Page 1 of 19 

Appendix D. Extrapolation Factors for Derivation of Acute Aquatic 

Life HC5s, with Emphasis on Acetylcholinesterase Inhibitors

Russell J Erickson 

National Health and Environmental Research Laboratory 

Mid-Continent Ecology Division 

Duluth, MN 

December 20, 2011 

 
Page 2 of 19 

1.0 INTRODUCTION 

 Risk assessments of chemicals to aquatic life often involve consideration of the statistical 

distribution of toxicity test results (e.g., median lethal concentrations, or LC50s) across various 

taxa of organisms (Posthuma et al. 2002). These distributions are commonly referred to as 

species sensitivity distributions (SSDs), because toxicity test results are often summarized at the 

biological species level, although other taxonomic levels can be used. A set of available toxicity 

test results is used to estimate the SSD, and, in so doing, this set is considered to be a 

representative sample of a larger set of possible toxicity tests results that define the SSD. Risk is

characterized based on one or more percentiles in the SSD, generically referred to here as HCps, 

"HC" standing for "hazard concentration" and "p" referring to the percentile of interest. 

 The nature of the risk characterized by SSDs depends on various requirements and 

assumptions in data selection and processing, including: the taxonomic composition of the data 

set; the nature of the toxicity test results; the assumed shape for the SSD; the percentile that is a 

level of concern; and how this percentile is calculated. For example, U.S.EPA Office of Water 

(OW) aquatic life criteria (ALC) for acute toxicity (Stephan et al. 1985) require median 

concentrations causing mortality or immobilization (LC/EC50s) for eight specific taxonomic 

groups ("minimum data requirements", or MDRs); impose various requirements about the nature 

of the toxicity tests; assume the distribution is locally log-triangular; and calculate the HC5

(termed the Final Acute Value, or FAV) from Genus Mean Acute Values (GMAVs) by specified 

procedures (Erickson and Stephan 1988). Other jurisdictions/entities have different 

characteristics for SSD development. 

 Directly characterizing an SSD from a set of toxicity test results requires sufficient data if 

HCp estimates are to be estimated well (see Appendix C of this document). The task described 

here concerns what can be done when there are insufficient data (too few tests, insufficient 

taxonomic diversity, etc.) to characterize an SSD for a chemical well enough based just on 

toxicity data available for that chemical. The general strategy employed here for doing this is the 

"extrapolation factor" (EF): 

, 

	



 
 




where f(toxicity data set) is a summary statistic for the set of available toxicity test results (e.g., 

the lowest toxicity test result) that can be computed regardless of the data set size and HCp,cl

refers to an estimate of the HCp at a designated confidence level (e.g., the maximum likelihood 

estimate or some lower confidence limit). 

 An EF is specific to the nature of the toxicity data, the summary statistic (f) that is used, 

the percentile of interest, and the desired confidence level. EFs are developed by examining the 

relationship between f(toxicity data set) and the HCp for data sets for which the HCp is known or 

can be reliably estimated. These EFs can then be applied to more limited data sets for chemicals 

for which the HCp is unknown and cannot be otherwise estimated. 

 The effort here will focus on HC5 estimation for acute LC/EC50s (hereafter referred to as 

acute values or AVs), although the methodology can be readily extended to other percentiles and 

endpoints. Furthermore, it will focus on HC5s that reflect the taxonomic and data requirements 

of OW ALCs, although again the methodology can be readily extended to other data 

requirements. 
Page 3 of 19 

 This kind of empirically-based factor was pioneered by Host et al. (1995), who developed 

"Final Acute Value Factors" (FAVFs) as part of U.S.EPA Great Lakes Initiative efforts to 

provide Tier II (screening) values for toxic chemicals without actual ALCs. Host et al. (1995) 

demonstrated two ways for deriving EFs: 

(1) "Resampling Approach". Using data for a single chemical for which there are a sufficient 

number of acute values to assess the SSD and to estimate an HC5 ("data-rich" chemical), 

multiple subsets of these data of a specified size and taxonomic composition are generated by 

repeated random sampling ("resampling") from the discrete acute values. For each subset, the 

ratio (EF) between the lowest acute value and the known HC5 is calculated. A large number of 

EFs so generated for a specific size and composition provide information on the central tendency 

and variability of EFs for that subset size/composition and chemical. EFs from multiple datarich chemicals can be combined to provide a more general distribution of EFs. This provides the 

basis for selecting an EF to apply to other chemicals that are "data-poor". 

(2) "Distributional Approach". Rather than using discrete acute values for a specific chemical, 

a probability distribution for acute values is specified with a shape and dispersion relevant to a 

chemical of interest. This distribution, rather than the discrete acute values for data-rich 

chemicals, is used to generate random samples with the specified size and taxonomic 

composition. EFs are calculated based on the lowest acute value in each sample and the known 

HC5 for the distribution. A sufficient number of samples are generated to describe the central 

tendency and variability of the EFs, which again can be applied to data-poor chemicals. 

 The current task expands on Host et al. (1995) in the following ways: 

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

chemicals in addition to those that have published ALC (Host et al.1995 only used data for 

published ALC). 

(2) It conducts separate analyses for different mechanisms of action (MOAs). This report 

addresses acetylcholinesterase (AChE) inhibitors as an initial example, with limited comparison 

to chemicals with narcotic action to illustrate how MOA might affect EFs. 

(3) It pays specific attention to EFs for minimum data requirements in certain U.S.EPA Office 

of Pesticide Programs (OPP) assessment procedures (i.e., acute tests for rainbow trout, bluegill 

sunfish, and Daphnia magna). 

(4) It extends the distributional approach by using a composite distribution reflecting the relative 

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

case for Host et al. (1995). 

2.0 METHODS

 This section will first discuss the issue of "reference" HCps, which are a necessary part of 

any definition/evaluation/use of EFs. It will next describe the database used in this effort, 

including some needs regarding further database development before any EFs are finalized. It 

will then present the protocols for the "resampling approach" of using subsets of acute toxicity 

values for data-rich chemicals (with established reference HC5s) to determine how EFs should be 

developed to apply to acute toxicity values for data-poor chemicals. It will finally present 

protocols for basing EFs on the "distributional approach" of randomly generating samples from 
Page 4 of 19 

acute value distributions inferred from all relevant chemicals, rather than sampling from discrete 

tests results for individual data-rich chemicals as in the resampling approach. 

2.1 Reference HC5s 

 Given a set of acute LC/EC50s for different aquatic species considered to be 

representative of a large distribution of species to be protected, the basic question being 

addressed here is to how to use this smaller set to estimate the HC5 for the larger distribution. 

Any EF methodology needs to start with defining what the nature of this distribution is and what 

percentile is of interest, which, as noted above, involves a variety of decisions regarding data 

requirements and analysis. For the effort here this "reference HC5" was the OW ALC FAV 

(U.S.EPA 1985), but the EF methodology will be readily adaptable to other percentiles or other 

taxa/endpoint compositions. 

 The ALC methodology requires a GMAV (and thus at least one acute toxicity test) for 

each of the MDRs in Table 1: 

Table 1. Minimum acute toxicity data requirements for derivation of final 

acute value for aquatic life criteria. 

MDR # MDR Description 

1 A salmonid fish 

2 A second family of fish 

3 A third family of fish or an amphibian 

4 A planktonic crustacean 

5 A benthic crustacean 

6 An insect 

7 A third phylum (other than chordata or arthropoda) 

8 A second family of insects or a fourth phylum 

 The resampling analyses conducted here also will only address datasets large enough that 

the FAV is near or above the lowest GMAV.1 For datasets with a number of genera <15, the 

FAV often is substantially below the lowest GMAV (e.g., for N=9, the expected value for the 

percentile of the lowest GMAV is 10%, so that the fifth percentile represents a substantial 

extrapolation below this value). This extrapolation allows for the likelihood of more sensitive, 

untested taxa, which is to be expected if the dataset is a random sample of a larger distribution. 

However, the purpose of this analysis is to determine whether, and to what degree, such 

extrapolation is appropriate for a particular MOA and dataset composition, so datasets were set 

to be large enough to eliminate or minimize extrapolation. 

 Although the ALC methodology requires representation from a diversity of taxonomic 

groups and thus provides some degree of coverage of the taxa occurring in aquatic communities, 

it has no further requirements about the proportional representation from different taxa for 

datasets larger than the minimum of 8 GMAVs. Thus, for large datasets, some variability in the 

meaning of the FAV is introduced when the relative frequencies of major taxonomic groups vary 

 

1

 This is a difference from the approach taken in Host et al. (1995). Using acute toxicity value databases for existing 

ALC, these authors (a) randomly sampled the database for each chemical to generate multiple datasets of 8 toxicity 

values meeting the ALC MDRs, (b) calculated the FAV (HC5) for each of these minimum ALC datasets, (c) 

sampled from these minimum ALC datasets to generate subsets of different sizes and taxonomic composition, and 

(d) calculated EFs as the ratio of the smallest toxicity value in each subset type to the FAV. Thus their reference 

HC5 for each subset was the FAV for the N=8 dataset from which subset was drawn, not the overall database. 
Page 5 of 19 

among the datasets and differ from the relative frequencies represented by the MDRs. However, 

current ALC derivations assume that such variations are not significant for defining the FAV, 

and this same assumption is also followed for this effort. Furthermore, in the analyses here, the 

effects of such differences on EFs will be (a) moderated in the resampling approach by using 

data sets for several chemicals and (b) addressed in the distributional approach by defining the 

reference HC5 using equal representation of the MDRs. 

2.2 Toxicity Database 

 The acute toxicity database used here was developed by the U.S.EPA's Gulf Ecology 

Division (GED) to support their Interspecies Correlation Estimations (ICE) efforts (Raimondo et 

al. 2010). The database associated with version 3.1 of Web-ICE was obtained from GED as a 

Microsoft Excel spreadsheet (Sandy Raimondo, U.S.EPA GED, personal communication). This 

database was further modified to support queries to generate subsets satisfying certain criteria 

regarding chemical, test conditions, and test species (U.S.EPA 2010). 

2.2.1 Datasets for Resampling Approach 

 The resampling approach requires individual datasets for chemicals with sufficient data 

for defining their individual reference HC5s. Because the Web-ICE database contained duplicate 

records (due to the use of multiple secondary sources covering the same primary sources) that 

could not be removed with the query builder, individual spreadsheets for AChE-inhibiting 

chemicals were extracted from the database using the query builder and the duplicate records 

were manually removed (Acute tox data – AChE Inhibitors – OPs.xlsx, Acute tox data - AChE 

Inhibitors – carbamates.xlsx; Kristina Garber, U.S.EPA OPP/EFED, personal communication). 

The spreadsheet for each of these individual chemicals was substituted for the master record set 

in the query builder to support further queries for each chemical, without the problem of 

duplicate records. Insufficient data were available to do this for the narcosis MOA, which 

therefore was not part of the resampling analyses. 

2.2.2 Datasets for Distributional Approach 

 The distributional approach requires conducting an analysis of variance using data for all 

the chemicals in an MOA, to provide the variances and mean differences for the various MDRs. 

This was conducted on a subset of the Web-ICE database of records specified as meeting data 

requirements for ICE model development, this subset also eliminating duplicate records. This 

subset of the database supported distributional analysis of acute values for both the AChEinhibition and narcosis MOAs. 

2.2.3 Data Quality Issues 

 Some errors and anomalies were discovered in these spreadsheets, such that the analyses 

in this report should only be considered to illustrate the procedures for developing EFs, and not 

to provide final EFs. This included some cases of LC/EC50 results entered in the database as 

mg/L rather than μg/L (and vice-versa) or reported erroneously in the original publication, and 

some records involving misidentified chemicals. It also included some instances of LC/EC50s 

ranging by 2-3 orders of magnitude within the same species (including some tests from the same 

laboratory) where there were no obvious errors in data transcription errors or in the original 

publication. For example, the bulk of the cladoceran data for carbaryl indicate acute LC/EC50s 

≤10 ug/L and that this taxa is orders-of-magnitude more sensitive than species in other phyla, but 

a few reports for D. magna have LC/EC50s>1 mg/L, indicating similar sensitivity to these other 
Page 6 of 19 

phyla (e.g., Johnson et al. 1993). This is indicative of either large variation among strains of this

species (with consequences to how EFs should be derived), or possible reporting errors that need 

to be evaluated. In another example, two separate papers from the same laboratory reported the 

sensitivity to methylparathion of different species from the same mollusc genus to be several 

μg/L in one case and several mg/L in another (Sukumar and Rao 1985; Padmaja et al. 1988). 

There are also cases where data are present for only a subset of the species or chemicals reported 

in the original publication, the reasons for which need to verified/documented before any 

finalization of the EFs. Although the rate of such errors is very low, if usable and defensible EFs 

are to be developed, it is imperative that this acute toxicity database be further validated to assure

that its quality be that which would be acceptable for aquatic risk assessments. It would also be 

useful to update this database by adding the most recent data. 

2.3 Extrapolation Factors by Resampling Toxicity Tests Results for Data-Rich Chemicals 

The edited spreadsheets for AChE-inhibiting chemicals (Kristina Garber, U.S.EPA 

OPP/EFED, personal communication) included nine AChE inhibitors for which there was 

sufficient data to satisfy the 8 MDRs for OW freshwater acute aquatic life criteria 

(organophosphates: chlorpyrifos, dichlorvos, fenitrothion, malathion, methylparathion, 

parathion; carbamates: carbaryl, methomyl, propoxur). This is subject to possible change 

contingent on further modifications of the database noted above. These spreadsheets were 

subject to the following actions: 

(1) Check for data anomalies (e.g., unusually wide variation within species or a broader taxa) and 

for compliance with ALC test requirements (e.g., test duration, lifestage). Delete/modify records

as justified, reviewing original publications as needed. 

(2) Create separate datasets for each chemical for (a) all species and (b) species resident to the 

continental United States and Canada. Although the ALC methodology specifies the use of the 

"resident-only" dataset, the "all-species" dataset was included to assess how much effect using 

non-resident species might have on EFs and to provide an alternative dataset if the resident-only 

dataset did not have sufficient data. 

(3) Compute the GMAVs and FAV (=reference HC5) for each dataset. To avoid appreciable 

extrapolation below the lowest GMAV (i.e., extrapolating to untested taxa that are possibly more 

sensitive), require that there be at least 15 genera and that the reference HC5 be >80% of the 

lowest GMAV. Also, to provide a variety of choices for the most sensitive acute value (so that 

no single value dictates an inordinate percentage of the EFs), require that there be at least 20 

individual arthropod tests. These requirements removed diazinon (resident-species only), 

propoxur, methomyl, and dichorvos from consideration. 

(4) Analyze each dataset (i.e., each chemical, with separate analyses for resident-only and allspecies) as follows: 

(a) Randomly generate 999 samples with 8 toxicity test results each (one for each MDR). 

(b) Generate a random subset of each N=8 sample meeting the specifications in Table 2. 

(c) For each subset specification in Table 2, compute the EF as the ratio of the lowest AV to 

the reference HC5, and describe the distribution of these EFs across all 999 samples. 
Page 7 of 19 

Table 2: Taxonomic requirements and dataset sizes used in generating EFs (MDRs per Table 1) 

Taxonomic Requirement Dataset Size 

All MDRs 8 

MDR 1 or 2; MDR 4; Other MDRs Random 2 

3 

4 

5 

6 

MDR 1, MDR 2, MDR 4, MDR 5 or 6; Other MDRs Random 4 

5 

6 

Rainbow Trout, Bluegill Sunfish, Daphnia magna 3 

MDR1, MDR2, MDR4 3 

MDR1 or 2, MDR 4, MDR 5 or 6 3 

2.4 Extrapolation Factors by Simulated Samples Based on Distributions of Toxicity Values 

 One limitation of the resampling approach described in the previous section is that the 

finite amount of data results in distributions with a limited number of discrete data (e.g., only 2 

or 3 acute values for some MDRs for some chemicals), thereby increasing uncertainties of some 

percentile estimates. Also, only a limited number of chemicals contribute to the analyses, 

ignoring relevant information from other chemicals on variability of toxicity within and between 

taxa. An alternative is to use the available toxicity tests, not as a source of discrete points in a 

resampling analyses, but rather as a source of information on the distribution of acute values 

within and between MDRs. These distributions can be based on data from all the chemicals 

within an MOA, not just a few data-rich chemicals. The individual MDR distributions in 

aggregate define a composite distribution from which a reference HC5 can be specified and 

samples drawn from a continuum rather than a limited number of toxicity test results. Host et al. 

(1995) did some theoretical calculations based on a simple overall distribution, but did not 

consider composite distributions that address variability both within and between MDRs. 

 The Web-ICE database was used to conduct an analysis of variance of the LC50s for 

various MOAs by EMR, Inc. The pooled variance (across chemicals) of log10(LC50)s within 

each MDR was determined, as well as the differences of mean log10(LC50)s between the different 

MDRs. The distribution for MDR 8 was not directly assessed, but rather specified to be a 

composite of MDRs 6 and 7 based on the relative frequency with which a second insect family 

or a fourth phylum appears in the database. This analysis was done on the subset of the WebICE database deemed acceptable by GED for supporting ICE modeling efforts, which had 

duplicate records removed and some additional data screening requirements used for ICE model 

development (Vivian et al. 2009). However, this subset of the database is still subject to some of 

the same uncertainties noted above, and also to the question of whether the ICE model data 

screening requirements eliminated some data that would be suitable for the current analysis. 

Therefore, there is also a need to update this analysis of variance based on a better qualityassured and filtered subset of the database before finalizing any EFs. There is also a need to 
Page 8 of 19 

expand the analysis of variance to address uncertainties in the composite distribution. However, 

the data and analysis used are still useful for illustrating the approach and providing preliminary 

results. 

 Composite distributions for AChE-inhibiting and narcosis chemicals were defined based 

on equal representation of the log10(LC50) distributions for each MDR. The fifth percentile of 

each composite distribution was used to define its reference HC5. It should be noted that the HC5

is thus based on the assumption that distributions based on individual acute toxicity test results 

are a suitable surrogate for the distributions of GMAVs that are the foundation for aquatic life 

criteria FAVs. This is a limitation in the efforts to date which also merits attention in further 

development of this effort. 

 Extrapolation factor development was based on 9999 samples of N=8 generated by 

randomly sampling one point from each of the eight MDR distributions. Analogous to the 

resampling of discrete acute values for the data-rich chemical datasets in the previous section, 

random subsamples of each N=8 sample was generated per Table 2, and used to generate EFs 

and describe their distributions. 

3.0 RESULTS

3.1 Extrapolation Factors by Resampling Toxicity Tests Results for Data-Rich Chemicals 

3.1.1 Datasets used in the analyses

 Table 3 summarizes attributes of each dataset analyzed. The unshaded rows indicate 

those datasets for which results are used in further development and description of EFs. 

Preference was given to the resident-only datasets in accordance with OW ALC procedures 

(Stephan et al. 1985), but the all-species datasets were also considered to assess how much effect 

using non-residents species might have and to allow for an alternative if the resident-only dataset 

did not meet the requirements of the analysis. Such an alternative was needed for diazinon 

because the FAV (0.23 μg/L) for the resident-only dataset was nearly two-fold below the lowest 

GMAV (0.40 μg/L). The all-species diazinon dataset was appropriate for this analysis because it 

had a taxonomic sensitivity distribution similar to the resident-only dataset and had enough 

additional data that the FAV did not have this undesired extrapolation. In contrast, the allspecies fenitrothion dataset included a species whose toxicity test result was 50-fold more 

sensitive than any other test, resulting in a low FAV of questionable utility for these analyses. 

For carbaryl, chloropyrifos, malathion, and methylparathion, using resident-only or all-species 

had little impact on the analyses – the FAVs were within 20% of each other and the acute values 

for the sensitive taxa were similar. 

3.1.2 EFs vs. # of MDRs, with a minimum requirement of one fish and one cladoceran test 

 For the selected datasets, Figure 1 summarizes EFs as a function of the number of ALC 

MDRs satisfied (one acute test per MDR) when there is a requirement of at least one fish test 

(either MDR 1 or 2) and a planktonic crustacean test (MDR 4), with the other MDRs being 

randomly selected. For each chemical, the symbols denote the geometric mean extrapolation 

factor over the 999 subsamples. The lines denote composite results across all chemicals, 

including not only the geometric mean, but also the 75th and 90th percentiles. 
Page 9 of 19 

Figure 1: Geometric mean extrapolation factors (for 999 samples) for six acetylcholinesterase-inhibiting 

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

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

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

chemicals. 

Number of MDRs Satisfied

2 3 4 5 6 7 8

Extrapolation Factor

2

5

20

1

10

Carbaryl 

Chlorpyrifos 

Diazinon 

Fentirothion 

Malathion 

Methylparathion 

Composite 

90th Percentile

Composite

75th Percentile

Composite

Geometric Mean

Table 3. Attributes of acute toxicity datasets analyzed regarding the generation of extrapolation factors by 

repeated subsampling of toxicity test results for data-rich chemicals. Nonshaded rows denote datasets used for 

reporting extrapolation factors. 

Chemical # of Genera # of Total Test 

Results 

# of Test Results 

for Arthropoda 

Reference HC5 

(μg/L) 

Carbaryl – Resident-Only 44 264 39 4.8 μg/L 

Carbaryl – All Species 53 281 42 5.3 μg/L 

Chlorpyrifos – Resident-Only 23 86 43 0.066 μg/L 

Chlorpyrifos – All Species 30 97 45 0.075 μg/L 

Diazinon – Resident-Only 17 57 22 0.230 μg/L 

Diazinon – All Species 28 78 30 0.55 μg/L 

Fenitrothion – Resident-Only 17 83 20 4.0 μg/L 

Fenitrothion – All Species 27 108 29 0.1 μg/L 

Malathion – Resident-Only 43 132 34 1.54 μg/L 

Malathion – All Species 50 140 34 1.80 μg/L 

Methylparathion – Resident-Only 22 45 22 0.75 μg/L 

Methylparathion – All Species 28 67 24 0.94 μg/L 
Page 10 of 19 

Figure 2. Geometric mean extrapolation factors (for 999 samples) for six acetylcholinesterase-inhibiting 

chemicals as a function of the number of MDRs satisfied, given a minimum requirement of one salmonid and 

one nonsalmonid fish test, one planktonic crustacean test, and one other arthropod test, with remaining tests 

being randomly selected to satisfy other MDRs for ALC derivation. Lines denote the geometric mean and the 

75th and 90th percentiles for composite results across all chemicals. 

Number of MDRs Satisfied

2 3 4 5 6 7 8

Extrapolation Factor

2

5

20

1

10

Carbaryl 

Chlorpyrifos 

Diazinon 

Fentirothion 

Malathion 

Methylparathion 

Composite 

90th Percentile

Composite

75th Percentile

Composite

Geometric Mean

 There is substantial variability both across chemicals and across subsamples, but the EFs 

generally are modest, exceeding a factor of 10 only at the 90th percentile for N=3. For all but one 

chemical, the geometric mean factor is <2 if there are ≥4 tests and is only slightly above 1 when 

there are 8 tests. For the other chemical (methylparathion) the geometric mean EF stays above 2 

because a low GMAV (0.37 ug/L) for one cladoceran genus based on one toxicity test results in 

an FAV (0.75 ug/L) below most of the other cladoceran tests (geometric mean of 13 tests = 2.7 

ug/L), so that the EFs based on a single planktonic crustacean test are often above 2.

3.1.3 EFs vs. # of MDRs with a minimum requirement of two fish and two arthropod tests 

 Figure 2 summarizes the EFs as a function of the number of MDRs satisfied when there 

is a requirement of including, at minimum, a salmonid fish test (MDR 1), a nonsalmonid fish test 

(MDR 2), a planktonic crustacean test (MDR 4), and a test with a benthic crustacean or an insect 

(MDR 5 or 6), with the rest of the MDRs for ALC derivation being randomly selected. Again, 

the symbols denote geometric mean EFs for the individual chemicals and the lines denote the 

geometric mean and the 75th and 90th percentiles of the composite results across all chemicals. 

 Because of requiring a minimum of four tests that included two taxa that are typically 

sensitive (arthropods), the EFs are moderately lower at N=4 than in Figure 1 and slightly lower 

at N=5 and N=6. Again, the notable result is that the EFs are relatively modest (because of the 

requirement to include taxa sensitive to AChE inhibitors), but have a large uncertainty (because 

of the variability of results within the sensitive taxa). 
Page 11 of 19 

3.1.4 EFs for N=3 under various sampling schemes 

 Table 4 summarizes the EFs for the four different sampling schemes with N=3 described 

in Table 2. Again, the mean factors are relatively small but show considerable variability. 

Interestingly, for most of the individual chemicals and for the composite results, specifically 

requiring D. magna results in slightly larger factors, because, on average, other planktonic 

crustaceans are more sensitive based on the available data. Requiring two arthropods in the set 

of 3 results in lower and less variable EFs, as expected given the sensitivity of most arthropods to 

AChE-inhibiting chemicals (i.e., including two arthropods increases the chance of a result nearer 

the reference HC5). 

 However, despite some effects of the taxonomic requirements, the differences between 

the four sampling schemes are not great. The composite geometric mean EF is reduced by less 

than two-fold by requiring a benthic arthropod in addition to a planktonic one. The reduction in 

uncertainty is also less than two-fold – with the two arthropod requirement still having a 10-fold 

ratio between the 10th and 90th composite percentiles, compared to an average of 18-fold for the 

other schemes. Again, the notable result is relatively modest EFs due to requiring at least one 

arthropod, but considerable variability because of the variability among individual tests even 

within the sensitive taxa. 

Table 4. Geometric means of extrapolation factors for samples of size 3 with different taxonomic requirements from 

datasets for selected acetylcholinesterase-inhibiting chemicals 

Taxonomic 

Requirements 

Rainbow Trout 

Bluegill Sunfish 

Daphnia Magna 

Salmonid Fish 

Nonsalmonid Fish 

Planktonic Crustacean 

Any Fish 

Planktonic Crustacean 

Benthic Arthropod 

Any Fish 

Planktonic Crustacean 

Any Other MDR 

Carbaryl 3.83 2.19 1.46 1.78 

Chlorpyrifos 6.07 1.91 1.36 1.70 

Diazinon 2.25 1.16 1.09 1.12 

Fenitrothion 2.74 5.57 1.36 2.94 

Malathion 1.07 1.28 1.07 1.21 

Methylparathion 3.83 3.07 2.73 2.93 

Composite Total 2.89 2.19 1.43 1.81 

Composite Total 

10th-90th percentile 1.04-16.0 0.64-12.1 0.49-4.84 0.53-11.0 

3.2 Extrapolation Factors by Simulated Samples from Distributions of Toxicity Values 

3.2.1 Definition of composite distributions

 Table 5 provides the standard deviations within and the mean differences between the 

MDRs for log10(logLC50)s for AChE-inhibition and narcosis MOAs, using MDR 4 as the 

reference for mean differences. (This choice of reference is arbitrary, because EFs will depend 

only on relative differences, not absolute values.) Table 5 also includes the median acute value 

on an arithmetic scale (relative to MDR 4=1.0) to better illustrate the relative differences 

between MDRs. The MDR 8 distribution is a composite consisting of 80% MDR 6 and 20% 

MDR 7, these percentages being based on the relative frequency with which a second order of 
Page 12 of 19 

Figure 3: Estimated distributions of acute values with each MDR, relative to the median value for MDR4, for 

chemicals with AChE-inhibiting and narcosis MOAs. 

AV for Designated MDR/Median AV for MDR 4

0.01 0.1 1 10 100

Frequency

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4 Narcosis

1 10 100 1000 10000

Frequency

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

MDR 1

MDR 2

MDR 3

MDR 4

MDR 5

MDR 6

MDR 7

MDR 8

ACHE Inhibition

insect and a fourth phylum appeared in the database. Figure 3 provides a graphical depiction of 

these distributions, assuming log normal distributions. 

Table 5. For chemicals with AChE-inhibition and narcosis MOAs, standard deviations of logLC50 within each MDR, 

mean logLC50 for each MDR relative to that for MDR4, and median LC50 for each MDR relative to MDR4. 

MDR Description 

AChE Inhibition Narcosis 

log10(LC50) Median 

LC50

(μg/L) 

log10(LC50) Median 

LC50 

(μg/L) Std Dev Mean Std Dev Mean 

1. A salmonid fish 0.32 2.14 138 0.25 0.02 1.0 

2. A 2nd family of fish 0.67 2.23 170 0.26 0.15 1.4 

3. A 3rd family of fish or an amphibian 0.67 2.23 170 0.26 0.15 1.4 

4. A planktonic crustacean 0.40 0.00 1.0 0.43 0.00 1.0 

5. A benthic crustacean 0.78 1.02 10.5 0.56 0.00 1.0 

6. An insect 0.67 0.55 3.5 0.50 -0.05 0.9 

7. A 3rd phylum (not chordata or arthropoda) 0.80 3.20 1580 0.32 0.21 1.6 

8. A 2nd family of insects or a 4th phylum Composite of 80% MDR 6 and 20% MDR 7 

 The distributions vary markedly between the MOAs. For the AChE MOA, the arthropod 

MDRs are 1-3 orders of magnitude more sensitive on average than the MDRs for other phyla. 

As would be expected, the variability for narrow taxonomic groups (MDR 1 and MDR 4) is 

smaller than for the more diverse MDRs. In contrast, for the narcosis MOA, there is no 

significant mean difference between the MDRs, and the variability within MDRs on average is 

smaller than for the AChE MOA. This represents a greater similarity of this mode of action 

across taxa. 

 Assuming normal distributions for each component distribution, the HC5 of the 

composite distribution for AChE-inhibiting chemicals, is -0.39 (base ten log scale), or 0.41 after 

antilog transformation. For the narcosis MOA, the respective numbers are -0.69 and 0.21. 
Page 13 of 19 

Figure 4: Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an 

analysis of the variance within and between MDRs for the LC50s of AChE-inhibiting chemicals, as a function of 

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

crustacean , with remaining MDRs being randomly selected. Thin lines denote the composite across all 

chemicals and samples from resampling analyses in Figure 1. 

Number of MDRs Satisfied

2 3 4 5 6 7 8

Extrapolation Factor

2

5

20

1

10

90th Percentile

75th Percentile

Geometric Mean

Again, note that these are on scale relative to MDR 4 within each MOA. The higher numbers for 

the AChE MOA do not reflect absolute sensitivity, but rather the use of a reference point (MDR 

4) that is highly sensitive within the AChE MOA and thus is closer to the HC5. 

 As noted earlier, one uncertainty here is defining the HC5 based on distributions with an 

assumed shape that are parameterized from toxicity tests, in contrast to the datasets of GMAVs 

used for the resampling effort which are more closely related to the ALC process. The reference 

HC5 (0.41) for AChE simulations here represents an FAV 60% below the mean for MDR 4. 

This is, in fact, similar to the FAVs for the six datasets used in the resampling effort, which 

varied from 22% to 68% (average 56%) below the mean for MDR 4. However, this agreement 

might be fortuitous for this case and should receive continuing attention in any further method 

development. 

3.2.2 EFs vs. # of MDRs with a minimum requirement of one fish and one cladoceran test

 For the AChE MOA, Figure 4 summarizes EFs as a function of the number of ALC 

MDRs satisfied (one acute test per MDR) when there is a requirement of at least one fish test 

(MDR 1 or 2) and a planktonic crustacean test (MDR 4), and when the other MDRs are 

randomly selected. The bold lines denote the simulation results from the composite distribution, 

using the geometric average and the 75th and 90th percentile EFs from the 9999 simulations. As 

for the resampling effort, the EFs are relatively modest but highly variable. 

 The thin lines in Figure 4 provide a comparison to the composite results from the 

resampling analyses (Figure 1) of the discrete tests for individual chemicals. The resampling-
Page 14 of 19 

Figure 5: Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an 

analysis of the variance within and between MDRs for the LC50s of narcosis chemicals, as a function of the 

number of ALC MDRs satisfied and given a minimum requirement of one fish test and one planktonic 

crustacean , with remaining MDRs being randomly selected. Thin lines denote simulation results for AChEinhibiting chemicals from Figure 4. 

Number of MDRs Satisfied

2 3 4 5 6 7 8

Extrapolation Factor

2

5

20

1

10

90th Percentile

75th Percentile

Geometric Mean

based EFs are usually lower and vary somewhat more with subsample size. This can be 

attributed in large part to species from MDRs 5 and 6 being somewhat more sensitive relative to 

MDR4 for the six chemicals used in the earlier analysis than for the overall analysis of variance 

of all the AChE-inhibiting chemicals (Table 5); i.e., as a greater number of sensitive arthropods 

are added, the EFs are more likely to drop to lower levels. However, the differences in the EFs 

are not great, the geometric mean EFs never differing by more than 30% and no difference being 

more than a factor of 2. 

 Figure 5 provides the same EF summary for the narcosis MOA, with the thin lines 

providing a comparison to the AChE MOA simulation results in Figure 4. The nearly identical 

mean EFs at N=2 reflects low EFs that arise (a) for the AChE MOA from inclusion of the AChEsensitive MDR 4 even at this lowest subset size and (b) for the narcosis MOA from the low 

variability of toxicity test results, causing most of the results to be relatively near the HC5. This 

low variability for the narcosis MOA results in lower EFs for the narcosis MOA at higher 

percentiles and values for N. However, the EFs for the narcosis MOA are at most a factor of 2 

lower from those for the AChE MOA, and usually much less. Despite the markedly different 

distributions (Table 5), the simple requirement that an AChE-sensitive taxon be included in EF 

evaluations resulted in the EFs for both MOAs not being extremely different. However, the 

differences are large enough, especially at the higher percentiles, to be of potential importance 

for assessments. Furthermore, because the limited differences are the result of rather specific 

circumstances, this comparison does not support more general conclusions about how EFs vary 

among MOAs. 
Page 15 of 19 

Figure 6. Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an 

analysis of the variance within and between MDRs for the LC50s of acetylcholinesterase-inhibiting chemicals, 

as a function of the number of ALC MDRs satisfied and given a minimum requirement of one salmonid and one 

nonsalmonid fish test, one planktonic crustacean test, and one other arthropod test, with remaining tests being 

randomly selected to satisfy the MDRs for ALC derivation. Thin lines denote the composite across all 

chemicals and samples from resampling analyses in Figure 2. 

Number of MDRs Satisfied

2 3 4 5 6 7 8

Extrapolation Factor

2

5

20

1

10

90th Percentile

75th Percentile

Geometric Mean

3.2.3 EFs vs. MDRs with a minimum requirement of two fish and two arthropod tests 

 Figures 6 and 7 summarize the EFs for both MOAs as a function of the number of MDRs 

satisfied when there is a requirement of including, at minimum, a salmonid fish test, a 

nonsalmonid fish test, a planktonic crustacean test, and a test with a benthic crustacean or an 

insect, with the rest of the MDRs for ALC derivation being randomly selected. Again, the bold 

lines denote the geometric mean and the 75th and 90th percentiles for the simulation results for 

the AChE MOA (Figure 6) and the narcosis MOA (Figure 7). The thin lines in Figure 6 allow 

comparison of the AChE simulation results to the resampling results of Figure 2, and the thin 

lines in Figure 7 allow comparison of the simulation results for the two MOAs. 

 For both MOAs, the lines in Figures 6 and 7 are nearly identical to those in Figures 4 and 

5. For the AChE MOA, this is because requiring a benthic arthropod in the first four data does 

not usually add a test more sensitive than just requiring a planktonic crustacean and randomly 

picking from other MDRs (which usually results in including a benthic arthropod). For the 

narcosis MOA, all the MDRs are so similar that requiring a second fish and a benthic arthropod 

to the first four data does not have much different effect than randomly selecting MDRs. 

Figure 6 again illustrates some modest differences between the two approaches – the 

resampling approach producing EFs that are a factor of 1.3-2.0 lower than the distributional 

approach. This reflects the more limited information data that is used in the resampling 

approach, but also includes some effects introduced by the assumed shapes in the distributional 

approach. Figure 7 again illustrates how narcosis EFs are moderately lower than those for 

AChE-inhibition, especially at the high percentiles, due to the lower variability among acute 

values for this MOA.
Page 16 of 19 

Figure 7: Extrapolation factors for 9999 simulated samples drawn from a composite distribution based on an 

analysis of the variance within and between MDRs for the LC50s of narcosis chemicals, as a function of the 

number of ALC MDRs satisfied and given a minimum requirement of one salmonid and one nonsalmonid fish 

test, one planktonic crustacean test, and one other arthropod test, with remaining tests being randomly selected 

to satisfy the MDRs for ALC derivation. Thin lines denote simulation results for AChE-inhibiting chemicals 

from Figure 6. 

Number of MDRs Satisfied

2 3 4 5 6 7 8

Extrapolation Factor

2

5

20

1

10

90th Percentile

75th Percentile

Geometric Mean

4.0 Comparison of Current Results to those of Host et al. (1995) 

Given that this work was intended as an extension of that of Host et al. (1995), it is 

worthwhile comparing EFs from these two efforts. This comparison does not pertain to the final 

recommendations in the GLWQG. Rather, the concern here is of the issue of MOA-specific 

versus generalized EFs and other methodological differences between the current work and that 

of Host et al. (1995). Table 6 summarizes EFs (by the resampling approach and for the same 

overall percentiles), for the analysis of AChE-inhibiting chemicals addressed in the current effort 

to the freshwater ALC chemicals analyzed by Host et al. (1995). In addition to differences in the 

chemical set and toxicity data, these efforts differ slightly regarding taxonomic requirements. 

The numbers from Host et al. (1995) required a daphnid test and a rainbow trout test while those 

from the current effort required a planktonic crustacean test (almost always a daphnid) and a test 

with any fish species. Because fish are tolerant to AChE-inhibiting chemicals, this taxonomic 

difference would have little or no effect on the EFs for the AChE-inhibitors. 

 The EFs from Host et al. (1995) range from 40% to 260% higher than the current effort. 

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

definition of the reference HC5 noted earlier. The current analyses based EFs on FAVs for large 

datasets from which the subsets were drawn, whereas Host et al. (1995) first sampled minimal 

(N=8) datasets from the larger datasets, calculated FAVs for each N=8 dataset, and based the 

EFs for smaller subsets on the FAVs for these N=8 datasets, not the FAV for the original, larger 
Page 17 of 19 

dataset. Current efforts did include calculating how the FAVs for such N=8 datasets would 

differ from the FAV of the larger dataset, which indicated that this might increase EFs by a 

factor of 2 or more for the AChE-inhibiting chemicals, but by very little for the narcosis 

chemicals. Efforts are needed to further resolve this issue. 

Table 6. Extrapolation factors (EFs) by resampling analyses from current efforts for AChE-inhibiting chemicals 

and from Host et al. (1995) for all freshwater ALC chemicals. Percentiles are for overall results across all samples 

and chemicals. 

MDRs 

Satisfied 

Median EF 75th Percentile EF 90th Percentile EF 

Current Effort Host et al. Current Effort Host et al. Current Effort Host et al. 

2 2.19 2.93 4.0 7.9 12.1 25.5 

3 1.81 2.47 2.7 6.6 11.0 20.5 

4 1.55 2.28 2.4 5.1 7.6 18.3 

5 1.37 2.16 2.2 3.9 4.9 12.6 

6 1.26 2.02 1.8 3.4 4.4 8.8 

8 1.13 1.86 1.5 2.7 3.5 4.9 

 To fully address the issue of MOA-specific versus generalized EFs, some other issues are 

also of concern to further efforts. Host et al. (1995) included 28 freshwater chemicals with 

available ALCs at the time, including 14 inorganic metals, 3 other inorganic chemicals 

(ammonia, chloride, chlorine), 7 industrial organics, 2 insecticides (parathion and 

methylparathion), and 2 other biocides (trybutyltin, pentachlorophenol). This dataset needs to be 

increased with new and additional data, and be targeted to generalized EFs for MOAs of interest 

for which there is insufficient data to develop MOA-specific EFs. In addition, because of intraand inter chemical (and MOA) variability, more information is needed on how this affects 

percentile selection to guide risk management decisions. Finally, consideration needs to be 

given to applying the distributional approach to the development of generalized EFs, in order to 

allow the use of more toxicity data. 

5.0 SUMMARY AND CONCLUSIONS 

The analyses described here demonstrated the following: 

(1) As described by Host et al. (1995), it is possible to use existing toxicity data to derive 

extrapolation factors to derive useful estimates of HC5s based on data sets with 3 to 8 toxicity 

values, and to provide useful information regarding aspects of the uncertainty of such estimates. 

(2) EFs will depend to some degree on adverse outcome pathways (toxicity mechanisms) which 

affect the variability of toxicity within and differences between taxonomic groups. For the two 

MOAs examined here (AChE-inhibition and narcosis), EFs were similar for the median result at 

N=2, but differences increased with percentile and sample size so that the narcosis EF was up to 

2-fold lower. Furthermore, this degree of similarity was in part due to different attributes of 

these toxicity sets that might not be true for other MOAs. This warrants further evaluation of 

MOA effects. 

(3) EFs can be developed by (a) resampling discrete toxicity test results from individual "data 

rich" chemicals within an MOA or (b) sampling from a composite distribution of the relative 
Page 18 of 19 

toxicity of different taxa, estimated by analysis of variance of toxicity tests for all chemicals 

within an MOA. The latter approach has the advantage of using a greater amount of data across 

more chemicals, but has uncertainties in assumptions concerning distributional shapes. For the 

analyses here, EFs were similar by either approach, but do have some differences which merit 

further resolution before finalizing an approach. 

(4) A critical aspect of this process is the definition of the desired reference HC5. This involves 

the definition of the composite distribution used in the simulation approach and sample 

size/composition issues in the resampling approach. In particular, it involves risk management 

judgments regarding assumptions about the possibility of untested, sensitive taxa. Further efforts 

to resolve this issue are needed. 

(5) When insufficient data are available for developing MOA-specific EFs, further efforts are 

warranted to update the generalized EFs of Host et al. (1995) to include new and additional data, 

consideration of the distributional approach, and additional information on percentiles. 

 As noted earlier, the analyses and results presented here are preliminary because of a 

need to further refine the database upon which they are based. This database needs to be 

expanded with additional available data, and records for taxa sensitive to chemicals important to 

an analysis need to be thoroughly reviewed. The datasets for the resampling approach should 

include more chemicals, including those that have sufficient amounts of data for sensitive taxa, 

but might lack data for taxa reasonably expected to be tolerant and which would thus not affect 

results. The composite distribution for the simulation approach should be re-evaluated to address 

issues of uncertainty in the relative sensitivities of different taxa and better definition of the 

reference HC5. A final area of needed attention is to expand/refine the conditions to which EFs 

apply regarding taxonomic requirements, multiple tests per MDR, etc. to apply more broadly to 

different dataset compositions. Although these limitations make the results presented here 

illustrative and tentative, they still provide good information regarding the utility of the approach 

and the general magnitude of extrapolation factors appropriate for risk characterizations based on 

toxicity datasets with <8 toxicity values. 

6.0 REFERENCES 

Erickson, R.J. and C.E. Stephan. 1988. Calculation of the final acute value for water quality 

criteria for aquatic organisms. U.S. Environmental Protection Agency. Duluth, MN. 

EPA/600/3-88-018. 

Host GE, Regal RR, and Stephan CE. 1995. Analyses of acute and chronic data for aquatic life. 

U.S. Environmental Protection Agency, Duluth, MN. 

Johnson IC, Keller AE, and Zam SG. 1993. A method for conducting acute toxicity tests with 

the early life stages of freshwater mussels. in Landis WG, Hughes JS, and Lewis MA 

(eds) Environmental Toxicology and Risk Assessment. ASTM STP 1179. American 

Society of Testing and Materials, Philadelphia, PA. 

Posthuma L, Traas TP, and Suter GW. 2002. General introduction to species sensitivity 

distributions. in L Posthuma, GW Suter, and Traas TP (eds.) Species Sensitivity 

Distributions in Ecotoxicology. Lewis. Boca Raton, FL. USA. 
Page 19 of 19 

Raimondo S, Vivian DN, and Barron MG. 2010. Web-based Interspecies Correlation 

Estimation (Web-ICE) for Acute Toxicity: User Manual. Version 3.1. EPA/600/R10/004. Office of Research and Development, U. S. Environmental Protection Agency. 

Gulf Breeze, FL. 

Sukumar RV and Rao MB. 1985. Toxicity of γ-HCH, methyl parathion and carbaryl to two 

varieties of a tropical frshwater gastropod, Bellamya bengalensis. in Dalela TC and 

Mane UF (eds). Proceeding of the 5th National Symposium on the Assessment of 

Environmental Pollution, 1984, Aurangabad, India. 

Padmaja K, Nagalakshmi P, and Rao MB. 1988. A compartive evaluation of the toxicity of 

Metacid (methyl parathion), quinalphos, and Nuvan (DDVP) to the freshwater snail 

Bellamya dissimilis. Indian J Comp Anim Physiol 6:34-38. 

USEPA. 1985. Guidelines for deriving numerical national water quality criteria for the protection 

of aquatic organisms and their uses. EPA Report 822/R-85-100 (NTIS Report PB85-

227049. 

USEPA. 2010. Query Builder (Web-ICE CQB v20100820.xls). US Environmental Protection 

Agency, Office of Research and Development, National Health and Environmental 

Research Laboratory, Mid-Continent Ecology Division, Duluth, MN. 

Vivian, D.N., S. Raimondo, and M.G. Barron. 2009. Web-ICE Aquatic Database 

Documentation. US Environmental Protection Agency, Office of Research and 

Development, National Health and Environmental Research Laboratory, Gulf Ecology 

Division, Gulf Breeze, FL. 36 p.