:20 216:2 217:9

218:3,22 378:6 439:21

initially 50:5 205:24 219:25 227:18 236:6 391:20

653:5

initiate 14:15

initiation 183:7 187:10 192:12 269:21

initiative 202:7 285:14

initiatives 274:19 450:9

injection 34:4

injections 206:11

innocuous 153:24

input 8:3 17:21,24 48:16 81:2 84:24 225:6 226:24

229:20 231:4,22 253:12 365:16 393:2 468:21

493:4 498:16 506:21 572:25 609:1 671:9 674:6

694:12

inputs 680:8

insect 5:26 6:12 23:9 24:15 153:25 170:13 251:3

336:2 342:10 373:8 376:25 450:10,12 461:22

516:22 517:3 625:1 631:6 692:7

insecticidal 628:21

insecticide 28:15 112:23 268:3 293:18 298:10

418:1 623:15 632:8

insecticides 120:10 123:15 217:4,9,11,13,18 218:3

290:10 291:4,6 297:25 398:19 483:8 566:22

insects 6:13 217:11,24 286:25 340:8 443:9,11

467:3 513:19,23 515:6 516:6 692:8

inside 38:13,25 145:20 266:21 351:4 484:24

511:26 527:23 528:12 531:7 534:11 690:13

insight 197:4 213:23 230:19 240:23 396:5 418:21

426:18 436:10 462:25 464:14 509:14 543:23

561:13 645:24 655:15 657:20 666:24 671:25,26

672:10 673:23 676:24 677:1 679:18 684:13,18

insights 11:3 12:17 231:3 240:17 241:20 246:5,20

419:3 527:14 553:5 608:10 646:8 647:14 657:15

671:26 672:4 683:24 694:22

insignificant 548:15
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insofar 32:2

insoluble 135:25 431:13,15

Inspection 303:20,21

inspections 313:9

inspector 236:18 283:10 304:1

inspirational 691:17

instance 84:16 93:12 100:13 121:24 144:11 331:10

331:12 347:12 390:14,18 413:15,16,26 445:23

460:16 463:16 518:16 520:18 523:25 524:4 525:9

525:12 528:8,23 529:6 542:7,12 557:3,6,26

574:20 575:7,14,16 592:6 602:19,25 603:6 607:4

607:5,16 627:23 628:20 632:7,14 633:1,5 634:24

638:3,24 672:11 682:8,13

instances 323:17

instar 159:18

instill 254:20

instilling 279:18

instinct 450:20

Institute 250:9

institutes 429:4

Institution 413:8

insufficiency 224:3

insufficient 19:5 39:17 113:19 223:25

insuring 563:21

intact 144:24 165:20

intake 134:9 262:14 264:20 384:19 385:3,5

integral 541:7

integrate 11:8,15 175:12,19 177:15 179:5 180:7

185:7 223:8 371:7 404:15 428:18 493:8 643:4,7

679:5

integrated 209:3 210:25 413:12

integrates 203:14 207:15 210:20

integrating 22:12 166:8 229:8

integration 414:11 571:7

integrity 196:2

intellectual 295:8

intelligent 650:9

intend 88:7 154:14 503:5

intendancy 625:3

intended 13:24 25:25 26:9,10,13 27:8 30:19 35:23

35:26 41:26 42:1 56:16,20 90:13 93:25 95:26

140:22,23 143:4 144:20 152:6 157:12 162:2

165:14 172:20 174:1 180:20 181:7 182:12 194:5

194:9 203:21 204:14 206:1 208:20 209:25 210:4

210:23 213:19 214:15 215:13 218:24 224:12

225:13 228:13 260:24 525:20,20 567:11

intense 689:23

intensity 27:25 191:18,19 214:9

intensive 489:24

intensively 283:1 287:2

intent 22:4 137:6 143:6 147:2 172:8 196:13 210:6

211:11 429:20 539:2 622:16 623:2

intention 101:13

intentional 230:24

intentionally 352:7

interact 651:9,16 677:18 694:4

interacting 232:7

interaction 165:21 220:7 233:10 461:22

interactions 6:13 8:17 219:18 230:14 238:25 450:4

450:12 473:20 504:6 514:25 531:12 616:14

669:12,14,18

interactive 219:23 246:4 566:1

interactively 667:6

interagency 283:7

intercept 58:2

intercepted 135:19

intercepting 41:12 367:15

interception 375:8 376:15,20 377:13

interconnected 473:22

interdependent 221:23 222:8

interest 2:24 6:11 155:16 168:20 186:24 189:20

193:22 235:10 314:4,5 406:4 412:10 563:21

567:6 569:23 577:11 588:16 594:4 597:5,5

599:15 600:12 601:24 642:8

interested 106:1,4 128:22 129:1,4,16 221:14,20

229:12 230:4,21 254:6 267:10 271:19 274:13

285:3 314:6,14 315:4,6 422:13 488:21 506:26

511:16 599:13,14 666:16

interesting 16:16 129:24 135:22 198:12 235:3

449:20 646:8 686:15

interfere 461:17,18 470:9

interim 156:11 589:17

intermediate 518:9 520:14 522:6

internal 177:15

internation 617:14

international 8:16 10:14 15:11,12,15 16:2 17:16

17:19 22:18,20 64:14 247:13 640:19

internationally 8:12 246:10

internationally-common 267:16

internet 324:15

interpersonal 611:6

interpret 12:3 152:2,10 181:16 185:13 194:14

223:7 234:15 235:15 236:5 268:20 275:17 467:8

479:1 518:25 529:13 541:20 565:3 575:26 620:21

651:3 667:25

interpretable 275:16 477:14 481:14 482:16

interpretation 189:15 191:13 200:9 213:17 229:7

243:1 452:5 482:22 530:5 549:16 552:14 553:1

575:6 626:21 639:10 640:10 679:3

interpreted 194:13 254:8 256:11 269:10 272:8
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488:12 563:14 617:9 619:22

interpreting 222:15 223:2 233:25 235:14 477:7

484:9 614:2 630:15 632:4

interpretive 206:21

interval 134:1 145:3 169:13 479:6 484:18

interventions 177:23 189:25

inter-colony 225:24,26 227:19

intolerance 522:18

intraglandular 388:23

intricate 651:8

intriguing 464:8

introduce 3:19,26 145:7 154:20 199:1 352:12

453:7,9 466:18 535:3 539:6 624:4 669:3

introduced 169:20 173:3 301:19 533:3

introduces 443:2 595:26

introducing 430:21 570:3

introduction 6:15 13:5 19:25 20:1 33:23 89:3

235:24 249:10 286:2 288:10 430:3 569:25

introductions 19:24 436:1 622:6

introductory 252:26

intuit 555:4

intuition 555:13

invent 627:12

inverse 657:6,15

invertebrate 293:21

invertebrates 24:15

investigate 225:21 232:11 326:20 405:3

investigated 45:10,12 79:3 508:5 560:9

investigating 20:25 325:23 340:11 557:5

investigation 5:14 250:10 509:23 560:11 565:4

627:23

investigations 416:7

investing 271:17,20

investment 181:18 317:8 479:2

inviable 560:12

invidivudal 167:17

invitation 689:13 695:13

invitations 695:14

invite 575:23

invited 690:22

inviting 15:5 16:22

involve 26:10 40:18 54:13,15 56:13 61:18 64:19

90:25 91:20 123:8 127:3 146:19 168:21 230:16

involved 15:26 108:24 109:2 120:8,20 188:13

245:21 254:2 277:8 289:19 290:4 369:23 383:10

404:21 419:7 431:13 692:7 696:4

involves 31:8 60:8 93:18 106:24 116:9 120:3

126:21 127:10 128:6 139:21 158:20 284:25 672:8

involving 42:3 56:1 104:6 162:11,24 293:2 383:4

532:17

in-contact 351:12,13

in-depth 72:3

in-hive 96:2 465:2 537:3

in-life 313:7

ionic 125:12,25

ionizing 398:18

Iowa 418:7,8 419:14

IPM 322:12 324:11

IQA 281:17,24

irrelevant 452:3

irreplaceable 451:12

irrespective 390:25

irresponsible 292:1

irretrievable 500:15

irrigated 596:7

irrigation 82:6,11,12 424:24 578:20,21 598:12

isolate 178:14 198:22

isolating 444:19

isolation 68:3 194:13 219:21 246:3 561:7,10 598:4

issue 9:9,20,24,25 14:22 17:8 19:8 46:4 50:25

77:19 83:10,15 84:3 178:2 202:4 234:1 235:6,6

235:11 253:4 278:21 279:12 282:8 283:5,8,9,12

283:18,22 284:4 287:17 290:20 292:4 293:13

307:25 308:10 314:22 331:23 339:13 340:10

343:19 375:14 380:3 388:3 410:13 412:13,21

414:10 417:5,7 421:24,26 426:18 427:24 431:16

434:3,4 437:21 440:9 441:4 442:19 479:19

496:21,25 497:23,24 498:7 503:4 517:3 525:13

526:8 528:7 540:14 545:18 560:22 561:17 564:14

591:23 593:5 595:13 609:13 620:22 629:7,25,26

635:9 639:19 655:14 660:9 663:8 682:14 686:24

688:18 690:16 694:13

issued 283:11

issues 2:16 9:12,14 11:15 23:11 26:15 136:12

168:7 237:10 247:2 250:23 252:2 274:16 280:26

285:14 287:18,26 295:1 315:19 319:20,26 323:4

323:4 349:18 380:2 383:23 387:25 406:6 408:17

414:13 423:13 428:20 437:25 445:15 448:10

453:17 469:18 471:3 503:13 525:12,25 590:12

595:16 623:24,25 625:15 630:18 645:18 653:3

657:18 687:25 689:8,18 690:25 693:2 694:9

Italian 623:12

Italians 635:10

Italy 291:24

item 338:5

items 2:8 74:13 75:11 341:20

iterative 25:26 26:10 30:26 56:16 140:22 155:25

204:15 206:1 208:21 661:6 663:10

it'd 371:23

it'll 70:2 152:9 560:10
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IV 140:14

i.e 31:17 365:24 373:6 409:9 410:24 423:1 497:23

J

Jacket 303:7

James 4:19 5:24,24 32:12,13 44:13,21 45:6,15,19

45:21,23,24,24 46:7,13 68:17,25 70:4 81:8,9

131:16,17,20 137:17,24 138:15,19 150:11,12

151:1 232:16,24 235:4 236:8 237:18 239:7

249:19 250:18,18 275:25 288:21 296:18 302:9,10

309:11,15,25 310:9,11,14,21,25 311:6,12 324:1,2

324:6 325:1 329:6,7,7 333:25,26,26 337:14,16,17

339:26 340:1,1,26 341:15,17 345:7 350:17,18

354:10,11,26 355:1,15,15 356:20,21 357:23,25

357:25 358:3,19,20,20 364:4 365:7,8 368:15,16

370:3,5 375:17,24,25,25 381:3,4 382:9,14,20

385:12,13 387:6,7 389:14,15,16 419:24,25 434:6

434:7,24 440:14,16 441:6,6 455:2,20,21 457:8,9

467:24 468:4,5 469:4,8,8 470:5 472:12 473:8,26

474:2 475:20,26 476:1,22,23 478:2 480:9,11

481:17,18,19 482:24,25 484:20,21 485:18,19

486:22,23,25 489:3,15,16 490:12,13,22,23 492:4

492:5 495:18,19 496:25 500:21,22 504:17,18

506:9 507:4,5,16,17 510:6,7 527:19,20 529:16,17

535:11,12 537:15,16 559:4,5,22,23 565:21,22

566:14 571:15,16 574:7,8 577:25 578:7,8,8

579:18 580:12,13,23,24 581:15,18 583:17,17

584:8,12,13 585:3 596:5,6 598:5,6 601:22 618:12

618:14 619:6,11 633:14,15 641:14,20,26 645:25

651:20 652:3,6,14,22 654:16 665:7 667:1 669:22

674:18 679:15,23 680:15 683:5 686:4 690:14

James's 46:25 364:10 486:1 599:21

January 283:10 623:12

Jay 253:1 254:26 265:26 266:1,2 275:4,23 276:9

276:23

Jeff 5:17,17 70:10 236:17,20,21,21 242:21 250:12

250:12 276:17,17 311:8 314:18,25 328:9,9

336:20 339:15 343:12 364:8 369:14 379:9,9

380:15,24,24 381:9,25,26 383:17 384:13 386:1

386:17 387:1 388:18 391:22 394:11 400:13 403:8

405:10 406:23 408:23 412:9 415:17 417:2 427:21

431:8 433:25 439:8 452:15 462:2 469:14,14

470:24 481:23 483:16 485:4 488:14 492:17 501:3

505:11 508:8 510:20,23 517:10 518:25 523:5

532:25 534:23,23 537:8 540:22 541:23 545:1

546:10 547:11 550:17 551:13 555:24 556:3

559:15 562:12 566:11,26 567:21 573:19,19 577:3

579:14 581:8 582:19 583:16,24 586:2 588:9

591:1,14 592:22 593:23 595:18 596:21 599:8

600:18 606:26 609:6 612:2 614:25 616:1 625:19

627:1 631:24 634:4,19 636:22 641:8 642:17

683:9 689:12 693:1

Jeff's 571:11

jelly 39:1 44:23 74:2,12,16 75:13 76:17 93:5,14

95:5,7,8 99:23 101:18 102:26 103:24 104:7,12

105:14 137:21 138:1,9,16 159:19 160:8 338:8,10

355:9 356:7,8,9,17 378:24 379:11,20,23,26 380:4

380:6 381:14 382:21,25 383:13,22,25 388:5,15

388:21 389:5 391:24 433:16 465:4,6,15,17 466:4

466:4,12 469:5,6,6,11,15,16,17,20,20 470:1,9,12

470:17,20 471:2,4,7,12,15

Jenkins 2:1,5 248:4 251:7,9 288:24,26 289:1,6

435:15 693:10 695:7

Jens 5:11,11 83:4 84:14 196:7,21 214:22 250:8,8

309:7,12,19 315:24 320:25 321:4 331:7 336:22

339:18 343:24 348:13,19,25 364:18 365:3,11

372:19 381:8 382:3 390:8,17 391:2 396:26

409:23 413:1 414:25,25 416:4,4 421:23,23

428:17 431:24 437:25 440:5,23 441:8 446:2

456:25 458:3 460:10 463:13 478:8 479:9 483:17

485:10 493:6 495:6 497:8 499:5,12 502:11,26

503:12 506:8 518:4 519:18,20 521:9 522:17,26

523:23 525:7 526:1 528:6 529:24 530:12 534:1

535:7 538:8 542:5 546:21 556:22 565:11 566:18

574:14 580:4 581:18 585:1,5 592:2 597:24

602:18 607:3 610:16 623:22 627:22 629:12,19

632:2 633:15 634:19 637:24 658:14 659:6 660:14

662:7 675:15 681:18 687:24

jeopardy 295:13

Jim 4:19 236:8 237:18 239:7 249:19 326:17 455:22

job 4:7 257:6 258:18 292:3,11 308:17 334:3 442:3

557:5 584:22 589:14 644:13 690:4 692:25 695:9

Joe 134:18 195:17 198:25 205:21 209:23 213:22

222:21 441:19 446:11 458:26 463:7 464:23

471:23 472:22 474:14 476:9 477:1 480:23 482:6

484:1 489:2 490:4 491:8 499:17 503:20 508:22

512:7 513:2 526:11 531:1 535:22 538:23 543:4

543:17 547:23 562:7 567:16 573:10 582:4 585:22

588:1 593:12 598:19

John 374:25 419:16

Johnson 475:14

join 6:19

joined 302:19

joint 205:8 279:5 356:10

jointly 16:5

Joseph 154:8,9,10 441:19 446:11 458:26 463:7

464:19,23 471:20,23 472:19,22 474:10,14 476:9

477:1 478:16 480:23 482:6 484:1 489:2 490:4

491:8 499:17 503:20 508:22 512:7 513:2 526:11

531:1 535:22 538:23 543:4,17 547:23 562:7

567:16 573:10 582:4 585:22 588:1 593:12 598:19
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696:4,16

journal 162:14 281:11,13 375:2

Joyce 696:16

JR 2:1

judge 301:15

judgement 263:26 287:16 552:8 638:26

judgment 196:1 629:14 643:5

Julius 5:12 250:9

July 19:8 292:14 413:10 418:7,7 430:12

jump 445:17 609:10

jumping 328:5 371:18

jumps 556:7

June 306:8 310:10

jurisdiction 297:22

jurisdictions 255:7

justice 8:21 548:21

justification 518:10 585:19

justifications 580:22 585:15

juvenile 161:7 655:5

K

Kansas 581:13

Kaplan-Meier 492:1

Kd 120:26

keep 4:8 155:8 215:6,7,8 218:23 239:2 248:22

252:15 275:13 299:25 302:11 330:14 336:12

337:26 359:7,21 363:26 364:2 387:11 412:23

429:15 448:18 449:20 459:22 516:7 528:11,20

535:2 545:10 564:11 568:17,20 571:20 574:11

604:13 623:1 625:26 631:14,21 640:25 645:12

keepers 445:14

keeping 228:8 268:20 288:21 338:11 430:13,22

459:25 556:22 565:16 568:19 592:19 610:21

keeps 559:24

Keith 33:1,2 42:20,26 43:7,17 44:4,17 45:1,7,18,20

46:3,12,17 47:16 48:7 49:6 53:3,4 66:26 69:15,25

82:22 89:14 91:6 152:12 199:9 200:25 204:7,13

214:18 220:23,24,26 230:7,23,23 231:17,20

232:9,9 240:1,8,8 244:4,4 330:10,10 345:12,12

353:18,18 354:2,2,23 355:5 356:26 357:1 422:12

422:13 432:7,7 436:5 437:12,12 441:14 482:4

497:21,21 527:12,12 530:7 538:15,15 551:26

552:2,2,21,21 553:19,19 561:25,26 624:8 626:7

630:13 643:11,16 669:26 670:19 676:14,19

683:18 696:3

Keith's 68:6

Kellum 474:21

Kelly 280:22,23,24 284:13 285:2

Kenaga 259:22

Kenega 113:15

Kenneth 4:23,24 249:22 458:9 686:14

kept 147:12 169:25 176:15 302:18 359:17

kernel 299:14,14

key 27:12 53:12 183:7 225:6 255:23 271:10 293:17

295:8 366:10 631:17 661:18

keys 515:6

kg 402:21

Khory 225:18

Khoury 227:5 500:3 674:26

kick 654:3

kicked 309:26

kill 23:17 163:2 283:3 292:12,26 293:5,10 295:1

302:24 303:3,25 304:20 308:21 310:8 326:18

528:15 581:3 692:8

killed 308:20

killing 653:15

kills 292:24 303:16

kilogram 104:14 107:2 110:13 114:14 115:21

117:8,10,19 118:4,8,11,19 119:23 123:2,4,7,19

123:20 126:5 127:25 129:12 371:16 374:18

393:21 401:25 402:10,17 403:18 404:9 436:26

438:25

kilometer 575:12

kilometers 191:1 568:16

kind 44:16,24 49:26 50:22 66:25 82:5,21,23 87:22

88:13 130:8 196:26 197:6,25 198:1,10 199:22

233:2,5,11,12,21 240:26 241:11,12 243:24

245:10 246:15 264:23 312:6 315:11,12 328:9

329:19 332:2,10 344:9,20,24 351:24 352:26

353:10 358:21 361:26 362:11,12 368:26 369:3,18

375:3,23 377:26 391:23 398:20 400:6 434:21

437:24 444:6 446:22 447:3 448:4 454:20,21

461:25 469:7,19 478:23,24 487:18 488:6,21

492:7 498:6,12,15 501:3 511:25 513:16 514:8

515:1,19 531:6 533:1 538:4 539:13,16 545:1

546:1 547:17 548:12 549:11 553:17 556:2 560:15

568:11 582:26 583:7 584:10 592:24 593:23

595:15 596:1 603:24 604:6 606:8 608:12,14

615:21 616:16 617:5 631:8 634:11 639:3 640:20

641:16 643:22 644:26 649:13 650:5,17 654:6

658:3 665:10,18 677:11 678:22 691:4

kinda 421:6 522:7

kinds 7:5 10:25 233:15 344:15 360:8 373:21

396:19 406:17 418:20 434:2 447:15 498:12

505:15,24 511:9 609:2 639:8 650:19,20 683:1

684:10

Kingdom 397:18

Kiss 478:7

Klaine 4:16,17 249:16,16 462:13,20,20 464:3,4

486:12,13 487:5,5 488:3,11,11 496:17,18 498:18

498:19 583:12,14,21 609:10,11,26 610:4 611:7,8
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611:24 613:4,5,5 620:16,17 635:23 636:7 641:23

642:2 685:15

Klaine's 686:5

Kleibacker 696:17

Kleier 125:26

knew 303:23

knock 323:18

knockdown 170:16

know 3:10 7:7 8:5,7,24 9:6 12:11,12 17:22 43:11

47:20 49:22 50:17 67:15 68:4,4 82:16,18 83:19

88:8,18 131:26 132:10 136:25 139:18,24 197:17

197:20 220:6 232:25 233:6,14,21 236:5 237:7

239:9,12 241:19 242:8 243:24 257:23 259:12

261:25,25 262:10 263:16,17 264:1,1,22 265:4

268:26 275:7 277:17 279:11 283:3 285:3 286:1

286:11,22 288:10 289:7 304:9,26 305:10,14,25

306:14 307:1,10,10,12,14,19,25,26 308:6,7,15,17

311:14 312:6 313:3 314:8 319:26 320:15 324:22

325:13 327:12 328:4,20 329:5 330:4,12 334:15

346:6 347:23 350:1 352:6,15 353:6,12 354:16

359:10 361:13 363:26 365:14 367:23 368:16

370:6 374:26 375:9,21,23 376:17 377:9,12 378:8

380:16,26 386:10,22 387:19 388:2 389:7,9,11,20

389:26,26 390:1,4 394:20 396:15 407:17 410:2,4

412:1 413:26 417:5 419:11 421:3,11 427:4

429:17,21,26 431:5 432:13 436:1,13 437:14,15

437:17 438:2,12,25 439:15 450:9 452:12 453:8

454:21 455:16 458:12 459:8 461:16 463:10

468:15 469:26 470:11 476:15,16 479:3,7,13

482:26 485:21 487:13 488:4,6,23 492:20 493:20

493:22,24 494:2,4,7,21 495:2,14,23,24 496:2,3,6

498:2,10,26 503:5 507:14 508:16 510:14 511:20

518:15 523:12,21 524:16 525:19 527:3,8 529:8

530:5 534:10,19,26 535:8,12 537:18 539:13

541:26 542:8,12,21,22 543:19 544:1,23 548:20

549:8 551:13 560:13 565:15,16 571:24 573:1,23

574:5,12 575:3,11 576:6,11,22 578:11 579:11,17

580:17 582:7,8 584:23 594:23 595:10,14,15,17

596:12,16,18,19 597:20 599:20 600:4 601:14

602:9 603:7 604:5 607:22 609:11 610:11 611:13

611:20 613:5,7 618:3,17 619:19,20 629:12

635:24 636:6,10 638:17,25 639:8 641:3 646:15

646:23 656:9 662:21 665:22 669:16 672:12

673:18,24,25 674:12 676:2 682:4 684:17 687:10

688:1,5,5,14,21 692:5 693:26 694:19 695:20,22

knowing 132:18 133:24 301:22 395:16 577:22

599:9 610:17 619:26 627:17 631:12 645:20,22

651:3

knowledge 166:22 199:24 281:16 286:19 315:15

473:5 510:1 552:25 553:6 564:8 674:16 684:11

knowledgeable 425:25

known 22:14 30:3 159:20 170:10 171:7 213:6

286:14 299:6 336:11,12 398:23,25 404:11 448:26

451:13 469:21 481:9 486:16 496:14 504:15 533:3

539:3 653:25 659:10 672:10

knows 314:12 325:14 389:23

Koc 120:21,25 121:1 125:14 128:8

Koch 106:13 108:18,23 109:16 110:10 111:7,20,23

112:10,19 126:25 129:3 131:4 217:1 257:16

366:1 369:23

Kow 120:5,11,18 121:14 124:16,17 125:9,13 128:7

128:12 397:23,26

Kows 397:24

KQhn-Institut 5:13

Kris 45:11 46:26 58:8 70:25 205:20,23 216:26

221:10 327:3,3,22 328:15 329:16 331:4 365:21

365:21 372:8,8 373:4,4 377:19,19 378:21,22,22

382:7,17,17 383:2 384:6,6 386:9,19,19 388:11,11

392:7,11,11 397:8,8 401:17 402:6,6 403:13,16

405:16,19,19 407:11 478:19,19 493:14,14,19

Kristina 81:16 82:10 86:9 87:26 88:11,24,26 130:7

130:13,26 131:7,19 132:7,25 133:6,13,17,26

134:7,16,20,24 135:11 138:6 202:18 392:5 696:3

Krupke 292:5 298:11 299:12 332:4 416:17

kudos 694:10

Kuehn 250:9

L

L 315:12

lab 5:19 304:13 312:2 380:16 469:18 476:18

649:24

label 77:6 183:14 206:25 216:5,8 276:7,10,12,15

286:24 323:10,12 331:19 418:3,5,24 567:23

568:5

labeled 82:17 156:5

labeling 254:14

labels 295:19,24 296:2 325:22

labor 225:20 353:13 489:24

laboratories 315:25 463:19

laboratory 5:8,26 22:10 24:12 54:12 59:6 64:21

143:19 156:2 158:11 160:5 163:5 165:3 166:1,5

167:2,11,18 169:1 175:15 176:1 179:4,15 181:4

194:16 196:16 211:15 213:20 246:22 247:7 250:6

250:14,20 266:13 270:23 312:25 313:2 315:17,20

336:8 352:5 361:12 414:26 415:6 418:15 441:1

446:5 489:6 503:13 542:12 615:6

laboratory-based 160:12 609:21 622:16

labs 313:12,25 314:6,9,13,13 316:5 380:17 381:2

381:10 597:26

lab's 314:4

lacewing 162:25
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lack 153:12 159:4 193:11 201:20 220:14 223:24,26

224:22 231:15 254:8 294:22 366:9 392:21 498:26

505:23 564:14 599:18 640:5

lacked 691:20

lacks 290:1 642:9 675:19

laden 409:21

ladies 308:7

lady 303:14

lag 411:18

laid 619:10

land 284:10,12 297:8 299:11 351:4 580:15 642:26

landed 604:4

landing 603:21

lands 348:17,19

landscape 190:22 223:25 228:4 574:23 575:22

576:20 600:10

landscapes 293:22 598:1

language 77:6 334:9 486:3 691:18

large 20:13 43:4 96:25 112:25 144:17 173:9 176:8

201:22 237:2 244:7 287:16 300:25 330:21 335:20

358:21 359:25 393:3 416:23 421:25 469:17

495:13 497:16 515:2 517:13 536:9 548:17 566:19

568:10,14 570:9 574:4 576:10 577:15 578:23

582:20 592:19 596:13,25 598:9,14 629:14

largely 54:22 66:9 132:17 256:15 277:9 536:4

larger 108:13 132:21 173:12 188:8,11 293:6

318:15 322:9 368:22 446:6 462:21 497:17 501:24

503:2 517:17 549:3,6 564:4 573:24 574:3,15,21

575:10 576:26 578:1 580:22 581:9 588:26 598:3

606:5

largest 17:13 322:10

larva 346:6,7,11,19 360:14,16,17 371:9 381:15

389:3 467:8 479:1 480:6 485:11 620:10

larvae 44:13,16,17 45:12,13,16 58:26 62:23 63:4

68:19 70:7 81:26 89:21 91:21 92:2 93:14,20,24

94:8 101:14,15,17,20,24,25 102:2,6,11,19,21,24

103:3,4,5,6,13,15,16,18,19,19,22,23 104:3,4,4,12

104:16,17,18,19,26 105:18 110:20 115:25 117:25

127:2,6,7,13 128:3 131:22,26 132:3,5 137:19,25

137:25 138:1,17 159:3,5,18 160:5,7,9,19 267:10

269:17 328:18 338:15 342:2 345:26 350:26 351:4

351:13,14,14,15 353:25 354:7 356:8,23,24

359:13 360:12 361:15 363:19 365:12,13 370:7,14

370:22,23 371:24 372:3 378:23 379:2 439:21

457:4,10,13 465:1,3,5,10,16 466:2,3,3,8 467:10

467:12 468:10,14 472:6,25 473:21 474:16,20,21

478:23 479:11,12 480:13 485:1,2 486:10 487:15

487:16,19 489:12,18 490:21 544:14 545:9 560:12

610:8 661:17,17 662:14,14,20 691:20,23

larvae's 104:20

larval 25:8,10 59:3,5 67:1,2 72:12 74:13,14,14,17

75:1,16 77:25 81:26 90:7 94:2 103:12,24 104:22

104:24 105:4,17 106:19 117:22 129:25 153:23,26

156:15 159:3,8,15 160:2,22,25 161:18,19,23

176:13 217:16 226:6 267:7 270:24 299:4 300:16

341:8,24,25 342:5,7,14,20 346:1,10,13,22 363:18

371:22 379:3,6,21,22,25,25 380:1,4,4,8,16,25

381:1,5 382:2,11 434:14 456:12,16 465:3,12

466:16 467:5 468:6 469:19 472:3 475:8,10 477:5

477:8,12 479:22 482:8,13 484:2,6,16 485:12,20

488:15,17 489:20,24 490:6,8 504:22 612:22

619:1

lasting 558:1

lastly 36:8 39:2 53:17 55:21 64:7 66:3 226:8

227:10 228:6 279:16 453:21 564:17 589:25 649:8

late 137:6 234:18 282:6 288:23 372:20 453:19

462:10 488:17 607:2

lately 533:18

latitude 554:12

Laura 696:15

law 281:24 284:16

laws 2:24 28:13 313:24 421:12

lay 342:9 648:17

layer 47:12,15 50:20 81:11 459:16

layers 50:15 306:23 365:5 453:17 487:22

laying 225:10 226:10 231:14 307:8 344:25 369:18

453:4 457:20 560:12,26

layout 30:25

lays 47:25

lazy 571:18

LC50 68:20 69:17

LD50 58:10,15 59:2 68:18,20 69:17 134:2,5 138:25

138:25 157:8,12,23 158:16,17,20 159:22 162:1

206:17 262:1,4,7 266:13 272:23,24,25 273:4,5,9

273:13 298:19,22,24 299:15 307:11,13,16 495:11

495:12,15,16 506:17 513:20 612:9,17

LD50s 264:22 309:1 514:10,12

lead 20:11,25 21:4 62:9,10 88:24 185:22 209:18

222:4 224:18 256:2 275:19 283:9 284:14 299:2

317:1,5 325:22 329:2 333:24 337:15 341:15

345:17 366:2 379:7,9,24 392:18 408:3 437:5

439:17 441:24 465:7 498:15 513:12 527:2 543:14

550:19 551:20,24 562:5,11 564:8 605:1 624:12

627:18 643:20 647:15 696:17

leader 695:26

leaders 279:3

leadership 693:2 696:15

leading 125:9 239:11 245:20 627:15 692:22 695:10

leads 176:12 318:3,11 647:9 649:22 682:6

leaf 146:22 259:10 265:19 277:19 315:3 347:12,13
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347:15 350:25 351:1,1 359:12 400:19 559:25

560:2 572:22 584:14

leaf-cutter 350:25

leaf-cutting 101:2,6 105:2

leap 378:7

learn 12:16,20 46:25 48:12 359:11 461:18 541:16

654:18

learned 452:11 687:17 691:10

learning 298:4 314:6,7 450:19 451:1,4,16,16

454:12 461:12,20 504:4 687:1

lease 660:12

leave 43:3 72:23 130:3 202:26 282:1 412:7 432:3

492:2 511:22 548:9 571:21 621:12 633:5 643:4

679:14

leaves 117:15 146:20 259:7 276:25 323:26 368:4

376:3 406:7

leaving 20:9 439:24,25

led 14:10 108:8 254:8,10 291:24 696:1

left 3:20 26:8 36:18 44:13,26 45:8 165:13 204:12

215:6 282:7 304:5 320:5 424:23 430:1 508:18

624:5 664:1

legal 284:9 313:6

legs 186:2

lend 640:20

length 139:17 257:10 661:17

lenient 289:7

lesser 459:15

lessor 38:3

lethal 112:23 157:8 175:23 262:10 298:2 301:12

463:18 491:26 511:6

lethality 51:11 207:21 301:8

letter 292:15 333:3 365:20 384:4 392:9 397:7

405:18 409:4 422:9 432:19 441:18 464:21 474:13

476:26 488:26 499:16 508:20 535:21 538:22

543:2 547:22 562:6 584:5 585:21,21 587:26

593:10,11 607:25,26 614:10 615:2 622:10 669:7

669:25 670:18,19 682:3

letting 692:15

lettuce 265:19 571:24

let's 48:24 68:12,13,13 87:14,22 140:4 154:4

236:26 249:9 281:15 289:9 296:12 332:17,23

336:25 337:1 341:2 347:20 365:19 373:2,26

378:17,17 384:2,4 388:9 392:9 405:18 409:4

417:14 422:17 432:18 435:6 437:15 446:9 448:20

464:20 471:21 475:7 476:25 480:18 482:3 483:26

488:26 491:7 499:16 503:18 512:19,26 523:26

524:4 526:5 535:21 538:21 562:2,3 567:14 576:4

579:19 580:11 585:21 593:10 604:15,20 607:25

621:20 626:5 630:11 643:13 655:24 667:6,13

669:8,24

level 26:23 28:23,25 35:6 46:16 48:17,19 50:6,9,11

51:1,16,22,23 52:1,2,6,11,17,20,22 54:6,11,20

59:9 61:4 63:1 64:21,22 65:8 67:10,24 68:22

69:18,21,22 70:22 83:18 84:22 91:1 93:19 100:18

105:8 106:6 108:5 113:9 118:18 126:9 127:20

136:13 137:6 139:1,2,22 140:22 145:19 150:15

153:5,13,19 155:14 156:1,13 159:12 162:2,7

164:10,10,26 165:2 166:6,9 167:1,11,15,19

172:15 174:8,10,18 175:14 177:7 178:1,11,16,16

178:17,19 179:3 180:17 181:17 184:23 186:7

188:3,18 190:4,6 191:6,11 193:17 195:22 196:4

196:10,12,20 198:7,12,21 199:3,11 200:13 201:6

201:8,14 205:13,16,24 206:9,20 207:3 208:1,7,8

208:13,24 210:5,13,18 213:22 214:16 215:25

217:17 218:14 219:10 220:13 221:7 222:14,18,19

223:10 224:25 227:19,19 228:22 229:22 232:1,13

240:7 241:1 243:3,6,9 247:7 268:26 269:1,21,26

270:4,5,6,8 271:1,4,5 282:23 285:7 297:7 305:9

307:26 308:1 325:16,16 331:11 332:11 337:6

341:7,19 358:14,15 366:8 370:18 391:26 404:9

415:6 424:3 425:11 434:26 440:24 449:3 454:20

459:21 461:1,4 466:5 470:21 477:21 493:22

494:14 495:13,20,22,23 496:4,6,12 500:6,12

501:6 502:21,21 505:1,4,4,9 507:13,17,26 509:3

509:23 511:6,7 512:12 514:17,19 515:16 516:24

517:1 522:15 523:9 531:13,17,21 532:4,7 535:14

536:8 539:19 546:14 548:15,16,19 551:3,4 556:8

556:24 557:8,20,23 559:20 563:6,7,13,14,17

576:2 579:12,16 581:11 582:23 584:1 586:21

588:4,14 590:22 593:20 594:6,11,14,20 599:3

600:24 606:11,20 608:2,12,18 609:6,12,15,20,22

609:22 610:24 614:18 615:20 616:8,11 617:8,9

618:5 619:3,14 620:19 624:16 625:2,3 626:19,21

626:24 630:25 633:11 634:1,8 636:25,26 639:7

641:10,12,12 642:22 644:8,16 645:18 647:4

648:15 649:1,7,15,22,23,26 650:2 653:11 654:7

662:12,19 666:5,13 668:6,10 671:18 678:17

693:6

levels 26:25 28:10 67:19 138:3 141:17 145:16

148:9 153:15 164:20 165:14 166:11 170:20 172:9

173:19 174:3,15,19,21 179:9 192:10 193:12

194:19 197:20 198:17 201:24 205:12 206:20

210:24 229:4,9 256:21 268:23,25 270:6,21

276:24 290:1 293:21,26 298:6,20 299:4,5 301:11

305:3,16 308:13,23 309:2 338:10 339:12,12,13

366:6 386:3 396:22 412:16 423:26 434:25 449:6

455:24 462:8 466:2 487:21 491:20,22 497:10

514:19 539:3 540:3 587:7 590:7 606:5,7,8,14,14

608:13,14,15 630:24 632:10 637:6,17 649:11

667:25 669:4 672:8,9
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liaison 2:8

lib 443:26

libitum 172:4

license 597:15

lies 527:7

lieu 435:26 481:21 490:11,23

life 31:11 66:23,24 67:8 70:13 74:12 93:10 94:2

96:1 102:22 115:24 133:4 154:1 198:3,3 343:5

360:15 369:7 370:8,9,26 382:11,21 387:19

390:23 416:24 451:12 475:4 486:24,26 487:6

524:8 530:21 570:20 614:19 629:2

lifecycle 130:2 184:5 370:13

lifespan 385:7

lifespans 447:21

lifestage 159:10

lifestyle 346:6

lifetime 372:2

Life-cycle 301:2

life-stages 657:2

lift 358:11

light 28:2 202:2 614:19

lignaria 361:19 362:15

likelihood 22:13 83:23 205:9 207:12 208:4 527:15

likewise 335:21

limit 3:13 173:1 191:21 210:8 248:19 273:2,24

294:19 375:26 393:4 402:19 513:4 593:16 675:24

limitation 97:12 99:19 124:25 125:8,15 146:9

166:6 384:8 539:4 544:16 549:7 568:11 590:23

596:19

limitations 21:23 23:26 105:25 112:10 119:20

124:5 126:2 129:2 142:21 153:22 175:25 179:26

180:3,11 196:23 203:19 212:16 365:23 366:14

379:4 402:9 403:18 404:20 407:23 446:18 494:9

513:10 515:1 518:15 519:7 539:23 540:1 562:9

567:14 607:14 610:16

limited 39:15 61:6 66:10 111:25 124:16 125:6,12

185:19 201:23 216:6 218:4,5 226:20,23 228:24

255:17 275:12 294:20 387:25 399:23 404:22

406:18 410:18 414:17 460:15,19 513:9,10 523:18

528:19,20 544:25 546:6 563:26 565:17 568:6

576:8,13 611:22 627:25 680:25

limiting 607:20

limits 68:23 69:5 395:11

line 37:10 49:2 67:14,22,23 69:13 114:2 121:12

139:20 143:14 158:5 209:3 229:1 348:1,14,25,26

356:10 372:21 376:11 378:1 438:2 468:7 496:5

523:5 539:9 546:10 635:9,22

linear 133:21 367:19 372:10,11,14 394:1

linearity 374:3

lines 25:24 30:26 37:18 45:9 47:17 48:14 52:5

55:23 59:18,21 60:5,10 61:11,20 62:6 63:24 66:7

113:26 153:6,11 163:12 166:19 167:14 174:25

178:24 181:1,21 203:15 209:9,12 210:20,26

211:3,8,19 212:2,23 213:7 214:14 219:7,21 220:3

287:16 330:24 340:23 347:5,5 356:16 364:10

387:7 513:20 542:10 547:12 565:22 605:18 621:7

621:14 625:20 634:14

link 84:18 164:14 227:20 327:10 459:9 460:11,14

460:14,19 628:19 645:4 672:14

linkage 155:10 193:25 223:24 284:26 460:6,7

512:11

linkages 51:5,15 154:19 165:24 460:25 508:25

509:6 621:4

linked 30:11 300:15 446:15 451:19 459:11 535:26

linking 167:14 178:24 222:17 511:5 513:17

links 164:3 178:18 451:18 509:20,26

lipophilic 134:12

liquid 293:14 376:6 395:26 415:5

liquids 356:3

liquifilic 433:17,17

list 224:21 227:11 277:24 302:4 338:7 390:19

406:15 538:9,12 553:12 557:18 561:25 566:4

569:11,17 571:17 572:3 653:8 687:26

listed 3:15 24:17 142:13,22 163:14 223:23 224:18

224:20 226:4 324:14 338:2 341:20,25 354:4,6,9

564:20 569:9,17 591:15

listening 314:3 470:18 603:17

listing 569:12

lit 642:4

liter 171:4 520:19

literally 451:10 600:11

literature 51:8 53:19 58:23 66:8 95:22 107:26

110:18 111:13 114:9 135:20 137:26 150:24

151:17 153:7 162:11,13,15,16 171:26 172:11

194:17 196:17 211:2 212:12 219:23 221:18

224:11 227:6 300:13 327:4,6,9,14 374:22 389:9

410:19 426:23 431:1 447:26 449:14 450:5 451:3

452:7,16,16 467:4 474:19 490:15 492:23 500:18

517:23 520:17 538:3 622:20 644:15 652:12

691:20

litigation 421:7

little 6:24 13:4 54:2 62:20 68:1 70:13 86:2 88:1

173:4 185:21 202:18 221:2 237:19 246:4 262:3

266:18 267:1 277:16 284:7 286:2 298:14 312:4

321:6,12,16 333:1 344:20 350:19,23 356:14

361:26 363:23 368:17 371:19 372:19 376:23

389:4 391:7 395:8 410:17 411:1 414:22 417:26

420:9,24 421:14 428:2 429:13 436:6 443:17

454:8 456:7,10,14 460:20 461:9 463:8 485:20

489:20 490:1 493:15,16 494:13 507:5 519:10,11
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520:15 528:13 529:7 535:3 542:5 552:22 563:11

575:10 576:26 580:7 585:17 598:3 614:19 618:8

621:21 628:8 636:12 639:4 644:5 645:26 660:20

664:19 672:10 674:20 679:25 687:5 689:8 690:18

littler 651:13

live 230:10,11 302:10 303:24 360:20 361:15

444:16 475:2 516:13 596:15 597:25,26

lived 380:6

livelihood 308:23

livelihoods 295:7

lives 359:25 387:17 693:26

livestock 284:8

living 73:22 75:10 324:10,12 558:13 640:14

load 248:9 297:1 438:17

loaded 32:26 251:14

loading 536:13 582:7,11,13 593:5

loads 537:21,24 538:1,16

LOAEC 161:11 172:8

LOC 59:9,10,12,14,17,21 63:22 67:13,18,20,23

68:2,11 70:1 81:21 208:5,16 217:20 219:26 437:2

439:1 608:3 610:7 612:15

local 52:21 591:23 595:10 673:5

localized 294:18,21

locally 506:3 604:14

located 41:22 78:19 99:20 109:1 110:25

location 57:12 146:15 148:7 301:1 303:7 353:21

567:24 682:11

locations 112:5 149:15,18,24,25 294:20 346:19

566:5,8 591:17,19

LOCs 201:15 209:1

log 120:11 124:16,17 125:9 313:17 397:23,26

Logan 5:26 250:20

logged 313:17,20

logic 331:1 390:9 654:26

logical 68:14 444:3 448:21 647:19,21

logistic 467:19

logistical 189:7 515:2,10 549:7

logistically 371:24 459:18

logistics 192:17 444:17

log-normal 261:16 262:25 263:4

long 14:8 52:9 83:8 139:3,19 198:8 240:18 274:9

279:1 290:24 334:2 340:23 361:14 380:7 409:16

416:24 444:15 456:18 485:24 524:8 544:19 557:2

558:1 570:3 571:21 580:17 591:3 605:21 610:26

615:22 647:8 649:9 656:17 665:3 671:12 673:6

689:24 695:22

longer 17:6 74:3,24 132:2,3 161:20 170:4 173:13

199:15 222:3,12 316:2 413:2 456:11 483:18

520:20 539:3 540:3 544:4 589:12,19 592:3,9,12

610:9 634:12 640:6 644:24 649:26

longevity 222:6 226:11 341:9 343:4,8 344:8,10

394:16 444:10,10,15 456:10 500:4 537:13 586:18

587:8,13 634:7,15,18 648:15 656:26

longhorn 259:14

long-term 52:4 173:16 177:19 199:12 231:4

295:11 318:19 340:19 500:2 524:2,16 586:10

634:22

look 9:20 17:23 33:7 44:14 48:19 68:5,13 85:20,23

86:2,5,6,9 87:9 88:6 131:14 152:3 198:1 202:19

202:23 233:11,12 235:9 245:23 249:5 258:11

260:15 261:5,6,8,24 263:10 269:26 277:16

282:19 303:16 308:26 309:1 320:19,21 340:21

346:12 349:4 361:12 365:10 367:8 371:16 374:21

387:3 392:23 413:24 423:13 426:23 430:5 437:19

438:26 447:7,12 452:23,26 453:13,25 456:1,13

486:18 492:24 506:6 508:10 518:11,23 521:15

524:5 525:12 537:5 538:1 541:25 543:18 545:3

546:18 547:4 552:10 556:5,12,16,18 569:10

573:4 575:7 585:10 589:7 595:8 597:1 603:10

604:7 616:3,12 632:5 633:6 636:25 637:19 638:4

638:5,13 645:9 646:26 648:24 652:10 654:23

671:20 680:23 684:20,26 685:18 686:18

looked 85:13,17 86:20 226:18 232:2 260:14 262:16

304:17 307:24 319:7 330:19 346:2 406:12 411:20

425:3 426:14 428:22 431:22 452:21 455:14

460:15 477:12 533:4 587:16 638:11

looking 13:1 15:9 16:13 42:24 43:13 51:3 67:10

70:21 85:21 131:8 132:5,11 135:24 150:25

152:13 198:20 200:23 201:19 224:23 232:8 237:6

237:9 243:12,16,24 259:23 260:12,13 266:7,20

267:2 270:12 277:14 279:21 283:21 285:6 286:21

295:15 328:10 334:19 339:21 340:17,22 341:18

347:22 348:6 370:12,21 371:15,17 376:14,25

380:1 396:21 397:15 407:5 411:13 412:15 413:2

413:20 415:9 416:15 420:26 421:1 424:17 425:12

431:21 437:11 442:8 444:9,24,25 447:2,4,10,16

448:15,22 450:1 453:20 454:5 455:24 458:14

461:14 476:16 479:16 493:17 494:7 496:6 505:1

508:3 513:18 514:9,12 532:19 536:5,17,24

537:10 540:4 542:23 544:20,24 549:13 553:17

555:26 564:13 572:21 573:16 587:1 591:22

597:12 598:22,24 604:15 605:23,26 607:9 609:7

616:2,10 620:11 629:22 634:22 635:22 636:23

638:4 639:18 645:12 653:11 657:13 665:3 680:10

686:12 687:23

looks 46:4 47:13 50:6 159:11 199:24 215:7 233:3,7

233:9,19 235:15 258:14 392:3 483:2 546:19

554:3

loops 221:25

lose 486:14 583:19 675:2
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losing 267:1 307:5

loss 9:12 20:5,9,18 39:14 221:26 222:3 236:26

238:9 239:11 243:7 253:20 285:4,6 303:1 306:6

306:23 500:4 501:5,14,14,16,24,26 502:21 503:9

503:10,11 559:17 569:26 665:14,15

losses 19:26 20:2,6,7,8,13 21:1 23:2,6 234:22,25

237:4 238:26 239:2 297:14,14 305:11 500:15

lost 238:7 306:2 317:22 420:2 502:7 563:16

lot 8:20,20,21 12:8,9 32:15 47:19 52:26 129:24

134:17 151:10 235:10 237:10 248:26 256:2,8,11

258:4,24,24 259:3,3,7 261:11,23 262:10,15,21

265:8 274:12 277:8 278:3 281:11,14 282:7

286:13 287:7 306:7 309:5,9,14 311:9,13,14,20

312:5 313:22 314:18 315:5,17,24 316:22 321:8

321:20,20 322:15,25 329:4,5 330:5 332:23 334:4

345:2 346:10,22 365:17 366:13,14,20 369:7,11

380:17 391:26 392:4 395:24 397:21 411:17,24

412:10 414:1,3 424:17 428:8,21 429:4 433:15

435:12,15,18 436:10 440:12,20 441:15 442:14

443:2,9,14 444:20 446:25 448:7 449:15 450:11

451:18 452:2,6 453:26 454:17 455:4 462:25

464:6,14 489:25 493:24 495:16 498:23,23 505:6

507:13 513:24 514:1,24 515:8,16 520:26 521:10

527:4 528:1 533:13 539:16 547:13 552:8 556:23

557:13 571:25 574:25 578:15 598:11 601:17

605:8,12,16 606:3 608:11 610:8 635:18 636:16

637:14 638:22 643:3 658:16 660:18 663:7 668:23

681:21 685:5,6 689:11 693:4 694:13,22 695:18

696:11

lots 9:26 281:5 310:3 345:20 351:10 571:23

love 315:6 560:18 689:9

lovely 49:11

low 43:21 77:9 119:17 124:2 128:12 162:6 165:10

172:22 184:19 191:23 261:17 263:4 273:4,5

286:16 328:12 335:11 424:2,4,20 425:14 430:16

463:19 481:9 491:22 495:14 497:15,16 534:5

549:21 560:6 563:25 577:4,17 628:9 632:9

lower 31:16 41:17 56:18 69:21 79:18 80:15 96:24

99:24 103:10 104:8,20 112:18 118:6 119:18

124:2 162:18 166:26 168:19 173:19 175:1 180:21

181:3 194:7,9,10 198:21 267:21 268:24 270:5,6

271:19 298:20 318:3 321:12 376:4 379:11 381:14

381:16,17,19 382:1,25 390:12 391:6,12,12,25,26

425:7 429:1 490:3 512:1 549:22 562:24 605:12

606:14 608:13 622:19 628:7 632:5,11 638:22

651:26 658:5,11

lowest 470:21

low-end 99:15

lubricants 77:11

lubricating 77:10

luck 660:8

luckily 443:19

lunch 87:14 88:1 325:4 332:23,25 360:25 427:10

508:19 512:20,21

luncheon 87:17

Lu-Ann 696:17

lying 511:17

M

machine 331:14 369:18

machinery 293:7 414:5

machines 291:19

magic 584:10 601:15

magically 287:18

magnify 616:11

magnitude 22:13 60:1 68:9 75:7 90:19 96:17,24

100:5 109:19,26 111:22 112:17 114:15 118:10,25

118:26 121:21 122:15 123:18 141:22 194:3 205:9

206:4 207:18 258:13 259:10 260:4,26 261:2

277:20 293:6 299:15 329:3 398:12 402:23 548:1

554:1

main 36:16 82:4 198:8 227:14 255:13 257:5

259:19 287:20 353:2 526:4 546:2 582:5 598:4

600:1 677:3

maintain 39:17 77:24 99:4 108:3 307:5 318:24

528:11

maintained 231:4 559:2

maintaining 84:8

maintenance 198:2 313:17

major 22:8 36:6 41:4 42:1 55:4 57:25 71:15 73:7

75:2,25 76:25 77:15 78:4 83:24 89:17 92:22

120:12 145:19 162:10,20 163:1 175:11 179:24

180:11 182:7,11 185:8 188:17 189:5,18,21

227:17 257:7 285:25 286:22 290:7 294:5 297:24

309:25 318:9 401:9 422:23 429:9 438:7 439:12

460:13 466:22 469:18 470:24 479:19 536:21

554:4 556:25 564:14 575:13,23,24 589:26 590:1

597:13,14,15,19 660:13

majority 78:18 96:22 112:16 218:10 285:20,22

286:25 351:5,9 353:4 407:25 488:15 598:7

Makela 224:26 225:25

making 7:19 51:4 68:8 91:12 242:13 246:1 252:12

252:25 264:9 287:25 299:24 318:8 325:26 328:3

378:14 412:2 463:3 521:17 541:12 571:23 586:25

598:21 617:25 640:24 643:6 645:4 687:26 692:17

693:25

males 511:17 635:17

mammals 150:21 182:13 264:21 271:2 336:2

man 28:17

manageable 145:13

managed 19:3,9,11,19 20:2,15 21:1 221:24 234:20
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management 8:9 9:12 13:25 14:13 15:2 21:2 23:16

26:14 27:1,12,20 28:2,5,13 29:24,26 30:4 32:14

32:14,15,17 42:8 55:23 67:21,25 77:11 83:11,12

83:23 84:3,5 139:16 141:3 145:12 183:8 196:11

200:17 213:13 220:19 253:20 254:15 274:17

297:9 331:25 362:21,23 414:12,12 418:25 419:1

448:10 588:6,11 614:1 629:7,20,25 630:9 669:5

management-based 139:10

manager 18:5 27:1,3 67:19 84:9 139:8 153:15

164:26 166:17,25 174:24 175:1 178:23 179:17,20

180:24 193:22 197:6 204:1,17 239:21 242:2

613:21 668:24

managerial 640:24

managers 26:11,14 35:1 60:18 203:15,22 218:20

281:26 282:1 572:16

managing 545:20

mandated 235:7

manifested 48:9

manipulate 198:26 385:1

manipulating 198:24 199:5

manipulation 570:2

manner 253:15 368:4 496:24 531:18

manufacturers 77:4 291:13 312:12 331:18 417:19

417:20

mapping 633:8

March 303:7

margin 294:22 436:26 578:15

margins 145:6

mark 344:10 392:3

marked 20:8 444:7

markers 455:7,17

marking 544:13

Martha 4:10,11,11 136:22 390:3 635:3 661:4

685:13 686:1

marvelously 417:13

Mary 14:13,23,25,26,26 542:17 696:7

Maryann 304:10

Maryland 5:20 250:14

mason 101:2,6 105:1 334:26 362:15 434:16

mass 118:21,24 119:9 126:13 128:1 259:5 263:8

327:11,12 369:12,16 370:15,16 371:8 508:4

massive 682:21 692:6

match 191:26 241:19 393:12

matched 91:26 122:1 132:12

mate 512:1 531:10,12

mated 635:16

material 39:1 89:6,8 177:11 300:7 355:12 368:7

416:1 418:16 419:20 433:7 437:25 448:5 453:23

468:20 540:15,20 565:12 567:6 590:15

materials 3:15 14:9 78:10 251:20,22,24,25 252:9

350:15 355:22 410:25 565:17

maternal 184:16

math 651:17

mathematical 6:7 221:4 230:5,8,10,13 250:25

406:17 606:2,10 664:20

Mathematically 560:24

mathematics 684:10

mating 37:22 74:9 457:19 635:16,17

matings 306:22

matrices 41:9 144:1 147:3,26 148:20 177:12

347:23 387:19

matrix 402:17 471:25 472:10

Matten 696:16

matter 153:23 239:9 281:24 283:21 284:16 287:5

310:4 329:11 394:6 415:2,4 441:9 452:8 495:12

502:13 550:15 576:6 598:2 664:13

matters 385:18 387:8 510:12

mature 467:10

maximal 439:5

maximize 169:24 192:12 398:11 614:5

maximized 317:9 549:7 576:9

maximizing 190:13 317:7

maximum 79:12 106:12 111:14 112:10 114:7,21

115:6,8 116:3 117:11 123:5 126:23 129:3 149:1

169:12 183:13 187:23,25 188:1 208:8,14 210:14

216:4 260:17 365:26 378:9 394:4 396:22 466:6

645:16 681:25

maximum-labeled 145:2

maze 320:26 421:24 429:5 574:20,21 576:25

McAllister 252:12,17,18

McFadden 322:2,3,6 323:26 324:4,7

McManaman 4:19,20 44:12,13,21 45:6,15,19,21

68:15,17,25 70:3,4 137:16,24 138:15,19 232:15

232:16,24 236:8,8 237:17,18,18 239:7,7 249:19

249:20 455:1,2 467:23,24 469:4 470:4,4,5 472:11

472:12 473:7,8 475:18,20 478:1,2 665:7 667:1

686:4

MCMANANMAN 137:17

Meade 302:14

mean 45:8 46:13 49:22 50:13,13 54:8 69:3 82:26

90:9 111:16,19,19,21 112:1 113:13 114:7,8,10,12

114:16,18,19,23,24,25,26 115:1,2,4,5 116:14

132:26 133:14 138:2 207:7,8,22 221:7 239:23

240:14 243:5 263:1 271:13 280:11,15 282:16,19

286:18,25 287:3,4,5 305:18 307:7,21 308:7,9

319:19,23 320:20 321:13 334:13,14 336:1,11

344:22 348:16 349:3 350:2 354:14,16 355:3,16

363:3 365:9 367:3 368:17 378:2 381:5 394:4
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399:2 414:4,5 420:6 424:18 433:18 434:21

438:14 445:17 458:12 469:6 475:2 478:22 484:22

489:17 508:5 524:4,11 525:9 528:11 529:25,26

533:6 548:10 550:24 551:2 554:15 566:6 579:20

582:2 583:18 599:9 601:14 603:10,11 617:6

627:10,11 636:5 637:21 639:20 644:1 646:15

649:20,25 650:4 652:2 654:23 666:6 679:1 684:5

690:21,22

meaning 129:7 220:11 260:25 343:6 444:21 484:10

680:5

meaningful 17:18 240:23 443:6 445:6 457:22

477:16 549:6 550:3 553:3 589:2 627:13 640:7,7,9

679:19

meanings 32:15 627:12

means 132:22 139:1 151:12 154:1 155:23 166:3

167:9 174:20 181:2 187:8 200:13 211:6 222:10

261:16 263:1 271:14 280:9 286:10 299:8,17

300:18 317:21 320:3 326:13 331:24 334:25,25

335:11 338:3 342:20 349:26 350:1 355:6 417:4

444:12 447:13 451:1 459:24 483:10 513:26

515:11 522:10 532:2 536:14 547:19 562:19 606:9

615:15 619:24 630:26 639:13 646:5 684:3 694:13

meant 155:23 160:12 167:8 171:16 181:24 335:3

342:12 354:16 457:18 499:11 538:3 562:13,23

568:3 652:15 659:5 666:3

measurable 29:25 654:10

measure 49:15 50:16,20 52:21 58:17 69:7,9 142:14

143:23 146:23 148:1 174:8 186:22 191:14,16,18

207:24 285:3 335:12 339:21 340:13 341:24

342:11,18,24 343:17,18,21 344:9 359:18 370:8

371:24 393:19 418:14,17 429:14 440:3 447:21

450:7 452:13 453:15 455:18 456:18 457:13,22

491:11 493:21 499:19 506:3,6 521:7 525:22

535:16 556:11,13,14,14,15,15,16,21 565:19

566:2 569:18 572:3,5,14 579:16 581:5,20 586:22

594:20 596:9 597:7 601:11 602:4,10 603:3

610:24 619:21 637:21 640:10 646:13,15,22,26

647:15,22 654:12,19 666:18 671:12 674:13 683:3

measureable 647:16 679:20

measured 19:10 54:11 109:6,14,22 110:19,24

112:1,17,24 113:18 117:12 118:3 119:8 121:17

123:17 124:13 126:25 128:17 135:21 147:23,26

164:19 174:4,17 175:4,18 184:24 186:6 193:3

199:21 201:4 202:12,16 206:3 210:2 214:4 219:3

221:22 232:12 267:13 293:26 330:12 342:26

375:4 439:18 446:26 452:13 532:3 535:24 536:20

540:6 547:26 562:25 585:25 586:3 587:1,5,9

606:5,7 607:10 615:6 625:9 631:10 644:20 660:1

660:3 662:2 666:25 671:20 682:10

measurement 27:11 29:17 30:8,10,14,18,21 39:7

49:19,26 50:18 51:13 70:6 114:13 155:7,10,24

161:17 163:6 164:2 166:9 173:24 174:25 175:2

175:21 176:5 178:12,18 185:8 189:19 190:4

193:12,25 201:8 213:26 214:5 223:3 227:21

330:26 337:6,12,18,22 338:17 339:5,10,25

340:18 341:6,13,19,20 342:8 343:13,26 346:22

371:17 394:4 406:5,8 446:13 456:19 457:20

459:11 491:10 493:8 496:14 503:22 538:17 540:3

540:18 543:20,22 544:4,5 553:16 604:24 627:16

640:8,9 648:14 649:1 651:6 662:6 674:7

measurements 49:25 70:7 107:24 112:21 113:1

132:2 141:21 171:10 185:11 230:17 258:11 260:4

260:16 261:16 328:2 340:17 373:17 374:8 378:14

391:9 393:5 395:2 406:3,24 449:9,16 456:6 480:4

482:16 506:25 513:20 515:20 536:10 549:10

551:21 554:14 627:16 636:13 645:20 646:9 655:2

661:23 671:9

measures 22:17 68:21 70:18,25 77:6,8 83:12 84:15

84:19,20 96:6 102:2 116:15 128:21 141:4,5

142:17 145:26 155:18 158:26 165:5 186:10,20,21

191:12 204:19 261:7 328:19,23 331:9 335:23

340:22 359:6 410:7 417:4,7,11 448:19,21,21

459:20 475:10 480:14 492:22 493:3 494:22 510:3

511:10 536:7 567:7 572:3 586:4 616:15 628:26

631:3 633:20,22 636:15 666:18 688:13

measuring 49:18 59:5 108:24 185:25 201:11

230:26 288:6 337:23 342:19 443:3,18 444:10

447:4 449:1 450:3 458:6 459:18 480:13 514:23

532:5 537:24 538:16 550:22 552:20 556:12 563:5

569:16 583:26,26

mechanics 651:10

mechanism 47:6 355:6 360:21 398:18 440:2

505:15 616:7

mechanisms 164:10 178:20 563:15 648:7

mechanistic 181:25 668:16

mechanistically-based 228:15

media 36:21 37:6,19 41:15 47:18 292:22 346:3

347:9 348:2,4,5,7 353:25 354:5,6,9,13,21 355:14

355:17,18,22 356:1,18 642:8,10

median 97:24,26 98:1,3 120:17 121:6 207:23,25

208:2,4

mediation 6:12

medicine 640:19

Mediterranean 22:25 107:4

medium 120:16,24 295:9 481:7 628:11

medium-sized 285:23

meet 17:19 93:10,15 101:23 147:21,24 211:17

239:4 248:1,2 256:24 333:22 337:19 357:19

622:2

meeting 2:3,3,6,13,14,22 3:3,14,21 7:4 21:20
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395:1 418:7,8 419:14,21,21 435:8,23 436:9 622:3

686:2 695:8,9,19,24 696:19,20,20,23

meetings 7:12 641:3

meets 341:19

Megachile 363:9 364:11 365:6

meld 494:17

mellifera 314:19 481:5 562:20 631:23 690:23

melon 121:16 122:10

member 11:6 23:11,13 253:11 585:15

members 2:23 6:19 7:10,26 13:11 16:8 23:4 87:22

89:1 251:22 278:7,10 285:22,22 287:1 292:9

316:17 336:26 340:26 344:6 350:10 352:23

353:16 364:22 368:26 377:6 380:14 385:12

396:13 407:10 409:2 415:11 425:25 455:15 468:4

472:25 475:26 478:5 482:1 483:25 485:17 492:19

533:10 538:7 551:9 565:8 571:14 587:24 596:4

602:7 607:1 614:26 616:17 626:2 627:2 631:26

658:13 667:15 675:14 681:17 691:8 693:19

men 305:10 451:10

mental 558:22

mention 132:20 156:26 274:8 292:8 293:25 340:17

416:5 423:17 460:23 656:8 678:7

mentioned 52:7 161:19 173:2 175:19 185:1 192:13

193:18 219:6 225:24 247:17 252:13 256:26 266:5

267:19 270:21 271:20 273:23 274:4,7 275:24

372:9 382:10 397:3 416:17,19 449:22 456:22,23

459:4,8 463:9,10 464:5 465:13 476:2,10 478:11

486:14 489:11 504:2 519:14,22 523:24 544:6

573:2,12 592:2 598:20 603:19 618:11 624:1

632:15,20 633:1 671:5 688:2 692:2,17

mentioning 516:21 540:8 681:19

mentions 182:11 183:23

merely 294:12

merge 549:20

merit 366:14 571:3

mess 308:10

message 668:3

messier 690:7

messy 356:16 686:21 690:7

met 2:11 3:2 190:19 195:15,19,20 210:9 254:20

279:18 287:15 290:15 407:17 428:9 664:5 688:18

meta 549:23 550:1 551:11,18

metabolic 97:20 101:23 390:4 458:13 508:3,4

metabolism 4:6 124:22 148:18 264:25 467:3 472:3

metabolized 265:5

meta-models 516:24

meteorologist 673:17

meter 523:7 575:17 585:7 603:7

method 39:25 64:6 68:18 73:7 76:7 78:3 80:7,18

80:20 81:3,5 83:20 86:10 89:2,13,26 90:11,13,16

90:21 91:7,20 92:18,23 93:17 95:10,14,16 97:5

98:4,8 105:8 107:5,10,12,14 108:16,20 110:4

111:6,24 113:4,22 114:4 116:8,17 117:6 119:26

120:2 122:25 123:23 126:19,23 127:10,23 128:4

128:23 130:7 143:9 144:10 146:10,16 158:11

159:15,18,26 160:18 169:19 170:6 171:1 183:13

197:11 260:24 273:7 315:26 316:1 329:17 330:21

344:1 373:15 399:16 403:21 406:13 407:24 409:7

413:4,23 416:14 423:1 431:10 455:7 457:2,6

464:25 465:25 466:2 471:26 479:10,18 480:7

489:21 490:14,15,25 491:4,19 495:1 519:2,8,9,23

520:16,25 524:3 533:24 534:24 607:7,8 633:6,8

640:3 691:26

methodologies 75:21

methodology 11:9 72:17 89:11 91:22 124:25

159:25 235:25 239:24 261:21 262:18 270:19

271:26 538:25 539:2 557:13 644:4

methods 15:14,18,19 18:11,12 21:18 22:6,23,24

24:1,2 25:11 33:25 36:26 40:7 43:8 55:1 63:11,15

64:5,26 65:7 76:2 82:24 83:6 90:1 106:9,16,20

107:15,19 108:7,9,11 109:9 126:18 127:3,17

128:13,14,16 129:7,16,18,18 130:23 131:13

140:11 146:14 148:7,8,9 149:25 154:11 155:13

155:14 158:19 161:23 166:4 170:24 196:24 230:6

230:21,21 254:5 267:8 271:9 272:6 314:6 406:1

406:16,17 421:20 422:13 432:8 434:12 446:17

453:26 458:6 467:15 474:15,20 475:16 482:9

490:14,18 492:24 507:7 513:11 520:12 521:24

588:2 624:21 632:24 640:4,16 641:6 655:18

691:24

methyl 302:25 303:2,3

methylation 465:18

metric 477:8 645:21 666:10

metrics 647:10,11 649:19 650:3 672:5 679:11

Metro 289:5

Metroplex 322:10

metropolitan 322:10

mg 299:13 402:21 436:25 438:25

mg/kg 258:7,14 262:1

mic 456:6 597:11

microbial 467:20

microcapsules 151:4

microclimatic 568:18

microencapsulated 151:8

microencapsulation 151:3

microgram 104:13 110:8 115:25 117:24 126:24

128:2 373:6 375:11 377:23

micrograms 24:24,26 92:4 108:19 109:19 127:12

127:13 138:23 157:23 272:19 273:12 365:25
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374:19 375:6

microliters 425:16 426:3

micropipetting 474:22

micro-environment 635:13

mid 235:23 321:8 418:7 523:14

middle 336:10 543:25 571:22 579:2,3

midgut 427:17

Midland 322:7

midpoint 545:6,11

midpoints 99:11

midwest 320:21,21 321:13 411:26 578:10,11,12,18

597:20 600:26

might've 153:8 198:21

migration 225:26

Mike 311:23,25,26 314:21 315:2,14 316:3,10

mild 234:24

mile 574:12,12

miles 187:22 191:2 322:11,20 323:20 324:20

military 558:21

milligram 114:14 115:21 117:8,10,19 118:4,8,11

118:19 119:3,5,10,23 123:2,4,7,19,20 126:5

127:24 129:12 132:24 369:1,9 371:16 401:24

402:9,17 403:18 404:9 442:23

milligrams 93:23,24 97:23 101:25 102:3,10,12,12

105:16,18 110:12 127:7 133:2 393:21

milliliter 298:18

milliliters 161:15 426:3

million 19:13,13,23 234:18,18,21 276:14 299:11

305:10 322:10,21 324:26 425:11

mimic 177:10 368:5 522:8,13 524:16,18 533:1

539:20

mimicking 522:10

mind 3:26 44:16 134:20 155:8 218:23 230:9 252:7

276:8 296:19 299:25 315:19 330:14 336:12

337:26 338:11 345:10 357:16 359:7,17,21 364:1

364:2 387:12 412:23 439:17 449:20 459:22,25

464:10 498:2 506:1 514:21 570:17 619:26 623:1

625:26 631:14,22 640:25 643:26 648:26 659:24

663:10,16 671:4 678:14 683:23

mine 285:23

mingling 503:13

minimal 26:23 57:24 59:23 60:7 61:14 62:10,21

63:25 80:3 170:2 177:14 187:17 193:1,7 201:16

489:19 585:7

minimally 187:7 195:2

minimize 77:4,16 90:25 172:21,23 175:8 185:4

294:25 318:21,22 436:21 564:9 570:5 589:10

639:18

minimized 570:2

minimizing 189:24 543:21 544:15

minimum 145:3 169:13 183:23 184:5 227:3 568:16

573:15 583:6,7 603:6 645:14 646:12,13 656:9

Minister 16:6

ministry 311:20

minor 40:26 71:15 80:24 140:18 142:23 589:26

597:14,16,18 618:10 633:16

minority 344:24

minute 215:5,7 252:9 322:4

minutes 2:7 3:13 248:20,23,24,24,25 279:17 287:9

296:13 303:18 322:24 378:17 379:12 437:26

493:11 546:9 547:17 585:13 643:7 667:14 670:12

miscarriage 493:2

mismatch 604:10

misread 610:1

missed 194:25 309:8 607:22

misses 644:9,16

missing 283:6,22 284:2 385:6 510:24 538:9 541:9

674:23 675:21 676:4

misspoke 652:1

mistake 551:23 555:4 560:23 597:16 640:26

650:23

mistakes 262:24 597:20

misunderstand 81:15,16

misunderstanding 242:4 659:3

misuse 630:8

misused 630:7

Mitchell 14:13,25,26 15:1 696:7

mite 231:24 232:11 305:3 637:17

mites 19:25,25 20:1 21:2 231:24 232:8,10 233:6

235:24 237:7 303:22 305:2 306:13 471:6 586:16

637:18,21

miticide 471:5

miticides 533:19

mitigate 63:13 238:21 291:11

mitigated 179:19

mitigating 276:15 331:24 414:14

mitigation 60:9,12 61:15,18,20 62:5,7 77:6,8 84:7

84:11,15,19,19 85:4 166:22 179:17 193:23

204:19 276:2,4,8 287:25 331:8,19 410:7 416:18

417:4,6,11 419:11 429:14 525:22 628:26 688:13

mixed 186:15 417:24

mixes 220:13

mixing 307:23

mixture 145:6 159:20 160:8 311:1 421:4

mixtures 300:14,18 678:2,3

Mm-hmm 134:23

mobile 128:12

mobility 119:17,19 124:2,4 298:1

mode 59:19 68:4 170:9 182:26 285:26 412:4

607:17 634:25 635:2 668:15
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model 11:18 27:15 32:16 34:3,12,19,22 35:5 36:14

37:1 39:20 40:3,23 41:1 42:21 43:2,9,23,25 44:14

44:15,16,18 45:5,16 47:17,21,23 48:5,13 63:5

68:7 71:17,23 83:1 86:2 88:14,15,17 106:25

107:6,8 110:7,16 113:7 120:3,4,18 121:4,18,26

122:1,12 125:21 126:3,11,14 128:6,7 129:13

131:1 132:8 135:16,18 164:19 199:24 202:13,16

206:15 207:11,14,17,26 211:9 216:5 222:9 223:5

223:25 224:1,3,4,7,21,26 225:1,6,6,7,8,12,12,14

225:18,21,24 226:2,3,4,8,23,24,25 227:1,1,6,7,8

227:15,20 228:2,6,14,17,19 229:21,22 231:15,18

231:23,23,25 232:10,11 240:6 241:10,11,12,13

241:14,15,16,17,21,23 243:20 244:3,8,21,22,24

245:1 257:1,3,22 258:18 259:21 260:5 271:20

356:15 366:16 373:25 392:21 393:1 394:1,12

397:11,17,20,25 398:2,5,17 399:3,4,9,14 400:3

401:5,13,25 403:19,19 404:8,14,18,21,26 405:3,4

408:16 411:12 424:12,13,13,15,15 425:1 426:7

426:15 502:9 527:6 550:13 560:24 619:23 644:5

644:22,22 645:7,8,23 647:5,13 648:24 650:2,26

651:1,13,16 653:4 655:17 656:23 657:15 658:8

658:22,23 659:18 660:22 661:15,20 665:5,16

666:14,16,22,23 670:4,5 671:10,22 673:2,4,9,9

673:14,15,17,20,22,26 674:1,5,15,22,24,26 676:3

676:24,26 677:13 678:13,25 679:24 680:6,9,18

681:5,8,19,24 683:1,7,7 684:2,18

modeled 411:12

modeler 250:26

modelers 239:18

modeling 6:7 47:2,11 48:17 100:18 199:20 225:17

231:8 239:17,25 242:22 243:5,19 262:22 264:15

264:18 271:16,18 291:2 373:12 398:10 406:9,10

406:16 461:26 500:23 501:9 502:1 511:7 516:24

551:11,17,19 553:4 555:11 606:3,10 627:7 644:7

644:10,18,23 647:25 649:9,13,26 650:24 654:7

654:21 656:16 657:6,13 658:3,5,15 659:4,12

660:5,7,19 662:1,4,8 663:12 664:4,9,14,25

665:12,13,18,23 672:19 675:9

models 27:12 28:7 32:6 33:11,13,18,19,21,24 34:9

34:15,26 35:12,19,22,25 36:4,9,13,17,19 41:24

42:6,12 44:6 49:9,19 55:20 75:20 107:17 125:25

130:21 165:4 199:22 205:25 214:17,18 219:10

221:4,7,7,14,17,20,21 222:14,19 223:1,7,11,16

223:22 224:10,13,17,17,24 226:12,18,20,22,26

227:10,24 228:12,16,23,25,25,26 229:13,17

230:3,3,5,8,8,10,11,11,13,18,19,25 231:2,12

232:18 240:3,4,16 241:3 243:25 244:1,17,19,23

244:23 263:22 271:21 296:26 340:20 345:14,20

345:25 351:10 387:21 401:11 550:9 643:18 644:6

644:26 648:3,13 649:15,22 650:8,19,20,25

651:13 654:22 655:4,19 656:4,19,22 660:11,26

661:10 662:18 663:14 666:6,13 667:8,11,13,17

670:21,24 671:3,4,4,11 672:3,7,13,14,18,18,19

672:20,23 673:6 674:23 675:19 676:7,22 677:7

677:11,15,24 678:5,8 679:2,9,10,13,17 680:1,3

682:11,23 683:6,10,24 684:4,5,8,8,20,25 685:1,1

691:13

model-estimated 219:1

moderate 682:18

moderately 157:22

modern 15:25 141:11 279:9

modes 216:12,17,21 218:17 631:5

modification 9:12 88:13 107:6 142:15 171:20

398:23 409:13 574:15 691:25

modifications 57:1 63:14 126:3 140:18 141:12

142:24 190:8 191:23 192:13 193:18 294:13 357:3

446:14 520:18 567:17 618:26

modified 36:2 42:3 124:6 128:6 129:12 259:22

398:2 399:24 405:1 410:7 416:19

modify 83:1 120:19 130:15 244:9 534:24

modifying 60:12 291:18

modular 648:8 651:5 656:8

modularity 678:19

modularly 648:13,18 678:25

modulated 608:23

module 426:13 679:1

modules 679:6

moisture 398:24

moment 366:17 373:24 378:5 400:8 481:14 503:6

526:10 610:4 658:16 659:8 675:20 676:4,7

moments 390:9 449:22

money 181:15 192:23 283:19 303:16 306:7 668:23

monitor 326:16 485:1

monitored 183:25 192:2

monitoring 23:5 128:17 146:13 161:20,22 165:6

170:3,18 172:12 174:4,5 192:4 206:5 238:5

313:18 429:2,4 592:8

Monte 97:18,21 230:16 262:18 263:19

month 7:3 252:23 637:7 641:19

months 523:15 586:11

Moore 263:23 641:25

Moriarity's 232:17 234:22 238:14

Moriarty 18:1,3,5 32:17,19 204:7 210:22 213:18

333:7,7,12,15,15 337:4,5 341:4,5 357:15,17

358:7 696:2

morning 2:1 3:6,9,23 4:16,19 5:6,17 6:2 8:2 11:25

13:11 14:12,26 17:26 33:9 34:5 35:20 55:15

71:12 79:12 204:7 247:17,17 248:8,9 249:5,17,19

249:25 250:12,21,24 251:7,12,20 252:25 261:24

277:26 297:3 302:9 303:14,25 304:21 316:13,16
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435:21,25 436:2 437:9 441:19 442:1 486:2

513:18 531:12 532:9 604:15 621:22,23,24,26

622:2 624:14 627:4

mortality 39:14 61:6 158:4,11,17 161:17 163:2,7

163:17 167:24 171:12 175:3 178:15 207:13,16

208:2,5,8,8 231:14 269:16,19,21,25 284:23,26

285:4,6 289:26 316:1 360:4 428:25 429:7 441:23

444:25 446:6 447:3,15,23 456:20 457:5 459:10

479:22 480:14 485:1 490:3 499:3 502:13,15

513:20 535:25 545:5,19 550:23 553:23 554:3

557:1,6,9,15,18 560:22 561:4 565:25 570:1 576:3

586:6,17 607:10 610:7 612:4,5,5,6,12,18,18,21

620:20 621:9,12 638:24 645:4,5 655:13 661:14

661:17 666:5 671:24 672:1 692:3

Moshameem 14:10

mosquitos 323:18 363:4

moth 110:20

motility 450:4 504:5

motivate 419:10

motivation 366:11 454:13

mottling 420:17

move 32:5 51:16 52:1 59:15 60:11 65:13 88:21

89:25 92:25 106:8 117:3 139:14 156:3,3 167:19

168:18 177:14 192:20 196:13 198:16 203:6

269:25 277:10,18 278:23 279:11 280:3,16 314:26

333:13 345:9 353:20 357:14 368:19,19 384:4

392:9 401:19 422:17 428:3 431:18 432:18 441:17

446:9 462:22 464:21 471:22 472:21 473:18

476:26 482:3 483:26 488:26 491:7 499:16 503:18

523:7 535:21 538:22 567:15 572:7 585:21 587:26

589:21 593:10 604:20 607:26 609:12,16 616:13

620:21 622:8,9 624:16 630:12 643:13 644:1

651:25 653:12 668:6 685:23 695:1

moved 170:2 321:11

movement 84:2,4 87:1 143:20 167:4 189:14

394:16 411:13 420:18 620:24 668:4

movements 298:9

moves 56:2 91:19 533:8

moving 10:16,20 12:14,22 21:24 55:21 100:8 141:2

154:21 170:25 173:6 198:19 240:15 266:7,10,18

294:23 320:7 341:3 354:13 356:19 365:19 509:15

545:10 609:13 694:25

mud 347:21 357:13 434:17

muddy 318:9

mull 325:7

mulling 247:21

multifactorial 237:10

multilateral 247:5

multiple 20:23 25:23 28:2 30:26 48:14 55:23 56:9

109:2,10 116:5 149:21,24 167:14 171:22 178:24

180:24 181:1 210:20 211:19 214:14 219:7 221:8

223:13 224:2 225:2 229:4,9 238:13 239:11

244:12 245:20 267:17,18 268:23 299:26 300:11

318:16 359:25 365:5 379:15 399:12 447:14

545:13,16 548:23 566:7,8 583:14,19 584:3 588:7

591:5,6 594:6 624:10,19 625:16 662:9 663:2

667:25 669:4

multiplied 117:21 206:16

multiply 260:20 423:20 425:16 672:15

multiplying 108:21 110:11 116:3 127:4

multitudes 307:15

multivariable 233:9

multiyear 301:14

multi-dimensional 656:2

multi-faceted 9:25

mundane 691:16

muscle 461:11

muster 282:14

mute 13:8 49:1 69:13

myopic 494:16

myriad 513:22

N

N 87:19,19,19 497:6

NAAA 285:18,20,23

NAFTA 16:3

nah 677:16

name 2:5 4:1,3 5:1,4,11,21,24 6:2 16:20 18:5 32:21

33:1 71:8 140:6 154:9 203:10 215:11 220:26

249:11,12,16 251:9 252:18 266:2 278:1 280:24

285:15 297:4 302:9 311:26 316:13 373:24 486:25

names 609:10

Nancy 5:21,21 250:16,16 336:24 339:20 340:10

344:3 345:19 348:23,23 353:24,25 354:19,19

355:20,20 356:13,13 357:7,7 364:23,23 371:14

371:14 376:23 380:12 385:10 387:15 389:1

445:19 451:24 457:24 461:8 462:15 468:1 471:10

472:14 473:11 475:23 478:4 479:25 481:26

483:22 485:7 488:2 492:14 497:14 500:26 502:19

503:8,15 504:25 510:12 516:20 532:23 537:2,8

540:13 550:8 555:19 564:23 565:10 570:25

577:20 587:19 590:11 591:2 592:16 595:1 600:3

617:19 629:11 635:8 636:11 637:26 660:24

689:22

nanogram 298:14

nanograms 298:18,19,20,22,24

narrow 286:13 631:16 672:17,22

narrower 362:13

narrowest 68:24

NAS 19:4 235:12

NASS 19:11,17 234:9 237:1
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nasturtium 122:9

nation 84:22

national 4:24 8:16 9:6 10:14 19:1,10 139:22

249:23 252:19 285:10 292:11 294:26 316:12,14

326:14 477:20 593:20 668:1,2 692:4

nationally 8:12

nation's 254:19 297:12

native 70:20 253:7 289:25 294:14,17 295:12 336:6

336:7 338:12,13 339:3,24 340:12 347:25 362:19

389:2

natural 177:12 199:5 267:2 338:14 401:24 450:20

489:21 490:1 518:16,17 541:15 563:4 650:13

naturally 180:6 266:22 267:2 501:23

nature 26:20 36:11 37:17 54:7 57:7 62:17 68:9

139:13 142:26 165:16,21 173:26 174:24 183:5

194:1 214:9 223:14 235:20 236:13 328:26 385:4

472:3 498:4 504:13 522:5,19 548:1,12 586:20

589:3 593:2

navigation 298:2

NC 315:10 368:24 446:20 489:8 513:14 605:3

NCGA 316:16

near 84:17,22 169:17 171:5 281:18 456:1

nearby 5:19 299:20 318:22

nearly 184:20 347:15

neat 690:13

nebulous 448:8 606:17

necessarily 47:21 49:22 112:22 171:16 241:4

257:26 258:25 265:6 271:13 282:22 329:26 330:7

354:7 367:21 368:5 377:24 378:2,3 387:18

397:21 415:9 438:17 455:25 561:7 586:21 608:20

625:9 628:18 636:18 648:20,21 649:2 653:26

666:17

necessary 26:24 36:2 60:19 63:26 89:19 93:20

94:22 111:9 113:21 144:14,18 149:24 150:3

157:16 171:23 177:26 179:20 191:9 208:6 222:24

227:3 253:7 268:12,14 291:6 333:22 337:13

341:13 357:6 366:11,16 399:2 478:12 488:9

518:26 574:22 638:10 648:16 659:19 679:16

necessitate 449:6 568:14

necessitated 392:21

necessity 647:19,21

nectar 25:6 37:8 38:2,15 39:26 40:12,26 41:6,18

44:9 45:3,4,26 46:8,8,9,9,14,15 55:10 58:14

60:23,26 73:1,5,10,13 74:1,5,15 75:6,12,14 76:2

76:15 78:7 79:23 80:22 90:2,5 93:2,2,4,15 94:7

94:11,19,22,23,26 95:2,2,12,14,17,21 96:7,14,15

96:17,20,23 97:1,2,3,6,6,11,20,24,24 98:13,15,22

98:26 99:1,3,5,13,14,15,16,18 100:1,14,15,23

102:12,14,14 103:22 104:14 105:9,13,15,16

106:17,22,25 107:2,20 108:13 113:19 114:7,13

114:14 115:11,18,24 116:11,16,22,25,25 117:4,8

117:18 118:2,9,10,14,20 119:6,8,17,18,24 120:1

122:6,8,26 123:10,17 124:1,3,10,14 126:15 127:4

127:12,23,25 128:5,9,18,21 129:8,14 134:11

135:3,13,19,26 137:3,8,13,15,20,21 138:12 141:4

142:15,17 143:13 144:14,15 145:4,23,26 146:8

146:23,26 147:19,21,23 148:22 149:2,2,8,15

169:14,16 170:19 172:13 174:3,8,12,17,22 186:5

186:15,17,18,19 191:16,17,20 201:11,22 202:20

206:6 258:25 260:2,6,6,9,10,15,21 262:26 263:3

263:6,8 264:16 273:17,18 276:19,22,25 277:3,4

277:21 299:10,17 301:4 313:16 326:25 327:19,24

328:11,13,16,22,23 329:3,10,14,18,25 330:2,4,6

330:21,25 337:8 348:2,5,15 349:1,5 353:7 361:1

361:2,9,23 370:24,25 379:1,18,23 380:9 381:18

382:22 384:9,14,19 385:16,20 386:4 387:9,12

388:13,19 389:4,10,19,19 390:9,13,26 391:4,9,10

391:11,15 392:16,24,25 393:6,25 394:2,4 395:2

395:14,25 396:2,9 397:13 399:5 400:7,15,18

402:11,16 405:22 406:4 407:3,4,6 412:24 413:25

426:22,26 428:10 465:5 466:3,5 520:2 523:15

524:7,20 525:11,19,21 528:2,9,21 529:18,22

534:9,10,25 535:1,9,16 537:5,21,24,26 538:1,16

538:17 541:1,4 543:8 545:14 556:10 575:23

nectaree 330:17

nectarees 330:19

nectaries 148:23

nectar-equivalent 100:21

nectar-specific 116:18

need 50:8 54:1,22 55:5 59:6 65:9 67:24 85:20

88:21 91:12 117:19 142:2 151:16 177:13 179:17

180:25 187:1 202:25,26 206:22 218:9,26 231:3

233:24 234:5 239:5 241:24 244:24 245:12 248:2

249:2 251:22,23,25 252:2 255:22 258:5 259:3

264:11,11,18 265:11,14 267:13 268:9 270:8

272:23,24 273:3 274:12 276:9,10 277:16 278:24

279:19 280:14 281:16 282:16 283:24 284:3,17

286:23 288:11 294:19 306:18 308:13 313:26

317:4,26 319:1 328:26 329:5 330:25 345:6 363:1

377:1 378:13 380:25 387:11 395:21,22 396:6,6

396:10 400:4 406:24 410:11,26 411:7 412:4,23

413:14 415:4 416:1,14 421:21 423:13,20 426:23

431:9 433:5 434:9 442:12 443:18 450:8 460:13

461:19 462:18 463:2,6 471:3,10,19 480:15 482:3

488:6 498:15,24 499:16 503:17 509:15,25 525:6

528:1,3 530:25 545:7,20 548:3 557:5 561:16

564:11 565:1 567:1,5 568:17,19 575:2 576:26

583:1 595:7,15 598:14,26 600:21 607:16,19

608:14 610:20 613:2 618:8 619:25 622:1 631:2

634:1 635:21,25 637:8 638:6,12,16 639:9,21
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650:15 656:9 657:1 658:6,21,24 659:19 660:1,3

662:9,18 665:5 668:11 669:7,15 672:23 673:15

673:22 674:2,7,8 680:12

needed 26:17 28:24 29:1 31:3 66:1 78:2 140:26

153:16 160:1 166:12 207:18 222:25 401:13

409:10 413:5 414:18 423:3 474:9 480:7 506:20

549:4 552:9 569:3 632:12 686:18 693:7

needing 318:6

needs 15:18 23:13 27:1,4 130:4,6 139:1 141:3

145:12 164:2 166:25 168:9 182:10 187:26 251:5

262:16 265:22 283:23,23 286:22 307:16 331:2

337:25 346:2 348:3 398:25 401:5,9 412:4 426:9

428:11 430:18,18 433:19 437:23 451:21 457:3

486:18 502:5 505:6 506:14,19 507:13 520:18

549:7 561:18 574:15 605:14 631:9 646:12 680:1

680:4

negate 617:23

negative 161:10 232:6 240:24 305:7 339:23 342:9

515:17 595:24 666:19

negatives 268:5 275:19 527:15 611:22 626:13

neglecting 520:25

neighboring 188:22

neither 561:6,10

neonicotinoid 290:10 291:4,8,25 293:1,18 294:5

297:25 317:12 318:20 411:11 421:11

neonicotinoids 298:6 319:8 373:23 416:23 421:4

nervous 463:21

nest 38:12 188:12 342:21 433:8 510:9 531:10,12

572:2,4

nesting 38:11 71:1 347:20 359:13 433:8 434:16

nests 430:14

net 222:7 572:11

Netherlands 557:10,10

network 23:3

neural 512:2

neurotoxic 298:10

neurotoxicity 509:19 511:15

neutral 125:25

never 304:15 306:15 362:4 520:9 554:9 639:6

640:15 683:11 693:5,23

nevertheless 367:10 413:12,22 592:9 627:25

628:16 676:1

new 12:17,17 56:5 77:9 191:22 211:14 214:23

271:26 289:8 302:10 303:18,20,21 311:13 323:21

326:19 336:7,9,10 430:11 440:2 452:19 476:7

506:1 528:25 559:25 642:23 655:11,14 681:26

691:23

newer 131:14 279:8 683:7

newest 314:6

newly 161:7 299:3 384:17 443:25 458:19,19

483:19 537:11 634:17

news 324:15

NGOs 289:19 292:16

nice 86:2 233:14 255:20 546:5 579:16 601:1,2

674:24 696:21

niche 10:11

Nichelle 296:22,23 297:3,5

night 242:3 287:4,10,13,14,16 288:13 322:23

463:1 464:8 476:2 479:5 623:10

nights 322:21

Nile 322:12,14 324:11

Nina 6:5,5 47:9 48:3 49:8 50:12 51:20 52:15

129:23 130:10,18 197:16 229:26 230:9 231:6

239:15,23 240:14 242:16 250:24,24 309:21

330:23 340:15 371:4 375:18 390:22 429:17

439:11 452:1 457:17 461:24 470:15 492:20 502:4

505:14 507:12,12,19 511:2 516:24 551:17 554:22

560:18 603:16 604:8,8 613:17 619:13 627:4,9

635:20 639:24 643:22 651:21 652:1,5,14,24

654:20 656:8 659:2,23 661:8 663:9 665:8,25

670:8,15 671:1 674:25 676:23 682:25 683:13

691:7 693:12

nine 109:23 115:2 290:15 303:10 304:3 322:17

520:21 622:2

nitrogen 420:12

NOAEC 58:20 59:3 159:24 161:11,21 172:8

208:16 266:13 491:9 493:24 494:9,20 495:8

496:19 497:9,15,22,23 612:13,16

noble 338:25

nodding 82:15 345:3 369:12 388:9 512:17

noise 13:7

nomogram 259:22 261:6

non 43:11

nonchemical 223:14 588:7

Noncompliance 313:24

nonessential 451:11

nonexistent 467:6

nonionic 397:20 398:17

nonlinear 133:21

nonsignificant 549:24

nonsocial 510:8

nonsystemic 351:19 431:25 440:17,20,20,24 441:3

441:10

nontarget 245:24 301:23 466:21 558:10,11,12,15

573:3

nontoxic 273:24

nontrivial 445:7

non-Apis 18:21,23,24 22:6,18 23:24 31:6,15,18,19

31:23 32:3 38:11 50:4 56:26 65:23 66:2 70:18,23
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71:2 101:10 129:6 163:9 213:25 257:11 270:22

279:25 294:10 301:3 314:22 350:21,22 351:22

352:12 358:8,10,13,16,22 360:6 361:22 373:18

385:14 498:3 510:10

non-beekeeper 534:3

non-comparable 564:15

non-EIIS 292:22

non-energetic 130:6

non-existent 79:19

non-grafting 691:26

non-guideline 211:1 212:17

non-honey 347:2,11 364:24

non-hot 428:8

non-insecticides 218:10

non-ionic 124:1

non-irrigated 596:7

non-normal 130:2

non-pesticide 648:2

non-precautionary 290:20

non-risk 648:2

non-robust 294:21

non-spiked 542:1

non-stable 655:3

non-systemic 13:19 33:26 36:15,24 39:21 40:5

42:18,21 43:15,17 44:1 75:22 116:23 142:1

non-systemics 43:12 57:5

non-target 24:4 162:21,25 165:10 194:17 206:22

206:26

non-tillage 320:26

non-tilling 331:12

non-toxic 157:21

norm 323:21

normal 129:26 297:14 306:18 386:5 415:7 527:25

527:26 528:18 553:25 560:5 588:13 603:9 629:21

660:21

normalization 367:17,19 374:1

Normalize 393:26

normalized 107:24 109:20 114:14 115:22 367:1

377:23

normalizing 369:12

normally 365:1 380:7 384:18 385:6 564:18 588:17

588:19 639:24 647:20

norms 610:14

North 5:2,3 18:10,16 19:1,2,16 24:19 77:1 123:15

187:18 190:11 235:13,18 249:26 250:2 255:8

273:15 294:14 442:1

northern 580:9 683:11

northwest 684:22

nose 653:26

nosema 299:6 303:23 305:6,7,9,13 306:13

notable 92:20 124:5 146:9

notably 192:15

notation 374:23

note 2:17 38:9 39:9 41:19 59:4 64:17 73:6 85:16

88:4 101:10 102:23 120:12,25 123:8 155:3 160:3

166:5 170:23 175:25 192:14,20 194:12 202:22

224:20 228:11 301:3 303:17 340:2 448:6 495:3

501:19 536:8,9 579:7 588:24 652:13 691:16

692:2

noted 13:22 47:17 54:23 58:21 62:26 63:5 65:12

100:19 103:5 104:5 121:7,22 122:16 161:4

171:24 176:2 190:2 298:21 424:25 438:4 466:12

506:12 547:9 563:13 586:24 587:12,14

notes 24:19 162:10 164:5 172:18 177:21 183:10,17

183:21 188:7,14,17 601:6 641:21 645:2 646:1

648:6 676:6 691:14

notice 37:10 330:15 550:23,25 645:10

noticed 352:25 545:16

noting 66:15 99:12 100:9 317:19

notion 515:25

notoriously 516:7

not-for-profit 297:6

novel 368:2

Nowadays 575:8,10

no-adverse 496:12

no-effect 495:20,23 496:6

no-observed 58:19 208:15 495:14

no-till 317:19,20,22 319:17 320:1,5,6,10 321:12

NRC 235:17

nuances 50:10,13 52:7

nucleus 147:13 168:14 169:15 173:10 517:14

nukes 306:3

nullified 615:14

number 18:25 19:18,22 20:19 21:11,17 29:9 42:5

64:17 71:20,20 83:17 97:9 111:25 125:6 145:18

152:13 160:4 161:9 163:8 166:17 169:5 171:25

175:8 182:3 185:4,25 186:26 188:13 190:8 191:8

192:22 201:23 216:13 218:4 221:19 227:3,8

229:12 231:11 232:24 234:1,20 237:4 261:3

264:4 270:1 271:17,25 273:5 287:6 321:19 323:1

323:16 326:5 328:2 335:14 338:16 346:18 358:23

359:26 365:12 368:6 369:4,10 372:4 373:7 375:1

375:11,11 404:20,22 410:6 421:25 422:20 435:11

446:6 447:5 448:23 452:19 453:25 454:5 459:2

460:15 463:13,18 491:20 495:13 496:23 497:26

500:7 510:22 515:4 531:12 533:3 543:21 544:15

559:21 560:3 569:3 575:7 576:13 582:6,8,14

583:4 584:10 585:7,10,18,18 590:6 593:13

598:14 600:1 601:15 603:4 610:10,11,12,22,23

611:11,25 613:3,14 628:10,14,16,20 629:14
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656:13 672:15,15 680:25 682:19,21 692:17

694:20

numbers 88:9 237:21,24 239:12 258:17 259:23,24

260:8 273:12 292:24 335:5 360:26 361:2,12,13

438:15,15 453:2 558:25 575:19 602:22 603:6,10

603:11 638:6,6,17,22

numerator 201:12 206:16 648:18

numeric 54:9

numerical 230:13 585:16

numerous 63:8 76:26 165:24 177:6 193:3,10

221:23 292:10

nurse 20:10 73:23 77:25 93:7,12 95:8 99:25 104:8

160:10 188:16 299:5 346:2 353:11 355:11 360:1

383:12 489:25 505:22 544:10 545:7 556:10

nurses 353:10

nursing 464:9,9 505:23,23

nutrients 55:14

nutrition 9:1 21:2 253:20

nutritional 9:12 190:12,19 369:18 383:24 539:7

541:10

O

o 87:19,19,19 448:4

OACD 147:15

Obama 16:5

objective 27:9 146:4

objectives 28:26 32:11 140:13 143:26 147:21,24

212:9

objectivity 312:13 314:5

objectors 28:5

obligatorily 442:26

obscure 565:18 665:5

obscures 692:10

observable 496:16

observation 61:7 176:23 188:12 531:9 610:9 642:6

observations 120:7 430:21 531:10

observe 227:1,4 228:20 534:15

observed 95:6 109:11 161:16 198:21 210:5 217:25

222:11 223:5 227:9 269:21 298:19 327:12 383:7

446:4 463:11 493:22 495:15 496:13 541:24 588:3

obstacles 287:15 291:20 597:8

obstetrics 4:20 249:21

obtain 142:24 143:7 147:22 185:17 191:22 236:18

267:3 623:17

obtained 73:11,13,15 78:8,10 121:3 171:12 186:17

206:3 269:12 624:10

obviated 644:18

obvious 221:21 403:2 433:14 542:2 552:10 652:25

656:11 685:19

obviously 65:21 99:3 198:13 244:1 266:7,26

286:26 288:4 312:22 367:8 373:13 377:7 384:26

402:24 419:7 424:18 430:17 432:15 442:4 454:10

454:26 513:26 514:22 515:10 533:12 549:6 552:5

553:4 571:5 586:6 600:18 606:1 608:15,18 617:6

624:21 630:24 656:7,26 657:9,22 666:20 687:15

693:21 695:9

occasional 292:8

occasionally 240:16

occasions 222:16 548:23

occur 26:13 35:3 40:16 74:4 77:13 96:3 104:2

116:26 162:4 164:11 168:24 174:2 194:11 210:13

240:7 265:15 268:5,7 297:14 338:26 359:22

430:15 446:5 455:5,6 505:4 588:5,11 590:14

677:4

occurred 239:15 293:2,5 386:2 578:9 594:21

627:24 677:6

occurrence 191:23

occurring 78:24 92:12 283:25 284:20 292:19

457:14

occurs 75:4 346:21 418:17 487:10

odor 450:24,26

OECD 11:5,7 16:4 23:1,13 25:9 157:6 158:14

176:2 241:8 247:4 296:24 446:3 457:2 463:11

477:21 481:10 483:4,6,9,9 493:7 499:6 518:12,20

519:2,12 521:12 542:10 547:2,13 552:24 618:7

633:8

OECD's 23:7

offending 326:7

offer 168:25 397:4 419:8 436:6 438:1 528:9 689:12

offering 254:22 524:7

offers 404:13 491:2 520:2 525:18,21

office 3:1 4:13 6:16 13:13 18:6 21:10 33:4 71:10

89:5 140:8 211:4,24 323:7 411:19 420:17 656:18

695:22,26

officer 2:26

offices 326:19

Official 2:6 251:10

officially 687:11 696:21

officials 2:21 291:15

offs 580:1

offsite 40:19,20 41:4 43:24 63:17

off-crop 414:9

off-label 166:23

off-target 416:14

oftentimes 44:24 220:11

oh 53:2,2 232:22 309:6 354:16,26 370:3 372:17

392:3 434:18 464:3 469:3 478:6,17 493:5 497:20

530:25 587:26 602:12,17 607:2 670:15 671:20

673:8 674:11 678:11 691:9

Ohio 411:22
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oilseed 524:6,17 528:8 574:20,22 575:2,9,17

576:20 585:11 594:7 603:6

okay 13:3 17:26 32:25 42:14 45:21 48:20,26 49:7

51:20 53:1 68:15 70:9 71:6 82:3,14 87:13 88:23

129:23 130:19 131:16 133:10,26 134:8,19 136:19

136:20 138:20 140:1,4 154:3,7 194:24 197:15

198:9 203:6 215:4,7,8 220:1,21 232:15 237:16

247:15,24 248:4 249:9 252:1,11 257:14 259:19

265:25 270:16 275:26 277:22,23 284:4 285:8,9

288:18,24 296:12,17,18,18,20 297:2 302:7,8

309:5,19 310:12 311:2,23 315:8 316:9 321:24,25

324:6 325:2 332:18,21,22 333:2,14,24 336:16

337:1 339:14,26 340:16,26 341:1,3,3 343:10

344:2,5 345:4,5,9,17 348:18,21,25 349:10,20

350:9,17 353:15,16 354:23,25 356:20,25 357:15

357:23 358:19 364:4,21 365:16,16,18 366:2,4

367:25 368:15 371:18 372:7,16,24 373:10 377:5

377:17 378:16 379:7 380:13 382:4,5 383:19

384:4,12 385:9,11,26 386:7,19 387:6 388:7,8,26

389:6,13 391:21 392:2,3,5,8,18,20 397:6,7,15

400:25 401:16,26 402:4,13 403:7,12,15 404:3

405:14,17 407:9,10,12,19 408:4,8,14,18 409:3,4

409:12,19 412:8,26 416:26 417:8,14 419:4 422:9

422:9,16,17 423:6,12 427:5,20 428:25 432:1,10

432:17 433:24 434:18 435:2,3,6 436:4 440:4,14

441:12,16,16 446:1,8,9 451:7,26 456:21 458:7,23

458:24 461:7,23 462:13 464:16,20 466:25 467:22

468:3 469:2 470:14 471:18,21 472:11,17,20

473:7 474:8,18 475:18,25 476:7,25 478:6,14,15

479:24 480:9,18 481:16 482:2,3 483:15,24,25,26

486:12 488:26 491:6,7 492:18 493:4,10,11,12,19

498:14,18 499:15 501:10 503:16,17,18 504:17

505:9 506:21 508:14,17,18 510:6,15,19 512:18

512:23,26 516:18 518:3 520:8 521:8,21 524:7,13

524:23 525:5,14,23 526:8 527:19 528:5 530:6,23

530:24 532:21 535:19,20,21 537:1 538:6,11,12

538:21 539:11 540:12 543:1,2 544:7 546:18

547:9,20,21 551:8,26 554:6 555:18 556:20,26

557:16,25 561:24 565:5 566:16 567:14 573:5,6

574:6 575:8,10 576:6 577:1,19 580:2,11,12 581:6

581:8 582:15,16 583:12 584:7 585:6,12,21

587:18,25 591:10,13 592:12 593:9,10 598:17

600:7 602:6,16 603:25 604:18,20 607:21,25

608:25 609:9,26 610:5 614:7,10 615:2,2 616:18

618:1 619:11,12,23 621:18,19,25,26 622:1 624:4

624:5 626:5 629:7 631:10,11 638:15 643:3,8,13

644:2,3 646:12 647:7,8,8,17 652:6 653:2,14

654:11,14 655:24 659:13,15 660:21 662:17,24

663:1,4 664:7 667:3,10,18 669:6,8,11 670:15,17

671:22 672:14 673:16 676:12 683:16 685:8

old 105:18 130:22 214:25 306:17 367:3 388:2

497:9 574:16

older 96:3 131:4 211:16 388:1 505:23

olfactory 298:4

omission 290:8

omitted 289:21

once 29:16 59:8 61:10 91:9 130:12 171:9 197:24

204:22 228:25 248:14 265:4 311:12 435:3 457:19

482:9 487:1 490:10 517:5 520:13 604:3 661:20

668:14 677:5 680:20

ones 35:7,11 193:16 230:11,13 260:17 295:23

353:10 395:7 518:20 520:24 533:14 546:24

552:19 555:23 557:24 558:6 600:1 606:17 633:22

637:10,16 649:22 653:8,9 663:13 674:11

one's 243:25

one-half 576:19

one-milligram 107:2

one-third 435:10

one-time 444:6,11 467:9 468:12 486:2 501:14

503:9

ongoing 15:16 421:6 482:23 491:3 501:14,16

onset 56:9 225:20,22 226:10 227:5

onsite 185:17

Ontario 293:4 411:24

Oomen 171:1 196:26 197:11 519:23 520:13

open 51:8 150:7,24 151:17 153:7 162:13,15,16

171:26 173:6 176:10 194:17 196:17 211:2 212:11

219:23 246:17 275:2 327:4,8 336:25 365:10

395:13 413:15 418:8 468:3 496:12 543:22 562:16

621:12 622:20

opened 304:2

opening 175:9 185:5 210:22 246:14,15 395:13

530:17 569:24

openness 7:23

operate 285:16 302:10

operating 313:8 314:10

operation 7:18 285:17 287:10 288:21 306:25

operational 467:18

operationally 29:21

operations 41:22 285:23 307:4 317:20,22

opine 684:16

opinion 23:22,25 29:11 31:24 64:10 65:18 163:19

188:6,14 190:25 192:1,7 390:14,18 397:1,1

412:11 429:9 433:19 437:22 485:14 502:12

519:15 520:11 557:24 610:20 638:9 644:9 663:14

OPP 18:2 24:2 205:17 212:9 695:19,20

opponent 623:23

opportunities 27:3 272:12 288:11

opportunity 3:3 26:13 33:5 71:11 140:9 203:9

247:19 248:13,15 253:11 270:25 271:5 285:13
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545:13 595:4 613:10 621:10 669:24 695:23

opposed 45:3 47:3 50:18,20 51:22 139:10 199:4

205:6 220:12 266:24 281:25 331:20 351:4 439:20

465:5 473:21 555:5

opposite 376:7 424:8

OPP's 17:1,9

opt 593:6

optical 602:20

optimal 263:18 482:12

optimized 254:9

optimizing 254:6

option 60:12 61:20 159:10,14 166:23 244:13 265:9

275:5,15 276:5,13 413:22 506:19 521:19 607:20

621:12

options 60:9 61:15,18 62:5,7 84:5,11 166:22

179:18,18 193:23 271:21 356:26 599:10 632:21

oral 3:8,8,9 25:5,9 58:12,14,15,15,25,26 59:1,2,2

62:23 63:3 65:3 72:8 73:4,7,15,17,26 75:2 76:1

76:14,18 78:4 80:21,24 81:3,12,13 82:1 92:1

156:10,14,18 158:9,10,12 159:1,8 161:2 167:13

173:21 206:17 207:5,9,20,24 208:9 215:19 216:6

216:20,21,23 217:26 218:6,14,17 248:15,17,22

252:24 267:6 273:24 289:2,12 307:15 346:5,8

413:16,24 414:26 415:19 436:18 441:1 464:26

495:11 506:10 518:21 520:3 521:16,16,19,21

607:11 612:11 662:13,20

orange 302:15,16

orchard 305:24 361:20 363:23

orchards 109:1,23 111:20 302:12 305:20 307:22

order 89:18 90:7 93:15 95:19 96:17 100:2 101:22

108:3 109:18,26 111:22 112:17 114:15 121:21

122:15 149:25 154:26 164:20 168:15 170:20

171:2,17 172:22 183:7 184:9 185:13 191:8 194:6

216:17 224:1 227:15 228:3 233:13 258:13 260:4

263:7 269:7 277:20 279:17 284:17 331:2 367:5

396:7 398:12 401:20 425:19 426:3 449:6 450:6

450:13 561:1 635:21,24 650:2 664:1

orders 90:18 96:24 100:5 118:10 123:18 259:9

260:26 261:2 293:5 299:14 402:23

ordinary 283:3

organic 124:1 297:10 322:26 398:4,25 407:6 471:4

organism 24:7 163:14 167:12 199:16 314:24 374:8

443:2 449:26 459:26 462:22 473:25 497:4 504:13

514:23 527:5 533:9 625:13 642:25

organisms 4:6 24:5 165:10,16 168:23 199:20,23

206:22,26 207:3 213:2 245:24 266:14 301:23

423:19 462:26 497:5 510:4 514:5 532:20 558:12

558:14 587:4 609:13 611:13 620:24 624:25 635:1

675:23

organization 11:5 22:25,26 51:2,17 54:12 107:4

229:4,10 268:25 270:6,7 280:25 289:16 292:15

297:6 312:2,10 324:3 442:12 506:5 606:9 624:17

667:26 668:7 669:5

organizations 8:20 9:26 213:13 246:9 268:24

312:24 313:4 331:17

organized 36:8

organophosphate 472:8

organophosphates 373:22 389:24

orientation 37:22 74:10

origin 160:2 186:13,18

original 94:19 397:25

originally 232:8 258:7 474:5 578:3 664:5

originate 121:24

or/and 632:9

Osmia 361:19 362:15 364:11 365:6 562:21 570:13

570:17 572:23

Ostiguy 5:21,21 250:16,16 336:23,24 339:19,20

344:2,3 345:18,19 348:21,23,23 350:23 352:21

353:23,24,25 354:19,19 355:19,20,20 356:4,12

356:13,13 357:7,7 364:22,23,23 371:13,14,14

376:22,23 380:11,12 385:9,10 387:14,15 388:26

389:1 445:3,10,18,19 451:22,24 457:23,24 461:7

461:8 462:14,15 467:26 468:1 471:8,10 472:13

472:14 473:10,11 475:22,23 478:3,4 479:24,25

481:25,26 483:21,22 485:6,7 488:1,2 490:26

492:13,14 497:13,14 500:25,26 502:18,19 503:7

503:8,15 504:24,25 510:11,12 516:19,20 532:22

532:23 537:1,2 540:12,13 550:7,8 555:18,19

564:22,23 565:9,10 570:23,25 577:2,19,20

587:18,19 590:10,11 592:15,16 594:26 595:1

600:1,3 617:18,19 629:10,11 635:8 636:11

660:24 689:22

ought 353:2 359:20 396:2,9 432:16 450:12 532:1

572:19,21 617:9 626:21

outbreak 322:19

outcome 51:1 61:13 147:10,19 157:20 229:3

230:17 231:1 290:18 436:20 439:17 440:6,12

447:19 488:24 509:7 511:14 522:12 536:19

576:21 614:2 628:17 636:13 647:6,15,20 648:19

655:4 666:2,15,17,26 668:14 673:1 674:4 677:5

679:19 680:6 687:19

outcomes 147:18 240:18 443:19 651:3,15 666:15

667:12 679:6

outdoor 57:19

outdoors 144:25 169:9 183:18

outliers 261:18

outlined 154:13 193:5 536:5 659:11

outlines 142:7

outlining 689:14
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output 224:25 227:8

outputs 644:21

outrageous 580:15

outreach 664:6,13

outset 14:6 274:8 664:23

outside 37:21,24 96:3 160:5 176:1 177:4 366:21

467:10 484:10 523:17 532:13 581:2 586:15

588:19 589:16

outwards 516:9,9

outweigh 671:22

ovarial 448:23

overall 19:18 20:14 25:16 32:11 42:6 49:5 53:22

65:6 66:4,14 71:14 98:4 104:18 112:14 119:11

154:17 190:17 203:22 222:8 223:15 224:25

225:11,13 236:11 353:12 535:26 536:6 569:20

599:5 626:8 672:2 678:25

overcomes 180:10

overestimate 258:2 260:10 262:9

overlap 79:12 91:13 386:22 387:2

overlapping 568:20

overloading 583:11

overlooked 294:25

overly 256:2 260:3 272:13

Overmyer 253:1 254:26 265:26 266:1,2 275:21,23

276:23

oversight 325:17

overspray 352:1,9 438:11

overview 13:19 18:9,15,17 32:8 33:19 89:11 90:10

154:15 156:12

overweighs 545:21

overwhelm 582:21

overwhelming 168:16 629:13

overwinter 177:24 634:11

overwintering 62:1 139:18,19 173:10,17 176:21

176:24 185:10 189:23,26 192:5 227:4 589:7,8

634:8,22 657:2

overworked 659:7

over-predict 138:13

owed 284:9

owned 636:24

owner 288:21

o'clock 622:2

P

P 550:19

package 220:4 519:25 637:5 662:12,16

packet 289:17

packets 292:5,6

paddies 425:3 521:7 524:14 530:2

paddy 425:2

page 47:22 269:13 291:9 295:16 296:1 304:17

305:2,12,14 306:9 334:17 407:21 553:13 554:14

paint-marked 634:17

pan 572:11 601:22

panel 2:2,9,13,17,23 3:23 4:7 6:19 7:11,13,17,22

13:12 14:4,6,24 16:8,9,24 17:21 18:4 23:21 31:21

31:25 33:6 71:11 84:24 87:22 89:1 150:8 194:23

203:9 215:5 221:21 244:10 247:26 249:10 251:16

251:22,23,26,26 252:8 263:16 274:24 279:15

280:5 281:18 288:18 296:12 299:24 301:13,24

302:7 313:1 318:24 325:5,10 334:9 336:26

340:26 342:25 344:5 350:9 352:23 353:16 357:2

364:21 365:16 372:6 377:6,17 380:13 382:5

385:11 396:13 403:22 405:15 407:9,24 409:2,6

415:10 417:5 422:25 425:24 435:7,16,19 436:8

436:14 444:3 446:7 455:3,15 459:2 466:24 468:4

469:5 475:25 478:5 482:1 483:24 485:17 492:18

493:12 503:25 507:1 512:8,14 513:24 533:10

538:6 539:15 543:9,23 551:8 561:16 565:8

571:14 573:14 582:5,12 584:19 585:15,19 587:24

596:4 598:20,24 599:1 602:6 607:1 608:9 614:26

616:17,23 617:3 620:8,18 626:2 627:2 631:26

643:8 658:13 667:11,14 669:12 671:6 675:14

681:17 683:16 685:10 686:7 687:4,18 689:13

690:23 691:3,8 693:2,19 694:19 695:10,11,13

Panelists 290:19 517:19

panels 489:5 515:15 692:20

Panel's 302:5 422:10 435:5 511:16 582:12

paper 7:1 8:8,14 10:17,19,21 11:4,10,12 12:4,11

13:1 14:8,18,20 16:26 30:13 33:10,14,17 34:4

41:26 48:8 63:6 64:16 65:15 66:4 70:11 71:17

78:14,16 79:5,26 80:14 81:18 82:19 89:8 93:1

99:10 101:11 108:2 121:14 126:1,4 128:25

140:15 152:4,26 154:13 155:3 156:17 201:20

202:21 205:6 206:7 208:3,19 211:20 212:13

213:19 214:16 215:12 221:10,12 224:9,11 230:1

231:21 240:9 255:4,16 256:14,20 257:2,15,18

258:21 265:22 267:12 269:13 270:14 272:9

278:11 279:15 285:26 288:4,14 289:14 293:17,25

327:21,24 332:4,5 333:20 334:3 335:22 336:4

338:26 340:3 357:20 358:15 376:12 377:11 395:6

395:10 397:3 398:13 399:21 402:14 403:20

410:15 411:12 422:22 425:15 438:20 442:3 443:1

443:23 450:18 465:14 476:16 482:9 490:20 491:9

500:3 503:21 508:23 513:25 515:26 522:22 523:6

536:5 538:24 540:8 543:21 545:16 547:24 548:23

553:13 562:9,23 567:12,19 588:26 593:14 618:20

619:10 620:5 621:3 622:17 623:5,19,24 636:22

644:12 647:24 650:21 651:14 668:14 670:22

671:5 672:18 674:11,21 678:6,8 686:3,5 687:1,14

689:4,20 690:20 691:3,22 692:10 693:22 694:7
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696:5

papers 251:21 395:5,5 452:16 462:9 498:1 690:25

691:22

paradigm 641:4

parallel 659:12,21

parameter 667:18 671:9 674:6,7,8,15 681:25

parameterization 670:26

parameterize 679:11

parameterized 227:25

parameters 97:13,17,19 224:2 225:6 226:24

240:10,20 343:3 393:2 398:2,7,22 453:25 554:11

625:7 644:20 655:1 672:26 682:10 683:2,3

paramount 448:13 449:26

paraphrasing 656:3

parasite 299:6

parasites 177:7 184:20 189:17 253:19

parasitic 20:1 21:2 586:16

parasitoid 162:25

parathion 302:25 303:2,3

Pardon 546:19

pared 450:23

parent 122:4,5,22,23 143:23,23 157:4 390:5

parentheses 102:25 216:15

parenthesis 117:15

parries 684:22

part 6:1,21 7:17 9:1,13 11:14 15:5,16 21:9,12

28:13 44:18 48:15 57:26 60:11 67:7 69:20 71:17

79:23 128:18 130:13 132:12 133:6 136:8,10

140:14,17,17 142:10,22 144:21 152:17 153:9

156:6 165:12,18 167:20 168:2 188:21 197:5

201:26 209:11 223:17 229:22 233:18 234:8,12

240:15 243:26 246:13 276:12 283:17,18 290:4,19

314:10 320:4 333:4 337:11 341:11 350:4 351:26

359:4 363:9 366:16,22 373:5 378:23 383:9 384:7

392:12 397:9 402:7 410:13 413:4,13 414:17

417:21 418:5 423:12,17 424:10 425:22 439:19

446:23 451:5 456:9 458:10 484:2 515:24 526:17

531:3,21 538:20 541:24 557:4 577:24 580:7

590:16 598:24 619:25 627:9,11 629:4,12,20

639:12 640:12 657:3 664:12 681:6 682:6 687:18

689:13 692:9,15,20 693:20 694:3

partial 215:21 225:12 521:11

partially 221:24 222:1 516:22 675:4

participant 2:25

participants 21:22,24 228:22

participate 16:8,24 355:12

participated 256:17

participating 267:11 271:19 274:14 696:13

participation 7:24 14:17

particles 41:12,20 299:22 349:15 434:2

particular 2:3 3:23 4:7 12:4 26:16 36:10 43:21

49:20 50:13 91:7,20 109:8 114:1 134:14 136:2

148:16 150:22,26 151:9 152:26 153:21 165:1

207:18 220:16 222:16,26 232:10 253:4 276:11

278:8 279:3,12,22 282:22,23 283:1 284:3 297:23

321:21 325:8,9 333:25 334:10 338:8 342:8 345:6

345:18 346:20 355:22 359:23 360:14 363:2

366:23 367:18 368:15 370:3 381:23 399:22

401:13 402:13,23 410:21 411:11 421:15 442:17

444:11,24 445:22 449:4 451:15 461:20 472:10

486:16 493:23 512:3 526:8 540:6,10 544:23

548:21 549:11 553:16 561:17 601:25 605:18

608:8 610:10 615:23 622:18 623:2 624:15 629:16

630:3,19 636:13 642:8,14 666:5 667:17 669:7

670:23 672:11 678:15 679:7 689:15,20 694:8,9

particularly 41:21 137:7 139:17 151:9 193:3

199:13 217:19 238:3 260:15 272:9 273:5 284:15

295:11 317:6 358:13 364:2 387:26 398:23 401:7

411:14 431:3 437:24 442:13 467:18 475:1,5

516:6 517:25 606:4 613:13 625:17 657:7 668:9

680:23 681:13

particulars 359:21 644:2

parties 419:11

partitioning 120:21

partitions 347:21

partly 338:11 552:22

partners 10:15 21:13 25:19 31:24

parts 113:11 117:12,14,15 247:12 256:13 258:12

298:12 332:5,7 402:20 404:4 411:6,13 423:22

424:3 425:11 596:25 600:10 643:24 656:13

pass 14:23 103:25 142:18 214:24 217:6,9,14 218:3

272:22 273:1,10 282:14 287:21 427:17 610:19

649:17,17

passed 294:7

passes 165:9 166:16

passing 242:3 436:20,21

passive 125:16

patently 666:19

path 50:20 378:15 619:2

pathogens 299:2 471:11 505:3

pathologist 5:25

pathway 38:21 51:1 58:24 63:10,14 77:4 80:8,17

135:4 173:22 180:13 396:10 411:2 424:21 425:21

511:15,18 512:3 648:9 657:8

pathways 37:12 41:9 47:24 55:4,6,7,11,17 71:16

79:3 81:21 171:22 229:3 408:7,11,11 436:15,17

465:21 509:8 511:14 513:22 648:8 668:14 672:12

677:26

patience 687:2

pattern 57:8 144:20 251:24 255:22 283:24 569:15
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569:16,18

patterns 36:9 146:14,15 225:16 228:4 265:15

317:1 586:2

patties 172:4 174:14

paucity 395:2

Paul 5:4,4 231:10,19 232:5 250:3,3 349:22 366:4,5

372:12,26 373:11 392:20,20 397:15 401:19,23

402:3,13 404:3 405:26 686:25

pause 68:12 333:8

pay 304:11 308:10 310:4 311:4

paying 310:5

peak 19:12 169:23 227:2 234:17 545:14

peaks 235:14

peer 2:15 7:5 107:17 281:13 312:21 395:5,8 575:1

peer-reviewed 162:14

PEIP 23:8,10,12

Pellston 10:22 22:3,8 29:5 65:25 156:21 202:23

223:17 224:6 228:21 247:6 255:15 256:16 257:10

257:18 688:19

Pellston-style 64:15

pending 290:18,21

penetrants 606:16

penetration 394:16

Penn 5:22 250:17 304:10

Penncap-M 151:9

people 3:4 10:23 16:11 234:6,7 238:26 262:10

263:15 276:8 282:7 311:15 312:4 313:25 314:1,6

314:9 322:10,21 323:21 324:9,26 340:10 342:20

342:24 418:20 419:18,21 430:23,25 475:2 546:6

547:17 558:12,13 629:21 664:1 685:6 692:6

694:4,14

peptides 449:17

perceived 201:20

percent 19:26 20:3,6 69:22 79:24,24 96:15 102:5

102:15,16 109:17,24 135:5 157:8 158:3 178:15

186:13 187:25 191:19 208:2,5,7 217:7,13 234:23

260:8,9 262:3,7 263:1,3 286:5 297:15,16 306:24

307:2 317:14 320:15 321:11,16,22 332:12 337:20

337:20 375:8 377:12 389:20,21 395:12,26 425:19

425:19 438:13,14 471:1,1 477:9 482:15 494:15

499:18 500:10,17 501:5,7,13,17 502:13,15,20,24

503:1,9 548:7 552:13 554:3 599:11,19,20,23,24

600:23 601:13 608:18 610:7 612:4,6,6,18

percentage 79:20,22 186:2 320:26 321:13 600:1

601:9,15

percentages 447:15 503:2

percentile 90:15 98:11,16 110:16 121:11 134:5

261:7,9,12 398:13 399:16 494:25,25,25

perception 287:17

Percival 696:17

Perdue 6:3

perfect 514:18 534:25 605:11

perfection 12:22

perfectly 522:13 592:26

perform 312:11 472:9 667:8

performance 173:4,17 177:18 178:10 298:4

performed 176:3 226:15

performing 151:14

period 3:5,9 92:11 101:16,21 131:23 132:14

135:23 159:21 169:25 170:4 176:16,23 183:26

190:17,18 192:2,3,5 222:12 238:7,10 247:18

248:14 252:7 289:6 297:15 317:2,17 352:8

370:19 379:1 444:2 453:15 468:11 477:8,10

479:10 482:14 483:2 485:20,22 488:25 523:16

530:15,20,20 532:14 534:15 537:9,12 543:25

544:18,19,23,24 545:2,4,6,12 569:13 586:14,15

588:18 589:7,8 590:8 594:19 637:7 662:4 685:9

periods 52:9 173:24 176:21 428:2 488:23 523:14

589:19

permanent 87:21

permanently 197:25

permitted 208:9 216:4,8

persist 506:18

persistence 199:13 634:24 656:25

persistent 184:2 188:25 192:4 199:17

person 410:3 469:15 545:19 557:9 651:15 660:15

664:16 688:20,25

personal 263:21 363:9 557:18 573:25 574:24

588:24 688:17 690:3

personally 46:21 47:20 316:5 518:8 523:1 557:17

574:21 696:14

perspective 295:3,6 312:4 406:18 428:12 502:16

611:6 614:1

pertain 377:21

pest 13:24 14:13 15:2,25 32:15 191:23 200:17

213:13 317:21 318:6 319:17 320:1,9,17 566:1

587:7 589:15

pesticide 2:16 5:9,16 6:16 8:9,10 10:3,10,19 11:19

13:14,26 14:15 15:17,24 16:12,21,23 18:1,6,6

21:10,14 22:3,5 23:9 24:3 28:4,19 31:23 33:4,8

33:12 34:17 35:21 36:1,5,9,11,24 37:6,9,17,18,25

38:9,19 39:12,22,26 40:14,24 41:5,6,12,25 42:1,2

42:17 46:19 49:23 50:16 53:7 54:16,17 55:6,19

56:1,6 57:7,17,22 58:1 59:19 60:13,22,23,25 61:4

62:1,17 63:11,19 64:5 65:22 71:10 72:25 73:1

76:2 78:15,26 79:5,16 80:1,5 82:8 85:22 86:14

88:16 89:5,18,26 90:2,12 91:9,11,14 93:21 94:5

94:18,19,23,25 95:10,12,13 98:12 99:24 100:2,13

104:1,7,19 105:12,13 106:9,16,20,22,25 107:8

110:15,19 111:2 113:10,14,23 116:11,12,15
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117:3,4,12 118:1,3,16,21,25 119:5,9,15,22

120:22 122:26 123:25 124:9,22,24 125:19,21

126:7,15,22 127:2,11,14,25 128:5,19 129:8,13

132:5 137:19,20 140:9,26 142:14 143:13 144:1,6

144:13,14 145:22 146:25 147:2,5 148:5 149:10

149:26 156:24 157:15 158:12 162:9 163:3 165:1

165:25 166:26 167:6 168:20 169:21,26 170:2,9

171:7 172:22 174:3 183:5,16 188:13 189:19,24

193:22 200:16 206:25 210:12,13 213:11,24 221:4

221:15 223:20 224:15 226:9,16 227:12,15,21

228:4 229:2,8,13,15,18,23 232:6 237:7 246:7,12

250:7 254:14 263:8 268:3 279:2,4 282:24,25

283:1,26 284:12,25 299:9 300:2,6,22 301:5,18

323:12 326:14,25 327:11 329:12,13 332:3 335:11

350:2 351:19,21 352:9,10,18 358:12 360:15,16

360:17,18,20 362:24 370:24 376:2 382:25 385:19

387:26 389:8 390:24 392:16 396:2,19 397:11,12

400:5 401:11 402:15,19 404:12 405:21 406:6,11

407:25 409:21 410:24,26 415:26 417:22 418:18

419:26 422:22 424:14 425:18 426:17 429:26

430:3 431:4 432:24 452:18 454:6 455:8 465:16

466:2,11,16 467:2,9 468:11 470:18,20 473:4

474:6 477:24 482:16 484:4 485:15 486:15,17

487:9 525:19 563:9 568:1,5,9 572:17,17 578:17

589:17 592:5 593:18,20,25 596:13 600:14,24

624:11 625:17 656:18,23 665:17 666:26 678:1,3

692:3 695:26 696:8

pesticides 4:6 5:13 7:17,19 8:6 9:13 10:5,8,12 12:3

13:19,20 16:15,25 17:7 18:13,13,17 21:3,12,19

22:23 23:7 25:7,8 28:13,16 33:16,22,26 34:2 35:2

36:15 38:1,2,24 39:19,21,24 40:5,6,8,11 41:3,8

41:10,19 43:18 53:14,15,25 54:1 56:8 57:4,13

65:2 71:13 72:24 75:25 77:18 78:25 84:21 85:18

89:3,17 90:23 94:15 95:4,6 104:6 108:2,3 110:24

111:15,17 113:8 116:23,24 117:17 118:13 120:9

120:10 121:16 122:9 123:1,6,17,22 125:4,7,8,10

126:10,13 127:21,22 128:17,21 139:21 141:6,16

142:17,23 148:11,16 154:12 157:21 158:22,24

159:4 160:15,20 167:21 168:2 169:23 170:12,14

172:20,23 177:8 181:5 184:22 188:19,23,25

198:6 206:10,13 211:4 213:12 214:17 215:16,17

218:25 231:13 232:2,6,7 236:10 238:12,19,20

239:10 245:22,23 250:10,11 253:23 254:11

278:13,18 289:19,26 290:14 294:19 295:5 296:20

297:5,6,11,21,23 299:1 300:1,12,16 301:11,20,22

302:3 308:21 312:16 318:20 323:7,11 331:9

338:14 342:1,10 349:16,18 352:11,19 362:1,4,5

366:10,22 367:8,9 368:6,11 373:9,21 387:17

389:11,24 392:17 394:4 396:1 397:20,22,25

402:22 404:11 405:23 406:3,9 423:15 425:3,10

427:18 434:11,13,15 439:26 440:17,21 465:1,10

465:22 466:5,6,13,20 468:17 469:12 470:8

471:24 472:9,26 474:3,22,24 487:13 498:11

499:22 537:6 550:24 562:19 579:10 592:14

652:11 665:15,22 666:10 675:10 686:13

pesticide's 57:10 128:7 144:20 301:16

pesticide-contaminated 426:7

pesticide-free 335:11

Pesticide-intensive 299:10

pesticide-specific 141:21 143:7 144:18 227:26

pesticide-spiked 167:7 168:23

pests 184:21 191:22 316:25 317:16 319:21 320:15

363:5 565:23

Petelle 277:25,26 278:1 280:11,19

Peter 288:26,26

petition 292:15

Pettis 5:17,18 70:9,10 236:17,21,21 242:20,21

250:12,13 276:16,17,17 311:7,8 314:17,18,25

328:8,9,9 336:19,20 339:14,15 343:10,12 364:7,8

369:13,14 379:8,9,9 380:24,24 381:22,25,26

383:16,17 384:12,13 385:26 386:1,16,17 387:1

388:17,18 391:20,21,22 394:10,11,21 400:12,13

401:2 403:7,8 405:9,10 406:22,23 408:22,23

412:8,9 415:17 417:1,2 427:20,21 431:7,8 433:24

433:25 439:6,8 452:14,15 462:1,2 469:13,14,14

470:23,24 481:22,23 483:15,16 485:3,4 488:13

488:14 492:16,17 501:2,3 505:10,11 508:7,8

510:19,20,23 517:8,10 523:4,5 532:24,25 534:22

534:23,23 535:8 537:7,8 540:21,22 541:22,23

544:26 545:1 546:9,10 547:11 550:16,17 555:23

555:24 556:3 559:14,15 562:10,12,12 566:11,16

566:25,26 567:20,21 572:6 573:11,19,19 577:1,3

577:25 579:13,14 581:7,8 582:19 583:16,23,24

585:14 586:1,2 588:8,9 591:13,14 592:21,22

593:22,23 596:20,21 599:7,8 600:16,17,18

606:25,26 609:4,6 612:1,2 614:24,25 615:26

616:1 625:19 627:1 631:24 634:4,20 636:22

642:17 683:9 689:12 693:1

Pettis's 382:8 383:3 561:8 566:4 601:12

ph 14:10 673:6,8

phacelia 108:26 109:12,15 110:2 111:20 136:25

137:4,9 169:9 528:9 529:13 563:19 567:4 575:15

575:18 577:5 585:8,9 594:5 602:19,23,24 603:9

phase 18:18 26:1,2,3,4 27:8 34:7 46:22 48:1 57:9

59:24 73:16,19 74:18 75:18 76:21 204:9,9,11,16

204:22,23,24 206:19 209:4,5,15 229:6 479:11

607:9 673:11

phases 25:19 134:13 204:8,14 209:9

PhD 290:1

phenoloxidase 449:16
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phenomena 658:8,11

phenomenal 692:25

phenomenon 443:3 444:5 445:24 446:25 499:25

639:19

phenotype 448:13,14,26 449:3,19 451:19 460:6

514:4,26 522:12 531:14 532:4,17 536:7,11

605:23,24 606:17

phenotypes 460:25 515:16 536:19 540:9 553:13

605:26 606:4,6 615:20

phenotypic 514:16 532:2 630:25

phenylurea 120:10

phenylureas 398:20

pheromone 452:4,8,17 507:21

pheromones 452:5

phloem 125:19,22 277:13

phone 13:7,9 49:1

phonetic 287:21 681:20 682:3 683:7

phosphorus 420:12

photo 306:9

photographs 344:11

phrase 439:13

physical 57:10 119:14 123:25 126:12 135:1 396:11

400:11 558:22 683:24 684:5,20,25 685:1

physics 645:1 673:10

physiological 488:5 508:6,16

physiologically 445:2,4 455:26 487:12

physiologies 330:18

physiologist 137:18 376:16,17

physiology 197:18

phytochemical 468:21

phytochemicals 389:10 465:20 467:2,4

pick 434:2 457:11 635:9,10

picked 690:3

picking 263:12 644:14

pictorial 35:11

picture 168:13,20 245:19 304:17 309:16 421:11

pictures 309:9

piece 265:25 368:1

pieces 350:26 351:1,1 359:12 480:8 517:6

piecing 319:11

Pierre 277:24,26 278:1 280:11,19

pile 306:11

piles 365:14

piling 303:26

pin 405:8 448:9

pipe 640:13

PISTORIOUS 309:12,19

Pistorius 5:11,12 83:3,4 84:13,14 196:6,7,21

214:21,22 250:8,8 309:6,7,7 315:23,24 320:24,25

321:4 331:6,7 336:21,22 339:17,18 343:23,24

348:12,13,19,25 364:17,18 365:2,3,11,15 372:17

372:19 381:7,8 382:3 390:7,8,17,20 391:2 396:25

396:26 409:23 412:26 413:1 414:23,25,25 416:4

416:4 421:22,23,23 428:15,17 431:23,24 440:4,5

440:22,23 441:8 446:1,2,22 456:24,25 458:2,3

460:9,10 463:10,13 478:7,8 479:8,9 483:3,5

485:9,10 493:5,6 495:5,6 497:7,8 499:4,5,12

502:10,11,25,26 503:12 506:7,8 518:3,4 519:20

521:8,9 522:16,17,26 523:22,23 524:25 525:5,7

525:26 526:1,14 528:5,6 529:24 530:8,12 533:24

533:26 534:1 535:6,7 538:7,8 540:23 542:3,5

546:20,21 556:21,22 566:17,18 574:13,14 580:3

580:4 581:10,17,18 584:25 585:1,5 592:1,2

597:23,24 602:17,18 607:2,3 610:15,16 618:1,2

623:21,22 627:22 629:18,19 632:1,2 634:19

637:24 658:14 659:2,6 660:14 661:26 662:7

675:15 681:18 682:26 687:24

pitfall 650:22

pitfalls 262:23 562:15

pivot 578:21

place 45:25 47:1 64:7 151:6 170:24 197:5 234:21

256:9 291:16 302:13,15 304:8,18 350:22 356:16

366:12 451:20 493:2 511:7 515:18 519:19 524:17

540:17 552:10 572:7 661:23 678:3

placed 148:14 169:16 171:4,13 172:22 180:11

183:24 251:21 301:1 303:6 458:20 585:13

placeholders 54:26

placement 182:23

places 19:22 234:19 301:13 302:12 354:18 430:12

460:25 515:25 564:26 566:6 578:15,19 596:16

601:3 626:23 683:26 684:3

placing 161:8 281:2 313:16 355:21,24 443:24

568:23,26 571:22

plan 27:14,15 34:9 36:14 51:13

planes 322:23

planet 253:8

planned 669:1

planning 26:8,12 48:1 137:25 197:4 333:2 551:12

plans 50:3

plant 22:18,25 23:21 29:12 37:7 38:6 39:22 40:9

40:12 41:7,15 55:11 73:2 75:5 78:24 106:21

107:4,6,9 113:11 115:15 116:18 117:3,5,12,14

118:25 119:16 120:3,5,15 121:7 123:26 124:8,9

124:21,26 128:6,8,20 137:5 142:1,4 148:17 149:7

149:8,12 258:1 268:23 276:20,24 277:10 299:9

299:17 300:7 317:7 330:16 338:12 340:6 352:7

362:12,12,17 376:3 397:12 398:20 399:8 400:1

400:21,21 402:17,20 404:22,26 405:2 406:4,6,9

411:5,13 412:19 419:20 527:25 529:19 571:4

596:24
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plantars 331:21

planted 299:11 305:20 317:14 332:8 348:16 349:2

349:3,24 593:25

planter 416:20 417:25

planters 318:21 410:5,7 416:18 417:19

planting 40:16 41:22 63:15 291:13,19 292:25

293:3 300:7,22 304:25 316:26 317:1 318:2,10

411:15 414:15 417:7 418:11 419:15 597:3 600:21

plantings 597:18

plants 6:13 37:2 41:5,21,21 44:8 84:25,26 91:15

113:11,12,15,24 114:11,17 115:2,7,12 117:8,16

120:9,23 123:18 124:20 125:1,11,19,22 126:7

142:26 147:24 148:19,23 217:20,23 257:24

276:26 277:5 286:26 299:20 300:5,8 330:18

331:13 351:26 352:7 362:18 400:22,23 404:11,16

416:14 436:19 528:1,3 564:15

plant's 120:6

plastic 443:26,26

plateau 233:4,5,8,19,19

plateaued 236:16

plateaus 235:14

plates 159:19

plausible 292:26

play 10:5 18:13 49:19 68:7 151:6 246:13 247:11

280:14 330:19 417:23 418:24 560:15 589:13

596:8 678:20

played 279:14 294:25 484:24

playing 9:18 238:13,21 289:24

plays 139:6 429:22

plea 334:9

please 3:9,13 13:8 49:1,7 53:3 69:13,13 88:25

101:10 102:23 247:26 248:7,11,17,19,22,24,24

248:25 249:1,3 251:13,16,26 252:13,15 289:17

292:3 296:18 308:26 309:1,1,2 333:16,21 334:13

337:5,11 341:4,6,12 345:13 347:12 357:16,18

358:8,12 365:22 373:5 379:1,3 384:7 388:12

392:12 397:9 402:7 403:17,24,26 405:18,20

407:23 409:9 422:18,20 423:2 432:21 435:19

441:21 446:12,16 464:24 471:24 472:21,23

474:13,15 477:3 480:24 482:6,25 483:26 484:3

491:10 493:14 499:18 503:23 507:11 509:3

512:22 513:7 519:22 530:26 531:2 535:23 539:8

548:1 562:8 567:17 585:21,23 588:2 593:17

604:22 608:2 614:12 615:5 623:1 624:8 626:8

630:14 643:6,17 667:7 669:21 670:20,23 676:18

676:20 688:8,12

pleased 14:12 66:13 289:13 374:23 496:24

pleasure 693:20 695:16

plenty 16:10 571:20

plot 182:24 185:21 187:16,20,26 188:3 189:11,12

189:14 192:15 268:3 563:26 564:2,4,14 565:23

567:24 568:6,8,12 569:5 570:11 573:11,15,17,18

573:19,21,22 574:2,9 577:13,14 578:1,5 579:8,19

579:21,23,24,25 580:15,25,25 581:2,9,11 582:5,6

582:8,9,12,13,14,20,21 583:4,5,6,15,22 590:4

594:16,16 596:12,17 597:9 598:4,22

plots 183:25 188:24 189:9,11,13,15 190:9,21,23

191:2 193:8 269:5,5,6 564:11,13,17,19 567:7

568:15 570:4,12,12,13 573:13,24 574:12 577:7

577:15,24 579:20,22 580:21,22 582:21 584:3

589:21,21 596:24 597:4 600:25

plot's 581:5

plug 550:9 648:13,18 678:25 679:1

plug-in 651:5

plus 95:17 122:23 213:15 544:19

PMRA 9:19 12:25 23:9 25:4,19 105:21 128:26

253:10 272:4 279:3,14 280:1 696:7

PM10 410:21 420:14,15,18

pneumatics 417:20

pods 113:12,24 114:20 115:5,9

point 3:19,25 6:15 24:5 36:1 45:9 51:10 52:6,12

65:11 66:21 68:24 69:7 70:10 83:22 88:8 107:25

130:15 131:8 132:10 135:7 150:13 161:23 162:20

163:1 195:21 205:2,6,11,19,20,24 206:8 218:13

221:21 224:11 237:19 248:7,10,21 251:12 257:23

257:25 258:22 259:11 260:1 261:15 267:26 269:4

269:20 283:7,23 284:2,4 290:25 293:10 295:14

295:17,21 296:2,3,23 303:2,4,23 304:5,9,25

305:25 309:26 314:14 319:16,23 320:11 322:8

325:2 328:1 330:11 331:5 345:25 347:6,14 348:8

353:3 369:3 371:22 374:3 378:12 386:9 387:1

392:24 393:16,20 395:25 397:16 421:15 430:9

432:17 441:17 444:23,23 447:1,13,15 448:6

449:21 450:14 451:14 454:1 460:13 461:15

463:22 466:19 469:16 472:17 486:1 488:5 495:2

495:3 497:23 500:14 501:6 502:12,21 505:19

513:17 517:18 518:11 529:25 537:3 541:23 545:6

546:23 547:21 549:24 552:15 559:5,17,20 563:2

572:6 574:15 576:2,12 581:9 596:21 601:12

602:14 608:16,19,22 609:17 612:20 616:20

617:20 618:23 620:26 621:25 622:14 624:3,15

630:5 633:16 636:26 637:26 640:18 641:3,5

649:3,12 658:22 660:10 666:8 670:10 671:19

673:15 679:4 681:23 682:5,9 683:26 685:8

687:12 688:6 691:26 693:1

pointed 70:23,24 139:7 153:4 195:17 204:13

219:20 229:5 234:16 236:22 240:2 278:20 284:22

330:5 384:25 453:1,13 454:23 568:12

pointing 219:23

points 35:22 55:26 149:3 173:25 175:4 188:7 199:3
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271:25 301:15 369:9 374:6 379:15 401:2 430:2

442:16 460:23 465:26 470:6 536:16 549:18 595:5

605:4 618:9,10,23 660:25 670:11 687:26

poison 5:14 130:11

poisoning 250:10 628:21

policies 693:24

policy 206:21 213:16 237:23 252:19 261:8 316:14

421:14

policy-driven 282:4

politics 640:19

poll 215:4

pollen 25:6 37:8 38:2,15,25 39:25 40:12,26 41:6,18

44:9 45:3,4,13,14 55:10 60:23,25 73:1,5,10,13

74:1,5,15 75:6,12,14 76:1,13,15 78:7 80:22 90:2

90:5 93:1,2,3,13 94:11 95:2,12,15,17,20 96:5,16

96:23 98:13,23 99:2 100:14,15,22 101:19 102:1,2

102:12 103:1,6,7,9 104:14 105:9,13,15 106:17,23

106:26 107:3,20 108:12 113:19 114:24 115:1,3,4

115:6,11,18,23 116:11,16,18,22,25,26 117:3,8,18

118:2,3,14,20 119:6,8,16,22 121:15 122:7,26

123:7,17 124:1,10,14 126:15 127:4,12,23,26

128:5,9,18,21 129:8,13 131:21 134:10 135:19

137:3,8,13,14,20,21 138:8,12,16,19 141:4 142:14

142:17 143:13 144:14,15 145:4,23,26 146:8,23

146:26 147:19,20,20,23 148:21,22 149:1,2,8,15

151:4 161:13 168:20 169:14,16 170:19 172:4,12

172:26 173:1 174:3,8,12,14,17,22 177:4 186:1,2

186:3,5,12,13,14,14,17 191:15,15,17,20 201:11

201:22 202:20 206:6 258:25 259:24 260:9,15,20

276:19,22,25 277:3,4,21 299:10,17 301:4 305:14

305:14,16 308:5,7 313:16 326:25 327:19,23

328:11,11,13,16,17,18,20 329:9,14,18,24 330:4,6

330:21 332:3,3,5,6,6,11,13 337:8 338:8,10 348:2

348:5,15 349:1,5 353:8,11 360:9,11,24,24 361:10

361:10,13,15,18,21,22,23 370:23,25 371:1

378:26 379:2,18,24 380:9 381:18 382:22 384:19

384:19 385:3,15,18,19,24 386:3,5 387:4,9,12

388:13,19 389:3 391:15,26 392:1,16,24 393:16

393:25 394:2 395:3,14 396:20 397:13 399:5

400:16,18 402:10,16 405:22 406:4 411:16 412:6

412:19,24 416:16 439:4 465:5 466:2,5 488:16

519:25 520:2,4,8 521:6,6 523:13 524:7,14,20

525:11,19,22 528:2,14,21,22,24,25 529:19,22

530:2,19 534:9,10,25 535:1,9,16 537:5,10,26

539:1 541:1,4 543:8 545:14 556:10 560:13 573:4

575:24 576:25 577:9,10 579:16 594:10,19,22

597:7 599:11,16,17,19,24 600:13,13,23 601:15

601:16,18,19 602:10,21,21,22,23 603:2 636:24

pollinate 17:15 347:15

pollinated 340:7 689:18

pollinating 5:26 250:20 300:2 340:8 363:21 423:19

559:19

pollination 9:5 17:13 29:7,14 239:4,5 253:6 269:2

278:15 286:12,24 297:18 301:7 303:6 326:8

334:23 340:6 347:14 357:20 516:10 527:24 529:6

558:24 559:8,9,18,19 561:9 574:11 582:26

pollinator 2:3 3:24 8:24 9:8,10 10:2,5,6,9 15:15

18:9,14,26 19:3 20:21,22,26 21:9,12 23:11,19

29:9 39:6 137:7,10 163:19 169:8 182:5,7,8,21

188:6 210:14 233:4,10,14,26 234:1,14 235:12,19

235:22 236:4,11 238:13,15 244:12 245:19 251:8

253:3,10,16,17 254:12,16 271:7 272:1,4,5 274:19

278:22 282:10 283:2,5 285:13 286:16 287:26

290:12,20 297:18 300:20 301:26 335:14 340:5

357:22 396:16 572:19,20 622:24 649:5 666:11

pollinators 8:6 9:3,15 11:16 12:3,15,24 13:17,22

14:3 17:8 18:13 19:2 21:10,15 22:4 23:8,9,14

24:15 70:12 132:21 133:5 168:4 235:17 237:25

238:3 245:24 253:7,26 257:9 272:15 276:11

278:12,18 287:3 297:12,20 301:23 312:9 314:20

316:23 318:23,26 336:1,1,2 343:16 373:9,14

376:26 377:1,3 442:10

pollinator-dependent 239:6

pollution 308:18

polyethism 481:6 505:17

pond 82:13 424:17

ponds 78:18 82:5

pool 15:8

poor 21:2 292:11 560:3 565:24,25

poorly 295:12 336:11 642:8

pop 683:7,7

Poppins 542:17

populated 448:19

population 52:9 221:8,22 225:1 227:19 232:18

233:4,10 268:25 271:1,16,18 333:17 335:2,3,4

340:20,21 445:15 461:13 536:6 540:5 555:21

556:13 561:2 581:22 586:3 611:12,15 637:6

645:15,16,17 646:3,8 649:5,6,23 650:1 653:7,11

653:15,19 654:5,7,8,13,13 655:10,11,16 666:12

670:25 671:23 677:20,21 682:16 690:8

populations 10:2,6 39:13 188:8 195:7 318:14

335:5 445:12 501:23 595:9 631:22 650:12

population-level 655:19 658:9

populus 350:14

portal 326:16

portion 34:19,22 39:25 40:1 47:10 79:11 143:2

208:21 293:20 294:9 506:13 520:7 576:10

portions 94:2 117:5 321:4 679:13

portrayed 84:4

ports 654:21
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pose 90:23 215:16 272:14 274:3 300:14

posed 17:7 54:2 285:5 517:23

posing 666:26

position 84:22 85:2 184:8 242:6 267:12 278:10

640:21

positions 404:10

positive 184:22 411:21,24 440:1 515:17 564:17

positives 268:6 272:14 275:8 527:16 580:21

possibilities 68:19 359:10 547:1 591:18

possibility 85:22 86:3 171:2 343:25 344:11 350:12

370:12 453:12 504:26 506:25 521:18 533:15

546:22 549:8 675:25

possible 14:22,23 15:19 37:22 38:14,20 41:14

83:12,20 84:16,18 115:17 125:20 128:19 141:18

146:12 149:20 171:18 184:7 185:10 191:1 196:7

196:11 199:5 209:17 214:8,11 217:18 219:3

220:6 232:19 238:22 248:7,18 267:11 304:14

315:19 331:14 341:26 348:6 367:4 372:1 393:13

398:5,6 440:19 453:7 465:11 471:11 472:6

481:20 491:11 523:6 524:12 529:25 532:9 533:13

540:13 563:20 564:18,26 566:1 569:25 575:21

579:26 588:10,23 589:24 593:2 603:12 629:21

634:13 635:12 636:5,17 638:2 657:12 680:26

possibly 198:3 222:12 340:18 376:1 472:4 500:19

531:16 540:19 541:17 542:23 571:3 650:1

post 176:22 265:20 290:23

post-exposure 191:18

potential 10:8 12:3 13:17 14:3 16:15,25 18:12,16

20:20 21:19 22:14 23:25 24:3,14 25:5,8 27:25

30:3 31:5,13,22 40:25 47:6 54:2,3 55:26 57:17,23

61:12 62:19,20,22 63:16 70:22 71:16 72:19,22

73:21 74:18,21,25 75:9,17 76:11,25 78:12 79:1

80:23 84:6 91:1,11,13,18 94:12 119:15 123:26

125:11 127:19 129:5 135:2 140:19 143:9,24

144:22 149:25 150:6 160:1,14 164:6 165:9 166:7

166:13,22 167:24,25 171:26 172:19,21 174:21

178:7 181:24 185:22 186:21,23 187:18,25 189:24

190:5 198:20 201:2 203:16,22 204:19 205:12

206:21,25 210:8 211:12 213:23 214:8,16,17

216:18 219:23 221:3 223:20 224:15 229:18

233:12 268:18,22 270:9 276:8 297:21 312:8

316:23 317:25 318:15 331:24 344:22,26 358:11

358:17 368:13 373:5 375:7 376:14 388:14 396:3

408:12 410:15 411:8 414:7 423:14,23 424:5,12

424:19 426:11 431:26 433:9 437:19 438:8,10

440:1 441:3 443:12 448:16 449:9 451:4 454:4

455:19 465:22 482:7 499:20 505:14 512:2 529:13

532:5,6,26 536:26 562:14,15 565:1 569:9,10

571:6 581:2 591:24 606:1 619:2 658:9,20 659:14

668:22 670:22 676:22

potentially 46:18 47:1 72:26 73:9 78:5,15 81:19

82:17 86:26 92:13 117:23 124:20 130:14 132:4

142:8 145:17 146:21 157:4 159:23 164:8 177:22

186:17 192:22 195:26 201:5,14 215:18 220:17

223:11 229:3 235:5 246:19 326:6 347:11 389:4

410:17 418:22 425:20 438:3 505:5 513:7 530:8

537:3 657:16 675:20

Potter 5:6,7 42:15,16,23 43:3,10,26 67:11,12 85:6

85:7 86:12 87:6,26 88:7 134:25,26 135:17 136:15

136:19 151:21,22,26 200:1,7,20,21 201:17,19

202:22 243:14,15,15 250:5,5 275:26 276:1

319:13,15 326:22,23 327:16,26 330:5 349:10,11

367:24,25 374:4,5 376:10 377:8,9 378:5,6 394:19

394:20 400:26 401:1 403:9,10 405:12,13 407:1,2

408:2,4,9,15 409:11,12,19 415:14,22,23 416:19

417:9,9 419:6 422:3 423:5,6 426:19 427:6,9,9

432:4,12,12 433:1,2,13 434:6 437:4,7,9,14

506:22,23,23 564:24,25 565:15,20 567:9,12,13

570:26 571:1 587:20,21 590:26 591:1,15,21,26

592:2 595:2,3 600:7,8 601:4,6 623:4,6 655:25,26

664:21 675:8 677:3 680:14 687:13

Potter's 87:25

pound 108:19 109:20 110:9 114:14 115:22,26

126:20,24 272:18,23,26 273:3,13 365:25 373:7

374:17 377:23 393:22,26

pounds 108:21 123:13 272:24

pound-per-acre 273:11

power 152:20 191:5,7,11,12 248:7,10 251:12

287:16 291:17,20 313:19 331:8 531:8 548:22

549:22 550:18 551:7 554:16 569:2,5,7,7 571:13

630:22 635:24 636:14,19,21,24 638:16 639:11,16

640:1 641:7

powerful 606:9

PPM 258:7

practical 254:12 257:9,11 286:18 291:20 446:18

453:8 479:14 499:1 525:1 627:25 629:8 639:22

639:25,25

practicality 498:24 577:15

practically 157:21

practice 116:4 133:11 188:21 193:11 313:2,15

315:17,20 569:4 579:8,9 640:18 650:24

practices 9:13 63:15 77:12,14 83:23 145:2 148:10

169:12 183:22 253:21 254:1,15 274:18 276:2

280:7,14 297:9 300:22 312:26 317:20 318:18

319:18 331:25 410:10 414:15 418:25 448:10

479:17 545:18 588:6,11 598:23

practitioners 239:18

Prallethrin 322:22 323:15,17

precedence 284:9

precedents 467:19
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precedes 409:4

preceding 61:18 204:6 211:10

precise 68:22 69:18 680:8

precision 69:5

preclude 190:22

precocious 499:26

predators 648:12

predict 83:19 225:3 263:18 266:13 276:18,20

582:25 616:14 641:13

predictability 272:2

predictable 314:23 666:15

predicted 106:6 125:13 227:2,5 398:11 399:14

predicting 63:11 231:24

prediction 83:20 259:8 647:9

predictions 121:18 122:12 463:3 606:11 647:4

672:5

predictive 394:14 658:5

predictor 569:20

predicts 120:4

predominant 157:19

predominantly 309:16 310:26 320:16

predominately 46:9 110:26

preface 513:15

prefer 583:10

preferable 468:25 497:17 581:14

preferably 90:18 248:9

preference 263:12 497:15

preferred 286:7 373:14 589:1 638:17,22 670:6

preliminary 70:24 80:13 218:7,23

premature 449:20 450:15

premium 450:13 460:25

preparation 14:20 589:4

prepare 14:9 252:23

prepared 8:8 14:7

preparing 77:25 276:12 436:9

prescribe 331:15

prescribing 331:11

prescription 255:21

prescriptive 246:17

presence 14:17 79:13 91:14 149:3 194:2 339:23

554:15 588:6

present 14:2 34:3 42:12 43:22 44:14 79:11 86:26

89:2 91:16 94:22 97:11 140:16 148:24 154:14

177:8 212:16 219:15 234:13 252:3 292:19 294:6

297:4 298:6,11 343:8 344:8,14 409:21 437:6

449:25 453:5 477:20 482:21 510:1 546:5 578:6

582:17

presentation 14:21 17:26 18:8,23 24:18 32:6,26

33:11,15 37:15 38:8,22 46:4,25 47:4 48:13 53:5

60:3 66:16 71:20 72:2 76:8 88:25 89:10,20 114:5

128:24 140:2,5,17 141:7 148:3 150:11 151:12

154:8,24 159:13 178:5 187:4 194:21 200:26

203:4,12 205:10 210:22 212:20 214:12,18 215:2

215:12 219:7,9,20 220:22 230:2 232:17,26

234:16,22 238:14 248:7,10 251:13,14 252:13

413:9 435:20 611:3,4 693:21

presentations 3:17 13:2,15 14:8,18 25:13 26:5,19

27:14 30:21,24 33:10 49:2,4 53:10 70:24 155:13

204:7 211:10 215:6,14 222:16 247:26 325:6,7

353:6

presented 36:4 37:15 41:25 42:21 48:10 53:10

55:20 64:8,10 71:17,23 75:20 78:13 79:5,26

82:20 88:15 89:14 91:5 97:14 99:9,11,12 128:24

140:14 141:7,25 143:5 146:24 148:3 156:16

215:11,14,15,26 216:26 251:22 272:16 282:11,12

351:11 379:10 392:13 397:10 402:8 413:10

567:18 678:6,13

presenter 277:24 280:20 285:10 288:20 311:23

316:11

presenting 8:19 34:5,15,23 35:20,21 45:11 69:19

71:22 82:23 254:26 255:1

presents 189:22 191:23 481:4

preservation 295:6

preserving 295:6 340:6

president 16:5 278:1 285:18

press 324:22 693:3

pressed 410:4

pressure 220:22 317:23 319:17 320:1

pressures 317:21 320:9

presumably 70:19,22 71:1 133:26 137:10 195:8

198:18 239:20 284:14 323:3 389:23 501:26 637:6

presume 60:7 133:18

presumed 40:15 43:21 137:11

presumes 43:4

presumption 43:1 57:23 59:22 61:13 62:10,10,21

63:25 165:10 198:14 201:16

pretend 417:21

pretreated 320:16

pretty 82:24 261:25 273:21 306:25 328:19 371:23

397:24 438:16 448:12 449:14 455:4 472:17 533:5

537:18 538:12 546:19 554:3 604:6 605:11 614:20

615:12,14 634:16 693:7

prevent 168:16 187:15 321:9 645:17

preventing 79:14

prevention 23:2

previous 155:12,24 158:25 179:23 181:23 182:1

183:4 213:11 218:2 219:6 221:3 257:19 295:15

332:2 342:16 481:20 486:5 512:25 531:22 605:7

614:21 639:9 658:2 681:16,22

previously 39:21 40:4,23 41:2 55:20 63:18,23 72:8
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100:19 122:16 140:21 161:19 166:18 173:2

175:19 187:11 190:2 192:13 193:18 218:1 278:12

630:20

pre-emergence 43:20

pre-emergent 43:5

pre-exposure 191:17

pre-pupation 692:1

prices 236:25

primacy 325:22

primarily 4:25 38:1,26 43:18 63:10 73:25 93:6

99:22 165:3 181:11 195:25 245:9 411:13 426:24

427:22 441:22 505:18

primary 8:6 10:3 21:1 31:4 37:20,24 40:14 72:4

76:3 77:22 79:8 80:19 97:26 161:17 183:19

239:21 361:9 368:3 396:10 407:26 408:24 417:4

572:18 604:24 621:10

prime 16:5 286:14

primitively 516:12

principally 478:12

principle 400:16 536:12 607:12

principles 359:11

prior 3:7,12 19:25 40:16 74:14 161:24 183:6 186:8

187:9 248:16 269:21 290:9 523:10 568:26 595:7

priori 63:11

prioritize 556:6 630:14 632:3 639:4

priority 303:18 422:14 460:26 477:23 509:18

631:1 692:4

prism 88:14 121:4

prison 313:26

pristine 589:23

private 312:2

PRMA 16:23 292:13

proactive 274:16

probabilistic 494:24 495:4 496:24

probability 205:8 207:16 318:3

probably 66:16 134:3 234:4 259:8 261:18,24

263:15 277:16 278:3 305:4 307:2 313:19,26

319:19,21 322:19,24,25 329:9 334:15 335:5,10

336:9 337:20 344:20 346:7,18,20 347:14 349:9

350:21 358:26 360:26,26 361:10,21,22,24 362:4

362:10 363:7,13 364:19 367:10 371:11 372:4

373:23 377:14 385:15 410:21 412:12,24 419:1

420:11,14,17 428:7,9 434:7 437:8 449:23 452:17

456:7 468:26 470:24 488:9 501:24 516:16,23

522:12 524:26 532:14 533:14 545:21 546:8 548:8

561:22 563:17,24 578:10 581:11 582:23 583:2

584:17 590:22 591:8 594:19 596:8 601:13 602:4

602:26 603:12 610:10 636:1,8 638:23 645:2

647:6 650:17 656:15 659:3 683:8 684:24 687:15

689:22 694:12

probe 237:6

probit 207:2,7,9,15

problem 10:1 18:18 25:20 26:2 27:7,16 34:7 57:8

88:20 134:23 198:24 199:23 204:9,16 229:6

239:3 253:19 256:14 275:10 276:15 281:11,21

282:17,20,20 283:6,19,22,25 285:5 302:20 306:1

307:10 352:11 378:9 395:17 399:6 403:2 404:22

411:14 413:18 431:25,26 496:19 497:9 502:22

515:4 544:16 554:2 566:24 598:7 600:5 630:9

638:21 650:18 656:2,7,10,13 657:3,12,23 683:10

problematic 416:6 649:12

problems 274:26 307:8,9,11 314:12 319:21 320:17

344:9 448:7,8 475:1 477:7 517:7 527:21 533:18

558:22 570:11 618:15 658:9 665:3 688:14

proboscis 450:19 451:15 460:7

procedure 262:18

procedures 254:18 278:25 313:9 480:25

proceed 26:25 59:15 61:21 82:21 88:22 173:13

184:5 273:26 479:4 660:12

proceeding 60:14 657:24

proceedings 428:20

proceeds 204:23

process 7:21 12:10,19 13:16,20 18:16,18,22 24:2

25:17,17,18,23,25 26:6,9,13,20 27:4,17 31:14

32:10 33:7 38:12 45:14 48:16 50:2,5,23 51:3,14

51:24 52:11 53:18,26 54:5,6 55:17 56:11,16 57:6

58:3 59:7 60:8,15 61:19 63:10 64:20 65:11,15

66:14,16 67:16 76:6 81:17,22 84:6,10 130:6,25

136:5,8 137:2 139:9,10,11,14 140:22 143:15

144:21 152:9 153:9,22 154:17 155:2,21,25

163:23 165:12,18 175:7 185:3 197:5 199:17,19

200:23 201:26 202:2,26 203:13,25 204:4,8,18,23

205:16,26 208:20 209:16 211:11 212:22 214:14

214:19,24 219:11 240:19,20 253:12 254:21 255:4

267:11 274:14 278:24 279:10,15 280:1,2,12

282:5 289:15,15,21,22 290:4,19 294:18 300:17

318:25 323:23 327:1 386:11 391:7,8,13 395:23

396:4 399:2 406:26 414:12 436:11 438:19 453:2

509:11 534:13 536:22 537:17 545:7 567:11

607:25 618:25,26 622:26 629:6 643:18 647:19,21

653:19 654:8,20 657:21 659:11,15 660:22 661:6

663:10 664:26 665:18 681:14 686:19 687:21

696:9,13

processed 31:22 38:25 45:4 47:1 74:14,15 75:13

93:4 361:8 378:24 388:16,20,21,22 391:23

467:11 487:20 488:15 537:11

processes 23:23 39:10 46:18,22 77:13 130:1,2

135:10 221:23 222:8 225:6 226:5,10 227:17

254:5,22 272:5 279:6,13 280:13,17 648:3

processing 38:14,16 46:13 74:1 75:12 145:25
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160:9 186:8 355:12 369:16 389:8,17

produce 27:9 93:13,26 95:8 148:23 174:16 188:9

230:18,19 234:6 267:13 268:4,6 272:14 312:15

497:18 569:3 678:18

produced 93:7 267:20 300:7 313:14 380:6,9 528:2

producer 235:19

producers 188:9

produces 148:21,22 157:7 158:3

producing 77:10 84:23 234:11 383:14 529:19

567:2 594:12

product 17:19 91:9 152:15 183:12 220:12 265:18

276:5 286:13 301:18 307:22 317:13,15 320:19,20

321:21 331:20 357:21 394:15,15 417:22 421:5

529:9,10,14 560:5,6,6 590:2 594:4 597:12 668:26

production 8:25 29:8 38:17,21 227:23 234:2,8,12

234:20 235:22 236:7,11 245:26 253:15 282:13

301:7 302:16 326:9 335:7 338:5 343:18 357:21

358:24 452:4,17 471:2 556:8,14,17 557:17,20

559:12 572:15 586:3 681:26 686:23

productive 19:3 73:12 448:18

productively 474:26 481:12

products 8:26 10:10 15:25 23:21 27:10 29:8,12,14

75:14 138:24 148:1 151:9 188:20 189:1 206:14

227:23 245:26 254:15 262:12 268:23 276:8 279:8

285:19 286:6,23 288:7 293:7 307:18,20 308:1,20

312:12 317:3,10 318:4 321:19 333:18 335:7,8,9

338:6,7,9 339:11 342:13 388:24 390:4 419:3

558:3 559:12 565:17 569:23 590:5 598:8 610:17

Profession 251:2

professional 196:1 552:8

professor 4:4,17,20 6:10 249:13

profound 245:7 548:15 558:10

profoundly 558:26 642:13

program 10:3 211:4,21 246:7 287:21 288:3,5

304:11 322:12 324:11 647:6

programs 6:16 13:14 18:6 21:10 33:4 71:10 77:5

89:5 140:9 259:14 274:17,18 287:20 316:19

656:18 695:26

progress 283:11 681:12 689:10

progressing 256:4

progression 165:13 385:4

progressively 155:25

prohibiting 291:25

project 396:17 418:23 684:2

projecting 420:16

projection 404:16

projections 226:25 684:20

projects 279:5

prolong 245:6

prolonged 25:1

prolonging 691:19

prominent 65:3 175:13

promote 228:9 254:1 465:19

promoting 297:9

promotion 278:22

prone 626:13,16 642:14

pronounce 373:23

propagate 47:12

propagated 401:12

propagates 619:16

propagation 165:23 340:6

proper 346:12 515:19 522:8 616:21 628:2,3,19

633:25 636:19

properly 185:14 193:20 222:9 269:9 517:13

562:18 630:7

properties 57:10 117:20 119:15 121:2,26 122:18

123:25 126:12 135:2 185:12 277:13,17 367:9

396:11 400:11 472:9 634:24

property 608:17

prophylaxis 616:7

propolis 38:17,25 73:2 74:7 75:15 76:13 338:8,10

350:13,15 352:17,19 353:20 354:6,13,20 355:8

355:21 356:9,17

proponent 350:21

proportion 46:1 501:24 573:22 602:11

proposal 15:8 17:21 113:5 191:8 272:6 334:5

428:17 509:3

propose 58:17 111:4 409:13 468:19,23 523:1

613:18,19

proposed 13:16,20 16:14 18:22 21:14 29:6 30:25

31:2,4,14,22,25 33:15 42:8,12 48:6 53:6,8,11,14

53:18,26 54:10,21 55:3,18 57:3 58:3,24 59:9,10

59:11 62:14 64:2,8 65:15 66:3 67:13 70:16 72:3

80:6,18,20 81:1,2,17,22,25 89:21,25 90:6 93:17

93:22 94:1,10 95:16 97:22 98:5,15,20 99:14,16

99:17 100:1,6,12,25 101:3,7 102:9,18 104:21,24

105:3,8,15,17,22 106:8,11,18,23 107:1,5,11

108:11,15 109:5,19,26 110:2,4,10 111:7,24

112:13 113:2,17 115:17 117:6 118:18 120:2

122:25 123:3 125:16 126:4,14,17,21 127:1,6,9,16

127:23 128:4,13,14,16,23 129:2,7,17,18 130:7

138:7 141:9 143:2 156:16 158:19 160:18 163:6

163:10 192:6 201:24 203:16 205:15 206:8 208:12

208:19 210:14 214:13,23 215:25 217:2 218:13

223:6 227:22 235:4 257:15 258:7 259:20 270:20

272:12 279:20 289:15,21 290:6 291:1 292:7

293:24 294:24 330:21 365:23 376:12 377:6,11,22

379:1 403:19 436:11 446:15 466:1 467:15 477:13

480:3 484:3 491:19 504:19 508:16 567:17 568:3

585:25 613:16 620:5,6,7,12 624:9 635:2 641:4
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643:18 650:21 652:18 667:8

proposes 50:3

proposing 33:12 54:4 63:9 95:10 152:4 154:1

195:6 200:24 201:8 260:19 378:2,3 417:4

props 148:7

protect 28:12 29:15 271:3 289:25 297:7 308:17

316:25 317:9 685:20 692:4,13

protected 29:21 119:1 165:11 188:18 297:20

308:19 393:22

protectin 252:20

protecting 30:18 269:2 301:7 559:11

protection 4:15 18:19,24 22:22,26 23:12,19,21

28:8,11,20,22,24,25 29:2,3,5,6,7,8,12,16,18

30:11,12,15,16,17 31:26 32:1 39:6 107:5 155:3,4

174:26 178:1,16 210:9 253:5,14 254:19 256:15

266:3 267:15 268:16,23 269:2 270:12,13 278:2

278:15,21 279:18 285:14,19 286:5,12,21 301:6

301:26 333:20,22 334:19,21 335:17,17,25 337:19

340:4 341:20 343:13,15 345:15 357:19,20,21,22

364:14 419:20 536:1 556:24,25,25 558:3 559:6,7

559:16,17,21 563:11 585:26 586:5,5 689:15,15

689:20

protective 18:22 76:15 78:9 95:3 101:9 105:5

106:7 137:12 290:24 367:1 403:3 557:13

protein 161:12 331:2 385:5 523:7,11

protocol 147:4 191:8 301:25 483:9

protocols 21:21,23,24,25 55:2 58:22 141:12 142:24

161:1 175:17 183:6 267:9 268:13 275:16 313:6

346:12 446:14 476:11

protracted 198:18

prove 628:1

proven 55:14 200:12 628:18 640:24

provide 9:5 17:21 18:15,17 28:14,23 33:19 55:16

61:3 72:15 130:16 140:26 144:5 145:16 146:21

146:22 154:15,21,24 155:10,15,23 158:5,26

159:16,22,23 163:11,15,25 165:8 166:3 167:13

174:8,9 178:25 179:8 181:20 182:8 186:11,23

188:12 190:14,20 192:19 193:21,24 197:4 199:3

203:15,22 208:25 211:6,11 213:23 214:3,18

215:13 216:1 222:10 229:1 230:22 231:3 240:22

241:20 247:3 248:13,15,17 253:12 268:15 285:24

290:3 301:15 312:13 327:9 357:5 377:24 380:1

396:23 403:26 404:6 425:5 426:18 440:18 444:16

461:10 467:10 476:21 477:14 482:17 491:25

499:18 513:6 516:25 517:5 523:11 527:15 539:18

549:5 567:11 575:22 585:15,16 608:9,19 617:24

646:7 648:4 672:3 676:25,26 677:21 683:24

684:17,18 691:4,13

provided 2:23 8:1 24:1 29:7 60:3 61:9 66:7 68:2

102:24 108:6 113:13 121:5,13 126:1,18 147:14

156:12 161:13,15 168:23 170:20 171:8 176:26

211:17 225:7 240:17 253:6 309:9 374:23 422:21

696:11

provides 2:15,18 3:3 7:15 11:22 12:1 30:7 31:15

72:18 73:20 74:20 95:22 145:3 158:2,16 164:16

168:19 169:13 180:17 182:9,14,20 206:7 211:25

218:15 300:9 325:21 375:9,14 475:10 500:1

623:14

providing 18:9 23:18 62:12 167:9 178:24 192:26

202:9 205:3 221:11 290:11 316:16 317:6 405:6

509:14 523:17 647:13 649:5 682:26

provision 131:21 235:21 360:14 370:15,16 559:8,9

560:13

provisionally 591:22

provisioned 389:3

provisions 2:10,24 38:24 44:22,22 313:2 334:23

355:8 356:9,17 360:12 361:14

provoking 281:15

proxies 466:26 514:19

proximate 511:10 649:3 666:18

proximity 41:22 80:10

proxy 132:22 390:1 442:9,22 451:18 454:19 514:3

605:11 615:13

pro's 30:22

PRZM 397:17 426:15

pseudo-replication 579:4 583:15

psychological 507:23

public 2:13 3:4,5,14 7:23,26 35:1 242:5,13 247:18

248:5,6,11,13,14,15,17,18,22 249:1 251:6,11,12

252:3,5,7,11 254:20 277:23 279:18 284:3 296:14

297:7 312:25 316:14 322:2 323:10 324:19 325:3

329:20 351:6,7 412:11 418:7 435:17 499:7,12

660:9,12,15 663:16,22 664:3,5,13,14,16,19 691:8

694:11 696:10,11,12,13

publically 228:8

publication 224:19 235:12 254:9 668:2

publications 156:20 550:19 641:24 676:2

published 19:5 64:9,13 96:6,8,13 97:5 106:12

107:7 108:17 120:18 202:5 212:9 223:17 224:14

224:26 225:15 226:3,9 228:11 231:25 281:14

299:15 319:6 339:23 375:2 379:14 383:4 390:16

390:17 396:20 400:17 410:18 414:2 428:18,20

443:23 466:14 490:14 498:1 623:11 641:25 682:4

puddle 86:7 88:2,20

puddles 78:19 85:18,22 86:3 88:16 424:23 425:6,9

425:18 426:7,14,17,24 431:4,16 438:16 687:8

pull 7:4 8:14 10:19 235:5 259:17 303:23 311:17,20

550:2

pulled 135:20 262:17 305:6 311:15 375:3,4

pulling 305:1
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pulse 198:6,8,8

pulsed 487:8

pumpkin 121:16 122:10

punch 69:26

pupae 269:17 342:5 467:18 606:13,15

pupal 467:19 474:23 476:10 477:15 482:15

pupate 475:3

pupation 456:17 477:14,15,15,21 479:6,11,15

480:6 482:14,15,15 484:17 486:20 490:7,10,10

504:22,22 607:9 610:8 620:11 691:20

pupil 176:13

Purdue 250:22 292:5

pure 247:11 511:25

purely 406:17 551:23

purple 602:20,21

purported 34:21,23

purpose 33:21 89:12 143:6 182:17 190:13 192:18

215:15 236:6 241:17 245:4 267:25 313:11 315:20

566:23 681:25

purposeful 230:4

purposes 31:21 112:4 133:11 257:12 268:14

466:20 648:2 672:6

pursue 511:15

pursuing 229:14

pursuitness 369:24

purview 150:25 668:25

push 237:19 308:5 310:15 437:2 614:6

pushed 479:19

pushing 693:10

put 7:2 8:22 31:9 52:24 88:10 136:4 231:16 235:11

260:16 263:15,21,23 270:23 295:11 296:1,6

304:7 306:3,24 307:1,22 310:18 322:12 324:11

327:11 352:3 355:14 365:9 370:6 376:12 378:8

389:13 399:24 400:4 409:17 419:24 428:4 432:4

463:15 476:2 493:19 506:5 508:17 534:3,4,6,7

537:12 546:8 552:9 554:4 560:20,21 561:18

570:9 579:1,2,3 583:14,18 584:15 585:14 589:23

595:15 596:16 634:16 636:10 641:20 645:26

652:10 668:22 670:12 687:14 689:19 691:2

putatively 602:1

puts 263:24 377:13 613:23

putting 49:10 52:18 352:22 354:20 355:17 484:24

579:23 582:20 593:5 655:14 691:5

puzzled 395:10

pyloric 427:16

pyrethroid 576:5,18

pyrethroids 542:13 688:8,11

p-values 640:2

p.m 87:20

Q

qualifications 286:3

qualify 553:12

qualitative 24:2 52:13,19 186:20 579:12 607:24

610:11

qualitatively 164:8,13,22 179:13 194:6 509:4

512:15 614:14 621:6,13

quality 139:23,24,26 176:9 191:24 201:21 202:10

212:4,7,8 253:13 281:2,3,8,12,14,19 282:14

313:7 314:5 321:9 331:11 333:18 335:8,9,10,12

338:6 339:10,12,13 340:22 395:17 414:4,14,15

421:26 455:18 625:14,15 674:6

quantifiable 144:6 459:19 482:15 625:8,16

quantification 126:22 143:10 160:19

quantified 24:4 55:6 60:23 72:5 76:4 80:20 109:4

112:26 133:9 144:15 146:26 149:10 164:23 210:3

213:3 446:26

quantifies 127:2 410:2

quantify 51:13 55:14 130:16 138:9 143:13 144:1

149:25 170:8 284:17 293:25 343:4 421:18 448:13

449:4,25 451:1 454:20 455:17 464:10,13 474:23

520:6,9,26 521:4 536:8,25 537:21 540:9 609:15

611:17

quantifying 13:20 83:17 147:2 201:15 432:8

447:23 448:16 449:11 496:14 536:11,23,24

618:24

quantitative 12:23 54:7 83:5 146:9 164:3,18

174:19 179:14 186:21,22 192:8 209:21 210:24

241:9 410:9 474:16 475:10 491:25,26 509:25

512:11 614:12 619:17

quantitatively 13:17 158:4 161:3 164:6 193:4

222:17 227:20 408:12 409:7,10 413:6 414:19

422:26 423:4 432:26 433:10 474:20 509:1 512:14

538:4 621:5 637:21

quantities 159:20 346:15

quantity 85:12 176:9 263:8 333:18 335:8,9,10

338:9 346:23 393:3 558:3

quarter 562:3

Quebec 293:4 411:24

queen 20:9,18 37:22 44:23,25 72:14 73:24 74:8,11

93:14 100:2,3 103:15,16,19,24 104:1,3,16,18,19

175:3 184:14,15 186:8 225:10,17,17 226:10

341:8 342:23 343:19,21,24 344:21,22,26 355:7

356:22,23 369:15 384:25 385:17 446:16 448:14

448:16,16 449:2 452:8,20,23,25 453:1,14,14,16

453:21,22,24 455:8,18,19 456:4 457:19 466:8

502:8 533:3 534:3,7,8,12,14,16,17 535:4,5,25

536:17,18 554:11,14,15,19 569:26 586:8,8

616:11 637:4 648:16,17 675:5 678:22,23

queenless 535:5

queens 38:26 99:21,22,26 100:8 132:3 184:15
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306:22,24 307:4 331:2 356:7,10 368:22 446:17

446:18 448:2,3,8,11,17,23 449:5,7 452:2,24

453:8,9,10,11,17,20 455:5,5,11 456:5 466:4

470:10 477:16 531:24 532:1,3,5,11 533:18,20,22

536:18 547:8 555:20 560:11 561:23 605:25 635:6

635:11,16 639:13

queen-less 306:23

query 676:3

question 32:13 42:5,11,16 45:2,2,25 46:25 49:12

52:16 57:16 67:13 69:16 70:8 71:20 81:10 83:4

83:11 84:14 85:8,21 86:6,12,19 87:25 105:21

106:1 128:26 129:7 133:23 134:8,21,26 135:23

136:22 137:17 139:15,16 151:2,22 152:9 187:26

196:4 197:17,26 200:7,9,11 208:25 209:2 215:1

229:12 230:1 232:17,21 233:18 235:2 236:15

238:4 239:4 241:18,24 242:22 243:16,18,23

244:2,5 260:22 280:15 281:19 315:15,26 321:2

325:7,10,12 326:23,25 329:8,15 331:7 332:2,14

332:26 333:2,2,16,25 334:1,16,17,20,22 335:15

336:26 337:11,15 340:16 341:5,12 345:6,9,13,16

345:18 350:21 353:19 354:8 357:14,17 363:7

365:18,20,22 368:15,18,25 369:4,4,9 370:3,23

371:15 372:4 373:4 377:22 378:23 379:10 381:10

381:12,23 382:8,9 383:3,16,18,22,26 384:2,7

386:20 387:10 388:12 392:3,9,12 393:1 396:16

397:9,22 400:6,7 402:7 403:16 405:19 406:6

407:10,16 408:5 409:5,13,17 411:7 413:2,4,13,15

414:1,17 415:4,10,13 419:5 421:14,16 422:5,10

422:12,20 423:18 426:13 429:7,24 432:21 433:9

434:20 435:3 436:3,7,7,13 437:5 438:9 440:6

441:17,18,21 442:17,18 444:7,24 446:12 448:1

452:4,12 456:23,26 458:9,24 459:1,7 460:5

461:25 464:1,21,24 465:8 468:13 469:4,10,26

470:5,7,7,16 471:22,23 472:6,18,22 473:3,12

474:14 476:6,10 477:1 480:21,23 481:20 484:1

485:11,12 486:2 491:8,15,16 493:15 494:13,14

494:14 495:9,20,26 496:13 497:10,14,20,22

499:17 500:14,24 503:18,20 504:3,19,21 508:19

508:22 512:25 513:2,24 514:2,21 516:4,23

517:11 522:8,10 525:8,10 526:3,12,13,18 527:9

530:26 531:1,12 533:23 535:22 537:16 538:16,23

540:1 543:5,10,15 547:23 548:10,22 549:11

552:3 556:23 557:8 560:20 562:5,7 564:3 566:15

567:15,16 573:11 579:18 580:5,7 581:18,26

582:5 584:9,24 585:1,22 588:1 591:3,4,14 593:12

595:7 598:20 599:1 600:15 602:2,9 603:17,17,25

604:2,19,22 605:17,17 608:2 610:1 612:2 613:1,3

613:25 614:12,21 615:5,23 619:16 623:7,7 624:6

624:7 625:5 626:6,7 630:3,13,19 631:16 635:3

639:4,9 643:14,16,21,23,24 644:23 648:23 649:4

650:7 651:4 652:10 665:10,16,21,26 670:1,9,19

671:2 675:9 676:9,17,19 677:4,8,17 679:16,23

680:16 684:15

questionable 294:1 368:10 677:12

questioning 351:18

questionnaires 237:11

questions 4:8 6:20,22 7:2 13:3 18:23 32:5,8,9,11

32:23 42:15 45:22 47:8 48:21 49:5,6,7 62:18

66:14,15,18 71:4 81:6,8 85:7 87:8,11 89:8 129:20

132:17 134:17 140:2 145:11 150:8,9 152:3,6,18

154:4,23 169:4 194:23 197:3 198:12 203:3

214:19,21 215:3 219:12 220:2,22 229:24 237:5

237:16,17 243:13 245:18 246:6,18,20 247:12,16

247:19,20,23 252:9 267:24 268:10,14,18 274:24

277:23 280:4 284:5 285:9 288:16,17 289:11

296:9,10,12 302:7 309:5 311:22 312:20 314:16

315:9,22 316:8,22 319:2 320:23 321:25 323:25

324:1 325:5,9 326:21 332:20,22,24 337:2 341:2

350:20 353:17 358:21 365:17 367:22 368:2

371:19 372:7 377:17 384:3 386:8 392:6 395:1,9

397:6 401:16 406:3 408:7 413:3 432:6 435:4,11

435:12 436:3 437:5,8 441:13 446:9 449:8 454:18

456:8 458:25 464:2,17 473:23 476:8 478:15,16

480:17 483:25 489:1 491:7 493:13 503:4 505:26

511:5 512:6,24,25 526:9 530:24 531:23 535:20

538:13 542:9 543:3 551:26 562:1,2 573:8 582:16

584:21 585:20 587:25 593:9 598:17 604:19 605:7

614:9 615:2,11 616:18 622:7,10,22,26 626:5

630:3 633:3 643:9,24,26 644:7,11,14 647:24

649:23 650:10 651:22 656:26 666:24 667:5,20

669:24 670:3 675:22 676:12,23 677:23 678:14,15

678:24 679:7 681:4 683:16 692:18,25 693:6

694:20 695:10

quick 32:21 67:12 151:22 245:18 321:15 395:7

538:16 694:18

quickly 17:8 130:4 196:15 304:14 371:4 405:26

473:18 484:22 502:4 528:3 584:20 636:7 693:12

quite 9:10 11:12 16:16 49:10 67:4 153:25 198:25

240:22 251:11 258:10 278:5 281:1 304:9 329:14

340:21 352:26 364:26 371:11 372:23 380:18

381:2 391:2,12 400:21 419:21 429:1,11 436:10

442:22 448:8 449:13 459:5,5,6 463:15 468:14

493:16 495:12 503:3 507:25 510:23 515:13

534:13,18,18 536:14 539:13 574:17 575:4 628:9

650:21 659:6 682:7 686:25

quits 621:20

quotations 281:5

quote 283:12

quotient 24:7 54:9 58:9 59:1 146:9 201:12 205:22

205:23 207:14,17 219:21 273:6,10,11,16,21
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386:23 478:24

quotients 63:1,22 81:19,20 92:2,7 140:23 141:13

143:17 153:4 157:14 158:21 162:18,22 164:1,20

179:12 201:15 206:9 208:22 209:2 210:4,24

215:24 265:24 378:4 509:4

quoting 402:15

R

R 87:19

RA 289:15,21 291:1,5,9 292:7 293:10,24 294:2,13

294:18,24 295:16 296:1

race 369:23,24

races 445:13,15 684:3

radius 187:18,24

rain 424:24 673:12,16

rainfall 318:9

raise 369:9 410:14 540:15 596:21

raised 61:25 168:7 181:3 198:11 202:4 351:4

460:24 536:16 540:5,10 542:24 543:7 549:19

558:18 562:23,26 591:14 609:7 634:5

raising 532:12 533:25 547:8

ramification 634:7

ramifications 139:2 548:16

ramp 457:21 502:5,6

ran 88:9 304:14 323:14

random 402:26 483:18

randomly 263:12 384:16

range 21:6 30:21 95:25 125:9,10 145:9 169:2

177:1 184:24 187:6,20 242:8 261:10 262:1

298:11 299:13 305:3 322:4 330:20 362:13,14

397:23,24 432:2 438:7 564:7 573:20 673:2,5

ranged 20:5 120:11

ranges 79:24 99:10,12 294:18 627:15

ranging 187:21 312:20

rank 553:12 561:21

Ranking 558:5

rape 524:6 528:9 574:20,22 575:2,9,17 576:20

585:11 594:7 603:6

rapid 170:16 323:18

rapidly 286:13 320:7 501:25 533:23

rare 474:21 487:2

raring 38:17

rat 514:9,11

rate 65:6,19,21 71:19 78:16 82:17 96:24,25 97:1,6

97:22 98:14,20,26 99:6,6,15,16,17,26 100:1,7,26

101:4,8,14,20,22 102:6,9,18 103:4,12,18 104:16

104:21,24 105:4,7,15,17 107:25 108:21 116:3,4

117:20 118:5,21 123:9,12 127:6,15,17 128:1,7

130:5 145:2 169:12,19 178:8 183:13 197:25

210:15 225:10 226:10 236:26 262:14 273:4,8,12

274:10 327:9,15 328:10,12 331:3 344:25 367:20

367:20 368:21 370:7,22 372:10 374:17,21 375:7

375:8 376:2,20 379:2 382:12,16 448:17 453:4

457:18,21 477:15,24 508:3,4 533:5 541:3,25

552:25 568:5 582:7,13,22 594:13 599:13,14

602:12,15 645:6 646:22 675:4 681:26 682:17

rates 60:13 79:19,22 82:19 89:21,23 90:6 93:19,22

94:1,10 95:3,16,20,23 96:5,7,11,13,16,18,20,21

98:7,23 99:2,10,20 100:10,12,15,16,20,22 101:1

101:24 102:1,7,21,25 103:3,6,8,9,10,15,16,21

104:26 105:5,23 106:5,18 115:24 117:22 119:2

123:14 127:5 129:26 130:17 131:9,12,25 148:16

182:26 267:2 299:13 328:16,17,22 361:15 384:9

386:21 410:4 448:24 507:9 536:23 538:5 540:26

552:13 553:23 560:22 561:3 577:5 582:11 586:18

587:8,13 603:21

ratio 24:6 58:10,15 65:16,19 120:14 205:22 208:14

276:21 330:21 361:23 370:25 387:12 560:25

561:1,4,11 645:3,5,9,9,12,20 646:17,21 647:7

rationale 71:25 87:9 242:13

ratios 277:4 370:26

rats 514:12,13

raw 313:6

Ray 252:18 254:24 278:20

RAYMOND 252:17

reach 9:26 204:3 625:25

reached 195:21 478:26

read 137:26 247:13 264:6 289:9 332:24 333:2,3

335:26 337:3 338:9 341:4 348:4 357:16 358:3,5

365:20 378:21 381:13,13 388:9 392:9 395:7,9

397:7 402:5 403:15 405:18 407:20 422:17 441:18

464:21 474:12 480:21 482:3 483:26 503:19

508:20 512:26 530:25 547:22 624:6 626:6 643:14

669:25 676:17 689:4 690:25 691:11,16

readily 24:11 166:1 227:25 443:22 625:8,15

reading 7:1 85:10 333:1,4 345:10,24 354:1 357:15

687:1 690:20

ready 71:6 228:23 252:5 357:14 436:14 437:20

441:17 456:2 469:11 472:15 484:12 518:7 534:24

539:13 547:12 562:5 591:11 595:21 604:3 605:5

607:5 655:21 661:26 670:17

real 32:21 138:4 234:24 236:13 237:25 239:3

249:10 256:21 266:19 268:5,7 287:17 290:5

291:17 293:26 300:21 321:15 403:1,4,4 406:25

412:21 448:6 450:7,13 460:25 463:16 483:6

496:26 515:10 524:4,8 530:21 538:8 556:14

562:13 571:11 586:12 590:7 591:20 592:8,24

604:16 605:21 615:18 626:23 629:2 631:2,17

636:7 639:7 683:24,25 684:25 694:18

realism 54:15 165:15 192:21 266:11,23 513:5

539:8,20 694:13
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realistic 140:26 155:18,26 166:4 174:21 179:4

181:8 183:15 211:12 240:19 245:2 288:12 301:15

346:15 428:24 438:18 440:10 460:12 462:8,10

468:16 496:23 497:4 503:14 515:4 522:10 524:14

530:1,21 563:17

realistically 539:19 615:16

reality 139:25 236:15 270:11 394:26 524:11

541:21 554:13 584:2 590:6,16 592:25 605:23

617:17

realize 267:22 311:9 649:8

realized 17:6 586:13 616:2,15

realizes 557:14

realizing 12:15 555:12

really 13:1 15:7 16:9,13 48:5 49:10 50:7 52:7

82:12 129:24 130:12 131:17,21 138:4 197:17

220:14 231:15 233:14,22 234:13,26 236:3,12

237:21,23,24,25 239:12 247:11 254:26 255:22

256:3 259:21 260:7 261:7 262:16 265:11,11

269:5 277:15 281:15,21 282:8,16 283:6,22,26

290:24 293:16 303:4 305:19 306:19 319:24

327:10 335:3,4,10,15,23 337:23 338:25 339:12

344:22,24 345:8 347:1,21 349:26 352:6 353:4,6,7

353:12 355:2,16 359:20 361:10 363:12 364:15

367:7 372:12 385:18 388:2 394:6,7,8 399:3,11,18

400:2 403:4 405:8 407:13,16 414:10 430:11

433:14,25 434:3 436:11 437:2 442:3 443:11,14

447:12,20 449:6 452:8 455:17 457:11 459:19

462:19 463:15 464:7 468:15 469:17,26 470:11,20

471:3 472:6,6 486:15,16 487:2 493:3 494:5

495:24,25 496:2,24 504:21 506:2,12 507:6,24

510:1 511:4,20 513:21,23 514:3 516:10 518:10

519:5,15 520:24 521:24 524:2 527:26 528:1,3

531:7 535:13 536:25 539:14 542:22 544:21

546:12 547:2 548:19,19,21 549:4,15 550:15

551:13 555:10,13 556:11 557:19 560:10 561:5,21

565:18 566:9,15 571:11 573:23 575:12 579:16

583:25 584:15 596:1 597:12 600:4 603:18 605:12

605:22 608:10 609:12,14 610:22 611:9,13 616:14

616:14 619:14,25 626:3 630:20,26 634:1 636:15

638:12 639:19 642:14 644:9 651:8,16 653:8,24

655:7 658:1,16 660:16,26 661:18 662:24 663:18

663:24,24 664:2,6 665:7,8,10 672:5 673:15

675:17,20 677:8 680:4 682:20 684:25 685:17,18

686:2,24 687:11,18 688:9,26 689:1,5,6 692:3,8

694:13 695:14,20

realm 331:23 401:7 501:7 574:2

real-work 272:15

real-world 269:8 270:24 274:4

reanalysis 416:22

reapply 655:10

rear 533:4,5,22 535:5

reared 380:7

rearing 222:5 225:23 226:11 469:19 490:20 534:12

691:23

reason 6:21 43:23 47:21 81:14 163:18 320:4

351:19 391:3 457:15 466:23 486:9 498:5 502:5

521:12 556:8 578:23

reasonable 12:2 22:17 31:7 32:3 204:1 228:10

257:13,25 286:24 291:21 358:10 361:3 367:10,26

377:24 389:16 418:15 438:17 466:10 501:8 574:2

577:14 593:7 594:15 599:17,25 607:14 623:9

reasonableness 375:11,15

reasonably 90:12,17 93:26 98:9,10 107:21 116:15

119:21 123:21 124:12 126:6 128:14 256:3 257:23

259:24 260:24 262:12 377:15 593:1 596:14

reasoning 434:15 438:2

reasons 45:8 77:22 79:8 98:1 221:14,19 223:23

270:18 325:26 347:8 355:21 366:14 427:22

467:19 546:7 555:9 583:20 592:17 638:16

reassessment 438:3

reassuring 496:15

rebound 198:20

recalculate 141:13 143:17 439:2

recalculated 60:5 121:10

recall 155:2 676:5

recapitulate 522:5 605:6 630:20

Recapitulating 531:15

receive 14:6 44:24 78:12 94:4 100:2 112:9 123:1

191:25 330:17 590:7

received 90:8 94:26 95:1 96:26 104:1,12,18 106:10

115:23 117:23 127:2 238:6 621:19 622:20

receiving 123:18 145:2 347:7 542:25

receptor 37:20 353:26 355:3

receptors 11:21 35:16 36:21 37:19 40:4 41:2 47:18

347:7,9 353:2 354:4,4,6,17,22

recess 48:25 87:17 154:6 296:16 378:19 480:20

562:4 669:10

rechemical 461:12

recipe 56:22

recognition 280:10 505:20

recognize 65:9 253:3 281:10,13 359:3 540:24

551:3 578:4 583:10 657:23

recognized 142:21 190:16

recognizes 14:25 253:5

recognizing 78:5 540:22 680:24

recommend 335:25 338:6 340:9 342:7,14,21

434:14 511:9

recommendation 499:9 500:9 580:19 585:7

recommendations 2:18 24:1 133:2 192:6 208:10

299:24 421:20 482:5 551:21,24 573:15 582:12
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585:13 591:16 599:2 630:18

recommended 65:17,25 163:21 255:15 425:15

506:13 582:13 618:20

recommending 343:15 504:20 598:24 664:24

recommends 25:14 66:4 255:16

reconcile 9:22 371:22 548:20 549:23

reconciling 548:7

reconstraints 280:8

record 32:22 52:23 88:10 241:7 289:10 326:17

388:9 392:10 405:18 407:20 422:18 464:22

474:13 483:26 503:19 507:11 512:26 582:18

623:13,20 640:11 643:15 669:14,15,25 676:18

685:11 687:11 691:12,17 692:17 694:20 695:9

recorded 126:25 531:19 669:18

recording 313:5 504:5

recordings 450:10

recover 501:15 503:1

recovery 27:26 214:11 502:21 503:10

recruitment 222:1 499:26 560:25

rectified 407:7

recur 487:25

recycled 355:22 435:20

recycling 439:22

red 39:23 76:21 113:25 121:12 164:22 303:7

redirecting 283:19

reduce 201:5 294:20 298:15 390:25 519:9 523:9

540:25 626:15 635:9 639:6

reduced 39:15 83:24 144:11 185:23 188:15 317:3

318:1 513:11 528:4 558:25 625:25

reduces 317:24,26 642:13

reducing 129:19 318:5 390:26 410:8 417:18

529:21 530:4 573:24 597:9

reduction 39:16,18 219:4 222:4,5 266:9 390:25

500:4 518:16 523:13 530:3

reductionistic 514:7 606:5

redundancy 435:9

Reetz 298:16

reevaluate 128:16

reevaluated 258:5

Reevaluation 18:1,7

reexamined 399:24

reexamining 617:16

refer 261:8 325:21 506:9 605:7

reference 161:10 170:5,8,16 269:5 391:18 400:3

468:20 478:10 493:11 499:11 641:15,20

referenced 116:8 131:3

references 476:11,21 642:2 648:4 692:9

referred 24:6 147:11 159:24 167:4

referring 135:17 143:3 231:17 489:14 522:25

601:20 619:7

refers 212:26 526:12

refine 18:11 26:1 44:6 51:25 54:18 56:18 60:25

65:7 116:17 140:19 141:12,15,18 142:11 143:16

146:6,8 167:9 180:14 181:22 196:13 218:20

432:16

refined 26:4 48:18 60:4,5 88:21 140:25 144:6

146:16 152:9 155:14 165:5 166:12 180:20 193:3

193:14 206:2 209:19 211:10 533:7 562:22

refinement 26:26 52:12 59:15 60:2 67:7 139:14

141:20,24 152:5 153:16 179:18 180:17 196:13

198:17 217:5,8 218:9 271:22 381:18 608:5

refinements 21:23 26:24 31:1 63:26 91:2 142:19

144:23 146:7 155:17 165:19 171:24 196:18

209:17 210:4 218:18,19 219:5 652:20

refines 155:26

refining 21:18 59:25 141:10 142:8 287:19 288:7

426:11 438:20

reflect 8:14 19:15 27:12 28:20 29:26 31:11 41:26

56:11,16,23 64:4 145:5 165:14 174:2 183:14

195:8 213:16 235:20 240:6 245:7 300:21 373:17

590:2

reflected 11:3 373:25

reflecting 171:26 213:2 236:14 258:19

reflection 8:20 611:11

reflective 171:16 190:10 209:26 234:4 236:3

240:19 384:15 385:3 617:17

reflects 8:8 10:17 24:13 28:2 95:26 155:25 175:22

211:13 212:13 224:13 285:26 611:14

reflex 450:20,23 451:15 460:7

reformulate 654:22

refused 290:8 303:14 518:7

refute 118:16 209:10

regard 27:1 85:17 86:13 135:10 244:2 246:11

276:1,18 334:16 349:12 395:11 410:10 421:9

423:23 425:26 437:24 438:1 462:2 562:15,25

582:3 611:22 674:6

regarded 292:14 457:3 520:14

regarding 22:5 26:17 31:25 34:20 35:23 43:5

55:26 57:7 135:1 137:12 145:16 150:10 155:3

164:26 179:17 182:7 200:8 201:21 213:16 312:16

313:5 316:22 319:16 415:4 425:2 458:3 465:4

478:9 497:2 538:16 652:9 667:24 670:20

regardless 35:12 463:24 553:3 579:8 645:24

646:21

regards 374:6 411:15 436:13 527:13 604:19 623:7

regime 162:2

region 226:21 228:3 303:14 320:14 321:6,8,11

366:20,21 574:25 581:13 682:8

regional 321:7 326:19

regions 149:21 293:19 320:17 375:20 581:12
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591:23 595:11

register 529:9,9

registered 108:4 123:14 148:14 290:10 301:19

421:5 572:16,17,21,22

registering 142:23

registrant 27:5 153:8 197:13 215:20 327:5,18,23

410:19 417:23 680:13

registrants 77:3 118:16 124:23 183:5 216:19 220:6

291:12 325:20 331:18 406:26 418:12 419:17

registrant-submitted 108:1 110:18 141:22 162:19

162:24 202:6,19

registration 15:24 142:10 147:5 201:26 276:6

284:15 290:14 292:17 298:21 592:7

registrations 161:6 290:21 301:21

regression 397:26 491:19

regression-based 162:1

regualations.gov 696:12

regular 83:19 356:10

regularly 457:12

regulate 28:14,16 77:23 465:20

regulated 15:21,22 17:18 197:8 202:7 325:17,19

668:25

regulating 84:21 139:21 238:19 247:8 507:25

regulation 8:10 10:19 13:26 14:15 16:21,24 200:16

416:22 696:8

regulations 2:21 323:12

regulator 170:13 559:25,26 560:9

regulators 153:25 279:7 342:10

regulatory 2:19 7:20 8:9 10:10 12:7 13:25 14:14

15:2,21 16:4 22:7 23:13 28:9,21 31:20 54:23 55:1

58:21 59:4 63:12 69:8,8 107:18 139:26 159:2

161:24 181:18 191:25 200:17 206:22 213:14

223:20,22 228:13,23 229:15 245:9 252:19 254:20

255:5 267:18 271:22 272:3 279:2,4,9 280:21,25

280:26 284:15 312:3,9,9,17,18 416:21 420:5

421:12 434:4 562:21 668:10 670:23

regurgitated 147:20

regurgitating 427:13

reinforce 292:6

reins 32:26

reiterate 49:1 78:3 278:24 334:22 351:17 361:25

385:13 419:26 430:8 442:19 486:8 624:16 643:5

678:26

reiterated 434:8

reiterating 457:10 501:3 513:17 593:23 616:16

reiteration 279:16

reject 604:3,5 659:20 660:5,23

rejected 603:21

rejecting 604:3

rejection 274:10

relate 70:20 147:9 154:16 175:8 185:3 246:12

269:2 289:22 397:21 511:5 525:25 554:11 622:16

677:24

related 3:16 23:11,13 32:11 36:21 65:21 66:15

76:26 88:12 94:9,18 112:5 134:17 148:4 158:6

164:24 166:7,20 172:1,24 176:7 178:26 180:5,15

182:4 188:7 190:3 192:7 193:4,14 203:4 214:1,6

225:20 232:18,18 246:6 252:9 266:4 267:3

268:10 274:16 325:5 386:23 418:26 437:5 455:25

456:26 461:10 470:7 509:1 573:18 585:26 586:4

595:16 670:25

relates 18:23 71:20 136:12 162:20 178:3 268:8

281:19 332:2 384:11 494:13 543:18 612:3 677:8

677:17 690:25

relating 155:13 507:7 554:15 582:13

relation 155:1 177:19 229:17 309:13 460:17

585:25 670:22

relational 671:17

relationship 27:18 30:14 69:26 134:3 166:14 212:5

213:8,10 257:16 296:25 367:19 399:22 449:19

613:9 672:1

relationships 22:19 27:13 31:10 34:13,21,24 39:2

164:9 222:10 240:21 241:9 318:22 511:5 647:3

666:9 668:15 671:11,13 675:7

relative 40:26 46:5 49:25 55:9 71:14 78:20 79:8

98:19 100:21 102:17 104:10,11 105:25 124:13

128:10 172:15 173:25 178:7 181:14,20 182:23

187:17,23 190:17 191:6 192:19 193:15 200:9,11

214:13 235:13 254:11 266:16 369:19 383:23,25

383:26 384:22 403:17 411:7 412:10,22 424:4

479:2 499:21 504:9 513:8 540:26 555:17 563:3

589:15 614:1

relatively 50:6 137:11 143:20 165:7 167:3 168:25

176:16 190:4 195:8,18 202:5 204:3 205:25

209:24 293:3 295:12 370:19 374:12 407:7 410:25

424:20 425:9 426:15 538:26 572:4

relativize 515:18

relativized 380:22

relayed 67:18

release 23:22

relevance 209:12 379:21 496:22 531:13 552:18,19

554:10 555:17 606:19

relevancy 164:1

relevant 23:18 29:14 30:2,3 35:15 56:8 57:7 63:2

71:1,2 77:21 80:8,11 89:8 107:12 114:3 129:8

132:11 144:2 147:3,26 149:12 163:10 171:17

172:11,14 173:25 210:18 284:11 301:18 302:4

354:5 396:16 409:8 410:8 413:11 423:1 431:10

465:15 478:20 479:3 486:10 494:1,2,5 499:19

509:12 539:14 542:25 549:12 550:2 553:18
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554:19 555:5,6,7,16 557:1,16 558:6,9 575:13

586:7 615:22 617:4 632:6 641:26 647:20 661:11

692:12

reliability 293:9 312:13 404:13

reliable 68:26 261:19 315:1 343:8 395:19 443:22

455:17 458:5 477:14 480:4 481:14 495:13 509:11

519:2 589:2 624:21

reliably 13:26

reliance 301:14 539:6 668:7

reliant 668:18

relied 207:10 471:26

relies 24:2,5 54:8 68:21 110:23 126:12 128:7

197:17 199:20

reluctance 326:6

rely 50:5 54:22 55:13 291:11 293:11 313:13 465:5

493:20 546:24 639:10 661:21 671:1 676:10

relying 291:26 585:14 587:6

remain 12:9 157:26 166:11 179:19 188:26

remainder 26:5 40:3 41:1 64:2 102:3

remained 318:13 332:10

remaining 30:20 174:26 179:23 317:2

remains 2:19 75:2 187:26 481:15 484:10

remark 499:5 618:6

remarkably 410:16

remarks 8:13 252:26 253:2 316:15 691:21

remedied 256:6

remedy 323:10

remember 334:11 382:14,26 565:10 611:3 619:17

619:22 636:23 661:5 672:21

remembering 619:20

remind 32:20 292:9 435:19 561:15 584:19 661:4

669:11

reminder 248:16,19 561:19

remiss 696:14

remote 411:3 507:5

removal 192:11 331:13

remove 216:18 303:8 343:6 565:14

removed 170:1 303:12 313:20 347:6

removes 318:7

removing 338:6

render 472:25

repeat 342:16 374:7 386:17 566:8 608:26

repeatability 590:21

repeatable 450:7 459:18 463:19 496:15 624:21

626:19

repeated 204:3 222:16 338:19 404:9 492:9 566:5,7

692:9

repeatedly 355:23

repellency 542:7,14

repertoire 451:17,21

repetition 278:4 567:22

repetitive 451:16

replace 77:10 128:19 188:16

replacement 490:6,9 586:8 646:19

replacing 307:4

replanted 317:4

replicable 314:23

replicate 583:21 584:2 592:17

replicated 554:11 596:14 634:2

replicates 145:8 161:9 598:10 635:25

replication 159:24 188:2 189:8 190:1 192:16

193:11 222:24 551:4,6 569:5 574:4 579:23,25,26

581:10 583:7 584:3 630:24 631:21,21 633:25

635:19,21 639:17

replications 518:19

report 2:25 7:4 9:7 18:26 19:4,7 86:10 161:4

191:10 246:7,8,8 247:14 257:10 283:11,13

298:21 305:13 325:19 326:7 357:3,4 512:3

527:13 602:11 623:23,25 624:1 641:17 683:22

696:19

reported 18:25 19:7 51:7 77:1 111:19 112:2 114:9

163:8 178:11 211:1 234:23 292:20 297:13 298:26

325:24 326:6 346:26 365:26 366:26 395:11

402:22 463:23 497:10 621:11 623:23

reporting 260:16 292:13 293:10 311:9,11 325:15

326:12

reports 3:1 9:7,8 20:16 59:19 63:8,24 66:8 163:1,3

163:4 194:18 238:24 246:8 292:22 313:8 325:26

326:2 411:17,20,22,23,24,26 412:2 448:7 452:25

474:19 622:19

represent 29:25 36:6 39:5 83:24 98:2,13 109:10

121:9,10 122:23 125:7 126:19 127:18,25 129:13

129:17 132:9 138:12 142:11 143:8 148:20 149:12

149:19 163:24 180:20 181:8,18 206:20 216:24

285:20 326:26 330:12 337:13 341:13 359:1 373:8

379:18 388:16,20 392:15 397:12 451:9 540:24

646:2 692:10

representation 35:11 72:18 73:20 74:21 164:16

225:16 323:23 582:22

representations 72:16 94:12 388:14

representative 92:11 99:18 101:9 105:4 111:11

112:5,8 125:3 136:26 144:19 169:18 187:17

190:10 301:14 517:12,15 592:24 593:26 594:2

678:9

representativeness 580:8 593:19

representatives 325:9

represented 44:18 45:4 53:23 92:8,15 96:23 97:2

102:26 114:1 150:1 216:22,23 229:15 234:13

358:14,16 407:26 591:20

representing 158:21 252:20 297:5 359:18
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represents 16:26 25:16 35:14 99:21 102:11,25

109:5 110:16 116:21 122:3 138:24 150:6 187:20

187:24 271:26 316:17 359:3 368:13 373:16

451:15 466:17 473:5 477:9

reproducibility 21:26

reproducible 254:7 509:11

reproduction 51:11 214:2 301:9 452:10 509:2

reproductive 4:22,25 167:26 344:22,26,26 446:16

448:4,16,21 455:9,19 532:5,5 606:1 655:6 675:4

reproductives 449:5 531:24 561:22

request 13:23 357:26 401:20 680:12

requesting 129:10 187:2

require 26:15 90:24 146:10 150:25 156:7 158:9

161:24 197:13 217:5 218:18 286:20 398:7 475:16

535:14 540:16 573:13 679:10 685:22

required 24:25 25:3 97:7,8 121:2 142:6,11 146:13

147:4 157:24 158:8,15 160:13 177:23 181:19

189:25 219:6,24 220:5 272:8 275:3 281:9 286:15

290:9 325:19 621:8 668:25

requirement 79:25 96:12 156:17 180:26 195:16

291:18,21 301:3 662:13

requirements 3:2 10:24 15:18 17:6,19 24:23 54:13

93:10 97:15,20 99:5 101:23 139:6 140:18 142:22

156:7,12 167:21 168:1 190:12,19 239:4 255:15

267:16 279:13 286:24 312:9 328:25 369:19 428:9

492:25 596:12 661:22 663:26

requires 83:17 156:9 165:21 284:16 482:19 515:9

requiring 217:7 274:2

reregistration 323:15

research 4:5,21,25 5:7,8,15,19,19,22,26 6:1,3,6

21:6 23:5,18 77:7 88:1 164:14 211:24 246:8

247:11 249:24 250:6,11,13,14,19,20 259:3

274:13,17,21 283:16 300:15 311:24,26 312:1,1,6

312:9,10,10,18,19,24 313:4 322:3 324:5 340:11

343:20 346:26 352:5 358:17 410:3 413:7 443:21

460:14 492:25 509:18 555:8 623:12 668:1

researched 410:17

researcher 420:26 683:11

researchers 254:6 299:3 451:1 533:17

researches 215:17

reserve 290:18 563:15

reserved 66:16,20 67:9

reserves 20:10 39:16 187:9,11 360:20 534:5,6

reservoir 653:22

resident 323:14

residual 167:23 181:5

residually 430:1

residue 59:26 60:1,22 110:22 113:6 114:1,19 119:8

141:23 143:5 145:15 146:18,25 147:19 150:2

157:25 164:20 174:10,15,19,21 179:9,10 192:9

192:10 194:19 206:5 261:7,16 329:18 339:11,12

339:13 349:24 373:6 378:14 381:18 386:3 392:14

395:2 396:19 397:2 428:22 439:2,3 453:5 535:1

539:3 571:4 594:21

residues 23:22 24:25 25:6 37:6,7,11,18,26 38:10

39:25 40:12,26 41:5 46:26 60:22 72:25 73:1 86:3

86:14 98:17 109:4,14,18,22,25 110:19,24,26

111:2,5,11,16 112:15 113:14 117:12 130:24

133:9 137:12 143:13,23 144:18 145:20,23,26

146:20,22,23,26 147:2,23,26 155:15 157:26

158:6 170:19 172:12 174:4,8 181:11 186:4,7,10

186:16 191:15,17 192:4 201:11 202:13 206:6

277:4 298:11 299:9,16,21,26 300:4,7,24 301:5

335:11 338:9 348:2,4,6,15,16 349:1,2,5 351:1,11

352:19 366:7 373:26 390:10 393:5 394:14 402:15

404:13 405:22 412:5 431:21 439:25 535:16

538:17 656:24 657:14,16

resilience 188:15

resins 73:2 338:13 350:13

resistance 473:4 664:6

resolve 661:3

resolved 12:10 360:25

resolving 9:25 85:5

resource 314:10

resources 26:17 31:3 50:7 146:11 175:4 181:19

192:23,25 242:4 244:7 255:13 271:21 283:16,20

352:8 475:12 498:22 523:17,17 541:3 668:11

resource-intensive 181:14

respect 28:12 31:2 33:18 203:24 212:7,25 213:4

256:14 258:6 275:9 292:17 482:20 517:26 546:21

respective 91:26

respectively 128:3 129:15 217:7

respiration 506:25 507:8,13

respirator 653:23

respiratory 41:13 653:25

respond 32:20 139:4 379:13 505:1

responding 344:7 670:3

response 21:9 32:24 47:25 48:12,22 68:5 69:26

71:5 87:12 140:3 173:15 174:3 194:8 198:2 199:4

199:4 202:8 203:5 207:2,8,9,15,19,22,23,25

208:2 243:24 245:11 292:15 378:6 379:16 382:6

389:13 435:5 439:20 449:17 450:21,24 454:13

455:22 458:14,15 473:3 475:21 491:23 492:22

496:14 504:3 505:16,21,22 506:4 511:16 520:16

520:23 550:24,25 584:1 588:12 595:7 604:9

619:18 656:12,14 675:8 677:8 680:17 691:4

responses 35:16 39:11 48:9 67:4 386:12 465:22

470:9 482:17 499:24 505:2 509:14 677:25

responsibilities 313:7 629:23

responsibility 238:19 245:9 597:16
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responsible 2:9 53:24 238:5,18 297:18 325:23

526:7

rest 97:15 114:5 507:1 571:18 585:19 644:12

687:26

restated 291:15

resting 97:20

restrict 294:19 409:14

restricted 143:21 167:5 666:3

restricting 230:20

restriction 230:24 276:10,15

restrictive 344:21

restroom 248:2

result 13:23 20:23 25:6 28:17 36:7 37:8 39:26 41:4

41:6,17 53:25 54:15 64:17 76:24 77:2,17,20 87:4

125:19 138:4 148:9 149:23 165:24 176:15 187:10

190:7 207:18 208:2 217:16 219:4 243:1 245:2

264:3,16 273:9 298:17 300:23 313:24 366:6

367:13 514:25 563:25 568:6 569:4,25 594:15

631:7 655:22

resulted 17:17 207:7 425:5

resulting 37:5 74:26 75:4 76:19 79:22 80:21 95:3

111:11,15 121:6 126:19 127:12 128:25 299:20

results 15:13,24 29:4 54:9 62:3 66:5,10 78:17 79:4

90:1 95:5 97:21 110:1,3 122:7 139:13 152:10

164:26 165:17 170:6 174:1,23 193:7 217:25

218:7,23 223:2,5,5,8,8,26 224:3 227:1 228:2,19

232:2 254:7 268:20 270:23 271:7 275:16,17

305:12 306:12 308:22 310:2 343:7 383:9,10

469:22 486:15 509:24 529:13 533:6 548:25

563:13 569:26 575:25 576:14 588:15,20 589:2,18

591:7,20,25 595:24 633:23 638:21 663:23 684:26

resume 87:15

retribution 326:9

return 112:24 141:8 186:3,21 298:7 435:19 462:9

479:2 553:24 560:3,5

returning 146:1 462:6,11,17 536:22,24 537:17

569:11,12 601:15

returns 49:26

Reuben 140:5,6,7 206:1 209:18 214:12 215:8,10

215:11 219:20 696:3

reveal 174:14

reverse 207:17 401:20

review 2:16 17:4 75:24 77:12 97:2 111:13 113:14

151:24 159:26 162:16 181:19 192:25 219:1

221:11 223:18,21 224:7,10,11 228:24 229:22

244:10 254:22 271:24 279:9 281:13 395:5,8

674:21,23 678:7 683:6

reviewed 3:1 25:26 107:17 223:16 224:17 263:23

274:11 312:21 404:8

reviewing 155:19 318:25 406:1 552:6 676:21

687:18

reviews 7:5 279:4,5

revise 661:21

revised 293:10 357:4

rewarding 17:23

rewritten 335:26

re-apply 654:26

re-asked 456:8

re-looked 307:17

re-queening 307:2

ribosomal 475:15

rice 425:2,3

rich 451:3,17 467:4

Richard 14:14 16:18,19,20 696:7

richness 333:19 335:22,23 337:8 338:19,24 339:4

648:10

rid 637:18 639:14,15

ride 360:2

ridiculous 574:9 638:6

ridiculously 639:7

right 3:11 34:6 43:7 46:12,17 48:23 51:12,17 60:16

68:11 71:7 113:26 126:18 131:18 132:6 133:17

133:21 138:20 165:13 219:22 241:13 247:24

252:5 256:11 277:12 295:9 296:13 307:5 308:19

309:11 310:4,9,11 312:5 315:20 320:8 321:23,26

327:26 332:19 334:6 336:25 337:3,3,16 339:6

341:2,3,17 353:26 354:22 357:13 365:19 373:2

378:17,20 382:14,16,26 383:1,26 387:2 388:9

391:16 392:8 427:14 430:11,12 434:22 437:14

441:7 460:2 471:21 474:12 476:4,6,16,25 480:2

480:19 493:17 494:16 501:17 502:9 503:18

508:14 510:21 512:5,19,22 522:6,14,24,26

523:24 532:10 536:17 542:18 545:4 562:10

565:26 566:9,15 572:10 582:1 583:3 587:25

599:22 601:16,19 609:26 611:23,25 612:22 613:2

618:19 621:17 627:7 630:11 634:20 641:18 645:8

646:11,20 651:26 652:4,5 661:26 667:4,19

671:19 673:11,12 676:16 684:12 693:5,10

rigid 56:21

rigor 253:13

rigorous 280:2 292:2

ring 159:25 316:6 380:26

ripe 184:14

risk 2:4 3:24 5:13 6:23 7:16,16,19 10:6,13,25 11:9

11:11,13,14,17,26 12:1,3,5,6,10,15,18,23 13:17

15:14 18:5,11,17,22 21:14 22:3,6,14,17 23:15,16

23:22 24:7 25:16,21,22,22 26:1,2,3,9,10,10,12,14

26:14,20,23,26 27:1,2,2,9,19,20 28:2,4,23 29:6

29:12,24 30:6,22,25 31:3,14,23,25 32:17 33:8,12

33:20,22 34:10,11,20,25,26 35:1,18,21,23 36:1,3
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42:2 46:15 48:1,15 49:12,13 50:8,23 51:25,26

53:5,7,24,26 54:2,3,9 55:17,22,22 56:1,6,10,21

57:3,24 58:9,18 59:1,10,11,22,23,23 60:7,7,9,12

60:18 61:12,14,14,14,19 62:4,6,8,9,10,11,13,14

62:21 63:1,9,21,25 64:9,19,25 65:10,24 66:4,8,14

66:25 67:19,20,25 81:19,20 83:7,11,11,12,13

84:4,5,9,15,16,18,19 90:22,23,26 91:3,10,12,19

92:2,7,7 93:17,18 100:11,14 106:3 111:6 113:8

136:8,9,10,10,11,11,14 139:8,10,15,16,22 140:21

140:23 141:3,13 143:17 145:11,12,14 146:9

150:4 151:13 153:4,13,15 154:16 155:1,5,9,21

157:14,14 158:5,19,21,21 160:18 161:3 162:18

162:22 163:4,12,19,23 164:1,13,17,20,23,23,23

164:25 165:9,10 166:11,13,17,25 168:8,9 169:4

174:14,24 175:1 178:23,26 179:12,13,14,17,19

179:20 180:24 181:26 182:1,10 183:8 188:6

192:19 193:1,22 194:6,15,16 196:11,19 197:5,6,6

199:17 200:22 201:7,12,15,15,16 202:24,26

203:12,13,14,15,16,18,21,22,25 204:1,2,8,11,14

204:17,18,22,23,24,25,26,26 205:1,1,11,12,15,17

205:18,23,26 206:9,9,18,19,21 207:4,14,17

208:13,14,17,19,21,22,26 209:1,2,4,4,5,7,8,8,11

209:14,15,15,16 210:3,6,11,16,20,22,23,25 211:9

211:11,12,18,23,26 212:6,8,15,19,22 213:5,8,10

213:12,15,18 214:2,7,13,15,19,23,25 215:16,24

218:10,20 219:4,8,11,15,21 220:19 221:4,15

223:6,9,20,23 224:15 226:16 227:12,16 228:10

229:1,9,14,18,23 237:20,20,25 239:20,21 242:1,2

242:2,9,10,12 246:12,25 247:1 250:9 251:8

253:10,13,26 254:5,10,13,18,21 255:4,10 258:8

258:22 260:7 262:7,13 265:9,24,26 266:6 267:24

268:14 270:16,26 271:8,13,13,19 272:4,7,15,17

274:4 278:25 279:13,20,23 280:12 281:22,25,26

282:1,4 284:11,14,24 289:14,24 291:16 293:26

294:7,20 295:10 300:17 312:16 318:25 331:8

357:18 359:5 362:21,21,22,23 363:1 378:4

381:17 386:23 395:23 401:10 413:12 414:11

420:26 428:18 437:2 440:7,11,12,16 463:23

478:24 479:3 480:26 493:9 502:16 504:11 506:14

506:19 508:24 509:4,5 521:21 525:22 539:10

551:14 559:2 562:19 572:15 575:26 576:5 586:25

593:16,18 595:26 607:15,18 610:21 613:20,20

614:2,14 618:24 622:21 624:10 626:9,15 627:6

627:24,26 628:3,17 629:2,5,7,20,24,25 630:8

640:23 641:4,15 643:18 644:11 650:25,26 651:14

652:3,4 658:20,23,26 659:1,9,10,14,18,21 660:21

660:21 661:5 662:10,19,24,25 663:4 664:26

665:19 666:7,26 667:8 668:24 669:5 670:23

672:6 675:17,18 676:8,22 677:2 678:5,16,18

679:6,19 681:7,8 687:21,23 688:12,13 691:23

692:13 695:21

risks 8:6 13:20 16:25 24:4 28:3 32:18 33:16 43:5

49:20,23 157:17 180:21 194:10 203:23 214:8

215:18 219:17 284:16,17 291:11 294:10,15

414:14 619:2 624:11 668:21

risk-benefit 272:8

Riverside 4:5 249:14

RNA 475:15

road 322:8

roads 664:1,10

Roberts 374:25

robust 15:13 112:20 137:10 162:15 178:26 190:9

193:19 459:23 556:15 566:12 586:3 587:9 626:14

626:18

robustness 446:26 672:25 673:1,5

rocket 308:8

Rod 316:11,13,13 319:10 320:3,18 321:2,6,18,23

321:26

Rodenticide 28:16

role 9:18 10:5,8 18:13 21:11 23:25 236:9 238:12

238:21 279:14 280:1 289:23 451:20 551:20

554:13 596:8 651:20 669:2

rolling 684:7,9

room 3:26 138:16 198:1 238:2 248:1 257:20

296:23 375:13 410:22 681:10

rooms 248:3

root 253:23 317:6 632:6

roots 120:6,16 149:14 291:6 350:6

Rortais 96:8,13 97:3,5,14 101:24 262:19

Rosalind 5:24,24 32:13 45:24,24 46:7,13 81:9

131:17,20 150:12 151:1 235:4 250:18,18 324:2,6

325:1 329:7,7 333:26,26 337:16,17 340:1,1

341:17 350:18 354:11 355:1,15,15 356:21 357:25

357:25 358:3,20 365:8 368:16 370:5 375:25,25

381:4 382:14,20 385:13 387:7 389:15,15 419:25

433:3,6 434:7,20,24 440:16 441:6,6 455:21 457:9

468:5 469:8,8 474:2 476:1,23 480:11 481:18,18

482:25 484:21 485:19 486:23,25 489:16 490:13

490:23 492:5 495:19 500:22 504:18 507:5,17

510:7 527:20 529:17 535:12 537:16 559:5,15,23

565:22 566:14 571:16 574:8 578:8,8 580:13,24

581:15 583:17,17 584:8,13 585:3 596:6 598:6

618:14 619:6,6,11 633:15 641:14,20,26 651:20

652:3,6,22 654:16 669:22 674:18 679:23 683:5

690:14

Rose 469:15 636:23

rotundata 363:9

rough 218:24 376:9 584:10

roughly 19:6,13,23 20:2 147:13 234:17 395:26

612:3
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round 385:1 632:20

route 38:5 40:7,15,25 58:11 63:7 73:3,7,17,26

74:10,11,19 75:2,4,17 76:10,25 77:16,17 78:4

80:11,15,17 83:24 85:1,3 89:17 92:22 157:19

167:12 183:19 291:8,11,23 292:1,3,25 293:14,16

294:3 407:18 409:6,10 413:5,11 414:18 422:6,23

422:26 423:3,8 429:9 433:4,15,20,23 438:24

440:11,18 525:2 603:20

routes 31:17 35:15 37:13,24 38:3 40:22 41:16 55:9

57:25 65:3 71:13,15,22,26 72:1,4,7,9,11,16,19

73:21 74:22 75:20,25 76:3,4,11 80:16,19,23,24

80:26 81:3,4,18 82:4,9 156:25 167:14 178:24

257:7 291:3 294:25 299:26 407:26 408:24,26

429:21 432:21,22 437:13 518:24 522:8 536:26

631:5 677:13,14

routine 57:8 85:3 153:23 316:1

routinely 50:26 60:1 142:9 158:9 160:13 178:10

443:20

row 104:25 225:9,15,19,25 226:5,8 322:21

rows 601:25

royal 39:1 44:23 74:2,11,16 75:13 76:17 93:5 95:5

95:6 99:23 101:18 102:26 103:24 104:7,12

105:14 137:21 138:1,9,16 159:19 160:8 338:8,10

355:9 356:7,8,9,17 378:24 379:11,20,23,26 380:3

380:6 381:13 382:21,25 383:13,22,25 388:5,15

388:21 389:5 391:24 433:16 465:3,6,14,17 466:4

466:4,12 469:5,5,6,11,15,16,16,20,20 470:1,9,12

470:17,20 471:2,3,6,11,15

RQ 58:9,14 59:8 60:4 63:23 67:17 201:2,4,5

206:14,18 208:13 209:8,14,19 210:10,25 217:16

217:19 218:14 219:25 256:24 491:12 492:10

RQs 206:2 346:5

RT25 158:3,4

rubber 322:8

rule 277:19 420:14,15 584:18

ruled 59:24 382:25

rules 574:10 643:1

run 161:9 216:3 304:12 310:3,4 324:8 426:16

428:19 453:18 456:11,16 462:3 480:1 566:22

693:14

running 198:1 215:4 230:16 288:22 305:10 306:24

310:5 506:2 541:2 684:2 691:9

runny 653:26

runoff 40:24 82:5 318:4 321:9 426:14

runoff/erosion 37:9

runs 307:12

rural 306:13

rush 139:20

Rutgers 6:6 250:25

Ryan 120:20 126:3 398:1,22

Ryan/Burghardt 406:11

S

s 87:19,19,19 127:26

Sacramento 684:23

sacrifice 453:24

sacrificing 453:21

sacs 147:20

safe 10:10 256:3 262:12 273:25 288:3 323:5,6,8,13

324:22,25 529:10 668:26

safeguarding 254:16

safely 693:18

safer 15:25 279:8

safety 11:2 23:20 29:13 64:11 156:22 213:15

287:20 288:25 289:2,16 292:16 324:23 436:26

663:20

sake 605:6

salamander 664:3

salamanders 664:2

salesmanship 663:17

salient 653:6

sample 149:2,5 182:19 261:20 301:2 305:6 313:16

313:17,20 453:22 497:15,16,17 498:12,20,22

515:2,9 536:21 537:17 538:3 540:16 541:17

548:26 549:1,3,6,21,26 572:10 580:25 625:24

630:22 636:25 639:7,17

sampled 293:20 581:19

samplers 434:1

samples 149:12 303:23 304:12 305:1,6 310:3,3

311:15,17,20 327:12 396:21 412:6 537:3 635:21

sampling 148:25,26 149:14 186:16 191:16 224:13

332:13 439:18 537:25 572:11,12 580:26

Sandy 4:11,11 136:21,22 390:2,3 635:3 661:4

663:10 685:12,13 686:1,4

SAP 2:14 3:10,21 7:11,13 48:16 70:17 81:2 105:22

106:2 128:22 129:1,4,10 140:10 197:4 199:16

208:25 209:2 229:12,16 249:15 251:8,10 283:23

285:13 290:19,19 291:11 292:2,9 294:7 641:3

693:17 696:15

Sappington 32:5,25 33:1,2 42:20,26 43:7,17 44:4

44:17 45:1,7,18,20 46:3,12,17 47:16 48:7 53:3,4

66:26 69:15 71:24 84:3 89:15 91:6 143:5 152:12

199:9 200:25 204:7 208:18 214:18 219:9 220:24

221:1 230:7,23,23 231:17,20 232:9,9 240:1,8,8

244:4,4 330:10,10 345:12,12 353:18,18 354:2,2

354:23 355:5 357:1 422:12,13 432:7,7 436:5

437:12,12 441:12,14 497:20,21,21 527:11,12,12

530:6,7 538:14,15,15 552:2,2,21,21 553:19,19

561:26 624:8 626:4,7 630:3,13 643:9,11,16

669:26 670:19 676:12,14,19 683:18 696:3

Sappington's 48:12 482:4
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satisfactory 477:21

satisfiable 240:25

satisfied 2:22 54:14

satisfy 187:26

save 308:24 309:2 333:1 335:19 430:23

saw 43:10 69:23 129:25 302:23 303:2 324:14

437:16 499:5 619:24 661:6

saying 16:13 48:12 90:8 132:10 136:15 239:13

277:14 321:16 327:22 329:11,13 335:18 344:16

352:17 389:26 424:18 427:4 461:21 466:18

482:18 495:26 519:18 557:9 559:11 601:22

607:18 609:10 620:9 635:4 637:25,26 645:8

647:24 650:10 652:9 653:14 679:12 681:9 685:2

says 283:13 323:5 334:24 335:2 340:4 402:15

414:23 439:2 554:1 648:14 652:2 659:13,18

673:16

scale 27:25 149:17 222:23 421:6 462:21 511:5,11

588:6 598:9 606:6 623:24 649:26 650:11,11

673:7 677:19,20

scaled 116:2

scales 190:24

scaling 372:10 619:16

scarce 177:5

scary 470:22

scenario 20:11 83:2 190:15 245:11 246:25,25

268:2 295:3,5 379:18 382:2 388:20,25 404:25

424:17 437:10 438:10,12 487:24 568:7 677:4,5

scenarios 121:3 130:9 156:5 213:3 246:13 265:17

268:19,19 295:9 403:24 404:5 430:16 437:10,13

488:18 568:4 590:1

scene 85:25

schedule 87:13 407:13

scheduled 87:14

scheduling 26:17

schematic 44:2

scheme 64:12 65:4 83:7 148:25 223:7 659:1

schemes 64:9,18,25 65:4,9,24,26 66:9 262:8

Schertz 285:10,12,15,16 288:18,19

Schlenk 3:20,22 4:3 6:14,18 13:6 14:24 16:17

17:25 32:7,19 42:14 44:11 45:22 47:7 48:20,26

52:26 66:18 67:11,26 68:15 69:12,23 70:3,9 71:3

81:7 82:3 83:3 84:12 85:6 87:11,21 88:23 89:1

129:21 130:19 131:16 132:15 134:25 136:21

137:16 138:21 140:1 150:9 151:21 152:11,23

154:3,7 194:24 195:10 196:6 197:15 199:7 200:1

200:5,20 201:18 203:3 214:20 215:3 219:12

220:1,21 229:24 231:9 232:15,20 236:20 237:16

239:14 242:20 243:11 245:5,14 247:15 249:9,13

251:4,18,19 252:5,6 274:23 275:21,25 276:16

277:22 280:4,18,20 284:6,21 285:8 288:17,20,24

296:11 302:6 309:4,20 311:3,7,22 314:16 315:8

315:22 316:8,11 319:2,13 320:23 321:14,24

322:1 323:25 325:2 326:21 328:8 329:6 330:9,22

331:6,26 332:14,17,20,26 333:10,14,24 336:16

336:19,21,23,25 337:14 339:7,14,17,19,26

340:14,25 341:15 343:2,10,22 344:2,5,12,18

345:2,17 348:9,12,18,21 349:7,20 350:9,16

352:14,21 353:15,22 354:10,25 355:19,25 356:4

356:12,20,25 357:10,23 358:2,5,19 364:4,7,17,21

365:2,7,15 366:2 367:24 368:14 369:11,21 370:1

371:3,13,18 372:5,16,24 373:2,10 374:4 375:16

375:24 376:22 377:5,16 378:16 379:7 380:11,13

381:3,7,21 382:4 383:15,19 384:2,12 385:9,11,21

385:26 386:7,16 387:6,14,23 388:7,17,26 389:6

389:12 390:2,7,15,20 391:14,19 392:2,8,18

394:10,19 396:13,23 397:5,14 400:12,25 401:15

401:18,19,22 402:1,4,12 403:7,9,11,14 404:2

405:9,12,14,17,25 406:22 407:1,9,12 408:2,8,14

408:18,22 409:2,11,16 411:9 412:8,26 414:21

415:8,15,20 416:9,26 417:8,14 419:4,23 420:3,22

421:13 422:2,8,16 423:5 426:19 427:2,5,8,20

428:15 429:16 430:6,19 431:7,23 432:1,10,17

433:1,12,22 434:5,18 435:2,25 436:5 437:4

438:21 439:6,10 440:4,14,22 441:11,16,24 445:8

445:16 446:1,7,19 451:7,22,26 452:14 454:2,15

455:1,20 456:21 457:8,16,23 458:2,7,23 459:13

460:3,9,21 461:7,23 462:1,13 463:4 464:2,16,20

465:7 467:22,26 468:3,18 469:2,13,23 470:3,14

470:23 471:8,13,18,21 472:1,11,13,16,20 473:1,7

473:10,13,16,26 474:8,12,18 475:18,22,25 476:5

476:20,25 477:6,26 478:3,5,14,17 479:8,24 480:9

480:17 481:2,16,22,25 482:1,11,24 483:15,21,24

484:7,20 485:3,6,8,17,25 486:6,12,22 487:11

488:1,13,20 489:7,15 490:12,22,26 491:6,13

492:4,13,16,18 493:4,10,18 494:11,19 495:18

496:10,17 497:7,13,19 498:8,14,18 499:4,10,15

499:23 500:21,25 501:2,10,16 502:3,10,18,25

503:7,16 504:1,17,24 505:8,13 506:7,21 507:3,10

507:16 508:1,7,13 509:9 510:6,11,15,18,21,26

511:13,23 512:5,17 513:12 516:18 517:8,19

518:3 519:16 521:1,8,25 522:16 523:4,22 524:23

525:5,23 526:5,25 527:11,19 528:5 529:16 530:6

530:23 531:4 532:21,24 533:10,26 534:20,22

535:6,11,19 536:2 537:1,7,15 538:6,11,21 539:11

540:12,21 541:6,22 542:3,17 543:1,13 544:26

545:23 546:4,17 547:4,9,16 548:5 550:7,16 551:8

551:16,25 552:17 553:8 554:6,21 555:18,22

556:1,20 558:7 559:4,14,22 560:17 561:15,24

562:1,10 564:22,24 565:5,8,20 566:16,25 567:13

567:20 570:23,26 571:9,14 572:25 573:5 574:6
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574:13 577:1,19 578:7 579:6,13 580:2,11,18

581:6,17,26 582:15 583:12,23 584:5,12,19

585:12 586:1 587:18,20,22,24 588:8 590:10,26

591:10,13,26 592:15,21 593:8,22 594:26 595:2

595:19 596:4,10,20 597:23 598:5,16 599:7,26

600:7,17 601:7,10 602:6,16 603:15,24 604:18

605:1 606:22,25 607:1,23 608:6,25 609:4,9,19,24

610:2,5,15 611:7,18,26 612:19,26 613:12 614:8

614:15,22,24,26 615:9,24,26 616:17,25 617:11

617:18 618:1,12 619:12 620:2,15 621:1,16 622:5

622:12 623:4,21 624:2,5,12 626:2 627:2,21 629:9

629:17 630:2,11,16 631:26 633:14 641:2,17,22

643:3,13,20 655:24 658:13 667:3 669:6,11,20,23

669:26 670:7,13,17 675:14 676:11,16 681:17

683:4,15 685:8 692:16 693:9,16 695:8

Schmickl 226:4 227:7

Schneider 298:13

Schonherr 399:25 405:1

Schwab 5:4,4 231:9,10,19 232:5 250:3,3 349:21,22

366:3,4,5 367:26 370:2 372:9,12,25,26 373:10,11

374:6 392:19,20,20 394:21 396:24 397:14,15

400:14 401:2,19,23 402:3,12,13 404:2,3 405:11

405:25,26 686:25

science 6:23 7:13,15,20 10:12 12:14 14:22 15:12

16:10 19:1,8 20:22 22:5,20 23:15 28:5 33:2 51:19

139:21,23,24,26 203:10 221:16 223:15 229:17

244:15,16 253:10 256:24 274:16,22 278:23 279:9

280:12 301:10 308:8 414:3 555:10 623:11 639:26

640:20 664:19,20 668:2 670:20 675:10 682:3

695:5

sciences 4:4 9:7 56:24 249:14

science-based 270:20 692:12

scientific 2:2,15 7:8 8:17 12:9 14:19 15:20 16:24

18:4 22:22 23:22 28:21 29:11 33:6 64:10 65:18

95:22 107:26 110:18 111:13 162:10 163:19 188:6

188:14 190:25 192:1,7 196:1 203:9 240:12 242:8

272:1 278:17,26 279:6 289:20 296:9 300:13

302:4 309:22 312:11 322:3 324:4 390:18 413:14

493:25 689:10

scientifically 162:13 195:24 210:18 228:7 253:25

254:4,17 272:6 278:24 279:19 280:2 318:25

scientist 140:7 664:17

scientists 12:26 23:3 256:17,18 281:25 300:15

560:8 696:4

scope 53:22 286:3 336:3 544:25 550:4

Scott 285:10,12,15 288:19

scout 604:15

scouting 573:2 604:4

screen 13:26 26:21,23 54:1 77:18 118:8 119:1

123:10 129:12 139:7 142:19 153:26 165:9 166:17

171:18 175:15 199:22 215:14,15 216:2,3,25

217:3,5,6,10,14 218:3,8,8,16,22,24 255:12

258:26 261:4 262:11,12 272:15 384:8 403:25

441:23 461:21 484:6 490:9 608:4 610:25 611:21

616:23

screened 134:15 265:17,23 271:11

screening 50:6 52:1,11 54:6 59:24 63:1 65:7 67:23

68:21 69:1 83:5 107:3 113:9 117:7,9,26 118:6,9

118:18 119:7,23 123:3 126:5,9 127:24 136:13

137:6 150:15 153:5,13 155:14 156:1,13 159:16

164:26 165:2 166:6 167:1 169:1 175:14 178:16

194:1 196:10,12 198:12 201:14 205:16,24 206:9

208:24 211:21 218:9 272:12 385:2 402:16 413:23

424:13 443:10,12 447:21 456:2 475:17 619:3

624:24 626:21,24 668:9

screenings 443:7 461:6

screening-level 405:22 474:17 614:13

screens 103:26 403:20 514:17

screwed 324:18

se 20:14 135:18 233:26 284:23 353:14 429:18

sealed 342:22 556:14

searched 327:6

searches 327:6

season 160:2 317:1,7 318:10 338:4 344:15 502:13

588:14 631:21

seasonal 223:3 430:6 588:10 589:3

seasonality 445:1 588:12,19

seasons 359:14

seats 296:18 512:23

second 36:4 53:2,5 56:15 58:11 64:12,24 77:25

83:10 84:14 86:12 98:2 134:8 143:2 147:11

162:20 172:18 177:9 182:11 183:10 186:5 188:17

209:4,7 222:14 225:15 227:20 243:26 244:14

251:8 296:19 298:24 305:8 310:15 338:5,18

342:14 383:9,15 384:4 410:13 413:4 414:17

423:12,17 424:10 425:22 446:10 471:22 479:22

479:23 519:23 553:21

secondary 38:3,5 245:2

secondly 79:16 152:17 323:14 396:8 406:5 531:22

570:20

secreted 349:4

secretions 93:7 388:23

section 4:13 33:14,17 48:4,6 71:18,18 89:7 101:11

128:24 136:11 140:14,14 185:1 190:2 201:13

221:9 231:8 294:12 333:19 381:12 392:13 397:10

402:8,14 446:10,12 447:10 458:8 472:17 508:23

510:17 538:24 542:18 547:24 567:15,18 593:14

670:21

sector 688:10

security 287:25
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see 3:17 10:12 11:3 44:13 45:1 47:15 50:7 70:5

82:20 88:20 96:16,22 98:25 109:17,24 113:26

114:10 220:13 224:24 234:21 248:7,17 251:13,26

255:8 257:20 264:2,2 265:16 267:20 269:17,18

269:24 270:2 274:12 276:4,24,25,26 277:3 278:3

279:14 289:9 290:2 303:5 304:16 305:2,13 306:9

307:8,11 334:6 335:12 343:19 345:2 352:22

369:11 370:4 373:18,26 380:3 391:11 401:8

405:4 406:19 411:1,15 415:12 416:16 435:21

444:15 445:10,22 452:2 453:10 454:9 462:11,19

475:7 483:17 487:9 488:18 494:1,1 499:1 506:13

512:17 517:16,17 528:23 533:24 536:22 537:17

538:4 542:26 544:16,17 545:14,14,15,19 550:21

550:26 551:1 557:15 568:10,25 570:20 573:2,3

586:7 590:25 593:4 594:10 600:24 601:2 610:10

612:6 613:21 616:6 623:18 629:20 646:15 657:15

660:17 661:10 666:19 681:6 685:19 690:16,19

seed 34:1 41:3,5,11,17,22 53:16 62:15 63:6,6,12,14

63:15 64:26 65:5 72:10 74:22,23,25,26 75:3

76:25 77:1,3,9,19 80:12 83:16 84:2 86:22,24 87:4

90:3 92:17,24 106:15 107:1 117:6,9,13,17 118:20

118:22 119:2,3,4,5,11,12,22 123:3 126:26 127:21

127:23 128:1 129:14 138:23 142:20 147:9 149:13

157:17 158:23 206:10,13 220:4 258:6,8,9,23

291:12,13,25 293:1,7 300:8 307:20 316:21,24

317:1,5,8,9,12,24 318:2,5,26 319:5 320:10,16

321:17 331:10,11,17,17 348:1,15,26 349:23,25

350:1,3 402:7 405:24 409:9,14,22 410:10 411:11

411:11 412:3,13,16 414:4,14 415:25 416:5,13

417:19,23,25 418:11 419:15 421:4,8,11,26 423:2

431:11,18 436:25 437:10,26 438:23 525:8 560:1

572:21 574:10,10,11 581:15 623:15

seeding 87:1

seedlings 317:17

seeds 113:12,24 114:20 115:5,9 118:2,13 119:9

298:16 299:8 316:25 317:17,23 348:16 349:2,3,4

349:24 417:25 421:9 422:1

seed-treated 300:8 402:11

Seeger 67:17 81:24 612:8,8

seeing 6:21 8:23 249:5 411:14,17 439:5 486:2

533:17 619:17,22 653:16,17

seek 31:24 178:6 317:8,15

seeking 42:6 81:2 105:22 295:4

seeks 312:19

seemingly 442:23 548:14,17

seen 141:9 168:13,19 193:13 199:24 233:16 256:20

302:21 303:3 304:15 306:15,16 340:19 380:5

396:22 461:5 493:2 496:9 563:17 574:3 579:15

587:15 633:23 640:15

select 263:17 516:25

selected 69:4,16 97:26 121:14 148:12 149:5,11,19

170:17 172:9 221:17 224:10 226:23 268:15

397:18

selecting 402:26

selection 24:10 30:1 34:8 55:16 147:16 170:8

172:20 182:18,22 184:25 517:26 563:23 625:1

self 634:23

self-serving 291:14

sell 308:12 664:14,14

semi-field 48:18 60:22 61:2,3,8 66:6,10 137:6

144:3,25 147:21 159:12 160:16 166:2 167:4,13

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169:19 317:26 366:6 413:18,19

spread 19:24 272:22 376:3 474:3,6

spreaders 368:8

spring 292:24,26 302:13,15 303:8 501:25 541:4

543:26 575:11 597:1 682:16

square 187:22 322:11,20 323:20 324:20 374:19,21

375:5,6 575:17 585:7 603:7

squash 121:16 122:10

squeeze 508:19

squeezing 537:23

stab 556:6 599:8

stability 39:13 177:11 333:17

stable 561:2

stacking 307:18

staff 3:10 16:22 17:5,9,22 212:11 251:13,16,23

252:1 290:6 424:26 623:8 686:3 687:14 688:18

689:2 693:13,17 694:15

stage 8:7 49:21,24 57:23 66:25 102:22 153:26

156:15 276:5 346:1,10,13 371:8 379:3 382:11,21

456:18 465:3 488:17,22

stages 66:23 67:1 94:2 115:24 154:1 159:17 169:16

173:20 342:5 370:26 486:24 487:1,6,26 552:13

684:13

staggering 238:10 239:2

stake 255:7 292:10

stakeholder 27:6 204:1

stakeholders 9:26 239:22 418:5

standard 35:4 121:3 158:14 188:21 193:10 195:15

211:17 270:18 313:8 342:19 424:13 461:20

477:21 483:4 484:23 490:14,18,20 579:8 610:13

632:24 633:4 640:12 662:13,16

standardization 380:25 453:26

standardize 344:17 381:1 442:23 458:1 581:23

596:2 637:3

standardized 21:25 51:4 133:4 459:23 464:6

468:20,22 469:21 486:17 507:7

standardizing 368:26 372:1 442:20 639:13

standardly 647:2

standards 23:5 280:8 281:17 313:3,5 478:10

standing 431:21 438:11

stands 107:4 317:7

Starner 293:17

start 12:14,22,22 84:9 107:10 184:16 247:19 252:3

285:6 286:21 287:15 317:23 345:22 367:10

407:15 418:20 420:19 441:20 463:3 483:19

498:26 512:24 523:10,18 529:20 545:6 575:20,24

583:9 588:23 619:26 621:22,23,24,25 637:5,9

642:25 643:26 644:4 695:7

started 2:7 6:25 13:7 87:23,24 249:12 251:5

308:23 687:1 690:20

starting 7:1 20:4 35:26 56:13 257:23,25 259:10

260:1 315:3 455:23 523:10 574:1 605:8,9 608:16

608:19,22 628:7 636:26 637:5 643:25

starts 420:21 457:19 489:12 513:19

starve 528:15

starving 529:7

state 5:2,22 10:15 21:13 32:21 56:24 221:16

223:15 229:17 236:9 244:15,16 247:13 249:11,26

250:17 258:21,21 265:22 303:20,21 304:10 311:9

311:9,14,17 315:10 316:18 323:7,12 325:16,22

326:18 368:25 409:17 411:20 415:3 418:7,8

419:15 442:2 446:21 489:9 502:12 509:26 513:15

598:9 605:4 629:26 669:13,15 670:20 675:10

681:3

stated 28:10 29:18 265:22 270:14 292:17 301:26

402:21 484:16 568:2 588:21 591:2 680:15

statement 260:23 323:11,22 345:3 582:17 630:1

658:4

statements 28:8 34:20 291:10 316:21 350:8 555:2

642:5

states 9:9 17:14 173:9,23 189:18 230:16 234:3

236:5 238:25 252:21 282:22 283:14 285:22

286:15,20 301:6 308:5 316:17 325:21 396:18

424:1 578:12 593:21 629:23 684:22

state-of-the 289:24

statically 630:22

stating 4:1 237:26 656:11
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station 454:7

statistical 152:19,20 177:26 178:2 191:3,12 261:18

495:24 547:26 548:7 549:12,16 550:10,11,14,18

551:6 552:4,19 554:16,25 555:3,15 569:7 625:25

638:1,5,9,12 639:16 640:2 641:10 642:9,12,12

651:9

statistically 495:2 536:12 549:4,24 550:3,26 569:4

625:8,22 642:7,15

statisticians 638:18 640:14

statistics 19:11 447:7,18 551:18 554:10 638:14,24

641:12 642:5

stats 553:3

status 235:17 297:12 359:26 504:9

statute 7:14 420:20

statutory 28:14,20 418:2

stay 197:24 249:3 344:13 570:15,19

steadies 233:22

steadily 19:19

steady 233:3

stealing 560:19

STEEEGER 52:24

Steeger 46:24 47:7 48:11 50:1,24 51:24 59:13

62:12 68:11,20 69:2,19 70:15 71:3 83:15 84:21

86:23 136:7,17,20 137:4,23 139:5 150:15 151:8

151:25 152:1 153:3,20 195:5,14 196:9 197:2

198:11 200:3,3,6,24 202:3 203:6,8,10 214:21,21

214:26,26 215:26 219:6,19,19 220:9 233:23,23

235:18 238:1,1 239:20 240:1 281:7 325:18

331:16 604:21,21 608:1,1 609:17,19,20 610:18

612:24 614:11,11 615:4,4 616:20 618:22,22

619:9,9 620:26 621:2 622:11,12 630:4,5 667:23

696:2

steep 235:23 687:2

steering 283:17

Steiger 282:12

stem 277:15,19 399:4 400:15,18,19

stems 276:19,25 397:12 406:6

step 49:14 57:6,8 60:2,8 62:13 68:14 154:15

204:26 334:6 399:3 411:1 519:1 567:25 568:23

569:1 639:3 656:9

Stephen 4:16 6:18 241:6 245:16 249:16 322:2,6

324:4,7 462:20 464:4 486:13 487:5 488:11

496:18 498:19 583:14,21 609:11,26 610:4 611:8

611:24 613:5 620:17 635:23 636:7 641:23 642:2

685:15

stepping 57:2

steps 21:11 204:8 694:26

Steve 4:16 13:22 153:3 249:16 374:25 420:22

462:20 487:5 488:11 613:5

Steven 6:17 68:2 69:25 417:17 419:13 420:7

683:20 694:18 695:25

stewardship 77:5 274:18

stick 130:10 450:21,25 648:5 655:2

sticker 323:10

stickers 368:8

sticking 417:22 651:16

sticky 417:26

stimulate 568:3

stimulation 644:7

stimuli 541:16

stimulus 450:23 505:24

stochastic 650:13

stochasticity 627:15 651:7

stocks 192:11

stomach 191:16 427:15,17,19

stoned 506:1

stop 335:19 359:18 506:2

stopped 592:7

storage 93:4 192:10 360:9 487:2 541:10

store 191:17 322:18 324:18 359:25 539:5 540:14

543:8,11

stored 78:1 94:16 105:11 186:5,6,11,16,17 388:24

389:22 392:1 448:22 528:25 657:14

stores 169:16 184:2 453:14 523:9 540:25 589:4

stories 303:10 311:19 690:4

storing 186:7

storm 418:9

straight 473:2 533:8 538:12 600:25

straightforward 9:11

strain 457:25 469:7 470:13

strange 613:18

strategies 358:11

stream 120:13 398:14 399:5

streamline 515:11

strength 61:7 144:8 164:3 175:3,11,24 176:24

184:16 222:9 224:26 225:11 226:7 227:3,23

262:2 267:14 270:1 337:7,22 338:2,18,21 397:21

535:26 536:6 556:13 563:9 588:22 637:11

strengthen 16:14 17:22 136:5 400:2

strengths 23:26 105:25 107:14 129:1 365:22

366:18 373:15 379:4 384:7 392:23 397:16 402:9

402:20 403:17 407:23 494:9 513:8 514:21,22

516:16 526:16 562:8 563:2,3,22 564:21 567:14

stress 173:3,8 175:10 176:11 180:2,11 253:18,20

275:12 279:22 280:2 289:23 385:25 506:26

507:14,23,25 508:6 511:22

stresses 360:8

stressor 27:13 30:3 34:16,22 35:15 36:19,23

223:12 224:2

stressors 9:17 21:1,8 34:14 157:1 223:13 244:12
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strict 314:11 646:20

strikes 305:19

striking 548:17

stringency 315:13

strive 242:14 480:7

stroke 631:9

strong 195:26 393:26 410:23 551:6 606:16 668:3

687:20

stronger 193:19

strongly 11:10 15:11 21:8 349:15 371:5 378:12

396:6 409:25 411:6 426:9 591:7 617:20 656:21

681:11

struck 85:10 201:20 232:25 351:24 425:26

structural 27:22 49:11

structure 35:12 145:10 169:3 241:9 296:25 575:4

structured 145:4 213:12

structures 472:9

stuck 296:6 370:18

students 690:1,4,25 694:2

student's 693:26

studied 295:12 367:14 517:3 648:9

studies 15:21 21:21 25:14 31:19 51:7 54:14,22,26

60:1,18,19 61:3,5,9,11,24 62:4,11 63:3 64:21

66:6,7,11 68:23 69:17 95:6 104:6 108:1 110:18

110:19 111:16,19,26,26 112:4,8,12 114:13 120:8

122:1 123:5 124:22 128:18 130:22 131:4,8,12,14

132:23 135:20,22 136:2 137:6,7 139:12,19

140:13,16,19 141:3,6,11,14,23,25 142:2,5,13,16

142:19,21 143:4,5,12,18,20,22,26 144:2,3,4,9,25

145:13,15,19 146:5,13,19 147:7,25 148:6,14,26

149:2,9,18,20,24 150:25 151:23 152:2,8,10,14,14

153:7,8 154:15,16 155:1,8,9,20,20 156:2,3,13,14

156:19 158:25 159:1 160:11,16,25 161:1 162:5

162:13,14,19,24 163:9,15,20 164:7 165:3,6,7,14

166:2,6,10,24,26 167:1,2,4,11,13,16,18,23 168:3

168:5,12,22,24,26 169:1,7 172:12 173:11,13,21

174:4,5 175:12,15,19,25,26 176:3,11,25 178:4,6

178:13,22 179:2,5,10,11,12,15,16,21 180:20,22

181:3,14,16,20,20 182:13 183:2,2,9 184:21 185:7

185:19 187:3,14,16,21 190:3 191:4,4,21,24

192:18,24 193:2,13,14,14,15 194:8,9,17,18,19

195:7,12,15,25 196:5,16,17 198:1,17,21 200:8,10

200:12,12,18,22 201:3,23 202:5,6 206:4,5,5

207:5,9,21,24 208:9 209:22,25 210:1,12,17 211:1

212:10,12 213:20,21 214:4 215:20 216:20 217:15

219:17,24 222:7,20,21 223:2,8 226:24 227:22

229:7 232:3 242:25 246:16,21,22 254:9 255:25

259:14 260:15 265:10 266:11,12,19 267:13,17,23

268:1,4,9,11,15,22 269:5,10,13 270:22 274:7,11

275:12 276:21 278:26 281:8,9 289:20 297:25

298:26 300:20 301:2 302:5 312:3,11,14,15

313:23 315:15 316:23 318:15 321:10 327:5,7,8

327:14,18,23 329:21 330:2 339:22 366:21 373:13

373:20 378:13 379:13 383:3 388:1 389:11 393:8

393:16 396:21 397:2 400:17 410:19 421:3 430:10

431:20 439:2,3 445:21 447:25 449:13 454:5

455:14 459:7 460:15 469:18 483:17 489:9 493:1

497:10,12 499:2 500:19 503:24 506:9,11 507:20

508:2,25 509:24 512:16 513:4,6,16 514:22

515:15,20,26 516:21,25 517:12,22 518:1,5,6,8,11

519:12,13,14,19,21 521:12 522:23 526:20 527:16

527:16,20 528:8 531:3,8,21 532:1,9,17 535:24

536:20,25 537:13 538:18 539:9,23 540:24 541:14

541:24 542:6,11 545:26 546:1,11,13,14 549:10

551:4 552:7,24 553:6 554:9 562:9,13,14,18,22,23

562:24 564:1 568:3 569:6,8,19 570:8,16,18 571:3

571:4,5 574:3,23,25 576:18 577:21 579:15

585:25 588:4,10 589:12,19 590:13,22 591:4,4,5

592:3 593:15,16,18 595:14 598:21,21 600:12

602:8 604:23,24 607:6 609:21,22 614:14 615:6,7

616:4,21,23 617:13 619:14 620:25 621:9 622:15

622:16,17,19,20,24 623:2 626:12,14,16,18 628:8

628:11,14 630:15,26 631:14 632:4,14,19,23

636:2 640:20 641:9 642:9,16 644:17,24 649:16

652:18 659:10,20 660:2,17,23 661:1 662:18

663:21,22 676:25 680:24,25 683:23

study 25:2 58:20,22 60:21,22,24 61:1,2,3,26

106:12 108:6,6,6,17,23 109:2,4,6,8,24 110:3

111:20 112:16,19,20 123:11 136:24 139:13,17

140:12 142:11,16,24 145:10 146:24,25 147:1,4,6

147:11,11,14,16,17,18,21,24 148:1,2 156:15,18

157:7,9,25 158:2,5,10,15,16,21 159:8,12,22

160:3 161:18,19,23,26 162:1 167:8 168:6,15,21

169:6 170:4,7,26 171:23 172:3,25,26 173:9,12,18

174:1,6,7,9,14,23 175:5,22,26 177:5,21 179:7,25

179:25 180:1,1,4,6,7,10,14,16,18 181:2,7,13,22

181:24,26 182:4,5,7,9,15,18,18,19,21 183:6,6,10

183:17 184:4,10,17,26,26 185:6,9,10,14,14,16

186:13,25 187:9 188:8,24 189:4,16,22,23 190:13

190:18,18,19,20 191:9,13,14,25 192:7,11,14,17

193:19,20,24 194:1,4,9,13,15 195:16,20,22,24

196:12,15 197:10,11 201:10 208:11 222:22,23,26

235:17 237:2 250:22 254:6 267:20,23 268:17,19

269:17,18,22,24 275:1 290:12,12 294:1 298:10

298:13 299:2 300:21 313:6 319:7,7 366:12,22

367:3,3,18,26 368:3,9 369:23 374:7 379:14 383:1

383:4,9 395:12,18 407:5 425:15 442:11 450:17

461:1 466:15 476:2 489:5 490:5,6,8,11,18 493:23

493:23 494:3,5,6 500:2,19 503:25 511:9 513:5,10
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543:19,24 544:1,3 545:3 546:24,25 547:1 549:25

549:26 550:3,22,26 551:5 562:24 563:23,25

566:4,23 567:18 568:24 570:9 573:14,16,18

576:19 584:6 587:11,13 588:23 589:6,8,12,15

590:23 592:4,17,19 593:13,20 594:2,14,18

595:24 598:25,26 619:24 628:12 630:24 632:16

632:22,24 633:12 634:10,23 635:6 636:2,9,10

637:9,10 639:11,12,17 642:13 649:18,21 650:1

659:16 662:11,26 677:25 680:26 683:26 690:9

studying 444:18

stuff 306:26 308:3 310:16 418:3 468:12 471:17

507:24 546:15 550:19 561:25 567:2 642:24

650:17

stupid 603:18 671:17

sub 333:16

subacute 346:14

subcomponents 227:7 426:15

subdividing 657:24

subgroup 23:8

subject 40:19 63:20 176:2 177:6 183:22 235:16

312:8 350:5 367:13,16 392:22 444:1 456:5

467:13 468:26 642:8 685:18

subjected 487:16

subjecting 266:14 268:1

subjective 500:16

subjects 287:24

sublethal 51:6 163:8,11,16,18,21,21 164:1,7,12

166:7 175:13,16,20,23 179:5 185:8 194:18 214:3

222:15,17 291:1 294:26 297:26 298:2,5 299:1,4

342:11 452:6 505:15 506:15 508:25,26 509:4,12

509:13,17 510:1 511:6,9 512:9,11,13 619:18,21

619:24 620:7,9,22 621:3,13

subletters 345:10

submit 118:16 124:23 183:6 276:6

submittal 312:11

submitted 17:5 60:1 124:21 142:9,10 150:17

151:24 201:26 206:4 207:6 216:19 281:4,9 288:4

288:15 289:14,20 302:3 323:16 327:5,7,18

622:25

subpart 287:11 341:6 345:13 357:17 365:22 409:5

422:20 432:21 441:21 464:24 471:24 472:23

474:15 477:2 480:24 491:9 499:18 503:21 508:23

513:3 526:24 527:13 531:2 535:23 538:24 547:24

562:8 567:17 585:23 588:2 593:13 676:20

subparts 424:3 435:12

subquestion 333:9,11,11 335:13 573:7

subsample 583:18

subsection 503:19

subsections 621:23

subsequent 41:8,13 53:10 60:3 149:11 224:16

240:5 408:7 544:20 623:16

subsequently 38:23 40:1

subset 91:4 92:16 216:10 228:25 395:18

subspecies 195:2

substance 148:18 181:9 185:26 187:11 192:12

415:1,2 441:1 463:17 523:26 529:9 557:21,26

628:4 629:16 634:24

substances 214:24 413:26 414:19 415:3 431:26

440:8,13,25 441:2,3,5,9 465:17 479:13 610:17,19

628:21 632:9,10

substantial 78:22 79:14 132:19 146:11 166:22

374:13 401:4,13 424:19 425:26 430:3,14 681:12

substantially 80:15 371:9 467:1 657:24

substantiate 668:22

subtle 606:18 631:6

subtleties 353:1,9

subvert 664:25

sub-colony 658:8

sub-lethal 632:15,16 677:25

sub-letters 333:5

sub-position 677:15

sub-question 593:11 605:5 608:8 621:17,20,24

625:5 626:12 630:12 644:19

sub-questions 442:18

succeed 647:18

success 39:16,18 62:1 231:4 341:8 342:13,15

343:16 344:26 481:10 484:15 504:22 507:14

515:6 533:4 565:2 586:12 601:11 634:9 645:5

646:3 649:4,5,7 657:2 660:4 666:10 692:1

successful 165:22 227:3 253:7 457:20 647:19

successfully 314:21 489:13 561:2

sucrose 159:20 160:8 161:12,14 171:4,5 172:4

174:13 177:4 450:22 454:14 539:1

sudden 20:8 324:17,18

suddenly 20:5 673:13

suffer 586:16

suffered 292:23

suffering 305:11 558:22

suffice 448:20 517:26

sufficient 31:9 55:5 92:13 124:23 131:9 150:4

159:24 161:9 176:24 205:3 333:4 335:10 417:7

490:11 491:20 508:25 509:5 515:4 548:2 630:22

674:15

sufficiently 162:15 239:26 462:18 480:25 512:19

596:13 617:25 619:18 621:14

sugar 47:3 94:20,21 96:10,13,15 97:7,8,11,15

102:5,5,14,15 135:6 234:6 262:26 263:7,13,17
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suggest 83:25 101:3 105:3 110:2 153:14 214:10

247:12 272:11 318:16 342:2 357:3 370:21 512:2

521:11 574:8 639:24 652:19 663:12 665:15 666:6

666:14 679:1

suggested 138:23 354:12 377:11 439:17 462:5

475:14 636:16 665:6

suggesting 294:12 478:22 620:13 640:11 649:14

661:8,10 663:11

suggestion 372:11 580:24 592:23

suggestions 120:19 187:3 475:8 610:8 635:14

suggests 20:22 25:25 99:23 101:7 116:14 124:12

439:26

suit 168:9

suitable 66:7 161:10 223:22 236:2 404:19 475:6

618:3 681:2

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suite 82:24

suited 185:11 403:23,25 404:5 405:2 499:26

suits 682:11

sum 175:23 374:3 657:22 681:9

Sumithrin 322:22

summarize 33:15 53:8 105:7 224:16 226:13

244:17 247:16 271:24 377:20

summarized 326:24 327:25 329:22

summarizes 126:17

summarizing 33:9 53:12 208:4

summary 80:19 100:6 104:21 180:10 202:23 206:7

223:17 224:5 252:24 275:11 674:24

summer 99:3 302:14 306:23,25 361:13 430:3

445:4 543:25 589:9 597:1

summer/winter 445:20

summing 263:8

super 443:1 514:5,23 532:20 533:9 694:23

superior 372:13

super-computing 235:5

super-organism 448:12 450:1 461:1,2 462:23

473:20 509:14

supplement 537:4

supplemental 212:17 516:4 517:22,23 518:1 522:7

523:11 529:22

supplementary 519:19,24

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supply 8:25 50:17 234:7 519:7

support 6:24 9:5 29:3,18,23 30:12 161:6 176:24

190:12 202:25 209:10 221:11 240:4 244:1 254:14

255:4,11,14,19 256:14,25 257:8,15 264:17 265:9

267:5,9,12 270:17 271:17 272:3 278:10 285:14

289:14 378:1 391:14,20 502:8 567:11 617:20

648:16 681:13

supported 22:24 32:2 167:20 210:17 253:24

300:13 324:23

supporting 37:14 202:1 502:6

supportive 278:22 279:12

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suppose 427:18 650:23

supposed 132:22 308:4,21 323:18 503:5 522:5,5

661:6 687:10

sure 13:6 16:16 68:12 88:5 130:18 151:6 196:14

201:18 221:20 231:6 244:24 246:1 255:23 264:15

284:26 290:22 307:16 319:10,18 320:22 334:24

335:3 337:23 340:26 349:26 352:3 357:8 358:2

371:3,6 374:9 377:20 380:21 381:11 386:12,14

386:24 391:2,12 400:10 401:22 402:4 420:1

426:21 427:8 429:11 430:7 432:17 457:18 469:10

471:16 476:18 493:18 496:21 500:9 501:13 503:3

512:8 519:17 526:5 538:11 547:4 567:7 571:23

574:17 575:4 585:17 595:8 596:7 598:22 609:14

609:19 611:17 618:21 621:1 623:19 627:19

628:13 629:11 634:1 635:13,22 651:24 652:7,8

654:24 659:6 664:22 667:13,20 669:13 670:3

682:7

surface 37:2,8,11 38:5 40:24 41:15 46:5 55:8

293:18,20 299:21 318:2 371:17 374:23 375:4,13

375:19,22 376:6,8,14 377:25 378:3,7 410:15

423:26 424:14

surfaces 37:7 38:6 411:5,5

surprised 263:14 321:16

surprising 655:22,23

surprisingly 328:2

surrogate 18:21 24:8,10 31:6,7 32:3 70:12 95:11

106:3,22,25 113:21 115:17 116:11,19 124:9

127:11 128:9 169:8 257:9,13 270:22 279:25

329:18 337:24,26 347:2,10,20 358:10 359:1,3

362:26 364:3,9 366:11,19 367:10 368:10,20

369:5 385:14 392:15 406:8 434:9 504:10 525:18

563:19 572:8

surrogates 13:18 50:4 115:17 128:20 359:17

418:15 514:19

surround 134:2

surrounding 37:10 69:6 223:25 300:3 564:9

595:13 603:1

surrounds 69:5

survey 19:11,17,18 23:13 237:4

survival 51:11 61:8 164:4 214:2 225:23 227:23

267:15 294:23 301:9 337:7 338:2,18,19,21 341:8

341:23,24 342:14 441:23 444:25 447:7,17 459:10

477:8 480:15 484:9,12 492:22 503:23 509:2

556:8,17 557:1 559:10,13 561:8,10 586:8,10,13
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586:22 604:24 610:9 655:14 666:11 672:2 686:10

survive 52:3 160:5 173:10 189:26 198:8

survivorship 447:7 492:1,1,6,11 636:19

susceptibility 284:25 359:2 445:14 473:4 488:21

488:23

susceptible 21:4 30:2 299:1 501:22

suspect 297:24

suspend 292:16

suspended 673:14

sustain 52:8,9

sustainability 295:11 650:5 666:12

sustainable 253:15 254:1 278:13 297:10 500:17

646:7 649:6

sustained 554:2

swarm 534:2 535:2

swarmed 306:22

swarming 130:4,9 225:26 645:17

sweet 677:23

sweetener 234:6

swimming 433:16

switching 505:17,23,24

symptoms 20:16,17 442:24 509:20

synchronizing 465:12

SYNDER 321:2

synergisms 566:23 688:9

synergistic 219:18 295:2 300:18 688:3,7

synergy 307:18

Syngenta 253:1 255:1 266:2 295:23 296:5

Syngenta's 296:1

synonymously 334:11

synthesize 22:4 624:19

synthesized 25:24

synthesizing 694:23

synthetic 159:19 471:25 472:10

syrup 520:4,20 523:7,11 526:4 542:1

system 45:16 144:26 145:5 169:9 183:15 198:9

211:5,6 256:7 276:13 277:11,11 278:14 292:9,13

317:6 320:2 401:12 442:11 444:5,21 529:8

541:12 640:5 645:24 673:7 686:24

systematic 407:5

systemic 13:18 25:7 33:26 34:2 39:19,24 40:6,10

41:10 75:3,22 116:23,24,26 117:2 118:13 119:16

122:18 123:26 124:24 125:9 126:7 137:14 142:1

142:4,26 148:16 149:8 184:1 257:24 258:8 277:9

277:13 291:4 299:8 300:1 351:21 411:13 440:24

441:9 524:1,5 525:8

systemically 124:15,18,20 125:11 127:22 258:23

300:6

systemicity 36:11 118:17

systemics 34:1 43:11 44:7 57:5 276:18 386:3

412:24

systems 6:8 82:7 230:14 250:26 269:8 277:19

293:10 297:10 320:1,6,26 331:12 352:12 442:11

473:22 514:7 516:14 605:13 609:14 626:20

633:26 668:14 672:4

T

T 87:19

table 7:7 30:13,17,19 95:22 96:16,22 102:8 104:25

113:13 126:17 128:11 142:7 155:2 197:12 206:7

206:12,13 208:3 216:24 217:12 224:16,20 225:5

226:3 231:12 241:4 258:11 260:13 333:19 334:17

334:18 337:9,10,17 340:2 341:9,10,18 345:16

358:15 397:3 571:26 572:1 610:18

tactile 450:24

tailor 228:2 246:20

tailored 64:25 168:9 646:2

tails 263:24

take 2:7 9:25 32:25 47:1 52:16 82:17 83:9,13 85:20

86:4 88:5 129:20 139:3,23 150:12,13 153:5

194:22 197:14 215:4,5 232:7 237:1 243:2 244:7

245:23 260:19 262:19 277:2 292:3 296:13,17

305:22 308:12 314:1 333:12 344:11 361:2,15

373:3 374:17 375:6 396:10 407:15 431:14 446:4

450:10 453:22,23 454:7 480:5,18 489:19 490:7

490:10 507:21 512:19,22 528:26 534:4,15 537:11

542:16 551:1 556:5 558:4 562:2 567:7 579:21

589:20 599:8 602:15 608:11 628:5,15 635:6

637:10 647:8 652:16 657:12 661:13,19 663:2

665:11,12 669:8 674:14 688:22 689:11 690:1,16

690:19 693:11

taken 21:11 87:17 141:21 171:10 173:24 206:13

255:23 265:14 283:8 290:24 295:26 332:25

335:24 362:20 378:19 386:4 387:1 401:5 406:18

442:12 445:7,21 483:13 512:21 537:3 549:11

559:5 562:4 568:23 586:24 587:2,14 589:5

605:14 633:20 686:11 688:5,15

takes 7:7,11 117:2 197:5 265:21 285:7 322:24

357:6 390:24 403:4 430:14 456:18 500:7

talc 77:11 332:9 412:3 417:24 418:11

talents 695:15

talk 50:10 68:13 87:8 100:9 121:8 134:18 143:3

151:11 154:14,19 220:10 231:7 295:15 302:20

307:18 309:8 321:20 329:9 366:17 367:21 379:13

401:24 437:25 440:20 456:7,14 461:24 468:9

506:3 511:11 516:22 523:6 530:8 584:6 642:18

688:21 695:14

talked 32:14 231:11 238:6 246:4 278:4 306:15

307:19 311:15,18 346:23 350:23 356:14 368:17

408:16 421:6 433:15 456:9 464:5 504:19 531:6

543:7 565:11 572:2 576:2 625:21 632:23 644:5
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687:6

talking 32:15 43:12 46:7,10 52:18 71:12 82:22

131:1 136:8 140:11 150:10 154:11 201:13 221:3

231:12 282:22 287:15 314:14 334:12,12 336:1

338:22 346:19 348:16 349:2 351:25 352:16

354:20 376:20 377:1,2,2,12,14 382:23 386:14

400:7 415:23,24,25,26 416:2,11 425:10 427:10

429:23 452:2 458:10 466:22 488:3 489:5 501:13

503:9 513:16 518:12 521:3 522:3 526:14,20

543:5,6,20,24 544:3 549:9 551:13 553:5 560:21

560:22 565:10 578:11,18,26 582:6,9,10 595:10

604:2 605:17 606:18 618:18,24,25 622:15 636:16

645:25 646:6 651:23 653:2 661:25 666:1 674:25

675:7 677:26 678:19 679:21 683:21 687:25

690:26

talks 70:2 241:10

tall 106:24 113:11,24 114:11,16 115:2,7,12,14,16

115:18 116:10,13 124:6 127:10 129:11 259:21

286:8 329:18 392:14 393:8,12,14,19 394:21

tanacetifolia 528:9

tank 220:13

tanks 428:2

tap 420:18

target 98:10 149:1 217:22 239:13 374:8 445:25

511:19 516:2 525:15,16 543:24 544:5 545:5

549:14 563:26 564:8 567:4,26 568:8 595:23

597:8 599:11,20,25 600:21 601:13,16,17 602:5

661:1

targeted 128:16 144:2,5 146:13,25 147:17,22,24

149:24 162:1 172:12 174:4,5 179:10 206:5

targets 98:6 162:6

Tarpy 5:1,1 82:3,4 132:15,16 133:1,10,15,18 134:6

134:8,19,23 152:24,25 153:17 154:2 249:25,26

315:9,10,10,21 344:18,19,19 352:23,24,24

368:24,24 369:14 371:20,21 380:14,15,15 384:23

430:7,8 441:25,26 442:1 446:19,20,20 451:24

452:23 454:15,16,16,24 455:10,10,22 458:17,17

459:4,13,14,14 460:21,22,22 473:16,17 476:2,13

476:14,14 485:25,26,26 489:7,8,8 490:13,17,17

501:17,18,18 504:2 511:23,24,24 513:13,14,14

516:21 521:25,26,26 522:21,21 526:6,26 527:1,1

530:24 531:4,5,5 536:2,3,3 537:16,20,20 538:19

538:19 539:11,12 542:23 543:13,15 544:6,7,7

548:5,6,6 553:8,10,11 561:17 565:5,6 571:9,10

571:10 587:22,23 591:10,11 595:19,20 601:7,8,8

601:12 605:2,3,3 608:6,7,7 614:9,15,16,16 615:9

615:10,10 616:25,26,26 624:13,14 626:11 630:16

630:17 639:2 643:3,6 657:26 675:12 681:15

686:16

Tarpy's 475:7 604:9

task 93:10 95:24,25,26 195:1 329:5 334:7 505:16

505:24

tasked 235:6

tasks 96:2,3,12 328:26

taxa 50:6 205:17 208:17 220:17,18 255:11 270:19

274:10 276:7 552:4 622:23

team 14:7,11 15:7 42:10 66:13 81:5 278:7 651:20

695:18 696:1,17

tease 549:16

teasing 433:3 549:12

technical 27:5 59:11 83:10 161:4 252:22 255:1

271:24 278:6,7 682:3 687:24 688:15 691:16

technical-grade 150:19,23

technique 10:7 264:18 331:14

techniques 10:13 11:8 12:15,18 19:18 131:6 136:2

443:10 555:12 624:24 641:1,5

technologies 77:5,9,13 131:5 450:8,13

technology 253:5 287:8 515:13 623:12

teleconference 13:8 323:9

telephone 69:13

tell 9:16 138:13 257:26 306:12,15 308:4 324:2,19

340:20 357:9 430:24 439:19 550:13 581:4 646:9

673:6,23,26 688:19,25 690:4

telling 311:18,19 327:20 453:23 533:6 570:18

tells 658:22 680:7

temperamental 553:15

temperature 77:23 225:9 313:18 320:8

temperatures 39:17

temporal 150:3 214:10 430:4 443:11 444:26 447:2

447:8 491:24 505:16 588:5

temporary 412:17,17 431:17,21

ten 19:26 69:22 114:18 169:25 173:5 176:16

520:21

tend 133:8 152:3 217:22 261:16 362:13,16 402:22

507:20 516:13 588:25 621:9 689:16

tendency 150:5 207:25 541:9

tends 343:5 402:21

tens 291:18

tension 376:8

tent 534:4

tents 530:17

teratogen 493:1

teratogens 493:1

term 199:15 220:15 222:3,4 322:15 323:6,8 324:25

324:26 340:23,23 344:21 349:24 557:2 580:21,22

termed 20:12 221:6

terminating 484:18

termination 552:12,25

terminology 220:9 432:16

terms 11:16 29:9 42:6 44:23 49:19 68:8 71:14
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85:11 116:7 135:3,18 139:6,25 155:19 165:16

181:15,19 182:21 183:3 192:23 193:1 195:3

197:8,12 202:1,4 205:13 214:9 226:25 231:14

232:3 233:25 235:1,13 236:9,11 237:20,23

240:12 245:18,23 270:16 279:7,8,9 280:12 285:5

314:5 315:18 328:25 335:5 343:7 349:17 354:3

365:17 368:10,12 369:12,13 371:20 374:16

375:10,19 387:24 388:4 389:21 391:15 392:23

393:2 395:17,24 396:3,12 398:26 399:8 404:24

405:6 406:1 407:13 412:22 414:23 415:11 416:2

416:3 418:3,4,26 420:20 423:22 426:11,16 435:5

435:11 437:15 459:2 462:21 466:9 468:21 470:21

479:2 486:19 487:7,15 488:5,7 495:3,26 498:16

499:2 505:18 540:17 541:20 546:4 547:17 553:22

554:10,12 561:10 565:16 571:17,17 573:14,16

575:18 582:7,11 595:9,10 599:4 610:12 611:20

613:9 639:26 641:24 645:7 650:13,14 656:18,24

656:25 657:1 665:3 667:14 681:5 686:12 692:24

693:24,24 694:13

terrestrial 24:14 113:9 147:5 182:14 199:23 571:4

terrible 573:23 645:13

territories 593:21

test 10:24 21:24,24 24:17,22,23 54:24,26 56:25

59:3 65:26 67:2 81:12,22 92:12 132:13 138:3

145:1,3,10 148:18 156:8,9,10 157:8 158:11,15

159:2,20 161:1,2,5,15 168:23 169:3,11,13,14,17

171:2,15 172:20,22 176:3 177:11 181:8,11,24

182:19 184:12,14 185:16,21,26 187:7,11,15

188:19,20,23,26 190:14,17,26 191:5 192:12

193:9 196:22,25,26 202:10 213:2 220:6 246:13

247:7 254:5 266:8 267:6,7,7,17 271:1,4,5 272:25

273:2,2,24 279:13 290:12,21 306:12 307:12

315:26 316:4,6 342:5 346:15,15 351:13 358:22

362:3 364:20 375:14 381:15 386:15 415:5 429:2

434:14 444:25 446:14 453:11 457:2,5,6 460:11

462:7 465:1 466:5 477:22 478:9,22,24 479:20,21

480:2 482:13,16,21 483:4,6,7,10,12,13,18,19

484:8,17 485:12,24 486:16,17 488:7,9,10 489:4

489:20 495:11,12 504:20 505:6 506:17,20 507:18

508:6,6 514:6,16 518:10,20 520:11,13,16 521:23

522:20 523:10 524:7,10,16 525:17 528:4 529:10

530:4 534:18 535:13 541:20,20 551:21 563:12

566:12 580:10 586:10,13,15 588:17,18 590:1,4,8

591:18 594:3 597:21,21 604:25 605:20 628:12

632:18,21 633:4 638:8,8,23,23 640:6,12 647:10

651:23 654:18 661:21,24 663:12 668:21 676:10

688:10

testable 647:9,16 666:14

tested 24:7 31:17 153:18 159:25 170:5 196:19

220:8 303:19 316:4,6 384:15 394:13 398:9 443:4

443:4 450:25 469:21 480:4 491:21 589:26 608:24

637:15 638:7

testing 21:21 22:23,24 23:26 24:8,12 48:18 52:2

66:22,24,25 81:26 137:25 144:21 151:20 153:9

153:22,23 155:23 157:24 160:13 161:11 162:5

165:15,18 166:5 167:19,21 168:4 182:14,16

198:13 211:1,15 212:12 217:16 219:5 223:25

240:5 255:20 256:8,26 257:12 266:4,6 267:5,9

270:23,26 274:2,5 275:1,3,6,15 291:5 314:19,19

315:1 343:17 358:8,25 364:12,12,15,25 380:26

384:16 385:5 399:9 439:16 440:9 444:20 446:18

463:26,26 465:10 466:2 474:15,20,25 478:10

482:18 495:8,10 497:23 498:11 551:14 555:11

563:1,6,7,10 566:23 567:22 581:13 587:3,15,16

588:13,16 591:16 593:26 608:5 615:15 618:26

621:8,15 626:24 630:25 651:25 665:6 668:3,4,8

668:18,25 669:1 672:25 681:13

testings 275:19

tests 24:15,19,20 25:9,10,15 64:4 92:1 133:7

156:24,26 157:3,21 160:22 161:7 199:21 231:26

255:17 256:10 261:4 266:13 274:9 275:7,8

301:15 303:1 307:13 314:23 316:2 317:10 351:13

363:18 364:20 386:14,25 414:20,26 441:2,22

446:3,5 458:5 461:3 462:3 477:2,4,18 481:11,11

481:20,21 482:8,26 483:4 484:3,14,16,23,23

495:9,15 498:7,12 504:4 506:15,20 509:25

519:26 522:4 523:3,16 542:12 551:22 562:13

563:10 566:21 591:19 598:1,14 607:13,17 618:19

619:3,4 633:2 638:2 658:21 659:12

Texas 5:5 250:3 322:7,9 323:8

text 39:4,4

texturally 48:7

thank 6:14,26 7:3,10,26 12:25 13:9 14:4 15:4 16:7

16:16,17,22 17:20,24,25 18:3,4 32:4,22 42:13,14

44:10 47:7 48:2 52:15,22 53:4 66:12,17 69:14

71:11 81:5,7,9 86:11 87:26 88:11,26 89:1 129:23

134:24 140:9 150:6 154:2 194:22 196:3 203:1,8

215:10 219:11 229:23 231:6 240:14 242:16,19

249:5 251:4 252:17 274:22,23 277:23 280:18,19

284:13 285:9 288:18,19 296:10,11 297:4 302:2,5

302:7 309:3,19 311:20 314:15,16 315:21 316:9

316:10 319:1 322:1 323:23 324:26 325:1 339:7

340:25 341:5 343:22 348:9 353:15 354:24 366:4

371:2 386:19 400:12 401:17 403:13 405:16

407:11 413:1 416:4 426:19 435:16 436:5,8 437:3

440:5 441:14,26 442:2 451:22 459:1 477:26

478:8 483:5 485:10 519:20 527:18 529:25 532:21

553:7 554:5 561:26 562:12 570:23 572:24 585:6

622:11 623:22 630:10 652:14 663:9 665:25

666:26 670:8 676:15 678:10 682:25 683:3,19
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685:16 686:3 687:13 688:22 689:1,14,19,21

690:23 691:1,7,14 692:15 693:15,17 694:11,19

695:4,11 696:6,8,10,12,15,22

thanking 695:8

thanks 2:12 3:20,22 6:18 14:24 48:23,26 51:20

52:23,26 87:15 88:23 129:21 130:18 136:20

214:20 220:24 231:8 236:20 237:17 247:25

251:19 252:4,10 254:24 266:1 275:21 279:26

284:6 302:6 309:4,5 311:2 321:26 322:5 330:23

332:23,24 333:6 337:3 345:7 348:12 349:20

352:14 364:4 367:24 397:5 400:25 402:12 432:13

439:11 467:22 470:15 474:11 481:16 488:12

516:18 517:8 541:22 550:7 564:22 590:26 601:7

609:24 611:7 667:1 685:7 689:11,13 693:9,19

695:25

theme 8:11 258:4 410:11 453:19

themes 22:8 23:14,14

theoretical 647:14 679:26

theoretically 418:23

theory 263:18 395:15 636:5

Thiamethoxam 290:11 297:26 298:20,23

thick 306:11

thin 240:26

thing 49:26 69:11 88:12 131:24 198:10 240:24

241:11 242:18 243:8,20 244:5,14 255:23 256:19

262:14 264:20 272:3 288:2 304:24 305:17 306:8

319:24 330:15 333:3 349:13 363:26 374:1 375:23

399:7 417:15 419:14 438:18 439:12 445:10,20

452:2,18 457:10 461:26 470:12 474:2 478:8

496:8 504:23,25 506:6 537:13 555:11 556:5

561:12 566:3 591:2 592:25 597:1 598:4 602:12

609:11 627:18 631:9 633:24 634:12 638:25 641:8

647:18,21 653:15 654:3,21 655:7 661:14 685:19

686:8 690:16

things 12:12 49:15,15,18 50:19 82:6 134:13 152:4

195:20 197:17,21,24 198:5 230:15,16 240:23

244:26 248:2,5 262:21,21 287:3 305:13,15,18

306:20 307:16,24 310:5 315:17 339:22 344:23

345:8,21 346:4,22 350:13 355:26 356:6 357:5

359:16 364:11 368:8 371:22 376:5,8,15 380:22

391:23 412:11 416:11 418:4,23,24,25 431:17

435:9 437:15,18 448:22,24 449:18 452:19,20

453:13 454:14 456:15 468:9 477:23 486:20 488:3

488:18 495:22 505:18,19 531:17 538:2 544:22

547:15 550:9,23 551:15,21 553:24 560:23 561:6

561:21 563:16 564:20 567:10,22 568:26 569:26

570:12 571:16 572:2,9 577:13 583:1 586:6,16

587:1,12 589:5,18 590:6 593:24 595:12 597:8,19

598:13 601:21 607:22 616:7,12 617:24 620:11

630:21 635:8 636:16,20 639:3,8,21 640:1,18,22

641:11 642:26 645:15,16 646:17,26 647:1,14

648:12 650:12 651:21,25 652:18 653:10,13,16,21

654:10,12 655:20 658:18 659:26 660:3 661:22

664:2 666:24 672:9 678:4 679:5 684:7,9 691:5,6

693:22,25 694:3

think 6:21 10:17,24 15:10 16:13 32:7 43:8 44:4

45:25 46:22 49:10 50:1,24 51:3,24 52:10,15,24

53:20 68:12 70:2 71:6 82:10,16 83:8,12,21 85:2

85:18 86:4 87:8,9 131:4,7,8 132:9 133:6 135:12

136:3,4,5 137:24 138:6 152:1,20 153:21 197:2

198:11 200:1 202:3 228:26 231:12 232:1 233:24

238:2,17,18 239:25 241:20 243:4,18,25 244:5

245:14 246:16 247:10,17 252:5 255:26 256:5,6

257:6,22 258:7,20 259:8,12,13,15 260:12 262:11

262:15 263:14 264:5,18 265:11,20 271:10,25

272:6 273:2,20 275:5,10,14 276:26 279:6,11

280:8,9,11,12 281:10 282:17 283:21 284:1 285:2

289:22 294:10 307:16 309:14 319:19,24,26

320:26 323:21 328:1,4,19 329:21,23 330:6,16

331:4 333:4,5 336:13,13 341:25 342:20 343:25

344:25 350:3 354:3,15,20 356:14 357:1 358:26

359:11 360:25 362:1,22 363:8 364:10 368:11

369:5,10 371:23,23 372:3,3,22 374:11,11 375:9

375:14 376:13 377:13 378:11 380:9,19,25 381:2

381:9,14,21,25 383:8 384:13,26 385:6,6,16 386:4

387:2,13 389:22 390:3,8,11,26 391:20 394:21

395:7,18,23 396:3 401:1,2,8,23 407:3,20 408:5

408:26 409:16,20,25 410:11,17,26 411:18 412:3

412:7,11,21,23 413:1,3,8,12,22 414:2,9,19,25

415:8 416:1,10,11,12,15,16 419:6,8,18,24 421:23

422:7,16 423:6,10,25 425:1,4,8,15,20 427:10

428:11,12 429:19,22 430:1 431:14 432:14,18

433:2,6,19 434:10 435:17,26 436:1 438:2,4,17,18

438:23 439:11 440:11,23 442:5,14 443:7,15

444:17,19 445:18,25 446:3,23 447:19 452:24,25

453:6 454:19,25 455:23 456:1,14 457:1,5 458:9

458:10,18 459:3,11,18 460:4,6,10,20 461:9,25

462:4,9,16,26 463:2 464:6,19 466:23 468:16

470:11 471:1 472:19 473:19 474:10 476:5,6

478:9 486:1 487:8 488:2,3,25 489:3,11 490:2,18

490:19,19,20 493:15,16 494:26,26 496:22,26

497:8,22,25 498:17 499:7 500:14 501:13 502:1

502:14,19,26 503:8,12 504:18 506:16 508:18

510:13,13 511:3,21 512:19 515:12 516:5 517:13

518:7,13,19 519:2,11,18,21,25 520:1,24 521:5,23

522:8,9 523:6,8,19,23,24 524:3,19,20,24 525:2

526:12,15,17 527:3 528:17 529:1 530:17 531:18

531:19 533:8 534:25 535:8 537:2 539:18,22,26

540:25 541:7 542:6,20 543:17,20,23 544:8

545:17,20,25,26 546:23,26 547:10 548:7 549:15
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549:18,19 550:20 551:1,22 554:22 555:2,10,14

556:4,5 558:4,6,8 561:24 566:19,24 567:1,9,11

567:23 568:1 569:7,11 570:4,5 571:2 574:9,11,14

574:19 575:1,7,18,19 576:19,22,24 577:21,24

578:9,21 579:14,21 580:1,4,16,26 581:9,14,19

582:5,8 584:6,9,17 585:8,10 589:1 591:7,15,22

592:14,23 594:11,13,17 595:6 596:22,25 597:1

597:12,15 598:6 599:12,14,22 600:3 601:10,13

603:3,9,24 604:4 605:5,16 607:5,14 608:18

610:22 611:19 612:17 613:8,10 614:16 615:11

616:5,12 617:2,7,24 618:2,8,15 619:13 620:4,6,8

620:12,21 622:6 623:4 625:26 628:6,8,15 629:15

629:19 631:11 632:4 633:10,11,16,17 634:20

636:13,25 637:24 638:11,13,15,21 639:8 643:25

644:17 652:22 653:6,8,9 657:4,4,20 658:4,25

659:3,23,24 660:3,6,6,13,14,24 661:6,8,9,14,25

662:7 663:7,10,13 664:18 665:6,14,22 667:12

669:8 670:2,13 674:21,23,25 675:19,21,21,24

676:1,7,9,14,17 678:1 680:3,11 681:11,13 682:7

683:18,20 684:9,11 685:3,4,6,22,25 686:8,9,9,17

686:22 687:19,21,22 688:14 689:2,5,10 690:3,7,8

690:10 691:2 692:10 693:22 694:22

thinking 49:24 51:21 131:24 198:5 242:6,17,18

368:19 371:19 374:16 378:8 387:16 421:10

436:23,24 462:21 498:13 574:3 590:3 595:15

601:1 610:3 685:23

thinks 381:22

third 56:20 58:24 65:2 172:24 177:13 183:17

189:5 222:19 225:5,19 227:24 337:18 406:8

424:22 435:10 536:21 564:19 572:15 675:2

thirdly 244:18 323:20

Thomas 5:6,6 18:1,3 32:17 42:16,23 43:3,10,26

46:24 48:11 50:1,24 51:24 52:24 67:12,17 68:20

69:2 70:15 81:24 83:15 84:21 85:7 86:12,23 87:6

88:7 134:26 135:17 136:7,15,17,19,20 137:4,23

139:5 150:15 151:8,22,25,26 152:1 153:3,20

195:5,14 196:9 197:2 198:11 200:3,6,21,24

201:17,19 202:3,22 203:8 214:26 219:19 220:9

233:23 238:1 239:20 240:1 243:15 250:5 276:1

319:15 325:18 326:23 327:16,26 331:16 333:7,12

333:15 337:5 341:5 349:11 357:17 358:7 367:25

374:5 376:10 377:8 378:5 394:20 401:1 403:10

405:13 407:2 408:4,9,15 409:12,19 415:14,22

417:9 419:6 423:6 427:6,9 432:12 433:2 437:7,14

506:23 564:25 565:15 567:9 571:1 587:21 591:1

591:21 595:3 600:8 601:4 604:21 608:1 609:17

609:20 612:8,24 614:11 615:4 616:20 618:22

619:9 620:26 621:2 622:11 623:6 630:5 655:26

664:21 667:23 675:8 680:14 687:13 696:2,2

Thompson 226:8 231:18 576:16

thorough 334:4 442:5

thoroughly 372:26 477:19 689:25

thought 47:20 64:7 81:9 135:8,10 136:4 227:13

257:2 272:16 281:15 329:26 334:4 363:7 376:18

386:1 415:15 438:2 473:2 507:6 529:4 547:14

596:3 599:2 604:9 624:20 688:1 689:4,20 694:7

thoughts 7:2 8:22 10:22 13:2 437:16 442:14 494:7

573:15 600:16

thousand 293:2

thousands 290:6 291:19 294:14 295:12

threat 672:8

threaten 308:23

threats 505:1,19

three 25:19 27:9 29:26 36:16 57:25 64:18 65:3

72:6 91:21 101:1,10,17 105:6,20 106:15,20

115:17 118:9 121:19 169:20 184:9 188:3 204:8

237:14 255:5 263:19 293:19 304:4 307:23 308:1

323:4 341:21 343:13 356:8 357:14 361:3 382:18

382:21 393:9,20 398:2 404:3 420:10 430:9

464:13 465:3 486:19 519:21 523:14 531:6 553:24

559:7 562:3 568:16 575:12 578:25 579:20 582:9

583:6 585:10 603:7 609:7 621:22 637:22 643:24

644:2 662:20 667:5 673:8 689:15,16

three-dimensional 376:2

three-quarters 578:12

threshold 68:10 230:15 240:23,25 454:13 613:24

640:6 646:20 648:24 666:5 675:2 679:18

thresholds 231:3 551:22 646:17 655:1 666:9

threw 361:1

thrilled 442:6

thrive 20:18

thriving 645:22 648:20 666:2

throughput 443:22 449:21 450:7 624:20

throw 139:3 272:17 399:23 407:2 515:25 578:12

throwing 533:12 633:20

thumb 435:19 584:18

thunder 560:20

tie 242:22 606:3

tied 175:21 267:14

tier 24:21 26:20 52:1 54:5,6,10,16,19 55:21 56:3

57:15,26 58:8,18,25 59:16,24 60:11,15,18,26

61:2,8,11,22,23 62:3,11,26 63:26 64:6 65:13,16

66:22,26 67:9,19,25 69:1 70:21 71:18,26 72:3,6

73:7 75:19 76:5,7 77:18 78:3 80:6,18,20 81:1,3,4

81:12 89:2,11,12 90:10,11,21 91:1,7,22 92:3,7,14

92:18,23 93:19,23,25 95:9,14,15 98:4,8 100:10

101:8 104:22 105:8,23 106:6,8 107:16 111:6

115:20 116:17 117:6 118:12 121:8 124:14 126:17

126:21 127:1,19 128:18,23 137:24 139:7,12,16

140:12,13,22,25 141:2,2,3,10,12 142:8,12,19
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143:3,7,9,15,16,18 144:8,10,12,16 145:12 146:3

146:5,10,24 147:18,25 148:2,6,26 151:5,20,23,24

152:2,26 153:1,15,18,18 154:21 155:26 156:23

156:26 157:14 158:19 160:18 165:2,12,18 166:2

166:5,6,10,11,14,14,21,24,26 167:1,8,9,18,19,23

168:6,25 169:1 173:19,20 175:14 179:7,11,12,15

179:19,21,22,25 180:5,14,15,16,19,23,23,25

181:3,14,26 183:2 184:21,23,25,26 185:1,2,6,7,9

186:25 187:14 190:2 192:20 193:19 194:9,10,26

194:26,26 195:4 196:8,8,10,22 199:11,11 200:8

200:12 210:1,4 211:1 213:21 215:14,15 216:3,25

217:2,9 218:3,7,15,20,21 222:20,20 223:2,2,9,10

223:10 242:24,24,24 243:2,2 244:26 255:14,20

255:20,20,26 256:8,10,26 257:4,23 258:4,12

260:23,24 262:18 265:16 266:6,7,7,9,10,10,10,12

266:18,23,26 267:5,6,19,21 268:9,10,11,22

269:12 271:10,19 272:15,17 274:1,5,7,9 275:1,3

275:6 276:1,2 281:21,22,23 282:18,25 283:4

290:12 291:5,5 314:19 329:10,17 358:25 363:1

364:11,20,26 365:24 384:8,16,16 385:2,5 386:22

403:20,21,25 405:22 407:24 409:7 413:4,13,23

414:20,23 415:11 421:17 422:15,26 423:8 424:15

426:10 428:13,18 432:26 433:20,23 436:16,20

437:22 438:24 439:19 440:7,9,13,24 441:23

442:13 443:7,10 445:21 446:14 449:3,6 451:6

452:24 453:11,12 454:18,18,19,20,25 457:3,6,7

458:4,5 460:26 461:3,5,5,21 462:6,7,16,16

463:26 464:26 466:6 474:17,23,23 475:6,17

479:4 484:6 488:6,6,9,9,10,10 489:10,12,12,13

490:9 496:7 499:26 501:19 506:20 507:18,25

509:15,25 511:9 512:16 513:4,16,18 514:17,22

515:15 516:17,20,25 517:4,4,4,6,11,11 518:9,9

518:10,20 519:1,15,18,26 520:11,14,14 521:15

521:23 522:4,15,18,20,23 523:2,7,8 524:25

526:19,21,21,22 527:7,18 531:3,7,15,17,21,23,23

532:1,9,16 533:8,9 535:13,14,24 536:20,25

538:26 539:18,19 546:2,12,13,15,15,17,23 547:7

547:8 548:3 549:10 550:22,25,25 553:21,21

562:13,18,22,24,26,26 563:7,7,10,12 564:1

570:16 577:21 587:3,3,14,15,17,17 588:9,9

589:12,19 590:1,12 595:23 604:23,23 605:11,18

606:15 607:6,13,17 608:13 609:21,25 611:21

614:13,13,14 615:21 616:2,13,22 617:20 618:5

618:20 619:13,18,19,24,25 620:7,25 621:8,9,15

622:15,17,19 623:2 625:3,24,24 626:12,14,18,24

628:7,8,8,12 630:15,15,24 631:13,13 632:4,11,14

632:14,19,19,22,23,24 633:11,19,23,23 634:6,15

634:15 639:19 641:9,10,10 644:17,23,24,24

649:16,17,20,21 650:1 651:23,25,26 652:2,13,18

652:18,19,20 654:18 657:6 658:21,23,24,24

659:4,10,11,13,15,16,18,26 660:11,22,23 661:1,9

661:21,22,24,24 662:2,12,19,25,25 663:5,5

664:26 665:1,1,2 668:24 669:1 676:10 680:24,24

681:13 683:23,23,25,26 688:12 691:2,20

tiered 13:16,20 24:21 26:20 30:26 54:4 64:19

141:5 142:2,5 144:4,21,23 146:4 154:17 155:22

165:14,19 175:7 180:21 211:11 219:5 255:10

270:17 279:23 286:15 439:1 495:22 526:19

610:20,23 619:4 668:21 691:4

tiers 26:24 31:16 54:12,15,18,19 56:3,9,13,17,18

61:25 64:4,23 65:13 66:5,9 91:3 152:5 154:20

156:3 168:7 175:1 179:23 181:23 182:2 183:4

192:20 194:7,13 223:9 229:9 245:3 255:24 256:4

273:26 509:16 532:19 548:24 605:12,13,15

607:20 608:4,23 609:16,22 624:10,19 628:7

632:5 649:11,12,25 651:25 658:5,5,11

tier-level 652:9

Tifton 5:8 250:7

tight 458:15

tighten 399:3

till 480:19

tillage 87:2 321:5,12 348:20

tilling 317:23

time 3:25 4:8 6:15 7:3 19:4,6 20:21 32:4 40:21

42:10 46:20 49:2,6 52:9 56:12 63:9 65:7 66:13

67:3 77:14 81:5 83:19 87:15 97:10 110:25 117:2

119:26 129:20 132:14 134:21 138:10 146:11

149:3,4 150:7 156:26 158:3,7 172:6,6 173:24,25

176:6,16,18,20 178:10 181:15 183:26 192:2,23

194:22 195:5 196:4 199:2 219:11 222:12 224:5,9

232:26 238:10 242:4 243:8 247:22 248:12,23

252:14,15 254:23 265:16 275:13 286:13,16,24

296:14 300:12 304:16 306:5 307:23 310:15

311:14,16 317:2 318:3,9,10 325:3,3,10 329:4

332:8,22,24 333:1 342:8,9,24 343:21 346:16

357:2,6 360:14,15,25 363:20 368:11 370:19,20

371:25 381:4,20 390:24 391:8 417:7,15 428:2,14

435:26 438:19 439:9,20,24 441:17 443:14 444:2

444:9,15 447:6,10,11,13,15,23 450:14 451:11

453:15 456:11,13 457:15 458:21 460:23 468:8

477:15,20 479:5,23 480:12,15 481:6 482:14

483:2 486:25 487:7,17 491:26 492:8 498:26

502:13 504:21,22 510:1,24 520:20 523:11 524:10

528:10 533:22 541:2 542:24 543:26 544:11,12,14

544:19,19,22,23,25 566:6 569:13 582:17 589:10

589:19 590:15 596:26 598:24 602:14,25 604:11

605:6 610:26 615:15 617:4 621:3 624:6 632:26

634:18 637:22 644:19 650:13 661:17 662:4

664:11 667:6 684:1 685:11 689:24 691:10 695:22

timeframe 249:3 291:21 687:16
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times 79:13 95:7 99:24,26 103:17 104:8,15 130:4

137:20 138:25 142:15 191:18 231:11 248:22

258:13 261:23 286:26 360:3 379:11 381:17,19

382:1,9 383:7,13 393:9,19 394:26 423:20 425:7

425:17 428:5 443:5 445:2 458:18 501:21 515:21

541:5 543:21 544:15 566:8 574:24 575:9 640:17

684:3 690:18

time-tell 493:1

time-weighted 92:10

timing 57:12 60:13 149:5 182:26 183:1 185:10

227:2 344:15 458:11 486:15 543:24 657:18

tiny 430:26 580:8

tip 413:10

tipping 501:5

tissue 142:4 276:20

tissues 39:22 40:1,2 41:15 75:5 399:8 406:4,9

titers 637:21

titled 182:13

toast 454:11

toasted 512:19

today 3:5,17 8:19 11:23 12:10,12,14 13:15,24

14:17 18:8 26:19 27:14 30:25 34:15 45:12 46:26

89:7 91:6 200:23 230:2 246:4 247:22,26 249:4

252:3 253:2 279:26 302:20 305:9 308:4,14

318:24 322:2 329:8,13 351:6 374:20 407:15

434:8 435:18 442:8,15 463:2 500:23 580:14

605:9 622:2 672:2 684:17

today's 205:10 245:22 388:1

told 263:23 303:15 304:24 540:19 688:20

tolerance 307:25 308:1,13 309:2 465:16

tom 18:5 62:12 200:3 203:10 204:7 210:22 213:18

214:26 219:19 233:23 234:22 238:1,14 243:15

250:5 333:7,15 367:23 377:9 378:6 400:7 415:23

416:19 417:9 419:5 427:9 432:12 437:9 506:23

567:12,13 601:6 604:21 608:1 612:8 614:11

615:4 618:22 619:9 622:12 687:6

tomato 121:16 529:6

tomorrow 3:5,9 8:2 11:24 243:12 247:18 248:6,9

248:12 407:16 435:6 500:23 622:2

Tom's 396:16

tongue 450:21

tongues 450:26

tonight 247:20

tool 83:5 380:1 456:2 569:2 571:13 656:20 657:5

681:6

toolkit 681:6

tools 23:17 34:26 126:6 228:10 253:14 648:22

651:9

top 26:8 27:7 36:19 56:3,4 104:25 168:13 224:18

224:20 244:26 323:7 355:3,5 476:15 557:18

561:22 583:3 599:19 646:11 691:5

topic 2:3 33:6 221:12 241:10 418:9 591:9 694:8,21

topics 140:13

top-down 658:4,10

tortoise 653:3,4,5,17,18,23 654:11,24,25

tortoises 653:7,10,15 654:24 655:7,12,13,13

tossing 364:24

total 95:18 96:19 102:20 113:1 131:22 134:9,13

161:16 170:3 346:13 370:22 372:2 435:11 573:22

579:12 586:22 587:1

totaling 263:6

totally 373:24 431:2 448:2 454:17 471:4 473:9

597:25 616:9 657:20 693:21

touch 8:12 18:20 141:10 154:24 526:13 536:16

668:13

touched 450:22 582:6 612:3 616:5

touches 549:11

towers 287:15

tox 498:7

toxic 150:19,19,23 157:4,22,22,26 161:22 170:18

175:12,20 179:5 185:7 199:18 220:17 261:26

273:1 390:5 413:26 414:19 415:1,2,3 431:25,26

440:26 441:4,9 523:26 541:19 564:19

toxicants 358:12

toxicities 266:16

toxicity 21:21 22:23 23:26 24:8,15,20,21,24,25,26

25:5,8,9,10,26 54:22,24 58:20 59:3,6 63:2,3

65:10,17,20 66:22 67:2 68:19,23 69:9 81:26

91:26 92:3,12 132:13,26 133:7,19 134:4,17

150:11,16 151:1 153:23 154:12,16,18 155:1,8,9

155:20,22 156:1,8,10,24,24,26 157:3,7,11,16,20

158:6,9,10,15,26 159:1,3,5,8,9,16 160:12,13,15

160:19,22,25,26 161:2,5,18,19,26 162:11,12,24

163:5,9,20 166:8,15,19 167:8,23 170:10,26

171:22 173:20 175:17 179:11 181:6 194:17

197:20 198:1,13 205:18,21 207:5,8,9,24 208:10

208:11 209:19 211:1 212:10 215:19,22 216:6,11

216:20,26 217:14,16 218:6,17 223:8 227:21

267:7,9 298:18,24 351:13,13 362:1,6 364:12

379:6,21 380:1 381:15 386:14,15,23,25 413:17

413:18 415:5,7,7,7,9 416:25 434:11,14 440:26

441:2,20,22 444:25 446:3 452:6 465:9,24 466:9

467:2,15,20 474:24 476:24 477:2,4,4,9,11,19

478:22,24 479:12 480:1,26 481:4,15 482:8,9

483:2 484:3,5,16 485:11 490:6,8 491:10,11 493:8

495:8 499:19 503:22,24 504:4,7,20 505:25

506:11,17 521:15,16,16 542:12 560:7 607:11

612:10,11,21 618:18 619:1,2 632:8,9 648:2,14

668:2 678:22

toxicity-testing 154:22
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toxicokinetics 199:25 458:13

toxicological 4:24 34:18 39:10 50:19 57:10 249:23

497:5 510:3

toxicologist 4:12,25 132:18 420:25 609:1

toxicologists 608:9,10

toxicology 4:12,26 22:2 64:13 197:18 451:13 452:4

655:21 692:7

toxins 475:10

trace 579:17 628:23,23

tracer 108:24 109:4 112:20 366:19,24

Tracheal 19:24

track 62:1 270:2 575:11

tracking 351:11 354:8,9 544:22

tractors 318:12

trade 252:20 580:1

traditional 35:10 68:21 69:7 407:18 510:3

traditionally 51:10 261:10 493:20

train 454:7

training 287:23

traits 374:10 475:13 504:10

trajectories 683:1

transcend 687:22

transcript 455:24

transcriptome 455:25

transcriptomes 475:8

transect 601:21 604:14

transects 601:24

transfer 74:5 75:5,11 137:14 315:18 472:24

616:11

transferred 75:10 159:19

transient 555:5 608:17

transition 51:1 67:19 152:5 166:13 167:22 179:22

180:23,25 196:9,12 260:13 548:3 604:23 607:13

608:4 673:11

transitioning 194:26 196:15 619:4 621:15 668:21

transitions 477:13

transitory 178:21 586:20

translatable 442:24

translate 133:4 369:7 380:19 414:7 423:14 448:25

606:20 626:19

translated 134:4

translates 500:8

translating 127:3

translocated 298:17 299:9

translocation 39:22 40:2,9 41:6 44:8 75:4 299:16

299:20 349:23 412:18

translocations 349:25

transmission 642:19,23

transmitted 411:18

transparency 7:23 212:14 228:9 239:16 240:3

241:26 244:8 256:22 272:2

transparent 12:2 203:24 228:7 239:17,26 271:21

transpiration 120:13

transport 5:10 11:18 73:10,13 78:7,10 116:24,26

117:2,4 119:16 122:18 123:26 125:13,16,20,22

126:7 141:26 142:1 149:7,8,14 277:10,11,11,19

401:11

transportation 398:14 399:5

transported 17:14 40:2 118:14 124:15,18,19

125:11 127:22

transporting 353:7

trap 186:13 557:13 577:9

Trapp 125:26

trapping 579:15 597:6

traps 147:20 171:13 172:26 186:1 191:15 332:6

572:11 601:22,23

travel 302:14

traveled 264:7

travelled 300:26

treadmills 508:9

treat 81:18 363:14 527:24 564:11

treated 37:3 41:18 60:14,24 78:19,19 80:11 83:16

84:2 86:24 94:23 95:14 106:21 108:25 110:21,25

111:2 118:2,20 119:22 127:26 141:21 143:14,22

147:1,12 167:6 168:14 170:1 173:1 177:16 180:8

180:9 182:23 183:25 185:18,20 188:2 189:11

190:21 191:2 193:8 298:16 300:6,8,22 321:17

332:12 349:14 395:16 410:16 415:25 416:1,13

447:5,6 471:16 474:21 485:23 513:9 516:2

529:23 560:5 563:5 568:15 570:4,12,19 572:13

577:12 589:21 600:15 612:14

treating 81:22 305:5 564:13,14 565:16 592:10

600:19 627:18

treatment 34:1 41:3 53:16 62:15 63:14 65:1 74:22

74:23,25 75:3 76:25 77:1,19 80:12 82:8 90:4

117:17 126:16 129:14 138:24 147:9 149:13 172:9

185:24 189:6,9,12,14 193:9 195:22 198:6 258:8,9

269:22 270:2 291:12 292:2 293:7 317:13 331:10

331:17 405:24 409:9 411:11 412:13,16 414:4,15

417:25 421:4,26 423:2,2 436:25 437:10 499:2

564:16 565:13 571:22 581:21,24 599:4 634:26

treatments 63:6 65:5 72:10 92:17,24 106:15 107:1

107:5 117:7,9,13 119:2,4 120:2 123:1,4 126:26

127:1,21,23 128:4 142:20 145:8 157:18 158:23

158:24 182:25 184:8,11 206:10,11,13 220:4

258:6,10,17,23 269:19 291:26 293:1 300:1

316:21,24 317:5,8,24 318:5,26 319:5 320:10

402:7 409:14,22 411:11 416:6 420:1 421:8,11

431:11,11,18,18 444:1 525:9 567:5

tree 34:4 92:22 141:9 143:2 157:18 158:23 206:11
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603:11

trees 352:18 362:16,16

trembling 463:21

tremendous 457:25 636:20 687:17 692:11,21

693:20 694:2

tremors 298:9

trend 630:8

trends 178:10 214:10

trespass 284:10

trial 110:2 112:26 146:25 184:9 569:21 572:9,19

trials 109:2,12,15,23 139:1,2 428:24 429:6 450:24

533:1 575:1,15 638:19

tricky 314:22

tried 8:14 232:13 296:2 306:3 311:11 315:3

tries 259:5

trigger 65:16,18 273:10 440:1 462:16 463:16,24

488:5 493:9 495:22 506:12,15,18 512:16 517:7

607:21 610:20 619:18 649:18 652:2,20 658:21

659:11 660:1,3

triggered 270:23 274:5 661:23

triggering 496:7 662:23

triggers 65:13 67:24 154:20 174:20 509:15 652:18

trip 97:10,11 324:13

triple 450:4

trips 97:9 263:2 318:8,10

trivial 671:26

troops 558:23

trophallaxis 472:24 473:4 487:2

trophyllaxis 513:9

trouble 555:21 681:21

trough 350:5

true 225:1 256:5 261:1 263:14 283:13 330:2 353:7

362:18 440:2 458:21 475:3 514:14 529:5 548:26

568:4 579:22 605:13 650:22 654:22 666:20

truly 335:4 399:18 406:20 449:6 461:3 473:12

695:16

trunk 34:4 157:18 158:23 206:11 259:2 423:2

431:11

trust 663:12

truth 151:13 602:10

truthful 237:12

truthing 240:18 549:13 595:22 601:21

try 10:1,4,11 11:7 52:2 154:4 199:6 233:11 237:7

237:10 241:7,22 255:11 262:19 295:21 315:7

324:10 353:23,24 378:17 381:1 399:17 404:5,15

404:16 420:17 421:15 448:13 452:26 461:21

506:6 540:9 548:20 551:2 555:4 565:18 583:11

584:20,22 589:6,23 590:13 615:16 619:21 627:6

631:16 639:12 651:10 656:6 657:6 667:4 671:7

672:21 674:19,19 678:3 684:9,11,17 685:4 695:1

trying 9:21 50:1 52:12 132:9 199:2 241:18 242:14

243:17 256:23 276:18 309:23 311:16 312:18

313:21 320:11 321:9 352:6,12 357:9 387:3

400:22 406:2 418:6,22 419:18,22 450:10,11

459:9 462:22 469:18 470:8 510:24 514:10 515:4

515:9 516:9 521:21 549:16,20,20 556:6 566:12

584:20,24 589:18 590:15 600:13 603:25 605:14

611:9,20,21,23 617:15 624:18 626:14 639:17

658:8,10 683:24 690:9

TSCF 120:14,17 121:5,6,10,13

tube 347:20

tubes 537:24

tune 196:19 345:21

tunnel 61:2 144:3 145:15 146:18 147:11,26 168:13

169:6,14,20,25 170:1 173:11 176:1,7,23 179:26

180:2,12,18 197:10 266:21 275:7,12 508:13

513:5 521:3,4 524:26 525:3 526:2,2,12,13,16,20

526:22 527:15,16,20,25 528:1,4 530:10,15

535:10 537:10,18 538:18 539:4,23 545:3,26

546:1 636:9,10

tunnels 170:24 172:17 176:14,15 180:1 522:23

525:24 528:19

turn 4:10 10:1 13:4 21:3 26:16 36:11 209:1 225:10

247:24 251:18 252:1 254:23 265:25 372:6 435:13

439:16 511:26 550:11 694:16

turned 455:12 560:11

turnover 540:5

turns 295:22 648:26 653:18,26 654:4 655:4,6,9

661:15

tweaking 406:19

Twenty 393:20

twice 291:15 322:20 323:20 411:15 435:3

twin-engine 322:23

twist 349:17

two 7:3 33:9 41:4 46:18 53:5 64:9 65:22 76:4 77:22

79:8 81:18,21 82:4,9 85:7 90:8,18 95:5 97:26

100:5 104:4,6 109:9 111:16,26 112:3,7,12 115:24

122:14 123:18 125:7 134:13 136:24,26 143:26

156:18,20 158:25 168:11 169:20 175:13 184:9

187:21 192:3 193:9 197:17 204:24 215:6 224:23

227:10 232:14 237:14 238:7 260:4,26 261:2

269:11 290:10 295:16 303:11 304:2,4,19 307:23

322:21 329:3,23 336:9 347:26 354:17 358:21

364:10 370:10 377:10 378:26 383:3 393:19

401:21 402:22 404:1,4 409:20 411:6 412:12

413:3 414:6 415:12 416:11 420:10 423:13,20,22

427:22 428:19 429:6 444:4 451:8 455:21 460:19

477:8 480:8 487:9 488:3 494:17 500:19 515:25

522:22 523:14 526:10 533:21 534:6 550:9 551:14
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570:21 572:9 575:12,20 576:19,24 585:10 603:7

618:23 625:6 627:13 631:3 637:7 656:13 658:25

659:21 676:23 677:2 688:7,10 694:17

two-fold 553:2

two-part 656:10

Two-thirds 598:8

type 27:24 29:1 49:12 50:16 52:12 55:19 57:12

60:21 61:1 72:9,12,17 87:2 90:25 91:1 104:2

124:26 141:24 142:12 146:24 147:8 151:23,23,24

161:5 168:15 169:19 172:1,3 177:5 212:4,16,23

212:26 230:22,26 266:19 270:8 331:19 368:9

408:25 416:2 431:12,20 436:21 438:12 444:4

450:6,8 451:15 458:1 463:9 486:3 492:1,11

501:26 507:18,20 511:9 515:15,26 516:11,15,17

522:23 531:15 532:9 539:20,21 544:3,9 549:15

571:6 577:22 578:6 579:20 590:17,19,23 594:12

596:1 600:5 617:26 626:13,15,16,16,25 642:1

644:14,25 645:18 647:13,25 649:23 651:13

665:23,24 672:18 675:6 694:5

types 49:13,25 60:13,19 72:7 77:21 96:11 104:4,5

106:16 125:1 133:5,12 136:13 143:26 145:19

148:10 150:17 161:1 166:18 175:16 178:6,12

196:18 198:6 216:14 245:12 287:3 346:16 358:13

358:16,25 369:8 384:11 403:23 409:9 423:1

431:11 442:13,22 444:5 445:5 447:1,9 448:24

449:17 450:13 473:22 509:24 515:26 516:14

517:22 519:21 522:23 531:2,16 535:17 536:20

539:25 546:24 591:4 606:3,6 624:23 630:26

644:10 649:16 650:9 656:3 678:14

typical 20:2 56:5 82:24,26 144:24 152:14 169:7

173:3 177:10 183:12 187:16,21 262:2 298:9

424:2 509:1 578:20

typically 24:4,8,21 27:19 35:20 40:18 54:14 79:11

143:19 167:2,12 170:14,26 173:11 178:11 188:3

195:7 206:4 276:24 277:3 287:12 352:18 378:24

477:2 513:3

typing 352:22 600:14

T-REX 106:24 110:7,11,16 111:4,10,21 113:3,7,7

113:10,13,23 114:1,19,21,24 115:5,11 116:5,20

124:6 127:10 129:5,11 217:24 259:21 260:5

261:6 327:2,13 373:5,16 377:24 378:10 392:14

392:21,25 393:7,17,18 394:5,12 395:19 396:7

399:6 406:10

U

ultimate 51:18 194:4 448:26 449:2 546:22 562:13

586:12 590:2 611:14 649:4 673:1

ultimately 15:13 21:4 35:16 47:18 145:12 166:13

194:9 242:11 243:23 396:12 423:14 462:26

486:20 544:1 565:18 668:24

um 683:14

unacceptable 301:20

unanswerable 596:11

unattainable 563:7

unattractive 564:4

unattributed 296:5

uncapped 379:1

uncertain 84:24 111:25 374:10,11 428:14

uncertainties 12:8 17:7 21:22 31:11 60:10 61:12

61:21,25 62:6 68:8,9 107:13 109:7 116:7 129:19

136:13 144:10 145:18 152:7 160:4 166:20 172:1

173:19 174:26 175:7,14 178:23 179:18,23,24

180:15,21 181:3,23 183:4 185:2 194:5,7 196:14

197:7,9 203:20 213:6 214:5 219:25 246:11

255:14,24 257:5 265:9 267:21 271:8,18 401:4

404:20 508:24 566:19 622:18 628:7,7 630:14,23

631:20 632:3 639:5 663:3

uncertainty 25:15 31:8 55:24 59:19 85:11 118:23

119:13 131:13,15 138:7 145:19 162:3 193:6

212:5,24,26 213:4 220:20 228:18 238:2 243:4

256:12 259:7 262:15 268:10 290:7 368:12 392:1

392:4 399:2,13 404:1,7 405:7,8 425:26 438:5

465:4 497:2,3 519:11 530:4 554:5 626:8,9,15

627:12,13,23 628:4,9,11,11,13 629:5 633:25

641:24 648:11 651:5,6,7 657:10

uncharacterized 465:14

unclear 112:4 238:12

uncomplimentary 690:17

uncontaminated 187:9 530:2

uncoordinated 298:9

undefined 291:18

underestimation 185:22

undergo 145:25 388:24

underlines 466:15

underlying 204:4

undermine 397:2

undermines 390:14

underscore 571:12 595:21

underscores 473:19,24

understand 10:5 45:2,5 46:19 56:24 67:2 70:4

83:16 89:19 154:26 159:9 160:26 203:26 204:2

224:1 233:13 235:1 241:23 242:3,13 244:8,8

283:18 293:4 314:22 333:8 348:24 354:3,24

357:1 358:22 362:22 386:12 411:25 420:18

421:18 449:18 470:8 508:26 519:17 524:24 552:7

604:1 619:5 631:2 651:24 658:10 659:23 678:21

understandability 242:1

understandable 659:22

understanding 21:11 26:26 44:23 45:7,10 47:5

52:8 85:1 88:13 140:26 150:5 151:13 152:18
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230:14,22 237:20 238:16 239:1 244:20 278:18

312:7 316:7 326:4,11 331:22 341:26 427:1,12

436:16 449:15 455:23 460:1 465:23 479:26 488:8

527:9 541:15 553:1,23,26 610:6 619:1 620:12

640:5,7 652:16,17 658:15 660:16 662:22 663:8

677:12

understands 242:5

understood 240:19 415:17 509:21 512:8 524:21,22

524:22 525:11 629:12 652:7 659:7

undertake 11:14

undertaken 599:18

undertaking 6:23 7:9,19 8:4 12:23 279:4 466:19

underway 159:7 160:24

under-predict 123:23

undesirable 27:21,22

undisclosed 202:14

undisturbed 596:14

undoubtedly 319:5

undue 294:6 570:2

unevaluated 300:19

uneven 281:13

unexpected 431:5

unexpectedly 653:22

unfortunate 326:11

unfortunately 282:7,11 290:1 300:17 395:20

404:19 595:18 637:13 674:10

unidentified 69:10 110:21 138:18 288:22

unidirectional 25:25 204:13

uniform 263:20,22,24 301:25 588:22 593:1

uniformly 320:13

unimportant 654:5

unintended 678:2

Union 22:20 158:9

unique 225:7,25 286:11 288:2,5,11 294:14 366:24

520:19 600:21

unit 88:3 444:9 446:16 447:13 514:6 533:5 578:20

583:26 605:21 625:1

United 9:9 17:14 234:3 236:5 238:25 252:21

283:14 308:5 397:18 424:1 578:12 593:21 684:22

units 85:19 92:4

universal 515:20

universally 645:21

universities 312:22

university 4:5,17,21 5:3,5 6:3,6,11 249:14,17,20

249:26 250:4,22,25 251:2 292:5 300:15 312:23

315:11 442:2

unkempt 101:16

unknown 79:10 103:6 112:7 399:12 421:2 473:5

511:5 535:17 542:25 627:8 672:8

unmitigated 419:10

unplug 648:9

unprecedented 297:14

Unpredictable 316:26

unprocessed 146:4 379:17

unpublished 108:1 215:20 216:19 327:5 383:9

410:19 466:15

unrealistic 268:2 487:8

unreasonable 28:17 260:1

unreasonably 120:1

unregulated 308:18

unreliability 404:13

unsafe 287:14

unscientific 430:21 431:1

unsettling 466:23

unsound 195:24

unsure 460:20

unsustainable 289:26

untreated 170:2 208:9

unusual 233:2 643:2

unwieldy 679:2

un-marketed 678:2

un-trodden 692:8

update 401:13 574:17

updated 113:15

updating 367:4 399:26 618:8

upper 90:13,14 98:11,13,14,17,18 109:7 110:6,11

110:15 113:6,12,18,20 114:21 115:6,8,16,20

116:9,14 117:18,23 121:9 124:7 127:10 129:11

135:7 144:16 150:5 190:14 320:21 375:26 376:3

393:4,7 399:17 400:10 402:18 605:13 608:15

658:5

upper-bound 258:9 373:6 376:11 392:14,15 393:4

393:10 394:6 397:12 398:14 402:10 405:21 407:3

upset 322:26 323:1,2

upstate 430:11

uptake 37:25 39:22 40:9 41:13 44:8 75:4 107:6

120:3,8 124:26 128:6 129:26 148:17 257:24

350:5 398:18 403:19 406:11

upward 400:4

upwards 658:8

urban 322:9 424:1 589:22

Urbana-Champagne 251:3

Urbana-Champaign 6:11

urge 293:15 318:24

urged 291:2

urgency 549:8

urging 617:4

usage 439:14 486:5

USDA 5:7,18 6:1 9:7 20:4,25 161:4 235:6 236:22
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299:2 304:13 418:25

USDA's 283:15

USDA-funded 21:20

USDA-led 156:20

use 10:10 16:12 25:13 28:17,19 32:2 33:19 35:23

36:6,9 42:1 48:16 49:23 50:3 51:6,10 52:3 53:6

56:5 57:8 68:12 71:26 77:25 82:7,18 91:9 95:10

98:2 100:11,15 106:24 108:17 110:23 111:4

112:20 113:6,21 114:4 118:18 120:3 123:3 124:6

125:24 127:14,24 128:6,19 137:4,5,7 138:23

141:1 143:21 144:16,20 147:16 148:5 149:19

150:1 151:14 155:21 156:13 161:24 164:18,26

167:5 170:2 172:20,22,26 173:12 177:3,6,14,20

179:12 181:10 182:25 183:11 184:15,20 185:12

188:18 190:26 191:15 192:8 193:26 194:11 195:6

196:25,25 197:10,11,11 199:22 203:16 205:7

206:3 209:26 211:7 213:16 216:17 221:3 228:4

228:13 229:18 230:3 237:9 241:17,23,23,24

244:3 247:8,14 255:22 257:1,3 258:22 259:19,20

261:11 263:20 265:15 266:6 267:6,8,17 270:21

270:22 271:22 278:25 279:24 283:15,26 284:12

284:25 294:19 317:24 320:16 328:3 331:15 334:9

334:10 339:25 344:16 346:21 347:20 361:1 364:3

364:20 378:1,2,3 379:1,23 380:3,9 382:1,1

384:13,16,19 385:1 386:21 392:20 393:3 400:15

401:10 402:9 414:6 423:24 424:13 425:3,11,14

426:15,26 427:23 428:6 434:9 438:14 439:2

442:10 443:23 444:6 457:15 461:3,21 470:26

474:16 475:12,15 478:20 482:7,20 483:18 484:13

491:11,12 492:5 493:9 494:18,20 495:1,1,8,17

496:22,26 497:1,11 509:3,13 512:14,15,15

516:23 518:8 521:11 525:18 529:13 530:1 533:19

540:15 547:2 563:8,19 565:1 567:4,4 568:1,9

569:5,7 570:8,12 578:17 579:9,25 590:2,5,14,17

597:13,14,15,17 604:22 614:13 616:24 617:15,20

620:6 624:9 627:25 628:2,2,4,19 629:13,16

634:23 639:25 641:12 647:6 649:3 651:11 652:23

654:26 657:6 659:4,25 660:6 661:10 667:11

668:11 673:9,9 674:12,16 675:20 676:1 680:2,26

681:2,7 684:20

useable 676:8

useful 34:26 48:1 49:15 64:7,15 126:6 142:8 162:8

163:4 167:16 181:4 182:14 200:18 227:13 242:19

243:6 244:23 245:3,4 267:24 340:24 342:23

346:17 357:3 364:25 406:26 407:7 413:3 445:11

461:10 467:17 475:11 487:13 507:2 517:5 518:2

542:23 544:3 546:8 547:1 569:2,9 571:13 595:26

609:15 620:8 649:14,17 650:21 657:5 670:9

671:11 678:16,18

usefulness 14:2 518:5

useless 306:4

user 241:16

uses 31:6 36:5 57:21 128:2 148:14 156:5 166:23

171:1,4 193:21 207:14 263:25 265:23 270:19

290:17 299:8 301:21 393:1 628:24 629:15 694:1

usual 82:21

usually 170:3 261:9 271:3 281:1 282:3 387:18

443:24 453:24 483:13 495:13 577:16 582:8

640:17 662:12 694:16

Utah 6:1 250:20 581:13

utilities 647:13

utility 110:6 125:12 129:5 200:8 212:11,19 214:16

224:15 342:26 373:5 522:9,11 526:24 539:8,16

539:16,16,21 571:12 593:16,18 595:22 614:4

642:13 644:10 663:14 672:6

utilize 531:8

utilized 327:1,20 328:6 476:17

utilizing 13:18 287:2

utterly 294:15 473:5

U.K 576:17,18

U.S 15:7,19 16:1,4,22 19:10,23 20:1 28:11 156:6

156:17 157:6 160:14 167:20 237:1,4 278:15

279:1 280:7,14 285:21 286:6 292:1,19,23 293:5

294:26 297:16 299:11 315:25 317:13 318:14

322:20 333:15 381:2 577:26,26 593:26 594:1

596:25 663:25 682:8 688:21

V

vaccine 663:20

vacuum 443:5 444:20 671:15

vague 291:14

valid 132:10 162:13 254:5 278:26 290:11 313:23

364:16 367:21 380:1,8 381:1 400:20,23 523:8

547:14 550:26 567:23 594:2 603:8 613:1 629:6

645:23 653:20 659:14 673:3 679:18

validate 76:6 644:21 647:2 666:16 679:12

validated 267:8 479:19 480:25 504:12 662:1

680:21 681:24

validating 504:15 666:21

validation 240:18 241:11 296:25 457:7 475:16

671:10 679:12 680:1

validity 240:26 339:11

validly 666:7

Valley 430:13

valleys 596:23,24

valuable 229:6 240:17 297:18 317:15 615:13

624:22 671:25,26 675:16,20 694:22

valuation 675:21

value 14:5 60:4 65:16,17,20 67:14,18 68:12,12

69:3 92:11 97:23,26 98:5 99:16 100:12 102:11
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115:26 116:14 117:11 118:5,6 119:7,11,23

122:21,22 123:7,19 126:5 127:7,24 140:23

157:14 158:16 201:5 206:17 207:18 208:13

256:24 258:7,12,14 259:21,25 260:5,19 261:12

273:10 297:19 344:14 346:17 366:26 367:4 373:6

375:15 377:11,14,22 378:10,11 379:5 382:1

383:24 393:10,15,18 394:23 395:20 401:25

402:10,16,19,21,23,26 403:3,4,18 404:14 405:5

406:13 425:14 432:26 436:25 493:26 494:21,24

495:3,4 497:16 541:1 571:3 585:16 612:3,9,12,15

612:16,16 623:18 646:19 653:18 655:2 672:26

values 28:9 29:15,20 59:1,8,12,17 63:22,23 67:13

67:14 69:4 91:26 92:5 103:25 105:20,24 107:24

113:18 114:7 120:17 121:10,11,13,20 122:14,15

122:17,23 124:17 125:9 126:18 128:11 131:10

143:15 144:17 157:12 162:17 165:5 201:2,4,5

202:16,17 205:25 206:2,3,14,18 207:15,22 209:8

209:14,17,19 210:10,25 217:17,20 219:2,3,25

227:4,6 261:6,17,18 282:3,4 299:16 330:12,12

391:25 393:4,14,17 394:2,3 397:23 399:14,15

406:2 425:11 439:5 463:24 491:11 493:9,9

495:17 497:9,12 550:19 627:17 671:9 673:2

674:2

valve 427:16

vanishingly 474:21

variability 97:13,16,19 133:14 134:1 150:3 160:1

176:5 177:6 178:11,13 181:17 190:2,3,4 192:21

193:12,15 198:26 212:5,26,26 269:9,25 276:26

330:20 403:26 404:7 405:7 470:1 495:24 496:2,4

496:25 498:13 499:2 515:7 549:2,3 591:24

608:21 631:22 636:3,9,21 637:14,23 639:11

682:7

variable 69:6 130:25 260:18 264:7 269:9 398:24

515:5 550:14 569:22 633:26 635:5 646:2

variables 97:7 145:7 263:19 264:13 307:15 415:12

536:13 550:11 589:11,13 591:6 638:1 666:9

variance 280:8

variation 132:19,19,20,21 133:11,14 148:4 311:10

340:19,24 369:24 515:8 537:26 568:24 569:26

596:15 635:24 640:5,9

variations 190:22,23 246:22 595:11

varied 20:6 108:6,7 426:2

varies 93:8

variety 11:8 19:15 77:5 213:2 218:16 246:12

247:10 253:18 325:26

various 11:25 20:24 37:6 47:25 48:8 56:13 58:22

78:15 91:13 107:18 110:19 152:5 154:11 155:3

156:13 165:22 174:3,9,22 175:4,15 179:9 180:2

191:17 194:20 204:14 214:4 224:19 227:21 229:9

232:2 237:9 241:14 243:22 255:7 276:7 282:20

291:25 294:25 360:2 397:24 399:10 406:9 411:5

444:1 458:12 552:13 583:19 684:13

varroa 19:25 231:24,24 237:7 683:7

vary 35:5,9 45:19 96:16 109:13 148:8 277:5 343:5

344:15 408:24 431:4 442:22 562:24 564:2 568:13

varying 9:18 190:23 438:6

vast 292:23 353:4 600:11

vegetable 302:13

vegetation 563:25 564:10 567:25 573:25 597:10

verification 228:19

verified 226:15 608:15

verify 601:23 606:8

verifying 226:25 601:25 602:4 606:10

versa 390:12 632:10

verse 541:16

version 110:7 445:17 574:16,17

versions 231:2 357:5

versus 45:14 68:3 71:15 134:10,12 237:7 259:23

265:4 276:22 277:15 307:15 328:11,13 370:23

387:9 392:1 418:24 444:6 486:3 494:9 496:12

497:22,23 501:25 537:26 549:12,16 553:14,18,21

583:4 592:17 597:13 600:24 603:20 617:8 644:6

655:5 664:14

vertebrates 113:9 336:2

vertical 399:1

vet 17:21

Vets 642:18

vetted 55:1 107:18 171:25 264:19 271:22 462:4

477:18 482:10 504:6 608:15

vetting 475:16 482:19

viability 9:4,14 10:6 448:23 453:14,16 466:10

482:21 536:18

viable 129:17 269:7 286:23 505:7 567:2 613:23

vice 278:1 390:12 632:9

vice-Chair 4:21

vice-versa 56:19

vicinity 185:20

video 450:10

view 45:15 68:17,26 284:8 351:24 392:24 393:16

397:16 679:25

viewed 153:5 219:21

views 690:23

violation 323:11

virtually 412:5 523:15

virtue 429:21 482:22

virulence 322:14

virus 322:12,14 324:11 637:16,19,21

viruses 637:14,15,22

visa-vis 152:19
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visible 557:11,12,22

visibly 601:25

vision 578:4

visit 180:8 265:19 431:1,9 573:23 576:10 589:17

604:13

visitation 577:5,14,14 594:9,12 597:7

visited 403:1

visiting 352:1 577:10 604:2,5

visual 34:12,22 72:15,18 73:20 74:20 499:14

visually 34:8 48:9

vital 253:4 451:12

vitality 163:22

vitro 81:25 132:22 156:15 159:14 161:18 315:26

316:4,6 443:3,21 447:22 449:10 454:21 461:3

464:9,25 465:10 471:26 478:9 479:9,18 482:8,13

489:9,12 506:9 531:26 532:10,18 533:1 615:16

617:5 691:23

vivo 443:3 514:23 615:16,17 626:25

volatile 38:1 80:9

volatilized 73:19 74:18 75:18 76:21

volume 425:12,23 427:25

volumes 424:5 426:1

vomiting 323:17,19

vote 499:13 547:6,12,19

vulnerable 144:5 317:18 472:26

W

wait 12:21 381:24 384:2 534:6

waiting 304:23 310:2 456:3 512:11

walking 604:14

walks 601:21

wall 48:24 218:25

walls 427:18

wall-to-wall 578:14

want 2:2,12 3:19 6:24,26 7:10,26 8:12 11:15,26

12:25 14:4,9 32:25 52:23 66:12 67:26 68:4 69:20

69:24 86:4 100:15 103:25 109:9 139:14,17

153:20 155:2 156:25 203:6 215:4 239:8 247:25

248:12,21 258:21 262:11 263:10 264:2,14,17

265:21 271:3 274:20 275:4,5,14,22 278:24

279:22 285:6 286:17 288:9 295:14 303:4 306:8

307:11 308:5,6 316:20 322:7 330:11 332:1,2

333:3,8 337:3,23 339:9 340:2 341:4 342:25 344:6

348:26 349:4,13 353:19,23 357:26,26 358:3,5

361:25 364:3,20 368:14,23 370:21 371:16 374:13

375:21 377:20 386:12,24 389:13 396:14 400:14

401:3 403:14 412:20 424:22 430:23 432:13

435:16 436:8 437:6 440:5 441:11 442:2 446:8

455:3 456:4,5 459:7 464:21 469:10 473:18

480:21 482:1,3 483:19 486:13 495:1,22 498:8

502:9 506:5,9 507:3,10 508:16,20 512:8 513:15

515:25 516:8 518:17,23 521:22 522:17 524:1,12

525:15,17,19 526:25 529:2 530:25 534:24 543:13

545:3,5,23 547:6,22 548:9 551:9 553:8 555:22

556:24 557:4,6,10,11,12 564:26 575:10 576:23

577:23 582:2,17 583:18,25 584:8 588:18 595:5

599:22 601:9 602:15 603:26 607:3 609:10 611:18

613:10,24 617:16,19 620:17 621:18 624:15 627:6

630:20 631:19 638:5 639:2 640:11 642:6,11,15

643:14 645:17,17 651:10 654:12,13,18 658:21

659:9 660:17 662:6 664:21 665:21 666:17 669:17

669:22 670:12 673:20,22 676:17 680:16 682:5,25

688:6 690:14,17,18 691:12 692:19 693:12,14

694:18 695:4,7

wanted 88:1,8 133:23 199:9 244:5,12 251:11,15

275:19 279:26 304:22 310:12 328:6 334:2,24

336:12 338:1,3,7 339:20 340:16 348:24 349:12

350:24 372:20 378:7 395:4,20 419:25 423:17

430:8 435:18 437:17 442:17 517:17 566:3 571:2

601:5 622:9 627:19 635:23 651:24 652:6 681:23

wanting 347:8 601:11 631:2

wants 139:4 353:22 417:26 445:3 479:7 580:14

606:13 624:6

war 234:5 308:23,24

warming 335:20

warn 638:20

warrant 42:4 621:14

Washington 245:8,17 692:15

wasn't 135:17 151:6 234:5 271:15 305:7 310:18

350:6 419:15 560:6 604:12

wasp 78:16 425:15

wasps 162:26 600:11

waste 306:4

wasting 265:16

watch 365:12 453:9

watching 322:18 324:18 580:13 604:10

water 37:3,8,11,20 38:5,20 40:24 41:15 46:1,5,9,10

47:3 55:8,13 71:19 73:2,5,10,14 74:2 75:6 76:19

77:22 78:1,7,13,15,16,18,18,19,23 79:9,15,19,20

79:22,25 80:3,4,6 82:5,8,19 85:9,12,16 102:5,16

120:6 135:13,25 277:10 293:13,24 294:3 300:8,9

318:4 321:9 387:18 389:20,21 398:4 400:5,9,9

416:24 422:20,23 423:15,18,24 424:5,7,14,15,18

424:24 425:12,18,23 426:8,22,24 427:3,11,17,22

427:26 428:1,2,6,9,26,26 429:8,18,19,21,22

430:1,15,17,18,18,25 431:2,2,9,9,13,14,15,17,19

431:21,21,22 432:15,15,16,23 436:18 437:1

438:1,5,11,15 439:13,14,25,25 440:12 472:8

520:8 653:22,24 654:1 657:19 673:13,13,14

waterproof 427:15

waters 293:18,20,23 423:26
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watershed 5:8 250:6 684:23

waving 691:21

wax 38:16,25 74:6 75:15 76:12 338:10 345:25,26

346:1 353:20 354:1,6,13,20 355:8,21 356:2,9,17

396:20 412:5 429:24,25 430:1,3 433:15,17

439:18,22,26 440:3 467:14

way 19:17 31:2 62:16 66:23 70:1 82:1,16 85:5,15

88:22 130:16 135:14 145:13 223:9 224:14 230:17

234:9 241:15 246:24 256:1 265:18 273:20 281:23

288:6 294:20 308:26 311:10 332:2 338:26 346:25

353:26 358:24 365:5 372:3 374:2,15 375:18

376:13 377:26 378:8 381:6 394:6 395:16 398:5

404:18,19 406:18 407:20 408:4 411:23 414:13

416:12 420:18 435:10,10 439:14,15 454:6,10

455:7,18 457:15 458:16,18 459:19 461:11 462:24

474:6 484:23 492:2 493:23 496:7 505:2 508:12

514:5,11 517:2 525:1 529:2 536:8,13 540:13

542:21 545:22 546:8 549:23 558:23 569:17

573:17 580:16,17 589:20 595:16 596:1,17 597:7

605:21 608:13 613:26 615:22 633:21 637:3,20

639:22 646:24 647:15 649:21 651:1,12,17 658:25

664:25 665:6,12 666:21,22 673:8 681:3,7 684:16

687:3 692:13 694:22

ways 47:25 48:8 185:25 237:6,9 247:10 264:14

286:7 329:10 342:19 343:20 371:10 375:9 412:12

418:21 421:17 447:14 448:15 452:26 453:6,20

456:9 496:7 504:12 514:15 517:3 527:4 532:4

536:12 544:18 558:20 579:22 590:18 602:3

617:24 619:17 631:16 635:7 636:17 637:11

646:16 649:3 650:9 651:8 658:26 659:21,25

671:7 677:25 678:16 684:9 686:12 690:10

weakest 294:11

weakness 393:26 394:12,17 402:24

weaknesses 367:2 373:19 393:24 398:16 514:22

526:16 563:3,23 564:21

weather 176:2,4 177:7 187:13,15 223:4 225:16

316:26 428:6 528:14 534:5 591:18

weathered 157:26

weave 687:3

website 86:1 297:1 323:5 324:8

weeds 145:6 349:6 352:4

week 6:20 7:3 14:5 16:11,16 235:8 249:1 499:7,14

521:5 624:23 631:4 644:6 685:25 686:15 690:26

692:22 695:16

weekend 4:9 696:21

weekly 171:9

weeks 138:1 238:8 428:19 524:17,18 532:14

533:21 544:12 586:11

week's 8:5

weigh 28:3 133:15 396:14 441:11 457:11 463:4

468:4,5 479:7 482:2 507:3 547:18 551:9 559:7

585:19 611:18 620:18 626:9 629:15 630:18

weighed 444:3

weighing 60:17 630:23

weight 66:5 110:13 119:12 133:20,20 134:4 194:20

211:21 253:24 260:17 262:2 263:24 346:21

368:17,21 369:1,20,25 371:6,7,8,24 372:1 384:23

442:20 457:12 482:15 496:18 552:23 567:23

587:2 621:25 663:7

weightof-evidence 624:18

weights 477:15

weight-of-evidence 624:9 625:3,10,16,22 626:1,20

662:8 663:2,5

Weisser 106:13 108:18,23 109:16 110:10 111:8,20

111:23 112:10,19 126:25 129:3 131:4 217:1

257:16 366:1

Weisser's 369:23

welcome 2:2 3:23 6:19 84:24 231:4 251:8 442:6

wells 489:23

well-balanced 589:1

well-designed 278:26 539:17

well-known 445:13

well-reasoned 10:9

well-suited 458:6

Wendel 37:16 53:2 55:16 71:7,8,9 89:16 212:21

407:22,22 409:5,8 422:10,19,19 432:20,20 696:3

went 47:16 88:9 135:9 244:9 334:5 399:16 417:12

424:11,26 425:3 637:15 653:5 654:23 664:6,7

671:20

weren't 82:10 88:20 264:6 305:3 335:2 366:22

487:17 587:2

west 322:12,14 324:11 578:1,11,13,20 580:14

598:6,15 601:2

western 24:16 577:26

westerner 578:3

wetter 319:22

we'll 12:15 87:7,15 136:8 151:11 201:12 407:15,18

408:6 423:17 424:9 442:14 443:16 456:7,9,14

461:24 500:23 511:3,11 547:4 554:6 605:7

621:22,23,24

we're 6:15 8:4,19 9:9 16:2 43:12 48:23 49:18,22

50:1 51:3,25 52:18 63:9 71:6 81:17 82:23 83:22

84:24 95:10 100:20 106:4 129:4,10,16 130:13

132:11,13 150:10 152:4 195:5 197:3 198:14

200:23 201:23 215:4,8 220:18 237:6 241:21

242:6,14 245:22 246:1 247:4 266:23,24,26

282:21 312:1 335:18 337:2 371:17 377:12 386:14

392:7 401:17 411:14,17 412:6 415:23 416:11

420:26 421:1,15 423:13 425:10 427:10 435:5

439:23 442:7 453:25 455:22,23,26 460:20 461:14
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463:1,2 464:19 470:7 472:19,21 473:20 474:10

476:3,16 480:18 486:2 493:16,16 494:6 495:25

498:6 500:9 501:13,19 503:5 510:14,14,21

512:10,19,25 513:18 522:3 523:19 526:20 527:5

533:12,17 535:21 543:5,6,20,24 544:3 546:18

548:20 551:13 552:11 554:22,24 556:12 560:22

562:5 573:17 578:26 582:6,9,10 584:20,20,24

587:25 589:6,6 590:18,19 595:8 597:12,14

603:24 609:14 612:17 613:7 614:9,10 618:15,18

620:13 621:11 622:1

we've 8:21,22 10:15,16 45:10,12 47:5 49:25 53:6

53:19 61:23 65:12 66:26 82:19,20 193:13 200:15

202:12 221:6 223:18 237:3 240:25 243:16 304:15

307:15 312:2 316:22 325:15 356:14 408:15 410:2

410:6 413:7 414:3 428:21 435:7 436:10 469:18

478:21 484:12 489:14 503:2 523:18 539:12

545:11 547:21 549:18 555:7 560:21 594:17

605:17 606:18 617:3 619:23 620:19

whatnot 656:26 657:19

whichever 652:17 666:10

white 7:1 8:8,14 10:17,19,21 11:4,10,12 12:4,11,26

14:7,18,20 16:26 30:13 33:10,14,17 34:4 41:26

48:8 63:6 64:16 65:15 66:3 70:11 71:17 78:14

79:5,26 80:14 81:18 89:7 93:1 99:10 101:11

108:2 121:14 126:1,4 128:25 140:15 152:4,26

154:13 155:2 156:17 201:20 202:21 205:6 206:7

208:3,19 211:20 212:13 213:19 214:16 215:12

221:9,12 224:9,11 230:1 255:3,16 256:14,19

257:2,15,18 258:20 265:22 267:12 269:13 270:14

272:9 278:11 279:15 285:26 289:14 317:16

320:12 327:21,24 332:4 333:20 334:3 335:22

336:4 338:26 340:3 357:20 358:15 376:12 377:11

397:3 398:13 399:21 402:14 403:19 410:15

411:12 422:21 438:20 442:3 443:1 450:18 465:13

479:13 482:8 491:9 500:3 503:21 508:23 513:25

515:26 522:22 523:6 536:5 538:24 540:8 543:21

545:16 547:24 548:23 553:13 562:9,22 567:12,19

588:26 593:14 618:20 619:10 620:5 621:2 622:17

623:19 644:12 647:24 651:14 670:22 671:5

672:18 674:11,21 678:6,8 686:3,5 687:1,14 689:4

689:19 690:20 691:3 692:10 693:22 694:7 696:5

whole-hive 614:14

whole-level 532:17

wide 125:9,10 421:6 599:9

widely 35:6 409:23 474:24 504:11 590:3 593:25

wider 211:16 623:24

widespread 280:26 590:5 597:18

wide-spread 293:23

wiggling 304:6

wild 300:4 351:9 358:24

Wildlife 182:14 663:25

William 280:21,23 284:13 285:2

willing 454:3

wind 349:14,15 410:16 492:21 652:25

windblown 86:15

window 286:13 458:15

windows 488:21

wind-blow 437:24

wind-blown 410:25 416:1

wing 637:16,18

wings 375:3,4 637:17

winning 586:12

winter 39:18 99:4,7,8 188:10 192:5 234:24 283:3

287:16 302:15 445:5,24 524:6,17 574:20,22

575:2,9,17 576:20 585:11 589:4 603:6 682:16

winterkill 20:17

wireworms 317:15 320:12

wish 3:8 55:25 65:11 243:20 301:24 558:1 658:14

693:10

wishes 417:15

withstand 360:3,8 449:12

wonder 232:16 263:13 274:26 323:17,21 635:5

wondered 69:10 494:16

wonderful 314:24 357:8 665:26 690:4

wonderfully 678:8

wondering 47:11 129:24 130:1 136:26 139:2

195:11 197:23 230:4,19 241:3 262:16 309:8

331:1 421:10 455:6 535:14 603:19

Woolly 259:15

word 32:14 338:6 342:2,4 430:26 516:23 561:20

627:12,12 642:15 694:17

wording 342:21

words 145:22 229:14 334:10 417:6 493:20 594:4

688:17

work 7:1,11 8:4,20 14:4,10,10 15:15 16:20 85:23

96:8 131:20 197:7 221:10 247:4,8 250:9 255:1

276:13,14 279:5 287:13 309:1 312:5,21,22,23

313:22 314:18,24 315:13 318:21 324:8,15 334:18

352:5 368:1,11 418:6 435:8 436:8 447:20 455:23

463:17 464:13 470:2 493:3 504:26 505:3 507:8

557:6 574:26 576:16 581:1 595:4,16 617:21

640:15 653:4 659:5 664:3,15,18,20 671:16 672:4

681:11,24,26 682:2,5 683:11 686:6 687:15 689:1

689:9 692:21 693:20 694:19,20 695:19,20,21,22

696:2

worked 2:20 14:8 15:7 252:22 315:2 694:21

worker 37:19 58:1 61:6 62:23 72:22 74:6 79:16,23

89:21 90:7 93:9,13 95:20,23 96:1,4,5,11,20 97:23

98:16,21,23,25 99:1 100:7,17,22,26 101:13,20

102:19,21 103:4,12,17,19,22,23 104:4,17,19
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105:4 110:14 115:23 117:22 158:10,12 159:1,10

160:20,26 169:15 215:20,22 216:7,10,20 218:1,6

222:5 226:11 269:16 301:12 341:9,23 343:4,8

344:8,10 346:2 355:11 356:10,23 371:2 384:11

384:22,26 385:4 450:21 453:3 465:21 466:7

482:7 500:4,15 513:8,21 560:25,25 563:15 587:8

612:25 634:7 645:4,5,5,11,11 646:18,18,22 666:5

671:24,24 672:1

workers 38:16 39:17 72:13 73:23 99:7,15 101:4

103:8 131:25 132:19 309:16 330:25 355:23

359:26 360:1,4 368:22 380:7 382:24 443:25

444:16 452:5 453:11 455:11 458:19,20 467:9,11

476:3 481:8 531:25 532:11 533:3 554:17 563:16

612:22

workforce 222:2 236:4 625:12 646:6

workgroup 23:9,10,12 252:22 255:1 274:13

working 5:12 9:20,25 10:15,18 11:1,4 23:7,10

63:13 77:2 161:4 200:15 218:19 232:20 270:12

278:6 283:18 289:1 305:25 307:14 314:1 331:16

337:17 346:8 374:20,26 412:6 414:3 418:11,12

418:12,20 419:16,17,18 420:16 421:7 438:19

483:1 511:8 565:19 619:26 653:7 658:16 689:8

692:23 695:3,16,23

works 245:8 297:6 649:21 656:23 659:25 673:15

695:20

workshop 10:22 22:3,8 29:5 64:15 65:25 156:20

156:21 223:18 224:6 228:21 255:15 256:16

257:10,18

world 10:24 17:13 138:5 255:9 266:20 279:3 290:5

293:26 300:21 335:19 503:14 562:13 590:7

591:20 592:8,24 683:25,25 695:3

worldwide 297:18

world's 471:2

worried 439:23

worry 305:5 541:9 603:21 643:22 645:17

worse 523:20

worst 295:9 322:19 379:18 382:1 388:20,25 419:15

421:9 424:16 428:24 429:5 430:16 438:10 518:14

519:5 520:10,26 521:22,24 524:19 568:4,7 628:9

worst-case 295:5

worth 87:9 99:12 100:9 229:14 244:6 317:19 324:9

611:19

worthless 642:16

worthwhile 486:17 606:2

wouldn't 3:26 135:25 138:4 264:8,11,11 305:5

314:25 329:11 345:10 357:16 366:23 369:20

377:24 440:16 488:24 492:10 517:17 567:5

582:21

wow 214:20 403:11 478:6 626:3

wraps 365:18

wrestle 548:11

wrestles 50:26

write 373:1 648:6 676:5

writing 242:12 334:3,5 476:16 546:4 684:16

writings 691:4

written 32:22 34:12,19 88:5 246:16 252:23 281:5

289:10,12 290:26 292:4 293:13,16 295:18 302:3

349:8 370:2 425:5 526:6 547:5 561:17 580:20

632:12 643:6 648:4 696:11,12

wrong 50:25 133:19 239:13 241:14 244:23 264:5

307:7 347:13 369:5 415:10 455:19 487:24 501:14

516:23 519:22 524:22 554:18 555:20 561:13

620:20 628:25 643:25 662:23

X

X 202:13 493:26 494:9,26

Xs 142:11

xylem 120:15 125:17,23

xylem-limited 277:9

X-axis 102:23

Y

yard 234:10 310:21,22,23 635:16

yeah 32:12 133:1,15 220:2 232:16 235:16 239:14

245:11,15 276:17 277:26 280:11 303:21 311:25

319:4,12,15 332:17 344:13 354:11 368:25 369:13

384:3 387:2 388:8 389:14 391:21 392:4 396:15

406:24 407:20 416:9 427:8 432:8 458:26 464:4

470:5 474:10 476:18,20 485:18 486:13 488:25

489:2 493:13,18 496:18 497:22 505:9 507:24,26

510:18 538:14,20 546:4,9 547:16 552:3 575:10

579:13 580:2,12 583:12 584:7 599:22 601:4

602:8,12 603:26 604:11,13 611:8,24 613:3,12,16

614:9 615:2 620:17 641:22

year 64:12 130:12 156:6 172:7 194:17 234:24

297:13 302:21,23,26 303:2,7 307:3 310:8 318:9

322:15,15,16,16 324:13 336:8,9,9 385:1 411:25

411:26 412:11 413:8,10 445:2 501:21 515:21

541:2,5 560:1 591:5 592:19 595:12,12 596:14,15

596:26 598:10 602:26 623:12 628:22 636:23

650:15 664:10,11

yearly 9:7

years 10:16,23 17:4 19:20 21:16 85:24 109:3 113:2

130:22 199:16 233:5 235:7 237:3 279:10 287:22

288:1 290:5,9,15 291:26 294:12 301:10 302:17

302:18 303:4 312:3 313:13 320:7 322:17 359:25

375:1 399:25 420:11 555:8 566:7 591:3,6 592:3

592:18 692:2

yellowish 603:2

yep 315:9 422:9 430:7 436:4 468:4 480:9 580:22

yesterday 281:7 282:7 291:15 325:6 346:4 351:18
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438:4 439:12 478:26 500:11 504:14 605:8 622:5

623:5 633:17 645:3 646:25 666:4 693:3,7,8

yesterday's 281:18

yield 317:5,11 421:2 460:18

yields 27:3 317:3 319:5

York 289:8 302:10 303:18,20,21 311:13 326:19

336:10 430:11

Yorker 506:1

young 24:22 156:8,10,14 157:7 158:11 161:2

217:15 384:17,17 385:3 386:5 479:10 574:25

612:25

younger 96:2

Y-axis 102:25

Z

zero 330:12 519:9 539:16

zillions 655:12

zone 554:5

0

0 142:24

0.05 298:14

0.1 258:14

0.25 299:13

0.33 260:20

0.35 500:5,10

0.4 59:10 208:5 217:17 608:3 610:7 613:1

003 118:11 672:15

004 187:22

0051 187:24

02 187:22

025 187:25

036 118:4

1

1 18:23 22:9 30:13 89:7 91:8 93:1 99:9 103:15

155:2 202:14 208:13 212:3 262:3 272:18,23,26

273:13 333:16,19 334:17,22 337:9,17 341:5,9,18

345:4,16,16 358:15 365:25 373:6 374:17,18

377:23 383:6 385:2 393:26 401:24 402:9,16,21

403:18 404:9 436:25 438:13,25 571:26 572:1

573:26 574:11,12 612:15

1(a) 334:16 337:1

1(b) 337:3

1(c) 341:4

1,000 53:24

1-hectare 573:21

1.0 59:11

1.25 299:13

1/10 273:3

1/100th 466:4,13

1:02:08 658:14

10 85:24 161:15,16 178:15 208:2,5,7 248:24,25

261:23 262:7 265:4 320:7 322:4 383:22,26 384:3

399:25 425:19 476:6 480:24 481:8 482:7 483:7

483:12 484:2 491:9 494:1,25 499:17 503:21

508:22 545:6,7 579:22,24 593:3 610:7 612:4,5,18

10B 482:3

10X 261:13 262:6

10(a) 480:22

10,000 172:5 183:24 554:17 637:4

10-day 161:2 482:20 484:2,8 495:9

10-day-old 544:11

10:35 48:24

10:45 296:14

100 79:24 95:7 99:24,26 104:8,14 137:20 261:23

272:25 307:2 377:12 379:11 381:14,17,19 382:1

382:9 383:7,12 426:4 438:7 533:2 548:7 555:8

569:16 576:3 599:22 608:18

100X 261:13 262:9 383:11

1000 302:13 306:24 324:20 383:8 425:6

11 24:24,26 61:16 157:23 374:19 375:6 393:16

512:25 513:3 531:1 535:22 538:23 547:23 605:17

11A 512:26

11:41 87:16

110 115:21 393:21

112 273:12

1150 322:20 323:20

117 102:12 692:2

12 110:8 117:25 128:2 154:23 295:16,20 373:6

375:11 378:10 429:4 562:5,8 567:15,16 578:24

578:25 585:23 588:2 593:13 603:9

12:45 87:15 512:20

12:50 87:20 332:24

120 93:24 101:25 102:3,9 105:17 127:7 292:20,26

430:13 578:22

128 110:14

13 62:3 115:25 127:13 187:22 208:25 209:2 295:17

295:20 298:19 604:20 625:5 630:12 651:4 684:15

685:4

13A 604:22

13B 608:2

13C 614:12

13d 615:5 624:6

13e 624:7

13f 626:6,7

13g 630:13

13.3 298:12

13.6 114:14

130 411:25
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132 553:13 554:14

133 553:13

14 115:8 229:12 243:16,18 287:22 292:18 293:4

323:16 361:14 373:20 411:20 435:11 516:23

560:20 631:16 643:14 670:19 676:19 684:15

685:4

14a 643:16

148 306:2,6 310:14

15 20:3 130:22 279:10 296:13 303:18 378:17

399:25

15-year-old 367:3

150 637:15

150-200 262:1

158 140:17 142:22 156:7 167:20 168:2 246:13

1600 302:12

17 123:12 258:11

17.6 297:14

17.9 298:24

170 269:14 295:16 296:1 518:13,19 519:3,12

521:12 618:7

1700 285:22

18 120:8 305:3 389:21

1895 466:21

1940s 234:19

1947 19:13

1950 233:3

1970 233:4

1980s 20:1 235:23

1982 107:7

1983 107:7

1986 474:21

1987 125:26 302:24

1988 120:20 125:26

1992 96:7 171:1 520:17

1993 225:1

1995 126:1 233:7

1997 106:13 108:18 366:1

1998 399:25

1999 375:2

2

2 22:10 42:5 71:20 78:14 79:5,26 85:10 91:8

152:25 208:19 212:4 294:2 298:14 322:21,24

324:26 345:9,10,13 373:21 422:21 430:10 436:22

2X 259:25

2.2.1 333:19

2.4 375:5,7

2.49 234:21

2.5 19:23 103:17 104:17 191:2 585:4,5

2.7 102:11 108:19,22 109:19,26 126:24 365:24

377:22 378:1,2,10

2.8 19:13 500:4

2:30 378:18

2:55 154:5

20 233:5 306:24 320:7 322:24 425:19 480:19 494:1

494:25 501:7 502:24 612:6

20s 234:24

20,000 593:3

20-day 545:2

200 576:21

2000 302:11 306:7,25

2002 324:9,11

2003 322:12 324:9,12

2005 96:9 226:9

2005/2006 233:20

2006 18:26 19:8,14 20:13 233:7 234:17 235:23

297:13

2007 9:7 20:4,13 226:4 472:7

2008 83:26 297:15

2009 21:20 117:11 297:15 443:23 475:14

2010 23:13 117:7 225:8 322:19 402:18

2011 19:22 22:1 64:14 156:21 234:19 322:19 500:3

688:18 689:8 691:25

2012 23:20 156:22 292:14,19,24 293:17 299:2

302:23 322:19 390:18

21 20:3

21st 668:3

21,000 238:8,9

213 446:3 481:10 483:9,10

214 446:3

22 206:7,13 273:13 298:19

24 141:22 158:18 208:3 330:13

24/7 313:18

25 158:3 286:5 292:16 303:4 575:18 603:9

26 312:3

27 292:14

27th 413:9

28 118:5 119:7 170:4 540:7

29 117:24 128:2 138:23 206:16 297:15

292 93:23 97:23 105:16 127:6

3

3 18:24 22:11 89:8 91:19 106:1 128:24 152:25

208:19 212:5 260:9 305:3 327:25 335:14 338:16

357:17 364:14 365:18 369:4 397:3 497:6

3X 260:14

3(a) 58:6 62:24

3(b) 58:14 62:24 358:7

3(c) 59:1

3,600,000 305:8

3.1.1.1 392:13

3.1.2 397:10
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3.1.3 402:8

3.2 207:25 208:3,4

3.2.2 402:14

3.4 207:10

3.6 611:5

3.6X 273:14,15

3.7 191:2

3.8 298:12

3.9 332:5

3.93 207:8

3:00 154:5

30 96:15 102:15 130:22 135:5 234:23 258:13 263:1

263:3 395:26 501:5,13,16 502:12,15,20 503:1,9

545:11 554:3 575:18

30,000 584:16,17 585:3

30-day-old 544:11

300 322:11,23,23

300,000 316:18

3000 584:10 585:2

31 20:5

32 116:1 127:12 217:7 272:19

34 234:23

35 500:10,17

355 675:2

36 20:6 258:12

37,000 316:17

39 68:3

4

4 22:12 67:14 89:8 91:25 92:6 103:8 128:26 217:7

327:25 345:14 365:20,22 373:4 497:6

4D 17:6

4(a) 58:9 62:26 208:22

4(b) 58:16 62:26 208:23

4.1.2.1 510:13

4.1.2.1.2 510:17

4.1.3 508:23 510:13

4.2.2 538:24

4.3 510:24

4.3.2 567:18

4.3.4 547:24

4.3.5 593:14

4.5 207:2

4.6 120:11

4:30 693:3

40 115:14 156:6 167:20 168:2 574:8 578:21,23

579:21 580:15 637:8 638:16

400 272:25

4000 336:6,11

41 67:15 68:3

42 611:26

43 109:17

43.9 298:22

44 302:18 334:17

45 102:5,16 375:8 426:3

46 285:22

47 306:10

48 158:18 316:17 443:13 447:2,12 477:3 480:14

482:26 484:24 599:21 618:18

48-hour 157:8 158:16,20 159:21

48-well 159:19

5

5 59:16 63:21 89:8 102:11 103:3,8,15,19,20 104:4

104:17 105:21 121:13 137:25 138:1,17 260:14

272:24 273:11 322:4 378:21,23 384:7 494:25

497:6 554:17 574:1

5A 380:10 384:3,3

5B 388:8,8

5C 388:9,12 392:5

5(a) 218:13

5,000 147:14 169:15

5-hectare 573:21

5-OH 575:17

5.3 101:11

5.4 221:9

5.4.2 670:21

5.5 234:18

50 157:8 161:8 184:5 273:10,11 305:10 310:2

363:25 395:12 411:22 438:13 443:25 555:8 576:3

576:3,7,16 582:10 599:11,19,20,23 600:23

601:13 637:9

50,000 188:4 238:9

5000 584:16

52 4:8 6:20

55 23:4

57 120:11 302:17

6

6 19:12 89:9 103:3 129:7 141:9 218:19 234:17

317:11 347:26 348:14 349:23 392:9,12 397:9

402:7 407:10 426:3

6A2 325:20

6C 403:16

6E 405:20

6(a) 201:1

6,000 109:4 113:1

6-bushel 319:6

6.5 322:10

60 19:19 317:14 320:15 321:16

60,000 188:4 582:10 593:5

66 244:22,24
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7

7 71:21 79:24 316:2 348:3 407:16 409:5 422:20

432:21 435:3 436:3,7,13 437:5 439:12 623:7

7A 407:20,22 409:4

70 138:24 430:12

71 109:24

72 443:13 447:3

75 305:10 518:12,20 519:2,12 521:12 552:24 618:7

633:8

8

8 60:8 154:23 273:4 303:14 345:14 369:10 372:4

441:18,18,21 446:12 458:24 486:2 504:3 514:2

531:12 605:17

8B 504:19,21

8:30 248:9

80 262:2 272:24 299:11 332:12 389:20 471:1 477:9

800,000 17:15

8000 298:17

82 131:3

85 217:13

850.2500 182:12

850.3020 156:9

850.3030 158:2

850.3040 168:3 195:17

88 332:7

9

9 248:12 305:3 381:12 435:21 456:26 464:21,24

471:22,24 472:23 474:14 477:2

9A 471:19,19

9(a) 143:3,4,6

90 321:11 471:1 696:20

90th-95th 261:9

95 430:14

95th 90:14 98:11,16 110:16 121:11 134:5 261:7,12

398:13 399:16

96 157:9 443:13 477:3 480:14 482:26

96-hour 158:17,20

97 110:12 131:4 260:8

98 321:22 337:20

99 291:9 337:20