### Page 1 of 54

Exploration of Methods for Characterizing Effects of Chemical Stressors to Aquatic

Animals

Draft 1 1 / 2 /2010

Kristina Garber 1



### Sandy Raimondo 2



### Patti TenBrook 3



### 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

### Page 2 of 54

Acknowledgement

The authors ack nowledge the contributions of Matt hew 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

g uidance provided by Thomas Steeger, Mark Corbin, Marietta Echeverria, and Don ald 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.

### Page 3 of 54

Table of Contents

List of Acronyms

................................ ................................ ................................ ............................ 4

1. Executive Summary

................................ ................................ ................................ ................ 5

2. Introduction

................................ ................................ ................................ ............................. 5

3. Methods for Deriving Measures of Effe ct 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 A ssociated with use of Extrapolation Factors

................................ ..... 19

3.2.3. Examples of Regulatory Applications of Extrapolation factors

............................. 19

3.2.4. Ext rapolation 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 E stimated HC

p 

### ................................ ............. 25

4.1.3. Analytical Steps

................................ ................................ ................................ .......... 25

4.1.4. Data

................................ ................................ ................................ ............................. 25

4.2. S pecific 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

### Page 4 of 54

List of Acronyms

ACE Acute

- to

- Chronic Estimation

AChE acetylcholinesterase

ACR Acute

- Chronic Ratio

ALS V 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

HC 5 

### The 5 th 

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

ICE Interspecies Correlation Estimation

LC 50 

### 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 Obse rvable 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 Ti me

- Concentration Event

USEPA U.S. Environmental Protection Agency

### Page 5 of 54

1. Executive Summary

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

United States Environmental Protection Agency ( US EPA) use s available tox icity 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 p opulations of the same species, populations of

similar genera/taxa, or to aquatic ecosystems

. T he goal of this work is to examine how limited

test results can best be used to characterize adverse effects on aquatic animals. T o that end, t his

paper explore s 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 sens itivity

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 percentil e

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 port ions 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 US EPA in order to

deter mine 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 Rode nticide Act

(FIFRA)

.

2. Introduction

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

upon which life depends. Consistent with the US EPA’s mission

, OPP and OW are both

responsible for evaluating the potential e ffects 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

liter ature, 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 chem ical on the environment . OPP and

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

discussed in this paper, i nvolves 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 result s to taxa

- based and community

- based (cross

- taxa) adverse effect thresholds has

not been consistent . In addition, the tools used in these process es 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 l evels of organization, i.e

. , single species, taxa

(population) and cross taxa (community), US EPA has conducted six regionally

- based public

meetings and has drafted three white papers . These papers describ e existing approaches and

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

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 US EPA, as well as states, local and tribal water manageme nt 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. Th is white paper

explores analytical approaches that rely on empirical t oxicity 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 G uidelines 1 

### to characterize

community

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

number of taxa

. T he available data for chemicals can vary considerably . Therefore, regulato ry

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 US EPA and will undergo peer

review and additional devel opment 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 endp oints that are related to aquatic ecosystems . An assessment endpoint is “an

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

### Specific 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. c ommunity

- 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 ef

fect, 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 t he 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

(NO EC ) values to represent effects to growth and reproduction of fish and invertebrate s pecies

resulting from chronic exposures. For OW, the measure of effect i s the HC 5 

### , or the 5 th 

### percentile

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

and invertebrates) as well as the HC 5 

### for chronic toxicity test results

.

This paper describes two types of methods tha t 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 distr ibutions 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 201 0)

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 US EPA in orde r 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 pathways 3



### 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 inter

species variability by con sidering 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 defin ed by its ability to predict a measure of

effect with a desired level of certainty.

As indicated in the scoping document for this project 4



### , “ O ne goal of a common effects

characterization method ology 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 con ducted 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 paper 5 

### and extrapolation met hods described in this paper.

T o that end, t his 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 US EPA, States

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

criteria established u sing 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 fo und 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

HC 5 

### of a sensitivity distribution, and the chronic ALSV is set to the HC 5 

### of the dis tribution of

chronic toxicity data. It is expected that as US EPA 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 OP P for

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

. The

fourth section of this paper describe s a proposed analysis that will be conducted by US EPA 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 provide s 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 e xposure 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 factor s are used by regulatory agencies for characterizing effects of chemicals on

aquatic animals.

3.1. S ensitivity Distributions

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

uncertainty in estimating HC 5 

### 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. Mos t 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 toxicit y 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 ch ronic

toxicity data for s imilar test results, such as NO ECs, or maximum acceptable toxic

concentrations (MATCs; defined as the geometric mean of the NO EC and the lowest observable

effect concentration (LO EC)) .

### 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. T he concentration at the lower 5 th



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

purpose s ( e.g., USEPA, OECD). The 5 th 

### percentile value is commonly termed the H C 5 

### , where

HC stands for “hazard concentration.” It should be noted that the H C 5 

### 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 dist ributions 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 car baryl

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

Web

- ICE 6 

### . These data are provided in Appendix A. These data are used throughout this white

paper to illustrate the applications of the methods that are descr ibed. 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 log 10 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 d istributions are provided in Appendix

B . In general, the quantile function provides an estimate of the concentration (in log 10 

### units)

associated with a given percentile of the distribution [ i.e.

, H C 5 

### = F - 1 

### (0.05)]. Thus, if the

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

deviation) , the H C 5 

### can be easily obtained

. HC 5 

### values from the f itted cumulative distribution

functions for log

- normal, log

- logistic , log

- triangular, and log

- gompertz distributions for car baryl

(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 log

gompertz distributions plotted against 49 log 10 GMAVs for carbaryl

.

Table 1. HC 5 values from fitted cumulative distribution functions for log

- normal, log

- logistic, log

- triangular,

and log

- gompertz distributions plotted against 49 log 10 GMAVs for carbaryl.

Distribution HC 5 ( μg/L) Proport ion of data < HC 5

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

M any other distributions have been used for sensitivity distribution approaches , including

weibull (Zajdlik & Associates 2005) and burr (Shao 200 0). 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 distribution s may al so 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 HC 5 

### When Data are Limited

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

parameters of the distribution is uncertain. Thus the precision with which the HC 5 

### 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 HC 5 

### (examples are provided in Appendix B). The l evel of

confidence (cl) in the estimated HC 5 

### can be noted as follows:

. "cl" denotes the confidence

with which the estimated HC

p 

### is no higher than the true HC

p 

### . A considerable body of literature

on sensitivity distributions employs a confidence level of 95% ( i.e., ) , 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 HC 5 

### ( i.e., )

. 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 concen tration is as a function of the sample mean ( , Equation 1),

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

Equation 1

Equation 2

Equation 3

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 sen sitivity

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 d erived 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, an d 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 HC 5 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 es timated HC 5



### . 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 cons tant corresponding to 95% confidence presumes

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

surrounding the estimated HC 5 

### 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, and:

Equat ion 4.

From the above, the equation for the sampling variance of the HC 5 

### is:

Equation 5.

### Page 13 of 54

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

resultin g 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 descr ibed 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 w ith sample means in deriving HC 5 

### values from sensitivity

distributions.

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

estimated means (from samples) to derive HC 5 

### values from sensitivity distributions. Thi s

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 che mical 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 pr oposed use of a logistic se nsitivity 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.

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

similar within a mode of action. Using this approach, the HC 5 

### is calculated using Equation 6.

Equation 6.

### Page 14 of 54

Table 2 . Mean β val ues 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 0.3 1 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 0.3 7 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

Pl ant 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 **



### 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 creat e chronic sensitivity distributions . In both approaches it is

possible to derive chronic H C 5 

### values using no chronic toxicity data.

de Zwart developed a regression for the acute and chronic mean values (termed α

acute 

### and α chronic 

### , 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 distribut ion 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. (de Zwart 2002)

In Duboudin et al. (2004)’s approach, acute mean (μ A 

### ) and standard deviations (σ A 

### ) based on

available invertebrate data are used to calculate mean values (μ

c 

### ) representative of chronic

e ffects 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; r 2 

### = 0.854) and invertebrates (15 chemicals, r 2 

### = 0.9 17). The test chemicals included

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

azinphos

- methyl, diazinon, atrazine).

Equation 8. (vertebrates)

Equation 9. (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 i n determining the HC 5 

### 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 o f

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

data requirements for pesticides. Under FIFRA, a cute 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 c arbaryl 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, t he average ( ) 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 95 t h 

### percentile HC 5 

### 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 2 003). 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, equati on 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 HC 5 

### values

calculated with these methods range 0.31

- 3.4 μg/L. It is notable that the 95 th 

### percentile HC 5 

### values are nine orders of magnitude lower than the median HC 5 

### value s. It is also notable that the

median HC 5 

### values are all below the HC 5 

### 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, resp ectively (Table 1).

Table 4 . Median and 95 th 

### percentile HC 5 

### values ( μg/L ) derived for subset of carbaryl data.

Distribution (method source) Median HC 5

Log

- normal (Aldenberg and Jaworska 2000) 0.46 7.3x10

- 10

Log

- logistic (Al denberg 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 HC 5 

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

chronic toxicity data ( α chronic 

### = 1.33). Using this with the β value of 0.71 and equation 6, the

resulting HC 5 

### 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 pes ticides to specific taxa

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

data for fish and aqu a tic 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 assessm ent 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 tox icity data to develop separate distributions for fish

and aquatic invertebrates. The model calculates the 5 th 

### , 50 th 

### and 95 th 

### percentiles of the

distributions to generate probit

- concentration response curves. These are used to determine the

magnitude of eff

ect to specific taxa resulting from an exposure concentration (USEPA 2004 b ).

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 5 th 

### percentile of the distribution , based on the

### Page 17 of 54

1985 Guidelines. The 5 th 

### 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 5 th 

### percentile EC50 value to an EC

“low” valu e 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 crite ria 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 in land surface waters as well as in transitional, coastal and territorial waters is

st atistical 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) “estim ated risk levels” are derived

using the SSD method of Aldenberg and Jaworska (2000). That is, HC 5 

### values are calculated

based on a log

- normal SSD.

3.1.3.4. OECD

The sensitivity distribution methodology for the O rganization of E conomic C ooperation and

D evelopmen t (OECD) method is similar to the E uropean U nion (EU) SSD methodology ;

however, n o 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

like ly. 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 5 th 

### percentile value

or the lower 95% confidence limit of the 5 th 

### percentile depending on t he 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 concentratio n . 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) ; h owever, appropriate QSAR values can be used as surrogates if laboratory data

are unavailable.

3.1.3.5. A ustralia 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 t hat 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 appro priate distribution is chosen, then the calculation to estimate the 95 th 

### 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 Methodo logy

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 pestici de 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 bifent hrin, chlorpyrifos, cyfluthrin, lambd a

- cyhalothrin,

diazinon, diuron and malathion 7



### .

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 Zeal and (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 5 th 

### 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 5 th 

### percentile by 2, and the chronic value is the 5 th



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

confidence intervals surrounding the 1 st 

### and the 5 th 

### percentiles using a bootstrapping procedure

that is described in detail within the methods manual and is also included with th e 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 factor s

3.2.1. Method Description

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

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

to measure s 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_p rojects/central_valley_pesticides/criteria_meth

od/index.shtml

### Page 19 of 54

3.2.2. Uncertainty Associated with use of Extrapolation F actor s

There is uncertainty associated with thi s 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

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

are tested and the adverse outcome pathway of the chemical.

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

individu als within a specie s due to different test conditions (intra

- specie s variability) and

sensitivity of test species and species of concern (inter

- species variability)

. EF s may be designed

to account for some of these sources of uncertainty and variability

.

3.2.3. Examples of Regulatory Applications of Extrapolation factor s

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

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

( e .g

., US EPA 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 EF s that are used by United Sta tes

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 diffe r in their methods and

intended measures of effect ( i.e., the lowest available toxicity data for a taxa and the HC 5 

### for a

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

literature by Pennington 2003, which is base d 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 Cr iteria. 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

( LC 50 

### ) and adverse effect concentrations for 50% of th e animals tested ( EC 50 

### ) values for

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

effect concentrations ( NO ECs ) for the same taxa . Benchmarks are calculated by multiplying

each lowest toxicity result for a t axon 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_ben chmark.htm

### Page 20 of 54

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

- target organisms

a nd 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 concent rations exceeding a benchmark

for a specific taxon have potential to cause adverse effects to that tax on

. 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 EF s, this is equivalent to dividing the lowest

acute toxicity t est result for a tax on 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 sectio n 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 Aq uatic 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. magn a 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

verteb rate 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 (US EPA 1995) was developed by USEPA with

participation from the eight Great Lakes states ( i.e ., Illinoi s, Indiana, Michigan, Minnesota, New

York, Ohio, Pennsylvania and Wisconsin) as part of the Great Lakes Water Quality Initiati ve.

The purpose of this g uidance is to help establish consistent, enforceable, long

- term protection

with respect to all types of pollutants. The g uidance 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 Guidelines 9 

### , and 2) Tier II values where there is an absence of the full set of da ta

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 cri teria 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 EF s

.

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 cal culate an acute Tier II criterion , the dataset must contain an acute

toxicity result for at least one daphnid species ( i.e., Ceriodaphni a sp.

, Daphnia sp. , or

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

approp riate EF (Table 5 ). When the EF s are applied to the lowest genus mean acute value, the

result is intended to approximate the 5 th 

### percentile of a species sensitivity distribution (triangular

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

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

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

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

of removing taxa on calculat ion of the 5 th 

### percentile (Host et al. 1995). T hese factors are

intended to address variability in interspecies sensitivity to chemical stressors.

Table 5

. Extrapolation factor s used by Great Lakes Water Quality Guidance

, M ichigan DEQ, Ohio EPA

, USDOE , and UC Davis in deriving criteria when data requ irements defined in 1985 Guideline s are not met.

Number of

data

requirements

that are met Great Lakes

Water

Quality

Guidance * Michigan

DEQ* Ohio EPA* USDOE* USDOE

(no

daphnid) UC

Davis*

1 21.9 Not applicable 21.9 20.5 242 570

2 13.0 13.0 13.0 or 7.9 ** 1 3.2 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 EF s that are

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

EF s are identical to the Great Lakes Water Quality Guidance , ex cept that the Michigan approach

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

different EF s 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 EF s for use when no

daphnid test results are available.

For the Great Lakes Water Quality Guidance and other similar method s 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 l owest 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 5 th



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

With the UC

- Davis Methodology (TenBrook et al. 2010), acute criteria are deriv ed u sing EF s

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

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 use d to determine the most appropriate SSD

data distribution. EF s 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 t hat one of the data values be from a daphnid species. The EF s 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 tax on 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 H C 5 

### 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 re quirements 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 Gr eat Lakes

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

3.2.4. Extrapolation factor s 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 EF s. Pennington considered the application of known β values in

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

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

relating the lowest sample toxicity test result to HC 5 

### values from log

- normal, log

- logistic and

log

- tri angular distributions in combination with the range of β values recommended by de Zwart

(0.24

- 1.4). Using this approach, HC 5 

### values could be estimated with 50% and 97.5% confidence

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

the sample of interest. Table 6 provides the EF s for estimating HC 5 

### values with 50% confidence.

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

inhibiton by organophosphates ( i.e. , β = 0.71). T able 7 provides the EF s for estimating HC 5 

### values with 50% confidence and assuming a log

- logistic distribution.

### Page 2 3 of 54

Table 6

. Extrapolation factor s reported by Pennington (2003) for estimating H C 5 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 factor s reported b y Pennington (2003) for estimating HC 5 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 ill ustrate 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 HC 5 

### 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 HC 5 

### values

of the triangular and burr distribution s, 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 200 3 ) 7.5 12 0.63

Lo g

- triangular (Pennington 2003) 7.5 9.4 0.80



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

Tests

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

adverse effects on aqu atic animals. T o 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 relev ant 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 HC 5 

### )

. ALSVs may be

### Page 24 of 54

used to screen concentrations of pesticides a nd 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 US EPA

proposes to conduct to determine the utility of these methods to estimate HC

p 

### based on "data

limited " 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 HC

p 

### 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 , where " p " denotes the proportion

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

confidently est imated 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 HC

p 

### , especially

for "data

- limited " chemicals, the proposed work will develop methods for the statistic (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 based on a value deri ved 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 . An EF might be applied to the lowest value among the available

data (s uch 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 th e available data (minimum,

mean), the confidence level to which the HC

p 

### 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 ou tlined below will be to evaluate the ability of different EF

derivation approaches to estimate , recognizing that this may vary depending on the nature

of available data.

Analysis of the continuum of available aquatic animal data fo r 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. B ecause US EPA will investigate several techniques for

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

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

“true” 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" HC

p

, ref 

### to provide a surrogate

for the true HC

p 

### for use in the evalu ations. Second, the statistical characteristics of data rich sets

will be used to develop distributions for which the true HC

p 

### 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 S ources of V ariation in the E stimated HC

p



### Many factors affect the valu e of an HC

p 

### 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

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

each of these potential influences.

4.1.3. Analytical S teps

The general analytical strategy will involve two steps. In the first step, HC

p 

### estimation will be

eva luated 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 HC p,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 analy sis relative to the HC p,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,

a nd their uncertainty described relative to the HC p,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 a nalyses 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 EC 50 and LC 50 values that are used to

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

AquaChronTox, which is currently under develop ment by US EPA. AquaChronTox contains

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

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

US EPA.

### Page 26 of 54

4.2. Specific Analyses

4.2.1. Analyses of Data

- Rich Sets

Regardless of what method is employ ed, the estimation of an HC

p 

### 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 f or 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 t he 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 H C p,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 Analys es of Data

- Limited Sets

4.2.2.1 Direct Application of Distributional Analysis

Given an estimate of the HC

p ,ref 

### ( i.e.

, from 4.2.1 above) USEPA will explore the accuracy of HC

p 

### 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 assume d that the best choices under “data rich” conditions will prove to be best under “data

limited ” conditions.

4.2.2.2 Extra polation 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 (i ncluding 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 an d 2 will be extended to consider the influence of

taxonomic diversity of the subsamples on the accuracy of 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 toxici ty 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 facto rs 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 pre dictive 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 Chroni c Toxicity

Two important issues arise in the estimation of an HC

p 

### 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 exposur e 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, c hronic test results (NOEC, LOEC and MATC) indicate an uncertain (or at

least var iable) 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.

, EC 2 0 s) that will be

subsequently used for HC

p 

### 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 acute

chronic 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

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

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

(often the 80 th 

### percentile) of the ACR distribution to specify a defa ult ACR (Host et al. 1995,

TenBrook et al. 20 10 ). 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 AC Rs to strengthen estimates of chronic HC

p 

### and :

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

### to HC p,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 EC 20 s from AquaChronTox to determine the degree to which

species

- specific ACRs are predictable within AOPs, with a particular focus on well

developed 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 a cross 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 t o provide a conceptu al 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 whic h USEPA Aquatic Life Water Quality Criteria are not yet

available, and to inte rpret 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

a re multiple approaches that may be used to accomplish this objective. Although the US EPA has

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

above, these methods may be considered to characterize the effec ts 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. Th is framework is intended as an illustration of how various

methods described in this paper and tools described in the tools white p aper 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, t wo separate ALSV frameworks are described

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

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

Guidelines are set to on e 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 HC 5 

### of a sensitivity distribution, and the

chronic ALSV is set to the HC 5 

### of the distribution of chronic toxicity data. Du e to uncertainty

associated with limited data used to derive HC 5 

### values, ALSV calculations should incorporate

conservative estimate of the HC 5

.



### 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 resul ts 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 thes e 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 HC 5



### . Extrapolation factor s

based on extrapolation from the most sensitive toxicity data to the HC 5 

### of a sensitivity

distribution may be used. Also, a n HC 5 

### may be derived from sensitivity distribution based on the

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

dis tribution 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

US EPA has evaluated the specific methods described above.

5.2. Derivation o f 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

outco me

pathway Can empirical toxicity

data be supplemented

with predicted data? If yes, u se appropriate

tools for chemical to

predicted toxicity data

.

If no, a cute 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 result s, 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 sensitivit ies 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., NO ECs, MATCs). Although limited tools are available for estima ting 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 fram ework for deriving chronic ALSV.



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

HC 5 

### . One option is to use a sensitivity distribution to derive the HC 5 

### . A second option would be

to apply a regression appro ach to derive the HC 5 

### ( 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 HC 5 

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

empirical t oxicity 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 ALSV s

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 sp ecies. 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

HC 5 

### of 12 μ g/L (95%CI = 1.1

- 89) for carbaryl. This value is similar to the HC 5 

### 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 HC 5 

### by 2,

resulting in a value of 6 μg /L. Note that this HC 5 

### estimate is based on estimated toxicity data for

42 species of freshwater and saltwater fish and invertebrates. The HC 5 

### estimate does not include Compile

empirical

toxicity data

Determin e

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 fro m 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 HC 5 

### from the acute data.

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



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

ACR of 8.9 were applied to the acute HC 5 

### , the resulting chronic HC 5 

### would be 1.3 μg/L. If the

90 th 

### percentile ACR were applied to the acute HC 5 

### , the chronic HC 5 

### would be 0.43 μg/L. One of

these chronic HC 5 

### values could be used to represent the chronic ALSV.

6. Conclusions

This paper identifies several different methods f or 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 US EPA 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 provide s a conceptual approach th at may

be used to integrate chemical

- specific data, tools and methods for deriving community level

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

defensible water quality criteria or in ecological risk assess ments . After the US EPA 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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http://www.epa.gov/ oppfead1/endanger/litstatus/effects/redleg

- frog/carbaryl/determination.pdf

USEPA. 2010. Office of Pesticide Programs’ Aquatic Life Benchmarks. United States

Environmental Protection Agency. Available online at:

http://www.epa.gov/oppefed1/ecorisk_ders/aquatic_life_benchmark.htm

Zajdlik and Associates, Inc. 2005. Statistical analysis of the SSD approach for development of

Canadian water quality guidelines. Project # 354

- 2005. Prepared for the Canadian Council of

Ministers of the Environment (CCME).

### Page 37 of 54

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 E lliersieck

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 Crus tacea 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

Appalac hian 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 LC50 S 1150 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 9 05 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 1430 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 1350 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 1180 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 4500 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 1000 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 900 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 1270 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 500 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 2010 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 1100 Mayer and Elliersieck

Atla ntic salmon Salmo salar Fishes 96h LC50 S 1000 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 2070 Mayer and Elliersieck

Atlantic salmon Salmo salar Fishes 96h LC50 S 250 Mayer and Elliersieck

Backswimmer Notonecta undulata Insecta 96h LC50 S 200 ECOTOX

Backswimmer Notonecta undulata Insecta 48h LC50 S 360 ECOTOX

Bayou darter Etheostoma rubrum Fishes 96h LC50 S 2020 ECOTOX

Black bullhead Ameiurus melas Fishes 96h LC50 S 20000 Mayer and Elliersieck

Black crappie Pomoxis nigromacul atus Fishes 96h LC50 S 2600 OPP

Black crappie Pomoxis nigromaculatus Fishes 96h LC50 S 2600 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 14000 OPP

Bluegill Lepomis macrochirus Fishes 96h LC50 S 5230 Mayer and Elliersieck

Bluegil l Lepomis macrochirus Fishes 96h LC50 S 5200 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 8200 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 6760 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96 h LC50 S 5600 ECOTOX

Bluegill Lepomis macrochirus Fishes 96h LC50 S 1000 ECOTOX

Bluegill Lepomis macrochirus Fishes 96h LC50 S 2200 ECOTOX

Bluegill Lepomis macrochirus Fishes 96h LC50 F 5047 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 6200 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 7400 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 2600 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 1800 Mayer and Elliersi eck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 5200 Mayer and Elliersieck

### Page 39 of 54

Bluegill Lepomis macrochirus Fishes 96h LC50 S 7000 Mayer and Elliersieck

Bluegill Lepomis macrochirus Fishes 96h LC50 S 16000 Mayer and Elliersieck

Bluegill Lepomis macrochi rus Fishes 96h LC50 S 5400 Mayer and Elliersieck

Bonytail Gila elegans Fishes 96h LC50 S 3490 Literature

Bonytail Gila elegans Fishes 96h LC50 R 2020 ECOTOX

Bonytail Gila elegans Fishes 96h LC50 R 650 ECOTOX

Bonytail Gila elegans Fishes 96h LC50 S 3500 ECOTOX

Bonytail Gila elegans Fishes 96h LC50 S 3490 ECOTOX

Bonytail Gila elegans Fishes 96h LC50 S 3580 ECOTOX

Bonytail Gila elegans Fishes 96h LC50 S 3400 ECOTOX

Bonytail Gila elegans Fishes 96h LC50 S 3044 Mayer 2008

Brook trout Salvelinus fontinal is Fishes 96h LC50 S 1130 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 2130 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 2100 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC 50 S 1200 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 4560 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 1290 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 1450 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 1070 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 1100 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 5400 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 2500 EC OTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 900 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 3700 ECOTOX

Brook trout Salvelinus fontinalis Fishes 96h LC50 S 680 Mayer and Elliersieck

Brook trout Salvelinus fontinalis Fishes 9 6h LC50 S 3000 Mayer and Elliersieck

Brown trout Salmo trutta Fishes 96h LC50 S 700 ECOTOX

Brown trout Salmo trutta Fishes 96h LC50 F 2000 Mayer and Elliersieck

Brown trout Salmo trutta Fishes 96h LC50 S 6300 Mayer and Elliersieck

Brown trout Salmo tru tta Fishes 96h LC50 S 1950 ECOTOX

Cape Fear shiner Notropis mekistocholas Fishes 96h LC50 S 4510 Literature

Cape Fear shiner Notropis mekistocholas Fishes 96h LC50 S 4264 Mayer 2008

Cape Fear shiner Notropis mekistocholas Fishes 96h LC50 S 4510 ECOTOX

Catla Gibelion catla Fishes 96h LC50 R 6400 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50 S 7790 Mayer and Elliersieck

### Page 40 of 54

Channel catfish Ictalurus punctatus Fishes 96h LC50 S 15800 Mayer and Elliersieck

Channel catfish Ictalurus punctatus Fis hes 96h LC50 NR 140 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50 S 1300 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50 S 1560 ECOTOX

Channel catfish Ictalurus punctatus Fishes 96h LC50 F 17300 Mayer and Elliersieck

Chinook sal mon Oncorhynchus

tshawytscha Fishes 96h LC50 F 2400 Mayer and Elliersieck

Climbing perch Anabas testudineus Fishes 96h LC50 R 5500 ECOTOX

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 4340 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fish es 96h LC50 S 2700 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 2400 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 1750 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 1150 Mayer and Elliersieck

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 997 ECOTOX

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 1300 ECOTOX

Coho salmon Oncorhynchus kisutch Fishes 96h LC50 S 764 ECOTOX

Colorado squawfish Ptychocheilus lucius Fis hes 96h LC50 SR 1310 Literature

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50 S 3070 Literature

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50 R 1310 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50 S 3100 ECOTOX

Colora do squawfish Ptychocheilus lucius Fishes 96h LC50 S 3070 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50 S 4060 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96h LC50 S 2320 ECOTOX

Colorado squawfish Ptychocheilus lucius Fishes 96 h LC50 S 3038 Mayer 2008

Common carp Cyprinus carpio Fishes 96h LC50 S 4220 ECOTOX

Common carp Cyprinus carpio Fishes 96h LC50 S 5280 Mayer and Elliersieck

Common carp Cyprinus carpio Fishes 96h LC50 S 1700 ECOTOX

Cutthroat trout Oncorhynchus clarkii F ishes 96h LC50 S 5000 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2221 Mayer 2008

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 1553 Mayer 2008

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2250 Literat ure

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 3950 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 6700 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 970 Mayer and Elliersieck

Cut throat trout Oncorhynchus clarkii Fishes 96h LC50 S 7100 Mayer and Elliersieck

### Page 41 of 54

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2169 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 1500 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fis hes 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 Oncor hynchus clarkii Fishes 96h LC50 NR 5000 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2300 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2250 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2530 ECOTOX

Cutt hroat trout Oncorhynchus clarkii Fishes 96h LC50 S 2000 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 6000 Mayer and Elliersieck

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 1600 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fish es 96h LC50 S 1550 ECOTOX

Cutthroat trout Oncorhynchus clarkii Fishes 96h LC50 S 1550 ECOTOX

Daphnid Ceriodaphnia dubia Crustacea 48h EC50 R 3.06 ECOTOX

Daphnid Ceriodaphnia dubia Crustacea 48h LC50 S 11.6 ECOTOX

Daphnid Daphnia magna Crustacea 48h EC5 0 R 10.1 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 S 9.5 ECOTOX

Daphnid Daphnia magna Crustacea 96h LC50 S 3280 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 S 16760 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 S 7.2 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 R 1900 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 R 5400 ECOTOX

Daphnid Daphnia magna Crustacea 48h LC50 NR 230 ECOTOX

Daphnid Daphnia magna Crustacea 48h EC50 S 5.6 Mayer and Elliersieck

Daphnid Daphnia obtusa Crustacea 4 8h EC50 S 0.0115 ECOTOX

Daphnid Daphnia pulex Crustacea 48h LC50 S 6.4 Mayer and Elliersieck

Daphnid Simocephalus serrulatus Crustacea 48h EC50 S 8.1 Mayer and Elliersieck

Daphnid Simocephalus serrulatus Crustacea 48h EC50 S 7.6 Mayer and Elliersieck

D aphnid Simocephalus serrulatus Crustacea 48h EC50 S 11 Mayer and Elliersieck

Desert pupfish Cyprinodon macularius Fishes 96h LC50 S 7710 ECOTOX

Dragonfly Austrolestes colensonis Insecta 48h LC50 S 3130.9 ECOTOX

Dragonfly Xanthocnemis

zealandica Insecta 48h LC50 S 600 ECOTOX

### Page 42 of 54

Fathead minnow Pimephales promelas Fishes 96h LC50 S 7000 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 13000 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 F 8930 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 F 6670 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 F 10400 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 F 9470 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 5210 Literature

Fathead minnow Pimeph ales promelas Fishes 96h LC50 S 5171 Mayer 2008

Fathead minnow Pimephales promelas Fishes 96h LC50 S 5200 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 5210 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 7430 ECOTOX

Fathead m innow Pimephales promelas Fishes 96h LC50 S 5860 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 5720 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 5240 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 4380 ECOTOX

F athead minnow Pimephales promelas Fishes 96h LC50 S 3940 ECOTOX

Fathead minnow Pimephales promelas Fishes 96h LC50 S 7700 Mayer and Elliersieck

Fathead minnow Pimephales promelas Fishes 96h LC50 S 14000 Mayer and Elliersieck

Fathead minnow Pimephales pr omelas Fishes 96h LC50 S 14600 Mayer and Elliersieck

Flying barb Esomus danricus Fishes 96h LC50 NR 34670 ECOTOX

Fountain darter Etheostoma fonticola Fishes 96h LC50 S 2020 Literature

Fountain darter Etheostoma fonticola Fishes 96h LC50 S 2020 ECOTOX

F ountain darter Etheostoma fonticola Fishes 96h LC50 S 1615 Mayer 2008

Gangetic mystus Mystus cavasius Fishes 96h LC50 R 4600 ECOTOX

Goldfish Carassius auratus Fishes 96h LC50 S 12800 Mayer and Elliersieck

Goldfish Carassius auratus Fishes 96h LC50 S 132 00 Mayer and Elliersieck

Gray treefrog Hyla versicolor Amphibia 96h LC50 S 2470 ECOTOX

Green frog Rana clamitans Amphibia 96h LC50 S 11320 ECOTOX

Green frog Rana clamitans Amphibia 96h LC50 S 17360 ECOTOX

Green frog Rana clamitans Amphibia 96h LC50 S 2 2020 ECOTOX

Green sunfish Lepomis cyanellus Fishes 96h LC50 S 9460 Mayer and Elliersieck

Green sunfish Lepomis cyanellus Fishes 96h LC50 S 11200 Mayer and Elliersieck

Greenthroat darter Etheostoma lepidum Fishes 96h LC50 S 2140 Literature

Greenthroat d arter Etheostoma lepidum Fishes 96h LC50 S 2143 Mayer 2008

Greenthroat darter Etheostoma lepidum Fishes 96h LC50 S 2140 ECOTOX

### Page 43 of 54

Greenthroat darter Etheostoma lepidum Fishes 96h LC50 S 2140 ECOTOX

Guppy Poecilia reticulata Fishes 96h LC50 S 6500 ECOTOX

G uppy Poecilia reticulata Fishes 96h LC50 S 9740 ECOTOX

Guppy Poecilia reticulata Fishes 96h LC50 S 3840 ECOTOX

Isopod Caecidotea brevicauda Crustacea 96h LC50 S 280 Mayer and Elliersieck

Kuncho river prawn Macrobrachium

lamarrei Crustacea 96h LC50 R 19 ECOTOX

Lake trout Salvelinus namaycush Fishes 96h LC50 F 2300 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50 S 872 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50 S 920 Mayer and Elliersieck

Lake trout Salve linus namaycush Fishes 96h LC50 S 690 Mayer and Elliersieck

Lake trout Salvelinus namaycush Fishes 96h LC50 S 740 Mayer and Elliersieck

Largemouth bass Micropterus salmoides Fishes 96h LC50 S 6400 OPP

Largemouth bass Micropterus salmoides Fishes 96h LC5 0 S 6400 Mayer and Elliersieck

Leon springs pupfish Cyprinodon bovinus Fishes 96h LC50 S 2017 Mayer 2008

Leon springs pupfish Cyprinodon bovinus Fishes 96h LC50 S 4540 ECOTOX

Midge Chironomus plumosus Insecta 48h EC50 S 10 ECOTOX

Mississippi grass

shri mp Palaemonetes

kadiakensis Crustacea 96h LC50 S 5.6 Mayer and Elliersieck

Mysid Mysis relicta Crustacea 96h LC50 R 230 ECOTOX

Oligochaete Lumbriculus variegatus Annelida 96h LC50 S 8200 ECOTOX

Oligochaete Lumbriculus variegatus Annelida 48h LC50 S 1300 0 ECOTOX

Paper pondshell Anodonta imbecillis Mollusca 48h LC50 R 25600 ECOTOX

Paper pondshell Anodonta imbecillis Mollusca 48h LC50 R 23700 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1880 Literature

Rainbow trout Oncorhynchus mykiss Fis hes 96h LC50 F 1330 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 3000 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1950 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1 450 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 780 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 3500 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1600 Mayer and Ell iersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2300 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2080 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1900 Mayer and Elliersieck

### Page 44 of 54

Rainbo w trout Oncorhynchus mykiss Fishes 96h LC50 S 1360 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1460 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1090 Mayer and Elliersieck

Rainbow trout Oncorhyn chus mykiss Fishes 96h LC50 S 1200 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1866 Mayer 2008

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1350 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1470 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 R 5400 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 R 4330 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 5400 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2830 ECO TOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 4340 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 800 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1500 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 800 E COTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2200 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 900 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2200 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 280 0 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1900 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1880 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 3110 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2260 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2070 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 2000 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1240 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC 50 S 1220 ECOTOX

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1200 Mayer and Elliersieck

Rainbow trout Oncorhynchus mykiss Fishes 96h LC50 S 1100 Mayer and Elliersieck

Razorback sucker Xyrauchen texanus Fishes 96h LC50 S 4350 Literature

Razorbac k sucker Xyrauchen texanus Fishes 96h LC50 S 4325 Mayer 2008

Razorback sucker Xyrauchen texanus Fishes 96h LC50 S 4400 ECOTOX

Razorback sucker Xyrauchen texanus Fishes 96h LC50 S 4350 ECOTOX

Razorback sucker Xyrauchen texanus Fishes 96h LC50 S 4420 ECOT OX

Razorback sucker Xyrauchen texanus Fishes 96h LC50 S 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 M ollusca 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 Chu b 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 Ins ecta 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 Ins ecta 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 Mys tus 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 vi rilis 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 Amphib ia 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 pe rch 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 96 h 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 Elli ersieck

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 f lavescens 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 s tatistical 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, whic h 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 distrib utions 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 and . For

example, the standard normal distribution has and . 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.

As sessment Factors

For standard forms in the location scale family, the distance between the mean and the HC 5 

### is

proportional to the standard deviation of the distribution. This fact has allowed the development

of distribution

- specific assessment factors t hat depend only on sample size and the desired level

of confidence with which the HC 5 

### 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:

.

More generally, letting K dist 

### 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

dist ribution, . Because μ and σ are unknown, the sample mean

(Equation 1) and standard deviation s (Equation 2) are substituted and it is important to

characterize, the expected sampling distribution of the statistic around a “t rue” fifth

percentile ( ). Assessment factors (k), at a given level of confidence (say 95%), are

chosen such that 95% of the expected distribution of would lie to the right of

. In the case of the standard normal, the sampling distribution of 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 of ten estimated through computer simulation

(Aldenberg and Slob 1993, Pennington 2003). With known assessment factors, the

### Page 48 of 54

concentration that represents the 5 th 

### percentile EC 50 

### value (untransformed) of the distribution

(HC 5 

### ) can be estimated with a given le vel of confidence (corresponding to the value of k) using

the following equation:

Goodness of Fit

Relatively little work has been done on assessing goodness of fit for fitted sensitivity

distributions. Newman et al. (2000) c alculated 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 describ es 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

. A ssuming the effects data have been log 10 

### transformed, the equations for

the pdf, cdf, and quantile function are:

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:

The standard normal distribution has mean 0 and unit var iance when:

Parameters for the normal distribution ( μ and σ ) can be estimated using the typical moment

estimators ( and s , as given in equations B1 and B2). Maximum likelihood estimates are:

### Page 49 of 54

When sample size is large, and s approach μ and σ , respectively and is a precise

estimator for . When the sample size is small ( i.e. , N<30), a student’s t

- distribution can

be used to describe the expected variat ion in around . Thus, for the log

normal distribution, assessment factors (k

s, 

### Aldenberg and Jaworska 2000), dependent on sample

size and desired confidence level, can be derived from a t distribution w ith N

− 1 degrees of

freedom. Table B1 provides assessment factors from Aldenberg and Jaworska (2000) for

generating 95% and 50% confidence limits on the 5 th 

### percentile of the sensitivity distribution.

Aldenberg et al. (2002) provide a more extensive table wit h many more sample sizes

represented.

Table B1. t

- value (one

- tailed probability) and lower k values (representing 95% confidence in resulting H C 5 )

and median k values (representing 50% confidence) for deriving H C 5 values from log

- normal distributions. k

v alues 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 concentr ations for available species exposed to the same

stressor. Later, Aldenberg and Joworska (2000) used the same method, but used a log 10 

### transformation. The two methods reached identical conclusions, as expected.

Logistic distribution

The logistic distri bution 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 5 th 

### percentile of the distri bution, 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 Stra alen and Denneman 1989; Aldenberg

and Slob 1993 ; de Zwart 2002).

Mean:

Variance:

The s tandard logistic distribution has mean 0 and variance when:

The logistic distribution is typically fit using the following moment estimators:

and

### 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 Sl ob (1993) used Monte

Carlo simulation to derive k values ( Figure 3

, Ta ble B2) that can be used along with the sample

mean and standard deviation to account for uncertainty in the HC 5



### .

Ta ble B2. Lower k values (representing 95% confidence in resulting H C 5 ) and median k values (representing

50% confidence) for deriving H C 5 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, respectiv ely). 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:

### Page 52 of 54

If:

Mean:

Variance:

A symmetric triangu lar distribution occurs when . Substituting these into the

above equations yields the equations for the symmetric triangular distribution.

Triangular distribution (symmetric)

If:

If:

if

If: p ≤ 0.5

If: p > 0.5

### Page 53 of 54

Mean:

Varia nce

The symmetric triangular distribution has mean zero and variance 1 when:

This is the form reported and used by van Straalen (2002). Erickson and Stephan used percentile

methods to fit the tria ngular distribution, which were used to generate Figure 1. Note that the

following moment estimators could also be used:

and

Van Straalen (2002) examined the quantiles of a symmetric triangular di stribution 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 50 th 

### and 95 th 

### percentile k values that varied based on N ( Table

B3 ).

Table B3. k values for calculating 50th and 9 5 th percentile H C 5 values from a triangular distribution

(Pennington 2003).

N 50 th percentile 95 th 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 othe r 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 χ 2 

### analysi s of fit). However,

Newman et al. (2000) further argued for the adoption of non

- parametric bootstrapping

approaches over all parametric methods.

Mean:

, where γ=Euler

- Mascheroni constant.

Variance

The standard gompertz distribution has:

Under these conditions, the gompertz distribution has mean γ and variance . The go mpertz

distribution is typically fit using maximum likelihood. Note the following moment estimators

could also be used: