Attachment I: 

Supplemental Information on 

Standard Procedures for 

Threatened and Endangered 

Species Risk Assessments on 

San Francisco Bay Species 

ENVIRONMENTAL FATE AND EFFECTS DIVISION 

OFFICE OF PESTICIDE PROGRAMS 

U.S. ENVIRONMENTAL PROTECTION AGENCY 

March 2, 2010
Table of Contents 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3.2.4

 24 

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

Impacts

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

2
List 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 

EC50 50% (or Median) Effect Concentration 

ECOTOX EPA managed database of Ecotoxicology data 

EEC Estimated Environmental Concentration 

EFED Environmental Fate 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 KOW (based) Aquatic BioAccumulation Model 

km Kilometer(s) 

Kd Solid-water Distribution Coefficient 

KOC Organic-carbon Partition Coefficient 

KOW Octanol–water Partition Coefficient 

LAA Likely to Adversely Affect 

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

LC50 50% (or Median) Lethal Concentration 

LD50 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 

3
NOAEC 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, Pesticides and Toxic Substances 

PCE Primary Constituent Element 

PRZM Pesticide Root Zone Model 

RQ Risk Quotient 

T-HERPS Terrestrial Herpetofaunal 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 

4
1.0 Introduction 

The purpose of this Attachment is to provide supplemental information on procedures and 

methodologies routinely used as part of the litigation-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 repetitive 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 comprehensive 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 endangered species, see the 

Overview Document (USEPA, 2004). 

2.0 Problem Formulation 

Section 2 describes elements of the problem formulation 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 species being assessed), assessment and measurement 

endpoints, on overview of common exposure models, and measures 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 and 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). Screening level methods include 

use of standard models such as the Pesticide Root Zone Model coupled with the Exposure 

Analysis Model System (PRZM-EXAM), Terrestrial 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 Overview Document (USEPA, 

2004), which specifies that “the assessment process 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 correspond to the Section numbers used in the chemical and 

species specific risk assessments. 

5
In 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 pesticide is based on an action area. The action area is the area 

directly or indirectly affected by the federal action, as indicated by the exceedence of the 

Agency’s Levels of Concern (LOCs). It is acknowledged that the action area for a national-level 

FIFRA regulatory decision associated with a use of a specific pesticide may potentially involve 

numerous areas throughout the United States and its Territories. However, 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 critical 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’ designated 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 indirect 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 specific pesticide “may affect” a listed species or its 

designated critical habitat, additional 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, including spatial analysis (to determine the 

geographical proximity of the assessed species’ habitat and a pesticide’s use sites) and further 

evaluation of the potential impact of a specific pesticide 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 affect 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 habitat. 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 habitat that are biological 

6
or that can be reasonably linked to biologically mediated processes (i.e., the biological resource 

requirements for the listed species associated with the critical habitat or important physical 

aspects of the habitat that may be reasonably influenced through biological processes). 

Activities that may modify critical habitat are those that alter the PCEs and appreciably diminish 

the value of the habitat. Evaluation of actions related 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 critical habitat have been 

identified by the Services and are discussed further below. 

2.2 Designated Critical Habitat 

Critical habitat are designated for the California Alameda Whipsnake, Bay Checkerspot 

Butterfly, Valley Elderberry Longhorn Beetle, California 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 geographic 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 management to protect the listed species. It may 

also include areas outside the occupied area at the time of listing if such areas are ‘essential to 

the conservation of the species.’ Critical habitat receives protection under Section 7 of the ESA 

through prohibition against destruction or adverse modification with regard to actions carried 

out, funded, or authorized by a federal Agency. Section 7 requires consultation on federal 

actions that are likely to result in the destruction or adverse modification of critical habitat. 

To be included in a critical habitat designation, the habitat must be ‘essential to the conservation 

of the species.’ Critical habitat designations identify, to the extent known using the best 

scientific and commercial data available, habitat areas that provide essential 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, air, 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 disturbance or are representative 

of the historic geographical and ecological distributions of a species. 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 environmental value that 

is to be protected.” Selection of the assessment endpoints is based on valued entities (e.g., San 

Francisco (SF) Bay species being assessed, organisms important in the life cycle of the assessed 

species, and the PCEs of its designated critical habitat), the ecosystems potentially at risk (e.g.,

waterbodies, riparian vegetation, and upland and dispersal habitats), the migration pathways of 

7
the pesticide (e.g., runoff, spray drift, etc.), and the routes by which ecological receptors are 

exposed to the pesticide (e.g., direct contact, etc.). 

2.3.1 Assessment Endpoints 

Assessment endpoints for the SF Bay species include direct toxic effects on the survival, 

reproduction, and growth of individuals, as well as indirect effects, such as reduction of the prey 

base or modification of its habitat. In addition, potential modification of critical habitat is 

assessed by evaluating potential effects to PCEs, which are components of the habitat areas that 

provide essential life cycle needs of the assessed species. Each assessment endpoint requires one 

or more “measures of ecological effect,” defined as changes in the attributes of an assessment 

endpoint or changes in a surrogate entity or attribute in response to exposure to a pesticide. 

Specific measures of ecological effect are generally 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 that assessment endpoints are limited to direct and indirect effects 

associated with survival, growth, and fecundity, and do not include the full suite of sublethal 

effects used to define the action area. According to the Overview Document (USEPA, 2004), the 

Agency relies on acute and chronic effects endpoints that are either direct measures of 

impairment of survival, growth, or fecundity or endpoints for which there is a scientifically 

robust, peer reviewed relationship that can quantify the impact of the measured effect endpoint 

on the assessment endpoints of survival, growth, and fecundity. 

As described in the Agency’s Overview Document (USEPA, 2004), the most sensitive endpoint 

for each taxon is used for risk estimation. Acute (short-term) and chronic (long-term) toxicity 

information is characterized based on registrant-submitted studies and a comprehensive review 

of the open literature on the chemical being evaluated. 

In order to address the risk hypothesis, the potential for direct and indirect effects to the SF Bay 

species, prey items, and habitat is estimated based 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 defined 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 effect. However, as outlined in the Overview 

Document (USEPA, 2004), the likelihood of effects to individual organisms 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 maximum labeled application rates and methods of 

application. The models used to predict aquatic EECs are the Pesticide Root Zone Model 

8
coupled with the Exposure Analysis Model System (PRZM/EXAMS). The models used to 

predict terrestrial EECs on food items are the Terrestrial Residue EXposure model (T-REX), 

Terrestrial Herptafaunal Exposure and Residue Program Simulation (T-HERPS), and KOW

(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 models 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 application sites receiving the 

pesticide through runoff and spray drift. PRZM simulates pesticide application, movement and 

transformation on an agricultural field and the resultant pesticide loadings to a receiving water 

body via runoff, erosion and spray drift. EXAMS simulates the fate of the pesticide and 

resulting concentrations in the water body. The standard scenario used for ecological pesticide 

assessments assumes application to a 10-hectare agricultural field that drains into an adjacent 1-

hectare water body, 2-meters deep (20,000 m3 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 exposure to fish; the 1-in-10-year 21-day mean is 

used for assessing chronic exposure 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 spray 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 amphibians 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) LD50/ft2

 analysis2, and 3) seed treatment analysis. 

For foliar applications, T-REX uses application information (application rate, number of 

applications, method of application) and the dissipation rate of a chemical applied to foliar 

surfaces to calculate pesticide residues on avian and mammalian food items. Upper-limit EECs 

and risk quotients are derived using the Kenaga nomograph, as modified by Fletcher et al.

(1994). The Kenaga nomograph is based on a large set of actual field residue 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/ft2 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. 

9
For modeling purposes, direct exposures to mammals 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 item 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 the highest RQs of the size and dietary categories 

in T-REX that are appropriate surrogates for the SF Bay species and some of the prey items. 

Estimated exposures to the pesticide by consumption of terrestrial insects use the dietary based 

EECs for small insects. 

T-REX includes the capability to calculate the LD50/ ft2 risk index values. Conceptually, an 

LD50/ft2

 is the amount of a pesticide estimated to kill 50% of exposed animals in each square 

foot of applied area. Although a square foot does not have defined ecological relevance, and any 

unit area could be used, risk presumably increases as the number of LD50/ft2

 increases. The 

LD50/ft2

 is used to estimate risk for granular formulations and row, banded, and in-furrow 

applications. It may also be used for broadcast applications. For example, it is commonly used 

to assess exposure to bare ground broadcast applications. For additional information on the 

LD50/ft2

 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 tool when there are exceedances to the SF Bay 

species in T-REX for amphibians and for estimated exposure for snakes. Currently, birds are 

used as surrogates for terrestrial-phase amphibians and reptiles. However, amphibians and 

reptiles are poikilotherms (body temperature varies with environmental temperature) while birds 

are homeotherms (temperature is regulated, constant, and largely independent of environmental 

temperatures). Therefore, amphibians and reptiles 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 reptiles on a daily dietary intake basis, assuming 

similar caloric content of the food items. Therefore, the use of avian food intake allometric 

equation as a surrogate to amphibians and some reptiles is likely to result in an over-estimation 

of exposure and risk for some reptiles and terrestrial-phase amphibians. Therefore, T-REX has 

been refined to the T-HERPS model (v. 1.0), which 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 when 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 grass 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 based on the maximum size prey item they could 

consume and is used to refine a risk estimate 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. 

10
developed by King 2002 was used to estimate the maximum size prey items for snakes (King, 

2002). 

1.015 = Mass Snake SizePrey 

The 95% confidence limits on the 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 freshwater aquatic food web for chemicals that are 

non-ionic, organic, have a Kow between 4 and 8, and have the potential to reach aquatic areas. It 

also evaluates subsequent risks that these residues 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 pesticide’s octanol-water partition coefficient (KOW) 

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 organisms. These estimated tissue concentrations are 

compared to toxicity values for various taxonomic groups that may eat aquatic organisms in 

order to evaluate potential risk. The current version of KABAM estimates risks only to terrestrial 

birds and mammals consuming aquatic prey. 

EECs for terrestrial plants inhabiting dry and wetland areas are derived using TerrPlant. This 

model uses estimates of pesticides in runoff and in spray drift to calculate 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 determination 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 data gaps. ECOTOX is a source for locating single 

chemical toxicity data for aquatic life, terrestrial 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 Laboratory's Mid-Continent Ecology Division. 

11
Unless data specific to reptiles and amphibians are available surrogate species are used to assess 

effects to amphibians and reptiles. The assessment of risk for direct effects to the terrestrialphase tiger salamander, Alameda whipsnake, and/or San Francisco garter snake makes the 

assumption that toxicity of the pesticide to birds 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 LD50, 

LC50 and EC50. LD stands for "Lethal Dose", and LD50 is the amount of a material, given all at 

once, that is estimated to cause the death of 50% of the test organisms. LC stands for “Lethal 

Concentration” and LC50 is the concentration of a chemical that is estimated to kill 50% of the 

test organisms. EC stands for “Effective Concentration” and the EC50 is the concentration of a 

chemical that is estimated to produce a specific effect 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-Effect-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-Concentration”) 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-Concentration. For non-listed plants, only acute 

exposures are assessed (i.e., EC25 for terrestrial plants and EC50 for aquatic plants). 

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

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

the full suite of sublethal effects used to define the action area. According the Overview 

Document (USEPA, 2004), the Agency relies on effects endpoints that are either direct measures 

of impairment of survival, growth, or fecundity or endpoints for which there is a scientifically 

robust, peer reviewed relationship that can quantify the impact of the measured effect endpoint 

on the assessment endpoints of survival, growth, and fecundity. 

2.4.3 Integration of Exposure and Effects

Risk characterization is the integration of exposure and ecological effects characterization to 

determine the potential ecological risk from agricultural and non-agricultural uses of a pesticide, 

and the likelihood of direct and indirect effects to SF Bay species in aquatic and terrestrial 

habitats. The exposure and toxicity effects data are 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 acute 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 assessed. If estimated exposures directly to the 

assessed species are sufficient to exceed the listed 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 LOCs are also used. If 

estimated exposures to the prey of the assessed species exceed the listed species LOC for a 

12
particular use, then the effects determination for that use is a “may affect.” If the RQ being 

considered also exceeds the non-listed species acute risk LOC, then the effects determination is a 

LAA. If the acute RQ is between the listed species LOC and the non-listed acute risk species 

LOC, then further lines of evidence (i.e. probability of individual effects, species sensitivity 

distributions) are considered in distinguishing between a determination of NLAA and a LAA. If 

the RQ being considered for a particular use exceeds the non-listed species LOC for plants, the 

effects determination is “may affect”. However, 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 would 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 are described in the risk assessment. 

3.1 Exposure Assessment Uncertainties 

3.1.1 Maximum Use Scenario

The screening-level risk assessment focuses on characterizing potential ecological risks resulting 

from a maximum use scenario, which is determined from labeled statements of maximum 

application rate and number of applications with the shortest time interval between applications. 

The frequency at which actual uses approach this 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 available to evaluate the effectiveness of a vegetative 

setback on runoff and loadings. The effectiveness 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 runoff and erosion from agricultural fields. 

Alternatively, a setback of poor vegetative quality 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 pesticide loadings becomes available, the aquatic 

exposure predictions are likely to overestimate exposure where healthy vegetative setbacks exist 

and underestimate exposure where poorly developed, 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 (EXAMS pond) used to calculate potential aquatic 

exposure to pesticides is intended to represent conservative estimates, and to avoid 

underestimations of the actual exposure. The standard scenario consists of application to a 10-

hectare field bordering a 1-hectare, 2-meter deep (20,000 m3

) pond with no outlet. Exposure 

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

vulnerable water bodies that occur at the top of watersheds including prairie pot holes, playa 

lakes, wetlands, vernal pools, man-made and natural ponds, and intermittent 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 larger 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 overflow 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 watershed is planted with a single crop that is all 

treated simultaneously with the pesticide. Headwater streams can also have peak concentrations 

higher than the EXAMS pond, but they likely persist for only short periods of time and are then 

carried and dissipated downstream. 

The Agency acknowledges that there are some unique aquatic habitats that are not accurately 

captured by this modeling scenario and modeling results may, therefore, under- or over-estimate 

exposure, depending on a number of variables. For 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 concentrations 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 pesticide in an agricultural field 

on a day-to-day basis. It considers factors such as rainfall and plant transpiration of water, as 

well as how and when the pesticide is applied. 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 saturation water content. The chemical transport 

component can simulate pesticide application on the soil or on the plant foliage. Dissolved, 

adsorbed, and vapor-phase concentrations in the soil are estimated by simultaneously considering 

the processes of pesticide uptake by plants, surface runoff, erosion, decay, volatilization, foliar 

wash-off, advection, dispersion, 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 the upper confidence bound on the mean values that 

are not expected to be exceeded in the environment approximately 90 percent of the time. 

Mobility input values are chosen to be representative of conditions in the environment. The 

natural variation in soils adds to the uncertainty of modeled values. Factors such as application 

date, crop emergence date, and canopy cover can also affect estimated concentrations, adding to 

the uncertainty of modeled values. Factors within the ambient environment such as soil 

temperatures, sunlight intensity, antecedent soil moisture, and surface water temperatures can 

cause actual aquatic concentrations to differ for the modeled values. In addition, the model 

assumption that granular applications have 0% spray drift may result in an underestimation of 

exposure for pesticides with this type of application. 

14
In order to account for uncertainties associated with modeling, monitoring 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 could be affected by the Federal action. The Federal 

action is the authorization or registration of pesticide use or uses as described on the label(s) of 

pesticide products containing a particular active ingredient. The 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 CFR §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 indirect toxic effect to 

the assessed species and any potential modification of its critical habitat, including reduction in 

survival, growth, and fecundity as well as the full suite of sublethal effects available in the 

effects literature. 

It is recognized that the scope of the SF Bay assessment limits consideration 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 purpose of defining the action area as the entire 

state of California is to ensure that the initial 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 influence the SF Bay species. Additionally, the 

concept of a state-wide action area 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, growth, 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 imply 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 rather to identify all areas that may 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 associated 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 throughout a landscape. It is well documented that 

runoff characteristics are highly non-uniform and anisotropic, and become increasingly so as the 

15
area under consideration becomes larger. The assumption made for estimating the aquatic 

potential area of LAA effects (based on predicted 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 different runoff characteristics of: a) undeveloped 

(especially forested) areas, which exhibit the 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 areas dominated by Hortonian and focused runoff 

(especially with row crops), a refined assessment 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 landscape; in the context of a risk assessment, the 

runoff potential that is assumed for the expanding area will be a crucial variable (since dilution at 

the outflow point is determined by the size of the 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 agricultural 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). Differences in runoff potential between 

urban/suburban areas and agricultural areas are generally less than between agricultural and 

forested areas. In terms of likely runoff potential (other variables – such as topography and 

rainfall – being equal), the relationship is generally 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 counteract the effects of the 

aforementioned issue. For example, the dilution model considers that 100% of the agricultural 

area has the chemical applied, which is almost certainly a gross over-estimation. Thus, there will 

be assumed chemical contributions from agricultural areas that will actually 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 other) 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 obtained from California’s Department of Pesticide 

Regulation Pesticide Use Reporting (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 for 1999 and earlier pesticide data. 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 diluted pesticide concentrations. In addition, it is 

possible that the data may contain reports for pesticide uses that have been cancelled. The 

CPDR PUR data does not include home owner applied pesticides; therefore, residential uses are 

16
not 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 conservative information was used. 

3.1.5.2 Organic Agriculture 

In this assessment, the National Land Cover Dataset (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 counties in California are devoted to organic agriculture where only 

certain pesticides are allowed5. According to the National Agricultural Statistics Service 

(NASS) 2007 census on National Organic Standards cropland, which includes pasture and 

grazing land, California ranks at 6th place in states with the most percentage organic cropland, 

with seven counties having more than 10% of cropland devoted to organic agriculture. Marin 

County has up to 84% of available cropland devoted 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 pesticides are used outside cropland 

areas under certification, such as for rodent control 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 assumptions 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 possible that much of these data reflect residues averaged over entire 

above ground plants in the case of grass and forage sampling. 

It was assumed that ingestion of food items in the field occurs at rates commensurate with those 

in the laboratory. Although the screening assessment process adjusts dry-weight estimates of 

food intake to reflect the increased mass in fresh-weight wildlife food intake estimates, it does 

not allow for gross energy differences. Direct comparison of a laboratory dietary concentrationbased effects threshold to a fresh-weight pesticide 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 before 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. 

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

items. 

Differences in assimilative efficiency between laboratory and wild diets suggest that current 

screening assessment methods do not account for 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 exposure may exist by assuming that consumption of 

food in the wild is comparable with consumption during laboratory testing. In the screening 

process, exposure may be underestimated because metabolic rates are 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 particular terrestrial species were not considered, and it 

was assumed that species occupy, exclusively and permanently, the modeled treatment area. 

Spray drift model predictions suggest that this assumption leads to an overestimation of exposure 

to species that do not occupy the treated field exclusively and permanently. 

3.1.6.2 T-HERPS 

T-HERPS evaluates potential exposures to terrestrial-phase herpetofaunal species resulting from 

consumption of terrestrial organisms. Consistent with the standard assessment process for 

terrestrial organisms, T-HERPS does not evaluate a number of potential exposure routes, 

including dermal exposures, water intake/submersion, or inhalation. For some pesticides, each of 

these exposure routes could be significant for terrestrial-phase animals. 

In the absence of data on terrestrial herpetofauna, T-HERPS uses avian toxicity data as a 

surrogate for risk estimation. Although differences in sensitivity may be expected, the lack of 

available toxicity data on reptiles and amphibians precludes a robust comparison to birds. This 

represents a source of uncertainty in the estimated 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 are expected to represent an over-estimate of 

exposure. However, for chemicals that are bioaccumulative 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 expected to increase over time for bioaccumulative 

chemicals, resulting in potential body burdens that exceed the estimated daily dose calculated by 

T-HERPS. In addition, potential residues on the surface of potential prey items (e.g., in the fur) 

were not estimated by T-HERPS. Additional residues would be expected to be on the prey item 

surface as well as within the prey item. Residues could be on prey items by several pathways, 

18
including direct deposition of spray 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. Allometric equations specific for terrestrial-phase amphibians have 

not been identified. To test the assumption that use of the iguanid lizard allometric equation 

results in a reasonable approximation of terrestrial-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 allometric equation incorporated into T-HERPS for the same body 

weight range. 

The analysis suggests that food intake values for juvenile bullfrogs in the Modzelewski and 

Culley (1974) study are reasonably approximated using the allometric equation for iguanid 

lizards. The data for juvenile bullfrogs reported daily food intake values 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 analysis suggests that use of the iguanid lizard allometric equation 

results in a reasonable approximation of food intake reported for terrestrial phase frogs. 

An additional uncertainty of T-HERPS is associated with temperature influence on the food 

intake allometric equation. Given that terrestrial-phase frogs are poikilothermic, temperature may 

impact feeding rate. Temperature has not specifically been incorporated into the food ingestion 

allometric equation, and is not directly 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 food 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 their daily diet from one food item for a particular day, especially 

if larger prey, such as a small mammal, is available. 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 food items for the purpose of giving a high-end and 

low-end bounding estimate. 

19
3.1.6.3 TerrPlant 

TerrPlant’s 10 to 1 ratio of target area to semi-aquatic non-target area is based on research 

indicating a pond located in Georgia with a six to seven foot typical 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 associated with the depth of the ponds used for 

modeling purposes and the expected depth of a semi-aquatic area. 

Consistent with a screening level approach, the application efficiency, which is the amount of 

applied pesticide reaching the target area, is assumed to be 100% for all applications. Application 

efficiency is considered separately from spray drift; where the sum of the two does not 

necessarily equal 100%. 

Spray drift is estimated based on application method alone, without consideration of other 

potentially influential factors related to application, such as droplet size, wind speed and release 

height. 

The model assumption that granular applications have 0% spray drift may result in an 

underestimation of exposure for pesticides with this type of application. 

TerrPlant assumes that drift and runoff concentrations 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 application site. If the dimensions (i.e., length and width) of the 

target area and non-target area where defined, 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 depth. 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 related 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 directly related to the 

depth to which the pesticide is incorporated into the ground. For further discussion of these 

procedures, the reader is referred to the TerrPlant User Manual (USEPA, 2006). 

There are several assumptions related to temporal factors of exposure and effects. First, the 

model assumes that a pesticide contained in drift and runoff reaches the non-target area at the 

same time. This assumption is conservative because it is unlikely that a pesticide would move at 

equal rates in drift and runoff. Second, the model does not consider the coincidence of drift 

pesticide and runoff pesticide reaching the non-target area in time to reach the emergence portion 

of the plant’s life cycle. If applied later in the plant’s life cycle, it is possible that a pesticide will 

reach the non-target plants at stages of different sensitivities. It is uncertain whether or not an 

exposure which occurs at a different life stage of the plant is relevant to the RQ derived based on 

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

indicated by the RQ). 

Modeled pesticide concentrations in runoff are dependent solely upon the solubility of a 

pesticide. The amount of pesticide in runoff does not consider other relevant transport properties 

of a pesticide (e.g., Kd). In addition, the model does not consider pesticide movement through 

the soil or contained in eroded soil. 

The model does not incorporate parameters that would allow for photolytic, hydrolytic, or 

microbial degradation. In cases where degradation occurs, this leads 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 TerrPlant 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 patterns). 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 runoff, 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 studies employ a foliar spray application that does not evaluate 

root uptake and consequently effects to plant roots. Therefore, these effects are not measured and 

cannot be incorporated into RQ development. 

There is an absence of data comparing the field concentrations of pesticides to EECs generated 

by TerrPlant. Therefore, the relevance of TerrPlant predictions to pesticide concentrations in the 

field is unknown. 

3.1.6.4 KABAM 

KABAM estimates risks to terrestrial birds and mammals consuming aquatic prey. There are 

several key assumptions and resulting uncertainties associated with modeling pesticide 

concentrations in tissues of aquatic organisms. These assumptions include the equations 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 with the equations of the bioaccumulation model, KABAM. A 

sensitivity analysis, which is included in Appendix A, was completed in order to explore 

exposure uncertainties associated with specific parameters 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 KOW value for the chemical of interest as well as water 

column and pore water estimated environmental concentrations (EECs). 

Given the influence of water column and pore water EECs on the model outputs, the use of 

PRZM/EXAMS for deriving surface and pore water EECs introduces the assumptions and 

uncertainties associated PRZM and EXAMS to KABAM. 

21
One major assumption of the model is that it assumes steady state, although sporadic peaks to 

aquatic organisms are expected, given the episodic nature of pesticide applications. For a 

chemical with a Log KOW 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 predictions are 

similar (USEPA, 2008) when a 60-d average is selected for water and sediment concentrations as 

input to the steady state model. This result suggests that steady-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 (kM) 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 kM for fish using empirical data provided in chemical-specific BCF 

studies. 

The Arnot and Gobas (2004) model is generally appropriate for chemicals with Log KOW values 

>4 to <8. Uncertainty increases as the value increases above 8 because the model has generally 

been validated using chemicals with Log KOW values within the range of 4 to 8. Use of the 

KABAM model for chemicals with Log KOW > 8 increases uncertainty in the model outputs 

because bioaccumulation predictions are based on extrapolations in its subroutines. For 

chemicals with Log KOW < 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 chemicals. 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 aquatic 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 applications, 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 nevertheless highly unlikely 

that plants in any specific area would receive the maximum amount of spray drift repeatedly. It 

22
appears that in most areas (based upon available meteorological data) wind direction is 

temporally very changeable, even within the same day. Additionally, other factors, including 

variations in topography, cover, and meteorological 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 single applications, especially as the distance increases from 

the site of application, since the model does not account for potential obstructions (e.g., large 

hills, berms, buildings, trees, etc.). Furthermore, conservative assumptions are often made 

regarding the droplet size distributions being modeled (‘ASAE Very Fine to Fine’ for orchard 

uses and ‘ASAE Very Fine’ for agricultural uses), the application 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 testing 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 overestimate 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 sensitivity 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 therefore, 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 pesticide. Additional environmental stressors may 

increase sensitivity to the herbicide, although there is the possibility of additive/synergistic 

reactions. Timing, peak concentration, and duration of exposure are critical in terms of 

evaluating effects, and these factors 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. 

23
3.2.3 Exposure to Pesticide Mixtures 

In accordance with the Overview Document and the Services Evaluation Memorandum (USEPA, 

2004; USFWS/NMFS/NOAA, 2004), the single active ingredient is considered. However, the 

assessed species and its environments may be exposed 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 assessments 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 exposure among contaminants, and the differential 

effects of other physical/chemical characteristics of the receiving 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 interaction 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 species by potentially impacting grassy/herbaceous riparian 

vegetation resulting in increased sedimentation. However, the characterization 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 assessed listed species and their 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. 

24
4.0 References 

Arnot, J. A., & Gobas, F. A. P. C. 2004. A food web bioaccumulation model for organic 

chemicals in aquatic ecosystems. Environmental Toxicology and Chemistry, 23(10), 

2343-2355. 

Fletcher, J. S., Nellessen, J. E., & Pfleeger, T. G. 1994. Literature review and evaluation of the 

EPA food-chain (Kenaga) nomogram, an instrument for estimating pesticide residues on 

plants. Environmental Toxicology and Chemistry, 13(9), 1383-1391. 

Hoerger, F., & Kenaga, E. E. 1972. Pesticide Residues on Plants: Correlation of Representative 

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