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 FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

 OPEN MEETING

 

SCIENTIFIC ISSUES ASSOCIATED WITH PROBLEM FORMULATION FOR THE

REASSESSMENT OF ECOLOGICAL RISKS FROM THE USE OF ATRAZINE 

 

 DOCKET NUMBER: EPA-HQ-OPP-2012-0230

 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

 JUNE 12TH 14TH, 2012
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1 DR. SHARLENE MATTEN: I would like to welcome

2 everyone to this meeting of the FIFRA Scientific Advisory Panel.

3 My name is Sharlene Matten and I am the Designated Federal

4 Official for this meeting. 

5 Today is the first day of the planned 3-day

6 meeting to consider and review a set of scientific issues

7 associated with problem formulation for the reassessment of

8 ecological risks from the use of atrazine, and this also

9 includes environmental fate, anything that relates to the

10 assessment. 

11 As the DFO for this meeting, I serve as a

12 liaison between the Panel and the Agency and I am responsible

13 for ensuring that the provisions of the Federal Advisory

14 Committee Act are followed. 

15 I want to thank Dr. Dan Schlenk on my left for

16 serving as the Chair of this SAP meeting. This is Dan's first

17 meeting as the official Chair of the permanent panel. 

18 I also want to thank our newest members for

19 joining the permanent panel, Dr. Jim McManaman and Dr. Marion

20 Ehrich, as well as the other permanent panel members, Steve

21 Klaine, and on the phone will be Dr. Martha Sandy, who will

22 introduce herself after everyone else has. 

23 I would like to thank the members of the

24 public for attending this meeting, the members of the panel and

25 my colleagues on the FIFRA Scientific Advisory Panel staff, and

26 if I forgot anyone else, I thank you for your participation, and
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1 the EPA Team, who will introduce themselves. It is an immensely

2 challenging activity to prepare for these meetings. 

3 The function of the Scientific Advisory Panel

4 is a Federal advisory committee that provides independent

5 scientific peer review and advice to the Agency on pesticides

6 and pesticide-related issues regarding the impact of proposed

7 regulatory actions on human health and the environment. 

8 The FIFRA Scientific Advisory Panel only

9 provides advice and recommendations to the EPA. The decision

10 making and implementation authority remains with the agency. 

11 As the Designated Federal Official for this

12 meeting, a critical responsibility is to work with the

13 appropriate agency ethics officials to ensure that all

14 appropriate ethics regulations are satisfied. In that capacity,

15 panel members are briefed on provisions of the Federal conflict

16 of interest laws. In addition, each participant has filed a

17 confidential financial disclosure statement.

18 I, along with our Deputy Ethics Officer for

19 the Office of Science and Coordination and Policy, and in

20 consultation with the Office of General Counsel, have reviewed

21 these reports to ensure that all ethics requirements are met,

22 both for financial conflicts of interest and the appearance of a

23 lack of impartiality. 

24 For this meeting, we have a number of public

25 commenters spread across 2 days starting this afternoon. There

26 is a set of approximately 3 hours of presentations this
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1 afternoon and approximately an hour-and-a-half on Wednesday

2 morning. 

3 I have a list of those who have already

4 contacted me as public commenters, if there is anyone in the

5 room who hasn't contacted me, please see my colleague who will

6 be sitting at this table over here on my right. Most of the

7 comments are in the 5 to 10 minute range. Others have arranged

8 with me to be 15 to 20 minutes as individual commenters. 

9 These proceedings are recorded for oral

10 transcription on a DVD, as well as a written transcription,

11 which will be formulated as a PDF. Our transcriber is in this

12 corner and our oral and sound people are over here, Carlos and

13 Linda, our wonderful transcriber.

14 We also have members of the media here for

15 everyone to know, and you might see a camera. The camera should

16 not be intrusive, but be aware that it is an open public

17 meeting. 

18 The materials for this meeting are placed into

19 the public docket. Most of the materials are in electronic form

20 in the docket that can be accessed through the open site:

21 www.regulations.gov. The Docket Number for this meeting is

22 EPA-HQ-OPP-2012-0230. 

23 All of the materials that are distributed

24 today in Public Comments and the information that the Panel

25 provides, any additional materials by the Agency or any other

26 information that relates to this meeting will be placed into the
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1 docket by the end of the week. Hopefully, those will also be

2 available at that time. There is a quality assurance process

3 that also is involved.

4 After this meeting is concluded, the SAP will

5 prepare a report consisting of responses to the questions, a

6 detailed summary. Those will have considered all of the

7 information discussed at the meeting, all of the information

8 that was provided prior to the meeting, and any information that

9 is provided during the meeting, including the public comments

10 reports. 

11 The report serves as the meeting minutes,

12 which must be completed within 90 days according to the Federal

13 Advisory Committee Act.

14 Again, I wish to thank everyone on the Panel

15 for their participation. I look forward to both a challenging

16 and interesting discussion over the next 3 days. If anyone has

17 any questions, please contact myself, or my wonderful colleagues

18 to my right or just outside the room. 

19 I want to remind the Panel members and the EPA

20 staff and the Public Commenters that we are having a little bit

21 of a challenge with the sound system, so please hold the mic

22 close to your mouth, don't eat it, but please introduce yourself

23 and your affiliation each time. 

24 With that, I am going to turn the meeting over

25 to our Chair, Dr. Dan Schlenk. Thank you.

26 DR. DANIEL SCHLENK: Thanks, Sharlene. Good
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1 morning, everyone. Welcome to Scientific Issues Associated with

2 Problem Formulation for the Reassessment of Ecological Risks

3 from the use of atrazine. 

4 My name is Dan Schlenk. I am basically the

5 timekeeper here to try to keep everybody on schedule. I am a

6 professor of Environmental Toxicology from the University of

7 California, Riverside, and my expertise is in mode of action of

8 legacy and emerging contaminants in aquatic organisms. 

9 What I would like to do right now is just go

10 around the room and have the panel members introduce themselves,

11 state your affiliation and just a couple of brief comments about

12 your area of expertise. So, begin on my left with Jim

13 McManaman.

14 DR. JAMES MCMANAMAN: Hi, I am Jim McManaman

15 and I am at the University of Colorado, Professor of

16 Reproductive Sciences and Vice-Chair for Research in the

17 Department of Obstetrics and Gynecology. My expertise is in

18 general Reproductive Biology with special emphasis on mammary

19 gland development. 

20 DR. MARION EHRICH: I am Marion Ehrich,

21 Virginia Tech. I am Professor of Pharmacology and Toxicology

22 with emphasis on pesticides. 

23 DR. STEPHEN KLAINE: Good morning, my name is

24 Steve Klaine. I am a professor at Clemson University and I

25 direct the Institute of Environmental Toxicology. I am an

26 ecotoxicologist and a member of the permanent Panel. 
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1 DR. CATHERINE PROPPER: I am Catherine

2 Propper. I am from Northern Arizona University. I am a

3 professor of Biological Sciences with expertise in Environmental

4 Endocrinology. 

5 DR. THOMAS LA POINT: Good morning. My name

6 is Tom La Point from the University of North Texas, a professor

7 in Environmental Science with expertise in Sediment Toxicology. 

8

9 DR. MICHELLE BOONE: Hi, I am Michelle Boone

10 from Miami University of Ohio and I am an ecologist who examines

11 maturation questions with amphibians. 

12 DR. JAMES FAIRCHILD: My name is Jim

13 Fairchild. I am an aquatic ecologist with the U.S. Geological

14 Survey in Columbia, Missouri, and my specialty is in the area of

15 fate effects of pesticides. 

16 DR. ANNE FETSCHER: My name is Anne Fetscher. 

17 I am a biologist with the Southern California Coastal Water

18 Research Project and I work on developing water quality

19 monitoring tools. 

20 DR. JOHN RODGERS: Good morning. My name is

21 John Rodgers and I am at Clemson University in South Carolina

22 and I am a professor of Environmental Toxicology and I work on

23 pesticides, organics, metals, and things of that sort in aquatic

24 systems.

25 DR. MICHAEL TWISS: My name is Michael Twiss. 

26 I am a Great Lakes biologist at Clarkson University and my
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1 expertise is phytoplankton. 

2 DR. BERNARD ENGEL: Bernie Engel, Purdue

3 University. Professor of Agricultural and Biological

4 Engineering with expertise in hydrologic waterfall remodeling. 

5 DR. WILLIAM EFFLAND: Good morning. My name

6 is Bill Effland and I am a soil scientist with the U.S.

7 Department of Agriculture Natural Resources Conservation Service

8 and I am primarily working on natural resource assessments.

9 DR. TIMOTHY ELLSWORTH: I am Tim Ellsworth

10 from the University of Illinois in the Department of Natural

11 Resources and Environmental Sciences and my areas of focus are

12 chemical fate and transport and geostatistics. 

13 DR. ROBERT GILLIOM: Bob Gilliom, U.S.

14 Geological Survey and I direct our national studies of

15 pesticides as part of the National Water Quality Assessment

16 Program. 

17 DR. LINDA YOUNG: I am Linda Young in the

18 Department of Statistics at the University of Florida. I

19 specialize in spatial and environmental statistics.

20 DR. KENNETH PORTIER: I am Ken Portier with

21 the American Cancer Society and I am a biostatistician. 

22 DR. DANIEL SCHLENK: Thanks. Martha, are you

23 on? Martha, do you want to introduce yourself? Martha Sandy

24 actually should be on the phone. She is a member of the

25 permanent Panel on the phone for our quorum purposes. She will

26 be here throughout the week as well.
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1 Thank you very much for the introductions. 

2 Let's get on our first item of the agenda, which is our opening

3 remarks from Stephen Bradbury, who is the Director of the Office

4 of Pesticide Programs for the EPA.

5 DR. STEPHEN BRADBURY: Thanks, Dan. I would

6 like to thank all of the members of the committee for joining us

7 for the next part of the week. I know it takes a lot of work

8 preparing for an SAP and a lot of work at the meeting, and then

9 during the 90 days afterwards pulling all of the information

10 together; so I greatly appreciate your contributions to this

11 effort and this is one of several SAPs we have had on atrazine;

12 some in the arena of human health, toxicology, exposure, and how

13 to help us with the risk assessment on human health aspects. 

14 This SAP, though, focuses on the ecological

15 issues and taking a look at issues with regard to aquatic

16 ecosystems in particular looking at both multiple species with

17 the ecosystem communities that we are focused on, as well as

18 amphibians as a particular species that humans have contact. 

19 What I would like to do with the next slide is

20 just help provide some context going into the week's meetings,

21 and in so doing, try to help frame what we are taking on. The

22 figure up here is out of the EPA's Ecological Risk Assessment

23 Guidelines. It is a framework for ecological risk assessment

24 that lays out the various steps in the Ecological Risk

25 Assessment. 

26 This figure and the document have been around
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1 for probably 15 years almost and it is deceptively simple. It

2 seems very straightforward, but when you start to really dig

3 into what is behind those boxes and arrows and words, it is

4 actually, I think, quite challenging, and having said that,

5 trying to use this as a guide to going forward I think is also

6 very instructive in how to undertake an ecological risk

7 assessment to better ensure that the science that is being

8 developed can help focus in on the questions that are at hand.

9 Today's meeting, and the next few days, are

10 going to focus on that top box, the problem formulation step of

11 the Ecological Risk Assessment, because that lays the foundation

12 for moving forward in the risk assessment itself. 

13 What I would like to do is spend a little time

14 talking about that box on the outside to the left, as well as a

15 little bit about the box that is on the right-hand side to help

16 provide some context for the risk assessment that we will be

17 undertaking. 

18 What the ecological risk assessment guidelines

19 try to help us all think through is the issue of science for a

20 purpose or science that has a question to answer. There are

21 many questions that can be asked, and in this case, in a risk

22 assessment we want to make sure we understand what are the

23 questions facing the regulators or the risk management aspect of

24 the overall task at hand. That helps guide the resource

25 investment, it helps guide the questions that need to be asked,

26 starts to get some insights on the kind of uncertainty that
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1 needs to be considered and evaluated as one moves forward

2 through the ecological risk assessment. 

3 So, the planning box on the left-hand side,

4 the figure talks about management goals and management options,

5 the scope and complexity and focus of the question at hand and

6 resources and scheduling. 

7 Let's take a look at some of those first

8 components of that planning exercise with regard to the

9 management goals. So, in this context, we are taking a look at

10 the reevaluation of atrazine under the FIFRA and the Federal

11 Insecticide, Fungicide, and Rodenticide Act. 

12 Every active ingredient in pesticides needs to

13 be reevaluated systematically over a 15-year cycle; and atrazine

14 is up, if you will, for reevaluations starting in 2013. Every

15 pesticide going through the reevaluation process has its first

16 step of problem formulation stage, in which we try to capture

17 what we know, where we are heading, and in an open and

18 transparent process, get additional information into that risk

19 assessment process. 

20 This SAP is helping us get a leg up, if you

21 will, in going into that reevaluation step for atrazine. I

22 think part of the reason is, there is a lot of data for atrazine

23 and it has a unique body of information compared to many

24 pesticides in terms of monitoring information, the breadth and

25 depth of the ecological effects of information. 

26 With the monitoring that is also associated
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1 with this compound it really creates a challenge in terms of the

2 all the information to process, but also establishes, I think, a

3 benchmark or some vision of what the future could be for

4 ecologically successful testing to go forth, depending upon the

5 amount of information that may be available on any given

6 compound. 

7 Every compound has a tenth of amount of

8 information as this one, but with this information I think we

9 can get some insights on challenges that aquatic toxicologists

10 and risk assessors have been thinking about for 35 or 40 years

11 in terms of mesocosms, single species studies, national risk

12 assessments, spatially specific risk assessments in specific

13 watershed in a specific state. 

14 How do you do that? How do you move in and

15 out of scale, how do you move in and out of time, and how do you

16 use different kinds of information to formulate the question and

17 figure out how to answer the question, and how to articulate the

18 uncertainties around you? 

19 We are also working with our colleagues in the

20 Office of Water and their roles and responsibilities under the

21 Clean Water Act. They are working on developing aquatic life

22 criteria for atrazine, and while the Clean Water Act and FIFRA

23 may have different words in the statutes in terms of how that

24 science gets used in the statutes, the goal of the EPA is that

25 underlying science should have a common theme to it. 

26 There should be a commonality in terms of what
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1 we think atrazine's fate and transport characteristics are. 

2 There should be a commonality in terms of how we think about

3 frequency of sampling and understanding the uncertainties around

4 different sampling frequencies, and there should be some

5 consistency at the Agency in terms of how to reevaluate

6 mesocosms or the individual species toxicology tests so that the

7 underlying science has a common basis to it. How it plays out

8 in terms of the statutes could have some differences, but they

9 should not be because the science is being interpreted

10 differently across the agencies. 

11 So, as we go forward our colleagues from the

12 Office of Water may come and help answer questions. Certainly,

13 colleagues from the Office of Research and Development at the

14 EPA will be part of this effort, partly because it spans more

15 than one statute and partly because of previous comments about

16 the depth of information and the opportunities that this

17 chemical has for answering some specific regulatory questions,

18 but also in helping to establish some insights for the future;

19 be it from molecular biology and the mode of action of atrazine

20 and endocrine-related effects on amphibians to how do you take a

21 look at complex ecosystem responses and how you interpret that. 

22

23 So, essentially we ultimately wanted to know

24 what are reasonable levels of atrazine that can be in water and

25 not cause unreasonable adverse effects to the aquatic ecosystems

26 and the species of organisms associated with those ecosystems? 
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1 And to the extent there needs to be litigation undertaken

2 through the registration of the product. How do you figure out

3 what an appropriate exposure level would be across space and

4 time to ensure that you don't have unreasonable adverse effects

5 associated, if you determine there are some places with adverse

6 situations?

7 So, that is sort of the management goal. How

8 much Atrazine in the water, or what kind of time course do we

9 expect to see adverse effects or not adverse effects. A lot of

10 the conversation over the next few days will be what is that

11 adverse effect and how do you quantify that? Realizing that

12 atrazine and most chemicals exist in the environment in a steady

13 state concentration; they have varying exposure patterns and how

14 do you interpret that? 

15 How do you take a look at responses to an

16 individual organism or a group of organisms in the context of a

17 complex ecosystem when all sorts of feedback groups? What does

18 that all mean? So, that is the regulatory goal, if you will.

19 The, management options I touched upon in

20 terms of how we are moving forward. Yes, it is a National

21 Ecological Risk Assessment or to the National Water Quality

22 Criteria for atrazine. But the options can range from the

23 national perspective down to very specific stream issues, or

24 specific a watershed in terms of how that information about

25 atrazine could be interpreted and to the extent the things that

26 need to be changed in a certain watershed and how the change can
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1 change the exposure profiles. 

2 So, in terms of complexity, which is one of

3 the concepts in the chart and scope, the idea of moving across

4 space and time, being able to zoom in or zoom out and zoom

5 across time, but understanding what happens as you change the

6 scale in terms of space and time for ecosystem responses. How

7 do we think through how atrazine behaves in terms of fate

8 transport in different kinds of water and types of watersheds? 

9 It probably does not behave the same way in every watershed in

10 the country. There are probably a bunch of factors that are

11 associated with that, and previous SAPs have given us insight

12 into how to take that into account in terms of the vulnerability

13 of watersheds to exposure to atrazine. 

14 So we have the ability to move across space

15 and time in terms of taking this risk assessment forward and the

16 management options that we can use as we go forward as a

17 meeting. 

18 Also, complexity, I think, in terms of the

19 kind of effects information that we have available to us for

20 this risk assessment, whether you've got mesocosm studies,

21 microcosm studies, as well as individual species tests; and I

22 think many of you around the table were involved in developing

23 the mesocosm study designs and how to use them with the idea

24 that, while these are still physical models, they are not

25 reality, but they are physical models that provides some more

26 insights into the complexity of these systems and perhaps
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1 single-species bioassay proof. 

2 The challenge is how to use that information

3 in the context of a risk assessment has been something we have

4 talked about in two previous risk assessment SAPs on this topic.

5 We hope that over the course of the next few days you can see

6 where we are and give us some of your advice in advancing some

7 of those concepts into that effect.

8 Finally, the idea of complexity in terms of

9 how this plays out at the end of the day. As I indicated

10 before, we want to come up with the ability through this risk

11 assessment to have a variety of options in terms of how we take

12 a look at atrazine. The idea can be spatially explicit as

13 needed in terms of the management of atrazine and using the kind

14 of information that we have here. 

15 It is inherently complex. It is more complex

16 than the water quality criteria with families tested in both

17 fairly acute and chronic calculation of what we do with

18 pesticide risk assessment when we take a look at the most

19 sensitive species tested and have that be the benchmark that

20 drives the ecological risk assessment. In this case, we are

21 trying to integrate a lot more information. It is more complex.

22 With that complexity, you would think, is this equally to have

23 flexibility in terms of how we carry it through our regulatory

24 system. That is hard; it is hard to communicate; it is hard to

25 do.

26 So, as we go forward, that complexity gives us
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1 some capability to think, but it also creates some challenges in

2 terms of how you explain it and how do you put it together in

3 such a way that it paints an adherent picture for everybody to

4 go forward.

5 One last point and I will turn it over to Don

6 and we will get on with the important part of this meeting. 

7 One of the concepts you will see playing out

8 in the conversation, and as you go through a risk assessment, is

9 talking about uncertainties and understanding what the

10 uncertainties are that you face. In problem formulation if you

11 read the Agency guidelines, it talks about when you start

12 capturing the thoughts about uncertainty during problem

13 formulation; to be thinking about that as you go through the

14 analysis phase and the risk characterization phase.

15 We will be asking directly or indirectly

16 questions around uncertainty and it is not to get a laundry list

17 of all of the things we don't know, because we always make

18 decisions about things we don't know. That is a fact of life.

19 The key is understanding what is the

20 significance of different classes of uncertainty within the body

21 of information that we have. Not all uncertainty is created

22 equal. Some really matter; some may not matter in the grand

23 scheme of things and it is getting some insight on things to

24 focus on, things to still acknowledge, but realize in the grand

25 scheme of things they may not have as big an impact. 

26 So, there is the first order, second order and
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1 third order kind of uncertainties that come up when we are

2 interpreting. 

3 That gets important when you take a look at

4 the box on the right-hand side in terms of when and what kind of

5 additional information may be useful to help refine the risk

6 assessment. It could be sort of this broad risk assessment

7 activity that we are at, or it could be if you are zooming into

8 this specific watershed, how you might need some additional

9 information to refine your thinking if you were in a specific

10 watershed and having to make some decisions about that exposure

11 profile and whether or not it would be reasonable to assume that

12 you're seeing adverse effects or unreasonable adverse effects. 

13 So, as we go through the next several days,

14 just think about that to serve as a backdrop of many of our

15 charge questions to see where they focus on.

16 So, that is the wrap up. I want to thank the

17 Panel for the 4 peer reviews leading up to this, for the issues

18 on amphibians, the issues around mesocosms and monitoring and

19 how do you interpret all of this information. I hope we have

20 done justice to your reports of the past and try to bring some

21 additional information together.

22 I also want to thank the DFO and all the staff

23 that has helped to put the Panel together and all the work with

24 all of you to help them. I also want to thank the public for

25 the comments that will be coming up. That's really critical, an

26 open, transparent process to get all of this information out on
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1 the table and open up rigorous peer reviews. The chief

2 foundation is to move forward with a solid risk assessment and

3 for regulatory decisions.

4 So, with that I will turn it over to Don Brady

5 to introduce the team.

6 DR. DONALD BRADY: Thanks, Stephen. I would

7 just like to add my acknowledgement of the Panel's work and the

8 activities that you'll go through the rest of the week with us

9 as we work through the agenda. 

10 I also want to add that personally I do not

11 know how many more SAPs on atrazine I can take. At one of the

12 previous ones, I had just come off rotator cuff surgery and

13 today I have an infected tear duct, so if I appear to be winking

14 or crying, please do not read anything into that I'm doing my

15 best, but something is going on with stress, I think.

16 My real goal here is that I have the privilege

17 of introducing Dana Spatz and the rest of the team who prepared

18 the White paper and have presentations to make for you today. 

19 So, without any further ado, I will just let Dana get started on

20 his framework presentation. 

21 DR. DANIEL SCHLENK: Sure, just before we

22 start, any general questions for Dr. Bradbury? Great, thanks.

23 MR. DANA SPATZ: Good morning, Dr. Schlenk and

24 the other Panel members. My name is Dana Spatz and I manage

25 Environmental Risk Assessment Branch in the Office of Pesticide

26 Programs in the EPA's Environmental Fate and Effects Division. 
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1 I want to thank everyone for your time and effort in reviewing

2 our white paper on atrazine. I look forward to the discussion

3 on the topics at hand. 

4 Our goal in the next few minutes is to simply

5 set the stage for the technical presentations you are going to

6 hear from Ms. Amy Blankinship, Dr. Frank Farruggia, Dr. Russell

7 Erickson, and Dr. James Hetrick. As you have heard, atrazine is

8 about to enter registration review next year. So a big part of

9 what we hope to gain from the next few days is the Panel's

10 perspective on the tools, methods, approaches and data that EPA

11 plans to use in conducting the ecological risk assessment for

12 atrazine. 

13 The white paper for this SAP is a little

14 unique in that it is a problem formulation for the atrazine

15 ecological risk assessment. This graphic shows the framework

16 for the ecological risk assessment and is published in the EPA's

17 guidelines for ecological risk assessment. The problem

18 formulation step shown in the top blue box is the first step of

19 the ecological risk assessment and it describes our current

20 understanding of what is known about the environmental fate and

21 ecological effects, the strengths and limitations in the data

22 and how the uncertainties will be addressed. 

23 The product of the problem formulation is the

24 analysis plan. This describes the tools and methods we plan to

25 use in conducting the updated ecological risk assessment.

26 We are going to come back to this slide in a
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1 couple of minutes and talk about where we are in terms of

2 Atrazine in this process. But, the presentations you will hear

3 today from my EPA colleagues will focus on 3 major areas:

4 Evaluation of amphibian toxicity data, the methodology for

5 determining the levels of concern for atrazine, and the methods

6 for comparing monitoring data to the aquatic plant community

7 level of concern. 

8 Pesticides have been registered in the U.S.

9 for over 50 years. There is a lot of history and a lot of data,

10 but for the purposes of this meeting, we only need to go back to

11 January 2003, at which time, EPA made its reregistration

12 eligibility decision for atrazine. 

13 That decision document, along with the

14 memorandum of agreement, required the registrants to implement

15 an extensive monitoring and mitigation program to protect the

16 aquatic plant community structure, function, and productivity. 

17 EPA considered this endpoint to be the most sensitive effect of

18 concern. 

19 By focusing on aquatic plant community

20 structural productivity changes, EPA intended to protect

21 invertebrates, fish, and amphibians from the direct effects of

22 atrazine, as well as the effects that atrazine could have on the

23 habitat and food sources of aquatic animals. 

24 This map from the USGS shows average annual

25 atrazine use from 1999 to 2004, expressed in average pounds

26 applied per square mile of agricultural land. My reason for
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1 showing you this map is simply to provide a visual depiction of

2 where atrazine is being used and thus where ecological impacts

3 may be greatest.

4 Among other things, 2003 IRED and memorandum

5 of agreement required the atrazine registrants to monitor water

6 bodies in corn, sorghum and sugarcane use areas, where changes

7 in aquatic plant community structure and productivity were most

8 likely to occur. You will hear today much more about the method

9 for determining the atrazine ecological level to discern what

10 that concentration level is, how EPA is using all of the

11 available monitoring data from various monitoring programs to

12 help pinpoint the watersheds that require more attention, as

13 well as an update of our state of knowledge regarding potential

14 effects on amphibians. 

15 Before we jump into this SAP meeting, I want

16 to provide a little history on previous SAP meetings on similar

17 topics. 

18 In June 2003, EPA summarized 17 studies

19 consisting of both open literature and registrant submitted

20 laboratory and field studies involving both native and

21 non-native species of frogs. The EPA concluded, and the SAP

22 concurred, that there was sufficient evidence to warrant further

23 evaluation of atrazine effects on gonadal development in

24 amphibians and that additional data would be required to reduce

25 uncertainties. 

26 The EPA subsequently issued a data call in for
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1 these studies. I will also note here that the Panel endorsed

2 the test design of the registrants' data call-in study in

3 addition to the general study evaluation criteria that the EPA

4 employed. 

5 In October 2007, a meeting was held to discuss

6 the results of these studies, submitted in response to the data

7 call in as well additional open literature studies published

8 since the 2007 SAP. These data suggested that gonadal

9 development of larvae Xenopus laevis was not affected by

10 atrazine concentrations tested, up to 100 μg/L  . In a few

11 minutes you will hear more about the body of literature on

12 atrazine and amphibians from Amy Blankinship.

13 In December 2007, the EPA discussed the

14 preliminary development of an ecological level of concern

15 methodology and presented an analysis of the monitoring program

16 using that methodology. This was the introduction of the

17 comprehensive aquatic system model or CASM to the Panel. 

18 This tool was used to determine a level of

19 concern that relates time-variable monitoring data to affects

20 identify in a series of microcosm and mesocosm studies. The

21 panel concurred with the conceptual approach, but recommended

22 that the model be revised to more accurately reflect second and

23 third order Midwestern stream characteristics, and ended up with

24 a more comprehensive sensitivity analysis. You will hear more

25 about this later from Dr. Russell Erickson. 

26 Then in May 2009, following up on the 2007
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1 meeting, the EPA presented new evaluations of the CASM model,

2 our concerns about proceeding with further development of this

3 CASM model, and introduced a simpler alternative to the

4 CASM-based approach. The Plant Assemblage Toxicity Index or

5 PATI, was introduced. 

6 Another major topic covered during this

7 meeting was the identification of the primary watershed

8 characteristics driving atrazine exposure and a preliminary

9 extrapolation of the results to the entire atrazine use area to

10 identify other areas where atrazine exposures may exceed the

11 level of concern. 

12 I said I would come back the ecological risk

13 assessment framework and discuss it in terms of where we are

14 with atrazine. 

15 In focusing in on the problem formulation

16 step, where we are integrating available data and developing an

17 analysis plan for conducting the risk assessment. Clearly one

18 focus of the risk assessment will involve the potential effects

19 on amphibians. 

20 The first presentation you will hear is from

21 Amy Blankinship, who will be updating you on our evaluation of

22 the available amphibian toxicity data that has been published

23 since the last amphibian SAP in 2007 and integrate with the

24 study results discussed at the previous SAP meetings. 

25 We will be asking for your feedback on our

26 interpretations of the body of data consistent with the advice
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1 and recommendations we have received from previous panels. This

2 data set will form the basis for the amphibian risk assessment. 

3

4 After discussion on amphibian toxicity, Dr.

5 Frank Farruggia will present our evaluation and analysis of the

6 microcosm and mesocosm database. These studies form the basis

7 of the aquatic plant community level of concern, and thus

8 underwent rigorous review by a team of EPA scientists who looked

9 at each study to determine if it met acceptance criteria, and if

10 so, determine if exposure to atrazine showed an effect in the

11 COSM or not. 

12 We have several charge questions for the panel

13 on the appropriateness of using the COSM studies to develop the

14 aquatic plant community level of concern for atrazine and the

15 reasonableness of our interpretations of these studies. 

16 The last data set we are going to discuss

17 today is the watershed monitoring data. This includes the

18 monitoring program required of the registrants, as well as state

19 monitoring, NCWQA, and other programs.

20 Dr. James Hetrick will present our analysis of

21 atrazine monitoring data and the use of these data to determine

22 whether or not concentrations in the watershed are of concern,

23 and how bias factors could be used to account for uncertainty in

24 the monitoring data. 

25 Earlier I mentioned that the Analysis Plan was

26 the final product of problem formulation. 
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1 Dr. Russell Erickson will update us on the

2 PATI tool we are planning to use to determine aquatic plant

3 community level of concern for atrazine, the work that has

4 been completed as a followup to the 2009 SAP and why we believe

5 this is a reasonable approach for determining the level of

6 concern. 

7 As I just mentioned, we will be discussing the

8 potential use of bias factors to account for uncertainty in the

9 monitoring data. With bias factors, a state for example, could

10 analyze their monitoring data. It might help less frequent

11 sampling and determine whether that watershed warrants further

12 attention or can be screened out. 

13 Which brings us to today's meeting. The

14 charge questions we have for the panel again focus on 3 major

15 topics: An update on our evaluation of the data available on

16 amphibian toxicity, the methods for determining the atrazine

17 level of concern for aquatic plant communities, and the method

18 for comparing monitoring data to the aquatic plant community

19 level of concern. 

20 We have a total of 14 charge questions and a

21 very full agenda, so let me stop here and introduce you to Ms.

22 Amy Blankinship, who will walk us through our evaluation of the

23 amphibian data for atrazine.

24 DR. DANIEL SCHLENK: Thank you Mr. Spatz. Any

25 questions of a general nature regarding the framework for Mr.

26 Spatz? Ms. Blankinship?
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1 MS. AMY BLANKINSHIP: Good morning. I am with

2 the Environmental Fate and Effects Division in the Office of

3 Pesticide Programs. I will be discussing the evaluation of

4 amphibian toxicity data for atrazine. I would like to start by

5 giving an overview of the presentation today. First, I will

6 provide some background information on previous evaluations of

7 amphibian toxicity data for atrazine. Second, I will discuss

8 the methodology for the evaluation of amphibian data. Third, I

9 will discuss the results and finally, the uncertainties and

10 overall conclusions.

11 As Dana Spatz indicated, there was an SAP in

12 2003 on the potential developmental effects of atrazine on

13 amphibians. At this SAP meeting, the EPA concluded, and the SAP

14 concurred that the registrant submitted and open literature data

15 supporting further evaluation of the effects of atrazine

16 exposure on amphibian gonadal development. 

17 The available data supported the formation of

18 a hypothesis that atrazine exposure resulted in gonadal

19 developmental effect in amphibians. However, the studies at

20 that time were insufficient to refute or confirm the hypothesis.

21 These insufficiencies are based on studies of design

22 deficiencies, such as inappropriate or uncontrolled husbandry or

23 environmental conditions. 

24 In order to address the uncertainties, a

25 tiered testing approach was identified in which tier 1 with a

26 laboratory study designed to test for apical effects on
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1 amphibian gonads. 

2 Based on the tiered approach, if the results

3 of lower tiered tests were positive, the more refined effects

4 studies, that is, tiers 2 through 4, were to examine the

5 potential effects of atrazine exposure on endocrine-mediated

6 processes, such as sex thyroid levels. 

7 These studies were intended to provide insight

8 into a plausible mechanism of action which could account for the

9 effects observed in lower tiered tests. 

10 The need to identify a plausible mechanism of

11 action is consistent with the EPA's efforts to identify and

12 characterize molecular-initiated events and other key events at

13 various levels of biological organization, which ultimately

14 resulted in adverse effects on the whole organism population or

15 community. 

16 Delineating such a series of events helps

17 define adverse outcome pathways, or AOPs, and this concept will

18 be discussing in several places within my presentation. 

19 In keeping with the idea of an adverse outcome

20 pathway, the SAP also considered the ecological relevancy of

21 forming a species. 

22 The SAP identified the test design elements

23 which were considered critical for future testing, especially in

24 regard to the tiered 1 laboratory study. These studies included

25 biological factors such as test species in developmental stage

26 and environmental factors such as biological loading rate, water
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1 quality and chemical measurements, and the use of a flow-through

2 study design. 

3 In 2004, the EPA issued a data call-in, or

4 DCI, for evaluating amphibian developmental effects from

5 Atrazine exposure. 

6 In 2007, another SAP was held to present the

7 data obtained in the 2004 DCI, as well as other relevant data,

8 particular to 10 laboratories and 9 field studies reported in

9 the open literature. 

10 Two of those laboratories and all of the 9

11 field studies with final reports of intern data presented at the

12 2003 SAP with respect to studies reported in the open literature

13 as of 2007. In general, the studies contained design elements,

14 which a previous SAP had concurred, limited their utility for

15 use in risk assessment as identified environmental conditions

16 impacted the studies' ability to reliably determine causality

17 from atrazine. 

18 While the study conducted in response to DCI

19 is referred to here as one study, it actually consisted of 2

20 studies conducted at 2 laboratories simultaneously using the

21 same protocols. Xenopus tadpoles were exposed for 6 days post

22 hatch through metamorphosis at nominal atrazine concentrations

23 at 0.01, 0.1, 1, 25, and 100 parts per billion, and an estradiol

24 positive control at 0.2 parts per billion.

25 The atrazine concentrations tested were based

26 on the range over which effects that had been noted in the open
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1 literature were also considered to be environmentally relevant. 

2

3 Each atrazine and estradiol treatment group

4 consisted of 8 replicates and there were 16 replicates of the

5 negative control. Each replicate tank contained 25 tadpoles and

6 the flow-through design was sufficient to yield loading rates of

7 less than 1 gram per liter per day, which is consistent with

8 ASTM standards, and helped to ensure appropriate environmental

9 conditions. 

10 Overall, each study utilized 1,600 tadpoles

11 and between the 2 studies, 3,200 tadpoles were exposed. 

12 However, not all animals were available for analysis due to

13 mortality or exclusion in some replicates.

14 At completion of metamorphosis or after 75

15 days of exposure, whichever came first, weight and length were

16 measured and gross organ morphology reported. Digital images

17 were taken of the gonadal surface area and histology of the

18 gonadal tissue was performed. There were no concentration

19 defending adverse effects reported at 100 parts per billion, the

20 highest concentration tested for all endpoints evaluated. 

21 Therefore, the EPA concluded that the DCI

22 study provided reliable information on amphibian toxicity and

23 was suitable for use in a risk assessment to evaluate survival,

24 growth and development. As the DCI study indicated, no adverse

25 effects at concentrations considered to be environmentally

26 relevant based on the tiered testing approach agreed upon by the
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1 SAP in 2003, no further testing was requested by the Agency.

2 While the 2007 Panel concurred that the EPA's

3 analysis of the open literature studies was appropriate, and as

4 the study in response to DCI provided reliable results for

5 Xenopus laevis, the Panel had concerns regarding the use of

6 Xenopus laevis as a surrogate for indigenous or native species,

7 as well as the sensitivity of the strain used in this study. 

8 However, Xenopus laevis was chosen because it

9 is a well established amphibian test species and the 2003 SAP

10 Panel could not identify data suggesting significant differences

11 in developmental pathways between Xenopus and native species. 

12 Also, while the 2007 SAP members may have had

13 concerns regarding strain sensitivity, the response of the

14 tadpoles to the positive control used by the DCI study resulted

15 in a shift in sex ratio consistent with the concentration of

16 estradiol used for gonadal conversion to female. 

17 What is the status of the atrazine amphibian

18 toxicity data? Additional studies have been published in the

19 open literature since the 2007 SAP. These new studies examined

20 many different endpoints, including sexual and metamorphic

21 development, as well as immune response and susceptibility to

22 infection, or biochemical or molecular or behavioral

23 modifications. 

24 The evaluation of these studies included

25 independent response to the 2007 SAP and consistent with EPA's

26 process for pesticides, the EPA stated that it will continue to
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1 monitor available data and in this process, EPA will apply the

2 recommendations and insights from the 2 previous SAPs for

3 evaluating studies so that a consistent standard is applied. 

4 The EPA recognizes that in addition to open

5 literature reports of original studies on atrazine, there were

6 also published reviews and analyses which attempt to interpret

7 or synthesize a number of atrazine-related studies on amphibians

8 since the 2007 SAP, and those sources are cited here. A short

9 discussion of these reviews is presented in the white paper. 

10 Therefore, in preparation for the problem

11 formulation written in support of the registration review of

12 atrazine, reviewers examined a large number of studies and

13 applied standards based in part from input on 2 previous SAPs,

14 which were intended to ensure quality and consistency across

15 chemicals. 

16 So, what are the objectives of the evaluation?

17 They are: 1) Identify critical test design elements. 2)

18 Classify each amphibian toxicity study. 3) Categorize endpoints

19 in each study. 4) Compare results across studies for an

20 endpoint category. 

21 Step 1 of the evaluation is to identify

22 critical test design elements. First, I will discuss the

23 preliminary screening criteria. The evaluation criteria

24 discussed here in the next few slides are consistent with those

25 identified in the ASTM Guidelines, the Office of Pesticides

26 Ecological Effects Open Literature Guidance, Appendix N of the
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1 white paper, as well as those presented at the 2003 and 2007

2 SAPs, with which the Panel has concurred.

3 All open literature studies for potential

4 evaluation by the Office of Pesticide Programs, or OPP, would

5 undergo a preliminary screening for further evaluation. In

6 addition to open literature studies, the criteria discussed here

7 and in the next few slides are also applied to

8 registrant-submitted studies. 

9 The study was included for further evaluation

10 if: 1) The treatments involved exposure to atrazine alone and

11 not mixtures including agricultural runoff or multi-active

12 ingredients. While complex mixtures may provide information on

13 potential effects, for the purposes here of examining a causal

14 relationship between atrazine and amphibians, these studies were

15 not included in this evaluation, but will be considered for use

16 in the risk assessment.

17 However, studies examining atrazine

18 formulations, or studies in which nitrate was added as an

19 additional stressor, were included. 

20 2) The effect was on a live, intact organism

21 in which test species were reported and verified. 3) The paper

22 is the primary source of the data and is published in English as

23 a full article. 4) The study type is reported. For example, a

24 field or laboratory study. 

25 If the study met preliminary screening

26 criteria, it was retained for further evaluation. Of the 75
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1 studies identified in the open literature, 64 laboratory studies

2 were retained for this further evaluation. Eleven micro- or

3 mesocosm studies that passed the screening were excluded from

4 this analysis in the 64 studies. 

5 The cosm studies were excluded due to

6 potential multiple interactions which may result in direct or

7 indirect effects, thereby confounding the ability to

8 discriminate direct effects. 

9 However, the cosm studies will be considered

10 for potential use in the risk assessment and were evaluated for

11 use in the development of the level of concern for the plant

12 community, which will be discussed later by Dr. Farruggia. In

13 addition to the 11 cosm studies, 9 field studies were also

14 excluded due to major confounding factors discussed at the 2003

15 and 2007 SAPs, with which the Panel concurred. 

16 Currently, there is no standardized guideline

17 or methodology for chronic developmental testing with

18 amphibians, such as the ASTM Guidelines. As such, the studies

19 evaluated here were conducted using 2 different test designs. 

20 The EPA identified critical test design

21 elements that could potentially confound or influence the

22 ability to discern a cause-and-effect relationship between

23 atrazine and a biological receptor such as controlled mortality

24 or biological loading rate, which influences water quality. 

25 The identified test elements were based on

26 ASTM Guidelines, elements concurred on by previous SAPs, and EPA
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1 Guidelines. The test design elements are considered to be

2 fundamental in providing a foundation that if followed will

3 allow for the evaluation of a cause-and-effect relationship with

4 minimal influences from the test design. 

5 Therefore, the criteria are intended to

6 evaluate whether the factors are known to influence the quality

7 of the study at acceptable levels.

8 However, if no information is provided in the

9 study to be determined if the criteria has been met, or if the

10 study did not follow the criteria, then it was considered an

11 uncertainty, which contributed to a decrease in confidence in

12 the reported results. 

13 The identified elements were further

14 classified into ones which would invalidate study results or one

15 which would raise concerns about the ability to discern

16 causality, but would by themselves, not invalidate the results. 

17

18 Test design elements that would invalidate the

19 study would include: 1) Atrazine concentrations were not

20 recorded. Minimally nominal concentrations have to be recorded;

21 however, measured concentrations at test initiation and during

22 the study are desired. 

23 2) A control group could not run concurrently.

24 If a study used a solvent carrier, then the study must contain

25 either a negative or a solvent control group; however, both are

26 desirable. 
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1 3) Atrazine was measured in the control

2 vessels. 

3 4) Control mortality was greater than 30

4 percent. This survival rate is based on the EPA PRIME fish

5 guidelines where the criterion is 70 percent or greater survival

6 at test termination in the controls. 

7 5) The solvent carrier concentration is

8 greater than 0.05 percent, which is the current maximum rate

9 recommended in ASTM and EPA Guidelines for acute static and

10 static-renewal aquatic toxicity tests. 

11 6) There was the presence of another stressor

12 expected to compromise the study. 

13 7) There was an observed aberrant effect in

14 the control with the occurrence of a high rate of an effect.

15 8) There was insufficient replication and

16 pseudo replication was not considered appropriate.

17 9) There was insufficient data to allow the

18 reviewer to understand or substantiate the study results. 

19 Summary information presented as graphs or in

20 tables, along with the statistical results is desired. As

21 stated previously, the criteria identified appeared to

22 invalidate study results reflect standards recognized by the

23 ASTM and the international research community, as well as those

24 identified in the OPP Ecological Effects Open Literature Guide. 

25 And these standards are routinely applied to open literature

26 studies as well as the registrant-submitted data. 
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1 While there are criteria considered to be

2 critical flaws discussed in the previous slides, there are other

3 factors that can have serious confounding effects because they

4 can interfere with the absorption, distribution, metabolism, and

5 excretion or adding of a chemical by the test organism. An

6 occurrence of one of these factors will not, in many cases,

7 invalidate a study, but will affect how the study is classified

8 in terms of its utility in the risk assessment. 

9 Test design elements that would raise

10 concerns, but not invalidate study results by themselves

11 included: 1) When a solvent carrier was used, both the negative

12 and solvent control groups were not included. 

13 2) There was a statistically significant

14 difference between the negative and solvent control groups or

15 any endpoint indicating a potential solvent effect. 

16 3) The solvent carrier concentration was

17 greater than 0.01 percent, which is based on the current maximum

18 rate for aquatic brine testing in the ASTM and EPA Guidelines. 

19 4) DMSO was used as the solvent carrier, which

20 is discouraged, as it interferes with (inaudible). 

21 5) Plastic was used as a test vessel material,

22 as there may be potential leeching of chemicals that may

23 interfere with the study. 

24 6) The study was not conducted using technical

25 grade atrazine, as the other ingredients in the formulation may

26 influence the response. 
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1 7) The loading rate was higher than

2 recommended, which when coupled with longer renewal periods, may

3 support poor water quality. 

4 8) Organisms used in the study were field

5 collected and prior exposure to a chemical, physical, or

6 biological structure may not be fully known, which may influence

7 the organism's response. 

8 One element I would like to discuss is

9 organism loading. It is noted that organism loading is the key

10 factor in the resulting water quality in the study. 

11 In the available open literature studies,

12 loading was not commonly reported as mass per water volume, but

13 rather the number of tadpoles per volume of water are in a

14 certain volume container without specifying those water volumes.

15

16 Loading was discussed at the 2003 SAP and the

17 recommended rate was 1 tadpole per liter or approximately 1 g/L

18 was presented. Weight data on Xenopus laevis was used to

19 support this recommended loading rate as well as the rates of

20 the ASTM Guidelines. 

21 As a point of reference, the loading rate at

22 ASTM is 1 g/L/day for flow-through designs, which is the loading

23 rate designated for the DCI study, in which it complied with;

24 and the ASTM rate for static and static-renewal studies based on

25 temperature is 0.5 to 0.8 g/L, and therefore, the use of 1

26 tadpole per liter or approximately 1 g/L in this evaluation is
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1 considered reasonable for static and static-renewal design. 

2 Weight data from the available open literature

3 studies for different amphibian species was extracted to better

4 understand their potential weight ranges in comparison to the

5 Xenopus laevis. 

6 In the following tables, the reported weights

7 generally reflect the wet weight at completion of metamorphosis.

8 These values may be approximate if data were taken in some

9 cases from summary grafts. As you can see, many species are

10 approaching or exceeding 1 gram. Therefore, it may be assumed

11 that the organisms reached 1 gram at some point while undergoing

12 metamorphosis in the study, and the use of 1 tadpole per liter

13 is deemed appropriate. 

14 The second objective on this evaluation was to

15 classify each amphibian study. Open literature and

16 registrant-submitted laboratory studies were evaluated using the

17 previously listed potential confounding test elements, and these

18 evaluations are in appendix C of the white paper.

19 Each study was classified according to a level

20 of confidence in the study results and the overall

21 classification was done by evaluating all identified test

22 elements in judging the confidence in the results as a whole. 

23 The classification indicates how the study may or may not be

24 used in a risk assessment, and provides a sense of a level of

25 confidence in the information contained within the study. 

26 Many studies evaluated more than one endpoint,
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1 and the level of confidence for each endpoint within the study

2 was not necessarily the same. Therefore, different endpoints in

3 the same study may have different classifications. 

4 Certain combinations for potential confounding

5 test elements were expected to lower the confidence in the study

6 report results, such as higher loading coupled with infrequent

7 test solution renewal and a lack of reporting of unacceptable

8 water quality. 

9 According to the OPP ecological effects open

10 literature guidance, appendix N, the studies were classified

11 into 3 categories: Quantitative, qualitative, or invalid. 

12 For this evaluation, qualitative was

13 subdivided into 3 more categories: Higher, medium, or lower

14 level of confidence. 

15 In the classification of quantitative, study

16 results may be used in calculations and risk assessment. For

17 qualitative studies, results could not be used in risk estimate

18 calculations, but can be used to characterize risk; and in this

19 evaluation, more emphasis on results would be favored from

20 studies of higher or medium level of confidence over information

21 provided in studies classified as quality of low risk because of

22 the low confidence that would be associated with the results of

23 these studies. 

24 The difference between a study with a

25 qualitative lower level of confidence and one with a medium may

26 be that with qualitative lower there was concerned about poor
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1 water quality, and for a medium study, water quality was deemed

2 to be within appropriate ranges, but the study lacked measured

3 atrazine concentrations in the control. The results of an

4 invalid study were considered to be scientifically unsound based

5 on failure to meet critical evaluation criteria and cannot be

6 used in the risk assessment. 

7 There were several common potential

8 confounding elements that reoccurred in the available data,

9 which included: 1) A lack of recording or measuring atrazine in

10 the control treatment. While some studies reported analytical

11 results for the atrazine treatment or in stock solutions, it did

12 not always report results in the controls. 

13 2) The use of field collected organisms. 

14 3) A higher than recommended loading rate

15 coupled with a lack of reporting water quality and infrequent

16 test solution renewal periods deemed here as greater than 48

17 hours. 

18 In the available data, it was common to only

19 record the temperature at which the study was conducted with no

20 other water quality parameters, such as dissolved oxygen or

21 ammonia concentrations recorded. Therefore, given the lack of

22 recorded water quality, coupled with other environmental

23 parameters deemed to influence water quality, there is

24 uncertainty in whether or not water quality standards were met. 

25

26 The majority of the available laboratory
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1 studies were classified as qualitative with a lower level of

2 confidence or invalid, and these numbers refer to studies with a

3 mean overall classification was qualitative lower or invalid. 

4 Many studies classified as qualitative with

5 lower confidence is due to the combinations described above for

6 the loading coupled with renewal period and reported water

7 quality. The importance attributed to the potential poor water

8 quality is consistent with input from the previous SAPs

9 regarding critical test design elements. 

10 However, it is pointed out that each reported

11 endpoint in the study was subjected to the study classification.

12 There were endpoints that had a qualitative medium or higher

13 level of confidence classification, which may have also

14 contained a classification of qualitative lower or invalid for

15 that particular endpoint. 

16 These figures summarize the main reasons for

17 studies having a qualitative lower level of confidence or

18 invalid classification. Please note that the scale on the Y

19 axis is different for each graph.

20 Given the number of confounding test elements

21 identified in the study and that each evaluated endpoint was

22 classified, the same study could be represented by multiple

23 factors affecting this classification. Many studies classified

24 as qualitative lower level of confidence resulted from concerns

25 about water quality, while invalid studies were for concerns

26 about the control group or a lack of reporting summary data for
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1 an endpoint.

2 One study was classified as quantitative. 

3 This is the study conducted as a result from the 2003 SAP and

4 was submitted by the registrant, which was later published as

5 Kloas et al. 2009. Again, this study was actually 2 studies

6 conducted simultaneously at 2 laboratories using Xenopus laevis

7 exposed at atrazine concentrations of 0.01 to 100 parts per

8 billion as young tadpoles through metamorphosis. 

9 This study was able to account for the study

10 design recommendations, which were intended to reduce

11 confounding effect, which limited the utility of previously

12 conducted study as of 2003. 

13 The study design was vetted before the 2003

14 SAP, with which the panel concurred, and the results were

15 presented at the 2007 SAP. The reported NOAEC was 100 parts per

16 billion for the endpoints of survival, growth, metamorphosis,

17 behavioral effects and sexual development. 

18 Although relatively rigorous study conditions

19 were maintained throughout the course of the DCI study, some

20 issues were identified. Issues including the measurement of

21 atrazine at 0.01 parts per billion in 1 cluster of 4 tanks out

22 the of 16 data control tanks that were in laboratory. However,

23 because of the (inaudible) measurements, it was determined that

24 contamination was limited to specific cluster of control tanks,

25 and those tanks were discarded for further analyses. 

26 Also at the same laboratory, a microbial bloom
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1 occurred in the addition cluster of 4 control tanks, and these

2 tanks were also discarded. Due to a combination of microbial

3 blooms and atrazine contamination, a total of 8 out of the 16

4 control tanks or replicates, were dropped from the study

5 analysis. Thus, at this laboratory, the number of control tanks

6 used in later analyses was the same as in the atrazine

7 treatments, that is 8 replicates. 

8 Also early in the study at the same

9 laboratory, a microbial bloom was observed in 1 of the 8 tanks

10 in the 1 part per billion treatment group resulting in high

11 mortality. This treatment tank was also dropped from the

12 analysis. 

13 The 2007 SAP Panel agreed with omitting these

14 tanks given that the analysis demonstrated negligible effect on

15 statistical analysis. 

16 Also, change in the methods used to analyze

17 histological samples in the use of 1 pathologist was discussed. 

18 The panel concurred with some of the changes and recommended

19 additional analyses including review by another pathologist. In

20 a subsample of slides that were later reviewed by a secondary

21 reader, the results did not differ substantially from the

22 initial review. 

23 The 2007 Panel also identified some of their

24 concerns regarding potential limitations of the study. These

25 included analyzing the results from each laboratory separately. 

26 The data were further combined and resulted in the same
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1 conclusion as when the data were analyzed separately. 

2 The Panel also expressed concern about the

3 lack of sensitivity in the Xenopus laevis strain used in the

4 study. Response data to estradiol to (inaudible) indicates that

5 the strain used in this study is sensitive compared to other

6 species. 

7 There was also concern that due to the use of

8 a flow-through system resulted in conditions that tadpoles did

9 not encounter that environment, especially as it relates to

10 degradates, although atrazine degradates were not specifically

11 measured in this study. The study was designed to ensure

12 exposures specifically to the parent test chemical, the

13 degradation half-life of atrazine measured in laboratory studied

14 indicate that none of the primary degradates of atrazine were

15 likely to have formed in appreciable quantities even under a 48

16 to 72 hour static renewal period. 

17 Water samples collected during this study were

18 later analyzed and indicated that out of the 3 primary atrazine

19 degradates, only deisopropylatrazine, DIA, and deethylatrazine,

20 DEA, were measured above the level of detection. The maximum

21 measuring concentrations for these 2 degradates was around 0.1

22 parts per billion in the high atrazine treatment group. 

23 The previous SAP also identified uncertainty

24 regarding the lack of evaluating reproductive fitness via a

25 grow-out phase. EPA acknowledged that evaluation of

26 reproduction function would be useful in demonstrating the
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1 potential effect of chemicals that impact gonadal development. 

2 Therefore, incorporating such studies, in a tier testing

3 approach agreed upon by the 2003 SAP. 

4 Based on the evaluation of the available data,

5 the need to proceed to higher level of testing, which would

6 include evaluating reproductive fitness, was not warranted given

7 the absence of effect at tier 1. Also, currently robust methods

8 to examine reproduction fitness do not appear to be available

9 based on trials which tried to examine such endpoints.

10 Nine studies were classified as qualitative

11 with either a higher or medium level of confidence. Four

12 studies evaluated mortality and/or teratogenesis for several

13 species, which encompass a large range of reported effects with

14 the lowest being LC50 of 410 parts per billion, which is based

15 on acute mortality as well as teratogenic effect expected to

16 cause invalidity. 

17 Evaluation of potential behavior modification,

18 such as responses to fear cues and soil preference were reported

19 in 2 studies after acute exposure to atrazine. 

20 Three studies examining subchronic or chronic

21 exposure examined effects on survival, metamorphosis, growth and

22 either sexual development or molecular modification. For 2 of

23 the chronic studies, the first 2 rows, no effect on

24 metamorphosis, growth, survival and sexual development for 1

25 study was reported at atrazine concentrations at 25 or 30 parts

26 per billion. 
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1 A limitation for both of these studies was

2 that neither study used a positive control in the study to

3 evaluate the sensitivity of the species. In the subchronic

4 study, effects on metamorphic stage at test termination was

5 reported at all concentrations tested them to 100 parts per

6 billion. However, the sample size of each treatment was low

7 with 10 organisms. In addition, there was an increase on flow

8 cytometric analysis meant to illustrate metamorphosis and

9 development. However, the results varied across trials. 

10 These data will be used to characterize risk,

11 but not change our overall understanding of atrazine effects on

12 amphibian development as the study response the DCI provided

13 definitive, quantitative toxicity endpoint of 100 parts per

14 billion. 

15 In summary, 64 studies obtained from

16 prescreened open literature and registrant submitted data were

17 available for further evaluation. Additional test method

18 evaluation criteria based on ASTM Guidelines, input from

19 previous SAPs and OPP's open literature guidance were applied to

20 these studies, which were then classified to denote how they

21 could be used in a risk assessment. 

22 Based on this classification, 24 studies were

23 invalid, 30 qualitative with a lower level of confidence, 9

24 qualitative with a medium or higher level of confidence and 1

25 quantitative.

26 Information from invalid studies, which are
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1 deemed to contain critical flaws were not to be use in a risk

2 assessment. The studies with the qualitative lower have a low

3 level of confidence in the reported information, and therefore

4 have low utility for use in a risk assessment. Results from a

5 qualitative medium and higher level of confidence represent

6 studies that could be used for characterizing risks in a risk

7 assessment. A quantitative classification indicates that the

8 results could be used to calculate risk estimates. 

9 The third objective was to categorize

10 endpoints in each study. Endpoints in each study were

11 categorized according to broad endpoint parameters. Categories

12 included survival, metamorphosis, growth, sexual development,

13 immune response and infection susceptibility, biochemical and

14 molecular modifications or behavioral modifications. 

15 For each study, the NOAEC and LOAEC values

16 were extracted for each reported endpoint and placed into one of

17 the mentioned categories. In this exercise, the lowest reported

18 facts are statistically significant. For some studies, both the

19 NOAEC and LOAEC values were not available due to either no

20 effect reported at the highest concentration tested or reported

21 effects at all concentrations tested. Also, not all studies

22 reported in all indentified endpoint categories. 

23 Once the studies had been classified as

24 quantitative, qualitative or invalid, and endpoints in the

25 studies categorized, the next step was to compare the results

26 across studies for an endpoint category.
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1 This comparison of endpoints included all

2 available laboratory studies, which represented different study

3 classifications. This comparison was done to examine the range

4 of concentrations where effects or no effects were recorded and

5 how the studies compared to each other regardless of

6 classification. 

7 The NOAEC and LOAEC values for a given

8 endpoint were compared across studies. However, results for

9 every endpoint category were not reported in each study and not

10 all studies had a corresponding NOAEC or LOAEC value for each

11 endpoint. 

12 In addition, endpoints within a study may have

13 different classifications. Endpoints with an invalid

14 classification while not usable in a risk assessment, were

15 retained here to illustrate where reported effects fell in

16 comparison to the other results. 

17 The first endpoint I will discuss is survival

18 of an examination of both acute and subchronic and chronic

19 exposures. 

20 First, I will discuss acute exposures

21 generally represented by acute LC50 and EC50 values. The LC50

22 values will be represented by the diamond and the EC50 values by

23 squares with accumulative frequency represented by the Y axis

24 and the corresponding atrazine exposure concentrations on the X

25 axis. Please note the long scale on the X axis. 

26 The different colors represent different
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1 classifications with the gray representing invalid studies, the

2 color green representing a classification of qualitative lower

3 level of confidence, and the yellow and brown shaded areas

4 representing qualitative medium and higher respectively. These

5 values represent observations across several species internally

6 at 96 hour or 96 hour post hatch exposure duration. EC50 values

7 reflect heterogenic effects. 

8 This graph, as well as the ones following

9 this, is meant to illustrate the breadth of atrazine

10 concentrations over which effects are reported and were ranked

11 in an effort to observe how studies of different classifications

12 compared to each other in terms of the overall rate of

13 concentration associated with a specific effect. However, the

14 graph is not meant to be used quantitatively. 

15 For survival, based on the available data,

16 amphibians do not appear to be particularly sensitive to

17 atrazine at an acute exposure basis, and this generalization has

18 also been reported in published reviews. The most sensitive

19 acute LC50 value was 410 parts per billion, which included both

20 mortality and teratogenic effects expected to result in

21 mortality. This was presented early during the slide on

22 classification. 

23 While there is not an LC50 or EC50 value

24 represented here for a quantitative classification, the one

25 study with a quantitative classification did inadvertently

26 examined acute exposures as the young tadpoles were exposed
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1 through metamorphosis and no effect on survival was observed in

2 atrazine concentration up to 100 parts per billion following

3 either short or long-term exposure. 

4 Effects on chronic or subchronic survival are

5 reported representing several species as well as varying

6 exposure durations in less than a month to several months or

7 even a year. In this graph and in the following graphs, the

8 no-effect values will be presented by the squares and the

9 effects values are the diamonds. The no-effect and effect

10 cumulative frequency represented by the Y axis and the

11 corresponding atrazine concentrations on the X axis. Please

12 again, note the long scale on the X axis. 

13 The different colors again are representing

14 the different classifications with gray being invalid, green a

15 classification of qualitative lower level of confidence and the

16 brown and red shaded data points representing qualitative higher

17 and quantitative, the red square, respectively. 

18 It was typical for the published studies not

19 to reexamine survival in terms of NOAEC or LOAEC values, but

20 rather to report on overall survival or mortality rates. 

21 However, some studies did report NOAEC and LOAEC. Therefore,

22 the results reported here are based on the presence or absence

23 effects rather than in statistical terms of NOAEC and LOAEC. 

24 Some studies evaluated more than 1 species at

25 a time and reported the same result for each species, as it was

26 considered to be important to represent each species so as to
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1 illustrate the number of reported endpoints and their position

2 relative to each other. Each species is represented on the

3 distribution, which skews the overall distribution emphasized by

4 the graph. It is not meant to be used in a quantitative manner.

5

6 Based on these data, there is a range of

7 reported effects on chronic survival endpoints across each of

8 the atrazine concentrations. This may be due to varying

9 exposure durations as well as varying environmental conditions

10 and organism source, especially in regards to the

11 field-collected organisms. 

12 A few of the effect values around 3 parts per

13 billion, the diamonds, the effect on survival for several

14 species varied with significant effects at 3 parts per billion

15 and that at 25 parts per billion of the same species in the same

16 studies. Other published reviews have also noted on the range

17 of toxicity estimates for survival. The study with quantitative

18 classification has a NOAEC value of 100 parts per billion. 

19 The next endpoint is metamorphosis. This

20 graph is set up in the same manner with the squares representing

21 NOAEC values and the diamonds with the LOAEC values with the

22 different colors representing different classifications. There

23 were different types of observed endpoints that were considered

24 to be reflective of metamorphosis, while the majority of the

25 endpoints represent time to metamorphosis or stage of test

26 termination, a few studies represent metamorphic organ
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1 malformation. 

2 This graph is represented by several different

3 amphibian species, including frogs and salamanders. As you may

4 notice, there are more squares or NOAEC values than LOAEC values

5 for diamonds on this graph. This is because not all NOAEC

6 values had a corresponding LOAEC value. The studies that did

7 have a corresponding NOAEC and LOAEC value are shown here

8 connected with a blue line. 

9 Note that the difference between the NOAEC and

10 LOAEC value is about a factor of 10 for some of these

11 combinations as it was common to have a treatment concentration

12 spacing of 10X in a study. Also note the wide range of reported

13 effects as they span several magnitude levels. 

14 The 1 quantitative NOAEC value, represented by

15 the red square, does not have a corresponding LOAEC value as it

16 is greater than 100 parts per billion, the highest concentration

17 tested. 

18 Here is a graph representing growth. These

19 endpoints are reflective of results on weight and length. 

20 This graph is set up in the same manner as

21 discussed before, and again, studies with a corresponding NOAEC

22 and LOAEC value are connected here by the blue bar. Again,

23 there is a wide range of atrazine concentrations representing

24 effects or no effects and between corresponding NOAEC and LOAEC

25 values. 

26 The NOAEC value for the quantitative study is
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1 100 parts per billion and a LOAEC value of greater than 100

2 parts per billion. 

3 This graph is for sexual development and is

4 illustrated in the same manner. Note that for the studies with

5 the corresponding NOAEC and LOAEC values, the spacing of 10X,

6 again, is not uncommon.

7 Shown here are the endpoints meant to be

8 reflective of sexual development. These endpoints reflect

9 gonadal development and/or other organs involved in sexual

10 development as well as measures of sex ratio. 

11 The quantitative NOAEC value for sexual

12 development is 100 parts per billion and is based on gonadal

13 development and sex ratio with a LOAEC value of greater than 100

14 parts per billion. 

15 Open literature studies have reported effects

16 on biochemical and molecular modifications after exposure to

17 atrazine, although the number of studies evaluating these

18 endpoints is less than for metamorphosis, growth or sexual

19 development. 

20 Biochemical endpoints inadvertently comes with

21 evaluation on hormones including testosterone, estradiol or

22 aromatase activity. There was a range of reported effects. For

23 example, for Xenopus laevis, decreased plasma testosterone was

24 observed at 2.5 with 258 parts per billion. However, no effects

25 were also reported at 25 and 107 parts per billion. 

26 Molecular modifications include gene
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1 expression, which was represented by different endpoints such as

2 changes in nuclear heterogeneity representing multiple genes for

3 things such as digestion and growth. 

4 The biochemical and molecular data demonstrate

5 that researchers are measuring perturbations in a broad range of

6 cellular and subcellular matrices with increasing

7 sophistication.

8 However, not all perturbations are

9 sufficiently significant to warrant using those results

10 quantitatively in a risk assessment as there can be uncertainty

11 regarding the extent of the perturbation required to call it

12 adverse. There is a need to develop appropriate languages that

13 enable transitioning from a measured perturbation at the

14 molecular or cellular level through a higher level of biological

15 organization to whole organisms to eventually adverse outcome

16 and an apical endpoint of impaired survival, growth and

17 reproduction that are used in a risk assessment. 

18 Although adverse effects on survival, growth

19 and reproduction in the intact animal may be more readily used

20 in a risk assessment, there is currently limited information

21 available to link many different types of measurement endpoints

22 with these apical endpoints. Developing plausible hypotheses

23 for potential mechanisms of action, which include key events in

24 adverse outcomes, is increasingly important to understand and

25 interpret the range of effects, which may result across multiple

26 levels of biological organization. 
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1 Complete delineation of such AOPs is not

2 needed. However, at this time, there is insufficient

3 information to enable the Agency to reliably extrapolate the

4 results from studies on endpoints such as immune modification to

5 apical assessment endpoints on which the EPA regulates. 

6 Open literature studies have also examined a

7 variety of potential behavior modifications that included

8 observations on swimming behaviors, whole organism activity,

9 water conserving behaviors, responses to fear cues or soil

10 preferences. While the studies listed here in this table

11 explicitly report behavioral observations, it is presumed that

12 many other studies observed some of these behavioral effects,

13 but did not analyze the results and report them. 

14 In the DCI study, test animal behavior was

15 observed and reported during the daily observations of the

16 tadpoles. No overall changes in behaviors were reported at

17 atrazine concentrations up to 100 parts per billion. 

18 There are some uncertainties involving

19 amphibian classes of data with atrazine. One of them are the

20 effects to indigenous or native species. The 2007 SAP Panel

21 raised concern about the suitability of Xenopus laevis as a

22 surrogate for native species, especially in regard to the study

23 that was conducted in response to the 2003 SAP. 

24 Although previous SAPs identified

25 uncertainties regarding sensitivity, the 2003 SAP could not

26 identify any differences between the Xenopus and native species
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1 in terms of underlying biochemical mechanisms for these species.

2

3 Additionally, the 2007 SAP indicated that to

4 their knowledge, testing methods have not been sufficiently

5 vetted that would enable testing with native species, which

6 would best reveal consistent results. 

7 While Xenopus is a well recognized surrogate

8 species because it lends itself to a laboratory setting and can

9 be readily manipulated, other species do not appear above well

10 established husbandry conditions such that they can be readily

11 used in laboratory studies. 

12 Based on the review of currently available

13 open literature, field collected-organisms were commonly used,

14 which suggests potential issues of reliable husbandry practices

15 in the laboratory that will allow for the culturing of the

16 native species.

17 However, there is uncertainty in whether the

18 use of field-collected organisms is reflective of issues with

19 husbandry culturing or whether it is common practice to field

20 collect the organisms without an effort to try to establish

21 laboratory cultures. 

22 The second uncertainty is effects on the

23 immune system or parasite susceptibility in amphibians. Data

24 from a limited number of studies suggests that exposure to

25 atrazine results in modifications to immune response or

26 infection susceptibility. 
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1 The studies report changes in immune system

2 endpoints such as white blood cells, phagocytic cells, plaques

3 representing different functions, or eosinophils as well as an

4 increase in parasite infection rate where organisms are exposed

5 to atrazine. 

6 Here is a table showing the different types of

7 immune system modifications reported. As you can see, there is

8 a variety of endpoints meant to be reflective of modifications

9 of the immune systems for different species. Also in terms of

10 infection susceptibility, there are differences in the reported

11 results within the same species. Currently, a defined adverse

12 outcome pathway, AOP for immune system modification is

13 considered insufficient. 

14 As discussed previously, the challenges in

15 identifying the sequence of events, which begin with a molecular

16 initiating event and spans multiple levels of biological

17 organizations, end in an adverse outcome. Along the way, there

18 may be a number of key events captured through entered endpoints

19 at various levels of biological organizations. 

20 These measured endpoints may one day serve as

21 alternate endpoints in a risk assessment provided that

22 appropriate linkages have been developed, which enable risk

23 assessments to transition from lower levels of biological

24 development organization to apical assessment endpoints, on

25 which regulatory decisions are based. 

26 With respect to atrazine and its potential
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1 immune effects in amphibians, while the number of measurement

2 endpoints have been identified, it is not know or established at

3 this time whether these endpoints are key events in an AOP that

4 has any common molecular initiating event that could be

5 associated with atrazine.

6 Based on the current evaluation of the

7 available studies, a majority of studies contained potential

8 confounding elements. 

9 The standards applied to evaluate these

10 studies are based on criteria identified in ASTM guidelines and

11 OPP, Open Literature Guidance, and are formed by input from both

12 the 2003 and 2007 SAPs. These standards are applied to both

13 open literature and registrant submitted data to ensure

14 consistency and reliability, and are applied to all of the taxa,

15 which EPA assesses ecological risk. This approach has been

16 consistent with the Data Quality Act. 

17 However, there are uncertainties regarding the

18 potential for effects on immune response and infections

19 susceptibility and the ability to examine effects to native

20 species due to the lack of suitably husbandry protocols. The

21 uncertainties related to these measurement endpoints speak to

22 the broader uncertainty that articulating a theory of mechanism

23 of action for atrazine in these animals, which link a molecular

24 initiating event to a broad range of endpoints across multiple

25 layers of biological organizations to an adverse outcome for an

26 intact animal population or community. 
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1 EPA acknowledges that it has applied rigorous

2 evaluation criteria to these studies. However, these criteria

3 are consistent with those applied to other taxa, and they are

4 consistent with the specific recommendations made by previous

5 SAPs with respect to factors, which could potentially confound

6 efforts to examine the potential effects of chemicals on

7 amphibians.

8 Of the studies considered, the study conducted

9 in response to the 2003 SAP, resulted in a quantitative

10 classification since it was able to account for the standards

11 used to evaluate studies. This study is therefore considered to

12 be sufficiently reliable for quantitative use in a risk

13 assessment. Although characterized in a single study, the 1

14 quantitative study is actually 2 studies that mirrored one

15 another's results. 

16 The conclusion of the studies were

17 characterized as medium to higher qualitative result in

18 endpoints which are less sensitive than the 2 quantitative

19 studies conducted in response of the DCI for data quality. 

20 Thank you. 

21 DR. DANIEL SCHLENK: Thank you, Ms.

22 Blankinship. Okay any questions or clarification for the

23 presentations this morning? Bob Gilliom? 

24 DR. ROBERT GILLIOM: One question that is a

25 little down a beat, goes back on the distribution plot where you

26 showed the percentages. Could you just clarify how the percents
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1 were calculated on the Y axis for those charts?

2 MS. AMY BLANKINSHIP: Where we used the

3 Weibull distribution methodology? 

4 DR. ROBERT GILLIOM: Was each group done

5 independently or something? It looks like they were lumped in

6 different ways. 

7 MS. AMY BLANKINSHIP: Well, what it was for

8 the NOAEC values, they were done along a distribution graph and

9 then the LOAEC values were done along their own distribution

10 graph. 

11 They do look like they are clumped together,

12 and that is the point I was getting to where in terms that --

13 for some studies, they tested more than one species at the same

14 atrazine concentration, and got the same results. 

15 So, on the distribution graph, they would not

16 be necessarily represented by the same concentration in the same

17 data point. So, there was an effort to sort of make each

18 individual observation. It is only a dependent point along the

19 distribution graph. 

20 Therefore, it sort of skews the overall

21 distribution. These graphs were sort of just meant to

22 illustrate the range of concentrations that were reported across

23 a particular endpoint. They are not necessarily meant to be

24 reflective of a quantitative value in terms of percent

25 distribution at any given point for an atrazine concentration. 

26 Did that help?
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1 DR. ROBERT GILLIOM: I think so. The main

2 thing is that they were separately ranked within the NOAEC and

3 LOAEC groups, and each study was rated equally, and used

4 plotting positions from Weibull. 

5 MS. AMY BLANKINSHIP: Exactly. Some studies

6 may have the same atrazine concentration representing the same,

7 because there were multiple species that, for example, reported

8 no effects at 25 parts per billion for 3 different species

9 within the same study. So, each study made up multiple data

10 points on the graphs.

11 DR. ROBERT GILLIOM: On the graph, but they

12 are on there.

13 DR. DANIEL SCHLENK: Dr. Young? 

14 DR. LINDA YOUNG: I just want to follow up to

15 be sure. So, percent rank is really accumulative percentage

16 according to the Weibull? 

17 MS. AMY BLANKINSHIP: Exactly.

18 DR. DANIEL SCHLENK: Dr. La Point? 

19 DR. THOMAS LA POINT: Just to ask a question

20 that is kind of interrelated. On the unrankings in the

21 probability distributions here, the invalid studies were used

22 along with the valid and qualitative, so really it is just a

23 description of the kind of range of what you found in all of the

24 papers.

25 MS. AMY BLANKINSHIP: Exactly, it is meant to

26 just sort of give a range or comparison of all the studies that
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1 were available for evaluation, meaning where they rank in

2 comparison to another study and maybe even another type of

3 classification.

4 DR. THOMAS LA POINT: Even if there may be

5 some questions on how they are reported?

6 MS. AMY BLANKINSHIP: Absolutely. So we

7 understand and recognize that all the studies do not have equal

8 weight in terms of their use in a risk assessment; but we

9 thought it was useful, in part, to show the range of effects,

10 including the studies that had been classified as invalid, just

11 so people can maybe understand that not all the invalid studies

12 were all at the really low end of distribution or at the high

13 end of distribution. But rather show that the classification

14 ranges are across the atrazine concentrations across the X axis.

15

16 They are not sort of clumped by classification

17 in terms of the invalid and the qualitative or the higher

18 numbers of study classifications. 

19 DR. DANIEL SCHLENK: Any other questions? 

20 DR. TIMOTHY ELLSWORTH: Yes, just one on slide

21 28, real quick I have a question. It looks like those numbers

22 do not add up with the slide before it. 

23 MS. AMY BLANKINSHIP: You are absolutely

24 right, they won't add up. It is because, in terms of 20 and the

25 34, it is because each endpoint in each of the studies - if a

26 study evaluated metamorphosis growth as a sexual development, I
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1 wanted to try and parcel out information that was useful from

2 these studies. 

3 So, not each endpoint in one study will have

4 the same classification because there may have been slight

5 differenced in the way the information is reported. I just

6 wanted to be very useful with the information. 

7 DR. DANIEL SCHLENK: Dr. Rodgers? 

8 DR. JOHN RODGERS: For the survival study, you

9 mentioned 96 hours as a duration of exposure. Could you attach

10 a duration of exposure to the other classes of studies like the

11 metamorphosis and growth, or is it fair to assume that there was

12 a range in duration of exposures in those studies? 

13 MS. AMY BLANKINSHIP: There is definitely a

14 range in exposure durations, partly because we were testing

15 different amphibian species. So, when we go through to

16 metamorphosis, they will have their own biological timeline by

17 which they will complete that metamorphosis stage. 

18 Also, some other study who I'll call

19 subchronic endpoints where maybe they exposed the organisms for

20 several weeks to maybe a few months, just being short of

21 complete metamorphosis. Therefore, yes absolutely, those

22 studies have a range of exposure duration. 

23 DR. DANIEL SCHLENK: Any other questions for

24 clarification? Dr. Klaine? 

25 DR. STEPHEN KLAINE: As a followup on that,

26 did you do any analysis to see if duration of exposure had any
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1 influence on how the studies were ranked? 

2 MS. AMY BLANKINSHIP: No, I did not. In terms

3 of any of the endpoints? 

4 DR. STEPHEN KLAINE: Well, just because the

5 range was different for all of the subequal endpoints, I

6 wondered if you had investigated that? 

7 MS. AMY BLANKINSHIP: I did not. Partly, I

8 will say that not all the studies had explicit time duration

9 attached to them. Some of the studies just had 2 metamorphoses.

10 So, establishing a chronological day exposure to some of those

11 studies was difficult. And I attempted to try to think about

12 that in terms of the different species, but I would had to have

13 make some assumption to what would be considered a normal rate

14 of development and then apply some standard to those studies

15 that did not report natural chronological test duration. 

16 DR. DANIEL SCHLENK: Any questions? I have

17 one question, and this may be for Mr. Spatz. In the

18 documentations that were provided with a host of other data

19 besides just amphibians and plants, I am curious, are you

20 looking for comments based upon other organisms? The questions

21 are geared for amphibians, plant and plant communities. Since

22 this is a problem formulation, I would assume you would want

23 some impact on some of the other receptors of interest? 

24 MR. DANA SPATZ: We are focusing in on the

25 amphibians and the plants. The rest of the taxa are there for

26 completeness because it is a problem formulation, but we are
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1 zeroing in on these three issues. 

2 DR. DANIEL SCHLENK: I noticed that the

3 registrants will also be presenting information on fish as well,

4 so it seems there is also that data too.

5 DR. STEVEN BRADBURY: I think in part of the

6 next presentation, part of the logic used that expound on that

7 is that the effects of which we are seeing -- concentrations, in

8 which there are changes in the plant community structure and

9 function tend to be lower than effects of concentrations that

10 cause direct effects on organisms higher than in atrophic

11 systems so that hypothesis going into the problem formulation is

12 that if we protect plant community structure and function, we

13 protect other species in that community in terms of direct

14 effects or indirect effects. 

15 The amphibian information has been one that

16 has been an important area to focus in on because as Amy pointed

17 out, depending on how you interpret that data as you are going

18 from 2003 to 2007, there were some studied that reported

19 concentrations below that, which would cause effects. So that

20 is what we have been focusing most on, those 2 hypotheses.

21 DR. DANIEL SCHLENK: If there are values that

22 are below the plant value, though, do you want that highlighted,

23 I guess, is the question? I noticed there were values below

24 that. 

25 DR. STEVEN BRADBURY: I think that is

26 certainly welcome to comment on. As you can sort of see the
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1 logic that we were using as we're going through our

2 presentations. It would be helpful if you could talk about

3 that.

4 DR. JAMES MCMANAMAN: This is a question for

5 Ms. Blankinship. Could you elaborate a little more about the

6 study that was deemed quantitative? I just want to make sure

7 that I understand the difference between it and the qualitative

8 studies is. 

9 MS. AMY BLANKINSHIP: That was the study that

10 was conducted in response to the 2004 DCI study. 

11 It actually was 2 independent studies

12 conducted under the same protocol. In that study, a large

13 number of organisms were used, 1600 per study were exposed at

14 atrazine concentrations of a wide range, which were meant to

15 encompass the range of effects as reported in the 2003 SAP. So

16 they ranged from 0.01, which was below the reported range of

17 effects, all the way up to 100 parts per billion, which again

18 with all the reported range of effects meant to be

19 environmentally developing. 

20 That study was conducted with young tadpoles,

21 6 days post hatch, which is meant to encompass the vulnerable

22 window, all the way through to metamorphosis. 

23 At termination of metamorphosis, the organisms

24 were evaluated for length and weight, and then organ morphology

25 was observed, and in particular, histology of the gonads was

26 performed. Then evaluation of sex ratio, gonadal development,
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1 metamorphic rate and the behavioral effects were also observed,

2 including survival. 

3 So those 2 studies were meant to sort of be

4 done at the same time to evaluate reproducibility. Since that

5 was one of the concerns raised in the 2003 SAP; since there were

6 reported effects over a range of different concentrations, it

7 was thought that it might be important to conduct 2 studies

8 simultaneously to observe reproducibility. 

9 DR. JAMES MCMANAMAN: Did you say that there

10 were positive controls contained within those studies? What was

11 the nature of those positive controls? 

12 MS. AMY BLANKINSHIP: There was. With an

13 estradiol positive control tested at 0.2 percent parts per

14 billion, and that was meant to be reflective of the dose meant

15 to result in an EC50 to female conversion, which the study did

16 not show. At the end of the study, the sex ratio in that group

17 was 75/25, female/male. 

18 DR. JAMES MCMANAMAN: So that was the only

19 positive control was same sex ratio, not for any of the other

20 parameters that were used? 

21 MS. AMY BLANKINSHIP: Exactly. The gonadal

22 development was the primary endpoint that was to be evaluated. 

23 Estradiol was used in positive control in that sense to evaluate

24 the sex ratio. 

25 DR. ROBERT GILLIOM: Just kind of a policy

26 clarification for Don or Steve. One of the critical things
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1 driving this stage of things is the criteria to determine which

2 studies are acceptable or not. 

3 The indication from the presentation is that

4 if the Agency is comfortable that the criterion standards apply

5 to these amphibian studies are comparable to ones used on

6 screening studies in general across risk assessments -- is

7 everybody comfortable with that, that those criteria had been

8 applied uniformly and we are applying the same standards that

9 are applied elsewhere? 

10 DR. STEVEN BRADBURY: Yes. The 2003 input

11 from the SAP tracks ASTM Guideline recommendations for tadpoles

12 as well as aquatic toxicology testing in general. The basic

13 attributes of this assay were also derived working with the

14 Office of Research and Development who were working our

15 Endocrine Disruptive Screening program and there is a lot of

16 data that we'll probably use; xenopus and all these various

17 factors we talked about, so that process we carried through OCD.

18 So it has not been a need for this specific

19 question. A lot of it is what's going on across amphibian

20 studies and applying toxicology studies in general, issues of

21 flow through and static-renewal. 

22 Again, I am not saying that static-renewal

23 testing cannot be used. But ASTM and others have talked about

24 the importance of static-renewal and static study in terms of

25 oxygen concentration and normal concentrations. 

26 The point is looking at the different style
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1 for which this study to be done is 20 or 30 years of experience

2 in toxicology when they would think about it in terms of

3 interpreting data quantitatively, qualitatively.

4 A big aspect of 2003 advice, and I am not a

5 statistician and never claimed to be one, but the nature of the

6 observations at the time and the challenges in interpreting

7 effects of doses and whether or not there were monotonic and

8 nonmonotonic does response relationships was a big part of the

9 dialogue in 2003. 

10 And that related to the recommendation to have

11 the doses that were use. Because above and beyond what we see

12 in typical guidelines to the lower doses, the range in the doses

13 and the number of animals that had to be in those experiments so

14 they could have some confidence with something going on, we

15 could pick it up. 

16 And that sort of leads back to some of the

17 conversation around species sensitivity in terms of any

18 toxicokinetically or toxicodynamic reason as a reason to expect

19 Xenopus to be wildly insensitive or sensitive to what is going

20 on. As a panel, we could think of this as a way across species,

21 but is there something that wildly that swing Xenopus one way or

22 the other in terms of sensitivity.

23 This goes back to using estradiol as a

24 positive control in study just to make sure we did not stumble

25 across a strain that was wildly sensitive or completely

26 insensitive. 
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1 It also determines the native species to get

2 back to the number of doses, to try to kick out a low dose

3 effect for the nonlinear dose response curve. The 2007 SAP

4 talked about to come up with the end to do that in the native

5 species just as the husbandry and culturing techniques.

6 MS. AMY BLANKINSHIP: Can I just follow up on

7 that in terms of applying criteria across taxa?

8 Appendix N of the white paper is the OPP

9 Ecological Effects Criteria Guidance. That is a generic

10 guidance for all pesticides evaluated in effect for an

11 evaluation of the open literature. So, that would go for any of

12 the taxa which would be used in a risk assessment. 

13 DR. DANIEL SCHLENK: Any other questions

14 before we take a break here? Okay, thank you very much; good

15 presentations this morning. Let's take our 15-minute break and

16 by this clock right here, I am going to go by that, we will be

17 back at 11:00.

18 (Whereupon, a luncheon recess was taken.)

19 

20 DR. DANIEL SCHLENK: Take your seats, please. 

21 Before we get going, I've been told that we have the technical

22 ability now for Dr. Sandy to introduce herself as our permanent

23 panel member who is on the phone, so Martha, are you there?

24 DR. MARTHA SANDY: I am. Good morning.

25 DR. DANIEL SCHLENK: Oh, great.

26 DR. MARTHA SANDY: Yes, this is Martha Sandy. 
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1 I am a toxicologist and I am Chief of the Cancer Toxicology and

2 Epidemiology section in the Office of Environment Health Hazard

3 Assessment, which is part of the California Environmental

4 Protection Agency. 

5 DR. DANIEL SCHLENK: Great. Thank you very

6 much, and if I am understanding correctly, if you have a

7 question, you are going to e-mail it to Sharlene here and we

8 will put you on, is that correct?

9 DR. MARTHA SANDY: That is correct.

10 DR. DANIEL SCHLENK: Okay, great. Thanks. 

11 Okay, next on our agenda is the presentation by Dr. Farruggia

12 and Dr. Erickson. Are you guys going to tag-team on that? So,

13 we will just hold questions until the end of both of your

14 presentations at the end and you have the fun job of standing

15 between us and lunch; so, no pressure.

16 DR. FRANK FARRUGGIA: Thanks, thanks for

17 putting the pressure on. Hello, I am Frank Farruggia, a

18 biologist in the Environmental Fate and Effects Division. 

19 This talk will provide an overview for the

20 methodology determining the level of concern for atrazine based

21 on aquatic plant communities. This slide is a summary of the

22 method for determining the level of concern for aquatic plant

23 communities, which was derived following the Interim

24 Reregistration Decision or IRED in 2003. We will be returning

25 to this flow chart throughout the remainder of the talk, walking

26 through the critical elements within each stage. 
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1 We will describe the changes made to the

2 methodology based on recommendations from the SAPs of 2007 and

3 2009, and finish with a discussion on the impact of these

4 changes on the concentration equivalent level of concern based

5 on the best data available to date.

6 Atrazine is mobile and persistent in the

7 environment. The main routes of dissipation are microbial

8 degradation, under erode conditions, runoff, and leeching. 

9 Because of its persistence and mobility,

10 atrazine will move into surface and ground water. This is

11 confirmed by the widespread detections of atrazine in surface

12 and ground water. Atrazine bonds with the protein complexes in

13 photosystem II in chloroplast photosynthetic membranes resulting

14 in a condition of the transfer of electrons that in turn

15 inhabits the light reaction to photosynthesis. 

16 This mechanism of action is essentially

17 causing effects similar to light limitation. So, oxygen

18 production, photosynthate for sugar production and ATP all would

19 produce.

20 The EPA is concerned with protecting

21 biological diversity from the harmful effects of atrazine. This

22 encompasses the protection of species, assemblage of ecosystem

23 and landscape levels of biological organization and the

24 structural, compositional, and functional components.

25 One of the goals of the upcoming

26 reregistration review for atrazine is to explore whether or not
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1 atrazine has the potential to reduce survival, reproduction

2 and/or growth of non-target threshold aquatic organisms, as well

3 as negatively affect the structure, productivity and function of

4 aquatic plant communities based on the current labeling

5 application rates and uses. 

6 The focus of the aquatic plant community

7 methodology ensures that atrazine concentrations from watersheds

8 do not cause significant changes of community structure,

9 function and/or productivity; and thus put at risk the food

10 chain for the entire ecosystem development. There are a large

11 number of single species and community toxicity studies

12 available to explore atrazine toxicity and to develop the level

13 of concern for aquatic plant communities. 

14 The potential effects of atrazine on plant

15 communities have been evaluated using available microcosm and

16 mesocosm studies. These cosms bring natural aquatic plant

17 communities under controlled conditions to allow for observation

18 of the direct effects of atrazine exposure to aquatic plant

19 populations and communities, as well as indirect effects to

20 higher trophic levels. 

21 Cosm studies conducted with atrazine reported

22 community-based endpoints that reflect aggregate responses to

23 multiple species, shifts in community composition and structure,

24 and relative impact of pressure. 

25 In addition, cosm studies, especially those

26 conducted in outdoor systems, incorporate partitioning,
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1 degradation and dissipation, all factors that are not usually

2 accounted for in laboratory toxicity studies, but may influence

3 the magnitude of ecological effects. 

4 Recovery from the effects of atrazine and the

5 development of resistance to the effects of atrazine in some

6 vascular and nonvascular aquatic plant species have been

7 reported both in single species studies, as well as cosm

8 experiments. However, the reports of recovery are often based

9 on differing interpretations. For the purposes of the upcoming

10 reregistration review of atrazine, recovery is defined as return

11 to preexposure conditions for the infected individual,

12 population, or community, not for a replacement population or a

13 community of more tolerate species. 

14 For example if in a cosm experiment exposed to

15 atrazine, the only fixed element is algae present with heavily

16 impacted loss from the cosm, but at the same time several

17 species of cyanobacteria bloom. There may no change in the net

18 primary productivity for that system; however, the structural

19 and functional role that the filamentous algae filled is not

20 likely to be filled by cyanobacteria; thus the community is

21 impacted by the change in both diversity as well as structure

22 and function.

23 To regain these components of the community

24 and recover from atrazine exposure, the replacement populations

25 or species would need to come from outside the cosm. 

26 Contrary to this example is another situation
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1 where in another cosm experiment there is a 10 percent reduction

2 in network primary productivity of the system for a short

3 duration. Say for example, 3 days. This short perturbation was

4 followed by a relatively quick recovery of primary productivity

5 to near controlled conditions over the next week. 

6 If the structural and functional elements of

7 the community were not significantly impacted by this

8 perturbation, then recovery is considered to have occurred, and

9 the transient effects to the community are considered minimal

10 and thus allowable under the method.

11 This type of endpoint would have been scored

12 as a Brock score of 2 under the old method and no effect under

13 the current methodology. I will get more into that discussion

14 this afternoon.

15 Based on the recommendations from the FIFRA

16 SAPs in 2007 and 2009, selection criteria were developed. The

17 criteria were derived using peer review resources from the USDA

18 Society for Environmental Toxicology and Chemistry, and the

19 Organization for Economic Cooperation and Development. As a

20 primary filter, the study was evaluated for inclusion in

21 developing the LOC only if treatments were exposed to only

22 atrazine, that is, not mixtures or multi-active ingredients,

23 exposure concentrations were reported, measured effects were

24 specific to aquatic plant communities, defined as 2 or more

25 species, and the study was written in English or available in

26 English. 
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1 In the evaluation process, the study would be

2 rejected as invalid if any of the following accepted mode of

3 criteria were not met. Controls must be present and run

4 concurrent with the study. The presence of other stressors,

5 physical, chemical or biological, any controls or treatments

6 must not be at levels that would be expected to compromise the

7 interpretation of atrazine as a stressor. 

8 While continued measurement of atrazine

9 concentrations throughout the experiment is ideal, the study was

10 minimally required to provide the initial exposure

11 concentrations. The experimental design had to have included at

12 least 2 replicates for treatment and controls regardless of the

13 statistical method used. In addition, if a solvent was to be

14 used, it must be less than 25 ppm in the static-renewal or

15 static testing and less than 0.1 ppm in flow-through testing,

16 and should be held at the same concentration across treatments

17 and solvents used in negative controls. 

18 If a regression-based approach is used, at

19 least 3 treatments, in addition to the controls must be tested. 

20 In the event that 2 replicates for treatment were not tested,

21 the study design must contain the minimum sample size of 5,

22 including the control, in order to fit a nonlinear regression

23 curve. Statistical analyses of the data, including numerical

24 and/or graphic presentation of the results must be clearly

25 described and presented in the publication. 

26 A total of 35 cosm studies were originally
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1 included in the 2003 Interim Reregistration Eligibility

2 Decision. An additional 38 cosm studies were identified by the

3 May 2009 SAP for a total of 73 studies. After the review

4 selections, criteria were applied, thirty-one of the original 35

5 studies remained for analysis. Fifteen of the 38 studies

6 recommended by the 2009 SAP remained. These results are

7 outlined in appendix D of the White paper. 

8 The majority of cosms were started from

9 natural communities collected from streams, lakes, reservoirs

10 and springs. Relatively few cosms were synthesized from the

11 addition of species taken from cultures. Cosms are considered

12 to be representative of the structure and function of the

13 aquatic plant communities in such water bodies. Taken together,

14 they provide useful and reasonable physical models for natural

15 aquatic plant communities exposed to atrazine in the United

16 States. 

17 Few studies provided the identity of

18 phytoplankton, periphyton and zooplankton. Those that did

19 reported great diversity of taxa with tested. That table shown

20 here illustrates some of that reported diversity. Estimates

21 from 1 study suggested there were 150 to 200 microorganism

22 species present in a single microcosm, source from Virginia lake

23 water. 

24 Most of the studies focused on atrazine

25 effects of phytoplankton, periphyton and macrophytes. However,

26 some also included measurements on animals. It is assumed that
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1 these studies represent biologically relevant natural

2 communities in the breadth of diversity found in North American

3 fresh water environments. 

4 A total of 87 endpoints were used in the

5 analysis to develop the level of concern for atrazine. Effects

6 observed in the cosm studies included changes in aquatic plant

7 biomass, chlorophyll A concentration, photosynthesis rate,

8 measured through 14C uptake and oxygen production, shifts in

9 aquatic plant community structure, such as species composition

10 changes and diversity. 

11 The durations of these studies range from a

12 few weeks to several years at constant or variable and declining

13 exposure concentrations ranging from 0.1 parts per billion to

14 10,000 parts per billion. 

15 Effects in the cosm study were scored using a

16 binary effect/no-effect score. In previous analyses, the cosm

17 endpoints were assigned Brock scores, which is a 5-point effect

18 scoring system. 

19 A Brock score of 1 was assigned to endpoints

20 that did not produce an effect and a Brock score of 5 was

21 assigned to endpoints that produced clear effects without

22 recovery for 56 days or more.

23 Endpoints with Brock score of 1, no effect or

24 2, slight transient effects were distinguished from endpoints

25 assigned a 3, which had a clear effect with recovery or higher

26 scores for the LOC analysis. 
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1 In response to the recommendations of the 2009

2 SAP, all cosm endpoints were reevaluated to ensure that each

3 endpoint was categorizing the appropriate effect or no effect. 

4 No effects, 1s and 2s, and effects 3s and 5s determinations of

5 the old set of cosms are essentially the same calls now as they

6 were in 2009. In 2009, there were 11 endpoints classified as

7 Brock score of 2. On current classifications, 3 of these

8 endpoints from 2 studies were considered invalid. Three

9 endpoints from another 2 studies were transferred to effects,

10 and the remaining 5 endpoints remained as no effects on final

11 analysis. 

12 I will discuss the impact of the 3 endpoints

13 that were changed to effects on the final analysis later in the

14 talk. 

15 A binary effect/no-effect system was

16 considered to be more clear and transparent, which is one of the

17 reasons for adopting it on this analysis. 

18 The studies were coded as an effect or no

19 effect for each concentration of atrazine tested in the study. 

20 For example, if the cosm study tested 5 different concentrations

21 of atrazine, a 0.5, 5, 100, 1000, 10,000 parts per billion, all

22 5 concentrations would be assessed for an effect or no effect. 

23 If we look to the figure, exposures at 7 days

24 represent this kind of data. The 2 lowest concentrations of 0.5

25 and 5 parts per billion were scored as no effects, but

26 concentrations above 5 parts per billion all show significant
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1 effects, and were scored as such. 

2 There were a total of 46 studies, included in

3 the current methodology representing 87 atrazine exposures, each

4 scored for effects/no-effects to the tested community. Those

5 are shown here.

6 Now that the cosms have been identified in

7 quality studies and the endpoints have been classified, we need

8 some way of comparing them. The first hurdle is that each cosm

9 had atrazine exposures that differed across concentrations as

10 well as differed within the studies, such that some study had

11 atrazine concentrations that were held constant through this

12 study, while others were steadily declining throughout the

13 study, such as that chemograph to the left. And still others of

14 short duration with rapidly declining concentrations, such as

15 the chemograph on the bottom right. 

16 The second issue comes in when trying to

17 extrapolate the cosm effects to the exposures typical in North

18 American fresh water bodies. Atrazine occurs in the aquatic

19 environment in episodic and often dramatically hidden place, as

20 you can see by the figure. 

21 The Missouri 01 watershed shown in the top

22 panel shows that there is a highly concentrated peak of atrazine

23 for a short period of time. This contrasts with the middle

24 chemograph of Missouri 07 watershed. It has 2 less concentrated

25 peaks. The lower chemograph, Missouri 02 watershed, had

26 multiple peaks and a longer exposure period. 
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1 It is important to note the difference in

2 scale across these 3 chemograph panels. It is also important to

3 keep that in mind throughout the remainder of this talk and

4 throughout Dr. Hetrick's talk following this. 

5 The basic goal of this methodology is to be

6 able the extrapolate effects across both concentration and

7 duration of exposure, thereby being able to compare effects that

8 occurred in cosms to each other, and to those expected in fewer

9 exposures. An index for the relative effects at different

10 concentrations and durations of exposure is therefore needed. 

11 The effects determined in the cosm studies are used to determine

12 an LOC using this index, which can then be applied to field

13 chemographs. 

14 We will return now to the overview. Up to

15 this point, we have completed stage 1 of the methodology by

16 characterizing the cosm exposures and effects. Now, I will pass

17 off to Dr. Erickson, research scientist in the Office of

18 Research and Development, who will discuss stage 2, developing

19 and history of the effects index based on single-species

20 toxicity. 

21 DR. RUSSELL ERICKSON: As discussed to this

22 point in our presentation, experimental cosm data are the

23 foundation for defining atrazine exposures that elicit

24 unacceptably adverse effects on aquatic plant communities. The

25 evaluations of the cosms are the first stage of the methodology.

26
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1 However, it has also been noted applying this

2 information entails comparison of the effects across different

3 exposure time series. The cosm data include exposures of

4 markedly different duration and involved concentrations that

5 declined at various rates. A few of the exposures to which this

6 information must be applied are even more time variable. 

7 As such, the second stage of this methodology

8 addresses development of an index to compare effects across

9 diverse exposure time series. I will be discussing the major

10 considerations that led to our proposed index.

11 As already noted, the general problem the

12 effects index must address is the need to compare effects across

13 different exposure time series. This index must be quantitative

14 and address the cumulative toxic severity of the exposures over

15 some defined assessment period. 

16 However, it does not need to provided the

17 basis for defining an absolute level of protection, because the

18 effects determinations in the cosm studies have already done

19 this. Rather, it needs only address the relative toxic severity

20 at different exposures. For example, is 100 micrograms per

21 liter for 1 day worse or better than 50 micrograms per liter for

22 2 days and by how much?

23 It should be emphasized that the only concern

24 here is that this be describe the effect of time series, that it

25 be a function only of exposure time series. We are not trying

26 to address other system properties. 
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1 We have looked at various candidates for such

2 an index. Part-per-billion days is simply the sum of daily

3 concentrations across the assessment time period. This measure

4 has received some attention in aquatic toxicity over the year as

5 a way of addressing time variable exposures, but has a

6 disadvantage of not considering what we do know about how

7 toxicity varies with concentration.

8 Species sensitivity distributions describe how

9 toxic concentrations for a specified level of effects, such as

10 50 percent mortality, varies across different taxa of organisms.

11

12 SSDs are used to specify what percentage of

13 species would be affected at this single effects level at a

14 specified exposure concentration. It had been an important and

15 accepted tool in aquatic risk assessments for 30 years, but did

16 not address the effect of time and only addressed one level of

17 effect. So, we considered how to more fully describe toxicity

18 relationships across multiple species using what is called here

19 the Plant Assemblage Toxicity Index, which I will be discussing

20 more on later.

21 A final more complex possibility for the

22 effects index is a mathematical community simulation model,

23 which is actually where our efforts started 10 years ago. But

24 before discussing specific efforts to develop an effects index,

25 I would like to discuss some other aspects of this methodology

26 framework, because they are important in evaluating the efficacy
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1 of these effects index candidates. 

2 Once we have an effects index, the next step

3 in this methodology in stage 3, is to ask what value for this

4 index should be considered a level of concern. Now remember, I

5 said this index is only looking at relative effects. It

6 inherently does not contain the information for us on what the

7 absolute level of protection should be, the cosm data do.

8 So, to do this in stage 3, effects index value

9 for each cosm exposure is calculated and the relationship to the

10 cosm effects characterizations to their index values is analyzed

11 to derive an LOC for the effects symptoms. Dr. Farruggia will

12 be giving more information on this process later, but the

13 important point here being that such an LOC is computed; that

14 is, it is on an effects scale, not a concentration scale and it

15 is determined by the cosm data set and not some judgement

16 inherent to the effects symptoms.

17 The next step then is to determine whether a

18 field exposure of interest exceeds a level of concern based on

19 this effects index and its LOC. To do this in stage 4, the

20 effects index for exposure is calculated and compared to this

21 LOC. In addition to simply determining whether the exposure

22 exceeds the level of concern or not, this stage can involve

23 quantifying the severity of the exceedance as defined on the

24 next slide. 

25 The Effects Exceedance Factor or EEF is simply

26 the ratio of an effects index, which describes a relative
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1 severity of toxicity for an exposure of interest to the LOC for

2 that effects index. An EEF of 1 means simply that the expected

3 effect of the exposure is right at the LOC. An EEF 2 means that

4 the effect is twice the LOC. This is analogous to a hazard

5 index that is often used in aquatic assessments, except that it

6 is components are on and effects scale rather than a

7 concentration scale. 

8 The Concentration Exceedance Factor puts this

9 back more related to concentrations. It is the factor by which

10 an entire chemograph or exposure time series must be reduced so

11 that the effects index is at its LOC. This can be expressed as

12 the ratio of the original chemograph or exposure series to one

13 that is proportionately adjusted so that the expected effects

14 are at the LOC. Because this EEF is a ratio based on

15 concentrations, it is even more analogous to a traditional

16 hazard index. 

17 With these definitions taken care of, which

18 will be referred to later, we can turn to the chronology of

19 development of our effects index. As I said before, it started

20 with community simulation models and our initial candidate was

21 the Comprehensive Aquatic Systems Model or CASM. 

22 CASM simulates biomass versus time for various

23 interrelated species that constitute a model community. Such

24 biomass versus time information can be used in an assessment by

25 first running a reference simulation with no atrazine exposure

26 to the model, and then running a simulation with an atrazine
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1 exposure of interest. 

2 The differences in plant biomasses or

3 community composition between these simulations provide a basis

4 for an effects index. These simulations are necessarily greatly

5 simplified compared to real ecosystems, but hopefully provide

6 pertinent information on the response of real systems, at least

7 for the purpose of this effects index. 

8 In CASM, each species is described by a

9 bioenergetics equation that addresses various biomass gains and

10 losses. For example, for plants, the biomass gain is a

11 photosynthesis term, and the losses include metabolism, death

12 and grazing by herbivores.

13 Each term in this bioenergetics equation is a

14 function of various other biomasses and input variables. For

15 example, photosynthesis is a function of light, temperature and

16 nutrients. CASM is just a linkage of various such equations.

17 For our atrazine assessments, CASM was

18 formulated to represent a generic corn-belt stream. This work

19 was done under a contract to Syngenta by Steven Bartell and

20 coworkers and reviewed and critiqued by EPA. Dr. Bartell was a

21 principal in developing CASM and its various past applications. 

22

23 A preliminary version of CASM atrazine was

24 used for an initial method development in 2003 and for the SAP

25 review in 2007. This version of CASM was not realistic in

26 several ways, especially with regard to the reference simulation
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1 biomass time series and how toxicity test information was used. 

2 As such for the 2007 review EPA, it was presented as a

3 preliminary component of a proposed assessment approach with the

4 SAP recommending further development. 

5 So, a more realistic version of CASM atrazine

6 2 was developed and evaluated for the 2009 SAP review. 

7 Unfortunately, the more realistic behaviors of this version of

8 CASM resulted in severe uncertainties with regard to the effects

9 index and its application. And EPA conclude that is was not

10 suitable for the generic type of assessment that was desired. 

11 Also on the 2009 SAP review, Syngenta

12 introduced a probabilistic version of CASM with claims about how

13 it could better inform being assessment. The SAP recommendation

14 was to further consider how CASM could contribute to atrazine

15 assessments, perhaps in a tiered manner. EPA did conduct

16 further evaluations of CASM, and again concluded it was not an

17 appropriate tool for the intended use, although it might be

18 useful for intensive site-specific assessments beyond the scope

19 and resources of this effort. 

20 Various EPA concerns about CASM can be found

21 in the 2009 SAP briefing materials, and then a critique of

22 probabilistic CASM provided in the briefing materials for this

23 review. I will discuss here only our most critical concern

24 about CASM. 

25 One important aspect of CASM atrazine is that

26 despite having 26 plant species with a broad taxonomic range,
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1 which is rather rich for such a model, simulations frequently

2 have only a few or even 1 species constituting most of the plant

3 biomass at any one time. Even without the issue of atrazine

4 effects, this raises a question of biological relevance to

5 natural communities that are more rich and diverse, although

6 this might be viewed in a better light if each individual

7 species in CASM is perceived as a surrogate for multiple species

8 with similar biological characteristics.

9 The key EPA concern about this is assigning

10 atrazine sensitivities to the various CASM species, especially

11 when it is dominated by so few species. This figure shows the

12 average relationship with growth rate to atrazine concentration

13 for a variety of aquatic plant species, included in our analysis

14 of available toxicity data. 

15 Obviously, there is considerable variability,

16 not just overall, but also within the broad taxa indicated here.

17 Not shown is the considerable variability within each species

18 as well. Even if an individual CASM species can be equated to a

19 particular species, for which toxicological information is

20 available, which is not always the case, the sensitivity of that

21 CASM species is highly uncertain. For this reason, CASM must be

22 run in a probabilistic fashion with respect to toxicity

23 relationships and this results in considerable uncertainty and

24 risk estimates. 

25 This uncertainty is worsened by the

26 aforementioned dominance of a few CASM species. Conclusions
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1 about the sensitivity of a plant community to atrazine based on

2 a single toxicity relationship assigned to a dominant model

3 species has uncertain relevance to risk in actual ecosystems. 

4 The impact of these model uncertainties are

5 illustrated in this next slide, showing the 3 chemographs

6 presented earlier. This figure shows the median and the 10th to

7 90th percentile range of CEFs, the Concentration Effects Factor

8 I mentioned earlier, resulting from 100 probabilistic CASM

9 simulations. These simulations differing just by what toxicity

10 relationship is assigned to each CASM species from the range of

11 possibilities for that species. 

12 If only the median results are considered,

13 these results might be viewed as informative. The first

14 chemograph has a median CEF well below 1, which means it is

15 below the level of concern. For the second chemograph, the

16 median CEF is well above 1, so it would be considered a

17 substantial concern. For the third chemograph, the median CEF

18 is near 1, and thus marginally of concern. 

19 However, for all the chemographs, conclusions

20 are highly uncertain if the range of CEFs is considered. In all

21 cases, the CEFs varied by about an order of magnitude, ranging

22 from well below 1 to well above 1. So what useful information

23 is the CASM actually providing here? 

24 At the 2009 SAP, discussion addressed the

25 important difference here between uncertainty and variability,

26 variability reflecting meaning differences among systems. 
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1 However, the variation shown here is

2 predominantly uncertainty, and positively exaggerated

3 uncertainty due to the uncertain realism of simulated model

4 communities dominated by a few species, the uncertain community

5 response based on the toxic response of just a few modeled

6 species, and the uncertainty of the toxicity in relationships

7 assign to those species. 

8 Even more important, whether this is

9 meaningful variability or uncertainty actually does not matter

10 much. If CASMs conclusion that some systems might be much more

11 sensitive than the median is actually a credible conclusion, a

12 risk management decision then becomes necessary on whether to

13 regulate at lower concentrations than indicated by the median,

14 again whether this is variability or uncertainty. The EPA was

15 just not confident enough in the mathematical community models

16 to accept such implications. So, we looked at alternatives. 

17 Despite our concerns about CASM, we did not

18 want to simply dismiss it, but rather consider what important

19 meaningful elements CASM contains regarding the effects index

20 and develop a simpler effects index based on those elements

21 while eliminating the uncertain and unnecessary elements.

22 Hence this question can the effects index be

23 based on well-established processes in CASM according to the

24 purposes of the effects index, while eliminating major

25 uncertainties and unimportant complexities? 

26 So what are the elements of CASM that are most
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1 important to the desired effects index? What are the elements

2 most important to the extrapolations it makes across different

3 exposure time series? 

4 The initial primary action in CASM with regard

5 to atrazine is simply the reduction of the growth of each

6 species on each day in accordance with the toxicity relationship

7 assigned to that species. This results in an overall reduction

8 in total plant community photosynthesis, weighted by the

9 relative biomasses and characteristics of each species. CASM

10 accumulates these daily effects over time, which does change the

11 relative biomasses. There are other interactions that will

12 affect the response, but these are secondary. 

13 So, can the effects index be formulated that

14 just reflects these primary effects in CASM; and can it be done

15 in a fashion that avoids the uncertainties about the screen

16 toxicity relationship assignments to the species, and the

17 uncertainties in the relative biomasses with the individual

18 species? These considerations led us to consider what we term

19 the Plant Assemblage Toxicity Index or PATI. The question then

20 is whether a simpler formulation produces similar value to what

21 we are getting out of CASM, or better value. 

22 In this slide, we again see the same set of

23 toxicity relationships presented before regarding CASM. PATI is

24 simply the average toxic effect across this assemblage of

25 species illustrated by this arrow at 50 micrograms per liter,

26 where the average percent for all 20 curves results in a PATI
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1 value of about 19 percent. It is basically averaging across

2 that assemblage of species to come up with an index value of the

3 relative severity of that atrazine concentration. 

4 This is not some radical new concept, but

5 rather a simple common sense representation of the primary

6 reaction of the plant community to a toxic exposure that was

7 already incorporated into CASM, but without the uncertain

8 assignment of different weights to species and the specific

9 toxicity relationships to dominant species. 

10 As we will discuss later, comparisons of risk

11 characterizations based on CASM and PATI indicate that the

12 average results are similar, suggesting that other processes in

13 CASM are of minor importance to the effects index. Again, the

14 effects index we are just concerned about relative toxic

15 severity extrapolating across different exposure time series. 

16 It also suggests that the important aspects of CASM appear to be

17 captured by the simple formulation of PATI. 

18 Before leaving this slide, I should note that

19 although PATI can be based on the average of discreet curves

20 such as these, I did this just to illustrate the process and

21 practice both CASM and PATI actually use statistical

22 distributions that summarize the variability of these data, CASM

23 randomly picks toxicity parameters from these distributions,

24 while PATI integrates the distributions. 

25 In addition to being a simple description of

26 the most important element of CASM, PATI is also a simple
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1 extension of the Species Sensitivity Distribution concept. In

2 contrast to SSDs addressing only 1 level of effect however, PATI

3 more completely uses available toxicity data by examining all

4 levels of effect, therefore providing a more complete and

5 informative measure of the overall effect on an assemblage

6 species. 

7 Again, this is not some radically new concept,

8 but a common sense evolutionary step beyond current accepted

9 assessment methodology. However, to this point in my

10 description, I have not addressed yet the issue of cumulative

11 effects over a time period of exposure. For this, we simply add

12 daily PATI values over a designated assessment period. This

13 results in units of percent to days where the percent refers to

14 the daily PATI value for the percent reduction of the average

15 specific growth rate of the species assemblage. 

16 Such an additive index represents a

17 perspective that we should be concerned as much with a 50

18 percent reduction in growth for 1 day as with 25 percent

19 reduction for each of 2 days. It is as simple as that and

20 extended to any number of days. This type of unit is simpler to

21 part-per-billion days, except incorporating information on the

22 toxicological relationships. 

23 It is also analogous to the concept of degree

24 days for the impact of temperature on heating and cooling. A 20

25 degree heating differential for 1 day and zero degrees for the

26 next is considered to have about the same effect as 10 degrees
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1 for both days. 

2 It should also be noted here that adding daily

3 PATI values based on just that day's concentration, assumes that

4 specific growth rates depend only on the current exposure with

5 no carryovers from previous days. Studies have generally shown

6 the specific growth rates of aquatic plants to recover rapidly

7 from atrazine exposure supporting this assumption. 

8 This simple summation that I provide is not an

9 assumption, but rather is dictated by the nature of the measure

10 used here for assessing toxicity, the specific growth rate. For

11 the same total effect of growth over an assessment period, if

12 specific growth rates vary over that period, the specific growth

13 rates for the different intervals within the period must by

14 definition sum to the same value. That is why we use the

15 summation. 

16 As noted earlier, cumulative PATI is limited

17 to an assessment period. We just do not monitor the

18 indefinitely accumulating small levels of effect over long

19 periods of time. The choice of 60 days for this and the

20 sensitivity of results to this choice are discussed in the

21 briefing material. Some important factors in selecting this

22 period rather than a shorter one are illustrated in the next 2

23 slides. This involves consideration of both the cosm data and

24 the field exposures. 

25 In regard to the cosm data, this slide

26 illustrates the implications of the 60-day assessment period
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1 versus the alternative of a 14-day period. 

2 A 14-day assessment period would basically

3 assume that most of the exposure for most of the cosms is

4 essentially of no consequence, that the effects observed out

5 here at the cosms only require a 14-day exposure and that we are

6 considering the massive exposure that occurs to elicit the

7 effects here. Even more importantly, we aren't considering that

8 there are no effects despite all this exposure for these cosms

9 here, but rather that we are relating these no effects just to

10 the exposure that occurs in 14 days. 

11 In short, the short assessment period has

12 rather low relevance for representing the effects of the long

13 test that constitute the bulk of the data here. 

14 But the reverse is actually not true. You

15 might say, well is the 60-day period relevant to the 14-day

16 period? The 60-day assessment period is relevant to effects at

17 the shorter exposure, because the evaluation of these shorter

18 exposures are considered significant in the evaluation in the

19 context of a longer time window. 

20 For example, effects observed during a 30-day

21 test are still of concern from a 60-day perspective because any

22 recovery that might have occurred between 30 and 60 days if it

23 was extended with no exposure does not eliminate the concern of

24 the original impact observed in that cosm. 

25 To put that in another way, this method is

26 trying to distinguish between exposures that elicit an effect
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1 and those that do not. The assessment period needs to encompass

2 the bulk of the data with the information about this

3 distinction. Just consider right here. This is where we are

4 trying to make the distinction in this bulk of data between no

5 effect and effect, taking a 14 day assessment period is

6 inappropriate for doing that. 

7 The 14 day period simply misses the important

8 mass of information in the 20 to 60-day time frame. In the case

9 of the 60-day period, we encompass all the no effects except we

10 slightly missed this one here and the effects out at longer

11 period, the effects determination is not sensitive to these

12 because they simply confirm an effect at a large PATI value. 

13 Now turning to the field data, one of the

14 implications of a 14-day versus a 60-day assessment period of

15 field data, some of the issues are even more evident when we

16 consider these chemographs. 

17 For the first chemograph, the 14-day and

18 60-day period would have nearly identical cumulative PATI values

19 because the single major exposure event is less than 14 days

20 long. However, for the other chemographs, the 14-day period

21 represents a risk management perspective that most of the

22 seasonal exposure does not matter. 

23 For the second plot, the 14-day period ignores

24 one of the 2 substantial events. Even if there is recovery

25 between the 2, together they represent more impact on the system

26 that either alone, but a 14-day period would essentially ignore
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1 that. 

2 For the third plot, even more of the exposure

3 is ignored by the 14-day period, which is only a small fraction

4 of the period's significant exposure. 

5 So with respect to the field data, this is

6 really not a technical issue regarding the logic or

7 appropriateness of the effects index, but rather a concern to

8 risk management decision about the timeframe for cumulative

9 effects. 

10 I would like to end my part of this talk with

11 a couple of comparisons of results using PATI versus CASM. This

12 graph addresses what limitations would be put on atrazine

13 concentrations if exposures were constant for a specified

14 duration and then were zero. These lines are based on median

15 results of the 2 different models. Although these are not

16 realistic exposure scenarios likely, they are useful in relevant

17 for making PATI versus CASM comparisons. 

18 I should also note that the exact

19 concentrations here depend on various aspects of the data and

20 methodology. The set of cosm data, which include the set of

21 cosm data generated in stage 1, the toxicity parameter

22 distributions developed in stage 2 and the LOC methodology used

23 in stage 3, because software for CASM has not been updated to

24 the most recent options regarding these factors, these graphs do

25 not represent the final results for current PATI-based

26 methodology, but again do represent the same conditions for CASM
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1 and PATI and allow this comparison. 

2 The major point to make here is that the

3 concentrations of concern for a constant exposure are very

4 similar, supporting the contention that PATI includes the

5 elements of CASM most important to the effects index, and that

6 other elements of CASM had relatively little impact on average

7 in addition to being highly uncertain. The maximum of

8 difference is about 1.5 at the shortest duration shown here

9 where PATI allows higher exposures. 

10 An important aspect of this is also that the

11 time dependence of these 2 models is also similar. This is

12 because on average, the same toxic responses are driving CASM

13 and PATI, and this toxic response is most important to the time

14 dependence of the effects characterization. This provides some

15 further support for the time dependence assigned to PATI. 

16 This final slide compares PATI and CASM for

17 the same 3 chemographs presented earlier, showing again that

18 these 2 models give similar average results. The first and

19 third chemographs, PATI results in CEF slight less than the

20 median for CASM, while for the second chemograph, the PATI

21 result is about 25 percent higher. 

22 We have made numerous comparisons, such

23 comparisons using various options for the cosm data, toxicity

24 data and the LOC method. Some of these comparisons were

25 reported at the 2009 SAP review. In all cases, the 2 model

26 produced similar results for median CEFs with PATI overall being
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1 slightly lower. However, if comparisons are based on mean CEFs

2 rather than median CEFs, CASM always predicts larger effects

3 reflective of its variability. 

4 Regarding effects index uncertainty, this

5 slide also shows how it was much smaller for PATI than for CASM.

6 This is because PATI only reflects the uncertainty and the

7 distribution of their collective toxicity data, where as CASM

8 also reflects additional uncertainties already mentioned. 

9 This PATI error is analogous to errors in SSD

10 distribution assessment, which also reflect the uncertainty in a

11 collective distribution of toxicity data and not in isolated

12 individual toxicity data, which we feel is more appropriate

13 here. 

14 The bottom line here regarding the desired

15 effects index is that PATI is proposed for use due to producing

16 similar average results as CASM without the latter's

17 uncertainties and complexities. The additional features of CASM

18 provided no substantiated added value for the purposes of the

19 effect index, although they could for some other purposes. 

20 PATI is also preferred over part-per-billion

21 days because although they produce similar results that we have

22 found, PATI does reflect the degree to which toxicity is

23 nonlinear with concentration. And regarding SSDs, PATI does

24 more fully describe the total toxicological response rather than

25 just reflecting a single large level of response such an EC50. 

26 I will now turn this presentation back to Dr.
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1 Farruggia, who will discuss the later stages of the methodology.

2

3 DR. FRANK FARRUGGIA: Now that we have an

4 index for extrapolating effects in cosms to the environmental

5 exposures, we can proceed to stage 3. 

6 In stage 3a, the effects index is applied to

7 each of the cosm endpoints. Each cosm study endpoint has a

8 concentration and duration of atrazine exposure represented by

9 an exposure chemograph, using an accumulative PATI value that is

10 calculate for a cosm endpoint by taking the daily exposure

11 concentration, getting a daily PATI value for that concentration

12 day, and then summing the daily PATI values across a duration of

13 the study. The figures on the right illustrate this calculation

14 using a chemograph from cosm endpoint classified as an effect. 

15 The chemograph shows an atrazine concentration

16 of approximately 80 parts per billion on day zero. This

17 concentration corresponds to daily PATI value of approximately

18 35 percent taken from the index on the bottom graph. 

19 When the chemograph has been completely scored

20 for daily PATI values, the cumulative PATI value is calculated,

21 in this case 1869 percent days. This value represents

22 concentration and duration on this particular effects endpoint. 

23

24 As another example, this chemograph is from a

25 cosm endpoint classified as no effect. The chemograph shows an

26 atrazine concentration of approximately 3.2 parts per billion on
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1 day zero, and was held constant for the duration of this study.

2 This concentration corresponds to a daily PATI

3 value of approximately 1.9 percent. When the chemograph has

4 been completely scored for daily PATI values, the cumulative

5 PATI value is calculated, in this case, 39.6 percent days. This

6 value represents the concentration and duration of this

7 particular no-effects endpoint. Each of the 87 endpoints are

8 processed in the same way to obtain cumulative PATI values. 

9 In the next stage, stage 3b, the effects-based

10 level of concern is defined. The cumulative PATI values that

11 have been calculated for all of the effects/no-effects endpoints

12 from the cosms are used to set the effects index-based LOC or

13 LOCPATI.

14 In the figure, the black triangles represent

15 effects endpoints, and the open triangles are no effects

16 endpoints, each being scored for cumulative PATI value that is

17 plotted along the X axis. The binary logistic regression is

18 fitted to the data. The LOCPATI is defined as cumulative PATI

19 value that has a 50 percent probability of an effect. 

20 For example, at cumulative PATI value of 140

21 percent days, there is a 50 percent probability of there being

22 adverse effects to the community. The PATI software also

23 supplies the standard deviation around the LOCPATI, from which

24 the 95 percent confidence intervals can be calculated. 

25 The next step is to apply the effects index to

26 each of the environmental monitoring site-year chemographs. 
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1 The Atrazine Ecological Exposure Monitoring

2 Program, AEEMP, also known and AEMP, implemented by the

3 registrants Syngenta started collecting 4-day interval atrazine

4 concentrations in primary Midwestern streams in 2004. Many of

5 these sites, outer samples were later added to collect daily

6 sample information. 

7 This environmental monitoring data is entered

8 into the PATI software to calculate the daily PATI values for

9 each year of sampling at each monitoring site. The process for

10 scoring the chemograph is the same as with cosm data shown in

11 stage 3. However, rather than using the entire chemograph,

12 which is roughly 120 days, the maximum rolling mean cumulative

13 PATI value for the 60-day assessment period was used to

14 characterize the total toxic severity of the chemograph. 

15 In the next stage in the methodology, 4b, the

16 watershed that exceed the LOCPATI are identified. An AEEMP

17 monitoring site with a cumulative PATI value that is greater

18 than LOCPATI of 140 percent days would have a greater than 50

19 percent probability of adverse effects happening to the aquatic

20 community. Those sites less than 140 would have less than 50

21 percent probability of adverse effects happening to the

22 community. 

23 The last stage of the methodology is to

24 convert the effects based LOC into a concentration-based LOC. 

25 The conversion of LOCPATI into a concentration

26 equivalent value will allow for any aquatic monitoring data to
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1 be compared to the LOC by simply calculating the maximum 60-day

2 running average. If the concentration step is not completed,

3 each water authority, state, etc., would need to process their

4 monitoring data running PATI software to derive a cumulative

5 PATI value. 

6 To make that conversion, the cumulative PATI

7 values for each of the AEEMP monitoring site years are divided

8 by LOCPATI to provide the effects exceedance factors described

9 earlier by Dr. Erickson. The EEF for each monitoring site was

10 plotted against the maximum 60-average concentration from the

11 site. The CE-LOC is determined as concentration of which the

12 monitoring site cumulative PATI value is equal to LOCPATI, or in

13 other words, the EEF equals 1. This is calculated using a

14 linear regression equation. 

15 The original screening concentration

16 identified in the 2003 IRED was 10 to 20 parts per billion over

17 an undefined period of time. As Dr. Erickson pointed out, there

18 were critical elements in the CASM index that were modified for

19 the 2007 SAP. It was at the time when different assessment

20 periods were investigated. 

21 For comparison in the present day, CE-LOC

22 values that changes to CASM based on a 60-day CE-LOC was 7.2

23 parts per billion. Following the SAP and the development of

24 PATI, the application of the aquatic community LOC methodology

25 with the available data in 2007 resulted in a CE-LOC of 8.1

26 parts per billion. 
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1 In 2009, the EPA considered 10 parts per

2 billion concentration for a 60-day assessment period, that the

3 above levels for adverse effects to the aquatic plant community

4 would be expected. In 2009, the SAP recommended several

5 changes, such as moving away from the Brock scores, exploring

6 alternatives to setting the index based LOC, as well as adding

7 to the pool of cosm studies. 

8 Based on these recommendations, the updated

9 methodology and the available data resulted in current CE-LOC of

10 4.2 parts per billion for a 60-day averaging period. Because of

11 the 60-day averaging period, the method allows for concentration

12 peaks much greater than 4.2 parts per billion for short

13 durations without triggering an exceedance for that site. 

14 Now that we have covered the calculation of

15 the CE-LOC, we can now discuss the impact of the assessment

16 period on the proportion of AEEMP sites exceeding. This table

17 shows the cumulative PATI values in CE-LOCs for 7, 14, 30 and

18 60-day assessment periods. As you can see, the cumulative PATI

19 values go up with increasing number of days included in the

20 assessment period. This translates to a decreasing CE-LOC for

21 increasing time periods, which is an expected result of

22 assessment effects over longer periods of time. 

23 The influence of the assessment period in

24 terms of exceeding sites is shown by the last row of the table. 

25 The shorter durations have fewer site-years exceeding the CE-LOC

26 for the respective assessment periods. This is most likely the
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1 result of the limited window of time assessed, thereby not

2 considering the mean seasonal atrazine exposures. 

3 As mentioned by Dr. Erickson, the 60-day

4 assessment period was selected because it represents cosm

5 endpoints durations well and better represents a typical

6 seasonal atrazine exposure seen in available monitoring data.

7 Lastly, it is protective of the shorter

8 durations in that those sites that exceed the shorter durations

9 are also identified at exceeding in the 60-day assessment

10 period. 

11 There are several calculations in the

12 degradation of the CE-LOC that incorporate uncertainty in the

13 final numbers. The greatest uncertainty comes from the

14 classification of the cosms with endpoints and the estimation of

15 the 50th percentile of the effects/no-effects distribution or

16 binary logistic regression. 

17 In the white paper, the LOC is presented as a

18 range of concentrations due to the uncertainty involved in the

19 classification of the few cosm endpoints. In 2007 and 2009, 3

20 of these endpoints classified as a Brock score of 2, defined a

21 slight transitory effect. One was a Brock score of 3. The last

22 was a Brock score of 4. 

23 Based on the feedback in 2009 SAP, these

24 endpoints along with all of the other 77 original endpoints were

25 re-reviewed. Sensitivity analysis was conducted to explore the

26 sensitivity of CE-LOC based on a classification of these 5
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1 endpoints. The results showed that there was an approximately

2 40 percent reduction and a revised baseline 60-day CE-LOC

3 between a no effect and effect classification from those

4 endpoints. If the classification of these 5 endpoints were

5 changed back to a no effect, the CE-LOC would be 7.4 parts per

6 billion. 

7 The EPA has determined the CE-LOC can be

8 arranged between 4 and 7 parts per billion based on the cosm

9 study reviews and endpoint classifications described in appendix

10 D in the white paper. 

11 This means that those freshwater and estuary

12 marine monitoring sites with a 60-day running average at or

13 above 4 parts per billion have atrazine concentrations that are

14 above the lower bound of the CE-LOC, and that adverse effects to

15 the aquatic plant community structure, function and/or

16 productivity will be expected. 

17 EPA recognizes that differing interpretations

18 of the cosm studies and their endpoints may change the CE-LOC,

19 but believes that the review provided in appendix D is

20 representative of the effects reported across these cosm

21 studies. 

22 In the process of selecting the LOCPATI, other

23 percentiles of the distribution of the effects and no-effect

24 data were considered. The CE-LOCs for the 25th, 50th and 75th

25 percentiles resulted in CE-LOCs of 1, 4 and 15 parts per billion

26 respectively for the 60-day assessment period. The uncertainty
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1 in the estimation of the LOCPATI is low surrounding the

2 calculation of the 50th percentile. This is the primary reason

3 for using the 50th percentile as the LOCPATI. 

4 The 95% confidence intervals around the

5 LOCPATI at the 50th percentile for both the 4 and 7 parts per

6 billion results were divided by LOCPATI to obtain an EEF in the

7 upper and lower confidence intervals. These EEFs were then

8 converted in concentrations using the same linear regression

9 equation for calculating CE-LOCs for each of these

10 concentrations. This results in a 2 to 10-part-per-billion

11 range of uncertainty around a 4-part-per-billion CE-LOC and a 3

12 to 15 range of uncertainty around the 7-part-per-billion CE-LOC.

13

14 To conclude, the cosms were comprised of

15 natural communities of periphyton and phytoplankton. In some

16 cases, vascular plants, invertebrates and vertebrates present in

17 those communities were included in the study. The source

18 communities for starting the cosms were generally described as

19 streams, lakes, reservoirs and springs, and are considered to be

20 representative of the diversity structure and function of

21 aquatic plant communities in such water bodies. 

22 EPA believes that these cosm studies, when

23 taken together, provide useful and reasonable physical models of

24 the natural aquatic plant communities exposed to atrazine in the

25 United States.

26 The aquatic plant community CE-LOC methodology
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1 for atrazine is a 4-stage process that has been modified based

2 on recommendations from SAPs in 2007 and 2009, and results in

3 CE-LOC for atrazine, which together with monitoring data can be

4 used to identify watersheds where concentrations may result in

5 adverse effects to aquatic plant community, structure, function

6 and/or productivity. 

7 The current analysis using the Plant

8 Assemblage Toxicity Index, or PATI, indicates a CE-LOC can range

9 from 4 to 7 parts per billion depending on the interpretation of

10 certain cosm endpoints. 

11 The EPA believes that this range reasonably

12 represents a range below which adverse effects in the aquatic

13 plant community structure, function and/or productivity due to

14 atrazine exposure would not be expected. 

15 The EPA believes that the CE-LOC is also lower

16 than atrazine concentrations observed to have produced

17 significant direct or indirect effects on invertebrates, fish

18 and amphibians, and is thus protective of these other organisms.

19

20 DR. DANIEL SCHLENK: Thank you Dr. Farruggia

21 and Dr. Erickson. Any questions for clarification? Yes, Dr.

22 Young? 

23 DR. LINDA YOUNG: When doing your logistic

24 regression, how was the adequacy of the model evaluated? 

25 DR. RUSSELL ERICKSON: Yes, the logistic

26 regression was done in response to the last SAP where Dr. Scott,
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1 in fact, suggested us to look at the seven literatures as far as

2 some of the assignments of LOC based on something that is

3 comparable in logic.

4 I did look at different models for that, but

5 the nature of the data is noisy enough that we cannot in all

6 honesty say that this is a definitive mode. Because of the

7 variability of the data, it does not allow the swaying between

8 similar models. Does that answer your question? 

9 DR. LINDA YOUNG: I guess I was wondering

10 whether when you did a goodness of fit type test, whether you

11 failed to reject.

12 DR. RUSSELL ERICKSON: Oh, yes, definitely

13 fails to reject, but there is no basis for rejecting, but there

14 are a range of models that could also do a good job here.

15 DR. LINDA YOUNG: Then as a followup, how was

16 the 95 percent confidence interval around the 50th percentile? 

17 How has that developed? 

18 DR. RUSSELL ERICKSON: I used a standard

19 maximum likelihood estimation for logistic binary regression,

20 and one of the parameters in the model is the EC50 and the 50th

21 percentile. I did the inverse of the information matrix as an

22 estimate for the standard there. 

23 DR. ANN FETSCHER: Can you comment a little

24 bit more -- you say on slide 58 on your selection criteria, I

25 know that your criteria included would not be a good description

26 of the source. I know that, et cetera, the cosm experiments. 
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1 You said that the majority of cosms were

2 started from natural communities, collected from streams, lakes,

3 reservoirs and springs. Do you have, you believe, sufficient

4 information on the level to which the sites, source, were

5 subjected to anthropogenic pressures, atrazine and others? 

6 DR. FRANK FARRUGGIA: Not off hand. I would

7 have to go back into the studies and look at those. However,

8 they do come from mostly a diversity of sites across the U.S. 

9 We can't assume that some anthropogenic impact has been, in

10 fact, placed on those communities. 

11 DR. ANN FETSCHER: Does this apply to both the

12 cosms and the single-species toxicity assays, or were those more

13 tending to be lab cultures, or how were those sourced? 

14 DR. FRANK FARRUGGIA: The single-species

15 toxicity studies are laboratory studies. 

16 DR. RUSSELL ERICKSON: Yes, for the

17 single-species tests, there were predominantly lab cultures, but

18 there were some cases where there were toxicity tests on

19 cultures that were derived from sites and that were in the

20 timeframe so that yes, they would probably still be reflecting

21 some adaptation with certain conditions. But most of them were

22 from long-term laboratory cultures. 

23 DR. MICHELLE BOONE: I am trying to understand

24 if you establish an LOC of 4 to 7 parts per billion, and what

25 does that mean besides increase in emotional angst from levels

26 if they see that in the field, are we going to be regulating at
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1 that level? 

2 DR. STEVEN BRADBURY: I will back up a little

3 bit and then I want to get you back into the loop.

4 So under the statute FIFRA, when you are

5 taking a look and in a context of ecological risk, we won't talk

6 about endangered species. what I am trying to get a handle on

7 is what range of exposure concentrations over varying times

8 periods, in this case, you have the advantage of digging into

9 that, or understand what the potential ecological effect could

10 be in the context of different types of exposure scenarios. 

11 Those different types of exposure scenarios

12 ultimately link back to the late registration of the product,

13 which specifies things like how much can be used, how often it

14 can be used, perhaps in what kind of settings need to be used

15 differently than other kinds of settings. 

16 Ultimately, that is how the exposure starts. 

17 So, we want to take a look at what is the likely water

18 concentration in this case for that product, and whether or not

19 those concentrations have a likelihood of causing an

20 unreasonable adverse effect that phrases out the statute. 

21 In the case of ecological effects, those

22 unreasonable adverse effects are in the context of social

23 benefits, economic benefits; so it is a risk to benefit

24 conclusion we have to make at the end of the day. 

25 What we are trying to get feedback from all of

26 you is getting insights as to that relationship between atrazine
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1 exposures, concentrations and duration of time factor, how much

2 atrazine over what different periods of time will likely cause

3 change in aquatic community structure or function. 

4 That is really important. How that

5 information is then used in the ultimate registration decision

6 about atrazine is going to bring in some of factors beyond that

7 relationship. But that relationship is the cornerstone in our

8 ultimate decision making process. 

9 One aspect of what we are trying to do here

10 and what we talked about in the opening presentations is that,

11 not just for atrazine but for chemicals generally, we are

12 increasingly able to make site specific decisions about the use

13 of a product. 

14 So, it is a national decision we have to make.

15 It is a Federal registration for a product. Increasingly, we

16 can specify how that product can be used in different parts of

17 the country. So we can start to move away from a one size fits

18 all approach to registering that product, to in some cases you

19 can go to web sites that indicate where your field is and get

20 specific instructions for how to use your product in that area,

21 based on studies of ecological success.

22 So, the 4 to 7 parts per billion, and you will

23 hear other ideas as we go through the next few days, is to think

24 about what range -- again, we got time and concentration to take

25 into account. We do expect an aquatic ecosystem to have an

26 irreversible change in its aquatic community structure and
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1 function? 

2 And as Dr. Farruggia said, we're not coming at

3 it from there can be absolutely no change. It is realizing -

4 we've move away from Brock scores of 1 and 2, effect/no-effect,

5 but -- realizing some no effects including some transient

6 change, with the idea of irreversible change. Or as Dr.

7 Farruggia described, if the biomass is the same, you can place

8 all of one class in a diversity of algae species.

9 This is not to dissimilar from the concept --

10 in fact I think it's very similar to the concept of an aquatic

11 life criteria and a notion of the species that are being tested

12 with the idea that you are not trying to reach 100 percent

13 confidence that there will never be a change in the aquatic

14 community and the stressor. But clearly speaking, you know, 95

15 percent of the time you expect it to be no more than a 5 percent

16 of species that closed to being described. That's what we are

17 getting at.

18 So I am trying to get feedback on that range

19 in which one would expect irreversible very significant change

20 in aquatic community structure and function. Then, how do you

21 sort that out given the fact that different watersheds are going

22 to have different chemographs and different kinds of exposures. 

23

24 We are thinking that with this kind of

25 information, we would be able to table our risk benefit analyses

26 to specific places or families of watersheds that have smaller
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1 attributes. 

2 DR. MICHAEL TWISS: What is the justification

3 for using effects from cosm studies that have long durations

4 greater than 60 days, and for those that used atrazine

5 concentrations that far exceed what we see in the literature? 

6 DR. FRANK FARRUGGIA: As for the longer

7 durations, we would expect the effects occurring in those cosms

8 at 60 days be similar to the effects that are seen at 260 days. 

9 Most of the time, these studies and duration of these studies

10 were looked at in the context of time throughout the study. So,

11 if you look at within a certain concentration, you can go

12 through the concentration time and evaluate effect amount.

13 The durations that were plotted in the figure

14 of all the cosm endpoints were the terminal durations at that

15 exposure. As for concentrations, I am assuming that this is the

16 10,000 parts per billion concentration, this is a weight of all

17 the evidence that is at hand, so they were included in the

18 analysis. 

19 DR. RUSSELL ERICKSON: If I could just add to

20 that point. Yes, the very long concentration cosms I made this

21 comment on, the very high concentration cosms over the same

22 time, if you recall, on the logistic regression curve, you have

23 some points way out on the upper leg. Those are those points,

24 and if you include them or not, you are not going to be changing

25 the basic decision about the LOC. The critical points are the

26 ones that are in the transition range of more moderate
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1 concentrations. 

2 As one of our chemographs pointed out, there

3 are cases where there are significant exposures extending over

4 60 days. Also, where there are multiple peaks extending over 60

5 days that might go up and down, but that is different than the

6 cosms, but not necessarily completely out of the ballpark as far

7 as what happens with cosms. Using the cosm studies, you do have

8 concentrations declining over time, they are not constant all

9 the time.

10 DR. JAMES FAIRCHILD: I had a question

11 regarding slide 54 where it talks about the difference between

12 recovery of a laboratory community exposed to atrazine compared

13 to a natural community. 

14 In your comment, you had indicated that a

15 switch to a different taxonomic group (inaudible).

16 I just wanted to clarify the difference

17 between laboratory and a field study, whereas in a laboratory

18 when you look at recovery, generally those are conducted, there

19 seems to be some tests under (inaudible) conditions. So if it

20 recovers, it has to recover to the same original species that is

21 present; as opposed to when you are in a natural environment,

22 you have constant turnover of species, there out of season. So

23 it seems to me, to expect it to return to the original community

24 might be somewhat unreasonable.

25 I'm thinking communities change constantly due

26 to atrazine or any other stressor. 
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1 DR. FRANK FARRAGGIA: That is a good point,

2 and the idea of recovering the single-spot species toxicity test

3 is pretty straight forward. It has been shown a lot. I

4 understand that within that sample, within that little system,

5 it has to recover at the same time. 

6 In a community and because communities are so

7 dynamic over time and within spacial constructs of the

8 hedgewater streams down into the larger streams, we consider

9 these shifts to be losses or potential losses of habitat or

10 structural elements of the community, as important as loss of

11 species or productivity that what happens when you lose, the

12 idea of the community is when you lose a structural element such

13 as the macrophyte, you are changing the entire community because

14 stream flow is going to increase, life is going to increase, so

15 your temperature is going to increase. You lose habitat for

16 fish reproduction, deposition of amphibian eggs, things like

17 that. 

18 The idea of looking at a study or talking

19 about recovery in the sense it is not a primary productivity

20 recovery, but we are really looking at recovery of structural

21 elements of that community. We find that to be a very important

22 part of the COSM experiments, because many of them represent

23 true solid communities, even because they are sampled from the

24 communities regardless of its anthropogenic effects on that

25 original source water, they still represent community that has

26 these elements that we are trying to protect. I hope that
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1 answers your question.

2 UNKNOWN SPEAKER: Do you have an idea of what

3 percent change in similarity might be considered ecologically

4 significant, relevant? 

5 DR. FRANK FARRAGGIA: I would have to look up

6 the way the COSMs were scored. Typically, when there was a 10%

7 or less change in the community that was considered okay under

8 the method. It allows for that kind of transitory effects of

9 slight effects happening to a community. If you lose 30% of

10 your diversity, that is a pretty significant loss. 

11 DR. JOHN RODGERS: I hope this is a simple

12 question. When you started out, I am trying to figure out how

13 many ages are on this stage. When you started out, you said we

14 are going to determine the level of concern for aquatic plant

15 communities that when we exceed this in monitoring programs,

16 this gives us a signal. This signals possible adverse effects

17 on aquatic communities. I was okay with that. Then, we put

18 another edge on that sword in the last conclusion that says now

19 if we are below that, we have no problems at all. I was

20 wondering where in the analysis both of those edges on that

21 sword come from. 

22 DR. FRANK FARRAGGIA: I should clarify that. 

23 When we are below the 4 parts per billion CE-LOC, we do expect

24 some transitory slight effects to be adherdent to the community.

25 The 50th percentile or that CE-LOC establishment was based on

26 that narrow uncertainty around estimating the 50th percentile. 
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1 When looking at the normalization of, like when you normalize

2 all the COSM data to a 60 day assessment period, we can look at

3 how protective the CE-LOC revealed those, taking in all the

4 issues of normalizing to a specific time period. All of these,

5 and the 4 part per billion based on our current classification

6 outlined in appendix B, would be protective of all its data

7 points, all but 10% of those data points in appendix B as

8 classified. 

9 UNKNOWN SPEAKER: I think the as classified

10 part means at the 50th percentile.

11 DR. FRANK FARRAGGIA: No. To clarify, the

12 50th percentile is the estimation of the 50% probability in

13 effect/no effect based on the COSM studies. But if we look at

14 the COSM studies individually and you look at these data points

15 and normalize with 60 day averaging period, its effect/no

16 effect, the whole part per billion is protective of 90 percent

17 of effects in humans. 

18 UNKNOWN SPEAKER: And that database was not

19 adjusted for nonnative species like either the laboratory data

20 (inaudible) because the invasive species that were on the list,

21 those were not weight at versus native species.

22 DR. RUSSEL ERICKSON: Yes, for the single

23 species toxicity test, no we did not pay attention to whether

24 they were (inaudible) or not. That is simply because in the

25 field, look at the materials provided. All we wanted out of

26 that toxicity data was a distribution of toxic sensitivities. 
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1 We do not have any reason to think that native versus nonnative

2 differ in terms of their toxic sensitivities. 

3 So again, they only contribute to the effects

4 index in terms of talking about, well it was 100 micrograms per

5 liter per 1 day worse or better than 50 microgram per liter for

6 2 days. That is where the single species toxicity test stops. 

7 There was no distinction between resident and native there

8 because there was no expectation that one or the other would be

9 more sensitive. 

10 Plus, if you look at a sensitivity analysis,

11 the end result is very insensitive to changes in the toxicity

12 data. That is if we just look at all greens, or if we did

13 different weightings of the different taxonomical groups, it did

14 not change the end result much at all because the entire process

15 is just asking, well if you go from this exposure to this

16 exposure, well maybe even if you have a sensitive organism, it

17 is still twice as bad for the sensitive organism even though

18 they are affected worse as it is for the tolerant organism. 

19 In the statistical sense, there is a very low

20 sensitivity of the entire process to the selection of the

21 toxicity data. So, that is the single species toxicity side of

22 it. 

23 Now, if I could just comment on the COSM side,

24 which is really the communities that are determining the level

25 of protection. Those are natives.

26 DR. FRANK FARRUGGIA: There may be cases where
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1 they were included in this system and the effects calls were

2 based on the totality of that system. 

3 If we had a COSM study with just 2 species and

4 (inaudible) we would expect very low concentrations of cause and

5 effects because they are pretty sensitive species. But, we

6 would also use that as a native community to North America. 

7 What is important is there are 2 COSM

8 experiments done in Europe. Based on European waterways, which

9 have similar systems that we have, they have different kinds of

10 pressures on them, and the evidence for the COSMs are in the

11 effects/no effects data is supposed to be representing the

12 totality of the datasets, which also include non-natives.

13 DR. JAMES MCMANAMAN: Based on what you

14 explained, it seems to me that that would tend to be weighted

15 towards the less sensitive species and that the more sensitive

16 species would look like they would be not quite normalizing

17 that. You are shifting that curve so that the first red line up

18 there where the arrow is coming from was completely affected at

19 one concentration using the original approach and is only

20 partially affected at the same concentration using the PATI

21 approach. 

22 How do you reconcile that with the notion of

23 maintaining no change in an aquatic assemblage, because

24 presumably that concentration, which the less sensitive species

25 is going to be not very affected, but the more sensitive species

26 will be affected? I am just a little confused about that. 
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1 DR. FRANK FARRUGGIA: I guess I am a little

2 confused about the question. Could you restate? 

3 DR. JAMES MCMANAMAN: The normalizing it, it

4 looks to me like it is shifting. You are tending to weigh it

5 toward the less sensitive species with this new PATI approach

6 weighting that the concentration is acceptable to the less

7 sensitive species. 

8 DR. FRANK FARRUGGIA: I guess I would have to

9 disagree with that final statement or that general statement. 

10 First of all, the PATI goes over all atrazine concentrations. 

11 That bottom curve means that what we are saying is that relative

12 to the totality of species, we have a low effect at low

13 concentrations and a high effect at high concentrations. 

14 So, PATI extends over all these

15 concentrations. Again, PATI only is used to make the assessment

16 of comparing the relative severity of toxicity at different

17 exposures, different exposure times, and different exposure

18 intensities. So, it is a more relative index than just saying

19 how severe that exposure is and does not make any value

20 judgement per se about relative sensitivities. 

21 It just takes the overall breath of

22 sensitivity and saying well at 20 micrograms per liter, its

23 value is low there because we are only partially affecting the

24 sensitive species. At 50, it gets higher because we are

25 completely affecting the most sensitive species, but not much

26 the tolerant species. At 500, it is high because we are
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1 affecting everything. That is all it is trying to do. I guess

2 I am not understanding you.

3 DR. JAMES MCMANAMAN: Well I got that part,

4 but what I do not understand is how do you use that as a gauge

5 to set the tolerance levels at which there is going to be no

6 effect? Because if you use this PATI approach and set it at one

7 level, let's say you set it at 100 parts per billion. Well,

8 there are 4 or 5 species which have been completely wiped out,

9 but overall, the assemblage is only partially affected. 

10

11 DR. FRANK FARRAGGIA: What the structure of

12 this methodology is that again, PATI only says what is the

13 relative severity of different concentrations? If I added it up

14 over days, what is the relative severity of the total exposure? 

15 We use the COSM data to make that value judgement about what is

16 bad or not, because the COSM data are independently assigned

17 effects determinations. We say that while this index value for

18 this COSM is at 100 or 200 or whatever it is, and it is the COSM

19 data that makes the value judgement of how high this index has

20 to be. It does not matter how much we look at this graph and

21 say, well you are affecting this number of species or you are

22 affecting this number of species. What matters is it enough of

23 an exposure to elicit the effects in the COSM dataset. 

24 I guess, I should have brought the slide

25 along. We could have used it as a supplemental thing. I had in

26 previous documents. Let's assume that we have a simple
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1 situation where there is only 1 COSM study that everybody around

2 the table, everyone in this room agrees is the definitive test

3 to say what should be regulated. Say that that COSM data was a

4 30 day exposure of 50 micrograms per liter and at that effect

5 level, we had an adverse effect, enough to say we want to

6 regulate at that level. That only defines doing it for a 30 day

7 exposure. What happens if we had a 15 day exposure, half the

8 exposure and twice the concentration? We would have to run that

9 test. We want to say, well is that a level of concern. 

10 All PATI is trying to do is to say that given

11 this toxicity data as a totality across the exposure, are we

12 going to say that 15 days of 100 micrograms per liter is as bad

13 as 30 days at 50 micrograms per liter, twice the exposure in

14 half the time. It is bad or worse? All we are doing is saying

15 that is that can be used as relative index to make that kind of

16 evaluation. 

17 Yes, what will happen with that shorter

18 exposure is that we are more intensely affecting the sensitive

19 species, we are more intensely affecting all the species. At 50

20 micrograms per liter, like right here, and we shift it up to 100

21 micrograms per liter, yes we are affecting more of the species

22 more intensely, but for a shorter period of time. Yes, I accept

23 that there is a qualitative difference between the species that

24 is being affected by the index in that shorter period of time. 

25 But does it provide us a metric to make those comparisons in a

26 reasonable manner? Looking at the plant community as a whole, I
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1 am certainly not going to say that this is a perfect

2 representation of all the myriad of effects that would differ

3 between those 2, but we do not know about myriad effects that

4 would be affecting those.

5 Is it a reasonable basis for making that

6 comparison? I guess it depends, if I understand your question

7 better now, I think that in some ways there is a qualitative

8 difference between the species affected at 100 versus 20 here. 

9 I could accept that, but again, looking at the community as a

10 whole, is it a reasonable way of comparing these 2 exposures as

11 far as the totality of the assemblage. What we have done is to

12 go on sensitivity analyses and basically say, well what would

13 happen if our assemblage was markedly more sensitive, not

14 extremely more sensitive or markedly more tolerant. How does

15 that affect the extrapolation you convey? Again, we are just

16 trying to say what is the relative severity of these 2 different

17 exposures, and is the index informative of that. Again, I am

18 not sure I am getting into your question.

19 DR. DANIEL SCHLENK: Maybe you guys can

20 discuss during lunch or something and come back to it. Ken has

21 promised me he has a very short question, and I believe Dr.

22 Boone has a request for a figure.

23 DR. KENNETH PORTIER: The sensitivity analysis

24 you reported on 93 and 94 really relate to these 5 COSMs that

25 were originally classified as low effect, and then in your

26 reassessment reclassified to effect right? And that drives that
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1 range of 4 to 7, moving down from 7.3 to 4. In reading the

2 White paper, I was trying to understand why, either we made a

3 mistake with the Brock Scores before or what did we change that

4 caused those 5 critical sites to change. Kind of be global. I

5 know you said some other things, but I did not see the big

6 picture on that.

7 DR. FRANK FARRAGGIA: One thing the White

8 paper does and does it poorly, is it describes these things and

9 does it incorrectly. I made a mistake when I was writing that

10 portion. I called them all a Brock Score of 2. That was a

11 mistake. When I went back in, I started looking at 2009

12 datasets that were presented. They were actually set up as

13 those 3 on the left there, they were the Brock Scores of 2. So,

14 DeNoyelles endpoint #52 and endpoint #58 were classified as

15 effects in 2009. So, they did not really change. Are current

16 classification is effect, unless we switch that over. Because

17 they were 2s, they were incorporated into the sensitivity

18 analysis, but it also shows kind of like the uncertainty around

19 some of those. You will hear about DeNoyelles endpoint and the

20 Lampert endpoint likely from Registrants later today. We will

21 be ready to discuss any follow-up questions you have on those. 

22 With endpoints #51, #59 and #60, the reason

23 why we are calling those effects today, to get to the 4 part per

24 billion CE-LOC, are listed here, a 25% reduction in

25 phytoplankton oxygen production for the Brockway study. 

26 Endpoint #59 is 35% decrease in dissolved oxygen with slight
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1 reduction in magnesium and calcium levels. Lastly, endpoint #60

2 is a 35% decrease in dissolved oxygen, 60% reduction in

3 chlorophyl A, a slight reduction to those nutrients. 

4 What that shows when we went back and reviewed

5 those studies, we see this and we say, well those are actually

6 pretty significant changes in the community. They are effects

7 and that is why we have this range because of that complexity. 

8 The history of this chemical is fascinating and very complex. 

9 DR. DANIEL SCHLENK: Dr. Boone, did you want

10 to make your request?

11 DR. MICHELLE BOONE: I was just wondering for

12 the end data if we could see a figure of the effects/no effects

13 data like with the plant data across the invalid or the data if

14 that is even possible. It might be too much to ask on this

15 short of a time scale, but it would be nice to see. 

16 DR. AMY BLANKINSHIP: Right, I think we can

17 work with that with the graphs that we have. That is why the

18 graphs were up there for the different endpoint even though they

19 were distribution. I sort of meant to somewhat illustrate that.

20 We're looking more on a time series sequence, and part of that

21 problem we had before was because not all of the studies had a

22 chronological timeline which to set each endpoint to. 

23 DR. DANIEL SCHLENK: Actually, if we could, we

24 are really late right now. In fact, I think we have to rush our

25 lunch to about 1:30. If we can get back at 1:30, then maybe we

26 can reconvene and have any other questions that you have at that
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1 time.

2 DR. DANIEL SCHLENK: Welcome back. If we

3 could have the left side of the panel, we will get those guys

4 back here I hope pretty soon. We do have a forum. I know there

5 are a bunch of questions still remaining. Let's go ahead. I

6 know Dr. Klaine has a couple of questions. Why don't you go

7 ahead and get those going and get those into the record.

8 DR. STEPHAN KLAINE: It was a great

9 presentation, Russ and Frank. I appreciated it. I think I know

10 how you got down to the number that you got to. I was just

11 trying to look for some sense of realism in terms of how that

12 number fits into the toxicology data that I look at. I was

13 looking at, can you go back to 74? So we are down to 4 to 7

14 micrograms per liter and that is not even on that graph. So, I

15 am thinking how does that fit in and it much be duration, maybe,

16 of exposure. So is that true before I go on? 

17 DR. FRANK FARRAGGIA: One thing to really

18 identify with this figure and to identify with PATI is that it

19 represents single-species toxicities. The endpoint 4 comes from

20 the concentrations in the COSM studies. It is the estimation of

21 the 50th percentile effect across all those COSM studies. That

22 is where that 4 is coming from. It is from the current

23 classification of all those studies. Near the end of the

24 presentation, we have 5 endpoints that were classified as no

25 effect, that takes us up to the 7. So in appendix D, those

26 endpoints are classified as effect and we see the 4. 
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1 DR. RUSSEL ERICKSON: I think even more to the

2 point is that we are talking about 4 as an average over 60 days.

3 In practice, the exposures are not that. If you go back a

4 couple of slides, that lowest one has over that 60 days is at

5 about the LOC for that. It is not quite as low as 4. It is for

6 an LOC situation around 7, but you have a lot of spikes well up

7 into that toxicity curve. What you are looking at is sort of

8 the integrated cumulative effect of shorter periods of higher

9 concentration. There is no field exposure out there that is

10 constant at 4 micrograms per liter. So, I mean that is another

11 estimate. 

12 DR. FRANK FARRAGGIA: Russ pointed out the

13 duration. The CE-LOC sensitivity to duration if we look at

14 that, we look at the CE-LOC for a 7-day, Russel correct me if I

15 am wrong, but the toxicity studies incorporated in the PATI

16 would be a 5 to 7 day exposure, correct? Okay, it varies. 

17 However, there are going to be a lot shorter than a 60 day

18 assessment period. If you look at an assessment period and

19 CE-LOC, you see the CE-LOC actually goes up. If you look at

20 that distribution of the species distribution and responses in

21 Russel's portion, slide #74 again, you think about that 18 in

22 that perspective, then you are talking about a reasonable

23 CE-LOC. 

24 If you look at the single species toxicity

25 studies that are in the tables within the single species

26 toxicity study section of the White paper, you will see a
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1 variety of endpoints talking about just the most sensitive

2 species within each broad category of taxonomy classification. 

3 In that, you can see that many species are below the CE-LOC for

4 a 5 day period. 

5 What the CE-LOC is meant to protect is the

6 community and the effects that seen in the community are based

7 on the totality of the systems and the responses to the systems

8 to the atrazine exposures. I hope I answered both of those

9 questions.

10 DR. RUSSEL ERICKSON: If I may add one more

11 thing. Slide 74 has a specific growth rate. It is a very short

12 duration effect. If you look at the actual tests here, you

13 might have an EC50 at 20 for specific growth rate if the test

14 extended over 4 o 7 days, EC50 for cumulative growth would be

15 down below 10. So, this graph tends to minimize the sensitivity

16 because we are expressing in terms of a very short-term effect. 

17

18 DR. STEPHEN KLAINE: I can understand

19 different endpoints. I can understand different durations. I

20 think you are trying to move with that. That discussion

21 includes the COSM data? Is that correct? You make a comment

22 about sort of desensitization by using just growth rate and the

23 different time, duration of exposure, is not taking that into

24 consideration. So you are basically just fine moving to the

25 COSM data to compute your numbers.

26 DR. RUSSEL ERICKSON: Yes, I mean we actually
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1 start with the COSM data. The decision was that the

2 single-species toxicity data is not something we want to just

3 take a bunch of single-species toxicity data and say this bad. 

4 What we wanted to do was to start with the COSM data as the

5 foundation for this whole thing, and really would not have to

6 consider the single-species toxicity data if we have COSM data

7 that we extrapolated in the field. But we felt that the

8 extrapolation that we needed to do of this erratic chemograph

9 required factoring in something that was again using the

10 single-species toxicity data to make just that extrapolation. 

11 DR. STEPHEN KLAINE: Okay, so then if we move

12 to 76, a couple more slides, you should see the COSM data. 

13 Again, I go back and say well, 4 to 7 micrograms per liter. I

14 look at 60 days, and I look to the left of 60 days and I see

15 very few studies, 10 or less, that have effects, and a whole lot

16 of studies above that that have no effects. 

17 Again, I do not think I have any real problems

18 with how you generated your number, I am just looking for a

19 sense of realism and how that number is actually a real number

20 that we can take to the bank. Because I think somewhere in this

21 document, it could actually say if we are above that, we are

22 standing a good chance of causing problems. 

23 DR. RUSSEL ERICKSON: The one factor to

24 consider in terms of this slide is that almost all those studies

25 had declining concentrations. So, a 60 day study, this is

26 average. I was thinking it was initial. I guess the one factor
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1 there is that I think it is being partly driven by the earlier

2 data that has the 20 to 30 day range as far as the summation of

3 it. Those have lower PATI values. 

4 DR. STEVEN KLAINE: I am not sure I

5 understand.

6 DR. FRANK FARRAGGIA: I ran an analysis to see

7 how the temporal situation on these endpoints was kind of

8 carried out. As before, I mentioned that I had mobilized to a

9 60 day period. They called those endpoints and mobilized them

10 to 60 days rather than having them staggered across time. When

11 you look at that CE-LOC of 4 and you look at the number, the

12 proportion of endpoints that are above 4, roughly 90% of the

13 endpoint effects and endpoints that are above 4, meaning that we

14 are being protected of 90% of the endpoint, 90% of those

15 community effects that in those COSMs based on our

16 classifications. There are all kinds of problems with

17 normalizing to 60 days, but it kind of paints a picture.

18 Also, if you draw a line across this at 4, you

19 can see that roughly 90% of the endpoints are above that. You

20 have a number of other endpoints that are below it in the

21 sensitive COSM studies. This method allows for that kind of

22 thing to happen and would be consistent with other offices

23 within.

24 DR. STEVEN BRADBURY: Russ and Frank, I have

25 to add, I think the COSM studies are the foundation. So, we

26 start with the COSM studies. What kind of empirical data do we
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1 have that gives us some insights as to how atrazine could

2 influence the ecosystems that we want to protect. We have the

3 problem with formulation. We could use the single-species data

4 that we have for animals and plants. We could also, in this

5 case, have some information from physical experimental models

6 and take a look at the interaction of different systems and

7 ecosystems. Their manifestations literally can give us some

8 insights into the real world. 

9 So, we think that that approach going back to

10 2003 is that this is a useful body of information. So far, we

11 have not had any indication to pitch it and try to figure out

12 how to use it in the most appropriate way. 

13 But that is how we are trying to figure out

14 how much atrazine and what periods of time may cause different

15 levels of change in these physical models to try to realize

16 interaction. Really, what Frank is getting at is that depending

17 on how you interpret some of these studies, it does not make a

18 big difference, but there is some wobble in the ultimate end

19 game in how you interpret some of the mesocosms studies. I

20 think that gets out 4 to 7 a bit. 

21 And then other part of one of the slides, it

22 showed what if you picked the 50th, or what if you used the 25

23 or used the 75, that gives you a sense of what these

24 concentrations would look like and how many of the COSM studies

25 could be above or below that. We are not coming at them trying

26 to think of value that resulted in no effects on any of the COSM
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1 studies. As Frank says, you realize there is some wobble in

2 there and get feedback on how to interpret the COSM studies, how

3 to think about the biological significance of interpreting the

4 COSM studies. 

5 The PATI score is just a way to figure out

6 part of the other problem. In the real world, there is not

7 steady state concentration of atrazine.

8 Most of the pesticides we deal with, none of

9 them are steady state. What has happened here is a more

10 explicit realization that in the real world, there are time

11 variable exposures and how to tackle that. In this case, we

12 have a lot of good information to help us try to figure out how

13 to tackle it. The PATI score is a relative way to try to sort

14 that out. We have no absolute mean at all other than a

15 definitive basis for logical thinking. 

16 The PATI does not drive LOC. The mesocosms

17 and microcosms drive the LOC. PATI is just a way to figure out

18 how to look at this exposure scenario and compare it to that

19 exposure scenario and try to normalize it in terms of whether or

20 not if we saw an effect here, we would expect to see an effect

21 there and realize they have different time sequences and

22 exposure. PATI is not the absolute. The absolute if there is

23 an absolute, is a physical model. That is the foundation.

24 DR. RUSSEL ERICKSON: Dr. Klaine said he did

25 not understand my answer. I probably should elaborate. What I

26 was trying to point out was that look at all the COSMs in the 14
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1 to 30 day range. If you would just look at the data around 14,

2 20 to 30 days, you would say maybe the 14 day average would be

3 10. What would the 60 day average then be? Ten divided by 6. 

4 What is happening here is that all these data

5 are being looked at and it happens that even though right at 60

6 days, you are right that all of the effects are over 10 and not

7 down at 4 when you factor in all the other data looking at sort

8 of an accumulative exposure for each of them. It comes out to

9 that value in the 4 to 5 range. Like I said, if you just looked

10 at the average, shall we just take a 10 parts per million

11 average for 10 days. Then if there were no other exposures, the

12 60 day average would be down around 2. 

13 So, it is looking at the totality of the data

14 in the context that ends up as that number 4. Again, like I

15 said before, in reality, almost all the exposures are much

16 higher for a shorter period. We are not looking at truly a

17 4-day or 60-day exposure. 

18 DR. STEPHEN KLAINE: My comments stem from

19 everything left of 60. So, I really was including all that in

20 my comment. But I think I understand where you are going. 

21 Frank, can I make a request to have the data that you say you

22 did that extra analysis? I would like to see that because I

23 think it might help explain some things better than these do. 

24 DR. FRANK FARRUGGLIA: I think you are

25 referring to the CE-LOC calculation and normalizing to the 60

26 day? 
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1 DR. STEPHEN KLAINE: Yes, looking at all the

2 endpoints you made a comment about. 

3 DR. FRANK FARRUGGIA: Yes I can do that.

4 DR. DANIEL SCHLENK: Okay, we really need to

5 wrap this up really quick, or else we are going to be here until

6 8 o'clock tonight. If you can, I would suggest if you can

7 contact the person you question maybe during the break. When

8 you present your comments, you can say, I talked to so-and-so

9 and we discussed this so we get past things so we can actually

10 get going. This is getting a little long. 

11 If you are going to exchange any written

12 material, Frank if you and Russ are sending material, send it

13 through Sharlene please, not directly to the panel member. So,

14 let's go ahead and move on to our last agency presentation, Dr.

15 Hetrick? 

16 DR. JAMES HETRICK: Good afternoon. The title

17 of this presentation is Method for Comparing Monitoring Data to

18 the Aquatic Plant Community CE-LOC for Atrazine. Before

19 starting the presentation, I want to thank my colleagues for

20 their assistance in review and technical support of this work. 

21 My name is James Hetrick. I am a chemist and senior science

22 advisor in the Environmental Fate Effects Division.

23 Atrazine is a unique pesticide because there

24 is an extensive amount of surface water monitoring and data as

25 detailed in the White paper. They are monitoring data from

26 registrants, states and federal agencies. To the extent
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1 possible, EPA would like to use these monitoring data to predict

2 levels in the environment. Everyone who recognizes that

3 monitoring data give information on what residues are present at

4 a particular location and at a particular time, and that residue

5 levels vary, often significantly, from time to time and place to

6 place. For example, depending on whether or not monitoring is

7 targeted, the atrazine (inaudible) is where the atrazine use

8 season could have a significant effect on detections. The

9 monitoring database includes results showing atrazine detection

10 ranging from 0.28% in California to 94% in this agenda Atrazine

11 Ecological Exposure Monitoring Program, also known as AEEMP. 

12 In view of the temporal and geographic

13 variability in residue levels, there is always some uncertainty

14 in predicting levels at other locations and other times when

15 extrapolating for measured levels. Quantifying the uncertainty

16 and atrazine concentrations for monitoring data is an important

17 component in assessing atrazine exposure. 

18 In the absence of monitoring data for specific

19 watersheds, environmental fate and transport will allow

20 identification of watershed vulnerability factors that have been

21 used to assess atrazine exposure for ecological risk

22 assessments. These approaches were presented to the FIFRA SAP

23 in 2009. 

24 When monitoring data are available however,

25 the characterization of atrazine exposure requires consideration

26 of sample frequency, monitoring location and sample time. In
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1 the 2010 and 2011 FIFRA SAP, the EPA presented methods to

2 account for uncertainty in estimating atrazine concentrations

3 from drinking monitoring data. These methods included bias

4 factors, kriging, conditional stochastic simulation and a

5 mixture of linking environmental fate and transport modeling

6 with monitoring data. 

7 The SAP recommended that bias factors were a

8 viable approach for addressing uncertainty in monitoring data. 

9 A bias factor is a multiplicative factor for adjusting

10 monitoring data to ensure the measured value is equal or greater

11 than the true atrazine concentrations. 

12 The objectives of this presentation are to

13 develop a bias factor method to address uncertainty in

14 estimating atrazine concentrations from various monitoring data,

15 and to illustrate a screening level process using a bias factor

16 method for assessing monitoring sites with potential atrazine

17 concentrations exceeding the CE-LOC. In this presentation, we

18 will provide bias factors from selected monitoring sites with a

19 maximum 60 day rolling mean concentration, different methods for

20 estimating bias factors for monitoring data, a performance test

21 of various bias factor estimation methods, and an illustration

22 of application of bias factors in screening monitoring data from

23 various sources. 

24 The problem formulation requires consideration

25 of available data for assessing toxicity and exposure. For

26 exposure assessments, these data include environmental fate
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1 data, environmental fate and transport modeling, and monitoring

2 data. In atrazine exposure assessment, there has been

3 considerable advancement of methods for identification of

4 vulnerable watersheds for atrazine exposure, as well as

5 consideration of watershed properties for predicting atrazine

6 exposure. For example, the work model.

7 Given the extensive monitoring data for

8 atrazine exposure also could be evaluated from the available

9 monitoring data. However, as detailed in the 2010 and 2011

10 FIFRA SAP, there is uncertainty in estimation of atrazine

11 concentration for monitoring data. This uncertainty can be

12 associated with sample frequency, sample location and sample

13 time. 

14 A screening level process using bias factors

15 is being considered in problem formulation as a mechanism for

16 screening out monitoring sites with no potential for atrazine

17 concentrations exceeding the CE-LOC. Additionally, it could be

18 used by state orders of atrazine monitoring data for assessing

19 monitoring sites for potential atrazine concentrations exceeding

20 the CE-LOC. In the context of the risk assessment framework,

21 the screening level process would be applied and the analysis

22 phase would be risk assessment process as detailed in the yellow

23 section on the diagram.

24 A comparison of the bias factor adjusted

25 atrazine concentration from the monitor view, would be compared

26 to the CE-LOC and the risk characterization phase of the risk
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1 assessment process as detailed in the green section on the

2 diagram. 

3 At this point, the monitoring sites for

4 potential atrazine concentrations exceeding the CE-LOC may

5 require additional session, which is indicated by the arrow

6 moving into the brown section of the risk assessment process. I

7 will just highlight that right here. This section is a

8 reevaluation of the data to ensure perfect refinements in the

9 risk assessment have been considered. In this case, it may

10 warrant an evaluation of the watershed properties or particular

11 monitoring site, watershed modeling such as use of the work

12 model or the need for additional monitoring data.

13 This process may require a series of iterate

14 actions with continuous monitoring of the results. 

15 The complex patterns of pesticides are

16 extremely complex because of the spatial and temporal dependent

17 processes and properties. Spatial dependent properties and

18 processes impacting pesticide occurrence, to name a few include

19 meteorology such as rainfall patterns, watershed properties such

20 as soil, hydrology cropping patterns and sampling location. 

21 Some of the temporal dependent properties and

22 processes impacting pesticide occurrence include seasonable

23 differences in rainfall, pesticide application timing and sample

24 frequency. 

25 The complexity of atrazine occurrence is

26 illustrated in this slide. The 2 chemographs illustrate
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1 atrazine occurrence in a large watershed such as the Maumee

2 River in 1995 from the Heidelberg University data as illustrated

3 in the chemograph on the left side, and a smaller watershed such

4 as the Missouri-01 in 2007 from the Syngenta Atrazine Exposure

5 Monitoring Program as illustrated on the right side. The

6 chemographs show different occurrence patterns and magnitude of

7 atrazine concentrations. 

8 The real challenge is developing a bias factor

9 method to accurately account for the temporal and spacial

10 variability of these atrazine chemographs.

11 This is a schematic of the bias factor

12 estimation process. This process is a boot-strapping

13 simulation, which is identical to the bias factor estimation

14 process presented to the 2011 FIFRA SAP. Excell Crystal Ball

15 was used for the simulations. Let me walk through this slide to

16 detail how we did the bias factor estimation. 

17 First, we started with the monitoring data up

18 in the left hand corner. Then what we do is the second step is

19 a daily chemograph as constructed using spare step info in

20 between the measured values, then sampling windows of 4, 7 and

21 28 days are overlaid on the daily chemograph. Each sampling

22 window on the chemograph has a third step down here. Each

23 sampling window on the chemograph is sub-sampled 10,000 times to

24 create essentially 10,000 chemographs. Each simulated

25 chemograph was created by linear interpretation between the

26 sample values. The fourth step down here is a distribution of
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1 maximum 60-day rolling mean was created from the 10,000

2 chemographs. The fifth percentile of this distribution was used

3 a reference concentration for estimation of the bias factor. 

4 The true 60-day rolling mean as determined in the original

5 chemograph is divided by the maximum fifth percentile 60-day

6 rolling mean from the boot strapping process to generate the

7 bias factor. 

8 Monitoring data from the bias factor analysis

9 were selected from the Atrazine Ecological Exposure Monitoring

10 Program were referred to previous presentations as AEEMP and the

11 Heidelberg University National Center for Water Quality

12 Research, commonly referred to NCWQR. The Atrazine Ecological

13 Exposure Monitoring Program was established to provide atrazine

14 occurrence data for use in ecological risk assessments. In

15 contrast, the Heidelberg University monitoring data was

16 established to monitor the water quality in major rivers in

17 Ohio. 

18 Both monitoring programs have high sample

19 frequencies over multiple years. A major difference in the

20 monitoring programs is the watershed areas. The Atrazine

21 Ecological Exposure Monitoring Program monitoring data

22 represents smaller watersheds when compared to the watersheds in

23 the Heidelberg University datasets. These data were selected

24 because of their high sampling frequency and length of sampling

25 period. Additionally, the data provide a range of watershed

26 properties. 
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1 A preliminary analysis of bias factors of

2 generations conducted on selected monitoring sites with 4 day

3 sampling intervals, shorter monitoring periods generally from

4 April to August and different sampling methods combined, such as

5 grab sampling and auto sampling. This data analysis deemed as

6 part preliminary because data infilling issues, such as between

7 the 4-day sampling intervals, their short monitoring periods

8 such as April to August and there is a mix of sampling

9 techniques. 

10 In order to reduce additional uncertainty from

11 issues identified in the preliminary dataset, another set of

12 monitoring data was selected with more extensive auto sampling

13 and longer sampling periods. These data are considered to

14 provide a more reliable representation of day-to-day variation

15 in atrazine concentrations. Therefore, these data are

16 considered as a definitive data for estimating bias factors in

17 its analysis. 

18 These were the monitoring datasets that were

19 used in the preliminary data analysis. The monitoring data

20 represent different midwestern watershed from Iowa, Missouri,

21 Tennessee, Nebraska and Ohio in different years from 1995 to

22 2008. The monitoring sites represent a wide range of watershed

23 area and average flow conditions. Additionally as expected, the

24 atrazine occurrence is different among the monitoring site

25 years. 

26 In contrast, definitive monitoring data are
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1 limited by their geographic distribution with watersheds in

2 Missouri, Indiana and Ohio, in limited temporal representation

3 in 1995 and then from 2007 to 2009. Again, the longer in sites

4 represent a range of watershed areas and average flood

5 conditions. Additionally as expected, the atrazine occurrence

6 is different among the various monitoring site years. 

7 This map shows the geographic distribution of

8 the monitoring sites for definitive monitoring data used in the

9 bias factor analysis. The definitive monitoring data is

10 highlighted in the red and are predominantly representative of

11 north east Missouri watersheds. Additionally, there is one

12 monitoring site in Indiana and Ohio. 

13 The monitoring data for the preliminary

14 analysis as highlighted in green are more geographically

15 dispersed in the midwestern United States. 

16 Some of the assumptions, limitations and

17 uncertainties associated with bias factor estimations are the

18 monitoring data ranged from April 1st to September 17th. This

19 range of monitoring data, we assume, to represent the second and

20 third quarters of the year, which would correspond Julian date

21 90 to 270. Using this assumption, the sample number for each

22 sample was estimated by dividing 180 days by the sampling

23 interval. Bias factors are expected to be dependent on sampling

24 frequency sampling strategy. For example, sampling according to

25 the hydrograph is expected to lower bias factors when compared

26 to systematic or random sampling shed. The extent of data
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1 infilling in the chemograph is expected to increase the

2 uncertainty in predicting bias factors. Their serial

3 correlation effects are expected to be dependent on sampling

4 interval. Longer sampling intervals are expected to exhibit

5 less effect.

6 Probably the most important point is that the

7 predication capabilities are really dependent upon the

8 underlying data. 

9 These are absolute values of bias factors

10 from the preliminary data. The bias factors generally express

11 negative values to represent the extent of underestimation. The

12 absolute value of the bias factor however, uses the

13 multiplicative factor. The bias factor estimates from the

14 preliminary data show that the bias factor increases with longer

15 sampling intervals as you would expect, and there is a also as

16 expected, variation in the bias factor estimates from site to

17 site. 

18 The definitive data showed a similar trend in

19 bias factors as the preliminary data as there is an increase in

20 the bias factor with longer sampling intervals. Again, there is

21 variation in the bias factor estimate from site to site as you

22 would expect. 

23 Forward stepwise regression analysis was

24 conducted to assess the importance of sample number, watershed

25 area and average flow in prediction of bias factors. Based on

26 the preliminary and definitive monitoring data, at least in this
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1 analysis, the analysis showed that sample number is a

2 significant explanatory variable in bias factors. In contrast,

3 watershed area and average flow were not significant explanatory

4 variables. From this point in the presentation, the bias factor

5 estimation method will consider only sample number.

6 This is just a graph of both preliminary and

7 definitive data the bias factors generated from those. It

8 showed relationship of bias factors and sample numbers. As

9 expected, the fewer samples in the second and third quarters

10 leads to larger bias factors. 

11 So, what we want to do is to try to capitalize

12 on that relationship and try to estimate bias factors as a

13 function sample numbers in the second and third quarters. One

14 of the estimation methods for bias factors for monitoring data

15 can be a simple categorical equation. The extended

16 quantification using this estimation method, depending on the

17 number of bias factors would be good for the various sampling

18 intervals. Additionally, interpolation of the bias factors in

19 unsampled or unpredicted areas is rather subjective. 

20 Another approach is to use regression equation

21 for estimation of bias factors from the monitoring data. In

22 this example, we fit the relationship of bias factors and sample

23 numbers using a nonlinear regression equation. In this case, it

24 is a 4 parameter exponential decay model. We use this equation

25 to fit all the data, 95% percent confidence interval data and

26 the mean data. 
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1 Another approach was to fit the relationship

2 of bias factor and sample number using a log linear regression

3 equation. From this analysis, we can see approximately 46% of

4 the variation of bias factors is explained by the sample number

5 alone. 

6 This is a comparison of predication

7 capabilities of the various methods for estimating bias factors,

8 categorical and the different regression equations. What we can

9 see is the various estimation methods predict a wide range of

10 bias factors for different sampling frequencies. Therefore, a

11 single method may not be suitable for predicting a high bias

12 factor across the board for different sampling frequencies. 

13 We wanted to test the performance of our

14 estimation methods on a dataset just to see what the impact is

15 when we place these according to regression equations over real

16 monitoring datasets. Using the AEEMP data, we used 190

17 chemographs for systematically sub-sampled at 4, 7, 10, 14, 20

18 and 28 day intervals. For each sampling interval, a daily

19 chemograph was constructed using stair-step interpolation and

20 continuous sample data. A maximum 60 day rolling mean was

21 estimated from the daily chemograph, estimating from daily

22 infill chemographs. These estimated 60 day rolling mean

23 concentrations were adjusted using predicted bias factors from

24 the various estimation methods and the appropriate sampling

25 number as determined by the sampling interval. Also, a maximum

26 60-day average concentration was estimated from the original
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1 chemograph that provided a true representation of the atrazine

2 concentration. 

3 So when we do this, what we see is without any

4 bias factor adjustment between 82% and 94% of the site-year

5 chemographs established from the data sampled at 4, 7, 10, 20

6 and 28 day intervals, under estimated the true atrazine

7 concentration. When we apply the bias factor from various

8 estimation methods however, the train is reversed with fewer

9 estimations from the bias factor adjusted data. 

10 The estimation method with the lowest

11 underestimation potential is the upper 95% confidence interval

12 of the log linear equation. We use this equation to evaluate

13 the bias factor estimation method upon various monitoring data. 

14

15 So the next question is how do you translate

16 bias factor into monitoring data? Well, first we start with the

17 monitoring data. Then what we do is we stair-step interpolation

18 to generate a daily chemograph. We also count the number of

19 samples that are taken in his chemograph. Remember, we are

20 really focusing on the sampling period in the second and third

21 quarters. That is from 90 to 270 Julian days. In this case, we

22 have 38 samples. Then we calculate the maximum 60-day rolling

23 mean. In this case, it is 3.14 micrograms per liter. Then we

24 input the number of samples, in this case 38 samples, into the

25 regression equation in the upper 95th percentile confidence,

26 log-linear regression equation to estimate the bias factors. 
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1 For 38 samples, we estimate a bias factor of 4.48. We take this

2 bias factor and multiply that by the maximum 60-day rolling mean

3 concentration to yield a bias factor adjusted concentration of

4 14.06. So, we take 4.48 times the 3.14 that we estimated on our

5 infill data chemograph, and we generate a bias factor estimated

6 concentration of 14.06 micrograms per liter. 

7 The bias factor adjusted concentration is

8 compared to the CE-LOC. Because the adjusted value is great

9 than the CE-LOC, there is maybe a CE-LOC exceedance for this

10 site here. A key point is that the unadjusted 60-day rolling

11 mean atrazine concentration would not exceed the CE-LOC. So,

12 the 3.14 when compared to the CE-LOC would not exceed 4. So,

13 the bias factor did make a difference in this case. 

14 The next question is how to implement a bias

15 factor to identify whether one of the sites will have potential

16 exceedance of the CE-LOC. We used the bias factor estimation

17 process described in the previous slides with these monitoring

18 data. The monitoring data used in his analysis represent data

19 from various sources, including state agencies, federal

20 agencies, universities and registrants. Some of the data were

21 targeted to atrazine use area, such as the Syngenta AEEMP data,

22 and some of the data are not targeted to atrazine use areas. 

23 The data represent different hydrologic

24 systems such as filling water and the more static water bodies,

25 such as reservoirs and lakes. The sample frequencies were

26 different for each monitoring program. 
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1 To assess the extent of CE-LOC exceedance

2 among the different monitoring sets, analysis was conducted on

3 non-adjusted atrazine concentration and bias factor adjusted

4 concentrations. For the non-adjusted atrazine concentrations,

5 there were 6917 site years representing monitoring data from

6 2006 to 2011. All site years with 4 or more samples in a year

7 were completed in this analysis. The reason we used 4 samples a

8 year because this is the minimum sampling frequency required to

9 support MCL Compliance for the Safe Drinking Water Act. 

10 For the monitoring data site, site years with

11 atrazine concentration exceeding CE-LOC of 4 parts per billion,

12 there were approximately 6% site years with 378 site years in

13 2006 to 2011 monitored. It is important to note that percentage

14 of site years does not represent and average weighted value of

15 land area. It is a simple percentage of site years considered

16 in this analysis. If the potential exceedance has occurred in

17 13 different states, and these states are known to have sorghum,

18 corn or sugar cane production, more importantly the analysis

19 indicates a 94% of the site years or 6502 site years do not have

20 atrazine concentrations exceeding the CE-LOC of 4. 

21 A similar trend can be observed for the

22 potential site years exceeding CE-LOC of 7. For assessing the

23 impact of bias factors, there were 2283 site years in this

24 analysis. All the site years with 6 or more samples in the

25 second and third quarter were included in this analysis. 

26 Predicted bias factors ranged from 3.36 to 7.94. There were
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1 approximately 62% of site years, or 1424 site years from bias

2 factor adjusted monitoring data for potential atrazine

3 concentrations exceeding the CE-LOC of 4 parts per billion. 

4 Again, it is important to note the percentage of site years does

5 not represent an area weighted value of land area. It is simply

6 a percentage of site years considered in his analysis. These

7 potential exceedances occurred in 22 states. Most of these

8 states are known for corn production, sugar cane production

9 areas, but it also includes some states where no atrazine

10 watershed vulnerability issues have been identified or atrazine

11 use intensity is low, such as in California. 

12 This analysis also indicates that 38% of the

13 site years, or 868 site years did not have atrazine

14 concentration exceeding the CE-LOC of 4. A similar trend was

15 observed for the southern parts per billion CE-LOC. 

16 Additional analysis was conducted to assess

17 the CE-LOC exceedance percentages from 2006 to 2011 monitoring

18 data with 6 or more samples in the second and third quarters of

19 the year, which corresponds to Julian day 90 to 270. This

20 analysis was conducted to evaluate the impact of sample

21 frequency and does not account for the differences in atrazine

22 use intensity in the various portions of the monitoring sites. 

23 The results show the percentage of monitoring

24 site years with potential atrazine concentrations exceeding the

25 CE-LOC of 4 parts per billion from bias factor adjustment was

26 greater when there were less than 25 samples taken in the second
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1 and third quarters. At higher sampling frequencies, the use of

2 bias factors did not substantially increase the percentage of

3 site years with potential atrazine concentration exceeding the

4 CE-LOC of 4 parts per billion. Therefore, the bias factor

5 approach appears to address the uncertainty associated with low

6 sample frequency monitoring data. 

7 An additional analysis was conducted to

8 surveil the geographic distribution of monitoring sites and

9 potential for atrazine concentration exceeding the CE-LOC of 4

10 parts per billion. The NAWQA data were used for this analysis

11 because it is a nationally distributed monitoring dataset with

12 geo reference monitoring sites. This is an illustration of

13 atrazine use in Tennessee as reported by Crop Reporting

14 Districts in shades of gray. NAWQA monitoring site years with

15 non-adjusted atrazine concentrations exceeding the CE-LOC is

16 represented by the blue circles, and NAWQA monitoring site years

17 with non-adjusted atrazine concentrations exceeding the CE-LOC 4

18 parts per billion as represented by the red circles. The size

19 of the circles relative to the number of years atrazine

20 concentration potential exceeds the CE-LOC. So the larger the

21 circle, the more years with the exceedance of the CE-LOC. 

22 Additionally, more and more watersheds for

23 atrazine runoff as identified in 2009 FIFRA SAP for a CE-LOC of

24 10 parts per billion are shown in bright yellow on the map just

25 as a point of reference. The watershed vulnerability readings

26 are expected to change with a CE-LOC of 4 or 7. 
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1 The geographical distribution of sites with

2 atrazine concentration potentially exceeding the CE-LOC of 4

3 parts per billion corresponds with high atrazine use intensity

4 areas and regions of the country where vulnerable watersheds

5 have been identified. These areas are associated with corn,

6 sorghum and sugar cane production. 

7 This is an illustration of atrazine use

8 intensity as reported by Crop Reporting Districts shown in

9 shades of gray bias factor adjusted monitoring data site years

10 with atrazine concentrations not exceeding the CE-LOC as

11 represented by blue circles and bias factor adjusted monitor

12 site years with atrazine concentrations exceeding the CE-LOC as

13 represented by red circles. Again, the size of the circle is

14 relative to the number of years atrazine concentration

15 potentially exceeds the CE-LOC. 

16 Additionally, the vulnerable watersheds for

17 atrazine runoff are shown in brown-yellow regions on the map. 

18 What we see in this case is that when we add the bias factor,

19 geographic distribution of sites of atrazine concentration

20 potentially exceeding the CE-LOC of 4 parts per billion

21 corresponded to high atrazine use intensity areas in regions of

22 the country with vulnerable watershed areas. However, there are

23 additional monitoring site years with atrazine concentrations

24 potentially exceeding a CE-LOC of 4 parts per billion were found

25 outside the major atrazine use intensity areas, and are not

26 associated with regions of the country with vulnerable
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1 watersheds. 

2 According to the proposed screening level

3 assessment, additional site-specific watershed analysis or

4 additional monitoring may be needed to evaluate the atrazine

5 exposure of these monitoring sites. 

6 Based on the data used in this analysis, we

7 concluded that the sample frequency is a significant explanatory

8 variable for bias factors, not unexpected. Watershed area and

9 average flow were not significant explanatory variables. Use of

10 the bias factor on monitoring data gives the potential for

11 underestimation of true atrazine concentration, and using NAWQA

12 data, monitoring data with potential atrazine concentrations

13 exceeded the CE-LOC are generally associated with regions of the

14 country with high atrazine use intensity and/or previously

15 identify vulnerable watersheds. Application of bias factor

16 showed some monitoring sites with potential atrazine

17 concentration exceeding the CE-LOC were outside the region that

18 we expected for CE-LOC exceedance with high use intensity or

19 regions previously identified vulnerable watersheds.

20 In conclusion, the bias factor method may be

21 an efficient screening process or tool to assess uncertainty in

22 estimating atrazine concentrations from monitoring data. More

23 importantly, it may serve as a useful screening tool for

24 identifying monitoring sites for higher resolution exposure

25 assessment. That concludes my presentation. 

26 DR. DANIEL SCHLENK: Thank you, Dr. Hetrick. 
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1 Any questions for clarification from the Panel? Dr. Portier? 

2 DR. KENNETH PORTIER: One of the associations

3 in methodology here is that when you are averaging over your 60

4 days and the samples you are getting are kinda of random, and

5 you listed a whole bunch of state and federal sampling programs,

6 do you know if all of those use something like a random sampling

7 plan or do they do more directed sampling? In other words,

8 sending people out after the largest rainfall in the last 2

9 weeks? 

10 DR. JAMES HETRICK: We did not do analysis of

11 that. My analysis of that data would suggest that most of this

12 is pretty systematic and in the sense it is on weekly, biweekly,

13 some type of intervals like that. There is not a lot, I do not

14 think any of the data that we have, maybe with the exception of

15 the Syngenta data, is really targeted around a hydrograph of the

16 watershed. But it is a good point. The sampling strategy

17 really dictates whether this is a successful method or not. 

18 DR. KENNETH PORTIER: Especially when you take

19 that average and multiply it by 7, that has an effect.

20 DR. JAMES HETRICK: Right, absolutely. 

21 DR. DANIEL SCHLENK: Any other questions? Dr.

22 Ellsworth? 

23 DR. TIMOTHY ELLSWORTH: I just have a

24 question. What watershed characteristics did you look at when

25 you were trying to establish if there was some relationship

26 between watershed characteristics and bias factors? 
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1 DR. JAMES HETRICK: The only thing we looked

2 at was watershed area and average flow. I know that we could

3 have done more in that area to look at that, and I think it is

4 probably is worthwhile doing that to be quite frank with you. 

5 One of the ideas was in my mind was to go back and if we would

6 have had more time, maybe to do some work analysis on each of

7 these watersheds and then compare, because there are a lot of

8 watershed properties from a work model that we could use to see

9 how they correlate, as well as a work predication that may have

10 some bearing on how that bias factor estimates.

11 DR. TIMOTHY ELLSWORTH: One more question, and

12 that is on your infilling approach, I would think that there

13 would be standard methods that you could look at to compare

14 different infilling methods and see how the mean square error

15 reduces given that you have these nice systematic datasets. Did

16 you look at trying to compare different infilling methods to

17 identify an optimal approach? 

18 DR. JAMES HETRICK: Well, in the 2011 SAP,

19 they actually went into a fairly in-depth analysis of various

20 methods for infilling, which including kriging techniques as you

21 are probably well aware of, and then we link that to conditional

22 simulation, stochastic simulation where we took the variogram

23 and actually put that into a stochastic simulation to generate a

24 time series, and have some probabilistic interpretation from

25 that. 

26 We also looked at the bias factor method, and
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1 we also looked a melding of trying to integrate modeling output

2 with monitoring data, such as the prism hybrid approach. 

3 The bias factor method, I think, was probably

4 the most universally approved method from the SAP Panel in 2011.

5 I know that the other methods are constrained by, well you have

6 to make some assumption of stationarity.

7 One of the things at least from my perspective

8 as a scientist, I mean we can come up with some really cool ways

9 of doing this, but we have to look at being able to apply this

10 on a large dataset and be able to do it in an efficient manner. 

11 This bias factor approach, if we could use these types of

12 regression equations to do that, I think it gives us a little

13 bit of insight as to being able to take these large data bases

14 of monitoring data and put some context around them. 

15 DR. TIMOTHY ELLSWORTH: Again, my comment was

16 just it would be nice to see that evaluation of comparing the

17 different methods and which ones are the best. I was concerned

18 definitely about the linear interpolation as the sampling day

19 goes up. There are some assumptions that did not seem to be

20 met. Anyway, we can talk about that later. 

21 DR. JAMES HETRICK: Yes, I do not disagree

22 with you that the data infilling process is definitely a point

23 of contention. 

24 DR. WILLIAM EFFLAND: Did you do any

25 calculations or looking at where you drive the bias factors and

26 then you looked at a watershed nearby that you also had
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1 monitoring data and then went back through and do like a

2 jack-knifing type process where you went through and saw if you

3 could predict the true concentrations in that adjacent or in

4 that nearby watershed, or a watershed of similar size or

5 sampling frequency or whatever? Do you see my question? 

6 DR. JAMES HETRICK: Yes I do, and no we did

7 not. I think the closest thing we did was we tested using a

8 systematic sampling scheme. We tested that on 190 AEEMP

9 monitoring data sites to see the extent of underestimation that

10 we have from the true atrazine concentration. I believe I

11 presented it, but I am not sure what slide that is. There is a

12 graph, it is in there. 

13 What was interesting to us at least is that

14 even at 40 sampling intervals, we are still underestimating or

15 82% of the site years are underestimating. when you do a 4, 7,

16 10, 20, 28 day sampling intervals, greater than 82% of the site

17 years with 60 day maximum rolling mean was underestimating from

18 the infill dataset from that sample data is underestimating the

19 true atrazine concentration that we had seen on the original

20 chemograph. So, by applying that bias factor, it tends to

21 normalize and takes into account that sampling frequency and

22 helps us to address that issue. 

23 DR. WILLIAM EFFLAND: That helps. And then

24 back to the watershed size, watershed characteristics, I think

25 that is my mind, looking at use intensity within watersheds

26 seems like the kind of information that would be valuable to
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1 look at as far as correlations and then something to do. The

2 way that I understand your environmental fate and transport

3 assessment, it looks like you are focused on runoff as your

4 primary route of movement. So, if you could really think of

5 factors that are going to be important for generating runoff, I

6 would think that that would be useful for looking at those

7 correlations. 

8 DR. DANIEL SCHLENK: Dr. Rodgers and then Dr.

9 Ellsworth.

10 DR. JOHN RODGERS: Just a quick question. Is

11 there any 303(d) TMDL Clean Water Act limited atrazine

12 watershed, and if there is, is that part of your sampling

13 program? 

14 DR. JAMES HETRICK: There are watersheds that

15 follow TMDL. They are in Kansas and Nebraska, I believe, out in

16 the Midwest. I believe that they are captured in our datasets,

17 in the Kansas as well as Nebraska dataset. 

18 DR. TIMOTHY ELLSWORTH: The question I have is

19 you are really leaking out a temporal bias factor here, it looks

20 like at one location. My question to you is have you thought

21 about a spacial bias factor? I mean, these samples in the AEEMP

22 were at the bottom of the stream so that you could have flow

23 year round, or most of them were situated in the bottom of the

24 stream location. How did that vary with the cross section of

25 the concentration at that time other than vary, if you had a

26 sampling site 20 meters upstream from that site predicting what
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1 was happening 20 meters upstream. There is a spacial bias

2 factor that is somehow related to the watershed characteristics.

3 Did you look at or think about not just this temporal bias

4 factor, but kind of a spacial bias factor, I guess? 

5 DR. JAMES HETRICK: We did not purse the data

6 out spatially to look at monitoring sites and do the regions of

7 the country we looked at as collectively. Like I said, the 2

8 datasets that really give us the ability to do this bias factor

9 analysis of the AEEMP data and the Heidelberg dataset. So, they

10 are predominantly Midwestern datasets. That my limit the

11 application of the method. If we could purse out regions within

12 that Midwestern area, we might be able to see some spatial

13 context. 

14 One thing, I think a key point though is we

15 are not looking at this to quantify an exact number. We are

16 looking at this as a screening toll to just allow us to focus in

17 and say here is a site that looks like it has a potential

18 exceedance. Let's go back and we may have to do some work

19 analysis on it. In that one diagram with the risk assessment

20 framework where we go down and we compare the bias factor

21 adjusted concentration to the CE-LOC, if we have an exceedance,

22 really what it says is that no we do not say if that site is an

23 absolute guaranteed exceedance. We say that we need to go back

24 and look at that site more closely and look at the watershed

25 properties and see if there is additional monitoring data that

26 may be necessary. That is all we are saying. 
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1 DR. DANIEL SCHLENK: Dr. Bradbury had some

2 comments.

3 DR. STEVEN BRADBURY: Quick clarification on

4 the TMDL question. My understanding is that those situations in

5 Kansas are not based on aquatic life criteria. TMDL is based on

6 the Human Health MCL value. 

7 DR. DANIEL SCHLENK: Dr. Portier? 

8 DR. KENNETH PORTIER: I may have answered my

9 own question. I was trying to make the connection between the

10 last presentation and this one, and why you are concentrating on

11 the maximum over the 60-day averages, the running average. Why

12 do you tie it back to the maximum 60-day? Why not the average

13 over the 60 days or something like that? 

14 DR. JAMES HETRICK: It is the maximum 60-day

15 average rolling concentration.

16 DR. KENNETH PORTIER: The rolling

17 concentration, and you pick the one that has the maximum. So,

18 my question is in the development of the CE-LOC, is there

19 something equivalent that related everything back to that 60-day

20 rolling average? I did not remember in the previous discussion

21 why you did that.

22 DR. JAMES HETRICK: I will tell you what, I am

23 going to direct that to my colleagues. 

24 DR. FRANK FARRAGGIA: Yes, it is a 60-day

25 assessment that we are really looking at. When we are looking

26 at a chemograph that is 120 days, 130 day, 360 days, we did a
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1 maximum PATI value across a 60-day period of time. So that

2 comes back to what is the maximum 60-day running average for

3 that PATI value and kind of makes that connection. That is how

4 we translate the CE-LOC. We put it in an application out in the

5 field. With Dr. Hetrick's talk here, this method kind of allows

6 us to look at the scope of potential exceedance, so we can find

7 out and try to figure out some more details about watersheds,

8 about the rivers, lakes, reservoirs, and kind of nail down where

9 we are really going to have to start entering into regulatory

10 action. That is what Steve talked more about.

11 DR. DANIEL SCHLENK: Okay, any other questions

12 for clarification? So, I assume that you guys will be available

13 tomorrow before we read in the questions. So, if you guys have

14 anything over this evening, and again, it is okay if you go and

15 talk to them. Just make sure when you come back to the table

16 that you say I spoke with so-and-so and we had that discussion. 

17 So, you guys it will all be before we read the questions into

18 the record tomorrow. With that, we will conclude the

19 presentations by the Agency, and move onto our public comment

20 period. Dr. Kriger, please come forward.

21 DR. KERRY KRIGER: Hi, my name is Dr. Kerry

22 Kriger. I am the founder and executive director of Save the

23 Frogs, which is America's first and only public charity

24 dedicated to protecting amphibians. Our mission is to protect

25 amphibian populations and to promote a society that respects and

26 appreciates nature and wild life. Some of my background I have
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1 a Ph.D. in environmental science. I study disease in amphibians

2 and amphibian ecology in Australia for 4 years. I published

3 over 15 papers on amphibian conservation and peer reviewed

4 internation scientific journals. I conceived and coordinate

5 Save the Frogs Day, which is the world's largest day of

6 amphibian education. This past April 28th, we had our 4th Save

7 the Frogs Day, which our supporters held over 200 educational

8 events in 39 countries. I have given over 200 presentations on

9 amphibians in 8 countries. 

10 As the founder and executive director of Save

11 the Frogs, I represent all 6978 amphibian species as well as all

12 humans who rely on a multitude of ecosystem services that

13 amphibians provide. So for financial disclosure, I do get pain

14 approximately 20 dollars an hour to Save the Frogs, which is far

15 less than I could make doing other work given my qualifications.

16

17 So, some background on what is happening to

18 amphibians in America and worldwide, at least 2000 amphibian

19 species are threatened with extinction. They are the most

20 threatened and rapidly disappearing vertebrate group on the

21 planet. About 200 species have gone completely extinct in the

22 last several decades, which is an extinction rate several

23 thousand times higher than it should be based on fossil record. 

24

25 Amphibians face a multitude of threats. These

26 threats are not mutually exclusive. Many of these amphibians
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1 face many of these threats all at the same time, habitat

2 destruction, urban expansion, suburbs getting built, parking

3 lots getting put down on top of swamps, rain forests getting

4 chopped down for cattle ranching, timber production, oil palm

5 plantation, sugar cane plantations. If we do not protect our

6 wilderness, we will end up getting places that look like this,

7 which are not habitat for much of anything. Over-harvesting for

8 the pet and food trades, several million frogs get imported into

9 America for use as pets each year. We do not keep record of how

10 many have been taken out of the wild in America each year. 

11 There is also a big black market trade. 

12 An even bigger problem is that up to a billion

13 frogs are taken out of the wild each year for use as food. 

14 Americans import 20% of the world's frog legs and our frog leg

15 consumption is on the rise. We are set to overtake France and

16 become the world's leading importer of frogs for food. 

17 Invasive species, such as non-native trout,

18 they get stopped by the hundreds of thousands into naturally

19 fishless lakes where they decimate amphibian populations. 

20 American bullfrogs, which are native the eastern half of the

21 U.S.A., but because they are the largest frog in North America

22 and usually grown in agriculture, are farmed in the American

23 west and worldwide and are now invasive species throughout the

24 American west and 15 countries where they do not belong. They

25 have very large mouths. Amphibians are gape-limited predators. 

26 They anything that fits in their mouth, and bullfrogs eat all
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1 kinds of native wildlife, including endangered frogs and

2 salamanders. 

3 Climate change is a big problem for frogs,

4 because frogs are dependent upon the proper precipitation

5 levels. If their cloud forest goes up mountains, what happens

6 is the earth warms and their habitats dry, their eggs dry, they

7 die. Yellowstone National Park, the oldest protected place on

8 the planet, is not protected from climate change. One-fourth of

9 the ponds have dried up in Yellowstone in the last 20 years or

10 so. Three of the 4 species there, even in the world's oldest

11 national park, are on the decline because they have lost their

12 habitat. 

13 Chytrid fungus is on the spread around the

14 world. America has zero regulations on chytrid fungus, 100%

15 legal import of chytrid-infected frog into the United States. 

16 Chytrid has driven about 100 amphibian species to complete

17 extinction in recent decades. It spreads because we ship about

18 100 million amphibians around the world each year for pets,

19 food, bait, laboratory use and zoos. 

20 Pollution and pesticides come out of our cars,

21 our homes, factories that we put near the rivers. Chemicals go

22 up into the air. They get carried by clouds. This includes

23 pesticides such as atrazine, which is one of 18,000+ registered

24 pesticides in the United States. Amphibians have permeable

25 skin. They absorb these pollutants and pesticides that

26 inevitably gradually bring down to the water bodies where they
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1 live and cause problems. 

2 As I said, these threats are happening. At

3 the same time, a pesticide may not kill a frog directly, but it

4 could weaken its immune defenses and a disease, which may just

5 make the frog sick usually, shows up and the population is able

6 to drive the population to extinction. 

7 Frogs are important for a variety of reasons. 

8 They eat ticks and mosquitos, flies that carry West Nile Virus,

9 dengue fever, leishmaniasis, malaria, Rocky Mountain spotted

10 fever and lots of other diseases we do not want. They are also

11 natural pesticides. They eat pests of our agricultural crops,

12 and therefore do us a big favor. They are also an important

13 food source for birds, fish, snakes, monkeys, dragon flies,

14 beetles. A lot of animals depend on amphions, their eggs and

15 tadpoles. When frogs disappear, other wildlife species have

16 trouble. 

17 10% of Nobel prizes in physiology and medicine

18 have come from research that depends on amphibians. A lot of

19 our pharmaceuticals, a lot of our medical advances from

20 amphibian species disappear as there goes any potential to

21 benefit human health through their continued existence of that

22 species. 

23 Frogs are bio-indicators. They are early

24 warning systems of environmental decay. Even though they are

25 sensitive, they have survived the past 250 million years and

26 more or less in current form. They have survived countless ice
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1 ages, asteroid crashes, volcanic explosions. They have outlived

2 the dinosaurs, yet in the last half century, we have managed to

3 drive about 1/3rd of them to the edge of extinction. 

4 Finally, frogs are cool. People like frogs. 

5 I like frogs. I know the vast majority of the thousands of kids

6 that I have spoken to about frogs like frogs. So, it is our

7 ethical and moral duty to protect them and in so doing, make the

8 world a cooler place to live than would be without them. So, it

9 is up to you to save the frogs. One way we could do that is to

10 ban atrazine, one of the most commonly used pesticides on the

11 planet and in the European Union since 2004. It consistently

12 turns up in American ground water, rain water, tap water. 

13 Numerous peer reviewed scientific publications

14 from laboratories around the world have demonstrated that

15 atrazine is extremely harmful to environmental and public

16 health. 

17 So, let's talk about some of the consequences

18 of atrazine use. I will say from going to scientific

19 conferences around the world for the past 9 years and talking a

20 lot to the amphibian biologists, the vast majority of the world

21 amphibian biologists agree that atrazine is extremely harmful to

22 amphibians. Abundant scientific literature from laboratories

23 around the world has demonstrated atrazine's harmful effects on

24 an array of wildlife. Atrazine's effects on wildlife include

25 demasculinizing and feminizing the gonads of exposed males,

26 testicular lesions, decreased penis size, disorders in accessory
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1 sex organs, severely impaired male reproductive behaviors,

2 impaired fertility, delayed puberty, induced abortion, reduces

3 anti-predator behaviors, diminished growth rates, reduced

4 survival rates, decreased cellular immunity, increased trematode

5 infections that result in malformations such as eight-legged

6 Pacific Chorus frogs, or even ones with 16 legs, which I have

7 seen pictures of. 

8 So, we hear lots of propaganda and lies that

9 come from Syngenta and the pesticide lobby, and I am sure we

10 will hear a lot of those today. So, let's talk about the

11 economics of atrazine, because that is why Syngenta is here. 

12 Let's talk about food security. 

13 So, to begin with the mission of the U.S. EPA

14 is to protect human and environmental health, it is not to

15 protect the profits of large corporations, nor is it to

16 guarantee consistent monetary income for those whose farm

17 methods wittingly or unwittingly pollute the land and deform

18 wildlife. With that being said, removing atrazine from the

19 market will be unlikely to significantly disrupt the American

20 economy. On the contrary, it will positively affect many

21 aspects of the American economy. For instance, an atrazine ban

22 would likely reduce the price differential between organic food

23 and pesticide laden conventional foods, benefitting organic

24 farmers who provide a valuable service to Americans by

25 delivering food that is safe to eat. An American ban will also

26 stop billions of dollars from leaving American shores at is
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1 currently is the Swiss manufactured atrazine, Syngenta, that is

2 the primary recipient of the billions of dollars Americans spend

3 on atrazine each year. 

4 Will those who choose to continue to spray

5 pesticides on their corn go bankrupt due to an atrazine ban? 

6 That is unlikely because atrazine only increases corn meal by 1%

7 to 6%. Atrazine is an outdated 50+ year old chemical. 

8 Technology can surely make up for the decrease of 1% to 6% of

9 corn if we put our minds to it. This is the 21st Century after

10 all, not 1956. In East Germany, they grow more corn now than

11 they did prior to banning atrazine in 1991. 

12 Humans have successfully grown corn, sugar,

13 sorghum and rice without pesticides for thousands of years. 

14 With modern organic farming techniques and investment in

15 technology and organic farming education, I have no doubt that

16 American farmers cannot only remain a world leader in food

17 production in the absence of atrazine, but we can grow food that

18 is safe for human consumption and safe for wildlife. As for

19 food security, we need so much corn.

20 The midwest has lost the vast majority of its

21 natural grasslands habitat and corn is the primary reason for

22 this. Less corn would actually allow a significant acreage of

23 land return to its natural state. This would be extremely

24 beneficial to environmental protection efforts. 

25 But will Americans starve to death with 1% to

26 6% less corn? No. Americans eat less than 10% of all the corn
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1 grown here, meaning there is a significant excess of corn. Over

2 40% of the corn grown in America is to feed livestock. 

3 Livestock trample habitats, down streams and rivers with manure,

4 producing significant greenhouse emitions in the form of methane

5 and are often the reason why forests are chopped down to create

6 space for grazing. Reducing the amount of American land being

7 dedicated to growing corn to raise livestock would be extremely

8 beneficial to the environment. 

9 About 4 percent of American corn goes to make

10 high-fructose corn syrup, which is a primary ingredient in soda,

11 candy, processed foods and a variety of extremely unhealthy food

12 products. These food are sold to people who are either

13 unconcerned about their health or are unaware of the health

14 problems caused by high-fructose corn syrup with the primary

15 problem being obesity, which costs American tax payers billions

16 of dollars in increased health care costs. Reducing the amount

17 of corn available for high-fructose corn syrup or increasing the

18 price of high-fructose corn syrup would benefit the vast

19 majority of Americans by improving the overall health of our

20 society and decreasing our skyrocketing healthcare costs. It

21 should also be noted that America with less carcinogens also

22 translates to decreased healthcare costs providing even more

23 reason to ban atrazine. 

24 Approximately 1/3 of the corn grown in America

25 goes to making ethanol, much of which is used in the cars we

26 drive. This abundance of live bio-fuel results in delayed
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1 innovation and fuel mileage reduction technology, meaning

2 Americans get left with cars that consistently have poorer fuel

3 mileage than cars in underdeveloped countries. Every gallon

4 extra that Americans purchase due to poor fuel standards is thus

5 a monetary setback every time a driver fills their tank.

6 Reducing the corn available for ethanol or a

7 similar increase in the price of the corn used in ethanol would

8 drive innovation and fuel efficiency technology and would

9 benefit the planet and the American people. Over 15 percent of

10 the corn grown in America gets exported. We should definitely

11 not be endangering American citizens and wildlife in order to

12 provide chemical-laden food for other country's citizens. 

13 What about the effect of atrazine on the EPA's

14 budget? Currently, only a small proportion of Americans have

15 ever heard of atrazine. There is no doubt however, that so long

16 as Atrazine is legal, groups interested in human and

17 environmental health will be working diligently to educate the

18 public about its effects and the movement to ban atrazine will

19 continue to grow. This issue will not disappear, and thus the

20 EPA will find itself dealing with this issue indefinitely until

21 a ban is ordered. With its limited budget, the EPA should not

22 squander its limited funds constantly trying to decide whether

23 atrazine should be legal. It is clear that atrazine is harmful.

24 Thus, the EPA should cancel atrazine for registration

25 immediately, and use the tax payer's funds on other important

26 environmental issues. 
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1 So in summary, the economics of atrazine, the

2 mission of the U.S. EPA is to protect environmental and human

3 health, not to protect the profits of those whose activities

4 harm the environment. Thus, the EPA should not be swayed by the

5 well-funded lobby that represents the multinational corporations

6 or the minority of Americans that benefit economically from

7 atrazine. Even if economics are considered, the American

8 economy as a whole stands to gain from a federal ban on the use

9 and production of atrazine, and it is unlikely that anybody but

10 the distributors of atrazine would suffer undue economic

11 hardship due to the necessary ban on atrazine. 

12 Let's talk about the relevance to 5 of

13 Administrator Lisa Jackson's 7 priorities for EPA's future. 

14 Banning atrazine has direct relevance to improving air quality. 

15 When sprayed, significant quantities are lifted into the air

16 where they get carried by clouds potentially hundreds of miles

17 from their point of application. Half a million pounds of

18 atrazine come down onto American soil each year via rain and

19 snow. As the jet stream carries the pesticide eastward from the

20 midwest corn belt, the millions of Americans living in the

21 mid-Atlantic region, the most densely populated area of our

22 country, become exposed to unwanted atrazine ingestion. Taking

23 atrazine off the market would clean up our air and protect

24 millions of Americans living downwind from areas of high

25 atrazine use. 

26 Assuring the safety of chemicals - there is
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1 just far too much scientific literature detailing the harmful

2 effects of atrazine to ever be able to assure Americans that

3 atrazine is safe in our air, water and food. Thus, the EPA

4 should ban atrazine. If it does not, it will fail to meet

5 Administrator Jackson's priorities. 

6 It should be clear that no amount of

7 industry-funded studies can assure us that atrazine is safe. 

8 Atrazine producers and distributors have billions of dollars at

9 stake. It is not difficult to find the result one seeks when

10 conducting studies. Similarly, it is extremely difficult to

11 find scientific effects when purposely point incompetent

12 scientists and no effect in an industry-funded study bears

13 little weight. Demonstrating direct carcinogenic effects on

14 humans is difficult because fortunately, our society agrees that

15 it would be unethical to test atrazine effects on live human

16 laboratory subjects. 

17 However, if any executives from Syngenta's

18 executive staff would volunteer for such a study and ingest a

19 daily dose of atrazine in their drinking water, I have no doubt

20 the study would produce some very disturbing data as atrazine

21 has already been linked to increased cancer rates in communities

22 surrounding high-use atrazine areas. 

23 We are long past the point where there will

24 ever be a consensus from both scientists and the pesticide

25 industry that atrazine is safe or unsafe. It is humans' nature

26 to protect sources of financial income. Thus, there will always
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1 be a minority contingent working diligently to produce data

2 and/or to introduce confounding data with the sole purpose of

3 protecting their profits and further disregard of human or

4 environmental health.

5 The precautionary principle states where there

6 are threats of serious or irreversible damage, lack of full

7 scientific certainty shall not be used as a reason for

8 postponing measures to prevent environmental degradation. The

9 EPA needs to bide by this extremely intuitive principle and side

10 with the people and the planet rather than placing our health at

11 extreme risk on the very low chance that atrazine, a chemical

12 designed to kill, which we put 80 million pounds a year of into

13 our ecosystem is harmless to all those target organism. 

14 Cleaning up our communities our communities

15 are not clean so long as atrazine, a known endocrine disruptor,

16 contaminates our food and water. The U.S. Department of

17 Agriculture consistently detects atrazine in groundwater, tap

18 water and even farm-raised catfish. Atrazine has a very long

19 half life, and is still detectable in European soil, even in a

20 place where it has not been used in over a decade. Banning

21 atrazine would help make our communities cleaner. 

22 Protecting America's waters atrazine was

23 banned in the European Union in 2004 because it was consistently

24 being detected in European tap water. The USDA conducted tests

25 in 2008 and 2009 to determine if atrazine was present in

26 American water. In 2009, 94% of the 618 samples they analyzed
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1 of American tap water were contaminated with atrazine with a

2 maximum concentration of 2.8 parts per billion, enough atrazine

3 to turn a male frog into a female. 

4 In 2008, 12.6% of the 238 ground water samples

5 they analyzed were contaminated with atrazine. Another federal

6 agency, the U.S. Geological Survey, has also demonstrated the

7 harm the atrazine can cause the American waters. Tillitt et al

8 showed that atrazine reduces reproduction in fathead minnows. 

9 Clearly, the EPA must ban atrazine if it was to succeed at

10 protecting America's waters. 

11 Expanding the conversation on environmentalism

12 and working for environmental justice - the people most commonly

13 exposed to atrazine are farm workers, a group of people with a

14 long history of exposure to injustice, both social and

15 environmental. Furthermore, atrazine usage is highest in the

16 Mississippi River Basin, and gravity sends millions of pounds of

17 atrazine flowing down the Mississippi River each year, taking

18 the chemical right into the heart of some of America's poorest

19 communities. There will not be environmental justice so long as

20 our poorest people are the most exposed to the pesticides that

21 the large companies make billions of dollars selling. 

22 I also want to point out the all of this, you

23 see that red that is flowing down the Mississippi River straight

24 into the Gulf of Mexico where a lot of the fish we eat come

25 from. If you look at South Florida, right north of the

26 Everglades and all that water flows straight through the
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1 Everglades, one of the most biologically important and amazing

2 places on the planet. 

3 It should be noted that the U.S. Department of

4 Agriculture does not even possess technology capable of

5 detecting atrazine on corn below 15 parts per billion. That

6 means that we have atrazine on our corn at 5 times the

7 concentration that can turn male frogs into females that then

8 mate with males and produce eggs that happen to not be viable. 

9 So, atrazine ban is supported by a wide

10 transect of Americans. Last year, we held a rally in front of

11 the EPA in DC. We had people come from all around the country

12 to help get atrazine banned, Dan Martin from the National

13 Geographic Society, lawyers, volunteers, kids. 

14 So, I have collected over 12,000 signatures

15 calling for a petition for a federal ban on the use and

16 production of atrazine and have gotten over 50,000 letters sent

17 to the EPA. So, what will you do? I am going to read the grand

18 prize winning poem from the 2009 Save the Frogs Poetry Contest

19 written by Brittany Bellanger, age 12, who beat out frog poets

20 of all ages from all around the world, her poem, All is Gone.

21 Frogs - the pivotal of our Earth, the binding

22 of our book, the balance of our world, almost gone. The wheel

23 has slowly stopped turning, the binding slowly unraveled, the

24 balance to serve. Frogs the glue holding our ecosystem

25 together, slowly beginning to fade like perfect morning, fade to

26 a thick canopy of darkness. Our world as we know will change. 
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1 The frogs are the answer, the key to turn the lock to cure many

2 diseases, but they are almost gone. What will you do? 

3 So, I suggest if you are here and you are

4 working for Syngenta or working for the pesticide lobby, then I

5 say go home and when you are laying in bed tonight, think about

6 what is the purpose of your life. Is it to make some money

7 doing something you probably do not care about and do not

8 believe in, and that is bad for society and the planet, or maybe

9 you can find something more worthwhile to do that your children

10 and grandchildren will be proud of. If you work for the EPA or

11 on the Scientific Advisory Panel, then take a leadership role

12 instead of people who are apathetic or trying to block progress,

13 then go ahead and take a stand and make things happen, get it

14 done. 

15 So, thank you for protecting human and

16 environmental health by placing an immediate federal ban on the

17 use and production of atrazine so that we can live in an America

18 where we are not afraid to drink tap water or eat food being

19 served in restaurants or supermarkets, an America where

20 pesticides do not rain down out of the sky on top of us, an

21 America where our wildlife do not ge deformed by the chemicals

22 we introduce into the habitats without their consent. 

23 I believe I have 1 minute remaining, so I am

24 going to take that minute to read some comments from various

25 people. Jenny Weber for Oregon: I am a wildlife biologist and

26 I have read numerous, reliable scientific papers about the
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1 negative effects of atrazine on the amphibians. Given this

2 knowledge and the fact that some of the amphibian populations

3 are struggling or declined, it is unacceptable that atrazine is

4 still in use in the U.S.

5 Dr. Mark Haze, Washington Department of Fish

6 and Wildlife: Contrary to data supporting the safety of

7 atrazine is riddle with fault in what can be most nicely

8 described as artistic science. Support to remove its use is

9 unquestionable. 

10 Dr. Howard DeGua, International Society for

11 the Study of Conservation of Amphibians: Very useful petition. 

12 I fully support. 

13 Dr. Dick Eickler, retired Professor of

14 Biology: Evidence of the endocrine disruptive effects of the

15 chemical atrazine indicate that it is too dangerous to be used

16 in the United States. It should be banned as other countries

17 have done. 

18 So, thank you very much and if anyone wants to

19 ask me questions, if I have time for that, I am happy to take

20 any. Thanks.

21 DR. DANIEL SCHLENK: Thank you for your

22 comments Dr. Kriger. Any questions for Dr. Kriger? Okay, thank

23 you very much. What we are going to do is we are going to take

24 a break right now. How about come back at 3:15 and the Syngenta

25 folks can come forward with their presentations. Can I have Dan

26 Campbell or Janice McFarland? 
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1 DR. JANICE MCFARLAND: Thank you very much. 

2 My name is Janice McFarland. I am the head Regulatory and

3 Stewardship for Syngenta crop protection. On behalf of

4 Syngenta, I would like to thank both the SAP and EPA for all of

5 the work, not only in preparing for this SAP, but also in all

6 the preparation and analyses that have been done over the years

7 on improving the Science-based Regulatory Brain Work. 

8 Even since we had the May 2009 Science

9 Advisory Panel hearing that was discussed earlier, Syngenta has

10 listened to the guidance very carefully of the Science Advisory

11 Panel. We provide the EPA with informed and accurate

12 environmental studies and work and assessments, including water

13 monitoring and speciality search studies, watershed stewardship

14 studies, and different methodologies to improve spacial

15 assessments and modeling. Much of this work has actually been

16 president. It improves testing and assessments not only for

17 atrazine, but other chemicals. There have been widespread use

18 of the improvements in methodologies and the science that has

19 been generated on based on guidance from the EPA and guidance

20 from the Science Advisory Panel. 

21 We will be hearing from the 3 main topics from

22 the SAP. Dan Campbell, the head of our Regulatory Herbicide

23 Team will be introducing the panel and leading the Syngenta

24 agenda for the afternoon. On the 3 major topics on amphibians,

25 the methods for determining the level of concern for aquatic

26 community structure and function, and bias factors analyses, as
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1 well as some alternative options we are really excited about, on

2 account of identifying local and global watershed steward. We

3 are targeting stewardship for understanding environmental

4 vulnerability. 

5 One of our key concerns of the White paper,

6 and you will be hearing some the information discussed, is that

7 proposed LOCs are lower than the underlying science works. In

8 fact, the weight of evident and proper study selection and

9 calibration concurrent proposed LOC to be in the range of about

10 40 parts per billion for a 30 day period. That would still be

11 protective. 

12 The practical context for the issues the EPA

13 has asked the panel to address is extremely important. The

14 input and guidance the Panel provides to the EPA and the public

15 and the EPA's subsequent work on the problem formulation as well

16 as the risk and benefit assessments that will be conducted for

17 atrazine under the Registration Review process as well as EPA's

18 office of Water Assessment for aquatic life criteria can have

19 substantial regulatory and real-world impacts. This is why it

20 makes it so critical and why we appreciate so much the work of

21 the Science Advisory Panel, looking at these data and methods to

22 get a science-based LOC taking into account the reversible mode

23 of action, the account recovery and taking into account biologic

24 relevance. 

25 With that, we want to thank you again for the

26 work before and after the Science Advisory Panel and thank EPA
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1 for all of the preparations, and look forward to great

2 discussions. We would be happy to provide any background

3 information or additional detail on studies that will be being

4 discussed by our Panel. With that, Dan Campbell will take over.

5 DR. DAN CAMPBELL: Thank you, Dr. McFarland. 

6 My name is Dan Campbell. I am the head of the Herbicide

7 Regulatory Group with Syngenta. I also want to thank the Panel.

8 We truly appreciate your expertise and the time you are taking

9 on these questions with the Scientific Advisory Panel.

10 Also, I would like to personally thank Dr.

11 Matten for her work with me during the submission of our public

12 comments. She is a designated federal official extraordinaire,

13 and I appreciate all her extra additional work on our

14 submissions. 

15 I am showing the agenda for our public

16 comments. After a few introductory remarks by myself, we will

17 have a section presented by Dr. Keith Solomon on the atrazine

18 mode of action and ecological effects. After that, we will get

19 into the 3 main topics of this Scientific Advisory Panel, the

20 Amphibian Tox Data, which will be presented by Dr. Solomon and

21 Dr. Glen Van Der Kraak. 

22 The second topic, the Method for Determining

23 the Level of Concern, the PATI function section and its

24 sensitivity will be presented by Dr. Richard Brain. The

25 important section on microcosm and mesocosm scoring and

26 selection will be presented by Dr. Jeff Giddings. Then Dr.
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1 Brain will come back and discuss the biological relevance of the

2 LOC, and in addition, we have some exciting new field work we

3 have begun in this past year that will be presented by Mr.

4 Lenwood Hall and Dr. Mark Hanson.

5 Then finally, the last topic will be on the

6 Bias Factors and Identifying Vulnerable Watersheds that will be

7 presented by Dr. Paul Hendley. 

8 Before I go into my opening remarks, I just

9 wanted to talk about the phasing. We have 6 chairs up here. 

10 What we hope to be able to do and I will ask the Chair if this

11 will be okay, is go through the first section, probably through

12 the end of Amphibian Tox Data, stop for questions if that is

13 okay and then bring the next group up and then run the next

14 section in a similar manner.

15 DR. DAN SCHLENK: Yes, I was thinking right

16 after A, B and C to do the switch out and then do your questions

17 during that switch out. That is appropriate. 

18 DR. DAN CAMPBELL: Okay, thank you Dr.

19 Schlenk. That will be great. A quick word on our submission 

20 we did submit a lot of scientific data and information. We

21 strove to do so in a way that would not confuse and hopefully

22 will be helpful in providing information as you go through the

23 different topics in your charge questions. 

24 I would like to point out that our summary

25 document, if you have not had a chance to see that, that is a 10

26 to 12 page document that summarizes all the materials that we
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1 have submitted and provides navigational guidance on going to

2 our more in depth technical documents if you choose to do so or

3 need to do so for your charge questions. Hopefully, you have

4 seen that. That document is called Summary of Documents

5 Submitted to the FIFRA Science Advisory Panel of June 12th

6 through 14th. 

7 A couple of comments on atrazine itself it

8 has been the backbone for weed control for more than 50 years. 

9 It is relied on by corn growers, sorghum growers, sugar cane

10 growers and other crop growers to provide control of weeds that

11 would otherwise devastate their farm operations. It is applied

12 on well over half of all U.S. corn, 2/3rds of U.S. sorghum acres

13 and 90% of all U.S. sugar cane. So, it is a key agricultural

14 tool and is relied on by growers around the United States.

15 It is available in at least 60 products from

16 many different companies. We have some of them listed up there.

17 This does not include the list of dozens of other products that

18 require a little bit of atrazine use.

19 

20 In addition, atrazine also is a key chemical

21 in preserving the open, transparent and science-based regulatory

22 system. So, not only is atrazine over the years been a

23 forefront of the science, it is also in the forefront of our

24 regulatory system. There have been 12 Scientific Advisory

25 Panels since 1998. This is the 13th. This is the 5th that is

26 addressing ecological and amphibian questions. Throughout the
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1 course of that time, thousands of studies have been evaluated by

2 panels and the EPA. 

3 One side note here on CASM, and we do not have

4 a lot of discussion in our new materials nor in our oral

5 comments today on the Comprehensive Aquatic Systems Model that

6 was discussed earlier by EPA and is discussed in the White

7 paper. We wanted to point out up front, we have actually held

8 off further development of this model since early 2010 mainly

9 for the reasons I list here. EPA has moved to PATI. It was

10 clear around that time that they were moving to PATI and away

11 from the CASM model. We do feel that the questions and issues

12 brought up in the appendix in the White paper on CASM can be

13 addressed, and probabilistic CASM or whatever version of CASM

14 can be parameterized successfully. But it also became obvious

15 to us around this time that it would be smart for us to put our

16 efforts into both PATI model and additionally a calibration

17 step. Microcosm and mesocosm calibration is the key step,

18 whether you are using CASM, PATI or even model naive approach. 

19 That was mentioned earlier by EPA. We will talk about it in

20 more detail. 

21 We do feel that ecological and bioenergetic

22 models, such as CASM, are important. They are important for our

23 industry. They are needed in higher tier assessments, in areas

24 and sites that could use more specific assessments. In addition

25 under restoration review for atrazine and possibly for other

26 products, such modeling can be really important. It can be a
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1 way, for example, to tease out potential effects on consumer

2 wildlife organisms from indirect effects to primary producers. 

3 So, PATI model currently cannot do these things. CASM can fill

4 that role in the future.

5 So, these are our key points by topic. I

6 guess a little hint, the length of these key points kind of

7 mirrors the length of our presentation. For topic A amphibians,

8 the weight of evidence supports and continues to support that

9 atrazine does not adversely affect amphibians and

10 environmentally relevant concentrations. 

11 Topic B, the LOC proposed in the White paper,

12 we feel is not supported by the science. We feel and propose

13 there need to be revisions to the selection and scoring of

14 microcosm and mesocosm studies, and also the assessment duration

15 compared to what is proposed in the White paper. The 4 to 7

16 parts per billion LOC lacks biological relevance and does not

17 account for atrazine's mode of action and recovery. If the best

18 available dataset is used, the LOC should be in the range of 40

19 parts per billion for 30 days and 25 parts per billion for 60

20 days.

21 For topic C, our key point is that proposed

22 bias factor methodology needs improvement and in fact should not

23 be used for identifying vulnerable watersheds as it does not

24 appear to be producing the correct answers. There are better

25 techniques that do exist that take advantage of and utilize the

26 large atrazine dataset that currently exists. 
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1 With that, we will turn to our panel of

2 presenters. What we need to do is open up the next slide set up

3 there if possible. 

4 DR. KEITH SOLOMON: Mr. Chairman, members of

5 the Panel, thank you very much for the opportunity to be here

6 today and to talk to you. My name is Keith Solomon. I am from

7 the University of Guelph, and I am here at the request of

8 Syngenta. I will be talking a little bit about some background

9 information and then on the weight of evidence. We went over

10 that quite a few times today, and we will more during my

11 presenting. 

12 We know that atrazine has been used for a long

13 time. We have always known about it. The mode of action is

14 well understood. It is an inhibitor of photosynthesis. It is a

15 specific mode of action. As Dr. Bradbury pointed out earlier,

16 it is a more potent and toxic to plants than it is to other

17 organisms. We have an excellent toxicological data base for

18 atrazine. There have been a number of previous risk assessments

19 done. We know that it is not bioaccumulative like DDT or other

20 compounds, and that it is rapidly excreted from organisms. We

21 also will hear later on that there have been a large number of

22 microcosm studies done on atrazine. 

23 First of all, atrazine is a herbicide and it

24 is an inhibitor of photosynthesis acting at the chloroplast at a

25 specific site in the chloroplast thylakoid membrane where it

26 binds to a receptor site on the D1 protein of the photosynthetic
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1 chain. This chain in involved in the transfer of electrons

2 through the photosynthetic system, which ultimately results in

3 fixation of carbon dioxide into carbohydrate materials and

4 release of oxygen. So, the plastoquinone here is a carrier of

5 electrons. It binds to a binding site in the D1 protein, and it

6 reacts the electron is passed on to convert that into

7 plastohydroquinone that then transfers the electrons to other

8 parts of the photosynthetic chain. 

9 Atrazine acts by binding to that same site,

10 except that it does not pick up an electron. While it is bound

11 to that site, it does not allow plastoquinone to approach that

12 site. So effectively, it acts as a competitor and reversible

13 blocker of photosynthesis, which then if the atrazine is

14 removed, by metabolism in the frog or by changes in the external

15 concentration, such as in the aquatic system, the atrazine will

16 diffuse out of that site, not permanently bonded, and the

17 mechanism of action is reversible and plastoquinone can the

18 reenter the active site and photosynthesis basically starts

19 again. In fact, it is static as well as stasis of

20 photosynthesis. 

21 You and the members of the SAP have been

22 charged with some questions. We know that we can offer a few

23 comments related to those, probably in great detail. You will

24 be made aware of some of the new studies with the thyroid and

25 sex ratios in fish and amphibians. There are no effects in the

26 more recent studies. We will talk about using quantitative
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1 weight of evidence to assist the studies. We believe that this

2 is an approach that could be very useful and should be something

3 seriously considered. 

4 When we apply weight of evidence analysis to

5 new reports and to some of the old reports, we see there is a

6 consistency in terms of what was observed in the DCI study,

7 which was referred to earlier by Ms. Blankenship earlier today,

8 and that there is no evidence to suggest that the conclusions

9 from that study are incorrect. We also see that the weight of

10 evidence supports consistency across many species in North

11 America and other locations, and the effects of the DCI study

12 will be extrapolated to these other species. Dr. Van Der Kraak

13 will talk briefly about some of the responses related to stress

14 and immune response that can also be looked at by weight of

15 evidence.

16 Historically, there have been a number of

17 reviews of data. The first 2 were probabilistic risk

18 assessment. I was real pleased some distributional data put on

19 slides here today. I was very fortunate to be involved in the

20 first assessment that was in 1996, and also as a coauthor to Dr.

21 Jeff Giddings's refined probabilistic assessment that was

22 published in book form in 2005. 

23 In 2008, we published a critical review with

24 focus on causality using the criterial guidelines. We came to

25 conclusions about the effects of atrazine on fish and amphibians

26 in relation to the at least nontraditional, at least at that
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1 time, response related to sub-equal effects on development,

2 sexual maturation, etc. 

3 Since then, there have been some additional

4 reviews of data. There was a qualitative meta-analysis in 2010

5 and some other reviews that was published in journals and some

6 books. 

7 The qualitative meta-analysis, just to focus a

8 rule on that, all of these have come to different conclusions,

9 but the used different methodologies. In fact, there is no such

10 thing as a qualitative meta-analysis. All meta-analyses have to

11 be qualitative. If you go back to the original ones by

12 (inaudible), you will see that they are quantitative and involve

13 reanalysis of data from previous studies.

14 Also, many of these reviews have excluded that

15 data. I am also noticing the EPA has excluded some data from

16 its reviews. I do not think that that is an entirely

17 appropriate to move ahead. We have decided to come forward and

18 propose a weight of evidence analysis that is more conclusive. 

19 The term weight of evidence is widely used,

20 but is seldom formalized. This is from a paper by Dr. Weed, who

21 used to be with the National Cancer Institute here in the DC

22 area. He pointed out that in most papers, it seems

23 metamorphically and not methodologically. In fact, in the human

24 health records, it is used very infrequently as anything other

25 than metaphorically. 

26 Interestingly enough in the eco-literature, it
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1 is being in a quantitative sense.

2 We have used weight of evidence in a

3 methodological context. We have included in this causal

4 criteria to include relevance to survival or reproduction as you

5 heard earlier today as adverse outcome pathways. We also

6 included the quality of the toxicological studies rather than

7 substituting information, we have included all the studies,

8 which obviously vary in terms of swing as well as relevance. 

9 This is currently under way. The authors that are in this room,

10 it will be published in the cited literature. 

11 In terms of questions to the SAP, we are in

12 general agreement with the statements expressed in relation to

13 the charge questions. We have a couple of qualifiers. The one

14 that we would like to address is the exclusion of studies based

15 on background incidence of testicular ovocytes as we call

16 testicular ovarian follicles. We believe that these exclusions

17 are not necessary, and that these studies can be incorporated

18 via weight of evident. 

19 Background incidence of tumors in organisms

20 does not exclude that data from cancer resistant specimens done

21 by EPA and other agencies, and we feel that the same conditions

22 should apply here. 

23 Testicular ovarian follicles are a natural

24 phenomenon. They are observed historically in frogs in Europe

25 and also here in the United States before the introduction of

26 atrazine and other pesticides. They have been reportedly in a
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1 number of locations around the world with no relationship to

2 exposures to atrazine. In the work we conducted in South

3 Africa, they were observed in exposed and unexposed Xenopus

4 laevis without any concentration response. The incidence of

5 these testicular ovarian follicles has shown to be related to

6 the phylogeny of the organisms and not the environment around

7 them. 

8 To cut to the chase, the organisms in the

9 circle stretching up to the upper right of this diagram here all

10 have testicular follicles, whether they are in areas where there

11 is exposure to atrazine used in maize production or not, where

12 as the ones found in the southwest part of the Cape of Good

13 Hope, which is on the other side of the barrier or the Cape Fold

14 Mountains, they possess no testicular ovarian follicles. 

15 An investigation of this using DNA, which was

16 published in 2009, this just shows data for mitochondrial DNA,

17 but there are also 2 nuclear markers that have also used it as

18 well, this shows a segregation, a quite significant segregation

19 in terms of the phylogeny of these organisms between those on

20 the northeast side of the Cape Fold Mountains and those of the

21 southwest side. A lot of individuals of these ones from the

22 Cape and these are also the ones that were used in the DCI

23 study. This was tested. They did not come from Chile as some

24 people recently suggested. They are from the Cape. The ones

25 that do not have a background incidence of testicular ovarian

26 follicles as opposed to the others in the northeast that do. 
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1 So now, the sub-conclusion here is that there

2 is no evidence to suggest that these testicular ovarian

3 follicles are related in any way to exposure to atrazine and the

4 natural occurrence should not exclude studies from

5 consideration. 

6 The weight of evidence analysis is a

7 quantitative system that uses scoring. It is inclusive of all

8 studies and like the method used by EPA, it looks at the

9 strength of the study in terms of experimental design. It uses

10 criteria developed from a number of sources including procedures

11 for incorporation of studies into a database. It is based on

12 quality assurance, number of concentrations used and range of

13 exposures that should be both realistic as well as upper bound

14 limits in terms of providing margins of exposure levels of

15 safety. 

16 Criteria for assessment of responses related

17 to what this means in terms of the adverse outcome that we were

18 hearing about earlier today, obviously statistical significance

19 is important, but so is concentration or dose response and the

20 relevance of adverse effects at realistic exposures as well as

21 when the effects are seen that these are explainable at least by

22 a plausible mechanism. 

23 Responses that we have looked at in the weight

24 of evidence approach are 41 responses and we're counting as

25 these studies get published. They are grouped here into several

26 categories, direct effect, indirect effects, direct effects on
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1 sexual differentiation and development, effects on hormonal

2 systems and then general category based on the immune function,

3 and then all the way up to the population level as well. 

4 The scores that we apply in these systems,

5 there are 2 scores. There is a computed score and where we have

6 effects for example, for large concentrations, they are internal

7 inconsistencies in studies, we address these in the narrative,

8 we use the consensus score, which may be lower or higher,

9 depending upon the nature of scientific opinion. The scores are

10 simply ranked 0 to 4. They are displayed graphically, which I

11 find easier to understand being a pictorial person, in a 2D

12 graph. I guess we could put a 3D if we wanted to incorporate

13 concentration as well. They are backed up by a detailed

14 assessment of each response. For the final version, it would be

15 possible to drill down to the individual assessment of the

16 studies, not the studies themselves, but the assessment of those

17 to make this as transparent as possible. 

18 This is an example of the 2D graph. Viewing

19 the studies in the top left quadrant with the higher mark

20 towards that corner, these are studies that have strength of

21 methodology and procedure. They should no relevance to adverse

22 effects. In other words, they show no effects. If on the other

23 hand they were on that part of the graph that is color coded,

24 then that would indicate strong evidence of adverse effects as

25 well as no adverse effects. 

26 Then you can drill down through that. This is
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1 just one example from one paper. This deals with deformities in

2 developing larvae in 3 clinical species. This is basically the

3 test, which looks at early larvae development in terms of the

4 deformities. You can see here there was statistical

5 significance. However, this was only at very large

6 concentrations. There was a concentration response. The

7 relevance was not specifically characterized, but we can assume

8 that by the importance, it is given a score of halfway score. 

9 Mechanism is not characterized, but is likely general toxicity. 

10 You will notice that the computed score comes 2.75, were as the

11 consensus score was reduced because this is only seen in pretty

12 large concentrations that are not environmentally realistic. 

13 The methodology used in the study is scored based on some of

14 criteria that we talked about early. 

15 Just a couple more examples. This is sex

16 ratio in fish, amphibians and reptiles. We have included all 3

17 of these, a lot of them focusing just on amphibians, but most of

18 the data is actually for amphibians. What you can see her is

19 that most of the information is on the left Y axis of the graph

20 indicating no adverse effects at all in the 2 studies that

21 showed some evidence of adverse effects. When you group these

22 together and you look at the consistency across the studies,

23 what you will see is that some of these are outliers and there

24 may be appropriate reasons for those you can describe in the

25 narrative that goes with it. 

26 In looking at another endpoint here, survival
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1 of fish and amphibians, this one is in terms of the number of

2 studies that are on the left Y axis. This is suggestive of

3 there are no adverse effects across the board. Looking at

4 incidence of testicular ovarian follicles in fish, amphibians

5 and reptiles, the same kinds of focus can be made there.

6 Just to go back to repeat the conclusions and

7 add a couple more to them, weight of evidence approach we think

8 is very useful and should be considered as an approach. There

9 is no strong evidence supporting a causal relationship between

10 exposure to atrazine and adverse effects in amphibians. If you

11 wanted to include fish and reptiles as well, you could in any

12 location including North America. At this point, I will hand

13 this over to Dr. Van Der Kraak, and he will continue with the

14 presentation. 

15 DR. GLEN VAN DER KRAAK: Thank you, Keith. My

16 name is Glen Van Der Kraak. I am a professor at the University

17 of Guelph in Ontario Canada. I am going to talk a little bit

18 about atrazine, the stress response in immune function. I am

19 going to talk not only about amphibians, but a little bit about

20 mammals and a little bit more about fish. 

21 As described this morning, the White paper had

22 a number of statements that related to immune function and

23 infection susceptibility, and then suggested that there were

24 some studies that reported potential for atrazine to have

25 effects in that area. However, the EPA, as described by Ms.

26 Blankenship early this morning, talked about this research and
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1 suggested that the research on these hypotheses does not provide

2 sufficient data to establish causal linkages across the

3 different levels of biological organization and are going to

4 result in adverse effects. The EPA concluded that a mode of

5 action or adverse outcome pathway leading to effects on

6 amphibian survival, growth or development could not be

7 established at this point in time. We agree with those

8 conclusions, but want to provide some additional information

9 that will help inform this discussion. 

10 As Ms. Blankenship talked this morning, she

11 talked about adverse outcome pathways, and one such pathway

12 leading to adverse health outcomes involves the interplay

13 between stress hormones, immune function, and then these

14 affecting susceptibility to disease or growth. As you see, the

15 activation of the synthetic nervous system and the activation of

16 corticosteroid hormone release has been shown to affect immune

17 function, and thereby provides a pathway by which when we look

18 at the interaction between stress hormones and immune function

19 in leading to potential adverse health outcomes. 

20 So, can we identify what has been done in the

21 mammal and can we get some generalizations just to how these

22 studies might inform what we would do in studies with amphibians

23 and fish. So, the studies that have been done in the mammal

24 have indicated that there is a transitory activation in the

25 hypothalamic pituitary adrenal axis by atrazine, and this leads

26 to an increased secretion of ACTH, the pituitary hormone
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1 stimulating the adrenal gland and corticosterone, the sex

2 steroid. 

3 However, when you look at the effects on

4 immune responses, these effects have been many, but they are

5 inconsistent. So, we see consistent effects of atrazine on

6 things like thymus weight, T cell-dependent antibody response

7 and natural killer cell function. 

8 In the studies in the mammals where there have

9 been evaluations of immune function, these effects were seen

10 most often at very large concentrations of atrazine. So in

11 fact, the U.S. EPA in 2010 has suggested that the immune system

12 is not proven to be a primary target organ affected by atrazine.

13 The observed effects have been well characterized in the adult

14 rat and these effects were seen at doses that produce overt

15 systemic toxicity.

16 If we look at what is happening in terms of

17 the data that is available for amphibians and fish, there is

18 limited evidence for atrazine effects on either the hypothalamic

19 adrenal or in the case of fish, the hypothalamic pituitary

20 interrenal axis, based at least on corticosteroid responses. 

21 There are inconsistent effects of atrazine on immune responses. 

22 When these effects, they were often seen at very large

23 concentrations of atrazine. Generally, these effects were seen

24 in concentrations that were greater than environmental levels. 

25 So, if we look at this weight of evidence type

26 approach that we have gone through to look at various data that
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1 looks at cortisol responses in fish and amphibians, we see a

2 variety of responses. Most of the responses are clustered in

3 part of the graph that says that there is weak evidence of

4 effects. Most of these studies have been ones where the

5 strength of the study has not been as strong as one might hope.

6 Interestingly enough, when you look at the

7 cortisol response, you can see this one outlier that as an

8 effect that you would say is tending toward showing some

9 evidence of a strong effect. The interesting part of that was

10 that particular study, if you looked at the totality of that

11 work, there was no effect on survival of this fish species in

12 terms of fresh water survival. So, I think what we are seeing

13 here is cortisol response is you are showing results that

14 indicate that there is a biological response in these animals,

15 yet the translation of those response to an adverse outcome is

16 clearly not demonstrated. 

17 If you look at immune function in both fish

18 and amphibians, again we see the bulk of the data is posturing

19 either along the Y axis or being shown in a portion of the graph

20 where it is showing weak evidence for known adverse effects. 

21 If you are trying to sum up what we have come

22 up with from the weight of evidence, we would say that that

23 analysis has shown limited evidence of effects of atrazine on

24 the stress and immune responses, but the experimental methods

25 that have been employed were perhaps not as robust as one would

26 like in trying to make that conclusion. So, one has to ask the
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1 question are there other models that are available to reduce

2 some of that uncertainty and understanding the risks poses or

3 potential risks posed by atrazine.

4 As we are talking about both fish and

5 amphibians, I would argue that if you even do a quick survey of

6 men, you will see that there are far more evidence for fish

7 models than there are for amphibian models. In fact, if you

8 look at the zebrafish, that represents one of those species

9 where there is a considerable amount of evidence and technique

10 that is available to investigate aspects of not only stress

11 physiology and immune function, but reproductive physiology so

12 that one could, using that one single species, go through the

13 totality of the adverse outcome pathway. 

14 We are proposing to do work in this particular

15 area. So, we are using the zebrafish to try to better

16 understand whether atrazine affects the HPI axis and the stress

17 response in fish. So, part of the reason why I am here is that

18 we have done quite a bit of work in this area. We have

19 published papers where we have looked at responses in the

20 hypothalamic pituitary adrenal axis in the zebrafish. In the

21 next couple of slides, we are just going to illustrate some of

22 the background work basing our work that we are basing our work

23 for the response patterns that we are going to look at with

24 atrazine. 

25 So, of course you all are interested in how

26 you stress a zebrafish. We have a developed a swimming stress. 
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1 We have shown that this leads to a cortisol response that is

2 proportional to the severity of the disturbance. The physical

3 setup of this assay is such that we can use it to look at how

4 environmental conditions such as changes in oxygen availability,

5 hormones, endocrine disruptors and contaminants that affect the

6 endocrine stress response. 

7 So what does this setup look like? The setup

8 looks like this that we obviously have zebrafish, we have them

9 in tanks. We set up the tanks so they sit over top of a stir

10 plate. We acclimate the fish for a period of about a week. We

11 can then, at set times, turn on the vortex current, and then we

12 can look at the cortisol response in fish, that is the mean

13 corticosteroid, and various gene responses that are quantified. 

14

15 So, I look at this as kind of a fish going to

16 see a physician and they are being put on a treadmill, only that

17 treadmill is still in the water. This is just to illustrate the

18 types of data that we get with this setup. 

19 This shows on the Y axis whole body cortisol

20 content as a function of in the first instance, the speed of the

21 vortex stirrer. We can see that with a 20 minute exposure to

22 this, you get a proportional increase in the concentrations of

23 cortisol in the body of the zebrafish, and in fact the bar on

24 the far right hand side shows what happens after air exposure,

25 which is a standardized stressor in fish studies. This shows

26 that the response that see with the vortex stressor is in fact
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1 proportional to that expected stress response.

2 This was very interesting data to us, at

3 least. It clearly measure whole body cortisol as a function of

4 time, the shaded bar between 0 and 60 minutes shows in this case

5 the duration of the vortex stressor. You will see that by 20

6 minutes after exposure, you see a very profound increase in

7 whole body cortisol and then feedback occurs. In fact, by 90

8 minutes, the responses return to the values that are seen

9 preexposure to this. 

10 Not shown on here because of time, we exceed

11 an increased expression in a variety of genes at the level of

12 hypothalamus of the zebrafish, pituitary and of the intrarenal

13 tissue in this species. So, the vortex stressor elicits a very

14 fast and transient whole body cortisol stress response in our

15 hands in adult zebrafish. 

16 So, the kinds of studies that we are doing

17 with the zebrafish and atrazine it to look at time course of

18 atrazine effects on both basal and stress-induced cortisol

19 levels. Subsequent to that, we propose that we are going to

20 look at the effects of atrazine and separately to look at the

21 effects of its metabolites on both basal and stress-induced

22 cortisol levels, and then we will do some further testing to

23 determine whether or not atrazine preexposure modifies the

24 ability of the fish to respond to repeated stressors. 

25 So, we believe that in doing these types of

26 tests, we will be able to characterize the aspects of the stress
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1 response in the zebrafish to atrazine. We will be able to view

2 this in a manner that will have a variety of the appropriate

3 husbandry techniques in place to make the data responsive to the

4 needs of the U.S. EPA. 

5 So if I was to conclude, I would say that

6 there is weak evidence for no adverse effects on the stress

7 physiology and immune function response in amphibians and fish. 

8 Because we have only weak evidence, we are going to try to

9 reduce that uncertainty by testing atrazine and its metabolites

10 on the activation of the hypothalamic pituitary intrarenal axis

11 in the zebrafish, and we are using the zebrafish because we

12 think it is a very useful model for evaluating not only the

13 stress response, but immune function and we can look at aspects

14 of reproduction and fitness. 

15 So, thank you and with that, that will

16 conclude the section of the presentation that is dealing with

17 the amphibian data. 

18 DR. DANIEL SCHLENK: Okay, let's go ahead and

19 take questions now for this particular section if you will. Dr.

20 La Point? 

21 DR. THOMAS LA POINT: Thank you. What I am

22 curious about, and actually I have the same question on the data

23 and what I meant to ask the EPA earlier, in a double blind

24 experiment with scoring, do you think, let's say somebody kriged

25 for the whole process, would the same scores come up? 

26 DR. KEITH SOLOMON: I will answer that
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1 question. Yes, probably because we have very distinct criteria.

2 If your concentration range includes the set value, it is

3 scored. If you have 5 concentrations, you get the top score

4 because that is usually the concentration range. The number of

5 concentrations used in a guideline study, we are trying to get

6 as close as possible to the guideline study. In terms of

7 consensus scoring, there may be some differences there. There

8 are some differences between experts. We have seen that in the

9 past.

10 DR. CATHERINE PROPPER: Do you have any plans

11 to evaluate any species besides zebrafish in this assay? I

12 really like the assay, but are there plans to look at

13 interspecies differences in responsiveness or have you done that

14 already with your stress response to see whether or not there

15 are there shifts in your curve in responsiveness? 

16 DR. GLEN VAN DER KRAAK: We do not have any

17 immediate plans for moving this test to other species, but in

18 the development of the test with the zebrafish, we did in fact

19 do a variety of comparisons with other standardized lab models

20 and we did work with, in particular, rainbow trout and a little

21 bit with goldfish. One of the things that we found that was the

22 most remarkable was the fact of the very rapid onset of a stress

23 response that we observed in the zebrafish. Perhaps, for those

24 of us that teach physiology, we have all heard of the Q10

25 effect. I suspect this is a perfect example of the Q10 effect

26 that in our hands, they are 28 degrees. At that speed and
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1 temperature, they are very speedy. So the stress response was

2 very much shortened in time and actually shortened in duration

3 in terms of the rapid turn off because of negative feedback. 

4 DR. CATHERINE PROPPER: That was just within

5 the zebrafish? 

6 DR. GLEN VAN DER KRAAK: Correct. With the

7 coldblooded fishes, their response to time course was much, much

8 more prolonged and the stress response continued for a longer

9 period of time before it returned back to baseline.

10 DR. CATHERINE PROPPER: Okay, do you have any

11 plans a similar kind of test with say, tadpoles? 

12 DR. GLEN VAN DER KRAAK: In the short term,

13 no, but I think based on the outcomes of the work, it is

14 certainly something that would be worthwhile considering we have

15 not specifically looked at tadpoles.

16 DR. LINDA YOUNG: I noticed statistical

17 significance was one of your criteria. How was sample size

18 considered in assessing the value of statistical significance? 

19 DR. GLEN VAN DER KRAAK: There are 2 parts to

20 the statistical significance. Number 1 would be in the weight

21 of evidence report. We typically went by the statistical

22 evaluation that was provided in the paper. We would make

23 comments in the narrative if in fact is seemed that the

24 statistical approach was inappropriate and using the test when

25 the Innova design was more appropriate. 

26 In terms of the work that we have done with
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1 the zebrafish, our determination of sample size was determined

2 on the basis including a power analysis based on what we knew

3 about variants from previous work.

4 DR. KEITH SOLOMON: If I could just add that

5 certainly the number of replicates, the number of animals in a

6 replicate, whether 2 are replicated or not, these are all

7 considered when we discuss the design of the study and the

8 hypotheses being tested. If they were at fault, then they were

9 basically reached the score for that particular point. 

10 DR. MICHELLE BOONE: So, am I understanding

11 correctly that in the weight of evidence approach, all the data

12 were included in the literature? 

13 DR. KEITH SOLOMON: We hope all the data were.

14 We have gone back and looked at the older studies and converted

15 those into weight of evidence analyses, which we have not done

16 formally before. We had done it in a sense, but not in a

17 qualitative sense. Dr. Weed put us on the right track and we

18 have been doing studies like you saw some, based on 2012, as

19 these come available, we put them in the system, add them to our

20 displays. That display or that graph in a supplemental

21 information context, you would click on the data point and take

22 it to the evaluation study so you can track back, pull down to

23 the original assessment. 

24 DR. MICHELLE BOONE: I guess I am just

25 wondering why all the points, if all the data available in the

26 literature used, all the points you are following on the left
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1 hand side of the graph suggesting that no effects are found,

2 since some of the literature suggests that there are effects

3 like changes in sex ratio in fish or amphibians or gonad

4 changes.

5 DR. KEITH SOLOMON: There was a study based on

6 fish that reported a change in sex ratio, but there are studies

7 that are not showing that. There is a recent study. It is

8 impressive. I have seen it on zebrafish that showed no change

9 in sex ratio. Both of those are scored. They come out

10 differently. The one is on the Y axis where there is no effect.

11 The other one is out to the right. That is talked about in

12 narrative sense the same way that you would in a critical

13 assessment topic.

14 DR. MICHELLE BOONE: So the work of Tyrone

15 Hayes, was that included? 

16 DR. KEITH SOLOMON: Oh, absolutely. All

17 Tyrone's published work, at least in the work that he has done

18 where he has actually done experimental research, we have not

19 included any of the overview papers and things like that because

20 they come out. I did not have any original work.

21 DR. MICHELLE BOONE: So whether or not you

22 accept the methods as being flawed or not, I mean he shows

23 effects. Would that not at least show up in the right-hand

24 quadrant? I am just trying to understand. 

25 DR. KEITH SOLOMON: Yes, it depends on the

26 seriousness of those effects. If they were linked to endless
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1 outcome pathways, which was explained earlier today, then yes

2 they would move over to the right side of the graphic. So,

3 depending on the strength of the study, they would be either

4 close to the X axis or far away from the X axis. I would be

5 happy to talk to you either now or at the Long Beach Convention.

6 DR. CATHERINE PROPPER: So, just like weight

7 of evidence has its problems in official definition, so does

8 adverse effect. So, who did you evaluate adverse effects? 

9 DR. KEITH SOLOMON: For instance with

10 biomarker effects. I think Ms. Blankenship this morning

11 explained that very well. Changes in the enzyme level in a

12 small amount or minor changes in weight, compared to control,

13 the inconsistent ones are some concentrations, mobs of other

14 concentrations observed, there are obvious concentration

15 response and with enough concentrations you can actually even

16 see that there is a U-shaped concentration response to be at

17 least more than 2 concentrations that do that. Those are

18 considered less relevant and something to do with survival or

19 the numbers of eggs laid that were obviously of more

20 significance because you can rate the gravity to adverse

21 outcomes of population. We are not very far away from the EPA

22 in the way we have done things.

23 DR. CATHERINE PROPPER: So, it is mostly

24 qualitative though?

25 DR. KEITH SOLOMON: No, we actually have

26 criteria change a portion, 10 percent change, 5 percent change. 
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1 We try to make it as transparent as possible so that anyone else

2 could come and look at the same numbers and come to the same

3 conclusion. We are also going through an extensive QA process

4 to make sure we are getting the number correct. 

5 DR. DANIEL SCHLENK: Any more questions? I

6 actually have a couple to throw out there. This is actually for

7 Glen. I think in slide 37, it is your dose response. I am just

8 curious, were there any other time points during that process

9 where you did not see those responses, like maybe you saw

10 nonmonotonic responses in the cortisol response? You are

11 looking specifically at 20 minutes out or 60 seconds. So I am

12 wondering if you looked at 40 minutes or you looked maybe down

13 at say like 30 or 40 minutes, did the dose response curve change

14 based upon the time that you actually took those measurements? 

15 DR. GLEN VAN DER KRAAK: In terms of looking

16 at the RPM speed, we did those specifically at the one time

17 point. We have done other studies where we have done exposure

18 to sex steroids that we looked at multiple time points. In

19 those situations, certainly you could see times where you would

20 have a change in the dose response curve in terms of threshold

21 so that right at the peak time point, you might see a step wise

22 increase whereas if you looked at when the response was

23 diminished after the peak had come, the highest concentration is

24 still showing effect whereas the lowest would be mapped down to

25 control level. So you stop. We did not see anything that I

26 would have deemed to have been a peculiar response. They follow
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1 an expected pattern of response or at least what we expected is

2 a pattern of response.

3 DR. DANIEL SCHLENK: Did you ever see

4 nonmonotonic tonicity in any of those responses?

5 DR. GLEN VAN DER KRAAK: So far no, not in

6 terms of stress related responses.

7 DR. DANIEL SCHLENK: Thanks. The other

8 question is for you Keith. I noticed the endpoints that you

9 selected, you looked at testes ova and you have had survival, I

10 guess. 

11 One study that came to mind, and I just know

12 this because while I was editor it came through, was the Tillitt

13 study. That was a reproduction study. I am wondering if you

14 did the weight of evidence in terms of, you know, some sort of

15 reproductive output, reproduction as an endpoint. Is that part

16 of the procedure that you plan to do, or is that included

17 somewhere? 

18 DR. KEITH SOLOMON: Yes, it is included. I

19 did not put that up as an example because of the endpoint that

20 we would be interested in there is a number of eggs laid or

21 number of spawning events. There were only very few studies

22 that actually measured that endpoint. There was one study

23 conducted by the U.S. EPA as part of its validation for the

24 endocrine screening techniques using the same species found in

25 following standard protocol for that endpoint of the short-term

26 study, based on no effects.
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1 There was another study published in the

2 literature, which did not show any effects, a smaller study, but

3 also looking at the same endpoint. The Pruett study did not

4 show the effect until later on in males, of the 2 males in each

5 container. The effects only became obvious after they were no

6 longer males, which is not part of the standard protocols. 

7 It is difficult to know whether that was just

8 a variance between the males coming in at the point or whether

9 it was truly an effect. It did score as an effect. It is

10 there, it is away from the X. There are 2 others lined up on

11 the left axis. At that point, it is out there. What you have

12 to do there is look at the clustering. We have more data there

13 obviously and have more data to that clustering. That

14 clustering once analyzed can be a false sense of variance in 2

15 directions of whatever it is. 

16 But of course when you do that, you are hiding

17 a lot of data, or not hiding, but it becomes obscured by 2

18 uniques.

19 DR. DAN CAMPBELL: Just to add, we could

20 provide additional information on that, written, if it would

21 help, on the materials that Keith just mentioned. 

22 DR. DANIEL SCHLENK: Not for me, that is fine.

23 I was just curious of how you would help with that particular

24 weight of evidence approach. It was question curiosity. 

25 DR. JOHN RODGERS: I just wanted to ask if the

26 weight of evidence approach is interesting form of data
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1 analysis, another way to look at that. Is there any plan to

2 expand that to look at maybe a thousand data? 

3 DR. KEITH SOLOMON: Dr. Rodgers, very

4 interesting question. I do not know at this point, but it

5 certainly could be used. It could be used for similar kinds of

6 criteria. There would be a different format allowing for

7 additional toxicity study as done in laboratories. There are a

8 number of concentrations used, range of concentrations, the fact

9 that the chemistry parameters were measured (inaudible) controls

10 all of these things common to both lab and crop studies used

11 that one could certainly use.

12 DR. JOHN RODGERS: Just for the record, I want

13 it to noted that it looks like you are using those weight of

14 evidence study to develop tests on hypotheses and then your

15 colleague is testing those policies, which is interesting way.

16 DR. KEITH SOLOMON: Not deliberately. I

17 think when Dr. Van Der Kraak was interested in space physiology,

18 the effects of chemicals, before we started this, it is in a

19 sense, the distribution of the studies on the diagram could be

20 used to indicate that there is uncertainty. The upper left

21 quadrant would be a quadrant to say of uncertainty. In the top

22 right and the top left, there might be a quadrant of certainty

23 for either adverse or non-adverse effects.

24 DR. MICHELLE BOONE: I just have a question

25 regarding the immune responses on amphibian and fish. You said

26 that when effects were seen, they were often seen at very large
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1 concentrations of atrazine. I was just wondering how you

2 defined very large concentrations? 

3 DR. GLEN VAN DER KRAAK: We had set off our

4 setting the criteria, we established a cutoff of 20 micrograms

5 per liter as being sort of the environmentally relevant

6 concentrations. What we were looking for in our analysis is we

7 were looking for tests that spanned that range, so some

8 concentrations below 20 and some concentrations above 20. So,

9 we have a set of criteria that we report in doing that. In

10 terms of the data, there are a number of studies where they have

11 looked at immune function and they have seen responses at

12 greater than 100 micrograms per liter. There are some studies

13 that show responses with 20 micrograms or less. So, it ranges

14 fairly widely, but I would say when you look at the vast

15 majority, many of them that are showing effects are showing

16 effects at concentrations that are greater than 20. 

17 DR. JAMES MCMANAMAN: I have a quick question.

18 Do we know what the binding cause of atrazine is to the D

19 protein or the IC50? 

20 DR. KEITH SOLOMON: It is certainly not very

21 high. So much can be explained. When you remove a plant,

22 talking about plants presumably, when you move them to a fresh

23 medium, within a very short time, the photosynthetic passthrough

24 is fully operational. It diffuses off relatively rapidly, but I

25 do not know the binding concentration.

26 DR. JAMES MCMANAMAN: Does anyone know that
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1 number? Is that notable? 

2 DR. KEITH SOLOMON: I think it is notable. I

3 will certainly be more than happy to chase down that number and

4 get it for you.

5 DR. DANIEL SCHLENK: Thanks a lot gentleman. 

6 I think we are going to go ahead and switch to topic B now and

7 move forward. Is it Dr. Brain first? 

8 DR. RICHARD BRAIN: Good afternoon. My name

9 is Richard Brain. I am the lead toxicologist with Syngenta. Of

10 course, I would like to thank the panel for taking the time to

11 listen to the information that we are presenting this afternoon.

12

13 Essentially, I will be providing overview of

14 the methodological approach developed by the EPA for specified

15 level of concern for atrazine in the midwestern streams,

16 specifically focusing on the sensitivity of this methodology to

17 calibration as well as assessment period, and investigating

18 whether or not you actually need a model based on the approach

19 taken. If so, the imputes for developing this methodology was

20 largely to develop a tool in order to translate exposures

21 measured in the environment into some metric of comparable

22 biological effect. 

23 The approach undertaken to do this is actually

24 employed on a model as well as the robust micro/mesocosm

25 dataset. To use this dataset and the model, you essentially

26 take 2 approaches. The first would be one of validation. In
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1 this case, you would take a complex ecosystem model and see if

2 that model can simulate the effects in the individual

3 micro/mesocosm studies if it was parameterized appropriately and

4 develop an LOC using that mechanism.

5 Another approach would be one of calibration. 

6 What I mean by that is you are essentially presequencing the

7 model to give a predefined output. The Agency's approach

8 reflects that of the later example, one of calibration, where

9 essentially is the calibration or the methodology, not the model

10 that defines the LOC. As Dr. Russel Erickson of the EPA

11 illustrated earlier quite openly, you get quite similar results

12 from both the CASM and the PATI model in terms of developing

13 LOC, which is surprising considering that these are

14 fundamentally different models. The reason you come up with

15 essentially the same answer is because you calibrate the exact

16 same thing in the exact same way. So, it is really the

17 calibration, which is defining the LOC and not the model. 

18 Because of the calibration approach,

19 essentially what this does is render the model highly sensitive

20 to changes in the calibration set. Because you have variable

21 durations in the micro/mesocosm studies, you also have disparity

22 in duration relative to the assessment period. This is a

23 concept that I have done, duration disparity. We will get into

24 the sensitivity analysis that looks at both of these concepts

25 throughout the presentation. 

26 Now because calibration is so fundamentally
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1 important, essentially what we did was several permutations. 

2 One of the permutations was to incorporate the collective

3 recommendations in the Giddings report in terms of calibrating

4 the model. When you do that, essentially what you come up with

5 an LOC of 40 parts per billion for 30 days or 25 parts per

6 billion for 60 days. Dr. Giddings will get into the

7 justification for the cause and selection of scoring that was

8 utilized in this calibration process later in the presentation. 

9

10 The third component is whether or not you

11 actually need a model in order to define the LOC. Because

12 calibration is the key, and that is the only step in the process

13 where you truly incorporate ecology and biology, you actually

14 need a model in order to develop an LOC. The analysis that we

15 conducted, in this case do not. We will show you why that is

16 later in the presentation. 

17 In order to sort of preface the sensitivity

18 analysis, I am going to run over basically the computational

19 mechanics to the PATI model. Dr. Erickson and Dr. Farruggia

20 already covered it this morning, but for clarity, I will just go

21 over the process again.

22 Initially, the first step in the process is to

23 develop an underlying toxicity distribution. This, in essence,

24 is a genus sensitivity distribution where each point on this

25 curve represents a geometric mean of multiple species with the

26 same genus. This data is expressed as specific growth rate and
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1 was developed by Dr. Erickson and is available in the appendices

2 of the White paper. 

3 So, you then have where you are continuing

4 your distribution from which the model sample is from. The

5 first step the model does is to take a predefined number of

6 subsamples from this distribution and essentially create a

7 number of hypothetical artificial species. From these

8 hypothetical species, you basically take an average to develop

9 what in essence is a hypothetical concentration response

10 distribution. What you are doing essentially is deconstructing

11 the original genus sensitivity distribution into artificial

12 components and then reconstructing it into a concentration

13 response period hypothetical. From this hypothetical

14 distribution, then what you do is you take 1000 subsamples for a

15 given day. For a specific concentration of atrazine on that

16 day, you would calculate the percent reduction in growth for

17 those 1000 species, and then calculate the average reduction in

18 growth across those 1000 species for a given day. That is what

19 is known as the average daily effect. 

20 For an assessment period, what you would do is

21 do this for every day in the assessment period and then take the

22 cumulative summation of those average daily effects over the

23 assessment period. That is what is known as the cumulative PATI

24 score, which is expressed as percent effect day. 

25 Now, the key step in this whole process is

26 then to plot the individual COSM study that you run through the
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1 model. You would plot to cumulative PATI score on the X axis,

2 or alternatively, you could simply provide the time integrated

3 concentrations of these COSMs. For the purpose of illustration

4 right now, we will focus on the PATI output. For each COSM

5 study, you plot the PATI outcome on the X axis and then

6 essentially, the COSM part delineated in a binary fashion

7 between effects and no effects, effects being a score of 1 and

8 no effects being a score of 0. This is subjectively defined. 

9 The COSM studies are in effect subjectively delineated into

10 categories of effect and no effect. 

11 Ultimately what happens then once you have

12 plotted this data is you apply a logistic regression curve to

13 the distribution and then calculate the 50th centile, which

14 represents the LOC. 

15 Once you have done this, the final step is to

16 then, you have developed your LOC, then you basically run each

17 of your chemographs from the sites of interest through the

18 model, and then that provides you with the accumulative PATI

19 score for that specific location at that specific time. The

20 ratio of that is to the LOC is then turned the effects

21 exceedance factor or effects exceedance factor is greater than

22 one would indicate a biological effect.

23 So, now that we have the mechanics of PATI

24 understood, the first aspect of the sensitivity analysis is to

25 look at duration. This is specifically looking at assessment

26 period, the effect of assessment period on the LOC relative to
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1 the duration of micro/mesocosm, which vary in length from 7 to

2 356 days. 

3 What are the attributes essentially of the

4 distribution of COSM studies? Well we will first indicate that

5 the Agency specified duration is for 60 days. Now, if you are

6 looking at the average duration for a microcosm study, that

7 would make some sense because the average duration is 57 days,

8 which is reflected on that specific assessment period. That

9 estimate is so misleading because you essentially got 6 IDs from

10 a single study, which are graded in 300 days, which is

11 artificially weighting the distribution to the longer duration. 

12

13 Now, a much more appropriate estimate of the

14 cycle tendency would be the median value. So, the median

15 duration of COSM studies is in effect 30 days. You can see a

16 big discrepancy between the average and the median here. It is

17 also worthwhile to point out that about 70% of the IDs are less

18 than 50 days and over half of them are less than 30 days. You

19 have quite a disparity between the duration of the vast majority

20 of COSMs and a select assessment period of interest. 

21 You might be asking yourself, what difference

22 does that make in terms of the LOC? Well, if you can in effect

23 plot the LOC versus duration, if all the COSM studies were at

24 least as long as or longer than the assessment period interval

25 of interest, we expect to see an increasing trend continually

26 with duration. So, that is as your accumulative PATI score
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1 increases for every day in that assessment period, you would

2 expect to see your computer change in proportion to the

3 duration. 

4 That is actually not what we see. What we see

5 is this plateauing effect. There is truly a good reason for

6 this. If you conceptualize if you have an assessment period of

7 60 days and you have a microcosm study that is 14 days,

8 essentially what you are doing for those 14 days is doing a

9 cumulative summation of the PATI output LOC for this. The first

10 14 days, you are looking at cumulative change. After the

11 balance of the 46 days of that 60 day period, that in effect

12 does not change because you have exceeded the duration of that

13 microcosm. 

14 So then what happens is when you start to

15 exceed the vast majority of the duration of COSM, essentially

16 what you do is start to plateau because you no longer increase

17 your accumulative score over time. This is the duration

18 disparity that I was discussing earlier. So, you get a

19 plateauing effect. This plateauing effect essentially the point

20 of inflection occurs around 30 days. 

21 Now to put this in the context of the

22 monitoring program, if you looked at the same exercise in terms

23 of the effects of exceedance factor, you see a similar trend,

24 although there is a slight discrepancy at the start. The reason

25 for this is that for small averaging periods for you site

26 chemograph, essentially what you got is a random variable
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1 atrazine concentration over short averaging periods, which can

2 lead to higher concentrations relative to LOC. So, that is why

3 initially start set are higher. As you averaging period

4 increases, you are essentially averaging some of that

5 variability out, and then you start to see that reduction down

6 to baseline. 

7 Essentially what happens is then you start to

8 see this increasing trend as you do for the LOC, except that the

9 point of inflections get further out because your site

10 chemographs are typically 120 days. So, you are getting a

11 cumulative increase in the PATI score for 120 days where as from

12 the mesocosm studies, it is quiet for the duration of the COSM

13 studies. So this COSM again, this point of inflection and your

14 plateauing effect. 

15 What this analysis essentially demonstrates is

16 that assessment period can have a profound impact on the LOC

17 relative to the duration of the vast majority of COSM studies. 

18 Now, in order to put this in the biological context, if you were

19 considering a study that was 14 days and asking whether or not

20 it was scientifically appropriate to extrapolate that effect out

21 to 60 days, there is a fundamental assumption there. That study

22 was designed, measured and analyzed specifically for a 14 day

23 window. Essentially once you have come in again and assume you

24 can extrapolate that effect is that the system is not dynamic

25 and you can extrapolate biology and ecology through time, which

26 is simply not scientifically appropriate. So, the assessment
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1 period needs to reflect the duration of the COSM studies of

2 interest. 

3 Another biological consideration would be to

4 look at what is the average time to effect and time to recover

5 even for the slow species. These would be rooted in aquatic

6 macrophytes. The typical test duration for these is 14 days. 

7 What you see is that within this window, you can have a

8 significant impact to the aquatic macrophyte, but within 14

9 days, it is able to return to control levels. Essentially, you

10 have a 28 day window to have exposure, effect and then recovery

11 even in the slowest growing macrophytes. 

12 As well, another thing to point out is that

13 even though they are constant exposures to atrazine, you do get

14 recovery with photosynthesis in low yields. What you get is

15 (inaudible) macrophyte even under prolonged exposure. So, 28

16 days seems to represent in the biological context, reflect the

17 primary producers as well as the studies of interest here. 

18 Again, the highlight would be that 30 days is a much more

19 appropriate and is much more representative of biological data,

20 which is being used to populate and calibrate the model. 

21 So intuitive activity in knowing the effect of

22 the assessment duration on the LOC, we need to consider if I am

23 only interested in a 30 day LOC, how about I just select those

24 studies that are 30 days or longer, or if I am looking at 60

25 day, just those studies that are 60 days or longer. When we do

26 this activity, essentially what you find is that based on the
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1 EPA's 87 COSMs, selecting those that are just the study duration

2 of interest or longer, you get 100% increase in the LOC, which

3 shows a very high degree of sensitivity to the LOC in respect to

4 COSM selection.

5 Similarly, if you look at the 60 day values or

6 30 to 60 day values for the cumulative effects from the

7 recommendations of the Giddings report, that change is anywhere

8 from 24% to 200%. So, again including just studies that are

9 reflective of the duration of interest has a profound effect on

10 the LOC. 

11 In order to illustrate the importance of

12 calibration, I have kind of come up with a diagram here. If you

13 would conceptualize the tap essentially is calibration where the

14 COSMs are defined with flow. The flow, if you will, is the LOC.

15 It really does not matter what model you put behind that tap. 

16 If you are calibrating the same thing in the same way, you will

17 get pretty much the same flow. So, that is why we have come to

18 the conclusion that calibration is the key to defining the LOC

19 and not the model. 

20 So what does this mean in terms of the

21 sensitivity to the calibration? Well, in order to evaluate

22 this, we look at various permutations based on the core set of

23 87 COSM IDs identified by the agency. We basically did 1

24 permutation, which was the interpretive suggestions from the

25 2009 SAP, which was the deletion of 3 data point, which would

26 have been flawed studies of poor quality, and then rescoring of
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1 2 points, which would have been slight transient or temporally

2 in the system effects to probable unaffected score of 1 to an

3 effect score of 0.

4 We also looked at an alternate set of

5 permutations that were recommended from the Giddings report that

6 Dr. Giddings will discuss here in just a second. Basically,

7 these were looking at the influence of deletions and restoring. 

8 These included those interpreted suggestions from the 2009 SAP

9 with additions as well as splitting. These would be previous

10 IDs that had about 2 groups, the periphyte and the macrophyte,

11 which were split into 2 separate IDs as well as the impact of

12 adding the COSM IDs from a single study, the Baxter study, and

13 then looking at the cumulative influence of all those alternates

14 collectively. Before I proceed with the sensitivity analysis to

15 demonstrate just how profound the calibration can have on the

16 LOC, Dr. Giddings is going to go through the justification for

17 essentially these permutations. 

18 DR. DANIEL SCHLENK: Actually, are you the

19 next 20 minutes? If we can, is it possible to actually have

20 questions now on what you presented before we go to Dr.

21 Giddings? Okay, let's do that and then we will come back after

22 a break and Dr. Giddings can go forward. Do we have any

23 questions for Dr. Brain? 

24 DR. JAMES MCMANAMAN: I have a question, you

25 are arguing that you cannot lose scale biological responses over

26 time, yet as a reason for focusing on a 30 day, looking back
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1 over the watershed data and it varies from 15 days to well over

2 60 days. How do you reconcile one you could do for 30 days when

3 you have already said, well I cannot scale over periods? How

4 would you incorporate those longer data where there are updated

5 levels? 

6 DR. RICHARD BRAIN: Essentially what we are

7 proposing is that the assessment interval of interest actually

8 has to be reflective of the durations of the micro/mesocosm

9 study that are being used to calibrate that dataset. So, what I

10 was simply prosing was that if you have a study that is a

11 predefined interval and it was assessed and evaluated over that

12 predefined interval, it is not scientifically appropriate to

13 then extrapolate those effects in terms of biology and ecology

14 through time. 

15 Now, it is true that you will have chemographs

16 that have longer durations, but essentially what you are doing

17 is for that chemograph, you are looking at the maximum

18 cumulative PATI score over that assessment period. Therefore,

19 the chemograph is assessed completely, but you are looking at

20 the maximum cumulative 60-day PATI score. 

21 DR. JAMES MCMANAMAN: Would you not expect the

22 PATI score to differ than, the PATI score does not take into

23 account time, but do you know that the effects on the COSM

24 effects over a period of 90 days or 60+ days would be taken into

25 account in your model? 

26 DR. RICHARD BRAIN: If you were including just
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1 those studies that were that assessment interval or longer, then

2 yes, you would be fairly sure that those specific effects were

3 representative of an exposure, predefined concentration in that

4 interval. 

5 DR. TIMOTHY ELLSWORTH: The question is why

6 just stick with one 28-day period? I am thinking of airborne

7 exposure guideline levels for example, which have 10 minute

8 intervals or 30 minute intervals. They go up to 8 hour

9 intervals. I am looking at the durations of the study. Are you

10 saying that there is insufficient data in the 30 to 60-day

11 duration to establish an exposure related to that? I can see

12 the median is 28 or whatever you showed there, but why not more

13 than 1 cutoff here? What is the argument against having what

14 they do in air exposure? 

15 DR. RICHARD BRAIN: That is certainly

16 conceivable, but again, if you are including just the studies

17 which are representing the duration of interest, obviously the

18 longer your assessment period the more micro/mesocosm studies

19 you can exclude from that activity because you have very few

20 study durations that are actually 60 days or longer. You could

21 look at using a shorter interval, but then you also have to

22 balance in terms of the biological context as well as averaging

23 the actual site chemographs. Because as you see with very short

24 averaging periods of time, you have a lot of variability in

25 terms of the concentrations that are reliable. So, you do need

26 an appropriate averaging period or you get the same average of
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1 the concentration. Over short periods, you will have very

2 variable distribution, whereas over longer averaging periods,

3 you get a much more consistent exposure. So, it is weighted

4 essentially the attributes of the monitoring data with the

5 biological data of the micro/mesocosm datasets. You could look

6 at multiple durations, but that has to reflect both of those

7 considerations. 

8 DR. DANIEL SCHLENK: Okay, let's take a break.

9 Let's go 10 minutes again, so we can get out of here at a

10 reasonable hour. Dr. Giddings, would you like to pick up where

11 we left off? 

12 DR. JEFF GIDDINGS: Thank you. I am Jeff

13 Giddings. I am consultant at Compliance Services International.

14 I am here speaking on behalf of Syngenta. I appreciate the

15 opportunity to talk to you. 

16 My background is pretty heavily involved in

17 the microcosms and mesocosms. As soon as I finished my

18 Doctorate in Aquatic Ecology, I went to Oak Ridge National

19 Laboratory and spent 10 years working on developing designs for

20 indoor pond microcosms and outdoor experimental ponds. I

21 followed that with a 10 year stint in a contract laboratory

22 doing different mesocosm studies that had just come on line at

23 the time, also developing methods for indoor and outdoor

24 microcosms and smaller scale versions of the different

25 mesocosms. During that time, I was involved in building a

26 guideline for COSM studies that eventually became the OECD
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1 method. I was involved in a couple of workshops in Europe, one

2 called HARAP, one called CLASSIC. These were workshops

3 specifically aimed at developing methods for designing and

4 interpreting COSM studies and other higher tier aquatic effect

5 studies. For the last long time, I have been doing risk

6 assessments, and a large portion of that is involved of

7 proactively viewing COSM studies, mining them for information,

8 interpreting them with a variety of chemicals. 

9 Beginning with atrazine, which I have been

10 working on for about 17 years now, when in was first asked to go

11 through all the COSM studies on atrazine and evaluate them,

12 which I did in 1994 during the special review of atrazine this

13 was incorporated into a report of 1995 and the Solomon et al.

14 publication in 1996 and another round of review in a follow-up

15 report in 2000. Around that time, during that development of

16 the IRED, the MOA Ecological Subgroup asked for help in

17 providing data and guiding the analysis of COSM data for the

18 MOA. The COSM review, in fact the whole risk assessment was

19 updated in 2005 and published in a book. Since then, we are

20 commenting on atrazine COSMs in the 2007 SAP and 2009 SAP and a

21 couple of reports that have been submitted to the EPA since

22 then.

23 Based on all of this, my conclusion based on

24 just looking at the COSM data is that there are very few

25 ecologically significant effect on plant communities below 30

26 parts per billion. That is the conclusion offered. I will now
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1 take you through some of reasoning behind that and give you a

2 few examples of the specific changes in the database the Dr.

3 Brain mentioned before I came on. 

4 These are the EPA scores that the COSM

5 database a whole. The bottom panel is just a blown up range up

6 to 50 parts per billion. It is the same data as the top. You

7 can see the COSMs that are scored at zeros on the bottom and

8 COSMs that are scored as 1 on the top. We went through every

9 one of those points. We went through the original source of the

10 data, evaluated it by using some criteria notes that I will

11 describe in a minute. 

12 Based on that, we came up with these

13 recommendations that Dr. Brain just summarized. Some of the

14 points would need to be rescored. Essentially, they were scored

15 as 1s as effects. Now we believe that the data supports scoring

16 those at zeros. Three of them are from studies that are

17 unreliable, actually 4 points from 3 unreliable studies. I will

18 explain the most important of those in a minute. There were

19 some data points that represented the multiple plant groups or

20 multiple studies. When the database was first put together, the

21 idea was that a given study generated results for different

22 taxonomic groups for phytoplankton, periphyton and microphytes

23 that each of those would get separate points in the database. 

24 That was carried out, but not consistently. For the 2009 SAP,

25 we pointed out a few of those situations where multiple plant

26 groups were represented by a single point. The SAP advised us
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1 to go back and go through that analysis for all the points and

2 split them out as appropriate. So, we have done that. Then

3 there were 2 recent studies that provide reliable data that

4 should be included in the database. 

5 Based on all that, we deleted some points,

6 rescored some points and we have added a bunch of points. We

7 end up with a revised COSM database that you see here. I will

8 go through some of the specific rescorings and deletions and

9 give you an idea of how this was done. 

10 The effect scoring was based on some objective

11 criteria. It still to some extent is a subjective process, but

12 the key is to have criteria that are as objective as possible so

13 that different people looking at the same study will reach the

14 same conclusions about the score.

15 Basically, what we are trying to do is

16 distinguish between no effects or effects that are a slighter

17 transient from effects that are pronounced. Those terms really

18 come from Brock's original story system. This was around 2000. 

19 The key then is to examine the results and make a decision as to

20 whether if there were effects, were they significant effects

21 whether they could be considered pronounced or there were slight

22 or transient. 

23 These are some of things that can be

24 considered in reaching that conclusion. Did the statistically

25 significant differences occur on at least 2 consecutive

26 observation dates or were they sporadic? Were they disconnected
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1 at times implying that it is not necessarily an effect of the

2 atrazine, but just random effects from the normal variability in

3 these communities? 

4 Were the effects observed during or shortly

5 after the treatment period, which is consistent with the rapid

6 mode of action of atrazine, or were they delayed, which implies

7 that they might not qualify? 

8 Were the effects consistent with the

9 reversible inhibition of photosynthesis? In other words, when

10 atrazine was delayed, did photosynthesis or whatever the

11 endpoint, return, which is consistent with atrazine reversible

12 mode of action. 

13 Were clear concentration-response

14 relationships observed? If not, it suggests the differences may

15 not have been effects of atrazine.

16 Were the effects transient and if so, when did

17 the recovery occur? This really did not enter into too many of

18 these decisions. If the effects were pronounced, then we would

19 score that at 1 in effects even if later on there was recovery. 

20 But in some cases, effects occurred for a day or 2 after the

21 treatment and then went away. We consider that evidence of a

22 slight or transient effect and we scored it at zero. 

23 So, I will give you some examples of the data

24 points. The details are provided in the report that we have

25 submitted to you. It is a long report and is not really meant

26 to be read end to end. It is really a reference for the most
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1 part, reference material on the studies so if you want to go

2 back and look at the details of some of these studies, why the

3 points were scored as they were, you have some of the

4 information there without having to go back to the original

5 article. 

6 So this is one of the points. Sequin et al.,

7 ID #86, which reported a slight effect at 2 micrograms per

8 liter. The EPA scored it as a 1. It should be rescored to a

9 zero. These are the results of effects on phytoplankton and

10 different phytoplankton classes that were observed in this

11 exposure. You can see that there were no effects on diatoms. 

12 There was an decrease in chlorophyl and chlorophytes and green

13 algae, but only on 1 day. This is supporting a study and

14 samples were taken about 8 times during the course of the study.

15 There was only 1 day and it was well into the study where there

16 was a statistical difference in green algae abundance, which

17 even if it is an atrazine effect, it would be a slight effect

18 over time. There was an increase in chrysophyceae, no effect on

19 cryptophycaeae and no effect throughout the study on

20 chlorophyll-a. 

21 The authors also calculated a similarity

22 index. This is integrating the abundances of all of the

23 phytoplankton taxa. In this analysis, they did identify 2

24 dates, which there were what they found to be significant

25 effects on similarity. In other words, the treated COSMs were

26 significantly different from the control COSMS. There are 2
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1 sets of bars there. There are the dark bars, which are

2 atrazine, and the light bar was a different compound, which is

3 irrelevant for our discussion now, done in separate COSMs. 

4 I have trouble understanding the magnitude of

5 the effects that are indicated by the similarity indices. I

6 have drawn that horizontal line up there to show what the

7 pretreatment level was. The first set of bars were the

8 similarity indices that were derived before the atrazine. You

9 see that there are 2 significant events on day 3 and day 7. We

10 are not very much different from the pretreatment difference. 

11 In fact, it levels thereafter in the study, but it looks to me

12 like they are certainly transient effects and compared to the

13 pretreatment differences, it looks like a slight effect. My own

14 conclusion on this is if this is a slight effect, then how

15 slight would it have to be to be considered slight and to be

16 scored as a zero? If the criteria were no effect at all, then

17 this might be borderline. The criteria for a zero is no effect

18 or slighter transient effect. This seems to be a pretty good

19 example of transient slight effect and should be scored as zero.

20

21 Another study, by Detenbenck et al., these are

22 EPA researchers at the Montezuma Facility as part of the Duluth

23 Lab. These were all artificial streams. It was an outdoor

24 realistic experimental system. They crammed an awful lot of

25 stuff into a publication that had to leave out most of the

26 supporting data. We tried to get that data, and I could not get
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1 it from them. Also had a somewhat strange experiment design

2 where they only had 2 treated streams and 2 controls. They

3 treated those streams with 15 parts per billion for a while, and

4 then ramped it up to 25, then up to 50 and the finally up to 79.

5 So, it makes the interpretation a little bit dicey. 

6 Nevertheless, just taking the results that

7 they reported at face value since we cannot go back to the

8 original data to verify those, but just taking their results as

9 reported, the EPA scored all 4 of these concentrations as a 1 as

10 an effect. So, this table shows the effects that were reported

11 on periphyton. You see at 15 parts per billion, they reported a

12 reduction in gross primary production and no effect on

13 respiration, chlorophyll, production of chlorophyll ratio or dry

14 weight. 

15 The effect on gross primary production did not

16 occur at the higher concentration. So really by our criteria,

17 this does not indicate a dose response, and really if I were to

18 do this again today, we would score that as zero. 

19 Conservatively at the time I did this, we gave that a score of 1

20 despite the fact that the effect that was observed just does not

21 show a dose response. 

22 At 25 parts per billion, the only effect was a

23 reduction in respiration, which was considered not to be a

24 relative endpoint for plant community and gave that a score of

25 zero. There were no effects on periphyton at 50 parts per

26 billion, so that score is zero.
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1 At 79 parts per billion, an increase in

2 respiration, which again I did not give much weight to, but a

3 reduction in productivity per chlorophyll ratio, which has been

4 observed in single-species tests and other studies that 1

5 response of a plant to atrazine exposure is to increase the

6 amount of chlorophyll because as you have seen, the effect of

7 atrazine is to reduce the efficiency of chlorophyll by producing

8 more chlorophyll, a plant can to a certain extent, overcome

9 that. So with an increase in productivity for chlorophyll is an

10 expected response, so I scored that as a 1. 

11 This study also reported effects on

12 macrophytes. This is an example where the 2 plant roots should

13 be split into 2 sets of points. There were effects on

14 macrophytes at the highest concentration, 75 parts per billion,

15 but not at the lower concentrations. These represent some of

16 the split point that you heard about.

17 Another example, the series of studies

18 expanding over several years by DeNoyelles et al., at the

19 University of Kansas, these took place in the late 1970s, early

20 1980s. They were large, highly realistic experimental systems

21 and experimental ponds. They really were the prototype for what

22 became the FIFRA COSM protocol. They presented these results on

23 a lot of different components of the system, on fish, on

24 insects, on plants and a number of species in reports. There

25 are 7 publications over the course of 15 years. Those

26 publications were sometimes contradictory with regard to the
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1 effects that were observed at 20 parts per billion. The actual

2 underlying data were difficult to find. The relative ones were

3 to the extent that they were available were in these theses. 

4 The results at 20 parts per billion were inconsistent across

5 experiments, which were repeated over several years with some

6 complications such as adding grass carp to the ponds in one of

7 those years to see what the combined effects of atrazine and

8 grass carp would be on aquatic plants. 

9 For reasons that you can read in our report,

10 if you want to get in deeper, we rescored these as zero, because

11 these effects are not well documented. They are not consistent

12 and they are slight despite the characterization of scoring from

13 the EPA. 

14 The study by Lampert et al., these are the 2

15 lowest points in the COSM dataset. These are at concentrations

16 of 0.1 and 1 parts per billion. Researchers took lake water,

17 enclosed in bottles, resuspended in the lake for up to 42 days. 

18 During that time, they measured effects on photosynthesis and

19 dissolved oxygen and chlorophyll of the phytoplankton. The EPA

20 scored both of these points at 1. Because these are outliers,

21 they are extremely low concentrations. They at the very least

22 warrant careful scrutiny. 

23 This is just to emphasize how low these

24 concentrations were. The curve on the right is the genus

25 sensitivity distribution, which you have seen before in the

26 EPA's numbers. They are EC50 were specific growth means for
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1 each of the 17 genre. Those 2 arrows are the concentrations

2 which Lampert claimed caused effects on phytoplankton in those

3 systems. 

4 This is an example of the results that we

5 reported that the WOC represent warm and cold. They apparently

6 repeated this study at different times of the year. They did

7 not provide all the details on this. They said they were only

8 attempting to represent the results, though it was hard to dig

9 any deeper. What it shows is that this was net productivity. 

10 The thing you can expect to respond immediately to atrazine

11 concentration if atrazine was causing an effect, on day 3 of the

12 warm study and day 3 and 7 in the cold study, there were no

13 differences in net productivity at 0.1 parts per billion. After

14 that, these effects started to show up. Now, this is net

15 productivity. So, this is actually measured as change in oxygen

16 concentration. It reflects both the algal photosynthesis and

17 respiration of the whole community. 

18 So, net productivity can go down either

19 because of photosynthesis going down or because of respiration

20 increasing. 

21 This is the dissolved oxygen results for that

22 0.1 parts per billion and also the 1 part per billion study. 

23 The same sort of thing, the steps were delayed, 7 to 12 days

24 before the effects on dissolved oxygen started to show up,

25 inconsistent with the immediate mode of action of atrazine on

26 plant photosynthesis. 
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1 Here are results for chlorophyll and

2 particularly organic carbon. They represent a 1 microgram per

3 liter study. You see there are really differences in

4 chlorophyll decline and a particular organic carbon increasing,

5 again with this delay of no effect for the first 7 days,

6 delaying the effects. The first shows up after 12 days of

7 exposure. What these results show is that there is a switch

8 from an autotrophic system with chlorophyll going down to a

9 heterotrophic system, which is the result indicated by the

10 particular organic carbon going up. There is a lot more

11 particular organic carbon showing up there than could be

12 explained just by the gap of the phytoplankton that were there

13 in the beginning. What is happening here is the phytoplankton

14 are being replaced by a microbial community. 

15 To cut to the chase, this is in fact a

16 response to the solvent that was used in the study, not to the

17 atrazine that was applied. Lampert et al., in their paper it

18 stated that no solvent was used. This paper reported results

19 from a whole series of studies, not just these microcosms. 

20 There really is no statement, the no solvent statement, applied

21 to the other studies because when you got to the primary source

22 of this data, which is the dissertation by Fleckner in German,

23 where it is clearly clear that ethanol was used as a solvent in

24 these COSM studies. There was no mention of a solvent control. 

25

26 When Brock et al., did their review of
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1 herbicide COSM studies in 2000, he cited this study as an

2 example of the enormous effects of ethanol, and they rejected it

3 in their analysis. They threw it out without reason. 

4 I should point out that this study violates 2

5 of the acceptance criteria that the EPA mentioned this morning. 

6 The primary source is not Lampert, but Fleckner, and Fleckner is

7 in German, for reasons that I do not agree with, but apparently

8 the EPA would not consider it if it was in English. On that

9 basis alone, these points should be included. Just the analysis

10 of the results indicate that this is not a valid study and

11 should be deleted from the database. 

12 Finally, Baxter et al., this is a new study,

13 which really should be added to the database. This study was

14 done at the University of Guelph a couple of years ago. It used

15 what are now modern 21st century COSM techniques with large

16 outdoor systems with sediment water of course, phytoplankton,

17 periphyton and macrophytes as well as a collection of

18 invertebrates and frogs, 3 replicates, 4 treatment levels, so it

19 is a good solid experimental design. Observations are made over

20 a period of 70 days. The only effects that were seen were

21 reductions in macrophyte biomass at 100 parts per billion, no

22 other effects at 1, 10 or 30. These results are sound and

23 should be included in the COSM database. 

24 What we have done then is deleted the 2 points

25 on the far left there of the effect score. Those were the 2

26 Lampert data points. There is another point that you see there
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1 with an X on it, which I will not go into here, but it is in the

2 report if you want to check on that. It was at a higher

3 concentration, so it does not show up there. It was also

4 suggested that it should be deleted. I think 3 of those 4 were

5 ones that the SAP suggested in 2009 should be deleted. 

6 There are a number of points that we scored

7 from 1s to 0s that we see, resulting in this revised database

8 that you see her, which read that the overlap between the 0s and

9 the 1s is certainly reduced. We see no effects below 10 parts

10 per billion. A few effects at the 10 to 30 parts per billion. 

11 Not many points between 30 and 50, and then pretty consistent

12 effects above 50 parts per billion. So, that is where the

13 revised COSM database will take us. 

14 Now we want to think about exposure duration. 

15 PATI as a CASM, assumes that longer exposure to a lower

16 concentration would give the same effect as shorter exposure to

17 a higher concentration. The COSM data do not support that. 

18 This is the EPA's dataset before the revisions that I have just

19 proposed. You can pretty much draw a horizontal line. I have

20 drawn one at the 10, but the point is that there is no

21 particular indication that the higher exposure duration, it

22 takes less atrazine to cause and effect. Looking at our revised

23 dataset, of course the number change, but the point is still

24 there that we do not see increasing effects with increasing

25 duration. This is consistent with the mode of action of

26 atrazine. If it has an effect, it acts quickly. If you expose
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1 a COSM to a concentration that does not cause an early effect,

2 watching it for the next 60 days, you are not likely to see

3 those effects. The effect either happens or it does not. It is

4 not duration dependent at least now based on the empirical data

5 we have here from COSMs. 

6 So, the conclusions from all of this is there

7 are certain points in the database that should be rescored or

8 deleted or split as I described. There are reliable data from

9 recent studies that should be added. The conclusion again is

10 that based on all of this, we do not see empirically very many

11 effects below 30 parts per billion in the COSM database. 

12 Thanks.

13 DR. DANIEL SCHLENK: I think you want to

14 finish that off, is that correct Dr. Brain? 

15 DR. RICHARD BRAIN: Yes.

16 DR. DANIEL SCHLENK: Why don't you just go

17 ahead and then just finish this one off until we break and then

18 you have your next presentation. 

19 DR. RICHARD BRAIN: Prior to Dr. Giddings's

20 talk, I introduced a number of permutations, which were

21 essentially designed to evaluate the sensitivity of the PATI

22 methodology to the micro/mesocosm selection and scoring. These

23 permutations were not random selections of the COSM dataset. 

24 They were rooted in rigorous scientific critical review as you

25 just heard in Dr. Giddings presentation. Really, this is the

26 key slide in my presentation. 
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1 What it demonstrates is a profound sensitivity

2 of the PATI to the inclusion/exclusion of specific studies, or

3 the additions we are scoring in specific studies. For example,

4 if you just consider the interpreted suggestions of the 2009

5 SAP, this would be removing 3 data points, a rescore of 2, you

6 get a 62% increase in the LOC, which is a pretty dramatic

7 increase. 

8 Additionally, if you look at just adding a

9 single study, this would be Baxter and using those 12 IDs

10 essentially with 3 taxonomic root of poor concentrations, you

11 also get a consistent increase of 60%. Adding a single study

12 results in a 60% increase in the LOC. If you look at the

13 cumulative changes suggested by Dr. Giddings's report, what that

14 results in is a 330% increase in the LOC. That is a pretty

15 dramatic increase. 

16 The EPA's own analysis indicated that even

17 deleting or rescoring a couple of studies can increase the LOC

18 from 4.2 to 7.4, which may not seem like a very large increase,

19 but if you consider it, that is nearly doubling the LOC. That

20 is a pretty profound increase. If that was, for example, 42

21 versus 74, that would be a much more dramatic change. What this

22 demonstrates is the extreme sensitivity of the model to

23 calibrations, specifically the study selection and scoring. 

24 Another point to consider is that you see this

25 same sort of duration dependent behavior where you get that

26 plateauing effect regardless of the datasets. 
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1 Another way to visualize the data would be to

2 essentially plot distribution of the COSM data points as they

3 were subjectively scored for each corresponding permutation,

4 where you have a cumulative PATI score percent effect on the X

5 axis for individual COSMs, and then binary delineation on the Y

6 axis. Here we see where the LOC falls out for the Agency's core

7 set of 87 COSMs now. If you incorporate in the interpreted

8 suggestions from the 2009 SAP, again just deleting 3 studies and

9 a score of 2, that LOC increases to 201. So, you can see

10 basically you are removing a couple of the flawed or low-quality

11 studies shown in the upper left-hand portion of the graph, which

12 really shows how much those couple of points can really

13 influence the progression as applied to this data. 

14 Now if you again look at the cumulative

15 changes suggested from the Giddings report, that LOC increases

16 to 537, again because essentially what you are doing is you are

17 removing the low-quality studies, which are in the upper

18 left-hand corner. Those would be studies that were previously

19 indicating effects at low concentrations, and then essentially

20 adding a number of studies, which do not show effects at higher

21 concentrations. What that does is really shifts that logistic

22 regression. Here you can see it on the 3 distributions overlay,

23 and you can see that. As you sequentially change the

24 micro/mesocosm dataset, that calibration just how much prime

25 influence that has on the LOC. 

26 Another way to visualize this data would be to
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1 essentially overlay the LOC relative to scatter plot

2 distribution of the micro/mesocosm dataset with duration on the

3 X axis and average study concentration on the Y axis. In this

4 first plot, we do this for the EPA's 87 set of IDs. 

5 Now, when you pick this cumulative figure and

6 then incorporate again the changes suggested by the 2009 SAP,

7 see those points that are very low indicating that that is a

8 very low concentration. This appears as distribution. Not only

9 does the LOC increase, but actually provides a much more

10 representative fit relative to the data. 

11 Again, when you do the same exercise

12 incorporating the recommendations from the Giddings et al.,

13 report, you see the same thing where you get an increase in the

14 LOC, but not just that. You get a much more representative fit

15 of the LOC relative to the distribution of the data, which

16 really indicates that this particular LOC is much more

17 reflective of the biology in the underlying calibration dataset.

18 Just to sum up the conclusions from this

19 particular exercise, you can see that the calibration has a

20 profound influence on defining the LOC economic change anywhere

21 from 30% to 300% depending on how the calibrating dataset is

22 scored and selected. Really, if you take into the consideration

23 the recommendations by Giddings and then calibrate the model

24 accordingly, what you come up with, whether you calculate the

25 constant exposure LOC as described by Dr. Erickson earlier, is

26 an LOC of 40 microgram per liter for 60 days or 25 parts per
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1 billion for 60 days. This is anywhere from 4 to 5 times higher

2 than the current level of concern defined by the Agency, which

3 speaks to a discrepancy represented in this of the LOC relative

4 to that underlying data and the importance of correct scientific

5 interpretation of the COSM dataset. 

6 When you essentially bring this back to the

7 field and you look to field chemographs and your effect

8 exceedance factors, you see the same sort of behavior, where

9 incorporation of the 2009 SAP suggestions can result in 45%

10 reduction in the amount of chemographs exceeding that value of

11 1. Similarly, adding a single study results in a 47% reduction.

12 If you look at cumulative changes recommended by Giddings in

13 the Giddings report, what you end up with is a 91% reduction in

14 the effect exceedance factors greater than 1. Again, this just

15 indicates a profound sensitivity of the model to calibration and

16 obviously a profound sensitivity of the effect exceedance

17 factors as a result of that. 

18 So, to sum up the sensitivity analysis,

19 basically what we have found is that through the nature of the

20 methodological approach essentially renders the PATI model

21 highly sensitive to changes in the micro/mesocosm scoring and

22 selection, where even slight changes can have a pronounced

23 influence on the LOC. Similarly, that also results in a

24 profound change in the number of effect exceedance factors that

25 are greater than 1. Another aspect of this is the profound

26 sensitivity of the model to assessment period relative to
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1 duration, which we discussed earlier. 

2 So, what we really want to highlight here is

3 this underscores the necessities for critical scientifically

4 viewed interpretation of the micro/mesocosm dataset in assuring

5 that that interpretation accurately reflects the biological

6 results of those studies, because it does have a significant

7 impact on the model. 

8 The last sensitivity analysis that we did was

9 to pose the question, do you actually need a model at all in

10 order to derive an LOC? The reason for this is that because

11 calibration is the key step in defining the LOC, and that in

12 essence is the only place in the entire methodology where you

13 are incorporating biology and ecology, do you actually need a

14 model to come up with an LOC. We basically did this activity

15 that instead of looking at the cumulative PATI score for each

16 micro/mesocosm, one could just look at the cumulative exposure

17 for each microcosm study. This is the concept known as Time

18 Integrated Concentration or as I introduced earlier as ppb-days.

19

20 So, if you were going to assume that you had a

21 high degree of correlation between the PATI LOC or the PATI

22 output and the time integrated concentration, you would expect

23 that the model output would be naive to the shape of the

24 chemograph. The only thing that would really matter would be

25 the cumulative exposure from that particular study of interest. 

26
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1 So, we basically just came up with a number of

2 synthetic profiles, if you will, ranging from a 5 day exposure

3 of 70 ppb-day or 2-day intervals for 10 events at 17.5 ppb-day. 

4 What we found is that the output from PATI, the percent effect

5 day, did not change whatsoever with respect the curve shape of

6 any of these profiles. Essentially, what this means is that you

7 can have the same PATI output for a very high concentration of

8 atrazine exposure over a short period or a very low

9 concentration for a long period. The model is essentially naive

10 to the shape of the chemograph. So that indicates that you have

11 a high degree of correlation between the model output and

12 exposure. 

13 To illustrate this in terms of a fear, you see

14 just what you expect when you plot the cumulative PATI percent

15 effect day on the Y axis versus the time integrated

16 concentration on the X axis. Here we have 7 and 60 days in the

17 top left and right panel respectively. You do see a very strong

18 correlation between these 2 variables. 

19 You do get some loss of linearity at the top. 

20 This is essentially due to the fact that you can have, for

21 example, an exposure at 1000 ppb that results in a 91% reduction

22 in your percent effect of growth that day, or you can also have

23 a 10X increase in that concentration that only results in, for

24 example, 93% reduction in growth. You can have very large

25 changes in your exposure to high concentrations resulting in a

26 relatively small difference in terms of the PATI output. That
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1 is where you get this loss of linearity at the top. 

2 More importantly, if you actually run the

3 chemographs for 2004 through 2011 collectively, and then plot

4 that versus the corresponding percent effect day for the PATI

5 model, what you end up with as you would expect is a correlation

6 and a near unity. That is a very strong association between

7 these 2 variables, which speaks to the concept of actually

8 whether or not you do in fact need a model to do this

9 interpretation. 

10 When you actually exclude the model or look at

11 the model naive approach, which just looks at the duration, the

12 cumulative exposure from each micro/mesocosm directly and you go

13 through the exact same logistic regression approach that you use

14 in your PATI, except not applying the X axis as percent effect

15 day, it is simply the cumulative exposure for the COSM dataset,

16 what you come up with is a surrogate model that gives you almost

17 the exact same output. For purposes of our activity, when you

18 calibrate the model to the recommendations of the Giddings

19 report, you end up with a 47 ppb for 30 days, or 30 for 60. 

20 This is very similar to the PATI output of 40 for 30 or 25 for

21 60 that we basically derive from the PATI model. 

22 In essence, what this suggests is that because

23 of the calibration is key to find the LOC, you do not in fact

24 need a model, because is it the methodology and not the model

25 that defines the LOC. 

26 In conclusion for this section just to keep
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1 reiterating this point is the calibration ultimately is key for

2 finding the LOC. Because of the methodological approach, it

3 essentially renders the PATI model high sensitive to scoring and

4 selection. Because you have disparity between the assessment

5 period and the vast majority of the micro/mesocosm duration, you

6 get this plateauing effect. When you calibrate the model to the

7 scientifically rigorous critical view as was presented by Dr.

8 Jeff Giddings earlier, what you come up with an LOC of 40 parts

9 per billion for 30 days or 25 for 60, which is anywhere from 4

10 to 5 times higher than that presented by the EPA earlier today. 

11 If you look at what we had done in the last SAP analysis is that

12 if you remove them all completely from this process and just

13 look at the cumulative exposure for each COSM individually, you

14 come up essentially with the same answer, which means that the

15 model is highly correlated between percent effect date to the

16 cumulative exposure in each of the COSM studies. Therefore, a

17 model is in fact not needed. You can do it directly from COSMs.

18 That is it for that particular section.

19 DR. DANIEL SCHLENK: Let's go ahead and take

20 questions now for Dr. Giddings and Dr. Brain. Dr. Klaine? 

21 DR. STEPHEN KLAINE: Jeff, when you looked

22 over the study data for Lampert et al., did they say how much

23 ethanol was used? 

24 DR. JEFF GIDDINGS: Yes they did, and I do not

25 remember off hand what it was. It was a small amount. It was

26 not responsible for loss of oxygen, just the ethanol was
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1 stimulating the microbial community. I think the details on

2 that point are in that report. 

3 DR. KENNETH PORTIER: Can you back up to slide

4 91? So, on the no effect studies, did you look particularly at

5 the 2 on the bottom that are all the way on the right, where you

6 have no effects that you would consider fairly high ppb day or

7 fairly high PATI days? Did you look at all of the studies of

8 just the ones? 

9 DR. JEFF GIDDINGS: We looked at them all. 

10 Those studies were highly artificial. Someone questioned

11 earlier this morning why we need them, why would anyone want a

12 COSM at that concentration, what is the relevance of it. I

13 think the answer that you got from Dr. Erickson was the same

14 answer that I give, which is they are not very rigorous studies.

15 This is a highly artificial system and really does not have

16 much effect on the LOC, but we looked at them all.

17 DR. WILLIAM EFFLAND: On that same slide, why

18 do you use the adverse effect level at 0.5? Why is that

19 selected? 

20 DR. RICHARD BRAIN: That methodology was

21 developed by the EPA. I think their reasoning for that is

22 because based on the distribution, you have the highest level of

23 confidence around the central value of that distribution whereas

24 the variability tend to increase to those details.

25 DR. ANNE FETSCHER: This splitting that you

26 did, sounds like you are potentially committing pseudo
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1 replication? 

2 DR. JEFF GIDDINGS: Well, it is a convention

3 that was used in the database to score the effects on each plant

4 community individually. Either that or we would lump them, but

5 we would not lump some of them and split some of them.

6 DR. ANNE FETSCHER: Yes, it just seems like if

7 they came in the same COSM, it would not really be appropriate

8 to have multiple points.

9 DR. JEFF GIDDINGS: Well, there are multiple

10 observations. There are distinct observations. This is

11 independent of the replication in the study. Those are distinct

12 observations. It is just like having 2 different endpoints. It

13 is having 2 endpoints for a single study. We could look at, I

14 suppose, the effects of the splitting versus lumping. I have

15 not done that. Take all the splits out or putting them all in.

16 DR. CATHERINE PROPPER: For that Lampert study

17 with ethanol, did they have a negative control so you can

18 definitively say that it was an ethanol effect? 

19 DR. JEFF GIDDINGS: They did not mention

20 whether they had an ethanol control. The implication is that

21 they did not, and certainly the results indicated that they did

22 not. 

23 DR. LINDA YOUNG: A quick question, one of you

24 made an argument that an effect seemed to be independent of time

25 or overall concentration. Was there any effort to relate

26 correctly the probability of an effect to concentration? 
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1 DR. JEFF GIDDINGS: I am not quite sure how to

2 answer that question except maybe going back to the old spotty

3 diagram and maybe ask you to look at that.

4 DR. LINDA YOUNG: So, what I would do is do a

5 logistic regression with 1s and 0s, effect/no effect and instead

6 of cumulative, which just used concentration, which I thought is

7 what you were heading for, but it never materialized.

8 DR. JEFF GIDDINGS: You are right. It did not

9 and that is because Dr. Brain did his work and I did my work. 

10 What I was leading up to was that you can just use the average

11 concentration during the course of the COSM study, which is what

12 is on the Y axis there, and use that.

13 Instead, Dr. Brain used parts per billion

14 days, which obviously is related to average concentration.

15 DR. RICHARD BRAIN: Just to follow up, because

16 you had such a high degree of correlation essentially between

17 the cumulative exposure in the individual or mesocosm study and

18 the percent effect data from PATI, again because the calibration

19 is defined in the LOC, you could then propose a methodology to

20 use the COSMs directly, using their individual cumulative

21 exposure as opposed to running it through a model and then using

22 the model output to do that. 

23 DR. JOHN RODGERS: A point of clarification in

24 these studies, when you say exposure duration, do you really

25 mean exposure duration or do you mean the duration of the study?

26 If you mean one or the other, did you distinguish that in your
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1 analysis? 

2 DR. JEFF GIDDINGS: That is a good point. How

3 we characterize duration for each study is basically the

4 observation period, although it is not always the case, but

5 generally is the observation period. Sometimes, concentrations

6 were constant throughout the observation periods. Other times,

7 they were a pulse and then it died away. So, the actual

8 duration of exposure is a little hard to pinpoint because of

9 time varying exposure like that.

10 DR. JOHN RODGERS: Then in that case, how can

11 anyone say that you would not expect the change of the duration

12 of exposure or duration of an experiment would give you

13 different answers? In other words, that answer would not change

14 over time, because you would think of all kinds of endpoints

15 that you could not possibly measure in a matter of hours or a

16 matter of days, things like macrophyte decomposition, quantities

17 of vascular plants. You guys know that degradation half-lives

18 of hundreds of days, 160 days.

19 DR. JEFF GIDDINGS: Good points, John. I

20 agree that in a more detailed analysis. Those kinds of

21 endpoints, I do not believe there were any decomposition

22 endpoints in any of these studies. Even if you are just looking

23 at biomass and microphytes, yes you are right. We take a period

24 of time of photosynthetic inhibition before you start seeing a

25 measurable decline in biomass perhaps. So, this is just taking

26 the results, I would not say at face value, but we are smudging
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1 the assignment of duration to individual studies. What you

2 really want to look at, I suppose, the telling point might be a

3 study that was conducted at say 2 concentrations, higher

4 concentration causing a median effect, lower concentration did

5 not cause a median effect, but then you went on for a long

6 period of time, and you did or did not see an effect. The PATI

7 model would predict that if you had half that concentration, you

8 would have to wait twice as long to see the effects, or

9 simplifying this essential what I would do, and then go back and

10 look at some studies to try to parse that out. 

11 DR. JOHN RODGERS: The 2 studies that you

12 mention that you thought were really unrealistic exposure to

13 concentrations, I take it that did not make your database. Did

14 you delete those? 

15 DR. JEFF GIDDINGS: You mean the high ones? 

16 No, we did not delete them. They are there. You can see them

17 up at the top. There is that 10,000 up there. 

18 DR. DANIEL SCHLENK: I think we are going to

19 try to squeeze in this last presentation by you, Dr. Brain, and

20 then we will probably call it a day. So, if you want to go

21 ahead and get that one in, that would be great.

22 DR. RICHARD BRAIN: So once you actually

23 define your LOC, the next step essentially is to ensure that the

24 LOC as you derive makes biological sense, and it accurately

25 reflects the data that was used to define that particular value.

26 Basically, we are introducing 2 concepts here.
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1 The first one is recovery. Keep in mind as

2 discussed by Dr. Solomon earlier, the mode of action of atrazine

3 really facilitates recovery. It is important to keep in mind

4 that although within the PATI mode routine, you could say the

5 model incorporates recovery. If you use each day of time

6 without consideration of the previous day of exposure, when you

7 consider that the model routine messes the cursory and that the

8 calibration is really defining the LOC, the only place in the

9 entire methodology where you could incorporate recovery

10 meaningfully in the process would be in a COSM scoring

11 selection. 

12 For example, if you had studies that had

13 pronounced effect and then recovery back to control levels on

14 endpoints of structure, and you classify those as 0s, then you

15 can say well we have incorporated recovery, but that was not

16 done. In each study that demonstrated a pronounced effect with

17 recover was categorized as a 1 according to the Agency's

18 criteria. So, what this indicates is that because recovery is

19 now being considered as an extra layer of conserves to the

20 model. 

21 The second element is that of biological

22 relevance. The output from the model being LOC, how do you

23 ensure that that is consistent with not only the laboratory

24 data, the underlying authenticity data and the model routine,

25 but also with the calibrating micro/mesocosm dataset? Thirdly,

26 in the context of the environment, what does that LOC mean in
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1 terms of what you are actually finding and measuring in these

2 systems? 

3 So, just to breeze through this rather

4 quickly, as discussed earlier by Dr. Solomon, the mode of action

5 of atrazine is competitive. The interaction is noncovalent and

6 reversible, which essentially facilitates recovery. We have

7 seen recovery demonstrated for a variety of plant species,

8 obviously from algae all the way to macrophytes. The key thing

9 to point out is that photosynthesis recovers in plants

10 immediately upon removal of exposure of atrazine. We can

11 probably give you an example of that here in just a second. 

12 The recovery of growth rate similarly occurs

13 rapidly, but obviously that varies depending on the specific

14 growth rate of the organisms where macrophytes with slower life

15 history characteristics recovery slower than the algae, which

16 has exponential growth rate characteristics. 

17 Other things to keep in mind are that based on

18 laboratory data available, what we see is that the potential for

19 recovery, that is potential for a plant to recover from atrazine

20 exposure, is largely dependent on both magnitude and duration up

21 to a point in which you get very high exposure to atrazine for

22 very long periods of time. Then you start to see a major impact

23 on recovery potential. Even under prolonged periods of atrazine

24 exposure, continual exposure without removing the plant from

25 that media, you still see recovery in the root of the macrophyte

26 in terms of photosynthesis within 28 days. You do not actually
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1 have to remove atrazine in order to see recovery. 

2 Third in terms of environmental context,

3 considering that the median duration of (inaudible) greater than

4 15 ppd is 2 days and the upper 90th percentile is 7 days,

5 really, that is the type of exposure, which would facilitate

6 recovery in these organisms. 

7 This is providing an example. This is a lab

8 study that we performed this last year where we exposed algae

9 for 48 hours to a pulse exposure of atrazine and then removed

10 the organisms into a control median and evaluated recovery. 

11 What you seen on the right-hand panel is that as soon as you

12 remove the particular organism from the median, you get

13 immediate recovery of photosynthesis back up to 10% of the

14 control levels. This is measuring using pulse amplitude

15 modulation fluorometry, which basically measures the efficiency

16 in which electrons are moving through PS2, which based on the

17 mode of action of atrazine is an appropriate biomarker effect. 

18 If you look at the left-hand panel, you

19 similarly see recovery in growth rate, although it is a slight

20 lag period. You get essentially this overshooting of growth

21 rate. The plants are essentially being held in stasis. As soon

22 as exposure is removed, you get this overcompensation of growth,

23 and then the growth rate of the treatment levels are larger than

24 that of the control. 

25 So, this demonstrates that you get recovery of

26 photosynthesis immediately as well as growth rate. This
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1 particular example is of the green algae pseudokirchneriella. 

2 We saw the exact same trends for blue-green algae as well as

3 diatoms. 

4 Just to provide another example, this study

5 was recently published in the 18C. What we found is that after

6 a 7 day exposure to atrazine of the floating macrophyte Lemna

7 Gibba, what you see again, the same thing. You get recovery,

8 growth rate occurred immediately and you get that overshoot

9 overcompensation above control levels.

10 So really, there is a robust dataset. Then we

11 provided recovery documents, which summarizes these collectively

12 that the body of literature with respect to recovery. It

13 consistently shows that you get recovery in atrazine regardless

14 of the growth form. 

15 The other element of this presentation is to

16 look at biological relevance. How can you measure the exposure

17 in a system and ensure that it has context in calibrating

18 dataset as well as a PATI model routine, and most importantly,

19 within the environment, in which you are taking these

20 measurements. Basically, the views that we look at are the

21 relative representation of the LOC compared to the distribution

22 of micro/mesocosm datasets. This was shown earlier and what we

23 found was that the calibration study, according to Giddings

24 recommendations, provided the best data and the most represented

25 fit of the LOC relative to the data. 

26 Another example would be to take that
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1 underlying toxicity distribution, a genus sensitivity

2 distribution that will develop using specific growth rate, and

3 then conduct essentially, calculate that as the hazardous

4 concentrations of 5% of the species. This would be considered

5 protective of 95% of species. In fact, this threshold is also

6 considered to be protective of structure and function of the

7 communities within the ecosystem. We use 5 different models for

8 the conservative being the Weibull to the less conservative of

9 the Fisher-Tippett model. What you find is that when you apply

10 these regressions to the dataset, you end up with a range of

11 hazardous concentration at the rate of 5% of species ranging

12 from anywhere from 37 to 70 parts per billion. It is important

13 to point out that the 30 day level of concern calculated

14 according to model calibration to the Giddings dataset provides

15 a value of 40 parts per billion. This is within and at the

16 lower end of this range of data points. 

17 Now if you take the 60 day LOC and end up with

18 a point of 25 micrograms per liter, then that is essentially

19 below the percentile, so that is even more conservative. 

20 What is striking is if you plot where the

21 Agency's 60 day LOC action falls out in this distribution, it

22 would off the plot to the left. This would equate to

23 essentially a 0.1 percentile of the underlying distribution used

24 within the PATI model routine. So, it just indicates how

25 conservative the Agency's LOC is relative to the underlying

26 toxicity data used within a model routine that is used to derive
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1 the LOC. 

2 Basically, what this shows is that the

3 cumulative LOC derived from the Giddings recommendations is both

4 consistent with the lab data as well as the micro/mesocosm data.

5 The final element of this is to put it in context, the LOC

6 relative to the actual environment, in which you are taking

7 these measurements. As you know, the structure and function of

8 primary producer communities is highly dynamic in change both

9 seasonally and in response to a number of variables, which are

10 themselves dynamic.

11 I am going to barrage you with a couple of

12 examples. For example, if you look at scour under a high flow

13 event where you have potentially abrasive TSS that can remove

14 periphyton from substrate, conditions of dry down where you have

15 low or no flow conditions, in addition to selecting the species

16 which will be tolerant to this type of conditions. You are also

17 potentially, this occurs in the early season, having a profound

18 effect on the ability of the alga community in the systems

19 obviously to prepare for that spring-summer bloom and establish

20 in those communities.

21 In addition, you have things such as light

22 intensity and increased turbidity. Because of the mode of

23 action of atrazine requires light as a method as a cofactor, if

24 you reduce the amount of light intensity, you therefore suspect

25 you also reduce the amount of toxicity. We see exactly this in

26 the study of (inaudible), where reduced light intensity also
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1 results in reduced impacts of atrazine on the growth of

2 macrophytes.

3 Another example would be runoff, where you

4 have essentially a lot of TSS and debris within a water column

5 being deposited onto a substrate that was previously being

6 occupied by periphyton that was actually covering surfaces. 

7 Last but not least, if you look at nutrients,

8 these are considered to be the primary driver for changes in

9 community dynamics within these systems. That was the

10 conclusion from Black et al., in a recent publication.

11 So, it is important to consider all of these

12 dynamic variables in the uses of interest. To summarize, the

13 mode of action of atrazine really facilitates for rapid recovery

14 in primary producers. That present recovery is not incorporated

15 in methodological approach. 

16 In the terms of biological relevance, the

17 Agency's LOC is not consistent with the underlying toxicity lab

18 data, it is not consistent with the micro/mesocosm data, and the

19 only way to actually assess whether or not the LOC is

20 biologically meaningful in these systems is to actually go out

21 into these systems and measure communities and how they change

22 over time relative to a number of these variables. 

23 We have done exactly that through a

24 biomonitoring program that Dr. Andrus from the water board is

25 going to touch on as well as a bio-survey of the habitat quality

26 in these systems by Lenwood Hall from the University of
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1 Maryland, and the community sensitivity and recovery analysis by

2 Dr. Mark Hanson of the University of Guelph. This is very

3 interesting data directly from the systems of interest. 

4 DR. DANIEL SCHLENK: Thank you. Any quick

5 questions that we have with regard to recovery for Dr. Brain? 

6 DR. JAMES FAIRCHILD: Where those studies, the

7 recovery studies, actually requested by the Agency? 

8 DR. RICHARD BRAIN: No they were not

9 requested. We simply look at recovery to evaluate the

10 efficiency with which recovery occurs at the biochemical level

11 in photosynthesis relative to growth rate. To my knowledge,

12 none of body of literature for the recovery data was actually

13 requested by the Agency.

14 DR. MICHAEL TWISS: Regarding the recovery,

15 was there any attempt, I see that you have given them some

16 relatively high concentrations and low to high concentrations,

17 and you returned to zero atrazine. But for the chemographs, we

18 see that there is usually a background concentration of

19 atrazine. You consider bringing it back to a lower, albeit 

20 elevated, concentration of atrazine as seen in impacted streams?

21 DR. RICHARD BRAIN: That is actually a really

22 good point. We would do that, but what we have seen is that

23 there is a study by Kemp et al., I think it was 1986. Basically

24 they had done a prolonged 28 day exposure to 100 micrograms per

25 liter of atrazine with looking at photosynthesis in anopia. 

26 They found that even under those high prolonged conditions of

27 atrazine exposure, you saw recovery of photosynthesis to control
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1 level. We know that recovery does occur in presence of atrazine

2 and also, I am not going to try to jump ahead too much, but Dr.

3 Hanson will be touching on that with actually analyzing the

4 recovery in mainstream communities where atrazine was not

5 removed completely, but we still see recovery. 

6 DR. JAMES MCMANAMAN: Do you think that your

7 recovery results would be applicable to all species? Do you

8 think that there might some species that might not recover fully

9 from atrazine? 

10 DR. RICHARD BRAIN: Thanks for that question. 

11 I would say that results at the biochemical level are highly

12 consistent and conservative among species. As soon as atrazine

13 is removed from the target site, you get resumption of

14 photosynthesis immediately. No obviously with respect to growth

15 rate, that is going to vary depending on life history,

16 characteristics of the organism of interest. Obviously, those

17 species with the slower growing life history characteristics

18 would potentially have a much slower response time. Therefore,

19 if you have had a very high exposure for a prolonged period,

20 those species would probably take longer to respond. In such a

21 situation, you may be then affecting the potential of the

22 organism to recovery. 

23 DR. JAMES MCMANAMAN: And consequently the

24 ecosystem.

25 DR. RICHARD BRAIN: Yes, but what we found is

26 that for macrophytes, the concentration at which basically the
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1 data demonstrates that the concentration at which duration is

2 irrelevant is basically up to and including 100 micrograms per

3 liter and beyond that. You base that to see some impact on

4 recovery potential, but not below that. For species with

5 exponential growth phases like Lemna and algae, the

6 concentrations are much higher. 

7 DR. DANIEL SCHLENK: Any other questions? 

8 Just for the record, I believe that Dr. Young has asked for

9 materials from the Agency that is going through Dr. Matten here

10 for some questions to make sure we put that on the record for

11 today. With that, we will call it quits for today and start

12 with Dr. Andrus tomorrow morning at 9 o'clock.

13 (Whereupon, the meeting was adjourned for the

14 day.)

15

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26
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1 **********************************************************

2 DUE TO TECHNICAL REASONS, AUDIO IS NOT AVAILABLE FOR THIS

3 SECTION OF THE TRANSCRIPT; THEREFORE, THIS SECTION OF THE

4 TRANSCRIPT WAS NOT PRODUCED FROM AUDIO. FOR THE FIVE SYNGENTA

5 PRESENTATIONS, THE SUMMARY SLIDES ARE PRESENTED BELOW. FOR THE

6 SIX TRIAZINE NETWORK PRESENTATIONS, REPRINTS OF THE WRITTEN

7 STATEMENTS READ ALOUD DURING THE MEETING ARE PROVIDED BELOW. 

8 SUMMARY SLIDES ARE PROVIDED FOR PRESENTATIONS. AFTER THIS

9 SECTION IS CONCLUDED, THE TRANSCRIPT PRODUCED FROM AUDIO IS

10 RESUMED:

11 **********************************************************

12 SYNGENTA PRESENTERS:

13 DR. RICHARD BRAIN: Recovery and Biological

14 Relevance Associated with Primary Producers and Atrazine

15 Exposure.

16 Recovery: Mode of action of atrazine facilitates rapid

17 recovery of primary producers (algae and plants) upon

18 cessation of exposure

19 Recovery is NOT considered in the EPAs methodological

20 approach; renders the calibration highly conservative

21 Biological Relevance: 

22 - The Agency's LOC does not provide the best

23 representation of the micro/mesocosm data 

24 - The Agency's LOC is extremely conservative relative

25 to the underlying toxicity data

26 - In-stream field research provides environmental
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1 context for the relevance of PATI output

2 DR. MALIA ANDRUS: Biomonitoring Pilot

3 Studies: Temporal Algal Community and Environmental

4 Variability.

5 Algal metrics, community, and environment are 

6 significantly different at the three test sites and over

7 time 

8 The major gradient of variation (generally CA1) is 

9 associated with typical seasonal changes (increased

10 nitrates, DO, windspeed in spring, increased water temp +

11 solar radiation toward fall)

12 Periphyton and phytoplankton at each site showed 

13 statistically similar patterns of seasonal variation

14 regardless of atrazine profile

15 No direct effects from atrazine were observed, for 

16 example, no major deviations observed for peak ~

17 70ppb (60dRA = 6.2 ppb)

18 Flow, TP, and TSS appear not to be associated with 

19 seasonal gradient, and effects from one or more of these

20 can be specifically seen in IA-03 periphyton community

21 changes (scour?)

22 Limitation: We did not collect any information about

23 other trophic levels or habit quality for this study.

24 MR. LENWOOD HALL: A Pilot Study for Assessing

25 Periphyton, Aquatic Macrophytes, Benthic Communities, and

26 Physical Habitats in Midwestern United States Streams with
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1 Historically Varying Atrazine Profiles. 

2 + Periphyton community metrics and percent of sampling area

3 occupied by aquatic macrophytes were not significantly

4 different at the 3 midwest sites with various degrees of

5 atrazine exposure. 

6 + Benthic communities at MO-05 were of lower quality when

7 compared to IL-10 but this difference is likely related to

8 poor habitat at MO-05 (i.e., poor substrate)

9 + Physical habitat conditions at IL-10 were of higher

10 quality than the other two sites

11 + These spatially and temporally limited biological data

12 from midwest stream sites, with historically varying

13 degrees of atrazine exposure, do not suggest that atrazine

14 has impacted these resident biological communities

15 DR. MARK HANSON: Biomonitoring Program: 

16 Periphyton Community Assessment 2011.

17 + Able to assess effectively response and recovery

18 + 2 h PSII EC50s (40 to 550 μg/L  ) are similar to those

19 observed by other atrazine studies with naïve lab

20 communities, e.g., 1 h EC50 of 194 Gμg/L [Schmitt-Jansen

21 and Altenburger, 2008 Aquatic Toxicology 86:49-58]

22 + EC50s consistent with genus species distributions

23 + 24 h PSII EC50s (19 to 478 μg/L  ) consistent with single

24 species lab algal studies (48 h EC50s 60 to 123 μg/L  )

25 [Brain et al., 2012 ET&C accepted]

26 + Recovery occurs rapidly (~2 h) once atrazine is removed
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1 + PSII recovery is in agreement with laboratory studies

2 (algae and duckweed)

3 + 2012 data support these conclusions 

4 DR. PAUL HENDLEY: Summary, identify

5 Potentially Vulnerable Watersheds using Ag Landscape

6 Characteristics.

7 Ag Landscape model from AEMP multi-variate data analyses

8 allows comparison of runoff potential between vulnerable

9 watersheds

10 - Extensive technology developed to refine

11 multi-variate analyses

12 - Identification of unmonitored watersheds potentially

13 similar to vulnerable monitoring sites best achieved

14 via modeling Ag landscape characteristics from PCA

15 Knowledge from AEMP Addresses Key Questions

16 1. AEMP/NCWQR daily datasets define magnitude/duration of

17 exposures in vulnerable watersheds.

18 2. Bias factors (BF) robust for moderate frequency monitoring

19 data but problematic for sparser data sets with longer

20 averaging intervals. 

21 a. Guidance needed to specify methodology for

22 developing and applying BFs.

23 3. Other approaches (e.g. PRZM-Hybrid) more appropriate for

24 augmenting sparse data.

25 4. Identifying potentially vulnerable additional watersheds

26 a. Current BF approach not suitable 
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1 b. Ag Landscape models based on AEMP multi-variate

2 data analyses offer sound approach 

3 QUESTIONS FROM THE PANEL MEMBERS WERE ASKED AND ANSWERED.

4 TRIAZINE NETWORK PRESENTERS:

5 MR. JERE WHITE, Executive Director, Kansas

6 Corn and Sorghum Grower Associations.

7 "PROBLEM FORMULATION FOR THE ENVIRONMENTAL FATE

8 AND ECOLOGICAL RISK ASSESSMENT FOR ATRAZINE"

9 THE FOLLOWING STATEMENT WAS READ:

10 Mr. Chairman, members of the committee, my

11 name is Jere White. I am the executive director of the Kansas

12 Corn Growers Association and Kansas Grain Sorghum Producers

13 Association and also serve as chairman of the Triazine 

14 Network.

15 The Triazine Network was formed in 1995 as a response by

16 growers of over 30 commodities and from over forty states, to

17 provide input to the US EPA special review of the triazine

18 herbicides. One of our objectives is to ensure that EPA has and

19 utilizes the best science. We have participated in every SAP

20 concerning atrazine since the beginning of the Special Review,

21 almost 18 years ago. Network membership encompasses farm groups

22 from across the nation. We believe the scientific weight of

23 evidence continues to show atrazine to be both safe and

24 effective. After 50 years, it truly is a classic compound that

25 our farmers know well. And by the way, we think farmers are

26 "cool" too.
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1 Regarding amphibians, our growers concur with the Agency

2 in that there is insufficient data to reach a

3 weight-of-the-evidence determination indicating hazards to

4 amphibians from the agricultural use of atrazine. Given the

5 significant amount of data available and the fact that our

6 growers live and farm in areas of significant amphibian

7 populations, they are confident in what they see and hear.that

8 frogs are doing fine on the farm. In the city, maybe not so

9 much. Perhaps OPP can commandeer one of Region 7's drones to do

10 a fly over of Midwestern streams in an effort to document the

11 reality that models fail to predict.

12 However, we are very concerned with the direction EPA

13 seems to be taking regarding their development of their

14 Concentration Equivalent Level of Concern (CE-LOC). In the past,

15 when EPA modeled plant growth for streams, it recognized that

16 numerous physical factors control whether or not plant growth

17 occurs. Many of these same factors influence whether atrazine

18 could cause a significant reduction in plant growth. Clearly

19 agriculturally located streams are subject to scour and high

20 turbidity that precludes growth from occurring during

21 significant runoff events and dry down conditions otherwise. 

22 Other sites have extensive tree canopy that controls the degree

23 of plant growth occurring. The proposed CE-LOC of 4-7 ppb is a

24 significant departure from previous proposed LOCs. Yet, the

25 inputs are the same, sometimes questionable, cosm studies and

26 their much debated scoring, that have clouded this process for
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1 decades. We are very concerned that the terminology itself,

2 Concentration Equivalent Level of Concern, invites

3 misinterpretation by others. In the past it was not uncommon to

4 reference an LOC de jour to the model that it was derived from,

5 such as CASM or PATI. It might seem like a small issue but we

6 live in a sound bite society that thrives on controversy. Words

7 are indeed important. 

8 For instance on page 43 of the Agency's White Paper, you

9 will note that the fourth criterion for including a cosm study

10 is that "the study was written in English" and further "If any

11 of these four criteria were not met the study was no longer

12 considered for use". The original work that is the subject of

13 cosm #58 Lampert et al. (1989) is actually a dissertation by

14 Fleckner (1988) in GERMAN. It is also a subject of an apparent

15 dispute regarding the use of ethanol as a solvent. 

16 "Zur Applikation in die SDcke mu5ten die Herbizide in ca.

17 5 ml Ethanol aufgelVst und mit destilliertem Wasser auf 100 ml

18 aufgefRllt werden. Mit einer Pipette wurden dann gleichmD5ig

19 wDrend des FRllvorgangs jeweils kleine Volumina der LVsungen in

20 die diagonal zueinander stehenden Tanks eingebracht." 

21 It appears that the Agency has pulled a switch on

22 requiring that the original be written in English; as they

23 stated yesterday that the study had to merely be reported in

24 English.

25 With almost two decades of intense review and

26 over five decades of use we believe that there is a high degree
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1 of certainty concerning atrazine. Uncertainty has been refined

2 down to possible, subtle, recoverable, and sub-lethal effects.

3 And while these potential effects and habitats are important, we

4 need to consider it on a larger scale and if we have

5 overestimated uncertainty necessary for sound regulatory

6 decision. Consider that there are six reservoirs in Illinois

7 listed as impaired for both atrazine and algae. And that is with

8 an atrazine standard of 9ppb. 

9 In spite of claims to the contrary that you

10 heard yesterday, atrazine is still critical on farms today. Yes,

11 for its economic value, but also for its environmental benefits

12 to farmers choosing to adopt conservation tillage. If

13 overregulated, conservation tillage, including no-till will

14 suffer, acreage will decline, and water quality will be further

15 impaired by issues of real concern to those that utilize our

16 water resources. 

17 It is clear that if a 4-7 ppb level of concern

18 was real, any impact that could occur because of agricultural

19 use would have occurred years ago. 

20 Our growers want to remind the panel and EPA

21 that the astounding amount of resources invested by the

22 registrants is in reality a cost to our farmers, whether it is a

23 model or monitoring, it is millions of dollars that are

24 ultimately removed from our rural communities, not some obscure

25 Swiss bank account.

26 Joining me today is:
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1 Laura Knoth, Executive Director, Kentucky Corn Growers

2 Association

3 Gary Marshall, CEO, Missouri Corn Growers Association

4 Mark White, Executive Director, Environmental Resources

5 Coalition

6 Scott Merritt, Executive Director, Nebraska Corn Growers

7 Association

8 Loren Larson, Caltha LLP

9 MS. LAURA KNOTH, Executive Director, Kentucky

10 Corn Growers Association.

11 THE FOLLOWING STATEMENT WAS READ:

12 Good morning, my name is Laura Knoth, the

13 Executive Director of Kentucky Corn Growers and Kentucky Small

14 Grain Growers Association.

15 My comments will be brief.

16 Your task is significant; the report that you

17 write and how it is evaluated and acted upon by EPA will have a

18 major impact on grain farmers.

19 While it is not at the top of your list of

20 concerns, please recognize the importance and benefits that

21 atrazine plays in food production. Atrazine makes no-till corn

22 and sorghum production possible. No-till in Kentucky and across

23 the country has and should continue to provide vast improvements

24 in water quality. It reduces sediment in not only the field

25 side streams that EPA is attempting to increase regulation on,

26 but also in our local community drinking water systems. Going
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1 backwards in soil conservation would be a tragic consequence of

2 regulating atrazine out of practical use. 

3 I have been tasked by the grain farmers I work

4 for and represent to monitor and participate in the atrazine

5 registration process. Farmers are frustrated with EPA's attempt

6 to arrive at an overly conservative ecological level of concern

7 for atrazine. When I first started monitoring the process a few

8 years ago we were being graded on a relatively transparent

9 process using rolling average numbers. Then we were given a

10 range of 10-20ppb for a level of Concern. Next, EPA simply

11 chose the lower number of 10ppb as the LOC and posted it on

12 their website. And now, they are proposing to go to a new range

13 of 4-7ppb for the latest LOC. They have asked the registrants,

14 who in turn asked the producers to mitigate watersheds and yet

15 keep moving the target or LOC to no productive or environmental

16 benefit.

17 It is disturbing to see on page 43 of the EPA

18 White Paper document "From these data, a LOC will be developed,

19 which together with monitoring data can be used to identify

20 watersheds where atrazine levels need to be mitigated consistent

21 with the 2003 Memorandum of Agreement signed by the EPA and

22 Syngenta." What is most concerning is that they have already

23 asked farmers to mitigate field side streams that are dry a

24 majority of the year, also streams that are full of algae,

25 aquatic plants, frogs and wildlife in general. It is puzzling

26 to me that we are at the lowest level of atrazine usage per acre
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1 in the 50+ year history of atrazine and we have an issue with

2 stream quality in particular the health of algae. Doesn't

3 common sense say that after 50+ years of use at rates that have

4 been steadily ratcheted down, we would have already seen any

5 damage to the streams, ponds and lakes on our farms. You would

6 expect that algae and aquatic plants would already be extinct in

7 the corn and sorghum production region given the historic usage

8 rates of atrazine. But the contrary is true, algae, plants,

9 fish and frogs are abundant.

10 I hope that you as panel members recognize the

11 importance of getting this right. After evaluation of the

12 Atrazine Ecological Exposure Monitoring Program and other data

13 the range of 4-7ppb is not even close to realistic. These are

14 field side streams that may go dry, others have issues of much

15 more concern than atrazine. Don't get me wrong, farmers have

16 environmental and economic reasons to apply best management

17 practices when applying atrazine or other pesticides. On page

18 97 of EPA's white paper, one of the charge questions says a lot.

19 "Please comment on the CE-LOC and whether it reasonably

20 represents a range below which permanent or irreversible change

21 in aquatic plant community structure, function, and/or

22 productivity due to atrazine exposure would not be expected." 

23 As I mentioned earlier with current atrazine rates and even

24 maximum label rate usage doesn't it make sense that we would

25 have already seen any permanent or irreversible change after 50

26 years? 
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1 I thank you for your consideration.

2 MR. GARY MARSHALL, Chief Executive Officer,

3 Missouri Corn Growers Association.

4 THE FOLLOWING STATEMENT WAS READ:

5 My name is Gary Marshall and I am the CEO of

6 the Missouri Corn Growers Association. I am pleased to have the

7 opportunity to address this Scientific Advisory Panel today. As

8 the CEO of the Missouri Corn Growers Association, I represent

9 over 15000 corn farmers in the state of Missouri. Farmers in

10 Missouri grow over three million acres of field corn annually. 

11 After 50 years, atrazine is still the

12 fundamental component in nearly all crop protection programs

13 used in Missouri. As I have related to this panel in the past,

14 the use of atrazine is critical to profitable corn production. 

15 You heard testimony yesterday that said if the

16 use of atrazine was lost the impact would be minimal. I am here

17 to tell you that is not true! In fact, if farmers in Missouri

18 were to lose the availability of atrazine it would cost them

19 over one hundred million dollars due to switching costs and

20 nationwide, that is a three billion dollar price tag. That's

21 real money, even in D.C. According to National Corn Growers

22 trials about 6 to 9 bushels could be lost per acre if atrazine

23 was not available. Add in the alternative herbicide expense and

24 it costs an additional $30 per acre. That is big money to a

25 grower as well!

26 The growers that I represent have asked me to
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1 try and convey to you how important this product is to their

2 livelihood.

3 The Missouri Corn Growers Association has a

4 long history of water quality and product stewardship. Between

5 1998 and 2006 MCGA and our partner organization the

6 Environmental Resources Coalition worked with state water

7 quality stakeholders and farmers to develop best management

8 practices that proved beneficial in keeping atrazine on the

9 field and out of the waterways.

10 Missouri farmers have proven their

11 willingness to improve their stewardship practices when

12 confronted with new challenges or new information. So in 2006,

13 when they were confronted with the prospect of the EPA requiring

14 Syngenta to monitor three watersheds in the state for ecological

15 effects of atrazine, the farmers in those watersheds approached

16 the project with a cautious yet willing attitude.

17 Missouri is currently host to seven

18 eco-monitoring sites stretched across the northern half of the

19 state. These watersheds represent over one hundred thousand

20 acres and hundreds of different farmers and landowners. Many of

21 these farmers grow corn within and outside of these

22 eco-monitoring watersheds. My organization has engaged the

23 farmers, agricultural retailers and agronomists in each of these

24 watersheds. 

25 While they have proven their willingness to

26 work with us to improve their product stewardship, they have
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1 also expressed to me their frustration with this eco-monitoring

2 process. To say the least, it is confusing. Most of these

3 producers have lived their whole lives in and around these

4 watersheds and they struggle to understand what exactly they are

5 being asked to protect. 

6 By their own personal accounts, these streams

7 have changed very little during their lifetimes. They know

8 atrazine has been used in these watersheds for nearly fifty

9 years and the streams still contain fish, algae, plant,

10 amphibians and sediments. I can remember when atrazine use rates

11 were closer to four pounds per acre decades ago, but the reality

12 today is that farmers are using a pound or less per acre on

13 average. 

14 Fifty years of use is more proof to my growers

15 than any model. Since the beginning of the eco-monitoring

16 project the ecological trigger numbers have been slowly

17 ratcheted downward as models have changed (CASM to PATI).

18 Regardless of the reason, farmers find it increasingly difficult

19 to meet the requirements of the pseudo ecological standard. 

20 The SAP needs to help the agency get the LOC

21 right. There are too many acres that could be negatively

22 impacted. Based on real world observation, we know the 4-7 ppb

23 LOC can't be right.

24 The burden to meet the ecological standard for

25 atrazine in these specific watersheds has created several

26 dilemmas. We know that one of the best practices to keep
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1 atrazine in the field is incorporation by tillage. Growers are

2 very resistant to adopt this as it is a regressive practice. 

3 Conservation tillage and no-till have been

4 widely adopted to meet the goals of soil and energy

5 conservation. A switch to conventional tillage for the purpose

6 of atrazine incorporation would undo many of the gains made in

7 soil conservation. 

8 Many farmers tell me that they farm in one

9 watershed that is being monitored in the study and in a

10 neighboring watershed that is not being monitored. The monitored

11 watershed costs the farmer more per acre depending on which best

12 management practice they are employing to help meet the

13 ecological WQ standard. 

14 Extra tillage passes cost more, two pass

15 herbicide programs generally cost more, reduced atrazine rates

16 drive up costs for weed control, and other cultural practices

17 are expensive as well. Farmers are historically low cost

18 producers and in these monitored watersheds they find themselves

19 at an economic disadvantage to their cohorts in the neighboring

20 watersheds.

21 Seemingly, the biggest issue is confusion over

22 compliance. In an eco-monitoring watershed such as MO-01,

23 eco-monitoring mitigation requires that farmers in the watershed

24 must adopt cultural practices that will limit atrazine run-off

25 even though the FIFRA label provides for a maximum single

26 application rate of two pounds per acre. In essence, this
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1 program has created an unofficial TMDL for atrazine in these

2 watersheds. Farmers are understandably confused and worried

3 about the implications. 

4 In closing, I would offer several

5 considerations for the EPA and this SAP.

6 1. It is of utmost importance that some

7 form of economic impact analysis be done when considering

8 what impact the ecological criteria will have on farmers

9 in the affected watershed. Agriculture is the single

10 largest economic driver in all of the states engaged in

11 the eco-monitoring program.

12 2. In an attempt to limit atrazine's

13 ecological impact there might be unintended consequences

14 that actually lead to greater negative impacts. These

15 might include an increase in soil erosion due to a move

16 away from conservation practices.

17 3. The SAP and the EPA should take into

18 consideration how the eco-monitoring program and any LOC

19 developed from it could be used incorrectly by states

20 seeking to promulgate aquatic life criteria for streams.

21 I would like to thank you for the opportunity

22 to address this panel today. I hope that you will consider the

23 farmer's reality when deciding how to implement the ecological

24 criteria in the future.

25 MR. SCOTT MERRITT, Executive Director,

26 Nebraska Corn Growers Association.
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1 THE FOLLOWING STATEMENT WAS READ:

2 On behalf of the Nebraska Corn Growers

3 Association we are pleased to have the opportunity to address

4 this Panel and express some thoughts and concerns we have on

5 EPA's process to update its ecological risk assessment for

6 atrazine. Atrazine remains a key tool for Nebraska farmers and

7 we have a long history of utilizing atrazine as a safe and

8 efficient crop protection product for corn and grain sorghum

9 production.

10 As our world's demand for food and fuel

11 continue to increase, farmers need to have management tools

12 available that are dependable and cost effective. One example

13 of the benefits of atrazine is its use in no-till and

14 conservation-tillage farming practices which is the preferred

15 practice in Nebraska and most areas of Midwest corn production.

16 Reduced tillage not only reduces the carbon footprint of

17 production agriculture it also makes a significant contribution

18 to reduced soil loss to improve water quality. 

19 An agreement between the registrants of

20 atrazine and the Environment Protection Agency reached in 2003

21 created an eco-monitoring program with sites at determined

22 locations across the country. Over the course of this atrazine

23 ecological monitoring program, nine sites have been monitored in

24 Nebraska, although several sites have been released by EPA from

25 monitoring. 

26 The eco-monitoring site we would like to focus
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1 on today is NE-04. This site over its history has been found to

2 approach or exceed the original CASM based triggers for more

3 than one year. The site is located in upper Gage County and

4 includes the village of Plymouth. It encompasses approximately

5 21 square miles and is 85% cultivated crop ground. The testing

6 equipment is located on a tributary of the Big Blue River that

7 is named Dry Creek, appropriately named as the creek is dry a

8 majority of the time in most years, including this year.

9 Despite the fact that "Dry Creek" does not

10 have continuously flowing water and therefore can only

11 sporadically support an aquatic plants in a given year. During

12 late 2010 representatives of NE Corn Growers, University of

13 Nebraska, Lower Big Blue Natural Resource District and other

14 stakeholders began a focused effort in the watershed to address

15 the EPA's concerns. From this, a Watershed Advisory Council was

16 formed to make recommendations to land owners and operators in

17 the watershed. This group also works in cooperation with

18 organizations and state agencies in Nebraska to address EPA's

19 issues. There is 100% participation of the landowners and

20 operators in the watershed in all activities required by EPA to

21 date. Stakeholders hold yearly meetings with representatives of

22 Environmental Protection Agency and Syngenta to provide reports

23 on activities. 

24 There are several underlying factors that the

25 landowners and Watershed Advisory Council have identified. The

26 first is the location and the properties of the water shed. Dry
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1 Creek is just that, an intermittent stream that has no

2 consistent flow. The Natural Resources District and University

3 technical experts have questioned the amount of resources

4 committed to evaluating a site that does not represent a stabile

5 aquatic community. They have recommended a cooperative effort

6 to relocate to a basin that better reflects the challenges in a

7 more typical Midwest stream. There are growing concerns and

8 questions from farmer operators regarding the "common sense"

9 approach to monitoring aquatic life criteria in a stream bed

10 that is dry for the majority of the year.

11 Additionally, the advisory group has brought

12 forward concerns regarding the model now proposed by EPA to

13 evaluate the monitoring results. The PATI (Plant Assemblage

14 Toxicity Index) model does not account for the inconsistent flow

15 and other natural occurrences such as sedimentation and

16 turbidity in the stream and watershed. Often times it seems we

17 are chasing a moving target that in the end does not reflect

18 reality in these watersheds. 

19 Another issue of concern we would like to

20 address with the Panel is the inclusion of certain data from

21 the 2002 Nebraska Department of Environmental Quality in the

22 EPA's White Paper. These high concentrations were the result of

23 an accidental spill or an intentional dumping of the product

24 according to the Nebraska Department of Agriculture (see

25 attached letter). These data are cited on pages 26, 28 and

26 Appendix G of the White Paper. Of major concern is the fact
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1 that the Agency is inappropriately using these data in

2 development of the bias factors.

3 The Watershed Advisory Council will continue

4 education, outreach and BMP implementation projects in this and

5 other Nebraska watersheds. Nebraska landowners and producers

6 are committed to continuing efforts to maintain and improve

7 water quality in the area. However, we would argue that the

8 current concerns coming from the Agency are misplaced by

9 focusing on extreme outliers not representative of most

10 watersheds in the Midwest. 

11 IN ADDITION TO MR. SCOTT MERRITT'S STATEMENT, HE PRESENTED A

12 SHORT POWERPOINT ILLUSTRATING STREAM CONDITIONS AT VARIOUS SITES

13 IN NEBRASKA.

14 MR. MARK WHITE, Executive Director,

15 Environmental Resources Coalition. 

16 SUMMARY SLIDE FROM PRESENTATION:

17 A Farmer's perspective. 

18 Eco Monitoring in Missouri.

19 Farmers are willing to comply with realistic label requirements,

20 however the newly proposed PATI 60 day CE-LOC of 4-7 ppb is

21 quite possibly unachievable on a year to year basis in many

22 Missouri watersheds. In Missouri this represents well over

23 200,000 acres, but in the Midwest it could reach several

24 million acres. The ecological risk being protected for does not 

25 seem to match what growers observe in the real world. Has the 

26 model been validated in any of the actual eco-monitoring streams?
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1 It is very important that the SAP give sound guidance to the 

2 agency to get the loc right because the negative ramifications

3 could have huge impact.

4 MR. LOREN LARSON:

5 Comments on Problem Formulation for the

6 Environmental Fate and Ecological Risk Assessment for Atrazine.

7 Mr. Larson discussed the use of cosm studies

8 that do not meet acceptance criteria: That the risk summary

 9 statement does not agree with toxicological data; the use of a

10 bias factor, and the regulatory implications of the LOC. 

11 ***************************************************************

12 THE REMAINDER OF THE TRANSCRIPT FROM THIS POINT WAS PRODUCED

13 FROM AUDIO:

14 ***************************************************************

15

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20

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1 DR. JERE WHITE: The proposals are more

2 realistic than what we talked about. Let me know. We would

3 like to have you in Kansas.

4 I know it can't be a part of this SAP. This

5 offer clearly extends to the SAP to the agency. We don't want

6 to get this wrong, but we think it's not near as wrong as what

7 you could be inferred by the weeks, the years of discussion that

8 we've had in this issue and we'll continue to have. So let's

9 make sure we do this right. Thank you.

10 DR. DANIEL SCHLENK: Thanks for your comments.

11 Any questions from the panel? Thank you very much. 

12 DR. CATHERINE PROPPER: I have a couple of

13 questions. The first is there has been a reduction from about 4

14 pounds an acre to 1 pound an acre; is that correct?

15 DR. JERE WHITE: Yes, that's correct.

16 DR. CATHERINE PROPPER: Have you seen any

17 economic impact based on that current reduction of atrazine use

18 over the last couple of years? 

19 DR. JERE WHITE: No, but one of the

20 interesting caveats of that is actually there has been, at

21 times, increases in treated acres with sonar rays. So what's

22 happened over the years is newer concentrates have been

23 introduced into marketplace. Typically, the next generation of

24 those neurochemistries, (inaudible) with atrazine. And the

25 reason is, is that the newer chemistries are much more

26 selective. They just simply have found that a pound of atrazine
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1 or a half-pound -- I mean, it's a range. It's not clearly a

2 pound for all. But that has become a very effective tool. So

3 it's a combination of chemistries. 

4 You also have new issues with (inaudible). 

5 The increased use of the (inaudible) for corn is probably

6 somewhat well understood, but the significant use of (inaudible)

7 as the main herbicide for a number of years has certainly

8 brought up resistance issues to the countryside. 

9 We understand this so part of the resistance

10 management is the use of other products like atrazine that can

11 very effectively be incorporated into that. So while the use

12 patterns have changed. The rates have definitely changed, the

13 importance of the product, even at the smaller rates is probably

14 as great as it's ever been.

15 DR. CATHERINE PROPPER: And then my second

16 question is has anybody done a systematic evaluation in the

17 amphibian populations in the areas of high versus low, how this

18 is used, to really evaluate whether there are effects that are

19 correlated with the shifts in the amphibian population. And I

20 know that's a little hard for them to do.

21 DR. JERE WHITE: You know, we probably comment

22 more on our observations, and perhaps, someone else can address

23 that better. 

24 I would truly say that it's unfortunate that

25 Mark's video did load up because I've seen it. And the noise

26 from the frogs and the sightings of the frogs are so typical of
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1 what we see. If you go out, even the edge of cornfield, on a

2 summer's evening and listen -- and that's what I was trying to

3 convey to you. If you're not sure, if you truly are concerned,

4 come out. 

5 Now, I realize that's not a controlled study,

6 but it is a reality and some of these areas have been highly

7 corn-farmed for a number of years, obviously. You know, again,

8 the impacts, we would expect to see. It's non-science base,

9 perhaps, but it's what the people we represent, live and see

10 every day.

11 DR. GARY MARSHALL: We actually did ask our

12 Department of Conversation that very question. Is there any

13 concern and have you done any studies? And their answer was no.

14 We have no concerns and we don't know of any studies. 

15 We tried to get that data. And again, all of

16 these agencies that we deal with are talking about robust

17 populations of amphibians and they just don't see any issues as

18 our growers don't as well.

19 DR. DANIEL SCHLENK: Okay. Any other

20 questions? Okay. Thank you very much.

21 To the audience, is it true Daren Coppock has

22 to leave early; is that correct?

23 MR. DAREN COPPOCK: That's correct.

24 DR. DANIEL SCHLENK: Yeah. Can you go ahead

25 and come forward then because we're probably going to break for

26 lunch soon? Introduce yourself and your affiliation.
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1 MR. DAREN COPPOCK: My name is Daren Coppock. 

2 I am the President and Chief Executive Officer of the

3 Agricultural Retailers Association. I appreciate not only the

4 chance to testify, but your flexibility in the timing to let me

5 get to another appointment. 

6 By way of personal background, nobody that

7 lives in Washington -- or at least not very many of us are

8 actually from Washington and that's true with me as well. I

9 grew up on a wheat and cattle farm in Eastern, Oregon and was

10 trained by my father at an early age of the importance of

11 following label directions on pesticide application and the

12 importance of crop protection in a sustainable farm operation. 

13 So I have some personal experience in using

14 some of these materials. As we raised wheat and not corn, we

15 didn't use atrazine, but we used plenty of other things.

16 The ARA is a non-profit trade association that

17 represents America's agricultural retailers and distributors. 

18 ARA members provide goods and services to farmers and ranchers,

19 which include: fertilizer, crop protection chemicals, seed, crop

20 scouting, soil testing, custom applications and pesticides and

21 fertilizers and development of comprehensive nutrient management

22 plans. 

23 Retail and distribution facilities are

24 scattered throughout all 50 states and range in size from small

25 family-held business or farmer co-ops to large national or

26 regional retailers with multiple outlets. 
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1 Our members are very much involved in helping

2 growers address the wheat challenges they have in finding

3 solutions that work for them on their operations. 

4 Atrazine is a critical crop protection tool. 

5 It's been used by agricultural retailers and commercial

6 applicators. According to some data from GfK Kynetec, in 2009,

7 atrazine was applied to more than $53 million acres. 

8 Atrazine is annually applied on well over half

9 of U.S. corn acres, two-thirds of U.S. sorghum acres, and up to

10 90 percent of U.S. sugar cane. It's also an important tank-mix

11 partner that adds another mode of action to improve

12 effectiveness and the performance of other herbicides. 

13 A report released by Professor of Agronomy,

14 Michael Owen, at Iowa State called "The importance of atrazine

15 in the integrated management of herbicide-resistant weeds" -

16 that's the name of the report -- highlighted atrazine's unique

17 mechanism of action that compliments the use of other herbicides

18 with other modes of action, like Jere White was just describing.

19

20 According to Professor Owen's study, "Without

21 atrazine, it is anticipated that a number of weeds could not be

22 effectively managed in corn, sweet corn, sorghum or sugar cane. 

23 And it is likely that these weeds would escalate more rapidly in

24 economic importance if atrazine were not available." 

25 EPA previously estimated that farming without

26 atrazine would cost $28 per acre in additional cost and
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1 alternative herbicides and reduced yields, which would result in

2 costing American farmers more than $2 billion, as you've heard

3 from other panelists earlier.

4 So while the decision that EPA makes is based

5 upon science, the economic impacts of the decision are not

6 trivial; they are quite significant.

7 The use of atrazine has allowed farmers to

8 utilize more conservation tillage to help them control weeds in

9 those systems and no-till systems that help protect top soil to

10 reduce erosion. 

11 According to the conservation Tillage

12 Information Center, conservation tillage can reduce erosion by

13 as much as 90 percent. It is estimated that 64 percent of

14 atrazine used in corn allowed for no-till or conservation

15 farming. Again, the source was GfK Kynetec. 

16 You've heard the benefits of soil erosion

17 prevention. There are some on our list on the paper I gave you.

18 I'm not going to read them all to you, but atrazine plays a

19 significant role in allowing farmers to receive those benefits.

20 Atrazine has been on the market for over 50

21 years and is one of the most heavily studied and tested crop

22 production products for safety with over 6,000 studies from

23 around the world. With this unsurpassed database and history of

24 safe use, ARA is concerned that EPA is considering a new, yet

25 scientifically invalid Level Of Concern for aquatic plants that

26 lacks biological relevance, ignores atrazine's mode of action,
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1 fails to reflect recoverability, and is far lower than the

2 levels at which the scientific data indicate that rapid and

3 complete recovery will occur. 

4 Also omitted from consideration are the

5 dynamic conditions in the monitored streams, including changes

6 in rate of flow, total suspended solids, nutrients, temperature,

7 and light. These streams show significant variability in

8 aquatic plant growth and community structure from day to day,

9 week to week and season to season.

10 

11 Atrazine is a cost effective, reliable and

12 essential tool for no-till and conservation tillage systems as

13 well as the integrated pest management systems. It's also

14 proven safe to humans and to the environment and it is essential

15 that the agricultural community continue to have it available. 

16 We believe this Scientific Advisory Panel

17 should encourage EPA to make its regulatory decisions in this

18 area, and all areas, based on the best available science while

19 taking into account the impacts on production and prices of

20 agricultural commodities, retail food prices, and other effects

21 on the agricultural economy. 

22 ARA members and their farmer customers have

23 safely used atrazine for decades and we support the continued

24 use of this product. With the use of valid science-based

25 information and the use of economic common sense, atrazine will

26 continue to safely provide significant benefits for retailers,
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1 farmers, and consumers. 

2 Petitions received by the EPA that are based

3 on hypothetical allegations, research that cannot be duplicated,

4 or simply consist of pictures and press releases should be

5 dismissed because the agency has higher priorities for its

6 scarce resources and those resources ought to be focused on

7 genuine inquiry and scientific analysis. 

8 I thank you for the opportunity to comment and

9 for your flexibility on the schedule. I would be happy to

10 answer questions if there are any.

11 DR. DANIEL SCHLENK: Any questions for Mr.

12 Coppock? Thank you very much.

13 We have one, just for the panel's note here;

14 there is one public presenter that didn't make it today. It's

15 Steven Bruckner, and you have that in the handouts that were

16 provided today, a sort of written testimony that he has, so just

17 to make note on that and get that into the record.

18 I think Sharlene has some comments that she

19 needs to make with regard to some of the materials that the

20 agency has provided.

21 DR. SHARLENE MATTEN: The environmental Fate

22 and Effects Division asked me to remind you that there are some

23 documents in this binder notebook, the white one with the green

24 paper. 

25 If you look at the notebook there are light

26 blue papers. Behind those light blue papers there is a table
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1 called cosm endpoint table taken from August 2011. They are

2 organized by number of endpoint; it relates to Topic B.

3 This is a paper copy. I've been sent the

4 Excel spreadsheet for that table. It looks like this; lots of

5 little writing and many pages of studies. 

6 Okay. The second document that is behind the

7 little light papers is something called "Errata Sheet for

8 Appendix C of the White Paper, Open Literature Review of

9 Amphibian Data." Can everyone find that? So I have the file

10 for that and it will also go in the docket. It looks like this.

11

12 Okay. The third thing that's in this notebook

13 is a copy of the white paper itself, which is already on the

14 docket. But just in case, if you really wanted to see it in the

15 paper form, and didn't bring it with you, here it is.

16 In addition, in response to a clarification

17 that was asked by the statistical group over here, Frank

18 Farruggia, of the Environmental Fate and Effects Division

19 provided for Topic B a 60-day PATI Output for Binary Log

20 Regression Calculation. It's an Excel spreadsheet. So I can

21 send that to all the panel members rather than printing it. 

22 I think that's it until the agency has some

23 clarification prior to the charge questions. So if anybody

24 needs those materials electronically, I have three Excel

25 spreadsheets that I can electronically provide for your work and

26 then I will put those in the public docket.
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1 DR. DANIEL SCHLENK: Okay. Thanks. What

2 we're going to do is we're going to break for lunch for an hour.

3 Just so you can be prepared, we're going to lead off with Rod

4 Snyder, who the next public commenter should be; and then we'll

5 continue through the public commenting period. So please be

6 back at 1:30.

7 (Whereupon, a luncheon recess was taken.)

8 * * * * *

9 DR. DANIEL SCHLENK: Before we lead off

10 with Rod Snyder, Sharlene has got a comment.

11 DR. SHARLENE MATTEN: Before lunch I mentioned

12 I had promised to give a location of some additional

13 information. I said three Excel files and for some reason I

14 only mentioned two. 

15 There is a third one related to a request

16 yesterday; called, "Normalization of cosm Endpoints to

17 Assessment Various Periods." And I think that when EPA comes

18 up, just before the charge questions, we can pop them open just

19 so everyone can see them; there is no secretiveness about what's

20 in them. Just flash them up there a little bit. 

21 Okay. If there are any other public

22 commenters who haven't already sought me out by e-mail or this

23 morning or before lunch, here is your last opportunity to come

24 forward. With that, I'll turn it back over to Dan Schlenk.

25 DR. DANIEL SCHLENK: So we have Rod Snyder. 

26 MR. RODNEY SYNDER: Good afternoon. My name
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1 is Rod Snyder. I'm Director of Public Policy for the National

2 Corn Growers. I'm not a scientist, but this is an issue that

3 our membership cares a lot about and has made remarks at all of

4 the SAPs for the last couple of years. So we do appreciate the

5 opportunity to offer comments today as well. 

6 The National Corn Growers Association was

7 founded in 1957 and it represents more than $37,000 members in

8 48 state, 27 affiliated state organizations and more than

9 300,000 corn farmers who contribute to state check-off programs

10 for the purpose of creating new opportunities and markets for

11 crown growers. 

12 Applied to more than half of all corn acres,

13 two-thirds of sorghum acres, and up to 90 percent of sugar cane

14 acres, atrazine is one of the most widely used and relied upon

15 herbicides and has been a backbone of weed control for farmers

16 for over 50 years. 

17 Atrazine boosts U.S. corn output by 600

18 million bushels per year and helps U.S. farmers produce an

19 average of 166 bushels of corn per acre, a significantly higher

20 yield than any other country in the world. 

21 Moreover, atrazine plays a crucial role in

22 conservation tillage and no-till farming, which reduces soil

23 erosion and decreases fuel usage, and improves water quality. 

24 It's important that sound science and real

25 world evidence serves as the basis for regulating atrazine and

26 addressing any potential issues, rather than a scientifically
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1 unsound and unsupported proposed Aquatic Plant Level of Concern

2 that is inconsistent with the biological reality and any

3 observed real world impacts.

4 For all the reasons I've stated, NCGA urges

5 the Scientific Advisory Panel to provide a rigorous,

6 science-based assessment of EPA's methodology and proposed new

7 LOC, clear input on the soundness of and support for the

8 methodology and proposed LOC, and clear direction on the steps

9 EPA must take to ensure that its methodology and the newly

10 proposed LOC are accurate and reliable.

11 NCGA considers EPA's assessment of the data on

12 potential atrazine impacts on amphibians is well supported,

13 consistent with sound science, reflects an accurate and explicit

14 evaluation of the validity and conclusiveness of each study, and

15 excludes studies and data points that do not meet rigorous and

16 appropriate scientific standards. 

17 EPA's evaluation appropriately focuses on the

18 relative and absolute rigor and reliability of the studies

19 rather than the mere existence and number of studies that assert

20 a potential negative adverse effect or the presence of

21 uncertainty or apparently conflicting results.

22 EPA's determination that there are "no

23 additional data" to alter the conclusion that atrazine does not

24 adversely affect amphibians is supported by real life condition,

25 as many of the earlier panelists were discussing at length. 

26 Frogs continue to thrive in areas where
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1 atrazine is routinely used, including in irrigation ditches next

2 to corn fields in the Midwest. 

3 NCGA appreciates the opportunity to comment on

4 the atrazine assessment. We are very interested in the outcome.

5 MR. DANIEL SCHLENK: Thank you. Any

6 comments for Mr. Snyder? Okay. Scott Slaughter is next on the

7 list. 

8 MR. SCOTT SLAUGHTER: My name is Scott

9 Slaughter and I'm commenting today on behalf of the Center for

10 Regulatory Effectiveness. I wanted to make three points. 

11 First, with regard to amphibian effects, we agree with EPA that

12 the data call-In study, which followed the recommendations in

13 the 2003 SAP, can and should be used to assess atrazine effects

14 on amphibians. 

15 We agree with EPA that we know of no other

16 study that meets that Data Quality Act requirement that could be

17 used. Yesterday, EPA stated that it believed that a proposed

18 regulation or proposed use of the DCI and only the DCI was

19 consistent with the Data Quality Act. 

20 We agree and we commend EPA for trying to

21 assure that regulation of atrazine complies with all of the Data

22 Quality Act requirements. The DQA is sometimes referred to as

23 the Information Quality Act also. 

24 This brings me to my second point. EPA's

25 proposed used of the PATI LOC model is inconsistent with the

26 Data Quality Act. It is inconsistent with EPA's Guidance for
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1 the Development and Use of Models and it is inconsistent with

2 the 2009 SAP's review of PATI. 

3 The Data Quality Act requirements, by the way,

4 in my written comments, the Data Quality Act requirements for

5 EPA are Footnote 1. There's a link to them, the written

6 comments. And there's a link to the EPA's model guidance in

7 Footnote 2.

8 EPA's council for Regulatory Environmental

9 Modeling has published model guidance, which is Footnote 2,

10 which governs EPA development and use of models. The CREM

11 Guidance was peer-reviewed by the National Academy of Sciences. 

12

13 It states, in part, "At EPA, quality is

14 defined by the Information Quality Guidelines. The IQGs apply

15 to all information that EPA disseminates, including models,

16 information for models and input data. According to the IQGs,

17 that quality has three major components: integrity, utility, and

18 objectivity. This chapter focuses on evaluating the third

19 component, objectivity, specifically how to evaluate the

20 objectivity of information by models by considering their

21 accuracy, bias and reliability."

22 The CREM guidelines in Appendix D contains

23 EPA's best practices for model evaluation. These best practices

24 state that EPA is supposed to compare its model predictions with

25 empirical observations to determine where the model is

26 consistent with reality, and reality is a term that's used in
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1 the CREM guidance. 

2 EPA has not done this with its PATI LOC model.

3 To our knowledge, EPA has not compared its PATI LOC adverse

4 effects predictions for a site with the actual conditions in the

5 real world at that site. 

6 Consequently, until it can prove that it has

7 validated that PATI LOC model by complying with the

8 corroboration and other requirements of EPA guidelines, the PATI

9 LOC model is not validated and should not be used for any

10 regulatory purpose by EPA. 

11 The 2009 SAP was very unkind to PATI. In

12 fact, the reviews were rather bad. The 2009 SAP stated several

13 concerns with PATI, and these concerns are quoted at pages 7-9

14 of our written comments, which you have before you.

15 We ask that this SAP review EPA's current

16 record for PATI to determine whether EPA has satisfactorily

17 addressed the 2009 SAP's concerns with PATI. We think not, but

18 once again, we ask you to reach and record your own conclusions.

19

20 I wish to emphasize the series Data Quality

21 Act comments about the PATI LOC model do not depend on which

22 COSMs are used to calibrate the LOC model. Our position is --

23 and we think we're on firm ground given EPA's quality standards,

24 which you have before you. Those standards require EPA to

25 properly validate this model regardless of what COSM studies

26 they use to calibrate it with. 
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1 To our knowledge based on this record they

2 have not complied with the best practice guidance, including the

3 corroboration requirement, which means correlating with real

4 world data that are imposed by the Data Quality Act guidelines

5 and by EPA's own model guidance.

6 I also wish to note that CRE made a similar

7 argument 10 years ago with regard to EPA's use of unvalidated

8 amphibian effects studies for atrazine. As of yesterday, at

9 least, EPA agrees with us. We see no reason why they shouldn't

10 agree with us on our PATI model comment too. Let's hope it

11 doesn't take 10 years, though.

12 My third and final point is that the real

13 world data that I've heard being presented to this SAP certainly

14 indicates that the PATI LOC model is inaccurate and unreliable

15 and shouldn't be used because as far as I can tell, the real

16 world data showed that there isn't a real problem with atrazine

17 effects on vegetation throughout the country, under current

18 label requirements. 

19 Thank you. I'll try to answer any questions.

20 DR. DANIEL SCHLENK: Any questions for Mr.

21 Slaughter? Okay. Thank you very much. Next on the list is Dr.

22 Leggett. 

23 Just for the panel, Dr. Leggett could not make

24 it. So his comments are provided in the documents that you guys

25 all have. 

26 Our next public commenter is actually a
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1 statement that needs to be read into the record on behalf of

2 Kaitlyn Bryer, who is a 10-year-old elementary student at

3 Cordova Gardens Elementary School in Rancho Cordova, California.

4 She sent this letter in and apparently she asked Dr. Matten to

5 read this into the record so we're going to do so today. You

6 should see it on the screen there.

7 "Hello. My name is Kaitlyn Bryer and I am 10

8 years old. The day I found out that frogs are disappearing I

9 knew I needed to make a difference. My first step was telling

10 everyone at school about this. Out of about 100 students, only

11 about five agreed with me. 

12 In the progress, I have been teased and made

13 fun of, but that didn't stop me. I've been called nature girl,

14 froggy, mother nature and lots of other names. I've been trying

15 to earn money to donate, but nobody seems to care. 

16 No matter what gets in my way, I'll keep

17 trying. Frogs are so important to me, especially my own Bubba. 

18 He is a baby pacman frog that means the world to me. 

19 The main point is that without frogs our world

20 would be terrible. So you should care. Thanks."

21 We have a picture here of Bubba. So there is

22 Bubba. It's the green frog there. All right. Our last public

23 commenter is Rick Robinson.

24 MR. RICK ROBINSON: Good afternoon. My name

25 is Rick Robinson. I'm the Environmental Policy Advisor for the

26 Iowa Farm Bureau in West Des Moines, Iowa. 
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1 I really didn't plan on speaking today, but

2 there is one other component, one other concern I think I want

3 to make sure you're aware of in your work here with the Science

4 Advisory Panel. 

5 You may have heard a little bit about

6 nutrients in the last couple of year, nutrients in the surface

7 water. In fact, EPA has made it a top priority. Administrator,

8 Lisa Jackson, was in Iowa a year ago talking about this and some

9 other regulatory concerns. 

10 EPA is pretty much on record talking about how

11 significant of a problem it is. It's becoming more costly to

12 rectify if we don't address it now, and how it is an EPA top

13 priority. 

14 We've actually seen some pretty aggressive

15 actions by EPA in some other states, the Chesapeake Bay, for

16 example, and Florida. Iowa farmers really don't want that kind

17 of regulatory action as they place denial. We want to address

18 nutrients in cost-effective, proactive voluntary fashion, and

19 there has been a lot of discussion about how we're going to do

20 that. 

21 In fact, we're a little bit concerned that

22 maybe we might not have regulatory or policy space to do that. 

23 Last year, last March there was a document issued by the EPA

24 called the "Stoner Memo." 

25 The Stoner Memo really outlines a path forward

26 for states like Iowa in the Midwest and the Mississippi River
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1 base, and to address nutrients through its existing state

2 programs with EPA in the background supporting those state

3 efforts.

4 So through the leadership of our Iowa

5 Secretary of Agriculture, Bill Northey, our Department of

6 Natural Resources, we're working on drafting a state nutrient

7 strategy for the State of Iowa to address nutrients in surface

8 water. 

9 Now, what does that have to do with atrazine? 

10 Well, you've heard a little bit about how important atrazine is

11 to no-till and conservation tillage systems. Well, especially

12 true in Iowa as the other states that have been on the panel

13 here today.

14 Iowa State University is currently analyzing

15 all of the published literature and putting together a plan for

16 the State of Iowa. That proposed plan will be out for public

17 comment probably in the month of July. We're, again, working

18 with EPA at the region 7 level to get that out there. 

19 So we're working in the State of Iowa to get a

20 plan out there for the public to comment on that will include

21 various conservation tillage approaches, strategies to address

22 phosphorus and (inaudible) in surface water. 

23 Now with respect to phosphorus, the

24 preliminary information is that conservation tillage and no-till

25 will be significant players to the tune of probably 30 to 90

26 percent reduction tools to address phosphorus. So we need to
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1 continue to make progress of no-till and conservation tillage. 

2 A key part of both successful conservation

3 tillage and no-till systems is the use of atrazine for all the

4 reasons that you've heard from others on the panel. We need to

5 continue to have that tool if we're going to address what EPA

6 says is a top priority for them on a national scale to address

7 nutrients in surface water. 

8 Now, it will be a challenge. It would be a

9 costly challenge as it is -- without the use of atrazine, it

10 would be even worse. But right now our preliminary estimates

11 are just to address a 45 percent reduction and total phosphorus

12 and total (inaudible) at the Gulf of Mexico, which is my

13 understanding of how the strategy is being drafted at this

14 point. 

15 It will probably cost Iowa in the neighborhood

16 of $1 billion a year to try to get close to that 45 percent

17 reduction goal. And it remains to be seen if we can make that

18 45 percent reduction goal. 

19 So you take away atrazine and you can start to

20 see the multiplying facts, the additional cost and challenges

21 we're going to have with addressing what EPA says is the top

22 priority for the country. 

23 So I want to make sure that we continue to

24 think about those other policy priorities for EPA as you work

25 very hard on this important issue. Thank you.

26 DR. DANIEL SCHLENK: Thank you. Any
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1 questions for Mr. Robinson?

2 Okay. Thank you very much. Okay. At this

3 point in time, this concludes the public comment period. At

4 this point, I think Dr. Brady is going to come and provide some

5 follow-up from pervious days' discussions, as well as introduce

6 charge questions. I believe we're going to show some of the

7 documents that were sent earlier.

8 DR. DONALD BRADY: Yes. Thanks very much, Dr.

9 Schlenk. I would like to just make a few clarifications to the

10 earlier presentation. Dr. Erickson will do so, as well as Dr.

11 Hetrick, in just a few minutes before the questions. Thank you.

12 DR. RUSSELL ERICKSON: Yeah. I've been asked

13 to address some of the registrants comments about the plant

14 community assessment methodology yesterday and this morning. 

15 I thought that their representation of it was

16 generally accurate and they presented some useful analysis and

17 data, but there were also some various errors in their

18 statements. I'd like to address some of those for the panel's

19 consideration during the rest of the meeting. 

20 First, regarding the current cosm data set,

21 EPA does not include multiple response endpoints as separate

22 data points, and feels that to do so is inappropriate because

23 any cosm treatment should have a single effect characterization

24 that reflects the entire plant community. I think there was a

25 panel comment yesterday about pseudo-replication. I don't know

26 if it's exactly that case, but I think that we would have
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1 concerns about including multiple endpoints. 

2 Thus far we were open to adding suitable

3 studies to the data set. We would disagree that the Baxter

4 study noted by the Registrant should contribute 12 new data

5 points. 

6 I should further note here that we consider

7 our COSM data set as a work in progress as part of our problem

8 formulation. It will evolve and we'll seriously consider

9 changes suggested by the Registrant and look to the SAP panel

10 for other recommendations in this regard. 

11 A second issue of the Registrant's

12 presentation that warrants some response is the use of part per

13 billion days as what was characterized as a model independent

14 approach, which might appear to add credence to its results. In

15 fact, part per billion days is still a model as much as PATI or

16 CASM is to extrapolate effects across time series, employing the

17 assumption that effects are linearly related to concentration.

18 PATI and part per billion days are closely

19 correlated because PAIT is based on -- currently at least -- is

20 based on log relationship and logistic toxicity relationships

21 and lognormal toxicity parameter distributions, both of which

22 are roughly linear when they are transformed back to the log

23 scales. This is the reason they are so closely correlated. 

24 This correlation is only a proximate and using part per billion

25 days thus ignores what we know about the non-linearity of toxic

26 responses. 
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1 As I indicated in my talk, we have looked at

2 part per billion days as an alternative and felt that by

3 ignoring the knowledge about toxicity was inappropriate to use

4 them. Furthermore, adopting the part per billion days approach

5 would preclude consideration of improved toxicity relationships

6 that might even be less linier concentrations. 

7 A third and serious problem with the

8 Registrant's presentation concerns their conclusions about the

9 assessment period duration. The assertion that the median cosm

10 duration of 30 days was scientifically justified showed poor

11 recognition of the role of the assessment period. 

12 As noted in my presentation yesterday, such as

13 short period basically ignores much of the exposure associated

14 with many of the cosms important to distinguishing exposures

15 with and without the facts. I don't see how that is

16 scientifically justified.

17 In contrast, the longer assessment period of

18 60 days is still relevant to effects in shorter cosm studies,

19 provided that these effects are significant in the context of

20 that longer window we're looking at. 

21 Even Dr. Giddings told you yesterday that

22 substantial effects in cosms with shorter exposures would not be

23 negated by recovery after the exposure period that you would

24 still count them as an effect. This is exactly why we feel that

25 a 60-day period is justified because the shorter periods where

26 they have significant effects are still relevant in the context
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1 of that longer window. 

2 We can honestly debate about which cosms are

3 appropriate, what levels of effects are appropriate, but I don't

4 think that this assessment period, this argument about the

5 30-day assessment period is, in fact, scientifically valid.

6 Also, as noted yesterday, this shorter period

7 basically constitutes a risk management decision to ignore most

8 of the seasonal field exposures at many field sites.

9 And so it's not surprising that it results in

10 lower effects assessments. 

11 This is a concern with me that there tends to

12 be a little muddying of the water between what the assessment

13 sciences -- which I'm perfectly willing to debate -- and what

14 risk management decisions are.

15 A fourth problem with the Registrants'

16 presentation involves recovery in their single species toxicity

17 test. On this side, I actually complimented the registrants. I

18 reviewed these reports and the toxicity tests were all fine

19 examples -- excellent examples of conducting this toxicity test

20 and reporting on toxicity test.

21 However, regarding recovery, it's been long

22 established that recovery of photosynthesis of plant growth from

23 atrazine exposures is rapid. This is nothing new to us. Our

24 assessment methodology has recognized this fact from its

25 inception 10 years ago.

26 However, this recovery does not involve
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1 compensatory growth as asserted yesterday by Dr. Brain and does

2 not mean that actual biomass in atrazine treatments will catch

3 up with the controls. This so-called compensatory growth is

4 actually an artifact of a common condition and toxicity test

5 which I would like to illustrate on this slide. 

6 This slide presents data from the same

7 toxicity test presented yesterday by Dr. Brain.

8 Basically, there are 48 hours of exposure and

9 48 hours of recovery. I blocked it out into day periods where

10 this is the average growth rate for the control of medium

11 atrazine level and the highest atrazine level. During the

12 exposure period you see how the control growth rate is up in the

13 .7 to .8 range. It drops somewhat at the medium level and

14 greatly at the highest level. I mean, it's showing the atrazine

15 effect. 

16 But look at what happens during the recovery

17 period for the control growth. It's dropping dramatically down

18 to the level of the atrazine level at the earliest time. Now,

19 what is happening here, what is common in toxicity tests, as the

20 control treatment grows, you get nutrient depletion, though

21 there was some replenishment of culture here, but you also get a

22 lot of self-shading. You get decreased control growth because

23 the control organisms are becoming limited by other factors. 

24 So if you look at the so-called compensation

25 here at the highest atrazine level, it's rate of growth does

26 pick up, but it only approaches what was already in the control
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1 at the beginning because the atrazine concentrations at both

2 plant ducts of these aren't being limited by the test conditions

3 that are present in the control. 

4 This is not compensatory action; it's simply

5 that the control rate of this treatment is achieving -- the

6 growth rates of these treatments are achieving what the control

7 already had earlier in the test. And that fact that it's

8 greater in the control is not indicative of some compensatory

9 mechanism, but rather just as a contrast of the -- now that

10 there's another stressor, another limitation, on the control

11 organisms.

12 So to characterize it as a compensatory

13 response with implications about how that might aid recovery is

14 misleading and inappropriate. This limiting condition and the

15 control versus the --

16 DR. DANIEL SCHLENK: Yeah. Let me just

17 interrupt a little bit. I'm being informed that this is not

18 supposed to be rebuttal --

19 DR. RUSSELL ERICKSON: Okay.

20 DR. DANIEL SCHLENK: -- in terms of what the

21 Registrant has actually produced and their presentation. 

22 Basically, this is just a clarification step in terms of if a

23 panel member had a particular question to respond to that

24 particular question and to provide new information in that

25 regard.

26 DR. RUSSELL ERICKSON: Okay. I was
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1 misinformed then.

2 DR. DANIEL SCHLENK: Just to clarify that.

3 DR. RUSSELL ERICKSON: Well, then I had a

4 couple of more point, but I guess I should shut up.

5 DR. DANIEL SCHLENK: Did Dr. Hetrick have

6 something.

7 DR. JAMES HETRICK: Very short clarification. 

8 I'm sorry. This is just a clarification on what we're really

9 doing here with an earlier group. A Registrant, in their

10 discussion of bias factor said that we were using it for

11 watershed mobility assessment. 

12 I don't know if this allowed to be said? No? 

13 Okay. I can't clarify that?

14 DR. DANIEL SCHLENK: You can only clarify if

15 one of the panel members ask for a specific question and you're

16 providing that data for that.

17 DR. JAMES HETRICK: Okay. That's fine. 

18 DR. DANIEL SCHLENK: Yeah. You can definitely

19 ask -- in fact, let me just point this out for the rest of the

20 panel. 

21 This is your opportunity right now to ask

22 clarifying questions that they can answer back to you, but you

23 have to ask the question and then they can answer back to you. 

24 Things that you might've missed. That type of things. 

25 DR. LINDA YOUNG: All right. So I have been

26 trying to understand the logistic regression that gave you the
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1 50 percentile where you had effect/no effect and you had the

2 60-day PATI, that logistic regression. 

3 DR. RUSSELL ERICKSON: Right.

4 DR. LINDA YOUNG: Now, my assumption is that

5 it is truly a logistic regression and that you did the base 10

6 model like in Appendix E, but then you just said something about

7 assumption in normality, which is inconsistent with logistic

8 progression.

9 DR. RUSSELL ERICKSON: What I was referring to

10 was the reason for the consistency between part per billion days

11 and the toxic response. I was referring not the logistic

12 regression to get the LOC, but to the logistic -- LOC logistic

13 relationship used for the original toxicity test and also the

14 LOC normal distribution used for the described distribution of

15 things like EC50.

16 Now, if you take the combined effects of those

17 and translate back to the linear scale from the LOC scale -- if

18 you look at a LOC normal distribution or a LOC logistic

19 relationship on its linear scale at its low end, it's

20 approximately linear. And that's the reason for that.

21 DR. LINDA YOUNG: All right. That's good. 

22 Thank you. 

23 DR. DANIEL SCHLENK: Okay. Any other

24 questions or clarification? Yes, Dr. Effland?

25 DR. WILLIAM EFFLAND: I guess this is for you.

26 Yesterday, we seen it several time where we've got your plot of
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1 cumulative PATI and then we've got the probability of adverse

2 effects and then we have that little interesting progression. 

3 There's a 50 percentile value that was chosen. 

4 I guess I would like to understand -- I talked

5 to the statisticians about it and I think one person said it was

6 the smallest uncertainty. Another person said it was the

7 greatest uncertainty. So I'm even more uncertain about why that

8 decision was made because that has a lot to do with -- it has

9 somewhat to do with what you finally decide as an endpoint. 

10 Can you help clarify that?

11 DR. RUSSELL ERICKSON: Well, it's the place of

12 smallest uncertainty of estimation that occurs.

13 DR. WILLIAM EFFLAND: So it is of the smallest

14 uncertainty.

15 DR. RUSSELL ERICKSON: It is of the smallest

16 uncertainty, but that isn't necessarily the reason though, I

17 mean, for picking this. That reason really has to involve

18 something beyond me, as assessors, and something that was more

19 of a risk management decision.

20 DR. WILLIAM EFFLAND: Okay.

21 DR. RUSSELL ERICKSON: And actually, what

22 probability you're willing to accept as far as in the context of

23 the cosm studies -- this is all in the context of the cosm

24 studies, defining a set of effects that you want to be

25 protected. And it's basically saying that you position yourself

26 at the point in exposure that you have a 50/50 chance. 
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1 Now, again, coincidentally that has the lowest

2 uncertainty, but that shouldn't be the driving force for that.

3 DR. WILLIAM EFFLAND: Thank you. That's good.

4 Thank you.

5 DR. DANIEL SCHLENK: Any other questions. 

6 Dr. Young?

7 DR. LINDA YOUNG: I just want to mention that

8 I have worked pretty hard to fit that logistic model with your

9 data and can't get the same numbers. So sometime, if you guys

10 can spend a little time with me, I would appreciate it. Okay?

11 DR. RUSSELL ERICKSON: Yes.

12 DR. LINDA YOUNG: Thanks.

13 DR. DANIEL SCHLENK: Okay. Dr. Rodgers?

14 DR. JOHN RODGERS: John Rodgers. Could I ask

15 for a clarification on the use of the CE-LOC? Because one thing

16 we were asked to do is to evaluate that and the sciences basis

17 for that. 

18 We were given our context in terms of how it

19 could be used in terms of identifying, in conjunction with the

20 monitoring data, sites that may be of interest or concern or

21 need further evaluation. And then I thought I might've heard

22 that it would be used for additional regulatory purposes. And I

23 just want to be really clear on what is the use of that CE-LOC.

24 DR. RUSSELL ERICKSON: Is there any question

25 about the definition of what its technical meaning is? You're

26 just interested in the use of it?
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1 DR. JOHN RODGERS: That could -- making sure

2 that we're clear on exactly -- if there is a specific definition

3 that you want us to use that would be good, too, I guess. I

4 don't think anything has changed, but I think my question was

5 more pointed at the regulatory use of that.

6 DR. STEVEN BRADBURY: I think we might get to

7 that. It might be helpful for Russ, just to recapitulate, from

8 a scientific analysis, clarification. If you don't know what it

9 is, then how do you understand how it's going to be used?

10 DR. RUSSELL ERICKSON: This is Russ Erickson. 

11 I know I'm long-winded, so...

12 Well, basically going back to whether the EEF

13 is that we have -- whatever the effects in this is whatever the

14 cosm data that we use are, we have an LOC that we don't want to

15 exceed. And the EEF is basically how much you exceed that LOC

16 at any one site. 

17 The desire is that that value stays at one or

18 below. And the CE-LOC is just an attempt to identify, not an

19 effects level on our test index, but a actual environmental

20 concentration that corresponds to an EEF of one. So it's trying

21 to meet the same goal based on monitoring the concentrations.

22 DR. STEVEN BRADBURY: Okay. Generically, we

23 have levels of concern that derive from our ecological species. 

24 The vast majority of the time those levels of concern are based

25 on single species eco-toxicological species, plants,

26 vertebrates, fish. 
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1 And we work through years and years with the

2 SAP of what those LOCs means. I won't go through that to kind

3 of save time, but the logic is if you take a look at your

4 expected or measured exposure concentration and you're below

5 that level of concern, we don't feel we have an issue in terms

6 of meeting the standard of avoiding unreasonable adverse effects

7 to the environment, given the context of the use of that

8 product.

9 In other words, by definition, the

10 risk/benefit analysis is done. It doesn't mean there's a zero

11 probability of something happening, but the odds of it happening

12 are very, very low. 

13 Now, if you exceed the level of the concern,

14 depending upon the degree of refinement to that risk assessment,

15 it doesn't necessarily mean there are going to be adverse

16 effects occurring at that measured or expected exposure

17 concentration, but it means that you need to roll up your

18 sleeves some more and dig in harder into the specifics. And you

19 may have to refine the risk assessment or it may allow you to

20 make some more site-specific determinations about where those

21 effects.

22 So in that sense, this LOC is no different

23 than any other LOC that we used. What's different is that this

24 LOC is attempting to take advantage of more than just single

25 species toxicity study. It's trying to take advantage of the

26 fact that ecosystems have more acting parts. 
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1 And to the extent the cosm studies gives us

2 some insight into what those relationships are, can we take

3 advantage of empirical data. It's getting at what those

4 single-species tests were supposed to be. 

5 The idea being none of the individual species

6 are going to be intoxicated to a degree of concern, and by

7 definition, the system should be okay. Maybe that's true, maybe

8 that's not; but in this case we've got information for those

9 interconnected parts. We're trying to take advantage of that to

10 see how do you use that as an LOC?

11 So the idea being that if you don't exceed

12 that LOC, we don't think there will be an effect on aquatic

13 structured function. If you exceed the LOC, there may be. And

14 that's when you have to take a look at those exposure situation,

15 define that, and then take a look at that LOC to then see if we

16 need to do additional mediation or not. 

17 So it's not a switch that turns on or turns

18 off. It's a switch that says you have to probably dig deeper

19 into what's going on, specifically with the LOC.

20 DR. DANIEL SCHLENK: Dr. Portier?

21 DR. KENNETH PORTIER: Ken Portier. So if

22 you're using this for a screening tool, kind of a first level

23 tool, which is worse for you, missing something that might have

24 a higher probability of being actually degraded or capturing a

25 bunch of locations that you might roll up your sleeve to do all

26 the work only to find that there is no effect?
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1 You know, it is the error of an issue, how are

2 you balancing that? How should we be thinking about that as we

3 think about this whole process, because I think that's kind of

4 buried in all of this?

5 DR. STEVEN BRADBURY: So generally speaking,

6 we want to be confident that if we're below the LOC, is it

7 really a likelihood (inaudible). 

8 It's not 100 percent confident, there's a zero

9 percent chance, but you can be more confident in whatever

10 scenario you're talking about. So we want to try to avoid the

11 false negatives. Because if you're above the LOC, we're

12 approaching it not that there is therefore adverse effects

13 occurring, but there are things that can be looked at. 

14 In another way, the LOC'S exceedances aren't

15 an estimation of risk, but as sort of a general feel to it would

16 be that if you're exceeding LOC by a little bit, maybe all

17 things (inaudible) probably not the same potential situation as

18 if you're exceeding LOC, blah, blah, blah. Because if it's

19 single species then it means you're further up the gross

20 response as being false. 

21 We don't have all sorts of sophisticated math;

22 I don't think you need sophisticated math to have sort of a gut

23 sense of what it means if you're getting higher and higher above

24 that LOC. Which gets back to John's question about how we can

25 use this, you can probably look at - I got a suite of monitoring

26 data that says I got a range of LOC, some being in the gray
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1 zone; some clearly from a regulatory perspective, that may give

2 us some insights and we can really roll up our sleeves first.

3 And where we may use different techniques to

4 try to figure out what's going on.

5 And the efforts can range from all different

6 sorts of different techniques that sort of -- in fact, the big

7 question is the 50th percentile. We put those in there to try

8 to bring out conversation. And the statistical part of that, is

9 what's the biological significance. And that gets back to the

10 questions you're asking me now, which is how we interpret that

11 in the Agency. 

12 They're actually getting into regulatory

13 stuff, but a risk assessment, those first graphs are clear

14 understanding, assumptions and implications of what those

15 assumptions are very helpful for risk so we can understand what

16 the consequences are.

17 DR. DANIEL SCHLENK: We're assuming you're

18 talking about Question 8, right? In terms of what you're

19 leading in to?

20 DR. STEVEN BRADBURY: Yeah.

21 DR. DANIEL SCHLENK: Okay. Any other --

22 Robert Gilliom.

23 DR. ROBERT GILLIOM: This is probably for Dr.

24 Erickson. Just a clarification back to this last point. So I

25 think you mentioned this earlier, but could you restate why you

26 think -- what's an estimate of the probability of adverse
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1 effects at the LOC?

2 I'm trying to get a clear sense of the

3 relation between the 50 percentile choice and how it translates

4 into the ---

5 DR. RUSSELL ERICKSON: Well, the LOC

6 regression or analysis of the cosm effects versus the cumulative

7 PATI or whatever effects index we use. Yeah, at the 50th

8 percentile, you're basically saying that in taking that variety

9 of cosms as sort of the universe you would be picking from that

10 you had a 50 percent chance of eliciting an effect here. 

11 DR. DANIEL SCHLENK: Okay. Does it relate

12 to a specific charge question?

13 DR. TIMOTHY ELLSWORTH: It relates to that

14 particular area.

15 DR. DANIEL SCHLENK: Okay. Can it wait

16 until we get to the charge question that relates to that?

17 DR. TIMOTHY ELLSWORTH: Yeah.

18 DR. DANIEL SCHLENK: Yeah. So we need to

19 get moving on charge questions. Okay. Can we just flash the

20 files real quick in terms of the new documentation just so

21 that's there for the record?

22 Okay. Let's just make a comment that they're

23 on the record and they're actually provided to the panel. So

24 can we read the first question into the record, please?

25 DR. DANA SPATZ: Sure. Many of the questions

26 begin with a statement. Would you like for me to read that
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1 statement or just the meat.

2 DR. DANIEL SCHLENK: Actually, you could

3 just read the sub part after the numeral would be fine.

4 DR. DANA SPATZ: Charge Question Number 1. Is

5 the SAP aware of any other laboratory-based or field-based

6 studies not included in this White Paper that should be

7 considered?

8 And this is Topic a. evaluation of the data on

9 amphibians.

10 DR. DANIEL SCHLENK: Okay. Our lead

11 discussant for that is Catherine Propper.

12 DR. CATHERINE PROPPER: All right. Thank you.

13 So I'm going to carefully read out my responses to each of

14 these questions. I do want to acknowledge that I think the EPA

15 and the Registrant have done an extraordinary amount of work in

16 doing this review and it's been very helpful in presenting and

17 preparing my responses.

18 So the EPA's search for papers prior to

19 mid-2011 was very thorough; however, since then several other

20 studies have come out and the public papers did not appear to be

21 evaluated and should be considered relevant to environmental

22 exposures to animals. Among these papers there were a wide

23 range of species and endpoints. 

24 While some studies would probably fit into the

25 designation of medium confidence as assigned by the EPA

26 evaluation of the design criteria, many probably did not. 
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1 Nevertheless, several suggested that there is

2 disruption in reproduction. All studies found anywhere from low

3 to high shifts in outcome measures associated without atrazine

4 exposure, including effects on physiological function, shifts in

5 gene expression, hormone levels, growth, gill function,

6 behavior, reproductive function and hatching success.

7 I'll briefly summarize the studies here. 

8 There are no effect on snail growth and mesocosms to do with

9 atrazine that's Baxter et al. (2011). And I do have a summary

10 of the references here too.

11 In a variety of fish species, atrazine

12 exposure impaired the stress response and glucose metabolism,

13 Nascimento et al, (2012), gill function, Alito et al. (2012)(a)

14 and (b); growth, Corvi et al. (2012); and behavior, Shenoy

15 (2012) and Tillitt et al. (2010).

16 A study by Tillitt and colleagues in 2010 is

17 mentioned on page 56 of the 2012 White Paper as being one that

18 will be further evaluated during inclusion in the atrazine risk

19 assessment; but I could find no further mention of it in the

20 White Paper or in Appendices B and C. 

21 In this paper they do note that decrease in

22 egg production resulting from fewer spawning events in fathead

23 minnows exposed to atrazine. 

24 And following Syngenta's presentation

25 yesterday I did go review that paper again last night and they

26 did have to remove a couple of the replicates; one from the
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1 control, I think, one from the .5 part per billion treatment

2 group, but that just reduced the sample size of those and did

3 not effect the overall outcome of the statistics. This was a

4 reduction in egg production which potentially has an effect

5 leading to fitness outcomes. 

6 Corvi et al. (2012), using the Zebrafish model

7 found no statistical effect of atrazine on gonadal development

8 and no effect on spine. 

9 There was only one amphibian study that was

10 not evaluated previously and that is by Tao, et al. (2012),

11 which found shifts in growth but not for infection rate. 

12 One of the study in amphibians evaluated the

13 effect of mixes and found that hatching success was

14 statistically correlated with atrazine exposure when evaluated

15 with a multivariate analysis. I'll come back to the issue of

16 mixes and how we need to interpret them when we get to the

17 design elements. 

18 Behavior was also affected in a study in mice,

19 suggesting neurological outcomes following exposure to atrazine.

20 Together with previous papers, also demonstrating behavioral

21 shifts in amphibians following exposure with relatively low

22 doses of atrazine, the possibility of neurological disruption

23 can't be dismissed without further research.

24 Similarly, some of the concern I have is in

25 the finding of a strong correlation between atrazine and

26 hatching success, as I mentioned before; but this was only found
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1 in some and not all amphibian species. 

2 So although not specific to the charge

3 questions issued for this SAP, a relatively recent paper in

4 humans does find, again, using a multivariate analysis,

5 statistical shifts in cycle length in women associated with

6 atrazine exposure. 

7 The outcome of these studies demonstrate

8 there's still variation in results across species, doses and of

9 course with design, which we'll get to in a moment. Thank you.

10 DR. DANIEL SCHLENK: Okay. Our first

11 associate discussant is Dr. Boone. 

12 DR. MICHELLE BOONE: Cathy really covered any

13 additional studies that I had found, but I wanted to indicate

14 the use of one or two studies to evaluate an entire species, let

15 alone a whole class of animals, is inadequate. 

16 Amphibians are not routinely used in toxicity

17 testing and the standards have not been developed. But there is

18 a decent amount available for the affects of atrazine on

19 amphibians, which has been reviewed; most of it has been

20 reviewed qualitatively.

21 In the studies that are available, they may

22 not be perfect, but the majority admitted or relegated with

23 lower confidence are not inherently flawed, and they allow

24 researchers to test the difference between atrazine-exposed

25 animals and controls, which is the basic question here with the

26 amphibian work. 
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1 Many of the studies available in the

2 literature could be used more fully to assess the effects of

3 atrazine and really the meta analysis of Rohr and McCoy (2010),

4 does a better job of evaluating the available data than the

5 White Paper currently does. 

6 DR. DANIEL SCHLENK: Okay. Dr. La Point.

7 DR. THOMAS LA POINT: Thank you. Tom La

8 Point, here. The only other aspects I can add is that I didn't

9 find any other papers, but we were made aware of the Baxter et

10 al. paper, which is good because I wanted to emphasize my

11 response to this; and it will come with other responses as well

12 that it's important to seek studies that include, predator,

13 parasitism and food chain as part of these.

14 As with the Baxter paper, Baxter, et al.

15 paper, and it will come up again in the discussion, I'm sure, as

16 we get to it, one of the things that I think may help these

17 discussions is to look for papers or look for data in papers to

18 be able to link something about statistical power and the

19 ability to separate the two means with a certain percent of

20 confidence. That's been kind of discussed, but we haven't seen

21 any quantification - well, not any -- there is not as much

22 quantification. Thank you.

23 DR. DANIEL SCHLENK: Thank you. Dr.

24 Klaine.

25 DR. STEPHEN KLAINE: Actually, the previous

26 three covered most of my points. I have nothing further to add.
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1 DR. DANIEL SCHLENK: Dr. Young.

2 DR. LINDA YOUNG: My only point was well

3 covered.

4 DR. DANIEL SCHLENK: Dr. Portier.

5 DR. KENNETH PORTIER: Nothing to add.

6 DR. DANIEL SCHLENK: Okay. That's the rest

7 of the panel. Any other panel comments for Question 1?

8 Okay. Let me go back to the Agency. Are you

9 fairly clear in terms of the responses provided?

10 DR. AMY BLANKINSHIP: This is Amy Blankinship.

11 I think as we go through this, in terms of looking at the

12 evidence and how to use it, there was some comment made by Dr.

13 Boone in terms of how to use the evidence, especially in regards

14 to McCoy and Rohr meta analysis. 

15 So we would be looking for input in exactly

16 how we would use those studies that maybe the agency had

17 determined as not, sort of, met the quantitative standard and

18 using -- as far as what sort of criteria would be maybe not

19 considered to be as strong an indication in terms of

20 classification and how to use that study?

21 DR. DANIEL SCHLENK: Okay. Dr. Portier

22 has some questions.

23 DR. KENNETH PORTIER: So Dr. Propper, one of

24 the things -- I was listening to what you were saying and one of

25 the words that caught me is "causal relationship." And I was

26 thinking about most of these studies were probably association
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1 relationships. Is anybody actually talking about causality,

2 litigating exposure through biochemical signaling pathways? I

3 hadn't seen any of that literature here.

4 DR. CATHERINE PROPPER: It's very difficult,

5 and I'll get into some of this when we discuss the design

6 elements.

7 DR. DANIEL SCHLENK: Okay. So we're ready

8 to move onto Question 2?

9 DR. DANA SPATZ: Please comment on the

10 completeness of EPA's list of pertinent test design elements.

11 Also, please comment on the degree to which

12 these test design elements, singularly or in combination, would

13 be expected to contribute towards confounding the test results.

14 DR. DANIEL SCHLENK: Our lead discussant

15 for that is Dr. La Point.

16 DR. THOMAS LA POINT: Okay. Thank you. I'll

17 just read what I have here; it's easiest so I don't mess up.

18 First, the caveat, prior to my response to

19 this question; for regulatory purposes, toxicological studies,

20 whether laboratory or field, need to have an auditable QA/QC

21 trail designed to generate data that are unequivocal; naturally,

22 the interpretation of results, will probably still lead to the

23 differences of opinion. 

24 The entire idea of requiring study protocol

25 that are as standardized as possible could only help better

26 determine cause and effect of exposure to a specific chemical;
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1 hence a design element as stated in the White Paper on page 62

2 and following, are absolutely critical to establish cause and

3 effect. They were well done. 

4 The review of published literature with any

5 type of study is absolutely important for putting the study into

6 context of what's already been studied and published; however,

7 it was entirely appropriate for the EPA to establish categories,

8 quantitative, qualitative, and invalid in their efforts to

9 establish a degree of confidence. 

10 Specifically as stated, I'll just paraphrase

11 some of these. And then there will be some other comments, I

12 know, from my co-respondents.

13 The sufficient replication is absolute,

14 allowing an adequate statistical power to detect difference from

15 controls. Screening for potential biological, chemical or

16 physical interferences in food and water sources, gets into what

17 Dr. Portier just referred to as cause and effect. How do we do

18 that?

19 Limited use of an appropriate solvent, as we

20 saw, has already led to a problem and requiring that is

21 excellent; and then using both negative insolvent controls

22 concurrently with treatment groups. 

23 Actual versus nominal measures of the test

24 chemical in control of treatment groups, again, is paramount,

25 which is wonderful to see and it should be in one that is

26 desirable as a test for this.
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1 Test equipment constructed of appropriate

2 materials so you don't have any conflicts in terms of potential

3 material moving in or leaching. Appropriate loading rates are

4 absolutely important and that's been something long-term in ASTM

5 and APHA that have been required. 

6 And something that is not as appreciated as

7 much as much as it should be by using organisms, understood

8 prior exposure history, joining those cultures were absolutely

9 wonderful.

10 In Section 7.2 (b), screening characteristics

11 are appropriate and necessary. This approach is ultimately

12 subjective any time screening is, but it is highly useful when

13 incorporating the weight of evidence approach and is defined as

14 one that chooses an accepted set of criteria. And this is

15 something that will come up again, too, when we talk about the

16 use of weight of evidence in the mesocosm section. I'll come

17 back to that in Part B.

18 DR. DANIEL SCHLENK: Dr. Boone.

19 DR. MICHELLE BOONE: So what ecological

20 studies have taught us is that species differ in their response

21 to natural and environmental variables in nature, where it's

22 tolerance for pH or for violet radiation, predators,

23 competitors, temporary environment or environmental

24 containments. 

25 One of the issues before us in this review is

26 whether two related studies published in a single paper, that
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1 met many of the EPA criteria, is acceptable to determine

2 susceptibility of amphibians to atrazine.

3 Bridge and Similich (phonetic) (2000) and

4 (2001) demonstrated that different anuran species, different

5 populations within a species and families within the same

6 population of a single species varied greatly in their

7 sensitivity to the insecticide carbaryl. And these studies

8 highlight the importance of including multiple species, multiple

9 populations and multiple clutches in any assessment evaluating

10 the impact of a contaminant. 

11 Naturally, EPA should use the best science

12 available. And in pursuit of that, the EPA established many

13 criteria to establish which studies should be valuable for

14 determining safe environmental concentrations of atrazine and

15 non-target species. Determining a cause/effect relationship

16 between atrazine and endpoints and metamorphosis during

17 reproductive measures requires manipulating one factor at a time

18 to compare for appropriate controls. 

19 Many of the studies excluded from

20 consideration have met this basic requirement for establishing

21 cause and effect relationships and have been published in top

22 tier peer review journals; but have been excluded from

23 consideration because they have not met EPA criteria.

24 With an objective to determine safe

25 environmental levels for non-target organisms, that criteria

26 have been set high; however, if that excludes all but one
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1 published manuscript, then such criteria are counterproductive

2 and it gives the appearance of bias, even if the intent was the

3 opposite. 

4 Most of the studies reviewed qualitatively as

5 well as those viewed as invalid should be reevaluated with

6 revised criteria so that more studies can be used explicitly

7 with particular attention for concentrations that impact

8 reproductive endpoints which appear sensitive to atrazine in

9 some populations and species. 

10 And these are the criteria that I believe are

11 essential to include which, to some extent, overlap with what

12 Dr. La Point said.

13 Sufficient replication to a control of power

14 analysis to evaluate if non-significant resolve for low

15 experimental power, which is not currently a criteria. A

16 solvent, if used, should not exceed the recommended ASTM rate. 

17 Measurement of test chemicals and experimental treatment, water

18 quality should be maintained, as indicated, by measuring the

19 endpoints or as by indicated by animals reaching natural sizes

20 and developmental endpoints in a timely fashion; and sufficient

21 data and statistical analysis is provided to enable reviewers to

22 understand and substantiate results. 

23 Although it's completely appropriate to

24 evaluate studies on the basis of their strength and

25 shortcomings, the current approach of selecting and eliminating

26 already published studies errs on the side of failing to detect
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1 an effect of atrazine if one exists. 

2 Selecting strict design elements makes sense

3 in theory, but not in practice if the effects of atrazine from

4 studies conducting sound science are eliminated from evaluation.

5 I can provide a list of the criteria that I think are

6 acceptable, many of which are common in practices by people who

7 have been conducting studies with amphibians in the lab. 

8 For instance, the loading rate of one tadpole

9 per one liter; field densities are often much higher in people

10 who successfully used other techniques in the lab. For

11 instance, using plastic containers, I agree is probably not

12 ideal, but those studies should be evaluated. For instance, to

13 see if atrazine does not affect a parameter in the controls, but

14 it does. And the atrazine exposure, I mean, that's an effect

15 that probably shouldn't be ignored. 

16 Using field animals, for instance, you know, I

17 think we want to know what happens to animals who live in the

18 field. So excluding animals collected from the field that no

19 doubt have been exposed to atrazine, given how pervasive it is,

20 I think is problematic. We need to look at a range of different

21 species. I addressed each of the EPA criteria and I can provide

22 that list.

23 DR. DANIEL SCHLENK: Okay. Yeah, if you

24 guys want it now, she can give that to you. It'll be in the

25 minutes, if that's okay.

26 DR. AMY BLANKINSHIP: Yeah, that's fine. I
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1 mean, do you have them right now? I mean it might help us form

2 the questions. That's why I'm asking for them now.

3 DR. DANIEL SCHLENK: Okay. Yeah. She's

4 going to get it to you, right, and you can distribute them. How

5 long would it take you to read it?

6 DR. SHARLENE MATTEN: It's better if you at

7 least touch on the major points. 

8 DR. DANIEL SCHLENK: How many pages is it?

9 DR. MICHELLE BOONE: Two and a half.

10 DR. DANIEL SCHLENK: Two and a half. Can

11 you hit the top sentences on each page instead of reading the

12 whole --

13 Dr. MICHELLE BOONE: So the EPA criteria that

14 it hashes contain both the negative and the solvent control that

15 met ASTM standards concurrently with the treatment groups. A

16 number of researchers have shown in previous research that there

17 is no difference. So some of those studies were relegated to

18 low competence and that may not be necessary, for instance.

19 DR. AMY BLANKINSHIP: Screening for potential

20 --

21 DR. MICHELLE BOONE: Screening for potential

22 contaminants in food and water sources. Again, of you have the

23 control and the atrazine exposure and there is a difference, you

24 attribute that difference to atrazine exposure and not the food.

25 You can do an experiment on the food to see if the food has

26 estrogenic compounds if that's really a concern, but that study
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1 should not be eliminated.

2 The EPA criteria that you must measure the

3 testing levels in both the control and the treatment group to

4 relay potential effects for exposure concentration and to check

5 for potential contamination of the control, you definitely have

6 to measure the treatments. However, controls, if there is no

7 effect on the control, then that may not mean that the study has

8 to eliminated or given low competence. If there are

9 unanticipated effects in the control then maybe that should be

10 an expectation. The test equipment should be constructed of

11 recommended materials and avoid the use of plastics is another

12 EPA criteria. 

13 Again, you're making the assumptions that

14 plastic and atrazine are going to synergistically interact. I

15 cannot find any studies that support that premise, therefore,

16 those studies should be included in the evaluation. 

17 And if the qualitative data look like they

18 took into account some of the effects seen in the literature

19 would be less concerning to me. And all these were evaluated,

20 but since they were qualitatively assessed and it made no impact

21 on the outcome of the EPA's recommendation.

22 Again, loading rates, natural densities are

23 much higher in water quality of those, changing water every

24 three to four days is a standard practice in amphibian ecology. 

25 Most people aren't measuring ammonia levels, which would, I

26 agree, would be great if they were doing it, but they're not. 
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1 But they raise animals comparable to the field. 

2 So I think you have to take that into account

3 at the state we are until you publish what the expectations are

4 or until standard practices are developed.

5 Another EPA criteria was use of organisms from

6 controlled environments so the whole lab-reared organisms --

7 again, we want to know what happens on animals in the field;

8 therefore, we need to, by weight, arrange species, including

9 Xenopus, which are, of course, not in the field around here,

10 ideally.

11 EPA criteria, the control mortality should not

12 exceed 30 percent. This, to me, it's a dependent one. If

13 you're creating conditions that are mimicking nature, where

14 presence of predators, disease or competitors are going to be

15 present, then mortality could be lower than 30 percent. 

16 In nature, survival is typically less than 5

17 percent. So this criteria may be dependent upon the

18 experimental venue and the approach taken.

19 Another EPA criteria was the presence of other

20 stressors and controls or treatments that compromise the

21 interpretation of atrazine as the causal factor. 

22 Again, if studies are mimicking nature, my

23 impression was those were viewed as confounding atrazine

24 effects, but if you're manipulating one thing during the control

25 and the treatment, and that thing is atrazine, then you should

26 be able to establish cause/effect relationships. And that's
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1 all.

2 DR. DANIEL SCHLENK: Okay. Thanks. Dr.

3 Propper.

4 DR. CATHERINE PROPPER: I'll try to only

5 repeat points briefly that Dr. Boone brought up, but I do have a

6 rather lengthy response.

7 And I do want to reiterate what Dr. La Point

8 said which is these design elements are rigorous and for

9 regulatory purposes, I do understand the necessity of evaluating

10 them.

11 I think the EPA reviewers did a very good job

12 of evaluating and fitting the studies into the categories based

13 on the EPA design criteria. And I know that that was a huge

14 job. It was a huge job reading it. 

15 The EPA should be encouraged to publish these

16 design criteria. I looked to see if they were published

17 somewhere for amphibians and I do have to say I was a little

18 surprised that it was the 2003 SAP that suggested these

19 criteria, as far as I can tell, and yet they weren't published

20 anywhere online for amphibians. They did seem to match pretty

21 well with the chronic exposure criteria test guidelines. And

22 there is a place to do this at the draft OSCPP test guidelines. 

23 And I have in my written response where that is.

24 The EPA does want a standard value for level

25 of concern and following these guidelines can help to provide

26 this type of data for regulatory purposes. However, if the
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1 guidelines aren't clearly followed, then each study needs to be

2 carefully and independently evaluated, which is what you tried

3 to do. They have identified issues which arise in each study. 

4 Collection of data that do not meet the design

5 elements made the evaluation of outcomes less capable of

6 providing clear-cut dose limits such as the NOAELs and LOAELs,

7 or any kind of Level Of Concern; however, the data still may be

8 useful for determination of concern when there are differences

9 among control and treated groups. 

10 Furthermore, for some of the elements such as

11 what Dr. Boone just talked about, more concern should really be

12 placed on negative data as representing false negatives than

13 necessarily on a positive scheme, false-positives.

14 This weight of evidence approach that actually

15 Syngenta proposed yesterday, I really do like that proposal. I

16 would like to see independent evaluation of the current

17 literature so that the bins in the scoring that goes into that

18 is evaluated by two different groups. I think that would be

19 useful.

20 The way that Syngenta did this was based on

21 their opinion, and they have that right, and it does lead to

22 weak evidence of no adverse effects, for the most part. So most

23 of the studies, and I went back through their slides yesterday

24 and took a look, most fall into that category. 

25 And I do think that weak evidence of no

26 adverse effects is not the basis for regulatory action or risk
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1 assessment. There are other factors that need to be taken into

2 account before decisions are issued regarding closure of this

3 issue for the EPA.

4 After evaluating carefully the White Paper

5 review and the literature, potentially confounding effects of

6 species and even population differences, insensitivity is a

7 concern. These differences preclude obtaining a straightforward

8 LOC value.

9 Furthermore, organisms exposed in the real

10 world are almost always exposed to complex mixes at various

11 times during their life history, and Dr. Boone has emphasized

12 this point, so I'll try to move on here. 

13 Violations of some of the design elements need

14 to consider whether such violations create, again, more risk for

15 false negatives than false positives.

16 Many of the design element restrictions are

17 not so strictly applied to the results reviewed in the

18 assessments evaluated for Sections B and C of the White Paper,

19 which we're going to be talking about soon in the plant

20 community. 

21 So I have to say, after sitting through this

22 panel, I'm a little bit confused as to why we have such

23 different criteria across our studies. I do have some comments

24 on the specific design elements. And again, I'll try not to

25 repeat what Dr. Boone said, but I will try to recognize whether

26 I agree or disagree.
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1 Sufficient replication, of course, is

2 critical. And without that, we can't make any kind of

3 evaluation on the effects. I have no further comment there. 

4 Screening for potential contaminants interferences in food and

5 water resources -- and I'll extend this to the type of container

6 that the animals are exposed to and/or loading issues. All of

7 those, again, as Dr. Boone said, if you have effects and you

8 only change or the difference among them is the atrazine level,

9 you still have to consider that there might be a causal

10 relationship, or at least an interaction between these factors

11 and atrazine. So eliminating them as irrelevant is not

12 necessarily appropriate. 

13 Nevertheless, I do understand that when coming

14 up with a risk assessment and a value, it becomes very difficult

15 to do because you can't say that there's a specific level of

16 concern when you have those kinds of interactions. 

17 The solvent issue also falls under that

18 category. So I won't spend a lot of time reading my comments

19 there, but I agree with Dr. Boone.

20 Measurement of the test chemical in the

21 control and treatment group relates to the potential effects to

22 an exposure concentration and to check for potential

23 contamination in the controls. Again, this is an issue of more

24 risk of a false negative than of a false positive if you have a

25 well-controlled study.

26 Test equipments; the test vessels, I think
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1 I've hit on. The loading; again, I think I've hit on. 

2 And the use of organism from a controlled

3 environment; again, the problem with eliminating these studies

4 is that if you have an appropriate controlled study, what you

5 might be seeing are confounding effects associated with

6 interactions, but it doesn't mean there's no atrazine effect. 

7 So again, eliminating them completely from consideration is more

8 problematic.

9 So it's not that I don't believe these design

10 criteria aren't very important, but I do hope that they get

11 published and that people can use them to design their

12 experiments to eliminate some of the confounding effects. I do

13 think that applying them post hoc to previously published

14 studies limits the value of those studies in risk assessment.

15 I have some thoughts on previous SAP

16 parameters used in evaluating amphibian development as well,

17 which again included experimental design protocols and data

18 quality assurance which, of course, is critical. The strength

19 of the cause effect and dose response relationships -- so these

20 next two (inaudible) plausibility, I think, get to what Dr.

21 Portier was mentioning.

22 So the EPA evaluations of the literature

23 repeatedly mentioned lack of monotonic responses. 

24 Unfortunately, that's the way it is with a lot of endocrine

25 disruption studies. The endocrine system is a complex set of

26 feedback loops. For better or for worse, we're going to have to
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1 deal with this issue; but just saying there's a lack of

2 monotonic response, therefore we can't use the study in any kind

3 of regulatory or risk assessment purpose, is invalid. 

4 I do understand, again, it makes adding those

5 no effect levels and effect levels very difficult to come up

6 with. 

7 Mechanistic plausibility; if only we knew the

8 mechanistic plausibility of even endogenous hormones, for all of

9 the endogenous hormones, life in medicine would be much easier. 

10 But unfortunately we don't. And so to expect for us to know

11 what the mechanism of action is for endocrine disruption for any

12 one compound, including atrazine, is unrealistic. And what we

13 need to do is look at overall responses of all animals that are

14 exposed, which, fortunately, we have been doing. But I don't

15 think we can eliminate studies because we don't know mechanism

16 of action. 

17 And then ecological relevancy of measured

18 endpoints, which, of course, is critical and it goes back to the

19 issue of what's an adverse effect. I wish there was a little

20 bit more clear-cut definition of adverse effect. I've looked

21 through all of the EPA's websites to see if I could find a very

22 clear definition. And I do appreciate that you are trying to

23 define that and gave some of a framework yesterday. But it

24 addresses the importance of understanding that any effects

25 founds are only relevant in the context, again, in complex mixes

26 because almost everybody in the world is now exposed to a mix of
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1 compounds.

2 Ecological relevancy goes to the heart of the

3 question, again, of adverse outcome. And unfortunately, trying

4 to base what is an adverse outcome or that there is a lack of

5 adverse outcome on the one very well-conducted study that came

6 with the DCI, doesn't evaluate shifts in fitness, which could

7 really help in determining adverse responses in an ecological

8 context. 

9 DR. DANIEL SCHLENK: Thank you. Dr.

10 Klaine.

11 DR. STEPHEN KLAINE: My colleague has done a

12 great job of capturing almost all the information. I only have

13 a few things to add. 

14 First, in regards to animal husbandry, it's

15 hard enough to do toxicology testing and get reproducible

16 results. If you layer on that an inadequate understanding of

17 the healthier organisms, it makes it almost impossible. So I

18 can't stress enough how important it is to understand the health

19 of your lab-reared organisms. That doesn't discount the

20 field-collected organisms. I'll get to that in a minute.

21 So having good quality food, uncontaminated

22 food, good quality water and good environment, and understanding

23 of how these organisms are behaving is very important before

24 starting the test. And you get the field-collected organism,

25 that I also endorse, in terms of understanding how the

26 difference between the cultures and how they respond. It
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1 provides a whole other level of complexity. That complexity

2 extends to not only understanding what these organisms were

3 exposed to in the environment, but also how the environmental

4 parameters that you collected them from affect their health.

5 It also extends to when you bring it back to

6 the laboratory, what are they not exposed to anymore that might

7 make them more acceptable to parasites as well as what are they

8 now more exposed to in the laboratory.

9 I guess all of this gets me to an idea that

10 maybe we should be thinking about, for all the cultures, whether

11 lab or field, we should maybe subject them to reference toxicant

12 test; if for no other reason than to get an idea of relevant

13 variability between how individuals respond, because that,

14 itself, may be the biggest source of your error, in terms of

15 these bio-assays. 

16 Once you know that then that facilitates doing

17 the power analysis to understand how many replicates you

18 actually need in order to determine a significant effect. I

19 think that's probably it. 

20 I'm going to reiterate, we don't usually

21 publish nominal concentration, so I'll just put my bid in for

22 ensuring that we measure how much atrazine these organisms are

23 exposed to.

24 DR. DANIEL SCHLENK: Okay. Dr. Young.

25 DR. LINDA YOUNG: Thanks. The main thing I

26 have to add is that our goal is to establish cause and effects
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1 relationships. And traditionally, that requires a randomization

2 of treatments to the subjects in the study. 

3 Although that may be implicit in the design

4 elements listed, it's not explicated stated as should be. Now,

5 more recently, consideration has been given to making causal

6 inference from observational studies and those studies conducted

7 without randomizing treatments to subjects.

8 In this case, care is taken to account for

9 differences in the subjects that could lead to observed

10 differences in treatments and control groups that are due to

11 differences in the subjects and not to treatments as an analysis

12 pretend to lead us to believe. 

13 So considering whether there's actual

14 randomization in this study or not is an important component,

15 and there are methods if that's not done, but they have to be

16 conducted with great care.

17 DR. DANIEL SCHLENK: Thanks. Dr. Portier.

18 DR. KENNETH PORTIER: You know statisticians

19 are trained all the same. So I will read my four paragraphs. 

20 So many of the seven items on the list are properties for

21 invalid studies the way statisticians would describe as

22 properties of good experimental design; sufficient replications,

23 use of adequate controls, standardizing the experimental unit. 

24 For example, using all laboratory-raised organisms or all field,

25 but not mixing them up.

26 Added to this would be the discussion of
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1 adequate randomization or lacking that, at least the lack of

2 obvious bias and the assignment of treatments to an experimental

3 unit. And then the other thing is the use of adequate blocking.

4 That is, making attempts to control for all other extraneous

5 factors through treating each experimental replicate; or

6 treating growth for a controlled replicate as one block of unit,

7 putting all treatment groups and controls into a block and then

8 doing things; for example, running concurrent treatments and

9 control is a concept for blocking.

10 Dr. Klaine mentioned good environment. I

11 would say for experiments where a complex test environments are

12 used to support the amphibians during that assay, it would be

13 nice to know that the test ecosystem is at some level of steady

14 state prior to the beginning of dosing. I think it's kind of

15 what you meant by a "good" environment. One could consider this

16 as further clarification of what we mean by lack of other

17 stressors.

18 I think it's critical that the study report be

19 clear on each of the seven items, as well on the randomization

20 of blocking and the test environment steady state status,

21 otherwise, the result can't be duplicated. As we know,

22 duplication is a key aspect of the scientific method. So

23 without duplication, we don't have science.

24 EPA is being very nice. They classifies some

25 of these studies as invalid. I'm kind of saying this

26 half-hearted, but I would say a better more than possibly
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1 actionable label would be useless for risk assessment as a

2 label.

3 I say this because I think a lot of scientists

4 think they're doing science that's going to be useful in

5 assessing risk. If we don't put that label on it, they don't

6 realize they're missing some key aspects of their study and

7 their effort is wasted and your effort is wasted in actually

8 having to assess those studies. So I think that an informative

9 label like useless for risk assessment helps the field

10 understand where they belong.

11 DR. DANIEL SCHLENK: Sure. Dr. Young.

12 DR. LINDA YOUNG: I just want to clarify what

13 Dr. Portier says. Blocking is nice but not essential for good

14 design -- I mean, for a design.

15 In other others it can explain some variation

16 that allows you to tell differences in treatment and controls

17 more precisely, but if you fail the block, you just put up a lot

18 more variation in this. Right? Okay. 

19 I just wanted to be sure that it's not

20 critical to block, it's just that you're a whole lot better off

21 if you block.

22 DR. DANIEL SCHLENK: Okay. Dr. Propper.

23 DR. CATHERINE PROPPER: So I do just want to

24 add, and I think I left it out by trying to limit my comments,

25 but a lot of those studies that were labeled invalid, I agree

26 with you, they were definitely invalid for regulatory reasons
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1 and sometimes I wonder how they past peer review. Not all of

2 them. Some of them I could see were invalidated for other

3 reasons.

4 DR. DANIEL SCHLENK: Anybody else? Dr.

5 Klaine.

6 DR. STEPHEN KLAINE: And a little more reason,

7 given past comments for these criteria to be put into some sort

8 of standardized fashion.

9 DR. DANIEL SCHLENK: Dr. Portier.

10 DR. KENNETH PORTIER: I was thinking about the

11 elements. There are design elements and then some of these

12 relate to the results like the non-monotonic dose response kind

13 of issue. And those you can't control for. But there may be

14 certain -- so those might still be useless for risk assessment

15 but not because they were a bad study; they just don't present a

16 result. 

17 So there are a lot of these studies where the

18 results bounce around; you know, it's high for the first dose,

19 low for the second one. You know, that's the reality of

20 experimentation at that point.

21 So again, that study is more of a negative

22 finding than it is of a useless study or an invalid study. If

23 that's the only thing that's bad with it, then it doesn't give

24 me a nice dose response.

25 DR. CATHERINE PROPPER: The problem you might

26 have with making that a criteria for risk assessment is that you
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1 see that more often than you don't with endocrine disruption

2 studies. So I'm a little concerned about us not saying there's

3 a clear-cut, straightforward dose response based on eco-tox

4 criteria, where that doesn't meet endocrine criteria.

5 DR. DANIEL SCHLENK: Anyone else?

6 No? Okay. Just to get a quick assessment,

7 what does (b) look like, in terms of length for you guys? Is it

8 fairly short? We can do 2(b) and then break?

9 DR. DANA SPATZ: Please comment on EPA's

10 conclusions about the level of confidence placed on each of the

11 study's results. 

12 DR. DANIEL SCHLENK: Okay. Dr. La Point.

13 DR. THOMAS LA POINT: Appendix C of the White

14 Paper describes the confidence with which the results of each

15 study were accepted. They acknowledge difficulties in

16 interpreting any kind of study. The variety of accepted studies

17 marked qualitative were, in large part, when I reviewed it,

18 because of the lack of measuring atrazine residues over time. 

19 That single component means a lot and should be measured to form

20 a goal to quantitative. 

21 Further difficulties arose in those studies

22 when which solvent used was not included or discussed. 

23 Finally, in my opinion, one of the major

24 weaknesses in the review studies leading to that qualitative

25 designation -- and this is a broader aspect that gets actually

26 to what we were just talking about -- it was the limited ability
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1 to link chemical biomarkers, perhaps best referred to as

2 biomarker exposure to measure the biological or ecological

3 effect. 

4 This remains a major area in which we can

5 expect to see differences in interpretation and can expect to

6 see more advances in the time ahead. It leads us back to the

7 value of published peer-reviewed literature. It is

8 self-correcting over time. We were just talking about the

9 utility of certain experiments that don't seem to follow along,

10 but from that, everyone knows in this room that you can take

11 those questions and generate a more focused study next time. It

12 just takes time and money. 

13 As questions arise from previous studies and

14 as published papers focused on the remaining questions, we

15 hopefully can come to a point in which we can prescribe

16 mitigation approaches to a small range of atrazine concentration

17 around our Level of Concern. That's all I have.

18 DR. DANIEL SCHLENK: Dr. Boone.

19 DR. MICHELLE BOONE: Based on the EPA

20 criteria, I felt like they were a factor of being categorized.

21 DR. DANIEL SCHLENK: Dr. Propper.

22 DR. CATHERINE PROPPER: I do agree that as a

23 pending exercise, they did a fairly consistent job of placing

24 most of the studies into the appropriate categories given the

25 weight placed on the design criteria. 

26 Again, I'm a little concerned about the
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1 control versus treatment groups when you see differences

2 discounting some of those studies; which I don't believe were

3 done in the White Paper, per se, as they did the whole

4 evaluation, but in the conclusion where we take into account

5 only that one study with two experiments; that makes me a little

6 more concerned.

7 I do think that the data are suggestive that

8 for most species there's no overt toxicity from an acute

9 exposure and rather limiting toxicity from chronic exposure. 

10 But unfortunately, overall, the lack of more than one strongly

11 controlled study in the literature makes extrapolation of the

12 effects of atrazine exposure, on general overall across the

13 entire plane of amphibians on their growth reproduction

14 monitoring survivorship, really impossible to evaluate with the

15 current data set that we have.

16 DR. DANIEL SCHLENK: Dr. Klaine.

17 DR. STEPHEN KLAINE: I have nothing to add.

18 DR. DANIEL SCHLENK: Dr. Young.

19 DR. LINDA YOUNG: I have nothing to add.

20 DR. DANIEL SCHLENK: And Dr. Portier.

21 DR. KENNETH PORTIER: The only comment I have

22 is that it's unclear in the paper the extent to which

23 information beyond what was in the published paper was looked

24 at. I know when you do a data call-in, you call them up,

25 hopefully, and talk to them and say, you know, "What exactly was

26 your sample size?" I've read a lot paper where they tend to
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1 forget to put that in the method.

2 So I think the confidence in the

3 classification is a function on how much effort demands of

4 really understanding what was going on in the experiment. And

5 unfortunately, the way things are published these days, a lot of

6 what went on in the experiment gets thrown out in the first

7 editing pass. We don't need to know all those stinking methods,

8 we just want to get the results, right. And it's the methods

9 that we're talking about here.

10 And then the second thing is that I think if

11 you're going to classify into levels, you need to classify the

12 levels. I was just looking at the errata and you had things

13 classified as qualitatively lower to invalid. Well, is it

14 qualitatively lower or is it invalid? You just created a new in

15 between level. You just use the criteria and put it in a bin

16 and be done with it. 

17 DR. DANIEL SCHLENK: Okay. Anybody else? 

18 Dr. Propper.

19 DR. CATHERINE PROPPER: Cathy Propper. So one

20 suggestion, just for the future is as I was going through the

21 table, it was inconsistent about whether or not there was an

22 effect or not. So there was a column that had what effects were

23 evaluated and sometimes they had in there whether or not there

24 was an increased rate of metamorphosis or a decreased rate of

25 metamorphosis. Sometimes it just listed metamorphosis. 

26 So as even reviewing and going through that, I
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1 had to go all the way back to the paper and try to dig out the

2 results quickly, instead of just taking a look at evaluating the

3 methods themselves and know what the result was. And so it was

4 really just inconsistent across that, but I found that I just

5 decided to go back and look at each paper, which took me a lot

6 of time.

7 DR. DANIEL SCHLENK: Okay. Any other panel

8 members want to jump in on this one?

9 Okay. Let me go back to the Agency. Do you

10 have what you need for 2(a) and 2(b) there?

11 DR. THOMAS STEEGER: I haven't been on this

12 panel as of yet, but I am the Senior Advisor in the

13 Environmental Fate and Effects Division and I have sat in on the

14 last two amphibian SAPs. 

15 As a Senior Advisor in the Agency, I recognize

16 the need to be able to provide consistent and clear guidance to

17 staff in terms of how to go about reviewing large amounts of

18 information. 

19 What we're trying to do here in the White

20 Paper is define the standards that we hold information to. And

21 while it's been said that many of these standards are

22 unpublished, they are actually drawn from well-established

23 published guidelines for other aquatic toxicity tests involving

24 fish. And the Agency has produced the guidance on how to review

25 open literature.

26 While a lot of this guidance is also formed by
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1 input from previous SAPs, and particularly as it relates to how

2 we look at amphibian data and studies. 

3 You're actually right. A lot of these data

4 have been published in the open literature, but as you've

5 acknowledged yourselves, many of the studies which are

6 identified as invalid, they were indeed published in the open

7 literature. It is amazing that they made it through the review

8 process. 

9 So the Agency has developed a series of

10 criteria that it has listed. I guess I'm very confused now

11 because, again, these criteria were developed from input from

12 previous SAPs that identify these as critical elements in term

13 of determining the cause/effect relationship and in terms of

14 eliminating confounding factors that would influence our

15 understanding of the utility of these studies, particularly in a

16 regulatory context where it's important that we have standards

17 that we apply to studies across the board. 

18 As it was mentioned earlier in the

19 presentations, it is important to understand that these criteria

20 are not just applied to amphibian studies, but they're applied

21 to all the taxa which we are required to review. 

22 We have thousands of studies that we have to

23 move through and there has to be a filtering process that

24 applies not just to registrants in those studies, but to open

25 literature as well; so that we have a level playing field and

26 that at the end, we have a clear and transparent process that
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1 does not involve subjective interpretations that this standard

2 is okay this time but it's not okay that time.

3 And while it does appear that different

4 standards were applied to field studies than were applied to

5 laboratory studies, the Agency recognizes that in the laboratory

6 setting, investigators are well able to control a number of

7 environmental factors that is not an option field studies. So

8 there has to be greater flexibility in the Agency's ability to

9 examine field studies versus laboratory studies.

10 So my confusion rests here. And I appreciate

11 your input and I very much appreciate what it is to identify

12 specific criteria, but understand that the --

13 DR. DANIEL SCHLENK: Dr. Steeger, is this

14 a question?

15 DR. THOMAS STEEGER: Well, what I'm trying to

16 --

17 DR. DANIEL SCHLENK: Because you're not

18 supposed to really comment. You're supposed to asking a

19 question.

20 DR. THOMAS STEEGER: Well, I'm asking for

21 clarification in why there seems to be a discrepancy in the

22 information that was provided in two previous SAPs that the

23 Agency incorporated its evaluation process and now we learn that

24 there is now a different standard that should be applied in the

25 interpretation and evaluation on these studies.

26 DR. DANIEL SCHLENK: So just to reiterate
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1 your question, you would like in the minutes to show

2 justification of why this particular panel felt differently than

3 other panels in terms of those criteria. Is that what I hear?

4 DR. THOMAS STEEGER: Yes. And I would like to

5 know which specific criteria should be weighed in order to

6 evaluate these studies.

7 DR. DANIEL SCHLENK: Okay. Dr. La Point?

8 DR. THOMAS LA POINT: I'm not the one to do

9 it. I think Dr. Propper wanted to comment.

10 DR. DANIEL SCHLENK: Right. Let me get

11 Dr. Bradbury first. I just want to make sure you understand.

12 DR. STEVEN BRADBURY: One of the things that

13 I'm confused about and think it would be helpful if there was a

14 question to help us understand.

15 One of the issues we went through in the 2003

16 SAP was, how you come up with the design that could capture low

17 dose effects in a non-monotonic dose response curves. So just

18 to clarify, it wasn't that we were ignoring these. But getting

19 back to Dr. Steeger's point, if you can help clarify, the

20 feedback from the 2003 SAP was to use four or five doses.

21 You got the statistical power of using a

22 positive control like estradiol, on and on and on, because that

23 was the feedback that could help us figure out whether or not

24 there could be low dose response relationship or non-monotonic

25 dose response relationship.

26 If this SAP thinks that the advice from 2003
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1 was inadequate to ask the very questions we're talking about

2 today, we need very exclusive, very detailed rebuttal of the

3 2003 SAP because nothing up until now has said that those design

4 elements that we were to use were inadequate. 

5 So we need clear, crystal clear, explanations

6 of what happened between 2003 and 2012 to say those approaches

7 are inadequate. 

8 Frankly, in the recent review article about

9 low dose response and non-monotonic dose response relationship,

10 it looks most of things in 2003 advised us to do like in that

11 recent review article, positive control; so we need explicitly

12 detailed explanations.

13 Another point of clarification that may help

14 in this, is whether or not your comments with regard of how to

15 sort out the effect of atrazine when we try to minimize other

16 stressors versus trying to understand what the effects of

17 atrazine can be in the context of other stressors. In my mind

18 those are two separate questions that probably require two

19 different kinds of experimental designs. 

20 So if you're telling us that you're -- this

21 where I'm confused; I can't tell what you're telling us -- are

22 you telling us that the design of 2003 is inadequate to figure

23 out the dose response relationship for atrazine when you try to

24 minimize other stressors? Or are you telling us that that

25 design is inadequate to understand the interaction of multiple

26 stressors?
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1 It's a completely different question and it

2 probably requires a completely different design context. And

3 that's okay, but as part of sorting out what you're telling us,

4 it would be very helpful to figure out if you're talking about

5 how to study interactions of multiple stressors or are you

6 talking about how to understand, as best we can, the dose

7 response curve for atrazine while you try to minimize other

8 stressors? That's where I'm confused.

9 DR. DANIEL SCHLENK: Did you want to go to Dr.

10 Propper first? Dr. Propper.

11 DR. CATHERINE PROPPER: So first let me be

12 clear. I thought we stated this. I really liked the design

13 elements and how they were developed. I think that that's how

14 you're going to get at whether or not any compound, including

15 atrazine, has an effect. It's really clear-cut and easy to

16 evaluate. I have no problem with that. 

17 The 2007 SAP, a report I did read, suggested

18 that you evaluate the literature and see what else is out there

19 and you justifiably and rightly applied the design elements. 

20 And as I said, those that were marked invalid, most of them were

21 invalid. Some of them for really invalid reasons, like this bad

22 design, bad sample size; some because they did miss several of

23 the design elements.

24 However, what I saw it in the final evaluation

25 of the report, and I'll get into this when we answer some of the

26 other questions, is that they were weighted so lowly that the
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1 only study that was taken into account in the assessment and the

2 decision that no further testing needs to be made on amphibians,

3 is one study that was nearly perfect. It was quite a good

4 study.

5 But, I don't think that we can base that kind

6 of decision on one study when -- and we can get into the weight

7 of evidence already, right? I don't think weight of evidence

8 has been met to support the null hypothesis, that there is no

9 effect. And that's because a lot of those studies did show

10 effect and it is harder to show effects, statistically, than it

11 is to show no effect.

12 Consistently the effects that were seen for

13 things like sex reversal or feminization are always in the

14 direction of feminization, never in the direction of

15 masculinization. That has be taken into account even if some of

16 those studies have self-limits. Does that help?

17 DR. STEVEN BRADBURY: That helps, especially

18 because part of what you're describing now is how you weight

19 evidence, which confirms what we're talking about. That's

20 helpful.

21 But also, aspects of those test designs, too,

22 were getting out whether or not the organisms were exposed to

23 multiple stressors or single stressors. So to the extent there

24 may be some indication of sex ratio changes, for example, were

25 those due to atrazine or those due to atrazine combined with the

26 other specimen? 
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1 And so that's one of our request; as you work

2 through your report, you help us understand the question of the

3 context of both the response.

4 DR. DANIEL SCHLENK: Okay. Let's go ahead to

5 Dr. La Point.

6 DR. THOMAS LA POINT: Okay. Thanks, Dr.

7 Bradbury. I mean those were -- and yours too -- they were good

8 questions. I'm acting in my capacity now more as a -- what do

9 they have me down as -- not lead discussant, but coordinator for

10 this part of the report. And if I may, and of course, correct

11 me right away, as they will, quickly, if I'm wrong.

12 But the thing is that I think everyone

13 recognizes, first and foremost, that those criteria used to

14 judge a valid study or certainly valid or invalid qualitative or

15 quantitative, everyone accepts those. And I think it would be

16 very explicit when it's stated that how some of the criteria may

17 be altered or changed, and why. 

18 Okay, but I don't think there's anybody in

19 here -- and we've had this discussion internally here about the

20 whole idea just as you said. And I think everyone recognizes we

21 need to form these conclusions, that in response to the

22 question, in hopes of helping register and get data that are

23 necessary to re-register atrazine as a chemical. 

24 So the whole idea of confounding effects and

25 any other effects with other chemicals, you know, we talked this

26 morning and someone mentioned about Roundup Ready and what are
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1 the interactions there.

2 Well, that's interesting; and again, it's

3 certainly, in our understanding of Agra ecosystems, ultimately

4 necessary; but that's not this kind of question. So I think

5 everybody is with you on that in terms of providing good advice

6 for them.

7 DR. DANIEL SCHLENK: Dr. Boone.

8 DR. MICHELLE BOONE: And just to clarify, I'm

9 fine with the test elements in terms of they are good ideas for

10 testing. But my concern is that if we're evaluating the

11 literature that's out there and we're saying only one study

12 meets all these criteria; and from this we're deciding that

13 atrazine has no effect on the amphibians period, when we've only

14 tested the Xenopus from one population, from ten fletches of one

15 lab. That is just not sufficient.

16 And it's certainly not one that a large

17 portion and the people working in amphibian ecotoxicology would

18 go comfortable with. So I'm not comfortable with that

19 conclusion. I think the test criteria is, you know, setting

20 what the standards are mostly, fine. I think the standards

21 really need to be nailed down. Amphibians are not fish and some

22 of the criteria, like flow through, may not be appropriate for

23 that taxonomic group.

24 So I think some of those methodologies need to

25 be defined and that should be a priority if the evidence is

26 going be used for re-registration or for just evaluation.
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1 DR. DANIEL SCHLENK: Dr. Bradbury, do you want

2 to comment?

3 DR. STEVEN BRADBURY: Sure. That would be an

4 example, with all due respect, of the confounding

5 recommendation. 

6 The 2003 SAP was very clear about these in the

7 flow through. They may have been wrong. You all may decide

8 that flow through as an (inaudible) may be wrong, and that's

9 okay, too. 

10 If you decided that now flow through is

11 inappropriate for that, we need some detailed explanation. I'm

12 not saying what's been done in the past is right; it could be

13 completely wrong. But we just need a lot of information as to

14 why we should shift. 

15 And then that's where the clarifying question

16 -- and I know it's hard, and I went to a statistician looking

17 for some advice. This is the only help we're asking for, is how

18 many studies have to show maybe the results, for lack of a

19 better word, before you have enough confidence that you can

20 prove the negative. Is it 2 more studies, 5 more studies, 10

21 more studies, 15 more studies? 

22 How do you even begin to talk about that, and

23 it's probably not to your statistics it's definitely to biology

24 and things like that, but some feedback. If these aren't enough

25 data, done a certain way, how much more data or what kind do you

26 need to determine -- not that there's any more scientific
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1 questions to ask, but given the context of what this science is

2 calling for, to say it's enough? 

3 I realize that's a real priority. That's

4 embedded in these questions. So doing more is helpful, and how

5 to do them is helpful, but how much is viewed appropriate?

6 DR. DANIEL SCHLENK: Okay. Dr. Young, do you

7 wish to add?

8 DR. LINDA YOUNG: I had ask to speak before

9 you ask that question, before I ask you that stuff. Because

10 that's really tough, and I think -- my concern is you have these

11 standards and if you meet the standards that the study

12 qualifies, then you can use it in your risk assessment.

13 Just because we don't have enough good studies

14 isn't a good enough reason to start lowering the bar. I mean,

15 that'd be the first thing I'd say.

16 Now, what do you do if you don't have enough

17 studies? Well, you've said we don't have enough evidence to say

18 there's an effect, but we don't really have enough studies. And

19 I think that's where you have to be. You don't have enough

20 evidence.

21 And what is it going to take? Well, you have

22 to back up where the biology comes in. What population are you

23 trying to draw inference to? You're really not trying to draw

24 inference to the lab, you're trying to draw inference to natural

25 populations.

26 So you have any studies on natural
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1 populations? Do those natural populations differ with

2 geographic area? If so, then you need different geographic

3 areas. 

4 You know, it all depends on what population

5 you want to draw inference to, which means it's probably going

6 be a while before you get a definitive answer. You know? 

7 That's life. It's hard.

8 DR. STEVEN BRADBURY: No. I understand. 

9 We'll come back to some of that, I think, in the next Charge

10 Question because there's some challenges in terms of

11 experimental design.

12 DR. LINDA YOUNG: Absolutely.

13 DR. DANIEL SCHLENK: Sure.

14 DR. STEVEN BRADBURY: We'll do that.

15 DR. DANIEL SCHLENK: Dr. Portier, would you

16 like to get in on that?

17 DR. KENNETH PORTIER: So in 2003, the panel --

18 and I didn't -- I read all the other panel -- I didn't go back

19 all the way to 2003, but my memory is very good. And I think if

20 we put -- we tried to put it in the positive. We said, you

21 know, a good study that would do cause and effect would have

22 these characteristics. Now you've turned it around to bad

23 studies have these characteristics.

24 So in terms of invalid versus valid, I think

25 we're very clear and we're in agreement. That's not the issue. 

26 I think the issue is in the gray area between low qualitative to
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1 high qualitative to excellent. 

2 And what I'm hearing is that within that range

3 there is, you know, suggestive situations or accommodations of

4 experiments that we don't want to see lost because there is a

5 super study up there that followed all our recommendations from

6 2003, and it says we don't see anything. And we don't want you

7 ignoring that second category of quantitatively challenged,

8 shall we say, but qualitatively okay studies. 

9 And I think, for example, the plastic

10 container issue; okay, that's a flag, but it maybe means you

11 need to be thinking what kind of plastic? Was it able to reach

12 bisphenol so that they were getting a double dose? I mean, we

13 want you to think that way rather than say, well, it's on the

14 side.

15 And I think Dr. Propper is shaking her head

16 yeah, so I think that's the thinking that was going on.

17 DR. LINDA YOUNG: But just to follow up, my

18 son tells me, "Slippery slope is not a valid argument." But

19 that is a slippery slope, so you have to be really careful with

20 doing that.

21 DR. DANIEL SCHLENK: That was Dr. Young. 

22 Okay. Yes?

23 DR. AMY BLANKINSHIP: It's Amy Blankinship. I

24 just sort of want to get some clarification from the panel. I

25 see a couple of different things sort of coming out here that

26 kind of reminds me of 2003 SAP; where we have some information
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1 that maybe suggests we have effects to atrazine maybe due to

2 multiple stressors or as a non-monotonic dose.

3 However, that information, at that time was

4 deemed not to be sufficient to refute or confirm the hypothesis.

5 So we asked for additional testing and then we went through

6 forward with that path. 

7 And so I sort of see where we're kind at again

8 today. So I kind of want to get some feedback from the Panel. 

9 The information we have today, so within that qualitative range

10 we're sort of talking about, is it useful for a weight of

11 evidence for us to move forward without further testing, and if

12 so, how can we do that? 

13 Or is it now back into the place where we sort

14 of aren't real sure if it's showing effect, or is it directly

15 atrazine? You know, is it due to some other things, and I would

16 just, as a point of clarification, although we've mentioned

17 plastics, it's not necessarily the driver for anything. 

18 But I kind of just want the Panel to give us

19 some clarification on -- I think it's going to be more for

20 Question Number 3, in terms of weight of evidence. But are you

21 sort of saying we can use the information we have, or the

22 information that we have now is for further testing? And if so,

23 what kind of testing do we need to do, especially in terms of

24 (inaudible) natural species? 

25 I just want us some clarification in how maybe

26 we can go about accounting for some of the visual effect from
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1 other stressor that maybe we can account for the natural

2 communities, and try and take that into account in terms of our

3 observed effects.

4 DR. DANIEL SCHLENK: Okay. I think we'll

5 probably -- if we don't address that in Question 3, you can

6 bring it back up. Okay? Does that work?

7 DR. AMY BLANKINSHIP: That's fine.

8 DR. DANIEL SCHLENK: Okay. So let's take a

9 much-needed break and be back at a quarter 'til four. 

10 (Whereupon, a break was taken.)

11 DR. DANIEL SCHLENK: Go ahead and read in --

12 you can just read the italics and comments -- or A and B, if

13 that's okay, for Question 3.

14 DR. DANA SPATZ: May I make one clarification

15 on something on Dr. Portier said?

16 DR. DANIEL SCHLENK: Sure.

17 DR. DANA SPATZ: The errata sheet where we

18 changed classification, we didn't create new classifications. 

19 When it says a qualitative low to invalid, we changed it from

20 qualitative lower to invalid. 

21 DR. DANIEL SCHLENK: Thanks for that.

22 DR. DANA SPATZ: Question 3(a). Given the

23 results of the DCI study and the input that the 2003 SAP had in

24 the development of that study, please comment on whether any new

25 information has become available that leads to a different

26 conclusion from the one which EPA reached in that the results of
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1 the DCI study were adequate to evaluate potential effects of

2 atrazine exposure to amphibians. 

3 DR. DANIEL SCHLENK: Tom La Point.

4 DR. THOMAS LA POINT: Thank you. I'll read my

5 response again; I think it's best that way. I'm not an

6 endocrinologist and I'm not aware of any new studies that would

7 have led to a different conclusion than one reached by the study

8 results between the Data Call-In, nor did I have time to

9 evaluate recent publications to determine if the criteria we've

10 just discussed hold in that publication.

11 However, I do put a lot of emphasis on studies

12 conducted for regulatory purposes if and when they are properly

13 conducted and follow strict QA/QC procedures as these studies

14 did. And it's my impression that the studies were sufficient to

15 evaluate potential effects of atrazine exposure on amphibians.

16 However, I would very much recommend the point

17 of approach, such as detailed by Dr. Solomon yesterday,

18 establishing the solid weight of evidence approach, much as saw

19 from the discussions of Syngenta that may be independently set

20 with criteria that both EPA and the Registrant could follow. 

21 The plots generated of the weight of evidence

22 where there was weak or strong evidence plotted out -- the

23 slides 23 through 26 from Syngenta -- reminded me of plots of

24 geo-located sources of pollution. One can use almost a GIS

25 approach, albeit, this one using toxicity response data; you can

26 plot points and be able to cue-in on any point, that was the
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1 drilling down, as referred to yesterday, to see the assumptions,

2 the effects of the publication, itself, and the data underlying

3 that particular point. 

4 This type of analysis can remove some more of

5 the subjectivity involved in assessing the degree of effects. 

6 However -- and this would be better discussed by my colleagues

7 in their responses -- so far there appears to be a great

8 reliance on the data set with Xenopus. I would hope that

9 further studies on native frogs or other amphibians would be

10 close at hand. Thank you.

11 DR. DANIEL SCHLENK: Dr. Boone.

12 DR. MICHELLE BOONE: I would just add that

13 really more studies are needed to fully evaluate what the level

14 of concern is to project amphibian reproduction and development

15 in nature. But that a qualitative assessment allows us to

16 determine that concentrations that are ecologically relevant in

17 nature appear to have effects on some populations and species in

18 the lab and in the field in both amphibians and fish.

19 DR. DANIEL SCHLENK: Dr. Propper.

20 DR. CATHERINE PROPPER: So I do think that the

21 White Paper identified several relevant studies that suggest

22 there maybe effects on amphibians. But none of these studies

23 demonstrate, as I said earlier, over toxicity of low

24 concentration exposures. Some do suggest that with subchronic

25 or chronic exposure there are effects of survival mode for

26 reproductive endpoints.
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1 However, again, I know that there are limits

2 to these studies. In reading the previous SAP report, it was

3 clear that the Panel was concerned that it was premature to come

4 to a strong conclusion regarding effects of atrazine exposure on

5 all amphibians, despite the high quality of research presented

6 by (inaudible) and colleagues, using Xenopus. The 2007 SAP

7 rightly suggested a full review of the public literature for all

8 species, and I think we've gone through what some of the issues

9 are associated with that report. 

10 So I do note that all the data presented and

11 in Data Call-In were conducted under the strict design elements

12 that were outlined. So I just have a few questions remaining

13 regarding the results of that study.

14 It states that the physiological evaluation

15 was conducted for testicular O sites and none were found, even

16 in the estradiol positive controls. And I know that some of

17 this was addressed yesterday by Syngenta, but this result does

18 suggest that the strain of animals used may be less sensitive to

19 feminization. 

20 I also know how hard it is to do that

21 evaluation, as we've done so in my laboratory. And we have

22 found that (inaudible) sense in controls as is suggested in

23 other studies as well.

24 The data presented yesterday by Syngenta

25 trying to address this issue suggested there may not be

26 differences in responsiveness of the animals from different
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1 Xenopus to atrazine. But nevertheless, a full blown controlled

2 study evaluating this hypothesis has not been conducted. 

3 And I do want to qualify my next statement is

4 that I think that a DCI study was conducted under very honest

5 and clear-cut and open conditions.

6 But a clear appearance of conflict of interest

7 -- and I going to emphasize appearance here -- in Syngenta

8 providing the atrazine used for this study and then Syngenta's

9 labs evaluating the compounds exposure concentrations is a

10 problem. 

11 Such an appearance of conflict of interest

12 does mean the data are invalid or that anybody acted

13 unethically. But it does lead to less public confidence in the

14 results, as we clearly saw yesterday, it led to confusion and

15 the ability for others to attack the data, whether or not they

16 were conducted under best lab practices.

17 DR. DANIEL SCHLENK: Dr. Klaine.

18 DR. STEPHEN KLAINE: I have nothing further to

19 add.

20 DR. DANIEL SCHLENK: Dr. Young.

21 DR. LINDA YOUNG: I have nothing further to

22 add.

23 DR. DANIEL SCHLENK: And Dr. Portier.

24 DR. KENNETH PORTIER: I don't really have any

25 additional information on studies, but I'll just make an

26 observation at something Dr. La Point said that got me thinking.
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1

2 You know in eco-studies, we don't do grading

3 like you do in cancer studies and say, you know, this is strong

4 evidence for effect, weak or suggestive evidence of effect,

5 inadequate data to evaluate, no evidence of effect in one, many,

6 or all strains, species. And I think of one of the issues here

7 is that there's a feeling that kind of gradation needs to be

8 used for some of these effects. And I know it's not typically

9 used in the eco-studies, but it might be something that we might

10 want to consider.

11 And the other one is that the one good Grade A

12 study is done in one species that we did have questions about,

13 at least in the 2011 study in terms of the representative of

14 Xenopus laevis for all amphibians.

15 And I think part of what we're hearing, again,

16 is that if you go beyond that one species, the data immediately

17 gets weaker and that's an issue here.

18 DR. DANIEL SCHLENK: Okay. Other panel

19 members, chance to chime in. Okay. 

20 Ms. Blankinship, does that meet your question

21 that you had raised earlier?

22 DR. AMY BLANKINSHIP: Yeah. I think we

23 contended that that study was sufficient for amphibians. So

24 we're having a lot of discussion about that compared it to a

25 native species and sort of just hearing some weight evidence and

26 how to use that to make some of those conclusions. So that's
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1 sort of what I was looking for, I think.

2 DR. DANIEL SCHLENK: Great. Okay. 

3 DR. THOMAS STEEGER: I have additional

4 comment.

5 DR. DANIEL SCHLENK: Oh.

6 DR. THOMAS STEEGER: A question. There seems

7 to be a continuing uncertainty about the extent to which Xenopus

8 laevis is a reasonably sensitive species for testing the effects

9 of atrazine on amphibians or even to serve as a surrogate for

10 amphibians at all, these native species. And it would be

11 helpful for the Agency to have feedback from this SAP, given the

12 difficulties that would be involved in replicating the study

13 that was conducted with Xenopus using native species.

14 If the issue is one where there is a

15 fundamental question as to whether there's a sensitivity

16 difference between Xenopus and native species. And I would add

17 that the positive control or the reference control for the

18 definitive DCI study didn't have particular sites and had

19 complete conversion, which suggests that the animals are

20 exquisitely sensitive. They weren't immediately sensitive or

21 exclusively sensitive.

22 But can this Panel recommend specific studies

23 that could get at whether the native species is either toxic

24 kinetically or toxic dynamically different than Xenopus? 

25 That would help us resolve this issue as to

26 whether it serves as an adequate surrogate or the sensitivity
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1 species somehow differ substantially. And how big of a

2 difference would be required to demonstrate our conclusions

3 regarding the least of our study are not applicable to a broader

4 range of amphibians.

5 DR. DANIEL SCHLENK: Okay. Dr. Boone.

6 DR. MICHELLE BOONE: There are some studies by

7 (inaudible) that evaluate for amphibian species with different

8 life histories that show that susceptibility to endocrine

9 disruption can be verbated to timing of somatic development. So

10 I think we know there is variation.

11 I think Xenopus is a useful species to

12 include, but given so many of the differences they have, they're

13 genetically, you know, they diverge from North American

14 amphibians a long time ago. So there's a lot of genetic

15 differences. Their life history, in terms of being completely

16 aquatic for all of their life, you know, there's a number of

17 issues that maybe make them different. But they're useful to

18 include; I just think we need to expand.

19 So you know the work of Tyrone Hayes, he's

20 using these Xopenex. And it could be a lot of different

21 reasons, differences in methodology, didn't meet all of your

22 criteria that you guys put forward. So it could be differences

23 in methodology, but it could simply be differences of strains. 

24 So some genetic variation within that species.

25 So in order to understand how pervasive the

26 effect of atrazine is, or if it is truly having an effect at
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1 some of these low concentrations that some studies have shown, I

2 think multiple species are going to have to be assessed; and

3 multiple populations are going to need to be assessed to really

4 have the answer.

5 DR. DANIEL SCHLENK: Dr. Propper.

6 DR. CATHERINE PROPPER: And I don't mean to

7 diss Xenopus as a model. It has clearly been a very useful

8 model for both development and in general for vertebrates and

9 for toxicology. 

10 The problem is, again, when we get a negative

11 result in one species, how you apply that to everything. If you

12 get a positive result, you can say, well, at least one species

13 is sensitive.

14 At this point, I just don't think the weight

15 of evidence lies in accepting the whole hypothesis. I do, and

16 I'll reiterate, I like the model that Syngenta put forward

17 yesterday for helping to evaluate the weight of evidence. I

18 think that is a more useful tool than the way the conclusion was

19 come to that we don't need to continue to evaluate amphibians,

20 based on the study.

21 DR. DANIEL SCHLENK: Yes, Dr. Young.

22 DR. LINDA YOUNG: Talking about this weight of

23 evidence, as I look at your criteria, here's one thing I would

24 definitely relax, and that's sufficient replication for power. 

25 Because if you have a bunch of small studies, none of which are

26 adequately powered, you can put those together to get an idea of
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1 whether together they give you enough information.

2 There is such a thing as equivalence testing

3 which actually lets you test whether or not the null is true. 

4 So it actually becomes the alternative that nothing's happening

5 is the alternative and that you have at least some kind of type

6 of an effect is the null hypothesis. And that's used

7 extensively in the development of generic drugs, bio-equivalence

8 testing. So that literature might be helpful for you to

9 consider at some point.

10 But especially this sufficient replication, if

11 you have a bunch of small studies, together, they could really

12 give you a lot of information.

13 DR. DANIEL SCHLENK: Okay. Dr. Portier. 

14 DR. KENNETH PORTIER: You know, I've heard

15 this conversation before, the National Toxicology Program, as

16 they talked about what test species to use in their screening

17 studies and, you know, they've been using a certain rat strain

18 and a certain mouse strain for 30 years. 

19 And the last time I was at a meeting they were

20 talking about well, you know, maybe we need to be looking at

21 generic susceptibility and maybe that mouse strain that we've

22 been using has certain characteristics which preclude certain

23 chemicals from expressing certain health outcomes.

24 So now they're into this massive study where

25 they're looking at the complete -- what's the right word --

26 Biology or the genealogy of their mice strains to understand
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1 which one expresses what characteristics so they're good for

2 what kind of study. It ups the complexity of the research, an

3 order of magnitude at least, and I think we all recognize that;

4 but it also is a recognition of that the fact that our

5 scientific understanding of the role of genetics in expressing

6 some of these outcomes has grown dramatically in the last

7 decade. 

8 I was sitting here thinking to myself if we

9 had talked about this, like immune responses in 2003 in

10 amphibians; I don't think we did. I don't think that was on the

11 board. 

12 That's only because in the last six or seven

13 years, it is almost as immune responses have become very

14 important because we're seeing that as a (inaudible) low dose

15 environmental chemical. 

16 So I think this discussion kind of fits into

17 that broader discussion of the need for a better understanding

18 of our animal model, I guess is the right word.

19 DR. DANIEL SCHLENK: Dr. Propper.

20 DR. CATHERINE PROPPER: And I do believe the

21 NIH requires folks now to include different ethnicities and both

22 sexes in their studies because of that variation, and so it is

23 important. Biological variation is clearly important. 

24 Secondly, Figure 10 in the White Paper does

25 look at the number of studies that showed effects versus the

26 number of studies that didn't. And it separates them out by
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1 whether or not there was a negative solvent control or not; and

2 finds a significantly -- and I went in and did the exact test --

3 the significant difference between those studies that showed

4 effects and those that didn't, which, again, is not really a

5 true metaanalysis type of analysis; but still, more studies

6 showed effects than didn't. And there was no difference whether

7 we used a solvent, negative solvent control or not. Zero. I

8 almost never see peak values that high.

9 DR. DANIEL SCHLENK: Okay. Thanks. Any other

10 comments before we go to (b)? 

11 Okay. Mr. Spatz, do you want to read Letter

12 (b) into the record?

13 DR. DANA SPATZ: If such information is now

14 available, please comment on how a threshold determination (a

15 concentration that is expected to cause no effect) may be

16 accomplished using the identified studies.

17 DR. DANIEL SCHLENK: Dr. La Point.

18 DR. THOMAS LA POINT: As I've found none, I

19 can't come up with -- I apologize-- I can't come up with the

20 answer to this question. I'm gonna have to pass it to my

21 colleague.

22 DR. DANIEL SCHLENK: Dr. Boone?

23 DR. MICHELLE BOONE: So I mean, if you use all

24 the data that were presented in Figures 7, 8, and 9, it suggests

25 effects are found at one part per billion and above on

26 metamorphosis or for sexual development. It's possible that
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1 concentrations below one part per billion can occur that could

2 impact reproduction as Tillitt, et al. (2010) demonstrated with

3 fatheaded minnows.

4 However, determination of the pervasiveness or

5 the likelihood of effects of these concentrations were below, as

6 well as above at ecologically relevant concentrations, really

7 needs to be determined and should be a research priority.

8 DR. DANIEL SCHLENK: Okay. Dr. Propper.

9 DR. CATHERINE PROPPER: So I found that the

10 effect ranges in the new studies range from fairly low, around

11 one part per billion to fairly high, around 400 parts per

12 billion, to no effect, depending on the study. 

13 The examination of the literature suggests

14 that there are inter and even intra specific variation in

15 response. They can range up to two orders of magnitude; so this

16 kept -- the models that we were just discussing. 

17 The chance of coming up with one value at this

18 time that can encompass all of the amphibians with an NOECL or

19 an LOECL is very low given the complexity of the species,

20 difference in environmental variation, leading to shifts in

21 responsiveness, even under mildly different conditions.

22 So it is going to be difficult because of the

23 biological variation inherent with genetics and the biological

24 variation inherent with the environment. 

25 However, reviewing the graphs on slides 39 to

26 43, that were presented by the EPA yesterday during the
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1 presentation of the White Paper, suggests that there are some

2 uses in effects found on at least some studies that were deemed

3 valid on several endpoints at/or just below 10 parts per

4 billion.

5 DR. DANIEL SCHLENK: Dr. Klaine.

6 DR. STEPHEN KLAINE: I have nothing further to

7 add.

8 DR. DANIEL SCHLENK: Dr. Young.

9 DR. LINDA YOUNG: I don't have anything to

10 add.

11 DR. DANIEL SCHLENK: And Dr. Portier.

12 DR. KENNETH PORTIER: Nothing to add.

13 DR. DANIEL SCHLENK: Okay. Any other panel

14 members want to add? Dr. Propper.

15 DR. CATHERINE PROPPER: Sorry. I have one

16 addition that I put in this afternoon, or this morning, which is

17 I agree with all of the different groups that we've heard over

18 the last several days -- today and yesterday -- that getting to

19 the CE-LOC level and getting it ready is critical; however, it

20 seems like the study is being determined differently for

21 amphibians and aquatic plant communities.

22 For amphibians, it's really gonna be hard to

23 set given the variety of exposure methods and durations in the

24 few available studies, where there is kind of quality data.

25 And so this is one of the questions I have,

26 and I think some of us have discussed it, is how do we mesh
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1 these two different data sets and the criteria applied to them;

2 and the complexity that we're seeing in the plant data versus

3 trying to limit that complexity in the amphibian data.

4 DR. DANIEL SCHLENK: Any other panel member? 

5 Okay. Let me go back to the Agency. Who is going to respond?

6 DR. AMY BLANKINSHIP: I'll respond for this.

7 DR. DANIEL SCHLENK: Okay. Ms. Blankinship,

8 do you have what you need for the question here?

9 DR. AMY BLANKINSHIP: I think kind of what I

10 understand from the clarification is that there is some concern,

11 obviously, for the one study that represents everything. So in

12 terms of establishing the threshold determination is kind of

13 where I go back to the weight of evidence. 

14 Is the evidence that we have now sufficient to

15 sort of refute what we have and come up with a value, or is it

16 just enough to sort of say, no, we kind of need to go back and

17 do x, y, and z study? Here's how you should do x, y, and z

18 study.

19 That's sort of, I think, what we're trying to

20 get out of this. I think we said the answer is there is that

21 available information, but want to know exactly how we use that

22 information.

23 DR. DANIEL SCHLENK: Dr. La Point, you got

24 that down? 

25 DR. THOMAS LA POINT: Yes. 

26 DR. DANIEL SCHLENK: Okay. Great. Let's move
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1 on into Question 4. Mr. Spatz, you can just read the italics, I

2 think, on the bottom.

3 DR. DANA SPATZ: Please comment on whether

4 there are suitable methods for testing native amphibians with

5 particular regard to husbandry and laboratory culturing

6 conditions, consisting with the design elements recommended by

7 the 2003 SAP.

8 DR. DANIEL SCHLENK: Okay. The lead

9 discussant for that is Dr. Propper.

10 DR. CATHERINE PROPPER: For species other than

11 Xenopus, I am not aware of really clear-cut husbandry protocols

12 that are available. There is an aquaculture, I believe, some

13 protocols available for Rana amphibians because they are often

14 (inaudible) through laboratory studies that maybe could be used

15 to help develop these protocols.

16 But unfortunately, we've got a lot of

17 amphibian species out there. If we decide that some are better

18 models to be used for future studies than, I think, working with

19 the folks, the researchers, that have the most experience with

20 those species to develop appropriate protocols within the design

21 element guidelines would be very useful.

22 And, again, maybe even just pointing people to

23 those fish/aquatic toxicology protocols clearly on the website

24 for people looking at amphibian, eco-tox would be helpful. I do

25 believe this should be a high priority.

26 DR. DANIEL SCHLENK: Okay. Dr. Boone.
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1 DR. MICHELLE BOONE: I don't have anything

2 additional to add regarding lab conditions, but I would like to

3 note that studies need to be conducted under more natural field

4 conditions, like mesocosms, to examine how natural factors can

5 influence the impact of atrazine; determining effects on animals

6 in the field is the ultimate goal. 

7 So more realistic studies need to be conducted

8 since environmental conditions could influence the impact of

9 atrazine, and comparing animals exposed to atrazine with

10 controls under more natural conditions will allow for the

11 determination of cause/effect relationships.

12 The presence of natural factors increases the

13 realism of the study and it doesn't confound the ability to

14 discern cause/effect relationships.

15 Methods for mesocosm studies exist, but if the

16 EPA has specific criteria they expect to be met, they should be

17 widely distributed and published like the (inaudible) data. I

18 think a number of us are part of eco-tox list servs. Like, if

19 there's a clear link, there is -- you know, amphibians haven't

20 been used in any ecotoxicology assessment. So, you've got a

21 bunch of ecologists doing toxicology. And I think it'd be

22 really helpful to get that information to the people do the

23 research so we don't lose more time.

24 DR. DANIEL SCHLENK: Dr. La Point.

25 DR. THOMAS LA POINT: I'm unaware of any

26 protocols for amphibian culturing of Xenopus.
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1 DR. DANIEL SCHLENK: Dr. Klaine.

2 DR. STEPHEN KLAINE: Nothing to add.

3 DR. DANIEL SCHLENK: Dr. Young?

4 DR. LINDA YOUNG: Nothing to add.

5 DR. DANIEL SCHLENK: And, Dr. Portier.

6 DR. KENNETH PORTIER: I don't have anything to

7 add about the study. 

8 I do want to comment on something Dr. Boone

9 said in just that, you know, it' typical that if you increase

10 the realism of the test environment, like using mesocosms, this

11 also results in a loss of control, increased opportunity for

12 effects from extraneous factors to impact the results; more

13 uncertainty unless you increase sample sizes. And then the

14 problem is you've got expensive cosms that you're trying to

15 understand, they're much more expensive. 

16 So the result is you want to have fewer

17 replicates. So there's kind of a negative feedback, I think,

18 going on with that that we have to somehow overcome. I think

19 you'd have to make a really strong case for that if you really

20 wanted to use that.

21 DR. DANIEL SCHLENK: Yes, Dr. Boone.

22 DR. MICHELLE BOONE: I think in amphibian

23 ecology there's a really strong precedent for using mesocosms. 

24 They've been used for decades to understand basic ecological

25 questions; and so three to five reps gets you typically 80

26 percent a power of (inaudible). So I think we have a good
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1 system that can be used to evaluate the effects of atrazine

2 under more realistic conditions.

3 So I don't disagree that more realism and more

4 variations, but we want to understand the effects in the real

5 world.

6 DR. DANIEL SCHLENK: And perhaps it might be

7 good to include, if you know those husbandry requirements, what

8 you're talking about, to include those in the minutes. I think

9 that would be very helpful.

10 So, any other panel member wants to weigh in

11 on this one? 

12 Okay, let's go ahead and move on.

13 DR. THOMAS STEEGER: I have a question --

14 DR. DANIEL SCHLENK: I'm sorry. I did not go

15 back to you --

16 DR. THOMAS STEEGER: -- in terms of responses.

17 This is Tom Steeger.

18 DR. DANIEL SCHLENK: -- I'm sorry. It's --

19 do.

20 DR. THOMAS STEEGER: I just want to have a

21 clear understanding. In 2003, there was a process, a tiered

22 process, described for transitioning the higher two studies that

23 are being alluded to now. And it would be helpful to have input

24 from this panel as to whether that tiered process is to be

25 abandoned; that you don't have a clear evidence of a

26 relationship that would enable you to move forward to higher
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1 tier testing.

2 Is that what we're hearing now? That we

3 should move to field studies when we don't even have laboratory

4 studies that can establish these potential relationships?

5 DR. DANIEL SCHLENK: Okay. Thank you. So

6 Tom, did you get that? Yeah. Okay. Great. Actually, Dr.

7 Propper answering on that one. Sorry. Okay. 

8 Okay. Let's move on. We can talk about that

9 when we break out. So we want to go on to Question 5?

10 DR. STEVEN BRADBURY: Sure. Dan?

11 DR. DANIEL SCHLENK: Sorry.

12 DR. STEVEN BRADBURY: Just to clarify a

13 question. I know we're asking for some clarifying questions,

14 and I realize some of the clarifying questions we're asking are

15 not simple; so now that we think about them, but I need to make

16 sure that tomorrow or something -- some additional feedback on

17 some of the thinking around that.

18 DR. DANIEL SCHLENK: Yeah. I think we'll

19 probably come back to this. What we were discussing is we'll

20 meet after the meeting and discuss -- we may have another

21 meeting between that group to bring out what they're planning to

22 put in the minutes so that it's actually transcribed.

23 Okay. Question 5(a), I think is next.

24 DR. DANA SPATZ: Please comment on whether the

25 data in the existing database reasonably support the hypotheses,

26 or demonstrates that atrazine effects immune function and/or
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1 infection susceptibility leading to adverse effects on survival,

2 growth or development. That is, are there sufficient data to

3 establish an adverse outcome pathway for atrazine effects on

4 immune function? 

5 Please provide a rationale for the Panel's

6 position and discuss the associated strengths and weaknesses

7 with the data supporting the rationale.

8 DR. DANIEL SCHLENK: Dr. Boone.

9 DR. MICHELLE BOONE: There are relatively few

10 studies to date examining atrazine effects on immune response

11 versus susceptibility to disease pathogen for parasites, which

12 makes it difficult to determine the importance of atrazine

13 effects. Given the importance disease appears to have in

14 amphibian population decline, this assessment could be very

15 important.

16 And there are a number of studies that

17 indicate that atrazine can influence responses, which at least

18 is a preliminary suggestion that it should be further

19 investigated, including phagocytic activity at 10 parts per

20 billion, acidophil changes at 330 and a 102 parts per billion,

21 liver (inaudible), macrophage counts at 102 parts per billion,

22 gene expression in immune systems, genes at 400 parts per

23 billion, disease susceptibility at 16 and 150 parts per billion,

24 susceptibility of the pathogens at 102 parts per billion.

25 And really Rorh et al. (2008) study published

26 in the journal, Nature provides the most compelling evidence. 
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1 They have a field survey that significantly

2 links atrazine exposure to trematode infections over 240 other

3 factors, which is a correlational study, but they combined it

4 with experimental manipulations that provided cause and effect

5 relationships linking the effects of atrazine to increase

6 trematode infection.

7 And, additionally the metaanalysis Rorh and

8 McCoy (2010) also -- atrazine negatively impacted immune system

9 responses and disease pathogen susceptibility at environmental

10 levels. 

11 So the available studies warrant further

12 investigation or suggest we should continue to evaluate this. 

13 And indicates atrazine may impact disease susceptibility and

14 immune function, but there is currently not enough information

15 to support a review of the EPA's hypothesis.

16 DR. DANIEL SCHLENK: Dr. Propper.

17 DR. CATHERINE PROPPER: I agree and have

18 nothing more to add.

19 DR. DANIEL SCHLENK: Dr. La Point.

20 DR. THOMAS LA POINT: My comment on this is

21 that I think the data are certainly beginning to be there, and

22 that's kind of a waffling way. But when I say that reasonably

23 support the hypotheses or demonstrate the atrazine effects,

24 immune function or infection susceptibility, I was looking back

25 at the presentation Dr. Solomon yesterday on the weight of

26 evidence that kind of classified those. And even there, there
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1 were 14 or 15 studies, which isn't very many, but it tended to

2 show that there is weak evidence in no effect or limited effect.

3 So we have that, but we're still left with the

4 fact that in spite of all the interest and stuff, if some

5 criteria is established to take that road further, and if that

6 is what's developed over the re-registration period coming up, I

7 think that would lead to some valuable insights. I think it's

8 necessary because I think we're getting to that.

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

10 DR. STEPHEN KLAINE: I agree and I have

11 nothing to add.

12 DR. DANIEL SCHLENK: Dr. Young.

13 DR. LINDA YOUNG: I'll just say that on this

14 weight of evidence that the one thing that I'd add is that often

15 it seems that everything was cast in terms of hypothesis

16 testing. And I think a lot more progress will be made if it's

17 in terms of confidence intervals, to the extent possible. 

18 Because you can have confidence intervals that cover zero, but

19 all of them tend to be a little on the plus, or a little on the

20 negative side, which then begins to give you stronger weight of

21 evidence in hypothesis testing, and that's just something to

22 keep in mind.

23 DR. DANIEL SCHLENK: Dr. Portier.

24 DR. KENNETH PORTIER: I was sitting here

25 thinking, have we ever really talked about what is the

26 sufficient data to establish an AOP. 
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1 We talked about the AOP, but I'm not sure

2 we've, as a panel, have ever talked about what's kind of the

3 level that establishes -- I won't even say to establish the AOP

4 -- to establish the need to further explore that AOP.

5 I mean, I think that's what I'm hearing here. 

6 You know, nobody has made that link, cellularly, molecularly

7 biokinetic pathway. It's more like epidemiology studies that

8 say, you know, we have an association here and it seems to be

9 repeating. Is that enough evidence to establish an adverse

10 outcome pathway? 

11 It establishes an adverse outcome as a

12 potential adverse outcome, but I'm not sure that it establishes

13 the pathway. And I'm just going to leave it there because I

14 don't know the answer to that.

15 DR. DANIEL SCHLENK: Okay. Any other panel

16 member wants to weigh in on this? 

17 This is Daniel Schlenk. Just one thing I do

18 recall from our Vermillion (phonetic) studies that that was

19 disregarded, I recall, as an adverse outcome pathway, the ACTH

20 interactions with the rats. I know that the panel at that time

21 thought that it was not an adverse outcome, just to add that in.

22 Okay. We can go back to the Agency. Are you

23 pretty clear on that one, I think?

24 MS. AMY BLANKINSHIP: Yes.

25 DR. DANIEL SCHLENK: Okay. Thank you. Okay. 

26 Do you want to read (b) in?
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1 DR. DANA SPATZ: If the panel concludes that

2 the existing data are sufficient to formulate hypotheses that

3 atrazine adversely affects immune function and infection

4 susceptibility, but are not sufficient to test the hypotheses

5 (refute or confirm), then please comment on specific study

6 protocols that can be used to test these hypotheses with

7 sufficient rigor to identify effects that can be directly and

8 quantitatively attributed to adverse impacts on amphibian

9 reproduction, growth and/or survival.

10 DR. DANIEL SCHLENK: Dr. Boone.

11 DR. MICHELLE BOONE: So in our discussions

12 we've concluded that the available data suggests atrazine could

13 increase susceptibility of amphibians in disease based on some

14 of the preliminary data that are out there. And the critical

15 endpoint and protocol need to be developed by immunologists for

16 biochemical assays. And although biochemical markers are

17 valuable endpoints, ultimately demonstrating susceptibility

18 through natural pathogens and parasites is necessary; but,

19 again, those protocols will need to be developed. 

20 And given the importance of disease,

21 especially the amphibian (inaudible) I think (inaudible) virus

22 and Rana virus influencing the populations in nature of those

23 would be a logical starting point to look at relevant pathogens

24 and disease.

25 DR. DANIEL SCHLENK: Okay. Dr. Propper.

26 DR. CATHERINE PROPPER: I only have a little
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1 bit more to add to that, which is that I think that a call for

2 directive research is warranted. 

3 I would like to see it in a published design

4 criteria and development of specific hypotheses to be tested. A

5 intramural or extramural funding -- I know things are tight --

6 this is like an impossibility, possibly, but the intramural or

7 extramural funding could be provided to appropriately address

8 the hypotheses.

9 Because having a funded peer-review

10 (inaudible) will increase the chances of obtaining sufficient

11 quality data that meet the criteria of the weight of evidence in

12 both laboratory and wild amphibian populations, both for the

13 immunological endpoints, reproductive and survival growth

14 endpoints.

15 At the moment, except for the one DCI study,

16 which you know was well done, the rest of the studies have been

17 rather haphazardly collected and conducted. 

18 So I think this would make for greater public

19 trust in the outcomes of the results from such things.

20 DR. DANIEL SCHLENK: Dr. La Point.

21 DR. THOMAS LA POINT: Okay. Thank you. I

22 concur totally with my two co-discussants, but have nothing

23 further to add.

24 DR. DANIEL SCHLENK: Dr. Klaine.

25 DR. STEPHEN KLAINE: I have nothing further to

26 add.
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1 DR. DANIEL SCHLENK: Dr. Young?

2 DR. LINDA YOUNG: I have nothing further to

3 add.

4 DR. DANIEL SCHLENK: And, Dr. Portier.

5 DR. KENNETH PORTIER: You should know better

6 to call Portier. So it strikes me that before the EPA goes out

7 for a research call, they might want to consider -- you know,

8 this panel really feels that this is a potential adverse

9 outcome. That might be sufficient justification, actually, to

10 begin a more focused discussion on the scientific community to

11 actually explore with them what hypotheses are reasonable. 

12 I think this is probably a too general a panel

13 to be able to actually get into that. I'm not sure how many,

14 you know, amphibian immunologists are on the panel at this

15 point. There are probably none.

16 So I would say that that's needed before going

17 to a focus research program, or some kind of challenge to the

18 scientific community for data to support or refute those

19 hypotheses. And I think that's actually a more reasonable

20 approach to moving forward with that.

21 DR. DANIEL SCHLENK: Yes, Dr. Young.

22 DR. LINDA YOUNG: I just reiterate that when

23 stating hypotheses, I would also encourage that you ask for

24 estimates of endpoints with associated confidence intervals. 

25 Because if you have multiple studies, that will be more helpful

26 than putting all the information together for a weight of
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1 evidence study.

2 DR. DANIEL SCHLENK: Dr. Rodgers.

3 DR. JOHN RODGERS: I would support an explicit

4 protocol that lists the criteria about which the study will be

5 evaluated to such that the study can be conducted thoroughly and

6 carefully. Because I think this is a critical question and

7 absolutely sufficient data to test the hypothesis. 

8 I think a clear statement of the hypothesis

9 and I'm confident that a clear-cut protocol could be developed

10 and the study could be conducted and you can get an answer for

11 it.

12 DR. DANIEL SCHLENK: Anyone else? Okay, it's

13 getting -- it's like 4:30, isn't it? 

14 Dr. Steeger.

15 DR. THOMAS STEEGER: I appreciate the Panel's

16 input. In the absence of a specific study design protocol, it

17 would be helpful if the Panel could provide specific study

18 design elements that would be identified for such a study. When

19 we ask the Registrants to conduct a study to address this

20 specific uncertainly, we don't tell them how to do a study. We

21 tell them study design elements and what we're looking for. 

22 So any input that you can provide for specific

23 study design elements that are consistent with the guidances

24 that are published, that would be very helpful. Thank you.

25 DR. DANIEL SCHLENK: Dr. Portier.

26 DR. KENNETH PORTIER: Ken Portier. So that's
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1 what you've done in the past, but I think what we're suggesting

2 is that kind of moving forward, it actually might be better to

3 sit down with the community and more frankly create that

4 protocol. 

5 I mean, this worked for you on the previous

6 discussion where you talked to a fairly good panel in 2003 and

7 we gave you some really good recommendations for a good study,

8 and you went off, and it was done, and it's sitting there as

9 that "A" study at the top. And I'm just saying the same thing

10 might be done with this key outcome. You know, talking with a

11 focus panel, working through all the hypotheses and then coming

12 up with some specific protocols. And then it's a slam-dunk that

13 you're going to get the good quality data and information to

14 answer that question and move on.

15 DR. DANIEL SCHLENK: Okay. Any other

16 comments? 

17 Dr. Matten has some things she needs to

18 clarify.

19 DR. SHARLENE MATTEN: So it's 4:30 in the

20 afternoon and I must say I should've brought more Tums. But I

21 feel the need -- it's not my passion to focus on that process,

22 but I feel that it's appropriate to do so. 

23 So the Agency has set up three SAP meetings. 

24 Each of those have three unique charges and three unique Panels,

25 each of which were selected based on expertise and review of

26 ethics in terms of financial conflict of interest and appearance
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1 of the lack of impartiality.

2 In this case, there are specific charge

3 questions. The Agency has asked for clarifications that relate

4 to previous SAPs which are not explicitly identified in the

5 charge questions. If you're going to consider what they've

6 asked, part of the process of open and transparent is to then

7 bring whatever ideas you are percolating back to the Panel in an

8 open and transparent way.

9 The second point is you're not locked into

10 agreeing with previous discussions that may relate to a topic in

11 a broad sense. If the charge, as you understand it, brings

12 forth comments, then please consider that as you understand the

13 charge and identify the rationale for why those comments are

14 based on your response. 

15 The Agency's asking for clarification, then

16 add that to your remarks and bring forth those additional

17 comments in the open discussion. If you need any additional

18 information, for example, if you feel overnight, you're going to

19 do this additional homework and you need the 2003 and 2007 and

20 2009 SAP reports in a more explicit fashion then referred to the

21 White Paper, please let me know, and I will provide them very

22 easily to you, and you can consider those.

23 The meeting we have this time has spent many

24 hours and many thousands and thousands of dollars of our public

25 monies to put this together and we want to make sure the Agency

26 has the best possible scientific advice; so take the time that
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1 you need. If you need these materials, let me know. They will

2 then go in the docket. 

3 The Agency will have their clarifications; the

4 public will have a transparent identification of the additional

5 information that was requested, and a logic on how you're going

6 to answer it.

7 DR. DANIEL SCHLENK: Okay. Any questions for

8 Sharlene on that process? Okay. All right. Barring any other

9 comments from the Agency, we're good to go on to 5(a) and (b)? 

10 All right. Let's move on to 6, then.

11 DR. DANA SPATZ: We're moving into cosm

12 studies?

13 DR. DANIEL SCHLENK: Yes.

14 MR. DANA SPATZ: Given the diversity of

15 sources and the described communities, please comment on the

16 extent to which these cosm studies taken together provide useful

17 and reasonable physical models of the natural aquatic plant

18 communities exposed to atrazine in the U.S.

19 DR. DANIEL SCHLENK: Okay. To lead the

20 discussion for Question 6 is Jim Fairchild. 

21 MR. JAMES FAIRCHILD: The 2009 SAP recommended

22 an increased focus on cosm studies since studies in natural

23 systems are difficult due to the lack of appropriate reference

24 conditions and the confounding factors of physical impacts due

25 to storm spates, associated scour, high levels of suspended

26 sediments, and varying nutrient concentrations.
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1 The Cosm studies is the gold standard of

2 evidence by which one can measure the fate and effects of

3 atrazine under a regulatory setting. Cosms, which the agency

4 uses to describe both microcosm and mesocosms, are usually

5 defined by size, with microcosms generally ranging from 1 to 10

6 m3 in volume; mesocosms generally exceed 10 m3 in volume can

7 range up to 10,000 m3 in volume or 1 ha in size.

8 I personally consider the cosms to be ideal

9 test systems for several reasons:

10 First, they can contain multiple trophic

11 levels which allow the measurement of direct and indirect

12 effects on aquatic ecosystem structure and function.

13 Secondly, each trophic level can contain

14 multiple species, which allows one to measure relative effects

15 across plant species. 

16 And third, they can be used to evaluate

17 factors that may mitigate the effects of atrazine in natural

18 systems, including species adaptation, species substitution and

19 functional redundancy, and recovery of plant species and

20 communities. 

21 And collectively, these endpoints are

22 considered of high ecological relevance when the goal is measure

23 the resistance, resilience and recovery of a aquatic ecosystem

24 exposed to atrazine under realistic fate conditions.

25 Some of the characteristics of high quality,

26 quantitative mesocosm studies include the following: 
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1 COSM studies should be conducted using

2 rigorous experimental design with a minimum of three replicates.

3 An example of an appropriate design might include the

4 examination of atrazine concentration, time, and their

5 interactions. 

6 The size of the cosms should be related to the

7 particular plant community of interest. For example, cosm

8 studies focused on phytoplankton can be as small as 15 liters,

9 where the community can be sampled several times a week for

10 biomass and species composition over a 14-21 day period without

11 incurring any study bias due to the small amount of water

12 removed for measurements. 

13 In contrast, cosm used to study macrophytes

14 should be greater than a cubic meter to eliminate problems with

15 nutrient depletion and self-shading, which can bias results.

16 Finally, cosms incorporating fish must not

17 exceed the maximum loading rate of 0.5 grams of fish per liter. 

18 And if one considers a study needs -- for example -- a study

19 that may need 100 fish of 1 gram size, including the subsequent

20 growth increment, then the minimum volume of the cosm needs to

21 be 10 cubic meters or greater.

22 Another attribute of high quality studies is

23 the duration of a quality cosm study should match the stimulated

24 chemograph of interest as well as incorporate the plant

25 taxonomic groups whose life history is relevant to that period. 

26
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1 A quality cosm study should also incorporate

2 the source of recolonization. A source of recolonization can be

3 internal. For example, natural sediments and associated

4 microbial systems, or perhaps external, the sequential addition

5 of algae, periphyton or macrophytes as the study progresses.

6 Natural sediments, with associated

7 bacterial, algal and periphyton communities are essential

8 because they allow the cosm to develop critical components of

9 structure, including species richness, diversity, biomass and

10 function; examples being productivity, system metabolism, and

11 nutrient cycling. Collectively, these attributes make the cosms

12 excellent physical and biological models to assess the fate,

13 effects and recovery of aquatic systems exposed to atrazine.

14 The Agency has responded to previous Panel's

15 advice to expand the number of cosm studies used in this

16 analysis. Twenty or so new cosm studies have been added to the

17 original data set. Potential further progress has been made by

18 the splitting of studies into separate taxonomic groups by

19 concentrations to add to the study population for modeling

20 purposes. The Agency has used this to build a more robust data

21 set that lends itself to more rigorous analysis. But we have

22 heard from other Panel members already that they are concerned

23 that the splitting of data may be considered a form of pseudo

24 replication, and so a sensitivity analysis with split data sets

25 would be in order. 

26 My primary concern with the cosm studies as
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1 evaluated lies in the actual scoring process of effect and no

2 effect which are provided by the Agency in Appendix D. In the

3 process of reviewing Appendix D, I found 11 discrepancies where

4 I believe cosms were incorrectly scored as "effects" when no

5 effects occurred. 

6 This is a subset of potential

7 mis-scoring of data since I focused my efforts on studies where

8 effects were listed at a concentration of less than 30

9 micrograms per liter, which is an area that I feel is

10 ecologically relevant. A list of studies and my concerns are

11 appended in these comments. A more exhaustive evaluation is

12 provided by Dr. Giddings in Syngenta Report #12709. 

13 Studies that I think need to be reevaluated or

14 perhaps not used, and I'm going to start off number one with

15 Lampert, et al. (1989).

16 You've heard this repeatedly, due to the

17 concern about the solvent effect. And I know that this was

18 covered in the 2009 Panel with concern of the quality of that

19 data. And that goes back to a quote by Dr. Giddings in 2005,

20 that was noted.

21 The fact that it's in German is not a

22 sufficient excuse not to have read that and confirm, not only

23 with whether or not solvent was used in the control as well as

24 the volume of the solvent.

25 Another group that I am concerned with, and

26 that is using separate studies from the original (inaudible)
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1 study from the University of Kansas from 1979 to 1984. I must

2 credit these studies because they're Ecotoxicological Classics

3 due to their hypothesis, study complexity and ecological

4 relevance. 

5 However, they were not conducted under any

6 semblance of GLPs. Individual notes for each study are listed

7 below, marked with an asterisk of those that were conducted at

8 the University of Kansas. 

9 I also want to note that this study basically

10 is accounted for five different times under five different

11 authorships, which, in my opinion, is five-fold accounting on

12 study and biases with the data in Figure 16 of the White Paper

13 at the 20 microgram per liter concentration. So that's going to

14 have quite an effect on reducing the number of cosm studies in

15 the database.

16 Specific studies, and I have these

17 specific notes appended to these, are Carney and DeNoyelles

18 (1986). This is perhaps one of the biggest issues that created

19 confusion across this series of studies due to the presence of

20 Grass carp were stocked at the 20 per acre, which is an order of

21 magnitude greater than common guidance. 

22 Macrophytes were totally denuded in one

23 control pond. However, these ponds may not have been the same

24 ponds examined by DeNoyelles et al. or Dewey et al. But this

25 is the exact direct levels on macrophytes that one would expect

26 at this extreme stocking level.
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1 Another study included in the Kansas studies

2 is Dewey et al. (1986) where they fail to account in the study

3 for microphyte biomass or the potential effect of Grass carp. 

4 In that study they're looking at insect emergence. 

5 I have a note there that there was zero Grass

6 carp survival in the controls as indicated by DeNoyelles, et al.

7 (1989), which resulted in high microphyte biomass and associated

8 insect emergence rates compared to the atrazine exposures. 

9 Also listed is DeNoyelles, et al. (1982). 

10 Again, in that study they haven't acknowledged the effect on the

11 presence of fish and that fish community was quite complex for a

12 mesocosm study containing gizzard shad at a total of 7 fish per

13 mesocosm, or 70 per acre; in addition to bluegill and channel

14 catfish. So in terms of predators, and possible indirect

15 predators, those studies have to be viewed with extreme caution.

16 Also listed on my list is DeNoyelles et

17 al. (1989), Detenbach et al. (1996).

18 One specific comment I had for that particular

19 study is that at the lowest concentration where they saw, they

20 said they saw effects that met productivity at 15 micrograms per

21 liter. And that paper explicitly describes those streams as

22 being heavily (inaudible) Missouri River and there was a lot of

23 tribal material there. And in my experience, when you have high

24 levels of detritus and flowing water, depending on the speed or

25 the current velocity, it can be very difficult to measure

26 dissolved on oxygen concentrations.
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1 Kettle et al. is another study from University

2 of Kansas. If you refer back to the original, I think that the

3 original or the Master's Thesis is probably a better reference,

4 where they note that there were no Grass Carp survival in the

5 controls.

6 Another study is concerning Kosinki et al.

7 (1984) with the 10 microgram per liter concentration. My notes

8 say the concentration listed in Attachment 3 should be 100

9 microgram per liter. Although some streams were colonized or

10 pre-treated at 10 microgram per liter, it is not sufficient data

11 to assign effects at this level.

12 Krieger, et al. (1985). They operated at two

13 temperatures and there were four studies at two temperatures,

14 only saw effects in one at one temperature, 10 degrees 0C, did

15 not see any effects till 250C. It's just not necessarily what

16 one would expect from exposure to atrazine. 

17 The last few studies were by Seguin, et al.

18 (2002). Seguin, et al. (2001b) and Seguin et al. (2001a). 

19 An example in Seguin, et al. (2001a), exposure

20 to 2 and 30 micrograms per liter had stimulatory effects on

21 periphyton production.

22 And so, when one goes through and

23 categorizes those studies, very often it's very easy, if one is

24 not paying close attention, to just look for one asterisk to

25 indicating effect on one day and that often seems to occur early

26 in the study, where just basically there's a high possibility of
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1 residual disturbance depending on how those mesocosms are being

2 viewed.

3 In terms of a path forward: Most of the

4 existing cosm studies have weaknesses that make their

5 interpretation difficult and subjective. It is important that

6 the Agency and the Registrant meet across the table to comb

7 through these cosm studies one more time to finalize a final

8 cosm data set for the assessment. And this should have been

9 done a long time ago, in my opinion. 

10 Second path forward is I feel that the

11 Agency should request a Data Call In for a cosm study that meets

12 the requirements of a high quality study in a manner similar to

13 what was done for amphibians. The design of the study should be

14 based on the mutual agreements of both the Agency and Syngenta,

15 and should be used to fill in the data gaps that currently exist

16 around the 4 to 7 microgram per liter Level of Concern and

17 higher to assure that both Levels of Concerns are met. 

18 The design in this study should be based on

19 exposure scenario for the 2-3rd order stream with an emphasis on

20 periphyton. 

21 The same set of measurement endpoints used in

22 Syngenta field reconnaissance studies conducted by Andrus et al.

23 in Missouri, Illinois and those measured endpoints are good

24 place to start, although in this high quality study, there's

25 going to be a reference condition as well as appropriated

26 replicated experimental design.
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1 A reasonable range of exposure concentrations

2 would bracket the current propose CE-LOC, for example, 0, 3.5

3 microgram per liter, 7, 14, 28, and 56, which is a level of

4 probable effects. It would be advisable to go even higher than

5 56, perhaps up to 128, which would be 7 total treatments. 

6 I feel that this study should resolve

7 many of the effects issues that continue to delay a final

8 decision on the re-registration of atrazine and the fact that

9 the mesocosm database is lacking in sufficient number of studies

10 that range from zero to 25 micrograms per liter.

11 DR. DANIEL SCHLENK: Okay. Dr. Twiss.

12 DR. MICHAEL TWISS: All right, I'll just add

13 on some brief comments on a few topics that I saw that were a

14 concern to me in the flow, time, and again, concentration. 

15 According to the cosm study listed in Appendix

16 D, only 30 percent of the cosms are running water. The rest are

17 stagnant or non-flowing systems. 

18 From an ecological perspective the majority of

19 these cosms are used to evaluate only lotic systems, which are

20 streams or flowing aquatic systems.

21 So regarding time, in figure 16 of the White

22 Paper, about 10 percent of the cosm studies are either greater

23 than 100 days -- and this is considered unduly long, given the

24 pattern of atrazine evident from the chemographs, available from

25 the monitoring data sets and you see those responses and their

26 understanding of recovery after the atrazine exposure.
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1 Moreover, about 10 percent of the cosm studies

2 have atrazine concentration that far exceed reserved peaks. 

3 These cosms, in my opinion, shouldn't be included on the basis

4 of exceedingly high concentrations that do not reflect reasonable

5 models of atrazine exposure. 

6 DR. DANIEL SCHLENK: Thank you. Dr. Rodgers.

7 DR. JOHN RODGERS: Just a few things to add. 

8 I applaud where my colleagues have landed in terms of supporting

9 a well-designed study. I've got several details that I've

10 written out here that I can certainly read into the record, or

11 just, I'll just allude to them in the interest of time. 

12 What I did is I made sure I qualified the

13 question and we have to make some assumptions in this question. 

14 We're talking about aquatic plant communities exposed to

15 atrazine in the U.S. today and from this time forward.

16 The physical models that we're talking about

17 are intended to provide some information relevant to the current

18 situation, to exposures that we're anticipating, as well as any

19 exposure history that these communities record. 

20 And third, that the species exposed are

21 reasonable representatives of sensitive species that may be

22 exposed throughout the range of atrazine use in the U.S.

23 There are some qualifiers in the question that

24 I define, things like useful, and I went to the old anomaly that

25 all models are used wrong, but some are useful. 

26 And representative, I define, reasonable, I
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1 define, and results I define in terms of what kinds of results

2 can these systems provide?

3 So going down through the data provided and

4 some of the information provided, I think -- and this is how two

5 independent scientists arrived at the same at the same place by

6 independent routes.

7 I think the studies in the database can be

8 best used at this point to design an appropriate study to test

9 the hypotheses generated. In other words, this is a

10 hypothesis-generating data set. It's not going to be useful to

11 evaluate what's going on today for lots of reasons. 

12 Many of these studies are 15 to 20 years old,

13 and we can't go back. The exposures in those communities that

14 we're talking about studying, for many places, they continue for

15 15 to 20 years. 

16 And so, in this study, in these studies, they

17 generally represent -- and we are asked to look at atrazine

18 responses that are acute -- I would define as short-term

19 responses. 

20 Things like oxygen elevation focused on the

21 light reactions, primary productivity, and even changes in

22 biomass. But changes in biomass aren't going to happen in the

23 short term, so if we limit these studies and we buy into the

24 mechanism of atrazine solely focused on light reactions, you can

25 see that in the first few days. But you're not going to see

26 these changes in biomass until you've given them a chance to be
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1 manifested, and so, all of those, to me, are short-term issues. 

2

3 Then the longer term issues are responses

4 resulting from long-term, low-level or repeated exposures. And

5 that would include things like resistance or enhanced tolerance

6 and a loss of the sensitive populations. We simply can't

7 measure that in a short-term study. 

8 So returning to the original question in

9 context, these studies provide guidance for problem formulation

10 and initial risk characterization for relatively short-term

11 exposures. They are inadequate for and do not speak to issues

12 for long-term exposures that I mentioned above.

13 DR. DANIEL SCHLENK: Thanks. Dr. Fetscher.

14 DR. ANNE FETSCHER: Well, my colleagues on

15 this question did a terrific job in encapsulating the

16 conversations that we had about this. I'm going to try not to

17 go over any of the same ground again. But I just wanted to add

18 a little bit of extra comments on some aspects of what has

19 already been expressed by these fellows.

20 One thing is I think that, I'm assuming that

21 maybe some new studies can be conducted to sort of better get at

22 the questions we have in determining the LOC. I think that in

23 order to conduct this kind of cosm analysis, it'll be really

24 important to include biota that have been sourced from a natural

25 community where it's very safe to assume that they have not been

26 exposed to atrazine in the past.
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1 Ideally, I'd say that they should come really

2 from what I would consider reference sites that represent to the

3 greatest extent possible the study region, while at the same

4 time, being exposed only negligible amounts of anthropogenic

5 stress, at the most. 

6 And I recognize -- as somebody who's dealt

7 with this problem a lot in the course of my own index

8 development efforts -- I recognize that this is not always easy,

9 but I do think it's important to have as a goal.

10 I think if we're not starting with

11 unperturbed natural communities, then that puts serious

12 constraints on our ability to measure severity of effects across

13 the representative natural biotic communities. And also calls

14 into question how meaningful the LOC would be for protecting

15 ecological integrity. 

16 I think that covers a lot of it. I also

17 wanted to interject that to the extent that some studies that

18 are tailored so our questions can actually be carried out, I

19 think it would be useful to take advantage of all the great

20 monitoring data that are now available and the chemographs that

21 are now available to actually come up with a few exposure

22 regimes in these mesocosm studies that reflect, let's say, a few

23 typical signatures, if you will, of exposure. 

24 So we can start actually getting some

25 experimental data on these pulses and these issues that people

26 have been, you know, really trying to wrap their brains around. 
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1 DR. DANIEL SCHLENK: Thanks. Dr. La Point.

2 DR. THOMAS LA POINT: Just a couple points,

3 because a lot has been said that I agree with. Obviously, the

4 best, closest fit with the mesocosm data we have were the ones

5 where most of them have been done in lentic, freshwater

6 pond-type systems, and fewer (inaudible) systems, and relatively

7 fewer than that for marine or brackish water for the

8 agricultural processes and that going on in the coast.

9 The nice thing about atrazine is it's

10 water-soluble and we need to remember that the responses in

11 mesocosms have always been meant to answer the question, is

12 there an ecosystem-level effect above and beyond that which we

13 can measure at the individual level, individual species or

14 general level? And the way these are set up -- I agree with Dr.

15 Rodgers totally, and actually, Fairchild's comments were spot on

16 that remember, these can be best used as our focus on a range

17 where we have the uncertainty of whether the LOC is actually at

18 7 or whether it varies from 4 up to 15 to 20 to 30. 

19 And so I think, in that sense, the ones that

20 are done have been conducted and reviewed. That's another

21 question we've come up with, Question Number 7, in terms of,

22 again, looking at the quality and calculating LOCs, but that

23 kind of question can be nicely used by these. 

24 Another one that actually Dr. Twiss talked

25 about, and I agree totally, the nice thing about a stream

26 mesocosms, there are fewer of them, but yet we're trying to
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1 protect in most cases the Midwestern streams and, let's say, the

2 agricultural areas, and look at the effect of atrazine. 

3 And so, the nice thing about some streams

4 mesocosms is they can be (inaudible) with a dose, just as Dr.

5 Fetscher said, and I think that'd be hugely valuable to look at

6 some of the hydrographs, the chemographs, and mimic that in

7 these systems; look at them for a period of several weeks and

8 see what happens with periphyton. 

9 But I think, again, that there was a lot of

10 work in reviewing all of these, going all the way back so many

11 years. And it's nice to see all these papers. But anyway, I'll

12 congratulate. I mean it was a good job of reviewing. That's

13 all I have.

14 DR. DANIEL SCHLENK: Dr. Klaine.

15 DR. STEPHEN KLAINE: First, I want to say I

16 really appreciate the job Jim did on reviewing these. This is

17 unbelievably detailed, and I agree with all of his assessments. 

18

19 And most of what I've got to say has been

20 said, so I'll be pretty brief. But one general comment, and I

21 can't quite rectify it in my mind, but it does appear to me that

22 the Agency has been less discriminating regarding which cosm

23 studies they accepted compared to the criteria used for

24 accepting the amphibian studies.

25 And I think that was raised a little earlier,

26 and it's just something to consider as a general comment. I
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1 really like the idea of utilizing the vast amount of atrazine

2 monitoring that we have. Decades, literally, to redesign

3 flow-through microcosm tests. I fully agree with both of your

4 comments. 

5 I think this is a path forward that really

6 helps us get away from averaging data, exposure data, and gets

7 into real world episodic exposures and the response of organisms

8 to these exposures. It's not a trivial question, but one that

9 going to have to be addressed sooner or later.

10 And I certainly support the Agency

11 putting together a team that can redesign both the mesocosm

12 study that can be acceptable, and I think that's already been

13 stated by Jim. So that's all I have. 

14 DR. DANIEL SCHLENK: Dr. Young.

15 DR. LINDA YOUNG: I've heard a lot of

16 consistency on that side of the table. 

17 On this side of the table, the big comment is

18 that as far as it impacts all of the other questions, the issue

19 of what is an acceptable study and what is not is paramount. 

20 All of the other statistics that are done depends on this

21 question, and that's why we're getting such different responses

22 from the Agency and from Syngenta. 

23 So we just build on what Mr. Fairchild says. 

24 It's important to get some consensus. I don't know how to do

25 it, but to get some consensus so that we've agreed these are the

26 studies that are under consideration, because all of the other
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1 analyses hinge on that. 

2 DR. DANIEL SCHLENK: Okay. Dr. Portier.

3 DR. KENNETH PORTIER: I focused on the

4 question that said, "Is the collection of cosm studies a

5 reasonable physical model of the natural aquatic plant

6 communities exposed to atrazine in the US?" 

7 So I asked the question, were any of the cosms

8 shallow in depth and allow the dry-down and then rehydrate, as

9 is common in these higher outer streams. The answer is, "No." 

10 So, by counterexample, it's not -- it -- clearly, this doesn't

11 cover the whole exposures base.

12 All right, it's got some limitations on it. I

13 know, we've pointed out that there's -- again, the cosms haven't

14 been linked to the hydrographs in terms of actually looking at

15 representation. 

16 I picked an extreme example, I know it, but I

17 started thinking that might be an interesting study.

18 DR. DANIEL SCHLENK: Okay. Any other input in

19 the panel?

20 MR. FRANK FARRUGGIA: First, I'd like to ask

21 what is meant by a short time? This goes back to your

22 statements, Dr. Rodgers. Also, where do you recommend for

23 reference sites, and what are negligible anthropogenic impacts? 

24

25 And lastly, we would like to have some very

26 specific design elements. I know you were getting into those
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1 with your criteria, but in your response, we would like to flush

2 out in very specific design characteristics for those ideal

3 cosms, and what the typical duration needs to be if there are

4 fluctuations in concentrations needing to be done, how many

5 replicates of each one of those. If dry-down needs to be

6 considered. 

7 These are a lot of elements that you're trying

8 to impart into this one ideal study, and that seems like a very

9 complex system to really test and tease out an effect from

10 atrazine.

11 MS. ANITA PEASE: Hi, my name is Anita Pease. 

12 I'm the associate director in the Environmental Fate and Effects

13 Division. I was involved in some of the review of the cosm

14 studies before Frank came on board. 

15 I just wanted to make one additional comment

16 that the ultimate analysis will involve whatever additional

17 studies we do or design, in addition to the available

18 information. I think it would be helpful for you to comment on

19 the existing quality criteria if you have specific comments on

20 the revisions of that so we can make use of the available

21 information that exists right now. Thanks. 

22 DR. DANIEL SCHLENK: Okay. Everybody got

23 that? Do you want the comment now, or do you want it tomorrow?

24 GROUP: Tomorrow.

25 DR. DANIEL SCHLENK: Because, I mean, if they

26 do what you guys are asking they would have to read it into the
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1 record. I mean, that's what I'm saying. That's the whole --

2 yeah. 

3 MR. DONALD BRADY: So just for clarification,

4 we'll get an answer tomorrow?

5 DR. DANIEL SCHLENK: Well, we're doing that on

6 Question 4, right? We have to do that on Question 4. If you

7 guys are asking additional questions that are not being

8 addressed here, they're going to have to meet and put that

9 information in the public docket, I would assume. So they will

10 have to address that, so, yes.

11 DR. THOMAS LA POINT: Can I ask the question

12 do we really have to come up with the criteria, or can we say

13 that we're working on it and will provide it in the report?

14 DR. DANIEL SCHLENK: Well, you can't -- it has

15 to be stated in the public record.

16 DR. THOMAS LA POINT: Okay.

17 DR. DANIEL SCHLENK: Yeah. 

18 MR. JAMES FAIRCHILD: Those criteria are

19 already stated, I believe, in appendix B, the mesocosm review. 

20 I think that it's a reasonably good set of parameters. I'm not

21 sure that it's perfect. If you'd like us to add to that by

22 tomorrow, I guess we could.

23 MR. FRANK FARRUGGIA: I think that would be a

24 good idea. It would really help us out to get a better

25 understanding how the cosm classification that we made, and that

26 you explicitly pointed out as being different. What additional
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1 criteria might be useful in order to characterize those kinds of

2 cosm effects, or what we call effects, a little bit better in

3 the secondary review prior to the final decision.

4 MR. JAMES FAIRCHILD: I'm not sure that we

5 have sufficient time to come up with that comprehensive a

6 document. I think that, you know, that speaks to not only

7 ecological but societal perceptions of what are significant and

8 what are not significant effects. And to do that one would

9 require a two to three-day workshop equivalent to what has been

10 previously done.

11 DR. DANIEL SCHLENK: Just to point out, and

12 Dr. Matten just whispered this in my ear, that if you do not

13 feel you have the information necessary to do that, that's okay.

14 So you can definitely respond that way, as well.

15 MR. JAMES FAIRCHILD: Yeah, I do not. 

16 DR. DANIEL SCHLENK: Yeah.

17 MS. ANITA PEASE: I just wanted to clarify

18 that I don't think it involves a whole, you know, workshop, just

19 really to comment on the existing criteria; if you agree or

20 disagree with any of them in particular, you could comment on

21 those. 

22 Like, for example, you said (inaudible)

23 replicates, and I think we accepted those two replicates, that

24 kind of thing.

25 MR. JAMES FAIRCHILD: Those actually are two

26 different questions. On one, I think he's asking for what
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1 should you actually study, what's the frequency, what's the

2 sampling methodology? That's totally different from what are

3 acceptance criteria for a quality quantitative study.

4 MR. FRANK FARRUGGIA: That is correct. The

5 Panel explicitly said that this ideal study exists, but there's

6 a lot of complexity to consider in that. Also, we have these

7 criteria for screening these studies and for calling an effect

8 no effect, and we'd like some feedback on those criteria.

9 DR. DANIEL SCHLENK: Yeah, Dr. Rodgers?

10 DR. JOHN RODGERS: I think our answer -- I

11 think we can come up with an answer if we get a chance to huddle

12 for a little bit. However, it's going to be a little bit like

13 evaluating children, or in my case, when I have to give a grade

14 in a class. And, you know, you guys have to give grades. 

15 And all of us think we know what a good child

16 is and a bad child is, and we think we have a criteria; but even

17 the bad children, the ones that we may say have ridiculous

18 concentrations or, you know, the not environmentally relevant

19 concentrations or two reps versus three reps, those are

20 information-rich. And when you give them an ultimate grade of

21 pass or fail or a zero or a one, you throw away a lot of

22 information. 

23 So we'll give you those criteria by which to

24 evaluate studies, but we can't give you the ultimate scientific

25 view that, you know, some of us invested more than three decades

26 in evaluating these studies and looking at these studies and
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1 conducting these studies; and every one of them provides

2 information of some sort or other. Whether or not that

3 information is valuable in a regulatory context is the

4 challenge. 

5 And we can't do that job simple. We can work

6 through the criteria, but that's not a simple job. Jim's right.

7 And a workshop may take us down the road toward putting some

8 more bells and whistles on that question, but ultimately, when

9 we go to evaluate these studies, even the bad children have

10 information to use.

11 DR. DANIEL SCHLENK: Do you guys understand? 

12 Is that okay with you? Do you have information that you can

13 use?

14 DR. STEVEN BRADBURY: This is Steve Bradbury

15 speaking.

16 DR. DANIEL SCHLENK: Yes.

17 DR. STEVEN BRADBURY: This is like dIj@ vu all

18 over again. Back in the 1970s and 80s.

19 I think some of these techniques that we're

20 using now can help us for a regulatory decision. Maybe they're

21 still not ready to be used. I don't know. 

22 I mean, it seems like we're always coming --

23 again, it's my stomach -- we're coming to the conclusion that

24 maybe we should just go back and do single species plant growth

25 and the restoration of these. I don't think that's what you're

26 getting at.
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1 But part of what I think you're getting at is

2 just, this is what we got. Do you have any additional advice on

3 how to take a look at what we've got, the things we should ask

4 ourselves when we look at those collections studies. Then I

5 think Mr. Fairchild gave us some things to think about, and

6 that's great? 

7 So I think just the -- it's all there, and

8 it's just, make sure you're thinking about these things as you

9 look at these studies, and not much more than that. 

10 And I think when you all start to talk about,

11 here's some ideas of mesocosm experiments that you had

12 discovered and done before, we can go through the door and you

13 opened the door, and you can open it as wide or keep it just

14 barely cracked open as you want to, but that's where, in the

15 time we've got, thinking about what that means. 

16 I'm not trying to make you think (inaudible)

17 straight, but just realize as you open that door, there's a

18 plethora of questions that come up. For example, well, why

19 don't you take some chemographs that you think would be

20 representative of -- the chemographs are changing every year, in

21 every place. There is no typical chemograph. So we're still

22 going to have to extrapolate across time and space and then

23 where do we go. 

24 Again, maybe just some, at a minimum,

25 philosophical, for lack of a better word, thoughts about what

26 that ultimate mesocosm study would be.
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1 DR. DANIEL SCHLENK: Dr. Portier?

2 DR. KENNETH PORTIER: I just kind of opened

3 that door for a point, but because I'm looking at the cosms

4 within kind of the environmental stage. 

5 When Dr. Rodgers mentioned that, you know,

6 there's a couple of cosms with extremely high concentrations

7 applied, and then I start thinking, well, those also maybe cosms

8 that are very influential in establishing the LOC. 

9 So I'd like to know which ones those are so

10 when we move into the next question we can look at sensitivity

11 of the next step in your process.

12 The other thing is that if we don't kind of

13 get some idea of this space, we don't know if the cosms all fall

14 on one line, which means they're all highly correlated or

15 multi-collinear with regards to what we think are the important

16 environmental space; which would then tell me that there's going

17 to be a lot more uncertainty than you think in our ability to do

18 that next step, because those cosms don't look at the full

19 environmental space, they only look at a co-linear phase. And

20 then we have to be thinking about all these co-linearities. 

21 So that's kind of what I was thinking when I

22 made my suggestion of a missed design; it was trying to put it

23 way out in the corner. But I think there's probably a lot more

24 realistic issues that need to be looked at. And one thing I

25 guess I would have expected in the report that I didn't see is a

26 clearer linkage between what happened in the cosms and what's
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1 happened in the field. 

2 And I'm not talking about a full season-types

3 of graph, I'm talking about maybe a hydro event. You know, the

4 dose occurs over five days. We have no -- maybe no cosms where

5 the dosing occurred over five days. It was either one day or it

6 was three months. So we're missing that kind of realism.

7 DR. DANIEL SCHLENK: Dr. Rodgers.

8 DR. JOHN RODGERS: Just a follow-up. It

9 wasn't my goal to leave you hanging or throw you under the bus,

10 the ecological bus. It was that I think our ability to design

11 these studies has improved significantly over the past decades. 

12

13 And many of these studies are -- I hate to say

14 it -- getting pretty old, but still familiar.

15 We've got better instrumentation, we've got

16 fluorescence techniques that are orders of magnitude more

17 sensitive than a lot of the acetone extractions, techniques that

18 we were using years ago and that are used in these studies. 

19 There are no really sensitivity sorts of analyses. There's none

20 of that analysis here, and we could just do better now, and it's

21 not necessarily more complicated. It's actually simpler. 

22 DR. DANIEL SCHLENK: So let me just go to Jim.

23 Do you understand what you need to do in terms of the write-up

24 for this particular question? Are you clear on what the Agency

25 wants? Do we need to meet tomorrow to add additional

26 information or do you think you have it in your write-up
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1 already?

2 MR. JAMES FAIRCHILD: It's not in my write-up,

3 so I'd like to state for the public record that in terms of the

4 requirements for a high quality mesocosm study, the Agency in

5 the White Paper has, I think, five different components that

6 come with sub-elements that constitute high quality study. 

7 Syngenta also has provided a document which

8 lists nine critical factors that must be met for it to be

9 considered high quality. So I think that it might be better if

10 you read the Syngenta document rather than repeat.

11 DR. DANIEL SCHLENK: You can just refer to

12 that. Dr. Twiss?

13 DR. MICHAEL TWISS: There's an exception in

14 that I didn't see any of the criteria talking about it; cosms

15 that were very long, some conducted for over a year, which I don't

16 think are relevant. And more cosms which had extremely elevated

17 concentration of atrazine.

18 MR. JAMES FAIRCHILD: I'd like to comment on

19 that, and I think you're specifically talking about the

20 University of Kansas studies, which are listed as being 365 days

21 a year with exposures up to 200 micrograms per liter. 

22 It's important to know that first of all, in

23 cosms like that, they receive no runoff. So basically, the only

24 precipitation that they get is instant precipitation and none of

25 the standard 10 to 1 model that's used tested runoff into a pond

26 system.
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1 And that's the reason that they had a

2 half-life of 195 days. Those are fairly -- well, I think they

3 are 10 hectare in size. They're 0.04 hectare in size, but

4 they're fairly deep, up to 2 meters. So rainfall is a primary

5 factor in terms of that 195-day half-life. So that really has

6 to be taken into regard. 

7 The other thing about those studies is that

8 they lasted 365 days, but very few of the endpoints are actually

9 reported as a 365-day (inaudible).

10 I mean, they make qualitative statements

11 throughout about changes in species, composition and such, and

12 ultimate findings at the end of the study; and what I recall

13 them saying is that they saw species replacement, but by the end

14 of the study, that they saw no changes in phytoplankton biomass

15 at 100 micrograms per liter.

16 And also, when those studies were scored, very

17 often, some of the sub-studies that were done are in the -- are

18 the, for example, some of the data was cited where there were

19 significant effects on phytoplankton at 20 micrograms per liter.

20

21 In those cases, those examples or those

22 short-term studies, I think 24-hour studies of C14 uptake,

23 collected phytoplankton from the actual mesocosms brought in. 

24 And exposed both those acclimated as well as

25 naEve populations to atrazine, and found that they did see an

26 immediate response in the naEve population but they did not in
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1 the adapted populations, which is similar to what they've shown

2 in the last set that they submitted.

3 So again, those were classical studies in

4 their time, but to include those in the database, as you say, I

5 think you're correct.

6 DR. DANIEL SCHLENK: That was Jim Fairchild. 

7 Bob Gilliom?

8 MR. ROBERT GILLIOM: To test my understanding

9 of what I am hearing from my colleagues; what I'm hearing is

10 that a useful step can be taken proceeding with the existing

11 studies as long as they are sifted through once more and get

12 consensus on those; and then that would be further improved by

13 adding some improved studies that meet these same criteria, or

14 -- that's what I'm hearing. But if you're saying everything

15 should stop until there's a collection of new,

16 carefully-designed studies that would be a different conclusion.

17 MR. JAMES FAIRCHILD: Jim Fairchild. I'm glad

18 you asked that point, because I have asked that the data be

19 reviewed and some of the data points potentially be dropped, as

20 well as some of the longer exposure times that Dr. Twiss was

21 concerned about. And that after those adjustments to the

22 database are made, they should rerun the models and determine

23 what that does to the LOC.

24 And I personally suspect that it's going to

25 raise the LOC to approximately 20 micrograms per liter. At that

26 point it would be the elective I think, whether or not they
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1 should continue with the mesocosm study. If it goes up to 20,

2 then there's probably other approaches that could be used.

3 MR. ROBERT GILLIOM: Well, I guess the only

4 thing I'd say, that's fine except for it doesn't seem like that

5 decision should be dependent upon the value of the LOC. I mean,

6 it's a separate logic that if we need the new studies, they

7 could push things either direction. We don't know. So my only

8 amendment would be, do it, see how long it's going to take to do

9 the other studies, plan that out. That's kind of an Agency

10 strategy decision, which, you know, I think in the next

11 questions, some of the issues will come up about stating the

12 conditions of the LOC and uncertainty and confidence in it that

13 are kind of related to this, and we don't need to get into them

14 now.

15 DR. DANIEL SCHLENK: That was Bob Gilliom. 

16 Dr. Portier?

17 DR. KENNETH PORTIER: So just following up on

18 what Bob's saying though, you know, an option, even if it went

19 to 20, they still have to make recommendations of where cosms

20 might be created to challenge or validate the further

21 development, right? 

22 So, I mean, the EPA can then move forward with

23 regulation, but they can also move forward with doing some

24 challenging studies that actually force the model to see whether

25 it's really good. 

26 I mean, that's just good science, and I think
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1 it is good regulation, as well. I don't think it depends on the

2 result.

3 MR. JAMES FAIRCHILD: Perhaps I wasn't clear. 

4 I think that both steps need to be taken. 

5 DR. DANIEL SCHLENK: So, good discussion. Let

6 me go back to the Agency for questions of clarity.

7 MS. ANITA PEASE: This is Anita Pease again. 

8 Sorry, I don't mean to beat a dead horse on this, but maybe not

9 quality but relevancy; if you could provide more input on the

10 relevancy, so I hear, like, longer-term studies, concentrations

11 that are not horribly relevant. When you talk about filtering

12 the data, if you could provide some guidance on exactly how you

13 want us to filter it based on relevance. 

14 I understand the quality criteria between what

15 we've documented and what Syngenta has in their report; it is

16 probably enough to go on. But in terms of how we're sifting

17 through the results would be helpful, if you could provide some

18 guidance on that. 

19 MR. JAMES FAIRCHILD: I would consider, well,

20 I would recommend sifting absolutely through any study that's

21 run at a concentration of 3 micrograms per liter or less.

22 DR. DANIEL SCHLENK: Do you have a question,

23 Russ?

24 DR. RUSSELL ERICKSON: Yeah, I have a couple

25 of points of clarification. 

26 The first is that maybe I misunderstood Dr.
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1 Fairchild, but the EPA cosm database currently does not have any

2 study where there are multiple points based on different

3 endpoints. And I thought you might be suggesting to doing the

4 opposite, and I think Dr. Giddings suggested that yesterday. So

5 I'm not asking for a response, but if that could be clarified in

6 your statement, or did I misunderstand you?

7 MR. JAMES FAIRCHILD: Well, as we said

8 yesterday, you have two choices, lumping or splitting. And just

9 out of basic curiosity, I would do both and see what the

10 difference in outcomes are.

11 DR. DANIEL SCHLENK: That was Jim Fairchild.

12 DR. RUSSELL ERICKSON: The other point of

13 clarification, again, if it's appropriate is that the -- this

14 relates more to the, how the 4 to 7 micrograms per liter came

15 about. 

16 We were forced into a compromise where you

17 have such diverse exposures where you might have peak post

18 exposures that have intense effects that are averaged over time,

19 except the 4 to 7 refers not so much to a constant 4 to 7

20 exposure as to the integrated effect of a more realistic

21 exposure that averages to that 4 to 7. 

22 And again, to what extent that that might be

23 factored into any kind of recommendations that do come out for

24 additional studies, that would be appreciated.

25 DR. DANIEL SCHLENK: Okay. Is that something

26 that you guys can address in this answer, that question?
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1 MR. JAMES FAIRCHILD: Dr. Rodgers says yes.

2 DR. DANIEL SCHLENK: All right. Dr. Rodgers,

3 there you go. Okay. Any other points of clarification from the

4 Agency?

5 DR. STEVEN BRADBURY: Just -- I think the back

6 and forth that Bob started, that helped a lot. I'd appreciate

7 it.

8 DR. DANIEL SCHLENK: Okay, we have one more

9 question to do and we'll actually be on schedule. Imagine that.

10 You want to read Question 7 into the record?

11 MR. DANA SPATZ: Please comment on the

12 methodology the EPA has used to derive the atrazine CE-LOC for

13 aquatic plant communities, and in particular, on EPA's

14 characterization of the uncertainties and assumptions in this

15 methodology.

16 DR. DANIEL SCHLENK: Okay, to lead the

17 discussant for that is Tom La Point.

18 DR. THOMAS LA POINT: Okay. The four stages

19 summarized in Figures 15, 16, and 17, beginning with an

20 evaluation cosm data characterizing effects versus no effects

21 from the Brock Scores and putting them into binary no effect and

22 effect numbers. 

23 Given the binary effects data, when PATI is

24 developed, which is analogous to an SSD, PATI is an average

25 toxicity relationship calculated as the main percent reduction

26 in growth rate across all aquatic plants.
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1 The PATI scores are iterated to establish

2 daily values and accumulated it over a 60-day period. Levels Of

3 Concern are established using transformed Brock Scores, Brocks 1

4 and 2 are no effect, Brocks 3, 4, and 5 equaling 1, in effect.

5 And then farther on in the process, watersheds

6 are identified with chemographs with accumulated PATI values

7 that exceed the LOC PATI, or Level Of Concern.

8 First, in this critique, I'm very impressed

9 with the amount of work and consideration put into the

10 calculations and estimates. A lot of data were collected and

11 much work put into these calculations. 

12 I was impressed by the discussion yesterday by

13 Syngenta, Dr. Brain, by the value of using -- well, I can see

14 there's some -- I was really -- it's a good discussion, even as

15 I say this, thinking about it. The logistic regression, having

16 the binary parts and the part per billion accumulation, was

17 valuable; and it really is good food for thought. And I was

18 pleased to hear earlier that this is an effort in progress, and

19 that it's going to be discussed more over the next coming year

20 or so.

21 I like the part per billion accumulation and

22 this is really more of a discussion part as I mention here;

23 because it seems it would make for a more robust model because

24 it's not so dependent on initial calibration and determinations

25 originally from the Brock Scores. 

26 As this is a work in progress, I suspect that
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1 more agreement will come about not only during this meeting, but

2 in the following year.

3 The use of a set of criteria, helpful weight

4 of evidence, effort, to determine the effects of a transient or

5 minor type versus more infinitive effects would be excellent.

6 This would really help in this aspect, and it

7 addresses the uncertainties. Because I think here, as I say,

8 reviewer should be able to approach the lines of evidence, and

9 given the criteria that were stated, there come to be agreed on,

10 or should come to be agreed on, then that should lead to fairly

11 concurrent judgment seen between two disparate groups.

12 The use of time-integrated exposures -- that

13 was the Syngenta slide 93 and following -- in my opinion are of

14 great use. I really like that. And then provide the reality

15 check on the resulting Levels Of Concern, others reflect the

16 SSDs for plants -- well, yeah, just for plants, in this case --

17 and allow reality check as we saw in predicted LOCs in field

18 situations and field studies.

19 The time-integrated part per billion approach

20 seems to provide a better look at actual effects of

21 concentrations of LOCs with those that are greater than 10 parts

22 per billion of atrazine. I think it should be pursued and

23 looked at more carefully.

24 DR. DANIEL SCHLENK: Dr. Twiss?

25 DR. MICHAEL TWISS: Yes. The approach of

26 using the individual species and using the specific growth rates,
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1 I found was a good approach, despite the fact that a lot

2 of these are phytoplankton. And again, these studies are

3 designed for streams and so I think if they were just strictly

4 periphyton that would be better. 

5 But nevertheless, using the specific growth

6 rate is an appropriate measure since growth rate is a very 

7 sensitive parameter that is included in the index to be used 

8 for excessive toxicity. The next stage, I think it was the 3-A,

9 using the 60-day time period, and one of the justifications of it

10 that was in the white paper was that selecting the 60-day time 

11 period encompassed all but a few of the cosm studies. And I do 

12 think that that was a valid reason. 

13 If the cosm studies have unrealistic

14 concentrations that I mentioned earlier, then that is not good. 

15 Also, the cosm studies should be used that have realistic 

16 exposure duration, if possible. 

17 DR. DANIEL SCHLENK: Dr. Rodgers?

18 DR. JOHN RODGERS: I'll just add a couple of

19 things real quickly. Jumping to the conclusion from the EPA's

20 characterization of the uncertainties and assumptions in this

21 methodology is generally (inaudible), and that was one of the

22 questions that we were asked to respond to. 

23 I went through stage by stage or step by step,

24 instead of steps, or tiers. And in the first step, there are

25 uncertainties associated, as the EPA's pointed out, with the use

26 of meta-analysis to query a database that wasn't specifically

27 designed to test the hypothesis that it's being used to test. 
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1 And I got some citations that went out, some of the risk

2 associated with meta-analysis. And then I think that at the end

3 of the day, that the process could arrive at an acceptable

4 conclusion if some of the discussions associated with the stages

5 and the methods are resolved.

6 I think that we should point out, however,

7 that this approach does not arrive at a bright line regulatory

8 endpoint, at the NTE or not to exceed or water quality criteria

9 endpoint.

10 And that's not its intent, so I would not want

11 this to be misused as several other folks have commented, given

12 their fears of what this committee is about. So I just wanted

13 to state that for the record.

14 DR. DANIEL SCHLENK: Okay. Dr. Fetscher?

15 DR. ANNE FETSCHER: I was pretty much on board

16 with the four-stage process that was employed. I think my main

17 concern was focused on -- and this is something in Appendix E

18 that shows the single species toxicity tests that were used in

19 the development of the PATI relationship. 

20 Basically, as I look at this table, a couple

21 things occurred to me -- and I'm focusing mostly on the algae

22 because that's sort of in the lines of my expertise. But a lot

23 of these species, or perhaps all of -- or, genera -- they're

24 grouped by genera. 

25 But I would tend to think that these are more

26 sort of what you would find in disturbed sites, more sort of the
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1 tolerant taxa. And so, I think that there's the potential for

2 some bias in terms of the relationship that has been developed,

3 relying on sort of this particular collection of taxa.

4 And another thing that I noticed that caused

5 me some concern is for selenastrum all of the data that are

6 provided in this table are coming from studies on the same

7 species, and yet you can see that there's a range in EC50 values

8 from 50 to 236, and this is essentially a five-fold range. 

9 To me, again -- I'm not a toxicologist, so I

10 might not have sort of the level of experience some of the other

11 folks in this room have at evaluating these kinds of data -- but

12 that, to me, is almost a little bit of a red flag.

13 And I can understand that there are all

14 different types of sources of error that can go into that, but

15 it does make me wonder whether some of the strains that were

16 used for some of these studies might be resistant strains or if

17 there is something going on here that could cause them to be

18 maybe suspect for inclusion in generation of the PATI

19 relationship upon which the LOC is derived.

20 DR. DANIEL SCHLENK: Dr. Klaine?

21 DR. STEPHEN KLAINE: I just have a few things

22 to add. I think I'm fine with the process. I think it probably

23 is overly complicated, given what else we've seen. But I guess

24 my biggest problem is with the cosm data, but I don't think we

25 need to rehash that. I think we addressed it in the last

26 question. 
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1 So, and I think that's particularly critical,

2 though, considering that the comment or the statement in the

3 White Paper that said based on the results of these analyses,

4 the EPA has determined the CE-LOC range to be between 4 and 7,

5 which means that those freshwaters and estuary and

6 marine-monitoring sites with a 60-day running average at or

7 above 4 micrograms per liter have atrazine concentrations that

8 are above the bounds; and that permanent or irreversible change

9 in a wide plate community structure watershed animal

10 productivity would be expected. And I still don't see data to

11 support that, so I have a real problem with that. 

12 The other issue is the 60-day running average,

13 and the idea of how to fill in data gaps, and I'm curious as to

14 why we don't use the models that we've spent years and years

15 developing to fill in those data gaps instead of simply

16 extrapolating. So those are really the only two things I have

17 to add.

18 DR. DANIEL SCHLENK: Okay. Jim Fairchild. 

19 MR. JAMES FAIRCHILD: No further comment.

20 DR. DANIEL SCHLENK: Okay. Dr. Young.

21 DR. LINDA YOUNG: I was hoping to wait till

22 tomorrow for this, but -- because I've put quite a bit of effort

23 into trying to replicate what you did, and to say it's overly

24 complicated is an understatement. 

25 I just never could get the same answers you

26 guys got. Okay, so -- and I was giving a pretty good effort,
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1 and Ken was trying to help me. So, and because it's overly

2 complicated, one of the things, the 4 to 7 is based on which

3 studies are included and not a true measure of uncertainty

4 associated with the estimation process. 

5 And what I got, I got kind of in the ballpark

6 of what you did, and I got -- we worked with, we didn't have

7 accuracy within twofold. So if you said a hundred, it went

8 from, like, a 0.05 to 200-and-something. 

9 You know, so these numbers weren't pinned down

10 very well, and I'm talking about the first logistic regression

11 of the PATI, okay. And then that is used in the next

12 regression, which is then used in the next thing, and so those

13 errors are propagated every time you go to a new process. And

14 I'm afraid that it's just that there's no precision left in

15 where you come out.

16 I can see an appeal in kind of following the

17 biology through -- but if we can make some simplification so

18 that it is in concentrations part per billion or something like

19 that instead of going through the PATI, I think you're going to

20 get overall a simpler model and more precision in your final

21 estimate. I don't understand why in Appendix E, you -- I see

22 the argument, but it doesn't ring true with me that there's a

23 lower asymptote of 0.05. 

24 And as a -- I think it's the substitute for

25 not having the lower competence bound. And I don't think you

26 have the lower competence bound because if you did, it'd be
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1 (inaudible), you know? And I think that's a major problem. 

2 Just from a statistical. 

3 I understand that biologically, it's

4 appealing, but from the statistical perspective, in the end,

5 what I'd like is a CE-LOC with a standard error, and I think the

6 standard error associated with this current process, which you

7 couldn't get through a bootstrapping process, would be

8 unacceptably large. So I have some concerns about the methods

9 used.

10 DR. DANIEL SCHLENK: Dr. Portier.

11 DR. KENNETH PORTIER: I also wish it were

12 tomorrow, but that's okay. So I thought the four-stage

13 processes clearly described in the overview in section 13 of the

14 White Paper, I think -- I won't write it here, but I think the

15 devil's in the details on this, right. The discussion's in the

16 details, and so a lot of the details are going to come out of

17 the subsequent questions, you know, 8 through 14, but so I

18 wanted to make a few points. 

19 One is in Figure 15, there is a minor error,

20 because I think stage 4-A, you're determining daily PATI values

21 for site chemographs, not for CASM chemographs, and then you

22 calculate the 60-day cumulative PATI values for each monitoring

23 chemographs. So I think it should have been site.

24 The way I view this whole process, pure and

25 simplistic statistician view, is that this is just a big

26 bioassay. What's the role of PATI? PATI plays the role of
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1 establishing the dose that was applied level. And that dose

2 integrates, in this case, something complicated, which is the

3 level and duration of exposure filtered through a synthetic

4 community of -- I think it's 21 species/genera. I'm not quite

5 sure, because it's never stated. But in a uniform distribution,

6 because we're just summing them up, so we're not putting in any

7 weight, and then we're somehow scaling down to a 60-day average.

8 And so that's our dose. 

9 And so you can assign a dose to every one of

10 the reps, every one of the cosms. The response is this effect

11 and no effect on a natural community, not on our synthetic

12 community of 21 genera. Now we have a real community in the

13 cosm that's a mixture of whatever is there, which we don't know.

14

15 All right? But it's in -- but that effect/no

16 effect, is integrated over a bunch of outcomes. Things, you

17 know, different kinds of outcomes. Reduced DO levels, biomass

18 reductions, extinctions of certain, you know 

19 So one of the issues we have on this side is

20 that, that effect/no effect and really understanding what that

21 is. 

22 And then, because this is a bioassay we're

23 going to do a logistic regression to link the dose, which is the

24 PATI score, to the effect. And then once we've got that

25 logistic regression, we're going to estimate the P50 value, the

26 probability, the .5 value, and use that as a decision point for
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1 discriminating effect from no effect based on dose, which is the

2 PATI. And in this case, that P50 dose is what we're calling the

3 LOC.

4 Then we're going to convert that LOC to the

5 CE-LOC by converting it back to some maximum 60-day average

6 concentration. Then we're going to use that CE-LOC to make

7 decisions on the site, and the sites are characterized by a dose

8 which is the PATI running through the 60-day averages that

9 somehow translate the chemograph available at that site. 

10 So, you know, I think that's the process, and

11 I think that's your four-step process. I'm hoping that's right.

12 That's the way I'm thinking as we're going to try to answer the

13 rest of the questions. I would like confirmation for whether I

14 missed something somewhere.

15 DR. RUSSELL ERICKSON: Yeah, basically, I

16 think you got it right. I mean, the cosms defined are like --

17 your analogy to a bioassay is right on. In fact, I have that

18 analogy in my Appendix.

19 That each of the cosm treatments is like an

20 organism either dying or not dying in a standard bioassay. And

21 PATI or part per billion days, or whatever, are a way of

22 establishing the dose, because we just can't do concentration we

23 have to somehow address the -- and so, you have the response in

24 your cosm, the dose being PATI or part per billion days, and

25 then you have what would amount -- you could use a logistic

26 equation, it amounts to a probit analysis. It is the same
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1 procedure as a probit analysis in the bioassay to come up with

2 an EC50, which is our LOC. 

3 The one thing I would maybe correct you on is

4 that once you have -- we have two alternatives there. We can

5 either run the field chemographs through part per billion days,

6 or PATI, and compare it to that LOC, or we can use a set of

7 chemographs to develop the CE-LOC. 

8 Once you have the CE-LOC, you don't have to

9 run any further chemographs through PATI. That's the whole

10 point of the CE-LOC is to go to strictly an average field

11 concentration. 

12 So that's the one correction I would make to

13 what you said. But otherwise, the analogy is spot on.

14 DR. DANIEL SCHLENK: That was Russ, Dr.

15 Erickson. 

16 MR. FRANK FARRUGGIA: Frank Farruggia here,

17 just a moment. I just want to say that we ran ppb days and the

18 CE-LOC calculated off of ppb days is 5, 5 parts per billion over

19 60 days, well within the error of CE-LOC based on PATI, at least

20 the error that we calculated.

21 DR. DANIEL SCHLENK: Do you have any further

22 comments now? Bob Gilliom?

23 MR. ROBERT GILLIOM: Bob Gilliom. So rather

24 than focus any more on the specific methods, the one thing is

25 kind of a request comment, is I think something that would help

26 in setting up the coming discussion of the monitoring approaches
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1 and modeling aspects of evaluating exposure, would be to ensure

2 that when you end up stating the CE-LOC, that it's really clear

3 what all of its assumptions are behind it in terms of frequency

4 of occurrence, probability of exceedance, and so forth. 

5 So what I mean is, like, what I'd like to

6 know, clearly -- and I think I got most of it now, but it wasn't

7 transparent, necessarily -- is that you've chosen a CE-LOC at a

8 50 percent chance level of effect or no effect. 

9 So you went to the middle in terms of

10 probability from the cosm studies you guys used. Okay. Then

11 the part that's kind of not stated is how often it's acceptable

12 for that to go over in terms of an annual basis, so if that's a

13 parameter to fit into the environmental exposure discussion, we

14 need to know it. 

15 In other words, is it okay if it's 1 in 20

16 years, or is it a every year thing that's a problem. And so

17 those kind of basic facts that structure the assessment of

18 exposure need to be known to make some of these other judgments.

19

20 And then the other kind of part of that is

21 within a year, for example, does it make any difference if it's

22 a 1-day exposure where the 60-day moving average goes over, or

23 does it make a difference whether it's 20 days of that average

24 exceeded at a site per year. 

25 And those kind of numbers are going to end up

26 influencing how we look at the exposure assessment.
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1 DR. DANIEL SCHLENK: Dr. Ellsworth?

2 DR. TIMOTHY ELLSWORTH: Yeah. The 2007 and

3 2009 SAPs discussed at length the challenges associated with

4 extending the cosm data to the real world. I don't think this

5 fundamental challenge is adequately addressed yet. 

6 The Agency is faced with the challenge

7 of using the exposure profiles to estimate the impact of these

8 complex, you know, chemographs. To this end, they, like has

9 been said, you estimate the probability of an adverse effect by

10 creating the cosm studies due to some atrazine exposure profile,

11 and then you use your logistic regression to estimate this B50

12 value. 

13 However, what these studies don't do, and I

14 know you know that, is they do not provide a measure of the

15 relative change in specific endpoints. They use a variety of

16 endpoints, and it's a yes or no effect -- but they don't provide

17 that relative change in the endpoint. And that's the leap of

18 faith that you make in going to the real world, is that PATI can

19 provide that, you know, relative change in adverse effects for a

20 given exposure. 

21 And there's, you know, there's no

22 observational data to support that extrapolation. There's no

23 confirmation that that extrapolation of the reduction in adverse

24 effect is correct. So, you know, I can't determine if that's a

25 reasonable extrapolation. 

26 If some of these cosm data could be used to
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1 estimate measures of relative reduction in relevant endpoints,

2 and I emphasize, you know, relative reduction in adverse

3 effects, because that's what you're trying to do with the

4 chemographs, and that might be a useful first step to this

5 validation.

6 The next step of it would be, you know, the

7 temporal variability in the chemographs and the very dynamic

8 flow and the physical chemical environment that's going on in

9 the real world, you know, but I don't know how we'd address

10 that. 

11 I know, you know, the Registrant tried to

12 provide some data on a field experiment looking at that, but it

13 has no controls, no replication, and I'm not, you know, you

14 can't really go there with that. 

15 But as pointed out in the 2009 SAP, a concern

16 with PATI is it ignored interactions among species. I think

17 what Dr. Portier, you know, referred to earlier is, say, if you

18 had -- and this was in a private discussion, he mentioned this,

19 and I think it's a point that should be put on record. You

20 know, if a hundred ppb short duration exposure wipes out a given

21 species and a new species overtakes that niche, will the system

22 ever regain its original structure? Well, PATI doesn't take

23 into account such a change in structure because it ignores

24 interactions. And it's -- I don't know that we've addressed

25 whether that's appropriate yet. 

26 PATI and ppb days both make the assumption
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1 that the adverse effects experienced by an aquatic community due

2 to atrazine exposure on any given day are independent of prior

3 exposure history. I have several concerns with this assumption

4 relative to the screen of adverse effect endpoints that are of

5 interest. 

6 For example, it seems that the potential for

7 an adverse effect will increase if prior exposure history has

8 altered community structure or increased the availability of

9 carbon, either killing off organisms, or nutrient availability

10 or decreased oxygen availability. So despite the apparent rapid

11 reversal of atrazine impact on photosynthesis, it seems like

12 there might be a carryover effect from prior day exposures

13 that's lost in the PATI summation of these chemographs. 

14 That latter assumption, you know, with what

15 you showed today, indicated SGR was not equal to the one-day

16 control. One to two days after cessation of exposure might want

17 to be some kind of cumulative instead of just a one-day PATI,

18 there might be a cumulative effect that needs to be carried

19 over, and that, I don't know whether that's appropriate anyway. 

20 So this assumption seems incongruent with the cosm study

21 requirement also of no exposure history. 

22 So you've already kind of acknowledged that

23 these chronic, low-level atrazine exposures result in a greater

24 sensitivity of organisms that atrazine places. So that seems

25 incongruent then with this assumption. 

26 Another issue here is SSDs derived for lab
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1 studies differ from SSDs that would be expected in streams due

2 to the difference in environmental factors. Physical chemical

3 variability, light, and et cetera. I'm concerned that PATI,

4 which relies on these laboratory-based SSDs and SGRs, may differ

5 significantly if used SSDs and SGRs relevant to second and

6 third-order streams, which we don't have, I know. But still,

7 that variability is important, I think, to account for.

8 So the PATI approach should include the impact

9 of physical chemical variability on predictions of adverse

10 effects if this variability will impact the probability of an

11 adverse effect in response to atrazine exposure profiles. 

12 At a minimum, variability in SGRs needs to

13 account not only for atrazine exposure, but also SGR variability

14 in response to environmental factors. In this regard,

15 consideration should be given to a maximum credible event. You

16 know, that would entail identification of a set of plausible

17 environmental factors that would impact a given exposure profile

18 and result in maximum potential for irreversible adverse

19 effects.

20 From what was presented by the Registrant --

21 this is my last paragraph. I know it's long-winded.

22 From what was presented by the Registrant,

23 there appears to be considerable uncertainty regarding the LOC

24 as a consequence of variability among the cosm results. This

25 kind of ties in to what we said earlier as maybe a different

26 approach. The bootstrap was one approach. Another approach
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1 would be to take a minimum subset of the cosm data, use that to

2 estimate your LOC, add one more cosm data value to that set in a

3 sequential random path, and look at a plot of the LOC value as

4 you add sequentially more data, and see if you're converging to

5 some value, or if it's just bouncing all over.

6 That also gives you an estimate of

7 uncertainty. You repeat that sequential random sampling a

8 number of times and you can come up with an estimate of how much

9 data you might need, or if this is a physically-relevant value

10 you're trying to get, because it should start to converge as you

11 get more and more data added to the estimation pool. And if it

12 isn't, well, that's a red flag. 

13 DR. DANIEL SCHLENK: Thank you. And any

14 other panel members? Dr. Portier?

15 DR. KENNETH PORTIER: I'm not sure whether

16 this comment goes here or in one of the other questions, but I

17 think I'll put it here. I did look at the relationship between

18 the ppd and the PATI, and I guess I expected them to be pretty

19 collinear. And I found that they were, but there were a number

20 of sites that didn't quite follow the relationship, and they

21 were primarily those cosms that had really long exposures, the

22 365-day. 

23 I'm not quite sure why that worked out because

24 I'm not quite clear how the PATI for the cosms were calculated. 

25 Maybe a clarifying question, if I may? Real quick.

26 DR. DANIEL SCHLENK: You sure you don't want
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1 to add that to a later question?

2 DR. KENNETH PORTIER: No, no this is a real

3 simple question. If you have a cosm that was just dosed on day

4 one and then never dosed again, is the PATI essentially

5 one-sixtieth of the first day's PATI value?

6 DR. RUSS ERICKSON: No, PATI is cumulative. 

7 The way PATI is devised is to sort of be a relative measure of

8 the cumulative impact, and so if you have a one-day exposure, it

9 accumulates a certain amount of toxic impact according to the

10 distribution of the -- and by the way, the -- and we've done it

11 both ways, and it doesn't matter, but we don't just use those 20

12 curves. 

13 But then after that one day, that's the

14 cumulative PATI. There's no more toxic stress for that 60 days.

15 It stays at that value, so if it's 10 after that day, it stays

16 10. And in fact, that 60 days is not so much, you know, an

17 averaging or normalizing period, it's just that we don't want to

18 accumulate a cumulative effect indefinitely, and so, you stop at

19 the end of the exposure, and that's the end of the -- that's the

20 representation of the total intensity of that exposure, whether

21 it's 1 day or 10 days or 30 days or 60 days.

22 DR. LINDA YOUNG: But just to follow up, the

23 normalizing to 60 days, if you had a PATI of 2 at 30 days, and

24 the study stopped, when you normalized to 60, it would become 4.

25 Is that right?

26 DR. RUSSELL ERICKSON: No, it would stay the
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1 same. It's basically an accumulation of toxic impact. 

2 And so when you get to the -- on each day, you

3 say what's the toxic impact, you add up the next day, the next

4 day, the next day. It's an additive index. When exposure

5 stops, it stops. And like I said, the 60 days is really

6 irrelevant to anything less than 60 days. It's just that we say

7 we don't indefinitely accumulate, because then you run into the

8 situation of giving it too long of a timeframe for a cumulative

9 impact.

10 DR. DANIEL SCHLENK: That was Dr. Erickson.

11 MR. FRANK FARRUGGIA: Just to clarify, from a

12 cosm endpoint, if the study was dosed on day one, half-life

13 decay was used to construct the chemograph, and then that

14 chemograph was scored for PATI values across the duration of the

15 study.

16 DR. KENNETH PORTIER: Thank you. That was the

17 missing connection, was how did the concentration decay? So

18 where a static system, (inaudible) system, you would have to use

19 some kind of a dilutional effect over time, right?

20 DR. RUSSELL ERICKSON: Yeah, I'm sorry, I

21 didn't understand the question. 

22 When you said one day, I thought you meant the

23 exposure just lasted one day, period. Here, it's whatever the

24 exposure was, including the declining concentrations of trying

25 to basically say that what's the relative severity of that one

26 versus this one versus something else.
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1 DR. DANIEL SCHLENK: Okay. Were you done,

2 Ken?

3 MR. FRANK FARRUGGIA: One follow-up comment

4 about multiple dosing. Some cosm studies did have multiple

5 dosing, and those were also incorporated into the chemographs

6 such that if you had 20 parts per billion on day one dose, 5

7 days later, you hit it again with another 20. Decay models was

8 added and then the dose was added. Okay? Thank you.

9 DR. DANIEL SCHLENK: Okay. Any other

10 comments? Yes, Dr. McManaman.

11 DR. MCMANAMAN: So what's the evidence that

12 the -- you're assuming that the cumulative effects are based on

13 these laboratories' cultures. How do you know that the

14 cumulative effects are the same in a cosm? I mean what's the

15 evidence for that? I may have missed that.

16 DR. RUSSELL ERICKSON: Frankly, there's no

17 direct evidence, and we say that in the report, that we don't

18 have direct evidence assigning a quantitative endpoint in a cosm

19 over different durations to say that that endpoint is affecting

20 them that way. 

21 It works in, you know, single species tests,

22 basically, that that's the kind of cumulative effect we see. 

23 And so when we add up PATI or if we want to use part per billion

24 days, we basically are saying that we know that there's that

25 kind of cumulative effect on the integrative toxic stress on

26 that system, but how that relates to the cosm points, there is
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1 no direct evidence for. 

2 And in fact, we are making, like I said

3 before, we make somewhat of a compromise within the mass of the

4 cosm data to assign, you know, what the LOC would be on that. 

5 But no, there's no direct evidence of field or

6 cosm data that would basically say that for this one study with

7 the consistent endpoint, that the endpoint follows the

8 cumulative exposure in this fashion. And it is an assumption of

9 the model that the kind of cumulative effect we see in single

10 species toxicity are irrelevant to what the toxic severity of

11 exposures is in the field. 

12 DR. MCMANAMAN: Well, I would think that that

13 would be important to know in terms of trying to find the LOC,

14 to find what a real accurate LOC should be.

15 DR. RUSSELL ERICKSON: I agree, but it's --

16 basically the issue here was that we had this in the absence of

17 the resources to do those kind of studies, we dealt with the

18 existing cosm data and basically said, what could we -- what's

19 the best thing we can say about what the, again, the relative --

20 how to compare different exposures as far as toxic severity so

21 that we can use the exposures to use the cosms in total as a

22 compromise among the variability in the cosms, which are quite

23 variable to say what that LOC was. Yeah, it could be much more

24 accurate with a lot more data, but we did not have that data.

25 DR. DANIEL SCHLENK: So in lieu of time, I

26 think what we're going to do is we'll reopen this question in
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1 the morning since there seems to be a little bit more

2 discussion, and it's 6:15, so we will keep this open. Dr.

3 Matten has to clarify questions, and the Panel needs to meet for

4 about five minutes in the back room.

5 DR. SHARLENE MATTEN: Okay, I just want to see

6 if I've captured all of the clarifications that the EPA has

7 asked for. Forgive me; I have to go page by page of my notes. 

8 Topic (a), Amy asked how to use the

9 meta-analysis in a quantitative fashion -- where did she go? 

10 Correct? That was for Question 1. Okay. 

11 Question 2(a). No, that's in your notes. 

12 Okay, so Michelle is going to -- she has the details for the

13 list of criteria that are acceptable studies expected with

14 amphibians and she provided a summary of her comments related to

15 each test design element. There wasn't any further question

16 about that. 

17 MS. AMY BLANKINSHIP: Not any further question

18 about the criteria.

19 DR. SHARLENE MATTEN: Tom Steeger asked, for

20 Question 2(b). The filtering process -- well, I wrote filtering

21 process and consistency and nothing more. It was a comment

22 related to how to -- can you explain? That's why I'm asking

23 this --

24 DR. THOMAS STEEGER: Yes. I mean, the Panel

25 agreed that the criteria that had been established in the Paper

26 for moving through open literature were appropriate criteria,
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1 that they had been applied appropriately to the different

2 studies that have been evaluated in the White Paper. 

3 But now there seems to be some uncertainty

4 that maybe some of these factors should no longer be applied and

5 that we need to have clarification as to which of those factors

6 need to somehow change in terms of how we apply them to these

7 studies. Which of those factors need to be changed?

8 DR. SHARLENE MATTEN: Okay, good. All right,

9 so that was 2(b). Now, 3, we're on 3. Nothing I have on 3(a). 

10 On 3(b), Amy asked how can you use the available information? 

11 And then I wrote, what was your question. So Amy, can you

12 restate your question?

13 MS. AMY BLANKINSHIP: I think it kind of goes

14 back to - related to the way we provide quantitative or

15 threshold analysis. Please provide (inaudible). If not, the

16 question (inaudible) evidence or provides for further testing. 

17

18 Please elaborate on what kinds of tests you

19 would like on the protocols designed, especially in regards to

20 the organisms and how you might account for stresses that maybe

21 we need to evaluate.

22 DR. SHARLENE MATTEN: Number 4, Tom Steeger

23 mentioned the 2003 SAP meeting and the tiered approach; and he

24 asked the panel to address whether that approach that was

25 mentioned in 2003 has changed. Is that correct?

26 DR. THOMAS STEEGER: That's correct. There
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1 was a tiered approach, and the idea was that if the first level

2 of studies was deemed adequate, there was no need to go on to

3 further testing, which would involve field testing. 

4 It seems that the panel now seems to be

5 recommending that field testing should be brought into our

6 requirements even though we don't have the criteria where it met

7 from going forward with field testing, and that we don't have

8 the necessary studies to determine those field tests, which

9 would be the basic laboratory studies to establish this

10 cause/effect relationship.

11 DR. SHARLENE MATTEN: Okay.

12 DR. DANIEL SCHLENK: Did you guys get that?

13 DR. SHARLENE MATTEN: Question 5, Tom Steeger.

14 What design elements to consider related to 5(b). Is there

15 something more specific as related to immune function?

16 DR. THOMAS STEEGER: The panel identified that

17 there were sufficient data possibly to establish a hypothesis

18 that atrazine exposure may result in effects to immune response,

19 and it would be very helpful if the panel could define study

20 design elements that we could propose to the Registrant to

21 explore those with the understanding that right now, there are

22 no firm protocols that could establish that type of

23 relationship.

24 DR. SHARLENE MATTEN: Question 6. I believe

25 that the question was related to focusing on part two of what

26 Jim had put in his recommendation, both one and the two about
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1 possible ideal sign of a mesocosm. And the response that I

2 understood you to make, just to make sure it's not a silly

3 question, was on Appendix D. 

4 Were you going to add more? Just trying to

5 capture whether it's still an open question or not.

6 MR. JAMES FAIRCHILD: In regards to the

7 question on what are the factors or further requirements of a

8 valid mesocosm study, I had stated that both the Agency and

9 Syngenta had provided separate lists. But I will provide a

10 document tomorrow summarizing those.

11 MS. ANITA PEASE: Can I just clarify, I was

12 also asking about, in terms of a sifting exercise, the relevancy

13 of which studies were not relevant, in terms of duration, how

14 long of a duration is too long, environmental development

15 contraction is too high, those factors as well.

16 MR. FRANK FARRUGGIA: This is Frank Farruggia.

17 There was also a question about recommendation for specific

18 high-quality sites for reference, as well as what are negligible

19 anthropogenic effect. And kind of clarify, specify some of

20 those criteria.

21 DR. RUSSELL ERICKSON: And this is Russ

22 Erickson. I also asked about the (inaudible) issue.

23 DR. DANIEL SCHLENK: Question 7 or 6?

24 DR. RUSSELL ERICKSON: That was Question 6. 

25

26 DR. SHARLENE MATTEN: Six. Okay. The panel
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1 understands everything that they've asked, or more or less. And

2 if you don't, well, they'll ask for more clarification tomorrow.

3

4 All right. So with that, we're ending the

5 meeting for today, and I'll start again at 9:00. Good bye. 

6 (Whereupon, the meeting was adjourned for the

7 day.)

8 * * * * *

9 DR. SHARLENE MATTEN: Good morning,

10 everyone. My name is Sharlene Matten. I'm the Designated

11 Federal Official for this FIFRA Scientific Advisory Panel. This

12 is the third and final day of our meeting to examine the

13 scientific issues associated with problem formulation for the

14 reassessment of ecological risks from the use of atrazine.

15 In my role, I am the liaison between the

16 federal agency, the federal part of our office that has brought

17 issues before the FIFRA Scientific Advisory Panel on pesticide

18 and pesticide-related issues. I am sure that all the provisions

19 of the Federal Advisory Committee Act are met. A report will be

20 provided within 90 days following this meeting.

21 All the materials that you have seen during

22 the meeting or heard about during the meeting will be provided

23 to the public docket. The docket can be accessed electronically

24 by going to www.regulations.gov and inserting the docket number,

25 EPA-HQ-OPP-2012-0230, and the number is on the agenda.

26 We've concluded the public comment period. 
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1 I'm still in the process of loading those comments into the

2 docket to make them available early next week. And with that, I

3 thank you and I'll turn that microphone over to Dr. Dan Schlenk,

4 to my left, Chair of the FIFRA Scientific Advisory Panel.

5 DR. DANIEL SCHLENK: Thank you, Dr. Matten. 

6 Again, just briefly for the public record, let's go around the

7 room and just state your name and your affiliations, and we'll

8 get started from that. My name's Dan Schlenk, and I'm a

9 professor at the University of California, Riverside.

10 DR. JAMES MCMANAMAN: Jim McManaman, professor

11 at University of Colorado.

12 DR. STEPHEN KLAINE: Steve Klaine, Clemson

13 University.

14 DR. CATHERINE PROPPER: Catherine Propper,

15 Northern Arizona University.

16 DR. THOMAS LA POINT: Tom La Point, professor

17 in biology, University of North Texas.

18 DR. MICHELLE BOONE: Michelle Boone, Miami

19 University.

20 MR. JAMES FAIRCHILD: Jim Fairchild,

21 Ecologist, U.S. Geological Survey in Columbia, Missouri.

22 DR. ANNE FETSCHER: Anne Fetscher, biologist,

23 Southern California Coastal Water Research Project.

24 DR. JOHN RODGERS: John Rodgers, professor at

25 Clemson University.

26 DR. MICHAEL TWISS: Michael Twiss, Clarkson
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1 University, New York.

2 DR. BERNARD ENGEL: Bernie Engel, Purdue

3 University.

4 DR. WILLIAM EFFLAND: Bill Effland, USDA-NRCS.

5 DR. TIMOTHY ELLSWORTH: Tim Ellsworth,

6 University of Illinois.

7 MR. ROBERT GILLIOM: Bob Gilliom, U.S.

8 Geological Survey.

9 DR. LINDA YOUNG: Linda Young, University of

10 Florida.

11 DR. KENNETH PORTIER: Ken Portier, American

12 Cancer Society.

13 DR. DANIEL SCHLENK: And I think on the phone,

14 we've got Martha. Are you on there?

15 DR. MARTHA SANDY: Yes, good morning. Martha

16 Sandy, California Environmental Protection Agency, Office of

17 Environmental Health Hazard Assessment.

18 DR. DANIEL SCHLENK: Thanks, Martha. So to

19 finish up, we left off yesterday with question 7. But before we

20 get going to that, a couple questions from the Panel. I've

21 allowed the Agency to put a few more -- a little bit more

22 information that's going to be posted up here on a couple of

23 slides.

24 So I believe the first slide is additional

25 information, and who is this for.

26 DANA SPATZ: In my remarks on Tuesday, I
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1 mentioned that we had a team of EPA scientists who evaluate each

2 of the cosm studies, and while we didn't ask questions on

3 individual cosm studies, discussions yesterday brought up

4 individual cosm studies.

5 I wanted to make everyone aware that our

6 evaluations of each one of those cosm studies has been available

7 in a different docket, the atrazine docket, and you can reach

8 those individual cosm studies by going to www.regulations.gov. 

9 And then the docket number is on the slide. I think we've

10 handed out the information to each member of the Panel.

11 MALE SPEAKER: Okay. Thanks. And I believe

12 in response to Dr. McManaman's question, Dr. Erickson has other

13 information.

14 DR. RUSSELL ERICKSON: Dr. McManaman asked

15 about any evidence or justification for how we treat cumulative

16 events. I thought I was rather incomplete in my answer and like

17 to expand on it. To use this cosm slide as a backdrop for the

18 answer, this is the cosm scattered plot that you have seen.

19 And as was shown yesterday and as was

20 evaluated back at the 2003 IRED, direct evidence from this data

21 doesn't really speak to the issue of time dependence and, in

22 fact, you might infer that there's just a flat relationship

23 here, as Dr. Giddings suggested yesterday.

24 If we stop there, this is the whole reason for

25 our methodology; and if we stop there and accepted a flat

26 relationship and the fact that even at the shortest exposures,
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1 we don't want to allow whether it's 15 or 20 or 30, we felt that

2 was unsatisfactory; that we had to consider that even if an

3 effect lasts for a short time versus a long time, we have to

4 take the long time more seriously and that effects would be

5 expected to increase with time if we had information on the same

6 endpoint in the same system at different durations.

7 And unfortunately, this data set does not have

8 that kind of information to make that inference. And so what

9 we're actually doing is using our effects models to impose a

10 relationship on the data such as this. Now this is only

11 approximate, and also it doesn't take into consideration that

12 these average concentrations are actually time-varying

13 concentrations.

14 And so it's only an accrued rough depiction of

15 the issue. But what our process does is basically impose a

16 relationship based in part on what we know happens in toxicity

17 tests when you do track the system at points through time and

18 also based on this general argument that we need to provide

19 different timescales of effects.

20 And this line that I show here is basically

21 the same, whether we use CASM, PATI, or parts per billion days. 

22 There's minor changes that you get much the same assumptions in

23 any of those about the human (inaudible). But the bottom line,

24 in going back to Dr. McManaman's question is we don't -- in

25 actual ecosystems, we don't have direct evidence for this kind

26 of time dependence. We have to go indirectly based on general
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1 common sense, theoretical arguments, or a judgment that effects

2 lasting a short period of time should be treated different than

3 a long period of time.

4 Or go back to the CASM model and say if we

5 look at some system dynamics using the CASM model, this is the

6 kind of relationship we expect. And we basically impose that on

7 the data, but use the cosm data then to say where that line is

8 going to fall in terms of concentrations.

9 I hope that makes things clearer.

10 DR. JAMES MCMANAMAN: Can you ask to do that?

11 DR. RUSSELL ERICKSON: I would certainly like

12 to. I mean, there are no plans. In Dr. Fairchild's suggestion

13 yesterday, or various Panel suggestion, that more definitive

14 cosm studies are desirable. I certainly agree with that, and I

15 was thinking at the time, even before your question came up,

16 yeah, but when we did that, it would be nice to, you know, have

17 different exposure periods.

18 We're talking about different exposure time

19 series, but just different exposure periods. And looking at

20 effects, consistently looking at effects in the same time

21 window, but with different exposure scenarios. That would

22 certainly be what I would urge for if those kind of tests were

23 wrong.

24 But whether they can be or not, there are no

25 plans for them and it remains to be seen.

26 DR. DANIEL SCHLENK: Yes, Dr. Effland.
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1 DR. WILLIAM EFFLAND: With this particular

2 curve that you fit, I'm not a cosm expert, I think our cosm

3 experts are over there, but I wonder if -- I heard yesterday,

4 long duration studies, and I think those are the ones on the

5 right; and then the high concentration studies, like the one

6 down there, that's at 10,000 micrograms per liter.

7 If those data points are not in that, does

8 that change that relationship any, or is it --

9 DR. RUSSELL ERICKSON: No, the relationship

10 change is very little. I mean, there will be some minor

11 changes. But if you recall, the LOC is based on that binary

12 relationship between the effects model and the effects

13 determination.

14 Well, if you have effects that are way off on

15 the -- at high concentrations to the right of the curve on the

16 upper arm, deleting a few of those or changing a few of those,

17 doesn't change it. Likewise, if you have no effects at the very

18 bottom of the curve, it has very little effects. What changes

19 things are what's in the middle as far as the determination.

20 So I would say that, you know, I would be

21 sympathetic to the arguments about, you know, extreme durations,

22 extreme concentrations not being part of the set. But it would

23 have minor effects on the bottom line.

24 DR. WILLIAM EFFLAND: That helps, and I

25 actually -- I have to read into the record. Some thoughts I

26 have on Question 6, which I think ties in with this. 
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1 DR. DANIEL SCHLENK: Actually, we're going to

2 be making the rounds again.

3 DR. WILLIAM EFFLAND: Are we going to touch

4 that? Okay.

5 DR. DANIEL SCHLENK: Well, we're going to do

6 every question. We're going to finish 7 and then we're going to

7 go through all the -- the agency is going to make their

8 questions of clarification again into the record. And then

9 everybody's going to respond to that, so you'll have your chance

10 when we come back. We will do that again.

11 DR. TIMOTHY ELLSWORTH: Just a quick question.

12 Actually, you want to go back to that slide that would be very

13 helpful. 

14 So from looking at that, I would kind of think

15 that just one 60-day dosage, if you will, isn't really telling. 

16 But you could have a 20-day or a 10-day exposure that might not

17 show up in your 60-day measure but would be an adverse effect

18 according to what I saw there. Is that true? In other words, a

19 20-day or a 20-ppb exposure for 20 days looks like it would

20 cause an adverse effect, from what you just showed; whereas if

21 that ended at 20 days and you averaged over a 60-day metric,

22 that might not show up as an effect. Is that true or no?

23 DR. RUSSELL ERICKSON: No, that's not true

24 because the way we treat that is that the effect is the effect. 

25 That cosm is considered the positive effect in the analysis, and

26 we're not averaging out the 20 days or the 60 days. And in some
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1 ways the 60 days should not be treated as a normalization

2 period, an averaging period, anything like that.

3 The measure we're applying, which applies to

4 the part per billion days, too, is that it's a cumulative

5 measure of exposure of effect. And what we're saying is that

6 the inclusion of that 20-day cosm study in that set means that

7 the effect is important to us regardless of the duration and

8 will be considered an important effect.

9 And what caused that effect was a cumulative

10 exposure over those 20 days, and then we're done with it. 

11 That's part of the data set. And the 60-day period is kind of

12 irrelevant to that 20-day period because there's no more

13 exposure. The 60-day period is only an upper limit on how long

14 we're willing to accumulate part per billion days, or PATI or

15 whatever.

16 Have I answered your question? I guess

17 there's a problem if we view that 60 days in terms of, you know,

18 normalization or averaging. It's more a risk assessment

19 judgment that we don't want to accumulate adverse effects more

20 than 60 days because if it was indefinite then very minor

21 effects stretched over multiple years would be considered; it

22 would add up to an adverse effect.

23 And that effect would be able to stop is at

24 where we're past the cosm data that we want to discriminate, so

25 that'll be really included in the exposures in that middle part

26 of the cosms.
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1 But the fact that 20 days is shorter than 60

2 days is really not important because we're still saying that

3 that's the significant effect. Regardless of whether you look

4 at it in terms of 60 days or 90 days or the whole year, that

5 effect is an effect and we're saying that it occurred because of

6 a 20-day exposure. And this effect -- at a certain

7 concentration -- this 40-day effect occurred because of 40 days

8 exposure.

9 And to take that information to say what

10 cumulative exposure does this data, as a whole, say is of

11 concern? 

12 DR. TIMOTHY ELLSWORTH: Yeah. Where I'm

13 coming at from this is with the experience in modeling

14 atmospheric transport in able hazard areas, if you will, for a

15 chemical exposure. And what you do there is you have -- you

16 know, you have a spatial map of the hazard area that is defined

17 by -- If I'm a mannequin standing there breathing in air and I

18 do it for 10 minutes, and I see this safe exposure level, that

19 area is flagged as a hazard area.

20 If I'm standing somewhere else and it's a much

21 lower dose, but it's over a longer period, I have reached an

22 adverse effect level at that point; and, you know, the model

23 actually looks at, "Did I violate the 10-minute period? Did I

24 violate a 30? Did I violate an 8-hour? 24-hour?"

25 It's not just this one period. It's really

26 looking at, "Did I get an adverse effect whether it was over a
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1 10-minute window or whatever?" And I'm confused here in kind of

2 just using this one 60-day with one rolling average because it

3 seems like we could have a shorter duration, and one could have

4 an adverse effect, and you can actually miss those metrics if

5 all you're looking at is, you know, a 60-day one. 

6 DR. RUSSELL ERICKSON: Yeah. I guess the

7 problem is we're talking about two different issues here. First

8 of all, how do you evaluate the effects data in terms of what to

9 say what the effect of concern is. I think the parallel with

10 your example would be to say that if we were in a situation

11 where the -- where you say like, if you breathe a high

12 concentration for 10 minutes, it's as bad as a lower

13 concentration or for a longer period of time.

14 And I think like, the curve up there kind of

15 agrees with that kind of perspective. But what we're doing here

16 is that because we translate to a cumulative scale, we can deal

17 with just one number. And so parallel would be if you're

18 inhalation data was like if you picked 10 minutes at 100 part

19 per million were as bad as 100 minutes at 10 part per million.

20 And that's the effect relationship we're

21 imposing on here. So we do end up with a single cumulative

22 effect metric for that way. But then there's another issue here

23 in that it's that there's a 60-day window here for that

24 cumulative effect.

25 And as Syngenta showed yesterday, it really

26 doesn't matter much whether that's 60 or 90 or 20 day as far as
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1 what the LOC is. Because you're past, you know, most of the

2 cosms so that, you know, if you go longer than 60 days, it

3 hasn't post out as it should.

4 But then there's another issue here in that

5 translation to the CE-LOC, and I think that's where your

6 question might more relate. I'll turn it over to Dr. Farruggia

7 to kind of to address that.

8 DR. FRANK FARRUGGIA: So in the white paper

9 and in my presentation, I went through that sensitivity analysis

10 and looking at different periods of time and what that CE-LOC

11 is. And that's this line here -- this curved line --

12 approximates those numbers.

13 And essentially, we gave an example of 20 days

14 and 20 parts per billion, but we didn't pass the 20-day period,

15 but we did look at 30-day and 14-day periods. The CE-LOC at 14

16 days was 14.90, and the CE-LOC at 30 days was 8.30. What that

17 says is at 30 days at 8.30 μg/L, there's a 50 percent

18 probability of there being adverse effects. So if we went and

19 ran a cosm experiment, we would have a 50 percent probability of

20 finding an adverse effect at that level for that duration, okay?

21 That's based on the data that's -- that is

22 available currently. And I think that's kind of coming back to

23 what you're talking about. So our predictive measure with that

24 CE-LOC, you could use it in PATI values and get to the same

25 thing where if we look at a concentration in duration of 20 μg/L

26 over 20 days, that's going to accumulate to a PATI value and
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1 we're going to compare that to the LOC PATI.

2 Okay. So the conversion to the CE-LOC is

3 really irrelevant. We can do it with PATI values; we can do it

4 with concentrations.

5 DR. RUSSELL ERICKSON: If I might just add,

6 again I think one of your concerns was -- and you spoke to this

7 previously -- was that why not multiple concentrations at

8 different averaging periods? And the bottom line is, is that

9 the way the CE-LOC came about -- well, first of all, as I said,

10 as far as directly doing things with the effects in this, that's

11 unnecessary because it's a cumulative index that that is a

12 necessary for.

13 For CE-LOC, I think, something else is as Dr.

14 Farruggia said or indicated. If we have a 60-day period any

15 shorter periods that we do become redundant.

16 DR. DANIEL SCHLENK: Okay. So I think that's

17 good. Yeah? So let's go ahead and finish up Question 7. I

18 believe Dr. Portier made that initial request, so we're going to

19 go to him and have him return to his comments.

20 DR. KENNETH PORTIER: When we left it

21 yesterday, I kind of go through this bi-assay view of this whole

22 process. And the point I wanted to make is just kind of go

23 through and make sure we understand where the uncertainties are

24 in this process and what's impacted because it's kind of a long,

25 multistep process and there is a lot of decisions that have to

26 be made. And I was trying to figure out where certain decisions
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1 -- where that process is very sensitive to decisions that are

2 made.

3 So we've talked about where we look at the

4 effect/no effect response in the cosms, we've seen that there's

5 uncertainty in assigning the response and the choice of cosms to

6 use in the subsequent analysis. So there's a need at that point

7 to be very clear what's the protocol for inclusion and exclusion

8 of cosms and what's the quality protocol that decides whether

9 that cosm is good or bad for this process.

10 You know, Syngenta had their view; you had

11 your view; some of the Panel had other views. And that just

12 tells me that that's not tied down at this point, so that's kind

13 of the comment there.

14 By estimation where we use the logistic of

15 PROBIT analysis to estimate the 50th percentile that's used as

16 basically defining the LOC, there's uncertainty from the PROBIT

17 analysis in that estimate that's not really been taken into

18 account. And I think I heard before we started this morning,

19 Dr. Erickson saying that there's quite a bit of variability in

20 that estimate. So that needs to be at least addressed in the

21 discussion.

22 And we've heard that there's sensitivity at

23 this calibrations that begin to which cosms are used in the

24 regression. So we're back to the issue of really being able to

25 tie down which cosms that we want to use. And the scoring of

26 those cosms is very important. We've seen -- you know, some
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1 people have argued that these ones should be zeros, and then

2 we've wondered if some of these zeros might be ones, and then

3 that leaves me uncertain at that step.

4 But the point is the PROBIT analysis is kind

5 of impacted by those decisions that lie and moves back and

6 forth. One cosm in the wrong place can shift this thing quite a

7 bit, and that's the sensitivity issue. So there's a stent where

8 we compute the PATI for each watershed using the available

9 chemograph data and using the maximum 90-day average

10 concentration.

11 So there's an issue there of what time periods

12 we use. Now we use standardized on the 180-day period in the

13 second and third quarters, right? And then the question that

14 came to my mind and something that was discussed in the 2011

15 SAP, that I didn't see in the white paper, was whether that time

16 period works with all geographic ranges, right? I mean, is

17 Atrazine applied later further north than the earlier further

18 south? I think you have it covered. I just think in the white

19 paper, you need to reiterate that that -- you know, that you've

20 really covered that time period.

21 I don't think anything else is affected. So

22 now we compare the watershed PATI to the LOC to compute the EFF,

23 right? So again, now we're seeing which watersheds are we going

24 to use because that step is really going to be used to compute

25 the CE-LOC, right?

26 So which watersheds are going to go into that
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1 computation? And again, looking back on the 2011 SAP, there was

2 discussion that, you know, some community water systems were

3 very variable and there was a lot of discussion about watershed

4 properties that we don't see here. Again, that didn't kind of

5 get carried over into this discussion.

6 It's like, all the AEEMP kind of went in there

7 and life's good. I don't want to lose that discussion. So

8 there may be some effect of the biases that are inherent in the

9 AEEMP set that affects the CE-LOC. And this is where I really

10 kind of lose it because I'm not quite sure how those biases work

11 out because that CE-LOC becomes important in the next step when

12 you apply the whole process to all the watersheds, right?

13 So the CE-LOC is not a good kind of

14 representation of all watersheds. There's going to be some bias

15 in there.

16 And then the final thing is the discussion

17 we're going to have in subsequent questions, which is the use of

18 the bias factors. And it's not clear in here whether the bias

19 factors are applied before or after the CE-LOC is computed. I

20 mean, it clearly -- the bias factors are going to reshuffle the

21 PATI values for low sample of sites, right? Because you're

22 going to apply a higher bias factor to the lower sample of

23 sites, and it's going to shift the 90-day average concentration,

24 which should increase, for those sites, the PATI value, right?

25 So there's going to be some reshuffling of the

26 sites kind of before and after bias factors are concerned, and I
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1 wasn't quite sure how that gets kind of translated to and

2 impacts the CE-LOC. It probably shouldn't. It just impacts

3 which sites are where.

4 So I guess that's kind of where I'll leave it,

5 and that is in my notes.

6 DR. DANIEL SCHLENK: Okay. Thank you. Dr.

7 Young.

8 DR. LINDA YOUNG: All right. I worked on this

9 last night, and I talked to Russ this morning and we're getting

10 close on having the same answers. 

11 So on the logistic analysis -- and I think I

12 would encourage the Agency to carefully look at fitting the

13 model because although it was said that the logistic model was

14 used, there's also mention that the normal distribution, which

15 has no role in the logistic model.

16 So their result would be very similar, but if

17 we're mixing the methods, then that could be problematic. And

18 in particular with the logistic model, you'd use the it for

19 (inaudible) response and actually take advantage of that

20 distribution and fitting the model; and the normal would have no

21 role. Whereas with the program model it does.

22 Regardless of the model used, and I think

23 either would be fine, the one presented in Appendix C would

24 naturally lead to the logistic model. I think it's important to

25 look at standard methods of assessing the model, in particular,

26 the influence of the different points on the estimated
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1 parameters. There were three that I found that had quite an

2 impact and were quite influential in the estimate of the model

3 parameters.

4 Now it may be that you want to go ahead and

5 leave those in, but if those are three that there is this debate

6 over whether they should be included or not, and they're having

7 a major impact on the LOC, then you need to think about that

8 carefully and whether to include them or not.

9 An effort was made to adjust for a 5 percent

10 significance level, and this again is on page 23 of Appendix E. 

11 That's highly unusual, but I think it was motivated to shift the

12 LOC up just a bit, but because it's significance testing. 

13 However, several of us were discussing in these cosm studies

14 were basically given Brock scores. The Brock scores were used

15 to determine effect/no effect, and it's not evident that

16 hypothesis tests were conducted to determine the Brock Score.

17 Therefore, I would recommend that you not try

18 to do that kind of adjustment; that you simply pick either the

19 logistic or the program model and report with that. In

20 addition, the model seems unduly complex in that there's so many

21 steps. And because it -- I talked to Dr. Erickson some this

22 morning and the uncertainty associated with the LOC is a primary

23 source of variation there.

24 And so ignoring some of the other error that

25 is present in the process doesn't really matter too much right

26 now. But if there's clarification on effect versus no effect,
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1 it could be that these other sources of variation become more

2 dominant. And I would encourage, if at all possible, to move to

3 skip some steps -- maybe using PPB instead of PATI and

4 consolidate it. And if that's not possible for any reason, then

5 to give careful consideration to error propagation in the whole

6 process.

7 DR. DANIEL SCHLENK: Thank you. Any other

8 comments on Question 7? Oh, yes, Dr. Young.

9 DR. LINDA YOUNG: I did say yesterday that

10 there were no standard errors and there were, so I apologize.

11 DR. DANIEL SCHLENK: Okay. So with that,

12 we'll turn it over to the Agency. Are you clear in terms of the

13 Panel's response? Any questions for clarification?

14 DR. FRANK FARRUGGIA: Frank Farruggia. I have

15 one major clarification for Dr. Portier. The AEEMP data was

16 used to establish the CE-LOC; that's 4 day or better sampling. 

17 So 4-day interval or better. The bias factor adjustments does

18 not apply prior to setting CE-LOC. So we're assuming that those

19 concentrations are good for setting the CE-LOC.

20 Bias factors are only applied to the

21 monitoring data after the CE-LOC's is established. That means

22 when we're looking at monitoring data from North Carolina, we

23 can apply -- we can look at frequency sampling and apply this

24 factor of uncertainty to that data based on that frequency of

25 sample.

26 And Dr. Hetrick -- we'll get into that
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1 conversation later in part C's discussions. My question after

2 this clarification is that does the Panel have a clearer

3 understanding of the methodology going from cosm classification,

4 fitting or making a relationship either with PATI or ppb-days

5 between the cosm exposure chemographs and the exposure

6 chemographs that are in the field in order to better understand

7 and calibrate that CE-LOC?

8 The LOC is just based on effects/no effects

9 from the cosm studies. So ppb-days or PATI will result in a

10 very similar index-based LOC, okay? So that calibration to

11 concentration is -- that's when the AEEMP site data comes in. 

12 That's when those field chemographs come in for helping to

13 calibrate what the concentration or what that index-based LOC

14 really means with few exposure data.

15 And so I just wanted to know if the Panel

16 understands this and have processed that.

17 DR. DANIEL SCHLENK: Anybody want to address

18 that, or do you think it's already addressed in terms of your

19 comments.

20 DR. THOMAS LA POINT: Thanks for that. That's

21 good. And I think, in my opinion, I think I have seen that the

22 LOC is based on -- in the critical discussion -- on where the

23 effects are. And I mean, that's a good thing when there's

24 strong effects and it's known. And like Dr. Erickson mentioned

25 this morning even, that in the middle range, that becomes more

26 critical in terms of whether it's affected.
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1 And so that gets into the discussion and I'm

2 much more comfortable with the idea, as Dr. Erickson mentioned,

3 it's a work-in-progress in evaluating some of those key studies

4 that are right there in the middle because that's what it's

5 going to come to for this, a lot of it.

6 And if that's to be used then it's kind of a

7 basis for determining the LOC, and I think that even puts more

8 emphasis on making sure (inaudible). That's all.

9 DR. FRANK FARRUGGIA: One follow-up to that:

10 so the Agency has thoroughly reviewed these through a team of

11 scientists who looked at each of these cosms and their endpoints

12 and made classifications decisions that we provided in Appendix

13 E as well as in the additional materials that'll sort out what

14 endpoint goes with what study, etcetera, and how we came to

15 that.

16 The DERs are available on the atrazine

17 registration docket that we now have information for it. You

18 have Syngenta's classification scheme and their conclusions

19 based on their studies. What do you see as the next step for

20 this data review? I know that Mr. Fairchild had recommended

21 sitting down with Syngenta and going through these endpoints bit

22 by bit and, you know, we have been doing this over the process

23 of the last year or so, having meetings and discussing these

24 endpoints. So what do you see as the next step for the cosm

25 reviews to really nail down the effects/no effects

26 classifications?
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1 DR. THOMAS LA POINT: That's a fair question,

2 and I think that's what we're working on, and the question is

3 understood.

4 DR. DANIEL SCHLENK: Thanks. All right. Bob

5 Gilliom.

6 DR. ROBERT GILLIOM: So just briefly, I wanted

7 to reiterate that I think I now understand and I think most

8 people on the Panel are saying they understand. I think it

9 would be a good exercise for the team to try to do a facts sheet

10 on one page that makes this clear to a wide range of audiences,

11 would be a good exercise just to go through because it's very

12 difficult. It's difficult to pick up. Okay? And there's a

13 number of policy interactions and decisions with the data, just

14 that really have never been done in the white papers or anything

15 I've seen.

16 DR. DANIEL SCHLENK: Okay. So one sentence.

17 DR. RUSSELL ERICKSON: Is this where I can

18 asked for, you know, whether information is adequate for the

19 question?

20 DR. DANIEL SCHLENK: Yeah, one question. 

21 Yeah, clarification question.

22 DR. RUSSELL ERICKSON: And this is just to Dr.

23 Young and Dr. Portier. I fully appreciate that we have four

24 stages in this process that each have their own certainty. And

25 my request is that when -- well, it's two-fold. One is that Dr.

26 Young mentioned perhaps simplifying the parts per billion days
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1 so that the uncertainty of PATI is not part of that.

2 But then there's also the model uncertainty in

3 parts per billion days, and I'd like you to comment on how

4 whether we're masking uncertainty there rather than actually

5 reducing it.

6 And then secondly, when it comes to the LOC

7 methodology, the standard errors are based on the (inaudible)

8 model is correct, and I think some of your comments reflected on

9 maybe doing a bootstrapping type evaluation of uncertainty

10 there.

11 And I guess I just request, you know, let that

12 be, you know, clearly identified.

13 DR. DANIEL SCHLENK: Okay. Thank you.

14 DR. LINDA YOUNG: This is just to clarify that

15 I do think bootstrapping should be part of it if that will be

16 part of the model.

17 DR. DANIEL SCHLENK: Okay. So Question 7,

18 we're done, okay? Right? All right. So what we're going to

19 do, based upon the comments of yesterday, is we're going to go

20 through everything we've gone through already up to Charge

21 Question 7. In two sentences or less, we're going to let the

22 Agency ask for a clarification question, okay? So that we are

23 specific on the record what those questions of clarification

24 are.

25 Then we're going to go to the lead discussant

26 and they're going to respond whether or not that's been
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1 accepted, we understand, we're going to be doing that or if

2 anything new needs to be added to the record so that the written

3 document doesn't have something that hasn't been discussed in

4 the whole Panel. Is that clear? Is everybody clear on that? 

5 DR. STEVEN BRADBURY: I think we worked out

6 the questions yesterday, so if the Panel just wants to go

7 through the answers to the questions I think we're good.

8 DR. DANIEL SCHLENK: Well, I just want to make

9 sure they know what the question is. Yeah. Do you guys want to

10 do that? I just want to make sure you know what the question of

11 clarification is.

12 DR. STEVEN BRADBURY: Sharlene did a very nice

13 job of it.

14 DR. DANIEL SCHLENK: All right. I just want

15 to make sure everything's clear. Okay. We had a question on

16 Question 1. Is there anything that needs to be added to the

17 record for Question 1?

18 DR. CATHERINE PROPPER: I would like to hear

19 Question 1 one more time.

20 DR. DANIEL SCHLENK: That's what I thought. 

21 We had a response on Question 1, the question of clarification. 

22 And please, one or two sentences so we can get moving.

23 MR. THOMAS STEEGER: The question that was put

24 to the Panel is looking at the fundamental relationship between

25 atrazine and amphibians. We realize there are a number of

26 factors that can influence that relationship. The
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1 classification of those studies in terms of how they provided us

2 information on the relationship just between atrazine and its

3 amphibian receptors, were those criteria appropriate, and were

4 they appropriately applied such that the classifications were

5 appropriate for withstanding a pure relationship between

6 atrazine and (inaudible) that we learned about confounding

7 effects of effects.

8 DR. DANIEL SCHLENK: Okay. Do we need to

9 change or add anything based on that question?

10 DR. CATHERINE PROPPER: This almost seems more

11 like a question appropriate to Question 2 in our Charge

12 Questions, which I do believe we addressed with some detail

13 yesterday.

14 DR. DANIEL SCHLENK: Okay. Great. Yes,

15 Question 1 was a different question. MS. Blankinship you had

16 meta analysis?

17 MS. AMY BLANKINSHIP: I believe some of the

18 questions started to go into another. I don't think Question 1

19 we had a lot of comment on in terms of I think within the terms

20 of asking for other tasks. That's where I know you had listed

21 other information that we can maybe then look at. And the

22 answer to that is through the registration review process we

23 review all available studies, so as you mentioned, would be

24 included in terms of that review, at least for consideration.

25 DR. CATHERINE PROPPER: I do believe the

26 question with regards to Question 1 was clarification on how to
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1 evaluate the meta analysis data?

2 DR. AMY BLANKINSHIP: And I think that what

3 happened was Question 1, 2, and 3 started to get pretty muddled

4 pretty quickly. And so there's a little bit of overlap with

5 what a clarifying would be. But we would like to have some

6 clarification in terms of how to quantify meta analysis and how

7 to use that.

8 DR. DANIEL SCHLENK: That's exactly. Okay. 

9 Great. And you've already addressed that in your spoken

10 comments, right? And you will address that in your written

11 comments, is that right?

12 DR. CATHERINE PROPPER: We have discussed it

13 among ourselves and we will continue to work towards adding that

14 into our written comments.

15 DR. DANIEL SCHLENK: Will there be anything

16 new that is in the written comments that we haven't discussed on

17 the Panel?

18 DR. CATHERINE PROPPER: I don't believe so.

19 DR. DANIEL SCHLENK: Okay. That's great. So

20 we've addressed 1 and 2(b), and we also have Question 3. Some

21 questions on 3(b). What was the question of clarification on

22 3(b)? Is that the meta analysis?

23 DR. AMY BLANKINSHIP: Well, I believe it was

24 in terms of what information did the wealth of certain

25 qualitative studies provide us? Was it in terms of providing

26 meta analysis, in terms of quantitative definitive answers, or
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1 was it more to support formation of even more testing. And if

2 so, provide details on that testing.

3 DR. DANIEL SCHLENK: Okay. Tom, did you

4 address that yesterday, or is there anything additional that

5 needs to be added to address that question of clarification?

6 DR. THOMAS LA POINT: We did not address that.

7 DR. DANIEL SCHLENK: Do you have the

8 information provided that you can address that today?

9 DR. CATHERINE PROPPER: I understand your

10 question. It's do we believe there's enough information to

11 justify determination of whether or not more test was needed?

12 MS. AMY BLANKINSHIP: What I sort of

13 understood from the Panel yesterday in the discussions that were

14 there multiple sort of lines of path forward. Some of them were

15 discussions in terms of the weight of evidence using the

16 Syngenta as a product in terms of how we view that information

17 quantitatively and with the definitive DCI study to go forward

18 in having affinitive no act value.

19 Or the other fact was the information is maybe

20 there that supports the further testing, maybe including such

21 things as native species or under natural conditions and if so,

22 how do we go about that fact?

23 DR. THOMAS LA POINT: Okay. Thanks for that. 

24 So that's important because the hesitancy that you're hearing

25 from us deals with the fact that no one had any questions about

26 the value of the David Cullen studies, okay, and the
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1 publications for that. I mean, they're high values; they're

2 good studies, they follow QA/QC and they provided really good

3 information on zero to 100 no effect levels.

4 The difficulty is that what we come to where

5 we are right now with the review and in the white paper is that

6 a lot of the evidence is based on this study. You know, and

7 then we've come to it with one of those two parallel studies

8 conducted.

9 And we did talk about it yesterday so it is in

10 the record. About the value because you asked about including

11 -- I don't want to mean this in a light way for public relations

12 purposes, but it's hard to base the atrazine standard on two

13 very well-done tests, right? I mean, that's it. It's just a

14 perception, perhaps, that we should have the test on Rana or

15 maybe hilA, some species of those.

16 It brings us back to a second genus at least

17 that comes to. I think that's the hesitancy, and we did talk

18 about that, but it's hard to come up with a -- as I understand,

19 I mean, here's what we're working with. But like I said, I

20 don't know that there's a protocol for that, except that I don't

21 think there is.

22 DR. DANIEL SCHLENK: Okay. Dr. Propper.

23 DR. CATHERINE PROPPER: This is Cathy Propper,

24 and I think some more of the response to this question for 3

25 will come up in our response to Question 4 for your question,

26 Question 4.
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1 DR. DANIEL SCHLENK: Okay. With that, let's

2 transition to Question 4, and then ask specifically to the

3 question of clarification for Question 4.

4 MS. AMY BLANKINSHIP: So the question is then

5 obviously if there is some uncertainty in protocol, we did ask

6 then for the Panel to provide some very clear -- or assistance

7 testing on elements, very much what was done in the previous one

8 with Xenopus.

9 DR. CATHERINE PROPPER: And that was the way I

10 had the question worded from yesterday in my notes was: should

11 the tiered approach from the 2003 SAP be the band leader? So

12 that's a little bit different from what you're asking, but my

13 understanding -- so I went back with 2003 SAP, and it is an

14 interesting read. And they do specifically recommend not just

15 testing in Xenopus, but testing in the native America's species.

16 And they suggest Rana amphibians, and they do

17 suggest husbandry protocols that can be used or that have been

18 developed. Furthermore, they also suggest that a grow-out phase

19 is started immediately so that they can evaluate the fitness

20 endpoints that I suggested yesterday.

21 And then last, they suggest that controlled

22 field studies be started up. And again, none of those things

23 have happened. So while that's from that tiered approach, I

24 would say -- they didn't call it tiered approach because I first

25 went searching through to see the tiered approach. They called

26 it a phased approach, and they actually suggested that some of
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1 the phases -- in fact, Phase 5 was the grow out -- be begun

2 immediately in Xenopus to start evaluating those fitness

3 endpoints as well.

4 So I'm a little confused as to what would be

5 abandoned or why haven't it started?

6 DR. DANIEL SCHLENK: So do you need a question

7 of clarification from the Agency then in that regard?

8 DR. CATHERINE PROPPER: No, I think I could

9 say go back to the 2003 SAP. They had some suggestions. For

10 husbandry techniques, there's a number of amphibian biologists,

11 including those in the room who have done some husbandry with

12 these and I can suggest other people, including Dr. Kloas who

13 has outstanding experience in husbandry with these animals.

14 It shouldn't be that hard to follow the

15 techniques.

16 DR. DANIEL SCHLENK: Okay. So let's move on

17 to Question 5(b) then.

18 DR. BRADBURY: I appreciate the last comment,

19 at least with the 2007 SAP, at that point in time, nobody knew

20 of techniques to raise enough animals to do the dose response

21 study required to look for low dose effects and non-modifying

22 dose response, which was part of the basis for that design.

23 And part of the rationale was Xenopus were

24 there only species for which we'd get enough in to do that. So

25 to an extent, protocol opportunities have changed to get that. 

26 So that's just - you don't have to - just do the report and
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1 that'll be great.

2 And assuming that's still the challenge, I

3 thought Dr. Portier brought up a good point yesterday when we

4 went back to our confrontation toxicology SAP and adverse

5 outcome pathways. Which is getting at maybe there's a way to

6 answer these questions without going to thousands of animals. 

7 Maybe there's toxicokinetics studies or toxicodynamics studies

8 looking at how different would the dosimetry have to be across

9 different species to explain differences. Or what is it about

10 physiology organisms of the biological systems? What did you

11 study with smaller and/or a different way to understand what the

12 species variability would be?

13 So I'm not asking you to do anything now, but

14 I think in your conversation yesterday, there may be multiple

15 solutions to the question.

16 DR. DANIEL SCHLENK: Okay. And again, this

17 was discussed yesterday, so it's on the public record. If you

18 have any other sort of endpoints or design issues that you can

19 recommend for that, then we can put that in the written records.

20 It should be fine. Okay.

21 All right. So 5(b), some study design

22 elements -- I believe this was for Dr. Boone. Are you clear

23 with the question that you were provided?

24 DR. BOONE: I'd be happy to hear the question

25 again.

26 DR. DANIEL SCHLENK: Okay. Can you please
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1 provide the question of clarification again for 5(b)?

2 MR. THOMAS STEEGER: What I was hearing from

3 the response was that the Panel seems to be recommending that

4 additional, more elaborate field studies are conducted. And as

5 we referred back to the 2003 SAP's recommendation for

6 higher-tiered studies would have been predicated on effects in

7 the lower tiered study.

8 There was no effect in the lower tiered study;

9 and so now I'm a little unclear in how we are -- is it the

10 Panel's recommendation that we transition the field studies

11 without the necessary fundamental understanding between the dose

12 response relationship of atrazine and its receptors?

13 DR. BOONE: I think that was the previous

14 question -- this is Michelle Boone -- which Cathy was just

15 addressing.

16 DR. DANIEL SCHLENK: So do you need to add

17 anything additionally to the public record?

18 DR. CATHERINE PROPPER: No, I believe

19 yesterday, I addressed the issue of adverse reaction quite

20 clearly.

21 DR. DANIEL SCHLENK: Okay. Okay. All right. 

22 So we'll move on to Question 6 then.

23 DR. BOONE: Well, I thought there was a

24 different question for Question 5.

25 DR. DANIEL SCHLENK: Okay. So ask them what

26 you thought the question was.
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1 DR. BOONE: We don't actually have an answer

2 for you, but we understood the question to be like, what are

3 specific protocols that could be developed regarding testing the

4 effect of measuring function (inaudible) by Atrazine.

5 Since there is data that suggests it's at

6 least a plausible outcome. And we feel that there is -- that

7 more time would be needed to be develop -- consulting with

8 immunologist to determine those, although some of the protocols

9 and testing that you've already established would probably be

10 acceptable to use, manipulating disease treatment and then

11 looking at the relevant endpoints.

12 And we're just not immunologists to know like

13 what those are; from an ecological standpoint looking at

14 infection would be a relevant endpoint and impact from exposure.

15 DR. DANIEL SCHLENK: Which I think you

16 discussed yesterday.

17 DR. BOONE: Which we discussed yesterday.

18 DR. DANIEL SCHLENK: So we don't need to add

19 anything further to the record. There's no additional comments

20 in the public record that you need to add that you haven't

21 already addressed.

22 DR. CATHERINE PROPPER: May I ask --

23 DR. DANIEL SCHLENK: Sure.

24 DR. CATHERINE PROPPER: Cathy Propper. So

25 this would be appropriate for me to include some of the wording

26 from the 2003 SAP for a response to the questions?
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1 DR. DANIEL SCHLENK: Let me go to the Agency

2 and say: would it help you in terms of answering the questions

3 to include wording from the 2003 report?

4 DR. THOMAS STEEGER: I believe that the Agency

5 is aware of what was in the 2003's report. There seems to be a

6 discrepancy between the Panel's understanding versus the

7 Agency's.

8 DR. DANIEL SCHLENK: Okay. So that sounds

9 like no, unless you want to put your interpretation of that. 

10 Yeah.

11 DR. SHARLENE MATTEN: The Panel, from a

12 process standpoint, the Agency is on the sideline at this point

13 theoretically. And you're discussing the responses to the

14 Charge Questions provided based on the information you had.

15 You can put in what you choose to put in with

16 your interpretation based on your understanding of the Charge

17 Questions. You don't have to ask for permission. If you want

18 the report to be 200 pages and add whatever you need to add,

19 then do so. Don't -- everybody's being very polite and very

20 professional, I understand that. And my colleagues over there -

21 I've been in the same shoes, it's very hard to sit on your

22 tushies for the whole time.

23 But if you have said what you said and it's

24 clear and you want to add more information, feel free to do so.

25 DR. CATHERINE PROPPER: I was just making sure

26 I don't need to read it into the record.
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1 DR. SHARLENE MATTEN: No, you do not have to

2 read it into the record.

3 DR. CATHERINE PROPPER: Okay. Thank you.

4 DR. DANIEL SCHLENK: Thanks for that

5 clarification, Ms. Sharlene. Okay. So we will move onto

6 Question 6, which we had, I believe, some questions of

7 clarification to Mr. Fairchild, I believe.

8 Any discussion for Question 6? And again,

9 please be brief.

10 MS. ANITA PEASE: Yes, this is Anita Pease. 

11 With regard to Question 6, we wanted further clarification on

12 the relevancy of the current body of information that was

13 evaluated in terms of the cosm studies, given that they are the

14 hedge point of this whole CE-LOC approach.

15 In terms of the dose and duration, what

16 studies are relevant, what studies are not? And then also some

17 clarification on the interpretation of effect versus no effect. 

18 I think we have some different interpretations, specifically

19 regarding the magnitude, what constitutes and effect. So what

20 percent effect is significant? And in terms of biological

21 significance, we require clarification on that as well.

22 DR. FRANK FARRUGGIA: Frank Farruggia. Yeah,

23 to add to that, we also wanted clarification on what was meant

24 by these time periods, long/short, was pretty vague, needs to be

25 polished out. 

26 What do you recommend for reference sites and
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1 what are negligible attributions of effects? Thank you.

2 DR. DANIEL SCHLENK: Okay. Do you -- are you

3 clear with that? Is there anything that you need to read into

4 the public record for that?

5 DR. JAMES FAIRCHILD: That was quite a lengthy

6 set of questions over a short period of time, and I wondered if

7 we could possibly get those in writing without having to wait

8 for an answer.

9 DR. SHARLENE MATTEN: If you can put together

10 a couple of bullet points, then we can share it with the group

11 and they have until whatever the timeframe that we'll close the

12 meeting and you could come back and address those. If you feel

13 you do not have time, you can comment in that regard.

14 So is that if you were asking for Frank and

15 Anita to put down those points they were asking for

16 clarifications -- specific clarification? Or are you asking for

17 something else? Because if it's the former, they can do that. 

18 They can share, and you can comment on it, as long as it's

19 before the meeting closes.

20 DR. JAMES FAIRCHILD: I would like a set of

21 bullet points, and we could comment on those if we have time.

22 DR. DANIEL SCHLENK: Okay. So we will have to

23 read those responses into the public record then. Yeah, we need

24 those bullets as soon as possible. We just pretty much have to

25 finish today.

26 DR. FRANK FARRUGGIA: As soon as we're done
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1 with my question, I'll get this typed up for you, or we could

2 send someone out to do it. I think there are some answers

3 coming for some of these questions.

4 DR. ANNE FETSCHER: So with respect to the

5 question of defining reference, and I made a suggestion that I

6 think that some of the biodata for cosm studies ideally should

7 be source from what could be considered reference for all of

8 these studies. And so there's the question of how do you define

9 reference, which is a very valid question and a tricky question.

10 It just so happens that there are folks at EPA

11 that have been working on this issue, especially within sort of

12 stream monitoring program type things, and also development of

13 tiered aquatic life uses reference plays a big role in that.

14 So there are actually some resources available

15 that can be tapped in to answer this question that are already

16 vetted through EPA scientists. So there's a document called

17 "Use of Biological Information to Better Define Designated

18 Aquatic Life Uses in State and Tribal Water Quality Standards

19 Tiered Aquatic Life Uses."

20 This document was drafted in 2005. 

21 Apparently, it's still a draft, and I don't know that it's been

22 finalized. But it does reference a paper that was under

23 development at the time and has since been published. The lead

24 scientist on the paper is John Stoddard, a research life

25 scientist with the Western Ecology Division.

26 That paper by Stoddard et al., was published
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1 in Ecological Indicators in 2006. It's called "Setting

2 Expectations for the Ecological Condition of Streams, The

3 Concept of Reference Condition." And they provided a number of

4 different scenarios, depending on the type of region they're

5 working in and what kind of challenges you're facing. And so I

6 think that that would be an excellent thing for you to look

7 into.

8 And another paper that provides some sort of

9 nuts and bolts of how you actual can go about selecting

10 reference sites was provided by Yates and Bailey, 2010,

11 Selecting Objectively Defined Reference Sites for Stream

12 Bioassessment Programs, published in Environmental Monitoring

13 and Assessment.

14 DR. DANIEL SCHLENK: Okay. So I think we

15 don't have any other questions in Question 7, right? I think

16 we're good with Question 7.

17 DR. FRANK FARRUGGIA: I did have a follow-up

18 for Dr. Ellsworth. Yesterday, you had discussed accounting for

19 the relevant change seen in the effects data. Could you

20 elaborate on that and provide some specific examples of how

21 (inaudible) the diversity of positive exposures and the

22 diversity of endpoints that we're really looking at?

23 DR. TIMOTHY ELLSWORTH: Sure. As I understand

24 what was done, and maybe I misunderstand what you've done or

25 what you are doing. It's a very real possibility. You're

26 taking the cosm data with the effect/no effect, you repeat
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1 having an effect and not having an effect.

2 And then you're applying that to a chemograph

3 in the real world, and you're saying, okay, here's my chemograph

4 and I'm going to compare chemograph A to chemograph B in the

5 real world. And I'm going to have, say, a 40 percent reduction

6 in adverse effect in chemograph B compared to chemograph A,

7 okay? It would be nice if there was some way to say, PATI's

8 really doing a good job estimating that relative change in

9 adverse effect. 

10 Okay. You've got all these different

11 endpoints in the cosm data. I know that, it's prevalent by, you

12 know, structure, function, diversity and biomass, whatever it

13 is. You know, and then you've just got this specific growth

14 rate relative reduction when you're going through the

15 application of PATI to chemographs.

16 My concern is that there's really no way to

17 say, that I can see, that this relevant reduction that you're

18 predicting is actually occurring. You know, there's no data

19 that say this chemograph gives me a 50 percent relative

20 reduction in adverse effect than chemograph A. I mean, there's

21 no objectivity to the model. There's no way to say is this

22 model outcome objective? That's my concern.

23 It's logical. I agree with Russ. It makes

24 sense that, you know, using the SGR. As I read through his

25 arguments there, it makes a lot of sense to me. But is it

26 objective? You know, can you really measure something in the
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1 real world that supports that model? Does that make sense?

2 DR. RUSSELL ERICKSON: What are you asking? 

3 Is that a question?

4 DR. DANIEL SCHLENK: Is that from the Panel?

5 DR. TIMOTHY ELLSWORTH: No. I guess -- I'm

6 sorry. I don't want to go on here. I know we need to move on,

7 but that was my concern yesterday was that there's not this

8 objective measure there of what you're predicting -- that I

9 understand.

10 DR. DANIEL SCHLENK: And so does the Agency

11 understanding of the response that the Panel provided you there?

12 DR. FRANK FARRUGGIA: We understand the

13 response. We feel that the totality of endpoints that cosms

14 represent, that we in fact seen in those are reflective of all

15 of those endpoints. And so when we come down to the LOC, we're

16 expecting an effect, it could be any of those effect endpoints

17 that can be seen. I've had species conversions, structural

18 loss, or percent reduction in photosynthesis, productivity, et

19 cetera. So any of those could be really reflective there.

20 And what I'm gathering, and maybe I'll read my

21 question, is whether or not that is the appropriate assumption

22 to make about that LOC.

23 DR. DANIEL SCHLENK: Actually, I don't think

24 we can do that.

25 DR. FRANK FARRUGGIA: Okay.

26 DR. DANIEL SCHLENK: I mean, it's becoming too
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1 interactive. This is not supposed to be interactive, so it's

2 basically do you understand the response or not? If you don't

3 understand the response, you don't have to agree with it. You

4 just have to -- do you understand the response or not, so.

5 MR. DANA SPATZ: We understand the response.

6 DR. DANIEL SCHLENK: Thanks. Okay. So we're

7 going to take a break for 15 minutes and then move on to

8 Question 8.

9 (WHEREUPON, a recess was taken.)

10 DR. DANIEL SCHLENK: Okay. Let's go ahead and

11 get going on Question 8. Do you guys want to read that on the

12 record, please?

13 MR. DANA SPATZ: The current analysis using

14 the Plant Assemblage Toxicity Index indicates the CE-LOC can

15 range from 4 to 7 micrograms per liter. Please comment on this

16 CE-LOC and whether it reasonably represents a range below which

17 permanent or irreversible change in aquatic plant community

18 structure, function, and/or productivity due to atrazine

19 exposure would not be expected.

20 DR. DANIEL SCHLENK: The lead discussant for

21 that is Jim Fairchild.

22 DR. JAMES FAIRCHILD: Who is in the process of

23 trying to boot up.

24 DR. DANIEL SCHLENK: Do you want to go ahead

25 and pass to the next person? Okay. Let's do that then. It's

26 Dr. Twiss.
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1 DR. MICHAEL TWISS: I had some difficulty

2 answering the question as it was posed because after reading the

3 white paper and understanding PATI, it was hard for me to have

4 any confidence in the model output. There were some confidence

5 intervals provided, but as Dr. Young has explained, in absence

6 of a full propagation of error from the very beginning of the

7 mathematical treatment all the way through the end.

8 So I was questioning whether or not there was

9 actually kind of a larger range of concentrations that are

10 really there rather than just this confidence intervals in terms

11 of the last probit analysis.

12 And so I essentially said that I can't really

13 address the question without having any confidence intervals in

14 showing this.

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

16 DR. JOHN RODGERS: Just to follow and

17 hopefully not duplicating anything that Jim Fairchild is going

18 to say, I just said as long as they're are plausible and

19 recognize -- I think this is a reasonable test of a hypothesis. 

20 And it's a hypothesis that's on the scientific table regarding

21 effects of either short-term or long-term lower concentration

22 responses to exposures of atrazine that degradation cause.

23 And as long as that's not been thoroughly

24 exposed, basically the idea of the notion that no effects can be

25 expected below the CE-LOC cannot be supported, there are many

26 plausible hypotheses, including but not limited to, the
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1 susceptibility of some species to exposures below that in terms

2 of the amphibian questions on the table.

3 And I think a general conclusion would shut

4 down and I think an affirmative answer and a general conclusion

5 that we can expect no effect would serve to discourage

6 scientific discussion, investigation, and production of needed

7 studies in those areas.

8 So I don't think that the data that we've been

9 provided provides us the information to draw the conclusion that

10 there could be no effect.

11 DR. DANIEL SCHLENK: And is your computer

12 ready Jim? Okay. You want to go ahead.

13 DR. JAMES FAIRCHILD: I apologize. My initial

14 response to that is based on my set of usual experience with

15 atrazine. I am fairly confident that no affirmative argument

16 regards to change in the structure of productivity will occur if

17 it has a below the 4 to 7 microgram per liter CE-LOC established

18 by the Agency. I feel this range is very conservative based on

19 what we know from laboratory and microcosm studies.

20 DR. MICHELLE BOONE: I had some concerns about

21 the single species toxicity test that I heard in response to an

22 earlier question, and I also had some concerns about the source

23 material for the cosm studies in so much as it wasn't clear to

24 what extent they represent communities that have already been

25 somewhat, let's say hammered.

26 And so I had some issues that fed into the
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1 determination of the CE-LOC. That having been said, if we could

2 take the cosm data at face value, then I would immediately

3 dismiss the 4 to 7 based on what data are available. So that's

4 actually a very heavily caveated, sorry, but that's kind of the

5 most comfortable way I feel responding to that particular

6 question.

7 There is another concern that I had about the

8 cosm studies that seems to be appropriate to bring up here, and

9 that is if we think about the fact that the LOC is intending to

10 be protective of primary productivity, which is kind of a more

11 direct parameter according to its development, but also being

12 protective of the plant community itself and higher trophic

13 levels.

14 That's actually a really tall order. We're

15 asking a lot of that LOC. And so we have to do it right. I

16 think everyone in the room can agree with that. And I just

17 wanted to emphasize that whether the LOC's stays at the proposed

18 level or changes, because it's intended to be protective not

19 only of primary productivity, but also a higher order of

20 ecosystem processes, land community dynamics are a very

21 important consideration and we shouldn't lose sight of this.

22 And I acknowledge that this played a role in

23 the LOC's determination insomuch as it was an endpoint in some

24 of the cosm studies, although it seems that the available data

25 impose limitations on the role that it could reasonably play.

26 Varying sensitivities of different plant
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1 species to atrazine could result in a scenario in which the most

2 tolerant species come to dominate a system. And if they happen

3 to be invasive this could give them a foothold resulting in a

4 major and lasting change of community structure and potential

5 confident with degradation to have that quality.

6 In addition to the extent that some plant

7 species are more nutritious or tolerable consumers than certain

8 other species, shifts in community structure could have adverse

9 effects propagating through higher trophic levels. And that's

10 just talking about the plant community and consumers.

11 So I'm just wanting to emphasize this was

12 touched upon in the white paper, but because a lot of things

13 still seem to be in flux and because the possibility that maybe

14 future studies could be conducted that are geared more towards

15 specifically answering questions we have, I really want to keep

16 the importance of the plant community dynamics, which ended up,

17 I think, getting diluted a little bit just out of necessity and

18 based on the current data available.

19 I just want to keep it on the table as

20 something very important.

21 DR. DANIEL SCHLENK: Dr. La Point.

22 DR. THOMAS LA POINT: Thank you. You know, my

23 response to this is very much more on the line of Jim Fairchild

24 in that my review indicates that it's a very conservative level

25 at 4 to 7 micrograms for the CE-LOC.

26 And I think the key for why I'm thinking this
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1 way is that the use of the cosm data, again, and the Brock

2 Scores to effect/no effect is very critical especially in those

3 areas just around from 10 to, let's say, 30 micograms per liter.

4 One of the aspects of that we've brought up and discussed

5 already in the presentation by Syngenta in terms of reversible

6 effects that we haven't really discussed much in general among

7 the Panel members here.

8 And I think that's something that might be

9 gained in -- I don't know whether it's having -- we're trying to

10 come up with ways of looking at how to get out of this. 

11 Yesterday even, Dr. Erickson mentioned that maybe a very focused

12 mesocosm study looking at differences in duration and

13 concentrations right around the effects/no effects. 

14 It's so easy, I guess, for me to call for more

15 data, and everybody always wants -- and I recognize the costs

16 and the time it takes to do that. But with some of these

17 critical questions about -- we know the way that atrazine works

18 and that after the exposure, there is recovery in

19 photosynthesis. And depending on how long the plants are

20 exposed to the atrazine, and so duration becomes a factor. 

21 And all we can do is say this develops -- I

22 think it's reasonable what the previous speaker just said about

23 requiring a few more focused cosm experiments, and it doesn't

24 have to be big, it doesn't have to be major. It should focus on

25 the (inaudible) and some of the more sensitive taxa to really

26 establish this. It would alter some concentrations, but it'd
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1 also be altering some durations, rather direct times of exposure

2 to the duration.

3 DR. DANIEL SCHLENK: DR. Klaine.

4 DR. STEPHEN KLAINE: I'm not going to repeat

5 everything that other folks have said. I pretty much agree,

6 especially with the last two speakers. But I will just state

7 that I felt that the model that went into producing this number

8 was extremely conservative. The data -- the interpretation was

9 conservative, and as a result, I think this number is

10 conservative. So the answer to the question, I believe I agree

11 that it will protect plant communities.

12 However, I think that it shouldn't stifle and

13 I hope it does stifle for the research on this area, but I think

14 that the research that we call for on previous questions should

15 address some of the glaring uncertainties, episodic exposure,

16 recovery, true community level endpoints versus inhibition of

17 species of population endpoints.

18 I think that will help address that, and I

19 think ultimately help refine that.

20 DR. DANIEL SCHLENK: Okay. Dr. Young.

21 DR. LINDA YOUNG: Well, I too think it's

22 conservative in the approach, but the 4 to 7 range was

23 determined by adjusting the affect/no effect on -- was it three

24 or five studies? Five studies. That shows you how sensitive

25 this range is to the determination of which studies are in,

26 which are out, as well as the effect/no effect decision. And
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1 because of that, I don't think we have a good measurement of

2 either the primary point or the uncertainty associated with

3 that, based on this analysis.

4 I do think it's likely to be conservative, but

5 I certainly do not think there's enough to do broad-base

6 inference on the results.

7 DR. DANIEL SCHLENK: Dr. Portier.

8 DR. KENNETH PORTIER: So I can agree. I agree

9 it's very conservative and I just want to reiterate that the

10 concept of the degree of conservative that Dr. Fetscher was

11 talking about. So if no individual of a community is harmed,

12 the whole community is not harmed, that's a given, right? So we

13 don't damage anybody.

14 So if you look at the species sensitivity

15 curves, which is Table 19 in the white paper, it seems that the

16 4 to 7 micrograms per liter level puts us in this situation. 

17 We're so far down that it's unlikely that at least any major

18 members of the community are going to be harmed.

19 When we give the degree, we have to start

20 asking questions, which some members of the community are

21 harmed, but in recoverable ways is the whole community harmed?

22 And I think we were thinking that the cosm

23 approach was trying to help us kind of get to that issue. I'm

24 not quite sure we'd gotten there with the cosm studies, and that

25 might be the focus of further cosm studies.

26 And in the next level is if some members of
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1 the community are actually eliminated by the exposure, if their

2 functional roles are replaced by other members -- that is the

3 niche that opens up is filled by others who are maybe resistant

4 to that exposure, is the whole community harmed.

5 And that's a big question in ecology. I mean,

6 there's a lot of people still talking about that. We've changed

7 the Everglade system in the '50s and we're trying to put it

8 back, and we already know it doesn't go back identically to the

9 same species composition that we had before. But does it go to

10 a functional state that operates like it did before? And I'm

11 not sure that's a scientific question. That's more a policy

12 question, you know? Are we going to allow exposures that change

13 the community composition but which don't change the functional

14 operation in the community to allow us to use atrazine?

15 That's a key question, and I'm not sure that's

16 one that we can answer with science. But I think that the focus

17 cosm stuff can help us to look at the recovery. 

18 And I think that's important for atrazine

19 because I really do feel that the exposure occurs in a periodic

20 way, not a long-term way, at least not -- even these 4 to 7

21 levels.

22 So the community is not exposed for long

23 periods of time. It's exposed for days, not months, at this

24 level. So I think there is a recovery issue.

25 DR. DANIEL SCHLENK: Okay. Any other

26 comments. Dr. Propper.
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1 DR. CATHERINE PROPPER: So there is a review

2 that's out of Yao et al., 2011, and I'll provide the whole

3 citation to the Panel that addresses environmental shifts

4 inducing genes to ecosystem level effects that might just be a

5 informatory for future reference.

6 DR. DANIEL SCHLENK: Anyone else have any

7 comments on Question 8? Okay. Let me ask the Agency for

8 questions of clarification.

9 DR. RUSSELL ERICKSON: I think all the

10 comments are clear.

11 DR. DANIEL SCHLENK: Thanks. Okay. Let's

12 move onto number 9, and Mr. Spatz you want to read that into the

13 record.

14 MR. DANA SPATZ: Given the current analysis of

15 the ecotoxicity data in the white paper, Chapter 1, Section 6,

16 and the Aquatic Plant Community LOC methodology, please comment

17 on how well the available database supports EPA's conclusion

18 that the CE-LOC is lower than exposures that result in

19 significant effects on the growth, survival and reproduction of

20 the aquatic animals.

21 DR. DANIEL SCHLENK: Lead discussant for that

22 is Dr. Klaine.

23 DR. STEPHEN KLAINE: Okay. I tried to

24 assimilate most of the comments from the other respondents on

25 this question. So I hope I get it right; if I don't, I'm sure

26 they'll tell me.
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1 Acceptance criteria for cosm studies, that is

2 the indicated the test must involve administration of atrazine

3 only and not mixtures of multi-active ingredients. The section

4 on stressors of concern, of the white paper explains that

5 quantitatively predicting the combined effects of multiple

6 variables on mixture toxicity of any given taxa with confidence

7 is beyond the capabilities of the available data.

8 The methodologies for that available toxicity

9 data for environmental mixtures with atrazine with other

10 pesticides represented as part of the ecological risk

11 assessment. And a qualitative discussion of implications of the

12 available pesticide mixture effects data on the confidence of

13 risk assessment conclusions will be addressed, as part of the

14 uncertainty analysis.

15 Isolation of atrazine for toxicity testing and

16 cosm studies likely renders direct assessment of atrazine

17 effects much more straightforward in the laboratory. But it

18 seems flawed in terms of its real world interpretation of

19 atrazine effects on growth, survival, and the reproduction of

20 aquatic organisms because of the possibility of synergistic

21 effects.

22 (inaudible) synergistic effects that can

23 exacerbate the effects of toxicity of a given measured

24 concentration of atrazine in the environment. Strong likelihood

25 that atrazine is often non-encountered in their isolation in the

26 environment further supports the need to consider likely
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1 synergies in the process of establishing meaningful effective

2 levels.

3 It would be of value when using published

4 literature to review them in context of QA/QC requirements. The

5 stressor of concern section also provides a rationale for not

6 including atrazine degredance in the analysis. And that

7 atrazine is more toxic when found in higher concentrations than

8 in any of its degradates argue that a LOC for the current

9 compound should be considered protective. 

10 This seems to overlook the possibility that a

11 given measured concentration of atrazine in the presence of its

12 degredates may be more toxic than the same concentration of

13 atrazine in the absence of metabolites.

14 Atrazine concentrations alone without

15 consideration of degredates is used for the calculation of the

16 daily PATI values leading to the determination of the effects

17 exceed factor.

18 Under the current effects index calculation,

19 any toxicity contributed by atrazine degredates are unaccounted

20 for to the extent that atrazine in combination with its

21 degredates results in toxic effects exceeding those and would be

22 anticipated for monitoring, other than accounting for atrazine

23 alone, this inhibition may result in underestimating risk.

24 EPA's conclusion based on the previous

25 analysis of (inaudible) toxicity data, the conclusion being that

26 the CE-LOC for atrazine is lower than the exposure results in
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1 the significant effects on growth and survival and reproduction

2 of aquatic animals.

3 This is supported by the (inaudible) data for

4 short-term acute exposure and responses such as survival. As of

5 this date, there are no consistent, published data indicating

6 that environmentally irrelevant concentrations of atrazine at or

7 below the 4 to 7 micrograms per liter are directly or acutely

8 toxic to invertebrate, fish or amphibians.

9 There are several references there that will

10 be in the report. However, there is compelling evidence that

11 reproductive endpoints in amphibians can be impacted at lower

12 levels in lab studies -- again, named several references -- and

13 as well as in natural field studies; communicating more research

14 is needed to evaluate the importance of atrazine within

15 populations between populations and across species.

16 EPA's conclusion that the CE-LOC for atrazine

17 is also an interesting and currently untested hypothesis that

18 deserves rigorous testing for longer term studies and for

19 effects other than survival. 

20 And that analysis of the data included in the

21 database provides some support for this hypothesis or they

22 simply facilitated the design in a more rigorous study to test

23 the hypothesis by (inaudible) the likely mechanisms and

24 diagnostic sensitivity and meaningful responses to exposure.

25 Review of published literature indicates that

26 atrazine exposure could lead to direct responses, e.g.
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1 behavioral changes, hyperactivity, et cetera, or to a cascade of

2 indirect responses, loss of populations due to decrease in food,

3 loss of populations due to endocrine alteration or changes in

4 renal function.

5 The hypothesis is sufficiently important that

6 it should be subjected to more than that analysis of data that

7 were not intended to test hypothesis. An experiment design to

8 test this hypothesis, one would not need to be constrained by

9 the estimated CE-LOC. A regression design could cover a range

10 of environmentally relevant exposures.

11 In this case, it is important to remember that

12 exposures also include duration and persistence through repeated

13 applications of atrazine. The data on long-term repeated

14 exposures and sub-lethal responses of animals, as well as the

15 consequences of those exposures and responses are not as

16 clear-cut.

17 It would be important to carefully consider

18 sub-lethal effects that previous published studies have

19 indicated. As long as there are plausible or reasonable

20 hypothesis on the scientific table regarding effects of

21 long-term, low concentration exposures of atrazine and

22 degradation products that have not been thoroughly explored, the

23 notion of no effects can be expected below the CE-LOC cannot be

24 supported.

25 Some of these hypotheses would include, but

26 not limited to, the susceptibility of amphibians, parasites, and
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1 a cascade of effects, immunosuppression in amphibians, and

2 alteration of amphibian gonadal development.

3 For example, few immunological studies have

4 looked at environmentally relevant exposures of atrazine. 

5 However, the little work that has been done on amphibians

6 suggests that atrazine can act as an immunosuppressant. The

7 concentrations 3 to 30 micograms per liter, atrazine apparently

8 significantly decreased circulating the acidophils in wood

9 frogs.

10 Also the data on indirect effects are

11 fundamentally lacking. For example, herbivore effects -- if you

12 kill the grass, then the cows die. And as our data on increased

13 tolerance or resistance developed by a near continuous exposed

14 wide communities.

15 It appears to be entirely plausible that if

16 plants are damaged by exposures, there would be adverse effects

17 on herbivores; that is the less herbivores are minor

18 contributors to the ecosystem structure and function.

19 Given the past, present, and anticipated use

20 patterns for atrazine as well as existing data on direct and

21 indirect effects, this and other hypotheses should be tested as

22 part of a stewardship and regulation of this herbicide.

23 Based on our ecotoxicological experience to

24 date, there are concerns and evidence of adverse effects that

25 emerge as we learn or gain knowledge of the questions to ask

26 about persistent (inaudible) substances that are biologically
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1 acted and relentlessly -- in relentless exposures, whether they

2 are persistent due to inherent chemical structure or due to use

3 patterns as well as persistence.

4 In order to provide a scientifically

5 defensible affirmation response to this question, one would need

6 to consider durations of atrazine exposure, frequencies of

7 exposures, as well as the actual magnitude or amplitude of

8 exposures that may not be detected in current monitoring

9 programs.

10 Until these exposures are resolved and other

11 emerging questions are also resolved, the careful designed and

12 executed studies with power and sensitivity and analysis, we are

13 best served by scientific vigilance and continuing

14 experimentation.

15 An affirmative answer to this question at this

16 point would only serve to discourage scientific discussion

17 investigators and production of needed studies.

18 DR. DANIEL SCHLENK: Okay. Dr Twiss.

19 DR. MICHAEL TWISS: I have nothing to add.

20 DR. DANIEL SCHLENK: Dr. Rodgers.

21 DR. JOHN RODGERS: I have nothing to add.

22 DR. DANIEL SCHLENK: Dr. Fetscher.

23 DR. ANNE FETSCHER: My responses are covered

24 by Dr. Klaine's.

25 DR. DANIEL SCHLENK: Okay. Dr. La Point.

26 DR. THOMAS LA POINT: Same here. Responses
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1 have been covered. Thank you.

2 DR. DANIEL SCHLENK: Okay. Jim Fairchild.

3 MR. JAMES FAIRCHILD: I have nothing to add at

4 this point.

5 DR. DANIEL SCHLENK: Dr. Boone.

6 DR. BOONE: Thank you. I have nothing to add.

7 DR. DANIEL SCHLENK: Dr. Young.

8 DR. LINDA YOUNG: I have nothing to add.

9 DR. DANIEL SCHLENK: And Dr Portier.

10 DR. KENNETH PORTIER: I have nothing.

11 DR. DANIEL SCHLENK: And Panel? Oh, you got

12 something to add. Okay. Sorry. Dr. Propper.

13 DR. CATHERINE PROPPER: I concur and have

14 nothing to add.

15 DR. DANIEL SCHLENK: I'm sorry. I skipped

16 you. Dr. Effland.

17 DR. WILLIAM EFFLAND: I think I heard it in

18 your comments, but I just want to make sure.

19 The one thing that I wonder about is the way

20 this assessment is, is atrazine only. And I would assume that

21 it would be atrazine and the degradates, since that's the normal

22 process. But in the real environment, in the natural

23 environment, exposure to just a single stressor like this is, I

24 don't think, realistic. So I just wanted to bring up that

25 point; that when we're talking about community effects and

26 things, I think it's important to consider that we're only using
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1 -- you know, we're saying that we just have a single stressor

2 when there's obviously a lot of things that can occur.

3 DR. DANIEL SCHLENK: Okay. Any other comments

4 provided there? Okay. Let's go to the Agency. Do you have any

5 questions for clarification?

6 MR. DANA SPATZ: No, we don't.

7 DR. DANIEL SCHLENK: Okay. Thanks. At this

8 point, I believe Dr. Twiss, you've got your bullet points from

9 Dr. Farruggia and are ready to respond to those, is that

10 correct? 

11 DR. MICHAEL TWISS: Yes, that's right.

12 Yeah, this is in response to a question

13 following up on Question 6. The Agency requested clarification

14 to provide suggested guidance on filtering criteria for

15 evaluating cosm studies. And I drafted three responses to

16 Fairchild to include, and they are -- the first criteria - there

17 are three criteria. One is the flow, second is the amounts of

18 time and then concentration.

19 So for criterion one, for instance, use only 

20 cosm data from lotic systems (artifical streams, flow through

21 systems) for use in evaluating responses to lotic systems.

22 Regarding criteria two and three, from the AEEMP

23 data sets, conduct a statistical analysis determines the mean

24 duration of peak atrazine concentrations in vulnerable watersheds,

25 the average time between multiple peak chemographs, and the 
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1 overall period during which atrazine in the stream water

2 is elevated. Make a reasonable decision on what the maximum

3 duration of a cosm study is a reasonable model of atrazine 

4 exposure to natural communities in vulnerable watersheds.

5 Importantly, from the seen data set in terms

6 of statistically meaningful determination of maximum

7 concentrations of atrazine that can expect to be encountered and

8 use this concentration to exclude cosms that have atrazine

9 concentrations that exceed this by some reasonable factor. 1.5,

10 ask engineers, the ones who's measuring stuff.

11 All cosm studies have some meaningful 

12 information to provide. Reasonable prudence should be

13 exercised to select cosm studies that are deemed reasonable 

14 models of atrazine exposure to natural communities in 

15 vulnerable watersheds based on these addtional selection 

16 (filtering) criteria.

17 DR. DANIEL SCHLENK: Okay. Thank you very

18 much. Okay. Let's go ahead and move forward to Topic C, I

19 guess. I'm assuming you guys need to change personnel. Is that

20 correct?

21 DR. FRANK FARRUGGIA: I'm sorry. I have a

22 question. Just a follow-up question in response to the email

23 question we just sent. I don't believe it was. Okay. Thank

24 you. It was Question 6. 

25 DR. DANIEL SCHLENK: We'll come back to that

26 after 10. Okay. Mr. Spatz, do you want to read Question 10

26 into the record?
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1 MR. DANA SPATZ: The FIFRA SAP in 2011 advised

2 that use of bias factors was a viable approach for addressing

3 uncertainty in monitoring data. The following question is

4 related to use of bias factors in identification of surface

5 water bodies exceeding the atrazine CE-LOC. Please comment on

6 the strengths and limitations of EPA's development and use of

7 bias factors for addressing uncertainties in monitoring data.

8 DR. DANIEL SCHLENK: Okay. Our lead

9 discussant on that Dr. Portier.

10 DR. KENNETH PORTIER: Thank you. Ken Portier.

11 We're not as organized as Dr. Klaine's. So we will be moving

12 the meat around a little bit more.

13 So that the purpose of the bias factor

14 adjustment is to address the expected underestimation of the

15 true maximum 60-day mean concentration in watersheds with

16 limited sample.

17 In the white paper, the 60-day period is

18 justified by reference to the longest cosm treatment with no

19 effect; and in fact almost all the periods of significant

20 exposure in the AEEMP monitoring data were less than 60 days. 

21 Beyond 60 days, there is limited cosm data, but the medium cosm

22 duration, as we heard from the Syngenta presentation, is closer

23 to 30 days and the mean being 57 days points to the impact of

24 the six studies with durations greater than 300 days.

25 So stated in the white paper is that using an

26 averaging period of less than 60 days leads to some invalid
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1 conclusions that less exposure is needed to elicit exposures

2 that are actually involved. So that's just kind of situation

3 centered. 

4 The development of bias factors in this

5 analysis is based on selected high sampling intensity monitoring

6 data from the AEEMP and the NCWQR monitoring program. As

7 expected, the application of bias factors to adjust the maximum

8 60-day means decreases the likelihood of underestimating the

9 true meaning, in some cases, but at the same time increases the

10 likelihood of overestimation in other cases.

11 The 2011 SAP report stated that, and I quote,

12 "The application of a bias factor to exposure statistics

13 calculated from simple linear interpolation of sparse monitoring

14 data is a potential, simple, and practical approach to

15 evaluating data of a variety of monitoring frequencies to get

16 either unbiased or conservatively high biased preliminary

17 estimates of exposure metrics, depending on how the factor is

18 derived.

19 The approach is primarily applicable to sites

20 with moderate frequency monitoring data, such as weekly or

21 biweekly, so that initial biased sampling estimates are more or

22 less in statistical control. Quarterly data, for example, would

23 be too sparsely used for short-duration monitoring and

24 sampling."

25 So note that it is not clear that the 2011 SAP

26 was considering use of this method for things like the 28-day
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1 sampling protocols that produce roughly one sample a month, or

2 even where we have the number of samples equal to four in the

3 sampling period.

4 In addition, in discussing the bias factor

5 method, it was expected that, and I quote, "that this approach

6 would use characteristics of a worst case group, such as small

7 basins to yield a conservatively high biased factor for

8 predictive screening that will trigger monitoring. This is a

9 practical approach that can be used now because there is

10 relevant confidence that floating water sites with small basins,

11 such as the AEEMP sites and other small basin sites, define the

12 worst case bias factors.

13 Both for larger flowing streams and for

14 reservoirs, at least regarding the short duration concentrations

15 in which the Agency is focusing on, there are a significant

16 number of community water systems within the watershed size

17 range of the AEEMP sites; and thus, the AEEMP sites are useful

18 and applicable for defining characteristics for small basin

19 community water system sites in vulnerable settings."

20 It's not clear in the white paper that the

21 seven site year data used to develop the bias factors do

22 represent the worst case group, but I'm going to assume that's

23 true for now. 

24 The white paper does state that the seven

25 sites are rather limited in geographic and hydrographic

26 variability, which I think is an important point that we need to
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1 consider.

2 So the observed bias factors were computed

3 using Monte Carlo resampling of the intensive single-day

4 sampling time series of the seven sites down to time series with

5 observation, say, every seventh, 14th, or 28th day.

6 Each of the 10,000 utilizations were then

7 interpolated to produce 10,000 simulated chemographs, which were

8 then processed at the 10,000 maximum 60-day mean atrazine

9 concentrations. From the distribution of these concentrations,

10 the 5th percentile maximum 60-day rolling mean atrazine

11 concentration was compared to the true rolling mean

12 concentrations to compute the bias factor for that site sampling

13 period.

14 So this whole process produced seven bias

15 factor estimates for each sampling interval. So seven times

16 four -- 28 pieces of data, all right? Note that there is

17 nothing that keeps us from sampling every third, fifth, ninth,

18 or 21st day using the Monte Carlo method. And since the

19 sampling interval is later translated into the number of samples

20 in the 60-day period, we could conceivably have scenarios of

21 two, three, four, up to, say, 30 samples in the 60-day period. 

22 So there's nothing that kept us from doing that.

23 If we had enough data, it's also possible that

24 this exercise might also have been attempted using more sites

25 that address a wider range of basin sizes, average flows, or

26 site times as kind of conceived of in the 2011 SAP.
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1 The seven bias factors for each sampling

2 interval, the next step is to come up with one bias factor for

3 each sampling interval. By grouping or regressing the bias

4 factors against sampling interval, it's possible to establish

5 one bias factor that has some probability of coverage associated

6 with it; that is we can kind of assign a probability statement

7 to say that we've got 95% likelihood that the true bias factor

8 is going to be below the value we choose. So we want to kind of

9 put a probability statement on there.

10 And I think that's all -- that's anything the

11 EPA did. I think that was their thinking. So let's talk about

12 the strengths. I look four strengths; and based on the actual

13 data, the many sites assumed really are worst case. So I think

14 most of us agree these are kind of worst case.

15 Operationalizing bias as the ratio of the 5th

16 percentile in the distribution for the maximum 60-day moving

17 average distribution from the Monte Carlo study data, comparing

18 those to the true value allows us to make a probability

19 statement about coverage; that is you would expect this bias

20 factor to be exceeded in only, say, one in 20 years at that

21 site.

22 So we're trying to not be overconservative,

23 but being able to establish a likelihood of this bias factor not

24 working. In limiting the focus to the second and third quarters

25 of the year, the periods with most difficult high atrazine use

26 in the field, I think is a strength. And limiting the data to
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1 sites with nearly complete daily data, and so allowing for low,

2 in filling or interpolation to be needed was, I think, a

3 strength.

4 The limitations of the method, there are a few

5 more of those boxes, I have six. It uses only a few calibration

6 site years. So there's seven site years for five sites, so

7 everything is based on five sites of data. 

8 What resulted, limited geography, limited

9 hydrology and limited typical representation. And again,

10 geography, hydrology and typical representation for one of those

11 at least three factors that the 2011 SAP kind of, I think,

12 expected to be addressed more completely in this analysis.

13 A minor limitation is it uses a stairstep

14 interpolation to infill missing data. I'm not sure that's a

15 major issue. The method doesn't take into account the

16 monitoring methods, so some of the data are draft samples, some

17 of them are auto samples; or the number of samples taken in the

18 day, and again, as pointed out in 2011, that has a big impact on

19 the estimate of the peak. How to take that into account, I'm

20 not sure I know how to do it. But I mean, I'm just pointing

21 that out.

22 Only four sampling intervals were considered,

23 whereas as many as, say, 28 possible sampling intervals could've

24 been created for the simulation; so I'm not too sure why we just

25 looked at four sampling intervals.

26 Limits on the range of basin sizes, average
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1 flows, and site types represented by the data results in these

2 factors not being useful in accounting for tempo-spatial

3 variability in the bias factors; and we'll come back to that in

4 the discussion of the regression methodologies in the next

5 question.

6 And the Monte Carlo does not take into account

7 serial correlations or something like a typical rainfall event

8 hydrograph structure. Something that we know would be more

9 important than sampling, say, below every fourth day. So I'm

10 not going to recommend that you go shorter frequency duration,

11 but if we did, the model would have to get significantly more

12 complicated.

13 It's clear that the true bias always tends to

14 be in the direction of underestimation because short-lived

15 high-concentration events have a higher likelihood of being

16 missed with sparser sampling. So in the non-worst case site

17 years, there's less tendency that extreme events will actually

18 occur.

19 So in other words, there's more (inaudible)

20 hydrographs and (inaudible) chemographs in the non-worst case

21 sites. So I think that the approach taken does provide a

22 significant level of conservativeness in the process. So those

23 are my comments, and I've integrated comments from a few of the

24 other members so don't be surprised if they pass. 

25 DR. DANIEL SCHLENK: Okay. Thanks for

26 bringing that up, Dr. Portier. Dr. Young.
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1 DR. LINDA YOUNG: I have nothing to add.

2 DR. DANIEL SCHLENK: Mr. Gilliom.

3 MR. ROBERT GILLIOM: I'm just trying to

4 rapidly filter what I might want to add compared to what was

5 covered. I think maybe one thing I'd want to add is that in

6 perspective this is part of a screening level step in the

7 process. So it doesn't necessarily have to be highly precise,

8 as long as it's well-characterized. And I think the general

9 approach to doing that is kind of laid out by example and then

10 there are some refinements that need to be made to make it fully

11 acceptable.

12 And those have to do, as I'm sure will come up

13 with some of the other questions so I won't elaborate, but will

14 have to do with how the data sets for applicability are

15 selective, what criteria there are, what data infilling methods

16 are used, and then how it's finally applied in regression with

17 larger data sets.

18 So I guess I'm comfortable with the overall

19 process with the idea in mind that those refinements would be

20 made; and a lot of what I think about the refinements have

21 already been addressed in last year's SAP. And some of it, I

22 think, hasn't been gotten to yet. So I think with that, I'll

23 just end here.

24 DR. DANIEL SCHLENK: Thank you. Dr.

25 Ellsworth. 

26 DR. TIMOTHY ELLSWORTH: Okay. And the one
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1 thing I guess I would like to hear on this question is maybe a

2 point of clarification, and that is exactly when and how the

3 Agency proposes to use these bias factors. And let me give a

4 little background on where I'm coming from.

5 I think Bob Gilliom here has mentioned several

6 times to me in private discussion that the monitored watersheds

7 are just a fraction of the percent of what's out there. So, you

8 know, how are you going to apply this when there's really --

9 it's not that big of an issue if you look at it from that

10 perspective.

11 But if, as I'm assuming you're going to do,

12 you're really going to use kind of the watershed characteristics

13 that were identified in 2007 and 2009 to identify vulnerable

14 watersheds, coupled with your LOC, to say, "Okay. For those

15 kinds of characteristics, we need to go in and monitor and see

16 if we have problems."

17 So you're going to go in and do some sparse

18 monitoring. And then you're going to come back and apply your

19 bias factors to see if you exceeded. So in that case, this

20 becomes very important, in my mind, to do the best job possible.

21

22 DR. JAMES HETRICK: The whole idea here is --

23 and we've touched on this before -- is to use this as a screen

24 level approach. That is to be able to -- unfortunately, it

25 characterizes the vulnerability assessment that we're doing with

26 this bias factor, which is not the case. 
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1 What we're doing is, we have extensive

2 monitoring data on atrazine and what we're trying to do is

3 utilize that monitoring data to its fullest extent, in doing our

4 exposure assessment and understanding where the potential

5 exceedance is. And this is the way that we see this being used.

6 If there is an exceedance of the CE-LOC, the

7 concept is to be able to go back -- essentially revert back to

8 those watersheds and look at the watershed properties and see if

9 they fall in line with the vulnerability characteristics that

10 have been identified in the 2009 vulnerability assessment that

11 has already previously conducted.

12 If they don't, we have to ask the question:

13 did we miss something in the vulnerability assessment, or is

14 there something unique to that particular watershed that we need

15 to take a closer look at?

16 So really, this is a way to really kind of

17 ramp up the resolution of our exposure assessment and be able to

18 -- it's kind of a model testing, too, in a way because we're

19 really testing if our vulnerability assessment is capturing the

20 vulnerable sites.

21 DR. TIMOTHY ELLSWORTH: So one more comment on

22 that. The goal as I see it is, you know, you want an optimal

23 bias factor estimation algorithm, and you want to minimize the

24 variance of the bias factors for a given sampling interval in

25 day rolling average.

26 And you want to minimize the bias of the bias
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1 factor. In other words, you want to minimize the magnitude of

2 the bias factor, right? Those are kind of the two guiding

3 criteria for selecting the optimal approach of estimating bias

4 factors.

5 I'm sorry. I shouldn't be asking you that

6 question, but that's what I see here as what's going on, is

7 that's the guiding goal.

8 And so, to me, the key part of that is really

9 going to focus -- and we'll talk about that, I think, later in

10 the subsequent questions, the infilling is really going to play

11 a critical role in trying to minimize, you know, the variance of

12 those bias factors and the magnitude of the bias factors so that

13 you get a better testing.

14 DR. DANIEL SCHLENK: Okay. Dr. Effland.

15 DR. WILLIAM EFFLAND: A couple of comments. I

16 agree with everything I've heard so far. One thing that I

17 learned, actually, was that this was all proposed through the

18 2011 SAP in supporting, and it was with community water supply

19 systems, so I want to make that point. I guess I was wondering

20 looking at some of the second and third order streams and the

21 photographs that we saw the last couple of days, those are

22 obviously not community water supplies where that information

23 was derived from. 

24 So I think that that's definitely very

25 conservative in a way that data has been collected, looking at

26 vulnerable watersheds and then at that level within the
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1 watershed.

2 The other thing that I would note is that the

3 function of the bias factors is interpolation of the available

4 monitoring data, and then identification of the watersheds with

5 postulated effects. And I want to make the point -- I did make

6 it earlier, but I would like to make the point and several other

7 of our colleagues here have made it -- that the discussion about

8 irreversible permit changes in a body plant community structure

9 function, that's a hypothesis.

10 It's stated very often in this white paper

11 that it sounds like it's a conclusion that's been reached, but I

12 really think it's a hypothesis at this point. I don't have any

13 evidence otherwise.

14 Another point I'd like to make is the bias

15 factor addresses the uncertainty associated with the

16 difficulties with monitoring, but it doesn't account for

17 temporal-spatial variability. And I think Dr. Portier mentioned

18 that when he talked about geography and I was glad to hear that

19 term. I think that's an important thing to consider.

20 The other point that I would like to make -- I

21 was having a discussion with Dr. Young on the bus last night. 

22 We were talking about this, and one of the things that came up

23 was where we're talking about applying this to where I think to

24 either moderate or sparse sampling, where there's more limited

25 sampling data rather than the AEEMP study that had very very

26 detailed information.
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1 And even though we would expect that this bias

2 factor -- we may not hit maximum concentration, just by chance,

3 a random or a systematic sparse or moderate sampling activity

4 could by chance hit the maximum concentration. So if you were

5 to be in a monitoring situation and you hit the maximum

6 concentration, and then you applied the bias factor to that, the

7 result is going to be under - or least I would expect it to be.

8 Another point that I'd like to make is that

9 the bias factor is an adjustment for monitoring problems because

10 of monitoring study constraints. And I'll actually have some

11 more comments to make about this later, but one of the things

12 that you mentioned is sampling frequency is, you know -- and

13 then of course event days for systematic sampling. I've heard

14 and I've seen quite often that major runoff events aren't

15 necessarily captured, and we're looking at runoff as the primary

16 driver in our transport assessment. So that's something to

17 consider. 

18 And then actually, I have a couple of comments

19 about modeling. There's a lot of modeling that's done in this

20 assessment process. I really appreciated somebody's comment

21 earlier this morning where they said if we can see a one-page

22 flow process of how this works and how -- where decisions --

23 there's a risk management decision on that 50/50 chance with

24 that one diagram that we've talked about several times.

25 So there's certainly things that are going on

26 beyond just risk assessment in your process, so I think that
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1 would be valuable. Well, we talk about a lot of your models,

2 but there's really not any discussion about -- there was

3 Syngenta's presentation on prism hybrid, and I really don't know

4 very much about that because I'm not even sure -- we're supposed

5 to get a document, but I don't have anything. I haven't seen

6 anything yet on that, so I can't really comment on that except

7 for there are a lot of process-based models that are used for

8 looking at water quality. And one that I'll mention, of course,

9 is one that EPA uses and has supported this whole water

10 assessment tool.

11 And there are maybe some shortcomings with

12 that as far as handling pesticides, some of the routines and

13 things, the algorithms may not be as robust as we'd like to have

14 them. So I'll just bring that up.

15 And then I guess I really just need a

16 clarification, and I know I'm not supposed to be asking any

17 questions, but I'm going to do this anyways. The document --

18 the white paper -- which I understand is a work-in-progress,

19 especially this section, there are a lot of this -- a lot of the

20 Topic C section that's -- well, we're still working on this and

21 we're still doing things, but somewhere in that document, it

22 says that the reason that EPA does not want to use PRZM hybrid

23 is because there's a hydrologic process that's not captured by

24 prism hybrid, and I'm assuming -- I've had some discussions with

25 my colleagues, and I'm assuming that's interflow, shallow

26 subsurface flow.
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1 I don't really know the answer to that because

2 I don't have the PRZM hybrid documentation.

3 DR. DANIEL SCHLENK: So you're asking a

4 specific question. What's your specific question for

5 clarification?

6 DR. WILLIAM EFFLAND: What hydrologic process

7 is missing from PRZM hybrid that EPA has decided makes the model

8 not applicable in this situation?

9 DR. NELSON THURMAN: I did a lot of that

10 analysis in 2009. And I think the issue was, it was actually a

11 slightly different way than Syngenta did. It was using one of

12 the water's vulnerability assessment. And essentially because

13 PRZM is a pure number based models, so it just a portion on

14 curve number. 

15 What we were identifying the shallow-draining

16 restrictive layers; that in the nuances of a runoff where you

17 hit this drain-restrictive layer, not only is your storage

18 capacity limited, but you do get that subsurface flow.

19 That nuance of that is not captured well in a

20 curved number approach. Now the way to do that would be to

21 adjust your curve for those, so that would be a way to do it. 

22 But the point of that was because we felt that

23 if you're looking at watershed characteristics to identify

24 vulnerable watersheds, you're better off looking at the nature

25 or soil-based characteristics that are actually measured as

26 opposed to a model-based characteristic. So that was the
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1 comment. That was the reason behind that.

2 DR. DANIEL SCHLENK: Okay. Thanks.

3 DR. WILLIAM EFFLAND: Okay. Thank you. I

4 appreciate that. Of course, a curved number, there -- it can be

5 problematic, but it's used very widely and it's very widely

6 accepted certainly as its values.

7 DR. DANIEL SCHLENK: Just to clarify, you guys

8 can ask questions. It's okay. It's the other way. You can

9 always ask questions of the Agency, okay? It's just that they

10 can't ask you questions unless you have a question.

11 DR. WILLIAM EFFLAND: Okay.

12 DR. DANIEL SCHLENK: And also, too, Dr.

13 Effland, can you just specify the information that you were

14 after? Is that what's just been provided to you?

15 DR. WILLIAM EFFLAND: I don't know. There was

16 a comment yesterday that there was going to be some information

17 put into the docket about PRZM Hybrid, and someone earlier this

18 morning said it was 70 pages and this doesn't seem to be 70

19 pages. But several of us were interested in that.

20 DR. DANIEL SCHLENK: Okay. Dr. Matten has it.

21 She'll get back to you electronically, okay? She's going to

22 email it to you. 

23 DR. WILLIAM EFFLAND: Yeah, I can't get to my

24 email.

25 DR. DANIEL SCHLENK: Sorry, she's got a flash

26 drive, so she'll give it to you by flash drive. I'm sorry. 
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1 Okay. Next on the list -- let's see. We had Dr. Engel.

2 DR. BERNARD ENGEL: My points have been

3 covered at this point, so I'll pass.

4 DR. DANIEL SCHLENK: Any other comments from

5 the other panelist?

6 MR. ROBERT GILLIOM: So I had one comment to

7 add to this one that could be worked in later that kind of fits

8 into the flow better here.

9 I think that the point is it's kind of related

10 to the application of this step in screening level assessments. 

11 And the idea is that the common thread of logic through all this

12 hierarchy of methods ought to be the probability of exceeding

13 the LOC that you set.

14 And if it's viewed that way, then every

15 method, whether it's bias factor, correction for a specific site

16 or extrapolation to another site or regression method to

17 extrapolate, the sites have no monitoring data, can all be put

18 in the same context.

19 So as we talk through the methods, I think

20 it's just important that we keep in mind that that probability

21 -- the criteria has to be embedded in the policy. So for

22 example, right now, in the derivation of the site-specific bias

23 factor that then go into the regression and all that is selected

24 at the 95th percentile of assimilations. That's fine, but

25 that's a conservative number, and it has policy implications

26 that's kind of saying, "Okay. We took the one-year data sets
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1 and did all this, and we want to make sure we don't misrepresent

2 them so we do a one 20-year type of an approach." That's

3 conservative, that's fine. 

4 And it's just that right now it's not always

5 clear through the whole hierarchy of each method how the

6 uncertainty and biases are explicitly incorporated. So that

7 would be my request, I think, is just to as this progresses,

8 clean up how those factors are represented and incorporated and

9 be certain that the whole chain of events relates back to that

10 probability.

11 DR. DANIEL SCHLENK: Okay. Dr. Young.

12 DR. LINDA YOUNG: I just want to build on that

13 because especially listening this morning, we had very

14 conservative approach to establishing the LOC. We have a

15 conservative approach here, and I think there's great value in

16 being conservative, but when you string it altogether, I wonder

17 if it's just simply conservative or unrealistically so.

18 So it's just something to keep in mind,

19 building upon what Dr. Gilliom just said.

20 DR. KENNETH PORTIER: I just wanted to kind of

21 clarify a little bit about what Dr. Hetrick said. So this is

22 used within and the CE-LOC, right? So as we heard today that

23 the CE-LOC is going to be used with PATI on the AMP data to

24 identify sites that they expect probably will have effect versus

25 probably won't have effect.

26 Then they're going to apply these risk factors
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1 to adjust the values to kind of determine which sites. It's

2 kind of a way of kind of adding to the sites that probably have

3 effect by adjusting for sample size, in this case. But then

4 they're going to take this to (inaudible) analysis, looking at

5 the site properly, try to address properties that relate to

6 potential sites that have impact.

7 So this is again an -- isn't that right -- an

8 intermediate step to looking more broadly as a GIS? I thought

9 that was the whole point is -- I'm sorry, but I thought that was

10 the whole point of Sections 16.2 and 16.3.

11 DR. JAMES HETRICK: The concept here is to be

12 able to identify sites that might have potential atrazine

13 concentrations that exceed CE-LOC. And if we have those

14 exceedances then it would kind of, as we showed in the risk

15 assessment framework slide, it would bounce into a zone where we

16 have to see what type of refinements that need to be considered

17 and go back and really do a higher resolution exposure

18 assessment on that particular site.

19 And that would really entail several -- it

20 could entail multiple things. It could entail going in and

21 doing some additional monitoring. It could entail looking at

22 the watershed properties of that site and seeing how they

23 compare to the vulnerability factors that have already been

24 determined. And it may even entail doing some watershed

25 modeling for that particular site.

26 DR. KENNETH PORTIER: Thank you for that
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1 clarification.

2 DR. DANIEL SCHLENK: All right. Any other

3 comments on -- yes, Dr. Ellsworth.

4 DR. TIMOTHY ELLSWORTH: One short one I just

5 want to make that Ken touched on, which was some of the sites

6 have this 24-hour sipping bottle sampler. And I'm just making

7 sure that, you know, in estimating the bias factor, that's a

8 completely different creature than this point sampling. Just

9 making sure that wasn't lumped in somewhere.

10 DR. DANIEL SCHLENK: Thank you. Any other

11 comments for Question 10? Okay. Let me go to Dr. Hetrick. Do

12 you have any questions for clarification?

13 DR. JAMES HETRICK: No, I don't.

14 DR. DANIEL SCHLENK: Thank you very much. 

15 Okay. I think I've been told by Dr. Portier that we can sneak

16 11 in here before lunch, so you can blame your hungry stomach on

17 him now. But we're going to start with 11a. Mr. Spatz, can you

18 read that into the record?

19 MR. DANA SPATZ: Please comment on EPA's

20 prediction of bias factors for monitoring data using categorical

21 or regression that it approaches.

22 DR. DANIEL SCHLENK: Dr. Portier.

23 DR. KENNETH PORTIER: I will point out Dan

24 asked that question 15 minutes ago, and he didn't ask me, "When

25 do you want to go to lunch?" 

26 It is clear and reasonable that the fewer the
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1 sampling intervals, the lower the likelihood of observing the

2 maximum 60-day average concentration. Since a greater

3 likelihood of missing important (inaudible) using the results of

4 the Monte Carlo study, three regression approaches and one

5 categorical approach were used to select the bias factors to be

6 applied depending on the number of samples available at the site

7 during the second and third quarters.

8 The categorical approach is the simplest

9 approach, using the upper 95 percent confidence interval with

10 the mean bias factor for the different sampling frequency

11 classes. And they identified, you know, 25 to 46 samples or --

12 it has a bias factor of like 1.87, which they rounded to 2; 12

13 to 26 samples have a bias factor of 2.94, which is rounded to 3,

14 and so on; all the way down to less than 6 samples and the bias

15 factor is like, 10.80, which they round up to 11.

16 So one of the issues that's not addressed is

17 what about sites with greater than 46 samples? And that's 46

18 samples out of a possible 180 days, which is maybe not

19 unreasonable for certain sites; is the assumed bias factor going

20 to be 1? And that's not defined in the white paper.

21 And the other question is, you know, I guess I

22 would've liked to have seen more sampling intervals simulated in

23 the Monte Carlo. I mean, it just bothers me that it's kind of

24 limited to 4 and you don't justify those 4. I kind of know why.

25 I think there's some practical considerations that are in

26 there, but you probably need to mention those if you're going
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1 move forward. 

2 The regression approaches use only one

3 explanatory variable, which is the number of samples in the

4 second and third quarter combined, and neither watershed size or

5 average flow are significant from the stepwise regression

6 analysis performed.

7 I have a couple of issues with this approach. 

8 So if only if a stepwise regression approach was used as

9 specified in the question formulation, and I think, in the white

10 paper, it's possible that the best regression model was actually

11 not observed or not tested.

12 When three factors it would of been possible

13 to look at all possible combinations, you know, number of

14 samples with average flow, number of watershed size, because,

15 for example -- and this is very common with stepwise approaches.

16 You might see that the best single model is number of samples,

17 but actually, the best two variable model might have been

18 watershed size combined with flow, and the stepwise approach

19 would never look at that combination, right? So I'm just kind

20 of pointing that out. 

21 I think the second kind of more important

22 issue is that it's almost preordained that the number of samples

23 would be the only factor that we could do in regression because

24 it's the only factor we could control in the Monte Carlo study,

25 right? So we could produce a range of sample sizes, and as we

26 know from regression, if we have a good range on a predictor,
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1 it's more likely to be a significant factor in the multiple

2 regression.

3 And so we come back to the fact that we only

4 have five sites and seven dates in the original data sets with

5 limited hydrology and limited geography, so we limited the two

6 other factors fairly tightly so it's almost certain that it

7 wouldn't show up in a multiple regression. So the fact that it

8 doesn't show up in the regression, to me, is an "Ah ha" moment. 

9 Okay. 

10 I think the more important issue is how do we

11 increase the sample size so we can get more range and flow, more

12 range in hydrology and geography so we can really test that

13 concept.

14 Now let me talk about your regressions one,

15 two, and three. So in regression one, we used the best/least

16 where it's not many of regression to fit the mean of the four

17 groups of data -- that's in figure 31 of the white paper -- to a

18 fourth parameter exponential decay model.

19 And that particular model, if you look at it,

20 the exponent for the parameter -- the sole parameter in the

21 exponential components is the same for both parts. It's -0.71

22 for both. So the coefficients of the two exponential components

23 are the same, and the intercept coefficients are about the same,

24 but are a different size.

25 So we kind of have two exponentials without

26 the same -- with the same shape, and you're kind of shifting
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1 them by a little bit. The bottom line is this is a terrible

2 model. It has terrible statistical properties. I don't know

3 how to say it any better than that.

4 It's what we call a poorly parameterized

5 model, so that's kind of the right way to look at it; but it's

6 not something I would want to really put a lot of confidence in.

7

8 So the form of the equation, describing the

9 upper 95 percent confidence interval; so from this parameterized

10 model you hit 95 percent confidence values for each of the

11 points, and then you fit another regression for those points,

12 which when you look at that -- and it was another two

13 exponential component regression. But when you look at the

14 parameters, it's pretty close to a simple exponential curve. 

15 Because one of the parameters has an exponent of minus one; the

16 other one has a -0.04, which means it's almost zero.

17 So, you know, I think the bottom point is with

18 the four groups and fitting four parameter exponentials in here,

19 you're really pushing it here, okay? I know Dr. Hetrick wants

20 to jump in, but I'm going to put all my critiques on the line

21 and then you can come back and say why you did it like you did

22 it, okay?

23 So regression two, this is a regression fit to

24 the average bias factor. So now instead of the seven for each

25 groups, we're going to fit a line through four means of those

26 groups. Well, now you get a R square of one. You fit four
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1 parameter, non-linear curve, the four data points, you get an R

2 square of one. So this is just a smoothing technique.

3 And here, you can't compute 95 percent

4 confidence interval, so you wouldn't be able to make the

5 probability statements that Dr. Gilliom was trying to relay. So

6 I'm going to throw that one out.

7 In regression three, you used a first-order

8 linear model on the log transformed bias factor values, so now

9 you have the log of the bias factors as a function of the sample

10 size and you did a linear regression. You did a R square of

11 about 0.46, which I would expect.

12 The fit, as far as I can tell by looking at

13 the figure in the white paper and looking at the output, in

14 appendix K was not bad. I mean, it's not bad. The white paper

15 suggests that the regression model for the 95 percent confidence

16 interval, so you use the model to get 95 percent confidence

17 interval and then you fit another curve through it. You fit

18 that as a linear regression through the points.

19 But really, the regression tells us that it

20 should be hyperbola. And you can look at the graph, Figure 33,

21 and you see that it is curved, so I guess that was just an

22 approximation to pick that 95 percent point to use a straight

23 line.

24 The bottom line is all of this is still, from

25 my point of view, these are all just smoothing curves to try to

26 come up with that. And I think you would've done better to use
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1 the Monte Carlo to generate more sampling intervals so you

2 would've had a little bit more confidence in any of these

3 regression approaches, allowing you to do a better job of

4 predicting the equation for the 95th confidence interval.

5 The problem is if you put more groups in

6 there, you might actually lower the confidence band a little

7 bit, so your 95th percentile estimates maybe a little bit

8 smaller, and I don't think it's going to have that much effect.

9 The only thing that's going to really affect

10 this regression is more sites that cover some of these other

11 factors, and I think that's my bottom line for this.

12 DR. DANIEL SCHLENK: Okay. Dr. Young.

13 DR. LINDA YOUNG: I just have a couple of

14 things to add. In the regressions you consistently used a 5

15 percent significance level. And generally, in developing model,

16 you want to let that up. You know, 0.25 or 0.30 -- if there's a

17 scientific foundation, and I see a 0.20, that variable goes in

18 the model, it would make it a better model and it would increase

19 R2, it would reduce the width of your intervals and so forth.

20 So that'd be one thing. And then the other is

21 I really think you should be using prediction intervals instead

22 of confidence intervals. Confidence intervals are on the mean

23 and prediction intervals predict for a watershed, and I think

24 that latter is what you want to do. So that would be another

25 approach.

26 DR. DANIEL SCHLENK: Okay. Thank you. Bob
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1 Gilliom.

2 MR. ROBERT GILLIOM: I think given the other

3 stuff that's said, I don't want to dwell on the methods of the

4 regression and all that. My main point, I think I'll come back

5 to, and the question is the categorical versus regression

6 question and basically say that if at all possible, you know, I

7 think this reflects the discussion that happened in the last

8 year's SAP. 

9 It would be preferable to get an adequate data

10 set developed and develop a sensible regression equation model

11 so you can incorporate some factors and allow a consistent

12 method to be used across all sites with some known degree of

13 reliability rather than have to fall back to a simple

14 categorical method.

15 So the categorical method has some advantages,

16 and then there is no viable regression model or variables that

17 explain anything. Maybe it comes to that when we do it all

18 completely from the beginning, but I don't think it will. I

19 think it'll probably support a regression model that's developed

20 from an improved data set.

21 DR. DANIEL SCHLENK: Dr. Ellsworth.

22 DR. TIMOTHY ELLSWORTH: And one thing I'm not

23 a statistician, so what I would do in this case, I think, if I

24 had the regression, I would use the actual original bias factor

25 estimates. You know, if you have 300 of them, calculate 300

26 chemographs for your sparse data set and actually look at the 95
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1 percent. In other words, you're using a multiplicative factors,

2 okay? And you're doing the smoothing before by fitting the

3 regression, and then trying to come up with one estimate. I

4 would want to just make sure whatever regression I used gave me

5 pretty much the same answer as if I took those 500 values and

6 multiplied them by that sparse data set and looked at the 95

7 percent value directly and said, "Okay. this smoothing that I'm

8 doing with the regression is giving me a pretty good estimate of

9 that." 

10 Because there's some upscheming issues going

11 on here and you've got a multiplicative non-linear model, so I'd

12 be nervous about some bias that would be introduced in doing the

13 regression first on that.

14 DR. DANIEL SCHLENK: Ken Portier.

15 DR. KENNETH PORTIER: I need to understand

16 that. So we don't have bias factors for anything but these

17 seven data points.

18 DR. TIMOTHY ELLSWORTH: As I understand this,

19 I've got a seven-day sampling interval, let's say, estimating a

20 60-day rolling average. For that maybe I had 600 site years of

21 data that I used to estimate bias factor using that stratifying

22 random sampling approach. So if you took those 600 bias factors

23 and applied them to a sparse data set that you were trying to

24 estimate the chemograph for -- is that --

25 DR. KENNETH PORTIER: I think the problem is

26 you don't have the true value to compute the bias factor for
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1 these other sites. The only reason these seven data points were

2 useful is that we have the actual daily sampling data; so you

3 know exactly what the true maximum 60-day value is. We don't

4 have that for the other sites where they didn't complete sample.

5 DR. TIMOTHY ELLSWORTH: No, I agree with you. 

6 All I'm saying is I would look at my comparison for a given

7 watershed where I know what the true value is and calculate the

8 95 percent to make sure I'm doing it right. I would compare

9 using all those bias factors and -- let me think about this,

10 actually. Actually, I'm not sure right now. Let me think about

11 it.

12 DR. DANIEL SCHLENK: Mr. Gilliom.

13 MR. ROBERT GILLIOM: I'll just add something

14 on the same topic. So I think the point taken out of the white

15 paper, to me, is, is that the demonstration regression on the

16 six sites or whatever it was really mostly that. And it was a

17 demonstration of the approach with very few sites that had a

18 really strict criteria. And I think it's true that when those

19 criteria are just relaxed a little bit and modified and with

20 more data coming in, that there's many more observations already

21 available to use for a better model development, if that's

22 right. I think I am.

23 So the concerns about just six sites don't

24 need to go too far because there's going to be way more to use

25 without compromising the integrity in the model development data

26 set once the full exercise is gone through.
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1 DR. DANIEL SCHLENK: Dr. Effland.

2 DR. WILLIAM EFFLAND: I don't have any further

3 comments.

4 DR. DANIEL SCHLENK: Dr. Engel.

5 DR. BERNARD ENGEL: Nothing further.

6 DR. DANIEL SCHLENK: Any other responses on

7 the other side of the table? Okay. Let me go to Dr. Hetrick. 

8 Do you have any questions?

9 DR. JAMES HETRICK: I just wanted to follow up

10 with Dr. Portier and what Dr. Bob Gilliom said. 

11 This is really an illustration of a process. 

12 And I realize that the other parameter applies the non-linear

13 regression models. I think I tried every non-linear regression

14 model that looked like it would fit out of sigmaStat just to see

15 if I could get one that fit that came up with something.

16 So this is not the end all as far as the

17 equation is concerned. It's really we're looking at a process

18 and trying to demonstrate with an illustrative process, that's

19 all.

20 DR. DANIEL SCHLENK: Okay. Do you want to do

21 b? You want to read b into the record.

22 MR. DANA SPATZ: Please comment on any

23 additional methods for estimating bias factors that would be

24 useful in this situation.

25 DR. DANIEL SCHLENK: Okay. I guess we'll come

26 back to that. I was misinformed. When I said 11, I meant a and
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1 b -- 11 -- so let's break for lunch, and Dr. Farruggia, we did

2 receive the email and Jim Fairchild will respond to your

3 questions of clarification after lunch.

4 (WHEREUPON, a lunch break was taken.)

5 DR. DANIEL SCHLENK: We're on 11b. Okay. So

6 let's go ahead, and I think Dr. Engel, you are going to start

7 off with this, correct?

8 DR. BERNARD ENGEL: So I guess what we -- sort

9 of get to the endpoint and then explain how I got there on this

10 one. So I think it'd be quite interesting to take a look at

11 hydrologic (inaudible) models, maybe something like the

12 PRZM-Hybrid item that was presented by Syngenta earlier this

13 week, to see how that might replace bias factors or how that

14 might be used to complement bias factors.

15 So how did I arrive there? If we think about

16 potential pesticide losses in watersheds, in largely a function

17 of watershed characteristics that are not changing very rapidly

18 or certainly changing quite slowly as well as a group of

19 temporal factors, largely rainfall and wind, pesticides might be

20 applied in a particular year.

21 I suppose if we carry that event further and

22 think about stewardship, Syngenta presentation called that out,

23 but it might be something that maybe falls in one or both of

24 those categories as well.

25 And think then about with bias factors we're

26 really trying to understand that temporal variation from these
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1 watersheds with the bias factors to some degree. And therefore,

2 we might use models combined with some of this approach. That

3 might provide for an opportunity to improve some of these

4 estimates.

5 I guess carrying that a step further, given

6 that most watersheds are unlikely to have monitored data or

7 certainly a large number are likely to have no monitor or very

8 limited monitor data available, sort of this hybrid approach

9 could also potentially allow one to span that gap between

10 monitored/unmonitored watersheds and allow then, you know,

11 development of technology or approach that would have utility of

12 both monitored and unmonitored watersheds.

13 I guess just a brief comment on some of the

14 stewardship or management practices. I would be inclined not to

15 worry about any of those in this kind of approach. Those that

16 seem to be something that might be considered later in a tiered

17 sort of approach or a more in-depth look, given the complexity

18 in trying to estimate which practices where they may be located

19 at this time; I don't think at this level it would be necessary

20 to pursue those.

21 Let me maybe stop there at this point and take

22 comment on some other questions then.

23 DR. DANIEL SCHLENK: Okay. Dr. Effland.

24 DR. WILLIAM EFFLAND: I agree very much with

25 Dr Engel's comments, and I appreciate those. I think maybe

26 that's where I was trying to go with some of my earlier comments
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1 about models. And I would just bring up one other point, and

2 that is one of the presentations -- I think it was in yours, Dr.

3 Hetrick, was a long list of monitoring data from states.

4 And I don't know the applicability of those

5 various data sets, I know they're not as detailed as the two

6 data sets that you use. But I would certainly encourage you to

7 take advantage of anything that you could glean from those as

8 far as maybe you could use that kind of information to -- if you

9 were to apply this bias factor, what would that tell you about

10 that particular region?

11 Because I completely agree with the earlier

12 comments about geographically limited with those data sets

13 because you've got -- I think it's Northwest Ohio, and then

14 you've got the various data sets in a few watersheds that were

15 specifically targeted for vulnerability.

16 So I think it would be worthwhile to look at

17 some of those other data sets. It would help to start to

18 approach what's it going to be like if they use, let's say, one

19 of those data sets -- if a state has to apply the process of a

20 procedure that's being proposed, whether they end up -- or just

21 using a data set?

22 I remember seeing -- I don't know what the

23 number was, but it must have been 20 different data sets, and I

24 thought, "Wow, what a great resource and, you know, why not take

25 advantage of that information?" So I guess I would encourage

26 that.
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1 DR. DANIEL SCHLENK: Okay. Dr. Ellsworth.

2 DR. TIMOTHY ELLSWORTH: The only thing I

3 wanted to say here is I think it might be possible to estimate

4 these BF distributions on the basis of watershed characteristics

5 and identify classes of watersheds.

6 And then you could estimate BF values for

7 those classes based on characteristics like (inaudible) or

8 something.

9 DR. DANIEL SCHLENK: Bob Gilliom.

10 MR. ROBERT GILLIOM: I think a number of ideas

11 for possible infilling methods were already spelled out in the

12 last SAP and I don't need to go over it again. A lot was stated

13 in that report about those pros and cons, including some of the

14 ones you haven't had a chance to apply yet. So I think, in a

15 way, a bit of a road map is in there already.

16 On the other hand, too, I think this

17 particular problem, I always say in a sense some care needs to

18 be taken not to let it sidetrack the rest of the process because

19 it's kind of the tail of the dog instead of the dog, you know

20 what I mean?

21 So it can get into all kinds of complicated

22 statistics stuff and everything, but the bottom line is it's

23 just a relatively straight ahead attempt to adapt existing

24 monitoring data, that means the basic standard, to be comparable

25 to the LOC on the same basis as a more intensely monitored data.

26 So I think it's that simple, in a way. 
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1 There's some issues to deal with, but they're not dealbreakers. 

2 They just need to be carefully dealt with. And in light of

3 that, I think it needs to be thought of in terms of a hierarchy

4 of the approaches you're going to need to use for sites and

5 locations in different situations. So instead of having just,

6 you know, black and white, this or that, I think you're going to

7 have a few different types of site situations with different

8 degrees of existing monitoring. And then you're going to have,

9 as has been said, most of the locations with no monitoring.

10 So I would just kind of think of it as a

11 hierarchy to try to develop the methods along those lines, and

12 keep in mind that the ultimate benchmark is the probability of

13 exceeding the LOC that came out from the other end.

14 DR. DANIEL SCHLENK: Dr. Young.

15 DR. LINDA YOUNG: I have nothing to add.

16 DR. DANIEL SCHLENK: And Dr. Portier.

17 DR. KENNETH PORTIER: Thank you. Basically,

18 when Dr. Gilliom mentioned that this was a demonstration

19 project, now my comments actually make sense to me; because I

20 sort of look at this and go, "Oh, that's what they were trying

21 to do. No wonder I made these comments." Because I really

22 don't have an additional methodology. My recommendations are

23 more improving the estimation of the bias factors.

24 So it's taking the methodology you developed

25 and expanding it to kind of those -- and a lot of the things I'm

26 talking about really are in line with what we talked about in
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1 the 2011 SAP. There we made a number of recommendations on the

2 use of modeling in relationships to produce more complete time

3 series that could be used in the development of bias factors

4 using the WARP, PRZM and SEAWAVE and all these different models

5 were mentioned.

6 We don't see that in this white paper, but if

7 this just was a demonstration of methodology, that explains why

8 it wasn't mentioned. The 2011 SAP did recommend, a little bit

9 like what Bob's talking about, is establishing acceptable

10 tradeoffs between theory and practice in the assessment here,

11 and I think enforcing that concept here? 

12 I think the next thing in your strategy is to

13 think about utilizing more of the AEEMP sites in this, but maybe

14 developing a strategy for which sites we're going to put effort

15 into. And that means thinking about the site characteristics

16 that you'd like to have, to explain variability in risk factors

17 like basin size, average flows, site types.

18 So, you know, I'm not saying run out and, you

19 know, theoretically fill out all of the profiles with

20 model-based data. That's a major undertaking. But I think you

21 could say select sites for their characteristics to expand the

22 range, and then go back to the multiple regressions -- keeping

23 it simple -- just to kind of predict the variability in the

24 factors.

25 That's the only thing I would say.

26 DR. DANIEL SCHLENK: Okay. Any other
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1 comments? Any Panel members? Okay. Dr. Hetrick, do you have

2 any questions regarding the Panel's comments?

3 DR. JAMES HETRICK: No, I think we're fine.

4 DR. DANIEL SCHLENK: Okay. So move onto

5 Question 12.

6 MR. DANA SPATZ: Stepwise regression analysis

7 indicates that watershed size and average flow rate in the

8 second and third quarters of the year are not significant

9 variables for prediction of bias factors. However, the number

10 of samples in the second and third quarters of the year was

11 found to be a significant variable, accounting for 46% of the

12 variation in bias factor. What other variables, if any, should

13 be considered in the prediction of bias factors?

14 DR. DANIEL SCHLENK: Okay. Dr. Ellsworth's

15 going to lead off on that.

16 DR. TIMOTHY ELLSWORTH: Okay. So the other

17 factors, I think, that need to be brought into play -- I'm going

18 to go through a list here in a second, but -- one thing that

19 came out in the discussion earlier actually when Bob Gilliom

20 suggested this was, you know, if you get something like 100 ppb

21 values, do you multiple that by a bias factor of 10 or 15? Or

22 what if you get a 10 ppb value? The idea coming up is, you

23 know, that's a pretty extreme event. You really want to

24 multiply all those values, that's something you need to

25 consider. 

26 I'm not sure what the right answer is at this
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1 point, but at least consider the fact that should the bias

2 factor -- you know, take into consideration the fact that you're

3 not only trying to infill, if you will. I'm not sure yet what

4 the answer is on that, but I'll think about it more and maybe

5 try and give you feedback in the report. 

6 I think you'd made a lot of progress, like

7 what Ken was talking about, developing this approach. And some

8 basic understanding has evolved. You got the systematic

9 sampling, which did better when you're, you know -- in other

10 words, better in terms of producing smaller bias factors

11 relevant to stratified random sampling.

12 And that was as the number of days and the

13 rolling average increased, it did better. And as the frequency

14 of sampling decreased, it did better. So those bias factors on

15 systematic sampling will be better than those that came from

16 stratifying random sampling when applied to data collected in

17 the systematic sampling design.

18 But I think something that comes up here, and

19 I concur with what you did in the white paper, is, you know, you

20 don't want to apply this to event-based sampling, and you said

21 that. But the other thing I think you want to think about here

22 is stratified random sampling bias factors are actually more

23 appropriate for instances where the monitoring truly isn't

24 sequential sampling.

25 And there may be times when you maybe need to

26 consider using more than one bias factor for a chemograph. If
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1 you've got, you know, sampling intervals that are four days and

2 some that are 10 days, you might want to consider using more

3 than one bias factor to get at that chemograph.

4 And as Paul Henley noted in his presentation,

5 the BF approach becomes very problematic as the interval gets

6 very large. And I think that's a real issue. You want an upper

7 bound -- you know, you need to maintain some acceptable level of

8 performance and confidence in the approach, and I'm not sure

9 exactly what that level of confidence and performance should be,

10 but there ought to be some kind of upper limit, I would think.

11 Again, coming back to this idea that

12 one-size-fits-all in the bias factor, there's an easier way to

13 go. These watershed characteristics need to be taken into

14 account. Most have been brought up, as you know, in previous

15 SAP's and that is a tremendous body of information. So maybe

16 the first step would be evaluating relationships between bias

17 factors and these watershed vulnerability factors.

18 Some of those, I think, you haven't looked at

19 yet, but I was really excited and wanted to look at. And I

20 mean, that's a tough one to look at where you have full data. 

21 This runoff propensity index, the 90th percentile flow versus

22 the 10th percentile flow -- that ratio is greater than two or --

23 there was some very good information on when that impacted

24 waterfall.

25 And the other one was just flow volume versus

26 watershed size was another one of those predictive variables
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1 that somehow got lost in this, that seemed like it would've been

2 a good one.

3 And then, you know, I think the intent here

4 for -- initially, I thought -- and I think you clarified this,

5 that you're really not going to be using this to identify

6 watersheds that need monitoring as a point of clarification

7 input, right? You're not going to be using these bias factors

8 in any type of approach where you're going to be designing

9 sampling events. 

10 And what I'm getting at with that is, you

11 know, if you're designing sampling, the sequential sampling

12 versus stratifying random sampling versus event-based sampling,

13 like if you only got limited resources, me, I want to spend for

14 event-based sampling, you know, if I've got some intelligence in

15 events.

16 But if that's not where you're going, then --

17 DR. JAMES HETRICK: And you're right. Again,

18 I want to emphasize that all we're doing is using this as a

19 screen to identify monitoring sites that may have potential

20 exceedances. If they do, then that really kicks into a higher

21 resolution assessment that requires to go back to that site and

22 look for watershed characteristics and things of that nature. 

23 It may also entail additional monitoring at

24 that site; maybe to see if there is a real problem there,

25 particularly if go back and look at the sample sizes, the sample

26 sizes are fairly small and sample frequency was kind of low,
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1 you'd probably increase that by a bit to get some more

2 confidence in its value.

3 DR. NELSON THURMAN: I just wanted to add one

4 other thing. Another reason that screening is if you look at a

5 PRZM hybrid of your data that's very time sensitive. So if we

6 were to do all 7,000 of those sites; so this is a way of

7 screening out which ones we really don't need to go back to.

8 DR. TIMOTHY ELLSWORTH: Okay. And the other

9 thing was -- so this runoff propensity index was one thing I

10 thought you should look at. Watershed flow versus watershed

11 size. And then flow event simulation in ungauged basins, okay? 

12 Which is a challenge. But there's (inaudible) actually has done

13 a lot of work on this and has made some serious progress on

14 this. You know, there's some literature on it.

15 And if you could get into that or you could do

16 a reasonable job on these small watersheds, you know, predicting

17 these flow events where you don't have actual data that would be

18 very good conditioning information for infill, especially when

19 you couple it with this planking window that they have, that

20 Syngenta had. They did a pretty darn good job of independently

21 predicting that planning window based on that radar information

22 and soil temperature.

23 So you could maybe help yourself out in that

24 approach. And you use this 10 meter DEM slope estimate for the

25 major soil groups, but somewhere also we lost this idea of

26 watershed conveyance of basin flow composite curve number is
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1 kind of an idea of looking at watershed characteristics as

2 another explanatory variable here in terms of grouping your bias

3 factors.

4 So I think what you're trying to do is find

5 kind of cluster analysis. This statistical distribution of bias

6 factors is different than this because of these watershed

7 characteristics. And, you know, going back to what Ken was

8 saying, trying to use all of the data you've got to help -- if

9 you've got a four-day sampling interval, I think you've got some

10 infilling that you could do there that would capture the

11 uncertainty in that chemograph.

12 With the four-day sampling interval, you're

13 going to do some conditional estimation there that's going to

14 bracket the range of uncertainty pretty well and you could still

15 use that data set to develop bias factors if you take that kind

16 of uncertainty into account.

17 Okay. The other thing I think I mentioned

18 earlier -- and, you know, Bob Gilliom and I talked about this,

19 and he's pretty much convinced it's not an issue, and so I don't

20 know that it is. I'm not convinced yet of his mindset. And

21 he's got a lot of experience on this, so I'm not putting that

22 down.

23 Just from the literature I've read and from

24 what I understand, it looks to me like there's a lot of

25 variability within these streams. There are some papers that

26 show 20-year spacings, 100-meter spacings, 500-meter spacing or
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1 even cross-sectional; in a stream you can get a lot of

2 variability.

3 And how's that taken into account here in

4 calculating your LOC? I'm not trying to open up another bag of

5 worms, but the idea is there's some uncertainty there. And, you

6 know, it's back to this concept of what is the regulatory scale

7 of interest here? You know, is it really small watersheds? Is

8 it a 15-square mile watershed?

9 I think somewhere in there, there needs to be

10 more of an explicit statement of what the spatial scale of

11 interest is. Because I could imagine a stream adjacent to a

12 cornfield might exceed this LOC, but maybe it's only for

13 100-meter stretch. Is that as significant?

14 You know, some monitoring group comes in and

15 says, "Hey, look, we've just detected over this stream reach

16 that you've exceeded." Are you going to say that's a

17 significant spatial scale? Do we even worry about it? I don't

18 think in any of the SAPs, we've actually resolved that issue

19 yet, and I would like to see it explicitly stated.

20 I know we kind of narrowed it down to second

21 and third order streams, but that's pretty broad. And I think

22 that the potential of exceeding the LOC is going to increase

23 maybe exponentially as you go to a smaller spatial scale. So

24 somewhere in there, you need to say this is enough, but anyway,

25 that's all.

26 DR. DANIEL SCHLENK: Dr. Portier.
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1 DR. KENNETH PORTIER: So what Tim Ellsworth

2 was just saying, I think this has been covered in the 2011 SAP. 

3 There's a couple of things that I want to say that I don't think

4 we talked about, but I'm pretty sure he hashed over -- I mean,

5 Dr. Gilliom probably couldn't support me on this, but I think we

6 hashed over all of that in that report. And so I'm not going to

7 go over that. 

8 I think the fact that -- and I'll reiterate --

9 because of the limited number of site years used in this

10 analysis and the way the data is created, it's not unexpected to

11 me that watershed size and average flow are not significant. 

12 And I really don't think there's going to be any other factors

13 in that small data set that are going come up significant. 

14 There's just not enough opportunity to cover a range of any

15 other factors. 

16 It's important that these factors be looked

17 at, and it brings us back to the previous discussion of the

18 importance of expanding that database before you go too much

19 further. And I think that's really all I have to say.

20 DR. DANIEL SCHLENK: Okay. Bob Gilliom.

21 MR. ROBERT GILLIOM: So I really do think on

22 this spatial question that it's not as important as the time

23 question. There's a time and a point at the bottom of the

24 stream segments, but let's keep our focus on it all, just to put

25 that on the record, which is what you've been doing.

26 To my way of thinking, the object of the
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1 resource that you're focused on is stream length for the

2 resource, but the spatial structure for explaining that is

3 watershed. So I think of it in terms of stream miles of a

4 certain condition, and each group of stream miles that has

5 basically similar attributing area is explained by that

6 watershed.

7 So what that's kind of meaning is a simple

8 example. If you have a tributary coming in, you start a new

9 segment, you go down, you don't have that much going on. You

10 have a new tributary coming in, and that's the end of that

11 segment. And that's how the hydrologic structure advantage T

12 system is set up with some modification.

13 So I think present-day modeling capabilities

14 will allow you to plug right into that and do it in terms of

15 stream length and watersheds associations.

16 I think everything else has been said. I

17 don't think we really have any shortage of variables to try, and

18 I guess the only specific thing I would say to that is the list

19 of variables already examined in both Syngenta reports and in

20 the USGS work probably have a comprehensive enough list

21 published that if you go through those and test them all in an

22 advanced data set, you'll cover the range.

23 DR. DANIEL SCHLENK: Okay. Dr. Young.

24 DR. LINDA YOUNG: I just want to be sure. I

25 think I misunderstood Dr. Ellsworth, but if not -- if you have

26 stopped every 4 days, no matter how you infill, you're never
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1 going to know whether you captured the peak or not. So it's

2 going to be problematic to use those to predict the bias factor.

3 DR. DANIEL SCHLENK: Okay. Dr. Effland.

4 DR. WILLIAM EFFLAND: Just a few additional

5 comments. From what I've seen and heard so far, and I guess I

6 propose or I believe that the bias factors could possibly vary

7 among watersheds. So I wonder about how you employ that kind of

8 process on the -- I'll just leave it at that for now.

9 And I would encourage you to look at the

10 watershed elevational characteristics. There's a lot of

11 available information, at least for elevation, modeling in GIS

12 format that's readily available. So if you use that with that,

13 that's certainly a very useful set of data.

14 And similar to what I said earlier, with

15 limited sampling, this is somewhat of a reverse of that. But

16 with limited sampling, you may miss the maximum concentration;

17 whereas earlier, I said just by chance, you may actually hit the

18 maximum concentration. So that's something to keep in mind with

19 the data analysis, especially as you look at some of your -- I

20 think the term I saw was sparsely sampled. I don't quite know

21 exactly what sparsely sampled is, but I guess the moderate was

22 daily or weekly maybe, so it's a pretty good data set. 

23 And then one of your statements here in your

24 opening to the question is: the number of samples accounts for

25 46 percent of the variation in your regression equation. I

26 guess the regression equation modeling in previous, in my time,
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1 and I think I got to 52 percent explaining -- when I was trying

2 to predict carbon in soils. And there was always the question

3 of: is that good enough? You would love it to be predicting 99

4 percent. So I guess I wonder how many of these variables do you

5 want to test?

6 How much of the percent of the variation do

7 you need before you're going to say, "We're there?" The reason

8 that I bring that up is there's a lot of very detailed, very

9 excellent data that's been produced to do the LOC and the same

10 with species, toxicity and all those things. And then you get

11 to this point, and it's like, you've got all that really refined

12 information and now you're starting to -- kind of starting to

13 chop at it with a machete, you know, rather than using a

14 surgical knife.

15 In my mind, from a practical standpoint, it

16 just seems like something that's going to be challenging as far

17 as analysis goes. I'll just stop with that.

18 DR. DANIEL SCHLENK: Okay. Dr. Engel.

19 DR. BERNARD ENGEL: I guess just a cautionary

20 note on using only number of samples in this two-quarter period.

21 That caution being that in reality that assumes that the

22 samples are somewhat equally spaced and therefore a sample set

23 that begins sampling once the pesticide has been applied and

24 continues that for a shorter period is probably going to be

25 equally valuable but result in a smaller number of samples from

26 that period.
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1 So there may be data sets and there may be

2 opportunities to certainly use this well beyond just looking at

3 total number of samples in this 180-day period.

4 DR. DANIEL SCHLENK: Okay. Any other comments

5 on Question 12? Dr. Ellsworth.

6 DR. TIMOTHY ELLSWORTH: Yeah, just coming back

7 through what was said about the four-day sampling interval. You

8 know, I think that type of infilling is done a lot in mining,

9 for example. And you're not going blind here -- you've got a

10 lot to condition on. I think you've got flow; they're

11 monitoring the flow with data loggers. And you've got flow

12 initiation in these watersheds when they're doing this four-day

13 sampling. You've got PRZM-Hybrid here, which is giving you some

14 estimates.

15 So I think there's a lot of conditioning data

16 to at least say, "What's the uncertainty in that four-day

17 window?" and come up with a reliable estimate of what the

18 uncertainty is. And then you could embed that in a stochastic

19 simulation and still come up with a good -- I mean, how much of

20 the data set is a four-day interval? That's the question.

21 Are you throwing out a lot of information if

22 you don't use it? So I would still urge you that you would want

23 to look into that.

24 DR. DANIEL SCHLENK: Dr. Portier. Or Dr.

25 Young, do you want to respond?

26 DR. LINDA YOUNG: Yeah. Just to follow up, if
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1 you had put all that in your explanation because the infilling

2 that we've seen here is much simpler in nature and wouldn't have

3 opportunity to (inaudible).

4 DR. DANIEL SCHLENK: Dr. Portier.

5 DR. KENNETH PORTIER: So I just wanted to kind

6 of give some insight on Effland's question about, inadequacy of

7 R2. You know, that 0.46, if they had continued once they met

8 and expanded their data set, I'd be happy with that. But I

9 suspect once you expand the data set, the R2 going to go down;

10 because number of samples is going to be less applicable as you

11 get more spatial-temporal variability included.

12 So I think that R2 is maybe more a function of

13 limited sample size at this point than actually the full utility

14 of number of samples. That's my concern, and that's what --

15 DR. WILLIAM EFFLAND: Okay. And I agree with

16 that. And I believe there are some statistical tests to look at

17 when that R2 is significant. I think maybe you did that, but I

18 just would also encourage that to be part of the analysis.

19 DR. DANIEL SCHLENK: Bob Gilliom.

20 MR. ROBERT GILLIOM: I guess just a couple

21 clarifications in my mind that you can tell me if I'm wrong on. 

22 One is there was some talk about the bias factor in relation to

23 individual samples, but the bias factor analysis you've done is

24 based on the 60-day moving average. It's not an individual

25 sample.

26 So it's just making the point that there's
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1 already been a lot of averages to that --

2 DR. JAMES HETRICK: That's correct.

3 MR. ROBERT GILLIOM: -- time interval. And

4 second, a major premise of this whole discussion about the

5 regression approach is based upon making "N" get a lot bigger. 

6 So you want to be up in the 50 to 100 site range at least. And

7 there's been somewhat conflicting messages about how much

8 relaxed the sample site of certain density requirements and so

9 forth to get an adequate data set for that using the fill in.

10 And I guess the only point I want to make is

11 it's kind of back to some of what we talked about with cosm

12 studies and which ones to include and which not. When you're

13 into that grey area, you guys just have some careful thinking to

14 do about how much to flex or change your site selection criteria

15 a little bit so you get "N" up. Because I think it's more

16 important for you to get "N" up and get a little bit on some of

17 the strictness of the criteria than it is to be really strict

18 and you site count way down; because we need the representation

19 of site types and geography in there more than we need the last

20 little bat's eyelash of the bias.

21 DR. DANIEL SCHLENK: Okay. Any other comments

22 for Question 12? Okay. Dr. Hetrick, any questions for

23 clarification?

24 DR. JAMES HETRICK: No, I don't think so.

25 DR. DANIEL SCHLENK: Okay. Thanks a lot. 

26 Okay. Let's move onto Question 13. Who's going to be leading
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1 that one? I believe Dr. Young.

2 MR. DANA SPATZ: EPA examined the performance

3 of various regression equations to assess the failure percentage

4 for identification of monitoring site-years with true maximum

5 60-day average concentrations exceeding the CE-LOC for atrazine.

6 This analysis showed that application of a bias factor, based

7 on sample number during the second and third quarter of the

8 year, substantially reduced the number of sites with

9 underestimation of true maximum 60-day means.

10 Given the EPA analysis, what other tests, if

11 any, should be conducted to assess the performance of regression

12 models for prediction of bias factors?

13 DR. DANIEL SCHLENK: Okay. Dr. Young.

14 DR. LINDA YOUNG: Well, we all know that the

15 regression model fits best on the data used to develop the

16 model. So what we have to do is to get some input in data for

17 validation purposes. Now that -- if we have trouble doing that,

18 you know, there's a tradeoff. You want all the data you can to

19 help develop the model, but at the same time, you need to

20 validate the model. So another approach that's often used is

21 like leave one out, by validation. So then if you take and

22 leave out each particular one and fit the model and then see how

23 close you predict the one that was left out and look at the

24 distribution of those errors that are associated with it, you

25 can begin to get a feel for how well the model fits.

26 The other thing that struck me is as we look
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1 at this, I mean, we have R2 in was way determining how well the

2 model fits based on using an R2. It's not just to use an R2,

3 but to actually use (inaudible) because you have replicate

4 samples and particular combinations of the variables, and you

5 know how much variation's there and how much total variation

6 there is.

7 And, you know, is the within variation and for

8 a particular combination of covariance, is it about the same as

9 you're seeing for the model? If so, you feel pretty good about

10 the model. So that's another thing that I've looked at in that

11 context.

12 DR. DANIEL SCHLENK: Okay. Dr. Portier.

13 DR. KENNETH PORTIER: To me, it's not so much

14 other tests, since I think the tests you use are adequate. The

15 issue is more to do with the impact of only considering the

16 reduction of the false negative rate; that is reducing the

17 number of sites where the true maximum of 60-day concentration

18 is underestimated as the only outcome you're examining, this is

19 an auto-building process.

20 We know that this number goes to zero, you

21 know, which will occur if you continue to increase bias factors.

22 The number of sites where this maximum is greatly exceeded will

23 go up as well. So as you drive that number of mistakes down,

24 your overestimation's going to go up. And it's a balancing act.

25 And I think getting this balance right is a trick, and I

26 haven't figured that one out yet. I was trying to figure out,
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1 "How do I say that?" And I don't know.

2 I think part of this is the general perception

3 that the Panel has that the current approach is likely to

4 produce a very conservative estimate. And so my warning here is

5 that you need to be thinking not just in terms of driving that

6 error rate down, but what is it meaning to be conservativeness

7 of the practice on the other end? Those are the two things that

8 have to be considered.

9 I've never really seen statisticians or

10 anybody else kind of take that into consideration. Dr. Young

11 might know because in the medical sciences, you know, false

12 positive, false negative rates and that stuff.

13 DR. DANIEL SCHLENK: Did you want to respond,

14 Dr. Young?

15 DR. LINDA YOUNG: Yeah, just to build on that,

16 I had it down here. One of the things that I would do is, if I

17 was doing this, leave one out or had a validation data set, I'd

18 look at the proportion of the samples that fell above the 95

19 percent prediction interval, it ought to be two and a half

20 percent. 

21 So if it's a lot larger than that, there's a

22 problem. But if there's not any above that prediction interval,

23 it's way conservative. And so that's one way of kind of getting

24 at that.

25 DR. DANIEL SCHLENK: Okay. Bob Gilliom.

26 MR. ROBERT GILLIOM: I think maybe the only
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1 thing I'll add is that I think that it's important to get your

2 site year (inaudible) if possible for the regression

3 development. So they'll leave one out. Those kind of methods

4 of retaining the data is important to do. I do think the

5 remaining data there are not perfect but pretty good can be used

6 very usefully in a qualitative validation by just basically

7 tracking whether the systematic differences are where you'd

8 expect them.

9 You know what to expect. If everything's

10 working right, you should be getting the kind of 5 percent rate

11 of underestimation because you use the 95th percentile. So just

12 use these numbers and build a case qualitatively in addition to

13 the more rigorous quantitative method that you use with

14 regression. That's it.

15 DR. DANIEL SCHLENK: Dr. Ellsworth.

16 DR. TIMOTHY ELLSWORTH: I just actually had

17 the same thing that Ken said. You know, the false positive,

18 false negative balancing act. I think it's really important

19 because, you know, looking at your log-linear results, they were

20 very conservative. I think there was 1 percent to 4 percent,

21 but mostly in the 1 to 2 percent.

22 DR. DANIEL SCHLENK: Dr. Effland.

23 DR. WILLIAM EFFLAND: I guess I just have one

24 comment, and that is in other modeling -- not statistical

25 modeling, but in other modeling, for example water quality

26 modeling or model evaluation guidance or guidelines, there's a
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1 lot of work that's been published, different kinds of basically

2 metrics of how well the predicted versus the measured. And I

3 think something like that could be used in this case if possibly

4 -- I think I talked about it earlier this week.

5 If you look at your watershed characteristics

6 and maybe you've got one of your areas, and you want to just

7 take the data out and make it -- you know, take that data set

8 and do some things with it to reduce the number of samples

9 because, let's say, you've got a watershed with a very good

10 sampling regime and you went back in and reduced the number of

11 samples. Then that watershed is somewhat analogous to another

12 one that you also have some pretty good data on. See how well

13 that works.

14 And I'm not really clear exactly how to do

15 that, but I think something that looks at those kinds of

16 comparisons; because in my mind, one of the things about models

17 is that they're abstracts of reality. And we're really trying

18 to predict -- we're really trying to estimate reality.

19 And so we want to look at, "Well, how close

20 are we doing? How well are we doing with that?" And so with

21 this process that you're outlining, I think that's -- I think

22 that would be a valuable thing to apply, so I just wanted to

23 comment. And then I guess that maybe gets into bootstrapping or

24 jackknifing, but a watershed type assessment, so that's just

25 what I'd like to say. Thank you.

26 DR. DANIEL SCHLENK: Dr. Engel.
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1 DR. JAMES ENGEL: Nothing further.

2 DR. DANIEL SCHLENK: Anyone else? Dr.

3 Portier.

4 DR. KENNETH PORTIER: I can't believe a

5 statistician would say this, but we know a lot about the

6 watersheds, right? So the proof is going to be in the practice.

7 So even though you -- you know, we've been talking about

8 quantitative ways of assessing. And somebody mentioned

9 qualitative ways, and the evaluation person that I am -- not

10 only as a statistician these days -- I've begun to realize

11 there's a lot of value in getting experts together to look at,

12 you know, what this approach is going to tell you about the bias

13 factor for a particular site. And then getting experts to look

14 at that same site who understand that site and say, "There's no

15 way that the maximum would ever reach what the bias factor would

16 tell you it would reach."

17 And if you get a lot of people saying that for

18 sites, and that's as qualitative as you can get, but it tells

19 you that your model just maybe not matching up with reality and

20 you're missing something. And that's sometimes a lot cheaper

21 than trying to go and collect another 15 years worth of daily

22 data to validate the model.

23 But it can be written up in such a way that

24 it's a convincing argument that your model does have support

25 without having to go and collect a lot of new data, do a lot of

26 analysis, try to argue for R2 and all this other kind of stuff. 
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1 I'll try to write that up and put that in the report.

2 DR. DANIEL SCHLENK: Thank you. Yes, Dr.

3 Effland.

4 DR. WILLIAM EFFLAND: I agree with that, and

5 actually that brings up something that I wanted to comment on

6 that Dr. Bradbury brought up earlier in the week -- I think at

7 the beginning -- where he was talking about, you know, we're

8 zooming in and out. We're talking about zooming in and out, and

9 you're basically looking at it from a very high elevation. You

10 know, you're not on the ground. You're not looking at it from

11 that level.

12 And I think what Dr. Portier's saying is, you

13 know, if you get those local people together and they all, you

14 know, agree that, "Hey, this isn't going to happen," or "It is

15 going to happen," and they agree with you, that's very

16 supportive and I think that that kind of evaluation is useful.

17 But you're trying to zoom in, but I think

18 you're still at a very high altitude. So I mean, the kinds of

19 data and things that you're working with, you've got -- I mean,

20 in looking at the watersheds and, you know, some of the soil

21 characteristics and all that, you're still out at a pretty high

22 level.

23 DR. DANIEL SCHLENK: Okay. Dr. Ellsworth.

24 DR. TIMOTHY ELLSWORTH: Yeah. One comment

25 here. You know, I'm kind of struggling right now. I'm not sure

26 what's the best way to go here in terms of a single regression
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1 model that takes into account all these watershed

2 characteristics that are important to predict the bias factor.

3 Or, you know, if you might actually be able to

4 group some of the watershed characteristics into groups and say,

5 "Let's do a regression equation for this group versus that," you

6 lose degrees of freedom. You're balancing power and, you know,

7 but if you're gaining more and you're -- I mean, that's

8 something you'll have to struggle with. I don't know the answer

9 to it. Anyway, just something to think about.

10 DR. DANIEL SCHLENK: Okay. Any other comments

11 on 13? Okay. Dr. Hetrick, do you have any questions regarding

12 the comments?

13 DR. JAMES HETRICK: No, we're fine.

14 DR. DANIEL SCHLENK: Great. Thanks. Okay. 

15 Mr. Spatz, do you want to read 14 into the record?

16 MR. DANA SPATZ: What are the strengths and

17 limitations of using a single regression model for prediction of

18 spatially and temporally explicit bias factors for a

19 nationally-distributed monitoring data set?

20 DR. DANIEL SCHLENK: Dr. Portier is leading

21 this one.

22 DR. KENNETH PORTIER: No, Dr. Gilliom.

23 DR. DANIEL SCHLENK: Bob Gilliom's got this

24 one?

25 MR. ROBERT GILLIOM: I think I've got this

26 one.
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1 DR. DANIEL SCHLENK: Okay. Oh, Bob. Bob

2 Gilliom's leading this one. Okay.

3 MR. BOB GILLIOM: So this maybe because it's

4 the last question, and I apologize to everyone sitting across to

5 us listening to all the bias factor things. We had to listen to

6 listen last time.

7 This will be a little bit repetitive with some

8 of the points, but I think it's worth doing because of the

9 decisions to be made about proceeding with the regression and so

10 forth.

11 So the regression approach illustrated in

12 Chapter 5 as both strengths and limitations, and these are

13 affected by the premises of applying the method. So when I

14 answer the question correctly, I made the assumption. One is

15 the CE-LOC has been determined it establishes concentrations,

16 statistic measures, and I'm going to assume it's the maximum

17 60-day rolling average during any particular year.

18 The duration of this average, whatever's

19 selected, will have a major effect on determining what data sets

20 you can qualify to use for the regression development set. 

21 So the significance of that for a tangent per

22 second is that if you stay at 60 days, then for example, some of

23 the monitoring data that's at four-day or two-day intervals

24 during part of that period is really pretty good for a 60-day

25 moving average.

26 But if you dropped that to 20-day, for some
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1 reason, it would change completely in the number of data sets

2 that qualify for doing the infilling for the model development.

3 Secondly, the model has been developed using

4 data for sites that has sufficient monitoring data recently

5 estimate was my assumption. True values of this concentration

6 for a particular site year represented the range of geographic

7 conditions and years necessary for applying the models. The

8 importance of including a fully representative range of

9 geography and years have been discussed in previous SAPs and

10 remains critically important. 

11 So to the strengths of the regression method,

12 it provides a consistent, uniform approach to using a wide range

13 of the existing and potential future data sets to estimate site

14 and years specific estimates of the maximum 60-day average for

15 comparison to the LOC.

16 It is simple and transparent. The regression

17 model can be used to estimate confidence bounds on the estimated

18 bias-corrected values, which can then be used to estimate the

19 probability of exceedance and compare to the reliability of

20 alternative methods.

21 However, part of this method that still need

22 to be worked out is how to incorporate the uncertainty in the

23 original uncorrected estimate for the monitoring site. So what

24 I mean there is you have a certain amount of the uncertainty

25 built into the regression model for fitting into your model

26 development data set, and those are your best data sets.
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1 When you apply this to sparsely monitored

2 sites, you not only have the error that's built into the

3 regression estimates, but you also have this varying degree of

4 uncertainty at the starting point because your data's so sparse,

5 you know, whether it be six samples during the period or 12.

6 So it's just -- I haven't thought that all the

7 way through, but it's something we've got to think about.

8 DR. DANIEL SCHLENK: Okay.

9 MR. ROBERT GILLIOM: Okay. Another to the

10 strengths. I think taking this approach has another ancillary

11 advantage of also requiring that you take a nationally

12 consistent approach to identifying what data qualify for the

13 monitoring site years. It's just good to get as much

14 systematic, repeatable evaluation going through this whole

15 process.

16 So to the limitations, the success of the

17 method ends on the representative and completeness of the model

18 development data set and the quality of the final model to

19 represent those conditions. Such a development data set needs

20 to include a much wider range of geography size and other

21 characteristics to meet this requirement and there need to be

22 more site years for model development. So that's kind of

23 repeating what we've been saying.

24 Applications of the model need to be

25 constrained to data sets that need specific data requirements

26 for such factors as the sampling intensity and seasonal coverage
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1 such as sufficient to yield and adequate initial estimate to be

2 bias-corrected.

3 Specific decisions on those kind of criteria

4 will need to be made. So then that brings me to kind of the

5 last major point, which is that the biggest overall limitation

6 to the approach is that most stream miles for which estimates

7 are needed don't have any monitoring data. So in the scale of

8 things, the most important problems you're facing is how to make

9 estimates for totally unmonitored segments of stream.

10 And that's where it kind of harkens back to

11 the discussions in the previous SAP and the need to do some kind

12 of multi-varying regional or national model that relates

13 watershed characteristics to the appropriate concentration

14 statistic that you want, the 60-day maximum moving average.

15 And I think that's doable, and there are

16 examples out there that are close and it's achievable. And I

17 think what you've got to do is just be sure it's kept in the

18 flowchart as a major component of your approach because it is

19 going to end up being the only way you can do most of the stream

20 miles of interest.

21 And related to that, the other thing I'd say

22 is depending on how that model comes out, if it's not -- it's

23 actually fairly likely that that model will actually be a better

24 estimator of some of these -- for some sites than the monitoring

25 data. So if you were looking at quarterly data from some

26 drinking water monitoring type of thing and looking at the
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1 model, the model's probably going to give you a better unbiased

2 estimate if the model's built on that data of that 60-day moving

3 average than the monitoring data.

4 So by looking at all of this in a more

5 probabilistic sense and judging it in relation to probability of

6 exceeding the LOC, you can put things on a level playing field

7 and be able to see where the crossover occurs of -- at those

8 sites.

9 Okay. I think that covers everything I had to

10 say.

11 DR. DANIEL SCHLENK: Great. Thanks. Let's

12 start at this end of the table. So Dr. Engel, and just go up

13 from here.

14 DR. JAMES ENGEL: Yeah, I guess just one

15 comment that I would like to make is that, again, I think it may

16 be possible to supplement some of the less frequently sample

17 size with model data. So keep that in mind as you move forward

18 and continue to consider that option.

19 DR. DANIEL SCHLENK: Dr. Effland.

20 DR. WILLIAM EFFLAND: Just a couple of

21 comments. With regression models, there's always a question

22 about their transportability, and I think that's what you're

23 really getting to here is can you have one model and use it for

24 the whole U.S.? And I guess the easy answer in my mind is

25 never, but that doesn't make it easy to do your assessment.

26 But that's certainly something that I think is
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1 testable, so it's something to consider. And I would also just

2 note that the geological surveys have been a lot of -- have

3 quite a bit of investment in these types of regression models

4 for various work, such as Sparrow and the hydrologic landscape

5 region that have been developed.

6 I believe this is correct that they're zooming

7 in now. They've got sparrow for smaller areas, so they're --

8 they have experience with that and they've seen -- you know,

9 seen that needs to be done.

10 And so I guess I'll just leave it at this.

11 DR. DANIEL SCHLENK: Dr. Ellsworth.

12 DR. TIMOTHY ELLSWORTH: Just coming back here

13 to this regression model, and I guess I'm going by -- to me, the

14 guiding criteria is really this infilling plays a critical role

15 as I see it here in this step.

16 So your objective, I think, again -- at least

17 my objective would be -- in your shoes -- would be to minimize

18 the variance of non-bias factors and to minimize the magnitude

19 of the bias factors, all right? And I'm thinking on the

20 infilling, the better job we do at picking a protocol for

21 infilling, that's exactly what's possible, the better you're

22 going to -- the more successful you're going to be in relating

23 watershed characteristics to bias factors.

24 You know, the less -- the poorer job you do

25 with this infilling, that uncertainty's going to go into trying

26 to relate bias factors to watershed characteristics, and you're
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1 going to lose a lot of that power. So it's going to be really

2 critical, I think, to identify an optimal protocol for that

3 infill, say among the suite of huge choices you've got in front

4 of you. 

5 And just kind of along that line, you know,

6 like the linear -- part of that would be, you know, when it

7 valid, when should it be used. You've got to -- you know, some

8 simple things that I might be looking at is I've got flow data. 

9 Like the background, I really shouldn't be using linear beyond

10 that. I should be trying to use the mean background for that

11 chemograph.

12 You know, there's some basic -- anyway, you're

13 going to need to go through and come up with what's the optimal

14 infilling method and use some criteria like Linda was talking

15 about to evaluate which one's best. And I would sure put some

16 effort in to making sure I did a good job there because that's

17 going to make this task much more meaningful.

18 DR. DANIEL SCHLENK: Okay. Dr. Young.

19 DR. LINDA YOUNG: I have nothing to add.

20 DR. DANIEL SCHLENK: Dr. Portier.

21 DR. KENNETH PORTIER: So I want to kind of

22 keep on the theme that Tim just mentioned in that the key thing

23 there is minimizing the variance of the bias factors and

24 maximizing the size of the bias factors.

25 So the simple regression works best when you

26 have only one factor. So in this case, sampling is used to
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1 modify the bias factors from site to site. Once we go to

2 multiple factors -- basin size, flow, whatever you put in that

3 -- you're now in a multiple regression. And the linearity

4 constraint in that multiple regression gets stronger as you have

5 more factors in there.

6 You're really limiting the kind of

7 relationship that you're looking at. And of course, you can add

8 in (inaudible) of the factors. You can add in mixtures of the

9 exponentials like you did to try to incorporate non-linearity. 

10 But actually, more recently statisticians have

11 been using things like regression trees which is to do exactly

12 what Tim said -- you know, kind of maximize that within and --

13 maximize the between the mean levels, minimize the within group

14 levels, but not be kind of restricted to what the groups are

15 defined as at the beginning.

16 So it's kind of a combination of clustering

17 and mean-fitting at the same time. And this is a well-developed

18 methodology. It's not going to be in your USGS all model

19 fitting package right now. It is available in R as a tool under

20 the tree procedure. I'll put some of those references in there.

21 The nice thing about this is that it will

22 allow -- it's kind of like a categorical approach that we talked

23 about before. But you don't get to specify the categories. The

24 variability and the relationship is what defines what goes into

25 the categories. And when you get into a multidimensional space,

26 the model ends up being a tree.
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1 If you have this situation, you've got to go

2 over here. And if there's other factors here -- use this bias

3 factor. If you get this other condition, use this bias factor. 

4 So it's a lot easier to use, even in a regression model, because

5 everybody can see the conditions. It's just a flowchart, right?

6 So I think that's going to be the methodology

7 that's going to be your next regression methodology is you add

8 more data in and try to build it out. And I'm not sure, but I

9 think maybe some of the methodology they're using in things like

10 SPARROW like USGS is doing.

11 I don't know. I haven't kept track of all

12 that stuff, but --

13 MR. ROBERT GILLIOM: Yeah, sorry. The

14 question was about whether that is what's in the USGS SPARROW

15 model. And the answer is it's kind of a hybrid combination of

16 flux balance and instream processes and regression techniques.

17 So I hesitate to draw the direct analogy to

18 this, but while mentioning SPARROW, the other thing I just want

19 to mention is that back to Dr. Effland's comment, I was just

20 going to clarify -- is that for the purpose of this specific

21 objective of modeling for a maximum 60-day moving average, the

22 -- basically, the warped type approach is built for that and the

23 SPARROW's not. It's a flow leaded mass flux type of approach,

24 which won't give you the right time weighted attributes.

25 So it's just a distinction on whether your

26 focus is on flux and one of the sources versus trying to
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1 estimate a concentration.

2 DR. DANIEL SCHLENK: Okay. Any other

3 comments? Okay. So what we would like to do now, we've -- Jim

4 Fairchild received some comments from Dr. Farruggia on some

5 questions for clarification. If you want to go ahead and read

6 those into the record now.

7 MR. JAMES FAIRCHILD: Yeah. Thank you. This

8 is Jim Fairchild. There were two questions to that for

9 clarification. One was regarding -- to interpret the difference

10 between what maybe be no effect and effect levels, and I think

11 especially related to the concept of diversity. And the second

12 question was talking about the subject of splitting or lumping

13 of these cosm data sets.

14 So changes in biomass of the primary endpoint

15 by which significant changes are determined to have occurred in

16 atrazine exposure. Rare and significant changes in biomass

17 would not necessarily be determined to be ecologically relevant

18 since they can and do occur by chance. Relevance is determined

19 as to whether or not a statistically significant effect is

20 observed consistently over the course of the exposure. In

21 practice, however consistency is dependent on how often biomass

22 is measured. 

23 So personally, I would suggest a level of

24 concern when changes are observed at 25 percent of observations

25 with special significance being given to changes that are

26 occurring at the end of exposure.
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1 An impact such as 25 percent biomass reduction

2 is a reasonable level of significant concern. However, even

3 this may not be a statistically significant effect, depending

4 upon the power of the test.

5 Recovery of biomass within (inaudible)

6 considered, algae should be expected to recover within 10 to 14

7 days post-exposure. Paraphyte in recovery usually occurs within

8 14 to 28 days, and microphyte recovery within one to two months

9 during the months of May, June, and July.

10 Total recovery of macrophytes may not occur if

11 the exposure occurs in August or September, since at this point

12 macrophytes begin to (inaudible). These recovery times should

13 be independent of exposure duration, since plants are known to

14 quickly return to normal function after estrogen stress is

15 removed.

16 Ecologic significance of changes in

17 biodiversity is more difficult to judge. There are currently no

18 commonly accepted models of criteria to define an unacceptable

19 change in biodiversity. There are, however, practical examples

20 that one could consider the changes as ecologically significant,

21 such as the loss of critical macrophyte species, important as

22 habitats to amphibians or maintaining the stability of a stream

23 bank to erosion.

24 Changes in paraphyte species composition may

25 or may not be ecologically significant. Paraphyte is an

26 assemblage of bacteria, algae, diatoms and Philomena algae that
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1 can take many different physical forms. It is generally

2 accepted that the diversity of invertebrates like any other

3 species will increase. 

4 If the paraphyte community is changed to a

5 different three-dimensional structure, physical structure for

6 example, from a venae, from a more vertical form to more

7 horizontal form, then changes may result in operational habitat

8 for micro- and macro-invertebrates.

9 This would be ecologically significant;

10 however, support to recognize these types of changes in

11 three-dimensional forms are the same responses you would see

12 (inaudible).

13 The hardest to judge is the ecological

14 significance of change in species composition of paraphyte and

15 three-dimensional habitat natures. Normally, this does not

16 alter micro-invertebrates in small streams, therefore is more

17 sophistically irrelevant than ecologically relevant.

18 Substitution of resistant diatoms for

19 sensitive algae in the paraphyte community also would not be

20 considered ecologically significant if biomass and productivity

21 are on the table.

22 Finally, I ask you all to realize that these

23 are my opinions based on the majority of my experience and

24 observation with aquatic plants. 10 biologists would likely

25 give you 10 different opinions on a continuum shape. That

26 continuum would be very, very interesting.
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1 That's why yesterday, I indicated that

2 (inaudible) senses could only be obtained with input from a much

3 larger group of individuals in a two- to three-day meeting or

4 conference dedicated to asking such questions. But even those

5 are difficult when you start talking about differences between

6 society and ecological significance and whether or not we're at

7 a point in this society where some of these things can be

8 changed, given the alterations and the arguments that's seen

9 here in streams.

10 In regard to splitting versus lumping, I think

11 I incorrectly stated yesterday that the Agency had done some

12 splitting in their analysis, and I was incorrect. So I

13 apologize for that. 

14 That splitting occurred Jeff Giddings portion

15 of the Syngenta's slide presentation, where they came up with a

16 conclusion that certain points in cosm database should be split,

17 deleted, rescored or added in order to populate our cosm

18 database, as described in Slide number 55.

19 These are all offered on that slide as

20 alternate cosm sets one through five. And again, these are both

21 qualitative, quantitative conclusions as derived by Dr.

22 Giddings. 

23 Graphical representation of the alternative

24 data sets provided on slide 89, splitting of the taxa reduce the

25 number of (inaudible) exceeding scores greater than one by 45

26 percent.
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1 Compared to the US EPA 2012 data set, which is

2 scaled at 100 percent, so splitting the data resulted in a 45

3 percent reduction and exceedances. 

4 It's interesting to note that this effect is

5 similar to the alternative using the SAP's 2009 data set. 

6 Deletions and additions, which ranged at a 40 to 60 percent

7 reduction in exceedances.

8 The most significant, a rescoring alternative

9 -- alternative four -- reduced the number of exceedances by 75

10 percent. Therefore, if the Agency follows the Panel's

11 instructions to rescore the cosm sets, then splitting is not

12 necessary. Any of the alternative data sets chosen will reduce

13 the number of exceedences remarkably. This is largely a factor

14 of the new scoring cosm sets the Agency has conducted for 2012,

15 which has lowered the EC-LOC by a factor of approximately two

16 compared to the one in 2009.

17 DR. DANIEL SCHLENK: Okay. Any other comments

18 from the Panel on Jim's input there? Okay. Dr. Farruggia, do

19 you understand his response?

20 DR. FRANK FARRUGGIA: Yes, I do.

21 DR. DANIEL SCHLENK: Thanks. Okay. So what

22 I'd like to do now is let's take a 10-minute break, and what

23 we'll do before we adjourn is I'd like to go around the Panel

24 for final comments. If there are other comments you'd like to

25 make at the very end, think about them for about 10 minutes and

26 get a nice hit of caffeine and get them out onto the public
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1 record.

2 So let's take a 10-minute break and come back

3 at 2:25.

4 (WHEREUPON, a recess was taken.)

5 DR. DANIEL SCHLENK: Okay. So what's

6 customary -- we normally do -- is go around one final time. If

7 there's comments that you've heard throughout the meeting that

8 you didn't get a chance to interject and would like them to be

9 part of the minutes, this is your last chance, basically. So if

10 there's certain things that you feel strongly about to get on

11 the public record, please do so at this time.

12 So what we'll do is we'll start to my left

13 with Dr. McManaman and go around the room this way.

14 DR. JAMES MCMANAMAN: Well, thank you. It's

15 been a stimulating couple of days, thanks to the Agency and

16 Syngenta for the nice presentations.

17 I think -- and regarding the aquatic animals,

18 we may be at the same point that we were in 2003. The

19 statements that were made at that time in that SAP, I think, are

20 still valid and are consistent with the recommendations of this

21 Panel. We still need to look at other species of frogs. And I

22 think that there's enough evidence in the literature and species

23 are available that those studies could be done.

24 However, I also think that we must be getting

25 close to an LOC for the compound because we're getting in an

26 area where there is no end results, and in the presentation by
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1 the Agency, it's clear that there's evidence in the literature

2 that suggest it, but we suggest it, but it's still not

3 quantitative data, it's not evidence.

4 So my recommendation would be that we follow

5 what was recommended in 2003 with another species and do more

6 quantitative studies.

7 DR. DANIEL SCHLENK: Dr. Klaine.

8 DR. STEPHEN KLAINE: I want to reiterate

9 appreciation for all the effort that went in both from the EPA

10 as well as Syngenta and others. And it was a very informative

11 -- it strikes me that as we move from, say, single species

12 tests, endpoint-driven risk assessments to ones that might be

13 more influenced by mesocosms and microcosms. That we're getting

14 into a level of complexity where single endpoints may not really

15 be adequate, and we need to be thinking about endpoints that

16 embrace higher levels of trophic organizations such as

17 community-level endpoints.

18 And I'd just like to leave you with that

19 thought because I think that might be the next stage in

20 evolution in this whole process.

21 DR. DANIEL SCHLENK: Dr. Propper.

22 DR. CATHERINE PROPPER: So I just want to add

23 that I deliberately didn't take a close look at the 2003 and

24 2007 report before evaluating all of the information. And the

25 recommendations there are still, and nearly 10 years later,

26 stand. And I don't see that most of those recommendations have
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1 had a follow through, and I really encourage the Agency to do

2 so.

3 I will point out, point-by-point, in my final

4 report where those recommendations fit into the current

5 questions, if they do, and then I will address those and

6 fine-tune our Panel's response to that as we go along.

7 DR. DANIEL SCHLENK: Thank you. Dr. La Point?

8 DR. THOMAS LA POINT: I'm happy to say that

9 just with the three prior statements that anything I needed to

10 say has been said already and I concur. Not only just that, I

11 concur with those recommendations.

12 DR. DANIEL SCHLENK: Dr. Boone.

13 DR. MICHELLE BOONE: I just want to reiterate

14 explicitly that we have been recommending additional test

15 species be tested within the lab and in the field, particularly

16 looking at rana amphibians, which spans North America as well as

17 the highland, which has been implicated from Africa to the

18 Northeast.

19 DR. DANIEL SCHLENK: Okay. Jim Fairchild.

20 MR. JAMES FAIRCHILD: I would just like to

21 thank the EPA staff for putting up with us and asking questions

22 over and over and over. But partly, it's because we admire the

23 expertise that you have in the Agency so much, and we're

24 learning every time we come in, and hope that we contribute some

25 small attribute to this atrazine's legislation.

26 DR. DANIEL SCHLENK: Dr. Fetscher.
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1 DR. ANNE FETSCHER: I'd just like to echo

2 everything that's been said.

3 DR. DANIEL SCHLENK: Dr. Rodgers.

4 DR. JOHN RODGERS: I'd like to specifically

5 thank each and every one of you for your participation, EPA and

6 the other folks that participated in the EPA for preparation and

7 their efforts in getting ready for this. Just a couple of quick

8 things that I think may have been said, but I maybe didn't

9 comment on at the time, but I probably should have because I was

10 still thinking.

11 One would be under discussion of ecological

12 significance when we talked about resistance and this phenomenon

13 of supposed recovery. There are many mechanisms of action for

14 any compound, and when we start talking about recovery -- if

15 you've got an herbicide and a species recovers, that's one

16 thing. We're particularly interested in perhaps that recovery

17 and perhaps the species that don't recover.

18 There are species that are tolerant and

19 perhaps resistant that we need to be aware of, and some of those

20 appear on the species sensitivity distribution. And we could go

21 and look at incidents of these species. There are many, many

22 toxin-producing species that are cropping up in this part of the

23 world that are causing ancillary issues, not necessarily

24 eco-risk issues, but certainly on the human health side. So we

25 need to be aware of the ecological context. 

26 And then a quick comment, if my colleagues
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1 across on the other side of the room will accept the next one. 

2 The concept of bias factor -- and I was particularly interested

3 in that. And I just didn't want the idea that every problem is

4 reasonably bounded and that if these bias factors are

5 proportional in concentration, that might be a simple way of

6 remembering a lot of differences in watershed types and things

7 like that; just something from the other side of the table for

8 you.

9 DR. DANIEL SCHLENK: Dr. Twiss.

10 DR. MICHAEL TWISS: Well, I'm still impressed

11 with the expertise of the EPA. I was holding them in high

12 regard after seeing all this work here and responses. It's

13 really impressive, so thank you.

14 I did make some comments on -- as I was

15 reading the white paper -- and these didn't sort of fill into my

16 questions, and so I'm going to forward that on. The one thing

17 that was difficult to me was the -- and this is my first

18 encounter with PATI. And then I'm looking through the list and 

19 I'm baffled because these are all cyanobacteria -- these aren't

20 plants. And others were algae -- these too aren't plants.

21 And there were very few macrophytes. And so

22 for a while, I was befuddled by what is this plant assemblage

23 really is. So you could change that and call it the 

24 Photoautotropic Assemblage Toxicity Index. I'd appreciate that. 

25 It might help you cmmunicating that. Thanks.

26 DR. DANIEL SCHLENK: Dr. Engel.
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1 DR. BERNARD ENGEL: Let me echo my thanks to

2 EPA as well as the public commenters as well for the

3 presentations and all the hard work in your document preparation

4 that has been done ahead of time.

5 I guess I would note in the white paper,

6 Chapter 5, Method for Determining vulnerable Watersheds, you

7 have really burning questions about that. 

8 And I guess in looking at that, there do seem

9 to be differences between the Agency's opinion and maybe the

10 registrant's opinion as to which watersheds may be vulnerable

11 and how to best approach that. So just an observation.

12 DR. DANIEL SCHLENK: Dr. Effland.

13 DR. WILLIAM EFFLAND: I'd also like to thank

14 EPA and Syngenta and all the public commenters. It's been very

15 educational. That's one of the reasons I agreed to help work on

16 these. I've got a few comments. One, I wanted to say earlier

17 but I guess now is my time -- about the use of cosms, mesocosms

18 and useful and reasonable physical models, I think they're

19 useful. As Dr. Rodgers said, all models are useful. Some

20 models are more useful than others.

21 I certainly think that they're useful. 

22 Whether they're reasonable, that's something I guess we could

23 convey. But I'd just make a few comments about what I see as

24 far as reasonable thinking that it had something to do with

25 approaching realistic.

26 One of the statements they made is that they
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1 represent U.S. Waters. That seems like a pretty big stretch,

2 but it seems like something that could be potentially tested. 

3 It's an assumption at this point.

4 The other thing is that they lack mixed

5 ingredients by design. I know that's to reduce some of the

6 interactions and all, but in the real world, in the streams that

7 you would be regulating -- that you might be potentially

8 regulating -- gets a mixture of active ingredients. So I think

9 that's something that needs to be kept in mind.

10 Canopy effects -- I don't know. As I said

11 earlier, I'm not a cosm expert. I had a tropical fish tank once

12 and the electricity went out and all my fish died; so I guess

13 that was a lesson I learned about 30 years ago. 

14 But Canopy is certainly an important thing on

15 most natural systems. I do a lot of fishing. I try claiming

16 myself as a fisherman, so I spend quite a bit of time on streams

17 and lakes.

18 And then the other thing with cosms is that

19 there's the land-water connection that's missing, in my mind. 

20 And I know that's also a way to help focus on what's going on in

21 that particular part of the environment, but I just think that's

22 a very important thing to consider is that land-water

23 interconnection. So I'll just leave that for that particular

24 question.

25 And then I just had a few additional comments

26 in five parts. One is that the white paper's a
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1 work-in-progress, and I mentioned that earlier and you said that

2 a number of people have said that.

3 And much like Dr. Engel said, I would like to

4 be able to submit written comments about the watershed

5 vulnerability part because there are some differences. I think

6 maybe even some differences between what Syngenta presented -- I

7 think where they brought up the soils. I don't necessarily

8 remember that -- and what was in the white paper.

9 So I need to go back. I really didn't have

10 time to very carefully look at that, but that's something that I

11 think I can help with as far as my expertise.

12 A couple other comments. One is the PATI

13 model seems to be extremely sensitive to the number of

14 acceptable studies. And so that's -- I think that's very

15 critical and that's been said multiple times. But I want to

16 reemphasize that because I think that is an important point. 

17 Just, you know, the robustness of that model, just it makes me

18 wonder about it.

19 And then another additional comment is the

20 overall assessment procedure seems to be very complex. There

21 seems to be a lot of different ways to look at things, and I

22 know this is still a procedure that's going through development,

23 but I would encourage you to think about, you know, the level of

24 complexity as far as being able to communicate that because

25 you're going to have to be able to communicate that to the

26 public and to the regulated community.
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1 The other thing that I would comment on is I

2 would encourage the Agency -- and this is a much bigger comment,

3 but whole occasion by peer review process allows for scrutiny by

4 the scientific community and it also increases the information

5 availability.

6 And so some of the comments that I heard

7 about, you know, a draft of a report that's seven or eight or

8 ten years old -- at least if some of the valuable components of

9 that, some of the real essence of that can be published and be

10 scrutinized by the scientific community and then also be

11 available because the Internet is just a wonderful way to

12 transmit that information. So that's something that I would

13 encourage.

14 And then also the fact that the Agency has a

15 lot of well-trained professional scientists, and that's part of

16 their overall development and continuing interaction with the

17 peer review publications.

18 And then finally, something that I brought up

19 in the 2009 SAP -- and I don't think it's really been touched

20 on, except for Syngenta mentioned it -- is the importance of

21 conservation practices within a watershed and things like buffer

22 strips. And Dr. Ellsworth mentioned it. And there's other -- a

23 variety of different things that are extremely important for

24 especially looking at things like runoff.

25 And in my opinion, this environmental

26 assessment is focused on runoff. I guess I have some concerns
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1 about that because there are certainly some watersheds, there

2 are certainly some areas of landscapes where subsurface load, or

3 just through naturally the developing piping and other types of

4 transport networks is important. And, you know, atrazine can

5 potentially leech through groundwater. So the focus on runoff is

6 something that I would comment on. 

7 And then finally, under the conservation

8 practice umbrella, conservation tillage, whether it's no till or

9 reduced till or whatever, is an extremely important part of our

10 agricultural production system. And I think that, you know,

11 I've heard several different people talk about, you know, the

12 value of being able to keep the soil on the land. And that's

13 one of the tools that we're using to try to be able to do that.

14 So there are some very important

15 considerations as far as what happens with this decision and

16 conservation tillage.

17 DR. DANIEL SCHLENK: Dr. Ellsworth.

18 DR. TIMOTHY ELLSWORTH: Well, it's my third

19 time serving on one of these panels, and my respect and

20 admiration for this EPA staff increases with every one of those.

21 I don't envy you at all, your jobs. I have a lot of respect

22 for what you're doing.

23 One thing I'd like to say, and I'm going to

24 gather some angst from my fellow members here, but regardless

25 I'll say it.

26 I think there's a reductionist tendency among
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1 scientist to always want more data. And, you know, I think

2 that's good, too. But I also think that there's a lot of

3 information on atrazine here, and I would encourage you to just

4 go ahead. I couldn't help but have the thought let's get those

5 studies in, and we'll be sitting -- and another Panel will be

6 sitting here in the future saying, "Well we need more studies."

7 The point is, you know, you're stuck with

8 doing the best you can with what you've got in front of you, and

9 I think you're doing that -- trying to do that obviously with

10 PATI, with the cosm data, with the spatial data, and the

11 information you have available. And I think you're really doing

12 a good job of that and just keep working at it.

13 One thing, kind of, I think that really came

14 out here was this idea of error propagation. You know, there's

15 a level of conservatism that's present in PATI. There's another

16 level in the watershed regressions. Make sure it's realistic;

17 that you're not propagating reality out of the picture that

18 they're -- that are going through that system.

19 And then the other thing I think, too, and I

20 think you do this, but the registrant's provided a lot of

21 valuable tools and information -- you know, tool, information,

22 and analysis to consider and I'm sure you did that. But I think

23 there's really some useful stuff there that came out from both

24 presentations -- you know, the calibration information, the EEE

25 data that you were aware of but it was brought forward through

26 that means and I think that's useful.
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1 I think these tools for watershed analysis --

2 you know, we've argued, in the 2009 SAP especially, to have

3 those be available in a format that's useable. You know, so

4 it's not this intractable thing that, you know, waterborne does

5 at extracting (inaudible) data and coupling that with the

6 (inaudible).

7 That's not going to be accessible, I don't

8 think, to you. But if you could be creative in a way that you

9 could get access to that information, wow, that would be really

10 useful.

11 So I would argue for the best sharing of

12 resources and tools that they have to help you. That's all.

13 DR. DANIEL SCHLENK: Bob Gilliom.

14 MR. ROBERT GILLIOM: I'll ditto all the

15 thanks, just like they all said. So I have just a couple things

16 to just reiterate that have come out of at least my spin on the

17 discussions.

18 So the first thing that stands out to me is

19 determining the LOC. This job of the other side of the table is

20 most difficult compared to over here. And it seems like there's

21 been support, to my mind, for both near-term and long-term

22 approaches; the near-term approach of focusing on the plant LOC,

23 reevaluating the studies, coming back through the process to

24 give an updated application of the method to compute and update

25 it and presumably, I won't say final, in LOC that goes into the

26 risk assessment process.
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1 If in the process of doing that evaluation of

2 the studies, it's determined that you just can't do that because

3 there's not enough good studies to do it, then that just falls

4 to the longer-term decision anyway, which means you've got to

5 bring in these other studies and then call to get more

6 defensible basis for the plant LOC.

7 I'm leaving the amphibians totally out of it

8 because I don't - that or fish. They may trump plants

9 eventually, but no one knows at this point.

10 To me, it's best if you can focus on setting a

11 single LOC from the effects point of view and not try to range

12 it or bound it and everything, but build in the policy decisions

13 into a single value because then it's set up to do a

14 probability-based assessment for all the different methods

15 you're going to have to use to compare exposure to LOC.

16 If you have too many thing floating loose and

17 unclear to people and people confused about ranges, it makes it

18 harder. So the next step would be to decide that probability of

19 exceedance. That's okay. And then I think you can just outline

20 a progression of methods to deal with the exposure end of it

21 that once that fixed LOC is there, you can evaluate whether they

22 need to measure out to that probability of exceedance that the

23 policy has to establish.

24 So that's my two bits, and I think a similar

25 vein of regime was addressed for the human health side of

26 things, too. And there's a lot of consistency across the SAP on
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1 most issues.

2 DR. DANIEL SCHLENK: Dr. Young.

3 DR. LINDA YOUNG: I want to build on Dr.

4 Ellsworth's comments. In the studies that have been considered

5 thus far, equal weight's been given to everything. Some are

6 actually better studies than others. They have more

7 observations, more application. And especially if we start

8 infilling some things, there's more studies associated with

9 those data sets.

10 And so at some point, to be more inclusive,

11 there's going to need to be methods put in place that integrate

12 the uncertainly associated with the studies in the review, which

13 is more sophisticated but certainly has a well-established

14 foundation for doing that.

15 So that's just one thing to keep in mind. Not

16 everything is equal and should not be treated as equal in your

17 analysis.

18 Aside from that, I truly appreciated the

19 presentations and the effort that went into all of the meeting,

20 both by EPA and Syngenta. And in addition, I want to thank my

21 fellow Panel members. You made it fun. I appreciate that.

22 DR. DANIEL SCHLENK: And Dr. Portier.

23 DR. KENNETH PORTIER: Thank you. I want to

24 first reiterate my thanks to especially Syngenta for hanging in

25 there. I'm probably the only one sitting here who sat in on

26 most of the seven or eight human health and ecosystem
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1 discussions that we've had on atrazine over the last -- since

2 2000 -- probably before 2003.

3 I may have missed one or two, but that same

4 team from Syngenta's been here with the same team from EPA

5 meeting after meeting, plus the industry people who comment

6 every time.

7 I think what I have appreciated is that

8 they're all committed to getting this right. Syngenta has their

9 view, EPA has their view, but they all agree they have different

10 views and they keep coming back and I think that's really helped

11 kind of move the discussion along.

12 The other thing that I've been thinking about

13 is the -- as I try to put these things together, the human

14 health and the ecosystem, I was looking for the similarities and

15 the differences. You know, when we talk about exposures to

16 humans or mammals that lead to cancer, you know, you have a lot

17 of discussion on changes and state at the molecular level.

18 But when you talk about exposures to

19 ecosystems, it seems much more complex than even cancer. It's

20 just a surprising thing to think about, but the ecosystems don't

21 have those kind of easily defined cells and -- it's a lot harder

22 to think about, you know, these issues with the ecosystem

23 integrity and food web stability and ecosystem health that are

24 kind of ill-defined at this point as endpoints in this kind of

25 ecosystem assessment where you kind of state, you know, I want

26 to maintain the health of the ecosystem, and it's going to
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1 define what do you mean by health? 

2 And the same thing with human health. You

3 know, we want to maintain the health of a person. What does

4 that mean? Not having disease. What does that mean? I think

5 what we're seeing here today is the same kind of things that

6 we're seeing in cancer. You know, we usually talk about cancer

7 -- like breast cancer. Now we talk about which form of the 20

8 forms of breast cancer because they're not all the same. They

9 all have different pathways leading to different kinds of

10 modifications.

11 And I think we have the same kind of thing

12 here that we're talking about ecosystem outcomes, but there's

13 maybe not one ecosystem ill-health outcome. There might be 20

14 different outcomes, and we may have to be thinking about the

15 pathways to those outcomes.

16 In both of these cases, there's a strong need

17 for a strong conceptual framework when thinking about these

18 complex issues. On the human health side, the adverse outcomes

19 pathway the EPA's working on now is really helping, I think,

20 kind of focus the discussion. And I know I find it very useful

21 and I think many other people find it very useful as a way of

22 thinking about this link from exposure to health outcome.

23 In this particular discussion, you know, when

24 you go back to Section 8.2, you find three conceptual models. 

25 One for the aquatic organisms, one for terrestrial plants, one

26 for terrestrial organisms, and they're not linked together. 
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1 So part of the complexity here may be we don't

2 have one kind of conceptual framework like the AOP on the

3 ecosystems side, and there might be need for something like that

4 to help kind of connect all these things together.

5 And the AOP is -- you know, the Adverse

6 Outcome Pathways -- is complex. It's not simplifying the issue.

7 What it's doing is it's helping communicate all the components

8 that go into making that link between an environmental exposure

9 and the human health outcome a little clearer to all the parties

10 involved, including the public.

11 I think the same thing has to be done here. 

12 The complexity of what we've been dealing with might be

13 simplified when we come up with this uniform framework that

14 helps not only us kind of communicate on a common language, but

15 communicate it out to the rest of the world why it's taken 15

16 years of discussion and 12 meetings to talk about atrazine and

17 it's not banned.

18 It's banned in Europe, right? But in the

19 U.S., we tend to base things on evidence, and there's a lot of

20 evidence here.

21 Anyway a lot about frameworks.

22 DR. DANIEL SCHLENK: So just so you know, this

23 is Dr. Portier's last meeting unless he gets invited back. But

24 he has served on the permanent Panel -- what -- for eight years.

25 Even longer than that. So anyway, he's been here forever. 

26 So I just wanted to say an extended personal
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1 thanks to him because he's been a great colleague, a great

2 person that we see eight times every year. It'll be great to

3 see you again this July, actually, so hopefully we'll have more

4 panels where we can get your expertise back, so I just wanted to

5 say thanks for that.

6 And to follow up, actually, you took exactly

7 what I was going to say. And one of the things we had talked

8 about earlier was adverse outcome pathways, and I think atrazine

9 represents a perfect example of how you would be able -- I mean,

10 the massive amount of data that you have, and there's still

11 those uncertainties that are there, I think this is a great

12 example of how you could use AOP in the ecological setting.

13 What I will try to do, at least in the

14 minutes, is try to integrate what I know about that process in

15 terms of maybe some of the endpoints that we were talking about

16 in the earlier questions.

17 And one of the things I think might behoove

18 the Agency is to maybe have a Panel that's focused a little bit

19 more on the amphibian side with an adverse outcome sort of

20 emphasis. I think that may provide a little bit -- and have the

21 expertise in those areas to maybe in the ad hoc component to get

22 through that. So just a recommendation there. 

23 Also, I think that the missing link, I see, is

24 the reproduction link, particularly on amphibian literature. I

25 think we need to really be a little bit more focused on it, more

26 so than, say, histological change and that's to do with whatever
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1 the other studies are focused on.

2 I'd say that's a change from the 2003 sort of

3 recommendations. So those are my scientific sort of comments. 

4 Again, I'd like to thank the Agency -- a fabulous job in terms

5 of which you presented the public commenters amazing sort of

6 information you provided us.

7 I'd definitely like to thank the SAP staff

8 that have all making us happy and all of the travel arrangements

9 that they have to go through to get us all here and happy and

10 caffein laden throughout the day.

11 And I'd like to thank each one of you for

12 coming and the hard work. As Ken had mentioned, we go through a

13 lot of these different things, and I'd have to say this was

14 probably one of the hardest working Panels that I've seen in

15 terms of the work and the emphasis that you guys put into this. 

16 Even as spur of the moments, where you've only had a couple of

17 hours to come up with answers to questions, I was very

18 appreciative of that, as I'm sure the Agency is as well.

19 So with that, I'll turn it over to -- oh, Dr.

20 Bradbury, I'm sure, has questions -- I'm sorry, comments.

21 DR. STEVEN BRADBURY: I just wanted to thank

22 all of you for all your efforts and good discussion. And thank

23 you for letting us ask questions and ask them back and forth

24 because I think it really helped us for sure.

25 I want to thank all the public commenters and

26 all the staff that helped. 
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1 Just to wrap up a few comments that I picked

2 up during the time and to be clear, what we're working on right

3 now is that problem formulation stuff, so the insights that

4 you're giving us now helps us start to pull the rest of that

5 information together and start to think about the approaches

6 we're going to use as we go into that risk assessment so we're

7 not making decisions today other than making decisions about

8 going forward. Within the guidelines, you'll see that this

9 assessment is an iterated process. This is an important step.

10 And clearly, atrazine is unique in a sense

11 where types of ecological risk assessment have been done not

12 only in the pesticide program, but I'd say EPA generally. There

13 aren't too many chemicals where there's this kind of information

14 that allows us to start to explore some of the things and try to

15 implement some of the things we've been talking about.

16 But having said that, I think it's important

17 to realize that all the things we've been talking about today

18 and over the last -- I don't know -- 10 years with atrazine --

19 after every single risk assessment we do all the time -- what

20 atrazine does is it helps make things that are implicit or

21 assumptions that are always there very visible. 

22 And so we have this great insight and tangible

23 information before us to start to grapple with things that we've

24 been grappling with since I got my Masters and PhD. And before

25 in terms of, "Well, how many species should we test? How do

26 those species tests reflect biological community?"
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1 I don't think we still know the answers to

2 that, but atrazine has given us more ways to think about that. 

3 We always know that ecological risk assessment is dealing with

4 the nation, but we know the nation is not the only thing. It's

5 all sorts of watersheds with all sorts of characteristics.

6 And so what atrazine is doing is, I think,

7 through these SAPs and others, that it's giving us insights to

8 think about the ecological risk assessments we do.

9 And realizing we're not always going to have

10 this kind of information. Perhaps it'll help us figure out

11 what's the appropriate question to ask.

12 So I just want to assure you we're going to

13 try to make sure we don't have blinders on.

14 Thanks again and thanks to Sharlene and the

15 SAP Panel.

16 DR. DANIEL SCHLENK: Cathy said you were going

17 to make some comments on 2003 and 2007, and 2007 is included in

18 the written document; you're going to do that? Yes? And you

19 guys had some comments on 2007? Oh and Chapter 5 -- was not

20 addressed? Are you going to be providing written comments as

21 well?

22 DR. WILLIAM EFFLAND: Yes.

23 DR. DANIEL SCHLENK: Okay.

24 DR. SHARLENE MATTEN: I have to pull out my

25 notebook here. This is the second notebook for this meeting,

26 that's to say I didn't have enough pages in the first notebook. 
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1

2 I'd like to thank everyone on the Panel for

3 their indulgence in the huge number of emails and other

4 explanatory notes that I hope were helpful on trying to

5 assimilate the information here. If you received them, some

6 files were very enormous and I had to try certain ways to try to

7 send them. 

8 For traveling here, of course, is always the

9 difficulties and nobody got on the list of planes stuck by rain,

10 or snow or sleet or anything like that this time so that was

11 very good. 

12 Also for not asking me when you could leave

13 before Wednesday night. You followed that very well; I really

14 appreciate that. You don't know how much I appreciate that. 

15 I wanted to thank the members of the EPA staff

16 and management. First of all Dana. This is Dana's second as

17 the Chief or key point person. I know I have to thank you for

18 all the interaction. And Jim is very very kind, very useful. 

19 Jim is the actual team group leader -- well that's what I call

20 him, the team leader. And Frank, Amy, Anita, Nelson, Russ,

21 Melanie, Don Brady and Tom Steeger. 

22 And then the members of the Public Commenters.

23 I had public comments by email, I had a huge age range, I must

24 say. At the moment there are 180 public comments. About 150

25 plus are very similar. I thank Syngenta for their insistence

26 and endurance and patience with the fact that I have them clear
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1 many of their items and reformat them and send them, clarifying.

2 So I appreciate that. They always set the records with me on

3 amount of information that's provided. 

4 Of course I'd like to thank my own colleagues,

5 Laura Bailey and Fred Jenkins and Joe Bailey and Don Wood,

6 Shirley, Joyce, Caite and Brie who work for Endyna and Carlos

7 and Linda.

8 So the report as I said will be done in 90

9 days approximately September 12th or 13th tying to figure out

10 the 31 day months.

11 As I've said before I'll try to put all the

12 materials in the docket as soon as I can. Next week is probably

13 your best bet.

14 Afterwards I'd like the Panel members to meet

15 next door to go over the next step. Thank you.

16 (Whereupon, at 3:00 p.m., the meeting was adjourned.)

17

18 

19

20

21

22

23

24

25

26
FIFRA SCIENTIFIC ADVISORY PANEL (SAP)

OPEN MEETING

SCIENTIFIC ISSUES ASSOCIATED WITH PROBLEM FORMULATION FOR 

THE REASSESSMENT OF ECOLOGICAL RISKS FROM THE USE OF 

ATRAZINE 

DOCKET NUMBER: EPA-HQ-OPP-2012-0230

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

JUNE 12TH - 14TH, 2012
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A

abandoned 384:25 483:5

aberrant 36:13

ability 15:14 16:10 29:16 34:7,22 35:15 59:19

71:22 160:8 201:24 259:18 325:19 348:26 354:8

370:15 382:13 409:12 420:17 421:10

able 15:4 43:9 60:10 82:6,7 113:12 114:25 157:9

157:10,13 160:12 166:5 173:2 182:10 201:26

202:1 221:9 266:17 325:18 335:26 352:16 354:6

364:11 367:26 392:13 430:8 462:23 463:14

467:24 517:23 521:24 522:7,17 531:12 537:4

570:3 575:7 592:4,24,25 594:12,13 602:9

abortion 168:2

abrasive 259:13

absence 46:7 51:22 137:18 169:17 393:16 449:16

495:5 505:13

absolute 83:17 85:7 134:14,22,22,23 145:9,12

160:23 296:18 328:13

absolutely 63:6,23 64:21 114:3 155:20 206:16

328:2,5 329:4,8 363:12 393:7 426:20

absorb 165:25

absorption 37:4

abstracts 567:17

abundance 170:26 231:16

abundances 231:22

abundant 167:22 274:9

Academy 298:11

accept 91:16 124:22 125:9 206:22 313:22 589:1

acceptable 35:7 69:2 122:6 330:1 332:6 343:7

412:12,19 432:3 440:11 450:13 486:10 520:11

548:9 551:7 592:14

acceptance 25:9 238:5 284:9 417:3 504:1

accepted 77:2 84:15 94:8 266:25 329:14 348:15,16

411:23 416:23 457:25 477:1 528:6 581:18 582:2

accepting 374:15 411:24

accepts 359:15

access 596:9

accessed 4:20 454:23

accessible 596:7

accessory 167:26

accidental 282:23

acclimate 200:10

acclimated 423:24

accommodations 364:3

accomplished 377:16

account 15:12 25:23 26:8 28:8 43:9 60:10 113:25

138:2 141:9 151:21 158:21 180:2,22,23,23

185:17 224:23,25 271:25 282:14 291:19 334:18

335:2 338:2 344:8 350:4 358:1,15 366:1,2 402:2

442:23 444:7,13 451:20 467:18 518:15,19 519:6

524:16 551:14 554:16 555:3 570:1

accounted 75:2 401:10

accounting 365:26 401:11 491:18 505:22 519:2

549:11

accounts 277:6 558:24

accrued 458:14

accumulate 446:18 447:7 462:14,19 465:26

accumulated 429:2,6

accumulates 92:10 446:9

accumulating 95:18

accumulation 429:16,21 447:1

accumulative 49:23 62:15 101:9 135:8 217:18

218:26 219:17

accuracy 298:21 435:7

accurate 179:11 296:10,13 305:16 449:14,24

accurately 23:22 141:9 245:5 253:24

acetone 421:17

achievable 574:16

achieved 267:13

achieving 310:5,6

acidophil 386:20

acidophils 508:8

acknowledge 17:24 321:14 348:15 497:22

acknowledged 45:25 353:5 402:10 443:22

acknowledgement 19:7

acknowledges 60:1

acre 273:26 275:22,24 277:11,12 278:11,26 285:14

285:14 289:26 295:19 401:20 402:13

acreage 169:22 271:14

acres 183:12 275:10 276:20 277:21 283:23,24

285:21 289:7,9,9 295:12,13,14

act 2:14 5:13 11:11 12:21,22 59:16 150:9 159:11

297:16,19,22,23,26 298:3,4 299:21 300:4 454:19

480:18 508:6 564:24 566:18

acted 272:17 370:12 509:1

ACTH 196:26 389:19

acting 186:24 316:26 359:8

action 6:7 13:19 28:8,11 55:23 59:23 73:16 92:4

162:10 180:23 181:18 185:17 186:13,15 187:17

196:5 230:6,12 236:25 239:25 254:2 255:4

256:17 258:21 259:23 260:13 264:16 289:11,17

289:18 290:26 302:17 310:4 337:26 341:11,16

588:13

actionable 346:1

actions 3:7 140:14 302:15

activation 196:15,15,24 202:10

active 11:12 187:18 591:8

activities 19:8 172:3 281:20,23

activity 3:2 18:7 54:22 56:8 221:21,26 225:19

245:14 247:17 386:19 525:3
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acts 187:9,12 239:26

actual 90:3 130:12 225:23 235:1 252:7 259:6 284:1

299:4 309:2 315:19 328:23 344:13 400:1 423:23

430:20 458:25 491:9 509:7 517:12 539:24 541:2

553:17 606:19

acute 16:17 36:9 46:15,19 49:18,20,21 50:17,19,26

350:8 407:18 506:4

acutely 506:7

ad 602:21

adapt 546:23

adaptation 111:21 397:18

adapted 424:1

add 19:7,10 63:22,24 94:11 115:19 130:10 132:25

153:18 195:7 205:4,19 210:19 275:23 306:14

325:8,26 326:5 342:13 343:26 346:24 350:17,19

362:7 368:12 370:19,22 372:16 379:7,10,12,14

382:2 383:2,4,7 387:18 388:11,14 389:21 391:1

391:23,26 392:3 395:16 399:19 405:12 406:7

408:17 415:21 421:25 431:17 433:22 434:17

445:2,4 446:1 447:3 448:23 453:4 462:22 466:5

478:9 485:16 486:18,20 487:18,18,24 488:23

509:19,21 510:3,6,8,12,14 520:1,4,5 529:7

538:14 541:13 547:15 553:3 566:1 577:19 578:7

578:8 579:7 586:22

added 33:18 100:18 103:5 123:13 229:6 238:13

240:9 344:26 399:16 445:11 448:8,8 477:2,16

480:5 583:17

adding 37:5 95:2 105:6 223:12 235:6 241:8,11

242:20 244:11 306:2 341:4 424:13 479:13 531:2

addition 3:16 23:3 32:4 33:6 34:13 44:1 47:7 49:12

74:25 77:13,19 78:11 85:21 93:25 99:7 182:2

183:20 184:24 259:15,21 283:11 293:16 379:16

399:4 402:13 414:17 471:20 498:6 515:4 566:12

598:20

additional 4:25 11:18 18:5,8,21 22:24 23:7 31:18

33:19 44:19 47:17 78:2 100:8,17 140:5,12 143:10

151:16 152:7 153:23 154:3,4 160:25 181:3,13

189:3 196:8 210:20 211:7 267:25 275:24 289:26

294:12 296:23 304:20 314:22 317:16 324:13

365:5 370:25 372:3 382:2 385:16 395:16,17,19

396:4 414:15,16 415:7,26 419:2 421:25 427:24

456:24 474:13 480:4 485:4 486:19 531:21 542:23

547:22 552:23 558:4 587:14 591:25 592:19

additionally 57:3 139:17 142:25 143:23 144:5,11

146:18 152:22 153:16 184:16 241:8 282:11 387:7

485:17

additions 223:9 241:3 584:6

additive 94:16 447:4

address 27:24 83:12,14,19,26 84:16 138:13 152:5

158:22 180:13 190:14 193:7 275:7 279:22 280:3

281:14,18 282:20 286:22 289:2 302:12,17 303:1

303:7,21,26 304:5,6,11 305:13,18 366:5 369:25

391:7 393:19 415:10 427:26 438:23 442:9 451:24

465:7 473:17 479:10 480:4,5,6,8 489:12 495:13

500:15,18 513:14 516:25 531:5 587:5

addressed 20:22 84:16 90:24 94:10 184:13 299:17

332:21 369:17 412:9 415:8 433:25 441:5 442:24

467:20 473:18 478:12 479:9,20 485:19 486:21

504:13 518:12 520:21 533:16 597:25 605:20

addresses 83:8 87:9 98:12 267:15 341:24 430:7

503:3 524:15

addressing 84:5 94:2 138:8 183:26 295:26 304:21

485:15 513:2,7

adds 289:11

adequacy 109:24

adequate 328:14 344:23 345:1,3 367:1 372:26

452:2 475:18 539:9 562:9 564:14 574:1 586:15

adequately 374:26 441:5

adherdent 118:24

adherent 17:3

adjacent 158:3 555:11

adjourn 584:23

adjourned 263:13 454:6 607:16

adjust 471:9 514:7 527:21 531:1

adjusted 86:13 119:19 139:24 147:23 148:9 149:3

149:7,8 150:3 151:2 153:9,11 160:21

adjusting 138:9 500:23 531:3

adjustment 148:4 151:25 471:18 513:14 525:9

adjustments 424:21 472:17

administration 504:2

Administrator 172:13 173:5 302:7

admiration 594:20

admire 587:22

admitted 324:22

ado 19:19

adopt 271:12 278:2,24

adopted 278:4

adopting 80:17 307:4

adrenal 196:25 197:1,19 199:20

adult 197:13 201:15

advanced 557:22

advancement 139:3

advances 166:19 349:6

advancing 16:6

advantage 112:8 185:25 316:24,25 317:3,9 409:19

470:19 545:7,25 557:11 573:11

advantages 539:15

adverse 13:25 14:4,5,9,9,11 18:12,12 28:14,17,19

30:19,24 55:12,15,18,24 58:11,17 59:25 82:24

102:22 103:19,21 105:3 107:14 109:5,12 112:20
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112:22 118:16 124:5 190:5 192:17,20 193:21,24

193:25 194:20,21 195:3,10 196:4,5,11,12,19

198:15,20 199:13 202:6 207:8,8,20 211:23

249:18 296:20 299:3 313:1 316:6,15 318:12

319:26 337:22,26 341:19,20 342:3,4,5,7 386:1,3

389:9,11,12,19,21 390:8 392:8 441:9,19,23 442:2

443:1,4,7 444:9,11,18 461:17,20 462:19,22

463:22,26 464:4 465:18,20 484:4 485:19 492:6,9

492:20 498:8 508:16,24 600:18 601:5 602:8,19

adversely 185:9 296:24 390:3

advice 3:5,9 16:6 24:26 70:4 355:26 360:5 361:17

395:26 399:15 419:2

advisable 405:4

advised 228:26 356:10 513:1

advisor 136:22 301:25 352:12,15

advisory 1:1 2:2,13,25 3:3,4,8 5:13 177:11 179:9

179:10,20 180:21,26 181:9,19 183:5,24 275:7

281:15,25 282:11 283:3 291:16 296:5 302:4

454:11,17,19 455:4

AEEMP 103:2,16 104:7 105:16 137:11 142:10

147:16 149:21 158:8 159:21 160:9 469:6,9

472:15 473:11 511:23 513:20 514:6 515:11,17,17

524:25 548:13

AEMP 103:2 267:7,15,16 268:1

affect 37:7 74:3 92:12 125:15 168:20 185:9 196:16

200:5 296:24 332:13 343:4 538:9

affect/no 500:23

affiliated 295:8

affiliation 5:23 6:11 287:26

affiliations 455:7

affinitive 480:18

affirmation 509:5

affirmative 496:4,15 509:15

aforementioned 89:26

afraid 177:18 435:14

Africa 191:3 587:17

afternoon 3:25 4:1 76:14 136:16 179:24 213:8,11

294:26 301:24 379:16 394:20

Ag 267:5,7,14 268:1

age 176:19 288:10 606:23

agencies 13:10 136:26 149:19,20 190:21 281:18

287:16

agency 1:12 2:12 3:5,10,13 4:25 13:5 17:11 31:1

56:3 69:4 72:4 136:14 162:19 175:6 218:5 222:23

244:2 261:7,13 263:9 269:1 270:21 277:20

280:20 281:22 283:1,8 284:3 285:5 292:5,20

293:22 319:11 326:8,16 352:9,15,24 353:9 354:5

354:23 372:11 380:5 389:22 394:23 395:3,25

396:3,9 397:3 399:14,20 400:2 404:6,11,14

411:22 412:10,22 421:24 422:4 425:9 426:6

428:4 441:6 453:8 454:16 456:16,21 461:7

470:12 472:12 474:10 476:22 483:7 487:1,4,12

493:10 496:18 503:7 511:4,13 515:15 521:3

528:9 583:11 584:10,14 585:15 586:1 587:1,23

593:2,14 602:18 603:4,18

Agency's 214:7 242:6 254:17 258:21,25 260:17

264:22,24 270:8 354:8 395:15 487:7 590:9

agenda 9:2 19:9 26:21 72:11 137:10 179:24 181:15

454:25

ages 118:13 167:1 176:20

aggregate 74:22

aggressive 302:14

ago 84:23 238:14 268:21 271:19 273:8 277:11

300:7 302:8 308:25 373:14 404:9 421:18 532:24

591:13

Agra 360:3

agree 167:21 196:7 238:7 252:20 284:10 297:11,15

297:20 300:10 332:11 334:26 338:26 339:19

346:25 349:22 379:17 387:17 388:10 410:3,14,25

411:17 412:3 416:19 449:15 459:14 492:23 494:3

497:16 500:5,10 501:8,8 517:14 523:16 541:5

544:24 545:11 561:15 569:4,14,15 599:9

agreed 30:26 44:13 46:3 301:11 412:25 430:9,10

450:25 590:15

agreeing 395:10

agreement 21:14 22:5 190:12 267:1 273:21 280:19

363:25 430:1

agreements 404:14

agrees 124:2 173:14 300:9 464:15

agricultural 8:3 21:26 33:11 166:11 183:13 269:4

271:18 276:23 288:3,17 289:5 291:15,20,21

410:8 411:2 594:10

agriculturally 269:19

agriculture 8:7 164:22 174:17 176:4 279:9 280:17

282:24 303:5

agronomists 276:23

Agronomy 289:13

Ah 535:8

ahead 128:5,7 136:14 177:13 189:17 202:18 213:6

240:17 248:19 253:21 262:2 287:24 349:6 359:4

366:11 384:12 466:17 471:4 494:10,24 496:12

512:17 543:6 546:23 580:5 590:4 595:4

aid 310:13

aimed 227:3

air 165:22 172:14,15,23 173:3 200:24 225:14

463:17

airborne 225:6

al 43:5 175:7 227:13 231:6 232:21 234:18 235:14

237:17,26 238:12 243:12 248:22 260:10 261:22

266:25 270:13 322:9,13,13,14,15 323:6,10
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325:10,14 378:2 386:25 400:15 401:24,24 402:2

402:6,9,17,17 403:1,6,12,17,18,18,19 404:22

490:26 503:2

albeit 367:25

alga 259:18

algae 75:15,19 114:8 231:13,16 255:8,15 256:8

257:1,2 263:5 264:17 267:2 271:7 273:24 274:2,6

274:8 277:9 399:5 432:21 581:6,26,26 582:19

589:20

algal 236:16 265:3,5 266:24 399:7

algorithm 522:23

algorithms 526:13

Alito 322:13

allegations 292:3

allow 35:3 36:17 57:15 74:17 99:1 103:26 110:7

137:19 160:16 169:22 187:11 316:19 324:23

382:10 397:11 399:8 413:8 430:17 458:1 502:12

502:14 539:11 544:9,10 557:14 578:22

allowable 76:10

allowed 290:7,14 311:12 456:21

allowing 211:6 290:19 328:14 518:1 538:3

allows 99:9 105:11 118:8 132:21 162:5 267:8

346:16 368:15 397:14 517:18 593:3 604:14

allude 406:11

alluded 384:23

ALOUD 264:7

Altenburger 266:21

alter 296:23 499:26 582:16

alteration 507:3 508:2

alterations 583:8

altered 359:17 443:8

altering 500:1

alternate 58:21 223:4 583:20

alternates 223:13

alternative 24:3 96:1 180:1 275:23 290:1 307:2

375:4,5 572:20 583:23 584:5,8,9,12

alternatively 217:2

alternatives 91:16 105:6 439:4

altitude 569:18

altogether 530:16

amazing 176:1 353:7 603:5

amendment 425:8

America 121:6 163:18 164:9,10,21 165:14 170:2

170:21,24 171:10 177:17,19,21 188:11 195:12

587:16

American 8:21 79:2 81:18 164:20,22,24 167:12

168:19,21,25,26 169:16 170:6,9,15 171:9,11

172:7,18 174:26 175:1,7 290:2 373:13 456:11

Americans 164:14 168:24 169:2,25,26 170:19

171:2,4,14 172:6,20,24 173:2 176:10

America's 162:23 174:22 175:10,18 288:17 482:15

ammonia 41:21 334:25

amount 12:5,7 115:12 136:24 170:6,16 173:6

199:9 207:12 234:6 244:10 248:25 259:24,25

269:5 271:21 282:3 321:15 324:18 398:11 412:1

429:9 438:25 446:9 572:24 602:10 607:3

amounts 352:17 409:4 438:26 511:17

AMP 530:23

amphibian 21:4 24:22,23 25:2,4 26:16,23 27:4,7,8

27:16 28:1 29:4 30:22 31:9,17 32:18 39:3,15

47:12 53:3 56:19 64:15 66:15 69:5,19 117:16

162:25 163:2,3,6,11,18 164:19 165:16 166:20

167:20,21 178:2 181:20 182:12 183:26 196:6

199:7 202:17 211:25 269:6 286:17,19 293:9

297:11 300:8 323:9 324:1,26 334:24 340:16

352:14 353:2,20 360:17 368:14 373:7 380:3

381:17,24 382:26 383:22 386:14 390:8,21 391:12

392:14 411:24 478:3 483:10 496:2 508:2 602:19

602:24

amphibians 7:11 9:18 13:20 18:18 21:21 22:14,24

23:12 24:19 27:13,19 32:7 33:14 34:18 50:16

57:23 59:1 60:7 65:19,21,25 109:18 162:24 163:1

163:9,13,18,25,26 164:25 165:18,24 166:18

167:22 178:1,11 179:24 185:7,9 187:25 188:25

194:16,17,18 195:1,4,10,19 196:22 197:17 198:1

198:18 199:5 202:7 206:3 269:1,4 277:10 287:17

296:12,24 297:14 321:9 323:12,21 324:16,19

330:2 332:7 336:17,20 345:12 350:13 358:2

360:13,21 367:2,15 368:9,18,22 369:5 371:14,23

372:9,10 373:4,14 374:19 376:10 378:18 379:21

379:22 381:4,13 382:19 390:13 404:13 450:14

477:25 482:16 506:8,11 507:26 508:1,5 581:22

587:16 597:7

amphions 166:14

amplitude 256:14 509:7

Amy 20:6 23:12 24:21 26:22 27:1 61:2,7 62:5,17

62:25 63:6,23 64:13 65:2,7 66:16 67:9 68:12,21

71:6 127:16 326:10,10 332:26 333:19 364:23,23

366:7 371:22 380:6,9 389:24 450:8,17 451:10,11

451:13 478:17 479:2,23 480:12 482:4 606:20

analogous 86:4,15 94:23 100:9 428:24 567:11

analogy 438:17,18 439:13 579:17

analyses 32:6 43:25 44:6,19 77:23 79:16 114:25

125:12 179:6,26 205:15 267:7,11 268:2 413:1

421:19 434:3

analysis 17:14 20:24 23:15,24 24:17 25:5,20,25

30:12 31:3 34:4 44:5,12,14,15 47:8 64:26 78:5

79:5,26 80:11,13,17 89:13 106:25 109:7 115:18

118:20 120:10 125:23 126:18 132:6 135:22

139:21 142:8 143:1,5,17,19 144:9,14 145:23
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146:1,1 147:3 149:18 150:2,7,16,18,24,25 151:6

151:12,16,20 152:7,10 154:3,6 155:10,11 156:6

156:19 160:9,19 188:4 189:18 192:6 198:23

205:2 211:1 212:6 214:24 215:14,18 217:24

220:15 223:14 227:17 229:1 231:23 238:3,9

241:16 244:18 245:8 248:11 252:1,20 261:1

279:7 292:7 305:16 315:8 316:10 320:6 323:15

324:4 325:3 326:14 331:14,21 343:17 344:11

368:4 377:5 399:16,21,24 408:23 414:16 421:20

438:26 439:1 451:15 461:25 465:9 467:6,15,17

468:4 470:11 478:16 479:1,6,22,26 494:13

495:11 501:3 503:14 504:14 505:6,25 506:20

507:6 509:12 511:24 514:5 518:12 527:10 531:4

534:6 549:6 554:5 556:10 558:19 559:17 561:18

561:23 563:6,10 568:26 583:12 595:22 596:1

598:17

analytical 41:10

analyze 26:10 44:16 56:13

analyzed 45:1,18 85:10 174:26 175:5 210:14

220:22

analyzing 44:25 262:3 303:14

ancillary 573:10 588:23

Andrus 260:24 263:12 265:2 404:22

and/or 46:12 54:9 74:2,9 77:24 107:15 109:6,13

154:14 174:2 274:21 339:6 385:26 390:9 484:11

494:18

angst 111:25 594:24

animal 55:19 56:14 59:26 342:14 376:18 434:9

animals 21:23 30:12 59:23 70:13 78:26 133:4

166:14 198:14 205:5 321:22 324:15,25 331:19

332:16,17,18 335:1,7 339:6 341:13 369:18,26

372:19 382:5,9 483:13,20 484:6 503:20 506:2

507:14 585:17

Anita 414:11,11 416:17 426:7,7 453:11 488:10,10

489:15 606:20

ANN 110:23 111:11

Anne 7:16,16 249:25 250:6 408:14 432:15 455:22

455:22 490:4 509:23 588:1

annual 21:24 440:12

annually 275:10 289:8

anomaly 406:24

anopia 261:24

another's 60:15

answer 10:20 12:17 13:12 110:8 134:25 177:1

202:26 214:15 248:14 249:13,14 251:2 252:13

287:13 292:10 300:19 311:22,23 357:25 363:6

374:4 377:20 380:20 389:14 393:10 394:14 396:6

410:11 413:9 415:4 417:10,11 427:26 438:12

457:16,18 478:22 484:6 486:1 489:8 490:15

496:4 500:10 502:16 509:15 527:1 540:5 549:26

550:4 570:8 571:14 575:24 579:15

answered 130:8 161:8 268:3 462:16

answering 13:17 385:7 487:2 495:2 498:15

answers 118:1 185:24 252:13 434:25 470:10 477:7

479:26 490:2 603:17 605:1

anthropogenic 111:5,9 117:24 409:4 413:23

453:19

antibody 197:6

anticipated 289:21 505:22 508:19

anticipating 406:18

anti-predator 168:3

anuran 330:4

anybody 172:9 286:16 293:23 327:1 347:4 351:17

359:18 370:12 473:17 501:13 565:10

anymore 343:6

anyway 157:20 411:11 443:19 555:24 570:9

577:12 597:4 601:21,25

anyways 526:17

AOP 58:12 59:3 388:26 389:1,3,4 601:2,5 602:12

AOPs 28:17 56:1

apathetic 177:12

APHA 329:5

apical 27:26 55:16,22 56:5 58:24

apologize 377:19 472:10 496:13 571:4 583:13

apparent 270:14 443:10

apparently 236:5 238:7 296:21 301:4 490:21 508:7

appeal 435:16

appealing 436:4

appear 19:13 46:8 50:16 57:9 93:16 185:24 265:18

306:14 321:20 331:8 354:3 368:17 411:21 588:20

appearance 3:22 331:2 370:6,7,11 394:26

appeared 36:21

appears 152:5 243:8 270:21 368:7 386:13 444:23

508:15

appended 400:11 401:17

appendices 216:1 322:20

appendix 32:26 39:18 40:10 71:8 78:7 107:9,19

119:6,7 128:25 184:12 282:26 293:8 298:22

312:6 348:13 400:2,3 405:15 415:19 432:17

435:21 438:18 453:3 470:23 471:10 474:12

537:14

applaud 406:8

applicability 520:14 545:4

applicable 262:7 373:3 514:19 515:18 527:8

561:10

application 74:5 88:9 104:24 138:22 140:23

154:15 160:11 162:4 172:17 278:26 288:11

492:15 514:7,12 529:10 563:6 596:24 598:7

applications 87:21 288:20 507:13 573:24

applicators 289:6
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Page 6

applied 21:26 32:3,13 33:7 36:25 47:19 59:9,12,14

60:1,3 69:8,9 78:4 82:12 83:6 101:6 139:21

183:11 237:17,20 242:13 289:7,8 295:12 338:17

353:20,20 354:4,4,24 357:19 380:1 420:7 437:1

451:1,4 468:17 469:19 472:20 478:4 520:16

525:6 533:6 540:23 543:20 550:16 559:23

applies 353:24 462:3 542:12

Applikation 270:16

apply 32:1 65:14 69:4 102:25 111:11 148:7 157:9

188:4 190:22 193:4 217:12 258:9 274:16 298:14

353:17 374:11 451:6 469:12,22 472:18,23,23

521:8,18 530:26 545:9,19 546:14 550:20 567:22

573:1

applying 69:8,20 71:7 83:1 158:20 247:14 267:22

274:17 340:13 462:3 492:2 524:23 571:13 572:7

appointment 288:5

appreciable 45:15

appreciate 9:10 180:20 181:8,13 226:14 288:3

295:4 314:10 341:22 354:10,11 393:15 411:16

428:6 475:23 483:18 528:4 544:25 589:24 598:21

606:14,14 607:2

appreciated 128:9 329:6 427:24 525:20 598:18

599:7

appreciates 162:26 297:3

appreciation 586:9

appreciative 603:18

approach 23:21 24:4 26:5 27:25 28:2 30:26 46:3

59:15 77:18 88:3 113:18 121:19,21 122:5 123:6

133:9 138:8 146:20 147:1 152:5 156:12,17 157:2

157:11 184:18 187:11 188:2 192:24 195:7,8

197:26 204:24 205:11 210:24,26 213:14,18,23

214:5,7,18 244:20 247:11,13 248:2 260:15

264:20 267:26 268:2 281:2 282:9 306:14 307:4

329:11,13 331:25 335:18 337:14 367:17,18,25

392:20 430:8,19,25 431:1 432:7 444:8,26,26,26

451:23,24 452:1 482:11,23,24,25,26 488:14

500:22 501:23 513:2 514:14,19 515:5,9 519:21

520:9 521:24 523:3 527:20 530:2,14,15 533:5,8,9

534:7,8,18 538:25 540:22 541:17 544:2,8,11,15

544:17 545:18 550:7 551:5,8 552:8 553:24 562:5

563:20 565:3 568:12 571:11 572:12 573:10,12

574:6,18 578:22 579:22,23 590:11 596:22

approached 276:15

approaches 20:10 137:22 213:26 267:23 303:21

309:26 349:16 356:6 425:2 439:26 532:21 533:4

534:2,15 538:3 547:4 596:22 604:5

approaching 39:10 318:12 590:25

appropriate 3:13,14 14:3 30:8 31:3 36:16 39:13

41:2 55:12 58:22 80:3 88:17 100:12 133:12

147:24 182:17 189:17 194:24 202:2 204:25

218:13 220:20,26 221:19 224:12 225:26 229:2

250:7 256:17 267:23 296:16 308:3,3 328:7,19

329:1,3,11 330:18 331:23 339:12 340:4 349:24

360:22 362:5 381:20 394:22 396:23 398:3 407:8

427:13 442:25 443:19 450:26 478:3,5,11 486:25

493:21 497:8 550:23 574:13 605:11

appropriated 404:25

appropriately 214:3 281:7 296:17 391:7 451:1

478:4

appropriateness 25:13 98:7

approved 157:4

approximate 39:8 458:11

approximately 3:26 4:1 38:17,26 101:16,17,26

102:3 107:1 147:3 150:12 151:1 163:14 170:24

281:4 312:20 424:25 584:15 607:9

approximates 465:12

approximation 537:22

April 143:4,8 144:18 163:6

aquaculture 381:12

aquatic 6:8 7:13,23 9:15 12:9,21 13:25 21:6,16,19

21:23 22:7 23:17 25:7,14 26:2,17,18 36:10 37:18

69:12 72:21,22 74:2,4,6,13,16,18 75:6 76:24

78:13,15 79:6,9 81:18 82:24 84:4,15 86:5,21

89:13 95:6 103:19,26 104:24 105:3 107:15

108:21,24,26 109:5,12 113:3,25,26 114:10,13,20

118:14,17 121:23 136:18 161:5 179:25 180:18

184:5 187:15 221:5,8 226:18 227:4 235:8 265:25

266:3,21 273:25 274:6,21 279:20 281:11 282:5,9

290:25 291:8 296:1 317:12 352:23 373:16 379:21

396:17 397:12,23 399:13 405:20 406:14 413:5

428:13,26 443:1 490:13,18,19 494:17 503:16,20

504:20 506:2 582:24 585:17 600:25

ARA 288:16,18 290:24 291:22

area 6:12 7:14 24:9 30:17 66:16 113:20 143:23

145:25 146:3 149:21 150:15 151:5,5 154:8 156:2

156:3 160:12 172:21 189:22 195:25 199:15,18

266:2 283:7 291:18 320:14 349:4 363:2,26 400:9

463:16,19,19 500:13 557:5 562:13 585:26

areas 8:11 21:3 22:6 24:10 50:3 142:20 144:4

146:19 149:22 151:9 153:4,5,21,22,25 172:24

173:22 184:23 191:10 269:6 280:15 286:17 287:6

291:18 296:26 363:3 411:2 463:14 496:7 499:3

567:6 576:7 594:2 602:21

arena 9:12

argue 199:5 283:7 505:8 568:26 596:11

argued 468:1 596:2

arguing 223:25

argument 225:13 250:24 300:7 308:4 364:18

435:22 458:18 496:15 568:24

arguments 459:1 460:21 492:25 583:8
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Arizona 7:2 455:15

ARLINGTON 1:16

arm 460:16

aromatase 54:22

arose 348:21

arrange 335:8

arranged 4:7 107:8

arrangements 603:8

array 167:24

arrive 273:6 432:3,7 543:15

arrived 407:5

arrow 92:25 121:18 140:5

arrows 10:3 236:1

article 33:23 231:5 356:8,11

articulate 12:17

articulating 59:22

artifact 309:4

artificial 216:7,11 232:23 249:10,15

artificially 218:11

artistic 178:8

Aside 598:18

asked 10:21,25 180:13 227:10,16 263:8 268:3

273:13,14,23 275:26 277:5 292:22 293:17 301:4

305:12 314:16 365:5 395:3,6 407:17 413:7

424:18,18 431:21 450:7,8,19 451:10,24 453:22

454:1 457:14 475:18 481:10 532:24

asking 17:15 24:25 120:15 218:21 220:19 319:10

333:2 354:18,20 361:17 385:13,14 395:15 414:26

415:7 416:26 427:5 450:22 453:12 478:20 482:12

484:13 489:14,15,16 493:2 497:15 501:20 523:5

526:16 527:3 583:4 587:21 606:12

aspect 10:23 70:4 88:25 99:10 113:9 217:24

244:25 345:22 348:25 430:6

aspects 9:13 84:25 93:16 98:19 168:21 199:10

201:26 202:13 325:8 346:6 358:21 408:18 440:1

499:4

assay 69:13 200:3 203:11,12 345:12

assays 111:12 390:16

assemblage 24:4 73:22 84:19 92:19,24 93:2 94:5

94:15 109:8 121:23 123:9 125:11,13 282:13

494:14 581:26

assemblaging 589:22

assert 296:19

asserted 309:1

assertion 307:9

assess 137:21 145:24 150:1 151:16 154:21 260:19

266:17 297:13 325:2 346:8 399:12 563:3,11

assessed 80:22 106:1 224:11,19 334:20 374:2,3

assesses 59:15

assessing 95:10 137:17 138:16,25 139:18 150:22

204:18 265:24 346:5 368:5 470:25 568:8

assessment 2:10 8:15 9:13,22,23,25 10:7,11,12,16

10:18,22 11:2,19 14:21 15:15,20 16:3,4,11,18,20

17:8 18:6,6 19:2,25 20:11,15,16,17,19,25 24:13

24:17,18 25:2 29:15 30:23 33:16 34:10 37:8

39:24 40:16 41:6 47:21 48:2,4,7 49:14 55:10,17

55:20 56:5 58:21,24 60:13 63:8 71:12 72:3 83:15

84:3 86:24 88:3,10,13 94:9,12 95:11,17,26 96:2

96:11,16 97:1,5,14 100:10 103:13 104:19 105:2

105:15,18,20,22,23,26 106:4,9 107:26 119:2

122:15 129:18,18 139:2,20,22 140:1,6,9 154:3,25

159:3 160:19 161:25 180:18 185:14 188:18,20,21

192:16 193:14,15,16 205:23 206:13 213:17

214:22 216:20,21,23 217:25,26 218:8,20,24

219:1,6 220:16,26 221:22 224:7,18 225:1,18

227:18 244:26 248:4 266:16 268:8 280:5 284:7

294:17 296:6,11 297:4 305:14 307:9,11,17 308:4

308:5,12,24 311:11 316:14,19 319:13 322:19

330:9 338:1 339:14 340:14 341:3 346:1,9 347:14

347:26 348:6 358:1 362:12 368:15 382:20 386:14

404:8 440:17,26 456:17 462:18 491:13 504:11,13

504:16 510:20 521:25 522:4,10,13,17,19 525:16

525:20,26 526:10 527:12 531:15,18 548:10

552:21 567:24 575:25 592:20 593:26 596:26

597:14 599:25 604:6,9,11,19 605:3

assessments 8:8 12:12,12 58:23 69:6 84:15 86:5

87:17 88:15,18 137:22 138:26 142:14 179:12,15

179:16 180:16 184:23,24 186:18 227:6 308:10

338:18 411:17 529:10 586:12 605:8

assessors 12:10 313:18

assign 91:7 403:11 437:9 449:4 517:6

assigned 79:17,19,21,25 90:2,10 92:7 99:15 123:16

321:25

assigning 89:9 448:18 467:5

assignment 93:8 253:1 345:2

assignments 92:16 110:2

assimilate 503:24 606:5

assimilations 529:24

assist 188:1

assistance 136:20 482:6

associate 324:11 414:12

associated 1:4 2:7 6:1 11:26 13:26 14:5 15:11

40:22 50:13 59:5 139:12 144:17 152:5 153:5,26

154:13 264:14 265:9,18 307:13 322:3 324:5

340:5 369:9 386:6 392:24 396:25 399:3,6 402:7

431:24 432:2,4 435:4 436:6 441:3 454:13 471:22

501:2 517:5 524:15 563:24 598:8,12

association 247:6 268:12,13 272:2,3,7,10,14 275:3

275:6,8 276:3 279:26 280:3 288:3,16 295:6

326:26 389:8
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associations 155:2 268:6 557:15

assume 18:11 64:11 65:22 96:3 111:9 123:26

144:19 162:12 194:7 220:23 245:20 408:25 415:9

510:20 515:22 571:16

assumed 39:10 78:26 517:13 533:19

assumes 95:3 239:15 559:21

assuming 115:15 319:17 408:20 448:12 472:18

484:2 512:18 521:11 526:24,25

assumption 65:13 95:7,9 144:21 157:6 220:21

306:17 312:4,7 442:26 443:3,14,20,25 449:8

493:21 571:14 572:5 591:3

assumptions 144:16 157:19 319:14,15 334:13

368:1 406:13 428:14 431:19 440:3 458:22 604:21

assurance 5:2 192:12 340:18

assure 173:2,7 297:21 404:17 605:12

assuring 172:26 245:4

asterisk 401:7 403:24

asteroid 167:1

ASTM 30:8 32:25 34:18,26 36:9,23 37:18 38:20,22

38:24 47:18 59:10 69:11,23 329:4 331:16 333:15

astounding 271:21

asymptote 435:23

atmospheric 463:14

ATP 73:18

atrazine 1:5 2:8 6:3 9:11 11:10,13,21,22 12:22

13:19,24 14:8,12,22,25 15:7,13 16:12,13 19:11

20:2,7,12,14 21:2,5,12,22,22,25 22:2,5,9,23

23:10,12 24:8,9,10,14 25:10,14,21 26:3,16,23

27:4,7,12,15,18 28:5 29:5,17,22,25 30:3 31:17

32:5,12 33:10,14,17 34:23 35:19 36:1 37:25 41:3

41:9,11 43:7,21 44:3,6 45:10,13,14,18,22 46:19

46:25 47:11 49:24 50:9,17 51:2,11 52:8 53:23

54:17 56:17,19 57:25 58:5,26 59:5,23 61:14,25

62:6 63:14 67:14 72:20 73:6,10,11,12,21,26 74:1

74:7,12,14,18,21 75:4,5,10,15,24 76:22 77:7,8

78:15,24 79:5 80:19,21 81:3,9,11,18,22 82:23

86:25,26 87:17,23 88:5,14,25 89:3,10,12 90:1

92:5 93:3 95:7 98:12 101:8,15,26 103:1,3 106:2,6

107:13 108:24 109:1,3,14,16 111:5 112:26 113:2

113:6,11 115:4 116:12,26 122:10 130:8 133:1,14

134:7 136:18,23 137:7,7,9,10,16,17,21,25 138:2

138:11,14,16 139:2,4,5,8,10,16,18,19,25 140:4

140:25 141:1,4,7,10 142:9,12,13,20 143:15,24

144:5 148:1,6 149:11,21,22 150:3,4,11,20 151:2

151:9,10,13,21,24 152:3,9,13,15,17,19,23 153:2

153:3,7,10,12,14,17,19,21,23,25 154:4,11,12,14

154:16,22 158:10,19 159:11 165:23 167:10,15,18

167:21 168:11,18,21 169:1,3,5,6,7,11,17 170:23

171:13,15,16,18,23,23,24 172:1,7,9,10,11,14,18

172:22,23,25 173:2,3,4,7,8,15,19,20,22,25

174:11,15,17,18,21,22,25 175:1,2,5,7,8,9,13,15

175:17 176:5,6,9,12,16 177:17 178:1,3,7,15

179:17 180:17 181:17 183:7,18,20,22 184:25

185:9,26 186:12,18,22,23 187:9,13,15 188:25

190:26 191:2,11 192:3 195:10,18,24 196:25

197:5,10,12,18,21,23 198:23 199:3,16,24 201:17

201:18,20,23 202:1,9 212:1,18 213:15 216:15

220:1 221:13 227:9,11,12,20 230:2,6,10,11,15

231:17 232:2,8 234:5,7 235:7 236:10,11,25

237:17 239:22,26 246:8 254:2 255:5,10,19,21,23

256:1,9,17 257:6,13 259:23 260:1,13 261:17,19

261:24,26 262:1,4,9,12 264:14,16 265:14,15

266:1,5,13,13,19,26 268:8,20,23 269:4,17 271:1

271:7,8,10 272:21,21 273:2,4,7,20,26 274:1,8,12

274:15,17,22,23 275:11,14,16,18,22 276:8,15

277:8,10,25 278:1,6,15,24 279:1 280:6,6,7,13,20

280:22 284:7 285:17,24,26 286:10 288:15 289:4

289:7,8,14,21,24,26 290:7,14,18,20 291:11,23,25

295:14,17,21,25 296:12,23 297:1,4,13,21 300:8

300:16 303:9,10 304:3,9,19 308:23 309:2,11,11

309:14,18,25 310:1 322:3,9,11,18,23 323:7,14,19

323:22,25 324:6,18 325:3 330:2,14,16 331:8

332:1,3,13,14,19 333:23,24 334:14 335:21,23,25

339:8,11 340:6 341:12 343:22 348:18 349:16

350:12 356:15,17,23 357:7,15 358:25,25 359:23

360:13 365:1,15 367:2,15 369:4 370:1,8 372:9

373:26 382:5,9,9 384:1 385:26 386:3,10,12,17

387:2,5,8,13,23 390:3,12 396:18 397:3,17,24

398:4 399:13 402:8 403:16 405:8,24,26 406:2,5

406:15,22 407:17,24 408:26 410:9 411:2 412:1

413:6 414:10 422:17 423:25 428:12 430:22 434:7

441:10 443:2,11,23,24 444:11,13 452:18 454:14

457:7 468:17 474:16 477:25 478:2,6 481:12

485:12 486:4 494:18 495:22 496:15 498:1 499:17

499:20 502:14,18 504:2,9,15,16,19,24,25 505:6,7

505:11,13,14,19,20,22,26 506:6,14,16,26 507:13

507:21 508:4,6,7,20 509:6 510:20,21 512:1,7,8

513:5 516:8,10 517:25 522:2 531:12 563:5

580:16 594:4 595:3 599:1 601:16 602:8 604:10

604:18,20 605:2,6

atrazine's 13:1 167:23,24 185:17 279:12 289:16

290:26 587:25

atrazine-exposed 324:24

atrazine-related 32:7

atrophic 66:10

attach 64:9

attached 65:9 282:25

Attachment 403:8

attack 370:15

attempt 32:6 261:15 273:5 279:12 315:18 546:23
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attempted 65:11 516:24

attempting 236:8 272:25 316:24

attempts 345:4

attending 2:24

attention 22:12 26:12 84:4 119:23 331:7 403:24

attitude 276:16

attribute 333:24 398:22 587:25

attributed 42:7 390:8

attributes 69:13 115:1 218:3 226:4 399:11 579:24

attributing 557:5

attributions 489:1

at/or 379:3

audience 287:21

audiences 475:10

AUDIO 264:2,4,9 284:14

auditable 327:20

auf 270:17

aufgefRllt 270:18

aufgelVst 270:17

augmenting 267:24

August 143:4,8 293:1 581:11

Australia 163:2

authenticity 254:24

authority 3:10 104:3

authors 190:9 231:21

authorships 401:11

auto 143:5,12 518:17

autotrophic 237:8

auto-building 564:19

availability 200:4 275:18 443:8,9,10 593:5

available 5:2 12:5 15:19 22:11 24:16,22 26:15

27:17 30:12 32:1 38:11 39:2 41:8,18,26 46:4,8

47:17 48:19 49:2 50:15 55:21 57:12 59:7 63:1

73:5 74:12,15 76:25 89:14,20 94:3 104:25 105:9

106:6 137:24 138:25 139:8 162:12 170:17 171:6

183:15 185:18 197:17 199:1,10 205:19,25 216:1

235:3 255:18 264:2 269:5 275:23 280:12 289:24

291:15,18 324:18,21 325:1,4 330:12 366:25

377:14 379:24 380:21 381:12,13 387:11 390:12

405:24 409:20,21 414:17,20 438:9 451:10 455:2

457:6 465:22 468:8 474:16 478:23 490:14 497:3

497:24 498:18 503:17 504:7,8,12 524:3 533:6

541:21 544:8 558:11,12 578:19 585:23 593:11

595:11 596:3

average 21:24,25 89:12 92:24,26 93:12,19 94:14

99:6,12,18 100:16 104:2 107:12 129:2 131:26

135:2,3,10,11,12 143:23 144:4 145:25 146:3

147:26 150:14 154:9 155:19 156:2 161:11,12,15

161:20 162:2 216:8,17,19,22 218:6,7,16 221:4

225:26 243:3 251:10,14 273:9 277:13 295:19

309:10 428:24 434:6,12 437:7 438:5 439:10

440:22,23 458:12 464:2 468:9 469:23 511:26

516:25 517:17 518:26 522:25 533:2 534:5,14

536:24 540:20 548:17 549:7 550:13 556:11

561:24 563:5 571:17,18,25 572:14 574:14 575:3

579:21

averaged 427:18 461:21

averages 161:11 427:21 438:8 562:1

averaging 93:1 105:10,11 119:15 155:3 219:25

220:1,3,4 225:22,24,26 226:2 267:20 412:6

446:17 461:26 462:2,18 466:8 513:26

avoid 318:10 334:11

avoiding 316:6

avoids 92:15

aware 4:16 156:21 187:24 302:3 321:5 325:9 367:6

381:11 457:5 487:5 588:19,25 595:25

awful 232:24

axis 42:19 49:23,25,25 51:10,11,12 61:1 63:14

102:17 194:19 195:2 196:25 197:20 198:19

199:16,20 200:19 202:10 206:10 207:4,4 210:11

217:1,5 242:5,6 243:3,3 246:15,16 247:14 251:12

B

b 119:6,7 182:16 185:11 213:6 268:1 293:2,19

322:14,20 329:10,17 338:18 348:7 366:12 377:10

377:12 389:26 396:9 415:19 492:4,6 542:21,21

543:1

baby 301:18

back 20:26 21:10 24:12 60:25 70:16,23 71:2,17

86:9 100:26 107:5 111:7 112:2,3,12 125:20

126:11 127:4,25 128:2,4,13 129:3 131:13 133:9

156:5 158:1,24 160:18,23 161:12,19 162:2,15

178:24 182:1 189:11 195:6 204:9 205:14,22

223:21,26 229:1 231:2,4 233:7 244:6 249:3 251:2

253:9 254:13 256:13 261:19 294:6,24 306:22

311:22,23 312:17 315:12 318:24 319:9,24 323:15

326:8 329:17 337:23 341:18 343:5 349:6 352:1,5

352:9 355:19 362:22 363:9,18 365:13 366:6,9

380:5,13,16 384:15 385:19 387:24 389:22 395:7

400:19 403:2 407:13 411:10 413:21 418:18,24

426:6 428:5 438:5 450:4 451:14 457:20 458:24

459:4 461:10,12 465:22 467:24 468:5 469:1

481:16 482:13 483:9 484:4 485:5 489:12 502:8,8

512:24 519:3 521:18 522:7,7 528:21 530:9

531:17 535:3 536:21 539:4,13 542:26 548:22

551:11 552:21,25 553:7 554:7 555:6 556:17

560:6 562:11 567:10 574:10 576:12 579:19 585:2

592:9 596:23 599:10 600:24 601:23 602:4 603:23

backbone 183:8 295:15

backdrop 18:14 457:17

backed 193:13
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background 27:6 162:26 163:17 181:2 186:8

190:15,19 191:25 199:22 226:16 261:18 288:6

303:2 521:4 577:9,10

backwards 273:1

bacteria 581:26 589:19

bacterial 399:7

bad 120:17 123:16 124:12,14 131:3 177:8 299:12

347:15,23 357:21,22 363:22 417:16,17 418:9

464:12,19 467:9 537:14,14

baffled 589:19

bag 555:4

Bailey 491:10 607:5,5

bait 165:19

balance 176:22,24 219:11 225:22 564:25 579:16

balancing 318:2 564:24 566:18 570:6

Ball 141:14

ballpark 116:6 435:5

ban 167:10 168:21,25 169:5 170:23 171:18,21

172:8,11 173:4 175:9 176:9,15 177:16

band 482:11 538:6

bank 131:20 271:25 581:23

bankrupt 169:5

banned 174:23 176:12 178:16 601:17,18

banning 169:11 172:14 174:20

bar 53:22 200:23 201:4 232:2 362:14

barely 419:14

barrage 259:11

barrier 191:13

Barring 396:8

bars 232:1,1,7

Bartell 87:19,20

basal 201:18,21

base 186:17 263:3 287:8 303:1 312:5 342:4 358:5

413:11 481:12 601:19

based 27:21 28:2 29:25 30:26 32:13 34:25 36:4

37:17 38:24 41:4 46:4,9,14 47:18,22 50:15 51:22

52:6 54:12 57:12 58:25 59:6,10 65:20 72:20 73:2

73:4 74:4 75:8 76:15 82:19 85:18 86:14 90:1 91:5

91:20,23 93:11,19 95:3 98:14 100:1 103:24

104:22 105:6,8 106:23,26 107:8 109:1 110:2

113:21 118:25 119:5,13 121:2,8,13 130:6 132:15

145:25 154:6 161:5,5 163:23 179:19 190:14

192:11 193:2 194:13 197:20 204:13 205:2,18

206:5 208:14 209:26 213:18 221:26 222:22

227:23,23 228:12 229:5,10 240:4,10 249:22

255:17 256:16 268:1 277:22 281:2 285:17 290:4

291:18 292:2 300:1 306:19,20 315:21,24 336:12

337:20 348:3 349:19 374:20 390:13 394:25

395:14 404:14,18 426:13 427:2 434:3 435:2

438:1 439:19 448:12 458:16,18,26 460:11 465:21

472:24 473:8,22 474:19 476:7,19 478:9 481:6

487:14,16 496:14,18 497:3 498:18 501:3 505:24

508:23 512:3 514:5 517:12 518:7 527:13 546:7

553:21 561:24 562:5 563:6 564:2 582:23

baseline 107:2 204:9 220:6

bases 157:13

basic 69:12 82:5 115:25 324:25 330:20 383:24

427:9 440:17 452:9 546:24 550:8 577:12

basically 6:4 93:1 96:2 125:12 130:24 187:18

194:2 205:9 215:18 216:8 217:16 222:23 223:6

229:15 242:10 244:19 245:14 246:1 247:21 252:3

253:26 256:15 257:20 259:2 261:22 262:26 263:2

307:13 308:7 309:8 310:22 313:25 315:12,15

320:8 401:9 403:26 422:23 432:20 438:15 447:1

447:25 448:22,24 449:6,16,18 458:15,20 459:6

467:16 471:14 494:2 495:24 539:6 547:17 557:5

566:6 567:1 569:9 579:22 585:9

basin 175:16 282:6 515:11,18 516:25 518:26

548:17 553:26 578:2

basing 199:22,22

basins 515:7,10 553:11

basis 13:7 25:2,6 50:17 83:17 87:3 110:13 125:5

134:15 205:2 238:9 283:21 295:25 314:16 331:24

337:26 406:3 440:12 474:7 483:22 546:4,25

597:6

bat's 562:20

Baxter 223:12 238:12 241:9 306:3 322:9 325:9,14

325:14

Bay 302:15

Beach 207:5

bearing 156:10

bears 173:12

beat 60:25 176:19 426:8

becoming 302:11 309:23 493:26

bed 177:5 282:9

beetles 166:14

befuddled 589:22

began 281:14

beginning 176:25 227:9 237:13 268:20 277:15

310:1 345:14 387:21 428:19 495:6 539:18 569:7

578:15

begins 388:20 559:23

begun 182:3 483:1 568:10

behalf 179:3 226:14 280:2 297:9 301:1

behave 15:9

behaves 15:7

behaving 342:23

behavior 46:17 56:7,14 241:25 244:8 322:6,14

323:18

behavioral 31:22 43:17 48:14 56:11,12 68:1
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323:20 507:1

behaviors 56:8,9,16 88:7 168:1,3

behoove 602:17

believe 26:4 111:3 125:21 158:10 159:15,16 177:8

177:23 188:1 190:16 201:25 228:15 252:21 263:8

268:22 270:26 291:16 305:6 331:10 340:9 344:12

350:2 376:20 381:12,25 400:4 415:19 452:24

456:24 457:11 466:18 478:12,17,25 479:18,23

480:10 484:22 485:18 487:4 488:6,7 500:10

511:8 512:22 558:6 561:16 563:1 568:4 576:6

believed 297:17

believes 107:19 108:22 109:11,15

Bellanger 176:19

bells 418:8

belong 164:24 346:10

belt 172:20

benchmark 12:3 16:19 547:12

beneficial 169:24 170:8 276:8

benefit 112:23 114:25 166:21 170:18 171:9 172:6

180:16 273:16

benefits 112:23,23 271:11 272:20 280:13 290:16

290:19 291:26

benefitting 168:23

Benthic 265:25 266:6

BERNARD 8:2 456:2 529:2 542:5 543:8 559:19

590:1

Bernie 8:2 456:2

best 19:15 57:6 73:5 157:17 185:17 257:24 264:22

267:13 268:19 274:16 276:7 277:26 278:11

291:18 298:23,23 300:2 330:11 349:1 357:6

367:5 370:16 395:26 407:8 410:4,16 449:19

509:13 521:20 534:10,16,17 563:15 569:26

572:26 577:15,25 590:11 595:8 596:11 597:10

607:13

best/least 535:15

bet 607:13

better 10:7 39:3 83:21 88:13 89:6 92:21 106:5

120:5 125:7 135:23 185:24 199:15 282:6 286:23

325:4 327:25 333:6 340:26 345:26 346:20 361:19

368:6 376:17 381:17 392:5 394:2 403:3 408:21

415:24 416:2 419:25 421:15,20 422:9 430:20

431:4 472:16,17 473:6 490:17 523:13 527:24

529:8 536:3 537:26 538:3,18 541:21 550:9,10,13

550:14,15 574:23 575:1 576:20,21 598:6

beyond 70:11 88:18 94:8 113:6 263:3 313:18

350:23 371:16 410:12 504:7 513:21 525:26 560:2

577:9

BF 267:18,26 546:4,6 551:5

BFs 267:22

bias 25:23 26:8,9 138:3,7,9,13,15,18,20,21,22

139:14,24 141:8,11,13,16 142:3,7,8 143:1,16

144:9,17,23,25 145:2,9,10,12,13,14,16,19,20,21

145:25 146:2,4,7,8,10,12,14,17,18,21,22 147:2,4

147:7,10,11,23 148:4,7,9,13,16,26 149:1,2,3,5,7

149:13,14,16 150:3,23,26 151:1,25 152:2,4 153:9

153:11,18 154:8,10,15,20 155:26 156:10,26

157:3,11,25 158:20 159:19,21 160:1,3,4,8,20

179:26 182:6 185:22 267:18 283:2 284:11 298:21

311:10 331:2 345:2 398:11,15 433:2 469:14,18

469:18,20,22,26 472:17,20 513:2,4,7,13 514:4,7

514:12 515:4,12,21 516:2,12,14 517:1,2,3,5,7,15

517:19,23 519:3,13 521:3,19,26 522:23,24,26,26

523:2,3,12,12 524:3,14 525:1,6,9 529:15,22

532:7,20 533:5,10,12,13,14,19 536:24 537:8,9

539:24 540:12,16,21,22,26 541:9 542:23 543:13

543:14,25 544:1 545:9 547:23 548:3 549:9,12,13

549:21 550:1,10,14,22,26 551:3,12,16 552:7

554:2,5,15 558:2,6 561:22,23 562:20 563:6,12

564:21 568:12,15 570:2,18 571:5 576:19,23,26

577:23,24 578:1 579:2,3 589:2,4

biased 514:16,21 515:7

biases 401:12 469:8,10 530:6

bias-corrected 572:18 574:2

bid 343:21

bide 174:9

big 17:25 20:8 70:4,8 126:5 133:18 164:11 165:3

166:12 218:16 275:24 281:6,13 319:6 373:1

412:17 436:25 490:13 499:24 502:5 518:18 521:9

591:1

bigger 164:12 562:5 593:2

biggest 278:21 343:14 401:18 433:24 574:5

Bill 8:6 303:5 456:4

billion 29:23,24 30:19 43:8,16,21 44:10 45:22

46:14,26 47:6,14 50:19 51:2 52:13,14,15,18

53:16 54:1,2,12,14,24,25 56:17 62:8 67:17 68:14

79:13,14 80:21,25,26 101:16,26 104:16,23,26

105:2,10,12 107:6,8,13,25 108:6 109:9 111:24

113:22 115:16 118:23 119:5,16 123:7 126:24

150:11 151:3,15,25 152:4,10,18,24 153:3,20,24

164:12 175:2 176:5 180:10 185:16,19,19 215:5,6

227:26 228:6 233:3,11,22,26 234:1,14 235:1,4,16

236:13,22,22 238:21 239:10,10,12 240:11 244:1

248:9 251:13 258:12,15 275:20 290:2 304:16

306:13,15,18,24 307:2,4 312:10 323:1 377:25

378:1,11,12 379:4 386:20,20,21,23,23,24 429:16

429:21 430:19,22 435:18 438:21,24 439:5,18

448:6,23 458:21 462:4,14 465:14 475:26 476:3

billions 168:26 169:2 170:15 173:8 175:21

bin 351:15

binary 79:16 80:15 102:17 106:16 110:19 217:6
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242:5 293:19 428:21,23 429:16 460:11

binder 292:23

binding 176:21,23 187:5,9 212:18,25

binds 186:26 187:5

bins 337:17

bioaccumulative 186:19

bioassay 16:1 436:26 437:22 438:17,20 439:1

Bioassessment 491:12

biochemical 31:22 48:13 54:16,20 55:4 57:1

261:10 262:11 327:2 390:16,16

biodata 490:6

biodiversity 581:17,19

bioenergetic 184:21

bioenergetics 87:9,13

biokinetic 389:7

biologic 180:23

biological 7:3 8:3 28:13,25,26 34:23,24 38:6 55:14

55:26 58:16,19,23 59:25 64:16 73:21,23 77:5

89:4,8 134:3 182:1 185:16 196:3 198:14 213:22

217:22 220:18 221:3,16,19 223:25 225:22 226:5

245:5 253:24 254:21 257:16 260:16 264:13,21

266:11,14 290:26 296:2 319:9 328:15 349:2

376:23 378:23,23 399:12 484:10 488:20 490:17

604:26

biologically 79:1 176:1 260:20 436:3 508:26

biologist 7:17,26 72:18 177:25 455:22

biologists 167:20,21 483:10 582:24

biology 6:18 13:19 178:14 215:13 220:25 224:13

243:17 245:13 361:23 362:22 375:26 435:17

455:17

biomarker 207:10 256:17 349:2

biomarkers 349:1

biomass 79:7 86:22,24 87:9,10 88:1 89:3 114:7

238:21 252:23,25 309:2 398:10 399:9 402:3,7

407:22,22,26 423:14 437:17 492:12 580:14,16,21

581:1,5 582:20

biomasses 87:2,14 92:9,11,17

biomonitoring 260:24 265:2 266:15

biostatistician 8:21

biota 408:24

biotic 409:13

bio-assays 343:15

bio-equivalence 375:7

bio-fuel 170:26

bio-indicators 166:23

bio-survey 260:25

birds 166:13

bisphenol 364:12

bit 5:20 10:15 110:24 112:3 133:20 157:13 183:18

186:8 195:17,19,20 199:18 203:21 233:5 294:20

302:5,21 303:10 310:17 318:16 338:22 341:20

391:1 408:18 416:2 417:12,12 433:12 434:22

450:1 456:21 467:19 468:7 471:12 474:21,22

479:4 482:12 498:17 513:12 530:21 536:1 538:2

538:7,7 541:19 546:15 548:8 553:1 562:15,16

571:7 576:3 591:16 602:18,20,25

bite 270:6

bits 597:24

biweekly 155:12 514:21

bi-assay 466:21

black 102:14 164:11 260:10 547:6

blah 318:18,18,18

blame 532:16

Blankenship 188:7 195:26 196:10 207:10

Blankinship 20:6 23:12 24:21 26:22,26 27:1 60:22

61:2,7 62:5,17,25 63:6,23 64:13 65:2,7 67:5,9

68:12,21 71:6 127:16 326:10,10 332:26 333:19

364:23,23 366:7 371:20,22 380:6,7,9 389:24

450:17 451:13 478:15,17 479:2,23 480:12 482:4

blind 202:23 560:9

blinders 605:13

block 177:12 345:6,7 346:17,20,21

blocked 309:9

blocker 187:13

blocking 345:3,9,20 346:13

blood 58:2

bloom 43:26 44:9 75:17 259:19

blooms 44:3

blown 228:5 370:1

blue 20:18 53:8,22 152:16 153:11 281:6,13 292:26

292:26

bluegill 402:13

blue-green 257:2

BMP 283:4

board 147:12 195:3 260:24 353:17 376:11 414:14

432:15

Bob 8:13 60:23 424:7 425:15 428:6 439:22,23

456:7 475:4 521:5 538:26 542:10 546:9 549:19

554:18 556:20 561:19 565:25 570:23 571:1,1,3

596:13

Bob's 425:18 548:9

bodies 22:6 78:13 81:18 108:21 149:24 165:26

513:5

body 11:23 17:20 23:11 24:26 133:10 200:19,23

201:3,7,14 257:12 261:12 488:12 524:8 551:15

bolts 491:9

bonded 187:16

bonds 73:12

book 176:22 188:22 227:19

books 189:6
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Page 13

Boone 7:9,9 111:23 125:22 127:9,11 205:10,24

206:14,21 211:24 324:11,12 326:13 329:18,19

333:9,13,21 336:5 337:11 338:11,25 339:7,19

349:18,19 360:7,8 368:11,12 373:5,6 377:22,23

381:26 382:1 383:8,21,22 386:8,9 390:10,11

455:18,18 484:22,24 485:13,14,23 486:1,17

496:20 510:5,6 587:12,13

boosts 295:17

boot 142:6 494:23

bootstrap 444:26

bootstrapping 436:7 476:9,15 567:23

boot-strapping 141:12

borderline 232:17

bothers 533:23

bottle 532:6

bottles 235:17

bottom 81:15 100:14 101:18 122:11 159:22,23

228:5,7 249:5 381:2 458:23 460:18,23 466:8

536:1,17 537:24 538:11 546:22 556:23

bounce 347:18 531:15

bouncing 445:5

bound 107:14 187:10 192:13 435:25,26 551:7

597:12

bounded 589:4

bounds 434:8 572:17

box 10:10,14,15 11:3 18:4 20:18

boxes 10:3 518:5

bracket 405:2 554:14

brackish 410:7

Bradbury 9:3,5 19:22 66:5,25 69:10 112:2 132:24

161:1,3 186:15 315:6,22 318:5 319:20 355:11,12

358:17 359:7 361:1,3 363:8,14 385:10,12 418:14

418:14,17 428:5 477:5,12 483:18 569:6 603:20

603:21

Brady 19:4,6 305:4,8 415:3 606:21

Brain 179:7 181:24 182:1 213:7,8,9 223:23 224:6

224:26 225:15 228:3,13 240:14,15,19 248:20

249:20 251:9,13,15 253:19,22 261:5,8,20 262:10

262:25 264:13 266:25 309:1,7 429:13

brains 409:26

Branch 19:25

breadth 11:24 50:9 79:2

break 71:14,15 136:7 178:24 223:22 226:8 240:17

287:25 294:2 348:8 366:9,10 385:9 494:7 543:1,4

584:22 585:2

breast 600:7,8

breath 122:21

breathe 464:11

breathing 463:17

breeze 255:3

Bridge 330:3

Brie 607:6

brief 6:11 272:15 405:13 411:20 488:9 544:13

briefed 3:15

briefing 88:21,22 95:21

briefly 188:13 322:7 336:5 455:6 475:6

bright 152:24 432:7

brine 37:18

bring 18:20 74:16 113:6 165:26 182:13 244:6

293:15 319:8 343:5 366:6 385:21 395:7,16 497:8

510:24 526:14 545:1 559:8 597:5

bringing 261:19 519:26

brings 26:13 297:24 395:11 481:16 556:17 569:5

574:4

Brittany 176:19

broad 18:6 48:11 55:5 59:24 88:26 89:16 130:2

395:11 555:21

broader 59:22 348:25 373:3 376:17

broadly 531:8

broad-base 501:5

Brock 76:12 79:17,19,20,23 80:7 105:5 106:20,21

106:22 114:4 126:3,10,13 237:26 428:21 429:3

429:25 471:14,14,16 499:1

Brocks 429:3,4

Brockway 126:25

Brock's 229:18

brought 123:24 184:12 282:11 286:8 336:5 394:20

423:23 452:5 454:16 457:3 484:3 499:4 549:17

551:14 569:6 592:7 593:18 595:25

brown 50:3 51:16 140:6

brown-yellow 153:17

Bruckner 292:15

Bryer 301:2,7

Bubba 301:17,21,22

budget 171:14,21

buffer 593:21

build 399:20 412:23 530:12 565:15 566:12 579:8

597:12 598:3

building 1:14 226:25 530:19

built 164:2 572:25 573:2 575:2 579:22

bulk 96:13 97:2,4 198:18

bullet 489:10,21 511:8

bullets 489:24

bullfrogs 164:20,26

bunch 15:10 128:5 131:3 155:5 229:6 317:25

374:25 375:11 382:21 437:16

burden 277:24

Bureau 301:26

buried 318:4

burning 590:7
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bus 421:9,10 524:21

bushels 275:22 295:18,19

business 288:25

buy 407:23

bye 454:5

B50 441:11

C

C 39:18 182:16 185:21 293:8 322:20 338:18

348:13 470:23 512:17 526:20

ca 270:16

caffein 603:10

caffeine 584:26

Caite 607:6

calcium 127:1

calculate 48:8 101:10 103:8 148:22 216:16,17

217:13 243:24 258:3 436:22 539:25 541:7

calculated 61:1 85:9,20 101:20 102:5,11,24 104:13

231:21 258:13 428:25 439:18,20 445:24 514:13

calculating 104:1 108:9 410:22 555:4

calculation 16:17 101:13 105:14 108:2 135:25

293:20 505:15,18

calculations 40:16,18 106:11 157:25 429:10,11

calibrate 214:15 221:20 224:9 243:23 247:18

248:6 299:22,26 473:7,13

calibrating 215:3 222:16 243:21 254:25 257:17

calibration 180:9 184:16,17 213:17 214:5,8,9,17

214:18,20,26 215:8,12 222:12,13,18,21 223:15

242:24 243:17,19 244:15 245:11 247:23 248:1

251:18 254:8 257:23 258:14 264:20 429:24

473:10 518:5 595:24

calibrations 241:23 467:23

California 6:7 7:17 72:3 137:10 151:11 301:3

455:9,23 456:16

call 22:26 23:7 55:11 64:18 190:15 253:20 263:11

350:24 391:1 392:6,7 404:11 416:2 482:24

499:14 500:14 536:4 589:23 597:5 606:19

called 84:18 126:10 132:9 183:4 227:2,2 289:14

293:1,7 294:16 301:13 302:24 482:25 490:16

491:1 543:22

calling 126:23 176:15 362:2 417:7 438:2

calls 80:5 121:1 409:13

call-in 23:2 29:3 297:12 350:24 367:8 369:11

Caltha 272:8

camera 4:15,15

Campbell 178:26 179:22 181:4,5,6 182:18 210:19

Canada 195:17

cancel 171:24

cancer 8:21 72:1 173:21 189:21 190:20 371:3

456:12 599:16,19 600:6,6,7,8

candidate 86:20

candidates 84:1 85:1

candy 170:11

cane 150:18 151:8 153:6 164:5 183:9,13 289:10,22

295:13

canopy 176:26 269:22 591:10,14

capabilities 145:7 147:7 504:7 557:13

capability 17:1

capable 176:4 337:5

capacity 3:14 359:8 527:18

Cape 191:12,13,20,22,24

capitalize 146:11

capture 11:16 355:16 453:5 554:10

captured 58:18 93:17 159:16 450:6 525:15 526:23

527:19 558:1

capturing 17:12 317:24 342:12 522:19

carbaryl 330:7

carbohydrate 187:3

carbon 187:3 237:2,4,10,11 280:16 443:9 559:2

carcinogenic 173:13

carcinogens 170:21

care 86:17 170:16 177:7 301:15,20 344:8,16

546:17

careful 235:22 364:19 472:5 509:11 562:13

carefully 179:10 321:13 337:2 338:4 393:6 430:23

470:12 471:8 507:17 547:2 592:10

carefully-designed 424:16

cares 295:3

Carlo 516:3,18 517:17 519:6 533:4,23 534:24

538:1

Carlos 4:12 607:6

Carney 401:17

Carolina 7:21 472:22

carp 235:6,8 401:20 402:3,6 403:4

carried 69:17 132:8 165:22 172:16 228:24 409:18

443:18 469:5

carrier 35:24 36:7 37:11,16,19 187:4

carries 172:19

carry 16:23 166:8 543:21

carrying 544:5

carryover 443:12

carryovers 95:5

cars 165:20 170:25 171:2,3

cascade 507:1 508:1

case 10:21 16:20 89:20 97:8 101:21 102:5 112:8,18

112:21 133:5 134:11 140:9 146:23 148:21,23,24

149:13 153:18 197:19 201:4 214:1 215:15 252:4

252:10 293:14 305:26 317:8 344:8 383:19 395:2

417:13 430:16 437:2 438:2 507:11 515:6,12,22

517:13,14 519:16,20 521:19,26 531:3 539:23

566:12 567:3 577:26
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cases 37:6 39:9 90:21 99:25 108:16 111:18 113:18

116:3 120:26 230:20 411:1 423:21 514:9,10

600:16

CASM 23:17 24:1,3 86:21,22 87:8,16,17,21,23,25

88:5,8,12,14,16,20,22,24,25 89:7,10,18,21,21,26

90:8,10,23 91:17,19,23,26 92:4,9,14,21,23 93:7

93:11,13,16,21,22,26 98:11,17,23,26 99:5,6,12

99:16,20 100:2,5,7,16,17 104:18,22 184:3,11,12

184:13,13,18,22 185:3 214:12 239:15 270:5

277:17 281:2 306:16 436:21 458:21 459:4,5

CASMs 91:10

CASM-based 24:4

cast 388:15

catch 309:2

catching 512:14

categorical 146:15 147:8 532:20 533:5,8 539:5,14

539:15 578:22

categories 40:11,13 48:11,17,22 192:26 217:10

328:7 336:12 349:24 543:24 578:23,25

categorize 32:18 48:9

categorized 48:11,25 254:17 349:20

categorizes 403:23

categorizing 80:3

category 32:20 48:26 49:9 130:2 193:2 337:24

339:18 364:7

catfish 174:18 402:14

Catherine 7:1,1 203:10 204:4,10 207:6,23 250:16

285:12,16 286:15 321:11,12 327:4 336:4 346:23

347:25 349:22 351:19 357:11 368:20 374:6

376:20 378:9 379:15 381:10 387:17 390:26

455:14,14 477:18 478:10,25 479:12,18 480:9

481:23 482:9 483:8 485:18 486:22,24 487:25

488:3 503:1 510:13 586:22

Cathy 324:12 351:19 481:23 485:14 486:24 605:16

cattle 164:4 288:9

caught 326:25

causal 33:13 190:3 195:9 196:2 326:25 335:21

339:9 344:5

causality 29:16 35:16 188:24 327:1

cause 13:25 46:16 66:10,19 74:8 113:2 121:4

133:14 166:1 175:7 212:18 215:7 239:22 240:1

253:5 269:18 327:26 328:2,17 330:21 340:19

343:26 363:21 377:15 387:4 433:17 461:20

495:22

caused 126:4 170:14 236:2 433:4 462:9

cause-and-effect 34:22 35:3

cause/effect 330:15 335:26 353:13 382:11,14

452:10

causing 73:17 112:19 131:22 236:11 253:4 588:23

caution 402:15 559:21

cautionary 559:19

cautious 276:16

caveat 327:18

caveated 497:4

caveats 285:20

CA1 265:8

CEF 90:14,16,17 99:19

CEFs 90:7,20,21 99:26 100:1,2

cell 197:7

cells 58:2,2 599:21

cellular 55:6,14 168:4

cellularly 389:6

cell-dependent 197:6

Center 1:13 142:11 290:12 297:9

centered 514:3

centile 217:13

central 249:23

century 167:2 169:9 238:15

CEO 272:3 275:5,8

certain 14:26 38:14 40:4 109:10 111:21 115:11

234:8 240:7 282:20 325:19 347:14 349:9 361:25

375:17,18,22,22,23 437:18 446:9 463:6 466:26

479:24 498:7 530:9 533:19 535:6 557:4 562:8

572:24 583:16 585:10 606:6

certainly 13:12 66:26 125:1 204:14 205:5 208:19

211:5,11 212:20 213:3 225:15 232:12 239:9

250:21 286:7 300:13 359:14 360:3,16 387:21

406:10 412:10 459:11,14,22 501:5 525:25 528:6

543:18 544:7 545:6 558:13 560:2 575:26 588:24

590:21 591:14 594:1,2 598:13

certainty 174:7 211:22 271:1 475:24

cessation 264:18 443:16

cetera 110:26 444:3 493:19 507:1

CE-LOC 104:11,21,22,25 105:9,15,20,25 106:12

106:26 107:2,5,7,14,18 108:11,12,26 109:3,8,15

118:23,25 119:3 126:24 129:13,14,19,19,23

130:3,5 132:11 135:25 136:18 138:17 139:17,20

139:26 140:4 149:8,9,9,11,12,16 150:1,11,20,22

151:3,14,15,17,25 152:4,9,15,17,20,21,23,26

153:2,10,12,15,20,24 154:13,17,18 160:21

161:18 162:4 269:14,23 274:19 283:20 314:15,23

315:18 379:19 405:2 428:12 434:4 436:5 438:5,6

439:7,8,10,18,19 440:2,7 465:5,10,15,16,24

466:2,9,13 468:25 469:9,11,13,19 470:2 472:16

472:18,19 473:7 488:14 494:14,16 495:25 496:17

497:1 498:25 503:18 505:26 506:16 507:9,23

513:5 522:6 530:22,23 531:13 563:5 571:15

CE-LOCs 105:17 107:24,25 108:9

CE-LOC's 472:21

chain 74:10 187:1,1,8 325:13 530:9
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Chair 2:16,17 5:25 182:10 455:4

chairman 186:4 268:10,13

chairs 182:9

challenge 5:21 12:1 16:2 141:8 304:8,9 392:17

418:4 425:20 441:5,6 484:2 553:12

challenged 364:7

challenges 12:9 17:1 58:14 70:6 276:12 282:6

289:2 304:20 363:10 441:3 491:5

challenging 3:2 5:15 10:4 425:24 559:16

chance 131:22 174:11 182:25 288:4 313:26 318:9

320:10 371:19 378:17 407:26 417:11 440:8 461:9

525:2,4,23 546:14 558:17 580:18 585:8,9

chances 391:10

change 14:26 15:1,5 44:16 47:11 75:17,21 92:10

107:18 113:3,26 114:3,6,6,13,19 116:25 118:3,7

120:14 121:23 126:3,4,15 133:15 152:26 165:3,8

176:26 206:6,8 207:26,26,26 208:13,20 219:2,10

219:12 222:7 236:15 239:23 241:21 242:23

243:20 244:24 246:5 252:11,13 259:8 260:21

274:20,25 339:8 434:8 441:15,17,19 442:23

451:6 460:8,10,17 478:9 491:19 492:8 494:17

496:16 498:4 502:12,13 512:18 562:14 572:1

581:19 582:14 589:23 602:26 603:2

changed 14:26 80:13 107:5 277:7,17 286:12,12

315:4 359:17 366:18,19 451:7,25 483:25 502:6

582:4 583:8

changes 21:20 22:6 44:18 55:2 56:16 58:1 66:8

73:1,4 74:8 79:6,10 104:22 105:5 120:11 127:6

187:14 200:4 206:3,4 207:11,12 214:20 228:2

241:13 242:15 243:6 244:12,21,22 246:25 260:8

265:9,21 291:5 306:9 358:24 386:20 407:21,22

407:26 423:11,14 458:22 460:11,18 497:18 507:1

507:3 524:8 580:14,15,16,24,25 581:16,20,24

582:7,10 599:17

changing 115:24 117:13 334:23 419:20 460:16

543:17,18

channel 402:13

chapter 298:18 503:15 571:12 590:6 605:19

characteristic 527:26

characteristics 13:1 23:23 24:8 89:8 92:9 155:24

155:26 158:24 160:2 255:15,16 262:16,17 267:6

267:14 329:10 363:22,23 375:22 376:1 397:25

414:2 515:6,18 521:12,15 522:9 527:23,25

543:17 546:4,7 548:15,21 551:13 552:22 554:1,7

558:10 567:5 569:21 570:2,4 573:21 574:13

576:23,26 605:5

characterization 17:14 99:14 137:25 139:26

235:12 305:23 408:10 428:14 431:19

characterizations 85:10 93:11

characterize 28:12 40:18 47:10 103:14 201:26

252:3 310:12 416:1

characterized 60:13,17 194:7,9 197:13 306:13

438:7

characterizes 521:25

characterizing 48:6 82:16 428:20

charge 18:15 25:12 26:14,20 182:23 183:3 190:13

274:18 293:23 294:18 305:6 320:12,16,19 321:4

324:2 363:9 395:2,5,11,13 476:20 478:11 487:14

487:16

charged 187:22

charges 394:24

charity 162:23

chart 15:3 72:25

charts 61:1

chase 191:8 213:3 237:15

chasing 282:17

cheaper 568:20

check 239:2 334:4 339:22 430:15,17

check-off 295:9

chemical 8:12 13:17 29:1 37:5 38:5 45:12 77:5

127:8 169:7 174:11 175:18 178:15 183:20 327:26

328:15,24 339:20 349:1 359:23 376:15 442:8

444:2,9 463:15 509:2

chemicals 14:12 32:15 37:22 46:1 60:6 113:11

165:21 172:26 177:21 179:17 211:18 227:8

288:19 331:17 359:25 375:23 604:13

chemical-laden 171:12

chemist 136:21

chemistries 285:25 286:3

chemistry 76:18 211:9

chemograph 81:13,15,24,25 82:2 86:10,12 90:14

90:15,17 97:17 99:20 101:9,14,15,19,24,25 102:3

103:10,11,14 131:8 141:3,19,21,22,23,25 142:5

145:1 147:19,21 148:1,18,19 149:5 158:20

161:26 219:26 224:17,19 245:24 246:10 398:24

419:21 438:9 447:13,14 468:9 492:2,3,4,4,6,6,19

492:20 512:3 540:24 550:26 551:3 554:11 577:11

chemographs 82:13 90:5,19 97:16,20 99:17,19

102:26 114:22 116:2 140:26 141:6,10,24 142:2

147:17,22 148:5 217:17 220:10 224:15 225:23

244:7,10 247:3 261:17 405:24 409:20 411:6

419:19,20 429:6 436:21,21,23 439:5,7,9 441:8

442:4,7 443:13 448:5 473:5,6,12 492:15 511:26

516:7 519:20 539:26

Chesapeake 302:15

chief 19:1 72:1 275:2 288:2 606:17

child 417:15,16

children 177:9 417:13,17 418:9

Chile 191:23

chime 371:19
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Page 17

chlorophyl 127:3 231:12

chlorophyll 79:7 233:13,13 234:3,6,7,8,9 235:19

237:1,4,8

chlorophyll-a 231:20

chlorophytes 231:12

chloroplast 73:13 186:24,25

choice 95:19,20 320:3 467:5

choices 427:8 577:3

choose 169:4 183:2 487:15 517:8

chooses 329:14

choosing 271:12

chop 559:13

chopped 164:4 170:5

Chorus 168:6

chose 273:11

chosen 31:8 313:3 440:7 584:12

chronic 16:17 34:17 46:20,23 49:18 51:4 52:7

336:21 350:9 368:25 443:23

chronological 65:10,15 127:22

chronology 86:18

chrysophyceae 231:18

chytrid 165:13,14,16

chytrid-infected 165:15

circle 152:21 153:13 191:9

circles 152:16,18,19 153:11,13

circulating 508:8

citation 503:3

citations 432:1

cited 32:8 190:10 238:1 282:25 423:18

citizens 171:11,12

city 269:8

claimed 70:5 236:2

claiming 591:15

claims 88:12 271:9

clarification 60:22 64:24 68:26 109:21 155:1 161:3

162:12 251:23 293:16,23 310:22 311:7,8 312:24

314:15 315:8 319:24 345:16 354:21 356:13

364:24 365:16,19,25 366:14 380:10 395:15 415:3

426:25 427:13 428:3 451:5 454:2 461:8 471:26

472:13,15 473:2 475:21 476:22,23 477:11,21

478:26 479:6,21 480:5 482:3 483:7 485:1 488:5,7

488:11,17,21,23 489:16 503:8 511:5,13 521:2

526:16 527:5 532:1,12 543:3 552:6 562:23 580:5

580:9

clarifications 305:9 395:3 396:3 450:6 489:16

561:21

clarified 427:5 552:4

clarify 60:26 116:16 118:22 119:11 311:2,13,14

313:10 346:12 355:18,19 360:8 385:12 394:18

416:17 447:11 450:3 453:11,19 476:14 528:7

530:21 579:20

clarifying 311:22 361:15 385:13,14 445:25 479:5

607:1

clarity 215:20 426:6

Clarkson 7:26 455:26

class 114:8 324:15 417:14

classes 17:20 56:19 64:10 231:10 533:11 546:5,7

classic 227:2 268:24

classical 424:3

Classics 401:2

classification 39:21,23 40:15 42:3,11,13,14,18,23

47:22 48:7 49:6,14 50:2,22,24,25 51:15 52:18

60:10 63:3,13,16 64:4 106:14,19,26 107:3,4

119:5 126:16 128:23 130:2 326:20 351:3 366:18

415:25 473:3 474:18 478:1

classifications 40:3 49:3,13 50:1,11 51:14 52:22

63:18 80:7 107:9 132:16 366:18 474:12,26 478:4

classified 35:14 37:7 39:19 40:10,21 42:1,4,22,23

43:2 46:10 47:20 48:23 63:10 80:6 81:7 101:14

101:25 106:20 119:8,9 125:25 126:14 128:24,26

351:13 387:26

classifies 345:24

classify 32:18 39:15 254:14 351:11,11

clean 12:21,22 159:11 172:23 174:15 530:8

cleaner 174:21

Cleaning 174:14

clear 79:21,25 80:16 171:23 173:6 184:10 230:13

237:23 271:17 296:7,8 314:23 315:2 319:13

320:2 326:9 341:22 345:19 352:16 353:26 356:5

356:5 357:12 361:6 363:25 369:3 370:6 382:19

384:21,25 389:23 393:8 421:24 426:3 440:2

445:24 467:7 469:18 472:12 475:10 477:4,4,15

482:6 484:22 487:24 489:3 496:23 503:10 514:25

515:20 519:13 530:5 532:26 567:14 586:1 604:2

606:26

clearer 420:26 459:9 473:2 601:9

clearly 24:17 77:24 114:14 175:9 198:16 201:3

237:23 269:18 285:5 286:1 319:1 337:1 370:14

374:7 376:23 381:23 413:10 436:13 440:6 469:20

476:12 485:20 604:10

clear-cut 337:6 341:20 348:3 357:15 370:5 381:11

393:9 507:16

Clemson 6:24 7:21 455:12,25

click 205:21

climate 165:3,8

clinical 194:2

clock 71:16

close 5:22 203:6 207:4 274:13 304:16 368:10

403:24 470:10 489:11 536:14 563:23 567:19

574:16 585:25 586:23
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closed 114:16

closely 160:24 306:18,23

closer 277:11 513:22 522:15

closes 489:19

closest 158:7 410:4

closing 279:4

closure 338:2

cloud 165:5

clouded 269:26

clouds 165:22 172:16

clumped 61:11 63:16

cluster 43:21,24 44:1 554:5

clustered 198:2

clustering 210:12,13,14 578:16

clutches 330:9

Coalition 272:5 276:6 283:15

coast 410:8

Coastal 7:17 455:23

coauthor 188:20

coded 80:18 193:23

coefficients 535:22,23

cofactor 259:23

cohorts 278:19

coincidentally 314:1

cold 236:5,12

coldblooded 204:7

colleague 4:5 211:15 342:11 377:21 602:1

colleagues 2:25 5:17 12:19 13:11,13 21:3 136:19

161:23 322:16 368:6 369:6 406:8 408:14 424:9

487:20 524:7 526:25 588:26 607:4

collect 57:20 103:5 265:22 568:21,25

collected 38:5 41:13 45:17 78:9 111:2 176:14

332:18 343:4 391:17 423:23 429:10 523:25

550:16

collected-organisms 57:13

collecting 103:3

collection 238:17 337:4 413:4 424:15 433:3

collections 419:4

collective 100:7,11 215:2

collectively 160:7 223:14 247:3 257:11 397:21

399:11

collinear 445:19

colonized 403:9

color 50:2 193:23

Colorado 6:15 455:11

colors 49:26 51:13 52:22

Columbia 7:14 455:21

column 260:4 351:22

comb 404:6

combination 44:2 286:3 327:12 505:20 534:19

564:8 578:16 579:15

combinations 40:4 42:5 53:11 534:13 564:4

combined 44:26 143:4 235:7 312:16 358:25 387:3

504:5 534:4,18 544:2

come 13:12 16:10 18:1 19:12 20:26 24:12 71:4

75:25 93:2 111:8 118:21 125:20 157:8 162:15,20

165:20 166:18 168:9 172:18 175:24 176:11

178:24,25 182:1 189:8,17 191:23 198:21 202:25

205:19 206:9,20 208:2,2,23 214:14 215:4 220:23

222:12,17 223:21 226:22 229:18 243:24 245:14

247:16 248:8,14 287:4,25 294:23 305:4 321:20

323:15 325:11,15 329:15,16 341:5 349:15 355:16

363:9 369:3 374:19 377:19,19 380:15 385:19

409:1,21 410:21 415:12 416:5 417:11 419:18

422:6 425:11 427:23 430:1,9,10 435:15 436:16

439:1 445:8 461:10 473:12 474:5 481:4,7,18,25

489:12 493:15 498:2 499:10 512:24 517:2 519:3

520:12 521:18 535:3 536:21 537:26 539:4 540:3

542:25 556:13 560:17,19 577:13 585:2 587:24

596:16 601:13 603:17

comes 54:20 81:16 106:13 128:19 135:8 162:2

194:10 294:17 362:22 473:11 476:6 481:17

539:17 550:18 555:14 574:22

comfortable 69:4,7 360:18,18 474:2 497:5 520:18

coming 18:25 114:2 121:18 128:22 133:25 210:8

283:8 339:13 364:25 378:17 388:6 394:11 418:22

418:23 429:19 433:6 439:26 463:13 465:22 490:3

521:4 541:20 549:22 551:11 557:8,10 560:6

576:12 596:23 599:10 603:12

commandeer 269:9

commend 297:20

comment 66:26 110:23 115:21 116:14 120:23

130:21 135:20 136:2 157:15 162:19 274:19

286:21 292:8 294:10 297:3 300:10 303:17,20

305:3,25 320:22 326:12 327:9,11 339:3 348:9

350:21 354:18 355:9 361:2 366:24 372:4 377:14

381:3 383:8 385:24 387:20 390:5 396:15 402:18

411:20,26 412:17 414:15,18,23 416:19,20 422:18

428:11 434:2,19 439:25 445:16 448:3 450:21

454:26 467:13 476:3 478:19 483:18 489:13,18,21

494:15 503:16 513:5 522:21 525:20 526:6 528:1

528:16 529:6 532:19 542:22 544:13,22 566:24

567:23 569:5,24 575:15 579:19 588:9,26 592:19

593:1,2 594:6 599:5

commented 432:11

commenter 294:4 300:26 301:23

commenters 3:25 4:4,8 5:20 294:22 590:2,14

603:5,25 606:22

commenting 227:20 294:5 297:9

comments 4:7,24 5:9 6:11 13:15 18:25 65:20
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135:18 136:8 161:2 177:24 178:22 181:12,16

183:7 184:5 187:23 204:23 272:15 284:6 285:10

292:18 295:5 297:6 298:4,6 299:14,21 300:24

305:13 326:7 328:11 338:23 339:18 346:24 347:7

356:14 366:12 377:10 394:16 395:12,13,17 396:9

400:11 405:13 408:18 410:15 412:4 414:19

439:22 448:10 450:14 455:1 466:19 472:8 473:19

476:8,19 479:10,11,14,16 486:19 502:26 503:7

503:10,24 510:18 511:3 519:23,23 523:15 525:11

525:18 529:4 532:3,11 542:3 544:25,26 545:12

547:19,21 549:1,2 558:5 560:4 562:21 570:10,12

575:21 580:3,4 584:17,24,24 585:7 589:14

590:16,23 591:25 592:4,12 593:6 598:4 603:3,20

604:1 605:17,19,20 606:23,24

commercial 289:5

committed 282:4 283:6 599:8

committee 2:14 3:4 5:13 9:6 268:10 432:12 454:19

committing 249:26

commodities 268:16 291:20

common 12:25 13:7 41:7,18 53:11 57:19 59:4 93:5

94:8 211:10 274:3 282:8 291:25 309:4,19 332:6

401:21 413:9 459:1 529:11 534:15 601:14

commonality 12:26 13:2

commonly 38:12 57:13 142:12 167:10 175:12

581:18

communicate 16:24 592:24,25 601:7,14,15

communicating 506:13 589:25

communities 9:17 26:17 65:21 72:21,23 74:4,13,15

74:17,19 76:24 78:9,13,15 79:2 82:24 89:5 91:4

108:15,17,18,21,24 111:2,10 116:25 117:6,23,24

118:15,17 120:24 173:21 174:14,14,21 175:19

227:25 230:3 258:7 259:8,20 260:21 262:4

265:25 266:6,14,20 271:24 366:2 379:21 396:15

396:18 397:20 399:7 406:14,19 407:13 409:11,13

413:6 428:13 496:24 500:11 508:14 512:15

community 21:6,16,19 22:7 25:7,14 26:3,18 28:15

34:12 36:23 59:26 66:8,12,13 74:6,8,11,23 75:12

75:13,20,23 76:7,9 79:9 81:4 84:22 86:20,23 87:3

90:1 91:4,15 92:8 93:6 102:22 103:20,22 104:24

105:3 107:15 108:26 109:5,13 113:3,26 114:14

114:20 116:12,13,23 117:6,10,12,13,21,25 118:7

118:9,24 121:6 124:26 125:9 127:6 130:6,6

132:15 136:18 179:26 233:24 236:17 237:14

249:1 250:4 259:18 260:9 261:1 265:3,5,20 266:2

266:16 272:26 274:21 282:5 291:8,15 305:14,24

338:20 392:10,18 394:3 398:7,9 402:11 408:25

434:9 437:4,11,12,12 443:1,8 469:2 494:17

497:12,20 498:4,8,10,16 500:16 501:11,12,18,20

501:21 502:1,4,13,14,22 503:16 510:25 515:16

515:19 523:18,22 524:8 582:4,19 592:26 593:4

593:10 604:26

community-based 74:22

community-level 586:17

companies 175:21 183:16

comparable 69:5 110:3 213:21 335:1 546:24

compare 32:19 48:25 82:7 83:8,12 134:18 156:7

156:13,16 160:20 298:24 330:18 439:6 449:20

466:1 468:22 492:4 531:23 541:8 572:19 597:15

compared 11:23 45:5 49:5,8 50:12 85:20 87:5

104:1 116:12 139:25 142:22 144:25 149:8,12

185:15 207:12 232:12 257:21 266:7 299:3 371:24

402:8 411:23 492:6 516:11 520:4 584:1,16

596:20

compares 99:16

comparing 21:6 26:18 81:8 122:16 125:10 136:17

157:16 382:9 517:17

comparison 39:4 49:1,3,16 62:26 63:2 83:2 99:1

104:21 125:6 139:24 147:6 267:8 541:6 572:15

comparisons 93:10 98:11,17 99:22,23,24 100:1

124:25 203:19 567:16

compelling 386:26 506:10

compensation 309:24

compensatory 309:1,3 310:4,8,12

competence 333:18 334:8 435:25,26

competitive 255:5

competitor 187:12

competitors 329:23 335:14

complement 543:14

complete 56:1 64:17,21 94:4 165:16 291:3 372:19

375:25 518:1 541:4 548:2

completed 5:12 26:4 82:15 104:2 150:7

completely 70:25 94:3 101:19 102:4 116:6 121:18

122:25 123:8 163:21 224:19 248:12 262:5 331:23

340:7 357:1,2 361:13 373:15 518:12 532:8

539:18 545:11 572:1

completeness 65:26 327:10 573:17

completion 30:14 39:7

complex 13:21 14:17 16:15,15,21 33:12 84:21

127:8 140:15,16 214:1 338:10 340:25 341:25

345:11 402:11 414:9 441:8 471:20 592:20 599:19

600:18 601:6

complexes 73:12

complexities 91:25 100:17

complexity 11:5 15:2,18,26 16:8,22,26 127:7

140:25 343:1,1 376:2 378:19 380:2,3 401:3 417:6

544:17 586:14 592:24 601:1,12

compliance 150:9 226:13 278:22

complicated 421:21 433:23 434:24 435:2 437:2

519:12 546:21

complications 235:6
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complied 38:23 300:2

complies 297:21

complimented 308:17

compliments 289:17

comply 283:19

complying 299:7

component 88:3 137:17 215:10 275:12 298:19

302:2 344:14 348:19 536:13 574:18 602:21

components 11:8 73:24 75:23 86:6 216:12 234:23

298:17 399:8 422:5 535:21,22 593:8 601:7

composite 553:26

composition 74:23 79:9 87:3 398:10 423:11 502:9

502:13 581:24 582:14

compositional 73:24

compound 12:1,6,7 232:2 268:24 341:12 357:14

505:9 585:25 588:14

compounds 186:20 333:26 342:1 370:9

comprehensive 23:17,24 86:21 184:5 288:21 416:5

557:20

comprised 108:14

compromise 36:12 77:6 335:20 427:16 449:3,22

compromising 541:25

computation 469:1

computational 215:18

compute 130:25 468:8,22,24 516:12 537:3 540:26

596:24

computed 85:13 193:5 194:10 469:19 516:2

computer 219:2 496:11

conceivable 225:16

conceivably 516:20

conceived 163:4 516:26

concentrated 81:22,24

concentrates 285:22

concentrating 161:10

concentration 14:13 22:10 30:18,20 31:15 36:7

37:16 48:20 50:13 51:2 53:11,16 61:14,16,25

62:6 69:25 73:4 77:16 79:7 80:19 82:6 84:7,14

85:14 86:7,8 89:12 90:7 93:3 95:3 100:23 101:8

101:11,11,15,17,22,26 102:2,6 103:25 104:2,10

104:11,15 105:2,11 112:18 113:24 115:11,12,16

115:20,21 121:19,20,24 122:6 124:8 129:9 134:7

138:19 139:11,25 142:3 147:26 148:2,7 149:3,3,6

149:7,11 150:3,11 151:14 152:3,9,20 153:2,14,19

154:11,17 158:10,19 159:25 160:21 161:15,17

175:2 176:7 187:15 191:4 192:19 193:13 194:6

203:2,4 207:14,16 208:23 212:25 216:9,12,15

220:1 225:3 226:1 233:16 234:14 236:11,16

239:3,16,17 240:1 243:3,8 245:18,22 246:7,9,16

246:23 249:12 250:25,26 251:6,11,14 253:4,4,7

258:11 261:18 262:26 263:1 269:14 270:2 306:17

315:20 316:4,17 334:4 339:22 343:21 349:16

368:24 377:15 398:4 400:8 401:13 402:19 403:7

403:8 405:14 406:2 422:17 426:21 438:6,22

439:11 447:17 460:5 463:7 464:12,13 465:25

468:10 469:23 473:11,13 495:21 504:24 505:11

505:12 507:21 511:18 512:8 513:15 516:11 525:2

525:4,6 533:2 558:16,18 564:17 572:5 574:13

580:1 589:5

concentrations 23:10 25:22 29:22,25 30:25 35:19

35:20,21 41:3,21 43:7 45:21 46:25 47:5 48:21

49:4,24 50:10 51:11 52:8 53:23 56:17 61:22

63:14 66:7,9,19 67:14 68:6 69:25 74:7 76:23 77:9

77:11 79:13 80:20,22,24,26 81:9,11,14 82:10

83:4 84:3,9 86:9,15 91:13 98:13,19 99:3 103:4

106:18 107:13 108:8,10 109:4,16 112:7,19 113:1

115:5,15 116:1,8 121:4 122:10,13,13,15 123:13

128:20 131:25 133:24 137:16 138:2,11,14,17

139:17,19 140:4 141:7 143:15 147:23 150:4,4,20

151:3,24 152:15,17 153:10,12,23 154:12,22

158:3 185:10 192:12 193:6 194:6,12 197:10,23

197:24 200:22 203:3,5 207:13,14,15,17 211:8,8

212:1,2,6,8,8,16 217:3 220:2 225:25 233:9

234:15 235:15,21,24 236:1 241:10 242:19,21

246:25 252:5 253:3,13 258:4 261:16,16 263:6

282:22 307:6 310:1 315:21 330:14 331:7 368:16

370:9 374:1 378:1,5,6 396:26 399:19 402:26

405:1 406:4 414:4 417:18,19 420:6 426:10

430:21 431:14 434:7 435:18 447:24 458:12,13

459:8 460:15,22 466:4,7 472:19 495:9 499:13,26

505:7,14 506:6 508:7 511:25 512:7,9 515:14

516:9,9,12 531:13 563:5 571:15

concentration-based 103:24

concentration-response 230:13

concept 28:17 93:4 94:1,7,23 114:9,10 214:23

245:17 247:7 345:9 491:3 501:10 522:7 531:11

535:13 548:11 555:6 580:11 589:2

concepts 15:3 16:7 17:7 214:24 253:26

conceptual 23:21 600:17,24 601:2

conceptualize 219:6 222:13

concern 21:5,7,18 23:14,19 24:11 25:7,14,22 26:3

26:6,17,19 34:11 45:2,7 56:21 72:20,22 73:4

74:13 79:5 83:23 85:4,18,22 88:23 89:9 90:15,17

90:18 96:21,23 98:7 99:3 102:10 118:14 124:9

179:25 181:23 213:15 244:2 258:13 269:14 270:2

271:15,17 273:6,10 274:15 282:19,26 287:13

290:25 296:1 302:2 308:11 314:20 315:23,24

316:5,13 317:6 323:24 333:26 336:25 337:7,8,11

338:7 339:16 349:17 360:10 362:10 368:14

380:10 399:26 400:17,18 404:16 405:14 429:3,7

430:15 432:17 433:5 442:15 463:11 464:9 492:16
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492:22 493:7 497:7 504:4 505:5 561:14 580:24

581:2

concerned 40:26 73:20 93:14 94:17 157:17 269:12

270:1 287:3 290:24 302:21 348:2 349:26 350:6

369:3 399:22 400:25 424:21 444:3 469:26 542:17

concerning 268:20 271:1 273:22 334:19 403:6

concerns 24:2 31:5,13 35:15 37:10 42:24,25 44:24

68:5 88:20 91:17 180:5 272:20 280:4 281:15

282:7,12 283:8 287:14 299:13,13,17 302:9 306:1

307:8 400:10 404:17 436:8 443:3 466:6 496:20

496:22 508:24 541:23 593:26

conclude 88:9 108:14 162:18 202:5,16

concluded 5:4 22:21 27:13 30:21 88:16 154:7

196:4 264:9 390:12 454:26

concludes 154:25 305:3 390:1

conclusion 45:1 60:16 91:10,11 112:24 118:18

154:20 198:26 208:3 222:18 227:23,26 229:24

232:14 240:9 247:26 260:10 296:23 350:4 360:19

366:26 367:7 369:4 374:18 418:23 424:16 431:18

432:4 496:3,4,9 503:17 505:24,25 506:16 524:11

583:16

conclusions 27:10 89:26 90:19 188:8,25 189:8

195:6 196:8 229:14 240:6 243:18 267:3 299:18

307:8 348:10 359:21 371:26 373:2 474:18 504:13

514:1 583:21

conclusive 189:18

conclusiveness 296:14

concur 269:1 391:22 510:13 550:19 587:10,11

concurred 22:22 23:21 27:14 29:14 31:2 33:2

34:15,26 43:14 44:18

concurrent 77:4 180:9 345:8 430:11

concurrently 35:23 328:22 333:15

condition 73:14 296:24 309:4 310:14 404:25 491:2

491:3 557:4 560:10 579:3

conditional 138:4 156:21 554:13

conditioning 553:18 560:15

conditions 27:23 29:15 30:9 43:18 45:8 52:9 57:10

73:8 74:17 75:11 76:5 98:26 111:21 116:19

143:23 144:5 190:21 200:4 259:14,15,16 261:25

266:9 269:21 283:12 291:5 299:4 310:2 335:13

370:5 378:21 381:6 382:2,4,8,10 384:2 396:24

397:24 425:12 480:21 572:7 573:19 579:5

conduct 68:7 88:15 258:3 393:19 408:23 511:24

conducted 29:18,20 34:19 37:24 41:19 43:3,6,12

56:23 60:8,19 67:10,12,20 74:21,26 106:25

116:18 143:2 145:24 150:2 151:16,20 152:7

174:24 180:16 191:2 209:23 215:15 253:3 344:6

344:16 367:12,13 369:11,15 370:2,4,16 372:13

382:3,7 391:17 393:5,10 398:1 401:5,7 404:22

408:21 410:20 471:16 481:8 485:4 498:14 522:11

563:11 584:14

conducting 20:11,25 24:17 173:10 308:19 332:4,7

418:1

conference 1:13 583:4

conferences 167:19

confidence 35:11 39:20,22,25 40:1,5,14,20,22,25

42:2,5,13,17,24 46:11 47:23,24 48:3,5 50:3 51:15

70:14 102:24 108:4,7 110:16 114:13 146:25

148:11,25 249:23 321:25 324:23 325:20 328:9

348:10,14 351:2 361:19 370:13 388:17,18 392:24

425:12 495:4,4,10,13 504:6,12 515:10 533:9

536:6,9,10 537:4,15,16 538:2,4,6,22,22 551:8,9

553:2 572:17

confident 91:15 269:7 318:6,8,9 393:9 496:15

498:5

confidential 3:17

confirm 27:20 97:12 365:4 390:5 400:22

confirmation 438:13 441:23

confirmed 73:11

confirms 358:19

conflict 3:15 370:6,11 394:26

conflicting 296:21 562:7

conflicts 3:22 329:2

confound 34:21 60:5 382:13

confounding 34:7,14 37:3 39:17 40:4 41:8 42:20

43:11 59:8 174:2 327:13 335:23 338:5 340:5,12

353:14 359:24 361:4 396:24 478:6

confrontation 484:4

confronted 276:12,13

confuse 182:21

confused 121:26 122:2 279:2 338:22 353:10

355:13 356:21 357:8 464:1 483:4 597:17

confusing 277:2

confusion 278:21 354:10 370:14 401:19

congratulate 411:12

conjunction 314:19

connect 601:4

connected 53:8,22

connection 161:9 162:3 447:17 591:19

cons 546:13

consecutive 229:25

consensus 173:24 193:8 194:11 203:7 412:24,25

424:12

consent 177:22

consequence 96:4 273:1 444:24

consequences 167:17 279:13 319:16 507:15

consequently 262:23 299:6

conservation 8:7 163:3 178:11 271:12,13 273:1

278:3,5,7 279:16 290:8,11,12,14 291:12 295:22

303:11,21,24 304:1,2 593:21 594:7,8,16
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conservation-tillage 280:14

conservatism 595:15

conservative 258:8,8,19,25 262:12 264:20,24

273:6 496:18 498:24 500:8,9,10,22 501:4,9,10

523:25 529:25 530:3,14,15,16,17 565:4,23

566:20

conservatively 233:19 514:16 515:7

conservativeness 519:22 565:6

conserves 254:19

conserving 56:9

consider 2:6 88:14 91:18 92:18 97:3,16 117:8

131:6,24 146:5 221:22 230:21 238:8 241:4,19,24

249:6 254:7 260:11 261:19 271:4,6 279:22 306:6

306:8 338:14 339:9 345:15 371:10 375:9 392:7

395:5,12,22 397:8 409:2 411:26 417:6 426:19

452:14 458:2 504:26 507:17 509:6 510:26 516:1

524:19 525:17 549:25 550:1,26 551:2 575:18

576:1 581:20 591:22 595:22

considerable 89:15,17,23 139:3 199:9 444:23

consideration 95:23 130:24 137:25 138:24 139:5

192:5 221:3 243:22 254:6 275:1 279:18 291:4

305:19 307:5 330:20,23 340:7 344:5 412:26

429:9 444:15 458:11 472:5 478:24 497:21 505:15

550:2 565:10

considerations 83:10 92:18 226:7 279:5 533:25

594:15

considered 5:6 11:1 21:17 28:20,23 30:1,25 33:15

34:9 35:1,10 36:16 37:1 39:1 41:4 51:26 52:23

58:13 60:8,11 65:13 76:8,9 78:11 80:8,16 84:17

85:4 90:12,16,20 94:26 96:18 105:1 107:24

108:19 118:3,7 139:15 140:9 143:13,16 150:15

151:6 172:7 188:3 195:8 204:18 205:7 207:18

229:21,24 232:15 233:23 254:19 258:4,6 260:8

264:19 270:12 321:7,21 326:19 397:22 399:23

405:23 414:6 422:9 461:25 462:8,21 490:7 505:9

518:22 531:16 544:16 549:13 565:8 581:6 582:20

598:4

considering 84:6 96:6,7 106:2 204:14 214:13

220:19 256:3 279:7 290:24 298:20 344:13 434:2

514:26 564:15

considers 296:11 398:18

consist 292:4

consisted 29:19 30:4

consistency 13:5 32:14 59:14 188:6,10 194:22

312:10 412:16 450:21 580:21 597:26

consistent 24:26 28:11 30:7 31:15,25 32:3,24 42:8

57:6 59:16 60:3,4 132:22 168:16 197:5 226:3

230:5,8,11 235:11 239:11,25 241:11 254:23

259:4 260:17,18 262:12 266:22,23 273:20 282:2

296:13 297:19 298:26 349:23 352:16 393:23

449:7 506:5 539:11 572:12 573:12 585:20

consistently 167:11 171:2 174:17,23 228:24

257:13 358:12 459:20 538:14 580:20

consisting 5:5 22:19 381:6

consolidate 472:4

constant 79:12 81:11 98:13 99:3 102:1 116:8,22

129:10 221:13 243:25 252:6 427:19

constantly 116:25 171:22

constitute 86:23 96:13 422:6

constitutes 308:7 488:19

constituting 89:2

constrained 157:5 507:8 573:25

constraint 578:4

constraints 409:12 525:10

construct 447:13

constructed 141:19 147:19 329:1 334:10

constructs 117:7

consultant 226:13

consultation 3:20

consulting 486:7

consumer 185:1

consumers 292:1 498:7,10

consumption 164:15 169:18

contact 5:17 9:18 136:7

contacted 4:4,5

contain 35:24 48:1 77:21 85:6 277:9 333:14

397:10,13

contained 29:13 30:5 39:25 42:14 59:7 68:10

container 38:14 210:5 339:5 364:10

containers 332:11

containing 402:12

containments 329:24

contains 91:19 298:22

contaminant 330:10

contaminants 6:8 200:5 333:22 339:4

contaminated 175:1,5

contaminates 174:16

contamination 43:24 44:3 334:5 339:23

contended 371:23

content 200:20

contention 99:4 157:23

Contest 176:18

context 9:20 10:16 11:9 14:16 16:3 96:19 112:5,10

112:22 115:10 135:14 139:20 157:14 160:13

180:12 190:3 205:21 219:21 220:18 221:16

225:22 254:26 256:2 257:17 259:5 265:1 307:19

307:26 313:22,23 314:18 316:7 328:6 341:25

342:8 353:16 356:17 357:2 359:3 362:1 408:9

418:3 505:4 529:18 564:11 588:25

contingent 174:1
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continual 255:24

continually 218:25

continue 31:26 169:4 171:19 195:13 272:23 280:11

283:3 285:8 291:15,26 294:5 296:26 304:1,5,23

374:19 387:12 405:7 407:14 425:1 479:13 564:21

575:18

continued 77:8 166:21 204:8 291:23 561:7

continues 185:8 268:23 559:24

continuing 216:3 283:6 372:7 509:13 593:16

continuous 140:14 147:20 508:13

continuously 281:10

continuum 582:25,26

contract 87:19 226:21

contraction 453:15

contradictory 234:26

contrary 75:26 168:20 178:6 271:9 274:8

contrast 94:2 142:15 143:26 146:2 307:17 310:9

398:13

contrasts 81:23

contribute 88:14 120:3 295:9 306:4 327:13 587:24

contributed 35:11 505:19

contribution 280:17

contributions 9:10

contributors 508:18

control 29:24 30:5 31:14 35:23,25 36:1,3,14 37:12

37:14 41:3,10 42:26 43:22,24 44:1,4,5 47:2 68:13

68:19,23 70:24 77:22 183:8,10 207:12 208:25

221:9 231:26 237:24 250:17,20 254:13 256:10,14

256:24 257:9 261:26 269:16 278:16 290:8 295:15

309:10,12,17,20,22,23,26 310:3,5,6,8,10,15

323:1 328:24 331:13 333:14,23 334:3,5,7,9

335:11,24 337:9 339:21 344:10 345:4,9 347:13

350:1 354:6 355:22 356:11 372:17,17 377:1,7

383:11 400:23 401:23 443:16 514:22 534:24

controlled 34:23 74:17 76:5 287:5 335:6 340:2,4

345:6 350:11 370:1 482:21

controls 36:6 41:12 68:10,11 77:3,5,12,17,19 211:9

233:2 269:22 309:3 324:25 328:15,21 330:18

332:13 334:6 335:20 339:23 344:23 345:7 346:16

369:16,22 382:10 402:6 403:5 442:13

controversy 270:6

convention 207:5 250:2

conventional 168:23 278:5

converge 445:10

converging 445:4

conversation 14:10 17:8 70:17 175:11 287:12

319:8 375:15 473:1 484:14

conversations 408:16

conversion 31:16 68:15 103:25 104:6 372:19 466:2

conversions 493:17

convert 103:24 187:6 438:4

converted 108:8 205:14

converting 438:5

convey 125:15 276:1 287:3 590:23

conveyance 553:26

convinced 554:19,20

convincing 568:24

cool 157:8 167:4 268:26

cooler 167:8

cooling 94:24

cooperation 76:19 281:17

cooperative 282:5

coordinate 163:4

Coordination 3:19

coordinator 359:9

Coppock 287:21,23 288:1,1 292:12

copy 293:3,13

Cordova 301:3,3

core 222:22 242:6

corn 22:6 150:18 151:8 153:5 169:5,6,9,10,12,19

169:21,22,26,26 170:1,2,7,9,10,14,17,17,18,24

171:6,7,10 172:20 176:5,6 183:9,12 268:6,12

272:1,3,6,10,13,21 274:7 275:3,6,8,9,10,14,21

276:3,21 279:26 280:2,8,15 281:12 286:5 288:14

289:9,22,22 290:14 295:2,6,9,12,17,19 297:2

corner 4:12 141:18 193:20 242:18 420:23

cornerstone 113:7

cornfield 287:1 555:12

corn-belt 87:18

corn-farmed 287:7

corporations 168:15 172:5

correct 72:8,9 129:14,16 130:21 185:24 204:6

208:4 240:14 244:4 285:14,15 287:22,23 359:10

417:4 424:5 439:3 441:24 450:10 451:25,26

476:8 511:10 512:19 543:7 562:2 576:6

correction 439:12 529:15

correctly 72:6 205:11 250:26 571:14

correlate 156:9

correlated 248:15 286:19 306:19,23 323:14 420:14

correlating 300:3

correlation 145:3 245:21 246:11,18 247:5 251:16

306:24 323:25

correlational 387:3

correlations 159:1,7 519:7

correspond 144:20

corresponded 153:21

corresponding 49:10,24 51:11 53:6,7,15,21,24

54:5 242:3 247:4

corresponds 101:17 102:2 151:19 153:3 315:20

corroboration 299:8 300:3
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corticosteroid 196:16 197:20 200:13

corticosterone 197:1

cortisol 198:1,7,13 200:1,12,19,23 201:3,7,14,18

201:22 208:10

Corvi 322:14 323:6

cosm 25:11,13 34:5,9,13 74:21,25 75:7,14,16,25

76:1 77:26 78:2 79:6,15,16 80:2,20 81:8,17 82:11

82:16,22 83:3,18 85:7,9,10,15 95:23,25 96:24

98:20,21 99:23 101:7,7,10,14,25 103:10 105:7

106:4,19 107:8,18,20 108:22 109:10 110:26

115:3,14 116:7 117:22 119:2,13,14 120:23 121:3

121:7 123:15,16,18,18,23 124:1,3 128:20,21

130:21,25 131:1,4,6,12 132:21,25,26 133:24,26

134:2,4 216:26 217:4,6,9 218:4,15,23 219:15

220:12,13,17 221:1 222:4,23 223:12 224:23

226:26 227:4,7,11,17,18,24 228:4 229:7 234:22

235:15 237:24 238:1,15,23 239:13,17 240:1,11

240:23 242:2 244:5 247:15 248:13,16 249:12

250:7 251:11 254:10 269:25 270:9,13 284:8

293:1 294:16 299:25 305:20,23 306:7 307:9,18

313:23,23 315:14 317:1 320:6 396:11,16,22

397:1 398:1,7,13,20,23 399:1,8,15,16,26 401:14

404:4,7,8,11 405:15,22 406:1 408:23 411:22

413:4 414:13 415:25 416:2 427:1 428:20 431:11

431:13,14 433:24 437:13 438:19,24 440:10 441:4

441:10,26 443:20 444:24 445:1,2 446:3 447:12

448:4,14,18,26 449:4,6,18 457:2,3,4,6,8,17,18

459:7,14 460:2,2 461:25 462:6,24 465:19 467:9

468:6 471:13 473:3,5,9 474:24 488:13 490:6

491:26 492:11 496:23 497:2,8,24 499:1,23

501:22,24,25 502:17 504:1,16 511:15,20 512:3

512:11,13 513:18,21,21 562:11 580:13 583:16,17

583:20 584:11,14 591:11 595:10

cosms 74:16 78:8,10,11 80:5 81:6 82:8,25 96:3,5,8

101:4 102:12 106:14 108:14,18 111:1,12 115:7

115:20,21 116:6,7 118:6 121:10 125:24 132:15

134:26 217:3 218:20 222:1,14 227:20 228:7,8

231:25,26 232:3 240:5 242:5,7 248:17 251:20

299:22 307:14,22 308:2 320:9 383:14 397:3,8

398:6,16 399:11 400:4 405:16,19 406:3 413:7,13

414:3 420:3,6,7,13,18,26 421:4 422:14,16,23

425:19 437:10 438:16 445:21,24 449:21,22

462:26 465:2 467:4,5,8,23,25,26 474:11 493:13

512:8 590:17 591:18

cost 271:22 275:18 278:14,15,17 280:12 289:26,26

291:11 304:15,20

costing 290:2

costly 302:11 304:9

costs 170:15,16,20,22 275:19,24 278:11,16 499:15

cost-effective 302:18

could've 518:23

council 281:15,25 283:3 298:8

Counsel 3:20

count 148:18 307:24 562:18

counterexample 413:10

counterproductive 331:1

counting 192:24

countless 166:26

countries 163:8,9 164:24 171:3 178:16

country 15:10 113:17 153:4,22,26 154:14 160:7

172:22 176:11 272:23 280:22 295:20 300:17

304:22

countryside 286:8

country's 171:12

counts 386:21

County 281:3

couple 6:11 21:1 98:11 128:6 129:4 131:12 183:7

190:13 194:15 195:7 199:21 208:6 227:1,21

238:14 241:17 242:10,12 259:11 285:12,18 295:4

302:6 311:4 322:26 364:25 410:2 420:6 426:24

431:17 432:20 456:20,22 489:10 523:15,21

525:18 534:7 538:13 553:19 556:3 561:20 575:20

585:15 588:7 592:12 596:15 603:16

coupled 38:2 40:6 41:15,22 42:6 521:14

coupling 596:5

course 14:8 16:5 43:19 184:1 199:25 201:17 204:7

210:16 213:10 231:14 234:25 238:16 239:23

251:11 280:22 324:9 335:9 339:1 340:18 341:18

359:10 409:7 525:13 526:8 528:4 578:7 580:20

606:8 607:4

covariance 564:8

cover 388:18 413:11 507:9 538:10 556:14 557:22

coverage 517:5,19 573:26

covered 24:6 105:14 215:20 324:12 325:26 326:3

400:18 468:18,20 509:23 510:1 520:5 529:3

556:2

covering 260:6

covers 409:16 575:9

coworkers 87:20

cows 508:12

co-discussants 391:22

co-linear 420:19

co-linearities 420:20

co-ops 288:25

co-respondents 328:12

cracked 419:14

crammed 232:24

crashes 167:1

CRE 300:6

create 141:24 170:5 216:6 338:14 366:18 394:3
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created 17:21 141:25 142:1 277:25 279:1 280:21

351:14 401:18 425:20 518:24 556:10

creates 12:1 17:1

creating 295:10 335:13 441:10

creative 596:8

creature 532:8

credence 306:14

credible 91:11 444:15

credit 401:2

creek 281:7,7,9 282:1

CREM 298:10,22 299:1

criteria 12:22 14:22 16:16 23:3 25:9 32:23,23 33:6

33:26 35:5,9,10 36:21 37:1 41:5 47:18 59:10 60:2

60:2 69:1,7 71:7,9 76:16,17 77:3 78:4 110:24,25

114:11 161:5 180:18 190:4 192:10,16 194:14

203:1 204:17 207:26 211:6 212:4,9 228:10

229:11,12 232:16,17 233:16 238:5 254:18 270:11

279:8,20,24 282:9 284:9 321:26 326:18 329:14

330:1,13,23,25 331:1,6,10,15 332:5,21 333:13

334:2,12 335:5,11,17,19 336:13,16,19,21 338:23

340:10 347:7,26 348:4,4 349:20,25 351:15

353:10,11,19 354:12 355:3,5 359:13,16 360:12

360:19,22 367:9,20 373:22 374:23 380:1 382:16

388:5 391:4,11 393:4 411:23 414:1,19 415:12,18

416:1,19 417:3,7,8,16,23 418:6 422:14 424:13

426:14 430:3,9 432:8 450:13,18,25,26 452:6

453:20 478:3 504:1 511:14,16,17,19,23 520:15

523:3 529:21 541:18,19 562:14,17 574:3 576:14

577:14 581:18

criterial 188:24

criterion 36:5 69:4 270:9

critical 3:12 18:25 28:23 32:17,22 34:20 37:2 41:5

42:9 48:1 68:26 72:26 88:23 104:18 115:25 126:4

180:20 188:23 206:12 240:24 245:3 248:7 271:10

275:14 289:4 328:2 339:2 340:18 341:18 345:18

346:20 353:12 379:19 390:14 393:6 399:8 422:8

434:1 473:22,26 499:2,17 523:11 576:14 577:2

581:21 592:15

critically 572:10

critique 88:21 429:8

critiqued 87:20

critiques 536:20

crop 152:13 153:8 179:3 183:10 211:10 275:12

280:8 281:5 288:12,19,19 289:4 290:21

cropping 140:20 588:22

crops 166:11

cross 159:24

crossover 575:7

cross-sectional 555:1

crown 295:11

crucial 295:21

crying 19:14

cryptophycaeae 231:19

crystal 1:15 141:14 356:5

cubic 398:14,21

cues 46:18 56:9

cue-in 367:26

cuff 19:12

Cullen 480:26

cultivated 281:5

cultural 278:16,24

culture 309:21

cultures 57:21 78:11 111:13,17,19,22 329:8 342:26

343:10 448:13

culturing 57:15,19 71:5 381:5 382:26

cumulative 51:10 83:14 94:10 95:16 97:18 98:8

101:20 102:4,8,10,16,18,20 103:12,17 104:4,6,12

105:17,18 129:8 130:14 216:22,23 217:1 219:9

219:10 220:11 222:6 223:13 224:18,20 241:13

242:4,14 243:5 244:12 245:15,16,25 246:14

247:12,15 248:13,16 251:6,17,20 259:3 313:1

320:6 436:22 443:17,18 446:6,8,14,18 447:8

448:12,14,22,25 449:8,9 457:15 462:4,9 463:10

464:16,21,24 466:11

cure 177:1

curiosity 210:24 427:9

curious 65:19 202:22 208:8 210:23 434:13

current 20:19 36:8 37:17 59:6 74:4 76:13 80:7

81:3 94:8 95:4 98:25 105:9 109:7 119:5 126:15

128:22 166:26 200:11 244:2 267:26 274:23 283:8

285:17 299:15 300:17 305:20 331:25 337:16

350:15 402:25 405:2 406:17 436:6 488:12 494:13

498:18 503:14 505:8,18 509:8 565:3 587:4

currently 34:16 46:7 55:20 57:12 58:11 169:1

171:14 185:3,26 190:9 276:17 303:14 306:19

325:5 331:15 387:14 404:15 427:1 465:22 506:17

581:17

cursory 254:7

curve 71:3 77:23 115:22 121:17 122:11 129:7

203:15 208:13,20 215:25 217:12 235:24 246:5

357:7 460:2,15,18 464:14 527:14,21 536:14

537:1,17 553:26

curved 465:11 527:20 528:4 537:21

curves 92:26 93:19 355:17 446:12 501:15 537:25

custom 288:20

customary 585:6

customers 291:22

cut 191:8 237:15

cutoff 212:4 225:13

cyanobacteria 75:17,20
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cycle 11:13 218:14 324:5

cycling 399:11

cytometric 47:8

C's 473:1

C14 423:22

D

D 78:7 107:10,19 128:25 212:18 298:22 400:2,3

405:16 453:3

daily 56:15 84:2 92:10 94:12,14 95:2 101:10,11,12

101:17,20 102:2,4 103:5,8 141:19,21 147:18,21

147:21 148:18 173:19 216:19,22 267:16 429:2

436:20 505:16 518:1 541:2 558:22 568:21

damage 174:6 274:5 501:13

damaged 508:16

Dan 2:15 5:25 6:4 9:5 176:12 178:25 179:22 181:4

181:5,6 182:15,18 210:19 294:24 385:10 455:3,8

532:23

Dana 19:17,19,23,24 27:11 65:24 320:25 321:4

327:9 348:9 366:14,17,22 377:13 381:3 385:24

390:1 396:11,14 428:11 456:26 494:5,13 503:14

511:6 513:1 532:19 542:22 549:6 563:2 570:16

606:16

Dana's 606:16

dangerous 178:15

Daniel 5:26 8:22 19:21 26:24 60:21 62:13,18 63:19

64:7,23 65:16 66:2,21 71:13,20,25 72:5,10

109:20 125:19 127:9,23 128:2 136:4 154:26

155:21 159:8 161:1,7 162:11 178:21 202:18

208:5 209:3,7 210:22 213:5 223:18 226:8 240:13

240:16 248:19 253:18 261:4 263:7 285:10 287:19

287:24 292:11 294:1,9,25 297:5 300:20 304:26

310:16,20 311:2,5,14,18 312:23 314:5,13 317:20

319:17,21 320:11,15,18 321:2,10 324:10 325:6

325:23 326:1,4,6,21 327:7,14 329:18 332:23

333:3,8,10 336:2 342:9 343:24 344:17 346:11,22

347:4,9 348:5,12 349:18,21 350:16,18,20 351:17

352:7 354:13,17,26 355:7,10 357:9 359:4 360:7

361:1 362:6 363:13,15 364:21 366:4,8,11,16,21

367:3 368:11,19 370:17,20,23 371:18 372:2,5

373:5 374:5,21 375:13 376:19 377:9,17,22 378:8

379:5,8,11,13 380:4,7,23,26 381:8,26 382:24

383:1,3,5,21 384:6,14,18 385:5,11,18 386:8

387:16,19 388:9,12,23 389:15,17,25 390:10,25

391:20,24 392:1,4,21 393:2,12,25 394:15 396:7

396:13,19 405:11 406:6 408:13 410:1 411:14

412:14 413:2,18 414:22,25 415:5,14,17 416:11

416:16 417:9 418:11,16 420:1 421:7,22 422:11

424:6 425:15 426:5,22 427:11,25 428:2,8,16

430:24 431:16 432:14 433:20 434:18,20 436:10

439:14,21 441:1 445:13,26 447:10 448:1,9

449:25 452:12 453:23 455:5 456:13,18 459:26

461:1,5 466:16 470:6 472:7,11 473:17 475:4,16

475:20 476:13,17 477:8,14,20 478:8,14 479:8,15

479:19 480:3,7 481:22 482:1 483:6,16 484:16,26

485:16,21,25 486:15,18,23 487:1,8 488:4 489:2

489:22 491:14 493:4,10,23,26 494:6,10,20,24

495:15 496:11 498:21 500:3,20 501:7 502:25

503:6,11,21 509:18,20,22,25 510:2,5,7,9,11,15

511:3,7 512:16,24 513:8 519:25 520:2,24 523:14

527:3 528:2,7,12,20,25 529:4 530:11 532:2,10,14

532:22 538:12,26 539:21 540:14 541:12 542:1,4

542:6,20,25 543:5 544:23 546:1,9 547:14,16

548:26 549:4,14 555:26 556:20 557:23 558:3

559:18 560:4,24 561:4,19 562:21,25 563:13

564:12 565:13,25 566:15,22 567:26 568:2 569:2

569:23 570:10,14,20,23 571:1 573:8 575:11,19

576:11 577:18,20 580:2 584:17,21 585:5 586:7

586:21 587:7,12,19,26 588:3 589:9,26 590:12

594:17 596:13 598:2,22 601:22 605:16,23

dann 270:18

Dan's 2:16

Daren 287:21,23 288:1,1

dark 232:1

darkness 176:26

darn 553:20

data 11:22 20:10,21 21:4,6,9 22:11,24,26 23:2,6,8

23:19 24:16,22,26 25:2,16,17,21,21,24 26:9,10

26:15,18,23 27:4,7,8,14,17 29:3,7,7,11 31:10,18

32:1 33:22 36:17,26 38:18 39:2,8 41:8,18 42:26

43:22 44:26 45:1,4 46:4 47:10,16 50:15 51:16

52:6 55:4 56:19 57:23 59:13,16 60:19 61:17 62:9

65:18 66:4,17 69:16 70:3 73:5 77:23 80:24 82:22

83:3 85:7,15 89:14 93:22 94:3 95:23,25 96:13

97:2,4,13,15 98:5,19,20,21 99:23,24 100:7,11,12

102:18 103:7,10,26 104:4,25 105:9 106:6 107:24

109:3 110:5,7 119:2,6,7,14,19,26 120:12,21

121:11 123:15,16,19 124:3,11 127:12,13,13,13

128:12 130:21,25 131:1,2,3,4,6,6,10,12 132:2,26

133:3 135:1,4,7,13,21 136:17,24,25 137:1,3,16

137:18,24 138:3,6,8,10,14,20,22,25,26 139:1,2,7

139:9,11,18 140:8,12 141:2,17 142:8,14,15,21,23

142:25 143:5,6,12,13,15,16,19,19,26 144:8,9,13

144:18,19,26 145:8,10,14,18,19,26 146:7,14,21

146:25,25,26 147:16,20 148:5,9,13,16,17 149:5

149:18,18,18,20,21,22,23 150:5,10 151:2,18

152:6,10 153:9 154:6,10,12,12,22 155:11,14,15

157:2,13,14,22 158:1,9,18 160:5,9,25 173:20

174:1,2 178:6 180:21 181:20 182:12,20 186:17

188:17,18 189:4,13,15,15 190:20 191:16 194:18

196:2 197:17,26 198:18 200:18 201:2 202:3,17
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202:22 205:11,13,21,25 210:12,13,17,26 211:2

212:10 215:26 217:12 221:19 222:25 224:1,4

225:10 226:4,5 227:17,17,24 228:6,10,15,19

229:3 230:23 232:26,26 233:8 235:2 237:22

238:26 239:17 240:4,8 241:5 242:1,2,13,26

243:10,15 244:4 248:22 251:18 253:25 254:24,24

255:18 257:24,25 258:16,26 259:4,4 260:18,18

261:3,12 263:1 264:23,25 266:11 267:3,7,19,19

267:24 268:2 269:2,5 273:18,19 274:12 282:20

282:25 283:1 284:10 287:15 289:6 291:2 293:9

296:11,15,23 297:12,16,19,21,26 298:3,4,16

299:20 300:4,4,13,16 305:17,20,22 306:3,4,7

309:6 311:16 314:9,20 315:14 317:3 318:26

321:8 325:4,17 327:21 331:21 334:17 336:26

337:4,7,12 340:17 350:7,15,24 353:2,3 359:22

361:25,25 367:8,25 368:2,8 369:10,11,24 370:12

370:15 371:5,16 377:24 379:24 380:1,2,3 382:17

385:25 386:2,7 387:21 388:26 390:2,12,14

391:11 392:18 393:7 394:13 399:17,20,23,24

400:7,19 401:12 403:10 404:8,11,15 405:25

407:3,10 409:20,25 410:4 412:6,6 423:18 424:18

424:19 426:12 428:20,23 429:10 433:5,11,24

434:10,13,15 441:4,22,26 442:12 445:1,2,4,9,11

449:4,6,18,24,24 452:17 457:20 458:7,10 459:7,7

460:7 462:11,24 463:10 464:8,18 465:21 468:9

472:15,21,22,24 473:11,14 474:20 475:13 479:1

486:5 491:19,26 492:11,18 496:8 497:2,3,24

498:18 499:1,15 500:8 503:15 504:7,9,12 505:25

506:3,5,20 507:6,13 508:10,12,20 511:20,23

512:5 513:3,7,20,21 514:6,14,15,20,22 515:21

516:16,23 517:13,17,26 518:1,7,14,16 519:1

520:14,15,17 522:2,3 523:25 524:4,25 529:17,26

530:23 532:20 535:4,17 537:1 539:9,20,26 540:6

540:17,21,23 541:1,2,20,25 544:6,8 545:3,5,6,12

545:14,17,19,21,23 546:24,25 548:20 550:16

551:20 553:5,17 554:8,15 556:10,13 557:22

558:13,19,22 559:9 560:1,11,15,20 561:8,9 562:9

563:15,16,18 565:17 566:4,5 567:7,7,12 568:22

568:25 569:19 570:19 571:19,23 572:1,4,4,13,26

572:26 573:12,18,19,25,25 574:7,25,25 575:2,3

575:17 577:8 579:8 580:13 583:24 584:1,2,5,12

586:3 595:1,10,10,25 596:5 598:9 602:10

database 25:6 119:18 137:9 192:11 228:2,5,20,23

229:4,7 238:11,13,23 239:7,13 240:7,11 250:3

253:13 290:23 385:25 401:15 405:9 407:7 424:4

424:22 427:1 431:25 503:17 506:21 556:18

583:16,18

dataset 123:23 143:11 147:14 152:11 157:10

158:18 159:17 160:9 185:18,26 213:25,25 224:9

235:15 239:18,23 240:23 242:24 243:2,17,21

244:5 245:4 247:15 254:25 257:10,18 258:10,14

datasets 121:12 126:12 142:23 143:18 147:16

156:15 159:16 160:8,10 226:5 241:26 257:22

267:16

data's 573:4

date 73:5 144:20 248:15 281:21 386:10 506:5

508:24

dates 229:26 231:24 535:4

David 480:26

day 2:5 16:9 30:7 58:20 65:10 83:21 92:6 94:18,25

97:5,7 101:12,16 102:1 104:21 112:24 119:2,15

120:5 124:4,6,7 129:16,17 130:4 131:25 132:2,9

135:1,2,3,12,26 138:19 143:2 147:18,20,22 148:6

151:19 157:18 158:16,17 161:26 163:5,5,7

180:10 216:15,16,18,21,24 219:1,11 220:22

221:10,23,25 222:5,6 223:26 230:20 231:13,15

232:9,9 236:11,12 246:2,5,15,22 247:4,15 249:6

253:20 254:5,6 257:6 258:13,17,21 261:23

263:14 283:20 287:10 291:8,8 301:8 309:9

398:10 403:25 421:5 432:3 443:2,12 446:3,13,15

446:21 447:2,3,4,4,12,22,23 448:6 454:7,12

464:26 472:16 516:5,18 518:18 519:9 522:25

603:10 607:10

days 3:25 5:12,16 9:9 10:9 14:10 16:5 18:13 20:9

29:21 30:15 67:21 76:3 79:22 80:23 83:22 84:2

94:13,19,20,21,24 95:1,5,19 96:10,22 97:19

100:21 101:21 102:5,21 103:12,18 105:19 113:23

115:4,8,8 116:4,5 120:6 123:14 124:12,13 129:2

129:4 130:14 131:14,14 132:10,17 135:2,6,11

141:21 144:22 148:21 155:4 161:13,26,26 185:19

185:20 215:5,6 218:2,5,7,10,15,18,18 219:7,7,8

219:10,11,20 220:10,11,19,21 221:6,9,16,18,24

221:25 224:1,2,2,24,24 225:20 235:17 236:23

237:5,6 238:20 240:2 243:26 244:1 246:16

247:19 248:9 249:7 251:14 252:16,18,18 255:26

256:4,4 305:5 306:13,15,18,25 307:2,4,10,18

312:10 334:24 351:5 379:18 405:23 407:25 421:4

421:5 422:20 423:2,8 438:21,24 439:5,17,18,19

440:23 442:26 443:16 446:14,16,21,21,21,23,23

447:5,6 448:7,24 454:20 458:21 461:19,21,26,26

462:1,4,10,14,17,20 463:1,2,4,4,7 465:2,13,16,16

465:17,26 475:26 476:3 502:23 513:20,21,23,23

513:24,26 523:21 525:13 533:18 550:12 551:1,2

557:26 568:10 571:22 581:7,8 585:15 607:9

day's 95:3 446:5

day-to-day 143:14

DC 176:11 189:21

DCI 29:4,7,18 30:21,24 31:4,14 38:23 43:19 47:12

56:14 60:19 67:10 188:6,11 191:22 297:18,18

342:6 366:23 367:1 370:4 372:18 391:15 480:17
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DDT 186:19

de 270:4

DEA 45:20

dead 426:8

deal 134:8 287:16 341:1 464:16 547:1 597:20

dealbreakers 547:1

dealing 171:20 202:16 601:12 605:3

deals 194:1 480:25

dealt 409:6 449:17 547:2

death 87:11 169:25

debate 308:2,13 471:5

debated 269:26

debris 260:4

decade 174:20 376:7

decades 163:22 165:17 270:1,25,26 277:11 291:23

383:24 412:2 417:25 421:11

decay 146:24 166:24 447:13,17 448:7 535:18

December 23:13

decent 324:18

deceptively 10:1

decide 171:22 313:9 361:7 381:17 597:18

decided 189:17 352:5 361:10 527:7

decides 467:8

deciding 279:23 360:12

decimate 164:19

decision 3:9 21:12,13 72:24 78:2 91:12 98:8 113:5

113:8,14 115:25 131:1 229:19 271:6 290:4,5

308:7 313:8,19 358:2,6 405:8 416:3 418:20 425:5

425:10 437:26 500:26 512:2 525:23 594:15 597:4

decisions 17:18 18:10 19:3 58:25 113:12 230:18

291:17 308:14 338:2 438:7 466:25,26 467:1

468:5 474:12 475:13 525:22 571:9 574:3 597:12

604:7,7

decline 165:11 237:4 252:25 271:14 386:14

declined 83:5 178:3

declining 79:12 81:12,14 116:8 131:25 447:24

decomposition 252:16,21

deconstructing 216:10

decrease 35:11 126:26 127:2 169:8 231:12 322:21

507:2

decreased 54:23 167:26 168:4 170:22 309:22

351:24 443:10 508:8 550:14

decreases 295:23 514:8

decreasing 105:20 170:20

dedicated 162:24 170:7 583:4

deemed 39:13 41:1,16,23 48:1 67:6 143:5 208:26

365:4 379:2 452:2

deep 423:4

deeper 235:10 236:9 317:18

deethylatrazine 45:19

defending 30:19

defenses 166:4

defensible 509:5 597:6

deficiencies 27:22

define 28:17 215:11 253:23,25 267:16 317:15

341:23 352:20 406:24,26 407:1,1,18 452:19

490:8,17 515:11 581:18 600:1

defined 58:11 75:10 76:24 83:15 85:23 102:10,18

106:20 212:2 217:8 222:14 244:2 251:19 298:14

329:13 360:25 397:5 438:16 463:16 491:11

533:20 578:15 599:21

defines 124:6 214:10 247:25 578:24

defining 82:23 83:17 214:17 222:18 243:20 245:11

254:8 313:24 467:16 490:5 515:18

definitely 64:13 110:12 157:18,22 171:10 286:12

311:18 334:5 346:26 361:23 374:24 416:14

523:24 603:7

definition 95:14 207:7 314:25 315:2 316:9 317:7

341:20,22

definitions 86:17

definitive 47:13 110:6 124:2 134:15 143:16,26

144:8,9 145:18,26 146:7 363:6 372:18 459:13

479:26 480:17

definitively 250:18

deform 168:17

deformed 177:21

deformities 194:1,4

degradates 45:10,10,14,19,21 505:8 510:21

degradation 45:13 73:8 75:1 106:12 174:8 252:17

495:22 498:5 507:22

degraded 317:24

degredance 505:6

degredates 505:12,15,19,21

degree 94:23,25 100:22 222:3 245:21 246:11

251:16 269:22 270:26 316:14 317:6 327:11 328:9

368:5 501:10,19 539:12 544:1 573:3

degrees 94:25,26 203:26 266:4,13 403:14 547:8

570:6

DeGua 178:10

deisopropylatrazine 45:19

delay 237:5 405:7

delayed 168:2 170:26 230:6,10 236:23

delaying 237:6

delete 253:14,16

deleted 229:5 238:11,24 239:4,5 240:8 583:17

deleting 241:17 242:8 460:16

deletion 222:25

deletions 223:7 229:8 584:6

deliberately 211:16 586:23

delineated 217:6,9
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Delineating 28:16

delineation 56:1 242:5

delivering 168:25

DEM 553:24

demand 280:10

demands 351:3

demasculinizing 167:25

demonstrate 55:4 223:15 324:7 368:23 373:2

387:23 542:18

demonstrated 44:14 167:14,23 175:6 198:16

254:16 255:7 330:4 378:2 512:11

demonstrates 220:15 241:1,22 256:25 263:1

385:26

demonstrating 45:26 173:13 323:20 390:17

demonstration 541:15,17 547:18 548:7

dengue 166:9

denial 302:17

denote 47:20

DeNoyelles 126:14,19 234:18 401:17,24 402:6,9,16

densely 172:21

densities 332:9 334:22

density 562:8

denuded 401:22

Department 6:17 8:7,10,18 174:16 176:3 178:5

282:21,24 287:12 303:5

departure 269:24

depend 95:4 98:19 166:14 299:21

dependable 280:12

dependence 99:11,14,15 457:21 458:26

dependent 61:18 140:16,17,21 144:23 145:3,7

165:4 240:4 241:25 255:20 335:12,17 425:5

429:24 580:21

depending 12:4 66:17 109:9 133:16 137:6 146:16

193:9 207:3 243:21 255:13 262:15 278:11 316:14

378:12 402:24 404:1 491:4 499:19 514:17 533:6

574:22 581:3

depends 125:6 166:18 206:25 363:4 412:20 426:1

depiction 22:1 458:14

depletion 309:20 398:15

deposited 260:5

deposition 117:16

depth 11:25 13:16 183:2 413:8

Deputy 3:18

der 181:21 188:12 195:13,15,16 203:16 204:6,12

204:19 208:15 209:5 211:17 212:3 270:19

derivation 529:22

derive 85:11 104:4 245:10 247:21 253:24 258:26

315:23 428:12

derived 69:13 72:23 76:17 111:19 232:8 259:3

270:4 433:19 443:26 514:18 523:23 583:21

DERs 474:16

des 270:19 301:26

describe 73:1 83:24 84:8,17 100:24 194:24 228:11

344:21 397:4

described 42:5 77:25 87:8 104:8 107:9 108:18

114:7,16 149:17 178:8 195:21,25 240:8 243:25

312:14 384:22 396:15 436:13 583:18

describes 20:19,24 85:26 126:8 348:14 402:21

describing 289:18 358:18 536:8

description 62:23 93:25 94:10 110:25

desensitization 130:22

deserves 506:18

design 23:2 27:21 28:22 29:2,13 30:6 32:17,22

34:20 35:1,4,18 37:9 39:1 42:9 43:10,13 77:11,21

192:9 204:25 205:7 233:1 238:19 321:26 323:17

324:9 327:5,10,12 328:1 332:2 336:8,13,16 337:4

338:13,16,24 340:9,11,17 344:3,22 346:14,14

347:11 349:25 355:16 356:3,22,25 357:2,12,19

357:22,23 363:11 369:11 381:6,20 391:3 393:16

393:18,21,23 398:2,3 404:13,18,26 407:8 413:26

414:2,17 420:22 421:10 450:15 452:14,20 483:22

484:18,21 506:22 507:7,9 550:17 591:5

designated 2:3 3:11 38:23 94:12 181:12 454:10

490:17

designation 321:25 348:25

designed 27:26 45:11 174:12 220:22 240:21

327:21 431:3,26 451:19 509:11

designing 227:3 552:8,11

designs 15:23 34:19 38:22 226:19 356:19 358:21

desirable 35:26 328:26 459:14

desire 315:17

desired 35:22 36:20 88:10 92:1 100:14

despite 88:26 91:17 96:8 233:20 235:12 281:9

369:5 431:1 443:10

destilliertem 270:17

destruction 164:2

detail 141:16 181:3 184:20 187:23 478:12

detailed 5:6 136:25 139:9,22 140:1 193:13 252:20

356:2,12 361:11 367:17 411:17 524:26 545:5

559:8

detailing 173:1

details 162:7 230:24 231:2 236:7 249:1,24 406:9

436:15,16,16 450:12 480:2

detect 328:14 331:26

detectable 174:19

detected 174:24 509:8 555:15

detecting 176:5

detection 45:20 137:9

detections 73:11 137:8

detects 174:17
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Detenbach 402:17

Detenbenck 232:21

determination 97:11 205:1 269:3 296:22 337:8

377:14 378:4 380:12 382:11 460:13,19 480:11

497:1,23 500:25 505:16 512:6

determinations 80:4 83:18 123:17 316:20 429:24

determine 14:5 23:18 25:9,10,21 26:2,11 29:16

69:1 82:11 85:17 118:14 174:25 201:23 298:25

299:16 327:26 330:1,24 343:18 361:26 367:9

368:16 386:12 424:22 430:4 441:24 452:8 471:15

471:16 486:8 511:24 531:1

determined 35:9 43:23 82:11 85:15 104:11 107:7

142:4 147:25 205:1 280:21 326:17 378:7 379:20

434:4 500:23 531:24 571:15 580:15,17,18 597:2

determines 71:1

determining 21:5 22:9 26:5,16 72:20,22 85:21

120:24 179:25 181:22 330:14,15 342:7 353:13

382:5 408:22 436:20 474:7 564:1 571:19 590:6

596:19

detritus 402:24

devastate 183:11

develop 25:13 55:12 74:12 79:5 84:24 91:20

138:13 211:14 213:20 214:4 215:14,23 216:8

258:2 276:7 381:15,20 399:8 439:7 486:7 515:21

539:10 547:11 554:15 563:15,19

developed 10:8 58:22 76:16 88:6 98:22 110:17

192:10 199:26 213:14 216:1 217:16 249:21

267:10 273:18 279:19 324:17 335:4 353:9,11

357:13 388:6 390:15,19 393:9 428:24 433:2

482:18 486:3 508:13 539:10,19 547:24 572:3

576:5

developing 7:18 12:21 15:22 24:16 55:22 67:19

76:21 82:18 87:21 141:8 194:2 213:19 214:12

226:19,23 227:3 267:22 434:15 538:15 548:14

550:7 594:3

development 6:19 13:13 22:23 23:9,14 24:2 27:16

30:24 31:21 34:11 43:17 46:1,22,24 47:9,12

48:12 54:3,8,9,10,12,13,19 58:24 63:26 65:14

67:26 68:22 69:14 74:10 75:5 76:19 82:18 83:8

86:19 87:24 88:4 104:23 161:18 184:8 189:1

193:1 194:3 196:6 203:18 227:15 269:13 283:2

288:21 298:1,10 323:7 340:16 366:24 368:14

373:9 374:8 375:7 377:26 386:2 391:4 409:8

425:21 432:19 453:14 490:12,23 497:11 508:2

513:6 514:4 541:21,25 544:11 548:3 566:3

571:20 572:2,26 573:18,19,22 592:22 593:16

developmental 27:12,19 28:25 29:4 31:11 34:17

331:20

develops 499:21

deviation 102:23

deviations 265:16

devil's 436:15

devised 446:7

Dewey 401:24 402:2

DFO 2:11 18:22

DIA 45:19

diagnostic 506:24

diagonal 270:20

diagram 139:23 140:2 160:19 191:9 211:19 222:12

251:3 525:24

dialogue 70:9

diamond 49:22

diamonds 51:9 52:13,21 53:5

diatoms 231:11 257:3 581:26 582:18

dicey 233:5

Dick 178:13

dictated 95:9

dictates 155:17

die 165:7 270:16,16,20 508:12

died 252:7 591:12

differ 44:21 120:2 125:2 224:22 329:20 363:1

373:1 444:1,4

differed 81:9,10

difference 37:14 40:24 53:9 67:7 82:1 90:25 99:8

116:11,16 124:23 125:8 133:18 142:19 149:13

218:21 231:16 232:10 246:26 266:7 301:9 324:24

328:14 333:17,23,24 339:8 342:26 372:16 373:2

377:3,6 378:20 427:10 440:21,23 444:2 580:9

differenced 64:5

differences 13:8 31:10 56:26 58:10 87:2 90:26

140:23 151:21 203:7,8,13 229:25 230:14 232:13

236:13 237:3 327:23 337:8 338:6,7 344:9,10,11

346:16 349:5 350:1 369:26 373:12,15,21,22,23

484:9 499:12 566:7 583:5 589:6 590:9 592:5,6

599:15

different 12:16,23 13:4 15:8 17:20 31:20 34:19

39:3 40:2,3 42:19 49:2,13,26,26 50:11 51:13,14

52:22,22,23 53:2 55:1,21 58:3,6,9 61:6 62:8

64:15 65:5,12 68:6 69:26 80:20 82:9 83:2,4,13,20

84:10 92:2 93:8,15 95:13 98:15 104:19 110:4

112:10,11 113:2,16 114:21,22,22 116:5,15

120:13,13 121:9 122:16,17,17 123:13 125:16

127:18 130:19,19,23 133:6,14 134:21 138:19

141:6 143:4,20,21,24 144:6 147:8,10,12 149:23

149:26 150:2,17 156:14,16 157:17 179:14 182:23

183:16 189:8,9 196:3 211:6 214:14 226:22,24

228:21 229:13 231:10,26 232:2,10 234:23 236:6

250:12 252:13 258:7 265:6 266:4 276:20 316:22

316:23 319:3,5,6 330:4,4 332:20 337:18 338:23

354:3,24 356:19 357:1,2 363:2 364:25 366:25
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367:7 369:26 372:24 373:7,17,20 376:21 378:21

379:17 380:1 401:10,10 412:21 415:26 416:26

417:2 422:5 424:16 427:2 433:14 437:17 444:25

448:19 449:20 451:1 457:7 458:6,19 459:2,17,18

459:19,21 464:7 465:10 466:8 470:26 478:15

482:12 484:8,9,11 485:24 488:18 491:4 492:10

497:26 527:11 532:8 533:10 535:24 545:23 547:5

547:7,7 548:4 554:6 567:1 582:1,5,25 592:21

593:23 594:11 597:14 599:9 600:9,9,14 603:13

differential 94:25 168:22

differentiation 193:1

differently 13:10 112:15 206:10 355:2 379:20

differing 75:9 90:9 107:17

difficult 65:11 173:9,10,14 210:7 235:2 277:18

327:4 339:14 341:5 378:22 386:12 396:23 402:25

404:5 475:12,12 517:25 581:17 583:5 589:17

596:20

difficulties 348:15,21 372:12 524:16 606:9

difficulty 481:4 495:1

diffuse 187:16

diffuses 212:24

dig 10:2 236:8 316:18 317:18 352:1

digestion 55:3

digging 112:8

Digital 30:16

dIj 418:17

dilemmas 277:26

diligently 171:17 174:1

diluted 498:17

dilutional 447:19

diminished 168:3 208:23

dinosaurs 167:2

dioxide 187:3

direct 6:25 8:14 21:21 34:6,8 66:10,13 74:18

109:17 161:23 172:14 173:13 192:26,26 265:15

397:11 401:25 448:17,18 449:1,5 457:20 458:25

497:11 500:1 504:16 506:26 508:20 579:17

directed 155:7

direction 269:12 296:8 358:14,14 425:7 519:14

directions 210:15 288:11

directive 391:2

directly 17:15 136:13 166:3 247:12 248:17 251:20

261:3 365:14 390:7 466:10 506:7 540:7

director 9:3 162:22 163:10 268:5,11 272:1,4,6,9,13

279:25 283:14 295:1 414:12

disadvantage 84:6 278:19

disagree 122:9 157:21 306:3 338:26 384:3 416:20

disappear 166:15,20 171:19

disappearing 163:20 301:8

discarded 43:25 44:2

discern 22:9 34:22 35:15 382:14

disclosure 3:17 163:13

disconnected 229:26

discount 342:19

discounted 406:3

discounting 350:2

discourage 496:5 509:16

discouraged 37:20

discovered 419:12

discreet 93:19

discrepancies 400:3

discrepancy 218:16 219:24 244:3 354:21 487:6

discriminate 34:8 462:24

discriminating 411:22 438:1

discuss 23:5 24:13 25:16 27:7,9 32:22 38:8 49:17

49:20 80:12 82:18 84:25 88:23 93:10 101:1

105:15 125:20 126:21 182:1 205:7 223:6 327:5

385:20 386:6

discussant 321:11 324:11 327:14 359:9 381:9

428:17 476:25 494:20 503:21 513:9

discussed 5:7 23:13 24:24 32:24 33:6 34:12,14

37:2 38:16 44:17 53:21 58:14 82:21 95:20 136:9

179:9 180:6 181:4 184:6,6 245:1 254:2 255:4

284:8 325:20 348:22 367:10 368:6 379:26 429:19

441:3 468:14 477:3 479:12,16 484:17 486:16,17

491:18 499:4,6 572:9

discussing 26:7 27:3 28:18 83:9 84:19,24 219:18

296:25 378:16 385:19 471:13 474:23 487:13

515:4

discussion 5:16 20:2 25:4 32:9 73:3 76:13 90:24

130:20 161:20 162:16 184:4 196:9 232:3 285:7

302:19 311:10 325:15 344:26 359:19 371:24

376:16,17 392:10 394:6 395:17 396:20 426:5

429:12,14,22 439:26 440:13 442:18 450:2 467:21

469:2,3,5,7,16 473:22 474:1 488:8 496:6 504:11

509:16 519:4 521:6 524:7,21 526:2 539:7 549:19

556:17 562:4 588:11 599:11,17 600:20,23 601:16

603:22

discussions 181:2 305:5 325:17 367:19 390:11

395:10 432:4 457:3 473:1 480:13,15 526:24

574:11 596:17 599:1

discussion's 436:15

disease 163:1 166:4 196:14 335:14 386:11,13,23

387:9,13 390:13,20,24 486:10 600:4

diseases 166:10 177:2

dismiss 91:18 497:3

dismissed 292:5 323:23

disorders 167:26

disparate 430:11

disparity 214:21,23 218:19 219:18 248:4
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dispersed 144:15

display 205:20

displayed 193:10

displays 205:20

dispute 270:15

disregard 174:3

disregarded 389:19

disrupt 168:19

disruption 322:2 323:22 340:25 341:11 348:1

373:9

disruptive 69:15 178:14

disruptor 174:15

disruptors 200:5

diss 374:7

disseminates 298:15

dissertation 237:22 270:13

dissimilar 114:9

dissipation 73:7 75:1

dissolved 41:20 126:26 127:2 235:19 236:21,24

402:26

distinct 203:1 250:10,11

distinction 97:3,4 120:7 579:25

distinguish 96:26 229:16 251:26

distinguished 79:24

distinguishing 307:14

distribute 333:4

distributed 4:23 152:11 382:17

distribution 37:4 52:3,3 60:25 61:3,8,9,15,19,21

61:25 63:12,13 94:1 100:7,10,11 106:15 107:23

119:26 127:19 129:20,20 141:26 142:2 144:1,7

152:8 153:1,19 211:19 215:23,24 216:4,6,10,11

216:14 217:13 218:4,11 226:2 235:25 242:2

243:2,8,15 249:22,23 257:21 258:1,2,21,23

288:23 312:14,14,18 437:5 446:10 470:14,20

516:9 517:16,17 554:5 563:24 588:20

distributional 188:18

distributions 62:21 84:8 93:22,23,24 98:22 242:22

266:22 306:21 546:4

distributors 172:10 173:8 288:17

District 281:13 282:2

Districts 152:14 153:8

disturbance 200:2 404:1

disturbed 432:26

disturbing 173:20 273:17

ditches 297:1

ditto 596:14

diverge 373:13

diverse 83:9 89:5 427:17

diversity 73:21 75:21 78:19,20 79:2,10 108:20

111:8 114:8 118:10 396:14 399:9 491:21,22

492:12 580:11 582:2

divided 104:7 108:6 135:3 142:5

dividing 144:22

Division 19:26 27:2 72:18 136:22 292:22 293:18

352:13 414:13 490:25

DMSO 37:19

DNA 191:15,16

doable 574:15

docket 1:7,9 4:19,20,21 5:1 293:10,14,26 396:2

415:9 454:23,23,24 455:2 457:7,7,9 474:17

528:17 607:12

Doctorate 226:18

document 9:26 21:13 131:21 182:25,26 183:4

269:10 273:18 293:6 302:23 416:6 422:7,10

453:10 477:3 490:16,20 526:5,17,21 590:3

605:18

documentation 320:20 527:2

documentations 65:18

documented 235:11 426:15

documents 123:26 183:2,4 257:11 292:23 300:24

305:7

dog 546:19,19

doing 9:21 19:14 97:6 109:23 124:6,14 156:4 157:9

163:15 167:7 177:7 201:16,25 205:18 212:9

216:10 219:8,8 224:16 226:22 227:5 242:16

269:8 311:9 321:16 334:26 341:14 343:16 345:8

346:4 362:4 364:20 382:21 415:5 425:23 427:3

458:9 464:15 466:10 474:22 476:9 477:1 491:25

492:8 512:14 516:22 520:9 521:25 522:1,3

526:21 531:21,24 540:2,8,12 541:8 552:18

556:25 560:12 563:17 565:17 567:20,20 571:8

572:2 579:10 594:22 595:8,9,11 597:1 598:14

601:7 605:6

dollar 275:20

dollars 163:14 168:26 169:2 170:16 173:8 175:21

271:23 275:19 395:24

dominance 89:26

dominant 90:2 93:9 472:2

dominate 498:2

dominated 89:11 91:4

Don 17:5 19:4 68:26 606:21 607:5

DONALD 19:6 305:8 415:3

donate 301:15

door 419:12,13,17 420:3 607:15

dosage 461:15

dose 68:14 71:2,3 173:19 192:19 208:7,13,20

233:17,21 337:6 340:19 347:12,18,24 348:3

355:17,17,24,25 356:9,9,23 357:6 364:12 365:2

376:14 411:4 421:4 437:1,1,8,9,23 438:1,2,7,22

438:24 448:6,8 463:21 483:20,21,22 485:11
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488:15

dosed 446:3,4 447:12

doses 70:7,11,12,12 71:2 197:14 323:22 324:8

355:20

dosimetry 484:8

dosing 345:14 421:5 448:4,5

double 202:23 364:12

doubling 241:19

doubt 169:15 171:15 173:19 332:19

downward 277:17

downwind 172:24

dozens 183:17

DQA 297:22

Dr 2:1,15,19,19,21 5:25,26 6:14,20,23 7:1,5,9,12

7:16,20,25 8:2,5,9,13,17,20,22 9:5 19:6,21,22,23

20:6,6,7 23:25 25:4,20 26:1,24 34:12 60:21,24

61:4 62:1,11,13,13,14,18,18,19 63:4,19,20 64:7,7

64:8,23,24,25 65:4,16 66:2,5,21,25 67:4 68:9,18

68:25 69:10 71:13,20,22,24,25,26 72:5,9,10,11

72:12,16 82:4,17,21 85:11 87:20 100:26 101:3

104:9,17 106:3 109:20,20,21,21,23,25,26 110:9

110:12,15,18,23 111:6,11,14,16,23 112:2 114:2,6

115:2,6,19 116:10 117:1 118:5,11,22 119:11,22

120:26 121:13 122:1,3,8 123:3,11 125:19,21,23

126:7 127:9,9,11,16,23 128:2,6,8,17 129:1,12

130:10,18,26 131:11,23 132:4,6,24 134:24,24

135:18,24 136:1,3,4,14,16 154:26,26 155:1,2,10

155:18,20,21,21,23 156:1,11,18 157:15,21,24

158:6,23 159:8,8,8,10,14,18 160:5 161:1,1,3,7,7

161:8,14,16,22,24 162:5,11,20,21,21 178:5,10,13

178:21,22,22 179:1 181:5,5,10,17,20,21,24,26,26

182:4,7,15,18,18 186:4,15 188:12,20 189:20

195:13,15 202:18,19,21,26 203:10,16 204:4,6,10

204:12,16,19 205:4,10,13,17,24 206:5,14,16,21

206:25 207:6,9,23,25 208:5,15 209:3,5,7,18

210:19,22,25 211:3,3,12,16,17,24 212:3,17,20,26

213:2,5,7,8 214:10 215:6,19,19 216:1 223:6,16

223:18,20,22,23,24 224:6,21,26 225:5,15 226:8

226:10,12 228:2,13 240:13,14,15,16,19,19,25

241:13 243:25 248:7,19,20,20,20,21,24 249:3,9

249:13,17,20,25 250:2,6,9,16,19,23 251:1,4,8,9

251:13,15,23 252:2,10,19 253:11,15,18,19,22

254:2 255:4 260:24 261:2,4,5,6,8,14,20 262:2,6

262:10,23,25 263:7,8,9,12 264:13 265:2 266:15

267:4 285:1,10,12,15,16,19 286:15,21 287:11,19

287:24 292:11,21 294:1,9,11,25 300:20,21,23

301:4 304:26 305:4,8,8,10,10,12 307:21 309:1,7

310:16,19,20,26 311:2,3,5,5,7,14,17,18,25 312:3

312:4,9,21,23,24,25 313:11,13,15,20,21 314:3,5

314:6,7,11,12,13,13,14,24 315:1,6,10,22 317:20

317:20,21 318:5 319:17,20,21,23,23 320:5,11,13

320:15,17,18,25 321:2,4,10,12 324:10,11,12

325:6,6,7,23,23,25 326:1,1,2,4,4,5,6,10,12,21,21

326:23,23 327:4,7,9,14,15,16 328:17 329:18,18

329:19 331:12 332:23,26 333:3,6,8,9,10,13,19,21

336:2,2,4,5,7 337:11 338:11,25 339:7,19 340:20

342:9,9,11 343:24,24,25 344:17,17,18 345:10

346:11,11,12,13,22,22,23 347:4,4,6,9,9,10,25

348:5,9,12,12,13 349:18,18,19,21,21,22 350:16

350:16,17,18,18,19,20,20,21 351:17,18,19 352:7

352:11 354:13,13,15,17,20,26 355:4,7,7,8,9,10

355:11,12,19 357:9,9,10,11 358:17 359:4,5,6,6

360:7,7,8 361:1,1,3 362:6,6,8 363:8,12,13,14,15

363:15,17 364:15,17,21,21,23 366:4,7,8,11,14,15

366:16,17,21,22 367:3,4,17 368:11,11,12,19,19

368:20 370:17,17,18,20,20,21,23,23,24,26

371:18,22 372:2,3,5,6 373:5,5,6 374:5,5,6,21,21

374:22 375:13,13,14 376:19,19,20 377:9,13,17

377:17,18,22,22,23 378:8,8,9 379:5,5,6,8,8,9,11

379:11,12,13,14,15 380:4,6,7,9,23,23,25,26

381:3,8,9,10,26,26 382:1,24,24,25 383:1,1,2,3,3

383:4,5,5,6,8,21,21,22 384:6,13,14,16,18,20

385:5,6,10,11,12,18,24 386:8,8,9 387:16,16,17

387:19,19,20,25 388:9,9,10,12,12,13,23,23,24

389:15,25 390:1,10,10,11,25,25,26 391:20,20,21

391:24,24,25 392:1,1,2,4,4,5,21,21,22 393:2,2,3

393:12,14,15,25,25,26 394:15,17,19 396:7,11,13

396:19 400:12,19 405:11,11,12 406:6,6,7 408:13

408:13,14 410:1,1,2,14,24 411:4,14,14,15 412:14

412:14,15 413:2,2,3,18,22 414:22,25 415:5,11,14

415:16,17 416:11,12,16 417:9,9,10 418:11,14,16

418:17 420:1,1,2,5 421:7,7,8,22 422:11,12,13

424:6,20 425:15,16,17 426:5,22,24,26 427:4,11

427:12,25 428:1,2,2,5,8,16,18 429:13 430:24,24

430:25 431:16,16,17 432:14,14,15 433:20,20,21

434:18,20,20,21 436:10,10,11 438:15 439:14,14

439:21 441:1,1,2 442:17 445:13,14,15,26 446:2,6

446:22,26 447:10,10,16,20 448:1,9,10,11,16

449:12,15,25 450:2,5,19,24 451:8,22,26 452:11

452:12,13,16,24 453:21,23,24,26 454:9 455:3,5,5

455:10,12,14,16,18,22,24,26 456:2,4,5,9,11,13

456:15,18 457:12,12,14,14,23 458:24 459:10,11

459:12,26,26 460:1,9,24 461:1,3,5,11,23 463:12

464:6 465:6,8 466:5,13,16,18,20 467:19 470:6,6

470:8 471:21 472:7,8,9,11,14,15,26 473:17,20,24

474:2,9 475:1,4,6,16,17,20,22,22,23,25 476:13

476:14,17 477:5,8,12,14,18,20 478:8,10,14,25

479:2,8,12,15,18,19,23 480:3,6,7,9,23 481:22,22

481:23 482:1,9 483:6,8,12,16,18 484:3,16,22,24

484:26 485:13,16,18,21,23,25 486:1,15,17,18,22
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486:23,24 487:1,4,8,11,25 488:1,3,4,22 489:2,5,9

489:20,22,26 490:4 491:14,17,18,23 493:2,4,5,10

493:12,23,25,26 494:6,10,20,22,24,26 495:1,5,15

495:15,16 496:11,13,20 498:21,21,22 499:11

500:3,3,4,20,20,21 501:7,7,8,10 502:25,26 503:1

503:6,9,11,21,22,23 509:18,18,19,20,20,21,22,22

509:23,24,25,25,26 510:2,5,5,6,7,7,8,9,9,10,11

510:12,13,15,16,17 511:3,7,8,9,11 512:16,20,24

513:8,9,10,11 519:25,26,26 520:1,2,24,24,26

521:22 522:21 523:14,14,15 524:17,21 527:3,6,9

528:2,3,7,11,12,12,15,20,20,23,25 529:1,2,4

530:11,11,12,19,20,21 531:11,26 532:2,3,4,10,11

532:13,14,15,22,22,23 536:19 537:5 538:12,12

538:13,26 539:21,21,22 540:14,15,18,25 541:5

541:12 542:1,1,2,4,4,5,6,7,9,10,10,20,25 543:1,5

543:6,8 544:23,23,24,25 545:2 546:1,1,2,9

547:14,14,15,16,16,17,18 548:26 549:1,3,4,14,14

549:16 552:17 553:3,8 555:26,26 556:1,5,20

557:23,23,24,25 558:3,3,4 559:18,18,19 560:4,5

560:6,24,24,24,26 561:4,4,5,15,19 562:2,21,22

562:24,25 563:1,13,13,14 564:12,12,13 565:10

565:13,14,15,25 566:15,15,16,22,22,23 567:26

567:26 568:1,2,2,4 569:2,2,4,6,12,23,23,24

570:10,11,13,14,20,20,22,22,23 571:1 573:8

575:11,12,14,19,19,20 576:11,11,12 577:18,18

577:19,20,20,21 579:19 580:2,4 583:21 584:17

584:18,20,21 585:5,13,14 586:7,7,8,21,21,22

587:7,7,8,12,12,13,19,26,26 588:1,3,3,4 589:9,9

589:10,26,26 590:1,12,12,13,19 592:3 593:22

594:17,17,18 596:13 598:2,2,3,3,22,22,23 601:22

601:23 603:19,21 605:16,22,23,24

draft 336:22 490:21 518:16 593:7

drafted 304:13 490:20 511:15

drafting 303:6

dragon 166:13

drain-restrictive 527:17

dramatic 241:6,15,21

dramatically 81:19 309:17 376:6

draw 132:18 239:19 362:23,23,24 363:5 496:9

579:17

drawn 232:6 239:20 352:22

dried 165:9

drill 193:15,26

drilling 368:1

drink 177:18

drinking 138:3 150:9 173:19 272:26 574:26

drive 1:15 134:16,17 157:25 166:6 167:3 170:26

171:8 278:16 528:26,26 564:23

driven 132:1 165:16

driver 171:5 260:8 279:10 365:17 525:16

drives 16:20 125:26

driving 24:8 69:1 99:12 314:2 565:5

drones 269:9

dropped 44:4,11 424:19 571:26

dropping 309:17

drops 309:13

drugs 375:7

dry 165:6,6 233:13 259:14 269:21 273:23 274:14

281:7,7,9,26 282:10

dry-down 413:8 414:5

duckweed 267:2

duct 19:13

ducts 310:2

due 30:12 34:5,14 42:5 44:2 45:7 48:19 52:8 59:20

91:3 100:15 106:18 109:13 116:25 169:5 171:4

172:11 246:20 264:2 274:22 275:19 279:15

344:10 358:25,25 361:4 365:1,15 396:23,24

398:11 400:16 401:3,19 441:10 443:1 444:1

494:18 507:2,3 509:2,2

Duluth 232:22

dumping 282:23

duplicated 292:3 345:21

duplicating 495:17

duplication 345:22,23

duration 50:6 64:9,10,12,22,26 65:8,15 76:3 81:14

82:7 83:4 98:14 99:8 101:8,12,22 102:1,6 113:1

115:9 128:15 129:13,13 130:12,23 185:14 201:5

204:2 214:22,23 217:25 218:1,5,6,7,11,15,19,23

218:26 219:3,12,15,17 220:12,17 221:1,6,22

222:1,9 225:11,17 239:14,21,25 240:4 241:25

243:2 245:1 247:11 248:5 251:24,25,25 252:3,8

252:11,12 253:1 255:20 256:3 263:1 307:9,10

398:23 414:3 431:15 437:3 442:20 447:14 453:13

453:14 460:4 462:7 464:3 465:20,25 488:15

499:12,20 500:2 507:12 511:24 512:3 513:22

515:14 519:10 571:18 581:13

durations 51:6 52:9 64:14 79:11 82:10 105:13,25

106:5,8,8 115:3,7,13,14 130:19 214:21 224:8,16

225:9,20 226:6 379:23 448:19 458:6 460:21

500:1 509:6 513:24

duty 167:7

DVD 4:10

dwell 539:3

dying 438:20,20

dynamic 117:7 220:24 259:8,10 260:12 291:5

442:7

dynamically 372:24

dynamics 260:9 459:5 497:20 498:16

D.C 275:21

D1 186:26 187:5
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E

E 312:6 432:17 435:21 471:10 474:13

ear 416:12

earlier 25:25 90:6,8 95:16 99:17 104:9 132:1 179:9

184:6,19 186:15 188:7,7 190:5 192:18 202:23

207:1 214:11 219:18 243:25 245:1,18 248:8,10

249:11 254:2 255:4 257:22 274:23 290:3 296:25

305:7,10 310:7 311:9 319:25 353:18 368:23

371:21 411:25 429:18 431:14 442:17 444:25

468:17 496:22 524:6 525:21 528:17 543:12

544:26 545:11 549:19 554:18 558:14,17 567:4

569:6 590:16 591:11 592:1 602:8,16

earliest 309:18

early 44:8 50:21 166:23 184:8 194:3,14 195:26

234:19 240:1 259:17 287:22 288:10 403:25 455:2

earn 301:15

earth 165:6 176:21

easier 193:11 341:9 551:12 579:4

easiest 327:17

easily 395:22 599:21

east 144:11 169:10

eastern 164:20 288:9

eastward 172:19

easy 357:15 403:23 409:8 499:14 575:24,25

eat 5:22 164:26 166:8,11 168:25 169:26 175:24

177:18

echo 588:1 590:1

Eco 283:18

Ecologic 581:16

ecological 1:5 2:8 6:2 9:14,22,23,24 10:6,11,18

11:2,25 14:21 16:20 20:11,15,16,17,19,21,25

22:2,9 23:14 24:12 28:20 32:26 36:24 40:9 59:15

71:9 75:3 103:1 112:5,9,21 113:21 137:11,21

142:9,12,14,21 181:18 183:26 184:21 227:16

268:8 273:6 274:12 276:14 277:16,19,24 278:13

279:8,13,23 280:5,23 283:25 284:7 315:23

329:19 341:17 342:2,7 349:2 383:24 397:22

401:3 405:18 409:15 416:7 421:10 454:14 486:13

491:1,2 504:10 582:13 583:6 588:11,25 602:12

604:11 605:3,8

ecologically 12:4 118:3 227:25 368:16 378:6

400:10 580:17 581:20,25 582:9,17,20

ecologist 7:10,13 455:21

ecologists 382:21

ecology 163:2 215:13 220:25 224:13 226:18 245:13

334:24 383:23 490:25 502:5

economic 76:19 112:23 172:10 243:20 271:11

274:16 278:19 279:7,10 285:17 289:24 290:5

291:25

economically 172:6

economics 168:11 172:1,7

economy 168:20,21 172:8 291:21

ecosystem 9:17 13:21 14:17 15:6 73:22 74:10

113:25 163:12 174:13 176:24 214:1 258:7 262:24

345:13 397:12,23 497:20 503:4 508:18 598:26

599:14,22,23,25,26 600:12,13

ecosystems 9:16 13:25,26 87:5 90:3 133:2,7 316:26

360:3 458:25 599:19,20 601:3

ecosystem-level 410:12

ecotoxicity 503:15

ecotoxicological 401:2 508:23

ecotoxicologist 6:26

ecotoxicology 360:17 382:20

eco-literature 189:26

eco-monitoring 276:18,22 277:1,15 278:22,23

279:11,18 280:21,26 284:1

eco-risk 588:24

eco-studies 371:2,9

eco-tox 348:3 381:24 382:18

eco-toxicological 315:25

EC-LOC 584:15

EC50 49:21,22 50:6,23 68:15 100:25 110:20

130:13,14 235:26 266:20 312:15 433:7 439:2

EC50s 266:18,22,23,24

edge 118:18 167:3 287:1

edges 118:20

editing 351:7

editor 209:12

educate 171:17

education 163:6 169:15 283:4

educational 163:7 590:15

EEE 595:24

EEF 85:25 86:2,3,14 104:9,13 108:6 315:12,15,20

EEFs 108:7

EFF 468:22

effect 14:11 16:7 21:17 25:10 27:19 33:20 36:13,14

37:15 43:11 44:14 46:1,7,15,23 48:20 50:13 51:1

51:9 52:12,13 71:3,10 76:12 79:17,20,23,25 80:3

80:3,18,19,22,22 83:24 84:16,17 86:3,4 92:24

94:2,4,5,26 95:11,18 96:26 97:5,5,12 100:19

101:14,25 102:19 106:21 107:3,3,5 112:9,20

115:12 119:13,16 122:12,13 123:6 124:4,5

125:25,26 126:16 128:21,25,26 129:8 130:12,16

134:20,20 137:8 145:5 155:19 171:13 173:12

192:26 198:8,9,11 203:25,25 206:10 207:8

208:24 210:4,9,9 213:22 216:19,24 217:9,10,10

217:22,26 218:15,22 219:5,11,19,19 220:14,20

220:24 221:4,10,21 222:9 223:3 227:4,25 229:10

230:1,22 231:7,17,17,18,19 232:13,14,16,17,18

232:19 233:10,12,15,20,22 234:6 236:11 237:5
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238:25 239:16,22,26 240:1,3 241:26 242:4 244:7

244:14,16,24 246:4,15,22 247:4,14 248:6,15

249:4,16,18 250:18,24,26 251:5,18 253:4,5,6

254:13,16 256:17 259:18 296:20 305:23 307:24

309:15 312:1 317:12,26 320:10 322:8 323:3,4,7,8

323:13 327:26 328:3,17 330:21 332:1,14 334:7

340:6,19 341:5,5,19,20 343:18 349:3 351:22

356:15 357:15 358:9,10,11 360:13 362:18 363:21

365:14,26 371:4,4,5 373:26,26 375:6 377:15

378:10,12 387:4 388:2,2 400:1,2,17 401:14 402:3

402:10 403:25 410:12 411:2 414:9 417:7,8

427:20 428:21,22 429:4,4 437:10,11,16,20,24

438:1,1 440:8,8 441:9,16,24 443:4,7,12,18

444:11 446:18 447:19 448:22,25 449:9 453:19

458:3 461:17,20,22,24,24,25 462:5,7,8,9,22,23

463:3,5,5,6,7,22,26 464:4,9,20,22,24 465:20

467:4 469:8 471:15,26,26 481:3 485:8 486:4

488:17,17,19,20 491:26 492:1,1,6,9,20 493:16,16

496:5,10 499:2 500:23,26 513:19 530:24,25

531:3 538:8 571:19 580:10,10,19 581:3 584:4

effective 268:24 280:12 286:2 291:11 505:1

effectively 187:12 266:17 286:11 289:22

effectiveness 289:12 297:10

effects 7:15 11:25 13:20,25 14:4,9,9 15:19 18:12

18:12 19:26 20:21 21:21,22 22:14,23 24:18 27:2

27:12,15,26 28:3,5,9,14 29:4,26 30:19,25 32:26

33:13 34:7,8 36:24 37:3 40:9 43:17 46:13,21 47:4

47:11 48:21 49:4,4,15 50:7,10,20 51:4,9,23 52:7

52:14 53:13,24,24 54:15,22,24 55:18,25 56:12,20

57:22 59:1,18,19 60:6 62:8 63:9 66:7,9,10,14,14

66:19 67:15,17,18 68:1,6 70:7 71:9 72:18 73:17

73:21 74:14,18,19 75:3,4,5 76:9,23 78:25 79:5,15

79:21,24 80:4,4,9,10,13,25 81:1,17 82:6,7,9,11

82:16,19,24 83:2,8,12,12,18 84:9,13,22,24 85:1,2

85:5,8,10,11,14,16,19,20,25,26 86:2,6,11,13,19

87:4,7 88:8 89:4 90:7 91:19,20,22,24 92:1,10,13

92:14 93:13,14 94:11 96:4,7,8,9,12,16,20 97:9,10

97:11 98:7,9 99:5,14 100:2,4,15 101:4,6,22

102:12,15,15,22,25 103:19,21,24 104:8 105:3,22

107:14,20,23 109:5,12,17 112:21,22 114:5 115:3

115:7,8 117:24 118:8,9,16,24 119:17 120:3 121:1

121:5,11 123:17,23 125:2,3 126:15,23 127:6,12

130:6 131:15,16 132:13,15 133:26 135:6 136:22

145:3 167:23,24 171:18 173:2,11,13,15 178:1,14

181:18 185:1,2 187:25 188:11,25 189:1 192:20

192:21,26,26 193:1,6,22,22,24,25 194:20,21

195:3,10,25 196:4,5 197:3,4,5,9,13,14,18,21,22

197:23 198:4,20,23 201:18,20,21 202:6 206:1,2

206:23,26 207:8,10 209:26 210:2,5 211:18,23,26

212:15,16 214:2 216:22 217:7,7,7,8,20,21 219:23

222:6 223:2 224:13,23,24 225:2 228:15 229:16

229:16,17,20,20 230:2,4,8,15,16,18,19,20 231:9

231:11,25 232:5,12 233:10,25 234:11,13 235:1,7

235:11,18 236:2,14,24 237:6 238:2,20,22 239:9

239:10,12,24 240:3,11 242:19,20 249:6 250:3,14

253:8 265:15,19 271:2,3 276:15 286:18 291:20

292:22 293:18 297:11,13 299:4 300:8,17 306:16

306:17 307:18,19,22,26 308:3,10 312:16 313:2

313:24 315:13,19 316:6,16,21 318:12 320:1,6,7

322:4 325:2 332:3 334:4,9,18 335:24 337:22,26

338:5 339:3,7,21 340:5,12 341:24 343:26 350:12

351:22 352:13 355:17 356:16 358:10,12 359:24

359:25 365:1 366:3 367:1,15 368:2,5,17,22,25

369:4 371:8 372:8 376:25 377:4,6,25 378:5 379:2

382:5 383:12 384:1,4 385:26 386:1,3,10,13 387:5

387:23 390:7 397:2,12,14,17 399:13 400:4,5,8

402:20 403:11,14,15,20 405:4,7 409:12 414:12

416:2,2,8 423:19 427:18 428:20,20,23 430:4,5,20

441:19 442:3 443:1 444:10,19 448:12,14 452:18

458:4,9,19 459:1,20,20 460:12,12,14,17,18,23

462:19,21 464:8 465:18 466:10 473:8,23,24

474:25 478:7,7 483:21 485:6 489:1 491:19

495:21,24 498:9 499:6,13 503:4,19 504:5,12,17

504:19,21,22,23 505:16,18,21 506:1,19 507:18

507:20,23 508:1,10,11,16,21,24 510:25 524:5

591:10 597:11

effects-based 102:9

effects/no 121:11 127:12 473:8 474:25 499:13

effects/no-effects 81:4 102:11 106:15

effect/no 119:13,15 251:5 312:1 437:15,20 467:4

471:15 491:26 499:2 500:26

effect/no-effect 79:16 80:15 114:4

efficacy 84:26

efficiency 171:8 234:7 256:15 261:10

efficient 154:21 157:10 280:8

Effland 8:5,6 157:24 158:23 249:17 312:24,25

313:13,20 314:3 456:4,4 459:26 460:1,24 461:3

510:16,17 523:14,15 527:6 528:3,11,13,15,23

542:1,2 544:23,24 558:3,4 561:15 566:22,23

569:3,4 575:19,20 590:12,13 605:22

Effland's 561:6 579:19

effort 9:11 13:14 20:1 50:11 57:20 61:17 88:19

250:25 269:10 281:14 282:5 346:7,7 351:3

429:18 430:4 434:22,26 471:9 548:14 577:16

586:9 598:19

efforts 28:11 60:6 84:23,24 169:24 184:16 283:6

303:3 319:5 328:8 400:7 409:8 588:7 603:22

egg 322:22 323:4

eggs 117:16 165:6 166:14 176:8 207:19 209:20

Ehrich 2:20 6:20,20
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Eickler 178:13

eight 593:7 598:26 601:24 602:2

eight-legged 168:5

einer 270:18

eingebracht 270:20

either 35:25 46:11,22 48:19 51:3 97:26 119:19

126:2 170:12 197:18 198:19 207:3,5 211:23

236:18 240:3 250:4 372:23 405:22 421:5 425:7

438:20 439:5 443:9 470:23 471:18 473:4 495:21

501:2 514:16 524:24

elaborate 67:5 134:25 451:18 485:4 491:20 520:13

elective 424:26

electricity 591:12

electron 187:6,10

electronic 4:19

electronically 293:24,25 454:23 528:21

electrons 73:14 187:1,5,7 256:16

element 38:8 75:15 93:26 117:12 254:21 257:15

259:5 328:1 338:16 381:21 450:15

elementary 301:2,3

elements 28:22 29:13 32:17,22 34:21,25,26 35:1

35:13,18 37:9 39:17,22 40:5 41:8 42:9,20 59:8

72:26 76:6 91:19,20,21,26 92:1 99:5,6 104:18

117:10,21,26 323:17 327:6,10,12 332:2 336:8

337:5,10 338:13,24 344:4 347:11,11 353:12

356:4 357:13,19,23 360:9 369:11 381:6 393:18

393:21,23 413:26 414:7 452:14,20 482:7 484:22

elevated 422:16 512:2

elevation 407:20 558:11 569:9

elevational 558:10

Eleven 34:2

elicit 82:23 96:6,26 123:23 514:1

eliciting 320:10

elicits 201:13

eligibility 21:12 78:1

eliminate 96:23 340:12 341:15 398:14

eliminated 332:4 334:1,8 502:1

eliminating 91:21,24 331:25 339:11 340:3,7

353:14

Ellsworth 8:9,9 63:20 155:22,23 156:11 157:15

159:9,18 225:5 320:13,17 441:1,2 456:5,5 461:11

463:12 491:18,23 493:5 520:25,26 522:21 532:3

532:4 539:21,22 540:18 541:5 546:1,2 549:16

553:8 556:1 557:25 560:5,6 566:15,16 569:23,24

576:11,12 593:22 594:17,18

Ellsworth's 549:14 598:4

email 512:21 528:22,24 543:2 606:23

emails 606:3

embed 560:18

embedded 362:4 529:21

embrace 586:16

emerge 508:25

emergence 402:4,8

emerging 6:8 509:11

emitions 170:4

emotional 111:25

emphasis 6:18,22 40:19 367:11 404:19 474:8

602:20 603:15

emphasize 235:23 299:20 325:10 370:7 442:2

497:17 498:11 552:18

emphasized 52:3 83:23 338:11

empirical 132:26 240:4 298:25 317:3

empirically 240:10

employ 558:7

employed 23:4 198:25 213:24 432:16

employing 278:12 306:16

enable 55:13 56:3 57:5 58:22 331:21 384:26

encapsulating 408:15

enclosed 235:17

encompass 46:13 67:15,21 97:1,9 378:18

encompassed 431:11

encompasses 73:22 268:21 281:4

encounter 45:9 589:18

encountered 512:7

encourage 291:17 392:23 470:12 472:2 545:6,25

558:9 561:18 587:1 592:23 593:2,13 595:3

encouraged 336:15

endangered 112:6 165:1

endangering 171:11

ended 23:23 461:21 498:16

endless 206:26

endocrine 69:15 174:15 178:14 200:5,6 209:24

340:24,25 341:11 348:1,4 373:8 507:3

endocrine-mediated 28:5

endocrine-related 13:20

endocrinologist 367:6

Endocrinology 7:4

endogenous 341:8,9

endorse 342:25

endorsed 23:1

endpoint 21:17 32:20 37:15 39:26 40:1 42:11,15

42:21 43:1 47:13 48:11,16,22,26 49:8,9,11,17

52:19 55:16 61:23 63:25 64:3 68:22 76:11 80:3

101:7,10,14,22,25 102:7 107:9 126:14,14,19,20

126:26 127:1,18,22 128:19 132:13,14 194:26

209:15,19,22,25 210:3 230:11 233:24 293:1,2

313:9 390:15 432:8,9 441:17 447:12 448:18,19

449:7,7 458:6 474:14 486:14 497:23 543:9

580:14

endpoints 30:20 31:20 32:18 40:2 42:12 43:16
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46:9 48:10,10,24 49:1,12,13 52:1,7,23,25 53:19

54:7,8,18,20 55:1,21,22 56:4,5 58:2,8,18,20,21

58:24 59:2,3,21,24 60:18 64:19 65:3,5 74:22 79:4

79:17,19,21,23,24 80:2,6,8,9,10,12 81:7 101:7

102:7,11,15,16 106:5,14,19,20,24,24 107:1,4,4

107:18 109:10 115:14 126:22 128:24,26 130:1,19

132:7,9,12,13,19,20 136:2 209:8 250:12,13

252:14,21,22 254:14 294:16 305:21 306:1 321:23

330:16 331:8,19,20 341:18 368:26 379:3 390:17

391:13,14 392:24 397:21 404:21,23 423:8 427:3

441:15,16 442:1 443:4 474:11,21,24 482:20

483:3 484:18 486:11 491:22 492:11 493:13,15,16

500:16,17 506:11 586:14,15,17 599:24 602:15

endpoint-driven 586:12

ends 135:14 573:17 578:26

endurance 606:26

Endyna 607:6

energy 278:4

enforcing 548:11

engaged 276:22 279:10

Engel 8:2,2 456:2,2 529:1,2 542:4,5 543:6,8

559:18,19 567:26 568:1 575:12,14 589:26 590:1

592:3

Engel's 544:25

Engineering 8:4

engineers 512:10

English 33:22 76:25,26 238:8 270:10,22,24

enhanced 408:5

enormous 238:2 606:6

ensure 3:13,21 10:7 14:4 30:8 32:14 45:11 59:13

80:2 138:10 140:8 253:23 254:23 257:17 268:18

296:9 440:1

ensures 74:7

ensuring 2:13 343:22

entail 444:16 531:19,20,20,21,24 552:23

entails 83:2

enter 20:8 230:17

entered 58:18 103:7

entering 162:9

entire 24:9 74:10 86:10 103:11 117:13 120:14,20

245:12 254:9 305:24 324:14 327:24 350:13

entirely 189:16 328:7 508:15

environment 3:7 14:12 45:9 72:2 73:7 81:19

116:21 137:2 170:8 172:4 191:6 213:21 254:26

257:19 259:6 265:5 280:20 291:14 316:7 329:23

340:3 342:22 343:3 345:10,15,20 378:24 383:10

442:8 504:24,26 510:22,23 591:21

environmental 1:11 2:9 6:6,25 7:3,7,22 8:11,19

19:25,26 20:20 27:2,23 28:26 29:15 30:8 41:22

52:9 72:3,18 76:18 101:4 102:26 103:7 136:22

137:19 138:5,26 139:1 159:2 163:1 166:24

167:15 168:14 169:24 171:17,26 172:2 174:4,8

175:12,15,19 177:16 179:12 180:3 197:24 200:4

256:2 264:26 265:3 268:7 271:11 272:4 273:15

274:16 276:6 281:22 282:21 283:15 284:7 292:21

293:18 298:8 301:25 315:19 321:21 329:21,23

330:14,25 343:3 352:13 354:7 376:15 378:20

382:8 387:9 414:12 420:4,16,19 440:13 444:2,14

444:17 453:14 456:16,17 491:12 503:3 504:9

593:25 601:8

environmentalism 175:11

environmentally 30:1,25 67:19 185:10 194:12

212:5 417:18 506:6 507:10 508:4

environments 79:3 335:6 345:11

envy 594:21

enzyme 207:11

eosinophils 58:3

EPA 3:1,9 5:19 9:4 12:24 13:14 20:10 21:3,11,17

21:20 22:10,18,21,26 23:3,13 24:1 25:8 27:13

29:3 30:21 31:26 32:1,4 34:20,26 36:4,9 37:18

45:25 56:5 59:15 60:1 73:20 87:20 88:2,9,15,20

89:9 91:14 105:1 107:7,17 108:22 109:11,15

137:1 138:1 168:13 171:20,21,24 172:2,4 173:3

174:9 175:9 176:11,17 177:10 179:4,11,19

180:12,14,26 184:2,6,9,19 189:15 190:21 192:8

195:25 196:4 197:11 202:4,23 207:21 209:23

213:14 214:10 227:21 228:4 231:8 232:22 233:9

235:13,19 238:5,8 248:10 249:21 268:17,18

269:12,15 271:20 272:17,25 273:10,17,21 276:13

279:5,17 280:24 281:20 282:12 289:25 290:4,24

291:17 292:2 294:17 296:9 297:11,15,17,20

298:5,10,13,15,24 299:2,3,8,10,16,24 300:9

302:7,10,12,15,23 303:2,18 304:5,21,24 305:21

321:14,25 328:7 330:1,11,12,23 332:21 333:13

334:2,12 335:5,11,19 336:11,13,15,24 338:3

340:22 345:24 349:19 366:26 367:20 378:26

382:16 392:6 425:22 427:1 428:12 434:4 450:6

457:1 490:10,16 517:11 526:9,22 527:7 563:2,10

584:1 586:9 587:21 588:5,6 589:11 590:2,14

594:20 598:20 599:4,9 604:12 606:15

EPAs 264:19

EPA's 9:22 19:26 20:16 28:11 31:2,25 171:13

172:13 180:15,17 222:1 235:26 239:18 241:16

243:4 273:5 274:18 280:5 281:15,18 282:22

296:6,11,17,22 297:24,26 298:6,8,23 299:15,23

300:5,7 321:18 327:10 334:21 341:21 348:9

387:15 428:13 431:18,24 503:17 505:24 506:16

513:6 532:19 600:19

EPA-HQ-OPP-2012-0230 1:7 4:22 454:25

epidemiology 72:2 389:7
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episodic 81:19 412:7 500:15

equal 17:22 63:7 104:12 138:10 443:15 515:2

598:5,16,16

equaling 429:4

equally 16:22 62:3 559:22,25

equals 104:13

equate 258:22

equated 89:18

equation 87:9,13 104:14 108:9 146:15,20,23,24

147:3 148:12,12,25,26 438:26 536:8 538:4

539:10 542:17 558:25,26 570:5

equations 87:16 147:8,15 157:12 563:3

equipment 281:6 329:1 334:10

equipments 339:26

equivalence 375:2

equivalent 73:4 103:26 161:19 269:14 270:2 416:9

Erickson 20:7 23:25 26:1 72:12 82:17,21 104:9,17

106:3 109:21,25 110:12,18 111:16 115:19 119:22

129:1 130:10,26 131:23 134:24 214:10 215:19

216:1 243:25 249:13 305:10,12 310:19,26 311:3

312:3,9 313:11,15,21 314:11,24 315:10,10

319:24 320:5 426:24 427:12 438:15 439:15 446:6

446:26 447:10,20 448:16 449:15 453:21,22,24

457:12,14 459:11 460:9 461:23 464:6 466:5

467:19 471:21 473:24 474:2 475:17,22 493:2

499:11 503:9

erode 73:8

erosion 279:15 290:10,12,16 295:23 581:23

errata 293:7 351:12 366:17

erratic 131:8

error 100:9 156:14 318:1 343:14 433:14 436:5,6

436:19 439:19,20 471:24 472:5 495:6 565:6

573:2 595:14

errors 100:9 305:17 435:13 472:10 476:7 563:24

errs 331:26

escalate 289:23

especially 28:23 45:9 52:10 56:22 74:25 87:26

89:10 155:18 301:17 303:11 326:13 358:17

365:23 375:10 390:21 451:19 490:11 499:2 500:6

526:19 530:13 553:18 558:19 580:11 593:24

596:2 598:7,24

essence 215:23 216:9 245:12 247:22 278:26 593:9

essential 253:9 291:12,14 331:11 346:13 399:7

essentially 13:23 73:16 80:5 96:4 97:26 141:24

213:13,25 214:6,9,15,19 215:1,4 216:6,10 217:6

218:3,9 219:8,15,19,26 220:4,7,15,23 221:9,26

222:13 223:17 224:6,16 226:4 228:14 240:21

241:10 242:2,16,19 243:1 244:6,20 246:6,9,20

248:3,14 251:16 253:23 255:6 256:20,21 258:3

258:18,23 260:4 433:8 446:4 465:13 495:12

522:7 527:12

establish 13:18 57:20 111:24 155:25 196:2 225:11

259:19 328:2,7,9 330:13 335:26 343:26 385:4

386:3 388:26 389:3,4,9 429:1 452:9,17,22 472:16

499:26 517:4,23 597:23

established 31:9 57:10 59:2 142:13,16 148:5 196:7

212:4 308:22 330:12 388:5 429:3 450:25 472:21

486:9 496:17

establishes 12:2 389:3,11,12 571:15

establishing 65:10 330:20 367:18 380:12 420:8

437:1 438:22 505:1 530:14 548:9

establishment 118:25

estimate 40:17 110:22 129:11 145:21 146:12

148:26 149:1 218:9,13 319:26 435:21 437:25

441:7,9,11 442:1 445:2,6,8 467:15,17,20 471:2

518:19 540:3,8,21,24 544:18 546:3,6 553:24

560:17 565:4 567:18 572:5,13,17,18,23 574:1

575:2 580:1

estimated 144:22 147:21,22,26 148:6 149:4,5

289:25 290:13 470:26 507:9 572:17

estimates 48:8 52:17 78:20 89:24 145:13,16

156:10 304:10 392:24 429:10 514:17,21 516:15

538:7 539:25 544:4 560:14 572:14 573:3 574:6,9

estimating 118:26 138:2,14,20 143:16 147:7,21

154:22 492:8 523:3 532:7 540:19 542:23

estimation 106:14 108:1 110:19 119:12 128:20

138:21 139:10 141:12,13,16 142:3 146:5,14,16

146:21 147:9,14,24 148:8,10,13 149:16 313:12

318:15 435:4 445:11 467:14 522:23 547:23

554:13

estimations 144:17 148:9

estimator 574:24

estradiol 29:23 30:3 31:16 45:4 54:21 68:13,23

70:23 355:22 369:16

estrogen 581:14

estrogenic 333:26

estuary 107:11 434:5

et 43:5 110:26 175:7 227:13 231:6 232:21 234:18

235:14 237:17,26 238:12 243:12 248:22 260:10

261:22 266:25 270:13 322:9,13,13,14,15 323:6

323:10 325:9,14 378:2 386:25 400:15 401:24,24

402:2,6,9,16,17 403:1,6,12,17,18,18,19 404:22

444:3 490:26 493:18 503:2 507:1

etcetera 474:14

ethanol 170:25 171:6,7 237:23 238:2 248:23,26

250:17,18,20 270:15,17

ethical 167:7

ethics 3:13,14,18,21 394:26

ethnicities 376:21

ET&C 266:25
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Europe 121:8 190:24 227:1 601:18

European 121:8 167:11 174:19,23,24

evaluate 30:23 35:6 47:3 59:9 60:11 68:4,23

115:12 148:12 151:20 154:4 203:11 207:8 222:21

227:11 240:21 261:9 282:13 286:18 298:19

314:16 324:14 331:14,24 342:6 350:14 355:6

357:16,18 367:1,9,15 368:13 371:5 373:7 374:17

374:19 384:1 387:12 397:16 405:19 407:11

417:24 418:9 451:21 457:1 464:8 479:1 482:19

506:14 577:15 597:21

evaluated 11:1 30:20 34:10,19 39:16,26 42:21

46:12 51:24 63:26 67:24 68:22 71:10 74:15 76:20

88:6 109:24 139:8 184:1 224:11 228:10 256:10

272:17 321:21 322:18 323:10,12,14 332:12

334:19 337:2,18 338:18 351:23 393:5 400:1

451:2 457:20 488:13

evaluating 29:4 32:3 39:21 45:24 46:6 54:17 84:26

202:12 282:4 298:18 325:4 330:9 336:9,12 338:4

340:16 352:2 360:10 370:2,9 417:13,26 433:11

440:1 474:3 483:2 511:15 514:15 551:16 586:24

evaluation 21:4 22:23 23:3 24:21 25:5 26:15,22

27:3,8,15 31:24 32:16,21,23 33:4,5,9,15,26 34:2

35:3 38:26 39:14 40:12,19 41:5 45:25 46:4,17

47:17,18 54:21 59:6 60:2 63:1 67:26 71:11 77:1

96:17,18 124:16 140:10 157:16 204:22 205:22

274:11 286:16 296:14,17 298:23 314:21 321:8,26

332:4 334:16 337:5,16 339:3 350:4 354:23,25

357:24 360:26 369:14,21 400:11 428:20 476:9

566:26 568:9 569:16 573:14 597:1

evaluations 24:1 27:6 39:18 82:25 88:16 197:9

340:22 457:6

evening 162:14 287:2

event 58:16 59:4,24 77:20 97:19 259:13 421:3

444:15 519:7 525:13 543:21 549:23 553:11

events 28:12,12,16 55:23 58:15,18 59:3 97:24

163:8 209:21 232:9 246:3 269:21 322:22 457:16

519:15,17 525:14 530:9 552:9,15 553:17

eventually 55:15 226:26 597:9

event-based 550:20 552:12,14

Everglade 502:7

Everglades 175:26 176:1

everybody 6:5 17:3 69:7 124:1 341:26 360:5

414:22 477:4 499:15 579:5

everybody's 461:9 487:19

everything's 477:15 566:9

evidence 22:22 115:17 121:10 178:14 185:8 186:9

188:1,4,8,10,15 189:18,19 190:2 192:2,6,24

193:24 194:21 195:7,9 197:18,25 198:3,9,20,22

198:23 199:6,9 202:6,8 204:21 205:11,15 207:7

209:14 210:24,26 211:14 230:21 268:23 295:25

326:12,13 329:13,16 337:14,22,25 358:7,7,19

360:25 362:17,20 365:11,20 367:18,21,22 371:4

371:4,5,25 374:15,17,23 380:13,14 384:25

386:26 387:26 388:2,14,21 389:9 391:11 393:1

397:2 430:4,8 448:11,15,17,18 449:1,5 451:16

457:15,20 458:25 480:15 481:6 506:10 508:24

524:13 585:22 586:1,3 601:19,20

evident 97:15 180:8 190:18 405:24 471:15

evolution 586:20

evolutionary 94:8

evolve 306:8

evolved 550:8

exacerbate 504:23

exact 98:18 160:15 214:15,16 247:13,17 257:2

377:2 401:25

exactly 62:5,17,25 68:21 259:25 260:23 277:4

305:26 307:24 315:2 326:15 350:25 380:21

426:12 479:8 521:2 541:3 551:9 558:21 567:14

576:21 578:11 602:6

exaggerated 91:2

examination 49:18 378:13 398:4

examine 28:4 46:8,9 49:3 59:19 60:6 229:19 354:9

382:4 454:12

examined 31:19 32:12 46:21 50:26 56:6 401:24

557:19 563:2

examines 7:10

examining 33:13,17 46:20 94:3 386:10 564:18

example 26:9 33:23 54:23 62:7 75:14,26 76:3

80:20 83:20 87:10,15 96:20 101:24 102:20 137:6

139:6 144:24 146:22 185:1 193:6,18 194:1

203:25 209:19 214:8 225:7 232:19 234:12,17

236:4 238:2 241:3,20 246:21,24 254:12 255:11

256:7 257:1,4,26 259:12 260:3 265:16 280:12

302:16 344:24 345:8 358:24 361:4 364:9 395:18

398:3,7,18 399:3 403:19 405:2 413:16 416:22

419:18 423:18 440:21 443:6 464:10 465:13 508:3

508:11 514:22 520:9 529:22 534:15 557:8 560:9

566:25 571:22 582:6 602:9,12

examples 194:15 228:2 230:23 259:12 308:19,19

399:10 423:21 491:20 574:16 581:19

exceed 24:10 103:16 106:8 115:5 118:15 149:11,12

201:10 219:15 281:2 315:15,15 316:13 317:11,13

331:16 335:12 397:6 398:17 406:2 429:7 432:8

505:17 512:9 531:13 555:12

exceedance 85:23,25 86:8 104:8 105:13 149:9,16

150:1,16 151:17 152:21 154:18 160:18,21,23

162:6 217:21,21 219:23 244:8,14,16,24 440:4

522:5,6 572:19 597:19,22

exceedances 151:7 318:14 531:14 552:20 584:3,7,9

exceeded 154:13 219:12 440:24 517:20 521:19
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555:16 564:22

exceedences 584:13

exceeding 39:10 105:16,24,25 106:9 138:17 139:17

139:19 140:4 150:11,20,22 151:3,14,24 152:3,9

152:15,17 153:2,10,12,20,24 154:17 244:10

318:16,18 505:21 513:5 529:12 547:13 555:22

563:5 575:6 583:25

exceeds 85:18,22 152:20 153:15

Excel 293:4,20,24 294:13

Excell 141:14

excellent 186:17 308:19 328:21 364:1 399:12

430:5 491:6 559:9

exception 155:14 422:13

excess 170:1

excessive 431:7

exchange 136:11

excited 180:1 551:19

exciting 182:2

exclude 190:20 192:4 225:19 247:10 512:8

excluded 34:3,5,14 189:14,15 330:19,22

excludes 296:15 330:26

excluding 332:18

exclusion 30:13 190:14 467:7

exclusions 190:16

exclusive 163:26 356:2

exclusively 372:21

excreted 186:20

excretion 37:5

excuse 400:22

executed 509:12

executive 162:22 163:10 173:18 268:5,11 272:1,4,6

272:9,13 275:2 279:25 283:14 288:2

executives 173:17

exercise 11:8 48:17 219:22 243:11,19 349:23

453:12 475:9,11 516:24 541:26

exercised 512:13

exhaustive 400:11

exhibit 145:4

exist 14:12 185:25 382:15 404:15

existence 166:21 296:19

existing 303:1 385:25 390:2 404:4 414:19 416:19

424:10 449:18 508:20 546:23 547:8 572:13

exists 185:26 332:1 414:21 417:5

expand 211:2 373:18 399:15 457:17 548:21 561:9

expanded 561:8

expanding 175:11 234:18 547:25 556:18

expansion 164:2

expect 14:9 70:18 113:25 114:15,19 115:7 116:23

118:23 121:4 134:20 145:15,22 218:25 219:2

224:21 236:10 245:22 246:14 247:5 252:11 274:6

287:8 341:10 349:5,5 382:16 401:25 403:16

459:6 496:5 512:7 517:19 525:1,7 530:24 537:11

566:8,9

expectation 120:8 334:10

expectations 335:3 491:2

expected 36:12 40:5 46:15 50:20 77:6 82:8 86:2,13

105:4,21 107:16 109:14 143:23 144:5,23,25

145:1,3,4,16 146:9 152:26 154:18 201:1 209:1,1

234:10 274:22 316:4,16 327:13 377:15 420:25

434:10 444:1 445:18 450:13 458:5 494:19 495:25

507:23 513:14 514:7 515:5 518:12 581:6

expecting 493:16

expense 275:23

expensive 278:17 383:14,15

experience 70:1 288:13 381:19 402:23 433:10

463:13 483:13 496:14 508:23 554:21 576:8

582:23

experienced 443:1

experiment 75:14 76:1 77:9 202:24 233:1 252:12

333:25 351:4,6 442:12 465:19 507:7

experimental 77:11 82:22 133:5 192:9 198:24

206:18 226:20 232:24 234:20,21 238:19 331:15

331:17 335:18 340:17 344:22,23 345:2,5 356:19

363:11 387:4 398:2 404:26 409:25

experimentation 347:20 509:14

experiments 70:13 75:8 110:26 117:22 121:8

235:5 340:12 345:11 349:9 350:5 364:4 419:11

499:23

expert 460:2 591:11

expertise 6:7,12,17 7:3,7 8:1,4 181:8 394:25

432:22 587:23 589:11 592:11 602:4,21

experts 203:8 282:3 460:3 568:11,13

explain 17:2 135:23 228:18 346:15 450:22 484:9

539:17 543:9 548:16

explainable 192:21

explained 121:14 147:4 207:1,11 212:21 237:12

495:5 557:5

explaining 557:2 559:1

explains 504:4 548:7

explanation 361:11 561:1

explanations 356:5,12

explanatory 146:2,3 154:7,9 534:3 554:2 606:4

explicated 344:4

explicit 16:12 65:8 134:10 296:13 359:16 393:3

395:20 555:10 570:18

explicitly 56:11 331:6 356:11 395:4 402:21 415:26

417:5 530:6 555:19 587:14

explore 73:26 74:12 106:25 389:4 392:11 452:21

604:14

explored 507:22
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exploring 105:5

explosions 167:1

exponent 535:20 536:15

exponential 146:24 255:16 263:5 535:18,21,22

536:13,14

exponentially 555:23

exponentials 535:25 536:18 578:9

exported 171:10

expose 239:26

exposed 29:21 30:11 43:7 50:26 58:4 64:19 67:13

75:14 76:21 78:15 108:24 116:12 167:25 172:22

175:13,20 191:3 256:8 322:23 332:19 338:9,10

339:6 341:14,26 343:3,6,8,23 358:22 382:9

396:18 397:24 399:13 406:14,20,22 408:26 409:4

413:6 423:24 495:24 499:20 502:22,23 508:13

exposure 9:12 14:3,13 15:1,13 18:10 24:8 25:10

27:16,18 28:5 29:5 30:15 33:10 38:5 46:19,21

49:24 50:6,17 51:3,6 52:9 54:16 57:24 64:9,10,14

64:22,26 65:10 74:18 75:24 76:23 77:10 79:13

81:26 82:7,10 83:3,9,13,25 84:14 85:9,18,20,21

86:1,3,10,12,25 87:1 92:3 93:6,15 94:11 95:4,7

96:3,5,6,8,10,17,23 97:19,22 98:2,4,16 99:3

101:8,9,10 103:1 106:6 109:14 112:7,10,11,16

115:15 120:15,16 122:17,17,19 123:14,23 124:4

124:7,7,8,11,13,18 128:16 129:9,16 130:23

134:18,19,22 135:8,17 137:11,17,21,25 138:25

138:26 139:2,4,6,8 141:4 142:9,13,21 154:5,24

175:14 191:11 192:3,14 195:10 200:21,24 201:6

208:17 221:10,15 225:3,7,11,14 226:3 231:11

234:5 237:7 239:14,15,16,21 243:25 245:16,25

246:2,8,12,21,25 247:12,15 248:13,16 251:17,21

251:24,25 252:8,9,12 253:12 254:6 255:10,20,21

255:24,24 256:5,9,22 257:6,16 261:23,26 262:19

264:15,18 266:5,13 274:12,22 307:13,23 309:8

309:12 313:26 316:4,16 317:14 322:4,12 323:14

323:19,21 324:6 327:2,26 329:8 332:14 333:23

333:24 334:4 336:21 339:22 349:2 350:9,9,12

367:2,15 368:25 369:4 370:9 379:23 387:2

403:16,19 404:19 405:1,26 406:5,19 409:21,23

412:6 424:20 427:20,21 431:15 437:3 440:1,13

440:18,22,26 441:7,10,20 442:20 443:2,3,7,16,21

444:11,13,17 446:8,19,20 447:4,23,24 449:8

452:18 459:17,18,19,21 461:16,19 462:5,10,13

463:6,8,10,15,18 473:5,5,14 486:14 494:19

499:18 500:1,15 502:1,4,19 505:26 506:4,24,26

509:6 510:23 513:20 514:1,12,17 522:4,17

531:17 580:16,20,26 581:11,13 597:15,20 600:22

601:8

exposures 24:10 45:12 49:19,20 50:26 64:12 80:23

81:3,9,17 82:9,16,23 83:3,5,14,20 84:5 95:24

96:18,26 98:13 99:9 101:5 106:2 113:1 114:22

116:3 122:17 125:10,17 129:3 130:8 134:11

135:11,15 191:2 192:13,20 213:20 221:13 267:17

307:14,22 308:8,23 321:22 368:24 402:8 406:18

407:13 408:4,11,12 412:7,8 413:11 422:21

427:17,18 430:12 443:12,23 445:21 449:11,20,21

457:26 462:25 491:21 495:22 496:1 502:12

503:18 507:10,12,14,15,21 508:4,16 509:1,7,8,10

512:14 514:1 599:15,18

expound 66:6

express 145:10 280:4

expressed 21:25 45:2 86:11 190:12 215:26 216:24

277:1 408:19

expresses 376:1

expressing 130:16 375:23 376:5

expression 55:1 201:11 322:5 386:22

exquisitely 372:20

extend 339:5

extended 94:20 96:23 130:14 146:15 601:26

extending 116:3,4 441:4

extends 122:14 285:5 343:2,5

extension 94:1

extensive 21:15 136:24 139:7 143:12 208:3 267:10

269:22 522:1

extensively 375:7

extent 14:1,25 55:11 136:26 144:26 145:11 150:1

158:9 229:11 234:8 235:3 317:1 331:11 350:22

358:23 372:7 388:17 396:16 409:3,17 427:22

483:25 496:24 498:6 505:20 522:3

external 187:14 399:4

extinct 163:21 274:6

extinction 163:19,22 165:17 166:6 167:3

extinctions 437:18

extra 135:22 171:4 181:13 254:19 278:14 408:18

extracted 39:3 48:16

extracting 596:5

extractions 421:17

extramural 391:5,7

extraneous 345:4 383:12

extraordinaire 181:12

extraordinary 321:15

extrapolate 56:3 81:17 82:6 220:20,24,25 224:13

306:16 419:22 529:17

extrapolated 131:7 188:12

extrapolating 93:15 101:4 137:15 434:16

extrapolation 24:9 125:15 131:8,10 350:11 441:22

441:23,25 529:16

extrapolations 92:2

extreme 174:11 241:22 283:9 401:26 402:15

413:16 460:21,22 519:17 549:23
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extremely 125:14 140:16 167:15,21 169:23 170:7

170:11 173:10 174:9 180:13 235:21 264:24 420:6

422:16 500:8 592:13 593:23 594:9

eyelash 562:20

e-mail 72:7 294:22

e.g 266:20 267:23 506:26

F

fabulous 603:4

face 17:10 163:25 164:1 233:7 252:26 497:2

faced 441:6

facilitate 256:5

facilitated 506:22

facilitates 254:3 255:6 260:13 264:16 343:16

facilities 288:23

Facility 232:22

facing 10:23 491:5 574:8

fact 17:18 110:1 111:10 114:10,21 127:24 178:2

180:8 185:22 187:19 189:9,23 197:11 199:7

200:23,26 201:7 203:18,22 204:23 211:8 227:18

232:11 233:20 237:15 246:20 247:8,23 248:17

258:5 269:5 275:17 281:9 282:26 299:12 302:7

302:21 306:15 308:5,24 310:7 311:19 316:26

319:6 376:4 388:4 400:21 405:8 431:1 438:17

446:16 449:2 457:22,26 463:1 480:19,22,25

483:1 493:14 497:9 513:19 535:3,7 550:1,2 556:8

593:14 606:26

factor 38:10 53:10 85:25 86:8,9 90:7 113:1 131:23

131:26 135:7 138:9,9,13,15,21 139:24 141:8,11

141:13,16 142:3,7,8 144:9,17 145:12,13,13,14,16

145:20,21 146:4 147:2,12 148:4,7,9,13,16 149:1

149:2,3,5,7,13,15,16 150:3 151:2,25 152:4 153:9

153:11,18 154:10,15,20 156:10,26 157:3,11

158:20 159:19,21 160:2,4,4,8,20 185:22 217:21

217:21 219:23 284:11 311:10 330:17 335:21

349:20 423:5 469:22 472:17,24 499:20 505:17

512:9 513:13 514:12,17 515:4,7 516:12,15 517:2

517:5,7,20,23 521:26 522:23 523:1,2 524:15

525:2,6,9 529:15,23 532:7 533:10,12,13,15,19

534:23,24 535:1 536:24 537:8 539:24 540:21,26

545:9 549:12,21 550:2,26 551:3,12 558:2 561:22

561:23 563:6 568:13,15 570:2 571:5 577:26

579:3,3 584:13,15 589:2

factored 427:23

factories 165:21

factoring 131:9

factors 15:10 25:23 26:8,9 28:25,26 34:14 35:6

37:3,6 42:23 60:5 69:17 75:1 95:21 98:24 104:8

113:6 137:20 138:4,7,18,20,22 139:14 143:1,16

144:23,25 145:2,9,10,19,25 146:2,7,8,10,12,14

146:17,18,21,22 147:4,7,10,23 148:26 150:23,26

152:2 154:8 155:26 157:25 159:5 179:26 182:6

244:8,14,17,24 267:18 269:16,17 281:24 283:2

309:23 338:1 339:10 345:5 353:14 354:7 382:4

382:12 383:12 387:3 396:24 397:17 422:8 444:2

444:14,17 451:4,5,7 453:7,15 469:18,19,20,26

472:20 477:26 513:2,4,7 514:4,7 515:12,21 516:2

517:1,4 518:11 519:2,3 521:3,19 522:24 523:4,12

523:12 524:3 530:8,26 531:23 532:20 533:5

534:12 535:6 537:9 538:11 539:11 540:1,16,22

541:9 542:23 543:13,14,19,25 544:1 547:23

548:3,16,24 549:9,13,17 550:10,14,22 551:17,17

552:7 554:3,6,15 556:12,15,16 558:6 563:12

564:21 570:18 573:26 576:18,19,23,26 577:23,24

578:1,2,5,8 579:2 589:4

facts 48:18 304:20 307:15 440:17 475:9

fade 176:25,25

fail 173:4 269:11 346:17 402:2 417:21

failed 110:11

failing 331:26

fails 110:13 291:1

failure 41:5 563:3

fair 64:11 475:1

Fairchild 7:12,13 116:10 261:6 396:20,21 412:23

415:18 416:4,15,25 419:5 422:2,18 424:6,17,17

426:3,19 427:1,7,11 428:1 434:18,19 453:6

455:20,20 474:20 488:7 489:5,20 494:21,22

495:17 496:13 498:23 510:2,3 511:16 543:2

580:4,7,8 587:19,20

Fairchild's 410:15 459:12

fairly 16:17 156:19 212:14 225:2 249:6,7 326:9

348:8 349:23 378:10,11 394:6 423:2,4 430:10

496:15 535:6 552:26 574:23

faith 441:18

fall 265:11 337:24 420:13 459:8 522:9 539:13

falls 242:6 258:21 339:17 543:23 597:3

false 210:14 318:11,20 337:12 338:15,15 339:24,24

564:16 565:11,12 566:17,18

false-positives 337:13

familiar 421:14

families 16:16 114:26 330:5

family-held 288:25

far 110:1 115:5 116:6 125:11 132:2 133:10 159:1

163:14 173:1 199:6 200:24 207:4,21 209:5

238:25 291:1 300:15 306:2 313:22 326:18 336:19

368:7 406:2 412:18 449:20 460:19 464:26 466:10

501:17 523:16 526:12 537:12 541:24 542:16

545:8 558:5 559:16 590:24 592:11,24 594:15

598:5

farm 168:16 175:13 183:11 268:21 269:6,8 278:8

288:9,12 301:26
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farmed 164:22

farmer 278:11 282:8 288:25 291:22

farmers 168:24 169:16 268:25,25 271:12,22

272:18 273:3,5,23 274:15 275:9,9,17 276:7,10,15

276:20,21,23 277:12,18 278:8,17,23 279:2,8

280:6,11 283:19 288:18 290:2,7,19 292:1 295:9

295:15,18 302:16

farmer's 279:23 283:17

farming 169:14,15 280:14 289:25 290:15 295:22

farms 271:10 274:5

farm-raised 174:18

FARRAGGIA 117:1 118:5,22 119:11 123:11

126:7 128:17 129:12 132:6 161:24

Farruggia 20:6 25:5 34:12 72:11,16,17 85:11

101:1,3 109:20 111:6,14 114:2,7 115:6 120:26

122:1,8 136:3 215:19 293:18 413:20 415:23

417:4 439:16,16 447:11 448:3 453:16,16 465:6,8

466:14 472:14,14 474:9 488:22,22 489:26 491:17

493:12,25 511:9 512:20 543:1 580:4 584:18,20

FARRUGGLIA 135:24

farther 429:5

fascinating 127:8

fashion 89:22 92:15 217:6 302:18 331:20 347:8

395:20 449:8 450:9

fast 201:14

fate 2:9 7:15 8:12 13:1 15:7 19:26 20:20 27:2

72:18 136:22 137:19 138:5,26 139:1 159:2 268:7

284:7 292:21 293:18 352:13 397:2,24 399:12

414:12

fathead 175:8 322:22

fatheaded 378:3

father 288:10

fault 178:7 205:8

favor 166:12

favored 40:19

fear 46:18 56:9 246:13

fears 432:12

features 100:17

fed 496:26

federal 2:3,13 3:4,11,15 5:12 11:10 113:15 136:26

149:19 155:5 172:8 175:5 176:15 177:16 181:12

454:11,16,16,19

feed 170:2

feedback 14:17 24:25 106:23 112:25 114:18 134:2

201:7 204:3 340:26 355:20,23 361:24 365:8

372:11 383:17 385:16 417:8 550:5

feel 100:12 184:11,21 185:12,12 190:21 307:24

316:5 318:15 394:21,22 395:18 400:9 404:10

405:6 416:13 486:6 487:24 489:12 493:13 496:18

497:5 502:19 563:25 564:9 585:10

feeling 371:7

feels 305:22 392:8

fell 49:15 565:18

fellow 594:24 598:21

fellows 408:19

felt 131:7 307:2 349:20 355:2 458:1 500:7 527:22

female 31:16 68:15 175:3

females 176:7

female/male 68:17

feminization 358:13,14 369:19

feminizing 167:25

fertility 168:2

fertilizer 288:19

fertilizers 288:21

Fetscher 7:16,16 110:23 111:11 249:25 250:6

408:13,14 411:5 432:14,15 455:22,22 490:4

501:10 509:22,23 587:26 588:1

fever 166:9,10

fewer 82:8 105:25 146:9 148:8 322:22 383:16

410:6,7,26 532:26

field 22:20 29:8,11 33:24 34:13 38:4 41:13 57:13

57:19 82:12 85:18 95:24 97:13,15 98:5 111:26

113:19 116:17 119:25 129:9 131:7 162:5 182:2

244:7,7 264:26 272:24 273:23 274:14 275:10

276:9 278:1 308:8,8 327:20 332:9,16,18,18 335:1

335:7,9 343:11 344:24 346:9 353:25 354:4,7,9

368:18 382:3,6 385:3 387:1 404:22 421:1 430:17

430:18 439:5,10 442:12 449:5,11 452:3,5,7,8

473:6,12 482:22 485:4,10 506:13 517:26 575:6

587:15

fields 297:2

field-based 321:5

field-collected 52:11 57:18 342:20,24

FIFRA 1:1,8 2:2,25 3:8 11:10 12:22 76:15 112:4

137:22 138:1 139:10 141:14 152:23 183:5 234:22

278:25 454:11,17 455:4 513:1

Fifteen 78:5

fifth 142:2,5 516:17

fifty 277:8,14

figure 9:22,26 11:4 12:17 14:2 80:23 81:20 89:11

90:6 102:14 115:13 118:12 125:22 127:12 128:18

133:11,13 134:5,12,17 162:7 243:5 319:4 355:23

356:22 357:4 376:24 401:12 405:21 436:19

466:26 535:17 537:13,20 564:26 605:10 607:9

figured 564:26

figures 42:16 101:13 377:24 428:19

filamentous 75:19

file 293:9

filed 3:16

files 294:13 320:20 606:6
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fill 185:3 404:15 434:13,15 548:19 562:9 589:15

filled 75:19,20 502:3

filling 149:24 518:2

fills 171:5

filter 76:20 426:13 520:4

filtered 437:3

filtering 353:23 426:11 450:20,20 511:14

final 25:26 29:11 80:10,13 84:21 98:25 99:16

106:13 122:9 193:14 217:15 259:5 300:12 357:24

404:7 405:7 416:3 435:20 454:12 469:16 573:18

584:24 585:6 587:3 596:25

finalize 404:7

finalized 490:22

finally 16:8 27:9 167:4 182:5 233:4 238:12 313:9

348:23 398:16 520:16 582:22 593:18 594:7

financial 3:17,22 163:13 173:26 394:26

find 117:21 162:6 171:20 173:9,11 177:9 193:11

221:26 235:2 247:23 258:9 277:18 278:18 293:9

317:26 322:19 324:4 325:9 334:15 341:21 432:26

449:13,14 554:4 600:20,21,24

finding 248:2 255:1 289:2 323:25 347:22 465:20

findings 423:12

finds 377:2

fine 130:24 210:22 269:8 308:18 311:17 321:3

332:26 360:9,20 366:7 425:4 433:22 470:23

484:20 529:24 530:3 549:3 570:13

fine-tune 587:6

finish 73:3 240:14,17 456:19 461:6 466:17 489:25

finished 226:17

firm 299:23 452:22

first 2:5,16 9:2 11:7,15 17:26 20:18 24:20 27:5

30:15 32:22 46:23 49:17,20 81:8 82:25 86:25

90:13 97:17 99:18 121:17 122:10 141:17 148:16

162:23 182:11 186:23 188:17,20 200:20 213:7,26

215:22 216:5 217:24 218:4 219:9 227:10 228:20

232:7 237:5,6 243:4 254:1 273:7 281:26 285:13

297:11 301:9 305:20 317:22 319:2,13 320:24

324:10 327:18 342:14 347:18 351:6 355:11

357:10,11 359:13 362:15 397:10 407:25 411:15

413:20 422:22 426:26 429:8 431:23 435:10 442:4

446:5 452:1 456:24 464:7 466:9 482:24 511:16

540:13 551:16 589:17 596:18 598:24 605:26

606:16

first-order 537:7

fish 21:21 36:4 66:3 109:17 117:16 166:13 175:24

178:5 187:25 188:25 194:16 195:1,4,11,20

196:23 197:17,19 198:1,11,17 199:4,6,17 200:10

200:12,15,25 201:24 202:7 206:3,6 211:25

234:23 274:9 277:9 315:26 322:11 352:24 360:21

368:18 398:16,17,19 402:11,11,12 506:8 591:11

591:12 597:8

fisherman 591:16

Fisher-Tippett 258:9

fishes 204:7

fishing 591:15

fishless 164:19

fish/aquatic 381:23

fit 77:22 110:10 128:15 146:22,25 147:1 243:10,14

257:25 314:8 321:24 410:4 440:13 460:2 535:16

536:11,23,25,26 537:12,17,17 542:14,15 563:22

587:4

fitness 45:24 46:6,8 202:14 323:5 342:6 482:19

483:2

fits 113:17 128:12 164:26 376:16 529:7 563:15,25

564:2

fitted 102:18

fitting 336:12 470:12,20 473:4 536:18 540:2

572:25 578:19

five 264:4 270:26 301:11 355:20 383:25 401:10,10

421:4,5 422:5 450:4 500:24,24 518:6,7 535:4

583:20 591:26

five-fold 401:11 433:8

fixation 187:3

fixed 75:15 597:21

flag 364:10 433:12 445:12

flagged 463:19

flash 294:20 320:19 528:25,26

flat 457:22,25

flawed 206:22 222:26 242:10 324:23 504:18

flaws 37:2 48:1

Fleckner 237:22 238:6,6 270:14

fletches 360:14

flex 562:14

flexibility 16:23 288:4 292:9 354:8

flies 166:8,13

floating 257:6 515:10 597:16

flood 144:4

Florida 8:18 175:25 302:16 456:10

flow 47:7 69:21 72:25 117:14 143:23 145:25 146:3

154:9 156:2 159:22 222:14,14,17 259:12,15

265:18 282:2,14 291:6 360:22 361:7,8,10 405:14

442:8 511:17 525:22 526:26 527:18 529:8 534:5

534:14,18 535:11 549:7 551:21,22,25 553:10,11

553:17,26 556:11 560:10,11,11 577:8 578:2

579:23

flowchart 574:18 579:5

flowing 175:17,23 281:10 402:24 515:13

flows 175:26 516:25 519:1 548:17

flow-through 29:1 30:6 38:22 45:8 77:15 412:3

fluctuations 414:4
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fluorescence 421:16

fluorometry 256:15

flush 414:1

flux 498:13 579:16,23,26

fly 269:10

focus 8:11 10:8,10 11:5 17:24 18:15 21:3 24:18

26:14 66:16 74:6 160:16 188:24 189:7 195:5

217:4 280:26 392:17 394:11,21 396:22 410:16

439:24 499:24 501:25 502:16 517:24 523:9

556:24 579:26 591:20 594:5 597:10 600:20

focused 9:17 78:24 159:3 281:14 292:6 349:11,14

392:10 398:8 400:7 407:20,24 413:3 432:17

499:11,23 557:1 593:26 602:18,25 603:1

focuses 9:14 296:17 298:18

focusing 21:19 24:15 65:24 66:20 148:20 194:17

213:16 223:26 283:9 432:21 452:25 515:15

596:22

Fold 191:13,20

folks 178:25 376:21 381:19 432:11 433:11 490:10

500:5 588:6

follicles 190:16,23 191:5,10,14,26 192:3 195:4

follow 35:10 62:14 71:6 159:15 208:26 251:15

349:9 364:17 367:13,20 445:20 446:22 481:2

483:14 495:16 542:9 560:26 586:4 587:1 602:6

followed 2:14 35:2 76:4 226:21 297:12 337:1 364:5

606:13

following 23:26 39:6 50:8 51:2,7 72:23 77:2 82:4

104:23 205:26 209:25 268:9 272:11 275:4 280:1

288:11 322:24 323:19,21 328:2 336:25 397:26

425:17 430:2,13 435:16 454:20 511:13 513:3

follows 449:7 584:10

followup 26:4 64:25 110:15

follow-up 126:21 227:14 305:5 421:8 448:3 474:9

491:17 512:21

food 21:23 74:9 164:8,13,16 165:19 166:13 168:12

168:22,25 169:16,17,19 170:11,12 171:12 173:3

174:16 177:18 272:21 280:10 291:20 325:13

328:16 333:22,24,25,25 339:4 342:21,22 429:17

507:2 599:23

foods 168:23 170:11

foothold 498:3

Footnote 298:5,7,9

footprint 280:16

force 314:2 425:24

forced 427:16

forefront 183:23,23

foremost 359:13

forest 165:5

forests 164:3 170:5

forever 601:25

forget 351:1

Forgive 450:7

forgot 2:26

form 4:19 25:2,6 166:26 170:4 188:22 210:26

257:14 279:7 293:15 333:1 348:19 359:21 399:23

536:8 582:6,7 600:7

formalized 189:20

formally 205:16

format 211:6 558:12 596:3

formation 27:17 480:1

formed 45:15 59:11 268:15 281:16 352:26

former 489:17

forming 28:21

forms 582:1,11 600:8

formulate 12:16 390:2

formulated 4:11 87:18 92:13

formulation 1:4 2:7 6:2 10:10 11:16 17:10,13

20:14,18,23 24:15 25:26 32:11 37:25 65:22,26

66:11 92:20 93:17 133:3 138:24 139:15 180:15

268:7 284:6 306:8 408:9 454:13 534:9 604:3

formulations 33:18

forth 12:4 395:12,16 428:6 440:4 468:6 538:19

562:9 571:10 603:23

fortunate 188:19

fortunately 173:14 341:14

forty 268:16

forum 128:4

forward 5:15 10:5,12 11:1 13:11 14:20 15:15,16

16:26 17:4 19:2 20:2 117:3 145:23 162:20 178:25

181:1 189:17 213:7 223:22 282:12 287:25 294:24

302:25 365:6,11 373:22 374:16 384:26 392:20

394:2 404:3,10 406:15 412:5 425:22,23 452:7

480:14,17 512:17 534:1 575:17 589:16 595:25

604:8

fossil 163:23

found 62:23 79:2 88:20 100:22 153:24 191:12

203:21 206:1 209:24 231:24 244:19 246:4 257:5

257:23 261:25 262:25 281:1 285:26 301:8 322:2

323:7,11,13,26 324:13 352:4 369:15,22 377:18

377:25 378:9 379:2 400:3 423:25 431:1 445:19

471:1 505:7 549:11

foundation 10:11 19:2 35:2 82:23 131:5 132:25

134:23 538:17 598:14

founded 295:7

founder 162:22 163:10

founds 341:25

four 46:11 270:11 277:11 334:24 344:19 355:20

366:9 403:13 428:18 475:23 515:2 516:16,21

517:12 518:22,25 535:16 536:18,18,25,26 537:1

551:1 584:9
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fourth 141:26 270:9 308:15 519:9 535:18

four-day 554:9,12 560:7,12,16,20 571:23

four-stage 432:16 436:12

four-step 438:11

fraction 98:3 521:7

frame 9:21 97:8

framework 9:23 19:20 20:15 24:13 26:25 84:26

139:20 160:20 341:23 531:15 600:17 601:2,13

frameworks 601:21

France 164:15

frank 20:6 25:5 72:16,17 101:3 111:6,14 115:6

117:1 118:5,22 119:11 120:26 122:1,8 123:11

126:7 128:9,17 129:12 132:6,24 133:16 134:1

135:21,24 136:3,12 156:4 161:24 293:17 413:20

414:14 415:23 417:4 439:16,16 447:11 448:3

453:16,16 465:8 472:14,14 474:9 488:22,22

489:14,26 491:17 493:12,25 512:20 584:20

606:20

frankly 356:8 394:3 448:16

Fred 607:5

free 487:24

freedom 570:6

frequencies 13:4 142:19 147:10,12 149:25 152:1

509:6 514:15

frequency 13:3 49:23 51:10 137:26 139:12 140:24

142:24 144:24 150:8 151:21 152:6 154:7 158:5

158:21 267:18 417:1 440:3 472:23,24 514:20

519:10 525:12 533:10 550:13 552:26

frequent 26:10

frequently 89:1 575:16

fresh 79:3 81:18 198:12 212:22

freshwater 107:11 410:5

freshwaters 434:5

FRllvorgangs 270:19

frog 164:14,14,21 165:15 166:3,5 175:3 176:19

187:14 301:18,22

froggy 301:14

frogs 22:21 53:3 162:23 163:5,7,11,14 164:8,13,16

165:1,3,4 166:7,15,23 167:4,4,5,6,6,9 168:6

176:7,18,21,24 177:1 190:24 238:18 269:8

273:25 274:9 286:26,26 296:26 301:8,17,19

368:9 508:9 585:21

front 176:10 184:7 577:3 595:8

frustrated 273:5

frustration 277:1

fuel 171:1,2,4,8 280:10 295:23

full 26:21 33:23 174:6 273:24 369:7 370:1 420:18

421:2 495:6 541:26 551:20 561:13

fullest 522:3

fully 38:6 84:17 100:24 178:12 212:24 262:8 325:2

368:13 412:3 475:23 520:10 572:8

fun 72:14 301:13 598:21

function 3:3 21:16 45:26 66:9,12 74:3,9 75:22

78:12 83:25 87:14,15 107:15 108:20 109:5,13

113:3 114:1,20 146:13 179:26 181:23 193:2

195:18,22 196:13,17,18 197:7,9 198:17 199:11

200:20 201:3 202:7,13 212:11 258:6 259:7

274:21 317:13 322:4,5,6,13 351:3 385:26 386:4

387:14,24 390:3 397:12 399:10 452:15 486:4

492:12 494:18 507:4 508:18 524:3,9 537:9

543:16 561:12 581:14

functional 73:24 75:19 76:6 397:19 502:2,10,13

functions 58:3

fundamental 35:2 220:21 275:12 372:15 441:5

477:24 485:11

fundamentally 214:14,26 508:11

funded 391:9

funding 391:5,7

funds 171:22,25

Fungicide 11:11

fungus 165:13,14

further 19:19 22:22 24:2 26:11 27:15 31:1 33:5,9

33:26 34:2 35:13 43:25 44:26 47:17 88:4,14,16

99:15 174:3 184:8 201:22 220:9 270:10 271:14

306:6 314:21 318:19 322:18,19 323:23 325:26

339:3 345:16 348:21 358:2 365:11,22 368:9

370:18,21 379:6 386:18 387:11 388:5 389:4

391:23,25 392:2 399:17 424:12 425:20 434:19

439:9,21 450:15,17 451:16 452:3 453:7 468:17

468:17 480:20 486:19 488:11 501:25 504:26

542:2,5 543:21 544:5 556:19 568:1

Furthermore 175:15 307:4 337:10 338:9 482:18

future 12:3 13:18 28:23 172:13 185:4 279:24

351:20 381:18 498:14 503:5 572:13 595:6

G

G 282:26

Gage 281:3

gain 20:9 87:10 172:8 508:25

gained 499:9

gaining 570:7

gains 87:9 278:6

gallon 171:3

game 133:19

gap 237:12 544:9

gape-limited 164:25

gaps 404:15 434:13,15

Gardens 301:3

Gary 272:3 275:2,5 287:11

gather 594:24

gathering 493:20
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gauge 123:4

ge 177:21

geared 65:21 498:14

gene 54:26 200:13 322:5 386:22

genealogy 375:26

genera 432:23,24 437:12

general 3:20 6:18 19:22 23:3 26:25 29:13 69:6,12

69:20 83:11 122:9 190:12 193:2 194:9 273:25

318:15 350:12 374:8 392:12 410:14 411:20,26

458:18,26 496:3,4 499:6 520:8 565:2

generalization 50:17

generalizations 196:21

generally 39:7 49:21 95:5 108:18 113:11 116:18

143:3 145:10 154:13 197:23 252:5 265:8 278:15

305:16 318:5 397:5,6 407:17 431:20 538:15

582:1 604:12

generate 142:6 148:18 149:5 156:23 327:21 349:11

538:1

generated 98:21 131:18 146:7 179:19 228:21

367:21 407:9

generating 159:5

generation 285:23 433:18

generations 143:2

generic 71:9 87:18 88:10 375:7,21

Generically 315:22

genes 55:2 201:11 386:22 503:4

genetic 373:14,24

genetically 373:13

genetics 376:5 378:23

genre 236:1

gentleman 213:5

genuine 292:7

genus 215:24,26 216:11 235:24 258:1 266:22

481:16

geo 152:12

geographic 137:12 144:1,7 152:8 153:19 176:13

363:2,2 468:16 515:25 572:6

geographical 153:1

geographically 144:14 545:12

geography 518:8,10 524:18 535:5,12 562:19 572:9

573:20

geological 7:13 8:14 175:6 455:21 456:8 576:2

geometric 215:25

geostatistics 8:12

geo-located 367:24

German 237:22 238:7 270:14 400:21

Germany 169:10

getting 17:23 61:12 92:21 101:11 112:26 114:17

125:18 133:16 136:10 155:4 164:2,3,3,6 208:4

220:10 274:11 317:3 318:23 319:12 355:18

358:22 364:12 379:18,19 388:8 393:13 409:24

412:21 413:26 418:26 419:1 421:14 470:9 484:5

498:17 552:10 564:25 565:23 566:10 568:11,13

575:23 585:24,25 586:13 588:7 599:8

GfK 289:6 290:15

Gibba 257:7

Giddings 181:26 215:3,6 222:7 223:5,6,16,21,22

226:10,12,13 240:25 242:15 243:12,23 244:12,13

247:18 248:8,20,24 249:9 250:2,9,19 251:1,8

252:2,19 253:15 257:23 258:14 259:3 307:21

400:12,19 427:4 457:23 583:14,22

Giddings's 188:21 240:19 241:13

gill 322:5,13

Gilliom 8:13,13 60:23,24 61:4 62:1,11 68:25

319:22,23 424:7,8 425:3,15 439:22,23,23 456:7,7

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554:18 556:5,20,21 561:19,20 562:3 565:25,26

570:22,25 571:3 573:9 579:13 596:13,14

Gilliom's 570:23 571:2

girl 301:13

GIS 367:24 531:8 558:11

give 16:6 62:26 99:18 133:7 137:3 160:8 214:7

228:1 229:9 230:23 234:2 239:16 249:14 252:12

255:11 284:2 294:12 319:1 332:24 347:23 365:18

375:1,12 388:20 417:13,14,20,23,24 472:5 498:3

501:19 521:3 528:26 550:5 561:6 575:1 579:24

582:25 596:24

given 12:5 15:11 41:21 42:20 44:14 46:6 49:7

61:25 114:21 124:10 139:7 156:15 163:8,15

178:1 194:8 216:15,18 228:21 261:15 269:4

273:9 274:7 281:11 299:23 314:18 316:7 332:19

334:8 344:5 347:7 349:24 362:1 366:22 372:11

373:12 378:19 379:23 386:13 390:20 396:14

405:23 407:26 428:23 430:9 432:11 433:23

441:20 442:20 443:2 444:15,17 471:14 488:13

501:12 503:14 504:6,23 505:11 508:19 522:24

539:2 541:6 544:5,17 563:10 580:25 583:8 598:5

605:2

gives 16:26 118:16 133:1,23 154:10 157:12 247:16

317:1 331:2 445:6 492:19

giving 27:5 85:12 434:26 447:8 540:8 560:13 604:4

605:7

gizzard 402:12

glad 424:17 524:18

gland 6:19 197:1

glaring 500:15

glean 545:7

gleichmD5ig 270:18

Glen 181:21 195:15,16 203:16 204:6,12,19 208:7
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208:15 209:5 212:3

global 126:4 180:2

GLPs 401:6

glucose 322:12

glue 176:24

go 6:9 12:4 13:11 15:16 16:26 17:4,8,13 18:13 19:8

21:10 64:15 71:11,16 105:19 111:7 113:19,23

115:11 116:5 120:15 125:12 128:5,6,13,16 129:3

131:13 136:14 156:5 160:18,20,23 162:14 165:21

169:5 177:5,13 182:8,11,22 189:11 195:6 199:12

202:18 213:6 215:20 223:16,20,22 225:8 226:9

227:10 229:1,1,8 231:1,4 233:7 236:18 239:1

240:16 247:12 248:19 253:9,20 260:20 273:12

274:14 287:1,24 293:10 316:2 322:25 326:8,11

352:1,5,9,17 357:9 359:4 360:18 363:18 365:26

366:11 371:16 377:10 380:5,13,16 384:12,14

385:9 389:22 396:2,9 405:4 407:13 408:17 418:9

418:24 419:12,23 421:22 426:6,16 428:3 433:14

435:13 439:10 440:12 442:14 450:7,9 452:2

455:6 458:26 459:4 461:7,12 465:2 466:17,19,21

466:22 468:26 471:4 475:11 476:19,25 477:6

478:18 480:17,22 483:9 487:1 491:9 493:6

494:10,24 496:12 502:8,9 511:4 512:17 519:10

521:15,17 522:7 529:23 531:17 532:11,25 541:24

542:7 543:6 544:26 546:12 547:20 548:22 549:18

551:13 552:21,25 553:7 555:23 556:7,18 557:9

557:21 561:9 564:23,24 568:21,25 569:26 575:12

576:25 577:13 578:1 579:1 580:5 584:23 585:6

585:13 587:6 588:20 592:9 595:4 600:24 601:8

603:9,12 604:6 607:15

goal 12:24 14:7,18 19:16 20:4 82:5 304:17,18

315:21 343:26 348:20 382:6 397:22 409:9 421:9

522:22 523:7

goals 11:4,9 73:25 278:4

goes 60:25 70:23 122:10 129:19 157:19 165:5

166:20 170:9,25 194:25 337:17 341:18 342:2

392:6 400:19 403:22 413:21 425:1 440:22 445:16

451:13 474:14 538:17 559:17 564:20 578:24

596:25

going 5:24 9:20 10:5,10 11:15,21 19:15 20:5,26

25:16 66:11,17 67:1 69:19 70:14,19 71:16,21

72:7,12 111:26 113:6 114:21 115:24 117:14,14

117:15 118:14 121:25 123:5 124:12 125:1 128:7

129:17 133:9 135:20 136:5,10,11 159:5 161:23

162:9 167:18 176:17 177:24 178:23,23 183:1

195:17,19 196:3 199:21,23 200:15 201:19 202:8

208:3 213:6 215:18 223:16 236:19 237:8,10

245:20 251:2 253:18 259:11 260:25 262:2,15

263:9 272:26 287:25 290:18 294:2,2,3 301:5

302:19 304:5,21 305:4,6 315:9,12 316:15 317:6

317:19 319:4 321:13 333:4 334:14 335:14 338:19

340:26 343:20 346:4 351:4,11,20,26 357:14

360:26 362:21 363:5 364:16 365:19 370:7 374:2

374:3 378:22 380:5 383:18 389:13 392:16 394:13

395:5,18 396:5 400:14 401:13 404:25 407:3,10

407:11,22,25 408:16 410:8 411:10 412:9 415:8

417:12 419:22 420:16 424:24 425:8 429:19

433:17 435:19,19 436:16 437:23,25 438:4,6,12

440:25 441:18 442:8 449:26 450:12 452:7 453:4

454:24 456:20,22 457:8 458:24 459:8 461:1,3,5,6

461:6,7,9 465:26 466:1,18 468:23,24,26 469:14

469:17,20,22,23,25 473:3 474:5,21 476:18,19,21

476:25,26 477:1 484:6 492:4,5,14 494:7,11

495:17 500:4 501:18 502:12 515:22 517:8 519:10

521:8,11,12,17,18 523:6,9,10 525:7,25 526:17

528:16,21 530:23,26 531:4,20 532:17 533:19,26

536:20,25 537:6 538:8,9 540:10 541:24 543:6

545:18 547:4,6,8 548:14 549:15,17 552:5,7,8,16

554:7,13,13 555:16,22 556:6,12,13 557:9 558:1,2

559:7,16,24 560:9 561:9,10 562:26 564:24 568:6

568:12 569:14,15 571:16 573:14 574:19 575:1

576:13,22,22,25 577:1,1,13,17 578:18 579:6,7,20

589:16 591:20 592:22,25 594:23 595:18 596:7

597:15 598:11 599:26 602:7 604:6,8 605:9,12,16

605:18,20

gold 397:1

goldfish 203:21

gonad 206:3

gonadal 22:23 23:8 27:16,18 30:17,18 31:16 46:1

54:9,12 67:26 68:21 323:7 508:2

gonads 28:1 67:25 167:25

gonna 377:20 379:22

good 5:26 6:23 7:5,20 8:5 19:23 27:1 71:14,24

110:14,25 117:1 131:22 134:12 136:16 146:17

155:16 191:12 213:8 219:5 232:18 238:19 252:2

252:19 261:21 272:12 294:26 301:24 312:21

314:3 315:3 325:10 336:11 342:21,22,22 344:22

345:10,15 346:13 358:3 359:7 360:5,9 362:13,14

363:19,21 371:11 376:1 383:26 384:7 394:6,7,7

394:13 396:9 404:23 411:12 415:20,24 417:15

425:25,26 426:1,5 429:14,17 431:1 434:26 451:8

454:5,9 456:15 466:17 467:9 469:7,13 472:19

473:21,23 475:9,11 477:7 481:2,2 484:3 491:16

492:8 501:1 534:26 540:8 551:23 552:2 553:18

553:20 558:22 559:3 560:19 564:9 566:5 567:9

567:12 571:24 573:13 577:16 595:2,12 597:3

603:22 606:11

goodness 110:10

goods 288:18

gotten 176:16 501:24 520:22
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governs 298:10

grab 143:5

gradation 371:7

grade 37:25 371:11 417:13,20

graded 218:10 273:8

grades 417:14

gradient 265:8,19

grading 371:2

gradually 165:26

grafts 39:9

grain 268:12 272:14,18 273:3 280:8

gram 30:7 39:10,11 398:19

grams 398:17

grand 17:22,24 176:17

grandchildren 177:10

graph 42:19 50:8,14 51:7 52:4,20 53:2,5,18,20

54:3 61:8,10,15,19 62:11 98:12 101:18 123:20

128:14 130:15 146:6 158:12 193:12,18,23 194:19

198:3,19 205:20 206:1 242:11 421:3 537:20

graphic 20:15 77:24 207:2

Graphical 583:23

graphically 193:10

graphs 36:19 51:7 61:21 62:10 98:24 127:17,18

319:13 378:25

grapple 604:23

grappling 604:24

grass 235:6,8 401:20 402:3,5 403:4 508:12

grasslands 169:21

gravity 175:16 207:20

gray 50:1 51:14 152:14 153:9 318:26 363:26

grazing 87:12 170:6

great 7:26 19:22 71:25 72:5,10 78:19 128:8 149:8

181:1 182:19 187:23 253:21 286:14 334:26

342:12 344:16 368:7 372:2 380:26 385:6 409:19

419:6 430:14 478:14 479:9,19 484:1 530:15

545:24 570:14 575:11 602:1,1,2,11 604:22

greater 36:3,5,8 37:17 41:16 53:16 54:1,13 103:17

103:18 105:12 115:4 138:10 151:26 158:16

197:24 212:12,16 217:21 244:14,25 256:3 279:14

310:8 354:8 391:18 398:14,21 401:21 405:22

430:21 443:23 513:24 533:2,17 551:22 583:25

greatest 22:3 106:13 313:7 409:3

greatly 9:10 87:4 309:14 330:6 564:22

green 50:2 51:14 140:1 144:14 231:12,16 257:1

292:23 301:22

greenhouse 170:4

greens 120:12

grew 288:9

grey 562:13

gross 30:16 233:12,15 318:19

ground 73:10,12 167:12 175:4 281:5 299:23

408:17 569:10

groundwater 174:17 594:5

group 14:16 30:3 35:23,25 42:26 44:10 45:22 61:4

68:16 116:15 163:20 175:13 181:7 182:13 194:21

281:17 282:11 293:17 311:9 323:2 334:3 339:21

360:23 385:21 400:25 414:24 489:10 515:6,22

543:18 555:14 557:4 570:4,5 578:13 583:3

606:19

grouped 192:25 432:24

grouping 517:3 554:2

groups 14:17 37:12,14 62:3 120:13 171:16 223:10

228:19,22,26 268:21 328:22,24 333:15 337:9,18

344:10 345:7 350:1 379:17 398:25 399:18 430:11

535:17 536:18,25,26 538:5 553:25 570:4 578:14

grow 169:10,17 171:19 275:10 276:21 483:1

grower 268:6 275:25

growers 183:9,9,10,10,14 268:12,16 269:1,6

271:20 272:1,3,6,10,13,14 275:3,6,8,21,26 276:3

277:14 278:1 279:26 280:2 281:12 283:26 287:18

289:2 295:2,6,11

growing 170:7 221:11 262:17 282:7

grown 164:22 169:12 170:1,2,24 171:10 376:6

grows 309:20

growth 30:24 43:16 46:21,24 48:12 53:18 54:18

55:3,16,18 63:26 64:11 74:2 89:12 92:5 94:15,18

95:4,6,10,11,12,12 130:11,13,14,22 168:3 196:6

196:14 215:26 216:16,18 235:26 246:22,24

255:12,14,16 256:19,20,22,23,26 257:8,14 258:2

260:1 261:11 262:14 263:5 269:15,16,18,20,23

291:8 308:22 309:1,3,10,12,17,22,25 310:6 322:5

322:8,14 323:11 345:6 350:13 386:2 390:9

391:13 398:20 418:24 428:26 430:26 431:5

492:13 503:19 504:19 506:1

grow-out 45:25 482:18

guarantee 168:16

guaranteed 160:23

Guelph 186:7 195:17 238:14 261:2

guess 66:23 110:9 122:1,8 123:1,24 125:6 131:26

160:4 185:6 193:12 205:24 209:10 311:4 312:25

313:4 315:3 343:9 353:10 376:18 415:22 420:25

425:3 433:23 445:18 462:16 464:6 470:4 476:11

493:5 499:14 512:18 520:18 521:1 523:19 526:15

533:21 537:21 542:25 543:8 544:5,13 545:25

557:18 558:5,21,26 559:4,19 561:20 562:10

566:23 567:23 575:14,24 576:10,13 590:5,8,17

590:22 591:12 593:26

guidance 32:26 40:10 47:19 59:11 71:9,10 179:10

179:19,19 180:14 183:1 267:21 284:3 297:26

298:6,9,11 299:1 300:2,5 352:16,24,26 401:21
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408:9 426:12,18 511:14 566:26

guidances 393:23

guide 10:5,24,25 36:24

guideline 34:16 69:11 203:5,6 225:7 226:26

guidelines 9:23 10:18 17:11 20:17 32:25 34:18,26

35:1 36:5,9 37:18 38:20 47:18 59:10 70:12

188:24 298:14,22 299:8 300:4 336:21,22,25

337:1 352:23 381:21 566:26 604:8

guiding 227:17 523:2,7 576:14

Gulf 175:24 304:12

gut 318:22

guys 72:12 125:19 128:3 162:12,13,17 252:17

300:24 314:9 332:24 348:7 373:22 414:26 415:7

417:14 418:11 427:26 434:26 440:10 452:12

477:9 494:11 512:18 528:7 562:13 603:15 605:19

Gynecology 6:17

g/L 23:10 38:17,25,26 266:18,20,23,24

g/L/day 38:22

H

h 266:18,20,23,24,26

ha 397:7 535:8

habit 265:23

habitat 21:23 117:9,15 164:1,7 165:12 169:21

260:25 266:8,9 582:7,15

habitats 165:6 170:3 177:22 265:26 271:3 581:22

half 124:7,14 164:20 167:2 172:17 174:19 183:12

218:18 253:7 276:18 289:8 295:12 333:9,10

565:19

halfway 194:8

half-hearted 345:26

half-life 45:13 423:2,5 447:12

half-lives 252:17

half-pound 286:1

Hall 182:4 260:26 265:24

hammered 496:25

hand 10:8,24 11:5 20:3 111:6 115:17 141:18

193:23 195:12 200:24 206:1 248:25 368:10

546:16

handed 457:10

handle 112:6

handling 526:12

handouts 292:15

hands 201:15 203:26

hanging 421:9 598:24

Hanson 182:4 261:2 262:3 266:15

haphazardly 391:17

happen 124:17 125:13 132:22 176:8 177:13 407:22

498:2 569:14,15

happened 134:9 285:22 356:6 420:26 421:1 479:3

482:23 539:7

happening 103:19,21 118:9 135:4 160:1 163:17

166:2 197:16 237:13 309:19 316:11,11 375:4

happens 15:5 116:7 117:11 124:7 135:5 165:5

200:24 217:11 219:14 220:7 240:3 309:16 332:17

335:7 411:8 458:16 490:10 594:15

happy 178:19 181:2 207:5 213:3 292:9 484:24

561:8 587:8 603:8,9

HARAP 227:2

hard 16:24,24,24 236:8 252:8 286:20 304:25 314:8

342:15 361:16 363:7 369:20 379:22 481:12,18

483:14 487:21 495:3 590:3 603:12

harder 316:18 358:10 597:18 599:21

hardest 582:13 603:14

hardship 172:11

harkens 574:10

harm 172:4 175:7

harmed 501:11,12,18,21,21 502:4

harmful 73:21 167:15,21,23 171:23 173:1

harmless 174:13

hashed 556:4,6

hashes 333:14

hatch 29:22 50:6 67:21

hatching 322:6 323:13,26

hate 421:13

Hayes 206:15 373:19

hazard 72:2 86:4,16 456:17 463:14,16,19

hazardous 258:3,11

hazards 269:3

Haze 178:5

head 179:2,22 181:6 364:15

heading 11:17 251:7

health 3:7 9:12,13 72:2 161:6 166:21 167:16

168:14 170:13,13,16,19 171:17 172:3 174:4,10

177:16 189:24 196:12,19 274:2 342:18 343:4

375:23 456:17 588:24 597:25 598:26 599:14,23

599:26 600:1,2,3,18,22 601:9

healthcare 170:20,22

healthier 342:17

hear 20:6 21:2 22:8 23:11,24 24:20 113:23 126:19

168:8,10 186:21 355:3 426:10 429:18 477:18

484:24 521:1 524:18

heard 20:7 171:15 190:5 203:24 234:16 240:25

271:10 275:15 290:2,16 300:13 302:5 303:10

304:4 314:21 375:14 379:17 399:22 400:16

412:15 454:22 460:3 467:18,22 496:21 510:17

513:22 523:16 525:13 530:22 558:5 585:7 593:6

594:11

hearing 179:9,21 180:6 192:18 364:2 371:15,25

385:2 389:5 424:9,9,14 480:24 485:2

heart 175:18 342:2
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hear.that 269:7

heating 94:24,25

heavily 75:15 226:16 290:21 402:22 497:4

hectare 423:3,3

hedge 488:14

hedgewater 117:8

Heidelberg 141:2 142:11,15,23 160:9

held 23:5 29:6 77:16 81:11 102:1 163:7 176:10

184:7 256:21

Hello 72:17 301:7

help 9:13,20,21 10:8,15,19 13:12 18:5,24 22:12

26:10 61:26 134:12 135:23 174:21 176:12 196:9

210:21,23 227:16 277:20 278:12 290:8,9 313:10

325:16 327:25 333:1 336:25 342:7 355:14,19,23

356:13 358:16 359:2 361:17 372:25 381:15

415:24 418:20 430:6 435:1 439:25 487:2 500:18

500:19 501:23 502:17 545:17 553:23 554:8

563:19 589:24 590:15 591:20 592:11 595:4

596:12 601:4 605:10

helped 18:23 30:8 428:6 599:10 603:24,26

helpful 67:2 182:22 315:7 319:15 321:16 355:13

357:4 358:20 362:4,5 372:11 375:8 381:24

382:22 384:9,23 392:25 393:17,24 414:18 426:17

430:3 452:19 461:13 606:4

helping 11:20 13:18 289:1 359:22 374:17 473:12

600:19 601:7

helps 10:24,25 28:16 158:22,23 295:18 346:9

358:17 412:6 460:24 601:14 604:4,20

Hendley 182:7 267:4

Henley 551:4

herbicide 179:22 181:6 186:23 238:1 275:23

278:15 286:7 508:22 588:15

herbicides 268:18 289:12,17 290:1 295:15

herbicide-resistant 289:15

herbivore 508:11

herbivores 87:12 508:17,17

Herbizide 270:16

hesitancy 480:24 481:17

hesitate 579:17

heterogeneity 55:2

heterogenic 50:7

heterotrophic 237:9

Hetrick 20:7 25:20 136:15,16,21 154:26 155:10,20

156:1,18 157:21 158:6 159:14 160:5 161:14,22

305:11 311:5,7,17 472:26 521:22 530:21 531:11

532:11,13 536:19 542:7,9 545:3 549:1,3 552:17

562:2,22,24 570:11,13

Hetrick's 82:4 162:5

Hey 555:15 569:14

Hi 6:14 7:9 162:21 414:11

hidden 81:19

hiding 210:16,17

hierarchy 529:12 530:5 547:3,11

high 36:14 44:10 45:22 63:12 115:21 122:13,13,26

123:19 142:18,24 147:11 153:3,21 154:14,18

172:24 212:21 222:3 245:21 246:7,11,25 248:3

249:6,7 251:16 253:15 255:21 259:12 261:16,16

261:25 262:19 269:19 270:26 282:22 286:17

322:3 330:26 347:18 364:1 369:5 377:8 378:11

381:25 396:25 397:22,25 398:22 402:7,23 403:26

404:12,24 420:6 422:4,6,9 453:15 460:5,15

464:11 481:1 514:5,16 515:7 517:25 569:9,18,21

589:11

higher 38:1 40:6,13,20 41:14 42:12 46:5,11 47:24

48:5 50:4 51:16 55:14 60:17 63:17 66:10 74:20

79:25 99:9,21 122:24 129:8 135:16 152:1 154:24

163:23 184:23 193:8,19 220:2,3 227:4 233:16

239:2,17,21 242:20 244:1 248:10 253:3 263:6

266:9 292:5 295:19 317:24 318:23,23 332:9

334:23 384:22,26 404:17 405:4 413:9 469:22

497:12,19 498:9 505:7 519:15 531:17 552:20

586:16

higher-tiered 485:6

highest 30:20 48:20 53:16 175:15 208:23 234:14

249:22 309:11,14,25

highland 587:17

highlight 140:7 221:18 245:2 330:8

highlighted 66:22 144:10,14 289:16

highly 81:22 89:21 90:20 99:7 214:19 234:20

244:21 248:15 249:10,15 259:8 262:11 264:20

287:6 329:12 420:14 471:11 520:7

high-concentration 519:15

high-fructose 170:10,14,17,18

high-quality 453:18

high-use 173:22

hilA 481:15

hinge 413:1

hint 185:6

histological 44:17 602:26

histology 30:17 67:25

historic 274:7

historically 188:16 190:24 266:1,12 278:17

histories 373:8

history 21:9 22:16 82:19 127:8 175:14 255:15

262:15,17 274:1 276:4 280:7 281:1 290:23 329:8

338:11 373:15 398:25 406:19 443:3,7,21

hit 333:11 340:1,1 448:7 525:2,4,5 527:17 536:10

558:17 584:26

hoc 340:13 602:21

hold 5:21 72:13 281:21 352:20 367:10
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holding 176:24 589:11

home 177:5

homes 165:21

homework 395:19

honest 370:4

honestly 308:2

honesty 110:6

hope 16:5 18:19 20:9 117:26 118:11 128:4 130:8

182:10 191:13 198:5 205:13 274:10 279:22

300:10 340:10 368:8 459:9 500:13 503:25 587:24

606:4

hopefully 5:1 87:5 182:21 183:3 349:15 350:25

495:17 602:3

hopes 359:22

hoping 434:21 438:11

horizontal 232:6 239:19 582:7

hormonal 193:1

hormone 196:16,26 322:5

hormones 54:21 196:13,18 200:5 341:8,9

horribly 426:11

horse 426:8

host 65:18 276:17

hour 45:16 50:6,6 163:14 225:8 226:10 294:2

hours 3:26 41:17 64:9 252:15 256:9 309:8,9

395:24 603:17

hour-and-a-half 4:1

Howard 178:10

how's 555:3

HPI 199:16

http 1:8

huddle 417:11

huge 284:4 336:13,14 577:3 606:3,23

hugely 411:5

human 3:7 9:12,13 161:6 166:21 168:14 169:18

171:16 172:2 173:15 174:3 177:15 189:23 458:23

588:24 597:25 598:26 599:13 600:2,18 601:9

humans 9:18 119:17 163:12 169:12 173:14,25

291:14 324:4 599:16

hundred 275:19 276:19 435:7 442:20

hundreds 164:18 172:16 252:18 276:20

hungry 532:16

hurdle 81:8

husbandry 27:22 57:10,14,19 59:20 71:5 202:3

342:14 381:5,11 384:7 482:17 483:10,11,13

hybrid 157:2 526:3,22,24 527:2,7 528:17 544:8

553:5 579:15

hydro 421:3

hydrograph 144:25 155:15 519:8

hydrographic 515:25

hydrographs 411:6 413:14 519:20

hydrologic 8:4 149:23 526:23 527:6 543:11 557:11

576:4

hydrology 140:20 518:9,10 535:5,12

hyperactivity 507:1

hyperbola 537:20

hypothalamic 196:25 197:18,19 199:20 202:10

hypothalamus 201:12

hypotheses 55:22 66:20 196:1 205:8 211:14 385:25

387:23 390:2,4,6 391:4,8 392:11,19,23 394:11

407:9 495:26 507:25 508:21

hypothesis 27:18,20 66:11 358:8 365:4 370:2

374:15 375:6 387:15 388:15,21 393:7,8 401:3

431:26 452:17 471:16 495:19,20 506:17,21,23

507:5,7,8,20 524:9,12

hypothesis-generating 407:10

hypothetical 216:7,8,9,13,13 292:3

I

IA-03 265:20

ice 166:26

IC50 212:19

ID 231:7

idea 15:3,23 16:8,12 28:19 114:6,12 117:2,12,18

118:2 228:21 229:9 317:5,11 327:24 343:9,12

359:20,24 374:26 412:1 415:24 420:13 434:13

452:1 474:2 495:24 520:19 521:22 529:11 549:22

551:11 553:25 554:1 555:5 589:3 595:14

ideal 77:9 332:12 397:8 414:2,8 417:5 453:1

ideally 335:10 409:1 490:6

ideas 113:23 156:5 360:9 395:7 419:11 546:10

identical 97:18 141:13

identically 502:8

identification 24:7 137:20 139:3 267:12 396:4

444:16 513:4 524:4 563:4

identified 27:25 28:22 29:15 32:25 34:1,20,25

35:13 36:21,24 39:21 42:21 43:20 44:23 45:23

56:24 59:2,10 78:2 81:6 103:16 104:16 106:9

143:11 151:10 152:23 153:5 154:19 222:23

281:25 337:3 353:6 368:21 377:16 393:18 395:4

429:6 452:16 476:12 521:13 522:10 533:11

identify 23:20 24:10 28:10,11 31:10 32:17,21

56:26 109:4 128:18,18 149:15 154:15 156:17

196:20 231:23 267:4 273:19 315:18 353:12

354:11 390:7 395:13 521:13 527:23 530:24

531:12 546:5 552:5,19 577:2

identifying 58:15 154:24 180:2 182:6 185:23

267:25 314:19 527:15 573:12

identity 78:17

IDs 218:9,17 222:23 223:10,11,12 241:9 243:4

ignore 97:26 308:7

ignored 98:3 332:15 442:16
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ignores 97:23 290:26 306:25 307:13 442:23

ignoring 307:3 355:18 364:7 471:24

II 73:13

Illinois 8:10 271:6 404:23 456:6

illustrate 47:8 49:15 50:9 52:1 61:22 93:20 101:13

127:19 138:15 140:26 199:21 200:17 222:11

246:13 309:5

illustrated 54:4 90:5 92:25 95:22 140:26 141:2,5

214:11 571:11

illustrates 78:20 95:26

ILLUSTRATING 283:12

illustration 138:21 152:12 153:7 217:3 542:11

illustrative 542:18

ill-defined 599:24

ill-health 600:13

IL-10 266:7,9

images 30:16

imagine 428:9 555:11

immediate 177:16 203:17 236:25 256:13 423:26

immediately 171:25 236:10 255:10 256:26 257:8

262:14 371:16 372:20 482:19 483:2 497:2

immensely 3:1

immune 31:21 48:13 56:4 57:23,25 58:1,7,9,12

59:1,18 166:4 188:14 193:2 195:18,22 196:13,16

196:18 197:4,9,11,21 198:17,24 199:11 202:7,13

211:25 212:11 376:9,13 385:26 386:4,10,22

387:8,14,24 390:3 452:15,18

immunity 168:4

immunological 391:13 508:3

immunologist 486:8

immunologists 390:15 392:14 486:12

immunosuppressant 508:6

immunosuppression 508:1

impact 3:6 17:25 46:1 65:23 73:3 74:24 80:12 90:4

94:24 96:24 97:25 99:6 105:15 111:9 147:14

150:23 151:20 220:16 221:8 223:11 245:7 255:22

263:3 271:18 272:18 275:16 279:7,8,13 284:4

285:17 330:10 331:7 334:20 378:2 382:5,8

383:12 387:13 441:7 443:11 444:8,10,17 446:8,9

447:1,3,9 471:2,7 486:14 513:23 518:18 531:6

564:15 581:1

impacted 29:16 75:16,21 76:7 266:14 277:22

387:8 466:24 468:5 506:11 551:23

impacting 140:18,22

impacts 22:2 180:19 260:1 279:14 287:8 290:5

291:19 296:3,12 390:8 396:24 412:18 413:23

470:2,2

impaired 55:16 168:1,2 271:7,15 322:12

impart 414:8

impartiality 3:23 395:1

implement 21:14 149:14 279:23 604:15

implementation 3:10 283:4

implemented 103:2

implicated 587:17

implication 250:20

implications 91:16 95:26 97:14 279:3 284:11

310:13 319:14 504:11 529:25

implicit 344:3 604:20

implies 230:6

implying 230:1

import 164:14 165:15

importance 42:7 69:24 93:13 145:24 194:8 222:11

244:4 272:20 274:11 279:6 286:13 288:10,12

289:14,24 330:8 341:24 386:12,13 390:20 498:16

506:14 556:18 572:8 593:20

important 17:6 18:3 51:26 55:24 66:16 68:7 82:1,2

84:14,26 85:13 88:25 90:25 91:8,18 92:1,2 93:16

93:26 95:21 97:7 99:5,10,13 113:4 117:10,21

121:7 137:16 145:6 150:13 151:4 159:5 166:7,12

171:25 176:1 180:13 181:25 184:22,22,26 192:19

215:1 228:18 254:3 258:12 260:11 270:7 271:3

276:1 284:2 289:10 295:24 301:17 303:10 304:25

307:14 325:12 328:5 329:4 340:10 342:18,23

344:14 353:16,19 376:14,23,23 386:15 404:5

408:24 409:9 412:24 420:15 422:22 444:7 449:13

462:7,8 463:2 467:26 469:11 470:24 480:24

497:21 498:20 502:18 507:5,11,17 510:26 515:26

519:9 521:20 524:19 529:20 533:3 534:21 535:10

556:16,22 562:16 566:1,4,18 570:2 572:10 574:8

581:21 591:14,22 592:16 593:23 594:4,9,14

604:9,16

importantly 96:7 150:18 154:23 247:2 257:18

512:5

imported 164:8

importer 164:16

impose 458:9,15 459:6 497:25

imposed 300:4

imposing 464:21

impossibility 391:6

impossible 342:17 350:14

impressed 429:8,12 589:10

impression 335:23 367:14

impressive 206:8 589:13

improve 179:14 276:11,26 280:18 283:6 289:11

544:3

improved 307:5 421:11 424:12,13 539:20

improvement 185:22

improvements 179:18 272:23

improves 179:16 295:23

improving 170:19 172:14 179:7 547:23
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imputes 213:19

inaccurate 300:14

inadequacy 561:6

inadequate 324:15 342:16 356:1,4,7,22,25 371:5

408:11

inadvertently 50:25 54:20

inappropriate 27:22 97:6 204:24 305:22 307:3

310:14 361:11

inappropriately 283:1

inaudible 37:20 43:23 45:4 116:15,19 119:20,24

121:4 137:7 189:12 211:9 221:15 256:3 259:26

261:19 285:24 286:4,5,6 303:22 304:12 318:7,17

340:20 361:8 365:24 369:6,22 373:7 376:14

381:14 382:17 383:26 386:21 390:21,21 391:10

400:26 402:22 405:19 406:4 410:6 411:4 416:22

419:16 422:15 423:9 431:6,6,13,15,20 436:1

447:18 451:15,16 453:22 458:23 470:19 474:8

476:7 478:6 486:4 491:21 495:4 499:25 504:22

505:25 506:3,23 508:26 511:21,25 512:12 519:19

519:20 531:4 533:3 543:11 546:7 553:12 561:3

564:3 566:2 578:8 581:5,12 582:12 583:2,25

589:24 596:5,6

inception 308:25

incidence 190:15,19 191:4,25 195:4

incidents 588:21

inclined 544:14

include 35:19 46:6 54:26 55:23 83:3 87:11 98:20

115:24 121:12 138:26 140:18,22 167:24 183:17

190:4 195:11 279:15 288:19 303:20 305:21

325:12 331:11 373:12,18 376:21 384:7,8 397:26

398:3 408:5,24 424:4 444:8 471:8 486:25 487:3

507:12,25 511:16 562:12 573:20

included 28:24 31:24 33:9,15,19 37:11,12 41:9

44:25 48:12 49:1 50:19 56:7 77:11 78:1,26 79:6

81:2 89:13 105:19 108:17 110:25 115:17 121:1

138:3 150:25 190:3,6,7 194:16 205:12 206:15,19

209:16,18 223:8 229:4 238:9,23 321:6 334:16

340:17 348:22 402:1 431:7 435:3 462:25 471:6

478:24 506:20 561:11 605:17

includes 2:9 25:17 99:4 130:21 137:9 151:9 165:22

203:2 281:4

including 5:9 31:20 33:11 43:20 44:19 53:3 54:21

63:10 68:2 77:22,23 114:5 135:19 149:19 156:20

165:1 179:12 192:10 195:12 205:2 222:8 224:26

225:16 263:2 270:9 271:13 281:8 291:5 297:1

298:15 300:2 306:1 322:4 330:8 335:8 341:12

357:14 386:19 397:18 398:19 399:9 447:24

480:20 481:10 483:11,12 495:26 505:6 546:13

572:8 601:10

inclusion 76:20 282:20 322:18 433:18 462:6 467:7

inclusion/exclusion 241:2

inclusive 192:7 598:10

income 168:16 173:26

incompetent 173:11

incomplete 457:16

incongruent 443:20,25

inconsistencies 193:7

inconsistent 197:5,21 207:13 235:4 236:25 282:14

296:2 297:25,26 298:1 312:7 351:21 352:4

incorporate 74:26 106:12 193:12 215:2,13 224:4

242:7 243:6 254:9 398:24 399:1 539:11 572:22

578:9

incorporated 93:7 126:17 129:15 190:17 227:13

254:15 260:14 286:11 354:23 448:5 530:6,8

incorporates 254:5

incorporating 46:2 94:21 243:12 245:13 329:13

398:16

incorporation 192:11 244:9 278:1,6

incorrect 188:9 583:12

incorrectly 126:9 279:19 400:4 583:11

increase 47:7 58:4 111:25 117:14,14,15 145:1,19

152:2 171:7 200:22 201:6 208:22 219:16 220:11

222:2 231:18 234:1,5,9 241:6,7,11,12,14,15,17

241:18,20 243:9,13 246:23 249:24 272:25 279:15

280:11 383:9,13 387:5 390:13 391:10 443:7

458:5 469:24 535:11 538:18 553:1 555:22 564:21

582:3

increased 168:4 170:16 173:21 196:26 201:11

259:22 265:9,10 286:5 351:24 383:11 396:22

443:8 508:12 550:13

increases 145:14 169:6 219:1 220:4 242:9,15

285:21 382:12 514:9 593:4 594:20

increasing 55:6 105:19,21 170:17 218:25 220:8

236:20 237:4 239:24,24

increasingly 55:24 113:12,15 277:18

increment 398:20

incurring 398:11

indefinite 462:20

indefinitely 95:18 171:20 446:18 447:7

indentified 48:22

independent 3:4 31:25 67:11 250:11,24 306:13

337:16 407:5,6 443:2 581:13

independently 61:5 123:16 337:2 367:19 553:20

index 24:4 82:9,12,19 83:8,10,12,13 84:2,19,22,24

85:1,2,4,5,8,10,19,20,26 86:2,5,11,16,19 87:4,7

88:9 91:19,20,22,24 92:1,13,19 93:2,13,14 94:16

98:7 99:5 100:4,15,19 101:4,6,18 102:25 104:18

105:6 109:8 120:4 122:18 123:17,19 124:15,24

125:17 231:22 282:14 315:19 320:7 409:7 431:7

447:4 466:11 494:14 505:18 551:21 553:9
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index-based 102:12 473:10,13

Indiana 144:2,12

indicate 45:14 93:11 113:19 178:15 193:24 198:14

211:20 217:22 218:4 233:17 238:10 291:2 324:13

386:17

indicated 16:9 27:11 30:24 45:18 57:3 89:16 91:13

116:14 140:5 196:24 232:5 237:9 241:16 250:21

307:1 331:18,19 402:6 443:15 466:14 504:2

507:19 583:1

indicates 39:23 45:4 48:7 109:8 150:19 151:12

243:16 244:15 246:10 254:18 258:24 300:14

387:13 494:14 498:24 506:25 549:7

indicating 37:15 194:20 242:19 243:7 269:3

403:25 506:5

indication 69:3 133:11 239:21 326:19 358:24

indicative 310:8

Indicators 491:1

indices 232:5,8

indigenous 31:6 56:20

indirect 34:7 66:14 74:19 109:17 185:2 192:26

397:11 402:14 507:2 508:10,21

indirectly 17:15 458:26

individual 4:8 13:6 14:16 15:21 61:18 75:11 89:6

89:18 92:17 100:12 193:15 214:2 216:26 242:5

251:17,20 253:1 317:5 401:6 410:13,13 430:26

457:3,4,8 501:11 561:23,24

individually 119:14 248:13 250:4

individuals 191:21 343:13 583:3

indoor 226:20,23

induced 168:2

inducing 503:4

indulgence 606:3

industry 173:25 184:23 599:5

industry-funded 173:7,12

inevitably 165:26

infected 19:13 75:11

infection 31:22 48:13 57:26 58:4,10 195:23 323:11

386:1 387:6,24 390:3 486:14

infections 59:18 168:5 387:2

infer 457:22

inference 344:6 362:23,24,24 363:5 458:8 501:6

inferred 285:7

infill 147:22 149:5 158:18 518:14 550:3 553:18

557:26 577:3

infilling 143:6 145:1 156:12,14,16,20 157:22

520:15 523:10 546:11 554:10 560:8 561:1 572:2

576:14,20,21,25 577:14 598:8

infinitive 430:5

inflection 219:20 220:13

inflections 220:9

influence 34:21 35:6 37:26 38:6 41:23 65:1 75:2

105:23 133:2 223:7,13 242:13,25 243:20 244:23

269:17 353:14 382:5,8 386:17 470:26 477:26

influenced 586:13

influences 34:24 35:4

influencing 390:22 440:26

influential 420:8 471:2

info 141:19

inform 88:13 196:9,22

information 4:24,26 5:7,7,8 9:9 11:18,23,24,25

12:2,5,8,8,16 13:16 14:24 15:19 16:2,14,21 17:21

18:5,9,19,21,26 27:6 30:22 33:12 35:8 36:19

39:25 40:20 47:26 48:3 55:20 56:3 64:1,5,6 66:3

66:15 83:2,6 85:6,12 86:24 87:6 88:1 89:19 90:22

94:21 97:2,8 103:6 110:21 111:4 113:5 114:25

133:5,10 134:12 137:3 158:26 180:6 181:3

182:20,22 186:9 190:7 194:19 196:8 205:21

210:20 213:11 227:7 231:4 265:22 276:12 290:12

291:25 294:13 297:23 298:14,15,16,20 303:24

310:24 317:8 342:12 350:23 352:18,20 354:22

361:13 364:26 365:3,9,21,22 366:25 370:25

375:1,12 377:13 380:21,22 382:22 387:14 392:26

394:13 395:18 396:5 406:17 407:4 414:18,21

415:9 416:13 417:22 418:2,3,10,12 421:26

451:10 456:22,25 457:10,13 458:5,8 463:9

474:17 475:18 478:2,21 479:24 480:8,10,16,19

481:3 487:14,24 488:12 490:17 496:9 512:4,12

523:22 524:26 528:13,16 545:8,25 551:15,23

553:18,21 558:11 559:12 560:21 586:24 593:4,12

595:3,11,21,21,24 596:9 603:6 604:5,13,23

605:10 606:5 607:3

information-rich 417:20

informative 90:13 94:5 125:17 346:8 586:10

informatory 503:5

informed 179:11 310:17

infrequent 40:6 41:15

infrequently 189:24

ingest 173:18

ingestion 172:22

ingredient 11:12 170:10

ingredients 33:12 37:25 76:22 504:3 591:5,8

inhabits 73:15

inhalation 464:18

inherent 85:16 378:23,24 469:8 509:2

inherently 16:15 85:6 324:23

inhibition 230:9 252:24 500:16 505:23

inhibitor 186:14,24

initial 44:22 77:10 86:20 87:24 92:4 131:26 408:10

429:24 466:18 496:13 514:21 574:1

initially 215:22 220:3 552:4
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initiating 58:16 59:4,24

initiation 35:21 560:12

injustice 175:14

Innova 204:25

innovation 171:1,8

input 32:13 42:8 47:18 59:11 69:10 87:14 148:24

180:14 268:17 296:7 298:16 326:15 353:1,11

354:11 366:23 384:23 393:16,22 413:18 426:9

552:7 563:16 583:2 584:18

inputs 269:25

inquiry 292:7

insect 402:4,8

insecticide 11:11 330:7

insects 234:24

insensitive 70:19,26 120:11

insensitivity 338:6

inserting 454:24

insight 15:11 17:23 28:7 157:13 317:2 561:6

604:22

insights 10:26 12:9 13:18 15:26 32:2 112:26 133:1

133:8 319:2 388:7 604:3 605:7

insistence 606:25

insolvent 328:21

insomuch 497:23

instance 168:21 200:20 207:9 270:8 332:8,11,12

332:16 333:18 511:19

instances 550:23

instant 422:24

Institute 6:25 189:21

instream 579:16

instructions 113:20 584:11

instructive 10:6

instrumentation 421:15

insufficiencies 27:21

insufficient 27:20 36:15,17 56:2 58:13 225:10

269:2

intact 33:20 55:19 59:26

integrate 16:21 24:23 157:1 598:11 602:14

integrated 129:8 217:2 245:18,22 246:15 289:15

291:13 427:20 437:16 519:23

integrates 93:24 437:2

integrating 24:16 231:22

integrative 448:25

integrity 298:17 409:15 541:25 599:23

intelligence 552:14

intended 21:20 28:7 32:14 35:5 43:10 88:17

406:17 497:18 507:7

intending 497:9

intense 270:25 427:18

intensely 124:18,19,22 546:25

intensities 122:18

intensity 151:11,22 153:3,8,21,25 154:14,18

158:25 259:22,24,26 446:20 514:5 573:26

intensive 88:18 516:3

intent 331:2 432:10 552:3

intentional 282:23

inter 378:14

interact 334:14

interaction 133:6,16 196:18 255:5 339:10 356:25

593:16 606:18

interactions 34:6 92:11 339:16 340:6 357:5 360:1

389:20 398:5 442:16,24 475:13 591:6

interactive 494:1,1

intercept 535:23

interconnected 317:9

interconnection 591:23

interest 3:16,22 65:23 85:18 86:1 87:1 217:17

218:20,25 221:2,17 222:2,9 224:7 225:17 245:25

260:12 261:3 262:16 314:20 370:6,11 388:4

394:26 398:7,24 406:11 443:5 555:7,11 574:20

interested 171:16 199:25 209:20 211:17 221:23

297:4 314:26 528:19 588:16 589:2

interesting 5:16 158:13 198:9 201:2 210:26 211:4

211:15 261:3 285:20 313:2 360:2 413:17 482:14

506:17 543:10 582:26 584:4

Interestingly 189:26 198:6

interfere 37:4,23

interferences 328:16 339:4

interferes 37:20

interflow 526:25

Interim 72:23 78:1

interject 409:17 585:8

intermediate 531:8

intermittent 282:1

intern 29:11

internal 193:6 399:3

internally 50:5 359:19

internation 163:4

international 36:23 178:10 226:13

Internet 593:11

interplay 196:12

interpolated 516:7

interpolation 146:18 147:19 148:17 157:18 514:13

518:2,14 524:3

interpret 13:21 14:14 18:19 32:6 55:25 66:17

133:17,19 134:2 319:10 323:16 580:9

interpretation 77:7 109:9 141:25 156:24 233:5

244:5 245:4,5 247:9 327:22 335:21 349:5 354:25

404:5 487:9,16 488:17 500:8 504:18

interpretations 24:26 25:15 75:9 107:17 354:1
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488:18

interpreted 13:9 14:25 223:8 241:4 242:7

interpreting 18:2 70:3,6 134:3 227:4,8 348:16

interpretive 222:24

interrelated 62:20 86:23

interrenal 197:20

interrupt 310:17

interspecies 203:13

interval 103:3 110:16 144:23 145:4 146:25 147:18

147:25 148:11 218:24 224:7,11,12 225:1,4,21

472:17 516:15,19 517:2,3,4 522:24 533:9 536:9

537:4,16,17 538:4 540:19 551:5 554:9,12 560:7

560:20 562:3 565:19,22

intervals 95:13 102:24 108:4,7 143:3,7 145:4,15

145:20 146:18 147:18 148:6 155:13 158:14,16

225:8,8,9 246:3 267:20 388:17,18 392:24 495:5

495:10,13 518:22,23,25 533:1,22 538:1,19,21,22

538:22,23 551:1 571:23

intoxicated 317:6

intra 378:14

intractable 596:4

intramural 391:5,6

intrarenal 201:12 202:10

introduce 2:22 3:1 5:22 6:10 8:23 19:5 26:21 71:22

174:2 177:22 287:26 305:5

introduced 24:3,5 88:12 240:20 245:18 285:23

540:12

introducing 19:17 179:23 253:26

introduction 23:16 190:25

introductions 9:1

introductory 181:16

intrusive 4:16

intuitive 174:9 221:21

invalid 40:11 41:4 42:2,3,14,18,25 47:23,26 48:24

49:13 50:1 51:14 62:21 63:10,11,17 77:2 80:8

127:13 290:25 328:8 331:5 341:3 344:21 345:25

346:25,26 347:22 351:13,14 353:6 357:20,21,21

359:14 363:24 366:19,20 370:12 513:26

invalidate 35:14,16,18 36:22 37:7,10

invalidated 347:2

invalidity 46:16

invasive 119:20 164:17,23 498:3

inverse 110:21

invertebrate 506:8

invertebrates 21:21 108:16 109:17 238:18 582:2

invested 271:21 417:25

investigate 199:10

investigated 65:6 104:20 386:19

investigating 213:17

investigation 191:15 387:12 496:6

investigators 354:6 509:17

investment 10:25 169:14 576:3

invited 601:23

invites 270:2

involve 24:18 85:22 189:12 308:26 313:17 354:1

414:16 452:3 504:2

involved 5:3 15:22 33:10 54:9 83:4 106:18 187:1

188:19 226:16,25 227:1,6 289:1 368:5 372:12

414:13 514:2 601:10

involves 95:23 196:12 308:16 416:18

involving 22:20 56:18 352:23

in-depth 156:19 544:17

In-stream 264:26

Iowa 143:20 289:14 301:26,26 302:8,16,26 303:4,7

303:12,14,16,19 304:15

IQGs 298:14,16

IRED 22:4 72:24 104:16 227:16 457:20

irrelevant 232:3 263:2 339:11 447:6 449:10

462:12 466:3 506:6 582:17

irreversible 113:26 114:6,19 174:6 274:20,25

434:8 444:18 494:17 524:8

irrigation 297:1

isolated 100:11

isolation 504:15,25

issue 10:19 81:16 89:3 94:10 98:6 158:22 171:19

171:20 270:5 274:1 278:21 282:19 285:8 295:2

304:25 306:11 316:5 318:1 323:15 338:3 339:17

339:23 341:1,19 347:13 363:25,26 364:10 369:25

371:17 372:14,25 412:18 434:12 443:26 449:16

453:22 457:21 458:15 464:22 465:4 467:24 468:7

468:11 485:19 490:11 501:23 502:24 518:15

521:9 527:10 534:22 535:10 551:6 554:19 555:18

564:15 601:6

issued 22:26 29:3 302:23 324:3 338:2

issues 1:4 2:6 3:6 6:1 9:15,15 14:23 18:17,18 43:20

43:20 57:14,18 66:1 69:20 97:15 119:4 143:6,11

151:10 171:26 180:12 184:11 271:15 274:14

281:19 286:4,8 287:17 295:26 329:25 337:3

339:6 355:15 369:8 371:6 373:17 401:18 405:7

408:1,3,11 409:25 420:24 425:11 437:19 454:13

454:17,18 464:7 484:18 496:26 533:16 534:7

540:10 547:1 588:23,24 598:1 599:22 600:18

italics 366:12 381:1

item 9:2 543:12

items 344:20 345:19 607:1

iterate 140:13

iterated 429:1 604:9

it'd 382:21 435:26 499:26 543:10

it'll 332:24 408:23 539:19 602:2 605:10

i.e 266:8
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J

jackknifing 567:24

Jackson 302:8

Jackson's 172:13 173:5

jack-knifing 158:2

James 6:14 7:12 20:7 25:20 67:4 68:9,18 116:10

121:13 122:3 123:3 136:16,21 155:10,20 156:1

156:18 157:21 158:6 159:14 160:5 161:14,22

212:17,26 223:24 224:21 261:6 262:6,23 311:7

311:17 396:21 415:18 416:4,15,25 422:2,18

424:17 426:3,19 427:7 428:1 434:19 453:6

455:10,20 459:10 489:5,20 494:22 496:13 510:3

521:22 531:11 532:13 542:9 549:3 552:17 562:2

562:24 568:1 570:13 575:14 580:7 585:14 587:20

Janice 178:26 179:1,2

January 21:11

Jeff 181:26 188:21 226:12,12 248:8,21,24 249:9

250:2,9,19 251:1,8 252:2,19 253:15 583:14

Jenkins 607:5

Jenny 177:25

Jere 268:5,11 285:1,15,19 286:21 289:18

jet 172:19

jeweils 270:19

Jim 2:19 6:12,14 7:12 396:20 411:16 412:13

421:22 424:6,17 427:11 434:18 452:26 455:10,20

494:21 495:17 496:12 498:23 510:2 543:2 580:3

580:8 587:19 606:18,19

Jim's 418:6 584:18

job 72:14 110:14 325:4 336:11,14,14 342:12

349:23 408:15 411:12,16 418:5,6 477:13 492:8

521:20 538:3 553:16,20 576:20,24 577:16 595:12

596:19 603:4

jobs 594:21

Joe 607:5

John 7:20,21 64:8 118:11 159:10 210:25 211:12

251:23 252:10,19 253:11 314:14,14 315:1 393:3

406:7 417:10 421:8 431:17 455:24,24 490:24

495:16 509:21 588:4

John's 318:24

joining 2:19 9:6 271:26 329:8

jour 270:4

journal 386:26

journals 163:4 189:5 330:22

Joyce 607:6

judge 359:14 581:17 582:13

judgement 85:15 122:20 123:15,19

judging 39:22 575:5

judgment 430:11 459:1 462:19

judgments 440:18

Julian 144:20 148:21 151:19

July 303:17 581:9 602:3

jump 22:15 262:2 352:8 536:20

Jumping 431:18

June 1:17 22:18 183:5 581:9

justice 18:20 175:12,19

justifiably 357:19

justification 115:2 215:7 223:16 355:2 392:9

457:15

justifications 431:9

justified 307:10,16,25 513:18

justify 480:11 533:24

K

K 537:14

Kaitlyn 301:2,7

Kansas 159:15,17 161:5 234:19 268:5,11,12 285:3

401:1,8 402:1 403:2 422:20

keep 6:5 82:3 164:9 247:26 254:1,3 255:17 273:15

277:26 301:16 388:22 419:13 450:2 498:15,19

529:20 530:18 547:12 556:24 558:18 575:17

577:22 594:12 595:12 598:15 599:10

keeping 28:19 276:8 548:22

keeps 516:17

Keith 181:17 186:4,6 195:15 202:26 205:4,13

206:5,16,25 207:9,25 209:8,18 210:21 211:3,16

212:20 213:2

Kemp 261:22

Ken 8:20 125:20 317:21 393:26 435:1 448:2

456:11 513:10 532:5 540:14 550:7 554:7 566:17

603:12

KENNETH 8:20 125:23 155:2,18 161:8,16 249:3

317:21 326:5,23 344:18 347:10 350:21 363:17

370:24 375:14 379:12 383:6 388:24 392:5 393:26

413:3 420:2 425:17 436:11 445:15 446:2 447:16

456:11 466:20 501:8 510:10 513:10 530:20

531:26 532:23 540:15,25 547:17 556:1 561:5

564:13 568:4 570:22 577:21 598:23

Kentucky 272:1,9,13,13,22

kept 378:16 516:22 574:17 579:11 591:9

Kerry 162:21,21

Kettle 403:1

key 17:19 28:12 38:9 55:23 58:18 59:3 89:9 149:10

160:14 177:1 180:5 183:13,20 184:17 185:5,6,21

215:12 216:25 222:18 229:12,19 240:26 245:11

247:23 248:1 255:8 267:15 280:6 304:2 345:22

346:6 394:10 474:3 498:26 502:15 523:8 577:22

606:17

kick 71:2

kicks 552:20

kids 167:5 176:13

kill 166:3 174:12 508:12
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killer 197:7

killing 443:9

kind 10:26 14:8 15:19 16:13 18:1,4 62:20,23 68:25

80:24 112:14 114:24 118:8 124:15 126:4,18

132:7,17,21,26 158:26 160:4 162:3,5,8 185:6

200:15 204:11 222:12 302:16 316:2 317:22 318:3

325:20 337:7 339:2 341:2 345:14,25 347:12

348:16 358:5 360:4 361:25 364:11,26 365:7,8,18

365:23 371:7 375:5 376:2,16 379:24 380:9,12,16

383:17 387:22,26 389:2 392:17 394:2 408:23

410:23 416:24 420:2,4,12,21 421:6 425:9,13

427:23 435:5,16 439:25 440:11,17,20,25 443:17

443:22 444:25 447:19 448:22,25 449:9,17 451:13

453:19 458:8,25 459:6,22 461:14 462:11 464:1

464:14,15 465:7,22 466:21,22,24 467:12 468:4

469:4,6,10,13,26 470:1,4 471:18 474:6 491:5

495:9 497:4,10 501:23 514:2 516:26 517:6,8,14

518:11 520:9 521:12 522:16,18 523:2 529:7,9,26

530:20 531:1,2,2,14 533:23,24 534:19,21 535:25

535:26 536:5 544:15 545:8 546:19 547:10,25

548:23 551:10 552:26 554:1,5,15 555:20 557:7

558:7 559:12 561:5 562:11 565:10,23 566:3,10

568:26 569:16,25 573:22 574:3,4,10,11 577:5,21

578:6,12,14,16,22 579:15 595:13 599:11,21,24

599:24,25 600:5,11,20 601:2,4,14 604:13 605:10

606:18

kinda 155:4

kinds 12:16 15:8 112:15 114:22 121:9 132:16

165:1 195:5 201:16 211:5 252:14,20 339:16

356:19 407:1 416:1 433:11 437:17 451:18 521:15

546:21 567:1,15 569:18 600:9

kinetically 372:24

Klaine 2:21 6:23,24 64:24,25 65:4 128:6,8 130:18

131:11 132:4 134:24 135:18 136:1 248:20,21

325:24,25 342:10,11 345:10 347:5,6 350:16,17

370:17,18 379:5,6 383:1,2 388:9,10 391:24,25

411:14,15 433:20,21 455:12,12 500:3,4 503:22

503:23 586:7,8

Klaine's 509:24 513:11

kleine 270:19

Kloas 43:5 483:12

knew 205:2 301:9 341:7 483:19

knife 559:14

Knoth 272:1,9,12

know 4:15 9:7 11:17 13:23 17:17,18 19:11 59:2

84:6 110:25,26 114:14 125:3 126:5 128:4,6,9

155:6 156:2 157:5 167:5 176:26 186:12,19

187:22 209:11,14 210:7 211:4 212:18,25,26

224:23 252:17 259:7 262:1 268:25 277:7,22,26

285:2,4 286:20,21 287:7,14 297:15 305:25

306:25 311:12 315:8,11 318:1 328:12 332:16,17

335:7 336:13 341:10,15 343:16 344:18 345:13,21

347:18,19 350:24,25 351:7 352:3 355:5 359:25

360:19 361:16 363:4,6,21 364:3 365:15 369:1,16

369:20 371:2,3,8 373:10,13,16,19 375:14,17,20

380:21 382:19 383:9 384:7 385:13 389:6,8,14,20

391:5,16 392:5,7,14 394:10 395:21 396:1 400:17

409:26 412:24 413:13,16,26 416:6,18 417:14,15

417:18,25 418:21 420:5,9,13 421:3 422:22 425:7

425:10,18 435:9 436:1,17 437:13,17,18 438:10

440:6,14 441:8,14,14,19,21,24 442:2,6,9,9,11,11

442:13,17,20,24 443:14,19 444:6,16,21 446:16

448:13,21,24 449:4,13 458:16 459:16 460:20,21

462:17 463:16,22 464:5 465:1,2 467:10,26

468:19 469:2 473:15 474:20,22 475:18 476:11,12

477:9,10 478:20 481:6,20 486:12 490:21 492:11

492:12,13,18,24,26 493:6 496:19 498:22 499:9

499:17 502:8,12 511:1 518:20 519:8 521:8

522:22 523:11 525:12 526:3,16 527:1 528:15

532:7 533:11,21,24 534:13,26 536:2,17,19

538:16 539:6,25 541:3,7 544:10 545:4,5,22,24

546:19 547:6 548:18,19 549:20,23 550:2,9,19

551:1,7,14 552:3,11,14 553:14,16 554:7,18,20

555:6,7,14,20 558:1,20 559:13 560:8 561:7

563:14,18 564:5,7,20,21 565:1,11,11 566:9,17,19

567:7 568:5,7,12 569:7,10,13,14,20,25 570:3,6,8

573:5 576:8,24 577:5,6,7,12 578:12 579:11 591:5

591:10,20 592:17,22,23 593:7 594:4,10,11 595:1

595:7,14,21,24 596:2,3,4 599:15,16,22,25 600:3

600:6,20,23 601:5,22 602:14 604:18 605:1,3,4

606:14,17

knowing 221:21

knowledge 22:13 57:4 178:2 261:11 267:15 299:3

300:1 307:3 508:25

known 20:20 35:6 38:6 103:2 137:11 150:17 151:8

174:15 186:13 198:20 216:19,23 245:17 440:18

473:24 539:12 581:13

knows 349:10 597:9

Kosinki 403:6

Kraak 181:21 188:12 195:13,15,16 203:16 204:6

204:12,19 208:15 209:5 211:17 212:3

Krieger 403:12

kriged 202:24

Kriger 162:20,21,22 178:22,22

kriging 138:4 156:20

Kynetec 289:6 290:15

L

La 7:5,6 62:18,19 63:4 202:20,21 325:6,7,7 327:15

327:16 331:12 336:7 348:12,13 355:7,8 359:5,6

367:3,4 370:26 377:17,18 380:23,25 382:24,25
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387:19,20 391:20,21 410:1,2 415:11,16 428:17

428:18 455:16,16 473:20 475:1 480:6,23 498:21

498:22 509:25,26 587:7,8

lab 111:13,17 203:19 211:10 232:23 256:7 259:4

260:17 266:19,24 332:7,10 343:11 360:15 362:24

368:18 370:16 382:2 443:26 506:12 587:15

label 274:24 278:25 283:19 288:11 300:18 346:1,2

346:5,9

labeled 346:25

labeling 74:4

laboratories 29:8,10,20 43:6 167:14,22 211:7

448:13

laboratory 22:20 27:26 28:24 33:24 34:1 39:16

41:26 43:22,26 44:5,9,25 45:13 49:2 57:8,11,15

57:21 75:2 111:15,22 116:12,17,17 119:19

165:19 173:16 226:19,21 254:23 255:18 267:1

327:20 343:6,8 354:5,5,9 369:21 381:5,14 385:3

391:12 452:9 496:19 504:17

laboratory-based 321:5 444:4

laboratory-raised 344:24

labs 370:9

lab-reared 335:6 342:19

lack 3:23 40:7 41:9,15,21 42:26 45:3,24 59:20

174:6 340:23 341:1 342:4 345:1,16 348:18

350:10 361:18 395:1 396:23 419:25 591:4

lacked 41:2

lacking 345:1 405:9 508:11

lacks 185:16 290:26

laden 168:23 603:10

laevis 23:9 31:5,6,8 38:18 39:5 43:6 45:3 54:23

56:21 191:4 371:14 372:8

lag 256:20

laid 207:19 209:20 520:9

lake 78:22 235:16,17

lakes 7:26 78:9 108:19 111:2 149:25 162:8 164:19

274:5 591:17

Lampert 126:20 235:14 236:2 237:17 238:6,26

248:22 250:16 270:13 400:15

land 21:26 150:15 151:5 168:17 169:23 170:6

281:16 497:20 594:12

landed 406:8

landowners 276:20 281:19,25 283:5

landscape 73:23 267:5,7,14 268:1 576:4

landscapes 594:2

land-water 591:19,22

language 601:14

languages 55:12

large 32:12 46:13 67:12 74:10 97:12 100:25 141:1

157:10,13 164:25 168:15 175:21 185:26 186:21

193:6 194:5,12 197:10,22 211:26 212:2 227:6

234:20 238:15 241:18 246:24 288:25 348:17

352:17 360:16 436:8 544:7 551:6

largely 213:20 255:20 543:16,19 584:13

larger 100:2 117:8 146:10 152:20 256:23 271:4

495:9 515:13 520:17 565:21 583:3

largest 155:8 163:5 164:21 279:10

Larson 272:8 284:5,8

larvae 23:9 194:2,3

lasted 423:8 447:23

lasting 459:2 498:4

lastly 106:7 127:1 413:25

lasts 458:3

late 112:12 127:24 234:19 281:12

latest 273:13

latter's 100:16

laundry 17:16

Laura 272:1,9,12 607:5

laws 3:16

lawyers 176:13

layer 254:19 342:16 527:17

layers 59:25 527:16

laying 177:5

lays 9:24 10:11

LC50 46:14 49:21,21 50:19,23

leaching 329:3

lead 213:9 220:2 279:14 294:3,9 321:10 327:14,22

337:21 344:9,12 359:9 370:13 381:8 388:7

396:19 428:16 430:10 470:24 476:25 490:23

494:20 503:21 506:26 513:8 549:15 599:16

leaded 579:23

leader 169:16 482:11 606:19,20

leadership 177:11 303:4

leading 18:17 164:16 179:23 196:5,12,19 251:10

319:19 323:5 348:24 378:20 386:1 505:16 562:26

570:20 571:2 600:9

leads 70:16 146:10 196:25 200:1 349:6 366:25

513:26

leaking 159:19

leap 441:17

learn 354:23 508:25

learned 478:6 523:17 591:13

learning 587:24

leave 232:25 287:22 389:13 421:9 470:4 471:5

558:8 563:21,22 565:17 566:3 576:10 586:18

591:23 606:12

leaves 468:3

leaving 93:18 168:26 597:7

led 83:10 92:18 328:20 367:7 370:14

leech 594:5

leeching 37:22 73:8
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135:19 141:3,18 171:2 193:19 194:19 195:2

205:26 210:11 211:20,22 226:11 238:25 246:17

258:22 346:24 388:3 435:14 455:4 456:19 466:20

563:23 585:12

left-hand 11:3 242:11,18 256:18

leg 11:20 115:23 164:14

legacy 6:8

legal 165:15 171:16,23

Leggett 300:22,23

legislation 587:25

legs 164:14 168:6

leishmaniasis 166:9

Lemna 257:6 263:5

lends 57:8 399:21

length 30:15 53:19 67:24 142:24 185:6,7 218:1

296:25 324:5 348:7 441:3 557:1,15

lengthy 336:6 489:5

lentic 410:5

Lenwood 182:4 260:26 265:24

lesions 167:26

lesson 591:13

letter 282:25 301:4 377:11

letters 176:16

letting 603:23

let's 9:2 11:7 71:15 123:7,26 128:5 136:14 160:18

167:17 168:10,12 172:12 202:18,24 223:21 226:8

226:9 248:19 285:8 300:10 320:22 359:4 366:8

380:26 384:12 385:8 396:10 409:22 411:1 455:6

466:17 482:1 483:16 494:10,25 496:25 499:3

503:11 511:4 512:17 517:11 529:1 540:19 543:1

543:6 545:18 556:24 562:26 567:9 570:5 575:11

584:22 585:2 595:4

level 1:13 14:3 21:7 22:9,10 23:14,18 24:11 25:7

25:14 26:3,5,17,19 34:11 39:19,24 40:1,14,20,25

42:1,13,17,24 45:20 46:5,11 47:23,24 48:3,5 50:3

51:15 55:14,14 72:20,22 73:4 74:12 79:5 83:17

84:9,13,16 85:4,7,18,22 90:15 94:2 100:25

102:10 111:4 112:1 118:14 120:24 123:7 124:5,6

124:9 138:15 139:14,21 154:2 179:25 181:23

193:3 201:11 207:11 208:25 213:15 232:7 244:2

249:18,22 258:13 261:10 262:1,11 269:14 270:2

271:17 273:6,10,26 290:25 296:1 303:18 309:11

309:11,13,14,18,18,25 315:19 316:5,13 317:22

336:24 337:7 339:8,15 343:1 345:13 348:10

349:17 351:15 353:25 368:13 379:19 389:3

397:13 401:26 403:11 404:16 405:3 410:13,14

429:7 433:10 437:1,3 440:8 452:1 463:18,22

465:20 471:10 497:18 498:24 500:16 501:16,26

502:24 503:4 519:22 520:6 521:24 523:26 529:10

538:15 544:19 551:7,9 569:11,22 575:6 580:23

581:2 586:14 592:23 595:15,16 599:17

levels 13:24 21:5 28:6,13 35:7 53:13 55:26 58:16

58:19,23 73:23 74:20 77:6 94:4 95:18 105:3

111:25 123:5 127:1 133:15 137:2,5,13,14,15

165:5 192:14 196:3 197:24 201:19,22 221:9

224:5 225:7 232:11 238:18 254:13 256:14,23

257:9 265:23 273:20 291:2 308:3 315:23,24

322:5 330:25 334:3,25 341:5,5 351:11,12 387:10

396:25 397:11 401:25 402:24 404:17 429:2

430:15 437:17 481:3 497:13 498:9 502:21 505:2

506:12 578:13,14 580:10 586:16

liaison 2:12 454:15

lie 468:5

lies 168:8 374:15 400:1

lieu 449:25

life 12:21 17:18 114:11 117:14 161:5 162:26

174:19 177:6 180:18 255:14 262:15,17 279:20

282:9 296:24 338:11 341:9 363:7 373:8,15,16

398:25 490:13,18,19,24

lifetimes 277:7

life's 469:7

lifted 172:15

light 73:15,17 87:15 89:6 232:2 259:21,23,24,26

291:7 292:25,26 293:7 407:21,24 444:3 481:11

547:2

liked 357:12 533:22

likelihood 110:19 112:19 318:7 378:5 504:24

514:8,10 517:7,23 519:15 533:1,3

Likewise 460:17

limit 160:10 278:24 279:12 346:24 380:3 407:23

462:13 551:10

limitation 47:1 73:17 265:22 310:10 518:13 574:5

limitations 20:21 44:24 98:12 144:16 413:12

497:25 513:6 518:4 570:17 571:12 573:16

limited 29:14 43:11,24 55:20 57:24 95:16 106:1

144:1,2 159:11 171:21,22 197:18 198:23 266:11

309:23 310:2 328:19 348:26 388:2 495:26 507:26

513:16,21 515:25 518:8,8,9 524:24 527:18

533:24 535:5,5,5 544:8 545:12 552:13 556:9

558:15,16 561:13

limiting 310:14 350:9 517:24,26 578:6

limits 192:14 337:6 340:14 369:1 518:26

Linda 4:13 8:17,17 62:14 109:23 110:9,15 204:16

250:23 251:4 311:25 312:4,21 314:7,12 326:2

343:25 346:12 350:19 362:8 363:12 364:17

370:21 374:22 379:9 383:4 388:13 392:2,22

412:15 434:21 446:22 456:9,9 470:8 472:9

476:14 500:21 510:8 520:1 530:12 538:13 547:15

557:24 560:26 563:14 565:15 577:14,19 598:3
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line 53:8 100:14 121:17 132:18 226:22 232:6

239:19 420:14 432:7 458:20,23 459:7 460:23

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537:23,24 538:11 546:22 547:26 577:5

linear 104:14 108:8 141:25 147:2 148:12 157:18

306:22 312:17,19,20 514:13 537:8,10,18 577:6,9

linearity 246:19 247:1 578:3

linearly 306:17

lined 210:10

lines 98:14 430:8 432:22 480:14 547:11

linier 307:6

link 55:21 59:23 112:12 156:21 298:5,6 325:18

349:1 382:19 389:6 437:23 600:22 601:8 602:23

602:24

linkage 87:16 420:26

linkages 58:22 196:2

linked 173:21 206:26 413:14 600:26

linking 138:5 387:5

links 387:2

Lisa 172:13 302:8

list 4:3 17:16 119:20 183:17 184:9 272:19 290:17

297:7 300:21 327:10 332:5,22 344:20 382:18

400:10 402:16 450:13 529:1 545:3 549:18 557:18

557:20 606:9

listed 39:17 56:10 126:24 155:5 183:16 271:7

344:4 351:25 353:10 400:8 401:6 402:9,16 403:8

405:15 422:20 478:20

listen 213:11 287:2 571:5,6

listened 179:10

listening 326:24 530:13 571:5

lists 393:4 422:8 453:9

liter 30:7 38:17,26 39:12 83:21,21 92:25 120:5,5

122:22 124:4,12,13,20,21 128:14 129:10 131:13

148:23 149:6 212:5,12 231:8 237:3 243:26

258:18 261:24 263:3 332:9 398:17 400:9 401:13

402:21 403:7,9,10,20 404:16 405:3,10 422:21

423:15,19 424:25 426:21 427:14 434:7 460:6

494:15 496:17 499:3 501:16 506:7 508:7

literally 133:7 412:2

literature 22:19 23:7,11 27:14 29:9,12 30:1 31:3

31:19 32:5,26 33:3,6 34:1 36:24,25 38:11 39:2,15

40:10 47:16,19 54:15 56:6 57:13 59:11,13 71:11

115:5 167:22 173:1 190:10 205:12,26 206:2

210:2 257:12 261:12 293:8 303:15 325:2 327:3

328:4 334:18 337:17 338:5 340:22 349:7 350:11

352:25 353:4,7,25 357:18 360:11 369:7 375:8

378:13 450:26 505:4 506:25 553:14 554:23

585:22 586:1 602:24

literatures 110:1

liters 398:8

litigating 327:2

litigation 14:1

little 5:20 10:13,15 20:13 22:16 60:25 67:5 99:6

110:23 112:2 117:4 121:26 122:1 136:10 157:12

173:13 183:18 185:6 186:8 195:17,19,20 203:20

233:5 252:8 277:7 286:20 293:5,7 294:20 302:5

302:21 303:10 308:12 310:17 313:2 314:10

318:16 336:17 338:22 341:19 347:6 348:2 349:26

350:5 388:19,19 390:26 408:18 411:25 416:2

417:12,12 433:12 450:1 456:21 460:10,18 479:4

482:12 483:4 485:9 498:17 508:5 513:12 521:4

530:21 536:1 538:2,6,7 541:19 548:8 562:15,16

562:20 571:7 601:9 602:18,20,25

live 33:20 166:1 167:8 170:26 173:15 177:17 269:6

270:6 287:9 332:17

lived 277:3

livelihood 276:2

liver 386:21

lives 277:3 288:7

livestock 170:2,3,7

living 172:20,24

LLP 272:8

load 286:25 594:2

loading 28:26 30:6 34:24 38:1,9,9,12,16,19,21,22

40:6 41:14 42:6 329:3 332:8 334:22 339:6 340:1

398:17 455:1

LOAEC 48:15,19 49:7,10 51:19,21,23 52:21 53:4

53:6,7,10,15,22,24 54:1,5,13 61:9 62:3

LOAELs 337:6

lobby 1:13 168:9 172:5 177:4

LOC 76:21 79:26 82:12 85:11,13,19,21 86:1,3,4,11

86:14 98:22 99:24 102:12 103:24,24 104:1,24

105:6 106:17 110:2 111:24 115:25 129:5,6

134:16,17 180:9,22 182:2 185:11,16,18 214:4,10

214:13,17 215:5,11,14 217:14,16,20,26 218:22

218:23 219:9 220:2,8,16 221:22,23 222:2,3,10,14

222:18 223:16 241:6,12,14,17,19 242:6,9,15,25

243:1,9,14,15,16,20,25,26 244:3,23 245:10,11,14

245:21 247:23,25 248:2,8 249:16 251:19 253:23

253:24 254:8,22,26 257:21,25 258:17,21,25

259:1,3,5 260:17,19 264:22,24 270:4 273:11,13

273:15,18 277:20,23 279:18 284:3,11 296:7,8,10

297:25 299:2,3,7,9,21,22 300:14 312:12,12,14,17

312:18,18 315:14,15 316:22,23,24 317:10,12,13

317:15,19 318:6,11,16,18,24,26 320:1,5 338:8

408:22 409:14 410:17 420:8 424:23,25 425:5,12

429:7 433:19 438:3,4 439:2,6 444:23 445:2,3

449:4,13,14,23 460:11 465:1 466:1 467:16

468:22 471:7,12,22 473:8,10,13,22 474:7 476:6
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493:15,22 497:9,15 503:16 505:8 521:14 529:13

530:14 546:25 547:13 555:4,12,22 559:9 572:15

575:6 585:25 596:19,22,25 597:6,11,15,21

local 180:2 272:26 569:13

located 269:19 281:3,6 544:18

location 137:4,26 139:12 140:20 159:20,24 195:12

217:19 281:26 294:12

locations 137:14 188:11 191:1 280:22 317:25

547:5,9

lock 177:1

locked 395:9

LOCPATI 102:13,18,23 103:16,18,25 104:8,12

107:22 108:1,3,5,6

LOCs 180:7 269:24 316:2 410:22 430:17,21

LOC's 318:14 497:17,23

LOECL 378:19

log 147:2 148:12 293:19 306:20,22 537:8,9

loggers 560:11

logic 66:6 67:1 98:6 110:3 316:3 396:5 425:6

529:11

logical 134:15 390:23 492:23

logistic 102:17 106:16 109:23,25 110:19 115:22

217:12 242:21 247:13 251:5 306:20 311:26 312:2

312:5,7,11,12,12,18 314:8 429:15 435:10 437:23

437:25 438:25 441:11 467:14 470:11,13,15,18,24

471:19

lognormal 306:21

log-linear 148:26 566:19

long 49:25 51:12 95:18 96:12 97:20 115:3,20

136:10 171:15 173:23 174:15,18 175:14,19

186:12 207:5 218:24 227:5 230:25 246:9 253:5,8

255:22 276:4 280:7 308:21 333:5 373:14 404:9

405:23 422:15 424:11 425:8 445:21 447:8 453:14

453:14 458:3,4 459:3 460:4 462:13 466:24

489:18 495:18,23 499:19 502:22 507:19 520:8

545:3

longer 38:2 81:26 96:19 97:10 105:22 115:6

143:13 144:3 145:4,14,20 204:8 210:6 218:11,24

219:16 221:24,25 222:2 224:4,16 225:1,18,20

226:2 239:15 262:20 267:19 270:11 307:17,20

308:1 408:3 424:20 451:4 463:21 464:13 465:2

506:18 601:25

longer-term 426:10 597:4

longest 513:18

long-term 51:3 111:22 329:4 408:4,12 495:21

502:20 507:13,21 596:21

long-winded 315:11 444:21

long/short 488:24

look 5:15 9:15 11:7,9 13:21 14:15 16:12,18 18:3

20:2 61:11 80:23 110:1,4 111:7 112:5,17 115:11

116:18 118:5 119:2,13,14,25 120:10,12 121:16

123:20 128:11,12 129:13,14,18,19,24 130:12

131:14,14 132:11,11 133:6,24 134:18,26 135:1

155:24 156:3,13,16 157:9 159:1 160:3,6,24,24

162:6 164:6 175:25 181:1 194:22 196:17 197:3

197:16,25,26 198:6,17 199:8,23 200:3,7,12,15

201:17,20,20 202:13 203:12 208:2 210:12 211:1

211:2 212:14 217:25 221:4 222:5,22 225:21

226:5 231:2 241:8,12 242:14 244:7,12 245:16

247:10 248:11,13 249:4,7 250:13 251:3 253:2,10

256:18 257:16,20 259:12 260:7 261:9 292:25

306:9 309:16,24 312:18 316:3 317:14,15 318:25

325:17,17 332:20 334:17 337:24 341:13 348:7

352:2,5 353:2 374:23 376:25 390:23 403:24

407:17 411:2,5,7 419:3,4,9 420:10,18,19 430:20

432:20 440:26 445:3,17 459:5 463:3 465:15,25

467:3 470:12,25 472:23 478:21 483:21 491:6

501:14 502:17 511:19,20,22 517:12 521:9 522:8

522:15 534:13,19 535:19 536:5,12,13 537:20

539:26 541:6 543:10 544:17 545:16 547:20

551:19,20 552:22,25 553:4,10 555:15 558:9,19

560:23 561:16 563:23,26 565:18 567:5,19 568:11

568:13 585:21 586:23 588:21 592:10,21

looked 25:8 84:1 91:16 115:10 135:5,9 156:1,26

157:1,26 160:7 188:14 192:23 198:10 199:19

204:15 205:14 208:12,12,18,22 209:9 212:11

219:22 223:4 248:21 249:9,16 307:1 318:13

336:16 341:20 350:23 420:24 430:23 474:11

508:4 518:25 540:6 542:14 551:18 556:16 564:10

looking 9:16 65:20 69:26 85:5 117:18,20 119:1

124:26 125:9 126:11 127:20 128:13 129:7 131:18

135:7,13,16 136:1 157:25 158:25 159:6 160:15

160:16 161:25,25 180:21 194:26 195:3 208:11,15

210:3 212:6,7 217:25 218:6 219:10 221:24 223:7

223:13,26 224:17,19 225:9 227:24 229:13 239:22

245:15 252:22 261:24 307:20 326:11,15 351:12

361:16 372:1 375:20,25 381:24 387:24 393:21

402:4 410:22 413:14 417:26 420:3 442:12 459:19

459:20 461:14 463:26 464:5 465:10 469:1 472:22

477:24 484:8 486:11,13 491:22 499:10,12 523:20

523:25 525:15 526:8 527:23,24 531:4,8,21

537:12,13 542:17 554:1 560:2 566:19 569:9,10

569:20 574:25,26 575:4 577:8 578:7 587:16

589:18 590:8 593:24 599:14

looks 61:5 63:21 122:4 159:3,19 160:17 192:8

194:3 198:1 200:8 211:13 214:24 232:11,13

247:11 293:4,10 356:10 461:19 463:23 554:24

567:15

loop 112:3

loops 340:26
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loose 597:16

Loren 272:8 284:5

lose 117:11,12,15 118:9 223:25 275:18 382:23

469:7,10 497:21 570:6 577:1

loss 75:16 117:10 118:10 246:19 247:1 248:26

280:18 383:11 408:6 493:18 507:2,3 581:21

losses 87:10,11 117:9,9 543:16

lost 165:11 169:20 275:16,22 364:4 443:13 552:1

553:25

lot 9:7,8 11:22 14:9 16:21 21:9,9 69:15,19 117:3

129:6,17 131:15 134:12 155:13 156:7 166:14,18

166:19 167:20 168:10 175:24 182:20 184:4

191:21 194:17 210:17 213:5 225:24 232:24

234:23 237:10 260:4 274:18 295:3 302:19 309:22

313:8 339:18 340:24 346:3,17,20,25 347:17

348:19 350:26 351:5 352:5,26 353:3 358:9

361:13 367:11 371:24 373:14,20 375:12 381:16

388:16 402:22 409:7,16 410:3 411:9 412:15

414:7 417:6,21 420:17,23 421:17 428:6 429:10

431:1 432:22 436:16 449:24 466:25 469:3 474:5

478:19 481:6 492:25 497:15 498:12 502:6 511:2

520:20 525:19 526:1,7,19,19 527:9 536:6 546:12

547:25 550:6 553:13 554:21,24 555:1 558:10

559:8 560:8,10,15,21 562:1,5,25 565:21 567:1

568:5,11,17,20,25,25 576:2 577:1 579:4 589:6

591:15 592:21 593:15 594:21 595:2,20 597:26

599:16,21 601:19,21 603:13

lots 164:3 166:10 168:8 293:4 301:14 407:11

love 559:3

low 40:21,22 47:6 48:2,4 63:12 71:2 96:12 108:1

120:19 121:4 122:12,12,23 125:25 129:5 151:11

152:5 174:11 221:14 235:21,23 242:19 243:7,8

246:8 259:15 261:16 278:17 286:17 312:19

316:12 322:2 323:21 331:14 333:18 334:8 347:19

355:16,24 356:9 363:26 366:19 368:23 374:1

376:14 378:10,19 469:21 483:21 507:21 518:1

552:26

lower 28:3,9 40:5,13,25,26 42:1,3,5,14,17,24 47:23

48:2 50:2 51:15 58:23 66:9 70:12 81:25 91:13

100:1 107:14 108:7 109:15 132:3 144:25 180:7

193:8 234:15 239:15 253:4 258:16 261:19 266:6

273:11 281:13 291:1 308:10 324:23 335:15

351:13,14 366:20 435:23,25,26 463:21 464:12

469:22 485:7,8 495:21 503:18 505:26 506:11

533:1 538:6

lowered 584:15

lowering 362:14

lowest 46:14 48:17 80:24 129:4 148:10 208:24

235:15 273:26 314:1 402:19

lowly 357:26

low-level 408:4 443:23

low-quality 242:10,17

lump 250:4,5

lumped 61:5 532:9

lumping 250:14 427:8 580:12 583:10

lunch 72:15 125:20 127:25 287:26 294:2,11,23

532:16,25 543:1,3,4

luncheon 71:18 294:7

LVsungen 270:19

M

machete 559:13

macrophage 386:21

macrophyte 117:13 221:8,15 223:10 238:21

252:16 255:25 257:6 581:21

macrophytes 78:25 221:6,11 234:12,14 238:17

255:8,14 260:2 262:26 265:25 266:3 398:13

399:5 401:22,25 581:10,12 589:21

macro-invertebrates 582:8

magnesium 127:1

magnitude 53:13 75:3 90:21 141:6 232:4 255:20

376:3 378:15 401:21 421:16 488:19 509:7 523:1

523:12 576:18

magnitude/duration 267:16

main 42:16 62:1 73:7 179:21 181:19 286:7 301:19

343:25 428:25 432:16 539:4

mainstream 262:4

maintain 283:6 551:7 599:26 600:3

maintained 43:19 331:18

maintaining 121:23 581:22

maize 191:11

major 21:3 24:6 26:14 34:14 83:9 91:24 97:19 99:2

142:16,19 153:25 179:24 255:22 265:8,16 272:18

282:26 298:17 333:7 348:23 349:4 436:1 471:7

472:15 498:4 499:24 501:17 518:15 525:14

548:20 553:25 562:4 571:19 574:5,18

majority 41:26 52:24 59:7 78:8 111:1 167:5,20

169:20 170:19 212:15 218:19 219:15 220:17

248:5 273:24 281:8 282:10 315:24 324:22 405:18

582:23

making 3:10 98:17 113:8 125:5 170:25 315:1

334:13 344:5 345:4 347:26 449:2 461:2 473:4

474:8 487:25 532:6,9 561:26 562:5 577:16 601:8

603:8 604:7,7

malaria 166:9

male 168:1 175:3 176:7 457:11

males 167:25 176:8 210:4,4,6,8

malformation 53:1

malformations 168:5

MALIA 265:2

mammal 196:21,23
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mammals 195:20 197:8 599:16

mammary 6:18

manage 19:24

managed 167:2 289:22

management 10:23 11:4,4,9 14:7,19 15:16 16:13

91:12 97:21 98:8 274:16 276:7 278:12 280:11

286:10 288:21 289:15 291:13 308:7,14 313:19

525:23 544:14 606:16

manifestations 133:7

manifested 408:1

manipulated 57:9

manipulating 330:17 335:24 486:10

manipulations 387:4

mannequin 463:17

manner 52:4,20 53:20 54:4 88:15 124:26 157:10

182:14 202:2 404:12

manufactured 169:1

manure 170:3

manuscript 331:1

map 21:24 22:1 144:7 152:24 153:17 463:16

546:15

mapped 208:24

March 302:23

marginally 90:18

margins 192:14

marine 107:12 410:7

marine-monitoring 434:6

Marion 2:19 6:20,20

mark 178:5 182:4 193:19 261:2 266:15 272:4

283:14

marked 348:17 357:20 401:7

markedly 83:4 125:13,14

markers 191:17 390:16

market 164:11 168:19 172:23 290:20

marketplace 285:23

markets 295:10

Mark's 286:25

Marshall 272:3 275:2,5 287:11

Martha 2:21 8:22,23,23 71:23,24,26,26 72:9

456:14,15,15,18

Martin 176:12

Maryland 261:1

masculinization 358:15

masking 476:4

mass 38:12 97:8 449:3 579:23

massive 96:6 375:24 602:10

Masters 604:24

Master's 403:3

match 283:25 336:20 398:23

matching 568:19

mate 176:8

material 37:21 95:21 136:12,12 231:1 329:3

402:23 496:23

materialized 251:7

materials 4:18,19,23,25 88:21,22 119:25 182:26

184:4 187:3 210:21 263:9 288:14 292:19 293:24

329:2 334:11 396:1 454:21 474:13 607:12

math 318:21,22

mathematical 84:22 91:15 495:7

matrices 55:6

matrix 110:21

Matten 2:1,3 181:11 263:9 292:21 294:11 301:4

333:6 394:17,19 416:12 450:3,5,19 451:8,22

452:11,13,24 453:26 454:9,10 455:5 487:11

488:1 489:9 528:20 605:24

matter 17:22,22 91:9 97:22 123:20 222:15 245:24

252:15,16 301:16 446:11 464:26 471:25 557:26

matters 123:22

maturation 7:11 189:2

Maumee 141:1

maximize 578:12,13

maximizing 577:24

maximum 36:8 37:17 45:20 99:7 103:12 104:1,10

110:19 138:19 142:1,5 147:20,25 148:22 149:2

158:17 161:11,12,14,17 162:1,2 175:2 224:17,20

274:24 278:25 398:17 438:5 444:15,18 468:9

512:2,6 513:15 514:7 516:8,10 517:16 525:2,4,5

533:2 541:3 558:16,18 563:4,9 564:17,22 568:15

571:16 572:14 574:14 579:21

McCoy 325:3 326:14 387:8

McFarland 178:26 179:1,2 181:5

MCGA 276:5

MCL 150:9 161:6

McManaman 2:19 6:13,14,14 67:4 68:9,18 121:13

122:3 123:3 212:17,26 223:24 224:21 262:6,23

448:10,11 449:12 455:10,10 457:14 459:10

585:13,14

McManaman's 457:12 458:24

meal 169:6

mean 14:18 42:3 100:1 103:12 106:2 111:25

129:10 130:26 134:14 138:19 142:1,4,6 146:26

147:20,22 148:23 149:2,11 156:14 157:8 158:17

159:21 200:12 206:22 214:6 215:25 222:20

251:25,25,26 253:15 254:26 286:1 309:2,14

313:17 316:10,15 332:14 333:1,1 334:7 340:6

345:16 346:14 359:7 362:14 364:12 370:12 374:6

377:23 389:5 394:5 411:12 414:25 415:1 418:22

423:10 425:5,22,26 426:8 438:16 440:5 448:14

450:24 459:12 460:10 468:16 469:20 473:23

481:1,11,13,19 492:20 493:26 502:5 513:15,23
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516:8,10,11 518:20 533:10,23 535:16 537:14

538:22 546:20 551:20 556:4 560:19 564:1 569:18

569:19 570:7 572:24 577:10 578:13 600:1,4,4

602:9

meaning 63:1 90:26 132:13 170:1 171:1 314:25

514:9 557:7 565:6

meaningful 91:9,19 260:20 409:14 505:1 506:24

512:6 577:17

meaningfully 254:10

means 86:2,3 90:14 107:11 119:10 122:11 176:6

192:17 235:26 246:6 248:14 300:3 301:18 316:2

316:17 318:19,23 325:19 348:19 363:5 364:10

419:15 420:14 434:5 462:6 472:21 473:14 514:8

536:16,25 546:24 548:15 563:9 595:26 597:4

meant 47:8 50:9,14 52:4 54:7 58:8 61:21,23 62:25

67:14,18,21 68:3,14,14 127:19 130:5 202:23

230:25 345:15 410:11 413:21 447:22 488:23

542:26

mean-fitting 578:17

measurable 252:25

measure 84:3 94:5 95:9 201:3 252:15 257:16

260:21 334:2,6 343:22 349:2 397:2,14,22 402:25

408:7 409:12 410:13 435:3 441:14 446:7 461:17

462:3,5 465:23 492:26 493:8 597:22

measured 30:16 35:21 36:1 41:2 45:11,13,20 55:13

58:20 76:23 79:8 137:15 138:10 141:20 209:22

211:9 213:21 220:22 235:18 236:15 316:4,16

341:17 348:19 404:23 504:23 505:11 527:25

567:2 580:22

measurement 43:20 55:21 59:1,21 77:8 331:17

339:20 397:11 404:21 501:1

measurements 29:1 43:23 78:26 208:14 257:20

259:7 398:12

measures 54:10 174:8 256:15 322:3 328:23 330:17

442:1 571:16

measuring 41:9 45:21 55:5 255:1 256:14 331:18

334:25 348:18 486:4 512:10

meat 321:1 513:12

mechanics 215:19 217:23

mechanism 28:8,10 59:22 73:16 139:15 187:17

192:22 194:9 214:4 289:17 310:9 341:11,15

407:24

mechanisms 55:23 57:1 506:23 588:13

mechanistic 341:7,8

media 4:14 255:25

median 90:6,12,14,16,17 91:11,13 98:14 99:20,26

100:2 218:14,14,16 225:12 253:4,5 256:3,10,12

307:9

mediation 317:16

medical 166:19 565:11

medicine 166:17 341:9

medium 40:13,20,25 41:1 42:12 46:11 47:24 48:5

50:4 60:17 212:23 309:10,13 321:25 513:21

meet 41:5 173:4 277:19,24 278:4,12 284:9 296:15

315:21 337:4 348:4 362:11 371:20 373:21 385:20

391:11 404:6 415:8 421:25 424:13 450:3 573:21

607:14

meeting 1:2 2:2,4,6,11,16,17,24 3:12,24 4:17,18,21

4:26 5:4,7,8,9,11,24 9:8 10:9 15:17 17:6 21:10

22:15 23:5 24:1,7 26:13 27:13 263:13 264:7

305:19 316:6 375:19 385:20,21 395:23 430:1

451:23 454:5,6,12,20,22,22 489:12,19 583:3

585:7 598:19 599:5,5 601:23 605:25 607:16

meetings 3:2 9:20 22:16 24:24 281:21 394:23

474:23 601:16

meets 297:16 360:12 404:11

Melanie 606:21

melding 157:1

member 6:26 8:24 71:23 136:13 310:23 380:4

384:10 389:16 457:10

members 2:18,20,23,24 3:15 4:14 5:19 6:10 9:6

19:24 31:12 186:4 187:21 268:3,10 274:10

288:18 289:1 291:22 293:21 295:7 311:15 352:8

371:19 379:14 399:22 445:14 499:7 501:18,20,26

502:2 519:24 549:1 594:24 598:21 606:15,22

607:14

membership 268:21 295:3

membrane 186:25

membranes 73:13

Memo 302:24,25

memorandum 21:14 22:4 273:21

memory 363:19

men 199:6

mention 237:24 250:19 253:12 314:7 322:19

429:22 470:14 526:8 533:26 579:19

mentioned 25:25 26:7 48:17 64:9 90:8 100:8 106:3

132:8 184:19 210:21 228:3 238:5 274:23 294:11

294:14 319:25 322:17 323:26 340:23 345:10

353:18 359:26 365:16 408:12 420:5 431:14

442:18 451:23,25 457:1 473:24 474:2 475:26

478:23 499:11 521:5 524:17 525:12 547:18 548:5

548:8 554:17 568:8 577:22 592:1 593:20,22

603:12

mentioning 340:21 579:18

mere 296:19

merely 270:23

Merritt 272:6 279:25

MERRITT'S 283:11

mesh 379:26

mesocosm 15:20,23 23:20 25:6 34:3 74:16 181:25
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184:17 185:14 220:12 226:22 251:17 329:16

382:15 397:26 402:12,13 405:9 409:22 410:4

412:11 415:19 419:11,26 422:4 425:1 453:1,8

499:12

mesocosms 12:11 13:6 18:18 133:19 134:16

226:17,25 322:8 382:4 383:10,23 397:4,6 404:1

410:11,26 411:4 423:23 586:13 590:17

mess 327:17

messages 562:7

messes 254:7

met 3:21 25:9 33:25 35:9 41:24 77:3 157:20 270:11

326:17 330:1,20,23 333:15 358:8 382:16 402:20

404:17 422:8 452:6 454:19 561:7

meta 325:3 326:14 478:16 479:1,6,22,26

metaanalysis 377:5 387:7

metabolism 37:4 87:11 187:14 322:12 399:10

metabolites 201:21 202:9 505:13

metals 7:23

metamorphic 31:20 47:4 52:26 68:1

metamorphically 189:23

metamorphoses 65:9

metamorphosis 29:22 30:14 39:7,12 43:8,16 46:21

46:24 47:8 48:12 51:1 52:19,24,25 54:18 63:26

64:11,16,17,21 67:22,23 330:16 351:24,25,25

377:26

metaphorically 189:25

meta-analyses 189:10

meta-analysis 189:4,7,10 431:25 432:2 450:9

meteorology 140:19

meter 398:14 553:24

meters 159:26 160:1 398:21 423:4

methane 170:4

method 22:8 26:17 47:17 72:22 76:10,12 77:13

87:24 96:25 99:24 105:11 118:8 132:21 136:17

138:13,16 141:9 146:5,16 147:11 148:10,13

154:20 155:17 156:26 157:3,4 160:11 162:5

181:22 192:8 227:1 259:23 345:22 351:1 514:26

515:5 516:18 518:4,15 529:15,16 530:5 539:12

539:14,15 566:13 571:13 572:11,21 573:17

577:14 590:6 596:24

methodological 190:3 213:14 244:20 248:2 260:15

264:19

methodologically 189:23

methodologies 179:14,18 189:9 360:24 504:8

519:4

methodology 21:4 23:15,16 27:8 34:17 61:3 72:20

73:2 74:7 76:13 81:3 82:5,15,25 83:7 84:25 85:3

94:9 98:20,22,26 101:1 103:15,23 104:24 105:9

108:26 123:12 155:3 185:22 193:21 194:13

213:16,19 214:9 240:22 245:12 247:24 249:20

251:19 254:9 267:21 296:6,8,9 305:14 308:24

373:21,23 417:2 428:12,15 431:20 457:25 473:3

476:7 503:16 547:22,24 548:7 578:18 579:6,7,9

methods 20:10,24 21:5 26:16 44:16 46:7 57:4

138:1,3,19,21 139:3 143:4 146:14 147:7,9,14,24

148:8 156:13,14,16,20 157:5,17 168:17 179:25

180:21 198:24 206:22 226:23 227:3 344:15 351:7

351:8 352:3 379:23 381:4 382:15 432:5 436:8

439:24 470:17,25 518:16 520:15 529:12,19 539:3

542:23 546:11 547:11 566:3 572:20 597:14,20

598:11

metric 124:25 213:21 461:21 464:22

metrics 265:5 266:2 464:4 514:17 567:2

Mexico 175:24 304:12

Miami 7:10 455:18

mic 5:21

mice 323:18 375:26

Michael 7:25,25 115:2 261:14 289:14 405:12

422:13 430:25 455:26,26 495:1 509:19 511:11

589:10

Michelle 7:9,9 111:23 127:11 205:10,24 206:14,21

211:24 324:12 329:19 333:9,13,21 349:19 360:8

368:12 373:6 377:23 382:1 383:22 386:9 390:11

450:12 455:18,18 485:14 496:20 587:13

micograms 499:3 508:7

micro 34:2 582:8

microbial 43:26 44:2,9 73:7 237:14 249:1 399:4

microcosm 15:21 23:20 25:6 74:15 78:22 181:25

184:17 185:14 186:22 218:6 219:7,13 245:17

397:4 412:3 496:19

microcosms 134:17 226:17,20,24 237:19 397:5

586:13

microgram 120:5 237:2 243:26 401:13 403:7,9,10

404:16 405:3 496:17

micrograms 83:20,21 92:25 120:4 122:22 124:4,12

124:13,20,21 128:14 129:10 131:13 148:23 149:6

212:4,12,13 231:7 258:18 261:23 263:2 400:9

402:20 403:20 405:10 422:21 423:15,19 424:25

426:21 427:14 434:7 460:6 494:15 498:25 501:16

506:7

microorganism 78:21

microphone 455:3

microphyte 402:3,7 581:8

microphytes 228:22 252:23

micro-invertebrates 582:16

micro/mesocosm 213:24 214:3,21 218:1 224:8

225:18 226:5 240:22 242:24 243:2 244:21 245:4

245:16 247:12 248:5 254:25 257:22 259:4 260:18

264:23

middle 81:23 440:9 460:19 462:25 473:25 474:4
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midwest 159:16 169:20 172:20 266:4,12 280:15

282:7 283:10,24 297:2 302:26

midwestern 23:23 103:4 143:20 144:15 160:10,12

213:15 265:26 269:10 411:1

mid-Atlantic 172:21

mid-2011 321:19

might've 311:24 314:21

mildly 378:21

mile 21:26 555:8

mileage 171:1,3

miles 172:16 281:5 557:3,4 574:6,20

million 135:10 164:8 165:18 166:25 172:17 174:12

275:10,19 283:24 289:7 295:18 464:19,19

millions 172:20,24 175:16 271:23

mimic 411:6

mimicking 335:13,22

mind 82:3 156:5 158:25 209:11 254:1,3 255:17

356:17 388:22 411:21 468:14 520:19 521:20

529:20 530:18 547:12 558:18 559:15 561:21

567:16 575:17,24 591:9,19 596:21 598:15

minds 169:9

mindset 554:20

minimal 35:4 76:9 275:16

minimally 35:20 77:10

minimize 130:15 356:15,24 357:7 522:23,26 523:1

523:11 576:17,18 578:13

minimizing 577:23

minimum 77:21 150:8 398:2,20 419:24 444:12

445:1

mining 227:7 560:8

minnows 175:8 322:23 378:3

minor 93:13 207:12 430:5 436:19 458:22 460:10

460:23 462:20 508:17 518:13

minority 172:6 174:1

minus 536:15

minute 4:7 177:23,24 200:21 225:7,8 228:11,18

342:20

minutes 4:8 5:11 20:4 21:1 23:11 201:4,6,8 208:11

208:12,13 223:19 226:9 305:11 332:25 355:1

384:8 385:22 450:4 463:18 464:12,18,19 494:7

532:24 584:25 585:9 602:14

mirrored 60:14

mirrors 185:7

misinformed 311:1 542:26

misinterpretation 270:3

misleading 218:9 310:14

misplaced 283:8

misrepresent 530:1

missed 97:10 311:24 420:22 438:14 448:15 519:16

599:3

misses 97:7

missing 317:23 346:6 421:6 447:17 518:14 527:7

533:3 568:20 591:19 602:23

mission 162:24 168:13 172:2

Mississippi 175:16,17,23 302:26

Missouri 7:14 81:21,24,25 143:20 144:2,11 272:3

275:3,6,8,9,10,13,17 276:3,10,17 283:18,22,23

402:22 404:23 455:21

Missouri-01 141:4

mistake 126:3,9,11

mistakes 564:23

misunderstand 427:6 491:24

misunderstood 426:26 557:25

misused 432:11

mis-scoring 400:7

mit 270:17,18

mitigate 273:14,23 397:17

mitigated 273:20

mitigation 21:15 278:23 349:16

mitochondrial 191:16

mix 143:8 341:26

mixed 591:4

mixes 323:13,16 338:10 341:25

mixing 344:25 470:17

mixture 138:5 437:13 504:6,12 591:8

mixtures 33:11,12 76:22 504:3,9 578:8

ml 270:17,17

MOA 227:16,18

mobile 73:6

mobility 73:9 311:11

mobilized 132:8,9

mobs 207:13

mode 6:7 13:19 77:2 110:6 180:22 181:18 185:17

186:13,15 196:4 230:6,12 236:25 239:25 254:2,4

255:4 256:17 259:22 260:13 264:16 289:11

290:26 368:25

model 23:17,22 24:1,3 84:22 86:21,23,26 89:1 90:2

90:4 91:3 99:25 109:24 110:20 134:23 139:6

140:12 146:24 156:8 184:5,8,11,16,18 185:3

202:12 213:18,24,25 214:1,2,7,9,12,17,19 215:4

215:11,14,19 216:4,5 217:1,18 221:20 222:15,19

224:25 241:22 243:23 244:15,20,26 245:7,9,14

245:23 246:9,11 247:5,8,10,11,16,18,21,24,24

248:3,6,15,17 251:21,22 253:7 254:5,7,20,22,24

257:18 258:9,14,24,26 267:7 270:4 271:23

277:15 282:12,14 283:26 297:25 298:6,9,23,24

298:25 299:2,7,9,21,22,25 300:5,10,14 306:13,15

312:6 314:8 323:6 374:7,8,16 376:18 413:5

422:25 425:24 429:23 435:20 449:9 459:4,5

460:12 463:22 470:13,13,15,18,20,21,22,24,25
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471:2,19,20 476:2,8,16 492:21,22 493:1 500:7

519:11 522:18 527:7 534:10,16,17 535:18,19

536:2,5,10 537:8,15,16 538:15,18,18 539:10,16

539:19 540:11 541:21,25 542:14 563:15,16,19,20

563:22,25 564:2,9,10 566:26 568:19,22,24 570:1

570:17 572:2,3,17,25,25 573:17,18,22,24 574:12

574:22,23 575:1,17,23 576:13 578:18,26 579:4

579:15 592:13,17

modeled 91:5 269:15

modeling 138:5 139:1 140:11 157:1 179:15 184:26

267:14 298:9 399:19 440:1 463:13 525:19,19

531:25 548:2 557:13 558:11,26 566:24,25,25,26

579:21

models 15:24,25 78:14 86:20 91:15 98:15 99:11,18

108:23 110:4,8,14 133:5,15 184:22 199:1,7,7

203:19 214:14 258:7 268:1 269:11 277:17 298:1

298:10,15,16,20 378:16 381:18 396:17 399:12

406:5,16,25 424:22 434:14 448:7 458:9 512:14

526:1,7 527:13 542:13 543:11 544:2 545:1 548:4

563:12 567:16 572:7 575:21 576:3 581:18 590:18

590:19,20 600:24

model's 575:1,2

model-based 527:26 548:20

moderate 115:26 267:18 514:20 524:24 525:3

558:21

modern 169:14 238:15

modes 289:18

modification 46:17,22 56:4 58:12 557:12

modifications 31:23 48:14,14 54:16,26 56:7 57:25

58:7,8 600:10

modified 104:18 109:1 541:19

modifies 201:23

modify 578:1

modulation 256:15

Moines 301:26

molecular 13:19 31:22 46:22 48:14 54:16,26 55:4

55:14 58:15 59:4,23 599:17

molecularly 389:6

molecular-initiated 28:12

moment 324:9 391:15 439:17 535:8 606:24

moments 603:16

monetary 168:16 171:5

money 177:6 275:21,24 301:15 349:12

monies 395:25

monitor 22:5 32:1 95:17 139:25 142:16 153:11

273:4 276:14 521:15 544:7,8

monitored 150:13 278:9,10,10,18 280:23 291:5

521:6 544:6,12 546:25 573:1

monitored/unmonitored 544:10

monitoring 7:19 11:24,26 18:18 21:6,15 22:11,11

23:15,19 25:17,18,19,21,24 26:9,10,18 102:26

103:1,7,9,17,26 104:4,7,9,12 106:6 107:12 109:3

118:15 136:17,24,25 137:1,3,6,9,11,16,18,24,26

138:3,6,8,10,14,16,18,20,22 139:1,7,9,11,16,18

139:19 140:3,11,12,14 141:5,17 142:8,9,13,15,18

142:20,21,21 143:2,3,7,12,18,19,22,24,26 144:6

144:8,8,9,12,13,18,19 145:26 146:14,21 147:16

148:13,16,17 149:17,18,26 150:2,5,10 151:2,17

151:22,23 152:6,8,11,12,14,16 153:9,23 154:4,5

154:10,12,16,22,24 157:2,14 158:1,9 160:6,25

179:13 219:22 226:4 267:13,18 271:23 273:7,19

274:12 280:23,25 282:9,13 283:18 314:20 315:21

318:25 350:14 405:25 409:20 412:2 436:22

439:26 472:21,22 490:12 491:12 505:22 509:8

513:3,7,20 514:5,6,13,15,20,23 515:8 518:16

521:18 522:2,3 524:4,16 525:5,9,10 529:17

531:21 532:20 545:3 546:24 547:8,9 550:23

552:6,19,23 555:14 560:11 563:4 570:19 571:23

572:4,23 573:13 574:7,24,26 575:3

monkeys 166:13

monotonic 70:7 340:23 341:2

Monte 516:3,18 517:17 519:6 533:4,23 534:24

538:1

Montezuma 232:22

month 51:6 303:17 515:1

months 51:6 64:20 421:6 502:23 581:8,9 607:10

moral 167:7

morning 4:2 6:1,23 7:5,20 8:5 19:23 27:1 60:23

71:15,24 176:25 195:21,26 196:10 207:10 215:20

238:5 249:11 263:12 272:12 294:23 305:14

359:26 379:16 450:1 454:9 456:15 467:18 470:9

471:22 473:25 525:21 528:18 530:13

morphology 30:16 67:24

mortality 30:13 34:23 36:3 44:11 46:12,15 50:20

50:21 51:20 84:10 335:11,15

mosquitos 166:8

mother 301:14

motivated 471:11

Mountain 166:9

mountains 165:5 191:14,20

mouse 375:18,21

mouth 5:22 164:26

mouths 164:25

move 12:14,15 15:14 19:2 73:10 113:17 114:4

130:20 131:11 136:14 162:19 189:17 207:2

212:22 213:7 279:15 327:8 338:12 353:23 365:11

380:26 384:12,26 385:3,8 394:14 396:10 420:10

425:22,23 472:2 483:16 485:22 488:5 493:6

494:7 503:12 512:17 534:1 549:4 562:26 575:17

586:11 599:11
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moved 184:9

movement 159:4 171:18

moves 11:1 468:5

moving 10:12 14:20 15:3 105:5 126:1 130:24

140:6 184:10 203:17 256:16 273:15 282:17

320:19 329:3 392:20 394:2 396:11 440:22 450:26

477:22 513:11 517:16 561:24 571:25 574:14

575:2 579:21

MO-01 278:22

MO-05 266:6,8

much-needed 366:9

mud 511:20

muddled 479:3

muddying 308:12

multidimensional 578:25

multinational 172:5

multiple 9:16 34:6 42:22 55:2,25 58:16 59:24 62:7

62:9 74:23 81:26 84:18 89:7 116:4 142:19 208:18

215:25 226:6 228:19,20,25 250:8,9 288:26

305:21 306:1 330:8,8,9 356:25 357:5 358:23

365:2 374:2,3 392:25 397:10,14 427:2 448:4,4

462:21 466:7 480:14 484:14 504:5 511:26 531:20

535:1,7 548:22 549:21 578:2,3,4 592:15

multiplicative 138:9 145:13 540:1,11

multiplied 540:6

multiply 149:2 155:19 549:24

multiplying 304:20

multistep 466:25

multitude 163:12,25

multivariate 323:15 324:4

multi-active 33:11 76:22 504:3

multi-collinear 420:15

multi-variate 267:7,11 268:1

multi-varying 574:12

mutual 404:14

mutually 163:26

mu5ten 270:16

myriad 125:2,3

m3 397:6,6,7

N

N 32:26 40:10 71:8 562:5,15,16

naEve 266:19 423:25,26

nail 162:8 474:25

nailed 360:21

naive 184:18 245:23 246:9 247:11

name 2:3 6:4,23 7:5,12,16,20,25 8:5 19:24 136:21

140:18 162:21 179:2 181:6 186:6 195:16 213:8

268:11 272:12 275:5 288:1 289:16 294:26 297:8

301:7,24 414:11 454:10 455:7

named 281:7,7 506:12

names 301:14

name's 455:8

narrative 193:7 194:25 204:23 206:12

narrow 118:26

narrowed 555:20

Nascimento 322:13

nation 268:22 605:4,4

national 8:14,15 12:11 14:20,21,23 113:14 142:11

165:7,11 176:12 189:21 226:18 275:21 288:25

295:1,6 298:11 304:6 375:15 574:12

nationally 152:11 573:11

nationally-distributed 570:19

nationwide 275:20

native 22:20 31:6,11 56:20,22,26 57:5,16 59:19

71:1,4 119:21 120:1,7 121:6 164:20 165:1 368:9

371:25 372:10,13,16,23 381:4 480:21 482:15

natives 120:25

natural 8:7,8,10 65:15 74:16 78:9,14 79:1 89:5

108:15,24 111:2 116:13,21 166:11 169:21,23

190:23 192:4 197:7 281:13 282:2,15 303:6

329:21 331:19 334:22 362:24,26 363:1 365:24

366:1 382:3,4,10,12 390:18 396:17,22 397:17

399:3,6 408:24 409:11,13 413:5 437:11 480:21

506:13 510:22 512:15 591:15

naturally 164:18 327:21 330:11 470:24 594:3

nature 26:25 68:11 70:5 95:9 110:5 162:26 173:25

193:9 244:19 301:13,14 329:21 335:13,16,22

368:15,17 386:26 390:22 527:24 552:22 561:2

natures 582:15

navigational 183:1

NAWQA 152:10,14,16 154:11

NCGA 296:4,11 297:3

NCWQA 25:19

NCWQR 142:12 267:16 514:6

NE 281:12

near 76:5 90:18 128:23 165:21 247:6 285:6 508:13

nearby 157:26 158:4

nearly 97:18 241:19 275:12 277:8 358:3 518:1

586:25

near-term 596:21,22

Nebraska 143:21 159:15,17 272:6 279:26 280:2,6

280:15,24 281:13,18 282:21,24 283:5,5,13

necessarily 40:2 61:16,23 87:4 116:6 230:1 313:16

316:15 337:13 339:12 365:17 403:15 421:21

440:7 520:7 525:15 580:17 588:23 592:7

necessary 91:12 160:26 172:11 190:17 271:5

329:11 333:18 359:23 360:4 388:8 390:18 416:13

452:8 466:12 485:11 544:19 572:7 584:12

necessities 245:3

necessity 336:9 498:17
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need 10:25 14:26 18:8 21:10 28:10 46:5 55:12

69:18 75:25 81:7 83:12,16 104:3 112:14 136:4

140:12 160:23 169:19 183:3 185:13 186:2 213:18

215:11,14 221:22 225:25 228:14 245:9,13 247:8

247:24 249:11 271:4 273:20 280:11 303:26 304:4

314:21 316:17 317:16 318:22 320:18 323:16

327:20 332:20 335:8 338:1,13 341:13 343:18

351:7,11 352:10,16 356:2,5,11 359:21 360:21,24

361:11,13,26 363:2 364:11 365:23 373:18 374:3

374:19 375:20 376:17 380:8,16 382:3,7 385:15

389:4 390:15,19 394:21 395:17,19 396:1,1

398:19 400:13 410:10 420:24 421:23,25 425:6,13

426:4 433:25 440:14,18 445:9 451:5,6,7,21 452:2

458:18 467:6 468:19 471:7 478:8 483:6 485:16

486:18,20 487:18,26 489:3,23 493:6 504:26

507:8 509:5 512:18 515:26 520:10 521:15 522:14

526:15 531:16 533:26 540:15 541:24 546:12

547:2,4 549:17,24 550:25 551:7,13 552:6 553:7

555:24 559:7 562:18,19 563:19 565:5 572:21

573:21,24,25 574:4,11 577:13 585:21 586:15

588:19,25 592:9 595:6 597:22 598:11 600:16

601:3 602:25

needed 16:13 56:2 82:10 131:8 154:4 184:23

248:17 267:21 301:9 368:13 392:16 480:11 486:7

496:6 506:14 509:17 514:1 518:2 574:7 587:9

needing 414:4

needs 11:1,12 14:1 83:19 97:1 174:9 185:22 202:4

221:1 277:20 292:19 293:24 301:1 337:1 358:2

371:7 378:7 394:17 398:18,20 414:3,5 443:18

444:12 450:3 467:20 477:2,16 480:5 488:24

546:17 547:3 555:9 573:19 576:9 591:9

negated 307:23

negative 30:5 35:25 37:11,14 77:17 145:11 178:1

204:3 250:17 279:14 284:3 296:20 328:21 333:14

337:12 339:24 347:21 361:20 374:10 377:1,7

383:17 388:20 564:16 565:12 566:18

negatively 74:3 277:21 387:8

negatives 318:11 337:12 338:15

negligible 44:14 409:4 413:23 453:18 489:1

neighborhood 304:15

neighboring 278:10,19

neither 47:2 534:4

Nelson 527:9 553:3 606:20

nervous 196:15 540:12

net 75:17 236:9,13,14,18

network 76:2 264:6 268:4,13,15,21

networks 594:4

neurochemistries 285:24

neurological 323:19,22

never 70:5 114:13 251:7 358:14 377:8 434:25

437:5 446:4 475:14 534:19 557:26 565:9 575:25

nevertheless 233:6 322:1 339:13 370:1 431:5

new 24:1 31:19 93:4 94:7 122:5 182:2 184:4

187:24 188:5 238:12 273:12 276:12,12 286:4

290:24 295:10 296:6 306:4 308:23 310:24 320:20

351:14 366:18,24 367:6 378:10 399:16 408:21

424:15 425:6 435:13 442:21 456:1 477:2 479:16

557:8,10 568:25 584:14

newer 285:22,25

newest 2:18

newly 283:20 296:9

NE-04 281:1

nice 127:15 156:15 157:16 345:13,24 346:13

347:24 410:9,25 411:3,11 459:16 477:12 492:7

578:21 584:26 585:16

nicely 178:7 410:23

niche 442:21 502:3

night 322:25 470:9 524:21 606:13

NIH 376:21

Nile 166:8

nine 46:10 280:23 422:8

ninth 516:17

nitrate 33:18

nitrates 265:10

NOAEC 43:15 48:15,19 49:7,10 51:19,21,23 52:18

52:21 53:4,5,7,9,14,21,24,26 54:5,11 61:8 62:2

NOAELs 337:6

Nobel 166:17

NOECL 378:18

noise 286:25

noisy 110:5

nominal 29:22 35:20 328:23 343:21

noncovalent 255:5

nonlinear 71:3 77:22 100:23 146:23

nonmonotonic 70:8 208:10 209:4

nonnative 119:19 120:1

nontraditional 188:26

nonvascular 75:6

non-adjusted 150:3,4 152:15,17

non-adverse 211:23

non-bias 576:18

non-encountered 504:25

non-flowing 405:17

non-linear 537:1 540:11 542:12,13

non-linearity 306:25 578:9

non-modifying 483:21

non-monotonic 347:12 355:17,24 356:9 365:2

non-native 22:21 164:17

non-natives 121:12

non-profit 288:16
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non-science 287:8

non-significant 331:14

non-target 74:2 330:15,25

non-worst 519:16,20

normal 65:13 69:25 230:2 312:14,18 470:14,20

510:21 581:14

normality 312:7

normalization 119:1 294:16 462:1,18

normalize 119:1,15 134:19 158:21

normalized 446:24

normalizing 119:4 121:16 122:3 132:17 135:25

446:17,23

normally 582:15 585:6

north 7:6 79:2 81:17 121:6 144:11 164:21 175:25

188:10 195:12 373:13 455:17 468:17 472:22

587:16

northeast 191:20,26 587:18

northern 7:2 276:18 455:15

Northey 303:5

Northwest 545:13

notable 213:1,2

note 23:1 42:18 49:25 51:12 53:9,12 54:4 82:1

93:18 98:18 150:13 151:4 184:3 270:9 292:13,17

300:6 306:6 322:21 369:10 382:3 401:9 402:5

403:4 514:25 516:16 524:2 559:20 576:2 584:4

590:5

notebook 292:23,25 293:12 605:25,25,26

noted 29:26 38:9 52:16 83:1,11 95:2,16 170:21

176:3 211:13 306:4 307:12 308:6 400:20 551:4

notes 228:10 401:6,17 403:7 450:7,11 470:5

482:10 606:4

nothing's 375:4

notice 53:4 194:10

noticed 66:2,23 204:16 209:8 433:4

noticing 189:15

notion 114:11 121:22 495:24 507:23

no-effect 51:8,9 107:23

no-effects 102:7

no-till 271:13 272:21,22 278:3 280:13 290:9,14

291:12 295:22 303:11,24 304:1,3

NTE 432:8

nuance 527:19

nuances 527:16

nuclear 55:2 191:17

null 358:8 375:3,6

number 1:7 3:24 4:21 32:7,12 38:13 42:20 44:5

52:1 54:17 57:24 58:18 59:1 67:13 70:13 71:2

74:11 94:20 105:19 123:21,22 128:10,12 131:18

131:19,19 132:11,20 135:14 144:21 145:24 146:1

146:5,17 147:2,4,25 148:18,24 152:19 153:14

160:15 186:18,21 188:16 191:1 192:10,12 195:1

195:22 203:4 204:20 205:5,5 208:4 209:20,21

211:8 212:10 213:1,3 216:5,7 234:24 239:6,23

240:20 242:20 244:24 246:1 259:9 260:22 273:11

286:7 287:7 289:21 293:2 296:19 321:4 333:16

354:6 365:20 373:16 376:25,26 382:18 386:16

399:15 400:14 401:14 405:9 410:21 445:8,19

451:22 454:24,25 457:9 464:17 475:13 477:25

483:10 491:3 500:7,9 503:12 515:2,16 516:19

518:17 527:13,14,20 528:4 529:25 533:6 534:3

534:13,14,16,22 544:7 545:23 546:10 548:1

549:9 550:12 553:26 556:9 558:24 559:20,25

560:3 561:10,14 563:7,8 564:17,20,22,23 567:8

567:10 572:1 583:18,25 584:9,13 592:2,13 606:3

numbers 42:2 63:18,21 106:13 130:25 146:8,13,23

207:19 208:2 235:26 273:9 277:16 314:9 428:22

435:9 440:25 465:12 566:12

numeral 321:3

numerical 77:23

numerous 99:22 167:13 177:26 269:16

nutrient 288:21 303:6 309:20 396:26 398:15

399:11 443:9

nutrients 87:16 127:3 260:7 291:6 302:6,6,18

303:1,7 304:7

nutritious 498:7

nuts 491:9

O

o 130:14 369:15

Oak 226:18

obesity 170:15

object 556:26

objective 39:14 48:9 229:10,12 330:24 492:22,26

493:8 576:16,17 579:21

Objectively 491:11

objectives 32:16 138:12 268:18

objectivity 298:18,19,20 492:21

obscure 271:24

obscured 210:17

observation 61:18 74:17 229:26 252:4,5,6 277:22

370:26 516:5 582:24 590:11

observational 344:6 441:22

observations 50:5 56:8,11,15 70:6 238:19 250:10

250:10,12 286:22 298:25 541:20 580:24 598:7

observe 50:11 68:8 283:26

observed 28:9 36:13 44:9 51:1 52:23 54:24 56:12

56:15 67:25 68:1 79:6 96:4,20,24 109:16 150:21

151:15 188:6 190:24 191:3 197:13 203:23 207:14

230:4,14 231:10 233:20 234:4 235:1 265:15,16

266:19 296:3 344:9 366:3 516:2 534:11 580:20

580:24
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observing 533:1

Obstetrics 6:17

obtain 102:8 108:6

obtained 29:7 47:15 583:2

obtaining 338:7 391:10

obvious 184:14 207:14 210:5 345:2

obviously 89:15 190:8 192:18 200:8 207:19 210:13

225:17 244:16 251:14 255:8,13 259:19 262:14,16

287:7 380:11 410:3 482:5 511:2 523:22 595:9

occasion 593:3

occupied 260:6 266:3

occur 22:8 229:25 230:17 233:16 262:1 271:18

291:3 378:1 403:25 496:16 511:2 519:18 564:21

580:18 581:10

occurred 44:1 76:8 82:8 96:22 150:16 151:7

230:20 257:8 271:19 400:5 421:5 432:21 463:5,7

580:15 583:14

occurrence 36:14 37:6 140:18,22,25 141:1,6

142:14 143:24 144:5 192:4 440:4

occurrences 282:15

occurring 115:7 269:20,23 316:16 318:13 492:18

580:26

occurs 81:18 96:6,10 201:7 219:20 255:12 259:17

261:10 266:26 269:17 313:12 421:4 502:19 575:7

581:7,11

OCD 69:17

October 23:5

odds 316:11

OECD 226:26

offer 187:22 268:2 279:4 285:5 295:5

offered 227:26 583:19

office 3:19,20 9:3 12:20 13:12,13 19:25 27:2 32:25

33:4 69:14 72:2 82:17 180:18 454:16 456:16

Officer 3:18 275:2 288:2

offices 132:22

official 2:4,17 3:11 181:12 207:7 454:11

officials 3:13

oh 71:25 110:12 206:16 372:5 472:8 510:11 547:20

571:1 603:19 605:19

Ohio 7:10 142:17 143:21 144:2,12 545:13

oil 164:4

okay 60:22 71:14 72:10,11 118:7,17 129:16 131:11

136:4 162:11,14 178:22 182:11,13,18 202:18

204:10 223:21 226:8 287:19,20 293:6,12 294:1

294:21 297:6 300:21 305:2,2 310:19,26 311:13

311:17 312:23 313:20 314:10,13 315:22 317:7

319:21 320:11,15,19,22 321:10 324:10 325:6

326:6,8,21 327:7,16 332:23,25 333:3 336:2

343:24 346:18,22 348:6,12 351:17 352:7,9 354:2

354:2 355:7 357:3 359:4,6,18 361:9 362:6 364:8

364:10,22 366:4,6,8,13 371:18,19 372:2 373:5

375:13 377:9,11 378:8 379:13 380:5,7,26 381:8

381:26 384:12 385:5,6,7,8,23 388:9 389:15,22,25

389:25 390:25 391:21 393:12 394:15 396:7,8,19

405:11 413:2,18 414:22 415:16 416:13 418:12

427:25 428:3,8,16,18 432:14 434:18,20,26

435:11 436:12 440:10,15 448:1,8,9 450:5,10,12

451:8 452:11 453:26 457:11 461:4 465:20 466:2

466:16 470:6 472:11 473:10 475:12,16 476:13,17

476:18,22 477:15 478:8,14 479:8,19 480:3,23,26

481:22 482:1 483:16 484:16,20,26 485:21,21,25

487:8 488:3,5 489:2,22 491:14 492:3,7,10 493:25

494:6,10,25 495:15 496:12 500:20 502:25 503:7

503:11,23 509:18,25 510:2,12 511:3,4,7 512:16

512:17,22,25 513:8 519:25 520:26 521:14 523:14

528:2,3,8,9,11,20,21 529:1,26 530:11 532:11,15

535:9 536:19,22 538:12,26 540:2,7 542:7,20,25

543:5 544:23 546:1 548:26 549:1,4,14,16 553:8

553:11 554:17 556:20 557:23 558:3 559:18 560:4

561:15 562:21,22,25,26 563:13 564:12 565:25

569:23 570:10,11,14 571:1,2 573:8,9 575:9

577:18 580:2,3 584:17,18,21 585:5 587:19

597:19 605:23

old 76:12 80:5 169:7 188:5 251:2 301:8 406:24

407:12 421:14 593:8

older 205:14

oldest 165:7,10

omitted 291:4

omitting 44:13

once 48:23 85:2 210:14 217:11,15 220:23 253:22

266:26 299:18 343:16 424:11 437:24 439:4,8

541:26 559:23 561:7,9 578:1 591:11 597:21

ones 19:12 35:14 50:8 69:5 115:26 157:17 168:6

189:11 191:12,21,22,24 198:4 207:13 235:2

239:5 249:8 253:15 410:4,19 417:17 420:9 460:4

468:1,2 512:10 546:14 553:7 562:12 586:12

one's 577:15

one-day 443:15,17 446:8

One-fourth 165:8

one-page 525:21

one-sixtieth 446:5

one-size-fits-all 551:12

one-year 529:26

online 336:20

onset 203:22

Ontario 195:17

open 1:2 4:16,20 11:17 18:26 19:1 22:19 23:7

27:14 29:9,12,26 31:3,19 32:4,26 33:3,6 34:1

36:24,25 38:11 39:2,15 40:9 47:16,19 54:15 56:6

57:13 59:11,13 71:11 102:15 183:21 186:2 293:8
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294:18 306:2 352:25 353:4,6,24 370:5 395:6,8,17

419:13,14,17 450:2,26 453:5 555:4

opened 419:13 420:2

opening 9:2 113:10 182:8 558:24

openly 214:11

opens 502:3

operated 403:12

operates 502:10

operation 288:12 502:14

operational 212:24 582:7

Operationalizing 517:15

operations 183:11 289:3

operators 281:16,20 282:8

opinion 193:9 327:23 337:21 348:23 401:11 404:9

406:3 430:13 473:21 590:9,10 593:25

opinions 582:23,25

OPP 1:9 33:4 36:24 40:9 59:11 71:8 269:9

opportunities 13:16 295:10 483:25 560:2

opportunity 186:5 226:15 275:7 279:21 280:3

292:8 294:23 295:5 297:3 311:21 383:11 544:3

556:14 561:3

opposed 116:21 191:26 251:21 527:26

opposite 331:3 427:4

OPP's 47:19

optimal 156:17 522:22 523:3 577:2,13

option 354:7 425:18 575:18

options 11:4 14:19,22 15:16 16:11 98:24 99:23

180:1

oral 4:9,12 184:4

order 17:26,26 18:1 23:23 27:24 77:22 90:21

143:10 171:11 213:20 215:11,14,17 220:18

222:11,21 245:10 256:1 343:18 355:5 373:25

376:3 399:25 401:20 404:19 408:23 416:1 473:6

497:14,19 509:4 523:20 555:21 583:17

ordered 171:21

orders 139:18 378:15 421:16

Oregon 177:25 288:9

organ 30:16 52:26 67:24 197:12

organic 168:22,23 169:14,15 237:2,4,10,11

organics 7:23

organism 14:16 28:14 33:20 37:5 38:9,9 52:10

56:8 120:16,17,18 174:13 256:12 262:16,22

340:2 342:24 438:20

organisms 6:8 13:26 14:16 38:4 39:11 41:13 47:7

52:11 55:15 57:18,20 58:4 64:19 65:20 66:10

67:13,23 74:2 84:10 109:18 185:2 186:17,20

190:19 191:6,8,19 255:14 256:6,10 309:23

310:11 329:7 330:25 335:5,6 338:9 342:17,19,20

342:23 343:2,22 344:24 358:22 412:7 443:9,24

451:20 484:10 504:20 600:25,26

organism's 38:7

organization 28:13 55:15,26 58:24 73:23 76:19

196:3 276:5,22

organizations 58:17,19 59:25 281:18 295:8 586:16

organized 293:2 513:11

organs 54:9 168:1

original 32:5 78:4 86:12 96:24 104:15 106:24

116:20,23 117:25 121:19 142:4 147:26 158:19

189:11 205:23 206:20 216:11 228:9 229:18 231:4

233:8 270:12,22 281:2 312:13 399:17 400:26

403:2,3 408:8 442:22 535:4 539:24 572:23

originally 77:26 125:25 429:25

OSCPP 336:22

ought 292:6 529:12 551:10 565:19

outcome 28:17,19 55:15 58:12,17 59:25 190:5

192:17 196:5,11 198:15 199:13 207:1 217:5

297:4 322:3 323:3 324:7 334:21 342:3,4,5 386:3

389:10,11,12,19,21 392:9 394:10 484:5 486:6

492:22 564:18 600:13,22 601:6,9 602:8,19

outcomes 55:24 196:12,19 204:13 207:21 323:5,19

337:5 375:23 376:6 391:19 427:10 437:16,17

600:12,14,15,18

outdated 169:7

outdoor 74:26 226:20,23 232:23 238:16

outer 103:5 413:9

outlets 288:26

outlier 198:7

outliers 194:23 235:20 283:9

outline 597:19

outlined 78:7 119:6 369:12

outlines 302:25

outlining 567:21

outlived 167:1

output 157:1 209:15 214:7 217:4 219:9 245:22,23

246:4,7,11,26 247:17,20 251:22 254:22 265:1

293:19 295:17 537:13

outreach 283:4

outside 5:18 10:14 75:25 153:25 154:17 276:21

outstanding 483:13

ova 209:9

ovarian 190:16,23 191:5,14,25 192:2 195:4

overall 10:24 27:10 30:10 39:20 42:3 47:11 50:12

51:20 52:3 56:16 61:20 89:16 92:7 94:5 99:26

122:21 123:9 170:19 250:25 323:3 341:13 350:10

350:12 435:20 512:1 520:18 574:5 592:20 593:16

overcome 234:8 383:18

overcompensation 256:22 257:9

overconservative 517:22

overestimated 271:5

overestimation 514:10
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overestimation's 564:24

overlaid 141:21

overlap 239:8 331:11 479:4

overlay 242:22 243:1

overlook 505:10

overly 273:6 433:23 434:23 435:1

overnight 395:18

overregulated 271:13

overshoot 257:8

overshooting 256:20

overt 197:14 350:8

overtake 164:15

overtakes 442:21

overview 27:5 72:19 82:14 206:19 213:13 436:13

Over-harvesting 164:7

ovocytes 190:15

Owen 289:14

Owen's 289:20

owners 281:16

oxygen 41:20 69:25 73:17 79:8 126:25,26 127:2

187:4 200:4 235:19 236:15,21,24 248:26 402:26

407:20 443:10

o'clock 136:6 263:12

P

Pacific 168:6

package 578:19

pacman 301:18

page 182:26 270:8 273:17 274:17 322:17 328:1

333:11 450:7,7 471:10 475:10

pages 282:25 293:5 299:13 333:8 487:18 528:18,19

605:26

pain 163:13

paints 17:3 132:17

PAIT 306:19

palm 164:4

panel 1:1 2:2,12,17,19,20,24,25 3:3,8,15 4:24 5:14

5:19 6:10,26 8:25 18:17,23 19:24 23:1,17,21

25:12 26:14 31:2,5,10 33:2 34:15 43:14 44:13,18

44:23 45:2 56:20 70:20 71:23 81:22 128:3 136:13

155:1 157:4 177:11 179:9,11,20,23 180:13,14,21

180:26 181:4,7,9,19 183:5 186:1,5 213:10 228:5

246:17 256:11,18 268:3 271:20 274:10 275:7,13

279:22 280:4 282:20 285:11 291:16 293:21 296:5

300:23 302:4 303:12 304:4 305:25 306:9 310:23

311:15,20 320:23 326:7,7 338:22 352:7,12 355:2

363:17,18 364:24 365:8,18 369:3 371:18 372:22

379:13 380:4 384:10,24 389:2,15,20 390:1 392:8

392:12,14 393:17 394:6,11 395:7 399:22 400:18

413:19 417:5 445:14 450:3,24 451:24 452:4,16

452:19 453:26 454:11,17 455:4 456:20 457:10

459:13 467:11 473:2,15 475:8 477:4,6,24 479:17

480:13 482:6 485:3 487:11 493:4,11 499:7 503:3

510:11 549:1 565:3 584:18,23 585:21 595:5

598:21 601:24 602:18 605:15 606:2 607:14

panelist 529:5

panelists 290:3 296:25

panels 25:1 82:2 183:25 184:2 355:3 394:24

594:19 602:4 603:14

panel's 19:7 20:9 292:13 305:18 386:5 393:15

399:14 472:13 485:10 487:6 549:2 584:10 587:6

paper 19:18 20:2,13 32:9 33:1,21 39:18 71:8 78:7

106:17 107:10 126:2,8 129:26 136:25 180:5

184:7,12 185:11,15 189:20 194:1 195:21 204:22

216:2 237:17,18 270:8 273:18 274:18 282:22,26

290:17 292:24 293:3,8,13,15 321:6 322:17,20,21

322:25 324:3 325:5,10,14,15 328:1 329:26 338:4

338:18 348:14 350:3,22,23,26 352:1,5,20 368:21

376:24 379:1 395:21 401:12 402:21 405:22 422:5

431:10 434:3 436:14 450:25 451:2 465:8 468:15

468:19 481:5 490:22,24,26 491:8 495:3 498:12

501:15 503:15 504:4 513:17,25 515:20,24 524:10

526:18 533:20 534:10 535:17 537:13,14 541:15

548:6 550:19 589:15 590:5 592:8

papers 62:24 163:3 177:26 189:22 199:19 206:19

292:26,26 293:7 321:18,20,22 323:20 325:9,17

325:17 349:14 411:11 475:14 554:25

paper's 591:26

paragraph 444:21

paragraphs 344:19

parallel 464:9,17 481:7

parameter 98:21 146:24 306:21 332:13 440:13

497:11 535:18,20,20 536:18 537:1 542:12

parameterized 184:14 214:3 536:4,9

parameters 41:20,23 48:11 68:20 93:23 110:20

211:9 340:16 343:4 415:20 471:1,3 536:14,15

paramount 328:24 412:19

paraphrase 328:10

paraphyte 581:7,24,25 582:4,14,19

parasite 57:23 58:4

parasites 343:7 386:11 390:18 507:26

parasitism 325:13

parcel 64:1

parent 45:12

park 165:7,11

parking 164:2

parse 253:10

part 8:15 9:7 11:22 13:14 17:6 20:8 32:13 44:10

63:9 66:5,6 70:8 72:3 98:10 117:22 119:5,10,16

123:3 126:23 127:20 133:21 134:6 143:6 159:12

191:12 193:23 198:3,9 199:17 209:15,23 210:6
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304:2 306:7,12,15,18,24 307:2,4 312:10 319:8

321:3 323:1 325:13 329:17 337:22 348:17 357:3

358:18 359:10 371:15 377:25 378:1,11 382:18

395:6 419:1 429:16,21,22 430:19 435:18 438:21

438:24 439:5 440:11,20 448:23 452:25 454:16

458:16 460:22 462:4,11,14,25 464:18,19 473:1

476:1,15,16 483:22,23 504:10,13 508:22 520:6

523:8 561:18 565:2 571:24 572:21 577:6 585:9

588:22 591:21 592:5 593:15 594:9 601:1

partially 121:20 122:23 123:9

participant 3:16

participate 273:4

participated 268:19 588:6

participation 2:26 5:15 281:19 588:5

particular 9:16,18 29:8 42:15 61:23 67:25 89:19

101:22 102:7 137:4,4 140:10 198:10 199:14

202:19 203:20 205:9 210:23 237:4,10,11 239:21

243:16,19 245:25 248:18 253:25 256:12 257:1

274:2 310:23,24 320:14 331:7 355:2 368:3

372:18 381:5 398:7 402:18 416:20 421:24 428:13

433:3 460:1 470:18,25 497:5 522:14 531:18,25

535:19 543:20 545:10 546:17 563:22 564:4,8

568:13 571:17 572:6 591:21,23 600:23

particularly 50:16 237:2 249:4 353:1,15 434:1

552:25 587:15 588:16 589:2 602:24

parties 601:9

partitioning 74:26

partly 13:14,15 64:14 65:7 132:1 587:22

partner 276:5 289:11

parts 29:23,24 30:19 43:7,15,21 45:22 46:14,25

47:5,13 50:19 51:2 52:12,14,15,18 53:16 54:1,2

54:12,14,24,25 56:17 62:8 67:17 68:13 79:13,14

80:21,25,26 101:16,26 104:16,23,26 105:1,10,12

107:5,8,13,25 108:5 109:9 111:24 113:16,22

115:16 118:23 123:7 135:10 150:11 151:3,15,25

152:4,10,18,24 153:3,20,24 175:2 176:5 180:10

185:16,19,19 187:8 204:19 215:5,5 227:26 228:6

233:3,11,22,25 234:1,14 235:1,4,16 236:13,22

238:21 239:9,10,12 240:11 243:26 248:8 251:13

258:12,15 316:26 317:9 378:11 379:3 386:19,20

386:21,22,23,24 429:16 430:21 439:18 448:6

458:21 465:14 475:26 476:3 535:21 591:26

part-per-billion 84:2 94:21 100:20

pass 82:16 278:14 351:7 377:20 417:21 465:14

494:25 519:24 529:3

passed 34:3 187:6

passes 278:14

passion 394:21

passthrough 212:23

path 302:25 365:6 404:3,10 412:5 445:3 480:14

pathogen 386:11 387:9

pathogens 386:24 390:18,23

pathologist 44:17,19

pathway 28:20 58:12 196:5,11,17 199:13 386:3

389:7,10,13,19 600:19

pathways 28:17 31:11 190:5 196:11 207:1 327:2

484:5 600:9,15 601:6 602:8

PATI 24:5 26:2 92:19,23,26 93:11,17,19,21,24,26

94:2,12,14 95:3,16 97:12,18 98:11,17 99:1,4,9,13

99:15,16,19,20,26 100:5,6,9,15,20,22,23 101:9

101:11,12,17,20,20 102:2,4,5,8,10,16,18,20,22

103:8,8,13,17 104:4,5,6,12,24 105:17,18 109:8

121:20 122:5,10,14,15 123:6,12 124:10 128:18

129:15 132:3 134:5,13,16,17,22 162:1,3 181:23

184:9,10,16,18 185:3 214:12 215:19 216:23

217:1,4,5,18,23 218:26 219:9 220:11 224:18,20

224:22,22 239:15 240:21 241:2 242:4 244:20

245:15,21,21 246:4,7,14,26 247:4,14,20,21 248:3

249:7 251:18 253:6 254:4 257:18 258:24 265:1

270:5 277:17 282:13 283:20 293:19 297:25 298:2

299:2,3,7,8,11,13,16,17,21 300:10,14 306:15,18

312:2 313:1 320:7 428:23,24 429:1,6,7 432:19

433:18 435:11,19 436:20,22,26,26 437:24 438:2

438:8,21,24 439:6,9,19 441:18 442:16,22,26

443:13,17 444:3,8 445:18,24 446:4,5,6,7,14,23

447:14 448:23 458:21 462:14 465:24,26 466:1,3

468:8,22 469:21,24 472:3 473:4,9 476:1 492:15

495:3 505:16 530:23 589:18 592:12 595:10,15

patience 606:26

PATI's 492:7

PATI-based 98:25

pattern 209:1,2 405:24

patterns 14:13 140:15,19,20 141:6 199:23 265:13

286:12 508:20 509:3

Paul 182:7 267:4 551:4

pay 119:23

payers 170:15

payer's 171:25

paying 403:24

PCA 267:14

PDF 4:11

peak 81:22 208:21,23 265:16 377:8 427:17 518:19

558:1

peaks 81:25,26 105:12 116:4 406:2

Pease 414:11,11 416:17 426:7,7 453:11 488:10,10

peculiar 208:26

peer 3:5 18:17 19:1 76:17 163:3 167:13 330:22

347:1 593:3,17

peer-review 391:9
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peer-reviewed 298:11 349:7

pending 349:23

penis 167:26

people 4:12 63:11 155:8 167:4 170:12 171:9

174:10 175:12,13,20 176:11 177:12,25 191:24

229:13 287:9 332:6,9 334:25 340:11 360:17

381:22,24 382:22 409:25 468:1 475:8 483:12

502:6 568:17 569:13 592:2 594:11 597:17,17

599:5 600:21

perceived 89:7

percent 36:4,5,8 37:17 61:24 62:15 68:13 76:1

84:10 92:26 93:1 94:13,13,14,18,18 99:21 101:18

101:21 102:3,5,19,21,21,24 103:18,19,21 107:2

110:16 114:12,15,15 118:3 119:16 146:25 170:9

171:9 207:26,26 216:16,24 242:4 246:4,14,22

247:4,14 248:15 251:18 289:10 290:13,13 295:13

303:26 304:11,16,18 318:8,9 320:10 325:19

335:12,15,17 383:26 405:16,22 406:1 428:25

440:8 465:17,19 471:9 488:20 492:5,19 493:18

521:7 533:9 536:9,10 537:3,15,16,22 538:15

540:1,7 541:8 558:25 559:1,4,6 565:19,20 566:10

566:20,20,21 580:24 581:1 583:26 584:2,3,6,10

percentage 62:15 84:12 150:13,15 151:4,6,23

152:2 563:3

percentages 60:26 151:17

percentile 90:7 106:15 108:2,3,5 110:16,21 118:25

118:26 119:10,12 128:21 142:2,5 148:25 256:4

258:19,23 312:1 313:3 319:7 320:3,8 467:15

516:10 517:16 529:24 538:7 551:21,22 566:11

percentiles 107:23,25

percents 60:26

perception 481:14 565:2

perceptions 416:7

percolating 395:7

perfect 125:1 140:8 176:25 203:25 324:22 358:3

415:21 566:5 602:9

perfectly 308:13

performance 138:20 147:13 289:12 551:8,9 563:2

563:11

performed 30:18 67:26 256:8 534:6

period 42:6 45:16 81:23,26 83:15 84:3 94:11,12

95:11,12,13,17,22,26 96:1,2,11,15,16,16 97:1,5,7

97:9,11,14,18,20,23,26 98:3 103:13 104:17 105:2

105:10,11,16,20,23 106:4,10 107:26 119:2,4,15

124:22,24 129:18,18 130:4 132:9 135:16 142:25

148:20 162:1,20 180:10 200:10 204:9 213:17

214:22 216:13,20,21,23 217:26,26 218:8,20,24

219:1,6,11 220:3,16 221:1 224:18,24 225:6,18,26

230:5 238:20 244:26 246:8,9 248:5 252:4,5,23

253:6 256:20 262:19 294:5 305:3 307:9,11,13,17

307:23,25 308:4,5,6 309:12,17 360:13 388:6

398:10,25 411:7 429:2 431:9,10 446:17 447:23

454:26 459:2,3 462:2,2,11,12,13 463:21,23,25

464:13 465:14 466:14 468:12,16,20 489:6 512:1

513:17,26 515:3 516:13,20,21 559:20,24,26

560:3 571:24 573:5

periodic 502:19

periods 38:2 41:16 95:19 104:20 105:18,21,22,26

112:8 113:2 129:8 133:14 143:3,7,13 219:25

220:1 224:3 225:24 226:1,2 252:6 255:22,23

294:17 307:25 309:9 459:17,19 465:10,15 466:8

466:15 468:11 488:24 502:23 513:19 517:25

period's 98:4

periphyte 223:10

periphyton 78:18,25 108:15 228:22 233:11,25

238:17 259:14 260:6 265:12,20,25 266:2,16

399:5,7 403:21 404:20 411:8 431:4

permanent 2:17,19,20 6:26 8:25 71:22 274:20,25

434:8 494:17 601:24

permanently 187:16

permeable 165:24

permission 487:17

permit 524:8

permutation 222:24 242:3

permutations 215:1,2 222:22 223:5,17 240:20,23

persistence 73:9 507:12 509:3

persistent 73:6 508:26 509:2

person 136:7 193:11 313:5,6 494:25 568:9 600:3

602:2 606:17

personal 277:6 288:6,13 601:26

personally 19:10 181:10 397:8 424:24 580:23

personnel 512:18

perspective 14:23 20:10 94:17 96:21 97:21 129:22

157:7 283:17 319:1 405:18 436:4 464:15 520:6

521:10

pertinent 87:6 327:10

perturbation 55:11,13 76:3,8

perturbations 55:5,8

pervasive 332:19 373:25

pervasiveness 378:4

pervious 305:5

pest 291:13

pesticide 9:4 11:15 16:18 19:25 27:3 33:4 136:23

140:18,22,23 166:3 168:9,23 172:19 173:24

177:4 288:11 454:17 504:12 543:16 559:23

604:12

pesticides 3:5 6:22 7:15,23 8:15 11:12,24 21:8

31:26 32:25 71:10 134:8 140:15 165:20,23,24,25

166:11 167:10 169:5,13 175:20 177:20 190:26

274:17 288:20 504:10 526:12 543:19
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pesticide-related 3:6 454:18

pests 166:11

pet 164:8

petition 176:15 178:11

Petitions 292:2

pets 164:9 165:18

pH 329:22

phagocytic 58:2 386:19

pharmaceuticals 166:19

Pharmacology 6:21

phase 17:14,14 45:25 139:22,26 420:19 482:18

483:1

phased 482:26

phases 263:5 483:1

phasing 182:9

PhD 604:24

phenomenon 190:24 588:12

Philomena 581:26

philosophical 419:25

phone 2:21 8:24,25 71:23 456:13

phonetic 330:3 389:18

phosphorus 303:22,23,26 304:11

photographs 523:21

photosynthate 73:18

photosynthesis 73:15 79:7 87:11,15 92:8 186:14

186:24 187:13,18,20 221:14 230:9,10 235:18

236:16,19,26 255:9,26 256:13,26 261:11,24,26

262:14 308:22 443:11 493:18 499:19

photosynthetic 73:13 186:26 187:2,8 212:23

252:24

photosystem 73:13

phrases 112:20

phylogeny 191:6,19

physical 15:24,25 38:5 77:5 78:14 108:23 133:5,15

134:23 200:2 265:26 266:9 269:16 328:16 396:17

396:24 399:12 406:16 413:5 442:8 444:2,9 582:1

582:5 590:18

physically-relevant 445:9

physician 200:16

physiological 322:4 369:14

physiology 166:17 199:11,11 202:7 203:24 211:17

484:10

phytoplankton 8:1 78:18,25 108:15 126:25 228:22

231:9,10,23 235:19 236:2 237:12,13 238:16

265:12 398:8 423:14,19,23 431:2

Ph.D 163:1

pick 70:15 161:17 187:10 226:10 243:5 309:26

471:18 475:12 537:22

picked 133:22 413:16 464:18 604:1

picking 313:17 320:9 576:20

picks 93:23

pictorial 193:11

picture 17:3 126:6 132:17 301:21 595:17

pictures 168:7 292:4

pieces 516:16

Pilot 265:2,24

pinned 435:9

pinpoint 22:12 252:8

Pipette 270:18

piping 594:3

pitch 133:11

pituitary 196:25,26 197:19 199:20 201:12 202:10

pivotal 176:21

place 81:19 114:7 137:5,6 147:15 165:7 167:8

174:20 202:3 234:19 245:12 254:8 302:17 313:11

336:22 365:13 404:24 407:5 419:21 468:6 598:11

placed 4:18,26 48:16 111:10 337:12 348:10 349:25

places 14:5 28:18 114:26 164:6 176:2 407:14

443:24

placing 174:10 177:16 349:23

plan 20:24,24 24:17 25:25 155:7 209:16 211:1

302:1 303:15,16,20 425:9

plane 350:13

planes 606:9

planet 163:21 165:8 167:11 171:9 174:10 176:2

177:8

planking 553:19

planned 2:5

planning 11:3,8 26:2 385:21 553:21

plans 20:11 203:10,12,17 204:11 288:22 459:12,25

plant 21:6,16,19 22:7 24:4 25:7,14 26:2,17,18

34:11 65:21,21 66:8,12,22 72:21,22 74:4,6,13,14

74:16,18 75:6 76:24 78:13,15 79:6,9 82:24 84:19

87:2 88:26 89:2,13 90:1 92:8,19 93:6 105:3

107:15 108:21,24,26 109:5,7,13 118:14 124:26

127:13 136:18 212:21 227:25 228:19,25 233:24

234:5,8,12 236:26 250:3 255:7,19,24 269:15,16

269:18,23 274:21 277:9 282:13 291:8 296:1

305:13,24 308:22 310:2 338:19 379:21 380:2

396:17 397:15,19 398:7,24 406:14 413:5 418:24

428:13 494:14,17 497:12,26 498:6,10,16 500:11

503:16 524:8 589:22 596:22 597:6

plantation 164:5

plantations 164:5

plants 65:19,25 87:10 95:6 108:16 133:4 186:16

212:22 234:24 235:8 252:17 255:9 256:21 264:17

273:25 274:6,8 281:11 290:25 315:25 428:26

430:16,16 499:19 508:16 581:13 582:24 589:20

589:20 597:8 600:25

plaques 58:2
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plasma 54:23

plastic 37:21 332:11 334:14 364:9,11

plastics 334:11 365:17

plastohydroquinone 187:7

plastoquinone 187:4,11,17

plate 200:10 434:9

plateau 219:16

plateauing 219:5,19,19 220:14 241:26 248:6

plausibility 340:20 341:7,8

plausible 28:8,10 55:22 192:22 444:16 486:6

495:18,26 507:19 508:15

play 497:25 523:10 549:17

played 497:22

players 303:25

playing 17:7 353:25 575:6

plays 13:7 16:9 272:21 290:18 295:21 436:26

490:13 576:14

please 4:5 5:17,21,22 19:14 42:18 49:25 51:11

71:20 136:13 162:20 272:20 274:19 294:5 320:24

327:9,11 348:9 366:24 377:14 381:3 385:24

386:5 390:5 395:12,21 396:15 428:11 451:15,18

477:22 484:26 488:9 494:12,15 503:16 513:5

532:19 542:22 585:11

pleased 188:18 275:6 280:3 429:18

plenty 288:15

plethora 419:18

plot 60:25 97:23 98:2 216:26 217:1,5 218:23 242:2

243:1,4 246:14 247:3 258:20,22 312:26 367:26

445:3 457:18

plots 367:21,23

plotted 102:17 104:10 115:13 217:12 367:22

plotting 62:4

plug 557:14

plus 120:10 388:19 599:5 606:25

Plymouth 281:4

poem 176:18,20

Poetry 176:18

poets 176:19

point 7:5,6 17:5 38:21 39:11 61:12,17,18,25 62:18

62:19 63:4 69:26 82:15,22 85:13 94:9 99:2

115:20 117:1 129:2 134:26 140:3 145:6 146:4

149:10 152:25 155:16 157:22 160:14 172:17

173:11,23 175:22 182:24 184:7 185:21 195:12

196:7 202:20,21 205:9,21 208:17,21 210:8,11

211:4 215:24 218:17 219:19 220:9,13 221:12

222:25 228:26 234:16 238:4,26 239:20,23 241:24

248:1 249:2 251:23 252:2 253:2 255:9,21 258:13

258:18 261:21 284:14 297:24 300:12 301:19

304:14 305:3,4 311:4,19 313:26 319:24 325:6,7,8

326:2 327:15,16 331:12 336:7 338:12 347:20

348:12,13 349:15 355:7,8,19 356:13 359:5,6

365:16 367:3,4,16,26 368:3 370:26 374:14 375:9

377:17,18 380:23,25 382:24,25 387:19,20 390:23

391:20,21 392:15 395:9 407:8 410:1,2 415:11,16

416:11 420:3 424:18,26 427:12 428:17,18 432:6

437:26 439:10 442:19 455:16,16 463:22 466:22

467:6,12 468:4 473:20 475:1 480:6,23 483:19

484:3 487:12 488:14 498:21,22 501:2 509:16,25

509:26 510:4,25 511:8 515:26 521:2 523:19

524:5,6,12,14,20 525:8 527:22 529:3,9 531:9,10

532:8,23 536:17 537:22,25 539:4 541:14 544:21

545:1 550:1 552:6 556:23 559:11 561:13,26

562:10 573:4 574:5 581:11 583:7 585:18 587:3,7

587:8 591:3 592:16 595:7 597:9,11 598:10

599:24 606:17

pointed 42:10 66:16 104:17 116:2 129:12 186:15

189:22 228:25 315:5 413:13 415:26 431:24

442:15 518:18

pointing 381:22 518:20 534:20

points 51:16 62:10 115:23,23,25 119:7,7,14 185:5

185:6 205:25,26 208:8,18 223:1 228:9,14,17,19

228:23 229:1,5,6,6 230:24 231:3,6 234:13 235:15

235:20 238:9,24,26 239:6,11 240:7 241:5 242:2

242:12 243:7 250:8 252:19 258:16 296:15 297:10

305:22 306:5 325:26 333:7 336:5 367:26 410:2

424:19 426:25 427:2 428:3 436:18 448:26 458:17

460:7 470:26 489:10,15,21 511:8 513:23 529:2

536:11,11 537:1,18 540:17 541:1 571:8 583:16

point-by-point 587:3

policies 211:15

policy 3:19 68:25 295:1 301:25 302:22 304:24

475:13 502:11 529:21,25 597:12,23

polished 488:25

polite 487:19

pollutants 165:25

pollute 168:17

pollution 165:20 367:24

pond 226:20 401:23 422:25

ponds 165:9 226:20 234:21 235:6 274:5 401:23,24

pond-type 410:6

pool 105:7 445:11

poor 38:3 40:26 42:7 171:4 222:26 241:10 266:8,8

307:10

poorer 171:2 576:24

poorest 175:18,20

poorly 126:8 536:4

pop 294:18

populate 221:20 583:17

populated 172:21

population 28:14 59:26 75:12,12 166:5,6 193:3
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207:21 286:19 330:6 338:6 360:14 362:22 363:4

386:14 399:19 423:26 500:17

populations 74:19 75:24 162:25 164:19 178:2

269:7 286:17 287:17 330:5,9 331:9 362:25 363:1

363:1 368:17 374:3 390:22 391:12 408:6 423:25

424:1 506:15,15 507:2,3

Portier 8:20,20 125:23 155:1,2,18 161:7,8,16

249:3 317:20,21,21 326:4,5,21,23 328:17 340:21

344:17,18 346:13 347:9,10 350:20,21 363:15,17

366:15 370:23,24 375:13,14 379:11,12 383:5,6

388:23,24 392:4,5,6 393:25,26,26 413:2,3 420:1

420:2 425:16,17 436:10,11 442:17 445:14,15

446:2 447:16 456:11,11 466:18,20 472:15 475:23

484:3 501:7,8 510:9,10 513:9,10,10 519:26

524:17 530:20 531:26 532:15,22,23 540:14,15,25

542:10 547:16,17 555:26 556:1 560:24 561:4,5

564:12,13 568:3,4 570:20,22 577:20,21 598:22

598:23

Portier's 569:12 601:23

portion 126:10 129:21 198:19 207:26 227:6 242:11

360:17 527:13 583:14

portions 151:22

pose 245:9

posed 199:3 495:2

poses 199:2

position 52:1 299:22 313:25 386:6

positions 62:4

positive 28:3 29:24 31:14 47:2 68:10,11,13,19,23

70:24 337:13 339:24 355:22 356:11 363:20

369:16 372:17 374:12 461:25 491:21 565:12

566:17

positively 91:2 168:20

positives 338:15

possess 176:4 191:14

possibilities 90:11

possibility 84:21 323:22 403:26 491:25 498:13

504:20 505:10

possible 118:16 127:14 137:1 186:3 193:15,17

203:6 208:1 223:19 229:12 271:2 272:22 327:25

377:26 388:17 395:26 402:14 409:3 431:15 453:1

472:2,4 489:24 516:23 517:4 518:23 521:20

533:18 534:10,12,13 539:6 546:3,11 566:2

575:16 576:21

possibly 184:25 252:15 283:21 345:26 391:6

452:17 489:7 558:6 567:3

post 29:21 50:6 67:21 340:13 427:17 465:3

posted 273:11 456:22

postponing 174:8

postulated 524:5

posturing 198:18

post-exposure 581:7

potent 186:16

potential 22:13 24:18 26:8 27:12 28:5 33:3,13 34:6

34:10 37:15,22 39:4,17 40:4 41:7 42:7 44:24 46:1

46:17 55:23 56:7 57:14 58:26 59:7,18 60:6 74:1

74:14 112:9 117:9 138:16 139:16,19 140:4

148:11 149:15 150:16,22 151:2,7,24 152:3,9,20

154:10,12,16 160:17 162:6 166:20 185:1 195:24

196:19 199:3 255:18,19,23 262:21 263:4 267:8

271:3 295:26 296:12,20 318:17 328:15 329:2

333:19,21 334:4,5 339:4,21,22 367:1,15 385:4

389:12 392:8 399:17 400:6 402:3 433:1 443:6

444:18 498:4 514:14 522:4 531:6,12 543:16

552:19 555:22 572:13

potentially 34:21 60:5 153:2,15,20,24 172:16

249:26 259:13,17 262:18 267:5,12,25 323:4

338:5 424:19 544:9 591:2,7 594:5

POTOMAC 1:14

pound 277:12 285:14,26 286:2

pounds 21:25 172:17 174:12 175:16 277:11 278:26

285:14

power 205:2 325:18 328:14 331:13,15 343:17

355:21 374:24 383:26 509:12 570:6 577:1 581:4

powered 374:26

POWERPOINT 283:12

ppb 246:21 247:19 249:6 265:17 269:23 271:17

277:22 283:20 439:17,18 442:20,26 472:3 549:20

549:22

ppb-day 246:3,3

ppb-days 245:18 473:4,9

ppd 256:4 445:18

ppm 77:14,15

practical 180:12 273:2 514:14 515:9 533:25

559:15 581:19

practice 57:19 93:21 129:3 278:2,12 280:15 300:2

332:3 334:24 548:10 565:7 568:6 580:21 594:8

practices 57:14 274:17 276:8,11 277:26 278:16,24

279:16 280:14 298:23,23 332:6 335:4 370:16

544:14,18 593:21

precautionary 174:5

precedent 383:23

precipitation 165:4 422:24,24

precise 520:7

precisely 346:17

precision 435:14,20

preclude 307:5 338:7 375:22

precludes 269:20

predator 325:12

predators 164:25 329:22 335:14 402:14,15

predefined 214:7 216:5 224:11,12 225:3
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predicated 485:6

predication 145:7 147:6 156:9

predict 137:1 147:9 158:3 253:7 269:11 538:23

548:23 558:2 559:2 563:23 567:18 570:2

predicted 147:23 150:26 430:17 567:2

predicting 137:14 139:5 145:2 147:11 159:26

492:18 493:8 504:5 538:4 553:16,21 559:3

prediction 145:25 532:20 538:21,23 549:9,13

563:12 565:19,22 570:17

predictions 298:24 299:4 444:9

predictive 465:23 515:8 551:26

predictor 534:26

predicts 100:2

predominantly 91:2 111:17 144:10 160:10

preexposure 75:11 201:9,23

preface 215:17

preferable 539:9

preference 46:18

preferences 56:10

preferred 100:20 280:14

preliminary 23:14 24:8 32:23 33:5,25 87:23 88:3

143:1,6,11,19 144:13 145:10,14,19,26 146:6

303:24 304:10 386:18 390:14 514:16

premature 369:3

premise 334:15 512:13 562:4

premises 571:13

preordained 534:22

preparation 32:10 179:6 588:6 590:3

preparations 181:1

prepare 3:2 5:5 259:19

prepared 19:17 294:3

preparing 9:8 179:5 321:17

prescreened 47:16

prescribe 349:15

presence 36:11 51:22 77:4 262:1 296:20 335:14,19

382:12 401:19 402:11 505:11

present 25:5,20 29:6 75:15 77:3 78:22 104:21

108:16 116:21 136:8 137:3 174:25 260:14 310:3

335:15 347:15 471:25 508:19 595:15

presentation 19:20 24:20 27:5 28:18 66:6 69:3

72:11 77:24 82:22 100:26 128:9,24 136:14,17,19

138:12,17 146:4 154:25 161:10 185:7 195:14

202:16 214:25 215:8,16 240:18,25,26 253:19

257:15 283:16 305:10 306:12 307:8,12 308:16

310:21 322:24 379:1 387:25 465:9 499:5 513:22

526:3 543:22 551:4 583:15 585:26

presentations 3:26 19:18 20:5 21:2 60:23 67:2

71:15 72:14 113:10 142:10 162:19 163:8 178:25

264:5,6,8 353:19 545:2 585:16 590:3 595:24

598:19

presented 23:15 24:1 29:11 32:9 33:1 36:19 38:18

43:15 50:21 51:8 77:25 88:2 90:6 92:23 99:17

106:17 126:12 137:22 138:1 141:14 158:11

181:17,20,24,26 182:3,7 223:20 234:22 248:7,10

264:5 283:11 300:13 305:16 309:7 369:5,10,24

377:24 378:26 444:20,22 470:23 543:12 592:6

603:5

presenter 292:14

presenters 186:2 264:12 268:4

presenting 66:3 186:11 213:11 321:16

presents 309:6

present-day 557:13

presequencing 214:6

preserving 183:21

president 179:16 288:2

press 292:4

pressure 72:15,17 74:24

pressures 111:5 121:10

presumably 121:24 212:22 596:25

presumed 56:11

pretend 344:12

pretreatment 232:7,10,13

pretty 117:3 118:10 121:5 127:6 128:4 155:12

194:11 222:17 226:16 232:18 239:11,19 241:6,14

241:20 302:10,14 314:8 336:20 389:23 411:20

421:14 432:15 434:26 445:18 479:3,4 488:24

489:24 500:5 536:14 540:5,8 549:23 553:20

554:14,19 555:21 556:4 558:22 564:9 566:5

567:12 569:21 571:24 591:1

prevalent 492:11

prevent 174:8

prevention 290:17

previous 13:15 15:11 16:4 19:12 22:16 24:24 25:1

27:6 29:14 32:2,13 34:26 37:2 42:8 45:23 47:19

56:24 60:4 79:16 95:5 123:26 142:10 149:17

161:20 186:18 189:13 205:3 223:9 254:6 269:24

323:20 325:25 333:16 340:15 349:13 353:1,12

354:22 369:2 394:5 395:4,10 399:14 482:7

485:13 499:22 500:14 505:24 507:18 551:14

556:17 558:26 572:9 574:11

previously 36:21 39:17 43:11 58:14 154:14,19

242:18 260:5 289:25 323:10 340:13 416:10 466:7

522:11

pre-treated 403:10

price 168:22 170:18 171:7 275:20

prices 291:19,20

primarily 8:8 445:21 514:19

primary 24:7 33:22 45:14,18 68:22 75:18 76:2,4

76:20 92:4,14 93:5 103:4 108:2 117:19 159:4

169:2,21 170:10,14 185:2 197:12 221:17 233:12
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principal 87:21

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printing 293:21

prior 5:8 38:5 169:11 240:19 293:23 321:18

327:18 329:8 345:14 416:3 443:2,7,12 472:18

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priorities 172:13 173:5 292:5 304:24

priority 302:7,13 304:6,22 360:25 362:3 378:7

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prism 157:2 526:3,24

private 442:18 521:6

privilege 19:16

prize 176:18

prizes 166:17

proactive 302:18

proactively 227:7

probabilistic 88:12,22 89:22 90:8 156:24 184:13

188:17,21 575:5

probability 62:21 102:19,21 103:19,21 119:12

250:26 313:1,22 316:11 317:24 319:26 437:26

440:4,10 441:9 444:10 465:18,19 517:5,6,9,18

529:12,20 530:10 537:5 547:12 572:19 575:5

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probable 223:2 405:4

probably 10:1 15:9,10 69:16 111:20 134:25 145:6

156:4,21 157:3 177:7 182:11 187:23 203:1

253:20 255:11 262:20 286:5,13,21 287:25 303:17

303:25 304:15 317:18 318:17,25 319:23 321:24

321:26 326:26 327:22 332:11,15 343:19 356:18

357:2 361:23 363:5 366:5 385:19 392:12,15

403:3 420:23 425:2 426:16 433:22 470:2 486:9

530:24,25 531:2 533:26 539:19 553:1 556:5

557:20 559:24 575:1 588:9 598:25 599:2 603:14

607:12

probit 438:26 439:1 467:15,16 468:4 495:11

problem 1:4 2:7 6:2 10:10 11:16 17:10,12 20:14,17

20:23 24:15 25:26 32:10 65:22,26 66:11 83:11

127:21 133:3 134:6 138:24 139:15 164:12 165:3

170:15 180:15 268:7 284:6 300:16 302:11 306:7

307:7 308:15 328:20 340:3 347:25 357:16 370:10

374:10 383:14 408:9 409:7 433:24 434:11 436:1

440:16 454:13 462:17 464:7 538:5 540:25 546:17

552:24 565:22 589:3 604:3

problematic 267:19 332:20 340:8 470:17 528:5

551:5 558:2

problems 118:19 131:17,22 132:16 166:1 170:14

207:7 398:14 521:16 525:9 574:8

procedure 193:21 209:16 439:1 545:20 578:20

592:20,22

procedures 192:10 367:13

proceed 46:5 101:5 223:14

proceeding 24:2 424:10 571:9

proceedings 4:9

process 5:2 11:15,18,19 12:2 18:26 21:2 31:26

32:1 69:17 77:1 85:12 93:20 103:9 104:3 107:22

109:1 113:8 120:14,20 138:15 139:14,21,22

140:1,6,13 141:12,12,14 142:6 149:17 154:21

157:22 158:2 180:17 202:25 208:3,8 215:8,12,21

215:22 216:25 229:11 248:12 254:10 269:26

273:5,7,9 277:2 280:5 318:3 353:8,23,26 354:23

384:21,22,24 394:21 395:6 396:8 400:1,3 420:11

429:5 432:3,16 433:22 435:4,13 436:6,7,24

438:10,11 450:20,21 455:1 458:15 466:22,24,25

467:1,9 469:12 471:25 472:6 474:22 475:24

478:22 487:12 494:22 505:1 510:22 516:14

519:22 520:7,19 525:20,22,26 526:23 527:6

542:11,17,18 545:19 546:18 558:8 564:19 567:21

573:15 586:20 593:3 596:23,26 597:1 602:14

604:9

processed 102:8 170:11 473:16 516:8

processes 28:6 91:23 93:12 140:17,18,22 410:8

436:13 497:20 579:16

process-based 526:7

produce 73:19 79:20 100:21 173:20 174:1 176:8

197:14 295:18 515:1 516:7 534:25 548:2 565:4

produced 79:21 99:26 109:16 264:4,9 284:14

310:21 352:24 516:14 559:9

producer 259:8

producers 173:8 185:2 221:17 260:14 264:14,17

268:12 273:14 277:3 278:18 283:5

produces 92:20

producing 100:15 170:4 185:24 234:7 500:7

550:10

product 14:2 20:23 25:26 112:12,18 113:13,15,16

113:18,20 276:1,4,26 280:8 282:23 286:13

291:24 316:8 480:16

production 73:18,18 79:8 126:25 150:18 151:8,8

153:6 164:4 169:17 172:9 176:16 177:17 191:11

233:12,13,15 272:21,22 274:7 275:14 280:9,15

280:17 290:22 291:19 322:22 323:4 403:21 496:6

509:17 594:10

productive 273:15

productivity 21:16,20 22:7 74:3,9 75:18 76:2,4

107:16 109:6,13 117:11,19 234:3,9 236:9,13,15

236:18 274:22 399:10 402:20 407:21 434:10
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289:13,20 455:9,10,16,24

profile 18:11 265:14 441:10 444:17

profiles 15:1 246:2,6 266:1 441:7 444:11 548:19

profitable 275:14

profits 168:15 172:3 174:3

profound 201:6 220:16 222:9 223:15 241:1,20

243:20 244:15,16,24,25 259:17

program 8:16 21:15 23:15 25:18 69:15 103:2

137:11 141:5 142:10,13,21 149:26 159:13 219:22

260:24 266:15 274:12 279:1,11,18 280:21,23

375:15 392:17 470:21 471:19 490:12 514:6

604:12

programs 9:4 19:26 22:11 25:19 27:3 33:4 118:15

142:18,20 155:5 275:12 278:15 295:9 303:2

491:12 509:9

progress 177:12 301:12 304:1 306:7 388:16 399:17

429:18,26 550:6 553:13

progresses 399:5 530:7

progression 242:13 312:8 313:2 597:20

project 7:18 276:16 277:16 368:14 455:23 547:19

projects 283:4

prolonged 204:8 221:15 255:23 261:23,25 262:19

promised 125:21 294:12

promote 162:25

promulgate 279:20

pronounced 229:17,21 230:18 244:22 254:13,16

proof 16:1 277:14 568:6

propaganda 168:8

propagated 435:13

propagating 498:9 595:17

propagation 472:5 495:6 595:14

propensity 551:21 553:9

proper 165:4 180:8

properly 299:25 367:12 531:5

properties 83:26 139:5 140:10,17,17,19,21 142:26

156:8 160:25 281:26 344:20,22 469:4 522:8

531:5,22 536:2

proportion 105:16 132:12 171:14 219:2 565:18

proportional 200:2,22 201:1 589:5

proportionately 86:13

proposal 337:15

proposals 285:1

propose 185:12 189:18 201:19 251:19 405:2

452:20 558:6

proposed 3:6 83:10 88:3 100:15 154:2 180:7,9

185:11,15,21 239:19 269:23,24 282:12 283:20

296:1,6,8,10 297:17,18,25 303:16 337:15 497:17

523:17 545:20

proposes 521:3

proposing 199:14 224:7 273:12

Propper 7:1,2 203:10 204:4,10 207:6,23 250:16

285:12,16 286:15 321:11,12 326:23 327:4 336:3

336:4 346:22,23 347:25 349:21,22 351:18,19,19

355:9 357:10,10,11 364:15 368:19,20 374:5,6

376:19,20 378:8,9 379:14,15 381:9,10 385:7

387:16,17 390:25,26 455:14,14 477:18 478:10,25

479:12,18 480:9 481:22,23,23 482:9 483:8

485:18 486:22,24,24 487:25 488:3 502:26 503:1

510:12,13 586:21,22

pros 546:13

prosing 224:10

prospect 276:13

protect 21:15,20 66:12,13 117:26 130:5 133:2

162:24 164:5 167:7 168:14,15 172:2,3,23 173:26

277:5 290:9 411:1 500:11

protected 132:14 165:7,8 283:25 313:25

protecting 73:20 162:24 174:3,22 175:10 177:15

409:14

protection 1:12 72:4 73:22 83:17 85:7 120:25

169:24 179:3 275:12 280:8,20 281:22 288:12,19

289:4 456:16

protective 106:7 109:18 119:3,6,16 180:11 258:5,6

497:10,12,18 505:9

protein 73:12 186:26 187:5 212:19

protocol 67:12 209:25 234:22 327:24 390:15 393:4

393:9,16 394:4 467:7,8 481:20 482:5 483:25

576:20 577:2

protocols 29:21 59:20 210:6 340:17 381:11,13,15

381:20,23 382:26 390:6,19 394:12 451:19 452:22

482:17 486:3,8 515:1

prototype 234:21

proud 177:10

prove 299:6 361:20

proved 276:8

proven 197:12 276:10,25 291:14

provide 9:20 10:16 22:1,16 27:6 28:7 33:12 72:19

77:10 78:14 87:3,5 95:8 104:8 108:23 124:25

138:18 142:13,25 143:14 163:13 168:24 171:12

179:11 181:2 183:10 196:1,8 210:20 217:2 229:3

236:7 257:4 264:22 268:17 272:23 281:22 288:18

291:26 293:25 296:5 305:4 310:24 332:5,21

336:25 352:16 386:5 393:17,22 395:21 396:16

406:17 407:2 408:9 415:13 426:9,12,17 430:14

430:20 441:14,16,19 442:12 451:14,15 453:9

458:18 479:25 480:2 482:6 485:1 491:20 503:2
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65:18 78:17 83:16 88:22 100:18 107:19 119:25

148:1 204:22 230:24 257:11,24 264:7,8 292:16

292:20 293:19 300:24 307:19 320:23 326:9

331:21 354:22 387:4 391:7 400:2,12 407:3,4

422:7 433:6 450:14 453:9 454:20,22 474:12

478:1 480:8 481:2 484:23 487:14 491:3,10

493:11 495:5 496:9 511:4 528:14 583:24 595:20

603:6 607:3

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183:1 196:17 217:18 243:9 258:14 264:26 278:25

343:1 386:26 418:1 451:16 491:8 496:9 505:5

506:21 572:12

providing 35:2 90:23 94:4 170:22 182:22 192:14

213:13 227:17 256:7 311:16 337:6 360:5 370:8

479:25 605:20

provisions 2:13 3:15 454:18

proximate 306:24

Pruett 210:3

PRZM 526:22 527:2,7,13 528:17 548:4 553:5

PRZM-Hybrid 267:23 543:12 560:13

pseudo 36:16 249:26 277:19 399:23

pseudokirchneriella 257:1

pseudo-replication 305:25

PSII 266:18,23 267:1

PS2 256:16

puberty 168:2

public 2:24 3:24 4:4,16,19,24 5:9,20 18:24 162:19

162:23 167:15 171:18 180:14 181:11,15 292:14

293:26 294:4,5,21 295:1 300:26 301:22 303:16

303:20 305:3 321:20 369:7 370:13 391:18 395:24

396:4 415:9,15 422:3 454:23,26 455:6 481:11

484:17 485:17 486:20 489:4,23 584:26 585:11

590:2,14 592:26 601:10 603:5,25 606:22,23,24

publication 77:25 227:14 232:25 260:10 367:10

368:2

publications 167:13 234:25,26 367:9 481:1 593:17

publish 335:3 336:15 343:21

published 20:16 23:7 24:22 31:18 32:6 33:22 43:4

50:18 51:18 52:16 163:2 188:22,23 189:5 190:10

191:16 192:25 199:19 206:17 210:1 227:19 257:5

298:9 303:15 328:4,6 329:26 330:21 331:1,26

336:16,19 340:11,13 349:7,14 350:23 351:5

352:23 353:4,6 382:17 386:25 391:3 393:24

490:23,26 491:12 505:3 506:5,25 507:18 557:21

567:1 593:9

pull 205:22 604:4 605:24

pulled 270:21

pulling 9:9

pulse 252:7 256:9,14

pulses 409:25

purchase 171:4

Purdue 8:2 456:2

pure 436:24 478:5 527:13

purpose 10:20 87:7 174:2 177:6 217:3 278:5

295:10 299:10 341:3 513:13 579:20

purposely 173:11

purposes 8:25 21:10 33:13 75:9 91:24 100:18,19

247:17 314:22 327:19 336:9,26 367:12 399:20

481:12 563:17

purse 160:5,11

pursue 544:20

pursued 430:22

pursuit 330:12

push 425:7

pushing 536:19

put 17:2 18:23 72:8 74:9 96:25 98:12 118:17

156:23 157:14 162:4 164:3 165:21 169:9 174:12

184:15 188:18 193:12 200:16 205:17,19 209:19

219:21 220:18 222:15 228:20 259:5 263:10

293:26 319:7 343:21 346:5,17 347:7 351:1,15

363:20,20 367:11 373:22 374:16,26 379:16

385:22 395:25 415:8 420:22 429:9,11 434:22

442:19 445:17 452:26 456:21 477:23 484:19

487:9,15,15 489:9,15 502:7 517:9 528:17 529:17

536:6,20 538:5 548:14 556:24 561:1 569:1 575:6

577:15 578:2,20 598:11 599:13 603:15 607:11

puts 86:8 409:11 474:7 501:16

putting 72:17 250:15 303:15 328:5 345:7 392:26

412:11 418:7 428:21 437:6 554:21 587:21

puzzling 273:25

p.m 607:16

P50 437:25 438:2

Q

QA 208:3

QA/QC 327:20 367:13 481:2 505:4

quadrant 193:19 206:24 211:21,21,22

qualifications 163:15

qualified 406:12

qualifiers 190:13 406:23

qualifies 362:12

qualify 230:7 370:3 571:20 572:2 573:12

qualitative 40:11,12,17,25,26 42:1,3,4,12,14,17,24

46:10 47:23,24 48:2,5,24 50:2,4 51:15,16 60:17

62:22 63:17 67:7 124:23 125:7 189:4,7,10,11

205:17 207:24 328:8 334:17 348:17,24 359:14

363:26 364:1 365:9 366:19,20 368:15 423:10

479:25 504:11 566:6 568:9,18 583:21

qualitatively 70:3 324:20 331:4 334:20 351:13,14
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42:8,25 59:16 60:19 81:7 142:11,16 172:14 190:6

192:12 222:26 260:25 265:23 266:6,10 271:14

272:24 274:2 276:4,7 280:18 282:21 283:7

295:23 297:16,19,22,23,26 298:3,4,13,14,17

299:20,23 300:4 331:18 334:23 340:18 342:21,22

369:5 379:24 391:11 394:13 397:25 398:22,23

399:1 400:18 404:12,24 410:22 414:19 417:3

422:4,6,9 426:9,14 432:8 467:8 490:18 498:5

526:8 566:25 573:18

quantification 146:16 325:21,22

quantified 200:13

quantify 14:11 160:15 479:6

quantifying 85:23 137:15

quantitative 40:11,15 43:2 47:13,25 48:7,24 50:24

50:25 51:17 52:4,17 53:14,26 54:11 60:9,12,14

60:18 61:24 67:6 83:13 187:26 189:12 190:1

192:7 326:17 328:8 348:20 359:15 397:26 417:3

448:18 450:9 451:14 479:26 566:13 568:8 583:21

586:3,6

quantitatively 50:14 55:10 70:3 364:7 390:8

480:17 504:5

quantities 45:15 172:15 252:16

quarter 150:25 366:9 534:4 563:7

quarterly 514:22 574:25

quarters 144:20 146:9,13 148:21 151:18 152:1

468:13 517:24 533:7 549:8,10

query 431:25

question 10:20 11:5 12:16,17 60:24 62:19 63:21

65:17 66:23 67:4 69:19 72:7 89:4 91:22 92:19

110:8 116:10 118:1,12 122:2 125:6,18,21 136:7

148:15 149:14 155:24 156:11 158:5 159:10,18,20

161:4,9,18 199:1 202:22 203:1 209:8 210:24

211:4,24 212:17 223:24 225:5 245:9 250:23

251:2 262:10 286:16 287:12 310:23,24 311:15,23

314:24 315:4 318:24 319:7,18 320:12,16,24

321:4 324:25 326:7 327:8,19 342:3 354:14,19

355:1,14 357:1 359:2,22 360:4 361:15 362:9

363:10 365:20 366:5,13,22 371:20 372:6,15

377:20 380:8 381:1 384:13 385:9,13,23 393:6

394:14 396:20 406:13,13,23 408:8,15 409:14

410:11,21,21,23 412:8,21 413:4,7 415:6,6,11

418:8 420:10 421:24 426:22 427:26 428:9,10

433:26 445:25 446:1,3 447:21 449:26 450:10,11

450:15,17,20 451:11,12,16 452:13,24,25 453:3,5

453:7,17,23,24 456:19 457:12 458:24 459:15

460:26 461:6,11 462:16 465:6 466:17 468:13

472:8 473:1 475:1,2,19,20,21 476:17,21,22 477:9

477:10,15,16,17,19,21,21,23 478:9,11,11,15,15

478:18,26,26 479:3,20,21 480:5,10 481:24,25,25

481:26 482:2,3,3,4,10 483:6,17 484:15,23,24

485:1,14,22,24,24,26 486:2 488:6,8,11 490:1,5,8

490:9,9,15 491:15,16 493:3,21 494:8,11 495:2,13

496:22 497:6 500:10 502:5,11,12,15 503:7,25

509:5,15 511:12,13 512:21,21,22,23,25 513:3

519:5 521:1 522:12 523:6 527:4,4 528:10 532:11

532:24 533:21 534:9 539:5,6 549:5 556:22,23

558:24 559:2 560:5,20 561:6 562:22,26 571:4,14

575:21 579:14 580:12 591:24 605:11

questionable 269:25

questioned 249:10 282:3

questioning 495:8

questions 5:5,17 7:11 10:8,21,23,25 13:12,17

17:16 18:15 19:22 25:12 26:14,20,25 60:22 63:5

63:19 64:23 65:16,20 71:13 72:13 109:21 126:21

127:26 128:5,6 130:9 155:1,21 162:11,13,17

178:19,22 181:9 182:12,16,23 183:3,26 184:11

187:22 190:11,13 202:19 208:5 223:20,23 248:20

261:5 263:7,10 267:15 268:3 274:18 282:8

285:11,13 287:20 292:10,11 293:23 294:18

300:19,20 305:1,6,11 311:22 312:24 314:5

319:10 320:19,25 321:14 324:3 326:22 333:2

349:11,13,14 356:1,18 357:26 359:8 362:1,4

369:12 371:12 379:25 383:25 385:13,14 395:3,5

396:7 408:22 409:18 412:18 415:7 416:26 419:18

425:11 426:6 431:21 436:17 438:13 445:16 450:3

456:20 457:2 461:8 469:17 472:13 476:23 477:6

477:7 478:12,18 479:21 480:25 484:6 486:26

487:2,14,17 488:6 489:6 490:3 491:15 496:2

498:15 499:17 500:14 501:20 503:8 508:25

509:11 511:5 520:13 523:10 526:17 528:8,9,10

532:12 542:8 543:3 544:22 549:2 562:22 570:11

580:5,8 583:4 587:5,21 589:16 590:7 602:16

603:17,20,23

quick 63:21 76:4 136:5 159:10 161:3 182:19 199:5

212:17 250:23 261:4 320:20 348:6 445:25 461:11

588:7,26

quickly 239:26 255:4 352:2 359:11 431:18 479:4

581:14

quiet 220:12

quite 10:4 121:16 129:5 156:4 186:10 191:18

199:18 214:11,11 218:19 251:1 283:21 290:6

358:3 401:14 402:11 411:21 434:22 437:4 445:20

445:23,24 449:22 467:19 468:6 469:10 470:1

471:1,2 485:19 489:5 501:24 525:14 543:10,18

558:20 576:3 591:16

quits 263:11

quorum 8:25
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quote 400:19 514:11 515:5

quoted 299:13

Q10 203:24,25

R

R 536:26 537:1,10 578:19

radar 553:21

radiation 265:11 329:22

radical 93:4

radically 94:7

rain 164:3 167:12 172:18 177:20 606:9

rainbow 203:20

rainfall 140:19,23 155:8 423:4 519:7 543:19

raise 35:15 37:9 170:7 335:1 424:25 483:20

raised 56:21 68:5 288:14 371:21 411:25

raises 89:4

rally 176:10

ramifications 284:4

ramp 522:17

ramped 233:4

ran 132:6 439:17 465:19

rana 381:13 390:22 481:14 482:16 587:16

ranchers 288:18

ranching 164:4

Rancho 301:3

random 144:26 155:4,6 219:26 230:2 240:23 445:3

445:7 525:3 540:22 550:11,16,22 552:12

randomization 344:1,14 345:1,19

randomizing 344:7

randomly 93:23

range 4:7 14:22 29:26 46:13 49:3 52:6,16 53:12,23

54:22 55:5,25 59:24 61:22 62:23,26 63:9 64:12

64:14,22 65:5 67:14,15,16,18 68:6 70:12 79:11

88:26 90:7,10,20 106:18 108:11,12 109:8,11,12

110:14 112:7 113:24 114:18 115:26 126:1 127:7

132:2 135:1,9 142:25 143:22 144:4,19 147:9

180:9 185:18 192:12 203:2,4 211:8 212:7 228:5

258:10,16 273:10,12 274:13,20 286:1 288:24

309:13 318:26 319:5 321:23 332:20 349:16 364:2

365:9 373:4 378:10,15 397:7 405:1,10 406:22

410:16 433:7,8 434:4 473:25 475:10 494:15,16

495:9 496:18 500:22,25 507:9 515:17 516:25

518:26 534:25,26 535:11,12 548:22 554:14

556:14 557:22 562:6 572:6,8,12 573:20 597:11

606:23

ranged 67:16 144:18 150:26 584:6

ranges 39:4 41:2 63:14 212:13 378:10 468:16

597:17

ranging 79:13 90:21 137:10 246:2 258:11 397:5

rank 62:15 63:1

ranked 50:10 62:2 65:1 193:10

rapid 203:22 204:3 230:5 260:13 264:16 291:2

308:23 443:10

rapidly 81:14 95:6 163:20 186:20 212:24 255:13

266:26 289:23 520:4 543:17

Rare 580:16

rat 197:14 375:17

ratcheted 274:4 277:17

rate 28:26 34:24 36:4,8,14 37:18 38:1,17,19,21,23

38:24 41:14 50:12 58:4 65:13 68:1 79:7 89:12

94:15 95:10 130:11,13,22 163:22 207:20 215:26

255:12,14,16 256:19,21,23,26 257:8 258:2,11

261:11 262:15 274:24 278:26 291:6 309:10,12,25

310:5 323:11 331:16 332:8 351:24,24 398:17

428:26 431:6 492:14 549:7 564:16 565:6 566:10

rated 62:3

rates 30:6 38:19 51:20 74:5 83:5 95:4,6,12,13

168:3,4 173:21 274:3,8,23 277:10 278:15 286:12

286:13 310:6 329:3 334:22 402:8 430:26 565:12

ratio 31:15 54:10,13 67:26 68:16,19,24 85:26

86:12,14 194:16 206:3,6,9 217:20 233:13 234:3

358:24 517:15 551:22

rationale 386:5,7 395:13 483:23 505:5

ratios 187:25

rats 389:20

rays 285:21

reach 114:12 229:13 269:2 283:24 299:18 364:11

457:7 555:15 568:15,16

reached 39:11 205:9 280:20 366:26 367:7 463:21

524:11

reaching 229:24 331:19

reaction 73:15 93:6 485:19

reactions 407:21,24

reacts 187:6

read 17:11 19:14 162:13,17 176:17 177:24,26

230:26 235:9 239:8 264:7 268:9 272:11 275:4

280:1 290:18 301:1,5 320:24,26 321:3,13 327:17

333:5 344:19 350:26 357:17 363:18 366:11,12

367:4 377:11 381:1 389:26 400:22 406:10 414:26

422:10 428:10 460:25 482:14 487:26 488:2 489:3

489:23 492:24 493:20 494:11 503:12 512:25

532:18 542:21 554:23 570:15 580:5

reader 44:21

readily 55:19 57:9,10 558:12

reading 126:1 333:11 336:14 339:18 369:2 495:2

589:15

readings 152:25

ready 126:21 327:7 359:26 379:19 418:21 496:12

511:9 588:7

real 19:16 63:21 87:5,6 131:17,19 133:8 134:6,10

141:8 147:15 188:18 271:15,18 275:21 277:22
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283:26 295:24 296:3,24 299:5 300:3,12,15,16

320:20 338:9 362:3 365:14 384:4 412:7 431:18

434:11 437:12 441:4,18 442:9 445:25 446:2

449:14 491:25 492:3,5 493:1 504:18 510:22

551:6 552:24 591:6 593:9

realism 91:3 128:11 131:19 382:13 383:10 384:3

421:6

realistic 87:25 88:5,7 98:16 192:13,20 194:12

232:24 234:20 274:13 283:19 285:2 382:7 384:2

397:24 420:24 427:20 510:24 590:25 595:16

reality 15:25 135:15 269:11 271:22 277:11 279:23

282:18 287:6 296:2 298:26,26 347:19 430:14,17

559:21 567:17,18 568:19 595:17

realization 134:10

realize 17:24 133:15 134:1,21 287:5 346:6 362:3

385:14 419:17 477:25 542:12 568:10 582:22

604:17

realizing 14:11 114:3,5 605:9

really 10:2 12:1 17:22 18:25 62:15,22 63:12 98:6

113:4 117:20 120:24 125:24 126:15 127:24

128:17 131:5 133:16 135:19 136:4,5 145:7

148:20 155:15,17 157:8 159:4,19 160:8,22

161:25 162:9 180:1 184:26 203:12 214:16 222:15

229:17 230:17,25,26 233:16,17 234:21 237:3,20

238:13 240:25 242:12,12,21 243:16,22 245:2,24

249:15 250:7 251:24 253:2,12 254:3,8 256:5

257:10 260:13 261:20 286:18 293:14 302:1,16,25

311:8 313:17 314:23 318:7 319:2 324:12 325:3

333:26 337:11,15 342:7 350:14 351:4 352:4

354:18 357:12,15,21 360:21 362:10,18,23 364:19

368:13 370:24 374:3 375:11 377:4 378:6 379:22

381:11 382:22 383:19,19,23 386:25 388:25 392:8

394:7 408:23 409:1,26 411:16 412:1,5 414:9

415:12,24 416:19 421:19 423:5 425:25 429:14,17

429:22 430:6,14 434:16 437:20 440:2 442:14

445:21 447:5 457:21 461:15 462:25 463:2,25

464:25 466:3 467:17,24 468:20,24 469:9 471:25

473:14 474:25 475:14 481:2 491:22 492:8,16,26

493:19 495:10,12 497:14 498:15 499:6,25 502:19

517:13 521:8,12 522:16,16,19 523:8,10 524:12

525:20 526:2,3,6,15 527:1 531:17,19 535:12

536:6,19 537:19 538:9,21 541:16,18 542:11,17

543:26 547:21,26 549:23 551:19 552:5,20 553:7

555:7 556:12,19,21 557:17 559:11 562:17 565:9

566:18 567:14,17,18 571:24 575:23 576:14 577:1

577:9 578:6 586:14 587:1 589:13 590:7 592:9

593:19 595:11,13,23 596:9 599:10 600:19 602:25

603:24 606:13

real-world 180:19

reanalysis 189:13

reason 11:22 21:26 70:18,18 89:21 108:2 120:1

126:22 150:7 169:21 170:5,23 174:7 199:17

214:14 219:5,24 223:26 238:3 245:10 277:18

285:25 294:13 300:9 306:23 312:10,20 313:16,17

343:12 347:6 362:14 423:1 431:12 457:24 472:4

526:22 528:1 541:1 553:4 559:7 572:1

reasonable 13:24 18:11 26:5 39:1 78:14 108:23

124:26 125:5,10 129:22 226:10 392:11,19 396:17

405:1 406:4,21,26 413:5 441:25 495:19 499:22

507:19 512:2,9,13,14 532:26 553:16 581:2

590:18,22,24

reasonableness 25:15

reasonably 109:11 274:19 372:8 385:25 387:22

415:20 494:16 497:25 589:4

reasoning 228:1 249:21

reasons 42:16 80:17 166:7 184:9 194:24 235:9

238:7 264:2 274:16 296:4 304:4 346:26 347:3

357:21 373:21 397:9 407:11 590:15

reassessment 1:5 2:7 6:2 125:26 454:14

rebuttal 310:18 356:2

recall 115:22 389:18,19 423:12 460:11

recapitulate 315:7

receive 290:19 422:23 543:2

received 25:1 84:4 292:2 580:4 606:5

receptor 34:23 186:26

receptors 65:23 478:3 485:12

recess 71:18 294:7 494:9 585:4

recipient 169:2

reclassified 125:26

recognition 307:11 376:4

recognize 63:7 272:20 274:10 338:25 352:15 376:3

409:6,8 495:19 499:15 582:10

recognized 36:22 57:7 269:15 308:24

recognizes 32:4 107:17 137:2 354:5 359:13,20

recolonization 399:2,2

recommend 367:16 372:22 413:22 426:20 471:17

482:14 484:19 488:26 519:10 548:8

recommendation 70:10 88:13 334:21 361:5

452:26 453:17 485:5,10 586:4 602:22

recommendations 3:9 25:1 32:2 43:10 60:4 69:11

73:2 76:15 80:1 105:8 109:2 215:3 222:7 228:13

243:12,23 247:18 257:24 259:3 281:16 297:12

306:10 364:5 394:7 425:19 427:23 547:22 548:1

585:20 586:25,26 587:4,11 603:3

recommended 23:21 36:9 38:2,17,19 41:14 44:18

78:6 105:4 138:7 223:5 244:12 282:5 331:16

334:11 381:6 396:21 474:20 586:5

recommending 88:4 452:5 485:3 587:14

reconcile 121:22 224:2

reconnaissance 404:22
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reconstructing 216:12

reconvene 127:26

record 41:19 128:7 162:18 163:23 164:9 211:12

263:8,10 292:17 299:16,18 300:1 301:1,5 302:10

320:21,23,24 377:12 406:10,19 415:1,15 422:3

428:10 432:13 442:19 455:6 460:25 461:8 476:23

477:2,17 481:10 484:17 485:17 486:19,20 487:26

488:2 489:4,23 494:12 503:13 512:26 532:18

542:21 556:25 570:15 580:6 585:1,11

recorded 4:9 35:20,20 41:21,22 49:4

recording 41:9

records 189:24 484:19 607:2

recover 75:24 95:6 116:20 117:5 221:4 254:17

255:19 262:8 581:6 588:17

recoverability 291:1

recoverable 271:2 501:21

recovering 117:2

recovers 116:20 255:9 588:15

recovery 75:4,8,10 76:4,8 79:22,25 96:22 97:24

116:12,18 117:19,20,20 180:23 185:17 221:10,14

230:17,19 254:1,3,5,9,13,15,18 255:6,7,12,15,19

255:23,25 256:1,6,10,13,19,25 257:7,11,12,13

260:13,14 261:1,5,7,9,10,12,14,26 262:1,4,5,7,22

263:4 264:13,16,17,19 266:17,26 267:1 291:3

307:23 308:16,21,22,26 309:9,16 310:13 397:19

397:23 399:13 405:26 499:18 500:16 502:17,24

581:5,7,8,10,12 588:13,14,16

rectify 302:12 411:21

red 51:16,17 53:15 121:17 144:10 152:18 153:13

175:23 433:12 445:12

redesign 412:2,11

reduce 22:24 43:10 74:1 143:10 168:22 199:1

202:9 234:7 259:24,25 290:10,12 538:19 567:8

583:24 584:12 591:5

reduced 86:10 168:3 194:11 239:9 259:26 260:1

278:15 280:16,18 290:1 323:2 437:17 563:8

567:10 584:9 594:9

reduces 156:15 168:2 175:8 272:24 280:16 295:22

reducing 170:6,16 171:6 401:14 476:5 564:16

reduction 76:1 92:5,7 94:14,18,19 107:2 126:24

127:1,2,3 171:1 216:16,17 220:5 233:12,23 234:3

244:10,11,13 246:21,24 269:18 285:13,17 303:26

304:11,17,18 323:4 428:25 441:23 442:1,2 492:5

492:14,17,20 493:18 564:16 581:1 584:3,7

reductionist 594:26

reductions 238:21 437:18

redundancy 397:19

redundant 466:15

reemphasize 592:16

reenter 187:18

reevaluate 13:5

reevaluated 11:13 80:2 331:5 400:13

reevaluating 596:23

reevaluation 11:10,15,21 140:8

reevaluations 11:14

reexamine 51:19

refer 42:2 403:2 422:11

reference 38:21 86:25 87:26 142:3 152:12,25

230:26 231:1 270:4 343:11 372:17 396:23 403:3

404:25 409:2 413:23 453:18 488:26 490:5,7,9,13

490:22 491:3,10,11 503:5 513:18

references 322:10 506:9,12 578:20

referred 29:19 86:18 142:10,12 188:7 297:22

328:17 349:1 368:1 395:20 442:17 485:5

referring 135:25 312:9,11

refers 94:13 427:19

refine 18:5,9 267:10 316:19 500:19

refined 28:3 188:21 271:1 559:11

refinement 316:14

refinements 140:8 520:10,19,20 531:16

reflect 23:22 36:22 39:7 50:7 54:8 74:22 100:10,22

221:1,16 226:6 282:17 291:1 406:4 409:22

430:15 604:26

reflected 218:8 476:8

reflecting 90:26 100:25 111:20

reflective 52:24 53:19 54:8 57:18 58:8 61:24 68:14

100:3 222:9 224:8 243:17 493:14,19

reflects 92:14 100:6,8 214:8 236:16 245:5 253:25

282:6 296:13 305:24 539:7

reformat 607:1

refute 27:20 365:4 380:15 390:5 392:18

regain 75:23 442:22

regard 9:15 11:8 28:24 56:22 87:26 88:8 92:4

95:25 234:26 261:5 292:19 297:11 300:7 306:10

310:25 356:14 381:5 423:6 444:14 483:7 488:11

489:13 583:10 589:12

regarding 3:6 22:13 26:25 31:5,13 42:9 44:24

45:24 55:11 56:25 59:17 91:19 92:23 98:6,24

100:4,14,23 116:11 211:25 261:14 269:1,13

270:15 282:8,12 305:20 308:21 338:2 369:4,13

373:3 382:2 405:21 411:22 444:23 486:3 488:19

495:20 507:20 515:14 549:2 570:11 580:9 585:17

regardless 49:5 77:12 117:24 241:26 257:13

265:14 277:18 299:25 462:7 463:3 470:22 594:24

regards 52:10 326:13 342:14 420:15 451:19 453:6

478:26 496:16

regime 567:10 597:25

regimes 409:22

region 154:17 172:21 269:9 274:7 303:18 409:3

491:4 545:10 576:5
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regional 288:26 574:12

regions 153:4,17,21,26 154:13,19 160:6,11

register 359:22

registered 21:8 165:23

registering 113:18

registrant 22:19 27:14 43:4 47:16 59:13 306:4,9

310:21 311:9 321:15 367:20 404:6 442:11 444:20

444:22 452:20

registrants 21:14 22:5 23:2 25:18 66:3 103:3

126:20 136:26 149:20 271:22 273:13 280:19

305:13 308:15,17 353:24 393:19

registrant's 306:11 307:8 590:10 595:20

registrant-submitted 33:8 36:26 39:16

registration 14:2 20:8 32:11 112:12 113:5,15

171:24 180:17 273:5 474:17 478:22

regressing 517:3

regression 77:22 102:17 104:14 106:16 108:8

109:24,26 110:19 115:22 145:23 146:20,23 147:2

147:8,15 148:25,26 157:12 217:12 242:22 247:13

251:5 293:20 311:26 312:2,5,12 320:6 429:15

435:10,12 437:23,25 441:11 467:24 507:9 519:4

520:16 529:16,23 532:21 533:4 534:2,5,8,10,23

534:26 535:2,7,8,15,16 536:11,13,23,23 537:7,10

537:15,18,19 538:3,10 539:4,5,10,16,19,24 540:3

540:4,8,13 541:15 542:13,13 549:6 558:25,26

562:5 563:3,11,15 566:2,14 569:26 570:5,17

571:9,11,20 572:11,16,25 573:3 575:21 576:3,13

577:25 578:3,4,11 579:4,7,16

regressions 258:10 535:14 538:14 548:22 595:16

regression-based 77:18

regressive 278:2

regulate 91:13 124:6

regulated 124:3 592:26

regulates 56:5

regulating 111:26 273:2 295:25 591:7,8

regulation 272:25 297:18,21 425:23 426:1 508:22

regulations 3:14 165:14

regulators 10:23

regulatory 3:7 13:17 14:18 16:23 19:3 58:25 162:9

179:2,7,22 180:19 181:7 183:21,24 271:5 284:11

291:17 297:10 298:8 299:10 302:9,17,22 314:22

315:5 319:1,12 327:19 336:9,26 337:26 341:3

346:26 353:16 367:12 397:3 418:3,20 432:7

555:6

rehash 433:25

rehydrate 413:8

reiterate 336:7 343:20 354:26 374:16 392:22

468:19 475:7 501:9 556:8 586:8 587:13 596:16

598:24

reiterating 248:1

reject 110:11,13

rejected 77:2 238:2

rejecting 110:13

relate 125:24 250:25 320:11 347:12 395:3,10

465:6 531:5 576:26

related 59:21 70:10 86:9 160:2 161:19 187:23

188:13 189:1 191:5 192:3,16 195:22 209:6

225:11 251:14 266:7 275:13 294:15 306:17

329:26 398:6 425:13 450:14,22 451:14 452:14,15

452:25 513:4 529:9 574:21 580:11

relates 2:9 4:26 23:19 45:9 293:2 320:13,16 339:21

353:1 427:14 448:26 530:9 574:12

relating 96:9 576:22

relation 188:26 190:12 320:3 561:22 575:5

relations 481:11

relationship 33:14 34:22 35:3 85:9 89:12 90:2,10

92:6,16 112:26 113:7,7 146:8,12,22 147:1 155:25

191:1 195:9 306:20 312:13,19 326:25 330:15

339:10 353:13 355:24,25 356:9,23 384:26 428:25

432:19 433:2,19 445:17,20 452:10,23 457:22,26

458:10,16 459:6 460:8,9,12 464:20 473:4 477:24

477:26 478:2,5 485:12 578:7,24

relationships 70:8 84:18 89:23 91:6 92:23 93:9

94:22 230:14 306:20 307:5 317:2 327:1 330:21

335:26 340:19 344:1 382:11,14 385:4 387:5

548:2 551:16

relative 52:2 74:24 82:9 83:19 85:5,26 92:9,11,17

93:3,14 122:11,16,18,20 123:13,14 124:15

125:16 134:13 152:19 153:14 214:22 217:26

220:2,17 233:24 235:2 243:1,10,15 244:3,26

257:21,25 258:25 259:6 260:22 261:11 264:24

296:18 397:14 441:15,17,19 442:1,2 443:4 446:7

447:25 449:19 492:8,14,19

relatively 43:18 76:4 78:10 99:6 212:24 246:26

261:16 273:8 323:21 324:3 386:9 408:10 410:6

546:23

relax 374:24

relaxed 541:19 562:8

relay 334:4 537:5

release 187:4 196:16

released 280:24 289:13

releases 292:4

relegated 324:22 333:17

relentless 509:1

relentlessly 509:1

relevance 89:4 90:3 96:12 172:12,14 180:24 182:1

185:16 190:4,8 192:20 193:21 194:7 249:12

254:22 257:16 260:16 264:14,21 265:1 290:26

397:22 401:4 426:13 580:18

relevancy 28:20 341:17 342:2 426:9,10 453:12
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488:12

relevant 29:7 30:1,26 79:1 96:15,16 98:16 118:4

185:10 207:18 212:5 307:18,26 321:21 341:25

343:12 368:16,21 378:6 390:23 398:25 400:10

406:17 417:18 422:16 426:11 442:1 444:5 453:13

486:11,14 488:16 491:19 492:17 507:10 508:4

515:10 550:11 580:17 582:17

reliability 59:14 296:18 298:21 539:13 572:19

reliable 30:22 31:4 57:14 60:12 143:14 177:26

225:25 229:3 240:8 291:11 296:10 560:17

reliably 29:16 56:3

reliance 368:8

relied 183:9,14 295:14

relies 444:4

relocate 282:6

rely 163:12

relying 433:3

remain 169:16

remainder 72:25 82:3 284:13

remained 78:5,6 80:10

remaining 80:10 128:5 177:23 349:14 369:12

566:5

remains 3:10 280:6 304:17 349:4 459:25 572:10

remarkable 203:22

remarkably 584:13

remarks 9:3 181:16 182:8 295:3 395:16 456:26

remember 85:4 148:19 161:20 248:25 277:10

410:10,16 507:11 545:22 592:8

remembering 589:6

remind 5:19 271:20 292:22

reminded 367:23

reminds 364:26

remodeling 8:4

removal 255:10

remove 178:8 212:21 248:12 256:1,12 259:13

322:26 368:4

removed 187:14 256:9,22 262:5,13 266:26 271:24

398:12 581:15

removing 168:18 241:5 242:10,17 255:24

renal 507:4

render 214:19

renders 244:20 248:3 264:20 504:16

renewal 38:2 40:7 41:16 42:6 45:16

reoccurred 41:8

reopen 449:26

repeat 195:6 336:5 338:25 422:10 445:7 491:26

500:4

repeatable 573:14

repeated 201:24 235:5 236:6 408:4 507:12,13

repeatedly 340:23 400:16

repeating 389:9 573:23

repetitive 571:7

replace 543:13

replaced 237:14 502:2

replacement 75:12,24 423:13

replenishment 309:21

replicate 30:5 205:6 345:5,6 434:23 564:3

replicated 205:6 404:26

replicates 30:4,4,13 44:4,7 77:12,20 205:5 238:18

322:26 343:17 383:17 398:2 414:5 416:23,23

replicating 372:12

replication 36:15,16 250:1,11 328:13 331:13 339:1

374:24 375:10 399:24 442:13

replications 344:22

report 5:5,11 40:6 41:12 51:20,21 56:11,13 58:1

65:15 204:21 212:9 215:3 222:7 223:5 227:13,15

230:24,25 235:9 239:2 241:13 242:15 243:13

244:13 247:19 249:2 272:16 289:13,16 345:18

357:17,25 359:2,10 369:2,9 400:12 415:13

420:25 426:15 448:17 454:19 471:19 483:26

487:3,5,18 506:10 514:11 546:13 550:5 556:6

569:1 586:24 587:4 593:7 607:8

reported 29:8,12 30:16,19 33:21,23 35:12 38:12

39:6 41:10 42:6,10 43:15 46:13,18,25 47:5 48:3

48:16,17,20,20,22 49:9,15 50:10,18 51:5,22,25

52:1,7 53:12 54:15,22,25 56:15,16 58:7,10 61:22

62:7 63:5 64:5 66:18 67:15,16,18 68:6 74:21 75:7

76:23 78:19,20 99:25 107:20 125:24 152:13

153:8 195:24 206:6 231:7 233:7,9,10,11 234:11

236:5 237:18 270:23 423:9

reportedly 190:26

reporting 40:7 41:15 42:26 152:13 153:8 308:20

reports 3:21 5:10 18:20 29:11 32:5 75:8 188:5,5

227:21 234:24 281:22 308:18 395:20 557:19

represent 48:5 49:26 50:5 51:26 52:25,26 79:1

80:24 87:18 97:25 98:25,26 102:14 117:22,25

143:20,22 144:4,19 145:11 149:18,23 150:14

151:5 163:11 221:16 234:15 236:5,8 237:2 273:4

275:8,26 276:19 282:4 287:9 407:17 409:2

493:14 496:24 515:22 573:19 591:1

representation 93:5 125:2 143:14 144:2 148:1

257:21 264:23 305:15 413:15 446:20 469:14

518:9,10 562:18 583:23

representative 78:12 107:20 108:20 144:10 221:19

225:3 243:10,14 283:9 371:13 406:26 409:13

419:20 572:8 573:17

representatives 281:12,21 406:21

represented 42:22 49:2,21,22,23 50:24 51:10 52:2

53:2,14 55:1 61:16 101:8 152:16,18 153:11,13

228:19,26 244:3 257:24 504:10 519:1 530:8
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572:6

representing 50:1,2,4 51:5,13,16 52:20,22 53:18

53:23 55:2 58:3 62:6 81:3 96:12 121:11 150:5

225:17 337:12

represents 94:16 97:21 101:21 102:6 106:4,5

109:12 128:19 142:22 172:5 199:8 215:25 217:14

274:20 283:23 288:17 295:7 380:11 494:16 602:9

REPRINT 264:6

reproducibility 68:4,8

reproducible 342:15

reproduction 45:26 46:8 55:17,19 74:1 117:16

175:8 190:4 202:14 209:13,15 322:2 350:13

368:14 378:2 390:9 503:19 504:19 506:1 602:24

reproductive 6:16,18 45:24 46:6 168:1 199:11

209:15 322:6 330:17 331:8 368:26 391:13 506:11

reps 383:25 417:19,19 437:10

reptiles 194:16 195:5,11

request 125:22 127:10 135:21 186:7 294:15 359:1

404:11 439:25 466:18 475:25 476:11 530:7

requested 31:1 261:7,9,13 396:5 511:13

require 22:12 96:5 140:5,13 183:18 299:24 356:18

416:9 488:21

required 21:14 22:5,24 25:18 55:11 77:10 131:9

150:8 281:20 329:5 353:21 373:2 483:21

requirement 297:16 300:3 330:20 443:21 573:21

requirements 3:21 277:19 283:19 297:22 298:3,4

299:8 300:18 384:7 404:12 422:4 452:6 453:7

505:4 562:8 573:25

requires 137:25 138:24 259:23 278:23 330:17

344:1 357:2 376:21 552:21

requiring 270:22 276:13 327:24 328:20 499:23

573:11

reregistration 21:11 72:24 73:26 75:10 78:1

rerun 424:22

resampling 516:3

rescore 241:5 584:11

rescored 228:14 229:6 231:8 235:10 240:7 583:17

rescoring 222:26 241:17 584:8

rescorings 229:8

research 6:16 7:18 13:13 36:23 69:14 82:17,18

142:12 166:18 195:26 196:1 206:18 264:26 292:3

323:23 333:16 369:5 376:2 378:7 382:23 391:2

392:7,17 431:6 455:23 490:24 500:13,14 506:13

researchers 55:5 232:22 235:16 324:24 333:16

381:19

reserved 406:2

reservoirs 78:9 108:19 111:3 149:25 162:8 271:6

515:14

reshuffle 469:20

reshuffling 469:25

resident 120:7 266:14

residual 404:1

residue 137:4,13

residues 137:3 348:18

resilience 397:23

resistance 75:5 286:8,9 397:23 408:5 508:13

588:12

resistant 190:20 278:2 433:16 502:3 582:18 588:19

resolution 154:24 522:17 531:17 552:21

resolve 331:14 372:25 405:6

resolved 432:5 509:10,11 555:18

resource 8:8 10:24 281:13 545:24 557:1,2

resources 8:7,11 11:6 76:17 88:19 271:16,21 272:4

276:6 282:2,3 283:15 292:6,6 303:6 339:5 449:17

490:14 552:13 596:12

respect 29:12 58:26 60:5 89:22 98:5 222:3 246:5

257:12 262:14 303:23 361:4 490:4 594:19,21

respective 105:26

respectively 50:4 51:17 107:26 246:17

respects 162:25

respiration 233:13,23 234:2 236:17,19

respond 201:24 236:10 262:20 310:23 342:26

343:13 380:5,6 416:14 431:21 461:9 476:26

511:9 543:2 560:25 565:13

responded 399:14

respondents 503:24

responding 497:5

response 23:6 29:18 31:4,13,21,25 37:26 38:7 45:4

47:12 48:13 56:23 57:25 59:18 60:9,19 67:10

70:8 71:3 80:1 87:6 91:5,5 92:12 99:13 100:24,25

109:26 188:14 189:1 191:4 192:19 193:14 194:6

195:18 197:6 198:7,13,14,15 199:17,23 200:1,6

200:12,26 201:1,14 202:1,7,13 203:14,23 204:1,7

204:8 207:15,16 208:7,10,13,20,22,26 209:1,2

216:9,13 233:17,21 234:5,10 237:16 259:9

262:18 266:17 268:15 293:16 305:21 306:12

310:13 312:11 318:20 322:12 325:11 327:18

329:20 336:6,23 340:19 341:2 347:12,24 348:3

355:17,24,25 356:9,9,23 357:7 359:3,21 367:5,25

378:15 386:10 395:14 412:7 414:1 423:26 427:5

437:10 438:23 444:11,14 452:18 453:1 457:12

467:4,5 470:19 472:13 477:21 481:24,25 483:20

483:22 485:3,12 486:26 493:11,13 494:2,3,4,5

496:14,21 498:23 509:5 511:12 512:21 584:19

587:6

responses 5:5 13:21 14:15 15:6 46:18 56:9 74:22

99:12 129:20 130:7 188:13 192:16,23,24 197:4

197:20,21 198:1,2,2,24 199:19 200:13 201:8

208:9,10 209:4,6 211:25 212:11,13 223:25

306:26 321:13,17 325:11 326:9 340:23 341:13
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342:7 368:7 376:9,13 384:16 386:17 387:9

405:25 407:18,19 408:3 410:10 412:21 487:13

489:23 495:22 506:4,24,26 507:2,14,15 509:23

509:26 511:15,21 542:6 582:11 589:12

responsibilities 12:20

responsibility 3:12

responsible 2:12 248:26

responsive 202:3

responsiveness 203:13,15 369:26 378:21

rest 19:8,17 65:25 305:19 311:19 326:6 391:16

405:16 438:13 546:18 601:15 604:4

restate 122:2 319:25 451:12

restaurants 177:19

restoration 184:25 418:25

restoring 223:7

restricted 578:14

restrictions 338:16

restrictive 527:16

rests 354:10

result 34:6 43:3 50:20 51:25 55:25 60:17 68:15

99:21 105:21 106:1 109:4 120:11,14 168:5 173:9

196:4 237:9 244:9,17 282:22 290:1 345:21

347:16 352:3 369:17 374:11,12 383:16 426:2

443:23 444:18 452:18 470:16 473:9 498:1 500:9

503:18 505:23 525:7 559:25 582:7

resulted 27:18 28:14 31:14 42:24 44:26 45:8 60:9

88:8 104:25 105:9 107:25 133:26 402:7 518:8

584:2

resulting 38:10 44:10 73:13 90:8 239:7 246:25

322:22 408:4 430:15 498:3

results 23:6 24:9,24 27:9 28:2 31:4 32:19 35:12,14

35:16 36:18,20,22 37:10 39:20,22 40:6,16,17,19

40:22 41:3,11,12 43:14 44:21,25 47:9 48:4,8,25

49:8,16 51:22 53:19 55:9 56:4,13 57:6,25 58:11

60:15 61:14 77:24 78:6 89:23 90:12,13 92:7,26

93:12 94:13 95:20 98:11,15,25 99:18,19,26

100:16,21 107:1 108:6,10 109:2 137:9 140:14

151:23 170:26 187:2 198:13 214:11 228:21

229:19 231:9 233:6,8 234:22 235:4 236:4,8,21

237:1,7,18 238:10,22 241:12,14 244:11,23 245:6

246:21,23 250:21 252:26 260:1 262:7,11 282:13

296:21 306:14 308:9 324:8 327:13,22 331:22

338:17 342:16 347:12,18 348:11,14 351:8 352:2

361:18 366:23,26 367:8 369:13 370:14 383:11,12

391:19 398:15 407:1,1 426:17 434:3 444:24

501:6 505:21,26 519:1 533:3 566:19 585:26

RESUMED 264:10

resumption 262:13

resuspended 235:17

retail 288:23 291:20

retailers 276:23 288:3,17,26 289:5 291:26

retained 33:26 34:2 49:15

retaining 566:4

retired 178:13

return 75:10 82:14 116:23 169:23 201:8 221:9

230:11 466:19 581:14

returned 204:9 261:17

returning 72:24 408:8

reveal 57:6

revealed 119:3

reversal 358:13 443:11

reverse 96:14 558:15

reversed 148:8

reversible 180:22 187:12,17 230:9,11 255:6 499:5

revert 522:7

review 2:6 3:5 20:8 25:8 32:11 44:19,22 57:12

73:26 75:10 76:17 78:3 87:25 88:2,6,11,23 99:25

107:19 136:20 180:17 184:25 188:23 227:12,14

227:18 237:26 240:24 268:17,20 270:25 293:8

298:2 299:15 321:16 322:25 328:4 329:25 330:22

338:5 347:1 348:24 352:24 353:7,21 356:8,11

369:7 387:15 394:25 414:13 415:19 416:3 474:20

478:22,23,24 481:5 498:24 503:1 505:4 506:25

593:3,17 598:12

reviewed 3:20 44:20 87:20 127:4 163:3 167:13

308:18 324:19,20 331:4 338:17 348:17 410:20

424:19 474:10

reviewer 36:18 430:8

reviewers 32:12 331:21 336:11

reviewing 20:1 351:26 352:17 378:25 400:3 411:10

411:12,16

reviews 18:17 19:1 32:6,9 50:18 52:16 107:9

188:17 189:4,5,14,16 299:12 474:25

revised 23:22 107:2 229:7 239:7,13,22 331:6

revisions 185:13 239:18 414:20

re-register 359:23

re-registration 360:26 388:6 405:8

re-reviewed 106:25

rice 169:13

rich 89:1,5

Richard 181:24 213:8,9 224:6,26 225:15 240:15

240:19 249:20 251:15 253:22 261:8,20 262:10,25

264:13

richness 399:9

Rick 301:23,24,25

riddle 178:7

Ridge 226:18

ridiculous 417:17

right 4:6 5:18 6:9 63:24 71:16 81:15 86:3 97:3

101:13 124:20 125:26 127:16,24 135:5,6 140:7
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141:5 155:20 175:18,25 178:24 182:15 191:9

200:24 205:17 206:11 207:2 208:21 211:22 217:4

235:24 246:17 249:5 251:8 252:23 274:11 277:21

277:23 284:3 285:9 301:22 304:10 311:21,25

312:3,21 319:18 321:12 333:1,4 337:21 346:18

351:8 353:3 355:10 358:7 359:11 361:12 375:25

376:18 396:8,10 405:12 413:12 414:21 415:6

418:6 425:21 428:2 436:15 437:15 438:11,16,17

446:25 447:19 451:8 452:21 454:4 460:5,15

468:13,16,23,25 469:12,21,24 470:8 471:25

474:4 475:4 476:18,18 477:14 479:10,11 481:5

481:13 484:21 485:21 491:15 497:15 499:13

501:12 503:25 511:11 516:16 523:2 529:22 530:4

530:22 531:7 532:2 534:19,25 536:5 541:8,10,22

549:26 552:7,17 557:14 564:25 566:10 568:6

569:25 576:19 578:19 579:5,24 599:8 601:18

604:2

rightly 357:19 369:7

right-hand 10:15 18:4 206:23 256:11

rigor 296:18 390:7

rigorous 19:1 25:8 43:18 60:1 240:24 248:7 249:14

296:5,15 336:8 398:2 399:21 506:18,22 566:13

ring 435:22

rise 164:15

risk 9:13,22,23,24 10:6,11,12,16,18,21,23 11:2,18

12:10,11,12 14:21 15:15,20 16:3,4,10,18,20 17:8

17:14 18:5,6 19:2,25 20:11,15,16,17,19,25 24:12

24:17,18 25:2 29:15 30:23 33:16 34:10 37:8

39:24 40:16,17,18,21 41:6 47:10,21 48:1,4,6,8

49:14 55:10,17,20 58:21,22 59:15 60:12 63:8

69:6 71:12 74:9 84:15 89:24 90:3 91:12 93:10

97:21 98:8 112:5,23 114:25 137:21 139:20,22,26

139:26 140:6,9 142:14 160:19 174:11 180:16

186:18 188:17 227:5,18 268:8 280:5 283:25

284:7,9 308:7,14 313:19 316:14,19 318:15

319:13,15 322:18 337:26 338:14 339:14,24

340:14 341:3 346:1,5,9 347:14,26 362:12 408:10

432:1 462:18 504:10,13 505:23 525:23,26 530:26

531:14 548:16 586:12 596:26 604:6,11,19 605:3

605:8

risks 1:5 2:8 6:2 48:6 199:2,3 454:14

risk/benefit 316:10

River 141:2 175:16,17,23 281:6 302:26 402:22

rivers 142:16 162:8 165:21 170:3

Riverside 6:7 455:9

road 388:5 418:7 546:15

Robert 8:13 60:24 61:4 62:1,11 68:25 319:22,23

424:8 425:3 439:23 456:7 475:6 520:3 529:6

539:2 541:13 546:10 556:21 561:20 562:3 565:26

570:25 573:9 579:13 596:14

Robinson 301:23,24,25 305:1

robust 46:7 198:25 213:24 257:10 267:18 287:16

399:20 429:23 526:13

robustness 592:17

Rocky 166:9

Rod 294:3,10,25 295:1

Rodenticide 11:11

Rodgers 7:20,21 64:7,8 118:11 159:8,10 210:25

211:3,12 251:23 252:10 253:11 314:13,14,14

315:1 393:2,3 406:6,7 410:15 413:22 417:9,10

420:5 421:7,8 428:1,2 431:16,17 455:24,24

495:15,16 509:20,21 588:3,4 590:19

RODNEY 294:26

Rohr 325:3 326:14

role 75:19 177:11 185:4 290:19 295:21 307:11

376:5 436:26,26 454:15 470:15,21 490:13 497:22

497:25 523:11 576:14

roles 12:20 502:2

roll 316:17 317:25 319:2

rolling 103:12 138:19 142:1,4,6 147:20,22 148:22

149:2,10 158:17 161:15,16,20 273:9 464:2

516:10,11 522:25 540:20 550:13 571:17

room 4:5 5:18 6:10 124:2 190:9 349:10 433:11

450:4 455:7 483:11 497:16 585:13 589:1

root 241:10 255:25

rooted 221:5 240:24

roots 234:12

Rorh 386:25 387:7

rotator 19:12

rough 458:14

roughly 103:12 132:12,19 306:22 515:1

round 159:23 227:14 533:15

rounded 533:12,13

rounds 461:2

Roundup 359:26

route 159:4

routes 73:7 407:6

routine 254:4,7,24 257:18 258:24,26

routinely 36:25 297:1 324:16

routines 526:12

row 105:24

rows 46:23

RPM 208:16

rule 189:8

run 35:23 77:3 89:22 124:8 182:13 215:18 216:26

217:16 247:2 426:21 439:5,9 447:7 548:18

running 86:25,26 104:2,4 107:12 161:11 162:2

251:21 345:8 405:16 434:6,12 438:8

runoff 33:11 73:8 152:23 153:17 159:3,5 260:3

267:8 269:21 422:23,25 525:14,15 527:16 551:21
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553:9 593:24,26 594:5

run-off 278:24

rural 271:24

rush 127:24

Russ 128:9 129:12 132:24 136:12 315:7,10 426:23

439:14 446:6 453:21 470:9 492:23 606:20

Russel 119:22 129:1,14 130:10,26 131:23 134:24

214:10

Russell 20:6 23:25 26:1 82:21 109:25 110:12,18

111:16 115:19 305:12 310:19,26 311:3 312:3,9

313:11,15,21 314:11,24 315:10 320:5 426:24

427:12 438:15 446:26 447:20 448:16 449:15

453:21,24 457:14 459:11 460:9 461:23 464:6

466:5 475:17,22 493:2 503:9

Russel's 129:21

R2 538:19 561:7,9,12,17 564:1,2,2 568:26

S

safe 150:9 168:25 169:18,18 173:3,7,25 268:23

280:7 290:24 291:14 330:14,24 408:25 463:18

safely 291:23,26

safety 172:26 178:6 192:15 290:22

salamanders 53:3 165:2

sample 47:6 77:21 103:6 117:4 137:26,26 139:12

139:12,12 140:23 141:26 142:18 144:21,22

145:24 146:1,5,8,13,22 147:2,4,20 149:25 151:20

152:6 154:7 158:18 204:17 205:1 216:4 323:2

350:26 357:22 383:13 469:21,22 472:25 513:16

515:1 531:3 534:25 535:11 537:9 541:4 552:25

552:25,26 559:22 561:13,25 562:8 563:7 575:16

sampled 117:23 148:5 398:9 558:20,21

sampler 532:6

samples 44:17 45:17 103:5 146:9 148:19,22,24,24

149:1 150:6,7,24 151:18,26 155:4 159:21 174:26

175:4 231:14 515:2 516:19,21 518:16,17,17

533:6,11,13,14,17,18 534:3,14,16,22 549:10

558:24 559:20,22,25 560:3 561:10,14,23 564:4

565:18 567:8,11 573:5

sampling 13:3,4 26:11 103:9 140:20 141:20,21,23

142:24,24 143:3,4,5,5,7,8,12,13 144:22,23,24,24

144:26 145:3,4,15,20 146:17 147:10,12,18,24,25

148:20 150:8 152:1 155:5,6,7,16 157:18 158:5,8

158:14,16,21 159:12,26 266:2 417:2 445:7

472:16,23 514:5,21,24 515:1,3 516:4,12,15,17,19

517:1,3,4 518:22,23,25 519:9,16 522:24 524:24

524:25 525:3,12,13 532:8 533:1,10,22 538:1

540:19,22 541:2 550:9,11,14,15,16,17,20,22,24

551:1 552:9,11,11,12,12,14 554:9,12 558:15,16

559:23 560:7,13 567:10 573:26 577:26

Sandy 2:21 8:23 71:22,24,26,26 72:9 456:15,16

SAP 1:1,8 2:16 5:4 9:8,14 11:20 20:13 22:15,16,21

23:8 24:23,24 26:4 27:11,13,13 28:20,22 29:6,12

29:14 31:1,9,12,19,25 32:8 38:16 43:3,14,15

44:13 45:23 46:3 56:20,23,25 57:3 60:9 67:15

68:5 69:11 71:3 78:3,6 80:2 87:24 88:4,6,11,13

88:21 90:24 99:25 104:19,23 105:4 106:23

109:26 137:22 138:1,7 139:10 141:14 152:23

156:18 157:4 179:4,5,22 187:21 190:11 222:25

223:8 227:20,20 228:24,26 239:5 241:5 242:8

243:6 244:9 248:11 268:19 277:20 279:5,17

284:2 285:4,5 297:13 299:11,12,15 300:13 306:9

316:2 321:5 324:3 336:18 340:15 355:16,20,26

356:3 357:17 361:6 364:26 366:23 369:2,6

372:11 381:7 394:23 395:20 396:21 442:15

451:23 468:15 469:1 482:11,13 483:9,19 484:4

486:26 513:1 514:11,25 516:26 518:11 520:21

523:18 539:8 546:12 548:1,8 556:2 574:11

585:19 593:19 596:2 597:26 603:7 605:15

SAPs 9:11 15:11 16:4 19:11 32:2,13 33:2 34:15,26

42:8 47:19 56:24 59:12 60:5 73:2 76:16 109:2

295:4 352:14 353:1,12 354:22 395:4 441:3

555:18 572:9 605:7

SAP's 298:2 299:17 485:5 551:15 584:5

sat 352:13 598:25

satisfactorily 299:16

satisfied 3:14

save 162:22 163:5,6,10,14 167:9 176:18 316:3

saw 134:20 158:2 205:18 208:9 257:2 261:26

328:20 357:24 367:18 370:14 402:19,20 403:14

405:13 423:13,14 430:17 461:18 523:21 558:20

saying 69:22 122:11,18,22 124:14 160:26 225:10

313:25 320:8 326:24 341:1 345:25 348:2 360:11

361:12 365:21 394:9 415:1 423:13 424:14 425:18

448:24 462:5 463:2,5 467:19 475:8 492:3 511:1

529:26 541:6 548:18 554:8 556:2 568:17 569:12

573:23 595:6

says 118:18 123:12 134:1 160:22 198:3 274:18

304:6,21 317:18 318:26 346:13 364:6 366:19

412:23 428:1 465:17 526:22 555:15

scale 12:15 15:6 42:18 49:25 51:12 82:2 85:14,14

86:6,7 127:15 223:25 224:3 226:24 271:4 304:6

312:17,17,19 464:16 555:6,10,17,23 574:7

scaled 584:2

scales 306:23

scaling 437:7

scarce 292:6

scatter 243:1

scattered 288:24 457:18

scenario 134:18,19 318:10 404:19 498:1

scenarios 98:16 112:10,11 459:21 491:4 516:20

schedule 6:5 292:9 428:9
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scheduling 11:6

schematic 141:11

scheme 17:23,25 158:8 337:13 474:18

Schlenk 2:15 5:25,26 6:4 8:22 19:21,23 26:24

60:21 62:13,18 63:19 64:7,23 65:16 66:2,21

71:13,20,25 72:5,10 109:20 125:19 127:9,23

128:2 136:4 154:26 155:21 159:8 161:1,7 162:11

178:21 182:15,19 202:18 208:5 209:3,7 210:22

213:5 223:18 226:8 240:13,16 248:19 253:18

261:4 263:7 285:10 287:19,24 292:11 294:1,9,24

294:25 297:5 300:20 304:26 305:9 310:16,20

311:2,5,14,18 312:23 314:5,13 317:20 319:17,21

320:11,15,18 321:2,10 324:10 325:6,23 326:1,4,6

326:21 327:7,14 329:18 332:23 333:3,8,10 336:2

342:9 343:24 344:17 346:11,22 347:4,9 348:5,12

349:18,21 350:16,18,20 351:17 352:7 354:13,17

354:26 355:7,10 357:9 359:4 360:7 361:1 362:6

363:13,15 364:21 366:4,8,11,16,21 367:3 368:11

368:19 370:17,20,23 371:18 372:2,5 373:5 374:5

374:21 375:13 376:19 377:9,17,22 378:8 379:5,8

379:11,13 380:4,7,23,26 381:8,26 382:24 383:1,3

383:5,21 384:6,14,18 385:5,11,18 386:8 387:16

387:19 388:9,12,23 389:15,17,25 390:10,25

391:20,24 392:1,4,21 393:2,12,25 394:15 396:7

396:13,19 405:11 406:6 408:13 410:1 411:14

412:14 413:2,18 414:22,25 415:5,14,17 416:11

416:16 417:9 418:11,16 420:1 421:7,22 422:11

424:6 425:15 426:5,22 427:11,25 428:2,8,16

430:24 431:16 432:14 433:20 434:18,20 436:10

439:14,21 441:1 445:13,26 447:10 448:1,9

449:25 452:12 453:23 455:3,5,8 456:13,18

459:26 461:1,5 466:16 470:6 472:7,11 473:17

475:4,16,20 476:13,17 477:8,14,20 478:8,14

479:8,15,19 480:3,7 481:22 482:1 483:6,16

484:16,26 485:16,21,25 486:15,18,23 487:1,8

488:4 489:2,22 491:14 493:4,10,23,26 494:6,10

494:20,24 495:15 496:11 498:21 500:3,20 501:7

502:25 503:6,11,21 509:18,20,22,25 510:2,5,7,9

510:11,15 511:3,7 512:16,24 513:8 519:25 520:2

520:24 523:14 527:3 528:2,7,12,20,25 529:4

530:11 532:2,10,14,22 538:12,26 539:21 540:14

541:12 542:1,4,6,20,25 543:5 544:23 546:1,9

547:14,16 548:26 549:4,14 555:26 556:20 557:23

558:3 559:18 560:4,24 561:4,19 562:21,25

563:13 564:12 565:13,25 566:15,22 567:26 568:2

569:2,23 570:10,14,20,23 571:1 573:8 575:11,19

576:11 577:18,20 580:2 584:17,21 585:5 586:7

586:21 587:7,12,19,26 588:3 589:9,26 590:12

594:17 596:13 598:2,22 601:22 605:16,23

Schmitt-Jansen 266:20

school 301:3,10

science 3:19 7:7 10:7,19,20 12:24,25 13:7,9 136:21

163:1 178:8 179:8,10,18,20 180:7,21,26 183:5,23

185:12 268:19 290:5 291:18 295:24 296:13 302:3

330:11 332:4 345:23 346:4 362:1 425:26 502:16

sciences 6:16 7:3 8:11 298:11 308:13 314:16

565:11

science-based 179:7 180:22 183:21 291:24 296:6

scientific 1:1,4 2:2,6,25 3:3,5,8 6:1 163:4 167:13

167:18,22 173:1,11 174:7 177:11,26 181:9,19

182:20 183:24 193:9 240:24 244:4 268:22 275:7

291:2,16 292:7 296:5,16 315:8 345:22 361:26

376:5 392:10,18 395:26 417:24 454:11,13,17

455:4 495:20 496:6 502:11 507:20 509:13,16

538:17 593:4,10 603:3

scientifically 41:4 220:20,26 224:12 245:3 248:7

290:25 295:26 307:10,16 308:5 509:4

scientist 8:6 82:17 157:8 295:2 490:24,25 595:1

scientists 25:8 173:12,24 346:3 407:5 457:1 474:11

490:16 593:15

scope 11:5 15:3 88:18 162:6

score 76:12 79:16,19,20,23 80:7 106:20,21,22

126:10 134:5,13 193:5,8 194:8,8,10,11 203:3

205:9 210:9 216:24 217:1,7,8,19 218:26 219:17

220:11 223:2,3 224:18,20,22,22 229:14 230:19

233:18,19,24,26 238:25 242:4,9 245:15 250:3

437:24 471:16

scored 76:11 79:15 80:25 81:1,4 101:19 102:4,16

118:6 194:13 203:3 206:9 228:7,8,14 230:22

231:3,8 232:16,19 233:9 234:10 235:20 239:6

242:3 243:22 400:4 423:16 447:14

scores 79:17,26 105:5 114:4 126:3,13 193:4,5,9

202:25 228:4 428:21 429:1,3,25 471:14,14 499:2

583:25

scoring 79:18 103:10 181:25 185:13 192:7 202:24

203:7 215:7 228:15 229:10 235:12 240:22 241:3

241:23 244:21 248:3 254:10 269:26 337:17 400:1

467:25 584:14

Scott 109:26 272:6 279:25 283:11 297:6,8,8

scour 259:12 265:21 269:19 396:25

scouting 288:20

screen 92:15 301:6 443:4 521:23 552:19

screened 26:12

screening 32:23 33:5,25 34:3 69:6,15 104:15

138:15,22 139:14,16,21 154:2,21,23 160:16

209:24 317:22 328:15 329:10,12 333:19,21 339:4

375:16 417:7 515:8 520:6 529:10 553:4,7

scrutinized 593:10

scrutiny 235:22 593:3

SDcke 270:16
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se 122:20 350:3

search 179:13 321:18

searching 482:25

season 116:22 137:8 259:17 291:9,9

seasonable 140:22

seasonal 97:22 106:2,6 265:9,13,19 308:8 573:26

seasonally 259:9

season-types 421:2

seats 71:20

SEAWAVE 548:4

second 17:26 23:22 27:7 39:14 57:22 81:16 83:7

90:15 97:23 99:20 141:18 144:19 146:9,13

148:20 150:25 151:18,26 181:22 223:6 254:21

255:11 286:15 293:6 297:24 306:11 347:19

351:10 364:7 395:9 404:10 444:5 468:13 481:16

511:17 517:24 523:20 533:7 534:4,21 549:8,10

549:18 555:20 562:4 563:7 571:22 580:11 605:25

606:16

secondary 44:20 92:12 416:3

secondly 376:24 397:13 476:6 572:3

seconds 208:11

Secretary 303:5

secretion 196:26

secretiveness 294:19

section 72:2 129:26 139:23 140:1,6,7 159:24

181:17,23,25 182:11,14 202:16,19 247:26 248:18

264:3,3,9 329:10,16 436:13 503:15 504:3 505:5

526:19,20 600:24

Sections 338:18 531:10

security 168:12 169:19

sediment 7:7 238:16 272:24

sedimentation 282:15

sediments 277:10 396:26 399:3,6

see 4:5,15 14:9 16:5 17:7 18:15 39:9 58:7 64:26

66:26 70:11 81:20 92:22 105:18 111:26 115:5

126:5 127:5,12,15 128:26 129:19,26 130:3

131:12,14 132:6,19 134:20 135:22 147:3,9,14

148:3 153:18 156:8,14 157:16 158:5,9 160:12,25

175:23 182:25 188:5,9 189:12 194:4,18,23

196:14 197:5 198:1,7,18 199:6 200:16,21,26

201:5,6 203:14 207:16 208:9,19,21,25 209:3

214:1 218:15,25 219:2,4,4,23 220:5,8 221:7

225:11,23 228:7 229:7 231:11 232:9 233:11

235:7 237:3 238:26 239:7,8,9,24 240:2,10 241:24

242:6,9,22,23 243:7,13,19 244:8 246:13,17 253:6

253:8,16 255:18,22,25 256:1,19 257:7 259:25

261:15,18 262:5 263:3 269:7 273:17 282:24

287:1,8,9,17 293:14 294:19 300:9 301:6 304:20

307:15 309:12 317:10,15 328:25 332:13 333:25

336:16 337:16 341:21 347:2 348:1 349:5,6 350:1

357:18 364:4,6,25 365:7 368:1 377:8 391:3

403:15 405:25 407:25,25 411:8,11 420:25 422:14

423:25 425:8,24 427:9 429:13 433:7 434:10

435:16,21 445:4 448:22 449:9 450:5 463:18

468:15 469:4 474:19,24 482:25 492:17 521:15,19

522:5,8,22 523:6 525:21 529:1 531:16 534:16

537:21 538:17 542:14 543:13 548:6 552:24

555:19 563:22 567:12 575:7 576:15 579:5 582:11

586:26 590:23 602:2,3,23 604:8

seed 288:19

seeing 18:12 66:7 198:12 252:24 340:5 376:14

380:2 468:23 531:22 545:22 564:9 589:12 600:5

600:6

seek 325:12

seeking 279:20

seeks 173:9

Seemingly 278:21

seen 106:6 115:8 130:6 158:19 168:7 183:4 192:21

194:11 197:9,14,22,23 201:8 203:8 206:8 211:26

211:26 212:11 234:6 235:25 238:20 255:7 256:11

261:21 265:20 274:4,25 285:16 286:25 302:14

304:17 312:26 325:20 327:3 334:18 358:12

430:11 433:23 454:21 457:18 459:25 467:4,26

473:21 475:15 491:19 493:14,17 512:5 525:14

526:5 533:22 558:5 561:2 565:9 576:8,9 583:8

603:14

segment 557:9,11

segments 556:24 574:9

segregation 191:18,18

Seguin 403:17,18,18,19

seldom 189:20

select 218:20 221:23 512:13 533:5 548:21

selected 106:4 138:18 142:9,23 143:2,12 209:9

243:22 249:19 394:25 514:5 529:23 571:19

selecting 95:21 107:22 222:1 259:15 331:25 332:2

431:10 491:9,11 523:3

selection 76:16 110:24 120:20 180:8 181:26 185:13

215:7 222:4 240:22 241:23 244:22 248:4 254:11

562:14

selections 78:4 240:23

selective 285:26 520:15

selenastrum 433:5

self-correcting 349:8

self-limits 358:16

self-shading 309:22 398:15

selling 175:21

semblance 401:6

send 136:12 293:21 490:2 606:7 607:1

sending 136:12 155:8

sends 175:16
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senior 136:21 352:12,15

sense 39:24 68:23 93:5 94:8 117:19 120:19 128:11

131:19 133:23 155:12 190:1 205:16,17 206:12

210:14 211:19 218:7 253:24 274:3,24 282:8

291:25 316:22 318:23 320:2 332:2 369:22 395:11

410:19 459:1 492:24,25 493:1 546:17 547:19

575:5 604:10

senses 583:2

sensible 539:10

sensitive 16:19 21:17 45:5 50:16,18 60:18 70:19,25

91:11 97:11 120:9,16,17 121:5,15,15,24,25 122:5

122:7,24,25 124:18 125:13,14 130:1 132:21

166:25 214:19 244:21 248:3 331:8 369:18 372:8

372:20,20,21 374:13 406:21 408:6 421:17 467:1

499:25 500:24 553:5 582:19 592:13

sensitivities 89:10 119:26 120:2 122:20 497:26

sensitivity 23:24 31:7,13 45:3 47:3 56:25 70:17,22

84:8 89:20 90:1 94:1 95:20 106:25,26 120:10,20

122:22 125:12,23 126:17 129:13 130:15 181:24

213:16 214:24 215:17,24 216:11 217:24 222:3,21

223:14 235:25 240:21 241:1,22 244:15,16,18,26

245:8 258:1 261:1 330:7 372:15,26 399:24

420:10 421:19 443:24 465:9 467:22 468:7 501:14

506:24 509:12 588:20

sent 176:16 293:3 301:4 305:7 512:22

sentence 475:16

sentences 333:11 476:21 477:22

separate 223:11 228:23 232:3 305:21 325:19

356:18 399:18 400:26 425:6 453:9

separately 44:25 45:1 62:2 201:20

separates 376:26

September 144:18 581:11 607:9

sequence 58:15 127:20

sequences 134:21

sequential 399:4 445:3,7 550:24 552:11

sequentially 242:23 445:4

Sequin 231:6

serial 145:2 519:7

series 23:20 28:16 83:3,9,13,24,25 86:10,12 88:1

92:3 93:15 127:20 140:13 156:24 234:17 237:19

299:20 306:16 353:9 401:19 459:19 516:4,4

548:3

serious 37:3 174:6 307:7 409:11 553:13

seriously 188:3 306:8 458:4

seriousness 206:26

serve 2:11 18:14 58:20 154:23 176:24 268:13

372:9 496:5 509:16

served 177:19 509:13 601:24

serves 5:11 295:25 372:26

service 8:7 168:24

services 163:12 226:13 288:18

serving 2:16 594:19

servs 382:18

session 140:5

set 2:6 3:26 20:5 25:2,16 52:20 53:20 80:5 85:15

92:22 98:20,20 102:12 123:5,6,7 126:12 127:22

143:11 164:15 186:2 200:9,11 203:2 212:3,9

214:20 220:3 222:22 223:4 232:7 242:7 243:4

305:20 306:3,7 313:24 329:14 330:26 340:25

350:15 367:19 368:8 379:23 394:23 399:17,21

404:8,21 407:10 410:14 415:20 424:2 430:3

439:6 444:16 445:2 458:7 460:22 462:6,11 469:9

489:6,20 496:14 512:5 529:13 539:10,20,26

540:6,23 541:26 545:21 554:15 556:13 557:12,22

558:13,22 559:22 560:20 561:8,9 562:9 565:17

567:7 570:19 571:20 572:26 573:18,19 584:1,5

597:13 607:2

setback 171:5

sets 150:2 232:1 234:13 267:19 380:1 399:24

405:25 511:24 520:14,17 529:26 535:4 545:5,6

545:12,14,17,19,23 560:1 571:19 572:1,13,26

573:25 580:13 583:20,24 584:11,12,14 598:9

setting 57:8 105:6 212:4 354:6 360:19 397:3

439:26 472:18,19 491:1 597:10 602:12

settings 112:14,15 515:19

setup 200:3,7,7,18

seven 110:1 276:17 344:20 345:19 376:12 515:21

515:24 516:4,14,15 517:1 518:6 535:4 536:24

540:17 541:1 593:7 598:26

seventh 516:5

seven-day 540:19

severe 88:8 122:19

severely 168:1

severity 83:14,19 85:23 86:1 93:3,15 103:14

122:16 123:13,14 125:16 200:2 409:12 447:25

449:10,20

sex 28:6 31:15 54:10,13 67:26 68:16,19,24 168:1

187:25 194:15 197:1 206:3,6,9 208:18 358:13,24

sexes 376:22

sexual 31:20 43:17 46:22,24 48:12 54:3,8,9,11,18

63:26 189:2 193:1 377:26

SGR 443:15 444:13 492:24

SGRs 444:4,5,12

shad 402:12

shaded 50:3 51:16 201:4

shades 152:14 153:9

shaking 364:15

shallow 413:8 526:25

shallow-draining 527:15

shape 245:23 246:5,10 535:26 582:25
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share 489:10,18

sharing 596:11

Sharlene 2:1,3 5:26 72:7 136:13 292:18,21 294:10

294:11 333:6 394:19 396:8 450:5,19 451:8,22

452:11,13,24 453:26 454:9,10 477:12 487:11

488:1,5 489:9 605:14,24

shed 144:26 281:26

sheet 293:7 366:17 475:9

Shenoy 322:14

she'll 528:21,26

shift 31:15 124:20 361:14 468:6 469:23 471:11

shifting 121:17 122:4 535:26

shifts 74:23 79:8 117:9 203:15 242:21 286:19

322:3,4 323:11,21 324:5 342:6 378:20 498:8

503:3

ship 165:17

Shirley 607:6

shoes 487:21 576:17

shores 168:26

short 32:8 51:3 64:20 76:2,3 81:14,23 96:11,11

105:12 125:21 127:15 130:11 143:7 204:12

212:23 220:1 225:23 226:1 246:8 283:12 307:13

311:7 348:8 407:23 413:21 442:20 458:3 459:2

489:6 515:14 532:4

shortage 557:17

shortcomings 331:25 526:11

shortened 204:2,2

shorter 95:22 96:17,17 105:25 106:7,8 124:17,22

124:24 129:8,17 135:16 143:3 225:21 239:16

307:18,22,25 308:6 463:1 464:3 466:15 519:10

559:24

shortest 99:8 457:26

shortly 230:4

short-duration 514:23

short-lived 519:14

short-term 130:16 209:25 407:18 408:1,7,10

423:22 495:21 506:4

should've 394:20

show 63:9,13 68:16 80:26 141:6 145:14 151:23

193:22 206:23 210:2,4 212:13 215:15 232:6

233:21 236:14,24 237:7 239:3 242:20 268:23

291:7 305:6 355:1 358:9,10,11 361:18 373:8

388:2 458:20 461:17,22 535:7,8 554:26

showed 25:10 60:26 107:1 133:22 145:18 146:1,8

154:16 175:8 194:21 206:8 225:12 265:12 300:16

307:10 376:25 377:3,6 443:15 461:20 464:25

531:14 563:6

showing 22:1 58:6 90:5 99:17 137:9 181:15 198:8

198:13,20 206:7 208:24 212:15,15 237:11 309:14

365:14 495:14

shown 20:18 53:7 54:7 78:19 81:5,21 89:17 91:1

95:5 99:8 103:10 105:24 117:3 152:24 153:8,17

191:5 196:16 198:19,23 200:1 201:10 242:11

257:22 333:16 374:1 424:1 457:19

shows 20:15 21:24 81:22 89:11 90:6 100:5 101:15

101:25 105:17 126:18 127:4 144:7 166:5 191:16

191:18 200:19,24,25 201:4 206:22 222:3 233:10

236:9 237:6 242:12 257:13 259:2 432:18 500:24

shut 311:4 496:3

sick 166:5

side 10:15 11:3 18:4 120:21,23 128:3 141:3,5

174:9 184:3 191:13,20,21 200:24 206:1 207:2

272:25 273:23 274:14 308:17 331:26 364:14

388:20 412:16,17 437:19 542:7 588:24 589:1,7

596:19 597:25 600:18 601:3 602:19

sideline 487:12

sidetrack 546:18

sifted 424:11

sifting 426:16,20 453:12

sight 497:21

sightings 286:26

sigmaStat 542:14

sign 453:1

signal 118:16

signaling 327:2

signals 118:16

signatures 176:14 409:23

signed 273:21

significance 17:20 134:3 192:18 194:5 204:17,18

204:20 207:20 319:9 471:10,12 488:21 538:15

571:21 580:25 581:16 582:14 583:6 588:12

significant 31:10 37:13 48:18 52:14 55:9 74:8

80:26 96:18 98:4 109:17 114:19 116:3 118:4,10

127:6 137:8 146:2,3 154:7,9 169:22 170:1,4

172:15 191:18 221:8 227:25 229:20,25 231:24

232:9 245:6 269:5,6,18,21,24 272:16 280:17

286:6 290:6,19 291:7,26 302:11 303:25 307:19

307:26 343:18 377:3 416:7,8 423:19 463:3

488:20 503:19 506:1 513:19 515:15 519:22 534:5

535:1 549:8,11 555:13,17 556:11,13 561:17

580:15,16,19 581:2,3,20,25 582:9,20 584:8

significantly 76:7 137:5 168:19 231:26 265:6

266:3 295:19 377:2 387:1 421:11 444:5 508:8

519:11

signs 589:19

silly 453:2

similar 22:16 73:17 89:8 92:20 93:12 99:4,11,18

99:26 100:16,21 110:8 114:10 115:8 121:9

145:18 150:21 151:14 158:4 171:7 182:14 204:11

211:5 214:11 219:23 247:20 265:13 266:18
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267:13 300:6 404:12 424:1 470:16 473:10 557:5

558:14 584:5 597:24 606:25

similarities 599:14

similarity 118:3 231:21,25 232:5,8

similarly 173:10 222:5 244:11,23 255:12 256:19

323:24

Similich 330:3

simple 10:1 93:5,17,25,26 94:19 95:8 118:11

123:26 146:15 150:15 385:15 418:5,6 446:3

514:13,14 536:14 539:13 546:26 548:23 557:7

572:16 577:8,25 589:5

simpler 24:3 91:20 92:20 94:20 421:21 435:20

561:2

simplest 533:8

simplification 435:17

simplified 87:5 601:13

simplifying 253:9 475:26 601:6

simplistic 436:25

simply 20:4 22:1 84:2 85:21,25 86:2 91:18 92:5,24

94:11 97:7,12 104:1 119:24 151:5 193:10 217:2

220:26 224:10 247:15 261:9 273:10 285:26 292:4

310:4 373:23 408:6 434:15 471:18 506:22 530:17

simulate 214:2

simulated 91:3 141:24 516:7 533:22

simulates 86:22

simulation 84:22 86:20,25,26 87:26 138:4 141:13

156:22,22,23 518:24 553:11 560:19

simulations 87:3,4 89:1 90:9,9 141:15

simultaneously 29:20 43:6 68:8

single 12:11 60:13 74:11 75:7 78:22 84:13 90:2

97:19 100:25 119:22 120:6,21 129:24,25 147:11

199:12 218:10 223:12 228:26 241:9,11 244:11

250:13 266:23 278:25 279:9 305:23 308:16

315:25 316:24 318:19 329:26 330:6 348:19

358:23 418:24 432:18 448:21 449:9 464:21

496:21 510:23 511:1 534:16 569:26 570:17

586:11,14 597:11,13 604:19

single-day 516:3

single-species 16:1 82:19 111:12,14,17 128:19

131:2,3,6,10 133:3 234:4 317:4

single-spot 117:2

singularly 327:12

sipping 532:6

sit 200:9 394:3 487:21

site 1:8 4:20 103:9,17 104:7,9,11,12 105:13 113:12

140:11 143:24 144:6,12 145:16,17,21,21 149:10

150:5,6,10,10,12,12,14,15,19,19,22,23,24 151:1

151:1,4,6,13,13,24 152:3,14,16 153:9,12,23

158:15,16 159:26,26 160:17,22,24 186:25,26

187:5,9,11,12,16,18 219:25 220:9 225:23 262:13

265:12 280:26 281:1,3 282:4 299:4,5 315:16

436:21,23 438:7,9 440:24 473:11 515:21 516:12

516:26 517:21 518:6,6 519:1,16 529:15,16 531:5

531:18,22,25 533:6 540:20 547:7 548:15,17

552:21,24 556:9 562:6,8,14,18,19 566:2 568:13

568:14,14 572:6,13,23 573:13,22 578:1,1

sites 103:5,20 105:16,24 106:8 107:12 111:4,8,19

113:19 126:4 138:16,18 139:16,19 140:3 143:2

143:22 144:3,8 149:15 151:22 152:8,12 153:1,19

154:5,16,24 158:9 160:6 184:24 217:17 265:6

266:4,10,12 267:13 269:22 276:18 280:21,23,24

283:12 308:8 314:20 369:15 372:18 409:2 413:23

432:26 434:6 438:7 445:20 453:18 469:21,23,24

469:26 470:3 488:26 491:10,11 514:19 515:10,11

515:11,17,17,19,25 516:4,24 517:13 518:1,6,7

519:21 522:20 529:17 530:24 531:1,2,6,12 532:5

533:17,19 535:4 538:10 539:12 541:1,4,16,17,23

547:4 548:13,14,21 552:19 553:6 563:8 564:17

564:22 568:18 572:4 573:2 574:24 575:8

site-specific 88:18 154:3 316:20 529:22

site-year 102:26 148:4

site-years 105:25 563:4

sitting 4:6 338:21 376:8 388:24 394:8 474:21

571:4 595:5,6 598:25

situated 159:23

situation 75:26 124:1 129:6 132:7 262:21 317:14

318:17 406:18 447:8 464:10 501:16 514:2 525:5

527:8 542:24 579:1

situations 14:6 161:4 208:19 228:25 364:3 430:18

547:5,7

six 264:6 271:6 376:12 453:26 513:24 518:5

541:16,23 573:5

size 47:6 77:21 113:17 152:18 153:13 158:4,24

167:26 204:17 205:1 288:24 323:2 350:26 357:22

397:5,7 398:6,19 423:3,3 515:16 531:3 534:4,14

534:18 535:11,24 537:10 548:17 549:7 551:26

553:11 556:11 561:13 573:20 575:17 577:24

578:2

sizes 331:19 383:13 516:25 518:26 534:25 552:25

552:26

skews 52:3 61:20

skin 165:25

skip 472:3

skipped 510:15

sky 177:20

skyrocketing 170:20

slam-dunk 394:12

Slaughter 297:6,8,9 300:21

sleet 606:10

sleeve 317:25
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sleeves 316:18 319:2

slide 9:19 20:26 50:21 63:20,22 72:21 85:24 90:5

92:22 93:18 95:25 99:16 100:5 110:24 116:11

123:24 129:21 130:11 131:24 140:26 141:15

158:11 186:2 208:7 240:26 249:3,17 283:16

309:5,6 430:13 456:24 457:9,17 461:12 531:15

583:15,18,19,24

slides 32:24 33:7 37:2 44:20 95:23 129:4 131:12

133:21 149:17 188:19 199:21 264:5,8 337:23

367:23 378:25 456:23

slight 64:4 79:24 99:19 106:21 118:9,24 126:26

127:3 219:24 223:1 229:21 230:22 231:7,17

232:13,14,15,15,19 235:12 244:22 256:19

slighter 229:16 232:18

slightly 97:10 100:1 527:11

slippery 364:18,19

slope 364:18,19 553:24

slow 221:5

slower 255:14,15 262:17,18

slowest 221:11

slowly 176:23,23,25 277:16 543:18

small 95:18 98:3 171:14 207:12 219:25 246:26

248:25 270:5 272:13 288:24 349:16 374:25

375:11 398:8,11 515:6,10,11,18 552:26 553:16

555:7 556:13 582:16 587:25

smaller 100:5 114:26 141:3 142:22 210:2 226:24

286:13 484:11 538:8 550:10 555:23 559:25 576:7

smallest 313:6,12,13,15

smart 184:15

smoothing 537:2,25 540:2,7

smudging 252:26

snail 322:8

snakes 166:13

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snow 172:19 606:10

Snyder 294:4,10,25 295:1 297:6

social 112:22 175:14

societal 416:7

society 8:21 76:18 162:25 170:20 173:14 176:13

177:8 178:10 270:6 456:12 583:6,7

soda 170:10

software 98:23 102:22 103:8 104:4

soil 8:6 46:18 56:9 140:20 172:18 174:19 273:1

278:4,7 279:15 280:18 288:20 290:9,16 295:22

553:22,25 569:20 594:12

soils 559:2 592:7

soil-based 527:25

solar 265:11

sold 170:12

sole 174:2 535:20

solely 407:24

solid 19:2 117:23 238:19 367:18

solids 291:6

Solomon 181:17,20 186:4,6 202:26 205:4,13 206:5

206:16,25 207:9,25 209:18 211:3,16 212:20

213:2 227:13 254:2 255:4 367:17 387:25

solution 40:7 41:16

solutions 41:11 289:3 484:15

solvent 35:24,25 36:7 37:11,12,14,15,16,19 77:13

237:16,18,20,23,24 270:15 328:19 331:16 333:14

339:17 348:22 377:1,7,7 400:17,23,24

solvents 77:17

somatic 373:9

somebody 202:24 409:6 568:8

somebody's 525:20

somewhat 116:24 127:19 233:1 286:6 309:13

313:9 449:3 496:25 558:15 559:22 562:7 567:11

son 364:18

sonar 285:21

soon 128:4 226:17 256:11,21 262:12 287:26

338:19 489:24,26 607:12

sooner 412:9

sophistically 582:17

sophisticated 318:21,22 598:13

sophistication 55:7

sorghum 22:6 150:17 153:6 169:13 183:9,12 268:6

268:12 272:22 274:7 280:8 289:9,22 295:13

sorry 311:8 379:15 384:14,18 385:7,11 426:8

447:20 493:6 497:4 510:12,15 512:20 523:5

528:25,26 531:9 579:13 603:20

sort 7:23 14:7 18:6 61:17,20,21 62:26 63:16 66:26

68:3 70:16 114:21 127:19 129:7 130:22 134:13

135:7 209:14 212:5 215:17 236:23 241:25 244:8

292:16 318:15,22 319:6 320:9 326:17,18 347:7

356:15 364:24,25 365:7,10,13,21 371:25 372:1

380:15,16,19 408:21 418:2 432:22,26,26 433:3

433:10 446:7 474:13 480:12,14 484:18 490:11

491:8 543:8 544:8,17 547:20 589:15 602:19

603:2,3,5

sorting 357:3

sorts 14:17 318:21 319:6 421:19 605:5,5

sought 294:22

sound 4:12 5:21 238:22 268:2 270:6 271:5 284:2

295:24 296:13 332:4

soundness 296:7

sounds 249:26 487:8 524:11

source 33:22 52:10 78:22 108:17 110:26 111:4

117:25 166:13 228:9 237:21 238:6 290:15 343:14

399:2,2 471:23 490:7 496:22

sourced 111:13 408:24
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sources 21:23 32:8 138:23 149:19 173:26 192:10

328:16 333:22 367:24 396:15 433:14 472:1

579:26

south 1:14,15 7:21 175:25 191:2 468:18

southern 7:17 151:15 455:23

southwest 191:12,21

so-and-so 136:8 162:16

so-called 309:3,24

space 14:3 15:4,6,14 170:6 211:17 302:22 419:22

420:13,16,19 578:25

spaced 559:22

spacial 117:7 141:9 159:21 160:1,4 179:14

spacing 53:12 54:5 554:26

spacings 554:26,26

span 53:13 544:9

spanned 212:7

spans 13:14 58:16 587:16

spare 141:19

sparrow 576:4,7 579:10,14,18

SPARROW's 579:23

sparse 267:24 514:13 521:17 524:24 525:3 539:26

540:6,23 573:4

sparsely 514:23 558:20,21 573:1

sparser 267:19 519:16

spates 396:25

spatial 8:19 140:16,17 160:12 463:16 555:10,17,23

556:22 557:2 595:10

spatially 12:12 16:12 160:6 266:11 570:18

spatial-temporal 561:11

Spatz 19:17,23,24 26:24,26 27:11 65:17,24 320:25

321:4 327:9 348:9 366:14,17,22 377:11,13 381:1

381:3 385:24 390:1 396:11,14 428:11 456:26

494:5,13 503:12,14 511:6 512:25 513:1 532:17

532:19 542:22 549:6 563:2 570:15,16

spawning 209:21 322:22

speak 59:21 362:8 408:11 457:21

speaker 118:2 119:9,18 457:11 499:22

speakers 500:6

speaking 114:14 226:14 302:1 318:5 418:15

speaks 244:3 247:7 416:6

special 6:18 227:12 268:17,20 580:25

speciality 179:13

specialize 8:19

specialty 7:14

species 9:16,18 12:11 13:6,26 15:21 16:19 22:21

28:21,25 31:6,9,11 33:21 39:3,9 45:6 46:13 47:3

50:5 51:5,24,25,26 52:2,14,15 53:3 56:20,22,26

57:1,5,8,9,16 58:9,11 59:20 61:13 62:7,8 64:15

65:12 66:13 70:17,20 71:1,5 73:22 74:11,23 75:6

75:7,13,17,25 76:25 78:11,22 79:9 84:8,13,18

86:23 87:8 88:26 89:2,7,7,10,11,13,17,18,19,21

89:26 90:3,10,11 91:4,6,7 92:6,7,9,16,18,25 93:2

93:8,9 94:1,6,15 112:6 114:8,11,16 116:20,22

117:2,11 119:19,20,21,23 120:6,21 121:3,5,15,16

121:24,25 122:5,7,12,24,25,26 123:8,21,22

124:19,19,21,23 125:8 129:20,24,25 130:2,3

163:11,19,21 164:17,23 165:10,16 166:15,20,22

188:10,12 194:2 198:11 199:8,12 201:13 203:11

203:17 209:24 215:25 216:7,8,17,18 221:5

234:24 255:7 258:4,5,11 259:15 262:7,8,12,17,20

263:4 266:22,24 308:16 315:23,25,25 316:25

317:5 318:19 321:23 322:11 324:1,8,14 329:20

330:4,5,6,8,15 331:9 332:21 335:8 338:6 350:8

365:24 368:17 369:8 371:6,12,16,25 372:8,10,13

372:16,23 373:1,7,11,24 374:2,11,12 375:16

378:19 381:10,17,20 397:14,15,18,18,19 398:10

399:9 406:20,21 410:13 418:24 423:11,13 430:26

432:18,23 433:7 442:16,21,21 448:21 449:10

480:21 481:15 482:15 483:24 484:9,12 493:17

496:1,21 498:1,2,7,8 500:17 501:14 502:9 506:15

559:10 581:21,24 582:3,14 585:21,22 586:5,11

587:15 588:15,17,18,20,21,22 604:25,26

species/genera 437:4

specific 12:12,12,13 13:17 14:23,24 18:8,9 43:24

50:13 60:4 69:18 76:24 84:24 93:8 94:15 95:4,6

95:10,12,12 113:12,20 114:26 119:4 130:11,13

137:18 184:24 186:15,25 215:26 216:15 217:19

217:19 218:8 225:2 228:2 229:8 235:26 241:2,3

255:13 258:2 277:25 311:15 315:2 320:12 324:2

327:26 338:24 339:15 354:12 355:5 372:22

378:14 382:16 390:5 391:4 393:16,17,20,22

394:12 395:2 401:16,17 402:18 413:26 414:2,19

430:26 431:5 439:24 441:15 452:15 453:17

476:23 486:3 489:16 491:20 492:13 527:4,4

529:15 557:18 572:14 573:25 574:3 579:20

specifically 45:10,12 194:7 204:15 208:11,16

213:16 217:25 220:22 227:3 241:23 265:20

298:19 317:19 328:10 422:19 431:25 482:2,14

488:18 498:15 511:21 545:15 588:4

specifics 316:18

specified 84:9,14 98:13 213:14 218:5 534:9

specifies 112:13

specify 84:12 113:16 267:21 453:19 528:13 578:23

specifying 38:14

specimen 358:26

specimens 190:20

speed 200:20 203:26 208:16 402:24

speedy 204:1

spelled 546:11

spend 10:13 169:2 314:10 339:18 552:13 591:16
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spent 226:19 395:23 434:14

spikes 129:6

spill 282:23

spin 596:16

spine 323:8

spite 271:9 388:4

split 223:11 229:2 234:13,16 240:8 250:5 399:24

583:16

splits 250:15

splitting 223:9 249:25 250:14 399:18,23 427:8

580:12 583:10,12,14,24 584:2,11

spoke 162:16 466:6

spoken 167:6 479:9

sporadic 229:26

sporadically 281:11

spot 410:15 439:13

spotted 166:9

spotty 251:2

spray 169:4

sprayed 172:15

spread 3:25 165:13

spreads 165:17

spreadsheet 293:4,20

spreadsheets 293:25

spring 265:10

springs 78:10 108:19 111:3

spring-summer 259:19

spur 603:16

squander 171:22

square 21:26 51:17 53:15 156:14 281:5 536:26

537:2,10

squares 49:23 51:8 52:20 53:4

squeeze 253:19

SSD 100:9 428:24

SSDs 84:12 94:2 100:23 430:16 443:26 444:1,4,5

stabile 282:4

stability 581:22 599:23

staff 2:25 5:20 18:22 173:18 352:17 587:21 594:20

603:7,26 606:15

stage 11:16 20:5 28:25 47:4 52:25 64:17 69:1

72:26 82:15,18,25 83:7 85:3,8,19,22 98:21,22,23

101:5,6 102:9,9 103:11,15,23 118:13 420:4 431:8

431:22,22 436:20 586:19

stages 101:1 428:18 432:4 475:24

staggered 132:10

stagnant 405:17

stairstep 518:13

stair-step 147:19 148:17

stake 173:9

stakeholders 276:7 281:14,21

stand 177:13 586:26

standard 32:3 65:14 102:23 110:18,22 156:13

209:25 210:6 271:8 277:19,24 278:13 316:6

326:17 334:24 335:4 336:24 354:1,24 397:1

422:25 436:5,6 438:20 470:25 472:10 476:7

481:12 546:24

standardized 34:16 200:25 203:19 327:25 347:8

468:12

standardizing 344:23

standards 30:8 32:13 36:22,25 41:24 59:9,12

60:10 69:4,8 171:4 296:16 299:23,24 324:17

333:15 352:20,21 353:16 354:4 360:20,20 362:11

362:11 490:18

standing 72:14 131:22 463:17,20

standpoint 486:13 487:12 559:15

stands 172:8 596:18

start 10:2 17:11 19:22 27:4 113:17 131:1,4 132:26

148:16 162:9 219:14,16,24 220:3,5,7 252:24

255:22 263:11 304:19 362:14 400:14 404:24

409:24 419:10 420:7 445:10 454:5 483:2 501:19

532:17 543:6 545:17 557:8 575:12 583:5 585:12

588:14 598:7 604:4,5,14,23

started 19:19 78:8 84:23 86:19 103:3 111:2 118:12

118:13 126:11 141:17 211:18 236:14,24 273:7

413:17 428:6 455:8 467:18 478:18 479:3 482:19

482:22 483:5

starting 3:25 11:14 108:18 136:19 342:24 390:23

409:10 559:12,12 573:4

starts 10:26 112:16 187:18

starve 169:25

stasis 187:19 256:21

state 6:11 12:13 14:13 22:13 25:18 26:9 104:3

134:7,9 139:18 149:19 155:5 169:23 275:9 276:6

276:14,19 281:18 289:14 295:8,8,9 298:24 303:1

303:2,6,7,14,16,19 335:3 345:14,20 422:3 432:13

455:7 490:18 500:6 502:10 515:24 545:19 599:17

599:25

stated 31:26 36:21 237:18 270:23 296:4 297:17

299:12 328:1,10 344:4 357:12 359:16 412:13

415:15,19 430:9 437:5 440:11 453:8 513:25

514:11 524:10 546:12 555:19 583:11

statement 3:17 122:9,9 237:20,20 268:9 272:11

275:4 280:1 283:11 284:10 301:1 320:26 321:1

370:3 393:8 427:6 434:2 517:6,9,19 555:10

statements 190:12 195:22 264:7 305:18 413:22

423:10 537:5 558:23 585:19 587:9 590:26

states 1:11 78:16 108:25 136:26 144:15 150:17,17

151:7,8,9 165:15,24 174:5 178:16 183:14 190:25

265:26 268:16 279:10,19 288:24 298:13 302:15

302:26 303:12 369:14 545:3
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static 36:9 38:24 39:1 45:16 69:24 77:15 149:24

187:19 447:18

static-renewal 36:10 38:24 39:1 69:21,22,24 77:14

stating 392:23 425:11 440:2

stationarity 157:6

statistic 571:16 574:14

statistical 36:20 44:15 51:23 77:13,23 93:21

120:19 192:18 194:4 204:16,18,20,21,24 231:16

293:17 319:8 323:7 324:5 325:18 328:14 331:21

355:21 436:2,4 511:24 514:22 536:2 554:5

561:16 566:24

statistically 37:13 48:18 229:24 265:13 323:14

358:10 512:6 580:19 581:3

statistician 70:5 361:16 436:25 539:23 568:5,10

statisticians 313:5 344:18,21 565:9 578:10

statistics 8:18,19 323:3 361:23 412:20 514:12

546:22

status 31:17 345:20

statute 13:15 112:4,20

statutes 12:23,24 13:8

stay 446:26 571:22

stays 315:17 446:15,15 497:17

steadily 81:12 274:4

steady 14:12 134:7,9 345:13,20

Steeger 352:11 354:13,15,20 355:4 372:3,6 384:13

384:16,17,20 393:14,15 450:19,24 451:22,26

452:13,16 477:23 485:2 487:4 606:21

Steeger's 355:19

stehenden 270:20

stem 135:18

stent 468:7

step 10:10 11:16,21 20:18,18 24:16 32:21 48:25

85:2,17 94:8 102:25 104:2 141:18,19,22,26

184:17,17 208:21 215:12,22 216:5,25 217:15

245:11 253:23 301:9 310:22 420:11,18 424:10

431:22,22,23 442:4,6 468:3,24 469:11 474:19,24

517:2 520:6 529:10 531:8 544:5 551:16 576:15

597:18 604:9 607:15

STEPHAN 128:8

Stephen 6:23 9:3,5 19:6 64:25 65:4 130:18 131:11

135:18 136:1 248:21 325:25 342:11 347:6 350:17

370:18 379:6 383:2 388:10 391:25 411:15 433:21

455:12 500:4 503:23 586:8

steps 9:24 236:23 296:8 426:4 431:23 471:21 472:3

stepwise 145:23 534:5,8,15,18 549:6

steroid 197:2

steroids 208:18

Steve 2:20 6:24 68:26 162:10 418:14 455:12

Steven 66:5,25 69:10 87:19 112:2 132:4,24 161:3

292:15 315:6,22 318:5 319:20 355:12 358:17

361:3 363:8,14 385:10,12 418:14,17 428:5 477:5

477:12 603:21

steward 180:2

stewardship 179:3,13 180:3 276:4,11,26 508:22

543:22 544:14

stick 225:6

stifle 500:12,13

stimulated 398:23

stimulating 197:1 249:1 585:15

stimulatory 403:20

stinking 351:7

stint 226:21

stir 200:9

stirrer 200:21

stochastic 138:4 156:22,23 560:18

stock 41:11

stocked 401:20

stocking 401:26

Stoddard 490:24,26

stomach 418:23 532:16

Stoner 302:24,25

stop 26:21 168:26 182:12 208:25 301:13 424:15

446:18 457:24,25 462:23 544:21 559:17

stopped 164:18 176:23 446:24 557:26

stops 120:6 447:5,5

storage 527:17

storm 396:25

story 229:18

straight 117:3 175:23,26 419:17 537:22 546:23

straightforward 10:2 338:7 348:3 504:17

strain 31:7,13 45:3,5 70:25 369:18 375:17,18,21

strains 371:6 373:23 375:26 433:15,16

strange 233:1

strapping 142:6

strategies 303:21

strategy 144:24 155:16 303:7 304:13 425:10

548:12,14

stratified 550:11,22

stratifying 540:21 550:16 552:12

stream 14:23 23:23 87:18 117:14 159:22,24

172:19 266:12 274:2 282:1,7,9,16 283:12 404:19

410:25 490:12 491:11 512:1 555:1,11,15 556:24

557:1,3,4,15 574:6,9,19 581:22

streams 78:9 103:4 108:19 111:2 117:8,8 170:3

213:15 232:23 233:2,3 265:26 269:10,15,19

272:25 273:23,24 274:5,14 277:6,9 279:20 284:1

291:5,7 402:21 403:9 405:20 411:1,3 413:9 431:3

444:1,6 491:2 515:13 523:20 554:25 555:21

582:16 583:9 591:6,16

strength 192:9 193:20 198:5 207:3 331:24 340:18
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strengths 20:21 386:6 513:6 517:12,12 570:16

571:12 572:11 573:10

stress 19:15 188:13 195:18 196:13,18 198:24

199:10,16,26,26 200:6 201:1,14,26 202:6,13

203:14,22 204:1,8 209:6 322:12 342:18 409:5

446:14 448:25 581:14

stresses 451:20

stressor 33:19 36:11 77:7 114:14 116:26 200:25,26

201:5,13 310:10 366:1 505:5 510:23 511:1

stressors 77:4 201:24 335:20 345:17 356:16,17,24

356:26 357:5,8 358:23,23 365:2 504:4

stress-induced 201:18,21

stretch 555:13 591:1

stretched 276:18 462:21

stretching 191:9

strict 332:2 367:13 369:11 541:18 562:17

strictly 338:17 431:3 439:10

strictness 562:17

strikes 392:6 586:11

striking 258:20

string 530:16

strips 593:22

strong 193:24 195:9 198:5,9 246:17 247:6 323:25

326:19 367:22 369:4 371:3 383:19,23 473:24

504:24 600:16,17

stronger 388:20 578:4

strongly 350:10 585:10

strove 182:21

struck 563:26

structural 21:20 73:24 75:18 76:6 117:10,12,20

493:17

structure 21:16 22:7 38:6 66:8,12 74:3,8,23 75:21

78:12 79:9 107:15 108:20 109:5,13 113:3,26

114:20 123:11 179:26 254:14 258:6 259:7 274:21

291:8 397:12 399:9 434:9 440:17 442:22,23

443:8 492:12 494:18 496:16 498:4,8 508:18

509:2 519:8 524:8 557:2,11 582:5,5

structured 317:13

struggle 277:4 570:8

struggling 178:3 569:25

stuck 595:7 606:9

student 301:2

students 301:10

studied 45:13 66:18 290:21 328:6

studies 8:14 12:11 15:20,21 22:18,20 23:1,6,7,20

25:6,13,15 27:19,21 28:4,7,24 29:8,11,12,13,16

29:20 30:11 31:3,18,19,24 32:3,5,7,12,19 33:3,6

33:8,14,17,18 34:1,1,3,4,5,9,13,13,18 36:26

38:11,24 39:3,16,26 40:10,17,20,21,23 41:10

42:1,2,4,17,23,25 43:5 46:2,10,12,19,20,23 47:1

47:15,20,22,26 48:2,6,18,21,23,25,26 49:2,5,8,10

50:1,11 51:18,21,24 52:16,26 53:6,21 54:4,15,17

56:4,6,10,12 57:11,24 58:1 59:7,7,10 60:2,8,11

60:14,16,19 61:13 62:5,21,26 63:7,10,11,25 64:2

64:10,12,22 65:1,8,9,11,14 67:8,11 68:3,7,10

69:2,5,6,20,20 74:11,16,21,25 75:2,7 77:26 78:2

78:3,5,5,17,24 79:1,6,11 80:8,9,18 81:2,7,10

82:11 83:18 95:5 105:7 107:18,21 108:22 111:7

111:15,15 113:21 115:3,9,9 116:7 119:13,14

127:5,21 128:20,21,23 129:15,25 131:15,16,24

132:21,25,26 133:17,19,24 134:1,2,4 173:7,10

179:12,13,14 181:3 184:1 185:14 186:22 187:24

187:26 188:1 189:13 190:6,7,14,17 192:4,8,11,25

193:7,16,16,19,20 194:20,22 195:2,24 196:22,22

196:23 197:8 198:4 200:25 201:16 205:14,18

206:6 208:17 209:21 211:10,19 212:10,12 214:3

214:21 217:9 218:4,15,23 220:12,13,17 221:1,17

221:24,25 222:8,26 225:1,16,18 226:22,26 227:4

227:5,7,11 228:16,17,20 229:3 231:1,2 234:4,17

237:19,21,24 238:1 240:9 241:2,3,17 242:8,11,17

242:18,20 245:6 248:16 249:4,7,10,14 251:24

252:22 253:1,10,11 254:12 261:6,7 265:3 266:19

266:24 267:1 269:25 284:8 287:13,14 290:22

293:5 296:15,18,19 299:25 300:8 306:3 307:18

313:23,24 317:1 321:6,20,24 322:2,7 324:7,13,14

324:21 325:1,12 326:16,26 327:19 329:20,26

330:7,13,19 331:4,6,24,26 332:4,7,12 333:17

334:15,16 335:22 336:12 337:23 338:23 340:3,14

340:14,25 341:15 344:6,6,21 345:25 346:8,25

347:17 348:2,16,21,24 349:13,24 350:2 353:2,5

353:15,17,20,22,24 354:4,5,7,9,9,25 355:6 358:9

358:16 361:18,20,20,21,21 362:13,17,18,26

363:23 364:8 367:6,11,13,14 368:9,13,21,22

369:2,23 370:25 371:3 372:22 373:6 374:1,25

375:11,17 376:22,25,26 377:3,5,16 378:10 379:2

379:24 381:14,18 382:3,7,15 384:22 385:3,4

386:10,16 387:11 388:1 389:7,18 391:16 392:25

396:12,16,22,22 397:1,26 398:1,8,22 399:15,16

399:18,26 400:7,10,13,26 401:2,14,16,19 402:1

402:15 403:13,17,23 404:4,7,22 405:9,22 406:1

407:7,12,16,23 408:9,21 409:17,22 411:23,24

412:26 413:4 414:14,17 417:7,24,26,26 418:1,9

419:4,9 421:11,13,18 422:20 423:7,16,22,22

424:3,11,13,16 425:6,9,24 426:10 427:24 430:18

431:2,11,13,14 433:6,16 435:3 440:10 441:10,13

444:1 448:4 449:17 450:13 451:2,7 452:2,8,9

453:13 457:2,3,4,6,8 459:14 460:4,5 471:13

473:9 474:3,19 478:1,23 479:25 480:26 481:2,7

482:22 484:7,7 485:4,6,10 488:13,16,16 490:6,8
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585:23 586:6 592:14 595:5,6 596:23 597:2,3,5

598:4,6,8,12 603:1

study 15:23 23:2,3 24:24 25:9 27:26 28:24 29:2,18

29:19 30:10,22,24 31:4,7,14 32:18,19 33:9,23,24

33:25 35:7,9,10,14,19,22,24,24 36:12,18,22 37:7

37:7,10,23,24 38:4,10,23 39:12,15,19,20,23,25

40:1,3,5,15,24 41:1,2,4,19 42:11,11,21,22 43:2,3

43:5,9,9,12,13,18,19 44:4,8,24 45:4,5,11,11,17

46:25 47:2,2,4,12 48:10,10,15 49:2,9,12 50:25

52:17 53:12,26 56:14,22 60:8,11,13,14 62:3,9,9

63:2,18,26 64:3,8,18 67:6,9,10,12,13,20 68:15,16

69:24 70:1,24 76:20,25 77:1,4,9,21 78:21 79:15

80:19,20 81:10,12,13 101:7,13 102:1 107:9

108:17 115:10 116:17 117:18 121:3 124:1 126:25

129:26 131:25 163:1 173:12,18,20 178:11 180:8

188:6,9,11 191:23 192:9 194:13 198:5,10 203:5,6

205:7,22 206:5,7 207:3 209:11,13,13,22,26 210:1

210:2,3 211:7,14 216:26 217:5 218:6,10 219:7

220:19,21 222:1 223:12,12 224:9,10 225:9,20

228:21 229:13 231:13,14,15,19 232:11,21 234:11

235:14 236:6,12,12,22 237:3,16 238:1,4,10,12,13

241:9,11,23 243:3 244:11 245:17,25 248:22

250:11,13,16 251:11,17,25 252:3 253:3 254:16

256:8 257:4,23 259:26 261:22 265:23,24 270:9

270:10,11,23 278:9 287:5 289:20 296:14 297:12

297:16 306:4 316:25 322:16 323:9,12,18 326:20

327:24 328:5,5 333:26 334:7 337:1,3 339:25

340:4 341:2 342:5 344:2,14 345:18 346:6 347:15

347:21,22,22 348:15,16 349:11 350:5,11 357:5

358:1,3,4,6 359:14 360:11 362:11 363:21 364:5

366:23,24 367:1,7 369:13 370:2,4,8 371:12,13,23

372:12,18 373:3 374:20 375:24 376:2 378:12

379:20 380:11,17,18 382:13 383:7 386:25 387:3

390:5 391:15 393:1,4,5,10,16,17,18,19,20,21,23

394:7,9 398:11,13,18,18,23 399:1,5,19 401:1,3,6

401:9,12 402:1,2,4,10,12,19 403:1,6,26 404:11

404:12,13,18,24 405:6,15 406:9 407:8,16 408:7

409:3 412:12,19 413:17 414:8 417:1,3,5 419:26

422:4,6 423:12,14 425:1 426:20 427:2 443:20

446:24 447:12,15 449:6 452:19 453:8 462:6

474:14 480:17 481:6 483:21 484:11,21 485:7,8

499:12 506:22 512:3 517:17 524:25 525:10 533:4

534:24

studying 407:14

study's 348:11

stuff 232:25 319:13 362:9 388:4 502:17 512:10

539:3 546:22 565:12 568:26 579:12 595:23 604:3

stumble 70:24

style 69:26

sub 321:3

subcellular 55:6

subchronic 46:20 47:3 49:18 51:4 64:19 368:24

subdivided 40:13

subequal 65:5

Subgroup 227:16

subject 269:19 270:12,14 343:11 580:12

subjected 42:11 111:5 507:6

subjective 146:19 229:11 329:12 354:1 404:5

subjectively 217:8,9 242:3

subjectivity 368:5

subjects 173:16 344:2,7,9,11

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submissions 181:14

submit 182:20 592:4

submitted 22:19 23:6 27:14 43:4 47:16 59:13

183:1,5 227:21 230:25 424:2

subsample 44:20

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469:17 523:10

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subset 400:6 445:1

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substantial 90:17 97:24 180:19 307:22

substantially 44:21 152:2 373:1 563:8

substantiate 36:18 331:22

substantiated 100:18

substitute 435:24

substituting 190:7

substitution 397:18 582:18

substrate 259:14 260:5 266:8

subsurface 526:26 527:18 594:2

subtle 271:2

suburbs 164:2

sub-conclusion 192:1

sub-elements 422:6

sub-equal 189:1

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sub-sampled 141:23 147:17

sub-studies 423:17

succeed 175:9

success 113:21 322:6 323:13,26 573:16

successful 12:4 155:17 304:2 576:22

successfully 169:12 184:14 332:10

suffer 172:10 271:14

sufficient 22:22 30:6 111:3 196:2 328:13 331:13

331:20 339:1 344:22 360:15 365:4 367:14 371:23
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sugar 73:18 150:18 151:8 153:6 164:5 169:12

183:9,13 289:10,22 295:13

sugarcane 22:6

suggest 136:6 155:11 177:3 188:8 192:2 266:13

368:21,24 369:18 387:12 482:16,17,18,21 483:12

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suggested 23:8 78:21 110:1 191:24 195:23 196:1

197:11 239:4,5 241:13 242:15 243:6 306:9 322:1

336:18 357:17 369:7,22,25 427:4 457:23 482:20

482:26 511:14 549:20

suggesting 31:10 93:12 206:1 323:19 394:1 427:3

suggestion 351:20 386:18 420:22 459:12,13 490:5

suggestions 222:24 223:8 241:4 242:8 244:9 483:9

suggestive 195:2 350:7 364:3 371:4

suggests 57:14,24 93:16 206:2 230:14 247:22

365:1 372:19 377:24 378:13 379:1 390:12 486:5

508:6 537:15

suitability 56:21

suitable 30:23 88:10 147:11 267:26 306:2 381:4

suitably 59:20

suite 318:25 577:3

sum 84:2 95:14 198:21 243:18 244:18

summarize 42:16 93:22 260:12 322:7

summarized 22:18 228:13 428:19

summarizes 182:26 257:11

summarizing 453:10

summary 5:6 36:19 39:9 42:26 47:15 72:21 172:1

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summation 95:8,15 132:2 216:22 219:9 443:13

summer's 287:2

summing 101:12 437:6

super 364:5

supermarkets 177:19

supplement 575:16

supplemental 123:25 205:20

supplies 102:23 523:22

supply 523:18

support 32:11 38:3,19 99:15 136:20 150:9 178:8

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supported 27:17 176:9 185:12 296:12,24 495:25

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supporters 163:7

supporting 27:15 95:7 99:4 178:6 195:9 231:13

232:26 303:2 386:7 406:8 523:18

supportive 569:16

supports 185:8 188:10 228:15 480:20 493:1

503:17 504:26

suppose 250:14 253:2 543:21

supposed 121:11 298:24 310:18 317:4 354:18,18

494:1 526:4,16 588:13

sure 10:22 19:21 62:15 67:6 70:24 125:18 132:4

158:11 162:15 168:9 208:4 225:2 251:1 263:10

285:9 287:3 302:3 304:23 315:1 320:25 325:15

346:11,19 355:11 361:3 363:13 365:14 366:16

385:10,16 389:1,12 392:13 395:25 406:12 415:21

416:4 419:8 437:5 445:15,23,26 453:2 454:18

466:23 469:10 470:1 474:8 477:9,10,15 486:23

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518:14,20,24 520:12 526:4 530:1 532:7,9 540:4

541:8,10 549:26 550:3 551:8 556:4 557:24

569:25 574:17 577:15,16 579:8 595:16,22 603:18

603:20,24 605:13

surely 169:8

surface 30:17 73:10,11 136:24 302:6 303:7,22

304:7 513:4

surfaces 260:6

surgery 19:12

surgical 559:14

surprised 336:18 519:24

surprising 214:13 308:9 599:20

surrogate 31:6 56:22 57:7 89:7 247:16 372:9,26

surrounding 108:1 173:22

surveil 152:8

survey 7:14 8:14 175:6 199:5 387:1 455:21 456:8

surveys 576:2

survival 30:23 36:4,5 43:16 46:21,24 48:12 49:17

50:15 51:1,4,19,20 52:7,13,17 55:16,18 64:8 68:2

74:1 168:4 190:4 194:26 196:6 198:11,12 207:18

209:9 335:16 368:25 386:1 390:9 391:13 402:6

403:4 503:19 504:19 506:1,4,19

survived 166:25,26

survivorship 350:14

susceptibility 31:21 48:13 57:23,26 58:10 59:19

195:23 196:14 330:2 373:8 375:21 386:1,11,23

386:24 387:9,13,24 390:4,13,17 496:1 507:26

suspect 203:25 259:24 424:24 429:26 433:18 561:9

suspended 291:6 396:25

sustainable 288:12

swamps 164:3

swayed 172:4

swaying 110:7

sweet 289:22
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swing 70:21 190:8

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switching 275:19

sword 118:18,21

sympathetic 460:21

symptoms 85:11,16

SYNDER 294:26

synergies 505:1

synergistic 504:20,22

synergistically 334:14

Syngenta 87:19 88:11 103:3 141:4 149:21 155:15

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186:8 213:9 226:14 264:4,12 273:22 276:14

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480:16 499:5 513:22 527:11 543:12,22 553:20

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Syngenta's 173:17 322:24 370:8 474:18 526:3

583:15 599:4

synthesize 32:7

synthesized 78:10

synthetic 196:15 246:2 437:3,11 589:23

syrup 170:10,14,17,18

system 5:21 16:24 23:17 45:8 57:23 58:1,7,12

75:18 76:2 79:18 80:15 83:26 97:25 117:4 121:1

121:2 183:22,24 187:2,15 192:7 196:15 197:11

205:19 220:24 223:2 229:18 232:24 234:23 237:8

237:9 249:15 257:17 317:7 340:25 384:1 387:8

399:10 405:20 414:9 422:26 442:21 447:18,18

448:26 458:6,17 459:5 498:2 502:7 515:19

557:12 594:10 595:18

systematic 144:26 155:12 156:15 158:8 286:16

525:3,13 550:8,15,17 566:7 573:14

systematically 11:13 147:17

systemic 197:15

systems 7:24 15:26 58:9 66:11 74:26 86:21 87:6

90:26 91:10 121:9 130:7,7 133:6 149:24 166:24

184:5 193:2,4 234:20 236:3 238:16 255:2 259:18

260:9,20,21,26 261:3 272:26 290:9,9 291:12,13

303:11 304:3 386:22 396:23 397:9,18 399:4,13

405:17 407:2 410:6,6 411:7 469:2 484:10 511:20

511:20,21,22 515:16 523:19 591:15

T

T 197:6 557:11

table 4:6 15:22 19:1 56:10 58:6 78:19 105:16,24

114:25 124:2 162:15 233:10 292:26 293:1,4

351:21 404:6 412:16,17 432:20 433:6 495:20

496:2 498:19 501:15 507:20 542:7 575:12 582:21

589:7 596:19

tables 36:20 39:6 129:25

tackle 134:11,13

tadpole 38:17,26 39:12 332:8

tadpoles 29:21 30:5,10,11 31:14 38:13 43:8 45:8

50:26 56:16 67:20 69:11 166:15 204:11,15

tag 275:20

tag-team 72:12

tail 546:19

tailored 409:18

take 11:7 13:20 14:15 15:12 16:11,18 18:3 19:11

71:14,15,20 112:17 113:24 131:3,20 133:6

135:10 149:1,4 155:18 157:13 177:11,13,24

178:19,23 181:4 185:25 202:19 205:21 213:26

214:1 216:5,8,14,21 224:22 226:8 228:1 239:13

243:22 248:19 250:15 252:23 253:13 257:26

258:17 262:20 279:17 296:9 300:11 304:19

312:16 316:3,24,25 317:2,9,14,15 333:5 335:2

349:10 350:4 362:21 366:2,8 388:5 395:26

409:19 418:7 419:3,19 425:8 442:22 445:1 458:4

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519:6 522:15 531:4 543:10 544:21 545:7,24

550:2 554:15 563:21 565:10 567:7,7 573:11

582:1 584:22 585:2 586:23

taken 30:17 39:8 71:18 78:11,13 86:17 101:18

108:23 148:19 151:26 164:10,13 213:19 224:24

231:14 293:1 294:7 335:18 338:1 344:8 358:1,15

366:10 396:16 423:6 424:10 426:4 467:17 494:9

518:17 519:21 541:14 543:4 546:18 551:13 555:3

585:4 601:15

takes 9:7 122:21 128:25 158:21 239:22 349:12

499:16 570:1

talk 21:1 67:2 72:19,25 80:14 82:3,4 98:10 112:5

157:20 162:5,15 167:17 168:10,12 172:12 182:9

184:19 186:6 187:26 188:13 195:17,19 207:5

226:15 240:20 307:1 329:15 350:25 361:22 385:8

419:10 426:11 481:9,17 517:11 523:9 526:1

529:19 535:14 561:22 594:11 599:15,18 600:6,7

601:16

talked 16:4 69:17,23 71:4 113:10 136:8 162:10

194:14 195:26 196:10,11 206:11 285:2 313:4

337:11 359:25 375:16 376:9 388:25 389:1,2

394:6 410:24 467:3 470:9 471:21 524:18 525:24

547:26 554:18 556:4 562:11 567:4 578:22 588:12

602:7

talking 10:14 17:9 117:18 120:4 129:2,22 130:1

167:19 186:8 199:4 212:22 287:16 302:8,10
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tank 30:5 44:11 171:5 591:11

tanks 43:21,22,24,25 44:1,2,4,5,9,14 200:9,9

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Tao 323:10

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targeted 137:7 149:21,22 155:15 545:15

targeting 180:3

task 10:24 272:16 577:17

tasked 273:3

tasks 478:20

taught 329:20

tax 170:15 171:25

taxa 59:14 60:3 65:25 71:7,12 78:19 84:10 89:16

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taxonomic 88:26 116:15 228:22 241:10 360:23

398:25 399:18

taxonomical 120:13

taxonomy 130:2

teach 203:24

team 3:1 19:5,17 25:8 179:23 412:11 457:1 474:10

475:9 599:4,4 606:19,20

tear 19:13

tease 185:1 414:9

teased 301:12

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techniques 71:5 143:9 156:20 169:14 185:25 202:3

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telling 253:2 301:9 356:20,21,22,24 357:3 461:15

tells 364:18 467:12 537:19 568:18

temp 265:10

temperature 38:25 41:19 87:15 94:24 117:15

204:1 291:6 403:14 553:22

temperatures 403:13,13

temporal 132:7 137:12 140:16,21 141:9 144:2

159:19 160:3 265:3 442:7 543:19,26

temporally 223:1 266:11 570:18

temporal-spatial 524:17

temporary 329:23

tempo-spatial 519:2

ten 135:3 360:14 593:8

tend 66:9 121:14 249:24 350:26 388:19 432:25

601:19

tended 388:1

tendency 218:14 519:17 594:26

tending 111:13 122:4 198:8

tends 130:15 158:20 308:11 519:13

Tennessee 143:21 152:13

tenth 12:7

teratogenesis 46:12

teratogenic 46:15 50:20

term 87:11,13 92:18 189:19 204:12 298:26 353:12

407:23 408:3 506:18 524:19 558:20

terminal 115:14

termination 36:6 47:4 52:26 67:23

terminology 270:1

terms 11:24 12:1,11,23,26 13:2,5,8 14:20,24 15:2,6

15:7,12,15,18 16:8,11,13,23 17:2 18:4 21:1 24:13

37:8 50:12 51:19,23 57:1 58:9 61:12,24 63:8,17

63:24 65:2,12 66:13 69:24 70:2,17,22 71:7

105:24 120:2,4 128:11 130:16 131:24 134:19

188:6 190:8,11 191:19 192:9,14,17 194:3 195:1

197:16 198:12 203:6 204:3,26 208:15,20 209:6

209:14 212:10 214:12 215:3 218:22 219:22

222:20 224:13 225:22,25 229:17 246:13,26 255:1

255:26 256:2 260:16 310:20,22 314:18,19 316:5

319:18 320:20 326:9,11,13,19 329:2 342:25

343:14 348:7 352:17 353:13 355:3 360:5,9

363:10,24 365:20,23 366:2 371:13 373:15 380:12

384:16 388:15,17 394:26 402:14 404:3 406:8

407:1 410:21 413:14 421:23 422:3 423:5 426:16

433:2 440:3,9,12 449:13 451:6 453:12,13 459:8

462:17 463:4 464:8 472:12 473:18,26 478:1,19

478:19,24 479:6,24,25,26 480:15,16 487:2

488:13,15,20 495:10 496:1 499:5 504:18 512:5

547:3 550:10 554:2 557:3,14 565:5 569:26

602:15 603:4,15 604:25

terrestrial 600:25,26
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40:5,7 41:16 42:9,20 45:12 47:4,17 52:25 56:14

65:15 88:1 96:13,21 110:10 117:2 119:23 120:6

124:2,9 130:13 138:20 147:13 173:15 194:3

203:17,18 204:11,24 221:6 265:6 308:17,19,20

309:4,7 310:2,7 312:13 315:19 324:24 327:10,12

327:13 328:23,26 329:1 331:17 334:10 336:21,22

339:20,26,26 342:24 343:12 345:11,13,20 358:21

360:9,19 375:3,16 377:2 383:10 390:4,6 393:7

397:9 407:8 414:9 424:8 431:26,26 450:15

480:11 481:14 495:19 496:21 504:2 506:22 507:7

507:8 535:12 557:21 559:5 581:4 587:14 604:25

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53:17 61:13 68:13 77:19,20 78:19 80:19,20 81:4

114:11 158:7,8 191:23 205:8 290:21 360:14

391:4 422:25 508:21 534:11 587:15 591:2

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testicular 167:26 190:15,16,23 191:5,10,14,25

192:2 195:4 369:15

testify 288:4

testimony 275:15 292:16

testing 12:4 27:25 28:23 30:26 31:1 34:17 37:18

46:2,5 57:4,5 64:14 69:12,23 77:15,15 179:16

201:22 202:9 211:15 281:5 288:20 324:17 334:3

342:15 358:2 360:10 365:5,11,22,23 372:8 375:2

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471:12 480:1,2,20 482:7,15,15 486:3,9 504:15

506:18 522:18,19 523:13

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tests 13:6 15:21 28:3,9 36:10 111:17,18 116:19

130:12 174:24 201:26 211:14 212:7 234:4 308:18

309:19 317:4 352:23 412:3 432:18 448:21 451:18

452:8 458:17 459:22 471:16 481:13 561:16

563:10 564:14,14 586:12 604:26

Texas 7:6 455:17

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20:1 26:24 60:20,21 71:14 72:5 109:20 136:19

154:26 177:15 178:18,21,22 179:1,4 180:25,26

181:5,7,10 182:18 186:5 195:15 202:15,21

213:10 226:12 261:4 275:1 279:21 285:9,11

287:20 292:8,12 297:5 300:19,21 304:25,26

305:2,11 312:22 314:3,4 321:12 324:9 325:7,22

325:23 327:16 342:9 367:4 368:10 385:5 389:25

391:21 393:24 406:6 445:13 447:16 448:8 455:3

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510:1,6 512:16,22 513:10 520:24 528:3 531:26

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585:14 587:7,21 588:5 589:13 590:13 598:20,23

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thanks 5:26 8:22 9:5 19:6,22 72:10,16,16 178:20

209:7 213:5 240:12 262:10 285:10 294:1 301:20

305:8 314:12 336:2 343:25 344:17 359:6 366:21

377:9 408:13 410:1 414:21 456:18 457:11 473:20

475:4 480:23 488:4 494:6 503:11 511:7 519:25

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589:25 590:1 596:15 598:24 602:1,5 605:14,14

that'd 362:15 411:5 538:20

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theoretical 459:1

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theory 59:22 332:3 548:10

theses 235:3

Thesis 403:3

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thing 62:2 123:25 126:7 128:17 130:11 131:5

132:22 156:1 158:7 160:14 189:10 214:16 221:12

222:16 236:10,23 243:13 245:24 255:8 257:7

293:12 314:15 335:24,25 343:25 345:3 347:23

351:10 359:12 362:15 374:23 375:2 388:14

389:17 394:9 408:20 410:9,25 411:3 416:24

420:12,24 423:7 425:4 433:4 435:12 439:3,24

440:16 449:19 465:25 468:6 469:16 473:23 491:6

510:19 520:5 521:1 523:16 524:2,19 538:9,20

539:22 546:2 548:12,25 549:18 550:21 553:4,9,9

554:17 557:18 563:26 564:10 566:1,17 567:22

574:21,26 577:22 578:21 579:18 588:16 589:16

591:4,14,18,22 593:1 594:23 595:13,19 596:4,18

597:16 598:15 599:12,20 600:2,11 601:11 605:4

things 7:23 14:25 17:17,18,23,23,24,25 22:4 55:3

68:26 69:1 112:13 117:16 126:5,8 135:23 136:9

157:7 177:13 185:3 197:6 203:21 206:19 207:22

211:10 229:23 252:16 255:17 259:21 288:15

311:24,24 312:15 318:13,17 325:16 326:24

342:13 345:8 351:5,12 355:12 356:10 358:13

361:24 364:25 365:15 391:5,19 394:17 406:7,24

407:20 408:5 419:3,5,8 425:7 431:18 432:21

433:21 434:16 435:2 437:16 459:9 460:19 466:10

480:21 482:22 490:12 498:12 510:26 511:2

514:26 524:22 525:11,25 526:13,21 531:20

538:14 547:25 552:22 556:3 559:10 565:7,16

567:8,16 569:19 571:5 574:8 575:6 577:8 578:11

579:9 583:7 585:10 588:8 589:6 592:21 593:21

593:23,24 596:15 597:26 598:8 599:13 600:5

601:4,19 602:7,17 603:13 604:14,15,17,20,23

think 10:4,5,19 11:22 12:2,8 13:1,2 15:7,18,22

16:22 17:1 18:14 19:15 62:1 65:11 66:5,25 70:2
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393:6,8 394:1 400:13 403:2 407:4,7 408:20,22

409:9,10,16,19 410:19 411:5,9,25 412:5,12

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438:10,11,16 439:25 440:6 441:4 442:16,19

444:7 445:17 449:12,26 451:13 456:13 457:9

460:2,4,26 461:14 464:9,14 465:5,22 466:6,13,16

467:18 468:18,18,21 470:11,22,24 471:7,11

473:18,21,21 474:7 475:2,7,7,8 476:8,15 477:5,7

478:18,19 479:2 481:17,21,24 483:8 484:14

485:13 486:15 488:18 490:2,6 491:6,14,15

493:23 495:19 496:3,4,8 497:9,16 498:17,26

499:8,22 500:9,12,13,18,19,21 501:1,4,5,22

502:16,18,24 503:9 510:17,24,26 515:26 517:10

517:11,13,26 518:2,11 519:21 520:5,8,20,22,22

521:5 523:9,24 524:12,17,19,23 525:26 527:10

529:9,19 530:7,15 532:15 533:25 534:9,21

535:10 536:17 537:26 538:8,11,21,23 539:2,4,7

539:18,19,23 540:25 541:9,10,14,18,22 542:13

543:6,10,15,22,25 544:19,25 545:2,13,16 546:3

546:10,14,16,26 547:3,6,10 548:11,12,13,20

549:3,17 550:4,6,18,21,21 551:6,10,18 552:3,4

554:4,9,17 555:9,18,21 556:2,3,5,8,12,19,21

557:3,13,16,17,25 558:20 559:1 560:8,10,15

561:12,17 562:15,24 564:14,25 565:2,26 566:1,4

566:18,20 567:3,4,15,21,21 569:6,12,16,17 570:9

570:25 571:8 573:7,10 574:15,17 575:9,15,22,26

576:16 577:2 579:6,9 580:10 583:10 584:25

585:17,19,22,24 586:19 588:8 590:18,21 591:8

591:21 592:5,7,11,14,16,23 593:19 594:10,26

595:1,2,9,11,13,19,20,22,26 596:1,8 597:19,24

599:7,10,20,22 600:4,11,19,21 601:11 602:8,11

602:17,20,23,25 603:24 604:5,16 605:1,2,6,8

thinking 12:10 17:13 18:9 114:24 116:25 128:15

131:26 134:15 182:15 225:6 318:2 326:26 343:10

347:10 364:11,16 370:26 376:8 385:17 388:25

413:17 419:8,15 420:7,20,21 429:15 438:12

459:15 498:26 501:22 517:11 548:15 556:26

562:13 565:5 576:19 586:15 588:10 590:24

599:12 600:14,17,22

thinks 355:26

third 18:1 23:23 27:8 48:9 90:17 98:2 99:19

141:22 144:20 146:9,13 148:20 150:25 151:18

152:1 215:10 256:2 293:12 294:15 298:18 300:12

307:7 397:16 406:20 454:12 468:13 516:17

517:24 523:20 533:7 534:4 549:8,10 555:21

563:7 594:18

Thirdly 254:25

third-order 444:6

thirty-one 78:4

THOMAS 7:5 62:19 63:4 202:21 325:7 327:16

348:13 352:11 354:15,20 355:4,8 359:6 367:4

372:3,6 377:18 380:25 382:25 384:13,16,20

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450:24 451:26 452:16 455:16 473:20 475:1

477:23 480:6,23 485:2 487:4 498:22 509:26

587:8

thorough 321:19

thoroughly 393:5 474:10 495:23 507:22

thought 63:9 68:7 159:20 251:6 253:12 305:15

314:21 357:12 389:21 427:3 429:17 436:12

447:22 457:16 477:20 484:3 485:23,26 531:8,9

545:24 547:3 552:4 553:10 573:6 586:19 595:4

thoughts 17:12 280:4 340:15 419:25 460:25

thousand 163:23 211:2 276:19

thousands 164:18 167:5 169:13 184:1 353:22

395:24,24 484:6

thread 529:11

threatened 163:19,20

threats 163:25,26 164:1 166:2 174:6

three 46:20 66:1 80:8 165:10 228:16 265:6 275:10

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417:25 421:6 471:1,5 500:23 511:15,17,23

516:21 518:11 533:4 534:12 535:15 537:7 587:9

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throwing 560:21

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THURMAN 527:9 553:3

thylakoid 186:25

thymus 197:6

thyroid 28:6 187:24

ticks 166:8

tie 161:12 467:25

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tier 27:25 46:2,7 184:23 227:4 330:22 385:1

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Tillitt 175:7 209:12 322:15,16 378:2

Tim 8:9 456:5 556:1 577:22 578:12

timber 164:4

time 5:2,23 10:13 12:15 14:4,8 15:4,5,6,15 20:1

21:11 27:20 51:25 52:25 56:2 59:3 65:8 68:4 70:6

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378:18 382:23 389:20 395:23,26 398:4 404:7,9

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419:22 424:4 427:18 431:9,10 435:13 447:19

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483:19 486:7 487:22 488:24 489:6,13,21 490:23

499:16 502:23 511:18,26 514:9 516:4,4 544:19

548:2 553:5 556:22,23 558:26 562:3 563:19

571:6 578:17 579:24 585:6,11,19 587:24 588:9

590:4,17 591:16 592:10 594:19 599:6 604:2,19

606:10

timeframe 98:8 111:20 447:8 489:11

timekeeper 6:5

timeline 64:16 127:22

timely 331:20

times 112:7 122:17 137:14 141:23 149:4 163:23

176:6 186:10 200:11 208:19 230:1 231:14 236:6

244:1 248:10 252:6 282:16 285:21 338:11 398:9

401:10 424:20 445:8 500:1 516:15,26 521:6

525:24 550:25 581:12 592:15 602:2

timescales 458:19

time-integrated 430:12,19

time-variable 23:19

time-varying 458:12

timing 140:23 288:4 373:9

TIMOTHY 8:9 63:20 155:23 156:11 157:15

159:18 225:5 320:13,17 441:2 456:5 461:11

463:12 491:23 493:5 520:26 522:21 532:4 539:22

540:18 541:5 546:2 549:16 553:8 560:6 566:16

569:24 576:12 594:18

tissue 30:18 201:13

title 136:16

TMDL 159:11,15 161:4,5 279:1

today 2:5 4:24 19:13,18 21:3 22:8 25:17 27:5

126:20,23 168:10 184:5 186:6,10 188:7,19 190:5

192:18 207:1 233:18 248:10 263:11,11 271:10,26

275:7 277:12 279:22 281:1 292:14,16 295:5

297:9 301:5 302:1 303:13 356:2 365:8,9 379:18

406:15 407:11 443:15 454:5 480:8 489:25 530:22

600:5 604:7,17

today's 10:9 26:13

told 71:21 307:21 532:15

tolerable 498:7

tolerance 123:5 329:22 408:5 508:13

tolerant 120:18 122:26 125:14 259:16 433:1 498:2

588:18

tolerate 75:13

toll 160:16

Tom 7:6 325:7 367:3 384:17 385:6 428:17 450:19

451:22 452:13 455:16 480:3 606:21

tomorrow 162:13,18 263:12 385:16 414:23,24

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totality 121:2,12 122:12 124:11 125:11 130:7

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totally 391:22 401:22 410:15,25 417:2 574:9 597:7

touch 260:25 333:7 461:3

touched 14:19 498:12 521:23 532:5 593:19

touching 262:3

tough 362:10 551:20

Tox 181:20 182:12

toxic 83:14,19 84:9 91:5 92:24 93:6,14 99:12,13

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372:23,24 446:9,14 447:1,3 448:25 449:10,20

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toxicant 343:11

toxicities 128:19

toxicity 21:4 24:4,22 25:4 26:16 27:4,7 30:22

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211:7 215:23 258:1,26 259:25 260:17 264:25

282:14 306:20,21 307:3,5 308:16,18,19,20 309:4

309:7,19 312:13 316:25 324:16 350:8,9 352:23

367:25 368:23 428:25 431:7 432:18 449:10

458:16 494:14 496:21 504:6,8,15,23 505:19,25

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toxicodynamic 70:18

toxicodynamics 484:7

toxicokinetically 70:18

toxicokinetics 484:7

toxicological 89:19 94:22 100:24 186:17 190:6

284:10 327:19

toxicologist 72:1 213:9 433:9

toxicologists 12:9

toxicology 6:6,21,25 7:7,22 9:12 13:6 69:12,20

70:2 72:1 76:18 128:12 266:21 342:15 374:9

375:15 381:23 382:21 484:4

toxin-producing 588:22

TP 265:18

track 205:17,22 458:17 579:11

tracking 566:7

tracks 69:11

trade 164:11 288:16

tradeoff 563:18

tradeoffs 548:10

trades 164:8

traditional 86:15

traditionally 344:1

tragic 273:1

trail 327:21

train 148:8

trained 288:10 344:19

trample 170:3

transcribed 385:22

transcriber 4:11,13

TRANSCRIPT 264:3,4,9 284:13

transcription 4:10,10

transect 176:10

transfer 73:14 187:1

transferred 80:9

transfers 187:7

transformed 306:22 429:3 537:8

transient 76:9 79:24 114:5 201:14 223:1 229:17,22

230:16,22 232:12,18,19 430:4

transition 58:23 115:26 482:2 485:10

transitioning 55:13 384:22

transitory 106:21 118:8,24 196:24

translate 148:15 162:4 213:20 312:17 438:9

464:16

translated 470:1 516:19

translates 105:20 170:22 320:3

translation 198:15 465:5

transmit 593:12

transparent 11:18 18:26 80:16 183:21 193:17

208:1 273:8 353:26 395:6,8 396:4 440:7 572:16

transport 8:12 13:1 15:8 137:19 138:5 139:1 159:2

463:14 525:16 594:4

transportability 575:22

travel 603:8
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traveling 606:8

treadmill 200:16,17

treat 457:15 461:24

treated 231:25 233:2,3 285:21 337:9 459:2 462:1

598:16

treating 345:5,6

treatment 30:3 41:10,11 44:10,11 45:22 47:6

53:11 77:12,20 230:5,21 238:18 256:23 305:23

309:20 310:5 323:1 328:22,24 331:17 333:15

334:3 335:25 339:21 345:7 346:16 350:1 486:10

495:7 513:18

treatments 33:10 44:7 76:21 77:5,16,19 309:2

310:6 334:6 335:20 344:2,7,10,11 345:2,8 405:5

438:19

tree 269:22 578:20,26

trees 578:11

trematode 168:4 387:2,6

tremendous 551:15

trend 145:18 150:21 151:14 218:25 219:23 220:8

trends 257:2

trials 46:9 47:9 275:22

triangles 102:14,15

triazine 264:6 268:4,13,15,17

tribal 402:23 490:18

tributary 281:6 557:8,10

trick 564:25

tricky 490:9

tried 46:9 232:26 287:15 337:2 363:20 442:11

503:23 542:13

trigger 277:16 515:8

triggering 105:13

triggers 281:2

trivial 290:6 412:8

trophic 74:20 265:23 397:10,13 497:12 498:9

586:16

tropical 591:11

trouble 166:16 232:4 563:17

trout 164:17 203:20

true 96:14 117:23 128:16 138:11 142:4 148:1,6

154:11 158:3,10,19 224:15 274:8 275:17 287:21

288:8 303:12 317:7 375:3 377:5 435:3,22 461:18

461:22,23 500:16 513:15 514:9 515:23 516:11

517:7,18 519:13 540:26 541:3,7,18 563:4,9

564:17 572:5

truly 135:16 181:8 210:9 215:13 219:5 268:24

286:24 287:3 312:5 373:26 550:23 598:18

trump 597:8

trust 391:19

try 6:5 9:21 10:19 11:16 18:20 57:20 64:1 65:11

71:2 133:11,15 134:12,13,19 146:11,12 162:7

199:15 202:8 208:1 253:10,19 262:2 276:1

300:19 304:16 318:10 319:4,7 336:4 338:12,24

338:25 352:1 356:15,23 357:7 366:2 408:16

438:12 471:17 475:9 531:5 537:25 547:11 550:5

557:17 568:26 569:1 578:9 579:8 591:15 594:13

597:11 599:13 602:13,14 604:14 605:13 606:6,6

607:11

trying 10:5 16:21 81:16 83:25 96:26 97:4 111:23

112:6,25 113:9 114:12,18 117:26 118:12 123:1

124:10 125:16 126:2 128:11 130:20 133:13,25

134:26 155:25 156:16 157:1 161:9 171:22 177:12

198:21,26 203:5 206:24 229:15 287:2 297:20

301:14,17 311:26 315:20 316:25 317:9 320:2

341:22 342:3 346:24 352:19 354:15 356:16

362:23,23,24 369:25 380:3,19 383:14 409:26

410:26 414:7 419:16 420:22 434:23 435:1 442:3

445:10 447:24 449:13 453:4 466:26 494:23 499:9

501:23 502:7 517:22 520:3 522:2 523:11 537:5

540:3,23 542:18 543:26 544:18,26 547:20 550:3

554:4,8 555:4 559:1 564:26 567:17,18 568:21

569:17 576:25 577:10 579:26 595:9 606:4

TSS 259:13 260:4 265:18

Tuesday 456:26

tumors 190:19

tune 303:25

turbidity 259:22 269:20 282:16

turn 5:24 17:5 19:4 73:14 86:18 100:26 175:3

176:7 177:1 186:1 200:11 204:3 273:14 294:24

455:3 465:6 472:12 603:19

turned 217:20 363:22

turning 97:13 176:23

turnover 116:22

turns 167:12 317:17,17

tushies 487:22

Twenty 399:16

twice 86:4 120:17 124:8,13 253:8

Twiss 7:25,25 115:2 261:14 405:11,12 410:24

422:12,13 424:20 430:24,25 455:26,26 494:26

495:1 509:18,19 511:8,11 589:9,10

two 16:4 29:10 266:10 270:25 278:14,26 294:14

324:14 325:19 329:26 333:9,10 337:18 340:20

350:5 352:14 354:22 356:18,18 378:15 380:1

384:22 391:22 403:12,13 407:4 416:9,23,25

417:19 427:8 430:11 434:16 439:4 443:16 452:25

452:26 464:7 476:21 477:22 481:7,12 500:6

511:23 516:21 523:2 534:17 535:5,15,22,25

536:12,23 545:5 551:22 565:7,19 580:8 581:8

583:3 584:15 597:24 599:3

twofold 435:7

two-day 571:23
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two-fold 475:25

two-quarter 559:20

two-thirds 289:9 295:13

tying 607:9

type 33:23 63:2 76:11 88:10 94:20 110:10 155:13

158:2 197:25 256:5 259:16 311:24 328:5 336:26

339:5 368:4 375:5 377:5 430:5 452:22 476:9

490:12 491:4 530:2 531:16 552:8 560:8 567:24

574:26 579:22,23

typed 490:1

types 15:8 52:23 55:21 58:6 112:10,11 157:11

200:18 201:25 433:14 519:1 547:7 548:17 562:19

576:3 582:10 589:6 594:3 604:11

typical 51:18 70:12 81:17 106:5 221:6 265:9 282:7

286:26 383:9 409:23 414:3 419:21 518:9,10

519:7

typically 118:6 204:21 220:10 285:23 335:16 371:8

383:25

Tyrone 206:14 373:19

Tyrone's 206:17

U

ȝJ/

ultimate 113:5,8 133:18 382:6 414:16 417:20,24

419:26 423:12 547:12

ultimately 13:23 28:13 112:12,16 187:2 217:11

248:1 271:24 329:11 360:3 390:17 418:8 500:19

umbrella 594:8

unacceptable 40:7 178:3 581:18

unacceptably 82:24 436:8

unaccounted 505:19

unachievable 283:21

unadjusted 149:10

unaffected 223:2

unanticipated 334:9

unaware 170:13 382:25

unbelievably 411:17

unbiased 514:16 575:1

uncertain 89:21 90:3,20 91:3,4,21 93:7 99:7 313:7

468:3

uncertainly 393:20 598:12

uncertainties 12:18 13:3 17:9,10 18:1 20:22 22:25

27:9,24 56:18,25 59:17,21 88:8 90:4 91:25 92:15

92:17 100:8,17 144:17 428:14 430:7 431:19,24

466:23 500:15 513:7 602:11

uncertainty 10:26 17:12,16,20,21 25:23 26:8 35:11

41:24 45:23 55:10 57:17,22 59:22 89:23,25 90:25

91:2,3,6,9,14 100:4,6,10 106:12,13,18 107:26

108:11,12 118:26 126:18 137:13,15 138:2,8,13

139:10,11 143:10 145:2 152:5 154:21 199:2

202:9 211:20,21 271:1,5 296:21 313:6,7,12,14,16

314:2 372:7 383:13 410:17 420:17 425:12 435:3

444:23 445:7 451:3 467:5,16 471:22 472:24

476:1,2,4,9 482:5 501:2 504:14 513:3 524:15

530:6 554:11,14,16 555:5 560:16,18 572:22,24

573:4

uncertainty's 576:25

unclear 350:22 485:9 597:17

uncommon 54:6 270:3

unconcerned 170:13

uncontaminated 342:21

uncontrolled 27:22

uncorrected 572:23

und 270:17

undefined 104:17

underdeveloped 171:3

underestimated 564:18

underestimating 158:14,15,17,18 505:23 514:8

underestimation 145:11 148:11 154:11 158:9

513:14 519:14 563:9 566:11

undergo 33:5

undergoing 39:11

underlying 12:25 13:7 57:1 145:8 180:7 215:23

235:2 243:17 244:4 254:24 258:1,23,25 260:17

264:25 281:24 368:2

underscores 245:3

understand 10:22 36:18 39:4 55:24 63:7,11 67:7

111:23 112:9 117:4 123:4 125:6 126:2 130:18,19

132:5 134:25 135:20 159:2 193:11 199:16 206:24

277:4 286:9 311:26 313:4 315:9 319:15 331:22

336:9 339:13 341:4 342:18 343:17 346:10 353:19

354:12 355:11,14 356:16,25 357:6 359:2 363:8

373:25 375:26 380:10 383:15,24 384:4 395:11,12

418:11 421:23 426:14 433:13 435:21 436:3

447:21 466:23 473:6 475:7,8 477:1 480:9 481:18

484:11 487:20 491:23 493:9,12 494:2,3,4,5

526:18 540:15,18 543:26 554:24 568:14 584:19

understandably 279:2

understanding 13:3 15:5 17:9,19 20:20 47:11 72:6

123:2 161:4 180:3 199:2 205:10 232:4 304:13

319:14 341:24 342:16,22,25 343:2 351:4 353:15

360:3 376:5,17 384:21 405:26 415:25 424:8

437:20 452:21 473:3 482:13 485:11 487:6,16

493:11 495:3 522:4 550:8

understands 454:1 473:16

understatement 434:24

understood 186:14 217:24 286:6 329:7 453:2

475:3 480:13 486:2

undertake 10:6

undertaken 14:1 213:23

undertaking 10:17 548:20
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underwent 25:8

undo 278:6

undue 172:10

unduly 405:23 471:20

unequivocal 327:21

unethical 173:15

unethically 370:13

unexpected 154:8 556:10

unexposed 191:3

unfortunate 286:24

unfortunately 88:7 340:24 341:10 342:3 350:10

351:5 381:16 458:7 521:24

ungauged 553:11

unhealthy 170:11

uniform 437:5 572:12 601:13

uniformly 69:8

unimportant 91:25

unintended 279:13

Union 167:11 174:23

unique 11:23 20:14 136:23 289:16 394:24,24

522:14 604:10

uniques 210:18

unit 94:20 344:23 345:3,6

United 1:11 78:15 108:25 144:15 165:15,24 178:16

183:14 190:25 265:26

units 94:13

unity 247:6

universally 157:4

universe 320:9

universities 149:20

University 6:6,15,24 7:2,6,10,21,26 8:3,10,18

141:2 142:11,15,23 186:7 195:16 234:19 238:14

260:26 261:2 281:12 282:2 303:14 401:1,8 403:1

422:20 455:9,11,13,15,17,19,25 456:1,3,6,9

unkind 299:11

UNKNOWN 118:2 119:9,18

unmonitored 267:12 544:12 574:9

unnecessary 91:21 466:11

unofficial 279:1

unperturbed 409:11

unpredicted 146:19

unpublished 352:22

unquestionable 178:9

unrankings 62:20

unraveled 176:23

unrealistic 253:12 341:12

unrealistically 530:17

unreasonable 13:25 14:4 18:12 112:20,22 116:24

316:6 533:19

unreliable 228:17,17 300:14

unsafe 173:25

unsampled 146:19

unsatisfactory 458:2

unsound 41:4 296:1

unsupported 296:1

unsurpassed 290:23

untested 506:17

unusual 471:11

unvalidated 300:7

unwanted 172:22

unwittingly 168:17

upcoming 73:25 75:9

update 22:13 26:1,15 280:5 596:24

updated 20:25 98:23 105:8 224:4 227:19 596:24

updating 24:21

upper 108:7 115:23 148:11,25 191:9 192:13

211:20 242:11,17 256:4 281:3 460:16 462:13

533:9 536:9 551:6,10

ups 376:2

upscheming 540:10

upstream 159:26 160:1

uptake 79:8 423:22

urban 164:2

urge 459:22 560:22

urges 296:4

usable 49:14

usage 175:15 273:26 274:7,24 295:23

USDA 76:17 174:24

USDA-NRCS 456:4

use 1:5 2:8 6:3 10:5 12:16 15:16,23 16:2 20:11,25

21:25 22:6 24:9 25:21 26:2,8 29:1,15 30:23 31:5

33:15 34:10,11 38:25 39:12 41:13 44:17 45:7

48:1,4 57:18 60:12 63:8 69:16 70:11 88:17 93:21

95:14 100:15 113:12,20 121:6 123:4,6,15 133:3

133:12 137:1,7 140:11 142:14 146:20,24 148:12

149:21,22 151:11,22 152:1,13 153:3,7,21,25

154:9,14,18 155:6 156:8 157:11 158:25 164:9,13

165:19 167:18 171:25 172:8,25 176:15 177:17

178:4,8 179:17 183:18 184:24 193:8 200:3

211:11 213:25 247:13 249:18 251:10,12,20 254:5

258:7 269:4 270:12,15,26 271:19 273:2 274:3

275:14,16 277:10,14 280:13 284:8,10 285:17

286:5,6,10,11 288:15 289:17 290:7,24 291:24,24

291:25 297:18 298:1,10 299:26 300:7 304:3,9

306:12 307:3 314:15,23,26 315:3,5,14 316:7

317:10 318:25 319:3 320:7 324:14 326:12,13,16

326:20 328:19 329:16 330:11 334:11 335:5 340:2

340:11 341:2 344:23 345:3 351:15 355:20 356:4

362:12 365:21 367:24 371:26 375:16 377:23

380:21 383:20 406:22 414:20 418:10,13 430:3,12
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430:14 431:24 434:14 437:26 438:6,25 439:6

441:11,15 445:1 446:11 447:18 448:23 449:21,21

450:8 451:10 454:14 457:17 458:21 459:7 465:24

467:6,14,25 468:12,12,24 469:17 470:18 479:7

486:10 490:17 499:1 502:14 508:19 509:2 512:8

513:2,4,6 514:26 515:6 517:25 521:3,12,23

526:22 534:2 537:16,22,26 539:24 541:21,24

544:2 545:6,8,18 547:4 548:2 553:24 554:8,15

558:2,12 560:2,22 564:2,3,14 566:11,12,13

571:20 575:23 577:10,14 579:2,3,4 590:17

597:15 602:12 604:6

useable 596:3

useful 18:5 45:26 63:9 64:1,6 78:14 88:18 90:22

98:16 108:23 133:10 154:23 159:6 178:11 188:2

195:8 202:12 305:16 329:12 337:8,19 346:4

365:10 373:11,17 374:7,18 381:21 396:16 406:24

406:25 407:10 409:19 416:1 424:10 442:4 515:17

519:2 541:2 542:24 558:13 569:16 590:18,19,19

590:20,21 595:23,26 596:10 600:20,21 606:18

usefully 566:6

useless 346:1,9 347:14,22

uses 74:5 94:3 145:12 192:7,9 260:12 379:2 397:4

490:13,18,19 518:5,13 526:9

USGS 21:24 557:20 578:18 579:10,14

usual 496:14

usually 75:1 164:22 166:5 203:4 261:18 343:20

397:4 581:7 600:6

utility 29:14 37:8 43:11 48:4 298:17 349:9 353:15

544:11 561:13

utilizations 516:6

utilize 185:25 271:15 290:8 522:3

utilized 30:10 215:8

utilizes 268:19

utilizing 280:7 412:1 548:13

utmost 279:6

U-shaped 207:16

U.S 7:13 8:6,13 21:8 111:8 168:13 172:2 174:16

175:6 176:3 178:4 183:12,12,13 197:11 202:4

209:23 289:9,9,10 295:17,18 396:18 406:15,22

455:21 456:7 575:24 591:1 601:19

U.S.A 164:21

V

vague 488:24

valid 62:22 238:10 291:24 308:5 359:14,14 363:24

364:18 379:3 431:12 453:8 490:9 577:7 585:20

validate 299:25 425:20 563:20 568:22

validated 284:1 299:7,9

validation 209:23 213:26 442:5 563:17,21 565:17

566:6

validity 296:14

valuable 158:26 168:24 330:13 388:7 390:17 411:5

418:3 429:17 526:1 559:25 567:22 593:8 595:21

value 49:10 50:19,23 52:18 53:6,7,10,14,15,22,26

54:1,11,13 61:24 66:22 85:3,8 92:20,21 93:1,2

94:14 95:14 97:12 100:18 101:9,11,17,20,21

102:3,5,6,16,19,20 103:13,17,26 104:5,12 122:19

122:23 123:15,17,19 133:26 135:9 138:10 145:12

149:8 150:14 151:5 161:6 162:1,3 203:2 204:18

218:14 233:7 244:10 249:23 252:26 253:25

258:15 271:11 313:3 315:17 336:24 338:8 339:14

340:14 349:7 378:17 380:15 425:5 429:13 437:25

437:26 441:12 445:2,3,5,9 446:5,15 465:26

469:24 480:18,26 481:10 497:2 505:3 511:21

517:8,18 530:15 540:7,26 541:3,7 549:22 553:2

568:11 594:12 597:13

values 39:8 48:15,19 49:7,21,22,22 50:5,6 51:8,9

51:19 52:12,21,21 53:4,4,6,25 54:5 61:8,9 66:21

66:23 85:10 94:12 95:3 97:18 101:12,20 102:4,8

102:10 103:8 104:7,22 105:17,19 132:3 141:20

141:26 145:9,11 201:8 222:5,6 377:8 429:2,6

433:7 436:20,22 447:14 465:24 466:3 469:21

481:1 505:16 528:6 531:1 536:10 537:8 540:5

546:6 549:21,24 572:5,18

Van 181:21 188:12 195:13,15,16 203:16 204:6,12

204:19 208:15 209:5 211:17 212:3

variability 89:15,17 90:25,26 91:9,14 93:22 100:3

110:7 137:13 141:10 220:5 225:24 230:2 249:24

265:4 291:7 343:13 442:7 444:3,7,9,10,12,13,24

449:22 467:19 484:12 515:26 519:3 524:17

548:16,23 554:25 555:2 561:11 578:24

variable 79:12 83:6 84:5 134:11 146:2 154:8

214:20 219:26 226:2 449:23 469:3 534:3,17

538:17 549:11 554:2

variables 87:14 146:4 154:9 246:18 247:7 259:9

260:12,22 329:21 504:6 539:16 549:9,12 551:26

557:17,19 559:4 564:4

variance 210:8,14 522:24 523:11 576:18 577:23

variants 205:3

variation 91:1 143:14 145:16,21 147:4 265:8,13

324:8 346:15,18 373:10,24 376:22,23 378:14,20

378:23,24 471:23 472:1 543:26 549:12 558:25

559:6 564:5,7

variations 384:4

variation's 564:5

varied 47:9 52:14 90:21 330:6

varies 84:7,10 129:16 224:1 255:13 410:18

variety 16:11 56:7 58:8 89:13 130:1 166:7 170:11

198:2 201:11 202:2 203:19 227:8 255:7 320:8

322:11 348:16 379:23 441:15 514:15 593:23

variogram 156:22
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various 9:24 22:11 28:13 58:19 69:16 83:5 84:1

86:22 87:9,14,16,21 88:20 89:10 98:19 99:23

138:14,21,23 144:6 146:17 147:7,9,24 148:7,13

149:19 151:22 156:19 177:24 197:26 200:13

222:22 266:4 283:12 294:17 303:21 305:17

338:10 459:13 545:5,14 563:3 576:4

vary 95:12 137:5 159:24,25 190:8 218:1 262:15

558:6

varying 14:13 51:5 52:8,9 112:7 252:9 266:1,12

396:26 497:26 573:3

vascular 75:6 108:16 252:17

vast 167:5,20 169:20 170:18 212:14 218:19 219:15

220:17 248:5 272:23 315:24 412:1

vegetation 300:17

vein 597:25

velocity 402:25

venae 582:6

venue 335:18

verbated 373:9

verified 33:21

verify 233:8

Vermillion 389:18

version 87:23,25 88:5,7,12 184:13 193:14

versions 226:24

versus 86:22,24 96:1 97:14 98:11,17 119:21 120:1

125:8 218:23 241:21 246:15 247:4 250:14 286:17

310:15 320:6 328:23 350:1 354:9 356:16 363:24

376:25 380:2 386:11 417:19 428:20 430:5 447:26

447:26 458:3 471:26 487:6 488:17 500:16 530:24

539:5 551:21,25 552:12,12 553:10 567:2 570:5

579:26 583:10

vertebrate 163:20

vertebrates 108:16 315:26 374:8

vertical 582:6

vessel 37:21

vessels 36:2 339:26

vetted 43:13 57:5 490:16

viable 138:8 176:8 513:2 539:16

Vice-Chair 6:16

video 286:25

view 137:12 139:25 202:1 248:7 417:25 436:24,25

462:17 466:21 467:10,11 480:16 537:25 597:11

599:9,9

viewed 89:6 90:13 245:4 331:5 335:23 362:5

402:15 404:2 529:14

viewing 193:18 227:7

views 257:20 467:11 599:10

vigilance 509:13

village 281:4

violate 463:23,24,24

violates 238:4

violations 338:13,14

violet 329:22

Virginia 1:16 6:21 78:22

virus 166:8 390:21,22

visible 604:21

vision 12:3

visual 22:1 365:26

visualize 242:1,26

volcanic 167:1

volume 38:12,13,14 397:6,6,7 398:20 400:24

551:25

volumes 38:14

Volumina 270:19

voluntary 302:18

volunteer 173:18

volunteers 176:13

vortex 200:11,21,26 201:5,13

vu 418:17

vulnerability 15:12 137:20 151:10 152:25 180:4

521:25 522:9,10,13,19 527:12 531:23 545:15

551:17 592:5

vulnerable 67:21 139:4 153:4,16,22,26 154:15,19

182:6 185:23 267:5,8,13,17,25 511:25 512:15

515:19 521:13 522:20 523:26 527:24 590:6,10

W

waffling 387:22

wait 253:8 320:15 434:21 489:7

walk 26:22 141:15

walking 72:25

want 2:15,18 5:19 8:23 10:22 16:10 18:16,22,24

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114:4 120:5 124:1 177:23 204:20 217:7 222:23

223:2 225:13 228:8 230:19 231:8,13,15 233:9,19

234:4,10 235:16,20 236:22 237:2 238:22 244:11

244:14,25 254:17 266:20 267:16 279:6 285:14

298:5 321:4 326:7 397:5,7 398:19 422:25 429:3,4

440:15 446:21 450:10 477:16,17,19,21 478:15,18

478:26 479:3,20 503:15 533:20 566:20,21

1s 80:4 228:15 239:7,9 251:5

1st 144:18

1% 169:6,8,25

1,600 30:10

1-day 440:22

1.5 99:8 512:9

1.87 533:12

1.9 102:3

1/3 170:24

1/3rd 167:3

1:30 127:25,25 294:6

10 4:7 29:8 47:7 53:10 76:1 84:23 94:26 104:16

105:1 130:15 131:15 135:3,6,10,11 147:17 148:5

152:24 158:16 182:25 207:26 225:7 226:9,19,21

238:22 239:9,10,20 246:3 300:7,11 301:7 308:25

312:5 361:20 376:24 379:3 386:19 397:5,6

398:21 403:7,10,14 405:22 406:1 422:25 423:3

430:21 446:15,16,21 463:18 464:12,18,19 499:3

512:25,25 532:11 549:21,22 551:2 553:24 581:6

582:24,25 584:25 586:25 604:18

10ppb 273:11

10th 90:6 551:22

10X 53:12 54:5 246:23

10% 118:6 119:7 166:17 169:26 256:13

10,000 79:14 80:21 115:16 141:23,24 142:1 253:17

397:7 460:6 516:6,7,8

10-day 461:16

10-minute 463:23 464:1 584:22 585:2

10-part-per-billion 108:10

10-year-old 301:2

10-20ppb 273:10

10.80 533:15

100 23:10 29:23 30:19 43:7,15 47:5,13 51:2 52:18

53:16 54:1,1,12,13 56:17 67:17 80:21 83:20 90:8

114:12 120:4 123:7,18 124:12,20 125:8 165:16

165:18 212:12 238:21 261:23 263:2 270:17

301:10 318:8 398:19 403:8 405:23 423:15 464:18

464:19 481:3 549:20 562:6 584:2

100% 165:14 222:2 281:19

100-meter 554:26 555:13

1000 80:21 216:14,17,18 246:21

102 386:20,21,24

107 54:25

11 34:13 80:6 400:3 532:16 533:15 542:26 543:1

11a 532:17

11b 543:5

11:00 71:17

12 176:19 182:26 183:24 236:23 237:6 241:9 306:4

533:12 549:5 560:5 562:22 573:5 601:16

12th 1:17 183:5 607:9

12,000 176:14

12.6 175:4

120 103:12 161:26 220:10,11

123 266:24

128 405:5
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13th 183:25 607:9

130 161:26

14 26:20 96:10 97:5,7,19 105:17 134:26 135:1,2

147:17 219:7,8,10 220:19,22 221:6,8 388:1 405:3

436:17 465:15 570:15 581:6,8

14C 79:8

14th 1:17 183:6 516:5

14-day 96:1,2,5,15 97:14,17,20,23,26 98:3 465:15

14-21 398:10

14.06 149:4,6

14.90 465:16

140 102:20 103:18,20

1424 151:1

15 4:8 10:1 107:25 108:12 124:7,12 163:3 164:24

171:9 176:5 224:1 233:3,11 234:25 256:4 361:21

388:1 398:8 402:20 407:12,15 410:18 428:19

436:19 458:1 494:7 532:24 549:21 568:21 601:15

15-minute 71:15

15-square 555:8

15-year 11:13

150 78:21 386:23 606:24

15000 275:9

16 30:4 43:22 44:3 168:6 386:23 401:12 405:21

428:19

16.2 531:10

16.3 531:10

160 252:18

1600 67:13

166 295:19

17 22:18 227:10 236:1 428:19

17th 144:18

17.5 246:3

18 129:21 268:21

18C 257:5

18,000 165:23

180 144:22 533:18 606:24

180-day 468:12 560:3

1869 101:21

19 93:1 266:23 501:15

190 147:16 158:8

194 266:20

195 423:2

195-day 423:5

1956 169:10

1957 295:7

1970s 234:19 418:18

1979 401:1

1980s 234:20

1982 402:9

1984 401:1 403:7

1985 403:12

1986 261:22 401:18 402:2

1988 270:14

1989 270:13 400:15 402:7,17

1991 169:11

1994 227:12

1995 141:2 143:21 144:3 227:13 268:15

1996 188:20 227:14 402:17

1998 183:25 276:5

1999 21:25

2

2 3:25 28:4 29:19,20 30:11 32:2,13,17 33:20 34:19

35:23 37:13 41:13 43:5,6 45:21 46:19,22,23

60:14,18 65:9 66:20 67:11 68:3,7 76:12,24 77:12

77:20 79:24 80:7,8,9,24 81:24 82:18 83:22 86:3

88:6 94:19 95:22 97:24,25 98:15,22 99:11,18,25

106:20 108:10 114:4 120:6 121:3,7 125:3,10,16

126:10,13 135:12 140:26 155:8 160:7 188:17

191:17 193:5 194:20 204:19 205:6 207:17 210:4

210:10,14,17 213:26 223:1,10,11 229:3,25

230:20 231:7,23,26 232:9 233:2,2 234:12,13

235:14 236:1 238:4,24,25 241:5 242:9 246:18

247:7 249:5 250:12,13 253:3,11,26 256:4 266:18

266:26 267:18 279:12 298:7,9 327:8 361:20

403:20 423:4 429:4 446:23 478:11 479:3 533:12

566:21

2D 193:11,18

2s 80:4 126:17

2(a) 352:10 450:11

2(b) 348:8 352:10 450:20 451:9 479:20

2-day 246:3

2-3rd 404:19

2.5 54:24

2.75 194:10

2.8 175:2

2.94 533:13

2/3rds 183:12

2:25 585:3

20 4:8 63:24 70:1 92:26 94:24 97:8 104:16 122:22

125:8 130:13 132:2 135:2 147:17 148:5 158:16

159:26 160:1 163:14 165:9 200:21 201:5 208:11

212:4,8,8,13,16 223:19 235:1,4 401:13,20 407:12

407:15 410:18 423:19 424:25 425:1,19 440:15,23

446:11 448:6,7 458:1 461:19,21,26 462:10 463:1

464:26 465:13,14,25,26 517:20 545:23 600:7,13

20% 164:14

20-day 461:16,19 462:6,12 463:6 465:14 571:26

20-ppb 461:19

20-year 530:2 554:26
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200% 222:8

200,000 283:23

200-and-something 435:8

2000 163:18 227:15 229:18 238:1 330:3 599:2

2001 330:4

2001a 403:18,19

2001b 403:18

2002 282:21 403:18

2003 21:11 22:4,18 27:12 29:12 31:1,9 33:1 34:14

38:16 43:3,12,13 46:3 56:23,25 59:12 60:9 66:18

67:15 68:5 69:10 70:4,9 72:24 78:1 87:24 104:16

133:10 273:21 280:20 297:13 336:18 355:15,20

355:26 356:3,6,10,22 361:6 363:17,19 364:6,26

366:23 376:9 381:7 384:21 394:6 395:19 451:23

451:25 457:20 482:11,13 483:9 485:5 486:26

487:3 585:18 586:5,23 599:2 603:2 605:17

2003's 487:5

2004 21:25 29:3,7 67:10 103:4 167:11 174:23

247:3

2005 188:22 227:19 400:19 490:20

2006 150:6,13 151:17 276:5,12 491:1

2007 23:5,8,13,26 24:23 29:6,13 31:2,12,19,25

32:8 33:1 34:15 43:15 44:13,23 56:20 57:3 59:12

66:18 71:3 73:2 76:16 87:25 88:2 104:19,25

106:19 109:2 141:4 144:3 227:20 357:17 369:6

395:19 441:2 483:19 521:13 586:24 605:17,17,19

2008 143:22 174:25 175:4 188:23 266:21 386:25

2009 23:26 26:4 43:5 73:3 76:16 78:3,6 80:1,6,6

88:6,11,21 90:24 99:25 105:1,4 106:19,23 109:2

126:11,15 137:23 144:3 152:23 174:25,26 176:18

179:8 191:16 222:25 223:8 227:20 228:24 239:5

241:4 242:8 243:6 244:9 289:6 298:2 299:11,12

299:17 395:20 396:21 400:18 441:3 442:15

521:13 522:10 527:10 584:5,16 593:19 596:2

201 242:9

2010 138:1 139:9 184:8 189:4 197:11 281:12

322:15,16 325:3 378:2 387:8 491:10

2011 138:1 139:9 141:14 150:6,13 151:17 156:18

157:4 247:3 266:16 293:1 322:9 371:13 468:14

469:1 503:2 513:1 514:11,25 516:26 518:11,18

523:18 548:1,8 556:2

2012 1:17 205:18 266:25 267:3 322:13,13,14,15,17

323:6,10 356:6 584:1,14

2013 11:14

21 281:5 437:4,12

21st 169:9 238:15 516:18

22 151:7

22202 1:16

2283 150:23

23 367:23 471:10

236 433:8

238 175:4

24 47:22 266:23

24% 222:8

24-hour 423:22 463:24 532:6

240 387:2

25 29:23 30:5 46:25 52:15 54:25 62:8 77:14 94:18

99:21 133:22 151:26 185:19 215:5 233:4,22

243:26 247:20 248:9 258:18 405:10 533:11

580:24 581:1

25th 107:24

25% 126:24

250 166:25

250C 403:15

258 54:24

26 88:26 282:25 367:23 533:13

260 115:8

27 295:8

270 144:21 148:21 151:19

2777 1:15

28 63:21 141:21 147:18 148:6 158:16 203:26

221:10,15 225:12 255:26 261:23 282:25 405:3

516:16 518:23 581:8

28th 163:6 516:5

28-day 225:6 514:26

3

3 3:26 5:16 21:3 26:14 32:18 33:21 36:1 37:16

40:11,13 41:14 45:18 52:12,14 62:8 76:3 77:19

79:25 80:7,12 82:2 85:3,8 90:5 98:23 99:17 101:5

103:11 106:19,21 108:11 126:13 179:21,24

181:19 194:2,16 222:25 228:17 232:9 236:11,12

238:18 239:4 241:5,10 242:8,22 266:4 267:23

279:17 365:20 366:5,13 403:8 426:21 429:4

451:9,9 479:3,20 481:24 508:7 533:13

3a 101:6

3b 102:9

3D 193:12

3s 80:4

3(a) 366:22 451:9

3(b) 451:10 479:21,22

3,200 30:11

3-A 431:8

3-day 2:5

3.14 148:23 149:4,12

3.2 101:26

3.36 150:26

3.5 405:2

3:00 607:16

3:15 178:24
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124:6,13 132:2 135:1,2 180:10 185:19 208:13

215:5 218:15,18 219:20 221:18,23,24 222:6

223:26 224:2 225:8,10 227:25 238:22 239:10,11

240:11 247:19,19,20 248:9 258:13 268:16 303:25

307:10 335:12,15 375:18 400:8 403:20 405:16

410:18 446:21,23 458:1 463:24 465:16,17 499:3

508:7 513:23 516:21 591:13

30% 118:9 243:21

30-day 96:20 308:5 465:15

300 218:10 513:24 539:25,25

300% 243:21

300,000 295:9

303(d) 159:11

305-5805 1:9

31 535:17 607:10

33 537:20

330 386:20

330% 241:14

34 63:25

35 12:10 77:26 78:4 101:18

35% 126:26 127:2

356 218:2

360 161:26

365 422:20 423:8

365-day 423:9 445:22

37 208:7 258:12

378 150:12

38 78:2,5 148:22,24 149:1

38% 151:12

39 163:8 378:25

39.6 102:5

4

4 18:17 28:4 32:19 33:23 36:3 37:19 43:21 44:1

85:19 106:22 107:8,13,25 108:5 109:9 111:24

113:22 118:23 119:5 123:8 126:1,1,23 128:13,19

128:22,26 129:2,5,10 130:14 131:13 132:11,12

132:13,18 133:20 135:7,9,14 141:20 143:2

146:24 147:17 148:5 149:12 150:6,7,11,20 151:3

151:14,25 152:4,9,17,26 153:2,20,24 158:15

163:2 165:10 170:9 185:15 193:10 228:17 233:9

238:18 239:4 244:1 248:9 267:25 285:13 381:1

404:16 410:18 415:6,6 427:14,19,19,21 429:4

434:4,7 435:2 446:24 451:22 472:16 481:25,26

482:2,3 494:15 496:17 497:3 498:25 500:22

501:16 502:20 506:7 533:24,24 557:26 566:20

4b 103:15

4th 163:6

4-A 436:20

4-day 103:3 135:17 143:7 472:17

4-part-per-billion 108:11

4-stage 109:1

4-7 269:23 271:17 277:22 283:20

4-7ppb 273:13 274:13

4.2 105:10,12 241:18

4.48 149:1,4

4:30 393:13 394:19

40 12:10 107:2 158:14 180:10 185:18 208:12,13

215:5 243:26 247:20 248:8 258:15 266:18 463:7

492:5 584:6

40% 170:2

40-day 463:7

400 378:11 386:22

41 192:24

410 46:14 50:19

42 235:17 241:20

43 270:8 273:17 378:26

45 304:11,16,18 583:25 584:2

45% 244:9

46 81:2 219:11 533:11,17,17 558:25

46% 147:3 549:11

47 247:19

47% 244:11

478 266:23

48 41:16 45:15 256:9 266:24 295:8 309:8,9

5

5 4:7 36:7 37:21 77:21 79:20 80:10,20,21,22,25,26

106:26 107:4 114:15 123:8 125:24 126:4 128:24

129:16 130:4 135:9 172:12 176:6 203:3 207:26

244:1 246:2 248:10 258:7 264:16,19,21 267:7

270:17 323:1 335:16 361:20 385:9 429:4 437:26

439:18,18 448:6 452:13 471:9 483:1 485:24

538:14 566:10 571:12 590:6 605:19

5s 80:4

5th 183:25 516:10 517:15

5% 258:4,11

5(a) 385:23 396:9

5(b) 452:14 483:17 484:21 485:1

5-point 79:17

50 21:9 83:21 84:10 92:25 94:17 102:19,21 103:18

103:20 120:5 122:24 124:4,13,19 169:7 183:8

218:18 228:6 233:4,25 239:11,12 268:24 274:1,3

274:25 275:11 288:24 290:20 295:16 312:1 313:3

320:3,10 433:8 440:8 465:17,19 492:19 562:6

50s 502:7

50th 106:15 107:24 108:2,3,5 110:16,20 118:25,26

119:10,12 128:21 133:22 217:13 319:7 320:7

467:15

50% 119:12

50,000 176:16
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500 122:26 540:5

500-meter 554:26

52 559:1

537 242:16

54 116:11

55 583:18

550 266:18

56 79:22 322:17 405:3,5

57 218:7 513:23

58 110:24

6

6 29:21 36:11 37:24 67:21 135:3 150:24 151:18

182:9 218:9 275:22 396:10,20 452:24 453:23,24

460:26 485:22 488:6,8,11 503:15 511:13 512:23

533:14

6% 150:12 169:7,8,26

6,000 290:22

6.2 265:17

6:15 450:2

60 95:19 96:22 115:4,8 116:4,4 119:2,15 129:2,4

129:17 131:14,14,25 132:9,10,17 135:3,5,12,19

135:25 138:19 147:20,22 155:3 158:17 161:13

183:15 185:19 201:4 208:11 215:6 218:5 219:7

219:11 220:21 221:24,25 222:5,6 224:2,24

225:20 240:2 243:26 244:1 246:16 247:19,21

248:9 258:17,21 266:24 283:20 307:18 439:19

446:14,16,21,23,24 447:5,6 461:26 462:1,17,20

463:1,4 464:26 465:2 513:20,21,26 571:22 584:6

60dRA 265:17

60% 127:2 241:11,12

60-average 104:10

60-day 95:26 96:15,16,21 97:8,9,14,18 103:13

104:1,22 105:2,10,11,18 106:3,9 107:2,12,26

135:17 142:1,4,5 147:26 148:22 149:2,10 161:11

161:12,14,19,24 162:1,2 224:20 225:10 293:19

307:25 312:2 429:2 431:9,10 434:6,12 436:22

437:7 438:5,8 440:22 461:15,17,21 462:11,13

464:2,5,23 466:14 513:15,17 514:8 516:8,10,20

516:21 517:16 533:2 540:20 541:3 561:24 563:5

563:9 564:17 571:17,24 572:14 574:14 575:2

579:21

600 295:17 540:20,22

618 174:26

62 328:1

62% 151:1 241:6

64 34:1,4 47:15 290:13

6502 150:19

6917 150:5

6978 163:11

7

7 36:13 38:1 80:23 105:17 107:8 108:5 109:9

111:24 113:22 126:1 128:13,25 129:6,16 130:14

131:13 133:20 141:20 147:17 148:5 150:22

152:26 155:19 158:15 172:13 185:15 218:1 232:9

234:25 236:12,23 237:5 246:16 256:4 257:6

303:18 309:13 377:24 402:12 404:16 405:3,5

410:18,21 427:14,19,19,21 428:10 434:4 435:2

453:23 456:19 461:6 466:17 472:8 476:17,21

491:15,16 494:15 496:17 497:3 498:25 500:22

501:16 502:20 506:7

7's 269:9

7,000 553:6

7-day 129:14

7-part-per-billion 108:12

7-9 299:13

7.2 104:22 329:10

7.3 126:1

7.4 107:5 241:18

7.94 150:26

70 36:5 238:20 246:3 258:12 402:13 528:18,18

70ppb 265:17

70% 218:17

703 1:9

72 45:16

73 78:3

74 128:13 130:11 241:21

75 30:14 33:26 133:23 234:14 584:9

75th 107:24

75/25 68:17

76 131:12

77 106:24

79 233:4 234:1

8

8 30:4 36:15 38:4 44:3,7,9 136:6 163:9 225:8

231:14 309:13 319:18 377:24 436:17 494:8,11

503:7

8-hour 463:24

8.1 104:25

8.2 600:24

8.30 465:16,17

80 101:16 174:12 383:25

80s 418:18

82% 148:4 158:15,16

85% 281:5

86:49-58 266:21

868 151:13

87 79:4 81:3 102:7 222:1,23 242:7 243:4

89 583:24
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9

9 29:8,10 34:13 36:17 47:23 167:19 263:12 275:22

377:24 503:12

9ppb 271:8

9:00 454:5

90 5:12 9:9 119:16 144:21 148:21 151:19 201:7

224:24 289:10 290:13 295:13 303:25 454:20

463:4 464:26 607:8

90th 90:7 256:4 551:21

90% 132:12,14,14,19 183:13

90-day 468:9 469:23

91 249:4

91% 244:13 246:21

93 125:24 430:13

93% 246:24

94 125:24

94% 137:10 148:4 150:19 174:26

95 102:24 110:16 114:14 533:9 536:9,10 537:3,15

537:16,22 539:26 540:6 541:8 565:18

95th 148:25 529:24 538:4,7 566:11

95% 108:4 146:25 148:11 258:5 517:7

96 50:6,6 64:9

97 274:18

99 559:3