### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 1

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 2

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 3

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 4

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 5

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 6

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 7

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 8

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 9

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 10

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 11

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 12

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 13

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 14

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 15

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 16

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 17

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 18

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 19

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 20

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 21

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 22

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 23

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 24

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 25

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 26

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 27

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 28

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 29

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 30

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 31

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 32

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 33

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 34

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 35

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 36

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 37

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 38

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 39

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 40

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 41

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 42

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 43

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 44

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 45

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 46

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 47

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 48

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 49

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 50

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 51

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 52

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 53

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 54

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 55

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 56

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 57

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 58

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 59

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 60

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 61

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 62

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 63

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 64

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 65

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 66

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 67

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 68

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 69

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 70

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 71

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 72

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 73

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 74

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 75

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 76

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 77

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 78

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 79

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 80

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 81

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 82

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 83

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 84

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 85

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 86

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 87

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 88

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 89

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 90

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 91

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 92

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 93

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 94

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 95

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 96

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 97

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 98

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 99

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 100

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 101

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 102

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 103

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 104

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 105

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 106

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 107

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 108

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 109

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 110

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 111

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 112

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 113

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 114

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 115

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 116

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 117

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 118

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 119

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 120

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 121

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 122

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 123

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 124

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 125

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 126

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 127

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 128

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 129

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 130

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 131

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 132

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 133

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 134

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 135

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 136

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 137

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 138

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 139

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 140

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 141

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 142

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 143

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 144

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 145

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 146

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 147

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 148

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 149

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 150

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 151

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 152

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 153

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 154

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 155

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 156

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 157

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 158

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 159

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 160

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 161

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 162

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 163

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 164

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 165

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 166

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 167

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 168

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 169

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 170

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 171

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 172

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 173

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 174

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 175

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 176

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 177

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 178

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 179

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 180

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 181

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 182

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 183

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 184

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 185

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 186

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 187

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 188

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 189

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 190

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 191

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 192

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 193

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 194

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 195

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 196

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 197

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 198

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 199

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 200

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 201

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 202

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 203

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 204

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 205

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 206

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 207

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 208

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 209

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 210

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 211

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 212

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 213

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 214

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 215

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 216

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 217

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 218

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 219

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 220

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 221

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 222

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 223

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 224

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 225

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 226

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 227

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 228

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 229

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 230

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 231

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 232

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 233

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 234

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 235

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 236

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 237

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 238

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 239

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 240

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 241

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 242

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 243

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 244

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 245

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 246

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 247

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 248

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 249

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 250

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 251

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 252

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 253

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 254

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 255

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 256

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 257

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 258

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 259

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 260

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 261

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. 

### 2 6 They found that even under those high prolonged conditions of 

### 27 atrazine exposure, you saw recovery of photosynthesis to control

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 262

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 263

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 16 17 18 19 20 21 22 23 24 25 26

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 264

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, REPRINT S** **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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 265

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**

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 266

1 **Historically Varying Atrazine Profiles.** 

### 2 + Periphyton community metrics and percentof 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 naïvelab 

### 20 G communities, e.g., 1 h EC50 of 194 μg/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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 267

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 268

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 

### 14Network. 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 269

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 270

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 271

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:

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 272

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 273

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 274

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 275

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 276

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 277

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 278

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 279

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.**

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 280

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 281

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 282

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 283

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 theMidwest it could reach several 

### 24 million acres.The ecological risk being protected for does not 25 seem to matchwhat growers observe in the real world. Has the 26 model beenvalidated in any of the actual eco-monitoring streams?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 284

1It is very important that the SAP give sound guidance to the 2agency to get the loc right because the negative ramifications 3could 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 

### 8that do not meet acceptance criteria :That the risk summary 

### 9 statement does not agree with toxicological data; the use of a 

### 10bias factor, and the regulatory implications of theLOC. 

### 11 ******************************************************************* 

### 12 **THE REMAINDER OF THE TRANSCRIPT FROM THIS POINT WAS PRODUCED** 

### 13 **FROM AUDIO:** 

### 14 ******************************************************************* 

### 15 1 6 17 18 19 20 21 22 23 24

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 285

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 286

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 287

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 288

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 289

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 290

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 291

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 292

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 293

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 294

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 295

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 296

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 297

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 298

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 299

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 300

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 301

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 302

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 303

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 304

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 305

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 306

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 307

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 308

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 309

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 310

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 311

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 312

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 313

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 314

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 315

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 316

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 317

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 318

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 319

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 320

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 321

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 322

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 323

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 324

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 325

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 326

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 327

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;

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 328

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 329

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 330

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 331

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 332

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 333

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 334

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 335

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 336

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 337

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 338

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 339

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 340

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 341

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 342

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 343

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 344

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 345

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 346

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 347

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 348

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 349

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 350

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 351

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 352

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 353

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 354

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 355

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 356

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 357

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 358

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 359

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 360

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 361

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 362

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 363

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 364

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 365

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 366

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 367

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 368

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 369

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 370

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 371

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 372

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 373

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 374

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 375

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 376

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 377

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 378

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 379

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 380

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 381

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 382

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 383

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 384

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 385

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 386

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 387

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 388

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 389

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 390

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 391

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 392

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 393

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 394

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 395

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 396

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 397

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:

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 398

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 399

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 400

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)

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 401

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 402

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 403

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 404

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 405

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 406

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 includedon the basis 

### 4 of exceedingly highconcentrations 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 407

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 408

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 409

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 410

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 411

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 412

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 413

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 414

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 415

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 416

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 417

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 418

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 419

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 420

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 421

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 422

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 

### 16think 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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 423

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 424

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 425

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 426

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 427

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 428

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 429

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 430

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 ,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 431

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 

### 7sensitive parameter that is included in the index to be used 

### 8 for excessive toxicity. The next stage, I think it was the 3-A, 

### 9using the 60-day time period, and one of the justifications of it 

### 10that was in the white paper was that selecting the 60-day time 

### 11period encompassed all but a few of the cosm studies. And I do 

### 12think that that was a valid reason. 

### 13 If the cosm studies have unrealistic 

### 14 concentrations that I mentioned earlier, then that is not good

. 

### 15Also, 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 

### 1 9 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 

### 2 6 of meta-analysis to query a database that wasn't specifically 

### 27 designed to test the hypothesis that it's being used to test.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 432

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 433

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 434

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 435

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 436

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 437

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 438

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 439

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 440

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 441

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 442

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 443

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 444

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 445

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 446

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 447

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 448

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 449

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 450

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 451

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 452

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 453

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 454

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 455

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 456

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 457

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 458

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 459

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 460

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 461

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 462

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 463

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 464

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 465

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 466

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 467

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 468

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 469

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 470

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 471

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 472

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 473

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 474

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 475

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 476

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 477

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 478

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 479

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 480

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 481

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 482

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 483

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 484

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 485

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 486

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?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 487

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 488

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 489

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 490

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 491

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 492

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 493

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 494

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 495

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 496

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 497

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 498

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 499

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 500

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 501

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 502

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 503

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 504

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 505

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 506

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 507

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 508

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 509

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 510

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 511

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 criterionone, for instance, use only 

### 20cosmdata 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 atrazineconcentrations in vulnerable watersheds , 

### 25 the average time between multiple peak chemographs , and the

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 512

1 overall period during which atrazine in the stream water 

### 2 is elevated . Make a reasonable decision on what the maximum 

### 3 duration of acosm study is a reasonable model of atrazine 

### 4exposure 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 

### 12information to provide. Reasonable prudence should be 13 exercised to select cosm studiesthat are deemed reasonable 

### 14 modelsof atrazine exposure to natural communities in 

### 15 vulnerable watershedsbased 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 

### 1 9 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 

### 2 6 after 10. Okay. Mr. Spatz, do you want to read Question 10 

### 26 into the record?

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 513

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 514

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 515

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 516

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 517

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 518

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 519

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 520

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 521

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 522

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 523

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 524

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 525

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 526

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 527

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 528

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 529

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 530

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 531

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 532

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 533

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 534

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 535

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 536

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 537

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 538

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 539

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 540

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 541

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 542

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 543

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 544

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 545

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 546

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 547

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 548

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 549

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 550

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 551

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 552

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 553

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 554

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 555

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 556

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 557

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 558

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 559

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 560

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 561

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 562

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 563

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 564

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,

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 565

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 566

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 567

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 568

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 569

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 570

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 571

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 572

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 573

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 574

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 575

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 576

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 577

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 578

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 579

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 580

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 581

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 582

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 583

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 584

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 585

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 586

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 587

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 588

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 589

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 throughthe list and 

### 19I'm baffled because these are all cyanobacteria-- these aren't 

### 20 plants. And others werealgae-- 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 

### 23really is. So you could change thatand call it the 

### 24Photoautotropic Assemblage Toxicity Index. I'd appreciate that. 25 It might help youcmmunicating that. Thanks. 

### 26 **DR. DANIEL SCHLENK:**Dr. Engel.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 590

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 591

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 592

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 593

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 594

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 595

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 596

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 597

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 598

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 599

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 600

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 601

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 602

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 603

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 604

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?"

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 605

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.

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 606

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 607

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**-0**230 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 12 TH** 

### - 14**TH**

### , 2012

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 1 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 absence46:7 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 accept91:16 124:22 125:9 206:22 313:22 589:1 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 accepted77:2 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 accessed4:20 454:23 accessible 596:7 accessory 167:26 accidental 282:23 acclimate 200:10 acclimated423:24 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 accounted75:2 401:10 accounting 365:26 401:11 491:18 505:22 519:2

549:11 accounts 277:6 558:24 accrued458:14 accumulate446:18 447:7 462:14,19 465:26 accumulated429:2,6 accumulates 92:10 446:9 accumulating 95:18 accumulation429:16,21 447:1 accumulative49:23 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 acetone421:17 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 acknowledged45:25 353:5 402:10 443:22 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 act2:14 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 2

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 adapt546:23 adaptation 111:21 397:18 adapted424:1 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 additional4:25 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 adjust471:9 514:7 527:21 531:1 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 adopt271:12 278:2,24 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 3 112:22 118:16 124:5 190:5 192:17,20 193:21,24 193:25 194:20,21 195:3,10 196:4,5,11,12,19 198:15,20 199:13 202:6 207:8,8,20 211:23 249:18 296:20 299:3 313:1 316:6,15 318:12 319:26 337:22,26 341:19,20 342:3,4,5,7 386:1,3 389:9,11,12,19,21 390:8 392:8 441:9,19,23 442:2 443:1,4,7 444:9,11,18 461:17,20 462:19,22 463:22,26 464:4 465:18,20 484:4 485:19 492:6,9 492:20 498:8 508:16,24 600:18 601:5 602:8,19 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 advisable405:4 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 affinitive480:18 affirmation 509:5 affirmative496:4,15 509:15 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 Ag267:5,7,14 268:1 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 agenda9:2 19:9 26:21 72:11 137:10 179:24 181:15

454:25 ages 118:13 167:1 176:20 aggregate74:22 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 agreement21:14 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 al43:5 175:7 227:13 231:6 232:21 234:18 235:14

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 4 325:10,14 378:2 386:25 400:15 401:24,24 402:2 402:6,9,17,17 403:1,6,12,17,18,18,19 404:22 490:26 503:2 albeit 367:25 alga259:18 algae75:15,19 114:8 231:13,16 255:8,15 256:8

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 allowable76:10 allowed 290:7,14 311:12 456:21 allowing211:6 290:19 328:14 518:1 538:3 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 allude406:11 alluded 384:23 ALOUD 264:7 Altenburger266:21 alter296:23 499:26 582:16 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 amendment425:8 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 ammonia41:21 334:25 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 AMP530:23 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 analogy438:17,18 439:13 579:17 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 5 146:1,1 147:3 149:18 150:2,7,16,18,24,25 151:6 151:12,16,20 152:7,10 154:3,6 155:10,11 156:6 156:19 160:9,19 188:4 189:18 192:6 198:23 205:2 211:1 212:6 214:24 215:14,18 217:24 220:15 223:14 227:17 229:1 231:23 238:3,9 241:16 244:18 245:8 248:11 252:1,20 261:1 279:7 292:7 305:16 315:8 316:10 320:6 323:15 324:4 325:3 326:14 331:14,21 343:17 344:11 368:4 377:5 399:16,21,24 408:23 414:16 421:20 438:26 439:1 451:15 461:25 465:9 467:6,15,17 468:4 470:11 478:16 479:1,6,22,26 494:13 495:11 501:3 503:14 504:14 505:6,25 506:20 507:6 509:12 511:24 514:5 518:12 527:10 531:4 534:6 549:6 554:5 556:10 558:19 559:17 561:18 561:23 563:6,10 568:26 583:12 595:22 596:1 598:17 analytical41:10 analyze26:10 44:16 56:13 analyzed45:1,18 85:10 174:26 175:5 210:14

220:22 analyzing44:25 262:3 303:14 ancillary 573:10 588:23 Andrus 260:24 263:12 265:2 404:22 and/or46:12 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 Anita414:11,11 416:17 426:7,7 453:11 488:10,10

489:15 606:20 ANN 110:23 111:11 Anne7:16,16 249:25 250:6 408:14 432:15 455:22

455:22 490:4 509:23 588:1 annual 21:24 440:12 annually 275:10 289:8 anomaly406:24 anopia261:24 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 anticipating406:18 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 AOP58:12 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 apparent270:14 443:10 apparently 236:5 238:7 296:21 301:4 490:21 508:7 appeal435:16 appealing436:4 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 appended400:11 401:17 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 applaud406:8 applicability 520:14 545:4 applicable 262:7 373:3 514:19 515:18 527:8

561:10 application74:5 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com 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 appointment288:5 appreciable45:15 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 appropriated404:25 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 arena9:12 argue 199:5 283:7 505:8 568:26 596:11 argued468:1 596:2 arguing 223:25 argument225:13 250:24 300:7 308:4 364:18

435:22 458:18 496:15 568:24 arguments459:1 460:21 492:25 583:8

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 7 Arizona7:2 455:15 ARLINGTON 1:16 arm460:16 aromatase 54:22 arose 348:21 arrange 335:8 arranged4:7 107:8 arrangements 603:8 array 167:24 arrive 273:6 432:3,7 543:15 arrived407:5 arrow92:25 121:18 140:5 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 assert296:19 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 assessment2:10 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 assigned79:17,19,21,25 90:2,10 92:7 99:15 123:16

321:25 assigning 89:9 448:18 467:5 assignment93:8 253:1 345:2 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 8 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 asterisk401:7 403:24 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 asymptote435:23 atmospheric463:14 ATP73:18 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 Attachment403:8 attack 370:15 attempt 32:6 261:15 273:5 279:12 315:18 546:23

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 9 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 attributed42:7 390:8 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 audiences475:10 AUDIO 264:2,4,9 284:14 auditable 327:20 auf270:17 aufgefRllt270:18 aufgelVst270:17 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 authorships401:11 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 averaged427:18 461:21 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 aware4:16 156:21 187:24 302:3 321:5 325:9 367:6

381:11 457:5 487:5 588:19,25 595:25 awful 232:24 axis42:19 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 back20:26 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 10 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 Bailey491:10 607:5,5 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 band482:11 538:6 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 bar53:22 200:23 201:4 232:2 362:14 barely419:14 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 Baxter223:12 238:12 241:9 306:3 322:9 325:9,14

325:14 Bay 302:15 Beach 207:5 bearing 156:10 bears 173:12 beat60:25 176:19 426:8 becoming 302:11 309:23 493:26 bed 177:5 282:9 beetles 166:14 befuddled 589:22 began281:14 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 behavior46:17 56:7,14 241:25 244:8 322:6,14

323:18 behavioral 31:22 43:17 48:14 56:11,12 68:1

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 11 323:20 507:1 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 bells418:8 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 Benthic265:25 266:6 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/least535:15 bet607:13 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 beyond70:11 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 biases401:12 469:8,10 530:6 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 biggest278:21 343:14 401:18 433:24 574:5 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 binary79:16 80:15 102:17 106:16 110:19 217:6

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 12 242:5 293:19 428:21,23 429:16 460:11 binder292:23 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 Bioassessment491:12 biochemical 31:22 48:13 54:16,20 55:4 57:1

261:10 262:11 327:2 390:16,16 biodata490:6 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 biologist7:17,26 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 biota408:24 biotic409:13 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 bit5:20 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-assay466:21 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 bloom43:26 44:9 75:17 259:19 blooms44:3 blown228:5 370:1 blue 20:18 53:8,22 152:16 153:11 281:6,13 292:26

292:26 bluegill402:13 blue-green 257:2 BMP283:4 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's425:18 548:9 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 bolts491:9 bonded 187:16 bonds73:12 book 176:22 188:22 227:19 books 189:6

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 13 Boone7:9,9 111:23 125:22 127:9,11 205:10,24

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 bootstrapping436:7 476:9,15 567:23 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 bouncing445:5 bound 107:14 187:10 192:13 435:25,26 551:7

597:12 bounded 589:4 bounds434:8 572:17 box 10:10,14,15 11:3 18:4 20:18 boxes 10:3 518:5 bracket405:2 554:14 brackish410:7 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 brains409:26 Branch 19:25 breadth 11:24 50:9 79:2 break71:14,15 136:7 178:24 223:22 226:8 240:17

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 breathe464:11 breathing463:17 breeze255:3 Bridge 330:3 Brie 607:6 brief6:11 272:15 405:13 411:20 488:9 544:13 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 broader59:22 348:25 373:3 376:17 broadly 531:8 broad-base 501:5 Brock76:12 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 Brocks429:3,4 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 brown50:3 51:16 140:6 brown-yellow 153:17 Bruckner292:15 Bryer 301:2,7 Bubba 301:17,21,22 budget 171:14,21 buffer593:21 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 bulk96:13 97:2,4 198:18 bullet489:10,21 511:8 bullets489:24 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 burden277:24 Bureau 301:26 buried 318:4 burning 590:7

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 14 bus421:9,10 524:21 bushels 275:22 295:18,19 business 288:25 buy407:23 bye454:5 B50441:11 C C 39:18 182:16 185:21 293:8 322:20 338:18

348:13 470:23 512:17 526:20 ca270:16 caffein 603:10 caffeine 584:26 Caite 607:6 calcium 127:1 calculate48:8 101:10 103:8 148:22 216:16,17

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 Caltha272:8 camera4:15,15 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 Carlos4:12 607:6 Carney401:17 Carolina7:21 472:22 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 carryover443:12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 15

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 categorized48:11,25 254:17 349:20 categorizes403:23 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 Catherine7:1,1 203:10 204:4,10 207:6,23 250:16

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 caught326:25 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 causing73:17 112:19 131:22 236:11 253:4 588:23 caution402:15 559:21 cautionary 559:19 cautious 276:16 caveat 327:18 caveated497:4 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's472:21 chain74:10 187:1,1,8 325:13 530:9

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 16 Chair2:16,17 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 channel402:13 chapter298:18 503:15 571:12 590:6 605:19 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 characterizing48:6 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 cheaper568:20 check 239:2 334:4 339:22 430:15,17 check-off295:9 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 child417:15,16 children 177:9 417:13,17 418:9 Chile 191:23 chime 371:19

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com 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 chloroplast73:13 186:24,25 choice 95:19,20 320:3 467:5 choices427:8 577:3 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 clarified427:5 552:4 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 Clarkson7:26 455:26 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 classical424:3 Classics401:2 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 clear79:21,25 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 clearer420:26 459:9 473:2 601:9 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 clock71:16 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 18 closed 114:16 closely 160:24 306:18,23 closer277:11 513:22 522:15 closes489:19 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 cluster43:21,24 44:1 554:5 clustered 198:2 clustering 210:12,13,14 578:16 clutches 330:9 Coalition 272:5 276:6 283:15 coast410:8 Coastal7:17 455:23 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 colleague4:5 211:15 342:11 377:21 602:1 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 collect57:20 103:5 265:22 568:21,25 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 collinear445:19 colonized403:9 color50:2 193:23 Colorado 6:15 455:11 colors49:26 51:13 52:22 Columbia7:14 455:21 column 260:4 351:22 comb 404:6 combination44:2 286:3 327:12 505:20 534:19 564:8 578:16 579:15 combinations 40:4 42:5 53:11 534:13 564:4 combined44:26 143:4 235:7 312:16 358:25 387:3

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 commandeer269:9 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 commented432:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 19 135:18 136:8 161:2 177:24 178:22 181:12,16 183:7 184:5 187:23 204:23 272:15 284:6 285:10 292:18 295:5 297:6 298:4,6 299:14,21 300:24 305:13 326:7 328:11 338:23 339:18 346:24 347:7 356:14 366:12 377:10 394:16 395:12,13,17 396:9 400:11 405:13 408:18 410:15 412:4 414:19 439:22 448:10 450:14 455:1 466:19 472:8 473:19 476:8,19 479:10,11,14,16 486:19 502:26 503:7 503:10,24 510:18 511:3 519:23,23 523:15 525:11 525:18 529:4 532:3,11 542:3 544:25,26 545:12 547:19,21 549:1,2 558:5 560:4 562:21 570:10,12 575:21 580:3,4 584:17,24,24 585:7 589:14 590:16,23 591:25 592:4,12 593:6 598:4 603:3,20 604:1 605:17,19,20 606:23,24 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 complement543:14 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 complicated421:21 433:23 434:24 435:2 437:2

519:12 546:21 complications 235:6

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 20 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 composition74:23 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 computation469:1 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 computer219:2 496:11 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 concept28:17 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 conceptualize219:6 222:13 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 21 492:22 493:7 497:7 504:4 505:5 561:14 580:24 581:2 concerned40:26 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 conclusion45:1 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 concur269:1 391:22 510:13 550:19 587:10,11 concurred 22:22 23:21 27:14 29:14 31:2 33:2

34:15,26 43:14 44:18 concurrent77:4 180:9 345:8 430:11 concurrently 35:23 328:22 333:15 condition73:14 296:24 309:4 310:14 404:25 491:2

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 conduct68:7 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 confident91:15 269:7 318:6,8,9 393:9 496:15

498:5 confidential 3:17 confirm 27:20 97:12 365:4 390:5 400:22 confirmation438:13 441:23 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 confrontation484:4 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 congratulate411:12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 22 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 consider2:6 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 consist292:4 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 consistent24:26 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 consolidate472:4 constant79:12 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 constraint578:4 constraints 409:12 525:10 construct447:13 constructed 141:19 147:19 329:1 334:10 constructs 117:7 consultant226:13 consultation 3:20 consulting486:7 consumer 185:1 consumers 292:1 498:7,10 consumption 164:15 169:18 contact5:17 9:18 136:7 contacted4:4,5 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 containing402:12 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 contamination43:24 44:3 334:5 339:23 contended 371:23 content200:20 contention 99:4 157:23 Contest 176:18 context9:20 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 23 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 continued77:8 166:21 204:8 291:23 561:7 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 contraction453:15 contradictory 234:26 contrary75:26 168:20 178:6 271:9 274:8 contrast94:2 142:15 143:26 146:2 307:17 310:9

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 converge445:10 converging445:4 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 conversions493:17 convert 103:24 187:6 438:4 converted 108:8 205:14 converting438:5 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 cooperation76:19 281:17 cooperative 282:5 coordinate 163:4 Coordination 3:19 coordinator 359:9 Coppock287:21,23 288:1,1 292:12 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 corner4:12 141:18 193:20 242:18 420:23 cornerstone 113:7 cornfield 287:1 555:12 corn-belt 87:18 corn-farmed 287:7 corporations 168:15 172:5 correct72:8,9 129:14,16 130:21 185:24 204:6

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 correction439:12 529:15 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 corresponding49:10,24 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 24 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 cost271:22 275:18 278:14,15,17 280:12 289:26,26

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 counterexample413:10 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 covers409:16 575:9 coworkers 87:20 cows 508:12 co-discussants 391:22 co-linear420:19 co-linearities 420:20 co-ops 288:25 co-respondents 328:12 cracked419:14 crammed 232:24 crashes 167:1 CRE 300:6 create 141:24 170:5 216:6 338:14 366:18 394:3

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 25 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 credit401:2 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 crown295:11 crucial 295:21 crying 19:14 cryptophycaeae 231:19 crystal 1:15 141:14 356:5 cubic 398:14,21 cues46:18 56:9 cue-in 367:26 cuff 19:12 Cullen480:26 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 current20:19 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 curve71:3 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 curved465:11 527:20 528:4 537:21 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 cutoff212:4 225:13 cyanobacteria75:17,20

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 26 cycle 11:13 218:14 324:5 cycling 399:11 cytometric47:8 C's473:1 C14423:22 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 Dan2:15 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 dann270:18 Dan's 2:16 Daren287:21,23 288:1,1 dark232:1 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 27 202:22 205:11,13,21,25 210:12,13,17,26 211:2 212:10 215:26 217:12 221:19 222:25 224:1,4 225:10 226:4,5 227:17,17,24 228:6,10,15,19 229:3 230:23 232:26,26 233:8 235:2 237:22 238:26 239:17 240:4,8 241:5 242:1,2,13,26 243:10,15 244:4 248:22 251:18 253:25 254:24,24 255:18 257:24,25 258:16,26 259:4,4 260:18,18 261:3,12 263:1 264:23,25 266:11 267:3,7,19,19 267:24 268:2 269:2,5 273:18,19 274:12 282:20 282:25 283:1 284:10 287:15 289:6 291:2 293:9 296:11,15,23 297:12,16,19,21,26 298:3,4,16 299:20 300:4,4,13,16 305:17,20,22 306:3,4,7 309:6 311:16 314:9,20 315:14 317:3 318:26 321:8 325:4,17 327:21 331:21 334:17 336:26 337:4,7,12 340:17 350:7,15,24 353:2,3 359:22 361:25,25 367:8,25 368:2,8 369:10,11,24 370:12 370:15 371:5,16 377:24 379:24 380:1,2,3 382:17 385:25 386:2,7 387:21 388:26 390:2,12,14 391:11 392:18 393:7 394:13 399:17,20,23,24 400:7,19 401:12 403:10 404:8,11,15 405:25 407:3,10 409:20,25 410:4 412:6,6 423:18 424:18 424:19 426:12 428:20,23 429:10 433:5,11,24 434:10,13,15 441:4,22,26 442:12 445:1,2,4,9,11 449:4,6,18,24,24 452:17 457:20 458:7,10 459:7,7 460:7 462:11,24 463:10 464:8,18 465:21 468:9 472:15,21,22,24 473:11,14 474:20 475:13 479:1 486:5 491:19,26 492:11,18 496:8 497:2,3,24 498:18 499:1,15 500:8 503:15 504:7,9,12 505:25 506:3,5,20 507:6,13 508:10,12,20 511:20,23 512:5 513:3,7,20,21 514:6,14,15,20,22 515:21 516:16,23 517:13,17,26 518:1,7,14,16 519:1 520:14,15,17 522:2,3 523:25 524:4,25 529:17,26 530:23 532:20 535:4,17 537:1 539:9,20,26 540:6 540:17,21,23 541:1,2,20,25 544:6,8 545:3,5,6,12 545:14,17,19,21,23 546:24,25 548:20 550:16 551:20 553:5,17 554:8,15 556:10,13 557:22 558:13,19,22 559:9 560:1,11,15,20 561:8,9 562:9 563:15,16,18 565:17 566:4,5 567:7,7,12 568:22 568:25 569:19 570:19 571:19,23 572:1,4,4,13,26 572:26 573:12,18,19,25,25 574:7,25,25 575:2,3 575:17 577:8 579:8 580:13 583:24 584:1,2,5,12 586:3 595:1,10,10,25 596:5 598:9 602:10 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 date73:5 144:20 248:15 281:21 386:10 506:5

508:24 dates 229:26 231:24 535:4 David480:26 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 DCI29:4,7,18 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 28 DDT 186:19 de270:4 DEA45:20 dead426:8 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 dealt409:6 449:17 547:2 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 December23:13 decent324:18 deceptively 10:1 decide 171:22 313:9 361:7 381:17 597:18 decided 189:17 352:5 361:10 527:7 decides467:8 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 declining79:12 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 deep423:4 deeper235:10 236:9 317:18 deethylatrazine45:19 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 definitive47:13 110:6 124:2 134:15 143:16,26 144:8,9 145:18,26 146:7 363:6 372:18 459:13 479:26 480:17 definitively 250:18 deform 168:17 deformed 177:21 deformities 194:1,4 degradates45:10,10,14,19,21 505:8 510:21 degradation45:13 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 deisopropylatrazine45:19 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 29 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 demonstrated44:14 167:14,23 175:6 198:16

254:16 255:7 330:4 378:2 512:11 demonstrates 220:15 241:1,22 256:25 263:1

385:26 demonstrating45:26 173:13 323:20 390:17 demonstration 541:15,17 547:18 548:7 dengue 166:9 denial 302:17 denote47:20 DeNoyelles 126:14,19 234:18 401:17,24 402:6,9,16 densely 172:21 densities 332:9 334:22 density 562:8 denuded401:22 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 dependent61:18 140:16,17,21 144:23 145:3,7 165:4 240:4 241:25 255:20 335:12,17 425:5 429:24 580:21 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 DERs474:16 des 270:19 301:26 describe73:1 83:24 84:8,17 100:24 194:24 228:11

344:21 397:4 described42:5 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 detection45:20 137:9 detections 73:11 137:8 detects 174:17

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 30 Detenbach402:17 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 detritus402:24 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 developing7:18 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 develops499:21 deviation 102:23 deviations 265:16 devil's436:15 devised446:7 Dewey401:24 402:2 DFO 2:11 18:22 DIA45:19 diagnostic 506:24 diagonal 270:20 diagram 139:23 140:2 160:19 191:9 211:19 222:12

251:3 525:24 dialogue70:9 diamond49:22 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 differ44:21 120:2 125:2 224:22 329:20 363:1

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 31 367:7 369:26 372:24 373:7,17,20 376:21 378:21 379:17 380:1 401:10,10 412:21 415:26 416:26 417:2 422:5 424:16 427:2 433:14 437:17 444:25 448:19 449:20 451:1 457:7 458:6,19 459:2,17,18 459:19,21 464:7 465:10 466:8 470:26 478:15 482:12 484:8,9,11 485:24 488:18 491:4 492:10 497:26 527:11 532:8 533:10 535:24 545:23 547:5 547:7,7 548:4 554:6 567:1 582:1,5,25 592:21 593:23 594:11 597:14 599:9 600:9,9,14 603:13 differential 94:25 168:22 differentiation 193:1 differently 13:10 112:15 206:10 355:2 379:20 differing75:9 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 difficulty481:4 495:1 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 diluted498:17 dilutional447:19 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 discarded43:25 44:2 discern 22:9 34:22 35:15 382:14 disclosure 3:17 163:13 disconnected 229:26 discount 342:19 discounted406:3 discounting 350:2 discourage496:5 509:16 discouraged 37:20 discovered419:12 discreet93:19 discrepancies 400:3 discrepancy 218:16 219:24 244:3 354:21 487:6 discriminate 34:8 462:24 discriminating411:22 438:1 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 disparate430:11 disparity 214:21,23 218:19 219:18 248:4

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 32 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 dissipation73:7 75:1 dissolved41:20 126:26 127:2 235:19 236:21,24

402:26 distinct203:1 250:10,11 distinction 97:3,4 120:7 579:25 distinguish 96:26 229:16 251:26 distinguished 79:24 distinguishing 307:14 distribute 333:4 distributed4:23 152:11 382:17 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 District281:13 282:2 Districts 152:14 153:8 disturbance 200:2 404:1 disturbed432:26 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 document9:26 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 doing9:21 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 dollar275:20 dollars 163:14 168:26 169:2 170:16 173:8 175:21

271:23 275:19 395:24 dominance 89:26 dominant90:2 93:9 472:2 dominate498:2 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 door419:12,13,17 420:3 607:15 dosage461:15 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 33 488:15 dosed446:3,4 447:12 doses 70:7,11,12,12 71:2 197:14 323:22 324:8

355:20 dosimetry484:8 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 downward277:17 downwind 172:24 dozens 183:17 DQA 297:22 Dr2:1,15,19,19,21 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 34 486:23,24 487:1,4,8,11,25 488:1,3,4,22 489:2,5,9 489:20,22,26 490:4 491:14,17,18,23 493:2,4,5,10 493:12,23,25,26 494:6,10,20,22,24,26 495:1,5,15 495:15,16 496:11,13,20 498:21,21,22 499:11 500:3,3,4,20,20,21 501:7,7,8,10 502:25,26 503:1 503:6,9,11,21,22,23 509:18,18,19,20,20,21,22,22 509:23,24,25,25,26 510:2,5,5,6,7,7,8,9,9,10,11 510:12,13,15,16,17 511:3,7,8,9,11 512:16,20,24 513:8,9,10,11 519:25,26,26 520:1,2,24,24,26 521:22 522:21 523:14,14,15 524:17,21 527:3,6,9 528:2,3,7,11,12,12,15,20,20,23,25 529:1,2,4 530:11,11,12,19,20,21 531:11,26 532:2,3,4,10,11 532:13,14,15,22,22,23 536:19 537:5 538:12,12 538:13,26 539:21,21,22 540:14,15,18,25 541:5 541:12 542:1,1,2,4,4,5,6,7,9,10,10,20,25 543:1,5 543:6,8 544:23,23,24,25 545:2 546:1,1,2,9 547:14,14,15,16,16,17,18 548:26 549:1,3,4,14,14 549:16 552:17 553:3,8 555:26,26 556:1,5,20 557:23,23,24,25 558:3,3,4 559:18,18,19 560:4,5 560:6,24,24,24,26 561:4,4,5,15,19 562:2,21,22 562:24,25 563:1,13,13,14 564:12,12,13 565:10 565:13,14,15,25 566:15,15,16,22,22,23 567:26 567:26 568:1,2,2,4 569:2,2,4,6,12,23,23,24 570:10,11,13,14,20,20,22,22,23 571:1 573:8 575:11,12,14,19,19,20 576:11,11,12 577:18,18 577:19,20,20,21 579:19 580:2,4 583:21 584:17 584:18,20,21 585:5,13,14 586:7,7,8,21,21,22 587:7,7,8,12,12,13,19,26,26 588:1,3,3,4 589:9,9 589:10,26,26 590:1,12,12,13,19 592:3 593:22 594:17,17,18 596:13 598:2,2,3,3,22,22,23 601:22 601:23 603:19,21 605:16,22,23,24 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 drawn232:6 239:20 352:22 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 dropped44:4,11 424:19 571:26 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-down413:8 414:5 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 duplicating495:17 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 DVD4:10 dwell 539:3 dying438:20,20 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 35 E E 312:6 432:17 435:21 471:10 474:13 ear416:12 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 early44:8 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 eat5:22 164:26 166:8,11 168:25 169:26 175:24 177:18 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 ecologist7:10,13 455:21 ecologists 382:21 ecology 163:2 215:13 220:25 224:13 226:18 245:13

334:24 383:23 490:25 502:5 economic76:19 112:23 172:10 243:20 271:11

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 EC5049:21,22 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 editor209:12 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 EFF468:22 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 36 238:25 239:16,22,26 240:1,3 241:26 242:4 244:7 244:14,16,24 246:4,15,22 247:4,14 248:6,15 249:4,16,18 250:18,24,26 251:5,18 253:4,5,6 254:13,16 256:17 259:18 296:20 305:23 307:24 309:15 312:1 317:12,26 320:10 322:8 323:3,4,7,8 323:13 327:26 328:3,17 330:21 332:1,14 334:7 340:6,19 341:5,5,19,20 343:18 349:3 351:22 356:15 357:15 358:9,10,11 360:13 362:18 363:21 365:14,26 371:4,4,5 373:26,26 375:6 377:15 378:10,12 387:4 388:2,2 400:1,2,17 401:14 402:3 402:10 403:25 410:12 411:2 414:9 417:7,8 427:20 428:21,22 429:4,4 437:10,11,16,20,24 438:1,1 440:8,8 441:9,16,24 443:4,7,12,18 444:11 446:18 447:19 448:22,25 449:9 453:19 458:3 461:17,20,22,24,24,25 462:5,7,8,9,22,23 463:3,5,5,6,7,22,26 464:4,9,20,22,24 465:20 467:4 469:8 471:15,26,26 481:3 485:8 486:4 488:17,17,19,20 491:26 492:1,1,6,9,20 493:16,16 496:5,10 499:2 500:23,26 513:19 530:24,25 531:3 538:8 571:19 580:10,10,19 581:3 584:4 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-effect79:16 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 effort9:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 37 Eickler 178:13 eight593:7 598:26 601:24 602:2 eight-legged 168:5 einer270:18 eingebracht270:20 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 elective424:26 electricity 591:12 electron 187:6,10 electronic4:19 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 elevated422:16 512:2 elevation407:20 558:11 569:9 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 emergence402:4,8 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 emphasize235:23 299:20 325:10 370:7 442:2

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 encapsulating408:15 enclosed 235:17 encompass46:13 67:15,21 97:1,9 378:18 encompassed431:11 encompasses 73:22 268:21 281:4 encounter45:9 589:18 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 Endocrinology7:4 endogenous 341:8,9 endorse 342:25 endorsed 23:1 endpoint21:17 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 38 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 enhanced408:5 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 entail444:16 531:19,20,20,21,24 552:23 entails 83:2 enter20:8 230:17 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 enzyme207:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 39 episodic 81:19 412:7 500:15 equal 17:22 63:7 104:12 138:10 443:15 515:2

598:5,16,16 equaling429:4 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 equipment281:6 329:1 334:10 equipments 339:26 equivalence 375:2 equivalent73:4 103:26 161:19 269:14 270:2 416:9 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 erode73:8 erosion 279:15 290:10,12,16 295:23 581:23 errata293:7 351:12 366:17 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 estimate40:17 110:22 129:11 145:21 146:12 148:26 149:1 218:9,13 319:26 435:21 437:25 441:7,9,11 442:1 445:2,6,8 467:15,17,20 471:2 518:19 540:3,8,21,24 544:18 546:3,6 553:24 560:17 565:4 567:18 572:5,13,17,18,23 574:1 575:2 580:1 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 et43:5 110:26 175:7 227:13 231:6 232:21 234:18

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 etcetera474:14 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 40 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 event58:16 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's461:9 487:19 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 evident97:15 180:8 190:18 405:24 471:15 evolution 586:20 evolutionary 94:8 evolve 306:8 evolved 550:8 exacerbate 504:23 exact98:18 160:15 214:15,16 247:13,17 257:2

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 examination49:18 378:13 398:4 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 41 555:16 564:22 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 excessive431:7 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 excuse400:22 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 exhaustive400:11 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 expecting493:16 expense 275:23 expensive 278:17 383:14,15 experience70:1 288:13 381:19 402:23 433:10

463:13 483:13 496:14 508:23 554:21 576:8 582:23 experienced443:1 experiment75:14 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 expert460:2 591:11 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 explore73:26 74:12 106:25 389:4 392:11 452:21

604:14 explored 507:22

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 42 exploring 105:5 explosions 167:1 exponent535:20 536:15 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 43 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 faced441:6 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 factored427:23 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 facts48:18 304:20 307:15 440:17 475:9 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 failure41:5 563:3 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 faith441:18 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 familiar421:14 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 44 farmed 164:22 farmer278:11 282:8 288:25 291:22 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 Farruggia20:6 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 farther429:5 fascinating 127:8 fashion 89:22 92:15 217:6 302:18 331:20 347:8

395:20 449:8 450:9 fast201:14 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 father288:10 fault 178:7 205:8 favor 166:12 favored40:19 fear46:18 56:9 246:13 fears432:12 features 100:17 fed496:26 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 fell49:15 565:18 fellow594:24 598:21 fellows408:19 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 fertilizer288:19 fertilizers 288:21 Fetscher7:16,16 110:23 111:11 249:25 250:6

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 Fifteen78:5 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 figures42:16 101:13 377:24 428:19 filamentous 75:19 file 293:9 filed 3:16 files 294:13 320:20 606:6

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 45 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 filter76:20 426:13 520:4 filtered437:3 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 finalize404:7 finalized490:22 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 findings423:12 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 finish73:3 240:14,17 456:19 461:6 466:17 489:25 finished 226:17 firm 299:23 452:22 first2:5,16 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-Tippett258:9 fishes 204:7 fishing 591:15 fishless 164:19 fish/aquatic 381:23 fit77:22 110:10 128:15 146:22,25 147:1 243:10,14

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-fold401:11 433:8 fixation 187:3 fixed75:15 597:21 flag 364:10 433:12 445:12 flagged463:19 flash 294:20 320:19 528:25,26 flat457:22,25 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 flow47:7 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 flowchart574:18 579:5 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 46 fluorescence421:16 fluorometry 256:15 flush414:1 flux498:13 579:16,23,26 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 follows449:7 584:10 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 foothold498:3 Footnote 298:5,7,9 footprint280:16 force 314:2 425:24 forced427:16 forefront 183:23,23 foremost 359:13 forest 165:5 forests 164:3 170:5 forever 601:25 forget 351:1 Forgive450:7 forgot2:26 form4:19 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 format211:6 558:12 596:3 formation 27:17 480:1 formed45:15 59:11 268:15 281:16 352:26 former489:17 forming 28:21 forms 582:1,11 600:8 formulate 12:16 390:2 formulated4:11 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 four46:11 270:11 277:11 334:24 344:19 355:20

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 47 fourth 141:26 270:9 308:15 519:9 535:18 four-day 554:9,12 560:7,12,16,20 571:23 four-stage432:16 436:12 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 free487:24 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 frequent26:10 frequently 89:1 575:16 fresh79:3 81:18 198:12 212:22 freshwater 107:11 410:5 freshwaters434:5 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 fullest522:3 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 fun72:14 301:13 598:21 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 gained499:9 gaining 570:7 gains 87:9 278:6 gallon 171:3 game 133:19 gap 237:12 544:9 gape-limited 164:25 gaps404:15 434:13,15 Gardens 301:3 Gary 272:3 275:2,5 287:11 gather594:24 gathering493:20

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 48

gauge 123:4 ge 177:21 geared 65:21 498:14 gene 54:26 200:13 322:5 386:22 genealogy 375:26 genera432:23,24 437:12 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 generic71:9 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 GfK289:6 290:15 Gibba257:7 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

319:22,23 424:7,8 425:3,15 439:22,23,23 456:7,7 475:5,6 520:2,3 521:5 529:6 530:19 537:5 539:1 539:2 541:12,13 542:10 546:9,10 547:18 549:19 554:18 556:5,20,21 561:19,20 562:3 565:25,26 570:22,25 571:3 573:9 579:13 596:13,14 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 gizzard402:12 glad424:17 524:18 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 49 208:15 209:5 212:3 global 126:4 180:2 GLPs401:6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 50

governs 298:10 grab 143:5 gradation 371:7 grade 37:25 371:11 417:13,20 graded 218:10 273:8 grades417:14 gradient265:8,19 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 graph42:19 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 graphic20:15 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 great7:26 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 greatest22:3 106:13 313:7 409:3 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 grew288:9 grey 562:13 gross 30:16 233:12,15 318:19 ground73:10,12 167:12 175:4 281:5 299:23 408:17 569:10 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 grower268:6 275:25 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-out45:25 482:18 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 51 408:9 426:12,18 511:14 566:26 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 gut318:22 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 habit265:23 habitat21:23 117:9,15 164:1,7 165:12 169:21

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-life45:13 423:2,5 447:12 half-lives 252:17 half-pound 286:1 Hall 182:4 260:26 265:24 hammered496:25 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 handed457:10 handle 112:6 handling 526:12 handouts 292:15 hands 201:15 203:26 hanging421:9 598:24 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 HARAP227:2 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 hardest582:13 603:14 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 hate421:13 Hayes 206:15 373:19 hazard72:2 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 hear20:6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 52 hear.that269:7 heating 94:24,25 heavily75:15 226:16 290:21 402:22 497:4 hectare423:3,3 hedge488:14 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 Herbizide270:16 hesitancy480:24 481:17 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-tiered485:6 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-quality453:18 high-use 173:22 hilA481:15 hinge413:1 hint 185:6 histological44:17 602:26 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 hit333:11 340:1,1 448:7 525:2,4,5 527:17 536:10

558:17 584:26 hoc 340:13 602:21 hold 5:21 72:13 281:21 352:20 367:10

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 53 holding 176:24 589:11 home 177:5 homes 165:21 homework 395:19 honest370:4 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 hoping434:21 438:11 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 horribly426:11 horse426:8 host65:18 276:17 hour45:16 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-half4:1 Howard 178:10 how's 555:3 HPI 199:16 http 1:8 huddle417:11 huge284:4 336:13,14 577:3 606:3,23 hugely411:5 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 hydro421:3 hydrograph 144:25 155:15 519:8 hydrographic 515:25 hydrographs411:6 413:14 519:20 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 hyperbola537:20 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-generating407:10 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 ideal77:9 332:12 397:8 414:2,8 417:5 453:1 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 identity78:17 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 54 ignores 97:23 290:26 306:25 307:13 442:23 ignoring 307:3 355:18 364:7 471:24 II73:13 Illinois 8:10 271:6 404:23 456:6 illustrate47:8 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 ILLUSTRATING283:12 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 imagine428:9 555:11 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 immunologist486:8 immunologists 390:15 392:14 486:12 immunosuppressant508:6 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 impart414:8 impartiality 3:23 395:1 implement21:14 149:14 279:23 604:15 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 importance42:7 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 impose458:9,15 459:6 497:25 imposed 300:4 imposing464:21 impossibility 391:6 impossible 342:17 350:14 impressed429:8,12 589:10 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 55 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 inclusion76:20 282:20 322:18 433:18 462:6 467:7 inclusion/exclusion 241:2 inclusive 192:7 598:10 income 168:16 173:26 incompetent 173:11 incomplete457:16 incongruent443:20,25 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 incorporate74:26 106:12 193:12 215:2,13 224:4

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 incorporating46:2 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 increase47:7 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 indefinite462:20 indefinitely 95:18 171:20 446:18 447:7 indentified48:22 independent3:4 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 index24:4 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 56 index-based 102:12 473:10,13 Indiana 144:2,12 indicate45:14 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 Indicators491:1 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 individual4:8 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 indoor226:20,23 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 infer457:22 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 infinitive430:5 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 influential420:8 471:2 info 141:19 inform 88:13 196:9,22 information4:24,26 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 infrequent40:6 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 inhalation464:18 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 initial44:22 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 57 initiating 58:16 59:4,24 initiation 35:21 560:12 injustice 175:14 Innova204:25 innovation 171:1,8 input32:13 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 insect402:4,8 insecticide 11:11 330:7 insects 234:24 insensitive 70:19,26 120:11 insensitivity 338:6 inserting454:24 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 insomuch497:23 instance 168:21 200:20 207:9 270:8 332:8,11,12

332:16 333:18 511:19 instances 550:23 instant422:24 Institute 6:25 189:21 instream 579:16 instructions 113:20 584:11 instructive 10:6 instrumentation421:15 insufficiencies 27:21 insufficient27:20 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 integrative448:25 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 intending497:9 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 interactive494:1,1 intercept535:23 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 Interim72:23 78:1 interject409:17 585:8 intermediate 531:8 intermittent282:1 intern 29:11 internal 193:6 399:3 internally 50:5 359:19 internation 163:4 international 36:23 178:10 226:13 Internet593:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 58 488:18 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 intrusive4:16 intuitive 174:9 221:21 invalid40:11 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 invalidity46:16 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 irrelevant232:3 263:2 339:11 447:6 449:10

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 iterated429:1 604:9 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 59 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's418:6 584:18 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 John7:20,21 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 jour270:4 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 judgment430:11 459:1 462:19 judgments440:18 Julian 144:20 148:21 151:19 July 303:17 581:9 602:3 jump 22:15 262:2 352:8 536:20 Jumping431:18 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 justify480:11 533:24 K K537:14 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 Kettle403:1 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 kick71:2 kicks 552:20 kids 167:5 176:13 kill 166:3 174:12 508:12

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 60 killer 197:7 killing443:9 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 Kloas43:5 483:12 knew205:2 301:9 341:7 483:19 knife 559:14 Knoth 272:1,9,12 know4:15 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 knowing221:21 knowledge 22:13 57:4 178:2 261:11 267:15 299:3

300:1 307:3 508:25 known20:20 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 Kosinki403:6 Kraak 181:21 188:12 195:13,15,16 203:16 204:6

204:12,19 208:15 209:5 211:17 212:3 Krieger403:12 kriged 202:24 Kriger 162:20,21,22 178:22,22 kriging 138:4 156:20 Kynetec 289:6 290:15 L La7:5,6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 61 387:19,20 391:20,21 410:1,2 415:11,16 428:17 428:18 455:16,16 473:20 475:1 480:6,23 498:21 498:22 509:25,26 587:7,8 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 labeling74:4 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 lacked41:2 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 lake78:22 235:16,17 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 landed406:8 landowners 276:20 281:19,25 283:5 landscape73:23 267:5,7,14 268:1 576:4 landscapes 594:2 land-water591:19,22 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 Larson272:8 284:5,8 larvae 23:9 194:2,3 lasted423:8 447:23 lasting459:2 498:4 lastly 106:7 127:1 413:25 lasts458:3 late 112:12 127:24 234:19 281:12 latest273:13 latter's 100:16 laundry 17:16 Laura272:1,9,12 607:5 laws 3:16 lawyers 176:13 layer254:19 342:16 527:17 layers 59:25 527:16 laying 177:5 lays 9:24 10:11 LC5046:14 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 leap441:17 learn 354:23 508:25 learned478:6 523:17 591:13 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 leaves468:3 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 62 left2:15 6:12 10:14 81:13 126:13 128:3 131:14 135:19 141:3,18 171:2 193:19 194:19 195:2 205:26 210:11 211:20,22 226:11 238:25 246:17 258:22 346:24 388:3 435:14 455:4 456:19 466:20 563:23 585:12 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 Lemna257:6 263:5 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 lentic410:5 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 lie468:5 lies 168:8 374:15 400:1 lieu449:25 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 light73:15,17 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 Likewise460:17 limit 160:10 278:24 279:12 346:24 380:3 407:23

462:13 551:10 limitation47:1 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 Linda4:13 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 63 607:7 line 53:8 100:14 121:17 132:18 226:22 232:6

239:19 420:14 432:7 458:20,23 459:7 460:23 465:11,11 466:8 498:23 522:9 536:1,20,25 537:23,24 538:11 546:22 547:26 577:5 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 list4:3 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 LLP272:8 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 LOAEC48:15,19 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 64 493:15,22 497:9,15 503:16 505:8 521:14 529:13 530:14 546:25 547:13 555:4,12,22 559:9 572:15 575:6 585:25 596:19,22,25 597:6,11,15,21 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 long49:25 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-term426:10 597:4 longest513:18 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/short488:24 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 65 loose 597:16 Loren272:8 284:5 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 lot9:7,8 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 low40:21,22 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 lower28:3,9 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 lowest46:14 48:17 80:24 129:4 148:10 208:24

235:15 273:26 314:1 402:19 lowly 357:26 low-level408:4 443:23 low-quality 242:10,17 lump 250:4,5 lumped 61:5 532:9 lumping 250:14 427:8 580:12 583:10 lunch72:15 125:20 127:25 287:26 294:2,11,23

532:16,25 543:1,3,4 luncheon71:18 294:7 LVsungen270:19 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 main42:16 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 maintained43:19 331:18 maintaining 121:23 581:22 maize 191:11 major21:3 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 majority41:26 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 66 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 manifested408:1 manipulated 57:9 manipulating 330:17 335:24 486:10 manipulations 387:4 mannequin463:17 manner52:4,20 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-monitoring434:6 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 Martha2:21 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 masking476:4 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 maturation7:11 189:2 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 67 516:8,10,11 518:20 533:10,23 535:16 537:14 538:22 546:20 551:20 556:4 560:19 564:1 569:18 569:19 570:7 572:24 577:10 578:13 600:1,4,4 602:9 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 meant47:8 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 measurement43:20 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 measuring41:9 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 media4:14 255:25 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 medium40:13,20,25 41:1 42:12 46:11 47:24 48:5

50:4 60:17 212:23 309:10,13 321:25 513:21 meet41:5 173:4 277:19,24 278:4,12 284:9 296:15

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 Merritt272:6 279:25 MERRITT'S 283:11 mesh 379:26 mesocosm 15:20,23 23:20 25:6 34:3 74:16 181:25

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 68 184:17 185:14 220:12 226:22 251:17 329:16 382:15 397:26 402:12,13 405:9 409:22 410:4 412:11 415:19 419:11,26 422:4 425:1 453:1,8 499:12 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 microbial43:26 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 microphone455:3 microphyte402:3,7 581:8 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 69 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 mimic411:6 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 mindset554:20 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 minor93:13 207:12 430:5 436:19 458:22 460:10

460:23 462:20 508:17 518:13 minority 172:6 174:1 minus 536:15 minute4:7 177:23,24 200:21 225:7,8 228:11,18

342:20 minutes4:8 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 misrepresent530:1 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 misunderstand427:6 491:24 misunderstood426:26 557:25 misused432:11 mis-scoring400:7 mit270:17,18 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 mobile73:6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 70 471:2,19,20 476:2,8,16 492:21,22 493:1 500:7 519:11 522:18 527:7 534:10,16,17 535:18,19 536:2,5,10 537:8,15,16 538:15,18,18 539:10,16 539:19 540:11 541:21,25 542:14 563:15,16,19,20 563:22,25 564:2,9,10 566:26 568:19,22,24 570:1 570:17 572:2,3,17,25,25 573:17,18,22,24 574:12 574:22,23 575:1,17,23 576:13 578:18,26 579:4 579:15 592:13,17 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 modification46:17,22 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 monitor22:5 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 monitoring7:19 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 monotonic70:7 340:23 341:2 Monte 516:3,18 517:17 519:6 533:4,23 534:24

538:1 Montezuma232:22 month 51:6 303:17 515:1 months 51:6 64:20 421:6 502:23 581:8,9 607:10 moral 167:7 morning4:2 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 motivated471:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 71 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 muddled479:3 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-collinear420:15 multi-variate 267:7,11 268:1 multi-varying 574:12 mutual404:14 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 naEve266:19 423:25,26 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's455:8 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 nationwide275:20 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 near76:5 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 necessarily40:2 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 72 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 needing414:4 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 negligible44:14 409:4 413:23 453:18 489:1 neighborhood 304:15 neighboring 278:10,19 neither47:2 534:4 Nelson 527:9 553:3 606:20 nervous 196:15 540:12 net75:17 236:9,13,14,18 network76:2 264:6 268:4,13,15,21 networks 594:4 neurochemistries 285:24 neurological 323:19,22 never70:5 114:13 251:7 358:14 377:8 434:25 437:5 446:4 475:14 534:19 557:26 565:9 575:25 nevertheless 233:6 322:1 339:13 370:1 431:5 new24:1 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 newer285:22,25 newest2:18 newly283:20 296:9 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 niche442:21 502:3 night322:25 470:9 524:21 606:13 NIH 376:21 Nile 166:8 nine46:10 280:23 422:8 ninth 516:17 nitrate 33:18 nitrates 265:10 NOAEC43:15 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 noncovalent255:5 nonlinear71:3 77:22 100:23 146:23 nonmonotonic70:8 208:10 209:4 nonnative 119:19 120:1 nontraditional 188:26 nonvascular75:6 non-adjusted 150:3,4 152:15,17 non-adverse 211:23 non-bias 576:18 non-encountered 504:25 non-flowing405:17 non-linear 537:1 540:11 542:12,13 non-linearity 306:25 578:9 non-modifying483:21 non-monotonic 347:12 355:17,24 356:9 365:2 non-native 22:21 164:17 non-natives 121:12 non-profit288:16

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 73 non-science 287:8 non-significant 331:14 non-target74:2 330:15,25 non-worst519:16,20 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 normalized446:24 normalizing 119:4 121:16 122:3 132:17 135:25

446:17,23 normally 582:15 585:6 north7:6 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 northern7:2 276:18 455:15 Northey 303:5 Northwest545:13 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-effect51:8,9 107:23 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 NTE432:8 nuance 527:19 nuances 527:16 nuclear55:2 191:17 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 numbers42:2 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 nutrient288:21 303:6 309:20 396:26 398:15

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 nuts491:9 O o 130:14 369:15 Oak 226:18 obesity 170:15 object556:26 objective 39:14 48:9 229:10,12 330:24 492:22,26

493:8 576:16,17 579:21 Objectively491:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 74 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 occur22:8 229:25 230:17 233:16 262:1 271:18

291:3 378:1 403:25 496:16 511:2 519:18 564:21 580:18 581:10 occurred44:1 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 October23:5 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 oh71:25 110:12 206:16 372:5 472:8 510:11 547:20

571:1 603:19 605:19 Ohio7:10 142:17 143:21 144:2,12 545:13 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 old76:12 80:5 169:7 188:5 251:2 301:8 406:24

407:12 421:14 593:8 older205:14 oldest 165:7,10 omitted 291:4 omitting44:13 once48:23 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-day443:15,17 446:8 One-fourth 165:8 one-page 525:21 one-sixtieth446:5 one-size-fits-all 551:12 one-year529:26 online 336:20 onset203:22 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 75 294:18 306:2 352:25 353:4,6,24 370:5 395:6,8,17 419:13,14,17 450:2,26 453:5 555:4 opened419:13 420:2 opening9:2 113:10 182:8 558:24 openly 214:11 opens 502:3 operated403:12 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's47:19 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 oral4:9,12 184:4 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 organization28:13 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 organized293:2 513:11 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 ought292:6 529:12 551:10 565:19 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 outdoor74:26 226:20,23 232:23 238:16 outer 103:5 413:9 outlets 288:26 outlier 198:7 outliers 194:23 235:20 283:9 outline 597:19 outlined78:7 119:6 369:12 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 outstanding483:13 ova209:9 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 76 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 overshoot257:8 overshooting 256:20 overt 197:14 350:8 overtake 164:15 overtakes 442:21 overview27:5 72:19 82:14 206:19 213:13 436:13 Over-harvesting 164:7 ovocytes 190:15 Owen289:14 Owen's 289:20 owners 281:16 oxygen41:20 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 panelist529:5 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 paragraph444:21 paragraphs 344:19 parallel464:9,17 481:7 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 paramount328:24 412:19 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 parent45:12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 77 217:6 231:1 232:22 236:22 285:4 286:9 298:13 304:2 306:7,12,15,18,24 307:2,4 312:10 319:8 321:3 323:1 325:13 329:17 337:22 348:17 357:3 358:18 359:10 371:15 377:25 378:1,11 382:18 395:6 419:1 429:16,21,22 430:19 435:18 438:21 438:24 439:5 440:11,20 448:23 452:25 454:16 458:16 460:22 462:4,11,14,25 464:18,19 473:1 476:1,15,16 483:22,23 504:10,13 508:22 520:6 523:8 561:18 565:2 571:24 572:21 577:6 585:9 588:22 591:21 592:5 593:15 594:9 601:1 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 partitioning74:26 partly 13:14,15 64:14 65:7 132:1 587:22 partner276:5 289:11 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 pathologist44:17,19 pathway28:20 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 PATI24:5 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's492:7 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 paying403:24 PCA 267:14 PDF4:11 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 Pease414:11,11 416:17 426:7,7 453:11 488:10,10 peculiar208:26 peer 3:5 18:17 19:1 76:17 163:3 167:13 330:22

347:1 593:3,17 peer-review 391:9

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 78 peer-reviewed 298:11 349:7 pending 349:23 penis 167:26 people4:12 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 perception481:14 565:2 perceptions416:7 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 period42:6 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 periphyton78:18,25 108:15 228:22 233:11,25

238:17 259:14 260:6 265:12,20,25 266:2,16 399:5,7 403:21 404:20 411:8 431:4 permanent2:17,19,20 6:26 8:25 71:22 274:20,25

434:8 494:17 601:24 permanently 187:16 permeable 165:24 permission487:17 permit524:8 permutation 222:24 242:3 permutations 215:1,2 222:22 223:5,17 240:20,23 persistence 73:9 507:12 509:3 persistent73:6 508:26 509:2 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 pest291:13 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 79 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 phased482:26 phases 263:5 483:1 phasing 182:9 PhD 604:24 phenomenon 190:24 588:12 Philomena 581:26 philosophical419:25 phone2:21 8:24,25 71:23 456:13 phonetic 330:3 389:18 phosphorus 303:22,23,26 304:11 photographs 523:21 photosynthate73:18 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 photosynthetic73:13 186:26 187:2,8 212:23

252:24 photosystem73:13 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-relevant445:9 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 pick70:15 161:17 187:10 226:10 243:5 309:26

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 Pilot265:2,24 pinned435:9 pinpoint22:12 252:8 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 placed4:18,26 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 plan20:24,24 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 plant21:6,16,19 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 80 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 play497:25 523:10 549:17 played497:22 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 please4:5 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 plethora419:18 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 point7:5,6 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 pointed42:10 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-point587:3 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 polished488:25 polite487:19 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-type410:6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 81 207:21 286:19 330:6 338:6 360:14 362:22 363:4 386:14 399:19 423:26 500:17 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 post29:21 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 power205:2 325:18 328:14 331:13,15 343:17

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 ppm77:14,15 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 precision435:14,20 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 82 predicated485:6 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 predictive465:23 515:8 551:26 predictor 534:26 predicts 100:2 predominantly 91:2 111:17 144:10 160:10 preexposure75:11 201:9,23 preface 215:17 preferable 539:9 preference46:18 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 prescreened47:16 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 present25:5,20 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 presenter292:14 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 pressure72:15,17 74:24 pressures 111:5 121:10 presumably 121:24 212:22 596:25 presumed 56:11 pretend 344:12 pretreatment232:7,10,13 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 prevalent492:11 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-treated403:10 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 83 233:15 237:21 238:6 259:8 260:8,14 264:14,17 399:26 407:21 423:4 471:22 497:10,19 501:2 525:15 580:14 prime 36:4 242:24 principal 87:21 principle 174:5,9 printing 293:21 prior5:8 38:5 169:11 240:19 293:23 321:18

327:18 329:8 345:14 416:3 443:2,7,12 472:18 587:9 priorities 172:13 173:5 292:5 304:24 priority 302:7,13 304:6,22 360:25 362:3 378:7

381:25 prism 157:2 526:3,24 private442:18 521:6 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

250:26 313:1,22 316:11 317:24 319:26 437:26 440:4,10 441:9 444:10 465:18,19 517:5,6,9,18 529:12,20 530:10 537:5 547:12 572:19 575:5 597:18,22 probability-based 597:14 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 probit438:26 439:1 467:15,16 468:4 495:11 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 proceed46:5 101:5 223:14 proceeding 24:2 424:10 571:9 proceedings4:9 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 produce73:19 79:20 100:21 173:20 174:1 176:8 197:14 295:18 515:1 516:7 534:25 548:2 565:4 produced79:21 99:26 109:16 264:4,9 284:14

310:21 352:24 516:14 559:9 producer259:8 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 production73:18,18 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 84 493:18 494:18 496:16 497:10,19 582:20 products 170:12 183:15,17 184:26 286:10 290:22

507:22 professional487:20 593:15 professor 6:6,15,21,24 7:3,6,22 8:3 178:13 195:16

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 project7:18 276:16 277:16 368:14 455:23 547:19 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 propagated435:13 propagating498:9 595:17 propagation472:5 495:6 595:14 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 Propper7:1,2 203:10 204:4,10 207:6,23 250:16

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 prospect276:13 protect21:15,20 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 protecting73:20 162:24 174:3,22 175:10 177:15

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 protein73:12 186:26 187:5 212:19 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 85 509:4 511:14 519:21 544:3 602:20 provided 5:8,9 30:22 31:4 35:8 40:21 47:12 58:21

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 provides 3:4,9 4:25 15:25 39:24 99:14 180:14 183:1 196:17 217:18 243:9 258:14 264:26 278:25 343:1 386:26 418:1 451:16 491:8 496:9 505:5 506:21 572:12 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 Pruett210:3 PRZM526:22 527:2,7,13 528:17 548:4 553:5 PRZM-Hybrid267:23 543:12 560:13 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 public2:24 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 publication77:25 227:14 232:25 260:10 367:10

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 pulses409:25 purchase 171:4 Purdue 8:2 456:2 pure436:24 478:5 527:13 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 pursued430:22 pursuit330:12 push425:7 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 putting72:17 250:15 303:15 328:5 345:7 392:26

412:11 418:7 428:21 437:6 554:21 587:21 puzzling273:25 p.m 607:16 P50437:25 438:2 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 qualified406:12 qualifiers 190:13 406:23 qualifies 362:12 qualify 230:7 370:3 571:20 572:2 573:12 qualitative40:11,12,17,25,26 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 86 364:8 566:12 quality 5:2 7:18 8:15 14:21 16:16 29:1 32:14 34:24

35:6 38:3,10 40:8,21 41:1,1,15,20,22,23,24 42:7 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 quantitative40:11,15 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 query431:25 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 questioning495:8 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 quiet220:12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 87 quote400:19 514:11 515:5 quoted 299:13 Q10 203:24,25 R R536:26 537:1,10 578:19 radar553:21 radiation 265:11 329:22 radical 93:4 radically 94:7 rain 164:3 167:12 172:18 177:20 606:9 rainbow203:20 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 range4:7 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 ranging79:13 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 reaction73:15 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 reader44:21 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 88 283:26 295:24 296:3,24 299:5 300:3,12,15,16 320:20 338:9 362:3 365:14 384:4 412:7 431:18 434:11 437:12 441:4,18 442:9 445:25 446:2 449:14 491:25 492:3,5 493:1 504:18 510:22 551:6 552:24 591:6 593:9 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 reasons42:16 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 reconnaissance404:22

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 89 reconstructing 216:12 reconvene 127:26 record41:19 128:7 162:18 163:23 164:9 211:12

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 recorded4:9 35:20,20 41:21,22 49:4 recording41:9 records 189:24 484:19 607:2 recover75:24 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 recovery75:4,8,10 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 redesign412:2,11 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 reduction76:1 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 reductionist594:26 reductions 238:21 437:18 redundancy 397:19 redundant466:15 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 refer42:2 403:2 422:11 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 reflect23:22 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 regain75:23 442:22 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 90 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 registrant22:19 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 regression77:22 102:17 104:14 106:16 108:8

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 rehash433:25 rehydrate413:8 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 relatively43:18 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 91 488:12 relevant29:7 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 relying433:3 remain 169:16 remainder72:25 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 render214:19 renders 244:20 248:3 264:20 504:16 renewal 38:2 40:7 41:16 42:6 45:16 reoccurred41:8 reopen449:26 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 replacement75:12,24 423:13 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 report5:5,11 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 reporting40:7 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 represent48:5 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 represented42:22 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 92 572:6 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 reproduction45:26 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 requirement297:16 300:3 330:20 443:21 573:21 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 rerun424:22 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 reserved406:2 reservoirs 78:9 108:19 111:3 149:25 162:8 271:6

515:14 reshuffle469:20 reshuffling469:25 resident 120:7 266:14 residual404:1 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 resolved432:5 509:10,11 555:18 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 respect29:12 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 responding497:5 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 93 342:7 368:7 376:9,13 384:16 386:17 387:9 405:25 407:18,19 408:3 410:10 412:21 487:13 489:23 495:22 506:4,24,26 507:2,14,15 509:23 509:26 511:15,21 542:6 582:11 589:12 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 return75:10 82:14 116:23 169:23 201:8 221:9

230:11 466:19 581:14 returned 204:9 261:17 returning72:24 408:8 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 revert522:7 review2:6 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 reviewing20:1 351:26 352:17 378:25 400:3 411:10

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 ridiculous417:17 right4:6 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 94 141:5 155:20 175:18,25 178:24 182:15 191:9 200:24 205:17 206:11 207:2 208:21 211:22 217:4 235:24 246:17 249:5 251:8 252:23 274:11 277:21 277:23 284:3 285:9 301:22 304:10 311:21,25 312:3,21 319:18 321:12 333:1,4 337:21 346:18 351:8 353:3 355:10 358:7 359:11 361:12 375:25 376:18 396:8,10 405:12 413:12 414:21 415:6 418:6 425:21 428:2 436:15 437:15 438:11,16,17 446:25 447:19 451:8 452:21 454:4 460:5,15 468:13,16,23,25 469:12,21,24 470:8 471:25 474:4 475:4 476:18,18 477:14 479:10,11 481:5 481:13 484:21 485:21 491:15 497:15 499:13 501:12 503:25 511:11 516:16 523:2 529:22 530:4 530:22 531:7 532:2 534:19,25 536:5 541:8,10,22 549:26 552:7,17 557:14 564:25 566:10 568:6 569:25 576:19 578:19 579:5,24 599:8 601:18 604:2 rightly 357:19 369:7 right-hand 10:15 18:4 206:23 256:11 rigor296:18 390:7 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 ring435:22 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 robust46:7 198:25 213:24 257:10 267:18 287:16

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 RODNEY294:26 Rohr 325:3 326:14 role75:19 177:11 185:4 290:19 295:21 307:11

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 room4:5 5:18 6:10 124:2 190:9 349:10 433:11

450:4 455:7 483:11 497:16 585:13 589:1 root241:10 255:25 rooted 221:5 240:24 roots 234:12 Rorh 386:25 387:7 rotator 19:12 rough458:14 roughly 103:12 132:12,19 306:22 515:1 round 159:23 227:14 533:15 rounded 533:12,13 rounds461:2 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 rows46:23 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 runoff33:11 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 95 553:9 593:24,26 594:5 run-off278:24 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 sample47:6 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 samples44:17 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 Sandy2:21 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 sat352:13 598:25 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 scaling437:7 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 96 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 score76:12 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 scored76:11 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 scoring79:18 103:10 181:25 185:13 192:7 202:24

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 scour259:12 265:21 269:19 396:25 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 97

se 122:20 350:3 search 179:13 321:18 searching482:25 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 secondary44:20 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 section72:2 129:26 139:23 140:1,6,7 159:24 181:17,23,25 182:11,14 202:16,19 247:26 248:18 264:3,3,9 329:10,16 436:13 503:15 504:3 505:5 526:19,20 600:24 Sections 338:18 531:10 security 168:12 169:19 sediment7:7 238:16 272:24 sedimentation 282:15 sediments 277:10 396:26 399:3,6 see4:5,15 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 segment557:9,11 segments 556:24 574:9 segregation 191:18,18 Seguin403:17,18,18,19 seldom 189:20 select218:20 221:23 512:13 533:5 548:21 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 selection76:16 110:24 120:20 180:8 181:26 185:13

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 selenastrum433:5 self-correcting 349:8 self-limits 358:16 self-shading 309:22 398:15 selling 175:21 semblance401:6 send 136:12 293:21 490:2 606:7 607:1 sending 136:12 155:8 sends 175:16

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 98 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 sentence475:16 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 separately44:25 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 Sequin231:6 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 set2:6 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 SGR443:15 444:13 492:24 SGRs444:4,5,12 shad402:12 shaded 50:3 51:16 201:4 shades 152:14 153:9 shaking 364:15 shallow413:8 526:25 shallow-draining 527:15 shape 245:23 246:5,10 535:26 582:25

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 99 share489:10,18 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 sheet293:7 366:17 475:9 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 shoes487:21 576:17 shores 168:26 short32:8 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 shorter95:22 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 shortest99:8 457:26 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 showed25:10 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 showing22:1 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 shown20:18 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 shut311:4 496:3 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 sideline487:12 sidetrack 546:18 sifted424:11 sifting426:16,20 453:12 sight497:21 sightings 286:26 sigmaStat542:14 sign453:1 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 silly453:2 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 100 267:13 300:6 404:12 424:1 470:16 473:10 557:5 558:14 584:5 597:24 606:25 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 simplest533:8 simplification435:17 simplified 87:5 601:13 simplifying 253:9 475:26 601:6 simplistic436:25 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 sit200:9 394:3 487:21 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 sitting4:6 338:21 376:8 388:24 394:8 474:21

571:4 595:5,6 598:25 situated 159:23 situation75:26 124:1 129:6 132:7 262:21 317:14

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 size47:6 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 Slaughter297:6,8,9 300:21 sleet 606:10 sleeve 317:25

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 101 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 slow221:5 slower255:14,15 262:17,18 slowest221:11 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 sneak 532:15 snow 172:19 606:10 Snyder294:4,10,25 295:1 297:6 social 112:22 175:14 societal416:7 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 solar265:11 sold 170:12 sole 174:2 535:20 solely407:24 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 solution40:7 41:16 solutions41:11 289:3 484:15 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 sonar285:21 soon 128:4 226:17 256:11,21 262:12 287:26

338:19 489:24,26 607:12 sooner412:9 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 sort7:23 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 sought294:22 sound4:12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 102

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 southern7:17 151:15 455:23 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 sparrow576:4,7 579:10,14,18 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 sparser267:19 519:16 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 spawning209:21 322:22 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/genera437:4 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 specifically45:10,12 194:7 204:15 208:11,16

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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 103 spent226:19 395:23 434:14 spikes 129:6 spill 282:23 spin 596:16 spine 323:8 spite 271:9 388:4 split223:11 229:2 234:13,16 240:8 250:5 399:24

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 spot410:15 439:13 spotted 166:9 spotty 251:2 spray 169:4 sprayed 172:15 spread 3:25 165:13 spreads 165:17 spreadsheet293:4,20 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 squares49:23 51:8 52:20 53:4 squeeze253:19 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 staff2:25 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 stagnant405:17 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 standing72:14 131:22 463:17,20 standpoint486:13 487:12 559:15 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 104 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 stay446:26 571:22 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 stent468:7 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 stimulatory403:20 stinking 351:7 stint226:21 stir200:9 stirrer 200:21 stochastic 138:4 156:22,23 560:18 stock41:11 stocked401:20 stocking401:26 Stoddard490:24,26 stomach418:23 532:16 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 105 517:26 518:3 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 student301:2 students 301:10 studied45:13 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 106 496:7,19,23 497:8,24 498:14 500:24,24,25 501:24,25 504:1,16 506:12,13,18 507:18 508:3 509:12,17 511:15 512:12,13 513:24 562:12 585:23 586:6 592:14 595:5,6 596:23 597:2,3,5 598:4,6,8,12 603:1 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 studying407:14 study's 348:11 stuff232:25 319:13 362:9 388:4 502:17 512:10

539:3 546:22 565:12 568:26 579:12 595:23 604:3 stumble70:24 style 69:26 sub 321:3 subcellular 55:6 subchronic46:20 47:3 49:18 51:4 64:19 368:24 subdivided40:13 subequal 65:5 Subgroup 227:16 subject269:19 270:12,14 343:11 580:12 subjected42:11 111:5 507:6 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 submission 181:11 182:19 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 subsample44:20 subsamples 216:6,14 subsequent 180:15 201:19 398:19 436:17 467:6

469:17 523:10 subsequently 22:26 subset400:6 445:1 substances 508:26 substantial 90:17 97:24 180:19 307:22 substantially44:21 152:2 373:1 563:8 substantiate 36:18 331:22 substantiated 100:18 substitute435:24 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 sub-lethal 271:2 507:14,18 sub-sampled 141:23 147:17 sub-studies423:17 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 sufficient22:22 30:6 111:3 196:2 328:13 331:13

331:20 339:1 344:22 360:15 365:4 367:14 371:23

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 107 374:24 375:10 380:14 386:2 388:26 390:2,4,7 391:10 392:9 393:7 400:22 403:10 405:9 416:5 452:17 572:4 574:1 sufficiently 55:9 57:4 60:12 507:5 sugar73:18 150:18 151:8 153:6 164:5 169:12 183:9,13 289:10,22 295:13 sugarcane 22:6 suggest 136:6 155:11 177:3 188:8 192:2 266:13

368:21,24 369:18 387:12 482:16,17,18,21 483:12 580:23 586:2,2 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 summarize42:16 93:22 260:12 322:7 summarized 22:18 228:13 428:19 summarizes 182:26 257:11 summarizing453:10 summary 5:6 36:19 39:9 42:26 47:15 72:21 172:1 182:24 183:4 264:5,8 267:4 283:16 284:9 322:9 450:14 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 supplement575:16 supplemental 123:25 205:20 supplies 102:23 523:22 supply 523:18 support32:11 38:3,19 99:15 136:20 150:9 178:8 178:12 185:8 239:17 267:3 281:11 291:23 296:7 334:15 345:12 358:8 385:25 387:15,23 392:18 393:3 412:10 434:11 441:22 480:1 506:21 539:19 556:5 568:24 582:10 596:21 supported 27:17 176:9 185:12 296:12,24 495:25

506:3 507:24 526:9 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 70:24 125:18 132:4 158:11 162:15 168:9 208:4 225:2 251:1 263:10 285:9 287:3 302:3 304:23 315:1 320:25 325:15 346:11,19 355:11 361:3 363:13 365:14 366:16 385:10,16 389:1,12 392:13 395:25 406:12 415:21 416:4 419:8 437:5 445:15,23,26 453:2 454:18 466:23 469:10 470:1 474:8 477:9,10,15 486:23 487:25 491:23 501:24 502:11,15 503:25 510:18 518:14,20,24 520:12 526:4 530:1 532:7,9 540:4 541:8,10 549:26 550:3 551:8 556:4 557:24 569:25 574:17 577:15,16 579:8 595:16,22 603:18 603:20,24 605:13 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 survey7:14 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 suspect203:25 259:24 424:24 429:26 433:18 561:9 suspended 291:6 396:25 sustainable 288:12 swamps 164:3 swayed 172:4 swaying 110:7 sweet289:22

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 108 swimming 56:8 199:26 swing70:21 190:8 Swiss 169:1 271:25 switch 116:15 126:16 182:16,17 213:6 237:7

270:21 278:5 317:17,18 switching275:19 sword 118:18,21 sympathetic460:21 symptoms 85:11,16 SYNDER294:26 synergies 505:1 synergistic 504:20,22 synergistically 334:14 Syngenta 87:19 88:11 103:3 141:4 149:21 155:15

168:9,11 169:1 177:4 178:24 179:3,4,9,23 181:7 186:8 213:9 226:14 264:4,12 273:22 276:14 281:22 337:15,20 367:19,23 369:17,24 370:7 374:16 400:12 404:14,22 412:22 422:7,10 426:15 429:13 430:13 453:9 464:25 467:10 474:21 480:16 499:5 513:22 527:11 543:12,22 553:20 557:19 585:16 586:10 590:14 592:6 593:20 598:20,24 599:8 606:25 Syngenta's 173:17 322:24 370:8 474:18 526:3

583:15 599:4 synthesize 32:7 synthesized78:10 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 table4:6 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 tailored409:18 take 11:7 13:20 14:15 15:12 16:11,18 18:3 19:11

71:14,15,20 112:17 113:24 131:3,20 133:6 135:10 149:1,4 155:18 157:13 177:11,13,24 178:19,23 181:4 185:25 202:19 205:21 213:26 214:1 216:5,8,14,21 224:22 226:8 228:1 239:13 243:22 248:19 250:15 252:23 253:13 257:26 258:17 262:20 279:17 296:9 300:11 304:19 312:16 316:3,24,25 317:2,9,14,15 333:5 335:2 349:10 350:4 362:21 366:2,8 388:5 395:26 409:19 418:7 419:3,19 425:8 442:22 445:1 458:4 458:11 463:9 470:19 494:7 497:2 518:15,19 519:6 522:15 531:4 543:10 544:21 545:7,24 550:2 554:15 563:21 565:10 567:7,7 573:11 582:1 584:22 585:2 586:23 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 109 318:10 319:18 327:1 338:19 348:26 349:8 351:9 356:1 357:4,6 358:19 365:10 374:22 375:20 384:8 394:10 406:14,16 407:14 421:2,3 422:14 422:19 435:10 459:18 464:7 465:23 498:10 501:11 502:6 510:25 524:22,23 547:26 548:9 550:7 568:7 569:7,8 577:14 580:12 583:5 588:14 600:12 602:15 604:15,17 talks 11:4 17:11 116:11 tall497:14 tangent571:21 tangible 604:22 tank 30:5 44:11 171:5 591:11 tanks43:21,22,24,25 44:1,2,4,5,9,14 200:9,9

270:20 tank-mix 289:10 Tao 323:10 tap 167:12 174:17,24 175:1 177:18 222:13,15 tapped490:15 target 174:13 197:12 262:13 273:15 282:17 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 taught329:20 tax 170:15 171:25 taxa59:14 60:3 65:25 71:7,12 78:19 84:10 89:16

231:23 353:21 433:1,3 499:25 504:6 583:24 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 Tech 6:21 technical 20:5 37:24 71:21 98:6 136:20 183:2

264:2 282:3 314:25 technique 199:9 537:2 techniques 71:5 143:9 156:20 169:14 185:25 202:3

209:24 238:15 319:3,6 332:10 418:19 421:16,17 483:10,15,20 579:16 technology 169:8,15 171:1,8 176:4 267:10 544:11 Telephone 1:9 tell 161:22 275:17 278:8 300:15 336:19 346:16

356:21 393:20,21 420:16 503:26 537:12 545:9 561:21 568:12,16 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 teratogenic46:15 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 110 terrible 301:20 536:1,2 terrific408:15 test23:2 27:26 28:22,25 31:9 32:17,22 33:21 34:19

34:20,25 35:1,4,18,21 36:6 37:5,9,21 39:17,21 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 testable 576:1 tested 16:16,19 23:10 29:25 30:20 47:5 48:20,21

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 testes 209:9 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 77:15,15 179:16 201:22 202:9 211:15 281:5 288:20 324:17 334:3 342:15 358:2 360:10 365:5,11,22,23 372:8 375:2 375:8 381:4 385:1 388:16,21 451:16 452:3,3,5,7 471:12 480:1,2,20 482:7,15,15 486:3,9 504:15 506:18 522:18,19 523:13 testosterone 54:21,23 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 thank2:15,18,23,26 5:14,25 9:1,6 18:16,22,24

20:1 26:24 60:20,21 71:14 72:5 109:20 136:19 154:26 177:15 178:18,21,22 179:1,4 180:25,26 181:5,7,10 182:18 186:5 195:15 202:15,21 213:10 226:12 261:4 275:1 279:21 285:9,11 287:20 292:8,12 297:5 300:19,21 304:25,26 305:2,11 312:22 314:3,4 321:12 324:9 325:7,22 325:23 327:16 342:9 367:4 368:10 385:5 389:25 391:21 393:24 406:6 445:13 447:16 448:8 455:3 455:5 470:6 472:7 476:13 488:3 489:1 498:22 510:1,6 512:16,22 513:10 520:24 528:3 531:26 532:10,14 538:26 547:17 567:25 569:2 580:7 585:14 587:7,21 588:5 589:13 590:13 598:20,23 603:4,7,11,21,22,25 606:2,15,17,25 607:4,15 thanks 5:26 8:22 9:5 19:6,22 72:10,16,16 178:20

209:7 213:5 240:12 262:10 285:10 294:1 301:20 305:8 314:12 336:2 343:25 344:17 359:6 366:21 377:9 408:13 410:1 414:21 456:18 457:11 473:20 475:4 480:23 488:4 494:6 503:11 511:7 519:25 528:2 562:25 570:14 575:11 584:21 585:15 589:25 590:1 596:15 598:24 602:1,5 605:14,14 that'd 362:15 411:5 538:20 theme 12:25 577:22 theoretical459:1 theoretically487:13 548:19 theory 59:22 332:3 548:10 theses 235:3 Thesis403:3 thick 176:26 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 111 70:20 113:23 114:10 119:9 120:1 125:7 127:16 127:24 128:9 129:1,21 130:20 131:17,20 132:1 132:25 133:9,20,26 134:3 135:20,23,24 155:14 156:3,12 157:3,12 158:7,24 159:4,6 160:3,14 177:5 189:16 195:7 198:12 202:12,24 204:13 207:10 208:7 211:17 213:2,6 239:4,14 240:13 249:1,13,21 252:14 253:18 261:22 262:6,8 268:25 285:6 292:18 293:22 294:17 299:17,23 302:2 304:24 305:4,24,26 308:4 313:5 315:4,4,6 317:12 318:3,3,22 319:25,26 321:14 323:1 325:16 326:11 332:5,17,20 335:2 336:11 337:18 337:25 339:26 340:1,13,20 341:15 343:19 345:14 345:18 346:3,4,8,24 350:7 351:2,10 355:9,13 357:13 358:5,7 359:12,15,18,20 360:4,19,20,24 362:10,19 363:9,19,24,26 364:9,13,15,16 365:19 366:4 367:5 368:20 369:8 370:4 371:6,15,22 372:1 373:10,11,18 374:2,14,18 376:3,10,10,16 379:26 380:9,19,20 381:2,18 382:18,21 383:17 383:18,22,26 384:8 385:15,18,23 387:21 388:7,7 388:8,16 389:5,23 390:21 391:1,18 392:12,19 393:6,8 394:1 400:13 403:2 407:4,7 408:20,22 409:9,10,16,19 410:19 411:5,9,25 412:5,12 414:18 415:20,23 416:6,18,23,26 417:10,11,15 417:16 418:19,25 419:1,5,5,7,10,16,19 420:15,17 420:23 421:10,26 422:5,9,15,19 423:2,22 424:5 424:26 425:10,26 426:1,4 427:4 428:5 430:7,22 431:3,8,11 432:2,6,16,25 433:1,22,22,24,25 434:1 435:19,24,25 436:1,5,14,14,20,23 437:4 438:10,11,16 439:25 440:6 441:4 442:16,19 444:7 445:17 449:12,26 451:13 456:13 457:9 460:2,4,26 461:14 464:9,14 465:5,22 466:6,13,16 467:18 468:18,18,21 470:11,22,24 471:7,11 473:18,21,21 474:7 475:2,7,7,8 476:8,15 477:5,7 478:18,19 479:2 481:17,21,24 483:8 484:14 485:13 486:15 488:18 490:2,6 491:6,14,15 493:23 495:19 496:3,4,8 497:9,16 498:17,26 499:8,22 500:9,12,13,18,19,21 501:1,4,5,22 502:16,18,24 503:9 510:17,24,26 515:26 517:10 517:11,13,26 518:2,11 519:21 520:5,8,20,22,22 521:5 523:9,24 524:12,17,19,23 525:26 527:10 529:9,19 530:7,15 532:15 533:25 534:9,21 535:10 536:17 537:26 538:8,11,21,23 539:2,4,7 539:18,19,23 540:25 541:9,10,14,18,22 542:13 543:6,10,15,22,25 544:19,25 545:2,13,16 546:3 546:10,14,16,26 547:3,6,10 548:11,12,13,20 549:3,17 550:4,6,18,21,21 551:6,10,18 552:3,4 554:4,9,17 555:9,18,21 556:2,3,5,8,12,19,21 557:3,13,16,17,25 558:20 559:1 560:8,10,15 561:12,17 562:15,24 564:14,25 565:2,26 566:1,4 566:18,20 567:3,4,15,21,21 569:6,12,16,17 570:9 570:25 571:8 573:7,10 574:15,17 575:9,15,22,26 576:16 577:2 579:6,9 580:10 583:10 584:25 585:17,19,22,24 586:19 588:8 590:18,21 591:8 591:21 592:5,7,11,14,16,23 593:19 594:10,26 595:1,2,9,11,13,19,20,22,26 596:1,8 597:19,24 599:7,10,20,22 600:4,11,19,21 601:11 602:8,11 602:17,20,23,25 603:24 604:5,16 605:1,2,6,8 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-order444:6 thirty-one78:4 THOMAS 7:5 62:19 63:4 202:21 325:7 327:16

348:13 352:11 354:15,20 355:4,8 359:6 367:4 372:3,6 377:18 380:25 382:25 384:13,16,20 387:20 391:21 393:15 410:2 415:11,16 428:18 450:24 451:26 452:16 455:16 473:20 475:1 477:23 480:6,23 485:2 487:4 498:22 509:26 587:8 thorough 321:19 thoroughly 393:5 474:10 495:23 507:22 thought63:9 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 three46:20 66:1 80:8 165:10 228:16 265:6 275:10

275:20 276:14 293:24 294:13 297:10 298:17 325:26 334:24 383:25 394:23,24,24 398:2 417:19 417:25 421:6 471:1,5 500:23 511:15,17,23 516:21 518:11 533:4 534:12 535:15 537:7 587:9 600:24

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 112 three-day416:9 583:3 three-dimensional 582:5,11,15 threshold74:2 208:20 258:5 377:14 380:12 451:15 threw238:3 thrive 296:26 thrives 270:6 throw208:6 417:21 421:9 537:6 throwing560:21 thrown 351:6 THURMAN527:9 553:3 thylakoid 186:25 thymus 197:6 thyroid 28:6 187:24 ticks 166:8 tie 161:12 467:25 tied467:12 tier27:25 46:2,7 184:23 227:4 330:22 385:1 tiered 27:25 28:2,3,9,24 30:26 88:15 384:21,24

451:23 452:1 482:11,23,24,25 485:7,8 490:13,19 544:16 tiers 28:4 431:23 ties444:25 460:26 tight 391:5 tightly 535:6 till403:15 434:21 594:8,9 tillage 271:12,13 278:1,3,5,14 280:16 290:8,11,12

291:12 295:22 303:11,21,24 304:1,3 594:8,16 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 75:16 81:23 83:3,6,9,13,24,25 84:3,5,16 86:10,22 86:24 88:1 89:3 92:3,10 93:15 94:11 95:19 96:19 97:8 99:11,13,15 104:17,19 105:21,22 106:1 113:1,2,24 114:15 115:9,10,12,22 116:8,9 117:5 117:7 119:4 124:14,22,24 127:15,20 128:1 130:23 132:10 133:14 134:10,21 137:4,5,5,26 139:13 156:6,24 159:25 162:1 164:1 166:3 171:5 178:19 181:8 184:1,10,15 186:13 189:1 196:7 201:4,10,17 204:2,7,9 208:8,14,16,18,21 212:23 213:10 217:2,19 219:17 220:25 221:4,4 223:26 224:14,23 225:24 226:23,25 227:5,15 231:18 233:19 235:18 245:17,22 246:15 250:24 252:9,14 252:24 253:6 254:5 255:22 260:22 262:18 265:7 281:8 305:3 306:16 309:18 312:26 314:10 315:24 316:3 329:12 330:17 339:18 348:18 349:6,8,11 349:12 352:6 354:2,2 365:3 367:8 373:14 375:19 378:18 382:23 389:20 395:23,26 398:4 404:7,9 405:14,21 406:11,15 409:4 413:21 416:5 419:15 419:22 424:4 427:18 431:9,10 435:13 447:19 449:25 457:21 458:3,3,4,5,17,26 459:2,3,15,18 459:20 464:13 465:10 468:11,15,20 477:19 483:19 486:7 487:22 488:24 489:6,13,21 490:23 499:16 502:23 511:18,26 514:9 516:4,4 544:19 548:2 553:5 556:22,23 558:26 562:3 563:19 571:6 578:17 579:24 585:6,11,19 587:24 588:9 590:4,17 591:16 592:10 594:19 599:6 604:2,19 606:10 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-varying458:12 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 told71:21 307:21 532:15 tolerable498:7 tolerance 123:5 329:22 408:5 508:13 tolerant 120:18 122:26 125:14 259:16 433:1 498:2

588:18 tolerate75:13 toll 160:16 Tom7:6 325:7 367:3 384:17 385:6 428:17 450:19

451:22 452:13 455:16 480:3 606:21 tomorrow 162:13,18 263:12 385:16 414:23,24

415:4,22 421:25 434:22 436:12 453:10 454:2

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 113 tongs 394:20 tonicity 209:4 tonight 136:6 177:5 tool 23:18 26:2 84:15 88:17 154:21,23 183:14

213:20 280:6 286:2 289:4 291:12 304:5 317:22 317:23 374:18 526:10 578:19 595:21 tools 7:19 20:10,24 280:11 303:26 594:13 595:21

596:1,12 top 10:10 20:18 81:21 164:3 177:20 193:19 200:9

203:3 211:21,22 228:6,8 246:17,19 247:1 253:17 272:19 290:9 302:7,12 304:6,21 330:21 333:11 394:9 topic 16:4 24:6 181:22 182:5 185:5,7,11,21 206:13

213:6 293:2,19 321:8 395:10 450:8 512:17 526:20 541:14 topics 20:3 22:17 26:15 179:21,24 181:19 182:23

405:13 total 26:20 44:3 77:26 78:3 79:4 81:2 92:8 95:11 100:24 103:14 123:14 291:6 304:11,12 402:12 405:5 446:20 449:21 560:3 564:5 581:10 totality 121:2,12 122:12 124:11 125:11 130:7 135:13 198:10 199:13 493:13 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 99:12,13 103:14 119:26 120:2 186:16 306:25 312:11 372:23,24 446:9,14 447:1,3 448:25 449:10,20 505:7,12,21 506:8 toxicant 343:11 toxicities 128:19 toxicity 21:4 24:4,22 25:4 26:16 27:4,7 30:22

31:18 32:18 36:10 47:13 52:17 74:11,12 75:2 82:20 84:4,7,17,19 86:1 88:1 89:14,22 90:2,9 91:6 92:6,16,19,23 93:9,23 94:3 95:10 98:21 99:23 100:7,11,12,22 109:8 111:12,15,18 117:2 119:23,26 120:6,11,21,21 122:16 124:11 129:7 129:15,24,26 131:2,3,6,10 138:25 194:9 197:15 211:7 215:23 258:1,26 259:25 260:17 264:25 282:14 306:20,21 307:3,5 308:16,18,19,20 309:4 309:7,19 312:13 316:25 324:16 350:8,9 352:23 367:25 368:23 428:25 431:7 432:18 449:10 458:16 494:14 496:21 504:6,8,15,23 505:19,25 559:10 toxicodynamic70:18 toxicodynamics 484:7 toxicokinetically 70:18 toxicokinetics 484:7 toxicological 89:19 94:22 100:24 186:17 190:6

284:10 327:19 toxicologist72:1 213:9 433:9 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 TP265:18 track 205:17,22 458:17 579:11 tracking 566:7 tracks 69:11 trade 164:11 288:16 tradeoff563:18 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 transcriber4:11,13 TRANSCRIPT 264:3,4,9 284:13 transcription4:10,10 transect 176:10 transfer73:14 187:1 transferred 80:9 transfers 187:7 transformed 306:22 429:3 537:8 transient76:9 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 translated470:1 516:19 translates 105:20 170:22 320:3 translation 198:15 465:5 transmit593:12 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 114 traveling 606:8 treadmill 200:16,17 treat457:15 461:24 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 triazine264:6 268:4,13,15,17 tribal402:23 490:18 tributary 281:6 557:8,10 trick 564:25 tricky490:9 tried46:9 232:26 287:15 337:2 363:20 442:11

503:23 542:13 trigger277:16 515:8 triggering 105:13 triggers 281:2 trivial 290:6 412:8 trophic74:20 265:23 397:10,13 497:12 498:9

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 Tuesday456:26 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 tushies487:22 Twenty 399:16 twice 86:4 120:17 124:8,13 253:8 Twiss7:25,25 115:2 261:14 405:11,12 410:24

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 twofold435:7 two-day 571:23

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 115 two-fold475:25 two-quarter559:20 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 typed490:1 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 Tyrone206:14 373:19 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 unacceptable40:7 178:3 581:18 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 unbelievably411:17 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 understands454:1 473:16 understatement434:24 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 116 underwent25:8 undo 278:6 undue 172:10 unduly405:23 471:20 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 uniform437:5 572:12 601:13 uniformly 69:8 unimportant91:25 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 unit94:20 344:23 345:3,6 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 unperturbed409:11 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 unsatisfactory458:2 unsound41:4 296:1 unsupported 296:1 unsurpassed 290:23 untested 506:17 unusual471:11 unvalidated 300:7 unwanted 172:22 unwittingly 168:17 upcoming73:25 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 uptake79:8 423:22 urban 164:2 urge459:22 560:22 urges 296:4 usable49:14 usage 175:15 273:26 274:7,24 295:23 USDA76:17 174:24 USDA-NRCS456:4 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 117 430:14 431:24 434:14 437:26 438:6,25 439:6 441:11,15 445:1 446:11 447:18 448:23 449:21,21 450:8 451:10 454:14 457:17 458:21 459:7 465:24 467:6,14,25 468:12,12,24 469:17 470:18 479:7 486:10 490:17 499:1 502:14 508:19 509:2 512:8 513:2,4,6 514:26 515:6 517:25 521:3,12,23 526:22 534:2 537:16,22,26 539:24 541:21,24 544:2 545:6,8,18 547:4 548:2 553:24 554:8,15 558:2,12 560:2,22 564:2,3,14 566:11,12,13 571:20 575:23 577:10,14 579:2,3,4 590:17 597:15 602:12 604:6 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 usual496:14 usually75:1 164:22 166:5 203:4 261:18 343:20

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 utilizing280:7 412:1 548:13 utmost279:6 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 vague488:24 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 value49:10 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 variable79:12 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 varied47:9 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 118 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 vascular75:6 108:16 252:17 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 velocity402:25 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 vetted43:13 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 violate463:23,24,24 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 visualize242:1,26 volcanic 167:1 volume 38:12,13,14 397:6,6,7 398:20 400:24

551:25 volumes 38:14 Volumina270:19 voluntary 302:18 volunteer 173:18 volunteers 176:13 vortex 200:11,21,26 201:5,13 vu418:17 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 wait253:8 320:15 434:21 489:7 walk26:22 141:15 walking72:25 want2:15,18 5:19 8:23 10:22 16:10 18:16,22,24 19:10 20:1 22:15 62:14 65:22 66:22 67:6 91:18 112:3,17 124:5,9 127:9 131:2 133:2 136:19 146:11 166:10 175:22 180:25 181:7 196:8 211:12 231:1 235:10 239:2,14 240:13 245:2 249:11 253:2,20 271:20 285:5 302:2,16,17 304:23 313:24 314:7,23 315:3,14 318:6,10 321:14 332:17,24 335:7 336:7,24 346:12,23 351:8 352:8 355:11 357:9 361:1 363:5 364:4,6,13,24 365:8,18 365:25 370:3 371:10 377:11 379:14 380:21 383:8 383:16 384:4,20 385:9 389:26 392:7 395:25 401:9 411:15 414:23,23 419:14 426:13 428:10 432:10 439:17 443:16 445:26 446:17 448:23 450:5 458:1 461:12 462:19,24 467:25 469:7 471:4 473:17 477:8,9,10,14 481:11 487:9,17,24 493:6 494:11,24 496:12 498:15,19 501:9 503:12 510:18 512:25 517:8 520:4,5 522:22,23,26 523:1 523:19 524:5 526:22 530:1,12 532:5,25 536:6 538:16,24 539:3 540:4 542:20,21 549:23 550:20 550:21 551:2,6 552:13,18 556:3 557:24 559:5

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 119 560:22,25 562:6,10 563:18 565:13 567:6,19 570:15 574:14 577:21 579:18 580:5 586:8,22 587:13 589:3 592:15 595:1 598:3,20,23 599:25 600:3 603:25 605:12 wanted 13:23 64:1,6 116:16 119:25 131:4 147:13 182:9 184:7 193:12 195:11 210:25 293:14 297:10 324:13 325:10 346:19 355:9 383:20 408:17 409:17 414:15 416:17 432:12 436:18 457:5 466:22 473:15 475:6 488:11,23 497:17 510:24 530:20 542:9 546:3 551:19 553:3 561:5 567:22 569:5 590:16 601:26 602:4 603:21 606:15 wanting498:11 wants 178:18 384:10 389:16 421:25 477:6 499:15

536:19 warm236:5,12 warms 165:6 warning 166:24 565:4 WARP548:4 warped579:22 warrant22:22 55:9 140:10 235:22 387:11 warranted46:6 391:2 warrants 26:11 306:12 Washington 178:5 288:7,8 wasn't355:18 421:9 426:3 431:25 440:6 450:15

470:1 496:23 532:9 548:8 Wasser270:17 wasted 346:7,7 watching240:2 water7:17,18 8:15 12:20,21,22 13:12,24 14:8,21 15:8 16:16 22:5 28:26 34:24 38:3,10,12,13,14 40:8 41:1,1,15,20,22,23,24 42:6,7,25 45:17 56:9 73:10,12 78:13,23 79:3 81:18 104:3 108:21 112:17 117:25 136:24 142:11,16 149:24,24 150:9 159:11 165:26 167:12,12,12 173:3,19 174:16,18 174:24,26 175:1,4,26 177:18 179:12 180:18 198:12 200:17 235:16 238:16 260:4,24 265:10 271:14,16 272:24,26 276:4,6 280:18 281:10,26 283:7 295:23 302:7 303:8,22 304:7 308:12 328:16 331:17 333:22 334:23,23 339:5 342:22 398:11 402:24 405:16 410:7 432:8 455:23 469:2 490:18 512:1 513:5 515:10,16,19 523:18,22 526:8,9 566:25 574:26 waterborne 596:4 waterfall 8:4 551:24 waters 174:22 175:7,10 591:1 watershed 12:13 14:24,26 15:9 18:8,10 24:7 25:17

25:22 26:11 81:21,24,25 103:16 137:20 139:5 140:10,11,19 141:1,3 142:20,25 143:20,22 144:4 145:24 146:3 151:10 152:25 153:22 154:3,8 155:16,24,26 156:2,8 157:26 158:4,4,24,24 159:12 160:2,24 179:13 180:2 224:1 278:9,10,11 278:22,23 279:9 281:14,15,17,20,25 282:16 283:3 311:11 434:9 468:8,22 469:3 515:16 521:12 522:8,14 524:1 527:23 531:22,24 534:4 534:14,18 538:23 541:7 543:17 546:4 549:7 551:13,17,26 552:22 553:10,10,26 554:1,6 555:8 556:11 557:3,6 558:10 567:5,9,11,24 570:1,4 574:13 576:23,26 589:6 592:4 593:21 595:16 596:1 watersheds 15:8,13 22:12 74:7 109:4 114:21,26 137:19 139:4 142:22,22 144:1,11 152:22 153:4 153:16 154:1,15,19 156:7 158:25 159:14 162:7 182:6 185:23 267:5,9,12,17,25 273:14,20 276:14 276:15,19,22,24 277:4,8,25 278:18,20 279:2 282:18 283:5,10,22 429:5 468:23,26 469:12,14 511:25 512:15 513:15 521:6,14 522:8 523:26 524:4 527:24 543:16 544:1,6,10,12 545:14 546:5 552:6 553:16 555:7 557:15 558:7 560:12 568:6 569:20 590:6,10 594:1 605:5 waterways 121:8 276:9 water's 527:12 water-soluble410:10 way 15:9 17:3 58:17 64:5 67:17,22 70:20,21 81:8

84:5 96:25 102:8 115:23 118:6 125:10 133:12 134:5,13,17 159:2 167:9 182:21 185:1 190:9 192:3 193:3 206:12 207:22 211:1,15 214:16 222:16 242:1,26 249:5 255:8 260:19 268:25 288:6 298:3 301:16 318:14 337:20 340:24 344:21 351:5 352:1 361:25 363:19 364:13 367:5 374:18 387:22 395:8 410:14 411:10 416:14 420:23 436:24 438:12,21 446:7,10 448:20 451:14 460:14 461:24 464:22 466:9 481:11 482:9 484:5,11 492:7,16,21 495:7 497:5 499:1,17 502:20,20 510:19 522:5,16,18 523:25 527:11,20,21 528:8 529:14 531:2 533:14 536:5 541:24 546:15,26 551:12 553:6 556:10,26 562:18 564:1 565:23,23 568:15,23 569:26 573:7 574:19 585:13 589:5 591:20 593:11 596:8 600:21 ways 61:6 87:26 125:7 157:8 446:11 462:1 499:10

501:21 568:8,9 592:21 605:2 606:6 wDrend270:19 weak 198:3,20 202:6,8 337:22,25 367:22 371:4

388:2 weaken 166:4 weaker 371:17 weaknesses 348:24 386:6 404:4 wealth479:24 web 1:8 113:19 599:23 Weber 177:25 website273:12 381:23

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 120 websites 341:21 Wednesday4:1 606:13 weed 183:8 189:20 205:17 278:16 295:15 weeds 183:10 289:15,21,23 290:8 week5:1 8:26 9:7 19:8 76:5 200:10 291:9,9 398:9

455:2 543:13 567:4 569:6 607:12 weekly 155:12 514:20 558:22 weeks 64:20 79:12 155:9 285:7 411:7 week's 9:20 Weibull 61:3 62:4,16 258:8 weigh 122:4 384:10 389:16 weighed 355:5 weight30:15 38:18 39:2,4,7 53:19 63:8 67:24 115:16 119:21 173:13 180:8 185:8 186:9 188:1,4 188:9,14 189:18,19 190:2,18 192:6,23 195:7 197:6,25 198:22 204:20 205:11,15 207:6,12 209:14 210:24,26 211:13 233:14 234:2 268:22 329:13,16 335:8 337:14 349:25 358:6,7,18 365:10,20 367:18,21 371:25 374:14,17,22 380:13 387:25 388:14,20 391:11 392:26 430:3 437:7 480:15 weighted 92:8 121:14 150:14 151:5 226:3 357:26

579:24 weighting 122:6 218:11 weightings 120:13 weights 39:6 93:8 weight's 598:5 weight-of-the-evidence 269:3 welcome 2:1 6:1 66:26 128:2 well-characterized 520:8 well-conducted 342:5 well-controlled 339:25 well-designed406:9 well-developed 578:17 well-done481:13 well-established 91:23 352:22 598:13 well-funded 172:5 well-trained 593:15 went 126:11 127:4 156:19 158:1,2 186:9 204:21

226:18 228:8,9 230:21 253:5 337:23 351:6 355:15 361:16 365:5 377:2 394:8 406:24 425:18 431:22 432:1 435:7 440:9 465:9,18 469:6 482:13 482:25 484:4 500:7 567:10 586:9 591:12 598:19 werden270:18 weren't336:19 372:20 435:9 west 164:23,24 166:8 301:26 Western490:25 wet39:7 we'll 69:16 285:8 294:4 306:8 324:9 363:9,14

366:4 385:18,19 415:4 417:23 428:9 449:26 455:7 472:12,26 485:22 489:11 512:24 519:3 523:9 542:25 584:23 585:12,12 595:5 602:3 we're 67:1 114:2 127:20 192:24 287:25 294:2,2,3

299:23 301:5 302:19,21 303:6,17,19 304:5,21 305:6 307:20 311:8 315:2 317:9 318:6,11 319:17 327:7 338:19 340:26 351:9 352:19 356:1 358:19 360:10,11,12 361:17 363:25,25 365:7,10 371:15 371:24 376:14 380:2,19 385:2,13,14 388:3,8 393:21 394:1 396:9,11 406:14,16,18 407:14 409:10 410:26 412:21 415:5,13 418:19,22,23 419:21 421:6 426:16 437:6,6,7,22,25 438:2,4,6 438:12 449:26 451:9 454:4 458:9 459:18 461:1,5 461:6,6,26 462:3,5,10,14,24 463:2,5 464:7,15,20 466:1,18 467:24 468:23 469:17 470:9,17 472:18 472:22 475:2 476:4,18,18,19,21,25 477:1,7 481:19 486:12 489:26 491:16,22 493:15 494:6 497:14 499:9 501:17 502:7 510:25,26 511:1 513:11 517:22 521:25 522:1,2,18 524:23 525:15 526:4,20,21 532:17 536:25 542:17 543:5,25 548:14 549:3 552:18 559:7 567:17,18 569:7,8 570:13 583:6 585:25 586:13 587:23 588:16 594:13 600:5,6,12 604:2,6,6 605:9,12 we've 114:4 285:8 302:14 312:26 313:1 317:8

359:19 360:13 365:16 367:9 369:8,21 375:21 379:17 381:16 389:2 390:12 410:21 412:25 413:13 419:3,15 421:15,15 426:15 433:23 434:14 437:24 442:24 446:10 454:26 456:14 457:9 467:3 467:4,22,26 468:2 476:20 479:20 481:7 496:8 499:4 502:6 517:7 521:23 525:24 555:15,18 561:2 568:7 573:7,23 580:3 596:2 599:1 601:12 604:15,17,23 whatever's 571:18 whatsoever246:5 wheat288:9,14 289:2 wheel 176:22 whichever 30:15 whispered416:12 whistles418:8 white 19:18 20:2,13 32:9 33:1 39:18 58:2 71:8 78:7 106:17 107:10 126:2,7 129:26 136:25 180:5 184:6,12 185:11,15 195:21 216:2 268:5,11 270:8 272:4 273:18 274:18 282:22,26 283:14 285:1,15 285:19 286:21 289:18 292:23 293:8,13 321:6 322:17,20 325:5 328:1 338:4,18 348:13 350:3 352:19 368:21 376:24 379:1 395:21 401:12 405:21 422:5 431:10 434:3 436:14 451:2 465:8 468:15,18 475:14 481:5 495:3 498:12 501:15 503:15 504:4 513:17,25 515:20,24 524:10 526:18 533:20 534:9 535:17 537:13,14 541:14 547:6 548:6 550:19 589:15 590:5 591:26 592:8

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 121 wide53:12,23 67:14 143:22 147:9 176:9 321:22

419:13 434:9 475:10 508:14 572:12 widely 189:19 212:14 278:4 295:14 382:17 528:5,5 wider516:25 573:20 widespread73:11 179:17 width 538:19 wild 162:26 164:10,13 391:12 wilderness 164:6 wildlife 165:1 166:15 167:24,24 168:18 169:18 171:11 177:21,25 178:6 185:2 273:25 wildly70:19,21,25 WILLIAM 8:5 157:24 158:23 249:17 312:25

313:13,20 314:3 456:4 460:1,24 461:3 510:17 523:15 527:6 528:3,11,15,23 542:2 544:24 558:4 561:15 566:23 569:4 575:20 590:13 605:22 willing276:16 283:19 308:13 313:22 462:14 willingness 276:11,25 wind543:19 window67:22 96:19 106:1 141:22,23 220:23 221:7

221:10 307:20 308:1 459:21 464:1,23 553:19,21 560:17 windows 141:20 windspeed265:10 winking 19:13 winning 176:18 wiped 123:8 wipes442:20 wise208:21 wish 5:14 299:20 300:6 341:19 362:7 436:11 withstanding478:5 wittingly 168:17 wobble 133:18 134:1 WOC 236:5 women 324:5 wonder347:1 433:15 460:3 510:19 530:16 547:21

558:7 559:4 592:18 wondered 65:6 468:2 489:6 wonderful4:13 5:17 328:25 329:9 512:12 593:11 wondering 110:9 118:20 127:11 205:25 208:12

209:13 212:1 523:19 wood508:8 607:5 word 182:19 361:19 375:25 376:18 419:25 worded482:10 wording486:25 487:3 words 10:3 12:23 104:13 155:7 193:22 230:9

231:25 252:13 270:6 316:9 326:25 407:9 440:15 461:18 519:19 523:1 540:1 550:10 work 3:12 7:18,22 9:7,8 18:23 19:7,9 26:3 87:18 127:17 136:20 139:6 140:11 156:6,8,9 160:18 163:15 177:10 179:5,7,12,15 180:15,20,26 181:11,13 182:2 191:2 198:11 199:14,18,22,22 199:22 203:20 204:13,26 205:3 206:14,17,17,20 251:9,9 270:12 273:3 276:26 289:3 293:25 302:3 304:24 306:7 316:1 317:26 321:15 324:26 359:1 366:6 373:19 411:10 418:5 429:9,11,26 469:10 479:13 508:5 553:13 557:20 567:1 576:4 589:12 590:3,15 603:12,15 607:6 worked 276:6 314:8 394:5 435:6 445:23 470:8

477:5 529:7 572:22 workers 175:13 working 8:8 12:19,21 69:13,14 171:17 174:1 175:12 177:4,4 226:19 227:10 303:6,17,19 360:17 381:18 394:11 415:13 475:2 481:19 490:11 491:5 517:24 526:20 566:10 569:19 595:12 600:19 603:14 604:2 works 180:7 281:17 448:21 468:16 499:17 525:22

567:13 577:25 workshop416:9,18 418:7 workshops 227:1,2 work-in-progress 474:3 526:18 592:1 world 133:8 134:6,10 165:14,18 167:8,14,19,20,23 169:16 176:20,22,26 191:1 277:22 283:26 290:23 295:20,25 296:3 299:5 300:4,13,16 301:18,19 338:10 341:26 384:5 412:7 441:4,18 442:9 492:3 492:5 493:1 504:18 588:23 591:6 601:15 worldwide 163:18 164:23 world's 163:5 164:14,16 165:10 280:10 worms 555:5 worried 279:2 worry544:15 555:17 worse 83:21 120:5,18 124:14 304:10 317:23

340:26 worsened 89:25 worst515:6,12,22 517:13,14 worth 568:21 571:8 worthwhile 156:4 177:9 204:14 218:17 545:16 wouldn't535:7 537:4 561:2 would've533:22 537:26 538:2 552:1 wow545:24 596:9 WQ 278:13 wrap 18:16 136:5 409:26 604:1 write272:17 436:14 569:1 write-up421:23,26 422:2 writing 126:9 293:5 489:7 written4:10 32:11 76:25 136:11 176:19 210:20

264:6 270:10,22 292:16 298:4,5 299:14 336:23 406:10 477:2 479:10,14,16 484:19 568:23 592:4 605:18,20 wrong 129:15 274:15 285:6,6 359:11 361:7,8,13

406:25 459:23 468:6 561:21

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 122

wrote450:20 451:11 wurden270:18 www.epa.gov/scipoly/sap 1:8 www.regulations.gov4:21 454:24 457:8 X x49:24,25 51:11,12 63:14 102:17 207:4,4 210:10

217:1,5 239:1 242:4 243:3 246:16 247:14 380:17 380:17 xenopus 23:9 29:21 31:5,6,8,11 38:18 39:5 43:6

45:3 54:23 56:21,26 57:7 69:16 70:19,21 191:3 335:9 360:14 368:8 369:6 370:1 371:14 372:7,13 372:16,24 373:11 374:7 381:11 382:26 482:8,15 483:2,23 Xopenex 373:20 Y y42:18 49:23 51:10 61:1 194:19 195:2 198:19

200:19 206:10 242:5 243:3 246:15 251:12 380:17 380:17 Yao 503:2 YARD 1:14 Yates491:10 yeah 287:24 305:12 310:16 311:18 319:20 320:7

320:17,18 332:23,26 333:3 364:16 371:22 385:6 385:18 415:2,17 416:15,16 417:9 426:24 430:16 430:25 438:15 441:2 447:20 449:23 459:16 463:12 464:6 466:17 475:20,21 477:9 487:10 488:22 489:23 511:12 528:23 560:6,26 565:15 569:24 575:14 579:13 580:7 year20:8 51:7 84:4 103:9 144:20 150:6,8 151:19 159:23 164:9,10,13 165:18 169:3,7 172:18 174:12 175:17 176:10 182:3 226:21 236:6 256:8 273:24 274:1 281:3,8,11 282:10 283:21,21 295:18 302:6,8,23 304:16 419:20 422:21 429:19 430:2 440:16,21,24 463:4 474:23 515:21 517:25 543:20 549:8,10 563:8 566:2 571:17 572:6 602:2 yearly 281:21 years 10:1 12:10 21:9 70:1 79:12 84:15,23 104:7 142:19 143:21,25 144:6 150:5,6,10,12,12,14,15 150:19,19,22,23,24 151:1,1,4,6,13,13,24 152:3 152:14,16,19,21 153:9,12,14,23 158:15,17 163:2 165:9 166:25 167:19 169:13 179:6 183:8,22 226:19 227:10 234:18,25 235:5,7 238:14 268:21 268:24 271:19 273:8 274:3,26 275:11 277:9,14 281:8 285:7,18,22 286:7 287:7 290:21 295:4,16 300:7,11 301:8 308:25 316:1,1 375:18 376:13 407:12,15 411:11 421:18 422:15 434:14,14 440:16 462:21 517:20 518:6,6 519:17 540:20 556:9 568:21 572:7,9,14 573:13,22 586:25 591:13 593:8 601:16,24 604:18 year's 520:21 539:8 yellow50:3 139:22 152:24 Yellowstone 165:7,9 yesterday 270:23 271:10 275:15 294:16 297:17

300:8 305:14,25 307:12,21 308:6 309:1,7 312:26 322:25 337:15,23 341:23 367:17 368:1 369:17,24 370:14 374:17 378:26 379:18 387:25 427:4,8 429:12 456:19 457:3,19,23 459:13 460:3 464:25 466:21 472:9 476:19 477:6 478:13 480:4,13 481:9 482:10,20 484:3,14,17 485:19 486:16,17 491:18 493:7 499:11 528:16 583:1,11 yield 30:6 149:3 295:20 515:7 574:1 yields 221:14 290:1 York456:1 young 8:17,17 43:8 50:26 62:13,14 67:20 109:22 109:23 110:9,15 204:16 250:23 251:4 263:8 311:25 312:4,21 314:6,7,12 326:1,2 343:24,25 346:11,12 350:18,19 362:6,8 363:12 364:17,21 370:20,21 374:21,22 379:8,9 383:3,4 388:12,13 392:1,2,21,22 412:14,15 434:20,21 446:22 456:9 456:9 470:7,8 472:8,9 475:23,26 476:14 495:5 500:20,21 510:7,8 519:26 520:1 524:21 530:11 530:12 538:12,13 547:14,15 557:23,24 560:25,26 563:1,13,14 565:10,14,15 577:18,19 598:2,3 Z z 380:17,17 zebrafish 199:8,15,20,26 200:8,23 201:12,15,17

202:1,11,11 203:11,18,23 204:5 205:1 206:8 323:6 zero94:25 98:14 101:16 102:1 165:14 230:22

231:9 232:16,17,19 233:18,25,26 235:10 261:17 316:10 318:8 377:7 388:18 402:5 405:10 417:21 481:3 536:16 564:20 zeroing 66:1 zeros 228:7,16 468:1,2 zone 319:1 531:15 zoom 15:4,4,4 569:17 zooming 18:7 569:8,8 576:6 zooplankton78:18 zoos 165:19 zueinander270:20 Zur270:16 $ $1 304:16 $2 290:2 $28 289:26 $30 275:24 $37,000 295:7 $53 289:7

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 123 # #12709400:12 #51 126:22 #52 126:14 #58 126:14 270:13 #59 126:22,26 #60 126:22 127:1 #74 129:21 #86 231:7 0 0 193:10 201:4 217:8 223:3 405:2 0C403:14 0s 239:7,8 251:5 254:14 0.01 29:23 37:17 43:7,21 67:16 0.04423:3 536:16 0.05 36:8 435:8,23 0.1 29:23 45:21 77:15 79:13 235:16 236:13,22

258:23 0.2 29:24 68:13 0.20 538:17 0.25 538:16 0.28 137:10 0.30 538:16 0.46 537:11 561:7 0.5 38:25 80:21,24 249:18 398:17 0.71 535:21 0.8 38:25 01 81:21 02 81:25 07 81:24 1 1 27:25 28:24 29:23 30:7 32:17,21 33:10 35:19

37:11 38:17,17,22,25,26 39:10,11,12 41:9 43:21 44:9,10,17 46:7,24 47:24 51:24 53:14 60:13 78:21 79:19,23 82:15 83:21 86:2 89:2 90:14,16 90:18,22,22 94:2,18,25 98:21 104:13 107:25 114:4 120:5 124:1 177:23 204:20 217:7 222:23 223:2 225:13 228:8 230:19 231:8,13,15 233:9,19 234:4,10 235:16,20 236:22 237:2 238:22 244:11 244:14,25 254:17 266:20 267:16 279:6 285:14 298:5 321:4 326:7 397:5,7 398:19 422:25 429:3,4 440:15 446:21 450:10 477:16,17,19,21 478:15,18 478:26 479:3,20 503:15 533:20 566:20,21 1s 80:4 228:15 239:7,9 251:5 1st 144:18 1% 169:6,8,25 1,600 30:10 1-day440:22 1.5 99:8 512:9 1.87 533:12 1.9 102:3 1/3 170:24 1/3rd 167:3 1:30 127:25,25 294:6 104:7 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 10,00079:14 80:21 115:16 141:23,24 142:1 253:17

397:7 460:6 516:6,7,8 10-day461:16 10-minute463:23 464:1 584:22 585:2 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 11a532:17 11b 543:5 11:0071:17 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 128405:5

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 124 13 150:17 436:13 562:26 570:11 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 14.90465:16 140 102:20 103:18,20 1424 151:1 154:8 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-minute71:15 15-square 555:8 15-year 11:13 15078:21 386:23 606:24 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-day468:12 560:3 1869 101:21 19 93:1 266:23 501:15 190 147:16 158:8 194 266:20 195423:2 195-day423:5 1956 169:10 1957 295:7 1970s 234:19 418:18 1979401:1 1980s 234:20 1982402:9 1984401:1 403:7 1985403:12 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 2-3rd404:19 2.5 54:24 2.75 194:10 2.8 175:2 2.94 533:13 2/3rds 183:12 2:25 585:3 204:8 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-day461:16,19 462:6,12 463:6 465:14 571:26 20-ppb461:19 20-year530:2 554:26

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 125 20078:21 123:18 163:7,8,21 422:21 487:18 200% 222:8 200,000 283:23 200-and-something435:8 2000 163:18 227:15 229:18 238:1 330:3 599:2 2001 330:4 2001a403:18,19 2001b403:18 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's487:5 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 236433:8 238 175:4 2447:22 266:23 24% 222:8 24-hour423:22 463:24 532:6 240 387:2 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 250C403:15 258 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-A431:8 3-day 2:5 3.14 148:23 149:4,12 3.2 101:26 3.36 150:26 3.5405:2 3:00 607:16 3:15 178:24

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 126 30 36:3 46:25 47:23 70:1 84:15 96:22 105:17 124:4 124:6,13 132:2 135:1,2 180:10 185:19 208:13 215:5 218:15,18 219:20 221:18,23,24 222:6 223:26 224:2 225:8,10 227:25 238:22 239:10,11 240:11 247:19,19,20 248:9 258:13 268:16 303:25 307:10 335:12,15 375:18 400:8 403:20 405:16 410:18 446:21,23 458:1 463:24 465:16,17 499:3 508:7 513:23 516:21 591:13 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 365422:20 423:8 365-day423:9 445:22 37 208:7 258:12 378 150:12 3878:2,5 148:22,24 149:1 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-A436:20 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-day463:7 400 378:11 386:22 41 192:24 41046:14 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 4841:16 45:15 256:9 266:24 295:8 309:8,9 5 54:7 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-point79:17 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

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 127 50/50 313:26 525:23 500 122:26 540:5 500-meter 554:26 52 559:1 537 242:16 54 116:11 55 583:18 550 266:18 5679:22 322:17 405:3,5 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 6:15450:2 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 7245:16 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-hour463:24 8.1 104:25 8.2 600:24 8.30465:16,17 80 101:16 174:12 383:25 80s418:18 82% 148:4 158:15,16 85% 281:5 86:49-58 266:21 868 151:13 8779:4 81:3 102:7 222:1,23 242:7 243:4 89 583:24

### FIFRA SCIENTIFIC ADVISORY PANEL DOCKET #EPA-HQ-OPP-2012-0230

www.transcriptionetc.com Page 128 9 9 29:8,10 34:13 36:17 47:23 167:19 263:12 275:22

377:24 503:12 9ppb 271:8 9:00454:5 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-day468:9 469:23 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