DRAFT EPA Proposed Revised Method for National Level Endangered Species
Risk Assessment Process for Biological Evaluations of Pesticides
Table of Contents
Introduction	2
Overview	3
Step 1 - Proposed Method to Differentiate May Affect (MA) from No Effect (NE) Determinations	6
la: Is the exposure pathway incomplete for all registered uses?	7
lb: Is the species most likely extinct or extirpated?	7
lc: Percent of species range that overlaps with the Action Area is <1%?	7
Species range	8
Identifying pesticide use sites	9
Off-site transport zone	11
Toxicity thresholds	12
Use of <1% Overlap for NE determinations	16
Id: Species range overlaps completely (>99%) with federal lands?	17
le: Are direct effects anticipated? And, If: Are indirect effects anticipated?	18
Step 2 - Proposed Method to Differentiate May Affect and Likely to Adversely Affect (LAA) from May
Affect and Not Likely to Adversely Affect (NLAA)	18
2a: Based on overlap and usage data, is it likely that no individual is exposed on any given year?	19
Species range	19
Agricultural crop uses	20
Non-crop uses	22
2b: Based on weight of evidence, is mortality likely for 1 (or more) individuals? And, 2c: Based on
weight of evidence, is it likely that > 1 individuals will have decreased growth or reproduction on any
given year?	23
Timing of Applications Relative to a Species' Dormancy State or Migration Pattern:	24
Precision of the Species Range Data (GIS Layers):	26
Dietary Considerations:	27
Confidence in the Exposure Assessments:	28
Confidence in the Toxicity Data (Surrogacy):	29
Other Factors:	29
Probabilistic Analysis	31
2d: Are indirect effects likely to impact apical endpoints of an individual?	36
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Introduction
Section 7(a)(2) of the Endangered Species Act (ESA) directs federal agencies to ensure that the actions
they authorize, fund and carry out are consistent with the Act. Specifically, it requires agencies to
consult with the Fish and Wildlife Service (FWS), National Marine Fisheries Service (NMFS), or both to
ensure agency actions are not likely to jeopardize federally listed threatened or endangered species or
destroy or adversely modify designated critical habitat of such species. The action related to pesticide
registrations subject to ESA consultation may be registration or registration review of a pesticide.
Potential risks a pesticide may pose to a listed species and any designated critical habitat associated
with the Agency's action are evaluated in a biological evaluation (BE) conducted by the action agency.
The BE determines whether the pesticide's registration will have 'no effect' on the species or designated
critical habitat or 'may affect' the species or designated critical habitat. The Services regulations provide
that the consultation obligation is triggered when an agency action 'may affect' one or more listed
species or designated critical habitat. May affect is not a defined term, but the Services have provided
guidance suggesting it is any effect on a listed species that is reasonably certain to occur. If EPA
determines the pesticide 'may affect' the species, it refines its assessment to determine whether the
pesticide's use:
•	"may affect, but is not likely to adversely affect" the species or designated critical habitat
(NLAA); or
•	"may affect and is likely to adversely affect" the species or designated critical habitat (LAA).
LAA determinations are made when an effect from a potential exposure that is reasonably certain to
occur is adverse. This may or may not be a quantitative determination and is described in more detail in
subsequent sections in this document.
Once the consultation obligation is triggered by a 'may affect' determination, agencies may engage in
either formal or informal consultation. Under the Services regulations, informal consultation can be
concluded if the action agency (1) finds that the action is Not Likely to Adversely Affect (NLAA) and (2)
the Services concurs in writing. Otherwise, the agency must engage in formal consultation to which the
Services will respond with their biological opinion addressing the likelihood of jeopardy and adverse
modification and establishing what, if any, reasonable and prudent alternatives are available for
engaging in the action in a manner that avoids jeopardy. If the Services find jeopardy, action agencies
must use their existing authorities (e.g., FIFRA) to meet the substantive requirements of the ESA.
This document describes proposed revisions to the interim methods used to conduct effects
determinations as documented in EPA's BEs for federally threatened and endangered species for
pesticides. This proposal is intended to describe methods that will generally be used in the evaluation of
potential risks from pesticides to listed species. However, the risk assessment methods are not a
regulation and, therefore, do not add, eliminate or change any existing regulatory requirements.
Conclusions within a BE will be made on a case- by- case basis that reflects the properties and use
patterns of each active ingredient. As such, every aspect of the proposal may not always be applicable in
a biological evaluation. The risk assessment process remains an iterative process. As such, if during the
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course of the evaluation information indicates that aspects of this method are not appropriate and
different assessment methods or assumptions are suitable, then they may be utilized as appropriate.
These methods are intended to be used by EPA for making effects determinations under registration
review, which will also be used to inform biological opinions from the Fish and Wildlife Service and the
National Marine Fisheries Service.
Overview
Consistent with the pilot process for developing methods and conducting national-level biological
evaluations (BEs), EPA has revisited the method used in the first three BEs (for chlorpyrifos, diazinon and
malathion)1 and has proposed refinements to the interim method. The revised method is summarized in
this document. In revising the interim methods for the BEs, EPA considered public comments provided
through stakeholder meetings and submitted to the docket for the pilot draft BEs, as well as National
Research Council (NRC)2 recommendations. In addition, EPA considered lessons learned during the
development of the first three BEs. The objective of the proposed revised method is to produce both a
sustainable and scientifically defensible risk assessment process to prepare BEs. This method is designed
to identify species that may be affected by the subject pesticide and whether they are likely to be
adversely affected. This method is consistent with the requirements of the ESA and its implementing
regulations, in a manner that remains consistent with the NRC recommendations, while being
responsive to regulatory mandates and public input and that will result in protections for those species.
As recommended by the NRC, the interim methods that were developed by EPA and the Services involve
a three-step consultation process to evaluate the potential risk to Federally-listed threatened and
endangered (listed) species3 (see Figure 1) under Section 7 of the Endangered Species Act. Steps 1 and 2
are represented by the BE, which evaluates whether an individual of a listed species is reasonably
expected to be exposed to a pesticide, and, if so, distinguishes effects that are likely to adversely affect
(LAA) an individual of a species from those that are not likely to adversely affect (NLAA) an individual.
Effects that result in a LAA determination are those that are measurable or observable, and likely to
occur. Steps 1 and 2 are focused on assessing risks to an individual of a listed species. Therefore, the
spatial scale of Steps 1 and 2 is relevant to an individual, which is considered the field level, including the
site of application and the potential areas around the application sites where effects may occur (Table
1). Because Step 2 also considers a distribution of exposures among individuals of a population, the
landscape scale is also relevant to Step 2. For the first three pilot assessments, a field scale assessment
was conducted without much consideration of the likelihood or extent of exposure occurring or the
potential variability and uncertainty in the underlying data used in the assessment. The proposed
advancements to the interim method attempt to consider systematically these factors in a meaningful
way in Steps 1 and 2 of the consultation process.
1	Copies of the first three biological evaluations (chlorpyrifos, diazinon and malathion) can be found at
https://www.epa.gov/endangered-species/implementing-nas-report-recommendations-ecological-risk-
assessment-endangered-and
2	National Research Council of the National Academies (NRC) (2013). Assessing Risks to Endangered and
Threatened Species from Pesticides. The National Academies Press. Washington, DC. Pp. 175.
3	These assessments will also consider those species that are currently proposed or candidates for listing.
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Step 3 is the "biological opinion", which determines whether an adverse effect will jeopardize the
continued existence of a species or destroy or adversely modify its designated critical habitat. The scale
is no longer at an individual level and is focused on assessing risks to the species' population that is
listed as endangered or threatened. The scale of Step 3 is the landscape that represents the range of a
listed species (also considered in Step 2). The BE informs the Services' biological opinion. For listed
species of which a pesticide is LAA for at least one individual, this analysis is structured to inform the
biological opinion, with appropriate modifications to account for population-level, landscape-scale
assessments. Since this document pertains to the BE, the approach presented here describes the
processes for conducting Steps 1 and 2; therefore, additional descriptions of Steps 1 and 2 are included
below.
EPA decides whether and under what
conditions to register pesticide
Figure 1. Three Step Section 7 Endangered Species Act Consultation Approach Based on a Figure in the
National Academies of Science National Resource Council (2013) Report4,
4 National Research Council. 2013. Assessing Risks to Endangered and Threatened Species from Pesticides.
Washington, DC: The National Academies Press, https://doi.org/10.17226/18344
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Table 1. Overview of the 3-Step Section 7 Endangered Species Act Consultation Process
TOPIC
STEP 1
STEP 2
STEP 3
Assessment
Biological Evaluation
Biological Evaluation
Biological Opinion
Scale1
Individual/field
Individual/field and landscape
Population/landscape/watershed
Determination
No Effect/May Affect
Not Likely to Adversely
Affect/Likely to Adversely Affect
No Jeopardy/ Jeopardy2
1	Although Step 1 and Step 2 are conducted at an individual level, consideration is given to the likelihood that exposure is
reasonably certain to occur in Step 1 and the potential consequence of that exposure in the effects determination in Step 2.
2	This is the determination for listed species. The determination for designated critical habitats is "No Adverse
Modification/Adverse Modification".
Step 1 identifies which species and designated critical habitats are reasonably expected to be affected
by the proposed action at the individual-level (warranting a "may affect" determination), and which
species would not be affected by the proposed action (warranting a "no effect" determination). Any
species and/or designated critical habitat that warrants a "no effect" determination is not considered
further. Any species and/or critical habitat that warrants a "may affect" determination in Step 1
continues to Step 2 for further analysis.
Step 2 determines whether use of the assessed pesticide is either "not likely to adversely affect" (NLAA)
or "likely to adversely affect" (LAA) a single individual of a listed species or designated critical habitat. A
NLAA determination can be made if the assessment finds that the effects of a pesticide on an individual
of a listed species is "insignificant," "discountable," or "wholly beneficial."5 These terms are defined by
the Services as follows:
Insignificant = based on best judgement, a person would not be able to meaningfully measure,
detect, or evaluate insignificant effects. Insignificant effects should never reach the level where
take6 occurs.
Discountable = those effects that are extremely unlikely to occur. Based on best judgement a
person would not expect discountable effects to occur.
Beneficial = are contemporaneous positive effects without any adverse effects (even short term)
to the species.
Based on these definitions, EPA concludes whether adverse effects on a single individual of a listed
species in the context of an effects determination are measurable, observable, and likely to occur. The
likely to adversely affect finding is an EPA determination that it is reasonable to conclude, based on the
weight of the evidence, that an individual is likely to be adversely affected. This may or may not be a
quantitative determination. The EPA determination requires concurrence from the Services.
In cases where a species determination is LAA, a Step 3 (population level, landscape scale) analysis is
conducted by the Services. When an analysis leads to an NLAA determination with the Services'
concurrence, no additional analysis is conducted for a species. Steps 1 and 2 are described in greater
detail in the following sections.
5	Based on the Endangered Species Consultation Handbook: Procedures for Conducting Consultation and
Conference Activities under Section 7 of the Endangered Species Act (FWS and NMFS, 1998).
6	From Section 3(18) of the Federal Endangered Species Act: "The term 'take' means to harass, harm, pursue, hunt,
shoot, wound, kill, trap, capture, or collect, or to attempt to engage in any such conduct."
(https://www.fws.gov/midwest/endangered/glossary/index.html)
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Each of the three steps of the process includes four components: problem formulation, effects
characterization, exposure analysis and risk characterization (Figure 1). This is based on the guidelines
for ecological risk assessment (EPA 19987). Although each step in the process has a similar framework
and relies largely upon a common data set, those data are used in a different manner in each step. Step
1 is intended to be a conservative screen that is heavily reliant upon overlap of areas of effect (based on
where the chemical being assessed is likely to be used) with species range/critical habitat. It uses
conservative assumptions and is intended to screen out species that are not reasonably expected to be
exposed and are, therefore, not of concern for the assessed pesticide. Step 2 uses a more refined
spatial-overlap with specific chemical use sites that considers life history information and detailed
toxicity data and potential exposure concentrations. Step 2 is intended to identify those species for
which it is likely that an individual will be adversely impacted. The method of Step 2 is designed to
screen out those species where impacts to an individual are not measurable, observable, or likely to
occur. This allows for a more focused list of species that will be carried forward to the more resource-
intensive analysis carried out in Step 3. The assessment processes proposed for use in the BEs for each
step are described below.
Step 1 - Proposed Method to Differentiate May Affect (MA) from No
Effect (NE) Determinations
This section provides details on how Step 1 is proposed to be conducted. Figure 2 depicts the decision
tree that represents the Step 1 method, by which species determinations are either "no effect" (NE) or
"may affect" (MA). This process is carried out one species at a time for each pesticide. The same process
is carried out for the designated critical habitat. Details on each part (la-lf) of Step 1 are provided
below.
7 USEPA. 1998. Guidelines for ecological risk assessment. United States Environmental Protection Agency, Risk
Assessment Forum. EPA/630/R-95/002F.
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Figure 2. Step 1 proposed decision framework for making No Effect (NE) and May Affect (MA)
determinations. Species with NE determination do not require additional analysis (red ovals indicate
stop). Species with MA determinations proceed to Step 2 (green ovals indicate proceed).
*NE determinations are made if any qualitative analysis (relevant to species) is not expected to
substantially increase the overlap of use sites with species range. The resolution of the available
spatial data does not allow for quantification of overlaps <1% (see lc for details).
la: Is the exposure pathway incomplete for all registered uses? In Step la, the assessor
considers whether the pathway to pesticide exposure is complete. This may consider species
characteristics and uses of the chemical. Examples of species characteristics that would result in an
incomplete exposure pathway (where applications and/or exposures are unlikely to occur) include:
species found only on uninhabited islands and species with habitats that are inconsistent with pesticide
exposure. Additionally, uses with incomplete exposure pathways (e.g., indoor uses; termiticide bait
stations; etc.) are not included in the Action Area for the assessed pesticide. An NE determination is
made for species for which all exposure pathways are incomplete, based on considerations of species
traits and/or registered pesticide use patterns.
lb: Is the species most likely extinct or extirpated? For species that are still listed but are most
likely extinct or extirpated from the United States, NE determinations are made. NE calls are made for
these species, as exposure from the action is not reasonably certain to occur, and, therefore, no effects
on the species are anticipated. A species is considered most likely extinct or extirpated if it:
is presumed by the Services to be extinct,
no longer occurs in the US,
has not been observed for decades in the US, or
exists only in captivity and has no designated critical habitat.
Whether or not a species is most likely extinct or extirpated is based on available information provided
by the Services (e.g., 5-year review). Once a species is identified as being "most likely extinct" or "most
likely extirpated", a NE determination will be made for that species for future pesticides assessed. This
list will be periodically updated as new information becomes available.
lc: Percent of species range that overlaps with the Action Area is <1%? For the remaining
species (that are not NE from parts a and b), an overlap analysis is conducted to determine the percent
overlap of the species range/designated critical habitat with the spatially defined Action Area8 (Figure
3). The Action Area is defined as "all areas to be affected directly or indirectly by the Federal action and
not merely the immediate area involved in the action." For pesticide use and applications, this is the
composite of all the areas where the chemical may possibly be used, based on the best available data,
and associated areas of potential effects. The spatially defined Action Area is a depiction of pesticide use
8 The spatially defined Action Area is composed of landcovers that can be spatially mapped and reliably represent
potential use sites that are on registered labels. These landcovers are from the best available data.
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sites (based on the registered labels and usage data) that can be mapped spatially in the US and its
territories, as well as the areas that potentially receive off-site transport at exposure levels that are of
toxicological concern (based on conservative exposure assumptions). Note that throughout this
document when reference is made to "direct effects" or "indirect effects", the term "direct effects"
refers to toxic effects of a chemical on an individual of the assessed listed species and the term "indirect
effects" refers to impacts to the prey and/or habitat upon which listed species rely. These terms do not
refer to the regulatory definitions of the terms under ESA, which describe the direct and indirect effect
of the action.
Additional details are provided below on the relevant components of the Action Area (i.e., the potential
use sites, application of usage data and calculation of off-site transport areas of concern). Also discussed
below is the source of the species range data.
Figure 3 is a simplification as, for many species, the overlap of range and Action Area will occur in
different areas and may not overlap in time. The overlap may take the form of several disconnected
areas, likely representing several different fields and off-site transport areas. The fields and surrounding
areas of effect will likely differ in size and shape.
Figure 3. Listed Species Range and Action Area [i.e., Pesticide Use Site Plus Off-site Transport Zone)
Overlap
Species range
The species range used in the overlap analyses are provided by the Services. As these spatial data layers
are expected to change over time, the date on which the files were received from the Services will be
referenced as part of the spatial analyses. When a critical habitat has been designated for a listed
species, the spatial file for the designated critical habitat will also be used to make the determination for
the critical habitat.
Off-site
transport zone
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Identifying pesticide use sites
Pesticide use sites are defined using two types of data. The first type are spatial data representing
potential use sites matched to uses defined on registered labels for the assessed pesticide. The second
type are usage data based on documented recent past applications of the assessed pesticide. These two
types of data are further discussed below.
Note that this section relies on two distinct terms: pesticide "use" and "usage." Use data are based on
registered labels and define crops or non-crop uses to which a pesticide may be applied, along with the
maximum application rates, method (e.g., aerial or ground spray), intervals and numbers of applications
that may occur according to the labels. Usage data describe documented applications of a pesticide,
including information such as actual application rates and timing, and spatial distribution of applications
(usually based on survey data). The key difference between use and usage is potential applications vs.
actual applications.
Potential use sites
Spatial data for locations of potential use sites are obtained from numerous sources, with different
sources providing data from different uses and locations. Agricultural crop uses in the 48 conterminous
states are represented by an aggregated dataset based on the Cropland Data Layer (CDL), produced by
the United States Department of Agriculture (USDA)9. The CDL is a land cover dataset that has over 100
cultivated crop classes. The spatial layer is interpreted from satellite imagery, which can be difficult to
interpret. Therefore, the Agency groups the individual CDL layers into 13 categories,10 referred to as Use
Data Layers (UDLs), to improve the accuracy of the data and to help ensure that agricultural fields that
are mis-identified with respect to the crop being grown are captured in the aggregated spatial layer.
These grouped CDL layers are referred to as UDLs (use data layers). In this approach, high confidence
crops (e.g., corn, wheat) are represented individually, while lower-confidence crops (e.g., onions,
tomatoes) are grouped in order to increase the confidence that the landcover represents the intended
crops (e.g., vegetable and ground fruits). The agricultural classes are further refined by comparing
county level National Agricultural Statistics Service (NASS) Census of Agriculture (CoA) acreage reports
to county level CDL acreages, and layers are adjusted to meet or exceed the acres reported in the
Census of Agriculture. This approach results in an overestimate of where a crop is likely to be found for a
given year due to common agricultural practices such as crop rotation and the aggregating of individual
CDLs. This is discussed further below.
Non-crop label uses (e.g., nurseries) include a wide range of land cover and land use categories
depending on the specific use to be spatially represented. Each label use is considered and represented
by the best available land cover data. Initially, the National Land Cover Dataset (NLCD)11 is considered to
9	USDA National Agricultural Statistics Service Cropland Data Layer. 2013-2017. Published crop-specific data layer
[Online], Available at https://www.nass.usda.gov/Research_and_Science/Cropland/SARSla.php (accessed 3/2018;
verified 3/2019). USDA-NASS, Washington, DC.
10	Categories include: corn, cotton, rice, soybeans, wheat, vegetables and ground fruit, other grains, other row
crops, other crops, pasture/hay, citrus, vineyards and other orchards.
11	Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and
Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-
Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v.
81, no. 5, p. 345-354
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represent non-crop label uses. When the NLCD is inadequate, other data sources are used as
appropriate.
Often there are uses for which reliable data are not available to map the locations of the use sites. For
example, a fly bait spread around enclosed dumpsters would not have a specific landcover class and
would need to be mapped using a larger class (such as the "Developed" landcover class) which would
significantly overestimate potential use sites. For these types of uses, for which one cannot reliably
define the spatial footprint of use, but complete exposure pathways are expected to occur for an
individual of a listed species, a qualitative spatial analysis will be carried out. For the fly bait example,
the spatial extent of the fly bait use would need to be evaluated in the context of the other labeled uses
that were assessed quantitatively (i.e., whether the use area is already accounted for by uses
quantitatively assessed).
Pesticide usage data
Usage data specify the location and magnitude of applications of a given pesticide. These data are
pesticide specific and vary by use site and by scale (e.g., state, national level). Agricultural crop usage
data are summarized at the state level. EPA uses best available pesticide usage data from public (e.g.,
USDA, California Pesticide Use Reporting) and proprietary sources (Agricultural Market Research Data)
and provides this data in a document called the National and State Summary Use and Usage Memo
(SUUM). Prior to incorporation into the biological evaluations, EPA evaluates the quality and relevance
of usage data by assessing numerous variables to determine applicability, utility and soundness of the
data. One criticism we have received on the utility of the usage data is that it does not necessarily
predict future use. EPA's method for forecasting relies upon the most recent usage data (generally the
last 5 years of available data) and uses those data to make regulatory decisions. The most recent 5 years
of data are still considered representative of current labeled uses.
In Step 1, the potential agricultural crop use sites are mapped, as described above, using the 13
agricultural UDLs. All relevant agricultural UDLs are identified based on labeled uses. Two data sources,
the NASS Census of Agriculture and the SUUM, are used to remove areas from the agricultural UDLs
prior to combining all potential use sites, both agricultural crop and the non-crop, into the Action Areas
to generate the initial footprint.
1.	The NASS Census of Agriculture is used to identify registered crops based on labeled uses that
are grown at a county level. If all labeled uses within a UDL are not grown in a county according
to the NASS Census of Agriculture, then the county is removed from the UDL.
2.	Based on the SUUM, if a state reported no usage (surveyed but no documented usage) for all
labeled uses in a UDL, the state is removed from the UDL.
In this approach, areas that have either not grown any of the labeled crop uses or that have not
reported usage for any of the currently labeled uses are not considered to meet the standard that the
effect of the action is reasonably expected to occur in those areas.
Not all agricultural crop uses are surveyed. In addition, the same crops are not surveyed in all states.
Thus, past usage data for the assessed chemical are not available for some crop-state combinations. In
these cases where there are no survey data, the potential use site corresponding to the appropriate
landcover category will remain in the pesticide use area footprint for the Step 1 analysis.
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The approach applied for non-crop uses cannot necessarily be applied in the same manner as described
previously for agricultural crop uses due to differences in available landcover and usage data. Examples
of non-crop uses include residential areas (e.g., applications to turf), nurseries (i.e., ornamental
applications), and forestry. For non-crop areas, usage data are available at different scales (e.g., state,
region or nation). Therefore, when usage data are available for a registered non-crop use, modifications
may not be able to be made to the potential use site footprint unless usage at a national level is de
minimis (i.e., very unlikely to occur) or if sufficient surveyed data are available that indicate no usage for
a given use occurs in the US. In that case, the UDL representing the entire use could be removed from
the pesticide use site footprint. There may also be some non-crop uses where data become available to
allow EPA to identify areas where a particular pesticide or labeled use is or is not reasonably expected to
occur.
After removing areas from the agricultural crop and non-crop UDLs using the above process, all UDLs are
combined into the Action Area. The Action Area is buffered for drift based on the process described in
the next section, off-site transport zone.
Off-site transport zone
Toxicity thresholds and spray drift transport are used to determine how far effects might extend from
the edge of a use site. The process for selecting these thresholds is described below.
In areas of overlap of the Action Area and the species range, we assume that taxa upon which a listed
species is dependent are also exposed. Taxa representing potential indirect effects endpoints will be
selected based on life histories of a given listed species (e.g., declines in invertebrate prey will be used
to assess indirect effects to insectivores). The endpoint that results in the farthest distance from the
treated field where any direct or indirect effect may occur relative to a specific species will be used to
determine the Action Area for that species. This distance is capped at 2600 feet (the aerial limit of the
AgDRIFT model; current version 2.1.1, December 2011). AgDRIFT is an empirical model based on
deposition studies that were conducted in the 1990s and upper-level drift estimates for aerial
applications derived from the AGDISP model12. EPA believes that spray drift deposition estimates of
AgDRIFT are conservative and the limits of the model are protective in considering downwind
deposition. While deposition beyond the limits of the models can occur under extreme circumstances,
estimation of deposition should be limited to the extent of the model because the AgDRIFT model is a
regression of interpolated values and going outside the bounds of that interpolation is uncertain.
Species with less than 1% overlap of direct use and drift (discussed further below) based on the most
sensitive relevant taxon will generally be an NE. The "less than 1% overlap" approach is discussed in a
following next section.
Standard EPA models will be used to calculate off site exposure concentrations. Measures of pesticide
exposure to aquatic animals and plants in surface water are simulated with the Pesticide in Water
Calculator (PWC, current version 1.52, February 201613), which generates estimated environmental
concentrations (EECs) that may occur from various uses, typically at maximum use rates allowed on the
label. AgDRIFT is used to assess exposures of terrestrial plants to pesticide deposited in terrestrial
12	Teske, M., Bird, S., Ray, S., Esterly, D., Perry, S. 2003. A User's Guide for AgDRIFT® 2.0.07: A Tiered Approach for
the Assessment of Spray Drift of Pesticides, Regulatory Version. CDI Report No. 01-02. February 2003.
13	USEPA. 2016. Pesticide in Water Calculator User Manual for Versions 1.50 and 1.52. February 25, 2016.
https://www.epa.gov/sites/production/files/2016-05/documents/pwc_user_manual_vl_50andl_52.pdf
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habitats by spray drift, simulating aerial and ground application, as well as spray blast applications to
orchard crops. AgDRIFT is also used to estimate the amount of drift of pesticide into adjacent
waterbodies. This area is represented by the farthest distance from a treated field based on the direct
and indirect effects endpoints included in Table 2.
For broadcast applications that occur for non-crop uses, AgDRIFT and PWC will also be used to estimate
off site transport due to runoff and spray drift. For non-crop uses that do not involve broadcast
applications (e.g., granular applications via a shaker can, spot applications via a spray wand), spray drift
will not be assessed, as the amount of pesticide being transported off site due to spray drift is
considered de minimis and the AgDRIFT model is not designed to assess such applications.
While downstream transport of a pesticide release into surface waters can occur, EPA does not currently
have a tool to accurately account for the advection, dispersion, and dilution expected to occur as the
pesticide mass moves downstream. The first three BEs did assess downstream effects by employing a
screening approach, implemented using the Downstream Dilution tool; however, the tool was
considered provisional [i.e., it has not been fully vetted (it has not been made available to the public) or
validated], and overly conservative (i.e., EPA used Bin 2 EECs as a starting point and assumed that, as the
concentrated mass of pesticide moved down the stream, there was no dissipation or dispersion of the
concentration, unless the next watershed had no use in it). For more information on the downstream
dilution methodology, consult Appendix 3-5 of the diazinon BE14. Where use patterns are extensive, and
thus would occur in a large area, the impacts of downstream transport are not expected to significantly
affect the removal of a species from consideration during Steps 1 and 2 of the BE and would only result
in additional resources being used. In place of the Downstream Dilution tool, EPA will qualitatively
evaluate the potential for downstream impacts to aquatic species in the medium and high-flowing bins
located in areas that have been removed from consideration during Steps 1 and 2 based solely on usage
data, as pesticide may be transported from upstream states where usage occurs to states where there is
no usage.
Toxicity thresholds
The toxicity values, or thresholds, will be based on those available from studies classified as Acceptable
or Supplemental (Quantitative) submitted to the EPA by registrants or from similarly classified open
literature studies identified through the ECOTOX15,16 database. Toxicity data used in the Step 1 and 2
analyses will be based on apical endpoints (i.e., survival, growth or reproduction) or other sublethal
effects that can be quantitatively linked to apical endpoints. EPA is using toxicity endpoints quantifying
effects to survival and reproduction of listed species. Because of the well-understood general links
between the effects of decreased growth on reproduction and survival, EPA believes that growth is an
important sublethal endpoint to consider under this framework. The reproductive and growth effects
that will be considered in the BE are the same as those required in EPA's ecological effects test
guidelines. The endpoints are broad and include, but are not limited to, the following: individual
parental and offspring growth, rate of maturation, embryo/egg production, embryo viability, egg
14	Available online at: https://www3.epa.gov/pesticides/nas/final/diazinon/appendix-3-5.docx
15	For additional information on ECOTOX see: https://cfpub.epa.gov/ecotox/
16	For information on how open literature studies are evaluated and classified, see:
https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-identifying-selecting-and-
evaluating-open
12

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abnormalities, time to hatch, time to swim-up, pathological and histological observations, lactation
performance, and development of secondary sexual characteristics. Additional sublethal effects will be
considered if they can be quantitatively linked to survival, growth or reproduction. This approach is
different from what was done in the Interim method for the first three BEs; however, the revised
approach is consistent with the recommendations of the NRC, which stated:
"An adverse effect should be defined by the degree to which an organism's survival or
reproduction is affected; thus, assessing the effects of a pesticide on a listed species requires
quantifying the effect of the pesticide on survival and reproduction of the species in the wild." [p.
132]
EPA will not be excluding information on other sublethal endpoints (e.g., changes to behavior or enzyme
levels). All endpoints related to survival and all sublethal effects from studies that pass the ECOTOX
screen will be provided in the BEs as an appendix and will be available to the Services for consideration.
However, due to the reasons discussed above, sublethal effects beyond reproductive and growth effects
that are not quantitatively tied to survival and fecundity will not be considered in the BE analyses.
For direct effects to an individual of a listed species, the mortality threshold is used to determine the
Step 1 spray drift distance, calculated as the concentration/dose that represents death to 1 out of the
population (i.e., the concentration likely to result in the death of at least one individual in the
population). For Step 1, the "no effect" threshold for sublethal effects in animals and plants will be
based on the lowest no-observed adverse effect concentration or level (NOAEC/NOAEL) for growth or
reproduction with a corresponding LOAEC available for the taxon being assessed. Table 2 summarizes
the toxicity endpoints used for assessing direct effects in Step 1, as well as Step 2 (the latter is discussed
below). The spray drift distance for Step 1 is based on the more sensitive endpoint of the mortality or
sublethal threshold. The mortality threshold for listed animals will be the concentration that results in at
least one predicted death based on: 1) the LD50/LC50 that corresponds to the lower fifth percentile of a
species sensitivity distribution (SSD; if available) or the most sensitive LD50/LC50 value available for the
taxon being assessed; 2) the slope of the dose-response curve (if a slope is not available, the standard
default slope of 4.5 will be used); and 3) the population size of the species being assessed (if the
population size is not known, a conservative estimate of the population will be made based on available
data). EPA has developed this method to be consistent with the ESA Section 7 regulations that task
action agencies with considering impacts of their actions on an individual of a listed species. In this
approach, if there are two species and all things are equal (e.g., percent of population exposed,
magnitude of mortality among exposed individuals), except their population sizes, the smaller
population would have fewer individuals impacted than the species with a larger population.
When considering indirect effects for listed species that rely on animals (e.g., as prey or pollinators),
effects will be focused on mortality endpoints for the taxa relied upon. For generalists, the endpoints
will be based on the LD50/LC50 that corresponds to the lower fifth percentile of an SSD (if available) or
the most sensitive LD50/LC50 value available for the animal taxa relied upon (using the most sensitive
taxon). The specific threshold for potential indirect effects for generalist species that rely on animals is
set at one-half (0.5) of the mortality endpoint concentration (i.e., there is a potential for indirect effects
when the ratio of the estimated concentration/mortality endpoint >0.5). This ratio is the same level of
concern for animal mortality used by EPA to conduct pesticide risk assessments under the Federal
Insecticide, Fungicide, and Rodenticide Act (FIFRA). For listed species that are obligates with an animal
13

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species (i.e., they cannot survive and/or complete their life-cycle without the obligate species), similar
endpoints are used for determining the potential for indirect effects; however more conservative
thresholds are used (to decrease the chance of failing to detect an effect that may be present). In the
case of obligates, the species threshold for potential indirect effects for obligate species that rely on
animals is set at one-tenth (0.1) of the mortality endpoint concentration (i.e., there is a potential for
indirect effects when the ratio of the estimated concentration/mortality endpoint >0.1).
Indirect effects to a listed species depending upon plants (e.g., for diet, habitat) is focused on growth.
For habitat and plants eaten as dietary items, for generalists, the indirect threshold will be based on the
most sensitive EC5o value for aquatic plants and the EC25 value for terrestrial plants. In this approach, it is
assumed that a 50% decline in biomass of the most sensitive tested aquatic species and a 25% decline in
the most sensitive terrestrial species would constitute an effect that is meaningful to the survival,
growth or reproduction of a listed species. Again, these are the same levels of concern used by EPA in
FIFRA pesticide risk assessments and is meant to be protective. Based on standard evaluation
procedures (SEP) developed by EPA in 198617 18 for aquatic plants and terrestrial plants, a 50% change in
plant growth or injury and a 25% detrimental effect, respectively, are the points at which plants will not
recover to their full aesthetic value, economic value, or reproductive potential, as in the case of the
maintenance of endangered or threatened species. It is notable that this threshold is only applied to a
generalist species and is still based on the most sensitive endpoint of the tested terrestrial or aquatic
plant. For obligates, similar to the direct endpoints for listed species, the NOAEC associated with the
lowest LOAEC will be used to address the potential for indirect effects. As discussed above, a more
sensitive endpoint is chosen for obligate relationships to decrease the likelihood for failing to detect an
effect. Table 2 summarizes the toxicity endpoints used for assessing indirect effects in Step 1.
Reduced animal testing is a priority for EPA. Scientific advancements exist and are being developed that
allow for better predictions of potential hazards for risk assessment purposes without the use of
traditional methods that rely on animal testing. EPA is aggressively pursuing these new approach
methodologies. As the methodologies mature, endpoints from studies using non-animal test methods
that are scientifically sound, fit for purpose in risk assessment, and represent toxicological thresholds on
apical endpoints will be incorporated into the BE process.
17	Hazard Evaluation Division, Standard Evaluation Procedure, Non-Target Plants. USEPA. Office of Pesticide
Programs. June 1986
18	Hazard Evaluation Division, Standard Evaluation Procedure, Non-Target Plants: Growth and Reproduction of
Aquatic Plants..,", OPP, June 1986
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Table 2. Description of toxicity endpoints used for Step 1 and 2 analyses.
Taxon
Exposure route(s)
Units of toxicity
Direct Effects (to taxon)

Indirect Effects (listed species relies upon


endpoints


taxon)




Mortality
Growth/Reproduction
Obligate relationship
General
Birds*
Diet
mg a.i./kg-bw
Lowest available
Step 1: NOAEC from
Lowest available
Lowest available

Dermal
mg a.i./kg-food
LDso/LCso or 5th
lowest LOAEC
LDso/LCso or 5th
LDso/LCso or 5th

Inhalation
lb a.i./A
percentile LD50/LC50

percentile LDso/LCso
percentile LDso/LCso

Drinking water

from SSD (if available)
Step 2: Geomean of
from SSD (if available)
from SSD (if
Mammals
Diet
Dermal
Inhalation
Drinking water
mg a.i./kg-bw
mg a.i./kg-food
lb a.i./A

the Lowest
quantitative NOAEC
and LOAEC

available)
Fish**
Respiration
contact
Uga.i./L




Aquatic
Respiration
Uga.i./L




invertebrates
contact





Terrestrial
Diet
lag a.i./individual




invertebrates
Contact
Mg a.i./g-diet
mg a.i./kg-bw
mg a.i./kg-soil
lb a.i./A




Aquatic plants
Contact
Uga.i./L
Not applicable
Not applicable (no
Step 1: NOAEC from
Lowest quantitative
- non-vascular



listed species)
lowest LOAEC
EC50
Aquatic plants
Contact
Uga.i./L
Not applicable
Step 1: NOAEC from


- vascular



lowest LOAEC
Step 2: Geomean of

Terrestrial
Contact (seedling
lb a.i./A
Not applicable

the Lowest
Lowest quantitative
plants -
emergence)


Step 2: Geomean of
quantitative NOAEC
EC25
monocots



the Lowest
and LOAEC

Terrestrial
Contact (seedling
lb a.i./A
Not applicable
quantitative NOAEC


plants - dicots
emergence)


and LOAEC


*Same endpoints used to represent terrestrial phase amphibians and reptiles, unless taxon-specific data are available.
**Same endpoints used to represent aquatic phase amphibians, unless taxon-specific data are available.
15

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Use of <1% Overlap for ME determinations
For Step 1, the overlap analysis is completed for all species range/designated critical habitat and the
Action Area including the potential areas of effect (using ArcGIS current version 10.3.1). The result of
this analysis is the percent of the species range/designated critical habitat that overlaps with the
spatially defined Action Area and is referred to as percent overlap. In this calculation, the denominator is
the area of the species range/critical habitat and the numerator is the area of overlap. The effects
determination for any listed species or designated critical habitat whose range overlaps <1% with the
area of effects, after considering the quantitative and qualitative (those not quantitatively defined in the
Action Area) analyses, will be an NE determination.
The cutoff of 1% is based on the precision of the available data. As recommended by the NRC, the
spatial analysis leverages authoritative geospatial data to increase accuracy and reliability. Authoritative
data was defined by the NRC as,
"...geospatial data on any scale need to meet three criteria: availability from a widely recognized
and respected source, public availability, and inclusion of metadata that are consistent with the
standards of the National Spatial Data Infrastructure (NSDI)—a federal interagency program [
Federal Geospatial Data Committee (FGDC)] to organize and share spatial data and to ensure
their accuracy [page 10]."
Even when relying on authoritative data sources there are limitations with GIS data. There are three
areas of the method impacted by these limitations: the species location files provided by the Services,
the Use Data Layers (UDLs), and the overlap analysis or quantitative spatial analysis conducted to
combine the species locations information with the UDLs. The accuracies of the available spatial data
need to be accounted for in evaluating the results of the overlap analysis. In this analysis, the 1% cutoff
is based on the level of accuracy of the UDLs, and includes conservative assumptions related to the
Action Area and drift. Additional details are provided below.
Species location files: At this time, the "best available species location information" is represented by
the files provided to EPA by the Services. The 1% cutoff is applied to the overlap based on the full extent
of the range; range files are not altered. There is no accuracy assessment available of the species
location files, as recommended in National Spatial Data Infrastructure provided by the Federal
Geospatial Data Committee (FGDC). The lack of an accuracy assessment introduces uncertainty related
to reporting accuracy of a spatial analysis, which should be based on the lowest level of accuracy among
the datasets used.
Use Data layers (UDLs): The primary spatial data source for the agricultural layers is the Cropland Data
Layers (CDL), which meets the NRC report definition of authoritative data as previously described. To
address some of the uncertainty inherent to the CDL, individual crops are combined into 13 general crop
categories or Use Data Layers (UDL), temporally aggregated across multiple years, and then expanded to
meet or exceed the area reported in the Census of Agriculture. These final UDLs represent anywhere the
crop could be found. However, this is an overestimate of where a crop is likely to be found for a given
year due to common agricultural practices such as crop rotation and the aggregating of individual CDLs
to form UDLs. For non-agricultural uses, a number of data sources were used, leveraging national level
GIS data with accuracy assessments when available. All agricultural and non-agricultural UDLs are
16

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combined into a composite layer to generate the Action Area. The Action Area is buffered to represent
the drift footprint in all directions. The 1% cutoff is applied only to the Action Area and includes drift in
all directions. The conservatism of the UDLs, Action Area, and drift assumptions, likely lead to high
estimates of overlap reducing the risk that a species will drop below the 1% overlap cut-off only because
it is artificially large, e.g. county range files.
Overlap analysis: The third area impacted is the quantitative overlap analysis, or the analysis performed
to combine species location and UDLs. The result of this analysis is the percent of the species
range/designated critical habitat that overlaps with the spatially defined Action Area or UDL and is
referred to as percent overlap. In this calculation, the denominator is the area of the species
range/critical habitat and the numerator is the area of overlap. When conducting this type of
quantitative spatial analysis, it is important to consider the limits of the GIS data used in the analysis, so
the results do not represent accuracy and precision beyond the limits of the data. Calculating the total
area of the species range is only one part of the overlap equation.
Of the two data sources included in the overlap (range data and CDL data), an accuracy assessment has
only been completed for the CDL, which followed the guidance on accuracy and precision of GIS data
outlined by the FGDC to assign the limits of the data. The CDL meets the standards for a 60-meter
accuracy to no decimal places (e.g., not to 60.0 meters). This accuracy value directs the number of
appropriate decimal places to report when conducting a quantitative spatial analysis. In this case, based
on the 60-meter accuracy, reporting overlap below whole numbers, or 1% overlap after rounding, would
be beyond the limits or exactness of the data. To report results down to multiple decimal places, the
accuracy of the underlying data would also need be accurate to a fraction of meter.
Use of 1% as a cutoff is conservative given the assumptions related to the Action Area and drift
discussed previously that lead to an overestimate of potential use areas. Also, because the FGDC
recommends reporting accuracy based on the least accurate dataset, in cases where species ranges may
be more accurate, 1% would still apply. However, in cases where species range data are at a county level
or other coarse scale, the accuracy of the overlap analysis would be lower [i.e., an appropriate cutoff
may be >1%).
Therefore, any overlap <1% is not considered reliable. Cases where overlap is <1% when considering all
the spatially defined uses combined will likely be represented by overlaps of clusters with only a few
pixels and EPA does not believe this constitutes reliable information.
Id: Species range overlaps completely (>99%) with federal lands? ESA Section 7 requires all
federal agencies to consult with the Services for any of their actions that may affect a listed species or
their designated critical habitats. Therefore, the determination for any listed species or critical habitat
that has a range occurring completely (i.e., >99%) on federal lands will be evaluated in the context of
existing consultation obligations, as appropriate protections may already be in place.
This will:
1)	conserve government resources by avoiding analysis that is not needed (i.e., not all federal
agencies will apply all pesticides to federal lands),
2)	minimize duplication of work and
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3) rely on the local expertise of the federal agencies applying pesticides to their lands to avoid
adversely affecting listed species.
le: Are direct effects anticipated? And, If: Are indirect effects anticipated? The purpose of
this part of Step 1 is to determine if a No Effect determination can logically be made for any species,
based on the mode of action and available toxicity data for the pesticide of interest. For example, a
pesticide that has no impact on plants at maximum application rates would not be expected to cause
direct effects to plants. With this example, if the pesticide impacts some animals, there could be indirect
effects to listed plant species that rely upon animals (e.g., for pollination), so an NE determination may
not be appropriate; however, for those listed plant species that do not depend upon animals, NE
determinations may be made (because direct and indirect effects would not be expected).
For species or designated critical habitats that have a MA determination as a result of parts le or If of
Step 1, more refined analyses will be carried out in Step 2. The Step 2 methods are described in the next
section.
Step 2 - Proposed Method to Differentiate May Affect and Likely to
Adversely Affect (LAA) from May Affect and Not Likely to Adversely
Affect (NLAA)
The framework depicted in Figure 4 represents the major parts of Step 2, in which 'likely to adversely
affect' (LAA) or 'not likely to adversely affect' (NLAA) determinations are made for species and critical
habitats with may affect determinations (from Step 1). Compared to Step 1, Step 2 includes more
information, refinement and effort to come to a determination. As discussed above, Step 1 relies upon
reasonably conservative assumptions to identify species for which no effect is expected and those
species for which an individual may be affected. Step 2 involves refinements to the conservative
approach employed in Step 1, with the intent of determining whether an individual of a species is or is
not likely to be adversely affected by the assessed pesticide. As discussed previously, adverse effects
that are measurable, observable, and likely to occur to a species results in a LAA determination. Details
on the Step 2 decision framework (Figure 4) are discussed below.
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Figure 4. Step 2 decision framework for making Likely to Adversely Affect (LAA) and Not Likely to
Adversely Affect (NLAA) determinations. Species with NLAA determinations do not require additional
analysis (red ovals indicate stop). Species with LAA determinations move on to step 3 (green ovals
indicate proceed). Once an LAA determination is made, no additional analyses are carried out for a
species.
2a: Based on overlap and usage data, is it likely that no individual is exposed on any given
year? As discussed previously, Step 1 broadly incorporated usage data by removing counties if the NASS
Census of Ag indicates no labeled uses in the UDL are grown and removing states with surveyed usage
data indicating no usage from those potential UDLs. The objective of this analysis is to go from all
possible use sites (in Step 1) to those sites where pesticide applications are likely (in Step 2) to result in
exposure to an individual of a listed species. Step 2 takes a more refined approach to incorporating
available usage data. As with Step 1, different approaches will be employed for crop and non-crop uses
due to differences in the nature of the available data.
Species range
In Step 2, regarding the species range and anticipated location in the range, consideration is given to a
species expected use of an agricultural crop or non-crop use site. For those species that are not found
19

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on and do not use potential use sites19 (e.g., agricultural fields, residential, forests) for habitat or
resources, those use sites will be removed from the overlap. In cases where a listed species is
determined not to use a crop or non-crop area, it is also assumed that the taxa relevant to indirect
effects are also not exposed on-site (i.e., exposure would only occur off site due to spray drift or runoff
transport). However, exposure may still occur to species and taxa relevant to indirect effects in areas
that receive spray drift and runoff and these areas are still considered in the analysis. In this approach, it
is assumed that indirect effects are only relevant to a listed species in areas where that listed species
occurs.
Agricultural crop uses
In addition to the methods applied in Step 1 with the application of the NASS Census of Agriculture and
the SUUM to remove areas from the agricultural UDLs where usage in not expected, Step 2 continues to
build on the usage data to apply it in a more quantitative manner. For agricultural crop uses, usage data
are available for many uses to quantify the percent of crop acreage that has been treated (PCT). The PCT
can be used to adjust the extent of an area that may overlap with a listed species. PCT data are available
for specific crops and states. These data are applied to the 13 agricultural UDLs discussed above. For
categories represented by single crops (e.g., corn, cotton), the available PCT data for a given state are
applied directly to the acres grown in that state to calculate the acres treated (acres treated = acres
grown x PCT). For those categories representing multiple crops (e.g., vegetable and ground fruit, non-
citrus orchards), an aggregated PCT is calculated. This is accomplished by first calculating the acres
treated for each crop relevant to the UDLs based on the available usage data, summing these treated
acres, then dividing by the total acres grown for the all crops relevant to the UDLs. For crops with usage
data, the acres grown are extracted from the SUUM, for crops without usage the acres grown are
extracted from the Census of Agriculture (USDA-NASS, 2012) 20. Acres treated of the UDL is calculated by
multiplying this aggregated PCT by the area of the UDL for the state. The application of the PCT occurs
after removing counties from a UDL if that county only includes acres for crops within a UDL that are not
registered.
As noted previously in Step 1, some uses are not surveyed at all and some uses are only surveyed in
some states. In such cases, a surrogate assumption may be used. For crops that are surveyed
somewhere in the US but not in the state of interest, a surrogate PCT (e.g., based on survey data for the
same crop in nearby states, or the national PCT for the crop) will be considered. For crops that are not
surveyed anywhere, a surrogate crop with surveyed data will be used if appropriate. For an unsurveyed
crop, the PCT may be derived from crops with similar agronomic practice.
The aggregate PCT is used to calculate the total number of acres treated within a state for each UDL
(PCT x total acres within a UDL = total acres treated for a UDL). The total acres treated will be compared
to the number of acres within a species' range that overlaps with that UDL. If the number of treated
acres in a state is > number of acres of UDL overlapping the species range, it will be assumed that all
acres within the species range that overlap with the UDL are treated. Treated acres are only placed in
counties where registered labeled use occur for the UDL as identified by Census of Agriculture.
19	This is determined by considering available life history of a species, particularly habitat as well as reported
observations of the species on these use sites. Life history and observations are from species-specific
documentation published by the Services (e.g., recovery plans, 5-year plans).
20	USDA NASS. 2012. Census of Agriculture. Available at: www.nass.usda.gov/AgCensus/ (accessed 3/2019, verified
3/2019). USDA-NASS, Washington, DC.
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If the number of treated acres is less than those overlapping with the species' range, it will be assumed
that all treated acres for that UDL in a state occur within the range of a species. Figure 5 illustrates how
this approach assumes that treated acres within a state are concentrated within the species range.
After treated acres are attributed to the species range, the overlap due to spray drift transport is
adjusted to account for the reduced number of treated acres. Basing drift on each individual use layer
greatly overestimates the drift overlap, as many of these areas will overlap with each other. Therefore,
as discussed in Step 1, overlap due to drift is based on buffering the Action Area in all directions.
However, in Step 2, a composite factor will be applied to the drift area based on the aggregated PCTs for
a state for agricultural UDLs, and any factor adjustments possible for the non-crop UDLs. This composite
factor will be used to scale the number of acres impacted by off-site drift and subsequently lower the
total predicted overlap with a species range due to drift. The off-site distance to which drift is buffered
will be based on the uses relevant to the species within the state. The UDL with the highest number of
acres treated in a state will be used to determine the method of application and maximum application
rates for calculating drift distances.
The total number of acres within the species range that are treated or receive spray drift for each state
will be added up and divided by the total number of acres represented by the species range. This can be
considered the percent of the species range that may be exposed to the pesticide of interest. That value
can be multiplied by the best available population size estimate to determine the number of individuals
potentially exposed. If <1 individual is exposed, a NLAA determination is made. If 1 or more individuals
are potentially exposed, then the weight of evidence analysis will be conducted as described in part 2b.
Note that part of the weight of evidence (discussed below) will employ alternative assumptions of how
treated acres may be distributed relative to the range of the species in cases where the species range is
small relative to the range of the state. For example, rather than assuming that all treated acres within
the state are concentrated within a species range, it could be assumed that they are uniformly
distributed throughout the state.
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State y
B
~
Agricultural
area
(potential use
site)
~
~ ~
~ I
Species x
range
Treated site
~
~ ~
~
~ ~
~
~
~
~ ~
~
Figure 5. Conceptual illustration of approach for assigning treated acres to area relative to species
range. In this example, PCT for the potential use site is 10%.
Non-crop uses
Pesticides are registered for a wide variety of uses that are not agricultural (e.g., turf and ornamentals in
residential areas, forestry, rangelands, and nurseries). Data for these types of uses are varied in their
availability and their characteristics. For example, usage data are available in the SUUMs for several of
these uses; however, they vary in scale (e.g., regional, national). For non-crop uses, EPA intends to
compile data from a combination of reliable sources, including production and sales data of formulated
products (reported under section 7 of FIFRA), reported usage from federal agencies (e.g., APHIS, US
Forest Service), and proprietary sources (Agricultural Market Research Data), as available and
appropriate to inform the extent and location of usage.
Of the uses described as "non-crop," those that are represented by the rangeland and forestry spatial
footprints have the greatest extent of overlap with the most listed species. For these cases, assuming
that all of these lands are treated (in the absence of usage data) potentially represents a gross
overestimate of overlap. This assumption could lead to erroneous conclusions when a chemical is not in
fact applied at a large scale to these UDLs. In cases where chemical specific usage data are not available
for rangeland, and forestry, EPA will consider using USDA census data for woodlands for broad classes of
pesticides. For example, available usage data reported in the census for all insecticides could be used to
represent a specific insecticide. This is assumed to be an overestimate, as this would represent
applications of multiple insecticides; however, it would be a more reasonable estimate than assuming
that all acres of these uses are treated. In this case, a similar approach as discussed above for
agriculture, would be used in applying state-level usage data to the rangeland and forestry footprints.
This approach may be supplemented or replaced with chemical specific usage data obtained from other
sources, such as formulated product sales data and usage data reported by federal agencies.
22

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For uses that are spatially represented by the developed landcover class [e.g., residential, gardens, turf,
ornamentals), usage data are also available at the national level. EPA is currently investigating an
approach for adjusting the percent of residential land expected to be treated by accounting for the
proportion represented by impervious surfaces (which are not expected to be treated). A treated area
assumption will be made for these areas based on the percent of a typical lot that is not represented by
impervious surfaces [e.g., houses, driveways). If product-specific sales data or other reliable survey
information are available to provide robust usage data for residential areas, the percent treated area
assumption may be lowered. In some cases, usage data are available on a regional basis for uses
relevant to the developed landcover class. In those cases, regional percent treated areas (PTAs) may be
developed.
For nurseries and other landcovers with a limited spatial extent, the 100% treated assumption will be
reconsidered for those species with >1% overlap. This is because the spatial extent of this use is small
and only overlaps with a limited number of species. Therefore, this use is not expected to lead to
substantial exposure with the majority of species. There are a handful of species with measurable [i.e.,
>1%) overlap with this use. In those cases, the percent of the area likely to be treated will be considered
as part of the weight of evidence. Factors to consider in the weight of evidence include the portion of
the use site (e.g., nursery) represented by surfaces expected to be treated and available usage data from
NASS or other sources discussed above.
2b: Based on weight of evidence, is mortality likely for 1 (or more) individuals? And, 2c:
Based on weight of evidence, is it likely that > 1 individuals will have decreased growth or
reproduction on any given year?
In keeping with the need to determine whether the use of a pesticide is likely or not likely to adversely
affect an individual of a listed species (through direct effects), this portion of the analysis relies upon a
weight of evidence that contains probabilistic elements. The goal of this approach is to account for
major factors that influence the potential exposure of an individual of a listed species and to account for
variation in individual sensitivity.
In addition to the probabilistic assessment of likelihood of exceedance of exposure concentrations
associated with a toxic effect (described further below), this approach focuses on: 1) information that
could impact potential exposure to the pesticide being assessed; 2) assumptions and uncertainties
associated with the current assessment process and available information; and, 3) consideration of the
confidence in the available information and tools related to the specific species/critical habitat being
assessed. The list of factors considered in this WoE approach is not exhaustive. The factors discussed
here were determined to have the greatest potential impact on the effects determinations and are
achievable with available resources (determined using a preliminary exploration of a subset of
approximately 50 species). The factors considered include: timing of application related to the dormancy
or migration pattern of the species, precision of the species range data, dietary considerations,
confidence in the exposure estimates and confidence in the toxicity data. These are discussed in detail
below. The process is set up as a tiered process, where conservative approaches are set up to identify
species for which NE or NLAA determinations can reasonably be made using limited effort and
refinement. As a species proceeds through step 2, refinements to the broad assumptions are made in
order to more clearly understand the species-specific risk picture and uncertainties associated with the
23

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available data and assessment. This is intended to decrease uncertainty in the effects determinations
and will increase the level of complexity, necessary data, and analysis as a species proceeds from lower
to higher tiers. This tiered framework is a standard practice, where resources (time, personnel, data) are
limited and must be used as efficiently as possible. Similar tiered frameworks have been discussed at
FIFRA SAPs21.
Timing of Applications Relative to a Species' Dormancy State or Migration Pattern:
Dormancy State:
Some listed species enter seasonal dormancy states normally related to low temperatures in the winter
(e.g., hibernation, torpor, or diapause). These dormancy states can impact potential exposures of
individuals to pesticides because they are associated with decreased metabolism and cessation of eating
or drinking. Additionally, animals that enter seasonal dormancy states normally are found in protected
areas (e.g., caves, underground burrows, tree crevices) that could limit pesticide exposures - especially
via spray drift. Because most dormancy states are related to late fall, winter, and spring, this factor
should be considered for any pesticide applications made during these times of year in areas where
individuals of a species may enter into one of the dormancy states.
For any pesticide uses that occur in late fall/winter/spring:
The assessor will determine if the species that overlaps with the use(s) enters into a dormancy
state during the time the applications are most likely made. This will be based on information
provided in FWS or NMFS documents (e.g., the species profiles included in the BE, recovery
plans or 5-year reviews) (Figure 6) and available pesticide use and usage information.
o If the species is most likely dormant during the expected pesticide applications to a
given use, the assessor will consider the exposure potential low and will remove this use
from consideration.
¦ The assessor will then determine if there are any other uses that overlap with
species range.
•	If there are no other uses, the assessor will make an NLAA
determination (because the overall exposure to the pesticide is likely
low)
•	If there are other uses, the assessor will continue the analyses with the
remaining uses
o If the species is not likely dormant during the expected pesticide applications, the
assessor will continue the analyses, considering other factors relevant to the Weight of
Evidence (discussed below).
21 FIFRA SAP. 2000. SAP Report No. 2000-02. A Set of Scientific Issues being Considered by the Environmental
Protection Agency Regarding: Session I. Implementation Plan for Probabilistic Ecological Assessment: A
Consultation. August 2. http://www.epa.gov/scipolv/sap/meetings/2000/april/freportapril572000.pdf
24

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Timing of
Application
Relative to a
Species Dormancy
State
during :ke!-.
No :: Co t in
yse?
• ¦?s - Con: "iue
•\hd ¦. ..fi I!;
Rning Jsei-
- Nl ¦\f\
Figure 6. Consideration of Dormancy state.
Migration Pattern:
Some listed species migrate (the seasonal movement of animals from one geographic area to another).
The species range data that are currently available for use in the overlap analyses include the entire
range of a species, without distinguishing seasonal variability. Therefore, for species that migrate,
further consideration will be made to determine if individuals of a species are expected to be in an area
when the pesticide being assessed is most likely applied. This will only apply to those species that have
all individuals of the population migrate during a given season (e.g., population of listed bird species
migrates from a wintering area to a separate breeding area), not to those species where only portions of
the population migrate (e.g., anadromous fish species where some individuals of the population move
between fresh and salt water, largely depending on age). In the former, areas of the range are
completely vacant of individuals of the population during parts of the year; whereas, in the latter,
individuals of a population can be found throughout the range throughout the year.
For all pesticide uses;
The assessor will determine if the species being assessed migrates. The initial focus will be on
birds, because many listed species within this taxon are known to migrate. In addition, this
analysis could also include species from other taxa where all individuals migrate within the same
season. Again, this will be based on information provided in FWS or NMFS documents (e.g., the
species profiles included in the BE, recovery plans or 5-year reviews) (see Figure 7).
o If the species migrates, the assessor will identify where the species is found during the
time when the pesticide applications are most likely made.
¦	The assessor will repeat and continue all of the overlap analyses, only including
the portion of the range with potential exposure (i.e., that part of the range
where the timing of the applications is expected to overlap with the locations of
individuals of the species).
¦	The effects determination will be based on the results of the analysis using only
the portions of the range where the species is when the applications are likely
made.
o If the species does not migrate (at all or at the same time), the assessor will continue
the analyses with the entire range.
25

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Figure 7. Consideration of Migration Pattern.
Precision of the Species Range Data (GIS Layers):
The available species range data varies in granularity (e.g., county-level, township-range level). In
addition, some of the available range data are delineated along geopolitical boundaries and include
specific areas where the species is not likely found. Because the proposed process is highly dependent
on an understanding of the overlap of the species range with likely pesticide applications, the assessor
will consider the precision of the available species range data.
For all pesticide uses:
The assessor will determine if the available species range is wholly or partially at the county
and/or state-level. The focus is on county and state-level ranges because these are geopolitical
boundaries that are easily identified, and an initial analysis indicated that a large portion of the
available range layers are at this level of granularity (see Figure 8).
o If yes, the assessor will determine if the species can be found in many habitat types,
meaning a refined range may look very similar to the state or county range data
available (e.g., bats, wide-ranging carnivores). Again, this determination will be based on
information provided in FWS or NMFS documents (e.g., the species profiles included in
the BE, recovery plans or 5-year reviews).
¦	If yes, the assessor will continue with the analyses
¦	If no, the assessor will continue with the following steps:
•	Explore the available Services' documents (e.g., recovery plans, 5-year
reviews) in ECOS (https://www.fws.gov/endangered/) to determine
where in the county(s)/state(s) the species is most likely located.
•	Determine if the species is only found on federal lands based on
available species location descriptions.
o If yes, the species will be treated as if the species is only found
on federal lands (i.e., no further analyses will be conducted)
o If no, determine if the species is found overlapping with or near
(within 2600 ft of) a potential use site(s). The 2600 ft limit is
based on the limits of the spray drift models available; beyond
this limit potential effects cannot be determined. Because the
overlap analysis using a supplemental species range (i.e., one
limited to specific habitats) relies on a suitable habitat crosswalk
26

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between described species habitats arid GAP22 landcovers
(which is not currently available), this analysis will be
qualitative.
*	If no, the exposure potential is considered low and the
assessor will make an NLAA determination
*	If yes, determine if the potential use site(s) overlapping
with or near the species range is wholly within a non-
federal protected area {e.g., state park, state forest). If
so, this will be considered qualitatively in the overall risk
determination (considering the totality of the available
data and all other analyses).
•	If no, continue with analyses
•	If yes (or it cannot be determined), determine if
the species is only found in a specific habitat. If
so, the overlap analysis will be repeated and will
be limited to the part of the species range that
contains the specific habitat. As a proof of
concept, the analysis will initially focus on
species found only on 'beaches' based on the
National Vegetation Classification Macro Group
and Ecological System/Land use classes of the
GAP Landcover layer. Other levels of the
National Vegetation Classification can be
considered if needed as the process expands to
other habitat types.
•	If no, continue with analyses
Precision of the
Species Range
Data
Is species range
layer wholly or
partially at the
county and/or
state-level?
Yes = Is it found in
many habitat
types?
No = Continue
with analyses
No - Is species
only found on
Federal lands
(using Services'
info)?
Yes = Continue
with analyses
No = Is species
likely within 2,600
ft from a use site
(determine
qualitatively)?
Yes = Is species
found in protected
areas?
Yes = Is species
primarily found in
one habitat-type?
No=NLAA
No = Continue
with analyses
Yes = Treat species
as if found only on
federal lands

Yes = Limit range to
that one habitat
type (start with
'beach'), and
repeat analyses
No = Continue
with analyses
Figure 8. Consideration of the Precision of the Species Range Data.
Dietary Considerations:
The quantitative analysis of Step 1 and the beginning parts of Step 2 (a-c) involving dietary exposures for
terrestrial species is based on the most conservative dietary item. In other words, if a species eats from
more than one dietary category, the category that results in the highest exposure value is used in the
exposure model. Although this is a conservative approach, it may not represent the most likely exposure
for a species that does not rely equally on each food category. Many species, even those that are
22 The USGS Gap Analysis Project (GAP) which consists of mapping three data layers — land cover, predicted
distributions of vertebrate species, and a stewardship layer depicting both location and conservation status of
protected areas. More information at: https://gapanalysis.usgs.gov/
27

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generalists, have food preferences and may rely more heavily on particular food items. This preference
can vary seasonally and/or by life stage. Therefore, for any species that has food preferences, a refined
dietary analysis that focuses on the preferred food items will be considered.
For all pesticide uses:
The assessor will determine if a species relies on specific dietary item(s) (throughout the year or
during times associated with pesticide applications). The identification of dietary preferences
will be based on information provided in FWS or NMFS documents (e.g., the species profiles
included in the BE, recovery plans or 5-year reviews) (see Figure 9).
o If yes, the assessor will focus the dietary exposure analysis on that particular food
item(s). If the risks associated with the preferred dietary item(s) are below the levels of
concern, then an NLAA determination will be made for the species. If the risks
associated with the preferred dietary item(s) are above the levels of concern, then
continue with the analyses [focusing on the preferred dietary category(ies)].
o If no, continue with analyses.
Dietary
Considerations
Does species rely
on one particular
dietary Item for at
least 3 portion of
the year or its life-
cycle?
Yes = focus dietary
exposure on
preferred food
Item, Are risks
below levels of
concefrt?
No - Continue with
analyses
Figure 9. Consideration of a Species Dietary Preferences.
Yes = NLAA
No = Continue
with analyses
Confidence in the Exposure Assessments:
The current exposure models used in this assessment were not designed to estimate exposures for all
types of pesticide applications; all habitat types; or for all potential exposure routes. Therefore, there
may be uncertainty in the exposure values being used for a particular species based on what potential
uses it may overlap with, what type of habitat it is found in, or what the main potential exposure
route(s) might be. Although the uncertainties associated with these factors cannot be quantitatively
assessed at this time, they should be considered qualitatively in the effects determination.
For species and critical habitats that have not been determined to be NE or NLAA based on the above
analyses, the assessor will consider how well the conceptual model of the relevant exposure model(s)
matches up with the specific species being assessed. For example, if an aquatic species is typically found
in a much larger water body than the one being modeled, the actual exposure values may be lower than
the estimated values. If it is determined that the estimated exposures may be higher or lower than
those being estimated for a species, it should be described, with a rationale, and considered in the
effects determination. An example for a terrestrial habitat would be a forest dwelling species. For
chemicals that do not have forestry use, the AgDRIFT model would be used to estimate pesticide
exposure via drift. The conceptual basis of AgDRIFT is a relatively flat, unimpeded field and adjacent
28

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area, where drift is not intercepted by trees or other geographic features. Therefore, AgDRIFT would be
expected to overestimate drift exposure to species that dwell in the interior of forest.
Confidence In the Toxicity Data (Surrogacy):
The toxicity endpoints used in the analyses are largely from broad taxonomic groups (e.g., all birds, all
mammals). Where the data allow, attempts are made to rely on more granular taxonomic groupings -
e.g., separating aquatic-phase amphibians out from fish; separating saltwater fish species from
freshwater fish; or separating mollusks from other aquatic invertebrates.
We explored the option of considering more specific taxonomic groupings (e.g., using salmonid toxicity
data to represent the toxicity to listed salmon species). What we found in our preliminary analyses with
several pesticides, is that for data-rich species that allowed for such an analysis (e.g., salmonids), even
species within the same genus were often found throughout the species-sensitivity distribution for a
broader taxonomic group. Additionally, the search and evaluation of toxicity data for all listed species,
even at the family-level, would be resource prohibitive.
Relying on toxicity data from broad taxonomic groups does introduce uncertainty into the assessments.
However, it is not possible at this time to quantify the uncertainty associated with this surrogacy
approach for each listed species; or to even determine if a specific species being assessed would be
more or less sensitive than the surrogate toxicity values being used would suggest. Therefore, we are
relying on toxicity data from the more sensitive species within each taxonomic group to help ensure we
are being protective of each listed species.
Other Factors:
The factors discussed above are general and are meant to apply to a broad range of species. There may
be other important factors that could impact the effects determinations that are specific to one or a few
species. For species and critical habitats that have not been determined to be NE or NLAA based on the
above analyses, the assessor will consider other species-specific information that could potentially
influence exposures and risks. If any of the species-specific factors are believed to limit potential
exposures and risks to a level that would result in an NE or NLAA determination, the NE or NLAA
determination should be made and a rationale for the call should be provided. The rationale should
clearly state the specific reasons why the factors considered are believed to limit potential exposures
and risk (see Figure 10).
29

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Timing of
Application
Relative to a
Species Dormancy
State
Fof pesticide uses
that occur in late
falt/winter/early
spring
Does use overlap
with a species that
enter* into a
dormancy stale
during likely
appJkdftiofi?
Ye* = femcve use
from further
consideration
Any other uses thai
overlap with
species?
Yes 3 Continue
Analyses with
flemoining Ltoes
No - Continue with
analyses
No = NLAA
Does entire species
migrate at the
same time?
Yes ¦ identify
where the species
Is found during the
time when the
pesticide
applications are
most likely made
No = continue
analyses with
entire range
Repeat and continue
all of the overlap
analyses only
including polygons
with potential
exposure
Precision of the
Species Range
Data
Is species range
layer wholly or
partially at the
county and/or
state-level?
Yes - Is it found in
many habitat
types?
—
j
No - Continue
with analyses
No ¦ Is species
only found on
Federal lands
(using Services'
Info)?
No = Is species
likely within 2,600
ft from a use site
(determine
qualitatively)?
Yes " Continue
with analyses
Yes = Treat species
as If found only on
federal lands
„ Yes s Is species
found in protected
areas?
v	
Yes £ Is species
primarily found in
one habitat-type?
No = NLAA
No = Continue
with analyses
\
Yes - limit range to
that one habitat
type (start with
'beach'), and
repeat analyses
No - Continue
with analyses
Does species rely
on one particular
dietary item for at
least a portion of
the year or its lif#-
eytle?
Yes = locus dietary
exposure on
preferred lood
item. Are risks
below levels of
concern?
Yes-NLAA
No = Continue
with analyses
Consider confidence in
Exposure Assessment
and other factors
No s Continue with
analyses
Figure 10. Additional Exclusion Criteria Approach.
30

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Probabilistic Analysis
The use of probabilistic methods was recommended by the NRC in all steps of the consultation process.
In addition, they recommended that uncertainty be integrated into the exposure and effects analyses so
that the impacts of uncertainty on risk can be recognized and considered. One of the major changes to
the interim method that are reflected here include a probabilistic analysis. It is the EPA's opinion that
this type of analysis is consistent with the NRC recommendations and allows for consideration of the
likelihood that an individual will be adversely affected, which is the goal of step 2 (i.e., to determine
whether a pesticide is likely or not likely to adversely affect an individual).
Probabilistic methods are incorporated as part of the weight of evidence to determine the likelihood of
exposure and effects to an individual of a listed species. The goal of the probabilistic analysis is to more
fully capture and characterize variability in the range of potential risks that can occur based on the
inherent variability in the most influential input parameters used in EPA's models. In contrast to
deterministic methods, the probabilistic analysis will consider distributions of exposure concentrations
as well as toxicological responses among individuals (i.e., differences in individuals sensitivities
influencing the likelihood of individual mortality). The method proposed herein draws conceptually from
previously described methods, including several EPA Scientific Advisory Panels and other documents
(USEPA, 200023-24'25; ECOFRAM, 199926-27'28). The method also employs algorithms described in the
USEPA Terrestrial Investigation Model (TIM; v. 3.029).
As described in the TIM technical manual (Appendix I; USEPA 2015), conceptually, an ecological risk
assessment, or in this case a biological evaluation, may be conducted using a Tiered framework (Tiers I-
IV) where the level of complexity of the analyses increases through the ascending Tiers. A deterministic
Tier I analysis, using a screen of the maximum exposure values to threshold ecological values, is
conducted in Step 1 of the process (Step le: Are direct effects anticipated? Step If: Are indirect effects
anticipated?). For a refined assessment of risks, Tiers ll-IV employ principles of probabilistic analysis with
increasing levels of complexity and specificity. The proposed method described herein can be
considered a Tier II probabilistic analysis. In this approach, variability in some of the more influential
input parameters is quantified, including potential EECs, exposure scenarios and individual species
sensitivities. The method is based on EFED's current standard, conservative field-based models and
23	U.S. Environmental Protection Agency (USEPA). 2000. Technical Progress Report of the Implementation Plan for
Probabilistic Ecological Assessments: Aquatic Systems.
http://www.epa.eov/scipolv/sap/meetines/2000/april/probaq.pdf
24	U.S. Environmental Protection Agency (USEPA). 2000. A Progress Report for Advancing Ecological Assessment
Methods in OPP: A Consultation with the FIFRA Scientific Advisory Panel. Overview Document.
http://www.epa.eov/scipolv/sap/meetines/2000/aprll/probover.pdf
25	U.S. Environmental Protection Agency (USEPA). 2000. Technical Progress Report of the Implementation Plan for
Probabilistic Ecological Assessments: Terrestrial Systems.
http://www.epa.eov/scipolv/sap/meetines/2000/040500 mte.htm
26	ECOFRAM, Aquatic Workgroup. 1999. ECOFRAM Aquatic Report. May 4,1999.
www.epa.gov/oppefedl/ecorisk/index.htm
27	ECOFRAM, Peer Input Workshop. 1999.
http://www.epa.gov/oppefedl/ecorisk/ecorisk ecofram.htm#EcoPeerlnput
28	ECOFRAM, Terrestrial Workgroup. 1999. ECOFRAM Terrestrial Draft Report. May 10,1999.
www.epa.eov/oppefedl/ecorisk/index.htm
29	USEPA. 2015. Technical Description and User's Guidance Document for the Terrestrial Investigation Model (TIM),
Version 3.0 BETA. United States Environmental Protection Agency, Office of Pesticide Programs. Available online
at: https://www.epa.gOv/pesticide-science-and-assessing-pesticide-risks/models-pesticide-risk-assessment#tim
31

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varies parameters that are influential to those models using known distributions. It is noted that there
are other factors (e.g., chemical properties, discrete distribution of aquatic species in water bodies,
simultaneous variation of application rates at a field scale, etc.) that could impact risks that are not
currently being integrated into the probabilistic approach. These types of higher-level Tier lll-IV analyses
may be developed in the future.
Probability simulation (Monte Carlo Analysis)
In the analysis, referred to as a Monte Carlo (MC) analysis, thousands of individuals of a species are
simulated in order to represent the full range of combinations of variables considered in the
probabilistic approach. In the MC analysis, one individual is simulated at a time, with a random value
being drawn from each distribution that is included in the probabilistic approach. This simulation is
completed over and over, each time using a different set of random input values drawn from the
probability functions. Depending upon the number of uncertainties and the ranges specified for them,
the simulation may require thousands or tens of thousands of recalculations to fully describe the
variability associated with an analysis. The Monte Carlo simulation produces a distribution of possible
outcome values, each with an associated probability of occurrence.
For the Monte Carlo analysis completed herein, the number of simulations completed will be
determined by the variables simulated (e.g., 10,000 runs may be completed to fully describe the
variability associated with an analysis, but less runs may be necessary to capture this variability). This is
not meant to represent specific individuals in the population; rather, we are trying to represent the
potential variability in terms of exposure and responses that are relevant to those individuals.
Therefore, it is necessary in some cases to simulate more individuals than are in the population. Impact
to individuals in the population will be calculated post analysis by applying the predicted impact to the
known population size. For many insecticides, which tend to have direct effects primarily on
invertebrate and other animal taxa, it is anticipated the probabilistic analysis will be conducted for only
terrestrial and aquatic animals as needed based on the tiered screening of species. For insecticides,
probabilistic analyses will also be utilized to assess indirect effects to listed plants or animals due to
impacts to animals on which those species depend. For herbicides, which are expected to impact plants,
probabilistic approaches to assessing exposure of animals will also be considered, where appropriate, as
well as consideration of alternative toxicity assumptions based on available data.
Different approaches are used for terrestrial and aquatic habitats due to differences in available models,
habitat characteristics and species behavior. Both approaches integrate exposure information with
dose-response acute toxicity data to determine the likelihood of mortality to an individual. For chronic
effects, this analysis calculates the likelihood that a sublethal effect endpoint (for growth or
reproduction) is exceeded. Details on the exposure and effect considerations of the probabilistic analysis
are provided below.
Exposure Analysis
In determining exposure concentrations, individuals of a species will be randomly assigned to areas of
their range proportional to the percent overlap of the species range with each zone, including being on
site, in the off-site transport (drift) zone or in an area of the species range not impacted by the pesticide.
The exposure will be based on a residue value selected from a distribution of concentrations relevant to
the diet of the organism or aquatic exposure concentrations and the organism's spatial location (e.g.,
on-field, 60-90 m from field). Exposure analyses are conducted differently for species that inhabit
terrestrial and aquatic environments. For those that inhabit both of these environments (e.g.,
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amphibians), each habitat is assessed separately and considered in the overall assessment of the
species.
Terrestrial habitats
In the terrestrial environment, dietary exposure will be drawn from a distribution of concentrations on
food items that are relevant to a species. These concentrations account for variability in residues on
food items located on treated use sites or in the spray drift area. On field, the concentrations will be
based on a residue value randomly selected from a distribution of exposure concentrations relevant to
the diet of the organism using the means and standard deviations as outlined in the TIM manual, which
are the same residue values incorporated in EFED's standard terrestrial exposure tools. Off field, the
same principle will apply, but the dose received by the individual will be decreased based on the
distance from the edge of the field (calculated according to AgDRIFT deposition curves; estimated
exposures would be reduced by the percent reduction estimates from AgDRIFT). For example, if a
species is assigned to a location that corresponds to a deposition of 50% of the application rate, then the
distribution of potential pesticide concentrations will be 50% lower than on-field concentrations.
The probability of an individual being in any zone (zones defined as on the use site, in the off-site
transport zone or in an unaffected area) will be simulated as proportional to the percent overlap of the
species range with each zone. Off-site drift will be analyzed in increments of 30-meter distances away
from a treated field, based on the size of a pixel in the GIS spatial analysis. The likelihood of an individual
of an assessed species being in that area would be proportional to the overlap of the species range with
that zone and the exposure concentration would be drawn from the distribution of predicted EECs at
that distance. For example, using the uniform distribution assumption, if there is a 7% overlap of the use
site with a species range, an individual of a species has a 7% chance of being in that area. Usage data, in
the form of the PCT, will be used to inform the total number of acres that could possibly be treated
within a species range. As described in the usage section above, different assumptions around how
treated acres are distributed within a state in relation to the species range will be considered (e.g., all of
the treated acres are within the area of overlap with the range, outside the area of overlap with the
range or uniformly distributed throughout the area).
The approach for assessing terrestrial exposure uses several approaches already incorporated into TIM,
which is EFED's Tier II and III model for assessing risks of pesticides to birds. The approach used here will
use a simplified version of the method integrated into TIM. Much of this method has been discussed at
several FIFRA SAPs (see Appendix I of TIM manual)30 and integrated into risk assessments used for FIFRA
decisions.
Aquatic habitats
In the aquatic environment, exposure concentrations will be drawn from predicted EECs within a
relevant size water body for a species. Relevant size water bodies are represented by aquatic bins
described in the biological evaluations (BEs) conducted for chlorpyrifos, diazinon, and malathion. For
this refined method, fewer bins will be modeled: bins 2 and 5 will be represented by edge of field runoff;
bins 3 and 4 are represented using the index reservoir; and bins 6 and 7 are represented using the
standard farm pond. Different distributions of EECs will be considered, including maximum annual daily
or time-weighted average (e.g., 21-day or 60-day) EECs and the distribution of EECs will represent the
variability in maximum EECs from year to year. Different methods are used for species depending on if
30 USEPA. 2015. Technical Description and User's Guidance Document for the Terrestrial Investigation Model (TIM),
Version 3.0 BETA. United States Environmental Protection Agency, Office of Pesticide Programs. Available online
at: https://www.epa.gOv/pesticide-science-and-assessing-pesticide-risks/models-pesticide-risk-assessment#tim
33

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they are in flowing or static waterbodies. For the static and low-flow flowing waterbodies, the
distribution of maximum daily EECs from the 30 years of data are used based on the assumption that a
species will not leave that static bin and could be exposed to the maximum exposure concentrations for
a given year. For medium and high-flow flowing waterbodies, there will be movement of the species, as
well as the water, within the water bodies and there is higher variability and uncertainty in the expected
exposure concentrations. In this case, the distribution of daily EECs based on the 90-day window around
the maximum annual daily concentration will be used in the analysis.
Numerous other factors can impact the actual concentrations in a water body under varied application
times, rates and conditions. To try and capture some of this variability, the influence of 2 additional
factors, application date and hydrologic soil group, will be considered in the distribution of EECs. These
factors were chosen as they can have a substantial impact on EECs and are expected to vary
considerably in real world applications. Simulations will be conducted with PWC to determine the EECs
for 1 application at the maximum application rate using the date associated with the month with the
maximum precipitation within a realistic application window for each scenario and bin (details on bins
and modeling assumptions provided in the exposure chapter analysis section of pilot BEs). The same
simulations are run using alternate application dates that would fall within a reasonable application
window (generally April to August, or the relevant application window for the area). Factors are
developed that relate the EEC associated with the original chosen application date to the randomly
selected application date. For example, if the EEC from the original analysis based on a May 1
application date was 80 ng/L and the randomly selected date yielded an EEC of 70 ng/L, the factor
applied would be 0.875 (70/80 = 0.875). A distribution of factors is created based on all the variable
dates modeled. Similarly, an analysis is conducted using a different hydrologic soil group. Original PWC
modeling used a hydrologic soil group that was conservative and generated high levels of runoff. The
sensitivity analysis will look at hydrologic soil groups which may reduce the runoff from a use site,
resulting in lower EECs. In the Monte Carlo analysis, EECs are then drawn from the distribution of
original EECs (modeled at the maximum application rates and wettest predicted month for the HUC) and
multiplied by a randomly drawn factor developed from a variable application date and variable
hydrologic soil group sensitivity analysis. Resulting EECs are defined by the equation below.
Exposure value EEC = EEC from max labeled rate run*app date factor*soil factor
This method is intended to be a more simplified approach to capturing the variability of these factors
without needing to conduct separate PWC runs for each variable every time an EEC is sampled from the
distribution in the Monte Carlo analysis.
Aquatic EECs resulting from spray drift only are estimated using the same algorithms employed in the
Tier I modules of AgDRIFT. Estimates of the average drift across the waterbody width at 30-meter
distances away from a treated field are developed and the product of this average drift and the
application rate, divided by the depth of the waterbody, results in a short-term average concentration in
the waterbody due to spray drift. Similar to the terrestrial analysis, the percent overlap is used as a
surrogate for the percent of the species exposed to an EEC in all water bodies. Locations of individuals
will be similarly modeled with the water body located next to the use site (receiving direct runoff) or in
the spray drift zone from 0 to 2600 ft. The probability of an individual being in any zone will be
simulated as proportional to the percent overlap of the species range with each zone. For individuals of
a species within the area of direct overlap with a use site, the individual would be considered to be
adjacent to the use site, and exposure EECs would be equal to those directly from the PWC output. For
individuals of a species within the spray drift area, EECs will be decreased based on the distance from
the edge of the field at 30 m increments and calculated with AgDRIFT as described above. It is important
34

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to note that aquatic species ranges are not based just on the water body a species occupies, but the
entire catchment that feeds that water body. Therefore, while any direct overlap of a use site with the
range could be anywhere in the catchment, the assumption is conservatively made that the water body
is directly next to the use site.
Toxicity Analysis
Under both the terrestrial and aquatic simulations, a distribution of sensitivities among individuals will
be considered when determining the likelihood of mortality. Toxicity data considered in the Step 2
analysis are listed in Table 2. The mortality endpoint of concern will be based on the dose-response
curve for a given toxicity endpoint (e.g., LD5o representing the 5th percentile of species sensitivity
distribution and associated slope). Additionally, "what-if" scenarios will bound possible results by
calculating the magnitude of mortality among exposed individuals at points on the species sensitivity
distribution (e.g., 5th, 50th percentile) and the confidence limits of the selected LD5o. Similar to the
method used in TIM, a sensitivity will be ascribed to each individual based on the LD50/LC50 and the dose
response curve for the selected LD50.
For sublethal effects, the geomean of the lowest quantitative NOAEC and LOAEC will be used to
determine the likelihood of exceeding this value, given the distribution of exposure concentrations. If
only a LOAEC is available, the LOAEC is used in the simulation.
As noted earlier in Step 1, given the priority for EPA to pursue incorporation of methods that reduce
whole animal testing, endpoints from studies using non-animal test methods that are scientifically
sound, fit for purpose in risk assessment, and represent toxicological thresholds on apical endpoints will
be incorporated into the BE process.
Effects Determinations
Probabilistic analyses will be completed using the most conservative and least conservative assumptions
initially to identify those that are clearly NLAA or LAA. If the most conservative assumptions (e.g.,
maximum labeled application rates, use of upper bound Kenaga values, all treated acres inside overlap
with the species range, etc.) in the probabilistic analysis predict less than 1 mortality or less than 1
individual exceeding the sublethal endpoint or less than the indirect threshold at greater than the 95th
percentile, then the call is NLAA and no further analysis is warranted. Conversely, if the least
conservative assumptions (e.g., typical application rate, single application per year, use of mean Kenaga
values, maximum number of treated acres outside overlap with the species range, etc.) predict greater
than 1 mortality or greater than 1 individual exceeding sublethal endpoint or exceedance of the indirect
thresholds at greater than the 95th percentile, the species call is LAA and no further analysis is
warranted. If the prediction is between these two extremes, then additional factors might be considered
to make the determination (e.g., output based on upper bound vs. mean EECs for terrestrial effects,
variation based on placement of treated acres within or outside the species range, impact of aquatic
scenario selection for crop groups, etc.). Aquatic scenario selection is important as some UDLs have
multiple PWC scenarios that could be applied, and due to differences in EECs among scenarios, exposure
could vary depending upon the scenario used to represent the UDL. Consideration may also be given to
additional output along the distribution (e.g., 90th and 70th percentiles).
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Output for the species will provide probability density functions. In the output, in addition to the
likelihood of impacting more than one individual being provided for a Step 2 determination, the most
likely number of impacted individuals will be provided. This will presumably provide useful information
to carry over into the Step 3 analysis where impacts across the population are considered.
Overall, this analysis is intended to introduce some basic components of probabilistic analysis into the
effects determinations and is not intended to capture all potential variables that could be considered.
Additionally, the results of this analysis are considered as part of the weight of evidence and will be
interpreted in light of other species considerations (e.g., suitability of conceptual model to species
habitat, representativeness of species range, behavior of species, etc.) to make the final effects
determination.
2d: Are indirect effects likely to impact apical endpoints of an individual? Since the focus of
the assessment is on impacts to an individual of a listed species, it is necessary for indirect effects to be
considered in the context of whether impacts to taxa relied upon by the species may result in an impact
to an individual of the listed species. Therefore, the focus of this analysis is on indirect effects that may
impact apical endpoints of a listed individual. For habitat requirements or for species with plants
included in their diets, a 50% decline in growth of aquatic plants or a 25% decline in growth of plants
(based on most sensitive tested species) is assumed to result in decreased cover/availability of food and
decreased likelihood of survival/growth of a listed individual (see indirect effects endpoints in Table 2).
For species that rely upon animals (e.g., for diet, pollination, seed dispersal), for non-obligate
relationships, the specific threshold for potential indirect effects is set at one-half (0.5) of the mortality
endpoint concentration (i.e., there is a potential for indirect effects when the ratio of the estimated
concentration/mortality endpoint >0.5). The mortality endpoint is based on the lower 5th percentile
LD50/LC50 of the SSD (if available) or the lowest available LD50/LC50 for the taxa that are relied upon (using
the most sensitive taxon). Again, this is meant to be protective at a community-level for non-listed
species.
For obligate relationships, more conservative assumptions are made, so the obligate species is treated
as if it were a listed species. The effects endpoints (mortality for animals and growth for plants), and
thresholds for potential indirect effects for obligate species that rely on animals is set at one-tenth (0.1)
of the mortality endpoint concentration (i.e., there is a potential for indirect effects when the ratio of
the estimated concentration/mortality endpoint >0.1). The 'range' of the obligate species will be
assumed to overlap with the range of the listed species to which it is obligated.
When assessing the potential for indirect effects to an individual of a listed species, EPA will consider
diet, habitat and other types of effects. When assessing exposure to terrestrial animal prey (vertebrates
or invertebrates) or pollinators, central tendency exposure estimates (i.e., mean) will be used to
represent potential exposure to dietary items located within the territory of an animal. The mean was
chosen as it is assumed to represent the concentration across the area where the prey may inhabit. For
aquatic food items, daily average EECs generated by PWC will be compared to toxicity endpoints. If an
animal's diet includes multiple food items, the food item representing the most conservative scenario
will be used in the assessment of indirect effects, although the magnitude of a reduction in food
availability as a whole may be considered as well. Impacts to habitat that may be relevant to animals
(e.g., dependency upon mammal burrows) will be assessed using the same method. Additionally, some
of the weight of evidence considerations discussed above (e.g., confidence in exposure estimates,
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confidence in toxicity data, probabilistic analysis, etc.) could be applied to the indirect effects
assessment in a similar manner.
If direct and indirect effects are not likely for an individual of a listed species, a NLAA determination is
made and no additional analyses are conducted. For all species with LAA determinations, Step 3
analyses will be carried out as part of the Biological Opinions (BO). In Step 3, the Services will determine
whether impacts to individuals are likely to result in population-level consequences (i.e., jeopardy, for
listed species, or adverse modification, for designated critical habitats).
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