# Attachment I:

Supplemental Information on

Standard Procedures for Threatened and Endangered Species Risk Assessments on San Francisco Bay Species

ENVIRONMENTAL FATE AN D EFFECTS DIVISION

OFFICE OF PESTICIDE PROGRAMS

U.S. ENVIRONMENTAL PROTECTION AGENCY



## March 2, 2010Table of Contents

1.0 ......................................................................................................................... 5 Introduction

2.0 .......................................................................................................... 5 Problem Formulation

2.1 ............................................................................................................................ 5 Purpose

2.2 ............................................................................................. 7 Designated Critical Habitat

2.3 ............................................ 7 Assessment Endpoints and Measures of Ecological Effect

2.3.1 ............................................................................................ 8 Assessment Endpoints

2.4 ..................................................................... 8 Measures to Evaluate the Risk Hypothesis

2.4.1 ............................................................................................. 8 Measures of Exposure

2.4.1.1 .................................................................................................... 9 Aquatic Exposure

2.4.1.2 ................................................................................................ 9 Terrestrial Exposure

2.4.2 ................................................................................................ 11 Measures of Effect

2.4.3 ..................................................................... 12 Integration of Exposure and Effects

3.0 ..................................................................................................................... 13 Uncertainties

3.1 ............................................................................. 13 Exposure Assessment Uncertainties

3.1.1 ........................................................................................ 13 Maximum Use Scenario

3.1.2 ............................................................ 13 Impact of Vegetative Setbacks on Runoff

3.1.3 ........................................................ 13 Aquatic Exposure Modeling of the Pesticide

3.1.3.1 ..................................................................................................... 13 PRZM/EXAMS

3.1.4 ............................................................................................. 15 Spatial Uncertainties

3.1.4.1 ................................................................ 15 Potential for Effects in the Action Area

3.1.4.2 ............... 15 Impact of Run-off Assumptions on the Potential Area of LAA Effects

3.1.5 .............................................................................................. 16 Usage Uncertainties

3.1.5.1 ................................................................................................... 16 CDPR PUR Data

3.1.5.2 .............................................................................................. 17 Organic Agriculture

3.1.6 ............................................................................. 17 Terrestrial Exposure Modeling

3.1.6.1 ................................................................................................................... 17 T-REX

3.1.6.2 .............................................................................................................. 18 T-HERPS

3.1.6.3 ............................................................................................................... 20 TerrPlant

3.1.6.4 ................................................................................................................ 21 KABAM

3.1.7 ............................................................................................ 22 Spray Drift Modeling

3.2 ................................................................................. 23 Effects Assessment Uncertainties

3.2.1 .................................................. 23 Age Class and Sensitivity of Effects Thresholds

3.2.2 .................................. 23 Impact of Multiple Stressors on the Effects Determination

3.2.3 ............................................................................ 24 Exposure to Pesticide Mixtures

3.2.4

24 Uncertainty in the Potential Effect to Riparian Vegetati on vs. Water Quality Impacts

4.0 ......................................................................................................................... 25 References

2List of Commonly Used Abbreviations and Nomenclature

BCF Bioconcentration Factor

CDPR California Department of Pesticide Regulation

CDPR PUR California Department of Pesticide Regulation Pesticide Use Reporting Database

CTS California Tiger Salamander

DS Delta Smelt

EC 50 50% (or Median) Effect Concentration

ECOTOX EPA managed databa se of Ecotoxicology data

EEC Estimated Environmental Concentration

EFED Environmental Fa te and Effects Division

ESA Endangered Species Assessment

e.g

. Latin exempli gratia (“for example”)

et al. Latin et alii (“and others”)

etc. Latin et cetera (“and the rest” or “and so forth”)

EXAMS Exposure Analysis Modeling System

FIFRA Federal Insecticide Fungicide and Rodenticide Act

ft Feet

i.e

. Latin for id est (“that is”)

KABAM K OW (based) A quatic B io A ccumulation M odel

km Kilometer(s)

Kd Solid-water Distribution Coefficient

K OC Organic-carbon Partition Coefficient

K OW Octanol–water Partition Coefficient

LAA Likely to Adversely Affect

lb a.i./A Pound(s) of ac tive ingredient per acre

LC 50 50% (or Median) Lethal Concentration

LD 50 50% (or Median) Lethal Dose

LOC Level of Concern

MA May Affect

NASS National Agricultural Statistics Service

NLAA Not Likely to Adversely Affect

NLCD National Land Cover Dataset

NMFS National Marine Fisheries Service

NOAA National Oceanic and Atmospheric Administration

3NOAEC No Observable Adverse Effect Concentration

NOAEL No Observable Adverse Effect Level

NOEC No Observable Effect Concentration

OPP Office of Pesticide Programs

OPPTS Office of Prevention, Pes ticides and Toxic Substances

PCE Primary Constituent Element

PRZM Pesticide Root Zone Model

RQ Risk Quotient

T-HERPS Terrestrial Herpetofau nal Exposure Residue Program

Simulation

T-REX Terrestrial Residue Exposure Model

USDA United States Department of Agriculture

USEPA United States Environmental Protection Agency

USFWS United States Fish and Wildlife Service

USGS United States Geological Survey

41.0 Introduction

The purpose of this Attachment is to provide supplemental information on procedures and methodologies routinely used as part of the lit igation-related threatened and endangered species risk assessments (ESAs) for the San Francisco Bay species. This Attachment is intended to reduce the size of the ESA by reducing repetiti ve information and providing all standard methodologies and language in one document to be used in association with the chemical- and species-specific ESAs. The information included in this Attachment is intended to provide standard procedures and language relative to three subsections of the ESA, including the problem formulation (Section 2), aquatic and terrestrial modeling approaches (Section 3), and generic uncertainties (Section 4). 1 This document is not comprehens ive and does not fully describe the

Environmental Fate and Effects Division’s (EFED’s) assessment methods. For a comprehensive discussion of the procedures used to evaluate potential risks to enda ngered species, see the Overview Document (USEPA, 2004).

2.0 Problem Formulation

Section 2 describes elements of the problem form ulation that are common to all ESAs conducted for the San Francisco bay species and includes general descriptions of purpose, designated critical habitat (if applicable for the speci es being assessed), assessment and measurement endpoints, on overview of comm on exposure models, and measur es to evaluate the risk assessment in Sections 2.1 through 2.4, respectively.

2.1 Purpose

In all assessments, direct and indirect effects to the specified species a nd potential modification to designated critical habitat are evaluated in accordance with the methods described in the Agency’s Overview Document (USEPA, 2004) and the U.S. Fish and Wildlife Service (USFWS) and National Marine Fisheries Service (NMFS) Endangered Species Consultation Handbook (USFWS/NMFS, 1998). The effects determinations for each listed species assessed is based on a weight-of-evidence method that relies heavily on an evaluation of risks to each taxon relevant to

assess both direct and indirect effects to the listed species and the potential for modification of their designated critical habitat ( i.e ., a taxon-level approach). Sc reening level methods include use of standard models such as the Pesticide Root Zone Model coupled with the Exposure Analysis Model System (PRZM-EXAM), Terre strial Residue Exposure Model (T-REX), TerrPlant, AgDRIFT, and AGDISP, all of which are described at length in the Overview

Document. Additional refinements are made on a case by case basis as needed. Use of such information is consistent with the methodology described in the Overvi ew Document (USEPA, 2004), which specifies that “the assessment pro cess may, on a case-by-case basis, incorporate additional methods, models, and lines of evidence that EPA finds technically appropriate for risk management objectives” (Section V, page 31 of USEPA 2004).

1 The Section numbers used in this document do not corres pond to the Section numbers used in the chemical and species specific risk assessments.

5In accordance with the Overview Document, provisions of the ESA, and the Services’ Endangered Species Consultation Handbook, the assessment of effects associated with registrations of a specified pe sticide is based on an action ar ea. The action area is the area directly or indirectly affect ed by the federal action, as indi cated by the exceedence of the Agency’s Levels of Concern (LOCs). It is ackno wledged that the action area for a national-level FIFRA regulatory decision associated with a use of a sp ecific pesticide may potentially involve numerous areas throughout the United States and its Territories. Howeve r, attention will be

focused on relevant sections of the action area including those geographic areas associated with locations of the species and their designated critical habitat within the state of California. As part of the “effects determination,” one of the following three conclusions will be reached separately for each of the assessed species in the lawsuits regarding the potential use of a specific pesticide in accordance with current labels:

• “No effect”;

• “May affect, but not likely to adversely affect”; or

• “May affect and likely to adversely affect”.

Some species have designated crit ical habitats associated with them. Designated critical habitat identifies specific areas that have the physical and biological features, (known as primary constituent elements or PCEs) essential to the conservation of the listed species. PCEs for each species are described in the main ESA.

If the results of initial screening-level assessment methods show no direct or indirect effects (no LOC exceedances) upon individuals or upon the PCEs of the species’ designate d critical habitat,

a “no effect” determination is made for use of a specific pesticide as it relates to each species and its designated critical habitat. If, however, potential direct or indi rect effects to individuals of each species are anticipated or effects may impact the PCEs of the designated critical habitat, a preliminary “may affect” determination is made for the FIFRA regulatory action.

If a determination is made that use of a specifi c pesticide “may affect” a listed species or its designated critical habitat, addi tional information is considered to refine the potential for exposure and for effects to each species and other taxonomic groups upon which these species depend ( e.g, prey items). Additional information, incl uding spatial analysis (to determine the geographical proximity of the asse ssed species’ habitat and a pest icide’s use sites) and further evaluation of the potential impact of a specific pe sticide on the PCEs is also used to determine whether modification of designated critical habitat may occur. Based on the refined information, the Agency uses the best available information to distinguish those actions that “may affect, but are not likely to adversely affect” from those actions that “may aff ect and are likely to adversely affect” the assessed listed species and/or result in “no effect” or potential modification to the PCEs of its designated critical habitat. This information is presented as part of the Risk

Characterization of the ESA.

The Agency believes that the analysis of direct and indirect effects to listed species provides the basis for an analysis of potential effects on the designated critical habita t. Because use of a specific pesticide is expected to directly impact living organisms within the action area, critical habitat analysis is limited in a practical sense to those PCEs of critical ha bitat that are biological

6or that can be reasonably linked to biologically mediated processes ( i.e ., the biological resource requirements for the listed species associated wi th the critical habitat or important physical aspects of the habitat that may be reasonabl y influenced through biological processes). Activities that may modify critical habitat are thos e that alter the PCEs and appreciably diminish

the value of the habitat. Evaluation of actions re lated to use of a specific pesticide that may alter the PCEs of the assessed species’ critical habitat form the basis of the critical habitat impact analysis. Actions that may affect the assessed species’ designated critic al habitat have been identified by the Services a nd are discussed further below.

2.2 Designated Critical Habitat

Critical habitat are designated for the Calif ornia Alameda Whipsnake, Bay Checkerspot Butterfly, Valley Elderberry Longhorn Beetle, Ca lifornia Tiger Salamander – Central California Distinct Population Segment (DPS), California Tiger Salamander – Santa Barbara County DPS, Delta Smelt, and Tidewater Goby. The California Clapper Rail, California Tiger Salamander Sonoma County DPS, California Freshwater Shrimp, Salt March Harvest Mouse, San Francisco Garter Snake, and the San Joaquin Kit Fox do not have designated critical habitat.

‘Critical habitat’ is defined in the ESA as the ge ographic area occupied by the species at the time of the listing where the physical and biological features necessary for the conservation of the species exist, and there is a need for special ma nagement to protect the listed species. It may also include areas outside the occupied area at th e time of listing if such areas are ‘essential to the conservation of the species.’ Critical habi tat receives protection unde r Section 7 of the ESA through prohibition against destruction or advers e modification with regard to actions carried out, funded, or authorized by a federal Agenc y. Section 7 requires consultation on federal actions that are likely to result in the destruc tion or adverse modificatio n of critical habitat.

To be included in a critical habitat designation, the habitat must be ‘esse ntial to the conservation of the species.’ Critical habitat designati ons identify, to the exte nt known using the best scientific and commercial data available, habitat areas that provide essen tial life cycle needs of the species or areas that contain certain primary constituent elements (PCEs) (as defined in 50 CFR 414.12(b)). PCEs include, but are not limited to, space for individual and population growth and for normal behavior; food, water, ai r, light, minerals, or other nutritional or physiological requirements; cover or shelter; sites for breeding, reproduction, rearing (or development) of offspring; and habitats that are protected from disturban ce or are representative of the historic geographical and ecological distributions of a sp ecies. Information specific to each species is provided in each ESA.

2.3 Assessment Endpoints and Measures of Ecological Effect

Assessment endpoints are defined as “explicit expressions of the actual envir onmental value that is to be protected.” Selection of the a ssessment endpoints is based on valued entities ( e.g., San Francisco (SF) Bay species being assessed, organisms important in th e life cycle of the assessed species, and the PCEs of its desi gnated critical habita t), the ecosystems potentially at risk ( e.g., waterbodies, riparian vegetation, and upland and dispersal habitats ), the migration pathways of

7the pesticide ( e.g., runoff, spray drift, etc .), and the routes by whic h ecological receptors are exposed to the pesticide ( e.g., direct contact, etc .).

2.3.1 Assessment Endpoints

Assessment endpoints for the SF Bay species incl ude direct toxic eff ects on the survival, reproduction, and growth of individua ls, as well as indirect effects, such as reduction of the prey

base or modification of its habitat. In additi on, potential modification of critical habitat is assessed by evaluating potential effects to PCEs, which are components of the habitat areas that provide essential life cycl e needs of the assessed species. Each assessment endpoint requires one or more “measures of ecological effect,” defined as changes in the attri butes of an assessment endpoint or changes in a surrogate entity or attri bute in response to exposure to a pesticide. Specific measures of ecological effect are ge nerally evaluated based on acute and chronic toxicity information from registrant-submitted guideline tests that are performed on a limited number of organisms. Additional ecological effects data from the open literature are also

considered. It should be noted th at assessment endpoints are limited to direct and indirect effects associated with survival, growth, and fecundit y, and do not include the full suite of sublethal

effects used to define the acti on area. According to the Over view Document (USEPA, 2004), the Agency relies on acute and chronic effects endpoi nts that are either direct measures of impairment of survival, growth, or fecundity or endpoints for which there is a scientifically robust, peer reviewed relationshi p that can quantify the impact of the measured effect endpoint on the assessment endpoints of surv ival, growth, and fecundity.

As described in the Agency’s Overview Docu ment (USEPA, 2004), the mo st sensitive endpoint

for each taxon is used for risk estimation. Ac ute (short-term) and chr onic (long-term) toxicity information is characterized based on registra nt-submitted studies and a comprehensive review of the open literature on the chemical being evaluated.

In order to address the risk hypothe sis, the potential for direct and indirect effects to the SF Bay species, prey items, and habitat is estimated ba sed on a taxon-level approach. Using a risk quotient (ratio of exposure concentration to effects concentration) approach, the use, environmental fate, and ecological effects of the pesticide are characterized and integrated to assess the risks. Although risk is often defi ned as the likelihood and magnitude of adverse ecological effects, the risk quotient-based approach does not provide a quantitative estimate of likelihood and/or magnitude of an adverse effe ct. However, as outlined in the Overview Document (USEPA, 2004), the likel ihood of effects to individual or ganisms from particular uses of the chemical is estimated using the probit dose-response slope and either the level of concern or actual calculated risk quotient value.

2.4 Measures to Evaluate the Risk Hypothesis

2.4.1 Measures of Exposure

Measures of exposure are based on aquatic and terrestrial models that predict estimated environmental concentrations (EECs) using maxi mum labeled application rates and methods of application. The models used to predict aq uatic EECs are the Pestic ide Root Zone Model

8coupled with the Exposure Analysis Model Sy stem (PRZM/EXAMS). The models used to predict terrestrial EEC s on food items are the T errestrial R esidue EX posure model (T-REX),

T errestrial H erptafaunal E xposure and R esidue P rogram S imulation (T-HERPS), and K OW (based) Aquatic BioAccumulation Model (KABAM). The model used to derive EECs relevant to terrestrial and wetland plants is TerrPlant. These models, which are discussed in further detail below, are parameterized using relevant reviewed registrant-submitted environmental fate data. Specific inputs used in the exposure mode ls are included in the risk assessment.

2.4.1.1 Aquatic Exposure

PRZM and EXAMS are screening simulation models coupled with the input shell PRZM EXAMS Model Shell, version 5.0 (file name PE5.pl ) to generate daily exposures and 1-in-10 year EECs that may occur in surface water bodies adjacent to applica tion sites receiving the pesticide through runoff and spray drift. PRZM simulates pesticide application, movement and transformation on an agricultural field and the re sultant pesticide loadings to a receiving water body via runoff, erosion and spray drift. EXAM S simulates the fate of the pesticide and resulting concentrations in the water body. The st andard scenario used for ecological pesticide assessments assumes application to a 10-hectare ag ricultural field that drains into an adjacent 1- hectare water body, 2-meters deep (20,000 m 3 volume) with no outlet. The measure of exposure for aquatic species is the 1-in- 10 year return peak or rolling mean concentration. The 1-in-10- year 60-day mean is used for assessing chronic expos ure to fish; the 1-in-10-year 21-day mean is used for assessing chronic exposur e for aquatic invertebrates.

2.4.1.2 Terrestrial Exposure

Terrestrial exposures are evaluated for both animal and plant taxa. Exposure to animals includes an evaluation of consumption through pesticide contaminated dietary items, whereas the modeled routes of exposure to plants include spra y drift and both sheet and channelized run-off. Four models provide estimates of exposure to terrestrial organisms. These models include TREX, T-HERPS, KABAM, and TerrPlant.

Exposure estimates for birds (surrogates for amph ibians and reptiles) and mammals assumed to be in the target area or in an area exposed to spray drift are derived using the T-REX model. TREX calculates risk quotients in three ways: 1) as exposure to dietary residues of pesticides applied to foliage, 2) LD 50 /ft 2 analysis 2 , and 3) seed treatment analysis.

For foliar applications, T-REX uses applica tion information (applic ation rate, number of applications, method of application) and the diss ipation rate of a chem ical applied to foliar surfaces to calculate pesticid e residues on avian and mammalia n food items. Upper-limit EECs and risk quotients are derived using the Ke naga nomograph, as modified by Fletcher et al. (1994). The Kenaga nomograph is based on a larg e set of actual field re sidue data. The upper limit values from the nomograph represent the 95th percentile of residue values from actual field measurements (Hoerger and Kenaga, 1972).

2 The LD50/ft 2 is most commonly used to estimate risk for granular formulations and for row, banded, and in-furrow applications. It can also be used to estimate risk to broadcast applications.

9For modeling purposes, direct exposures to mamm als and birds (also used as a surrogate for amphibians and reptiles) through contaminated food are estimated using the EECs for the most sensitive size class and dietary it em for that species. Dietary- based and dose-based exposures of potential prey are assessed using the small mammal (15 g) and small bird (20 g) which consume short grass because these categories represent th e highest RQs of the size and dietary categories in T-REX that are appropriate surrogates for th e SF Bay species and some of the prey items. Estimated exposures to the pesticide by consumpti on of terrestrial insect s use the dietary based

EECs for small insects.

T-REX includes the capability to calculate the LD 50 / ft 2 risk index values. Conceptually, an

LD 50 /ft 2 is the amount of a pesticide estimated to kill 50% of exposed animals in each square foot of applied area. Although a square foot doe s not have defined ecologi cal relevance, and any unit area could be used, risk presumab ly increases as the number of LD 50 /ft 2 increases. The

LD50/ft 2 is used to estimate risk for granular formulations and row, banded, and in-furrow applications. It may also be used for broadcas t applications. For exam ple, it is commonly used to assess exposure to bare ground broadcast a pplications. For additional information on the

LD 50 /ft 2 risk index, please refer to USEPA (1992). Finally, when pesticides are used as a seed treatment, T-REX uses the seeding rate to estimate exposure and risk.

The T-HERPS model is used as a refinement t ool when there are exceedances to the SF Bay species in T-REX for amphibians and for estimat ed exposure for snakes. Currently, birds are used as surrogates for terrestrial-phase amphi bians and reptiles. However, amphibians and reptiles are poikilotherms (body te mperature varies with environm ental temperature) while birds are homeotherms (temperature is regulated, cons tant, and largely independent of environmental temperatures). Therefore, amphibians and reptil es tend to have much lower metabolic rates and lower caloric intake requirements than birds or mammals. As a consequence, birds are likely to consume more food than amphibians and some rep tiles on a daily dietary intake basis, assuming

similar caloric content of the f ood items. Therefore, the use of avian food intake allometric equation as a surrogate to amphibi ans and some reptiles is likely to result in an over-estimation of exposure and risk for some reptiles and terres trial-phase amphibians. Therefore, T-REX has been refined to the T-HERPS model (v. 1.0), whic h allows for an estimation of food intake for poikilotherms using the same basic procedure as T-REX to estimate avian food intake.

T-REX may underestimate exposure to snakes wh en birds are used as a surrogate and are assumed to eat similar dietary items because of the large meal size a snake may consume on a single day. 3 That is why birds consuming short gra ss in T-REX are used as the screen to

determine whether further refinement in T- HERPS is needed for snakes. T-HERPS was modified to estimate exposure to snakes ba sed on the maximum size prey item they could

consume and is used to refine a risk estim ate when LOCs are exceeded for small birds consuming short grass based on RQs estimated in T-REX. The following allometric equation

3 When examining the same application rates and types, RQs calculated in T-REX for small birds consuming short grass are higher than or equal to the highest RQs estimated in T-HERPs for medium snakes consuming small herbivore mammals. Therefore, RQs calculated in T-REX for the small birds consuming short grass may be used as a screen for examining risk to snakes.

10developed by King 2002 was used to estimate th e maximum size prey items for snakes (King, 2002).

1.015 Mass Snake SizePrey =

The 95% confidence limits on th e coefficient are 0.959 and 1.071 (King, 2002). The upper limit was used in T-HERPS to estimate exposure to snakes.

The KABAM model (an aquatic bioaccumulation model) is used to estimate potential bioaccumulation of pesticide residues in a freshw ater aquatic food web fo r chemicals that are non-ionic, organic, have a K

ow between 4 and 8, and have the potenti al to reach aquatic areas. It also evaluates subsequent risks that these resi dues pose to SF Bay species via consumption of contaminated aquatic prey ( i.e ., aquatic invertebrates and fish). The bioaccumulation assessment in KABAM is based on an aquatic food web bioaccumulation model published by Arnot and Gobas (2004). The model relies on both a pestic ide’s octanol-water partition coefficient (K OW ) to estimate uptake and elimination constants through the respiration and diet of aquatic organisms and the predicted water and sediment concentrations from PRZM/EXAMS to estimate concentrations of the pesticide in aquatic organi sms. These estimated tissue concentrations are compared to toxicity values for various taxonom ic groups that may eat aquatic organisms in order to evaluate potential ris k. The current version of KABAM es timates risks only to terrestrial birds and mammals consuming aquatic prey.

EECs for terrestrial plants inha biting dry and wetland areas are derived using TerrPlant. This model uses estimates of pesticides in runoff a nd in spray drift to cal culate EECs from single pesticide applications. EECs are based upon solubility, application rate, and minimum incorporation depth.

2.3.2.3 Spray Drift and Downstream Dilution Exposure

Spray drift models, AGDISP and/or AgDRIFT (if applicable) are used to assess exposures of terrestrial animals to the pesticide deposited on terrestrial habitats by spray drift. AGDISP (Teske and Curbishley, 2003) is used to simulate aerial and ground applications. In addition to the buffered area from the spray drift analysis, the downstream extent of the pesticide that

exceeds the LOC for the effects dete rmination is also considered.

2.4.2 Measures of Effect

Data is obtained from registrant submitted studies or from literature studies identified by

ECOTOX in order to determine the direct and indirect effects to the SF Bay species. The ECOTOXicology database (ECOTOX) was searched in order to provide more ecological effects data and in an attempt to bridge existing da ta gaps. ECOTOX is a source for locating single chemical toxicity data for aquatic life, terrestr ial plants, and wildlife. ECOTOX was created and is maintained by the USEPA, Office of Research and Development, and the National Health and

Environmental Effects Research Labora tory's Mid-Continent Ecology Division.

11Unless data specific to reptiles and amphibians are available surroga te species are used to assess effects to amphibians and reptiles. The assessment of risk for dire ct effects to the terrestrialphase tiger salamander, Alameda whipsnake, an d/or San Francisco garter snake makes the assumption that toxicity of the pesticide to bird s is similar to or less than the toxicity to terrestrial-phase amphibians and reptiles (this also applies to potential prey items). The same assumption is made for fish and aquatic-phase tiger salamanders, as well as potential prey items.

The acute measures of effect used for animals in this screening level assessment are the LD 50 , LC 50 and EC 50 . LD stands for "Lethal Dose", and LD 50 is the amount of a material, given all at once, that is estimated to cause the death of 50% of the test orga nisms. LC stands for “Lethal

Concentration” and LC 50 is the concentration of a chemical that is estimated to kill 50% of the test organisms. EC stands for “Effective Concentration” and the EC 50 is the concentration of a chemical that is estimated to produce a specific e ffect in 50% of the test organisms. Endpoints for chronic measures of exposure for listed and non-listed animals are the NOAEL/NOAEC and

NOEC. NOAEL stands for “No Observed-Adverse-E ffect-Level” and refers to the highest tested dose of a substance that has been reported to have no harmful (adverse) effects on test organisms. The NOAEC ( i.e. , “No-Observed-Adverse-Effect-Concen tration”) is the highest test concentration at which none of the observed effects were statistically different from the control.

The NOEC is the No-Observed-Effects-Concen tration. For non-liste d plants, only acute exposures are assessed ( i.e. , EC 25 for terrestrial plants and EC 50 for aquatic plants).

Measures of effect for direct and indirect effects to the asse ssed species and their designated

critical habitat are associated with impacts to survival, growth , and fecundity, and do not include

the full suite of sublethal eff ects used to define the action area. According the Overview Document (USEPA, 2004), the Agency relies on effects endpoints that are e ither direct measures of impairment of survival, growth, or fecundity or endpoints for which there is a scientifically robust, peer reviewed relationshi p that can quantify the impact of the measured effect endpoint on the assessment endpoints of surv ival, growth, and fecundity.

2.4.3 Integration of Exposure and Effects

Risk characterization is the integration of e xposure and ecological effects characterization to determine the potential ecological ri sk from agricultural and non-agricultural uses of a pesticide,

and the likelihood of direct and indirect effects to SF Bay speci es in aquatic and terrestrial habitats. The exposure and toxicity effects data ar e integrated in order to evaluate the risks of adverse ecological effects on non-target species. For an assessment of the risks as a result of the use of a particular pesticide, the risk quotient (RQ) method is used to compare exposure and measured toxicity values. EECs are divided by ac ute and chronic toxicity values. The resulting RQs are then compared to the Agency’s levels of concern (LOCs) (USEPA, 2004).

Listed species LOCs are used for comparing RQ values for acute and chronic exposures of a pesticide directly to the SF Bay species being as sessed. If estimated exposures directly to the assessed species are sufficient to exceed the list ed species LOC from a particular use of the pesticide, then the effects determination for that use is “may affect”. When considering indirect effects to the assessed species due to effects to prey, the listed species LO Cs are also used. If estimated exposures to the prey of the assesse d species exceed the listed species LOC for a

12particular use, then the effects determination for that use is a “may affect.” If the RQ being considered also exceeds the non-listed species acut e risk LOC, then the effects determination is a LAA. If the acute RQ is between the listed species LOC and the non-lis ted acute risk species LOC, then further lines of evidence ( i.e . probability of individual effects, species sensitivity distributions) are considered in distinguishing between a determin ation of NLAA and a LAA. If the RQ being considered for a pa rticular use exceeds the non-lis ted species LOC for plants, the effects determination is “may affect”. Howeve r, for the Bay Checkerspot Butterfly and the Valley Elderberry Beetle (which are considered obligates with a dicot), any exceedance of the endangered species LOC for dicots woul d result in an LAA determination.

3.0 Uncertainties

Supplemental information relative to generic uncertainties for the exposure and effects assessments is provided in Sections 3.1 through 3.2, respectively. Additional uncertainties specific to the assessed chemical ar e described in the risk assessment.

3.1 Exposure Assessment Uncertainties

3.1.1 Maximum Use Scenario

The screening-level risk assessment focuses on char acterizing potential ec ological risks resulting from a maximum use scenario, which is determ ined from labeled statements of maximum application rate and number of applications with th e shortest time interval between applications. The frequency at which actual uses approach th is maximum use scenario may be dependant on pest resistance, timing of applications, cultural practices, and market forces.

3.1.2 Impact of Vegetative Setbacks on Runoff

Unlike spray drift, tools are currently not availabl e to evaluate the effectiveness of a vegetative setback on runoff and loadings. The effectivenes s of vegetative setbacks is highly dependent on the condition of the vegetative strip. For example, a well-established, healthy vegetative setback can be a very effective means of reducing r unoff and erosion from agricultural fields. Alternatively, a setback of poor vegetative quali ty or a setback that is channelized can be ineffective at reducing loadings. Until such time as a quantitative method to estimate the effect of vegetative setbacks on various conditions on pe sticide loadings becomes available, the aquatic exposure predictions are likely to overestimate exposure where healthy vegetative setbacks exist and underestimate exposure where poorly develo ped, channelized, or bare setbacks exist.

3.1.3 Aquatic Exposure Modeling of the Pesticide

3.1.3.1 PRZM/EXAMS

The standard ecological water body scenario (EXAM S pond) used to calculate potential aquatic exposure to pesticides is intended to repres ent conservative estimates, and to avoid underestimations of the actual expos ure. The standard scenario c onsists of application to a 10- hectare field bordering a 1-h ectare, 2-meter deep (20,000 m 3 ) pond with no outlet. Exposure

13estimates generated using the EXAMS pond are in tended to represent a wide variety of

vulnerable water bodies that occu r at the top of watersheds in cluding prairie pot holes, playa lakes, wetlands, vernal pools, man-made and natural ponds, and interm ittent and lower order streams. As a group, there are factors that make these water bodies more or less vulnerable than the EXAMS pond. Static water bodies that have larg er ratios of pesticide- treated drainage area to water body volume are expected to have higher peak EECs than the EXAMS pond. These water bodies will be either smaller in size or have larger drainage areas. Smaller water bodies have limited storage capacity and thus may overf low and carry pesticide in the discharge, whereas the EXAMS pond has no discharge. As watershed size increases beyond 10-hectares, it becomes increasingly unlikely that the entire wate rshed is planted with a single crop that is all treated simultaneously with the pesticide. Headwa ter streams can also have peak concentrations higher than the EXAMS pond, but they likely persis t for only short periods of time and are then carried and dissipated downstream.

The Agency acknowledges that there are some uni que aquatic habitats th at are not accurately captured by this modeling scenario and modeling results may, therefore, u nder- or over-estimate exposure, depending on a number of variables. Fo r example, some organisms may inhabit water bodies of different size and depth and/or are located adjacent to larger or smaller drainage areas than the EXAMS pond. The Services agree that the existing EXAMS pond represents the best currently available approach for estimating aquatic exposure to pesticides (USFWS/NMFS/NOAA, 2004).

In general, the linked PRZM/EXAMS model produces estimated aquatic con centrations that are expected to be exceeded once within a ten-year period. The Pesticide Root Zone Model is a process or “simulation” model that calculates what happens to a pe sticide in an agricultural field on a day-to-day basis. It consid ers factors such as rainfall and plant transpiration of water, as well as how and when the pesticide is applie d. It has two major components: hydrology and chemical transport. Water movement is simulated by the use of generalized soil parameters, including field capacity, wilting point, and satura tion water content. The chemical transport component can simulate pesticid e application on the soil or on the plant foliage. Dissolved, adsorbed, and vapor-phase concen trations in the soil are estimated by simultaneously considering the processes of pesticide upt ake by plants, surface runoff, erosion, decay, volatilization, foliar wash-off, advection, disper sion, and retardation.

Uncertainties associated with each of these individual components add to the overall uncertainty of the modeled concentrations. Additionally, model inputs from the environmental fate degradation studies are chosen to represent th e upper confidence bound on th e mean values that are not expected to be exceeded in the envir onment approximately 90 percent of the time. Mobility input values are chosen to be represen tative of conditions in the environment. The natural variation in soils adds to the uncertainty of modeled values. Fact ors such as application date, crop emergence date, and canopy cover can al so affect estimated concentrations, adding to the uncertainty of modeled values. Factors within the ambient environment such as soil temperatures, sunlight intensity, antecedent so il moisture, and surface water temperatures can cause actual aquatic concentra tions to differ for the modeled values. In addition, the model assumption that granular applica tions have 0% spray drift may re sult in an underestimation of exposure for pesticides with this type of application.

14In order to account for uncertain ties associated with modeling, m onitoring data (when relevant) may be compared to PRZM/EXAMS estimates of EECs.

3.1.4 Spatial Uncertainties

3.1.4.1 Potential for Effects in the Action Area

The action area is used to identify areas that c ould be affected by the Federal action. The Federal

action is the authorization or registration of pesticide use or us es as described on the label(s) of pesticide products containing a pa rticular active ingredient. Th e action area is defined by the Endangered Species Act as, “all areas to be affected directly or indirectly by the Federal action and not merely the immediate area involved in the action” (50 CF R §402.2). Based on an analysis of the Federal action, the action area is defined by the actual and potential use of the pesticide and areas where that use could result in effects. Specific measures of ecological effect for the assessed species that define the action area include any direct and i ndirect toxic effect to the assessed species and any potenti al modification of its critical habitat, including reduction in survival, growth, and fecundity as well as the fu ll suite of sublethal effects available in the

effects literature.

It is recognized that the scope of the SF Bay assessment limits c onsideration of the overall action area to those portions that may be applicable to the protection of the SF Bay species in question and their designated critical habitat within the state of California. As a result, the entire state of California is considered the action area. The pur pose of defining the action area as the entire

state of California is to ensure that the initia l area of consideration encompasses all areas where the pesticide may be used now and in the future, including the potential for off-site transport via spray drift and downstream dilution that could in fluence the SF Bay species. Additionally, the concept of a state-wide action ar ea takes into account the potential for direct and indirect effects and any potential modification to critical habitat based on ecological effect measures associated with reduction in survival, growt h, and reproduction, as well as the full suite of sublethal effects

available in the effects literature.

It is important to note that the state-wide action area does not impl y that direct and/or indirect

effects and/or critical habitat modification are expected to or are likely to occur over the full extent of the action area, but rath er to identify all areas that ma y potentially be affected by the action. The Agency uses more rigorous analysis including consideration of available land cover data, toxicity data, and exposure information to determine areas where the SF bay species and designated critical habitat may be affected or modified via endpoints as sociated with reduced survival, growth, or reproduction.

3.1.4.2 Impact of Run-off Assumptions on the Potential Area of

LAA Effects

An example of an important simplifying assumption that may require future refinement is the assumption of uniform runoff characteristics thro ughout a landscape. It is well documented that runoff characteristics are highly non-uniform and anis otropic, and become increasingly so as the

15area under consideration becomes larger. The assumption made for estimating the aquatic potential area of LAA effects (based on predic ted in-stream dilution) was that the entire landscape exhibited runoff properties identical to those commonly found in agricultural lands in this region. However, considering the vastly di fferent runoff characteri stics of: a) undeveloped (especially forested) areas, which exhibit th e least amount of surface runoff but the greatest amount of groundwater recharge; b) suburban/residential areas, which are dominated by the relationship between impermeable surfaces (roads, lots) and grassed/other areas (lawns) plus local drainage management; c) urban areas, that are dominated by managed storm drainage and impermeable surfaces; and d) agricultural area s dominated by Hortonian and focused runoff (especially with row crops), a refined assessm ent should incorporate these differences for modeled stream flow generation. As the zone around the immediate (application) target area expands, there will be greater variability in the la ndscape; in the context of a risk assessment, the runoff potential that is assumed for the expanding ar ea will be a crucial variable (since dilution at the outflow point is determined by the size of th e expanding area). Thus, it important to know at least some approximate estimate of types of land use within that region. Runoff from forested areas ranges from 45 – 2,700% less than from ag ricultural areas; in most studies, runoff was 2.5 to 7 times higher in agricultural areas ( e.g ., Okisaka et al ., 1997; Karvonen et al. , 1999;

McDonald et al ., 2002; Phuong and van Dam 2002). Diffe rences in runoff potential between urban/suburban areas and agricultural areas are generally less than between agricultural and forested areas. In terms of likely runoff pot ential (other variables – such as topography and rainfall – being equal), the relationship is gene rally as follows (going from lowest to highest runoff potential):

Three-tiered forest < agroforestry < suburban < row-crop agriculture < urban.

There are, however, other uncertainties that should serve to counter act the effects of the aforementioned issue. For example, the dilution model considers that 10 0% of the agricultural area has the chemical applied, which is almost ce rtainly a gross over-estima tion. Thus, there will be assumed chemical contributions from agricultur al areas that will actua lly be contributing only runoff water (dilutant); so some contributions to total contaminant load will really serve to lessen rather than increase aquatic concentrations. In light of these (and othe r) confounding factors, the Agency believes that this model gives us the best available estimates under current

circumstances.

3.1.5 Usage Uncertainties

3.1.5.1 CDPR PUR Data

When available, county-level usage data are obtai ned from California’s Department of Pesticide Regulation Pesticide Use Reporti ng (CDPR PUR) database. Data from 1999 to the most recent complete year available are included in this analysis because methodologies for removing outliers are provided by CDPR fo r 1999 and earlier pesticide da ta. CDPR PUR documentation indicates that errors in the data may include the following: a misplaced decimal; incorrect measures, area treated, or units; and reports of dilu ted pesticide concentrati ons. In addition, it is possible that the data may contain reports for pe sticide uses that have been cancelled. The

CPDR PUR data does not include ho me owner applied pesticides; th erefore, residential uses are

16not likely to be reported. As with all pesticide usage data, there may be instances of misuse and misreporting. The Agency made use of the most current, verifiable information; in cases where there were discrepancies, the most c onservative information was used.

3.1.5.2 Organic Agriculture

In this assessment, the National Land Cover Data set (NLCD) are used to identify potential areas where the pesticide can be used. 4 The NLCD data do not consider whether organic practices are commonly used in areas where crops are grown, as indicated by the NLCD data, or where endangered species may be found. Therefore, in areas where there is significant organic agriculture, the conclusions arrived at in the risk assessments are expected to be conservative. Significant portions of some coun ties in California are devoted to organic agriculture where only certain pesticides are allowed 5 . According to the National Agricultural Statistics Service (NASS) 2007 census on National Organic Standa rds cropland, which includes pasture and grazing land, California ranks at 6 th place in states with the most percentage organic cropland, with seven counties having more than 10% of cropland devoted to orga nic agriculture. Marin County has up to 84% of available cropland devote d to organic, the highest in the state and

nation. 6 The trend of increasing organic farming practices both in the number of farms and total acreage has been on the rise for many years. However, it is unknown how many organic farms revert back to regular farming practices and whether regular pest icides are used outside cropland areas under certification, such as for rodent co ntrol or rights of way management in peripheral

areas.

3.1.6 Terrestrial Exposure Modeling

3.1.6.1 T-REX

The Agency relies on the work of Fletcher et al . (1994) for setting the assumed pesticide residues in wildlife dietary items. These residue assu mptions are believed to reflect a realistic upperbound residue estimate, although the degree to which this assumption reflects a specific percentile estimate is difficult to quantify. It is important to note that the field measurement efforts used to develop the Fletcher estimates of exposure involve highly varied sampling techniques. It is entirely possi ble that much of these data refl ect residues averaged over entire above ground plants in the case of grass and forage sampling.

It was assumed that ingestion of food items in th e field occurs at rates commensurate with those in the laboratory. Although the screening assessment process ad justs dry-weight estimates of

food intake to reflect the increased mass in fres h-weight wildlife food intake estimates, it does not allow for gross energy differences. Direct co mparison of a laboratory dietary concentrationbased effects threshold to a fresh-weight pes ticide residue estimate would result in an

4 CA CDPR Usage data are also analyzed to characterize current use practices.

5 The National Organic Standards only allows certain approved pesticides under the National Organic Program for a minimum of three years be fore certification. See http://www.ams.usda.gov/AMSv1.0/nop for more information on the organic program and http://attra.ncat.org/pest.html for a summary of pesticide techniques and control methods.

6 NASS 2007 data summarized at the state and county level using NASS Tables 1, 43 and 48 on total cropland, and total acres used for organic production.

17underestimation of field exposure by food consum ption by a factor of 1.25 – 2.5 for most food

items.

Differences in assimilative efficiency between laboratory and wild diet s suggest that current screening assessment methods do not account fo r a potentially important aspect of food requirements. Depending upon species and dietary matrix, bird assimilation of wild diet energy ranges from 23 – 80%, and mammal ’s assimilation ranges from 41 – 85% (USEPA, 1993). If it is assumed that laboratory chow is formulated to maximize assimilative efficiency ( e.g. , a value of 85%), a potential for underestimation of expo sure may exist by assuming that consumption of food in the wild is comparable with consumption during laborato ry testing. In the screening process, exposure may be underestimated becau se metabolic rates ar e not related to food consumption.

For the terrestrial exposure analysis of this risk assessment, a generic bird or mammal was assumed to occupy either the treated field or adjacent areas receiving a treatment rate on the field. Actual habitat requirements of any particul ar terrestrial species were not considered, and it was assumed that species occupy, exclusively an d permanently, the modeled treatment area. Spray drift model predictions sugg est that this assumption leads to an overestimation of exposure to species that do not occupy the treate d field exclusively and permanently.

3.1.6.2 T-HERPS

T-HERPS evaluates potential exposures to terrestr ial-phase herpetofaunal species resulting from consumption of terrestrial organisms. Consiste nt with the standard assessment process for terrestrial organisms, T-HERPS does not eval uate a number of potential exposure routes, including dermal exposures, water intake/submersi on, or inhalation. For some pesticides, each of these exposure routes could be signifi cant for terrestrial-phase animals.

In the absence of data on terr estrial herpetofauna, T-HERPS us es avian toxicity data as a surrogate for risk estimation. Although differences in sensitivity may be expected, the lack of available toxicity data on rept iles and amphibians precludes a robus t comparison to birds. This represents a source of uncertainty in the es timated risks to amphibians and reptiles.

T-HERPS calculates EECs for terrestrial-phase herptiles that consume mammals and other terrestrial-phase herptiles. The amount of chemical estimated to be in the prey animal, in most cases, is thought to be a conservative estimate of potential dietary exposure because T-HERPS assumes that a small prey animal is consuming its daily intake of contaminated food before being consumed by the assessed species. Depuration of the pesticide from the prey item due to

excretion or metabolism has not been included in the estimation. Therefore, the EECs for

chemicals that are short-lived in an animal ar e expected to represent an over-estimate of exposure. However, for chemicals that are bi oaccumulative and are not readily degraded or excreted in an animal, the resulting exposure estimates could be low-end estimates because body burdens within the prey species would be expe cted to increase over time for bioaccumulative chemicals, resulting in potential body burdens th at exceed the estimated daily dose calculated by T-HERPS. In addition, poten tial residues on the surface of potential prey items ( e.g. , in the fur) were not estimated by T-HERPS. Additional residue s would be expected to be on the prey item surface as well as within the prey item. Residue s could be on prey items by several pathways,

18including direct deposition of spra y drift or by contact of the prey animal with contaminated soil or foliage.

The daily food intake is estimated in T-HERPS, using an iguanid lizard allometric equation as presented in U.S. EPA (1993). This equation is used in T-HERPS to estimate potential exposures to all herptiles. A llometric equations specific for te rrestrial-phase amphibians have not been identified. To test the assumption that use of the iguanid li zard allometric equation results in a reasonable approximation of terrestri al-phase amphibian food intake, measured food intake values reported for juvenile bullfrogs ( Rana catesbeiana ) of various weights reported by Modzelewski and Culley (1974, as cited in USEPA, 1993) were compared to estimates derived using the iguanid food intake a llometric equation incorporated into T-HERPS for the same body weight range.

The analysis suggests that food intake values for juvenile bu llfrogs in the Modzelewski and Culley (1974) study are reasonabl y approximated using the allometric equation for iguanid lizards. The data for juvenile bullfrogs reporte d daily food intake valu es that ranged from approximately 3% to 7% of their body weight. Estimates of daily food intake using T-HERPS for the same range of body weights (13 grams to 100 grams) ranged from approximately 3% to 5% body weight daily. This analys is suggests that use of the i guanid lizard allometric equation results in a reasonable approxima tion of food intake reported fo r terrestrial phase frogs.

An additional uncertainty of T-HERPS is asso ciated with temperatur e influence on the food intake allometric equation. Given that terrestrial -phase frogs are poikiloth ermic, temperature may impact feeding rate. Temperature has not specifi cally been incorporated into the food ingestion allometric equation, and is not di rectly considered in T-HERPS.

The allometric equation used to estimate daily food intake assumes a typical or constant food intake rate daily. In reality, the amount of food consumed (and, therefore, potential exposures to pesticides) may vary significantly from day to day, depending on a number of factors including availability of particular f ood items and energy needs.

T-HERPS estimates potential exposures for a number of food items. EECs for a particular food item are calculated with the assumption that one food item is consumed daily. Terrestrial-phase herptiles may receive 100% of thei r daily diet from one food item fo r a particular day, especially if larger prey, such as a small mammal, is avai lable. However, many terrestrial-phase herptiles may consume a variety of food items in a given day. T-HERPS estimates potential exposures resulting from consumption of a range of f ood items for the purpose of giving a high-end and low-end bounding estimate.

193.1.6.3 TerrPlant

TerrPlant’s 10 to 1 ratio of target area to se mi-aquatic non-target area is based on research indicating a pond located in Georgi a with a six to seven foot t ypical depth requires a two acre drainage area per foot of depth (USDA, 1997). Although the data are derived from observations of aquatic areas ( e.g ., farm ponds), it is assumed that this ratio is relevant to low-lying semiaquatic areas. There is some uncertainty asso ciated with the depth of the ponds used for modeling purposes and the expected depth of a semi-aquatic area.

Consistent with a screening leve l approach, the application effi ciency, which is the amount of applied pesticide reaching the target area, is assu med to be 100% for all applications. Application efficiency is considered separately from sp ray drift; where the sum of the two does not necessarily equal 100%.

Spray drift is estimated base d on application method alone, w ithout consideration of other potentially influential factors re lated to application, such as dr oplet size, wind speed and release height.

The model assumption that granular applicatio ns have 0% spray drift may result in an underestimation of exposure for pesticid es with this type of application.

TerrPlant assumes that drift and runoff concentr ations are uniform over the non-target area. In the field, decreasing concentration gradients are expected for each of these exposure pathways as the distance increases from the applic ation site. If the dimensions ( i.e ., length and width) of the target area and non-target area where define d, the uncertainties associated with these assumptions could be explored.

For pesticides that involve ground incorporation applications with incorporation greater than one inch, less of the pesticide applied is vulnerable to runoff. In TerrPlant, the application rate is divided by the incorporation dept h. The basis for calculation of effects of ground incorporation on pesticide runoff also originated from former assumptions related to modeling aquatic EECs. The assumption is that the incorporation depth in inches is directly rela ted to the proportion of runoff. For example, incorporation of a pesticide to a depth of two inches would result in half of the application rate being available for runoff. This proportion is considered relevant up to six inches. Thus, the model assumes that the amount of pesticide in runoff is di rectly related to the depth to which the pesticide is incorporated into the ground. For further discussion of these procedures, the reader is referred to th e TerrPlant User Manual (USEPA, 2006).

There are several assumptions rela ted to temporal factors of e xposure and effects. First, the model assumes that a pesticide contained in dr ift and runoff reaches the non-target area at the same time. This assumption is conservative beca use it is unlikely that a pesticide would move at equal rates in drift and runoff. Second, the m odel does not consider th e coincidence of drift pesticide and runoff pesticide reaching the non-targ et area in time to reac h the emergence portion of the plant’s life cycle. If applied later in the plan t’s life cycle, it is possible that a pesticide will reach the non-target plants at stages of different sensitivities. It is uncer tain whether or not an exposure which occurs at a different life stage of the plant is relevant to the RQ derived based on

20the early seedling stage of a plant’s lifecycle ( i.e ., this may have greater or lesser effect than indicated by the RQ).

Modeled pesticide concentrati ons in runoff are dependent so lely upon the solubility of a pesticide. The amount of pesticide in runoff does not consider other releva nt transport properties of a pesticide ( e.g ., K d ). In addition, the model does not c onsider pesticide movement through

the soil or contai ned in eroded so il.

The model does not incorporate parameters th at would allow for phot olytic, hydrolytic, or microbial degradation. In cases where degradation occurs, this l eads to an uncertainty and likely overestimation in the amount of pesticide that would be present in runoff and in drift.

The RQ values which are currently derived by TerrP lant represent the risk of effects for single maximum applications. It is assumed that each single application would expose different plants ( i.e ., due to different drift pattern s). The modeling of EECs from single pesticide applications rather than multiple applications could result in underestimating pesticide exposures to plants.

For defining RQ values for plants exposed to ru noff, measures of effect to seedling emergence are used; however, vegetative vigor could also be affected by runoff ( i.e ., effects to plant roots). Limitations in the testing methods add uncertainty to the effects to vegetative vigor from runoff. For example, vegetative vigor st udies employ a foliar sp ray application that does not evaluate root uptake and consequently effects to plant root s. Therefore, these effects are not measured and cannot be incorporated into RQ development.

There is an absence of data comparing the field concentrations of pestic ides to EECs generated by TerrPlant. Therefore, the releva nce of TerrPlant predic tions to pesticide c oncentrations in the

field is unknown.

3.1.6.4 KABAM

KABAM estimates risks to terrestrial birds a nd mammals consuming aquatic prey. There are several key assumptions and resulting uncerta inties associated with modeling pesticide

concentrations in tissues of a quatic organisms. These assumpti ons include the e quations of the model itself and the parameterization of those underlying equations. Appendix A of the KABAM User’s Guide ( http://www.epa.gov/oppefed1/models/water/ , USEPA, 2009) describes the assumptions associated w ith the equations of the bioa ccumulation model, KABAM. A sensitivity analysis, which is included in A ppendix A, was completed in order to explore exposure uncertainties associated with specific pa rameters of the equations and their influences on model outputs. Based on the sensitivity analysis, the parameters that have the greatest influence on model outputs include the K OW value for the chemical of interest as well as water column and pore water estimated envi ronmental concentrations (EECs).

Given the influence of water column and por e water EECs on the mode l outputs, the use of PRZM/EXAMS for deriving surface and pore water EECs introduces the assumptions and

uncertainties associated PRZM and EXAMS to KABAM.

21One major assumption of the model is that it a ssumes steady state, alt hough sporadic peaks to aquatic organisms are expected, given the episodi c nature of pesticide applications. For a chemical with a Log K OW of approximately 5, comparison of the fish tissue EECs predicted using the steady state and dynamic bioaccumulation modeling with PRZM/EXAMS and the

Arnot and Gobas bioaccumulation model (Arnot and Gobas, 2004) indicates pr edictions are similar (USEPA, 2008) when a 60-d average is select ed for water and sediment concentrations as input to the steady state model. This result suggests that stead y-state bioaccumulation modeling can provide useful predictions of bioaccumulation potential even with highly variable exposures, provided proper consideration of the averaging period associated with water and sediment

concentrations is considered.

In the KABAM default settings, it is assumed that the elimination of the pesticide from aquatic organisms through metabolism does not occur, i.e. , the metabolism rate constant (k M ) is zero. In cases where pesticide metabolism does occur ( e.g. , where fish rapidly depurate the pesticide in

bioconcentration factor-BCF-studies ), use of the default metabolism rate constant is likely to overestimate pesticide bioaccumulation. Appendix H of the KABAM User’s Guide provides methods for estimating k M for fish using empirical data pr ovided in chemical-specific BCF

studies.

The Arnot and Gobas (2004) model is genera lly appropriate for chemicals with Log K OW values

> 4 to < 8. Uncertainty increases as the value increa ses above 8 because the model has generally been validated using chemicals with Log K OW values within the range of 4 to 8. Use of the KABAM model for chemicals with Log K OW > 8 increases uncertainty in the model outputs because bioaccumulation predictions are based on extrapolations in its subroutines. For chemicals with Log K OW < 4, exposure from food becomes insignificant because uptake and depuration across the gills controls the residue concentrations in the organism. Therefore, it is not necessary to run a food web model for these ch emicals. In these cases, available BCF data are sufficient to predict residues in the aquatic species.

It is assumed that there is no predation within a trophic level of the a quatic food web in KABAM ( e.g. , medium fish cannot prey upon medium fish). It is also assumed that mammals and birds only consume organisms from the aquatic system.

3.1.7 Spray Drift Modeling

AgDRIFT and AGDISP are used in spatial analysis to evaluate risk to terrestrial and aquatic organisms due to exposure to spray drift. Although there may be multiple chemical applications at a single site, it is unlikely that the same organism would be exposed to the maximum amount of spray drift from every application made. In order for an organism to receive the maximum concentration of a pesticide from multiple applic ations, each application of the chemical would

have to occur under identical atmospheric conditions ( e.g. , same wind speed and – for plants – same wind direction) and (if it is an animal) the animal being exposed would have to be present directly downwind at the same distance after each application. Although there may be sites where the dominant wind direction is fairly consistent (at least during the relatively quiescent conditions that are most favorable for aerial spray applications), it is neve rtheless highly unlikely that plants in any specific area would receive th e maximum amount of spray drift repeatedly. It

22appears that in most areas (based upon avai lable meteorological da ta) wind direction is temporally very changeable, even within the same day. Additionally, other factors, including variations in topography, cover, and meteorologi cal conditions over the transport distance are not accounted for by the AgDRIFT/AGDISP model ( i.e ., it models spray drift from aerial and ground applications in a flat area with little to no ground cover and a steady, constant wind speed and direction). Therefore, in most cases , the drift estimates from AgDRIFT/AGDISP may overestimate exposure even from si ngle applications, especially as the distance increases from the site of application, since the model doe s not account for potential obstructions ( e.g ., large hills, berms, buildings, trees, etc .). Furthermore, conservativ e assumptions are often made regarding the droplet size distri butions being modeled (‘ASAE Ve ry Fine to Fine’ for orchard uses and ‘ASAE Very Fine’ for agricultural uses), the ap plication method ( e .g., aerial), release heights and wind speeds. Alterations in any of these inputs would change the area of potential

effect.

3.2 Effects Assessment Uncertainties

3.2.1 Age Class and Sensitivity of Effects Thresholds

It is generally recognized that test organism age may have a significant impact on the observed sensitivity to a toxicant. The acute toxicity data for fish are collected on juvenile fish between 0.1 and 5 grams. Aquatic invertebrate acute te sting is performed on recommended immature age classes ( e.g ., first instar for daphnids, second instar for amphipods, stoneflies, mayflies, and third instar for midges).

Testing of juveniles may overe stimate toxicity at older age classes for pesticide active ingredients that act directly without metabolic transformation because younger age classes may not have the enzymatic systems associated with detoxifying xenobiotics. In so far as the available toxicity data may provide ranges of se nsitivity information with respect to age class, this assessment uses the most sensitive life-stage information as measures of effect for surrogate aquatic animals, and is therefor e, considered as protective.

3.2.2 Impact of Multiple Stressors on the Effects Determination

The influence of length of exposure and concurrent environmental stressors ( i.e. , construction of dams and locks, fragmentation of habitat, change in flow regimes, increased sedimentation, degradation of quantity and quality of water in the watersheds of the action area, predators, etc .) will likely affect the species’ response to a pes ticide. Additional environmental stressors may increase sensitivity to the herbicide, although th ere is the possibility of additive/synergistic reactions. Timing, peak concentration, and dur ation of exposure are critical in terms of evaluating effects, and these factor s are expected to vary both temporally and spatially within the action area. Overall, the effect of this variability may result in either an overestimation or underestimation of risk. However, as previously discussed, the Agency’s LOCs are set to be protective given the wide range of possible uncertainties.

233.2.3 Exposure to Pesticide Mixtures

In accordance with the Overview Document and the Services Evaluation Memorandum (USEPA, 2004; USFWS/NMFS/NOAA, 2004), the single active in gredient is considered. However, the assessed species and its environments may be ex posed to multiple pesticides simultaneously. Interactions of other toxic agents with the specific pesticide could result in additive effects, more than additive effects, or less than additive effects. As previously discussed, evaluation of pesticide mixtures is beyond the scope of ESA asse ssments because of the myriad of factors that cannot be quantified based on the available data. Those factors include identification of other possible co-contaminants where some of the SF Bay species reside and their concentrations, differences in the pattern and duration of e xposure among contaminants, and the differential effects of other physical/chemical charac teristics of the r eceiving waters ( e.g ., organic matter present in sediment and suspended water). Evaluation of factors that could influence additivity/synergism/antagonism is beyond the nature and quality of the available data to allow for an evaluation. However, it is acknowledged that not considering mixtures could over- or under-estimate risks depending on the type of in teraction and factors discussed above.

3.2.4 Uncertainty in the Potential Effect to Riparian Vegetation vs. Water Quality Impacts

Effects to riparian vegetation are evaluated using submitted guideline seedling emergence and vegetative vigor studies. In cases where LOCs are exceeded for seedling emergence endpoints and/or vegetative vigor endpoints, it may be concluded that the chemical use is likely to adversely affect the SF Bay sp ecies by potentially impacting grassy/herbaceous riparian vegetation resulting in increased sedimentation. However, the char acterization of riparian areas

is uncertain due to a lack of readily available information on riparian areas.

In addition, soil retention/sediment loading is dependent on a number of factors including land management and tillage practices. Therefore, although an assessment may conclude that a chemical is likely to adversely affect the a ssessed listed species and th eir designated critical habitat by potentially impacting sensitive herbaceous riparian areas, it is possible that adverse impacts on sediment loading may not occur in areas where soil retention strategies are used.

244.0 References

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