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Review of Methods for Characterizing Effects of Pesticides and Other 

Chemical Stressors to Aquatic Organisms

United States Environmental Protection Agency

December 20, 2011
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Project Team:

Office of Pesticide Programs, Environmental Fate and Effects Division

Mark Corbin, Kristina Garber, Anita Pease, Sujatha Sankula, Thomas Steeger

Office of Water, Office of Science and Technology

Joe Beaman, Charles Delos, Diana Eignor, Wade Lehmann, Lisa Huff

Office of Research and Development

Russell Erickson, Matthew Etterson, Dale Hoff, Michael Lewis, David Mount, Sandy 

Raimondo, Cindy Roberts, Chris Russom, Charles Stephan, Glen Thursby

Region 9

Patti TenBrook
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1. Executive Summary

The mission of United States Environmental Protection Agency (USEPA) is to protect human 

health and the environment. Consistent with the USEPA‟s mission, the Office of Pesticide 

Programs (OPP) and the Office of Water (OW) are both responsible for scientifically evaluating 

potential effects of chemicals on aquatic life. This is accomplished by OPP through risk 

assessment and OW through aquatic life criteria (ALC) development. For pesticides, data are 

available to inform deterministic, and in some cases, probabilistic risk assessment used in 

pesticide registration by OPP; however, in many cases the data bases used by OPP contain fewer 

studies than OW uses to derive ALC according to OW‟s 1985 Guidelines1. Recent surface water 

monitoring reports (e.g., by the United States Geological Survey‟s National Water-Quality 

Assessment Program) for certain chemicals, including many pesticides, have prompted 

stakeholders to raise questions as to the possible risks these chemicals pose to aquatic 

communities. No ALC have been established for many of these chemicals, which impedes the 

ability of stakeholders to interpret the potential effects on aquatic communities where the 

chemicals are detected. OPP‟s Aquatic Life Benchmarks, which are based on toxicity values

used in OPP‟s most recent risk assessments developed as part of the decision-making process for 

pesticide registration, allow for comparison of measured concentrations to acute and chronic 

endpoints for fish, invertebrates and plants; however, they are not designed to represent 

community level benchmarks.

This effort involves exploration of several approaches that may be considered with varying 

amounts of data for OPP and OW to characterize the effects of chemicals on aquatic organisms. 

Emphasis is placed on situations where data that OPP would regard as sufficient are available to 

conduct risk assessments, but OW would not have a data set that, according to the 1985 

Guidelines, would support derivation of ALC. This paper analyzes the potential uses of 

predictive tools, i.e., Quantitative Structure Activity Relationships (QSARs) and Interspecies 

Correlation Estimation (ICE), for estimating the acute toxicity of aquatic animals exposed to 

pesticides. This paper also explores the potential applications of species sensitivity distributions 

(SSDs) and extrapolation factors (EFs) for characterizing the effects of chemicals within taxa 

and communities of aquatic animals and plants. In addition, this paper investigates the uses of 

acute-chronic ratios (ACRs) to estimate chronic effect thresholds for aquatic animals lacking 

chronic toxicity data. Particular emphasis is placed on the ability of these approaches to reliably 

predict the fifth percentiles of sensitivity distributions of toxicity data (termed the “HC5”), which 

are intended to be representative of more sensitive species in aquatic communities and serve as

the assessment endpoint used by OW for derivation of ALC. Potential influences of pesticidal 

modes of action (MOA) on the utility of these methods are also considered. 

This white paper describes analyses of some readily available QSAR tools that may be used to 

predict acute toxicity values for sensitive taxa. Three publicly available QSAR models were 

examined, including: Ecological Structure Activity Relationships (ECOSAR), Toxicity 

Estimation Software Tool (TEST), and the Organization for Economic Cooperation and 

Development (OECD) QSAR Toolbox. These tools are increasingly used by USEPA risk 

assessors to determine the potential toxicity of various chemicals, including pesticide degradates. 

 

1 Guidelines for Deriving Numerical National Water Quality Criteria for the Protection of Aquatic Organisms and 

Their Uses (USEPA 1985), referred to as the 1985 Guidelines
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The white paper‟s analyses of ECOSAR, TEST and the OECD Toolbox suggest that, at this time, 

1) the chemical domains for most models within these QSAR tools do not include sufficient data 

for pesticide active ingredients and therefore these models are not currently robust estimators of 

the toxicity for modes of action (MOAs) that are unique to pesticides (e.g., acetylcholinesterase 

(AChE) inhibitors), 2) current models are generally populated with data for species for which 

toxicity data are typically submitted under the Federal Insecticide, Fungicide Rodenticide Act 

(FIFRA), and generally, could not be used to fulfill other taxonomic data requirements defined in 

the 1985 guidelines, and 3) more toxicologically relevant molecular descriptors and structural 

alerts need to be identified for use in building models for MOAs associated with pesticide active 

ingredients.

The work involved investigation of the use of Web-based Interspecies Correlation Estimation 

models (Web-ICE) to predict acute toxicity values. The review of the current version of WebICE suggests that, when using the acute toxicity data for aquatic animals that are typically 

submitted to fulfill requirements under FIFRA (i.e., data for Daphnia magna, rainbow trout 

(Oncorhynchus mykiss) and bluegill sunfish (Lepomis macrochirus)), Web-ICE appears to 

generate the most precise predictions of toxicity for closely related fish species (e.g., within the 

same family). If toxicity data are available for either rainbow trout or bluegill sunfish, Web-ICE 

may be useful for predicting the toxicity of a chemical to some species of fish. The current 

review of this tool indicates that Web-ICE does not have sufficient data to fulfill the minimum 

data requirements across all taxa defined in the 1985 Guidelines when there are only acute 

surrogate data available for rainbow trout, bluegill and D. magna. 

For this effort, USEPA explored the uses of SSDs with varying amounts of empirical data for 

acute exposures to aquatic animals to estimate the HC5. Analyses performed by USEPA for this 

effort suggest that SSDs have limited utility where biological diversity is not sufficient to 

represent the distribution of taxa sensitivity. The analyses further suggest that SSDs applied to 

datasets that satisfy existing taxonomic requirements with extrapolation constants (not 

extrapolation factors) would likely result in lower and yet reasonable approximations of the HC5.

Several potential avenues for future work to improve the utility of SSDs are described, including 

i) extension of methods for ordered data to all continuous distributions ii) development of 

stepwise methods, including decision points, for data analysis to incorporate information on the 

relationship of MOA and taxa sensitivity, and iii) the use of random effects methods for handling 

cases in which more than one test result is available for a given taxon.

Of particular interest to this effort is the set of EFs developed by Host et al. (1995) and 

incorporated into the Great Lakes Water Quality Guidance (GLWQG: USEPA 1995), 

specifically for deriving Tier II values. The analysis by Host et al. (1995) included toxicity test 

datasets used in freshwater ALC that existed at that time; these analyses involved repeatedly 

subsampling (resampling) these datasets to assess the relationship of the lowest toxicity values in 

datasets of a specified size and composition to Final Acute Values (FAVs; approximation of an 

HC5 that is derived according to the methods described in the 1985 Guidelines) based on larger 

datasets. The analyses described in this white paper build upon the analysis conducted by Host 

et al. (1995) by (a) utilizing more recent aquatic toxicity data and data for chemicals that do not 

have an ALC, (b) developing EFs for different MOAs to reduce uncertainty, (c) paying specific 

attention to EFs for OPP minimum data requirements for acute toxicity testing of aquatic 
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animals, and (d) generating EFs using statistical distributions descriptive of acute toxicity values 

for different taxonomic groups and based on multiple chemicals, rather than resampling toxicity 

data for individual chemicals. These analyses established the feasibility of establishing EFs for 

certain MOAs, demonstrated how a distributional rather than a resampling approach can more 

effectively exploit data to develop MOA-specific EFs, and documented the relationship between 

OPP and OW benchmarks. In addition, the current efforts considered how EF applicability and 

meaning might be affected, relative to the GLWQG, by different strategies for assigning 

reference HC5 and for summarizing EF distributions. 

Chronic toxicity data are generally available for fewer species than are tested to determine acute 

toxicity. ACRs are a common tool used to estimate chronic effect thresholds for taxa lacking 

chronic toxicity data. Both OPP and OW currently use ACRs developed for specific chemicals 

to estimate chronic effect levels. ACR distributions have been developed for broad groups of 

chemicals without consideration of MOA or taxa. This white paper considers the application of 

ACRs developed for chemicals within the same MOA to estimate chronic effects for chemicals 

with the same MOA and lacking any chemical-specific ACRs. Analysis of ACR data,

specifically for AChE inhibitors, identified patterns in the ACR distributions. Most notably, 

ACRs for invertebrates were smaller than those for fish. Although fish had larger ACRs, this did 

not translate to higher chronic sensitivity of fish to AChE inhibitors, because of their very low 

acute sensitivity. In fact, analyses showed that concentrations protective of acute toxicity to 

invertebrates would simultaneously protect fish from chronic effects. By extension, for AChE 

inhibitors, applying an invertebrate-based ACR to the acute HC5 should provide a chronic HC5 

protective of both fish and invertebrates. When comparing ACRs developed for broad groups of 

chemicals and taxa to ACRs for aquatic invertebrates exposed to AChE inhibitors, they were 

approximately 2 fold different. 

This white paper explores potential uses of EFs and SSDs to estimate the HC5 relevant to aquatic 

plants. The primary analyses investigated the use of the standard aquatic plant species submitted 

to fulfill FIFRA data requirements in estimating the HC5. These species, which are referred to in 

the white paper as “the FIFRA-5” include: Pseudokirchneriella subcapitata (a freshwater green 

alga), Anabaena flos-aquae (a freshwater cyanobacterium), Navicula pelliculosa (a freshwater 

diatom), Skeletonema costatum (a saltwater diatom) and Lemna gibba (the aquatic vascular plant, 

duckweed). There was no pattern among the FIFRA-5 species for their relative sensitivity, and 

there was no species that was consistently the most sensitive. Of ten chemicals, the most 

sensitive of the FIFRA-5 ranged from the lower 4th to the lower 31st percentile of the SSDs for 

those chemicals. The analysis suggested that estimating the HC5 using the FIFRA-5 will result in 

a reasonable approximation of the HC5 of an aquatic plant community. Although use of EF

values consistently generates conservative estimates of these HC5 values, the EF approach is 

limited for predicting the HC5 for aquatic plant communities because it is based on a limited 

number of chemicals and, unlike EF approaches involving aquatic animals, the magnitude of the 

EF does not change with addition of other test species.
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Table of Contents

1. Executive Summary................................................................................................................ 3

2. List of Acronyms Used in this White Paper ........................................................................... 8

3. Introduction............................................................................................................................. 9

3.1. Current OW and OPP Procedures for Characterizing Effects of Chemical Stressors on 

Aquatic Organisms.................................................................................................................... 10

3.1.1. OPP ......................................................................................................................... 10

3.1.2. OW.......................................................................................................................... 12

3.2. Common Effects Project ................................................................................................ 13

3.3. Great Lakes Water Quality Guidance ............................................................................ 15

3.3.1. Extrapolation Factors.............................................................................................. 15

3.3.2. ACRs....................................................................................................................... 16

3.4. Purpose and Organization of this Document.................................................................. 17

4. Methods for Predicting the Toxicity of Chemicals to Species of Aquatic Animals............. 18

4.1. QSARs............................................................................................................................ 18

4.2. ICE ................................................................................................................................. 19

5. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data for 

Animals......................................................................................................................................... 21

5.1. HC5 Estimation Based on Characterization of Species Sensitivity Distribution........... 21

5.2. HC5 Estimation Based on Extrapolation Factors........................................................... 23

5.2.1. EFs for AChE Inhibition by Resampling Approach ............................................... 24

5.2.2. EFs by Resampling Approach versus Distributional Approach ............................. 26

5.2.3. Comparison of AChE-Inhibition and Narcosis MOAs........................................... 26

5.2.4. Comparison of AChE-inhibitor EFs to GLWQG EFs............................................ 27

6. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals.... 27

7. Methods for Estimating the HC5 for Aquatic Plants Using Data Submitted to Fulfill FIFRA 

Data Requirements........................................................................................................................ 31

7.1. Use of FIFRA-5 species to Estimate HC5 ..................................................................... 32

7.1.1. SSDs........................................................................................................................ 32

7.1.2. EFs .......................................................................................................................... 34

7.2. Standardization of endpoints.......................................................................................... 35

8. Conclusions........................................................................................................................... 35

8.1. Methods for Predicting the Acute Toxicity of Chemicals to Aquatic Animal Species 35

8.2. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data 

for Animals ............................................................................................................................... 36
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8.3. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals

36

8.4. Methods for Estimating the HC5 for Aquatic Plants using Data Typically Submitted to 

Fulfill FIFRA Data Requirements ............................................................................................ 37

9. References............................................................................................................................. 37

Appendices:

A. Review of predictive approaches for use in common effects framework: QSAR Tools

B. The Use of Interspecies Correlation Estimation (ICE) in Interpreting Chemical Risks to 

Aquatic Species

C. Analysis of Sensitivity Distributions for Estimation of Acute Hazard Concentrations to 

Aquatic Animals

D. Extrapolation Factors for Derivation of Acute Aquatic Life HC5s, with Emphasis on 

Acetylcholinesterase Inhibitors

E. Analysis of Chronic Toxicity Data and Acute Chronic Ratios (ACRs) in Support of 

Deriving Chronic HC5s: Acetylcholinesterase Inhibitors

F. Estimating Aquatic Plant Community Hazard Concentrations for Pesticide Effects
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2. List of Acronyms Used in this White Paper

AChE Acetylcholinesterase

ACR Acute-Chronic Ratio

AIC Akaike‟s Information Criterion

AICc Akaike‟s Information Criterion, corrected for small sample size

ALC Aquatic Life Criteria

CCC Criterion Continuous Concentration

CMC Criterion Maximum Concentration

CWA Clean Water Act

EC50 Median effects concentration

ECOSAR Ecological Structure-Activity Relationships

EEC Estimated Environmental Concentration

EF Extrapolation Factor

FACR Final Acute Chronic Ratio

FAV Final Acute Value

FCV Final Chronic Value

FIFRA Federal Insecticide, Fungicide and Rodenticide Act

FPV Final Plant Value

GLWQG Great Lakes Water Quality Guidance

GMAV Genus Mean Acute Value

GMCV Genus Mean Chronic Value

HC5 The 5th percentile of a sensitivity distribution, HC stands for “hazard

concentration”

ICE Interspecies Correlation Estimation

LC50 median lethal concentration

LOC Level of Concern

LOEC Lowest Observable Effect Concentration

MDR Minimum Data Requirement

MOA Mode of Action

NOEC No Observable Effect Concentration

OECD Office of Economic Cooperation and Development

OW Office of Water

OPP Office of Pesticide Programs

QSAR Quantitative Structure-Activity Relationship

RQ Risk Quotient

SACR Secondary Acute Chronic Ratio

SMAV Species Mean Acute Value

SSD Species Sensitivity Distribution

TEST Toxicity Estimation Software Tool

USEPA United States Environmental Protection Agency
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3. Introduction

The mission of the USEPA is to protect human health and the environment. Consistent with the 

USEPA‟s mission, OPP and OW are both responsible for scientifically evaluating potential 

effects of chemicals on aquatic life. This is accomplished by OPP through risk assessment and 

OW through ALC development. For pesticides, data are available to inform deterministic, and in 

some cases, probabilistic risk assessment used in pesticide registration by OPP; however, in 

many cases the data bases used by OPP contain fewer studies than OW uses to derive ALC 

according to OW‟s 1985 Guidelines (USEPA 1985). The data and methods used by the two 

offices for risk assessment and ALC development are described in the next section of this 

introduction.

Currently, OW has 47 established national recommended ALC2. Of these, 16 are pesticides that 

are currently registered for use in the US. Meanwhile, OPP has registered over 1000 active 

ingredients that may be used in the US. Surface water monitoring reports (e.g., by the United 

States Geological Survey‟s National Water-Quality Assessment Program) for certain chemicals, 

including many pesticides, have prompted stakeholders to raise questions as to the possible risks 

these chemicals pose to aquatic communities. No ALC have been established for many of these 

chemicals, which impedes the ability of stakeholders to interpret the potential effects on aquatic 

communities where the chemicals are detected. Stakeholders have expressed a need for guidance 

from the USEPA to allow them to gauge whether these chemicals represent a concern for aquatic 

life. To that end, OPP‟s Aquatic Life Benchmarks3allow for comparison of measured 

concentrations to acute and chronic endpoints for fish, invertebrates and plants; however, they 

are not designed to represent community level benchmarks. Although some ALC have been 

developed for pesticides (e.g., diazinon, acrolein), a major impediment to the development of 

ALC often exists: the amount of toxicity data that is generally available for most chemicals is 

insufficient to meet the minimum data requirements (MDRs) that are defined in the 1985

Guidelines for developing ALC. For pesticides, the only available data may be from the studies 

required under FIFRA for the registration of pesticides. Data defined by the 1985 Guidelines for 

the development of ALC are more extensive than the data required under FIFRA for aquatic 

animals, not plants. 

The purpose of this white paper is to explore approaches that may be considered with varying 

amounts of data to characterize the effects of chemicals on aquatic organisms. Emphasis is 

placed on situations where data that OPP would regard as sufficient are available to conduct risk 

assessments, but OW would not have a data set that, according to the 1985 Guidelines, would 

support derivation of ALC. Particular emphasis is placed on the ability of these approaches to 

reliably predict the HC5, which is intended to be representative of sensitive species in aquatic 

communities and serves as the assessment endpoint used by OW for derivation of ALC. 

Potential influences of pesticidal MOA on the utility of these methods are also considered.

 

2 Current National Recommended Water Quality Criteria are available online at: 

http://www.epa.gov/waterscience/criteria/wqctable/index.html 

3 Available online at: http://www.epa.gov/oppefed1/ecorisk_ders/aquatic_life_benchmark.htm
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3.1. Current OW and OPP Procedures for Characterizing Effects of Chemical 

Stressors on Aquatic Organisms

3.1.1. OPP 

The objective of FIFRA is to provide Federal control of pesticide distribution, sale and use. All 

pesticides used in the US must be registered by USEPA. Registration assures that pesticides will 

be properly labeled and that, if used in accordance with label specifications, will not cause 

unreasonable adverse effects on human health and the environment. Each registration specifies 

the crops or sites on which a pesticide may be applied, and each use must be supported by data

that the pesticide registrant is required to submit to the USEPA. Categories of required data 

include product chemistry, environmental fate, residue chemistry, dietary and non-dietary 

hazards to humans, and hazards to domestic animals and non-target organisms. All studies 

submitted by registrants must be conducted under conditions that meet Good Laboratory Practice 

regulations.

For ecological risk assessments of pesticides, OPP relies upon toxicity data submitted by 

registrants of pesticides and any data available from the open literature. Toxicity data are 

submitted in order to provide the USEPA with an understanding of the toxicity of a pesticide to 

animals and plants (non-target) that inhabit aquatic and terrestrial habitats. Since the focus of this 

effort is on aquatic habitats, only the data relevant to aquatic animals and plants will be discussed 

further. 

Most baseline datasets for chemicals submitted by a registrant are comprised of acute toxicity 

data for two freshwater fish, which are most commonly represented by rainbow trout and bluegill 

sunfish and one freshwater invertebrate (D. magna). Chronic toxicity data are generally 

available for a freshwater fish (rainbow trout or fathead minnow (Pimephales promelas)) and 

invertebrate (D. magna). Acute data for estuarine/marine species typically include one 

estuarine/marine fish, which is most commonly represented by sheepshead minnow (Cyprinodon 

variegatus) and two estuarine/marine invertebrates, which are most commonly represented by 

mysid shrimp (Americamysis bahia) and Eastern oyster (Crassostrea virginica). In addition,

chronic toxicity data are often available for the sheepshead minnow and mysid shrimp. For 

aquatic plants, data are submitted for one vascular plant, typically duckweed and four species of 

nonvascular plants, including blue-green algae, green algae and freshwater and marine diatoms. 

These plant species are referred to in this white paper as the “FIFRA-5”. It should be noted that 

these are the test species of animals and plants that are typically tested in registrant-submitted 

studies. For some pesticides, a broader range of data for other test species may be available from 

registrant-submitted studies and open literature; however, for new pesticides, typically available 

data are often registrant-submitted studies that fulfill the requirements under FIFRA4. 

OPP‟s aquatic risk assessments evaluate effects of pesticides to individual taxa and are based on 

the most sensitive taxa-specific endpoints for freshwater and estuarine/marine species. Reliance 

on the most sensitive species is intended to be conservative and does not reflect intra- and inter-

 

4 OPP's data requirements are set forth in Title 40 of the Code of Federal Regulations (CFR) Part 158. USEPA has 

authority to ask for additional data or waive requirements, as appropriate. FIFRA section 3(c)(2)(B) provides the 

authority to require additional data on currently registered products. 
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species variability (USEPA 2004). OPP‟s risk assessments are based on an integration of 

exposure and ecological effects characterizations to determine the potential taxa-specific 

ecological risks from the uses of pesticides and the likelihood of direct and indirect effects to 

non-target organisms in aquatic habitats. Exposure and toxicity data for pesticides are integrated 

in order to evaluate the risks of adverse ecological effects on non-target species. In this 

approach, risk is determined using risk quotients (RQs) which are calculated as the ratio of 

exposure to effect, i.e., estimated environmental concentrations (EECs) are divided by acute and 

chronic toxicity values. For acute exposures, the toxicity endpoint is the median lethal 

concentration (LC50) or median effect concentration (EC50) and for chronic exposures, the 

toxicity value is the No Observable Effects Concentration (NOEC). The resulting RQs are then 

compared to the Agency‟s LOCs (USEPA 2004; Table 1). This method is used to indicate when 

the use of a pesticide, as directed on the label, may have the potential to cause adverse direct or 

indirect effects to non-target organisms. 

 

Table 1. Agency risk quotient (RQ) metrics and levels of concern (LOC) for aquatic 

animals and plants.

Risk Class Risk Description RQ LOC

Acute -

Animal

Potential for effects to non-listed animals 

from acute exposures Peak EEC/LC50 0.5

Acute - Listed 

Animal

Federally-listed endangered and 

threatened (listed) species may be 

potentially affected by acute exposures

Peak EEC/LC50 0.05

Chronic -

Animal

Potential for effects to non-listed and 

listed animals from chronic exposures 

60-day EEC/NOEC (fish)

1

21-day EEC/NOEC 

(invertebrates)

Non-Listed 

Plant

Potential for effects to non-target, nonlisted plants from exposures EEC/ EC501

Listed Plant Potential for effects to non-target, listed 

plant species from exposures EEC/ NOEC or EC05 1

OPP routinely uses ACRs in ecological risk assessments for pesticides to predict chronic toxicity 

for aquatic animals when chronic toxicity data are not available for a species of interest, for 

example, when the acute toxicity profile indicates that the most sensitive surrogate aquatic 

species was not tested in a chronic study. The ACR is calculated using toxicity values from valid 

acute (LC50s or EC50s) and chronic studies (NOECs), ideally from the same test species. This 

ACR is then applied to species within a similar group (e.g., freshwater fish).

When sufficient data exist, assessments may be refined using probabilistic methods, including 

SSDs to provide a description of the range of sensitivities within and between taxa (e.g., USEPA 

2002, 2007). OPP‟s risk conclusions have not typically relied on SSDs to quantitatively 

characterize the potential effects of pesticides, but rather to qualitatively characterize the 

conservatism of the most sensitive toxicity endpoints. 

In response to recommendations and input from stakeholders, OPP developed a webpage of nonregulatory taxa-specific endpoints referred to as “OPP Aquatic Life Benchmarks.” OPP‟s 
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benchmarks may be useful for interpreting the potential effects of pesticides in surface water in 

cases where there is no existing ALC. These Benchmarks are based on the most sensitive acute 

and chronic toxicity test results for fish and invertebrates (considering registrant-submitted 

studies and the scientific literature) from OPP‟s ecological risk assessments of specific 

pesticides. In the context of OPP‟s risk assessment, any water concentrations exceeding a 

benchmark for a specific taxon have potential to cause adverse effects to that taxon. 

3.1.2. OW 

The objective of the Clean Water Act (CWA) is to restore and maintain the chemical, physical, 

and biological integrity of the nation's waters. CWA section 101(a)(2) sets an interim national 

goal of achieving, wherever attainable, water quality which provides for the protection and 

propagation of fish, shellfish, and wildlife and provides for recreation in and on the water (which 

is often referred to simply as "fishable and swimmable"). CWA section 304(a) requires USEPA 

to develop and publish ALC recommendations in the Federal Register to be used as guidance 

that can be adopted by States into water quality standards by a rulemaking process to protect 

designated uses. These criteria must reflect the latest scientific knowledge and do not consider 

the social and economic impacts, or the technological feasibility, of achieving that concentration 

of a chemical in the surface water. ALC define water column concentrations to protect against 

adverse ecological effects to aquatic life as a result of exposure from direct contact or ingestion 

of the chemical of concern in the water column. USEPA interprets ALC that protect the 

designated use for "fishable" waters as criteria that protect the aquatic assemblage in a water 

body, i.e., not the protection and preservation of every individual of every species in a water 

body. USEPA published the 1985 Guidelines (USEPA 1985) to provide uniformity and 

transparency to this process. ALC are usually derived from laboratory toxicity tests using 

specific standard procedures described in the 1985 Guidelines, but the 1985 Guidelines also have 

general provisions for deviating from these procedures as warranted by the available science.

ALC consist of two concentrations: the Criterion Maximum Concentration (CMC) that is 

intended to protect against severe acute effects, and the Criterion Continuous Concentration 

(CCC) that is intended to protect against longer-term effects on survival, growth, and 

reproduction. The acute criterion limits peak exposures by generally requiring that 1-hour 

averages of exposure concentrations not exceed the CMC more often than once in three years on 

average. The chronic criterion limits more prolonged exposures by requiring that 4-day averages 

of exposure concentrations not exceed the CCC more often than once in three years on average.

The CMC is based on available acute toxicity values for aquatic animals. These values are 

median lethal concentrations (LC50) or median effect concentrations (EC50 for detrimental acute 

effects such as immobilization) from acute toxicity tests (48- to 96-hrs duration) meeting certain 

data quality requirements. To compute the CMC, the 1985 Guidelines require that acceptable 

acute toxicity values be available for at least eight families to address a range of taxonomic 

diversity. For freshwater aquatic communities, these minimum data requirements (MDRs) 

include three vertebrates (a salmonid, another family of bony fish, and an amphibian or third 

family of fish) and five invertebrates (a planktonic crustacean, a benthic crustacean, an insect, a 

species from a phylum other than Chordata or Arthropoda, and a species from another order of 

insect or another phylum not already represented). Similarly, a saltwater CMC can be derived 
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using two vertebrates, a shrimp (Mysidae or Penaeidae; note that this is an arthropod), three nonvertebrates, a non-vertebrate species that is not an arthropod and a species from any other family 

that is not already represented. For both freshwater and saltwater criteria, acute toxicity values 

are averaged within species and genera to provide genus mean acute values (GMAVs). The fifth 

percentile of these GMAVS is estimated by procedures specified in the 1985 Guidelines and 

designated the FAV. The CMC is set equal to half of the FAV to represent a low level of toxicity 

(rather than 50%). It should be noted that the data that are required for pesticides (described in 

Section 2.1.1) fulfill 3 of the 8 acute MDRs for both freshwater and saltwater CMCs. 

The 1985 Guidelines specify that the CCC be set to the lower of either a final chronic value 

(FCV) for animals or a final plant value (FPV). The FCV is based on effects concentration 

determined for survival, growth, or reproduction of aquatic animals in chronic tests meeting 

certain data requirements. If the 8 MDRs are met, the FCV is calculated as the 5th percentile of 

Genus Mean Chronic Values (GMCVs) by the same methodology as for the FAV. If fewer 

MDRs are met, the FCV can be calculated by dividing the FAV by a final acute-chronic ratio 

(FACR), which is an average (or other extraction) of ACRs from paired acute and chronic tests 

meeting certain data quality criteria. The 1985 Guidelines specify that the FPV be the lowest 

toxicity values for important aquatic plant species and endpoints from tests meeting certain data 

quality criteria, but do not specify MDRs for the variety of test species or the magnitude of the 

effect. Aquatic plant toxicity data available for use in a criterion derivation are frequently 

absent, and no criteria to date have been published that use plant data in the calculation. 

3.2. Common Effects Project

This white paper is part of the development of a common effects characterization methodology 

that may be used by USEPA‟s OW and OPP in ecological assessments of chemicals, in particular 

of pesticides. In April 2009, USEPA developed a document entitled “Toward a Common Effects 

Characterization Methodology Scoping Document5” (hereafter referred to as the Scoping 

Document). The Scoping Document provided background for a proposal to develop a common 

effects characterization methodology for use in ecological assessments of chemicals, specifically 

pesticides, by USEPA. The resulting methodology was intended to integrate the USEPA‟s

aquatic effects characterization methods and provide a common basis for achieving the water 

quality protection goals established under the CWA and FIFRA statutes. The common effects 

characterization methodology focuses on situations where data that OPP would regard as 

sufficient are available to conduct risk assessments, but OW would not have a data set that, 

according to the 1985 Guidelines, would support derivation of ALC. Once sufficiently validated 

and vetted, the methods could then be used by state, local and Tribal water management agencies 

to interpret aquatic ecological risks associated with chemical exposure information, e.g., water 

monitoring data. In order to allow stakeholders to provide input on the Common Effects Project 

throughout the process, the USEPA opened a docket (ID: EPA-HQ-OPP-2009-0773) where 

comments could be submitted and later considered by USEPA. 

The approaches articulated in the Scoping Document were subsequently explored by USEPA and 

discussed in greater detail at stakeholder meetings in six regions throughout the country in 

 

5 Available online at: http://www.epa.gov/oppefed1/cwa_fifra_effects_methodology/scope.html
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January 20106. The purpose of those meetings was to solicit input on methods being evaluated 

as part of the common effects project to characterize effects from pesticides on fish, other aquatic 

organisms, and aquatic plants in aquatic ecosystems. 

In December 2010, USEPA hosted a national stakeholder meeting, where three white papers that 

built on the topics discussed at the regional stakeholder meetings were discussed.7The first

paper, titled “Predicting the Toxicity of Chemicals to Aquatic Animal Species” (USEPA 2010a), 

discussed different methods and tools available to predict the toxicity of a chemical to aquatic 

animals. The second white paper, “Exploration of Methods for Characterizing Effects of 

Chemical Stressors to Aquatic Animals,” (USEPA 2010b) identified possible means through 

which available empirical data sets could be either enhanced using predictive tools or employed 

along with adjustment factors to characterize the lower range of sensitivities to specific 

chemicals that may exist in an aquatic community. Additionally, a third white paper, “Deriving 

Plant Aquatic Life Screening Values for Pesticide Effects,” (USEPA 2010c) was developed to 

explore the uncertainties associated with and methods for characterizing adverse effects to the 

aquatic vascular and nonvascular plant community from chemical exposure. 

At this time, OPP and OW, with consultation from the Office of Research and Development, are 

in the process of developing a common set of guidelines for reviewing and categorizing studies 

for their utility in ecological effect assessments and ALC development. These guidelines are 

intended to enable scientists in both OPP and OW to have a consistent method for evaluating 

studies used in populating chemical-specific databases with empirical toxicity data. The 

guidelines for evaluating open literature identify critical study elements that should be 

considered by OPP and OW scientists in determining whether a study can be used quantitatively 

(i.e., for deriving risk quotients or ALC), qualitatively (e.g., for characterization of uncertainty), 

or to identify those studies where there is sufficient uncertainty such that the study is considered 

scientifically unsound and classified as unacceptable for use in risk assessment or ALC. OPP‟s 

Environmental Fate and Effects Division‟s current Guidance for evaluating studies from open 

literature is available online (USEPA 2011c) as a reference for informational purposes; however, 

this is not intended as the focus of this white paper.

The current white paper builds upon the approaches described in the scoping document and 

provides the results of USEPA‟s reviews of many of the methods described in USEPA 2010 a, b 

and c. One of these methods included the Tier II methodology incorporated into the Great Lakes 

Water Quality Guidance (GLWQG). In 1995, as part of the Great Lakes Initiative, USEPA 

published an alternate method (also known as the Tier II methodology) for calculating Tier II 

values when the minimum data requirements for deriving national recommended CWA section 

304(a) ALC are not met (40 CFR part 132, App. A. XII). These Tier II values represent estimates 

of ambient chemical concentrations that would likely be at or below ALC values had the ALC

data requirements been fully met. These Tier II values may be used as a basis for State or Tribal 

water quality standards in the Great Lakes States. The Tier II methods incorporate the use of EFs 

(for acute values) and ACRs (for chronic values). These methods are described in the following 

section.

 

6

See http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/cem.cfm#meeting1 for more information.

7

See http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/cem.cfm for more information.
Page 15 of 40

3.3. Great Lakes Water Quality Guidance 

3.3.1. Extrapolation Factors

In regulatory applications, EFs generally include default values applied to acute toxicity test

results to derive the desired assessment endpoint. Of particular interest to this effort is the set of 

EFs developed by Host et al. (1995) and incorporated into the Great Lakes Water Quality 

Guidance (GLWQG: USEPA 1995), specifically for deriving Tier II values. The GLWQG was 

developed by USEPA with participation from the eight Great Lakes states (i.e., Illinois, Indiana, 

Michigan, Minnesota, New York, Ohio, Pennsylvania and Wisconsin) as part of the Great Lakes 

Water Quality Initiative. The purpose of this guidance is to help establish consistent, enforceable, 

long-term protection with respect to all types of pollutants. The guidance includes a two-tiered 

approach to derive guidance values, including: 1) Tier I values for which data meet the minimum 

data requirement in the 1985 Guidelines, and 2) Tier II values where there is an absence of the 

full set of data meeting Tier I data requirements. Due to limited data used to derive Tier II 

values, these values are likely to be more uncertain compared to Tier I values. Tier II values are 

intended to be more conservative, on average, than what Tier I values would be for the same 

chemicals if sufficient data were available. Discussed below are the Tier II values incorporated 

into the GLWQG, which are derived using EFs as well as additional work on EFs by Host et al.

1995.

The GLWQG EFs constitute an existing methodology that can be used for predicting the HC5 

using limited data because they 1) are designed to conservatively approximate the FAV (which is 

an estimate of the HC5) that would be generated using the 1985 Guidelines; 2) can be used for 

chemicals with various amounts of empirical data; 3) have been used by states to derive Tier II 

values, which are similar in nature to the estimates of the HC5 desired here; 4) are derived from 

a variety of chemicals with several MOAs, some of which are pesticides; 5) have been adapted 

into methods used by several different regulatory agencies, including states (e.g., MI, CA and 

OH) and the Department of Energy; and 6) are derived from data sets that meet the acceptability 

standards employed by USEPA OW.

Host et al. (1995) developed EFs for acute toxicity by using toxicity datasets for chemicals with 

established ALC, generating 199 samples of 8 tests from these datasets that still satisfied the 

MDRs, computing an FAV for each of these samples, generating subsamples of these samples of 

various sizes and taxonomic compositions, and calculating EFs as the ratio of the lowest datum 

in each of these subsamples to the FAV for its respective sample. This produced 199 EFs for 

each chemical and subsample composition, from which distributions for each chemical and for 

the chemicals overall could be described. Table 2 provides some relevant summary EFs from 

Host et al. (1995) based on 29 freshwater ALC datasets, which included metals (e.g., cadmium, 

copper), inorganic chemicals (e.g., ammonia) and organic chemicals, including pesticides (e.g.,

parathion). Each EF in Table 2 represents the median across chemicals of the 95th percentile EF 

within each chemical, for the specified dataset composition. Comparison of the EFs in the 

second column, which were generated without consideration of taxonomic requirements, and the 

EFs in the third column, which was generated by assuming that at least one Daphnid species (i.e., 

Ceriodaphnia sp., Daphnia sp., or Simocephalus sp.) was present, indicate that there is a large 

influence of requiring an acute toxicity result for at least one Daphnid species. This is likely 
Page 16 of 40

because these species are often sensitive to chemical stressors. Comparison of the third and 

fourth columns of Table 2 indicates that when a daphnid species is required, the additional 

requirement of a salmonid species some additional, but limited impact on the magnitude of the 

EF for the smallest sample sizes. The EFs specified in the GLWQG for deriving Tier II values 

were those from the middle column of Table 2, requiring an acute toxicity test for a daphnid 

species.

Table 2. Extrapolation factors derived by Host et al. (1995). Values represent median of 

95th percentiles. EFs where daphnid is required (*) were incorporated into GLWQG for 

deriving Tier II values.

Number of

MDRs met Any MDR Daphnid required* Both salmonid and daphnid required

1 426 21.9 Not applicable

2 65 13.0 7.9

3 43 8.0 7.1

4 28 7.0 7.1

5 20 6.1 6.1

6 13 5.2 5.0

7 7.5 4.3 4.3

The utility of these EFs can be demonstrated using three recent ALC for pesticides, including: 

acrolein (aquatic herbicide; USEPA 2009), carbaryl (carbamate insecticide; USEPA 2011) and 

diazinon (organophosphate insecticide; USEPA 2005). For this evaluation, the HC5 for each 

chemical was estimated using only the minimum FIFRA acute toxicity data set for freshwater 

animals (i.e., D. magna, bluegill sunfish and rainbow trout). The lowest acute toxicity data value 

from these three species was divided by the GLWQG EF for N = 3 (i.e., 8.0). The estimated HC5 

for each chemical was then compared to the FAV calculated for the full quantitative data set 

reported in the ALC. The FAVs calculated in the ALC for these three pesticides are included in 

Table 3 as well as the means of the acute toxicity data (SMAVs) for the typical FIFRA test 

species. For all three chemicals, the estimated HC5 is lower than the FAV by a factor varying 

from 2.5 to 4.5, indicating that the EF approach provides a conservative estimate of the HC5.

Table 3. SMAVs (in ppb) for typical OPP test species from criteria for pesticides. Also 

included are FAVs from criteria and HC5 values estimated using lowest SMAV and 

GLWQG EF for N=3 (i.e., 8.0).

Chemical SMAVs Estimated 

HC5 FAV D. magna Rainbow Trout Bluegill sunfish

Acrolein 51 16 27 2.0 5.9

Carbaryl 7.5 860 6970 0.94 4.2

Diazinon 1.05 426 460 0.13 0.34

3.3.2. ACRs

The GLWQG also outlines an approach to calculate a chronic guidance value when fewer than 3 

appropriate Acute to Chronic Ratios (ACR) are available, by using a default value of 18 is used 
Page 17 of 40

to replace each missing ACR. This default value is based on an analysis conducted by Host et al.

(1995). The default value represents the 80th percentile of the distribution of FACRs from 

existing ALC. Under the GLWQG, a minimum of three ACRs are required to calculate a 

Secondary Acute-Chronic Ratio (SACR); if fewer than three experimentally-determined ACRs 

are available, values of 18 are added as needed to bring the total ACRs to 3, then the SACR is 

calculated as the geometric mean of those values; if no experimentally determined ACRs are 

available, the SACR is 18.

3.4. Purpose and Organization of this Document

The purpose of this white paper is to describe efforts to better characterize the fifth percentiles of 

toxicity endpoint distributions (HC5s) for acute and chronic toxicity to aquatic animals and for 

aquatic plants. Emphasis is placed on situations where data that OPP would regard as sufficient 

are available to conduct risk assessments, but OW would not have a data set that, according to 

the 1985 Guidelines, would support derivation of ALC. Consideration is also given to the 

uncertainty of HC5 estimates to allow designation of a desired level of conservatism.

For acute toxicity to aquatic organisms, efforts are being made in three areas: 

(1) Because HC5 estimates would be improved by a greater number of toxicity values across 

more diverse taxa (e.g., meeting more of the ALC MDRs), tools to predict toxicity values 

in the absence of measured values were evaluated. This included QSARs and ICE. 

These efforts are summarized in Section 4 and described in detail in Appendices A and 

B. 

(2) Methods for characterizing SSDs were evaluated regarding how well HC5s are estimated 

as a function of the number and composition of data, with particular emphasis on the 

number of toxicity values being between 3 and 8. These efforts are described in Section 

5.1 and Appendix C. 

(3) EFs to estimate HC5s based on the lowest of available measured toxicity values were 

evaluated. This work built on EFs developed by Host et al. (1995) for the GLWQG

(USEPA 1995) to provide conservative estimates of FAVs in the absence of sufficient 

data to derive FAVs according to the 1985 Guidelines. The current work expands on the 

efforts of Host et al. (1995) by considering new data, new analysis approaches, and how 

EFs might vary with MOA. The efforts are described in Section 5.2 and Appendix D.

Initial efforts have focused on AChE inhibiting chemicals as an important class of pesticides 

with a large amount of aquatic toxicity data for variety of chemicals and taxonomic groups.

For chronic toxicity to aquatic organisms, efforts focused on applying ACRs to estimate a 

chronic HC5 from an acute HC5. ACR data for AChE inhibitors were compiled and evaluated to 

assess whether MOA-specific ACRs might reduce uncertainty in estimating a chronic HC5, 

compared to generalized ACR approaches from the literature. These efforts are summarized in 

Section 6 and Appendix E.

For plant toxicity, the use of SSDs and EFs to estimate HC5 from typical test species submitted

to fulfill FIFRA‟s requirements were investigated. In addition, the utility of standardizing 

toxicity endpoints was explored. These efforts are summarized in Section 7 and Appendix F. 
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4. Methods for Predicting the Toxicity of Chemicals to Species of Aquatic Animals

To supplement available empirical data using predicted acute toxicity values, USEPA 

investigated the use of some available predictive toxicology tools, including 3 QSAR

applications and ICE. Through the use of algorithms, QSARs attempt to quantify relationships 

between an aspect of chemical structure and an activity (e.g., toxicological response) imparted by 

that structure. A detailed description of the methods and results of the USEPA‟s analysis of 

some available QSAR applications is provided in Appendix A. ICE uses empirical toxicity data 

for a given test species to predict the toxicity of that chemical to another species. This is done by 

correlating toxicity results (e.g., LC50s values) for two species for as many chemicals as are 

available for the species combination (e.g., rainbow trout vs. bluegill) regardless of chemical‟s 

type, use or MOA. A detailed description of the methods and results of the analysis of an 

application of ICE (i.e., Web-ICE) is provided in Appendix B. Information on additional 

methods and tools that may be used to predict the toxicity of chemicals to aquatic animals can be 

found in USEPA 2010a. 

4.1. QSARs

In theory, if acceptable robust models and data to build reliable models exist, the use of QSAR 

methods could be used to supplement empirical data to a larger toxicity set (e.g., 3 acute toxicity 

values to fulfill FIFRA requirements or the 8 MDRs for ALC). In order to determine whether a 

QSAR estimate for a chemical and species of interest is appropriate, a number of characteristics 

of the model need to be considered. These characteristics include the applicability of the model 

to the endpoint or parameter of interest; applicability to the chemical class, MOA, group and 

structure of the chemical of interest; the requirement for a quantitative or qualitative prediction; 

and whether the tool has inherent biases or limitations (OECD 2007). Many of the available 

predictive tools are qualitative and not suitable for deriving predicted values. Although 

qualitative tools may be useful in other aspects of the risk assessment, they are not considered in 

the current analysis. Conversely, quantitative models, which predict values of interest (e.g., 

LC50, EC50), are available, but the use of these tools may be limited when statistical rigor, 

predictive performance, domain of applicability, and the level of validation of the statistical 

algorithm/model are considered.

As described in detail in Appendix A, USEPA evaluated the ability of three QSAR tools to 

predict the acute toxicity of AChE inhibitors to fish and aquatic invertebrates. Since a critical 

evaluation was not conducted on all existing QSAR models and tools as well as MOAs, the 

limitations of QSARs that are discussed here and in Appendix A are based on the restricted 

evaluations that were undertaken. In this analysis, three publicly available tools were examined, 

i.e., existing QSAR models for fish and daphnids within ECOSAR (USEPA 2011a), TEST

(USEPA 2011b) applications, and the OECD QSAR Toolbox (OECD 2011), which was used to 

develop new QSAR models. These tools were selected because they are freely accessible, 

regulatory authorities frequently rely on these tools when chemical-specific empirical data are 

not available, and they have undergone some level of validation as described by the OECD 

validation principles (OECD 2007). These tools were evaluated by comparing their predicted 
Page 19 of 40

values to empirically based toxicity data. The purpose of this evaluation was to address the 

following questions as they related to the common effects framework:

What is the potential utility for each model/tool in developing quantitative toxicity values?

For each model, what are the strengths, weaknesses and uncertainties as they relate to use in 

predicting toxicity of a chemical to specific taxa?

How do model predictions compare to an independently compiled empirical data collection?

What future research could improve the utility of the predictive tools considered in this 

review? 

The analyses of ECOSAR, TEST and the OECD Toolbox would suggest that, at this time, (a) the 

chemical domain for most models does not include pesticide active ingredients and therefore 

models are insufficient for estimating the toxicity for modes of action that are unique to 

pesticides (e.g., AChE inhibitors), (b) existing validated QSAR models provide estimates of 

chemical toxicity for species that typically have sufficient empirical data sets (i.e., fish and 

daphnids), and (c) the development of reliable QSARs to fill these model gaps will require the 

availability of empirical toxicity data that meet rigorous acceptability requirements. 

Future work will require the development of methods to consistently identify empirical test data 

that are acceptable for use in QSAR model development. This acceptability evaluation should 

take into account the relevance (i.e., applicability and appropriateness) and the quality (i.e., 

reliability, validity, credibility, trustworthiness, accuracy, repeatability, and reproducibility) of 

the study. In addition, QSAR models within existing tools need to be expanded to better include 

pesticide active ingredients within their domain of applicability. Improvement of the reliable 

predictability of these tools will likely require development of specific models based on MOA 

and inclusion of parameters other than the octanol-water partition coefficient (log P) to describe 

toxicity. It would be helpful to have information related to molecular descriptors that work best 

in describing toxicity for specific chemical classes of pesticide structures. These descriptors 

should have some linkage to key events within the MOA, and would then provide a means to 

develop reliable, consistent, and toxicologically-defensible QSAR models. 

4.2. ICE

The Web-ICE provides ICE models for acute toxicity estimation for aquatic and wildlife species 

with known certainty. Almost 3000 ICE models are available for wildlife (birds and mammals), 

and aquatic (fish and invertebrates) species, genera, and families to predict toxicity in three 

separate modules. ICE calculator modules predict single toxicity values for a species, genus 

(aquatic only) or family. 

The Web-ICE user‟s manual (USEPA 2010d) provides recommendations for evaluating the 

quality of model regressions. These recommendations were used to evaluate the application of 

Web-ICE for estimating toxicity data for specific species. Termed as “Rules of Thumb”, they are 

recommendations for selecting models with low uncertainty. For best estimates, the authors 

recommend to select models that possess the following attributes:

1. Relatively low mean square error (<0.22)

2. Close taxonomic distance (≤ 3; i.e., species are within the same genus, family or order)

3. High cross-validation success rate (> 85%)
Page 20 of 40

4. High degrees of freedom (> 8 when N > 10)

5. High R2value (> 0.6)

6. Low p-values (<0.01)

7. Narrow confidence bands on the graph

When considering these rules of thumb, Web-ICE‟s predictions function best when estimating 

toxicity for closely related species (e.g., same genus or family) for which empirical data are 

available. Using the acute toxicity data for aquatic animals that are typically submitted to fulfill 

requirements under FIFRA (i.e., data for D. magna, rainbow trout and bluegill sunfish), this tool 

appears to generate the most precise predictions of the toxicity of closely related fish species. 

Specifically, when using data for rainbow trout and bluegill sunfish, predictions are most precise 

for the following species: yellow perch (Perca flavescens), largemouth bass (Micropterus 

salmoides), brown trout (Salmo trutta), lake trout (Salvelinus namaycush), cutthroat trout (O. 

clarkia) and Coho salmon (O. kisutch). The current version of Web-ICE does not have sufficient 

data to fulfill all of the MDRs of the 1985 Guidelines when there are only acute surrogate data 

available for rainbow trout, bluegill and D. magna. With future updates, the tool has the potential 

to use the typical data submitted under FIFRA to predict the toxicity of a chemical to species that 

may fulfill the MDRs of the 1985 Guidelines. If toxicity data are available for only one fish 

species that is typically submitted under FIFRA (i.e., rainbow trout or bluegill sunfish), it 

appears that this tool could be useful for predicting the toxicity of a chemical to the missing test 

species. 

The influence of chemical MOA, relative to taxa relatedness, in aquatic species ICE modeling 

(Raimondo et al 2010) has been tested using Web-ICE. Taxa relatedness proved to be a more 

important determinant in the precision of the ICE models than MOA up to the phylum taxonomic 

level. When species pairs were correlated across different phyla, MOA had a significant 

influence on the precision of ICE estimates. At this time, updates to Web-ICE are underway. 

One major update to the application involves the development of MOA-specific models. MOA 

specific models are intended to improve the accuracy of predictions for two species of greater 

taxonomic distance (Raimondo et al. 2010). While MOA-specific models may improve toxicity 

predictions, fewer species pairs will be available using the smaller datasets of specific MOAs. 

In addition to predicting the toxicities of individual species, the Web-ICE tool allows the user to 

derive an HC5 (from a logistic distribution) using a combination of empirical and predicted 

values. The predicted values may be derived for many species of fish and invertebrates; however, 

the user has the choice of which values to include in the SSD. The Web-ICE guidance suggests 

that the user consider the “rules of thumb” in interpreting the reliability of the results of the tool. 

If all of the Web-ICE rules of thumb are applied, the SSD application will be very limited in its 

ability to predict the HC5. For example, if the typical FIFRA acute toxicity data set for diazinon 

(an organophosphate insecticide) is used to derive a distribution and the rules of thumb are 

applied, the HC5 would be based on 9 species (8 fish and 1 invertebrate; see Table 4 of 

Appendix B). The user may expand the data set by relaxing the reliance on these rules of thumb 

and the data set will likely contain a mixture of data on fish and a greater number of

invertebrates, with a predominance of data for fish. 
Page 21 of 40

For insecticides, which are generally more toxic (by orders of magnitude) to aquatic 

invertebrates compared to fish, the predominance of reliable predictions of toxicity endpoints for 

fish are likely to result in an overestimation of the HC5 (less conservative value) by the WebICE tool. Based on this, the application of Web-ICE for estimating the HC5 appears to be limited 

for insecticides. For chemicals that do not have a marked difference in effects on fish and 

invertebrates (e.g., chemicals with a narcotic MOA, including many herbicides and fungicides)

the Web-ICE tool may provide a more reliable estimate of the HC5. 

5. Methods for Estimating the HC5 of Sensitivity Distributions of Acute Toxicity Data for 

Animals

The statistical distribution of toxicity endpoints (e.g., LC50s) for different biological taxa to the 

same stressor is a common tool used in the decision process for setting safe limits on toxic 

chemical concentrations in surface waters (USEPA 1985, Posthuma et al. 2002, Suter 2002, 

Chapman et al. 2007). These distributions are generically referred to as species sensitivity 

distributions (SSDs) because the toxic endpoints are typically summarized at the species level, 

although other taxonomic levels might be used. Risk is characterized based on one or more 

percentiles in the SSD, generically referred to here as HCps, "HC" standing for "hazard 

concentration" and "p" referring to the percentile of interest. Efforts here are concerned with 

HC5 estimation for acute toxicity (EC/LC50s) to aquatic animals, at a specified confidence level, 

for datasets with 3-8 species.

Two types of HC5 estimation are considered here. Section 5.1 concerns the direct 

characterization of SSDs from an individual dataset, evaluating how well HC5s are thereby 

estimated as a function of various attributes of the dataset and methodology. Section 5.2 

concerns the use of EFs, defined here as the ratio of the smallest toxicity value in the dataset to

the HC5. EFs are developed through analysis of acute toxicity data for chemicals with robust 

data sets for which the HC5 is known or can be reliably estimated; these EFs can then be used to 

estimate the HC5 for smaller data sets by dividing the lowest toxicity value by the EF. 

5.1. HC5 Estimation Based on Characterization of Species Sensitivity 

Distribution

Although methods for analysis and interpretation of SSDs vary widely, the basic methodology is 

quite general and can be summarized as a three-step procedure. First, all available acute toxicity 

data for a given chemical are compiled. Second, a distribution to which the test results are 

thought to conform is chosen and fit to the data. Third, the fitted distribution is used to infer a 

concentration that will be protective of a desired proportion of species in an (hypothetical) 

aquatic community (e.g., the HC5). As part of this effort, the direct analysis of SSDs for AChE 

inhibitors was evaluated in light of the following eight questions, which are discussed briefly 

here and in detail in Appendix C:
Page 22 of 40

1. How does sample size influence accuracy (bias and variance) of the estimated HC5?

The SSD analysis for AChE inhibitors indicates that the reliable calculation of a 

distribution‟s HC5 requires more than 3 to 8 data points (due to large bias and variance). 

Within this range, estimates of the HC5 may be affected by both bias and substantial 

uncertainty, when constraints on data composition, such as taxonomic requirements (see 

below), are not imposed. When data requirements are imposed (see below) estimates of 

the HC5 at sample sizes of 3 to 8 data points improve considerably.

2. How do minimum data requirements influence accuracy of the estimated HC5?

The imposition of MDRs (either the 3 test species typically available to fulfill FIFRA 

data requirements or the 8 MDRs included in the 1985 Guidelines) improved precision 

and tended to produce conservative estimates of the HC5 (i.e., estimates that, on average, 

would be lower than the „true population‟ HC5). This result is due to MDRs forcing 

inclusion of sensitive species (typically arthropods in the case of AChE inhibitors) in all 

data subsets, and thus the eliminating data subsets that contain only insensitive species. 

3. How do different estimation methods influence accuracy of the estimated HC5?

For all SSD estimation methods considered, bias decreased with increasing sample size. 

Within the range of sample sizes of interest (i.e., 3 ≤ N ≤ 8), moment estimators (e.g.,

estimating distribution parameters from the sample mean and variance, see Section 2.2.4 

of Appendix C) were the least biased. Conversely, graphical methods produced the most 

conservative estimates of the HC5 in this range. Estimation methods did not differ much 

in precision.

4. Are measures of fit (goodness-of-fit, Akaike‟s Information Criterion) reliable indicators 

of performance? 

Within the range of n=3 to 8, neither AIC nor goodness-of-fit measures were reliable 

indicators of the performance of a distribution, though both methods performed well at 

large sample sizes (N ≥ 50). In analyses of simulation data, AIC did not consistently 

identify the distribution used to simulate the data as the best distribution to fit the data 

with any sample sizes less than N = 50. Similarly, goodness-of-fit tests rarely showed 

sufficient power to reject alternative distributions with greater than 50% probability with 

sample sizes less than N = 50.

5. What are actual coverage rates of 95% confidence limits for subsamples?

Lower 95% confidence limits (calculated only for normal, logistic, and triangular 

distributions at N = 3 and N = 8) generally showed nominal coverage rates (i.e., the true 

or reference HC5 is above the confidence limit in 95% of data subsets) with simulated 

data. With empirical data, lower 95% confidence limits showed higher than nominal 

coverage rates when MDRs were imposed, but usually showed lower than nominal rates 

when MDRs were not imposed.
Page 23 of 40

6. How do different candidate distributions perform relative to each other?

No single distribution was preferred for either simulation data or empirical data. 

Performance of each distribution, based on AICc was very evenly distributed in the long 

run, with the BurrIII distribution being disfavored compared to the other six distributions 

considered, probably due to the larger number of estimated parameters for the Burr 

distribution. Judging by bias at small sample sizes, distributions on log-transformed data 

(normal, logistic, triangular, Gumbel) generally outperformed distributions on 

untransformed data (Pareto, Weibull, and BurrIII) and of the former, the log-normal, loglogistic, and log-triangular showed very similar performance. 

7. Does model-averaging across distributions improve estimates of the HC5?

Model averaging substantially reduced bias in estimates of the HC5 in empirical 

analyses, and thus shows some promise for application to data when sample sizes are 

limited. However, in theoretical analyses on simulated data, this pattern of improved 

performance was not observed. This topic is one among several identified for future 

research on the application of SSD for the conservative prediction of HC5 values using 

limited data sets (refer to future research section of Appendix C). 

8. What future research is suggested by these results? 

The results of these analyses suggest that additional work on sensitivity distributions 

could be valuable. In particular, extension of methods for ordered data to all continuous 

distributions might alleviate some of the large positive biases with limited data for other 

distributions than those currently amenable to graphical estimation. Similarly, 

development of Bayesian methods for hierarchical data analysis using information on 

MOAs and phylogenetic trees as underlying hypotheses for the distribution of

sensitivities could help to extend the benefits of MDR imposition to other, as yet 

unexplored MOAs that may not be as well covered by existing MDRs. Another potential 

improvement in SSD analyses might be to incorporate random effects methods for 

handling cases in which more than one test result is available for a given taxon.

5.2. HC5 Estimation Based on Extrapolation Factors

When there are insufficient data to derive ALC in accordance with the 1985 Guidelines, 

USEPA's GLWQG (USEPA 1995) specified extrapolation factors for deriving conservative 

estimates of the FAV for use in Tier II values (screening/advisory concentrations). These EFs 

require that the toxicity data include a test with a daphnid, and range from a value of 21.9 when 

just the daphnid test is available to 4.3 when all but one of the ALC MDRs are met. 

The GLWQG EFs were based on analyses by Host et al. (1995). Using acute toxicity value 

databases for existing ALC, these authors (a) randomly sampled the database for each chemical 

to generate multiple datasets of 8 toxicity values meeting the ALC MDRs, (b) calculated the 

FAV (HC5) for each of these minimum ALC datasets, (c) sampled from these minimum ALC 
Page 24 of 40

datasets to generate subsets of different sizes and taxonomic composition, and (d) calculated EFs 

as the ratio of the smallest toxicity value in each subset type to the FAV. For freshwater acute 

toxicity, this resulted in 199 EFs for each of 29 chemicals for each subset type, providing a 

composite distribution of EFs reflecting both intra- and inter-chemical variability. The GLWQG 

used the median value of the 95th percentiles EFs for the individual chemicals. In addition to this 

repeated sampling of discrete toxicity values ("resampling approach"), Host et al. also developed 

EFs by sampling from a statistical distribution representing toxicity values ("distributional 

approach").

To provide EFs more applicable to conservative estimates of acute toxicity HC5s for pesticides 

and to better defining the relationship of OW and OPP assessment procedures, the efforts in the 

current project expand on those of Host et al. (1995) as follows: 

(1) Using a larger database of toxicity test results, reflecting more recent data and data for 

chemicals other than those that actually have ALC.

(2) Conducting separate analyses for different MOAs. Results reported here are just for AChE 

inhibitors as an initial example, with limited comparison to chemicals with narcotic action to 

illustrate how MOA might affect EFs. 

(3) Paying specific attention to EFs for certain minimum data requirements in OPP assessment 

procedures (i.e., acute tests for rainbow trout, bluegill sunfish, and Daphnia magna). 

(4) Extending the distributional approach by using a composite distribution reflecting the 

relative sensitivities of different taxonomic groups, rather than a single simple distribution as was 

the case for Host et al. (1995).

In addition, this effort addresses how the reference (known) HC5 used to calculate EFs is 

defined. For Host et al. (1995) the reference HC5 was the FAV calculated for each of the N=8 

datasets satisfying the ALC MDRs, rather than the FAV for the complete dataset for a chemical. 

For the current effort, analyses use the HC5 of complete datasets as a more appropriate reference 

point for EFs.

Work to date is summarized here and discussed in more detail in Appendix D. As noted in this

appendix, there is a need for further improvements in the toxicity database used in these analyses 

and in assessing its distributional properties. Because of data uncertainties, the specific EFs 

calculated should be considered provisional, but they still are informative regarding the 

methodology and general outcomes. Overall, this work supports the approach of Host et al.

(1995), in that it is possible to use existing toxicity data to derive EFs that can be used to provide 

useful estimates of HC5s for datasets not meeting the ALC MDRs, and to address aspects of the 

uncertainty of such estimates. Summaries of more specific issues follow.

5.2.1. EFs for AChE Inhibition by Resampling Approach 

Available toxicity data for six AChE-inhibiting pesticides satisfied the ALC MDRs and had 

sufficient data for sensitive taxa to support the analyses. Random resampling was used to 

establish EF distributions for datasets satisfying from 2 to 8 of the MDRs (one test result per 

MDR) and various taxonomic requirements, including the OPP acute test minimums. Figure 1

summarizes the effects of dataset size when there was requirement of a test on a planktonic 
Page 25 of 40

crustacean (daphnid) and any species of fish. Evaluations for other taxonomic requirements are 

included in Appendix D. 

Number of MDRs Satisfied

2 3 4 5 6 7 8 Extrapolation Factor

2

5

20

1

10

Carbaryl 

Chlorpyrifos 

Diazinon 

Fentirothion 

Malathion 

Methylparathion 

Composite 

90th Percentile

Composite

75th Percentile

Composite

50th Percentile

Figure 1. Geometric mean extrapolation factors (for 999 samples) for six AChE-inhibiting chemicals as a 

function of the number of MDRs satisfied, given a minimum requirement of one fish test and one planktonic 

crustacean test, with remaining tests being randomly selected to satisfy other MDRs for ALC derivation. 

Lines denote the geometric mean and the 75th and 90th percentiles for composite results across all chemicals.

By requiring a test from a highly sensitive taxon (daphnids) in the datasets, composite median 

EFs (across all chemicals and samples) were small and did not change greatly with dataset size 

(Figure 1). However, due to the variability of toxicity values within sensitive taxa, the 

uncertainty on EFs was substantial, with the composite 90th percentile being more than 5-fold 

higher than the median for N=2 and 3. There was also considerable variability among chemicals, 

especially at the lowest sample sizes. This variability among chemicals highlights how the 

composite EF distribution contains both intra- and inter-chemical variability, so that the 

composite 90th percentile includes more than 90% of the EFs for some chemicals and less for 

others. This is an important factor in the selection of any regulatory EFs, and was considered in 

the work of Host et al. (1995) and its application to the GLWQG.

For the typical data submitted to OPP under FIFRA (i.e., Daphnia magna, rainbow trout, and 

bluegill sunfish), the composite median EF was determined to be 2.9. This is greater than the 

composite results for N=3 (and even for N=2) in Figure 1, because, on average, other daphnids 

that were tested were more sensitive than D. magna and because the OPP data requirements 

specify data for a second fish species, which are comparatively insensitive to AChE chemicals. 
Page 26 of 40

Results to date indicate that:

(1) Useful MOA-specific EFs can be developed by the resampling approach when there are 

sufficient data for several chemicals with that MOA; these EFs can be tailored to the 

taxonomic composition of the datasets to which they are applied.

(2) A data requirement that includes a taxon that is highly sensitive to AChE inhibitors (i.e.,

daphnids) leads to small EFs even for the smallest datasets of these chemicals.

(3) Care must be taken in selecting and expressing what percentile in the composite EF 

distribution (= the probability with which the HC5 estimate exceeds the desired value), 

because the EF distributions involve both intra- and inter-chemical variability.

5.2.2. EFs by Resampling Approach versus Distributional Approach 

The resampling approach requires that a sufficient number of chemicals within the MOA have 

sufficient data to support resampling, a requirement few MOAs are likely to meet. The 

distributional approach has the potential to support EF derivations for more MOAs because data 

from all chemicals within the MOA can contribute to the analysis, not just those that meet all 

ALC MDRs and are large enough for resampling. To compare these two approaches, the 

toxicity data for all chemicals with an AChE-inhibition MOA were analyzed to estimate the 

standard deviation of EC/LC50s within each MDR and the mean differences between MDRs. 

EFs were then generated as for the resampling approach, except that sampling was from these 

distributions rather than from the discrete toxicity test values.

Despite the markedly different toxicity databases used for the two approaches, the resultant EFs 

from the resampling and distributional approaches are similar. For analyses requiring one 

daphnid and one fish, the median EFs differed by no more than a factor of 1.3 and the 90th

percentiles were within a factor of 1.4. This work established a basic framework for the 

distributional approach and demonstrated its utility. Because the distributional approach could 

apply to more MOAs and include more data, it would be advantageous to standardize on it, using 

the resampling analysis only as part of uncertainty evaluations. To do this, work is ongoing to 

improve the distributional analysis, especially with regard to parameter uncertainty and 

distributional shape.

5.2.3. Comparison of AChE-Inhibition and Narcosis MOAs 

Although only one narcosis chemical had data sufficient for a resampling analysis, there were 

sufficient data across all chemicals with a narcosis MOA to estimate distributions for the MDRs. 

The composite of 8 MDR distributions differed greatly between the MOAs – for narcosis, the 8 

MDR distributions were very similar, while for AChE-inhibition, arthropods were 2-3 orders of 

magnitude more sensitive than other phyla and variability within MDRs was higher than for 

narcosis. Despite the large differences in the composite distributions for these two MOAs, 

differences between their median EFs were actually rather small – differing by no more than a 

factor of 1.2. At higher percentiles, the narcosis EFs were lower, especially at higher N, by up to 

a factor of 2.
Page 27 of 40

The similarity of median EFs for the two MOAs can be attributed to each MOA having 

characteristics that lead to a high probability that the lowest toxicity value is near the reference 

HC5. For AChE-inhibiting chemicals, requiring a planktonic invertebrate even when overall N 

was low ensured that a highly sensitive species was included in the dataset. For narcosis 

chemicals, the low variability and similar mean sensitivity across MDRs make it likely that at 

least one sampled value will be near the HC5. This lower variability for the narcosis chemicals 

also results in less variability of the EFs, and thus the lower 90th percentile EFs, especially at 

N=8. Because these underlying characteristics are not necessarily true for other MOAs, it would 

be inappropriate to presume EFs are widely generalizable based solely on analysis of these two 

MOAs.

5.2.4. Comparison of AChE-inhibitor EFs to GLWQG EFs 

Host et al. (1995) also reported overall percentiles for EFs based on a requirement of a daphnid 

and a rainbow trout test, which is similar enough to the requirement of a daphnid and a fish in the 

current work to warrant some comparison. The Host et al. (1995) median EFs range from 35 to 

65% greater than those for AChE-inhibitors reported above for the resampling approach, and the 

90th percentiles range from 40 to 160% higher. 

Although such differences might seem to substantiate an importance of considering MOAspecific EFs, these differences are confounded by the methodological differences regarding the 

definition of the reference HC5 noted earlier. The effects of these different references were 

investigated for datasets with N=8 and it was determined that the approach of Host et al. (1995) 

increased EFs by more than two-fold for AChE-inhibitors, but by very little for narcosis 

chemicals. Efforts are needed to resolve this issue to better establish the efficacy of MOAspecific EFs. Additionally, future work could be undertaken to address updating the work of 

Host et al. (1995) on broader groups of chemical for when MOA-specific EFs are not available. 

Issues that this work could address include the aforementioned reference HC5s, the implications 

of percentile selection, the use of the distributional approach, new data, and chemical selection 

relative to assessment needs. 

6. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to Aquatic Animals

As discussed in Section 3, ACRs are currently used by both OPP and OW to extrapolate from 

acute effect concentrations to chronic effect thresholds. In current practice, this is generally a 

“within chemical” extrapolation, meaning that an ACR developed for a chemical tested with one 

or more species is used to estimate chronic effect thresholds for other organisms exposed to the 

same chemical. In this section, we discuss applying the ACR concept to estimate chronic effect 

thresholds in cases were little or no chronic data are available for that chemical. The generalized 

scenario contemplated in this analysis is where an acute HC5 has been calculated or estimated, 

and an ACR will be used to estimate the chronic HC5 from the acute HC5; this ACR is termed 

the ACRHC5. The ACRHC5 is parallel in concept to the Final Acute-Chronic Ratio (FACR) under 

the 1985 Guidelines, which is used to relate the FAV to the FCV. While not a focus of this 

analysis, some of the same approaches could also be used to make taxa-specific predictions of 

chronic thresholds when chemical-specific ACRs are not available.
Page 28 of 40

The ACR is calculated as the ratio of the acute effect concentration to a chronic effect 

concentration. In all formulations used by OPP or OW, the acute effect concentration is defined 

as the LC50 or EC50. The chronic effect concentration is either the NOEC, the geometric mean of 

the NOEC and the Lowest Observed Effect Concentration (LOEC), or the chronic EC20 

(concentration reducing measures of survival, growth, development, or reproduction by 20%), 

depending on the context. For purposes of this discussion, ACRs calculated using different 

chronic effect concentrations will be noted with the appropriate subscript: ACRNOEC, ACRGM, 

and ACREC20 for ACRs calculated using the NOEC, geometric mean of NOEC and LOEC, and 

EC20, respectively.

Several authors have evaluated ACRs in an attempt characterize their distributions and/or define 

“default” values to use in the absence of empirical data for chemicals or organisms of interest 

(e.g., Raimondo et al. 2007, Ahlers et al. 2006, TenBrook et al. 2010). Despite the limited 

success of literature analyses to refine ACR prediction based on the chemical class or MOA, it 

seems a logical extension of the MOA concept that chemicals acting through the same toxicity 

pathway should show greater similarity in the expression of toxicity and, by extension in the 

relationship between acute and chronic effects (i.e., the ACR). Put differently, particular toxicity 

pathways should logically lead to consistently higher or lower ACRs compared to averages 

obtained across multiple mechanisms (as in the GLWQG). The idea of MOA-specific ACRs 

would plausibly extend also to the possibility of particular taxa having higher or lower ACRs 

because of the intersection of their physiology and the toxic mechanism of the chemical.

Toward that end, the analyses described in Appendix E were undertaken, focusing specifically 

on data for pesticides whose primary mode of action is AChE inhibition (i.e., carbamates and 

organophosphates). While Appendix E deals solely with AChE inhibitors, this analysis was also 

intended to be demonstrative of approaches that might be used for developing MOA-specific 

ACRs for other groups of chemicals in the future.

Initial evaluation of these data indicated that ACRs for fish were larger and somewhat more 

variable than those for invertebrates (arthropod invertebrates specifically; Figure 2).

Comparisons of data for carbamate and organophosphate chemicals did not suggest systematic 

differences in ACRs between those chemical groups so they were combined for all subsequent 

analyses. ACRs for freshwater and saltwater fish were well distributed across the overall range 

of values. In the case of invertebrates, saltwater ACRs (exclusively from tests with mysids) were 

more common in the upper tail of the combined invertebrate distribution; however, it is not clear 

that this necessarily indicates that mysid ACRs are intrinsically higher than daphnid ACRs for 

AChE inhibitors (see Appendix E for more discussion).
Page 29 of 40

Acute-Chronic Ratio (LC50/GM)

0.1 0.3 1 3 10 30 100 300 1000 3000 10000

Cumulative Percent

0.5

1

2

5

10

20

30

50

70

80

90

95

98

99

99.8

SW Invertebrates

FW Invertebrates

SW Fish

FW Fish

Figure 2. Distribution of ACRGMs for fish and invertebrates derived from acute and 

chronic toxicity data involving exposures of animals to AChE inhibitors.

While fish had generally higher ACRs, they are far less acutely sensitive to AChE inhibitors than 

are invertebrates. As a consequence, the higher ACRs for fish do not translate into greater 

chronic sensitivity – in fact, the opposite is true. To put this in perspective, the NOEC values 

from chronic studies with fish were compared to the acute LC50 for D. magna (Figure 3). A 

substantial majority of chronic fish NOECs were higher than the D. magna acute LC50 for the 

same chemical. If the applicable EFs for AChE inhibitors (Appendix D) are applied to the D. 

magna acute values, one sees that the resulting 50th or 90th percentile EF-based estimates of the 

acute HC5 for AChE inhibitors would be lower than all, or nearly all chronic fish NOEC values. 

This suggests it is the chronic sensitivity of invertebrates that would be the primary determinant 

of the chronic HC5, and that fish ACRs are not relevant to the chronic HC5 for AChE inhibitors
Page 30 of 40

Daphnia magna 48-h LC50 (ug/L)

0.1 1 10 100

NOEC from Fish Chronic Test (ug/L)

0.1

1

10

100

1000

ELS Fish NOEC

ELS Unbounded NOEC

Life Cycle Fish NOEC

Life Cycle Unbounded NOEC

NOEC = LC50

NOEC = Median acute HC5

NOEC = 90th% acute HC5

Fish chronic

less sensitive

Fish chronic

more sensitive

Figure 3. Comparison of chronic fish NOEC values to acute D. magna LC50 and acute HC5 

estimates for AChE inhibitors. 

Accordingly, an ACRHC5 for AChE inhibitors would logically be based on invertebrate ACRs. 

As indicated above, there is some uncertainty as to whether mysid ACRs are inherently higher 

than those for daphnids, which influences the decision of which invertebrate ACRs would be 

most appropriate to defining the ACRHC5. At present, the suggested approach is to select the 

ACRHC5 from the combined invertebrate ACR distribution. This suggestion stems from the 

precedent established in the 1985 Guidelines, under which ACRs for daphnids and mysids would 

likely be averaged to derive a FACR, if both ACRs are available, and both species are acutely 

sensitive (as is the case of AChE inhibitors). However, separating the distributions could also be 

considered.

As the FACR values underlying the Host et al. (1995) and TenBrook et al. (2010) ACRs are tied 

to the 1985 guidelines and therefore to ACRGM or ACREC20 values, those same ACR 
Page 31 of 40

distributions are the most appropriate points of comparison between generalized and AChE 

inhibitor-specific ACRs. As shown in Table 4, the 50th and 80th percentile ACRGM and ACREC20

values derived specifically for AChE inhibitors are up to 2-fold lower than the Host et al. (1995) 

values, and less different from the TenBrook et al. (2010) 80th percentile value of 12.4. The 

selection of a specific ACRHC5 value from among these values (or other points in their 

distribution) would be dependent on both the definition of the chronic toxicity threshold and the 

desired level of conservatism/uncertainty in the assessment.

Table 4. ACR distributional values from Appendix E and literature sources

ACR Distribution Source/Type

Percentile

50th 80th

Appendix E

 AChE inhibitors, invertebrates only ACRNOEC 7.98 17.9

 AChE inhibitors, invertebrates only ACRGM 5.91 13.6

 AChE inhibitors, invertebrates only ACREC20 3.88 8.63

Host et al. (1995)

 Combined chemicals, freshwater FACR 7.1 17.9

TenBrook et al. (2010)

 Mixed pesticides, freshwater FACR NR 12.4

Building on the experience of evaluating ACR data for AChE inhibitors, there are additional 

studies that might help advance the overall approach. Within AChE chemicals, it may be useful 

to explore whether additional data screening to more carefully “match” (e.g., same lab, same 

water, same time frame) the acute and chronic data would decrease variability in ACRs. In 

addition, conducting similar analyses for other MOA would help define the degree to which 

developing MOA-specific ACR distributions can reduce uncertainty relative to generalized ACR 

distributions. Finally, selecting default ACR values from within a distribution raises the issue of 

potential under-protection in cases where the “true” ACR lies higher in the distribution than the 

selected default value. Better understanding the circumstances that lead to unusually high ACRs 

might inform the development of computational or other approaches useful to flagging chemicals 

that might have unusually high ACRs (e.g., structural alerts proposed by Ahlers et al., 2006). 

7. Methods for Estimating the HC5 for Aquatic Plants Using Data Submitted to Fulfill 

FIFRA Data Requirements

The current guidance for derivation of ALC for aquatic plants is to use the lowest toxic effect 

value of the available data. Data acceptable for ALC are described only as “the result of a 96-hr 

test conducted with an alga or a chronic test conducted with an aquatic vascular plant” (USEPA 

1985). Although the establishment of a plant effects value is not required for the derivation of 

ALC, if such a value is desired, then the minimum data required is one acceptable test with a 

freshwater alga or vascular aquatic plant for the freshwater criterion. Similarly the result of only 

one saltwater species is required to establish a saltwater plant criterion. In the event that plants 

are likely to be more sensitive than animals to a given compound (e.g., herbicides), then the 

“results with a plant in another phylum (division) should also be available”. 
Page 32 of 40

The lack of specificity for plant toxicity data is due largely to the fact that at the time of the 

writing of the 1985 Guidelines the “procedures for conducting tests with plants and interpreting 

the results of such tests [were] not as well developed”. Current scientific understanding on how 

to conduct and interpret plant toxicity tests has improved. Nevertheless, standardization of 

procedures and selection of relevant endpoints are still issues. In addition, there is no published 

guidance on how many plant species and from which taxonomic groups they should be selected 

in order to reasonably represent the sensitivity of the aquatic plant community. The current 

analysis (detailed in Appendix F) seeks to investigate the relevance of some of these 

uncertainties, and to explore alternative approaches to characterizing effects of chemical 

stressors on aquatic plant communities when limited data are available.

As noted in the introduction, this effort focuses on the HC5 of a SSD of aquatic plant data. As 

with the acute animal toxicity data, two different methods were investigated for estimating the 

HC5, i.e., EFs and SSDs. For this effort, the EC50 value is used to represent effects of chemical 

stressors on plants. The primary analyses investigated the relative sensitivities of the standard 

aquatic plant species submitted to fulfill FIFRA data requirements (i.e., the FIFRA-5) to the HC5 

of a log normal distribution of toxicity data including other aquatic plant species. Detailed 

descriptions of the methods and results are provided in Appendix F. 

7.1. Use of FIFRA-5 species to Estimate HC5 

7.1.1. SSDs

The relative sensitivities of the FIFRA-5 species were compared to those of other aquatic plant 

test species using log normal SSDs. This distribution was deemed appropriate since the SSD 

analysis with acute animal toxicity data indicated that distributions of log-transformed data 

(normal, logistic, triangular, Gumbel) performed better than distributions with untransformed 

data (Pareto, Weibull and Burr) but no one distribution stood out as the best when considering 

various amounts of data. For this analysis, toxicity data for nine herbicides (atrazine, diquat, 

diuron, irgarol, linuron, metolachlor, metribuzin, metsulfuron methyl and prometryn) and one 

fungicide (pentachlorophenol) were used. These chemicals were selected because they had 

relatively large toxicity datasets (i.e., number of test species ≥ 10), that included the FIFRA-5 

species. The median and lower 25th percentile estimates of the HC5 values from the larger 

datasets were usually within a factor of 5 of the HC5 values of determined for the FIFRA-5 test 

species (Table 5). For the majority of these chemicals (7 of 10), the HC5 value estimated from 

the FIFRA-5 data set was lower than the HC5 of the full data set, suggesting that estimating the 

HC5 using the FIFRA-5 will usually be a conservative method for approximating the HC5.
Page 33 of 40

Table 5. Median and 25th percentile HC5 values from full data sets and FIFRA-5 for 9 

herbicides and 1 fungicide. Units are ppb.

Chemical

Full Data Set FIFRA 5

N Median HC5 HC5 25th

percentile

Median 

HC5

HC5 25th

percentile

Atrazine 54 15.30 14.51 12.66 9.99

Diquat 13 0.60 0.43 0.359 0.172

Diuron 32 1.57 1.40 5.77 4.96

Irgarol 38 0.066 0.055 0.034 0.027

Linuron 10 3.41 2.87 9.33 8.29

Metolachlor 17 22.98 20.73 14.08 10.13

Metribuzin 18 7.65 7.21 1.81 1.1

Metsulfuron 19 0.064 0.044 0.010 0.0024

Pentachlorophenol 27 12.26 9.73 16.61 14.02

Prometryn 13 1.25 1.05 0.53 0.34

There was no pattern among the FIFRA-5 species for their relative sensitivity, and there was no

one species (including the FIFRA-5 and all others) that was consistently the most sensitive. 

Toxicity data for Myriophyllum sp. were available for the majority of the chemicals. The SSDs 

for the full data sets were used to estimate the proportion of species expected to be more 

sensitive than the most sensitive FIFRA-5 species. These proportions of the SSD ranged from 

0.04 to 0.31 (Table 6). Nine of the ten FIFRA-5 most sensitive values are greater than the HC5.

Table 6. Lowest FIFRA-5 EC50 values and their relative positions (i.e., proportion) on the 

SSD for the full data sets.

Chemical Lowest FIFRA-5 

EC50 (μg/L) Proportion of SSD

Atrazine 31.46 0.19

Diquat 4 0.17

Diuron 7.09 0.31

Irgarol 0.1 0.08

Linuron 13.7 0.31

Metolachlor 61 0.13

Metribuzin 8.09 0.06

Metsulfuron 0.41 0.25

Pentachlorophenol 35.26 0.13

Prometryn 1.0 0.04
Page 34 of 40

7.1.2. EFs

The ratios of the lowest FIFRA-5 values to the respective HC5 were greatest for diquat and 

metsulfuron (6.6 and 6.4, respectively). The average of these values could potentially be used to 

derive an extrapolation factor to adjust the lowest FIFRA-5 values to minimize under-protection. 

Use of this extrapolation factor would result in a conservative estimate (i.e., lower) of the HC5 

that is within a factor of 1.4-8.1 of the HC5 values from the large data sets (Table 7). Division 

by an EF, which is commonly done, results in the FIFRA-5 data being conservative relative to 

those from the full data sets. 

Table 7. HC5 values estimated by dividing lowest FIFRA-5 value by 6.5 (EF), and median 

HC5 values from full data sets. Units are ppb. Chemicals which were used to derive EF are 

not included in this table.

Chemical

Lowest 

FIFRA 

EC50

Estimate 

HC5

(from EF)

Median 

HC5

Factor 

difference 

between 

HC5s

Atrazine 31.46 4.8 15.3 3.2

Diuron 7.09 1.1 1.57 1.4

Irgarol 0.1 0.015 0.066 4.3

Linuron 13.7 4.02 3.41 1.6

Metolachlor 61 9.4 22.98 2.4

Metribuzin 8.09 1.2 7.65 6.1

Pentachlorophenol 35.26 5.4 12.26 2.3

Prometryn 1.0 0.15 1.25 8.1

There are a couple of cautions with using these data to establish an EF. Since only ten pesticides 

were used, the maximum value that could be recommended is uncertain. Another issue is that 

there is insufficient information on how to adjust the EF when more phytotoxicity data than 

those for the FIFRA-5 are available. The analyses presented in this section considered a full data 

set to contain results for a minimum of ten species. Based on this, an EF would not be needed 

when similar numbers of toxicity results are available. However, it is uncertain what should be 

done when toxicity data are available for more than five species, but less than ten species.

One notable uncertainty associated with these analyses is that there is no established minimum 

data requirement for aquatic plants for deriving ALC. Because of this, the relationship between 

the range of sensitivities within the current “full” data sets to that from a more taxonomically 

diverse representation of an aquatic plant community is unknown. It is assumed here that the test 

species incorporated into the data sets for each of the ten chemicals considered in this analysis 

are representative of the range of sensitivities of aquatic plants. Until aquatic plant minimum 

data requirements are established, the accuracy of this assumption cannot be fully evaluated. 
Page 35 of 40

7.2. Standardization of endpoints

There have been some questions about whether or not aquatic plant toxicity results should be 

standardized to only growth rate. Sufficient toxicity data were available for three herbicides 

(atrazine, diuron and irgarol) to investigate the impact of standardizing endpoints to growth on 

estimates of the HC5. For each of these herbicides, two distributions were compared. The first 

distribution used to estimate the HC5 was a log normal distribution of all available EC50 values. 

For each chemical, this distribution incorporated all toxicity information for aquatic plant species 

and all endpoints, such as biomass, counts, length, weight, area, chlorophyll a concentration, 

photosynthetic rate or growth rate. If multiple endpoints were available for a given species, only 

the result for the most sensitive endpoint was included in the SSD. The second distribution 

constructed for each chemical was a log normal distribution using EC50 values based on growth 

rate alone. 

The HC5 values for the herbicides are provided in Table 8. The HC5 values from the full data 

sets were within a factor of 3 of the HC5 values from the distributions based on growth rate only. 

Although this suggests that standardization of endpoints to growth rate may not have a great 

impact on the estimate of the HC5, it should be noted that these three compounds all have the 

same mode of action (photosystem II inhibition). Different modes of action may have different 

conclusions. One limitation to this approach is that it may result in much smaller data sets for a 

chemical. For the main analyses, data sets for ten chemicals were considered (see Table 5 for 

full list); however, only three had sufficient data to derive distributions for growth rate. 

Table 8. Median HC5 estimates (in ppb) from SSDs based on full data sets and normalized 

to growth rate for atrazine, diuron and irgarol. 

Chemical HC5

(full data set)

HC5

(growth rate)

Factor 

different

atrazine 15 17 1.1

diuron 1.6 3.4 2.1

irgarol 0.066 0.024 2.8

8. Conclusions

8.1. Methods for Predicting the Acute Toxicity of Chemicals to Aquatic Animal 

Species

The analysis of several predictive tools suggests that, at this time, QSAR models are insufficient 

to confidently estimate the toxicity of pesticide active ingredients, and that models do not exist 

for species associated with missing MDRs defined by the 1985 Guidelines. It is expected that, as 

chemical domains within existing QSAR models expand, these tools may become more robust, 

which could increase their potential use in predicting the toxicity of a chemical to desired species 

and taxa (e.g., MDRs of 1985 Guidelines). Proper data curation, involving the evaluation of 

open literature and registrant submitted data could result in reliable data sets for use in expanding 

predictive QSAR models both from a chemical and a taxa perspective. Even with the 

compilation of reliable empirical data sets to build models, it is unlikely that sufficient high 
Page 36 of 40

quality taxa specific data will exist to develop rigorous QSAR models to predict the toxicity of a 

chemical to species that may fulfill all of the MDR defined in the 1985 Guidelines without 

further animal testing. Nevertheless, the proper curation and evaluation of open literature and 

registrant submitted data could result in reliable data sets for use in expanding predictive models 

for use in estimating the toxic effects of degradate products which typically have little or no 

available empirical data, and possibly FIFRA requirements for fish and invertebrate species. 

The current version of Web-ICE does not have sufficient data to fulfill all of the MDRs of the 

1985 Guidelines when there are only acute surrogate data available for rainbow trout, bluegill 

and D. magna. This may change with future updates. The precision of Web-ICE predictions are 

best for closely related species (e.g., within the same family), suggesting that this tool may be 

useful for predicting the toxicity of a chemical to some species of fish when empirical toxicity 

data are available for a species of fish, especially one of the typical test species submitted to 

fulfill FIFRA data requirements (i.e., rainbow trout or bluegill sunfish). The precision of WebICE is better for chemicals that do not target specific taxa in a distribution of species. 

8.2. Methods for Estimating the HC5 of Sensitivity Distributions of Acute 

Toxicity Data for Animals

A review of the use of available SSD methods in predicting HC5 values with limited empirical 

data indicate that these methods may generate reasonable estimates of HC5 values, but are likely 

to suffer from considerable uncertainty. The review suggested that the use of SSDs with limited 

data should be restricted to the log-normal, log-logistic, and log-triangular distributions, should 

employ graphical methods for parameter estimation, and should impose data requirements to 

ensure that sensitive taxa are included in the analysis. The review also suggested that the use of 

published extrapolation constants (in conjunctions with the previous constraints) would likely 

provide at least nominal coverage of 95% confidence limits around the HC5, but that these 

extrapolated limits were likely to be very conservative (i.e., on average much lower than the true 

HC5).

Extrapolation factors can provide an effective approach for estimating HC5s, with a defined level 

of uncertainty, for toxicity datasets with less than 8 values. The acute EFs recommended in the 

GLWQG (USEPA 1995) for a broad set of chemicals appear to provide conservative estimates 

for HC5s, but need to be further evaluated with regard to the reference HC5s used in their 

development, the chemical selection, and definition of uncertainties. Where sufficient data are 

available, useful MOA-specific EFs can be developed, and the number of MOAs for which this 

is possible can be improved by further development of the distributional approach for EF 

development rather than the approach of resampling of discrete values from toxicity datasets.

8.3. Use of ACR for Characterizing the Chronic Toxicity of Chemicals to 

Aquatic Animals

Chronic toxicity data are generally available for fewer species than are tested to determine acute 

toxicity. ACRs are a common tool used to estimate chronic effect thresholds for taxa lacking 

chronic toxicity data. Both OPP and OW currently use ACRs developed for specific chemicals 

to estimate chronic effect levels. ACR distributions have been developed for broad groups of 
Page 37 of 40

chemicals without consideration of MOA or taxa (e.g., mixed pesticides by TenBrook et al.

2010; all chemicals by Host et al. 1995). This analysis considers the application of ACRs 

developed for chemicals within the same MOA to estimate chronic effects for chemicals with the 

same MOA and lacking any chemical-specific ACRs. Analysis of ACR data specifically for 

AChE inhibitors indicated that ACRs for invertebrates were smaller than those for fish. 

Although fish had larger ACRs, this did not translate to higher chronic sensitivity of fish to 

AChE inhibitors, because of their very low acute sensitivity. In fact, analyses showed that 

concentrations protective of acute toxicity to invertebrates would simultaneously protect fish 

from chronic effects. By extension, for AChE inhibitors, applying an invertebrate-based ACR to 

the acute HC5 (i.e., fifth percentile of a sensitivity distribution) should provide a chronic HC5 

protective of both fish and invertebrates. When comparing ACRs developed for broad groups of 

chemicals and taxa to ACRs for aquatic invertebrates exposed to AChE inhibitors, they were 

approximately 2 fold different. 

8.4. Methods for Estimating the HC5 for Aquatic Plants using Data Typically 

Submitted to Fulfill FIFRA Data Requirements

There was no pattern among the FIFRA-5 species for their relative sensitivity, and there was no 

species that was consistently the most sensitive. The SSDs for the full data sets were used to 

estimate the proportion of species expected to be more sensitive than the most sensitive FIFRA-5 

species, which ranged from 0.04 to 0.31 for the ten chemicals considered. 

EFs were generated from the ratio of the most sensitive of the FIFRA-5 test species and the HC5. 

The highest of these EFs was approximately 6.5. Although this value consistently generates 

conservative estimates of these HC5 values, it is limited for predicting the HC5 for aquatic plant 

communities because it is based on a limited number of chemicals and, unlike EF approaches 

involving aquatic animals, the magnitude of the EF does not change with addition of other test 

species. This is because, due to a lack of defined species requirements (outside of the FIFRA-5), 

the magnitude of different EFs cannot be evaluated. 

The HC5 values generated using an SSD approach and the FIFRA-5 data set were generally 

within a factor of 5 and were more conservative than the HC5 from the larger data set. This 

method seems to be reasonably predictive and has the ability to incorporate the FIFRA-5 as well 

as any additional test species. Normalization of endpoints to growth rate does not appear to 

greatly improve the predictability of SSDs in predicting the HC5 for aquatic plant communities.

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