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Exploration of Methods for Characterizing Effects of Chemical Stressors to Aquatic 

Animals

Draft 11/2/2010

Kristina Garber1

Sandy Raimondo2

Patti TenBrook3

United States Environmental Protection Agency (USEPA)

1 Office of Pesticide Programs (OPP), Environmental Fate and Effects Division, Washington DC

2 Office of Research and Development (ORD), National Health & Effects Research Laboratory, 

Gulf Ecology Division, Gulf Breeze, FL

3 Region 9, San Francisco, CA
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Acknowledgement

The authors acknowledge the contributions of Matthew Etterson, Dale Hoff, Charles Stephan, 

David Mount and Russel Erickson of ORD and Charles Delos of the Office of Water (OW) in 

developing the concepts discussed in this paper. The team acknowledges the peer review and 

guidance provided by Thomas Steeger, Mark Corbin, Marietta Echeverria, and Donald Brady of 

OPP; Joe Beaman and Elizabeth Behl of OW; and Cindy Roberts of ORD. The team also 

acknowledges assistance provided under contract support by Keith Taulbee and Dennis McIntyre 

(of Great Lakes Environmental Center) in summarizing regulatory applications of sensitivity 

distributions and extrapolation factors.
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Table of Contents

List of Acronyms ............................................................................................................................ 4

1. Executive Summary................................................................................................................ 5

2. Introduction............................................................................................................................. 5

3. Methods for Deriving Measures of Effect from Results of Toxicity Tests ............................ 8

3.1. Sensitivity Distributions................................................................................................... 8

3.1.1. Method Description .................................................................................................. 8

3.1.2. Distribution Approaches With Limited Toxicity Test Results............................... 11

3.1.3. Examples of Regulatory Applications of Sensitivity Distributions........................ 16

3.2. Extrapolation factors...................................................................................................... 18

3.2.1. Method Description ................................................................................................ 18

3.2.2. Uncertainty Associated with use of Extrapolation Factors..................................... 19

3.2.3. Examples of Regulatory Applications of Extrapolation factors............................. 19

3.2.4. Extrapolation factors Described in the Scientific Literature................................... 22

4. Proposed Analyses of Methods for Deriving Measures of Effect from Results of Toxicity 

Tests.............................................................................................................................................. 23

4.1. Analytical Strategy............................................................................................................. 24

4.1.1 Definitions.................................................................................................................... 24

4.1.2. Anticipated Sources of Variation in the Estimated HCp ............................................. 25

4.1.3. Analytical Steps.......................................................................................................... 25

4.1.4. Data ............................................................................................................................. 25

4.2. Specific Analyses............................................................................................................... 26

4.2.1. Analyses of Data-Rich Sets ........................................................................................ 26

4.2.2 Analyses of Data-Limited Sets.................................................................................... 26

4.2.3. Use of Predicted Values.............................................................................................. 27

4.2.4 Considerations for Proposed Analyses of Chronic Toxicity........................................ 27

5. Potential Approach for Deriving Aquatic Life Screening Values for Chemicals with Limited 

Datasets......................................................................................................................................... 28

5.1. Derivation of Acute ALSV ............................................................................................ 29

5.2. Derivation of Chronic ALSV ......................................................................................... 30

5.3. Illustration of one approach for deriving ALSVs........................................................... 31

6. Conclusions........................................................................................................................... 32

7. References............................................................................................................................. 32

Appendix A. Acute toxicity data available for Carbaryl in Web-ICE master database. .............. 37

Appendix B. Equations for the Parametric sensitivity distribution Distributions ........................ 47
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List of Acronyms

ACE Acute-to-Chronic Estimation

AChE acetylcholinesterase 

ACR Acute-Chronic Ratio

ALSV Aquatic Life Screening Value

ALWQC Aquatic Life Water Quality Criteria

AOP Adverse Outcome Pathway

CWA Clean Water Act

EF Extrapolation Factor

FIFRA Federal Insecticide, Fungicide and Rodenticide Act

HC5 The 5

th percentile of a SSD, HC stands for “hazard concentration”

ICE Interspecies Correlation Estimation

LC50 Lethal Concentration of 50% of organisms

LOC Level of Concern

LOEC Lowest Observable Effect Concentration

MATC Maximum Allowable Test Concentration (geometric mean of NOEC and LOEC) 

NOEC No Observable Effect Concentration

OECD Office of Economic Cooperation and Development

OW Office of Water

OPP Office of Pesticides

ORD Office of Research and Development

QSAR Quantitative Structure-Activity Relationship

SSD Species Sensitivity Distribution

TCE Time-Concentration Event 

USEPA U.S. Environmental Protection Agency
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1. Executive Summary

In order to characterize potential adverse effects of chemicals in the aquatic environment, the 

United States Environmental Protection Agency (USEPA) uses available toxicity data from 

studies involving individual test species, which serve as surrogates for untested species. These 

data are collected for individual organisms exposed to chemicals (e.g., pesticides) and are then 

frequently extended to represent effects to populations of the same species, populations of 

similar genera/taxa, or to aquatic ecosystems. The goal of this work is to examine how limited 

test results can best be used to characterize adverse effects on aquatic animals. To that end, this 

paper explores two general types of methods that may be used to extrapolate from toxicity test 

results to taxa-specific and community-based measures of effect relevant to the Office of 

Pesticide Programs (OPP) and to the Office of Water (OW). These methods include sensitivity 

distributions and extrapolation factors (EFs) both of which may be used to account for 

uncertainty, particularly in situations where toxicity data are limited. A portion of this work will 

address the derivation of an “Aquatic Life Screening Value” that is related to the fifth percentile 

in a Sensitivity Distribution. ALSVs may be used to screen concentrations of pesticides and 

effluents in ambient waters and may be used by States and Tribes in the development of water 

quality standards. Other portions of this work will address other percentiles in sensitivity 

distributions that can be used to evaluate concentrations of pesticides in ambient water in other 

ways. This paper describes proposed analyses that will be conducted by USEPA in order to 

determine the utility of specific methods for development of a common effects characterization 

methodology for use in ecological assessments of chemicals by USEPA to meet the mandates of 

the Clean Water Act (CWA) and the Federal Insecticide, Fungicide, and Rodenticide Act 

(FIFRA).

2. Introduction

The mission of the USEPA is to protect human health and to safeguard the natural environment 

upon which life depends. Consistent with the USEPA’s mission, OPP and OW are both 

responsible for evaluating the potential effects of chemicals on aquatic life. The process for 

accomplishing this mission involves three general steps. The first is compiling available toxicity 

data. Currently, both OPP and OW rely on the same aquatic toxicity test results (e.g., scientific 

literature, registrant-submitted studies) to characterize the sensitivity of species in the aquatic 

environment to chemical stressors such as pesticides. The second step, which is the focus of this 

paper, involves characterization of potential effects of a chemical on the environment. OPP and 

OW currently use different methods to accomplish this. The third step, which will not be 

discussed in this paper, involves implementing the results of the effects characterization in order 

to safeguard the environment. This is accomplished by OPP through risk assessment and OW 

through criteria development.

In the effects characterizations of OPP and OW, the process used to translate single species 

toxicity test results to taxa-based and community-based (cross-taxa) adverse effect thresholds has 

not been consistent. In addition, the tools used in these processes have not been consistently 

applied across both offices. Toward developing a more transparent and consistent process for 
Page 6 of 54

characterizing chemical effects at various levels of organization, i.e., single species, taxa 

(population) and cross taxa (community), USEPA has conducted six regionally-based public 

meetings and has drafted three white papers. These papers describe existing approaches and 

potential tools that may be used by OPP and OW to characterize the distribution of sensitivities 

in fish, invertebrates and plants in aquatic communities exposed to chemicals. 

This particular white paper describes methods that may be used to characterize effects of 

chemicals on specific taxa (i.e., aquatic vertebrates and invertebrates) and across taxa (i.e., 

aquatic vertebrates and invertebrates combined). These additional methods may be used to 

augment the ability of the USEPA, as well as states, local and tribal water management agencies 

to derive taxa-based and cross-taxa (community-based) toxicity benchmark values for chemicals,

such as pesticides, for risk assessment, monitoring and diagnostic purposes. This white paper 

explores analytical approaches that rely on empirical toxicity test results, particularly in cases 

where the available data may be limited and there is uncertainty as to the extent that the full 

distribution of species sensitivities (either at the taxa level or across taxa) is adequately 

characterized. OW has relied on a process defined in the 1985 Guidelines1to characterize 

community-level effect thresholds (i.e., Aquatic Life Water Quality Criteria) using a defined 

number of taxa. The available data for chemicals can vary considerably. Therefore, regulatory 

agencies must have the flexibility to characterize the potential taxa-based and community-based

effects of chemicals using the available data even if those data are limited in quantity. Although 

many of the methods discussed in this paper are currently used to varying extents in both offices,

their consistent and integrated use by both offices has yet to be realized. The utility of the 

specific approaches described in this paper will be evaluated by USEPA and will undergo peer 

review and additional development prior to implementation by OPP and OW.

In order to characterize potential adverse effects of chemicals in the aquatic environment, it is 

necessary to use available toxicity test results from individual test species to extrapolate to 

assessment endpoints that are related to aquatic ecosystems. An assessment endpoint is “an 

explicit expression of the environmental value to be protected.”2Specific assessment endpoints 

relevant to this effort are based on those currently used by OPP and by OW. For OPP, 

assessment endpoints are taxa specific and include acute mortality and chronic survival, growth 

and reproduction of aquatic vertebrates (i.e., fish and aquatic-phase amphibians) and 

invertebrates. For OW, assessment endpoints also include acute mortality and chronic survival, 

growth and reproduction but these endpoints are expressed in terms of the aquatic animal

community (i.e., a combination of vertebrates and invertebrates). Although the assessment 

endpoints of OPP and OW differ (i.e., taxa-based vs. community-based, respectively), they both 

rely upon similar aquatic toxicity test results (See USEPA 1985, USEPA 1994, USEPA 2004). 

Therefore, it is possible to arrive at both sets of assessment endpoints using similar a common 

methodology.

Measures of effect, such as acute and chronic toxicity test results, are used to quantitatively 

represent assessment endpoints (USEPA 1998). For OPP, the measures of effect are the lowest 

available EC50 (or LC50) values to represent effects from acute exposures of fish and 

 

1

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

their Uses” (USEPA 1985).

2

http://www.epa.gov/OCEPATERMS/
Page 7 of 54

invertebrate species to stressors, as well as the lowest No Observable Effect Concentration

(NOEC) values to represent effects to growth and reproduction of fish and invertebrate species

resulting from chronic exposures. For OW, the measure of effect is the HC5, or the 5th percentile 

of a distribution of acute toxicity test results for genera of aquatic animals (i.e., fish, amphibians 

and invertebrates) as well as the HC5 for chronic toxicity test results. 

This paper describes two types of methods that may be used to extrapolate from individual 

species toxicity test results to taxa-based and community-based measures of effect relevant to 

OPP and OW. These methods are sensitivity distributions and extrapolation factors. Examples of 

how sensitivity distributions and extrapolation factors are used by regulatory agencies, 

particularly those in the United States, for characterizing effects of chemicals on aquatic animals 

are also discussed. For instance, OPP’s taxa-specific Aquatic Life Benchmarks (USEPA 2010)

for pesticides and OW’s community-based Aquatic Life Water Quality Criteria currently use a 

combination of these methods in characterizing effects to aquatic organisms.

This paper also describes proposed analyses that will be conducted by USEPA in order to 

determine the utility of specific sensitivity distribution and extrapolation factor approaches for 

characterizing effects of chemicals on aquatic vertebrates, invertebrates and animal communities.

These analyses will use large data sets for chemicals with various adverse outcome pathways3

that are representative of pesticides with differing modes of action (e.g., acetylcholinesterase 

inhibition and non-polar narcosis). These data sets will be used to evaluate intra- and interspecies variability by considering test results available for species exposed to the same chemical. 

The analysis will also consider the uncertainty that results from extrapolating from limited 

toxicity data to other species within aquatic taxa and across taxa (aquatic communities). 

Ultimately, the utility of a specific approach will be defined by its ability to predict a measure of 

effect with a desired level of certainty.

As indicated in the scoping document for this project4, “One goal of a common effects 

characterization methodology is to improve the tools and approaches available to States and 

stakeholders to derive scientifically defensible water quality criteria that can in turn be used to 

set water quality standards in a manner consistent with aquatic effects assessments conducted by 

OPP and OW in compliance with both the CWA and FIFRA.” This could involve use of 

empirical toxicity test results available for chemicals, predicted or estimated toxicity data using 

methods discussed in the tools white paper5and extrapolation methods described in this paper. 

To that end, this paper provides a general framework that may be used to conceptualize the 

development of community level benchmarks that may be considered by stakeholders to set 

water quality standards. The term “aquatic life screening value” (ALSV) is introduced here to 

represent community level benchmarks. Since the ALSV may be considered by USEPA, States 

and Tribes to derive scientifically defensible water quality standards, and because water quality 

criteria established using the 1985 Guidelines are set to one half of the fifth percentile of a 

 

3 An adverse outcome pathway describes the linkage between a molecular-level initiating event for the toxicity of a 

chemical of interest to an adverse outcome at a biological level of organization of interest for a risk assessment

(Ankley et al. 2010). More information about this term and its advantages relative to the conceptually similar 

“mode-of-action” and “mechanism-of-action” can be found in the tools white paper.

4 Available online at: http://www.epa.gov/oppefed1/cwa_fifra_effects_methodology/scope.html

5 Titled: “Predicting the toxicity of chemicals to aquatic animal species.”
Page 8 of 54

sensitivity distribution, the measure of effect for the acute ALSV is also set to one half of the 

HC5 of a sensitivity distribution, and the chronic ALSV is set to the HC5 of the distribution of 

chronic toxicity data. It is expected that as USEPA reviews the specific methods described in this 

paper and in the tools paper, the ALSV framework presented in this paper will evolve to 

represent the most useful approaches and the conditions under which they should be used.

Following this introduction, this paper has three primary sections. The next section of this white 

paper (i.e., section 3) serves to characterize available methods for extrapolating from available 

toxicity test results to taxa-specific and community-based measures of effect relevant to OPP for 

use in ecological risk assessments and OW for use in development of water quality criteria. The 

fourth section of this paper describes a proposed analysis that will be conducted by USEPA in 

order to determine the utility of the available methods for OPP’s and OW’s effects 

characterization. And the fifth section of this paper provides a conceptual approach that may be 

used to integrate chemical-specific toxicity test results, tools and methods for deriving 

community level benchmarks (i.e., ALSVs) that, 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., monitoring data).

3. Methods for Deriving Measures of Effect from Results of Toxicity Tests

This section provides descriptions of methods that can be used to extrapolate from available 

toxicity test results to measures of effect. These methods include sensitivity distributions and 

extrapolation factors. Also included are examples of how sensitivity distributions and 

extrapolation factors are used by regulatory agencies for characterizing effects of chemicals on 

aquatic animals.

3.1. Sensitivity Distributions

This section describes various distributions used for describing sensitivities of species as well as 

uncertainty in estimating HC5 values and applications of sensitivity distributions in limited data 

situations. This section also includes brief descriptions of regulatory applications of sensitivity 

distributions.

3.1.1. Method Description

“Sensitivity Distribution” is a generic term used to represent a distribution of sensitivities of 

different biological taxa to the same stressor. Most commonly the taxa are biological species 

(these distributions are termed: “species sensitivity distributions” and abbreviated SSDs); 

however, sensitivity distributions may also be developed for genera (e.g., USEPA 1985). For 

acute exposures, the toxicity test results of interest are EC50 and LC50 values of set durations of 

exposure (e.g., 48-hr for cladocerans, 96-hr for non-cladoceran invertebrates and fish). Separate 

sensitivity distributions may also be applied to chronic toxicity data by compiling chronic 

toxicity data for similar test results, such as NOECs, or maximum acceptable toxic 

concentrations (MATCs; defined as the geometric mean of the NOEC and the lowest observable

effect concentration (LOEC)) . 
Page 9 of 54

Toxicity data are log-transformed and then used to derive a cumulative distribution where 

sensitivities are ranked from most sensitive to least sensitive. The concentration at the lower 5th

percentile of the sensitivity distribution is most often the concentration of interest for regulatory 

purposes (e.g., USEPA, OECD). The 5

th percentile value is commonly termed the HC5, where 

HC stands for “hazard concentration.” It should be noted that the HC5 is discussed as a measure 

of effect throughout this document; however, other percentiles of sensitivity distributions could 

also be used. 

Many different probability distributions exist and are in use for describing the sensitivities of 

aquatic organisms to chemicals. For instance, Figure 1 depicts normal, logistic, triangular and 

Gompertz probability distributions of log-transformed, acute toxicity data for the pesticide 

carbaryl. Carbaryl is a carbamate insecticide and plant growth regulator. For aquatic animals, 

carbaryl’s mode of action is inhibition of acetylcholinesterase (AChE). Toxicity data for carbaryl 

were obtained from the database of empirical acute toxicity EC50 and LC50 values underlying 

Web-ICE6. These data are provided in Appendix A. These data are used throughout this white 

paper to illustrate the applications of the methods that are described. It should be noted that 

although these data were reviewed to determine suitability for inclusion in Web ICE, these data 

have not been reviewed for inclusion in OPP ecological risk assessments or OW’s aquatic life 

water quality criteria development. 

Figure 1. Probability densities of log-normal, log-logistic, log-triangular, and log-gompertz distributions fit to 

49 log10 GMAVs for carbaryl.

 

6 Model and documentation available online at: http://www.epa.gov/ceampubl/fchain/webice/
Page 10 of 54

Fitted cumulative distribution functions for the carbaryl data from Figure 1 are depicted in Figure 

2. Equations for the probability density function (pdf), cumulative distribution function (cdf), 

quantile function (F

-1

), mean, and variance of these four distributions are provided in Appendix 

B. In general, the quantile function provides an estimate of the concentration (in log10 units) 

associated with a given percentile of the distribution [i.e., HC5 = F

-1

(0.05)]. Thus, if the 

sensitivity data are drawn from a distribution with known parameters (i.e., mean and standard 

deviation), the HC5 can be easily obtained. HC5 values from the fitted cumulative distribution 

functions for log-normal, log-logistic, log-triangular, and log-gompertz distributions for carbaryl 

(depicted in Figure 2) are provided in Table 1. These values range 7.9 to 10.5 μg/L. 

Figure 2. Fitted cumulative distribution functions for log-normal, log-logistic, log-triangular, and loggompertz distributions plotted against 49 log10GMAVs for carbaryl.

Table 1. HC5 values from fitted cumulative distribution functions for log-normal, log-logistic, log-triangular, 

and log-gompertz distributions plotted against 49 log10GMAVs for carbaryl.

Distribution HC5 (μg/L) Proportion of data < HC5

Normal 9.6 0.13

Logistic 10.2 0.15

Triangular 10.5 0.15

Gompertz 7.9 0.11

N = 55 GMAVs
Page 11 of 54

Many other distributions have been used for sensitivity distribution approaches, including 

weibull (Zajdlik & Associates 2005) and burr (Shao 2000). The burr distribution tends to limit 

to either the reciprocal weibull (Tadikamalla 1980) or the pareto distribution 

(http://www.cmis.csiro.au/envir/burrlioz/). Sensitivity distributions may also be calculated using 

non-parametric, distribution-free methods (Newman et al. 2000). 

3.1.2. Distribution Approaches With Limited Toxicity Test Results

3.1.2.1. Uncertainty in the Estimated HC5 When Data are Limited

When a distribution is fit to a sample set with limited data, the precision of the estimated 

parameters of the distribution is uncertain. Thus the precision with which the HC5 is estimated 

will vary from chemical to chemical and will often be limited depending on the number of data 

points and the amount of variability in the data. Several approaches for handling this uncertainty 

have been developed for sensitivity distributions, typically with the objective of placing 

confidence limits around the estimated HC5 (examples are provided in Appendix B). The level of 

confidence (cl) in the estimated HC5 can be noted as follows: 

cl HC5

. "cl" denotes the confidence 

with which the estimated HCp is no higher than the true HCp. A considerable body of literature 

on sensitivity distributions employs a confidence level of 95% (i.e.,

95 HC5

), though in practice 

any level of confidence could be specified. In the 1985 Guidelines, a 50% confidence level 

(corresponding to the median estimate) is used as the best estimate of the HC5 (i.e.,

50 HC5

). 

While the methods for developing these confidence limits differ across distributions, most follow 

a similar procedure, often based on a “standard” form of the distribution. A common method for 

expressing the location of this concentration is as a function of the sample mean (

x

, Equation 1), 

standard deviation (s, Equation 2) and an extrapolation constant (k, Equation 3). 

Equation 1

1

1

N

i

i

x x

N

Equation 2

2

1

1

1

N

i

i

s x x

N

Equation 3

HC x ks cl

5

Extrapolation constants (not to be confused with extrapolation factors, which are described in 

section 3.2) have been developed for three distributions commonly used for sensitivity 

distributions, the log-normal distribution (Aldenberg and Jaworska 2000, Aldenberg et al. 2002), 

the log-logistic distribution (Aldenberg and Slob 1993) and the triangular distribution 

(Pennington 2003). These authors provide tables of k values derived either theoretically or 

through Monte Carlo simulation. These k values are provided in Appendix B. For distributions 

in the location/scale family, values of the extrapolation constants depend only on the parametric 

distribution, the sample size, and the desired level of confidence. Extrapolation constants 

increase with the level of confidence required and decrease with sample size (Figure 3). 
Page 12 of 54

Figure 3. Extrapolation constants (k) versus confidence bound on the HC5 for a standard log-logistic 

distribution of species sensitivities (k-values generated following methodology of Aldenberg and Slob 1993).

Extrapolation constants are not the only method for handling uncertainty in an estimated HC5. 

An important limitation to the use of extrapolation constants is that the choice of any given value 

for an extrapolation constant makes an implicit judgment concerning an acceptable level of 

uncertainty (e.g., the use of an extrapolation constant corresponding to 95% confidence presumes 

that the acceptable level of uncertainty is 5%). Another way to quantify the uncertainty 

surrounding the estimated HC5 is to estimate its standard error using the delta method (Seber 

2002). This requires an estimate of the covariance matrix for the estimated parameters and the 

quantile function (F

-1

). For example, let θ represent a vector of estimated parameters describing 

a distribution, then for a normal distribution, 

x s,

and:

Equation 4. 

var cov , ˆ ˆ

COVˆ

cov , var ˆ ˆ

x x s

x s s

From the above, the equation for the sampling variance of the HC5 is:

Equation 5. 

1 1 1 1

5

var HC , cov ,

T

F F F F

x s x s
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Where the partial derivatives are evaluated at 0.05. An advantage to the above method is that the 

resulting variance estimate can be applied to any level of uncertainty deemed acceptable using 

standard methods for generating confidence intervals.

3.1.2.2. Sensitivity Distribution Approaches Using Known Population Variance

The sensitivity distribution approaches described in Section 3.1.2.1 rely upon sample estimates 

of population parameters (i.e., sample mean and standard deviation). As discussed below, 

Aldenberg and Luttik (2002) and de Zwart (2002) have explored the applications of known 

population variance along with sample means in deriving HC5 values from sensitivity 

distributions. 

Aldenberg and Luttik (2002) proposed the use of known standard deviations in combination with 

estimated means (from samples) to derive HC5 values from sensitivity distributions. This 

approach requires knowledge of the standard deviation from the population of interest, 

preferably from “similar substances as the one under study.” This approach is limited in its 

application in that the standard deviation must be obtained from other chemical data sets, which 

does not allow for a simple application of this method for data limited chemicals. 

In order to account for population variability that may be represented by standard deviations of 

samples with small sample sizes, de Zwart 2002 proposed use of a logistic sensitivity distribution 

with known variance (β) that are based on a chemical’s mode of action (Table 2). de Zwart 

assumed that the intrinsic toxicity of each chemical within a mode of action would change (i.e., 

x

would vary); however, the variability of organism sensitivities to the same chemical will be 

similar within a mode of action. Using this approach, the HC5 is calculated using Equation 6. 

Equation 6. 

2.94 HC5

10
Page 14 of 54

Table 2. Mean β values for various modes of action reported by de Zwart 2002.

Mode of Action N Average β

Standard error of 

the mean β **

Nonpolar narcosis 34 0.39 0.03

AChE* inhibition (carbamates) 27 0.71 0.03

Photosynthesis inhibition 20 0.60 0.03

Polar narcosis 13 0.31 0.03

AChE* inhibition (organophosphates) 11 0.50 0.05

Uncoupler of oxidative phosphorylation 8 0.38 0.05

Multisite inhibition 6 0.62 0.07

Dithiocarbamates 6 0.57 0.05

Diesters 6 0.42 0.07

Systemic fungicide 5 0.46 0.04

Sporulation inhibition 5 0.37 0.05

Neurotoxicity (pyrethroids) 4 0.65 0.03

Neurotoxicity (cyclodiene-type) 4 0.61 0.01

Plant growth inhibition 4 0.52 0.06

Membrane damage by superoxide formation 3 0.69 0.01

Cell division inhibition 3 0.63 0.21

Systemic herbicide 3 0.52 0.12

Plant growth regulator 3 0.44 0.10

Neurotoxicant (DDT-type) 2 0.50 0.13

Amino acid synthesis inhibition 2 0.47 0.03

Germination inhibition 2 0.40 0.02

Quinolines 2 0.28 0.02

Reactions with carbonyl compounds 2 0.28 0.07

*AChE = acetylcholinesterase **

N

stdev of SEM ( )

3.1.2.3. Methods for Deriving Chronic Distributions Using Acute Toxicity Test 

Results

Two approaches are described in the literature (de Zwart 2002, Douboudin et al. 2004) that use 

available acute toxicity data to create chronic sensitivity distributions. In both approaches it is 

possible to derive chronic HC5 values using no chronic toxicity data.

de Zwart developed a regression for the acute and chronic mean values (termed αacute and αchronic, 

respectively; Equation 7). Using this equation, it can be concluded that the average acute toxicity 

for a chemical is a factor of 13-24x higher than the average chronic toxicity. de Zwart concluded 

that this analysis could be applied to assign surrogate chronic species distribution parameters in 

cases were acute toxicity data are available but chronic data are not available. Using this 

approach, a chronic HC5 could be derived using Equation 6 and the appropriate β from Table 2.
Page 15 of 54

Equation 7. 

chronic 1.053*acute 1.430

(de Zwart 2002)

In Duboudin et al. (2004)’s approach, acute mean (μA) and standard deviations (σA) based on 

available invertebrate data are used to calculate mean values (μc) representative of chronic 

effects using equations 8 and 9 for vertebrates and invertebrates, respectively. These equations 

were derived from regressions of empirical acute and chronic data available for vertebrates (22 

chemicals; r2= 0.854) and invertebrates (15 chemicals, r2= 0.917). The test chemicals included 

metals, inorganic chemicals and organic chemicals, some of which were pesticides (e.g., lindane, 

azinphos-methyl, diazinon, atrazine).

Equation 8. 

Cv 0.82*Av 0.62*Av 0.49

(vertebrates)

Equation 9. 

Ci 0.58*Ai 1.96*Ai 1.60

(invertebrates)

3.1.2.4. Illustration of Distribution Approaches Involving Limited Data

Toxicity text results for carbaryl were used to illustrate the different distributional approaches 

described in Section 3.1.1. Relative to many other pesticides, there are a great deal of aquatic 

toxicity test results available for carbaryl in the scientific literature, as well as from studies 

submitted by carbaryl registrants to fulfill FIFRA data requirements. In order to illustrate the 

uses of the methods described above in determining the HC5 of for a chemical using a small data 

set, a subset of aquatic toxicity test results for carbaryl from Appendix A is used here to simulate 

a chemical with limited toxicity data (Table 3). Since this effort is focused on applications of 

extrapolation factors that are relevant to OPP and OW, the subset of data is based on the FIFRA 

data requirements for pesticides. Under FIFRA, acute toxicity data are required for two 

freshwater fish (typically the rainbow trout, Oncorhynchus mykiss and the bluegill sunfish, 

Lepomis macrochirus) and one freshwater invertebrate (typically the waterflea, Daphnia magna).

Table 3. Subset of acute toxicity data for carbaryl used to illustrate use of sensitivity 

distribution approaches with limited data.

Taxa Species Test Result 

(μg/L)

Geometric mean of test result 

for species (μg/L)

Aquatic invertebrate Daphnia magna 5.6* 7.5 10.1*

Fish (warm water) Bluegill sunfish 5230** 5230

Fish (cold water) Rainbow trout 1090** 1841 3110**

*48-h EC50

**96-h LC50

For this small data set, the average (

x

) and standard deviation (s) of the log transformed 

geometric means of the toxicity test results for the species are 2.62 and 1.53, respectively. These 

values are used to generate median and 95thpercentile HC5 values (Table 4) using Equation 3 

and the k values from the log-normal distribution (Aldenberg and Jaworska 2000, Aldenberg et 

al. 2002), the log-logistic distribution (Aldenberg and Slob 1993) and the triangular distribution 

(Pennington 2003). The k values used for N=3 are provided in Appendix B. These values are 
Page 16 of 54

also used in the approaches where the variance is known (i.e., de Zwart 2002). For the de Zwart 

method, equation 6 was used with a β value of 0.71, which is based on the mode of action 

representative of carbaryl (i.e., AChE inhibition from a carbamate). Median HC5 values 

calculated with these methods range 0.31-3.4 μg/L. It is notable that the 95th percentile HC5

values are nine orders of magnitude lower than the median HC5 values. It is also notable that the 

median HC5 values are all below the HC5 values generated for the fitted distributions from the 

full carbaryl data set, which were 9.6, 10.2 and 10.5 μg/L for the log-normal, log-logistic and 

log-triangular distributions, respectively (Table 1). 

Table 4. Median and 95th percentile HC5 values (μg/L) derived for subset of carbaryl data. 

Distribution (method source) Median HC5

95 HC5

Log-normal (Aldenberg and Jaworska 2000) 0.46 7.3x10-10

Log-logistic (Aldenberg and Slob 1993) 0.31 1.5x10-10

Log-logistic (de Zwart 2002) 3.4 Not available

Log-triangular (Pennington 2003) 0.52 7.2x10-10

If no chronic toxicity data were available for carbaryl, de Zwart’s work could also be used to 

derive a chronic HC5. In this approach, equation 7 could be used to estimate a mean of the 

chronic toxicity data (αchronic = 1.33). Using this with the β value of 0.71 and equation 6, the 

resulting HC5 would be 0.17 μg/L. 

3.1.3. Examples of Regulatory Applications of Sensitivity Distributions

3.1.3.1. OPP

OPP has used species sensitivity distributions to characterize risks of pesticides to specific taxa, 

(e.g., fish) of concern. This approach involved generating separate distributions of acute toxicity 

data for fish and aquatic invertebrates. Estimated and measured concentrations of pesticides in 

surface water were compared to the two joint probability distributions to consider the proportions 

of fish and invertebrates that may be impacted by the assessed pesticide (USEPA 2007a, USEPA 

2007b). 

Sensitivity distributions are also incorporated into OPP’s aquatic level II risk assessment model 

(v.2.0, 3/16/2004). Although this model is still in development, it is intended for use in OPP to 

estimate the likelihood and magnitude of effect on aquatic species that are vulnerable to pesticide 

exposure. This model uses available acute toxicity data to develop separate distributions for fish 

and aquatic invertebrates. The model calculates the 5th, 50th and 95th percentiles of the 

distributions to generate probit-concentration response curves. These are used to determine the 

magnitude of effect to specific taxa resulting from an exposure concentration (USEPA 2004b). 

3.1.3.2. OW

OW currently derives Aquatic Life Water Quality Criteria (ALWQC) for communities of aquatic 

animals using a triangular distribution of toxicity test results for genera. This approach combines 

toxicity data for vertebrates (i.e., fish and amphibians) and invertebrates (e.g., cladocerans, 

insects). The acute criterion is set to half of the 5th percentile of the distribution, based on the 
Page 17 of 54

1985 Guidelines. The 5th percentile is calculated using the four most sensitive acute toxicity 

genus mean values. The 5

th percentile is divided by 2 to derive a final acute criterion (i.e., the 

criterion maximum concentration) in order to adjust the 5th percentile EC50 value to an EC 

“low” value which is intended to estimate a level of toxicity between 0 and 10% such that the 

final value is statistically indistinguishable from mortality allowable in control treatment and 

considered background. The overall intention of the acute and chronic criteria is to establish 

concentrations of chemicals that, if not exceeded, aquatic animals should not be unacceptably 

affected (directly).

3.1.3.3. Europe

The preferred approach of the EU for deriving environmental quality standards for substance 

concentrations in inland surface waters as well as in transitional, coastal and territorial waters is 

statistical extrapolation using a SSD (European Commission 2003). In this approach, it is also 

preferred that the dataset used in the SSD contain more than 15, but at least 10 NOECs from 

“long-term” toxicity tests, for different species covering at least 8 taxonomic groups.

The Netherlands requires at least four chronic NOECs for different taxonomic groups for a 

refined effects assessment. The Netherlands (RIVM 2001) “estimated risk levels” are derived 

using the SSD method of Aldenberg and Jaworska (2000). That is, HC5 values are calculated 

based on a log-normal SSD.

3.1.3.4. OECD

The sensitivity distribution methodology for the Organization of Economic Cooperation and 

Development (OECD) method is similar to the European Union (EU) SSD methodology; 

however, no data distribution type is assumed a priori. The SSD can follow any data 

distribution, although log-normal and log-logistic (e.g., Burr Type III) distributions are most 

likely. The best model is assessed using the Kolmogorov-Smirnov goodness-of-fit test. The 

“maximum tolerable concentration” is represented as either the median of the 5th percentile value 

or the lower 95% confidence limit of the 5th percentile depending on the goal of the end user. If 

sufficient chronic data exist to meet all of the taxonomic requirements, then a final chronic value

can be calculated. If species level data are used, then the final chronic value is equal to the 

maximum tolerable concentration. Unlike the USEPA method, however, the OECD method 

requires NOECs from eight taxonomic groups, and does not allow for use of Actue Chronic 

Ratios (ACRs); however, appropriate QSAR values can be used as surrogates if laboratory data 

are unavailable. 

3.1.3.5. Australia and New Zealand

Australia and New Zealand derive guideline trigger values from toxicity data. The Australia/New 

Zealand guidelines use the same SSD method as the Netherlands, but with a curve-fitting 

procedure that overcomes the problem of data that do not fit an assumed distribution. Using the 

program BurrliOZ v. 1.0.13 (CSIRO 2001; Campbell et al. 2000), data are first fitted to one of a 

family of Burr distributions (Burr 1942; the log-logistic distribution is in the Burr family). After 

an appropriate distribution is chosen, then the calculation to estimate the 95th protection level is 
Page 18 of 54

the same as the Netherlands’ methodology but utilizes extrapolation constants ( k ) derived for 

each of the distributions.

3.1.3.6. University of California, Davis Methodology

This methodology was developed by researchers at the University of California, Davis 

(TenBrook et al. 2010) for the derivation of pesticide water quality criteria for the Sacramento 

and San Joaquin Basins, although the method is applicable to pesticide water quality criteria

derivation for all freshwater aquatic systems. This methodology was first finalized as a 2009 

report to the Central Valley Regional Water Quality Control Board and has been used to derive 

aquatic water quality criteria for bifenthrin, chlorpyrifos, cyfluthrin, lambda-cyhalothrin, 

diazinon, diuron and malathion7.

The authors of this methodology evaluated many of the current water quality criteria derivation 

methodologies and incorporated elements from several of them, in addition to providing their 

own specific modifications of these elements, throughout the derivation process. The SSD 

procedure described in this approach is the result of a synthesis of elements from the SSD 

procedures of the Dutch (RIVM), Australian and New Zealand (ANZEC&ARMCANZ), and 

1985 Guidelines procedures, with additional modifications. Based on this analysis, the Burr III 

distribution should be used for the SSD method (acute and chronic). Similar to the 1985 

Guidelines approach, this SSD method uses the median value of the 5th percentile of the SSD, 

but species level data are used, rather than aggregating to the genus level. The acute value is 

determined by dividing the median of the 5th percentile by 2, and the chronic value is the 5th

percentile value without additional adjustment. The authors also recommend calculating 95% 

confidence intervals surrounding the 1st and the 5th percentiles using a bootstrapping procedure 

that is described in detail within the methods manual and is also included with the BurrliOZ 

freeware described above. The use of the SSD method is based on the availability of toxicity test 

results for species in 5 different families.

3.2. Extrapolation factors

3.2.1. Method Description

Extrapolation factors (EFs) are set values that are applied to available toxicity test results to 

account for various sources of uncertainty in extrapolating from individual species toxicity data 

to measures of effect. Several different names have been used to describe these factors, including 

assessment factors, safety factors, application factors and uncertainty factors. These factors are 

used as follows: available toxicity data are identified for a chemical and the lowest toxicity test 

result is divided by the EF. The advantage of this approach is that it is simple, easy to use and 

requires little expenditure of time or resources in terms of reviewing toxicity data or calculating 

the final measures of effect.

 

7

available at:

http://www.swrcb.ca.gov/rwqcb5/water_issues/tmdl/central_valley_projects/central_valley_pesticides/criteria_meth

od/index.shtml
Page 19 of 54

3.2.2. Uncertainty Associated with use of Extrapolation Factors 

There is uncertainty associated with this approach in terms of accurately characterizing effects of 

stressors on measures of effect given limited availability of data. Since the EF is applied to the 

most conservative toxicity data available for a chemical, the magnitude of the EF is typically 

dependant on the extent of available data. Important considerations for this include: species that 

are tested and the adverse outcome pathway of the chemical. 

Additional sources of uncertainty can be attributed to differences in: sensitivity among 

individuals within a species due to different test conditions (intra-species variability) and 

sensitivity of test species and species of concern (inter-species variability). EFs may be designed 

to account for some of these sources of uncertainty and variability.

3.2.3. Examples of Regulatory Applications of Extrapolation factors

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

results to derive water quality criteria. EFs are used by various agencies in the United States 

(e.g., USEPA 1995), Canada (e.g., MENVIQ 1990, Rev. 1992) and Europe (European 

Commission 2003) to establish levels of concern for aquatic species, taxa, communities and 

ecosystems. Described below are two approaches involving EFs that are used by United States 

regulatory agencies. Specifically, these include the OPP Aquatic Benchmarks and the Great 

Lakes Water Quality Initiative for deriving criteria with limited toxicity data. The OPP Aquatic 

Benchmarks and Great Lakes Water Quality Initiative methods differ in their methods and 

intended measures of effect (i.e., the lowest available toxicity data for a taxa and the HC5 for a 

community, respectively). Also described below is an approach presented in the scientific 

literature by Pennington 2003, which is based on various sensitivity distribution assumptions and 

considers chemical mode of action. It should be noted that the approaches described below are

not necessarily equivalent because they vary in the uncertainty, measures of effect and 

assessment endpoints they are intended to represent. 

3.2.3.1. OPP Aquatic Benchmarks

The OPP benchmarks were developed in response to recommendations and input from 

stakeholders, who were concerned about potential effects of pesticides with no existing Aquatic 

Life Water Quality Criteria. OPP developed a webpage of non-regulatory taxa-specific endpoints 

referred to as “OPP Aquatic Benchmarks”8. 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. OPP’s acute toxicity test results are lethal concentrations to 50% of the animals tested

(LC50) and adverse effect concentrations for 50% of the animals tested (EC50) values for 

freshwater vertebrates and invertebrates. Chronic toxicity test results are no observed adverse 

effect concentrations (NOECs) for the same taxa. Benchmarks are calculated by multiplying 

each lowest toxicity result for a taxon by its respective Level of Concern (acute risk LOC = 0.5;

chronic risk LOC = 1.0), which is based on OPP’s ecological risk assessment process. LOCs are 

 

8 OPP Aquatic Benchmark Table. Available online at: 

http://www.epa.gov/oppefed1/ecorisk_ders/aquatic_life_benchmark.htm
Page 20 of 54

the Agency’s interpretative policy and are used to analyze potential risk to non-target organisms 

and the need to consider regulatory action. LOCs are used to indicate when a pesticide use (as 

directed on the label) has the potential to cause adverse effects on non-target organisms (USEPA 

2004). 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. OPP’s benchmarks may 

be useful for interpreting the potential effects of pesticides in surface water in cases where there 

is no existing Aquatic Life Water Quality Criteria. If the FIFRA data requirements are fulfilled, 

then benchmarks should be available for freshwater fish and invertebrates. 

To put the OPP benchmark approach in terms of EFs, this is equivalent to dividing the lowest 

acute toxicity test result for a taxon by 2 and the lowest chronic toxicity test result by 1. For the 

acute benchmark, this is comparable to OW’s approach for calculating acute criteria value by 

dividing the 5

th percentile LC50 value 2 to establish an LC “low” (see section 3.1.3.2).

To illustrate the methods employed in deriving OPP Aquatic Benchmarks the same subset of 

aquatic toxicity data for carbaryl used to illustrate SSD methods is used here to simulate a 

chemical with limited toxicity data (Table 3). For the OPP Aquatic Benchmarks, two acute 

benchmarks are selected: one for aquatic invertebrates and one for fish. In this example, toxicity 

data are available for one species of invertebrate (i.e., D. magna). The lowest single toxicity test 

result available for D. magna is used to derive the freshwater invertebrate acute toxicity 

benchmark by dividing by 2. Therefore, the freshwater invertebrate acute benchmark is 2.8 μg/L. 

Although toxicity data are available for two species of fish, the lowest single result of all the tests 

is selected and then divided by 2 to calculate the fish benchmark. The resulting freshwater 

vertebrate benchmark value for this example is 545 μg/L. 

3.2.3.2. Great Lakes Water Quality Guidance (and similar approaches)

The Great Lakes Water Quality Guidance (USEPA 1995) 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 

criteria, including: 1) Tier I values for which data meet the minimum data requirement as given 

in the 1985 Guidelines9, and 2) Tier II values where there is an absence of the full set of data 

needed to meet Tier I data requirements. Due to limited data used to derive Tier II criteria, these 

values are likely to be more uncertain compared to Tier I criteria. Tier II criteria are intended to 

be more protective, on average, than what Tier I criteria would be for the same chemicals if 

 

9

For acute criteria, the data requirements include toxicity data for: 

- a salmonid fish

- a nonsalmonid fish

- a species from a third chordate family

- a planktonic crustacean

- a benthic crustacean

- an insect

- a species from a family in a phylum other than Chordata or Arthropoda

- a species from a family in another order of insect or in a fourth phylum
Page 21 of 54

sufficient data were available. Of interest here are the Tier II criteria values, which are derived 

using EFs.

For the Tier II method, all acute toxicity data are collected for a chemical. The geometric mean 

of the toxicity data for each genus is calculated. The number of data requirements that are met is 

determined. In order to calculate an acute Tier II criterion, the dataset must contain an acute 

toxicity result for at least one daphnid species (i.e., Ceriodaphnia sp., Daphnia sp., or 

Simocephalus sp.) The number of data requirements that are met is used to determine the 

appropriate EF (Table 5). When the EFs are applied to the lowest genus mean acute value, the 

result is intended to approximate the 5th percentile of a species sensitivity distribution (triangular

continuous probability distribution) as used in the 1985 Guidelines. With this method, the acute 

Tier II criterion is calculated by dividing the lowest genus mean acute value by the appropriate 

EF (this result is termed the “secondary acute value” abbreviated SAV) and then dividing that 

value by 2.The EFs were calculated using 29 toxicity datasets analyzed to determine the effects 

of removing taxa on calculation of the 5th percentile (Host et al. 1995). These factors are 

intended to address variability in interspecies sensitivity to chemical stressors.

Table 5. Extrapolation factors used by Great Lakes Water Quality Guidance, Michigan DEQ, Ohio EPA,

USDOE, and UC Davis in deriving criteria when data requirements defined in 1985 Guidelines are not met.

Number of 

data 

requirements

that are met

Great Lakes 

Water 

Quality 

Guidance*

Michigan 

DEQ* Ohio EPA* USDOE*

USDOE

(no 

daphnid)

UCDavis*

1 21.9 Not applicable 21.9 20.5 242 570

2 13.0 13.0 13.0 or 7.9 ** 13.2 64.8 36

3 8.0 8.0 8.0 8.6 36.2 7.8

4 7.0 7.0 7.0 6.5 20.1 5.1

5 6.1 6.1 6.1 5.0 12.9 3.8

6 5.2 5.2 5.2 4.0 9.2 NA

7 4.3 4.3 4.3 3.6 7.2 NA

*Data from at least one daphnid species is available.

** If the family salmoindae is not represented, this value is 13.0.If the family salmoindae is represented, this value is 

7.9.

NA = not applicable

A number of similar approaches are in use by state agencies (i.e., Michigan Department of 

Environmental Quality (MDEQ; MNDEQ 2006) and Ohio EPA (Ohio EPA 2008)) and the U.S. 

Department of Energy (USDOE; Suter and Tsao 1996). These approaches involve EFs that are 

modifications of the Great Lakes Water Quality Guidance (Table 5). The MDEQ) and Ohio EPA 

EFs are identical to the Great Lakes Water Quality Guidance, except that the Michigan approach

does not provide an EF when only one data requirement is met and the Ohio EPA has two 

different EFs available when two data requirements are met: one for when a salmonid test result 

is available, and one for when a salmonid test result is not available. The USDOE differs from 

the Great Lakes Water Quality Guidance in that it provides a separate set of EFs for use when no 

daphnid test results are available. 

For the Great Lakes Water Quality Guidance and other similar methods described above (i.e., MI 

DEQ, Ohio EPA, USEOE), the example data set for carbaryl (Table 3) fulfills 3 data 

requirements (i.e., a salmonid fish, a non-salmonid fish and a planktonic crustacean). Therefore, 
Page 22 of 54

the EF of 8.0 is used. For this guidance, the lowest mean value of toxicity test results available 

for a genus is selected. In this example, the most sensitive genus is Daphnia sp., with a mean 

toxicity test results of 7.5 μg/L. The SAV of 0.94 μg/L is calculated by dividing the daphnid 

toxicity value by 8.0. As indicated above, this value is intended to be representative of the 5th

percentile of a triangular distribution, as calculated according to the 1985 Guidelines.

With the UC-Davis Methodology (TenBrook et al. 2010), acute criteria are derived using EFs 

when toxicity data are not available for the 5 required species defined in this method. The EFs

for this method (Table 5) were empirically derived by applying the procedure of Host et al. 

(1995), using a database of ten of the pesticides also used to determine the most appropriate SSD 

data distribution. EFs based on having 1 through 5 of the required taxa were generated for each 

of the ten pesticides, and then a final EF was calculated for taxonomic samples sizes of 1-5. This 

approach requires that one of the data values be from a daphnid species. The EFs were cross 

validated by comparing them to criterion values derived using the SSD method. For 2-5 

taxonomic groups, the EF approach (lowest acute value in the dataset divided by the appropriate 

EF) yielded lower criteria than the SSD approach. The EF approach for one taxon yielded higher 

criteria estimates for two pesticides in the dataset. For these pesticides, daphnids were not the 

most sensitive species and the EF approach overestimated the HC5 by a factor of 8-10. Thus, an 

additional safety factor of ten was included for cases where data for only one taxon is available.

To illustrate the use of the UC-Davis methodology using the carbaryl dataset used previously

(Table 3), 3 of the 5 data requirements are met by the example data set for carbaryl, resulting in 

the use of 7.8 as an EF. The lowest species mean acute value (i.e., 7.5 μg/L) is divided by this EF 

to yield a value of 0.96 μg/L. This result is similar to the result produced by the Great Lakes 

Water Quality Guidance (i.e., 0.94 μg/L).

3.2.4. Extrapolation factors Described in the Scientific Literature

Pennington 2003 expanded upon the work of de Zwart (2002) who explored the use of a logistic 

sensitivity distribution with known variability (β) based on chemical mode of action (see section 

3.1.2.2). Pennington’s work is essentially an application of de Zwart’s work on SSDs but is 

expressed in the form of EFs. Pennington considered the application of known β values in 

combination with uncertainty associated with sample mean values based on varying N, where 

N≤8. Pennington determined EFs (note that the author termed them “application factors”)

relating the lowest sample toxicity test result to HC5 values from log-normal, log-logistic and 

log-triangular distributions in combination with the range of β values recommended by de Zwart 

(0.24-1.4). Using this approach, HC5 values could be estimated with 50% and 97.5% confidence 

by dividing the lowest available toxicity test result by the EF representing the appropriate N for 

the sample of interest. Table 6 provides the EFs for estimating HC5 values with 50% confidence. 

Pennington also determined EFs using β values for Quinolines (i.e., β = 0.28) and AChE 

inhibiton by organophosphates (i.e., β = 0.71). Table 7 provides the EFs for estimating HC5

values with 50% confidence and assuming a log-logistic distribution. 
Page 23 of 54

Table 6. Extrapolation factors reported by Pennington (2003) for estimating HC5 values with 50% confidence 

assuming different distributions. Values derived using range of β values recommended by de Zwart (2002).

N Log-normal Log-logistic Log-triangular

2 24 27 23

3 10 12 9.4

4 6.0 7.1 5.5

5 4.2 4.9 3.9

6 3.1 3.5 2.8

7 2.5 2.8 2.3

8 2.0 2.3 1.9

Table 7. Extrapolation factors reported by Pennington (2003) for estimating HC5 values with 50% confidence 

for quinolines and organophosphates.

N quinolines organophosphates

2 3.8 25

3 2.8 12

4 2.3 7.2

5 2.0 4.9

6 1.7 3.7

7 1.5 2.9

8 1.4 2.2

The carbaryl dataset in Table 3 is used to illustrate the application of Pennington’s EFs. When 

considering Pennington’s EFs for the log-normal, log-logistic and log-triangular distributions 

when considering the availability of 3 toxicity test results, the HC5 values range 0.63-0.80 μg/L 

(Table 8). These are comparable to the results of the Great lakes water quality guidance (0.94 

μg/L) and TenBrook et al. 2010 (0.96 μg/L), which are intended to approximate the HC5 values

of the triangular and burr distributions, respectively.

Table 8. Example OPP benchmarks and Tier II criterion for sub-set of carbaryl data.

Description Test result (μg/L) Extrapolation 

factor Value (μg/L)

Log-normal (Pennington 2003) 7.5 10 0.75

Log-logistic (Pennington 2003) 7.5 12 0.63

Log-triangular (Pennington 2003) 7.5 9.4 0.80

4. Proposed Analyses of Methods for Deriving Measures of Effect from Results of Toxicity 

Tests

The goal of this work is to examine how limited test results can best be used to characterize 

adverse effects on aquatic animals. To that end, Section 3 explores two general types of methods 

(i.e., sensitivity distributions and extrapolation factors) that may be used to extrapolate from 

toxicity test results to taxa-specific and community-based measures of effect relevant to OPP and 

to OW. A portion of this work will address the derivation of an “Aquatic Life Screening Value”

that is related to the fifth percentile in a Sensitivity Distribution (i.e., the HC5). ALSVs may be 
Page 24 of 54

used to screen concentrations of pesticides and effluents in ambient waters and may be used by 

States and Tribes in the development of water quality standards. ALSVs are further discussed

below in Section 5. Other portions of this work will address other percentiles in sensitivity 

distributions that can be used to evaluate concentrations of pesticides in ambient water in other 

ways (e.g., for ecological risk assessment). This section describes the analyses that USEPA 

proposes to conduct to determine the utility of these methods to estimate HCp based on "datalimited" sets. These proposed analyses will not simply compare published methods, but rather 

will use these methods as sources of ideas for development of improved methods for estimating 

the HCp to a specified level of confidence.

4.1. Analytical Strategy

4.1.1 Definitions 

The measurement of effect of interest to this effort is the

HCp

, where "p" denotes the proportion 

of species adversely affected by a pesticide under laboratory test conditions, which can be 

confidently estimated for "data-rich" chemicals (i.e., those with large amounts of toxicity test 

data available). Because there will be uncertainty about the “true” value of the HCp, especially 

for "data-limited" chemicals, the proposed work will develop methods for the statistic 

cl HCp

(See 

section 3.1.2.1). These methods will be developed to accommodate a range of values for p and 

cl, specific values for which can be established as appropriate for specific applications.

Because the term extrapolation factor has been used by different authors to mean different things 

(see Section 3), it is important to define its meaning for the analyses proposed here. Simply put, 

an EF is a factor applied to estimate the 

cl HCp

based on a value derived from the available data. 

In more specific terms, it is the expected ratio between a location statistic (e.g., mean) within the 

data set of interest and the

cl HCp

. An EF might be applied to the lowest value among the available 

data (such as in the Host et al. (1995) approach described in Section 3.2.3.2), or to some other 

characteristic of the data, such as the mean value. Accordingly, the magnitude of an EF will 

depend upon several factors, including the statistic used to describe the available data (minimum, 

mean), the confidence level to which the HCp is estimated (higher confidence will result in larger 

EFs), as well as the amount and type of available data (fewer data will generally result in larger 

EFs). The focus of the work outlined below will be to evaluate the ability of different EF 

derivation approaches to estimate

cl HCp

, recognizing that this may vary depending on the nature 

of available data.

Analysis of the continuum of available aquatic animal data for pesticides will allow for inference 

on data sufficiency that may be dependent on the taxonomic sensitivity to a given pesticide AOP, 

and provide a scientifically defensible basis for the use of available benchmark methods 

discussed in this white paper. Because USEPA will investigate several techniques for 

specifying

cl HCp

, it will be necessary to determine which techniques produce the most accurate 

estimates. This will be particularly challenging because, even with “data rich” pesticides, the 

“true” 

HCp

is unknown. The work proposed here will use two strategies for addressing this 
Page 25 of 54

unknown. First, data rich sets will be used to specify a "reference" HCp,ref to provide a surrogate 

for the true HCp for use in the evaluations. Second, the statistical characteristics of data rich sets 

will be used to develop distributions for which the true HCp is known and from which samples 

can be drawn to test methodologies for data-limited sets. All of the proposed analyses described

below will entail various evaluations of accuracy and precision. However, in the interests of 

readability and brevity, this is not separately noted for each task.

4.1.2. Anticipated Sources of Variation in the Estimated HCp

Many factors affect the value of an HCp estimate derived from test data, including (but not 

limited to): 1) total number of test results available for analysis; 2) taxonomic diversity of 

available test results; 3) degree of within species (or genus) test replication and associated 

variability; and 4) knowledge of the AOP of the pesticide. The proposed analyses will explore 

each of these potential influences.

4.1.3. Analytical Steps

The general analytical strategy will involve two steps. In the first step, HCp estimation will be 

evaluated under "data rich" conditions. This work will support decisions regarding the best 

distributional assumptions and estimation techniques for sensitivity distribution analysis. It will 

also allow specification of an HCp,ref to use in assessing method performance under "data 

limited" conditions. In the second step, data-limited samples of the data-rich sets will be 

constructed and their theoretical distributions will be used to test the performance of various 

options for sensitivity distribution analysis relative to the HCp,ref values. Extrapolation factors for 

data-limited conditions will also be developed by various approaches described in Section 3 

based on both distributional analyses and sampling of data-rich sets and theoretical distributions, 

and their uncertainty described relative to the HCp,ref values. Unless specifically noted below, 

the same methods will be applied regardless of whether the toxicity values pertain to acute 

versus chronic endpoints or pertain to aquatic invertebrates, fish, or both. 

4.1.4. Data

Proposed analyses will be conducted using acute and chronic toxicity data for chemicals with 

modes of action representative of pesticides (e.g., AChE inhibition). Acute toxicity values will 

be obtained from the Web-ICE database of empirical EC50 and LC50 values that are used to 

generate the ICE models. Chronic toxicity values will be obtained from a database called 

AquaChronTox, which is currently under development by USEPA. AquaChronTox contains 

chronic toxicity test results for aquatic animals from studies identified in the primary literature, 

OPP Data Evaluation Records, and the fathead minnow database managed by scientists at 

USEPA.
Page 26 of 54

4.2. Specific Analyses

4.2.1. Analyses of Data-Rich Sets

Regardless of what method is employed, the estimation of an HCp requires the estimation of a 

quantile from a sample of data. Further, it is desirable that the estimate be an unbiased estimate 

of the “population” quantile of interest (i.e., the quantile of the distribution of sensitivities of all

aquatic invertebrates, all fish, or both in a given aquatic community). As noted in Section 3, this 

requires choice of a distribution to fit to the sample and choice of a method for fitting the 

distribution. Specifically, the proposed analyses are:

1) Compare the fit of commonly used (log) distributions (Triangular, Normal, Logistic, 

Gompertz, Weibull, and Burr) to full sets of data for “data rich” chemicals and to composite 

distributions based on these data-rich sets.

2) Explore multiple methods for fitting the distributions to the data. In particular, the following 

will be compared:

a) linear regression techniques on transformed data (e.g., after Erickson and Stephan 

1988)

b) maximum likelihood (Edwards 1992), and

c) moment estimators (equating the sample mean and standard deviation with the 

parametric equations for the distribution of interest and solving for the distributional 

parameters). This is a commonly used method in the literature on Species Sensitivity 

Distributions (e.g., Aldenberg and Slob 1993, Aldenberg and Jaworska 2000)

3) In performing the above comparisons, the proposed analyses will include investigation of 

methods for using information contained in:

a) replicate tests on a given species (genus) beyond simply taking the geometric mean 

b) adverse outcome pathway of the pesticide (for example using AOP-specific variance 

estimates, de Zwart 2002).

c) taxonomic diversity of the “data rich” sample.

d) right-handed censoring of data sets.

4) Overall results will be used to assign an HCp,ref for each data-rich chemical

USEPA will also consider distribution free methods (e.g., Newman et al 2000) and compare the 

results of these methods to commonly used distributions and methods for fitting distributions to 

available data. 

4.2.2 Analyses of Data-Limited Sets

4.2.2.1 Direct Application of Distributional Analysis

Given an estimate of the HCp,ref (i.e., from 4.2.1 above) USEPA will explore the accuracy of HCp

estimates from distributional analysis of limited subsets of the same data. The same steps and 
Page 27 of 54

issues as in section 4.2.1 will be followed for this analysis of smaller sets. It will not necessarily 

be assumed that the best choices under “data rich” conditions will prove to be best under “data 

limited” conditions. 

4.2.2.2 Extrapolation Factors from Distributional Analysis

1) Toxicity values will be subject to analysis of variance to determine the variance of toxicity 

values for a chemical across all species (genera). For chemicals with the same AOP, an 

AOP-specific variance (including standard error of the variance estimate) will be estimated. 

The result of this task will be distributions of variances applicable to different AOPs, as well 

as a distribution of variances across all chemicals. 

2) Based on the procedures of de Zwart (2002) and Pennington (2003), analyses of “data 

limited” subsets will be conducted using the AOP-specific variances determined in Task 1 to 

derive extrapolation factors for different “data limited” sample sizes.

3) The proposed analyses in Tasks 1 and 2 will be extended to consider the influence of 

taxonomic diversity of the subsamples on the accuracy of 

cl HCp

estimates. This analysis will 

include consideration of species for which OPP typically receives toxicity test results. 

4.2.2.3. Extrapolation Factors from Statistical Re-sampling

1) For each “data rich” pesticide, extrapolation factors will be generated using the approach of 

Host et al. (1995), but expanding this approach to:

a) consider the impact of using individual toxicity test results rather than geometric mean 

values as the basis of the toxicity data subsets, and

b) describe how estimated extrapolation factors vary with taxonomic diversity of the 

underlying subsamples.

2) The relationship between the extrapolation factors for individual chemicals determined under 

Task 1 will be analyzed regarding their similarity within and across different AOPs.

4.2.3. Use of Predicted Values

The availability of toxicity values that comprise the data for these analyses (LC50, EC50, 

NOEC, etc.) differs widely among pesticides and may be very limited in some cases, especially 

for recently developed pesticides. To address this, the analyses proposed above (sections 4.2.1-

4.2.2) will be repeated in a second phase including the use of predictive methods (i.e., QSAR, 

Read-Across/Bridging, ICE, and TCE) to augment empirical data. The use of acute-chronic 

ratios (below) will also be evaluated for augmenting scarce data on chronic toxicity.

4.2.4 Considerations for Proposed Analyses of Chronic Toxicity

Two important issues arise in the estimation of an HCp for chronic toxicity. First, the definition 

of a “chronic effect” is not always consistent, with some researchers classifying any toxicity test 

with a duration longer than an acute exposure period as a chronic test, whereas others apply more 

stringent standards (e.g., only results of full or partial life-cycle tests, as defined in the 1985 
Page 28 of 54

Guidelines). Second, chronic test results (NOEC, LOEC and MATC) indicate an uncertain (or at 

least variable) degree of effect, though use of exposure-effect analysis and point estimates is 

increasing. To the extent possible, USEPA will re-analyze chronic toxicity data (currently being 

compiled in AquaChronTox) to obtain point estimates of effect (e.g., EC20s) that will be 

subsequently used for HCp estimation. Whatever the chronic test result (ECx, NOEC, etc.), 

distributional analysis of empirical data will proceed largely as described in sections above (4.2.1 

– 4.2.2). Proposed analyses that will be unique to chronic data include estimation of acutechronic ratios.

In general, fewer chronic test results are available compared to acute tests. This naturally leads 

to investigation of methods to supplement chronic data through analysis of the relationship 

between acute and chronic toxicity. With paired acute and chronic data this is a straightforward 

exercise and generates a distribution of ACRs. Historically, researchers have used quantiles 

(often the 80th percentile) of the ACR distribution to specify a default ACR (Host et al. 1995, 

TenBrook et al. 2010). When paired data do not exist, several methods may be applied, making 

use of predictive methods (see Tools paper). The proposed analyses will include the following 

evaluations of the potential for using ACRs to strengthen estimates of chronic HCp and 

cl HCp

:

1) Use “data rich” chemicals to compile a distribution of ratios of HCp,acute to HCp,chronic

and examine both the variance and quantiles of the resulting distribution.

2) Evaluate alternatives for estimating an ACR from chronic toxicity values for which 

paired acute values are not available, including ICE, QSARs, read-across, and using 

acute values from closely-related species (see Tools paper for a description of these 

methods).

3) Evaluate and develop guidance for applying read-across methods to estimate chronic 

values and ACRs for species having acute data but no chronic data.

4) Evaluate time-concentration effect (TCE) models (Mayer et al. 1994) for predicting 

results of chronic toxicity tests, including relationships between mortality and 

sublethal endpoints under chronic exposure.

5) Use screened chronic EC20s from AquaChronTox to determine the degree to which 

species-specific ACRs are predictable within AOPs, with a particular focus on welldeveloped methods for the narcosis AOP (Di Toro et al. 2000; Undine and DiToro 

2009).

6) Development of AOP-specific default ACRs for all chemicals within a specific AOP by 

using a percentile of the distribution of available empirical ACRs across all taxa for 

the most sensitive taxa for each chemical where valid ACRs are available. 

5. Potential Approach for Deriving Aquatic Life Screening Values for Chemicals with Limited 

Datasets

One of the objectives of this effort is to provide a conceptual approach that may be used to 

integrate chemical-specific data, tools and methods for deriving community level benchmarks 

(i.e., ALSVs) that may be used by States and Tribes to set scientifically defensible water quality 
Page 29 of 54

standards for pollutants for which USEPA Aquatic Life Water Quality Criteria are not yet 

available, and to interpret pesticide monitoring data. This is of particular interest for chemicals 

with limited toxicity data. Based on the information described above and in the tools paper, there 

are multiple approaches that may be used to accomplish this objective. Although the USEPA has 

not yet evaluated the specific sensitivity distribution and extrapolation factor methods described 

above, these methods may be considered to characterize the effects of chemicals to aquatic 

organisms. The appropriateness of specific methods may depend upon the amount of available 

data, the type of available data (e.g., specific species for which toxicity data are available) and 

the mode of action of the chemical. 

This section presents a potential framework for determining when to use different methods for 

chemicals with limited data. This framework is intended as an illustration of how various 

methods described in this paper and tools described in the tools white paper may be integrated to 

characterize the effects of a chemical on aquatic communities (not specific taxa). It is expected 

that once the methods described above are reviewed that this framework will be revised to 

incorporate the methods determined to have the greatest utility.

The term used to represent the community level benchmark that is derived from this framework 

is the aquatic life screening value (ALSV). Since different approaches are available for 

characterizing acute and chronic toxicity data, two separate ALSV frameworks are described 

below. Since the ALSV may be considered by USEPA, States and Tribes to derive scientifically 

defensible water quality criteria, and because water quality criteria established using the 1985 

Guidelines are set to one half of the fifth percentile of a sensitivity distribution, the measure of 

effect for the acute ALSV is also set to one half of the HC5 of a sensitivity distribution, and the 

chronic ALSV is set to the HC5 of the distribution of chronic toxicity data. Due to uncertainty 

associated with limited data used to derive HC5 values, ALSV calculations should incorporate 

conservative estimate of the HC5.

5.1. Derivation of Acute ALSV

A conceptual framework for deriving the acute ALSV is provided in Figure 4. The first step in 

deriving an acute ALSV is to compile all available acute toxicity data for aquatic animals. Data 

acceptability is based on standards established by OW (USEPA 1985) and by OPP (USEPA 

1994, USEPA 2004). As noted previously, acute toxicity test results for aquatic invertebrates are 

defined as 48-h or 96-h EC50 or LC50 values. For vertebrates, acute toxicity test results are 

defined as 96-h LC50s. 
Page 30 of 54

Figure 4. Conceptual framework for deriving acute ALSV.

Once the acute toxicity database has been developed for a chemical, it is necessary to determine 

whether the empirical data can be supplemented with predicted or estimated toxicity data. This 

can be determined by considering the chemical’s mode of action in the context of the utility of 

available tools. More information on specific tools and their applicability to chemicals with 

specific adverse outcome pathways is provided in the tools white paper. If toxicity data can be 

predicted or estimated for a chemical, then these data can be combined with available empirical 

data to represent the acute toxicity database for that chemical. If tools are not available to 

reliably predict or estimate the acute toxicity of the chemical, then the empirical data alone will 

represent the acute toxicity database for the chemical.

Once the acute toxicity database is available for a chemical, an ALSV can be derived. As noted 

above, there are two major approaches that may be used to derive an HC5. Extrapolation factors 

based on extrapolation from the most sensitive toxicity data to the HC5 of a sensitivity 

distribution may be used. Also, an HC5 may be derived from sensitivity distribution based on the 

mean and variance of the acute toxicity database. The specific extrapolation factors and 

distribution methods that may be most appropriate for various situations (considering number of 

available toxicity data and adverse outcome pathway of a chemical) will be determined after 

USEPA has evaluated the specific methods described above.

5.2. Derivation of Chronic ALSV

A conceptual framework for deriving the acute ALSV is provided in Figure 5. Like the acute 

ALSV, derivation of the chronic ALSV starts with compiling acceptable chronic toxicity test 

Compile 

empirical 

toxicity data

Determine 

adverse 

outcome 

pathway

Can empirical toxicity 

data be supplemented 

with predicted data?

If yes, use appropriate 

tools for chemical to 

predicted toxicity data.

If no, acute toxicity 

database contains only

empirical data.

Acute toxicity database 

contains empirical and 

predicted data.

Use extrapolation factor or sensitivity 

distribution to derive ALSV.
Page 31 of 54

results for aquatic animals. Chronic toxicity test results, which are based on various sublethal 

effects and exposure durations, differ much more from each other compared to acute toxicity test 

results, which are set durations of exposure most often based on mortality. In order to compare 

the relative sensitivities of species chronically exposed to a chemical, effort should be invested to 

make the test results as consistent as possible in terms of duration of exposure and the measure 

of effects (e.g., NOECs, MATCs). Although limited tools are available for estimating chronic 

toxicity data, it may be possible to supplement the empirical toxicity data using predicted or 

estimated chronic values (e.g., using ACRs or ACE models applied to acute toxicity data). 

Figure 5. Conceptual framework for deriving chronic ALSV.

Once the chronic toxicity database is established, several options are available for deriving the 

HC5. One option is to use a sensitivity distribution to derive the HC5. A second option would be 

to apply a regression approach to derive the HC5 (e.g., de Zwart 2002, Duboudin et al. 2004). 

Another option, which is similar to the one used in the 1985 Guidelines, would be to apply an 

ACR to the acute HC5, which is derived for the acute ALSV. The ACR may be based on 

empirical toxicity data, a combination of empirical and predicted or estimated data for specific 

species or a default values. Default values may be generically applied to all chemicals (e.g.,

Great Lakes Water Quality Guidance), or it may be possible to use a default ACR based on the 

mode of action of the chemical (e.g., Raimondo et al. 2007). 

5.3. Illustration of one approach for deriving ALSVs

Although there are many different tools and methods available for estimating an ALSV, one 

approach is used here to illustrate a potential way to derive acute and chronic ALSVs when data 

are limited. The approach for deriving the acute ALSV involves the use of the Web-ICE tool. 

Within Web-ICE there is an option for calculating a SSD with toxicity test results from as few as 

1 species. This SSD is a log-logistic distribution (de Zwart 2002) of estimated toxicity data. In 

this example, the example data set used previously in this paper (Table 3) were used to derive a 

HC5 of 12 μg/L (95%CI = 1.1-89) for carbaryl. This value is similar to the HC5 value generated 

for the fitted log-logistic distribution from the full carbaryl data set, which was 10.2 μg/L (Table 

1). Therefore, the acute ALSV for this example would be calculated by dividing the HC5 by 2, 

resulting in a value of 6 μg/L. Note that this HC5 estimate is based on estimated toxicity data for 

42 species of freshwater and saltwater fish and invertebrates. The HC5 estimate does not include 

Compile 

empirical 

toxicity data

Determine 

adverse 

outcome 

pathway

Select option for 

deriving HC5 (based 

on available data)

Use sensitivity distribution 

with empirical data to derive 

ALSV

Apply ACR to acute HC5 to 

derive ALSV

Use regression approach to 

derive ALSV
Page 32 of 54

estimated toxicity test results from species where the input values were not within model 

maximum and minimum values.

The chronic ALSV could be calculated by applying an ACR to the HC5 from the acute data. 

Raimondo et al. (2007) published ACRs based on mode of action. The median and 90th

percentile ACRs for AChE inhibition by carbamates are 8.9 and 28, respectively. If the median 

ACR of 8.9 were applied to the acute HC5, the resulting chronic HC5 would be 1.3 μg/L. If the 

90th percentile ACR were applied to the acute HC5, the chronic HC5 would be 0.43 μg/L. One of 

these chronic HC5 values could be used to represent the chronic ALSV.

6. Conclusions

This paper identifies several different methods for extrapolating from available toxicity data to 

measures of effect relevant to the survival, growth and reproduction of aquatic vertebrates, 

invertebrates and animal communities. These methods include: sensitivity distributions and 

extrapolation factors. This paper describes the analyses that USEPA proposes to conduct to 

determine the utility of the specific methods in characterizing effects to aquatic organisms both 

in data-rich and data-limited instances. This paper also provides a conceptual approach that may 

be used to integrate chemical-specific data, tools and methods for deriving community level 

benchmarks (i.e., ALSVs) that may be used by USEPA, States and Tribes to derive scientifically 

defensible water quality criteria or in ecological risk assessments. After the USEPA finishes its 

analysis of available tools and methods, this conceptual approach will be refined to incorporate 

specific methods and considerations for deriving ALSVs for chemicals.

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Appendix A. Acute toxicity data available for Carbaryl in Web-ICE master database.

Common Name Species Taxa Test 

Duration

Dose 

Type

Test 

Type

Acute Toxicity 

Value (ug/L)

Data Source 

Category

African clawed frog Xenopus laevis Amphibia 96h LC50 S 1730 ECOTOX

Amphipod Echinogammarus 

tibaldii Crustacea 96h LC50 NR 6.5 ECOTOX

Amphipod Gammarus fasciatus Crustacea 96h LC50 S 26 Mayer and Elliersieck

Amphipod Gammarus italicus Crustacea 96h LC50 NR 28 ECOTOX

Amphipod Gammarus lacustris Crustacea 96h LC50 S 22 Mayer and Elliersieck

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 7.2 ECOTOX

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 7 ECOTOX

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 13 ECOTOX

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 16 ECOTOX

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 16 ECOTOX

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 7.2 Mayer and Elliersieck

Amphipod Gammarus 

pseudolimnaeus Crustacea 96h LC50 S 7 Mayer and Elliersieck

Amphipod Pontoporeia hoyi Crustacea 96h LC50 R 250 ECOTOX

Apache trout Oncorhynchus gilae Fishes 96h LC50 S 1435 Mayer 2008

Apache trout Oncorhynchus gilae Fishes 96h LC50 S 1500 ECOTOX

Apache trout Oncorhynchus gilae Fishes 96h LC50 S 1540 ECOTOX

Apache trout Oncorhynchus gilae Fishes 96h LC50 S 1650 ECOTOX

Apache trout Oncorhynchus gilae Fishes 96h LC50 S 1430 ECOTOX

Appalachian crayfish Cambarus bartonii Crustacea 96h LC50 R 2240 ECOTOX

Appalachian crayfish Cambarus bartonii Crustacea 96h LC50 R 2900 ECOTOX

Appalachian crayfish Cambarus bartonii Crustacea 96h LC50 R 2410 ECOTOX

Appalachian crayfish Cambarus bartonii Crustacea 96h LC50 R 2230 ECOTOX

Appalachian crayfish Cambarus bartonii Crustacea 96h LC50 R 940 ECOTOX

Argentine common toad Bufo arenarum Amphibia 96h LC50 S 24640 ECOTOX
Page 38 of 54

Atlantic salmon Salmo salar Fishes 96h LC50S 1150 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S905 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1430 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1350 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1180 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 4500 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1000 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 900 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1270 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 500 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 2010 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1100 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 1000 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 2070 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50S 250 Mayer and Elliersieck

Backswimmer Notonecta undulata Insecta 96h LC50S 200 ECOTOX

Backswimmer Notonecta undulata Insecta 48h LC50S 360 ECOTOX

Bayou darter Etheostoma rubrum Fishes 96h LC50S 2020 ECOTOX

Black bullhead Ameiurus melas Fishes 96h LC50S 20000 Mayer and Elliersieck

Black crappie Pomoxis nigromaculatus Fishes 96h LC50S 2600 OPP

Black crappie Pomoxis nigromaculatus Fishes 96h LC50S 2600 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 14000 OPP

Bluegill Lepomis macrochirus Fishes 96h LC50S 5230 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 5200 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 8200 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 6760 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 5600 ECOTOX

Bluegill Lepomis macrochirus Fishes 96h LC50S 1000 ECOTOX

Bluegill Lepomis macrochirus Fishes 96h LC50S 2200 ECOTOX

Bluegill Lepomis macrochirus Fishes 96h LC50F 5047 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 6200 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 7400 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 2600 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 1800 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 5200 Mayer and Elliersieck
Page 39 of 54

Bluegill Lepomis macrochirus Fishes 96h LC50S 7000 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 16000 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50S 5400 Mayer and Elliersieck

Bonytail Gila elegans Fishes 96h LC50S 3490 Literature

Bonytail Gila elegans Fishes 96h LC50R 2020 ECOTOX

Bonytail Gila elegans Fishes 96h LC50R 650 ECOTOX

Bonytail Gila elegans Fishes 96h LC50S 3500 ECOTOX

Bonytail Gila elegans Fishes 96h LC50S 3490 ECOTOX

Bonytail Gila elegans Fishes 96h LC50S 3580 ECOTOX

Bonytail Gila elegans Fishes 96h LC50S 3400 ECOTOX

Bonytail Gila elegans Fishes 96h LC50S 3044 Mayer 2008

Brook trout Salvelinus fontinalis Fishes 96h LC50S 1130 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50S 2130 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50S 2100 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50S 1200 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50S 4560 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50S 1290 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50S 1450 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 1070 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 1100 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 5400 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 2500 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 900 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 3700 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50S 680 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes96h LC50S 3000 Mayer and Elliersieck

Brown trout Salmo trutta Fishes 96h LC50S 700 ECOTOX

Brown trout Salmo trutta Fishes 96h LC50F 2000 Mayer and Elliersieck

Brown trout Salmo trutta Fishes 96h LC50S 6300 Mayer and Elliersieck

Brown trout Salmo trutta Fishes 96h LC50S 1950 ECOTOX

Cape Fear shiner Notropis mekistocholas Fishes 96h LC50S 4510 Literature

Cape Fear shiner Notropis mekistocholas Fishes 96h LC50S 4264 Mayer 2008

Cape Fear shiner Notropis mekistocholas Fishes 96h LC50S 4510 ECOTOX

Catla Gibelion catla Fishes 96h LC50R 6400 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50S 7790 Mayer and Elliersieck
Page 40 of 54

Channel catfish Ictalurus punctatus Fishes 96h LC50S 15800 Mayer and Elliersieck

Channel catfish Ictalurus punctatus Fishes 96h LC50 NR 140 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50S 1300 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50S 1560 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50F 17300 Mayer and Elliersieck

Chinook salmon

Oncorhynchus 

tshawytscha Fishes 96h LC50

F 2400 Mayer and Elliersieck

Climbing perch Anabas testudineus Fishes 96h LC50R 5500 ECOTOX

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 4340 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 2700 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 2400 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 1750 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 1150 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 997 ECOTOX

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 1300 ECOTOX

Coho salmon Oncorhynchus kisutch Fishes 96h LC50S 764 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50 SR 1310 Literature

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50S 3070 Literature

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50R 1310 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50S 3100 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50S 3070 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50S 4060 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50S 2320 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50S 3038 Mayer 2008

Common carp Cyprinus carpio Fishes 96h LC50S 4220 ECOTOX

Common carp Cyprinus carpio Fishes 96h LC50S 5280 Mayer and Elliersieck

Common carp Cyprinus carpio Fishes 96h LC50S 1700 ECOTOX

Cutthroat trout Oncorhynchus clarkiiFishes 96h LC50S 5000 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2221 Mayer 2008

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 1553 Mayer 2008

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2250 Literature

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 3950 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 6700 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 970 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 7100 Mayer and Elliersieck
Page 41 of 54

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2169 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 1500 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 NR 3950 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 NR 970 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 NR 3950 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 NR 6000 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 NR 5000 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2300 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2250 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2530 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 2000 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 6000 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 1600 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 1550 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50S 1550 ECOTOX

Daphnid Ceriodaphnia dubia Crustacea 48h EC50R 3.06 ECOTOX

Daphnid Ceriodaphnia dubia Crustacea 48h LC50S 11.6 ECOTOX

Daphnid Daphnia magna Crustacea 48h EC50R 10.1 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50S 9.5 ECOTOX

Daphnid Daphnia magna Crustacea 96h LC50S 3280 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50S 16760 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50S 7.2 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50R 1900 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50R 5400 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 NR 230 ECOTOX

Daphnid Daphnia magna Crustacea 48h EC50S 5.6 Mayer and Elliersieck

Daphnid Daphnia obtusa Crustacea48h EC50S 0.0115 ECOTOX

Daphnid Daphnia pulex Crustacea 48h LC50S 6.4 Mayer and Elliersieck

Daphnid Simocephalus serrulatus Crustacea 48h EC50S 8.1 Mayer and Elliersieck

Daphnid Simocephalus serrulatus Crustacea 48h EC50S 7.6 Mayer and Elliersieck

Daphnid Simocephalus serrulatus Crustacea 48h EC50S 11 Mayer and Elliersieck

Desert pupfish Cyprinodon macularius Fishes 96h LC50S 7710 ECOTOX

Dragonfly Austrolestes colensonis Insecta 48h LC50S 3130.9 ECOTOX

Dragonfly Xanthocnemis 

zealandica Insecta 48h LC50

S 600 ECOTOX
Page 42 of 54

Fathead minnow Pimephales promelas Fishes 96h LC50S 7000 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 13000 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50F 8930 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50F 6670 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50F 10400 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50F 9470 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 5210 Literature

Fathead minnow Pimephales promelas Fishes 96h LC50S 5171 Mayer 2008

Fathead minnow Pimephales promelas Fishes 96h LC50S 5200 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 5210 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 7430 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 5860 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 5720 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 5240 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 4380 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 3940 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50S 7700 Mayer and Elliersieck

Fathead minnow Pimephales promelas Fishes 96h LC50S 14000 Mayer and Elliersieck

Fathead minnow Pimephales promelas Fishes 96h LC50S 14600 Mayer and Elliersieck

Flying barb Esomus danricus Fishes 96h LC50 NR 34670 ECOTOX

Fountain darter Etheostoma fonticola Fishes 96h LC50S 2020 Literature

Fountain darter Etheostoma fonticola Fishes 96h LC50S 2020 ECOTOX

Fountain darter Etheostoma fonticola Fishes 96h LC50S 1615 Mayer 2008

Gangetic mystus Mystus cavasius Fishes 96h LC50R 4600 ECOTOX

Goldfish Carassius auratus Fishes 96h LC50S 12800 Mayer and Elliersieck

Goldfish Carassius auratus Fishes 96h LC50S 13200 Mayer and Elliersieck

Gray treefrog Hyla versicolor Amphibia 96h LC50S 2470 ECOTOX

Green frog Rana clamitans Amphibia 96h LC50S 11320 ECOTOX

Green frog Rana clamitans Amphibia 96h LC50S 17360 ECOTOX

Green frog Rana clamitans Amphibia 96h LC50S22020 ECOTOX

Green sunfish Lepomis cyanellus Fishes 96h LC50S 9460 Mayer and Elliersieck

Green sunfish Lepomis cyanellus Fishes 96h LC50S 11200 Mayer and Elliersieck

Greenthroat darter Etheostoma lepidum Fishes 96h LC50S 2140 Literature

Greenthroat darter Etheostoma lepidum Fishes 96h LC50S 2143 Mayer 2008

Greenthroat darter Etheostoma lepidum Fishes 96h LC50S 2140 ECOTOX
Page 43 of 54

Greenthroat darter Etheostoma lepidum Fishes 96h LC50S 2140 ECOTOX

Guppy Poecilia reticulata Fishes 96h LC50S 6500 ECOTOX

Guppy Poecilia reticulata Fishes 96h LC50S 9740 ECOTOX

Guppy Poecilia reticulata Fishes 96h LC50S 3840 ECOTOX

Isopod Caecidotea brevicauda Crustacea 96h LC50S 280 Mayer and Elliersieck

Kuncho river prawn Macrobrachium 

lamarrei Crustacea 96h LC50

R 19 ECOTOX

Lake trout Salvelinus namaycush Fishes 96h LC50F 2300 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50S 872 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50S 920 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50S 690 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50S 740 Mayer and Elliersieck

Largemouth bass Micropterus salmoides Fishes 96h LC50S 6400 OPP

Largemouth bass Micropterus salmoides Fishes 96h LC50S 6400 Mayer and Elliersieck

Leon springs pupfish Cyprinodon bovinus Fishes 96h LC50S 2017 Mayer 2008

Leon springs pupfish Cyprinodon bovinus Fishes 96h LC50S 4540 ECOTOX

Midge Chironomus plumosus Insecta 48h EC50S 10 ECOTOX

Mississippi grass 

shrimp

Palaemonetes 

kadiakensis Crustacea 96h LC50

S 5.6 Mayer and Elliersieck

Mysid Mysis relicta Crustacea 96h LC50R 230 ECOTOX

Oligochaete Lumbriculus variegatus Annelida 96h LC50S 8200 ECOTOX

Oligochaete Lumbriculus variegatus Annelida 48h LC50S 13000 ECOTOX

Paper pondshell Anodonta imbecillis Mollusca 48h LC50R 25600 ECOTOX

Paper pondshell Anodonta imbecillis Mollusca 48h LC50R 23700 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1880 Literature

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50F 1330 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 3000 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1950 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S1450 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 780 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 3500 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1600 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2300 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2080 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1900 Mayer and Elliersieck
Page 44 of 54

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1360 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1460 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1090 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1200 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1866 Mayer 2008

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1350 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1470 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50R 5400 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50R 4330 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 5400 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2830 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 4340 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 800 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1500 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 800ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2200 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 900 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2200 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2800 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1900 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1880 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 3110 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2260 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2070 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 2000 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1240 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1220 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1200 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50S 1100 Mayer and Elliersieck

Razorback sucker Xyrauchen texanus Fishes 96h LC50S 4350 Literature

Razorback sucker Xyrauchen texanus Fishes 96h LC50S 4325 Mayer 2008

Razorback sucker Xyrauchen texanus Fishes 96h LC50S 4400 ECOTOX

Razorback sucker Xyrauchen texanus Fishes 96h LC50S 4350 ECOTOX

Razorback sucker Xyrauchen texanus Fishes 96h LC50S 4420 ECOTOX

Razorback sucker Xyrauchen texanus Fishes 96h LC50S 4290 ECOTOX
Page 45 of 54

Redear sunfish Lepomis microlophus Fishes 96h LC50 S 11200 ECOTOX

Shortnose sturgeon Acipenser brevirostrum Fishes 96h LC50 S 1810 Literature

Shortnose sturgeon Acipenser brevirostrum Fishes 96h LC50 S 1949 Mayer 2008

Shortnose sturgeon Acipenser brevirostrum Fishes 96h LC50 S 1810 ECOTOX

Snail Bellamya bengalensis Mollusca 96h LC50 S 9020 ECOTOX

Snail Bellamya bengalensis Mollusca 96h LC50 S 4320 ECOTOX

Snail Bellamya bengalensis Mollusca 96h LC50 S 13500 ECOTOX

Snail Bellamya bengalensis Mollusca 96h LC50 S 9600 ECOTOX

Southern leopard frog Rana sphenocephala Amphibia 96h LC50 S 7964 Mayer 2008

Southern leopard frog Rana sphenocephala Amphibia 96h LC50 S 8400 ECOTOX

Spotfin Chub Erimonax monachus Fishes 96h LC50 S 3410 Literature

Spotfin Chub Erimonax monachus Fishes 96h LC50 S 3416 Mayer 2008

Spotfin Chub Erimonax monachus Fishes 96h LC50 S 3410 ECOTOX

Stonefly Claassenia sabulosa Insecta 96h LC50 S 5.6 Mayer and Elliersieck

Stonefly Pteronarcella badia Insecta 96h LC50 S 29 Mayer and Elliersieck

Stonefly Pteronarcella badia Insecta 96h LC50 S 13 Mayer and Elliersieck

Stonefly Pteronarcella badia Insecta 96h LC50 S 11 Mayer and Elliersieck

Stonefly Pteronarcella badia Insecta 96h LC50 S 29 ECOTOX

Stonefly Pteronarcella badia Insecta 96h LC50 S 13 ECOTOX

Stonefly Pteronarcella badia Insecta 96h LC50 S 11 ECOTOX

Stonefly Pteronarcella badia Insecta 96h LC50 S 1.7 Mayer and Elliersieck

Stonefly Pteronarcys californica Insecta 96h LC50 S 2 ECOTOX

Stonefly Pteronarcys californica Insecta 96h LC50 S 4.8 Mayer and Elliersieck

Striped black fly Simulium vittatum Insecta 48h LC50 S 23.72 ECOTOX

Striped catfish Mystus vittatus Fishes 96h LC50 R 2400 ECOTOX

Striped catfish Mystus vittatus Fishes 96h LC50 S 11500 ECOTOX

Swamp lymnaea Lymnaea stagnalis Mollusca 48h LC50 S 21000 ECOTOX

Tiger frog Rana tigrina Amphibia 96h LC50 R 5680 ECOTOX

Two-spot barb Puntius ticto Fishes 96h LC50 S 3700 ECOTOX

Virile crayfish Orconectes virilis Crustacea 96h LC50 R 1890 ECOTOX

Western toad Bufo boreas Amphibia 96h LC50 S 12300 Literature

Western toad Bufo boreas Amphibia 96h LC50 S 12303 Mayer 2008

Western toad Bufo boreas Amphibia 96h LC50 S 12300 ECOTOX

Western toad Bufo boreas Amphibia 96h LC50 S 12310 ECOTOX

Yellow fever mosquito Aedes aegypti Insecta 96h LC50 S 336 ECOTOX
Page 46 of 54

Yellow perch Perca flavescens Fishes 96h LC50 F 1420 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 3750 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 5000 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 350 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 480 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 3800 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 3400 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 5400 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 5100 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 4200 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 4000 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 745 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 1200 Mayer and Elliersieck

Yellow perch Perca flavescens Fishes 96h LC50 S 13900 Mayer and Elliersieck
Page 47 of 54

Appendix B. Equations for the Parametric sensitivity distribution Distributions

In the equations below, we use standard statistical notation to describe common functions 

associated with probability distributions. The probability density function (pdf) will be referred 

to as f. The cumulative distribution function (cdf) will be referred to as F. The quantile 

function, which is the inverse of the cdf, will be referred to as F

-1

. We provide formulae for the 

mean and variance of each distribution as functions of the parameters of the distribution. While 

these equate to the parameters μ and σ for the normal distribution, this will not generally be the 

case, so we avoid using those symbols for the mean and variance, respectively. 

Location/scale family of distributions

The following four distributions (normal, logistic, triangular and gompertz) are from the family 

of distributions often referred to as the location/scale family. They are characterized by a 

location parameter and a scale parameter. The standard form is a reference form of the 

distribution with known parameter values, often chosen so that 

E x 0

and 

E s 1

. For 

example, the standard normal distribution has 

0

and 

1

. For a location/scale distribution, 

any parameterization can be expressed as a linear transformation of the standard form. Such 

transformations have played a large role in the development of the theory of assessment factors 

for sensitivity distributions, which are typically derived for the standard form and shown to apply 

to all other parameterizations.

Assessment Factors

For standard forms in the location scale family, the distance between the mean and the HC5 is 

proportional to the standard deviation of the distribution. This fact has allowed the development 

of distribution-specific assessment factors that depend only on sample size and the desired level 

of confidence with which the HC5 is to be bounded. For example, in a normal distribution, the 

fifth quantile is approximately 1.64 standard deviation units to the left of the mean. In other 

words, in a standard normal distribution:

1 F 0.05 1.64 . 

More generally, letting Kdist represent the scaled distance between the mean and the fifth 

percentile in the standard form of a specific distribution, then for any parameterization of that 

distribution, 

1

0.05 F Kdist

. Because μ and σ are unknown, the sample mean 

x

(Equation 1) and standard deviation s (Equation 2) are substituted and it is important to 

characterize, the expected sampling distribution of the statistic 

dist x K s

around a “true” fifth 

percentile (

Kdist

). Assessment factors (k), at a given level of confidence (say 95%), are 

chosen such that 95% of the expected distribution of 

dist x K s would lie to the right of 

x ks . 

In the case of the standard normal, the sampling distribution of 

x ks

follows a t distribution 

with N-1 degrees of freedom (Aldenberg et al. 2002). However, in practice, the sampling 

distribution of the fifth percentile is most often estimated through computer simulation 

(Aldenberg and Slob 1993, Pennington 2003). With known assessment factors, the 
Page 48 of 54

concentration that represents the 5th percentile EC50 value (untransformed) of the distribution 

(HC5) can be estimated with a given level of confidence (corresponding to the value of k) using 

the following equation:

5

10x ks HC

Goodness of Fit

Relatively little work has been done on assessing goodness of fit for fitted sensitivity 

distributions. Newman et al. (2000) calculated goodness-of-fit tests for 30 sets of effects data 

(NOEC, EC50 and LC50 values) fitted with a normal distribution and found that the assumption 

of (log) normality was often violated.

Normal distribution 

A normal probability distribution describes data that are dispersed symmetrically around a mean 

value, forming a characteristic “bell shaped” probability density. The normal distribution has two 

parameters, μ and σ . Assuming the effects data have been log10 transformed, the equations for 

the pdf, cdf, and quantile function are:

2

2

2

1

| , e

2

x

f x | ,

x

F x f z dz 1 F p p

Neither the cdf (F) nor quantile function (F

-1

) have explicit forms. However, both can be readily 

approximated to arbitrary precision in most mathematical software.

Mean:

Variance:

2

The standard normal distribution has mean 0 and unit variance when:

0

1

Parameters for the normal distribution (μ and σ) can be estimated using the typical moment 

estimators (

x

and s, as given in equations B1 and B2). Maximum likelihood estimates are:
Page 49 of 54

1

1

ˆ

N

i

i

x x

N

1

1 1 ˆ

N

i

i

N

s x x

N N

When sample size is large, 

x

and s approach μ and σ, respectively and 

x s 1.64

is a precise 

estimator for 

1.64

. When the sample size is small (i.e., N<30), a student’s t-distribution can 

be used to describe the expected variation in 

x s 1.64

around 

1.64

. Thus, for the log 

normal distribution, assessment factors (ks, Aldenberg and Jaworska 2000), dependent on sample 

size and desired confidence level, can be derived from a t distribution with N − 1 degrees of 

freedom. Table B1 provides assessment factors from Aldenberg and Jaworska (2000) for 

generating 95% and 50% confidence limits on the 5th percentile of the sensitivity distribution. 

Aldenberg et al. (2002) provide a more extensive table with many more sample sizes 

represented.

Table B1. t-value (one-tailed probability) and lower k values (representing 95% confidence in resulting HC5) 

and median k values (representing 50% confidence) for deriving HC5 values from log-normal distributions. k 

values from Aldenberg and Jaworska (2000).

N t-value

Aldenberg and Jaworska (2000) 

median lower

2 6.3138 2.3387 26.2597

3 2.9200 1.9384 7.6956

4 2.3534 1.8295 5.1439

5 2.1318 1.7793 4.2027

6 2.0150 1.7505 3.7077

7 1.9432 1.7318 3.3995

8 1.8946 1.7187 3.1873

9 1.8595 1.7091 3.0312

10 1.8331 1.7016 2.9110

11 1.8125 1.6957 2.8150

12 1.7959 1.6910 2.7363

13 1.7823 1.6870 2.6705

14 1.7709 1.6837 2.6144

15 1.7613 1.6808 2.5660

20 1.7291 1.6712 2.3960

30 1.6991 1.6620 2.2198

50 1.6766 1.6549 2.0650

100 1.6604 1.6498 1.9265

200 1.6525 1.6473 1.8372

500 1.6479 1.6458 1.7630

∞ 1.6449 1.6449 1.6449
Page 50 of 54

Wagner and Løkke (1991) used a logarithmic normal distribution in estimating percentiles on 

species sensitivity distributions. This approach involves using the mean and standard deviation 

of natural log transformed effects concentrations for available species exposed to the same 

stressor. Later, Aldenberg and Joworska (2000) used the same method, but used a log10

transformation. The two methods reached identical conclusions, as expected. 

Logistic distribution

The logistic distribution shares the “bell shape” probability density with the normal distribution, 

but differs from the normal distribution in the tails. This difference is particularly relevant for 

this analysis because it is concerned with the 5th percentile of the distribution, which lies in the 

tails. Like the normal distribution, the log-logistic distribution is described by two parameters, α 

and β. Various log-logistic sensitivity distribution approaches have been suggested in the 

sensitivity distribution literature (Kooijman 1987; Van Straalen and Denneman 1989; Aldenberg 

and Slob 1993; de Zwart 2002).

2

exp /

: ,

1 exp /

x

f x

x

1

: ,

1 exp /

F x

x

1

ln

1

p

F p

p

Mean:

Variance:

2

2

3

The standard logistic distribution has mean 0 and variance 

2

3

when:

0

1

The logistic distribution is typically fit using the following moment estimators:

ˆ

x

and

ˆ

3

s
Page 51 of 54

Closed-form maximum likelihood estimates do not exist, but are easily obtained numerically. 

However, Kooijman (1987:273) found that maximum likelihood estimates of α and β tended to 

“seriously underestimate” β for small sample sizes. Aldenberg and Slob (1993) used Monte 

Carlo simulation to derive k values (Figure 3, Table B2) that can be used along with the sample 

mean and standard deviation to account for uncertainty in the HC5. 

Table B2. Lower k values (representing 95% confidence in resulting HC5) and median k values (representing 

50% confidence) for deriving HC5 values from log-logistic distribution (from Aldenberg and Slob 1993).

N median lower

2 2.49 27.7

3 2.05 8.14

4 1.92 5.49

5 1.85 4.47

6 1.81 3.93

7 1.78 3.59

8 1.76 3.37

9 1.75 3.19

10 1.73 3.06

11 1.72 2.96

12 1.72 2.87

13 1.71 2.80

14 1.70 2.74

15 1.07 2.68

20 1.68 2.49

30 1.66 2.28

50 1.65 2.10

100 1.64 1.95

200 1.63 1.85

500 1.63 1.76

∞ 1.62 1.62

Triangular distribution (general)

The triangular distribution is so named for the triangular shape of its probability density function. 

The general triangular distribution is described by three parameters, a, b, and c (minimum, 

maximum and mode, respectively). Most applications of the triangular distribution to species 

sensitivity data use the two-parameter symmetric form (Fig. B1). The triangular distribution also 

underlies the calculations of final acute values under the 1985 Guidelines.

If: 

a x c

2 x a

f x

b a c a
Page 52 of 54

2

x a

F x

b a c a

If: 

c x b 2 b x

f x

b a b c

2

1

b x

F x

b a b c

Mean:

3

abc

Variance:

2 2 2

18

a b c ab ac bc

A symmetric triangular distribution occurs when 

2

a b

c

. Substituting these into the 

above equations yields the equations for the symmetric triangular distribution.

Triangular distribution (symmetric)

If: 

2

a b

a x 2

4

| ,

x a

f x a b

b a

2

2

2

| ,

x a

F x a b

b a

If:

2

a b x b 2

4

| ,

b x

f x a b

b a

if 

2

2

2

| , 1

x b

F x a b

b a

If: p ≤ 0.5

2

1

2

p b a

F p a

If: p > 0.5
Page 53 of 54

2

1

1

2

pba

F p b

Mean:

2

a b

Variance

2

24

b a

The symmetric triangular distribution has mean zero and variance 1 when:

6

6

a

b

This is the form reported and used by van Straalen (2002). Erickson and Stephan used percentile 

methods to fit the triangular distribution, which were used to generate Figure 1. Note that the 

following moment estimators could also be used:

ˆ

a x b ˆ 2

and

ˆ

b s x 6

Van Straalen (2002) examined the quantiles of a symmetric triangular distribution for estimating 

percentiles of species at risk. Pennington (2003) used Monte Carlo simulations to develop 

assessment factors (k, described above) applicable to all symmetric triangular distributions. 

Pennington (2003) recommended 50th and 95th percentile k values that varied based on N (Table 

B3). 

Table B3. k values for calculating 50th and 95th percentile HC5 values from a triangular distribution 

(Pennington 2003).

N 50th percentile 95th percentile

2 2.3 25.5

3 1.9 7.7

4 1.8 5.0

5 1.7 4.1

6 1.7 3.6

7 1.7 3.4

8 1.7 3.1

Gompertz distribution

The gompertz distribution is the only asymmetric distribution among the four considered here 

(Fig. B1), tending to be slightly right-skewed. Like the other three distributions it has two 

parameters (μ, β). Newman et al. (2000) compared the performance of the gompertz distribution 

to normal and logistic distributions for sensitivity distributions and generally found the gompertz 
Page 54 of 54

to be the best of the three parametric distributions (based on χ2analysis of fit). However, 

Newman et al. (2000) further argued for the adoption of non-parametric bootstrapping 

approaches over all parametric methods.

x

e

F x e

1

x x

e

f x e e

1 F p p ln ln

Mean:

, where γ=Euler-Mascheroni constant. 

Variance

2 2

6

The standard gompertz distribution has:

0

1

Under these conditions, the gompertz distribution has mean γ and variance

2

6

. The gompertz 

distribution is typically fit using maximum likelihood. Note the following moment estimators 

could also be used:

ˆ

6

s

ˆ

ˆ

x