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# Appendix B.

The Use of Interspecies Correlation Estimation (ICE) in Interpreting

Chemical Risks to Aquatic Species

Prepared For:

Ecological Fate and Effects Division

Office of Pesticide Programs

&

Health and Ecologica l Criteria Division

Office of Water

Prepared By:

Dale Hoff

Mid-Continent Ecology Division

Office of Research and Development

12/20/2011

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# 1.0 Background on Interspecies Correlation Estimation (ICE)

Interspecies correlation estimation (ICE) is a met hod used to estimate the t oxicity of a chemical

to a species that has no measured result from the known toxicity of another species. It is done by

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

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

type, use, or adverse outcome pathway (AOP). Once a correlation (mostly in Log/Log scale) is

developed for a species pair, the regression equati on can be used to estimate the toxicity of

additional chemicals for species on the y-axis of th e regression from the known toxicity result of

the species on the x-axis of th e regression. For any ICE model, the species for which toxicity

data are available is referred to as the surrogate species (x-axis) and the species for which no data

are available is the predicted species (y-axis). In the example below (Figure 1), rainbow trout

( Onchorynchus mykiss) is the surrogate species and bluegill ( Lepomis macrochirus ) is the

predicted taxon.

Figure 1. Interspecies Correlation Estimation model using LC50 toxicity data from rainbow

trout and bluegill. This model can be used to estimate an LC 50 value representative of one

species using empirical data from the other. Source: Web-I.C.E.

Although the concept of ICE has been around for d ecades, it was first recommended for use in a

regulatory context in 1978 (Kenag a, 1978). After the enactment of Toxic Substances Control

Act (TSCA) was passed in October of 1976, the US EPA was given the aut hority to require premanufacture testing of chemicals substances for i ndications of human and e nvironmental effects.

Kenaga argued that there were 2,131 species of fish and over 90,000 species of insects and an

unknown number of other invertebrates in the U. S. With so many species and the rapidly

increasing number of chemicals being produced, he suggested that tools like ICE should be

developed to screen chemicals for their potentia l toxicity. Kenaga st ated that many of his

correlations were highly variable, but that ICE could at least put a “fence around the ball park”.

Also in response to the passing of TSCA, Ma ki (1979) published a paper using ICE with

Daphnia magna and fathead minnow ( Pimephales promelas ). Maki cited TSCA data

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# requirements as reasons to use the shorte r in duration D. magna chronic test as a surrogate for the

longer and more expensive fathead minnow chr onic test. His analysis correlated 7 day D. magna

chronic NOECs with 21 day chronic fathead min now NOECs for 35 substances which yielded a

significant relationship (P ≤ 0.01) and spearman rank correlation of 0.71. In an effort to

“corroborate and expand” the findi ngs of Kenaga and Maki, Dohe rty (1983) correlated acute

toxicity data (LC50/EC50s) from rainbow trout, D. magna , bluegill and fathead minnow for a

broad array of chemicals including pe sticides. Again, many significant (p ≤ 0.05) and highly

correlated (R 2 

# ranging from 0.78 – 0.94) species combina tions were noted. The author argued

that the requirements of TSCA, FIFRA, and CWA for both a warm and cold water fish is

redundant as bluegill and rainbow trout are highl y correlated and “equisensitive”. However, the

author did point out that there were obvious outlying points in the regression analysis where the

species sensitivity of one of the species would not be adequately predicted by the other. This

was especially true when the invertebrate D. magna was used to as the surrogate for the fish. He

identified these points as “deviant materials” a nd suggested that as long as these materials could

be identified, they should not invalidate the use of ICE.

In an effort to determine when a “deviant ma terial” may be identified and to continue to

challenge the need for both warm and coldwate r fish acute toxicity testing, LeBlanc (1984)

conducted various interspecies comp arisons of acute toxicities of chemicals separated into three

categories: a.) Non-pesticide organic compounds from the USEPA’ s list of priority pollutants;

b.) Pesticides which exhibit specific toxicologi cal properties and which are lethal to certain

groups of organisms at low doses; c.) Metals that occur naturally in the aquatic environment but

which also have anthropogenic source s. There was strong correlation (R 2 

# =0.93, p ≤ 0.01)

between the warm water fish (b luegill) and coldwater fish (rainbow trout) for the non-pesticide

organic chemicals. Similar results for the non-pesticide organic chemicals were found

regressing freshwater and saltwater fish (blu egill vs. sheepshead minnow) and invertebrates

( D.magna vs. mysid shrimp). The same general conclusions were made for the metals group of

chemicals. However, several points worth noting came from the pesticide category. In an effort

to determine if regressing two closely related ta xa would improve the rela tionship, bluegills were

regressed with both largemouth bass ( Micropterus salmoides ) and channel catfish ( Ictalurus

punctatus ). Largemouth bass and bluegill are both in the family Centrarchidae, while channel

catfish are in the family Ictaluridae. The toxi city of the two centrarch ids was highly correlated

(R 2 

# =0.94, p ≤ 0.01), whereas the bluegill and catfish were much less so (R 2 

# =0.46, p ≤ 0.05).

Furthermore, there was no significant relationship between the bluegill and D.magna with a

reported R 2 

# of 0.04. The author conc luded that taxonomic relatedness highly influenced

comparative toxicity for all chemical categorie s. He also conclude d that pesticides are

engineered to be toxic to specifi c taxa at low doses, and therefore the effects of pesticides in one

phylum should not be extrapolated to another phylum.

One of the first USEPA reports (Mayer et al. 198 7) on ICE was submitted to the Ecological Risk

Assessment Program, Office of Pesticides and Toxi c Substances from authors in the Office of

Research and Development’s Envi ronmental Research Laboratory in Gulf Breeze, FL in 1987.

In it, the authors had the most extensive number of species and chemical combinations at that

time correlated and tested hundreds of regression models. Ultimately, interspecies correlation

models were derived for 39 aquatic organisms with acute static toxicity values. These models

included 1 alga, 16 invertebrates, 20 fishes and 2 amphibians in cluding both freshwater and

saltwater species. The report’s general conclusions supported t hose of the previous authors

(Kenaga 1978, LeBlanc 1984) that correlations of organisms within the sa me family are much

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# improved over those much further related. Als o, specific modes of action (MOA) for certain

pesticides make it difficult to extrapolate among different phylum.

Using the data set from Mayer et al., 1987 a nd supplementing it with several other datasets,

Asfaw et al. (2003) released software (ICE v 1.0) that would allow user s to easily choose

predicted and surrogate taxa. In total, 247 species, 661 chemicals, and 4778 tests were available

for regression analysis with acute results for both aquatic and wildlife species. This software and

subsequent versions allowed othe r authors to begin more rapidly exploring applications for ICE

models.

Dyer et al. (2006) evaluated ICE v. 1.0 for it’s use in developing species sensitivity distributions

(SSDs) that could in turn be us ed to calculate HC5s (hazard con centration of the 5% percentile of

the distribution). SSDs were produced by IC E using rainbow trout, fathead minnow and

D.magna as surrogate species and compared to SSDs from observed data for the chemicals

dodecyl linear alkybenzene sulfona te (LAS), nonylphenol, fenvalerat e, atrazine, and copper from

published sources. The effect on SSDs using all sp ecies available within ICE or only those with

strong model statistics and a minimum of 10 data points in the regression was examined. Using

the truncated data set did not influence the D.magna SSDs, but did influe nce the rainbow trout

and fathead minnow SSD shape. The increased vari ability in the larger data set for these two

species flattened out the distribut ion and resulted in lower HC5s. In general the species rank in

the predicted distributions was sim ilar regardless of which species was used as a surrogate. ICEpredicted SSDs, for the most part, were statisti cally different than the distributions generated

from measured data. The authors argue however , that “ despite modes of actions including a

neurotoxicant (fenvalerate), tw o narcotics (LAS and nonylphenol), a herbicide (atrazine), and a

metal (copper), ICE-based predicted HC5 values were generally within a f actor of 10 of the HC5

value based on measured data....” It was also noted that none of th e predicted SSDs from D.

magna were statistically similar, but yet predicted HC5s ranged only 1.3 to 6.4 fold from those

HC5s calculated from measured data from the same species.

Building on these results, Dyer (2008) used a da ta set of acute values from Ambient Water

Quality Criteria and reference documents from th e 1980s from 55 chemicals to test hypotheses of

taxa relatedness and the use of multiple surrogates for predicting toxicity. They determined that

correlation statistics for ICE mode ls were better when fish only predicted other fish, than when

used to predict both fish and i nvertebrates. Improved correlati on statistics were not found when

D. magna was used as the surrogate for inverteb rates only compared to both fish and

invertebrates. However, the best correlation statistics were observed when both a fish and D.

magna were combined as surrogates for all species.

Mayer et al. (2008) evaluated th e use of ICE in completing assessments on endangered species.

Acute testing was completed or compiled for 16 fish species which included both endangered

and commonly tested species for the ch emicals carbaryl, copper, 4-nonylphenol,

pentachlorophenol and permethrin. SSDs were developed and HC5s calculated for each

chemical utilizing the following information: 1.) experimentally determined data; 2.) data

derived by ICE software using the surrogate species (fathead minno w, rainbow trout, or

sheepshead minnow) having the best correlation statistics; 3.) data derived by ICE using only the

surrogate fathead minnow value; 4.) data de rived by ICE using the ECOSAR (Ecological

Structure Activity Relationship) fathead minnow value (USEPA 2001); 5.) data derived by ICE

using ASTER (Assessment Tools for the Evaluati on of Risk) fathead m innow value (Russom

1991, 1997). The four SSDs were using predicte d data were compared to the SSD with

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# experimentally determined data. Overall, pred ictability of LC50s among fish species was good,

with 96% of 70 estimated acute toxic ity values within a factor of 2.0. ( ≥ 0.5 ≤ 2.0). The

remaining 3 observations were only slightly greate r than 2 fold. The authors go onto cite several

inter- and intralaboratory studies where it was common to find up to 2 fold variability in single

species/chemical combinations. The most accurate predicted SSD, as compared to the SSD from

experimentally determined data, to determine the 5 th 

# percentile was obtaine d using the surrogate

species having the best correlation model in ICE. The HC5 estimate was slightly conservative

and averaged 0.94x that of the experimentally de termined data set. The other predicted SSDs

and HC5s were less conservative but were within an order of magnitude of the results found in

the experimentally determined data. The author s noted that phylogeny was again important, as

the best estimates of toxicity came from more clos ely related species. It is important to note that

these analyses only included fish, and did not include regressions that crossed phylum.

Raimondo et al (2010) tested the influence of sp ecies relatedness and ch emical mode of action

(MOA) in ICE modeling with aquatic species using ICE models provide on an updated webbased application (Web-ICE Ver 3.1, Released 1/1/10:

http://www.epa.gov/ceampubl/fchain/webice/). This application currently contains the largest

publically available collection of ICE models availabl e and is described in more detail in the next

section of this report. Predictions from the IC E models were validated by “leave-one-out crossvalidation.” In this approach, each pair of acute values for surrogate and predicted species was

systematically removed from the original model. A new model was then built with the

remaining data and used to estimate the toxicity value of the removed predicted species from the

respective surrogate species toxicity value. The N-fold difference between the observed and

predicted toxicity results were then used to judge the performance of the model. Taxa

relatedness again proved to be an important de terminant in the precision of the ICE models.

Models using data from two species in the same family predicted within 5 and 10-fold of the

actual value for 91 and 96% of the data points, resp ectively. This is in contrast to species pairs

that are only in the same phylum where 61 and 75 % of the predicted values were 5 and 10-fold

of the actual value. ICE mode ls were developed for several broad and specific MOAs and

subjected to the same cross validation approach as the taxa relatedness analyses. Mode of action

did not significantly influence the precision of the ICE models until the taxonomic distance of

species pairs was at the same phylum level. The authors concluded that taxa relatedness was

more important for accurately predicting toxicity than MOA.

As Kenaga (1978) initially argued, ICE models can be used to at least “put the fence around the

ball park” when in comes to predicting acute toxi city from surrogate species. As repeatedly

shown, there is often about a 90% probability of predicting acute values within 10-fold of

experimentally determined acute values when the following are incorporated into the analysis:

1.) Statistical considerations: Model II regressions need to be used to account for random

variation in both the X and Y axis. The re gression needs to have a significant slope,

preferably positive. The regression should have a high R 2 

# value with tight 95 th 

# percentile

confidence limits.

2.) The toxicity values being used in the regression are of a high data quality using

chemicals of high purity.

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# 3.) Taxonomic relatedness has been repeated ly shown to influence the precision of ICE

models. Whenever possible, species pairs wi thin the same family should be used. ICE

models with species pairs in different phyla should be avoided, especially if species

sensitivities between the phyl a are extremely different.

4.) Using multiple surrogate species toxicity values will provide more available models

from which to predict toxicity and improve correlation stat istics, and subsequently the

precision of the estimated toxicity value, by selection of the best possible value.

The purpose of this appendix is to evaluate the potential use of interspecies correlation

estimation (ICE) as a predictive toxicology tool for evaluating the potential toxicity of chemicals

to individual species and species distributions when little experimentally determined data is

available. Specifically, this work focuses on the Web-ICE tool. The following section presents

an illustration of the Web-ICE model using em pirical data for four chemicals, including:

diazinon (an organophosphate insecticide), chlo rpyrifos (an organophos phate insecticide),

acrolein (an aquatic herbicide) a nd cadmium (a metal). The final section of this appendix uses

the Web-ICE analysis to summarize the st rengths and weaknesses of this tool.

2.0 Description of Web-ICE

The Web-based Interspecies Correlation Estimat ion (Web-ICE) application provides ICE models

for acute toxicity estimation for aquatic and w ildlife species with known certainty. Web-ICE was

posted to the EPA’s Center for Exposure A ssessment Modeling website in June 2007, with

additional modules and improved functionality updates th rough 2010 and ongoing. Almost 3000

ICE models are available for wildlife (birds and mammals), and aquatic (fish and invertebrates)

species, genera, and families to predict toxicity in three separate modules. ICE calculator

modules predict single toxicity va lues for a species, genus (aquatic only) or family. The Species

Sensitivity Distribution (SSD) module within Web-ICE creates log-logistic cumulative

probability distributions of ICE- estimated toxi city and experimentally determined values.

Within this module, hazard levels ( e.g. HC5s) are derived from the SSD that can be used in risk

assessment to prescribe environmental concentrat ions of contaminants protective of a larger

number of species. The endangered species module uses ICE models to extrapolate toxicity to

untested endangered taxa at the species, genus, or family level using the suite of available

models for all available surrogates. At the ti me of this report, Web-ICE updates are in the

process of being develope d. These updates include:

1.) Modification to the SSD modul e that will provide an output that includes all predicted values

from all entered surrogate species values. Where multiple surrogates predict to the same species,

Web-ICE will continue to apply the current defau lt settings to determine which value is included

in the SSD (smallest confidence interval), however the user will have the option to select an

alternate predicted value based on professional ju dgement. The SSD output will be displayed in a

table format containing all model parameters, which is expected to be exportable to excelcompatible file.

2.) Preliminary MOA specific models will be ava ilable for all modules. In addition to having the

option of selecting surrogate and predicted taxa , additional menus will be provided to select

MOA. MOA specific models improve the accuracy of predictions for two species of greater

taxonomic distance ( e.g . between phyla; Raimondo et al. 2010 a 

# ) and may help to obtain all seven

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# MDRs. While MOA-specific models may improve toxicity predictions, fe wer species pairs will

be available using the smalle r datasets of specific MOAs.

3) Algal ICE models will be made available. Algal models include only plant-to-plant

predictions and will be available in a separate algal module, as well as within the aquatic SSD

module. The latter will allow users to include aquatic plants, invert ebrates, and vert ebrates within

on SSD.

3.0 Potential applications of Web-ICE in developing aquatic toxicity benchmarks

Web-ICE could potentially be used in the development of aquatic toxicity benchmarks by two

different means. First, the tool could be used to predict acute toxicity values for single species

for which empirical data are unavailable. This application could be used to fulfill minimum data

requirements (MDRs) of the 1985 Guidelines or be us ed to estimate the toxicity of a chemical to

a specific species of interest ( e.g ., endangered species). Second, the SSD module could be used

to calculate HC5s from experime ntally determined acute values.

These two potential applications of Web-ICE we re explored in the following analysis for 3

pesticides and one heavy metal: diazinon, chlor pyrifos, acrolein and cadmium. The 3 pesticides

were chosen because they have AWQC derived with methodology as per the 1985 Guidelines,

and the criteria documents provide high quality experimentally determined toxicity data to

compare with predicted values. Cadmium was chosen as a non-pesticide to contrast any patterns

seen in the other 3 chemicals, and again, becau se it has AWQC and its associated documentation

is useful for comparison purposes. Applications of Web-ICE for predic ting specific species

toxicity data and HC5 values were tested using empirical da ta for the 3 surrogate species

typically tested in acute toxicity studies us ed to fulfill FIFRA requirements for pesticide

registration, i.e., bluegill, rainbow trout and D.magna

.

The Web-ICE user’s manual (Raimondo et al. 2010 b 

# ) provides recommendations for evaluating

the quality of model regressions. These recommenda tions were used to ev aluate the application

of Web-ICE for estimating toxic ity data for specific species and for deriving HC5 values.

Termed as “Rules of Thumb”, they are r ecommendations for selecting models with low

uncertainty. For best estimates, the authors recommend to select models that possess the

following attributes:

1. Relatively low mean s quare error (MSE) (<0.22)

2. Close taxonomic distance ( ≤ 3)

3. High cross-validatio n success rate (> 85%)

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

5. High R 2 

# value (> 0.6)

6. Low p-values (<0.01)

7. Narrow confidence bands on the graph

3.1 Using Web-ICE to predict single species toxicity values

For freshwater criteria, the MD Rs needed to calculate an Final Acute Value (FAV) comprise:

1. a salmonid fish

2. a non-salmonid fish

3. a species from a third chordate family

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# 4. a planktonic crustacean

5. a benthic crustacean

6. an insect

7. a species from a family in a phylum other than Chordata or Arthropoda

8. a species from a family in another or der of insect or in a fourth phylum

Since the calculation of FAVs are completed us ing genus mean acute values (GMAVs), the

genus level option in the Aquatic Web-ICE module was chosen to search for available species

pairs to the 3 surrogate species of D. magna , bluegill and rainbow trout. Those available options

are illustrated in Tables 1, 2 a nd 3 respectively. An arbitrary su rrogate toxicity value of 50 ug/L

was used for all three species. This section of the appendix focuses on availability of predicted

estimates of specific species within Web-ICE, accuracy and precision of Web-ICE models are

discussed in more detail in the next section regarding SSD s and HC5 estimates.

FAV calculation using this method is currently limited by the paucity of data available to meet

the 7 th 

# MDR of the 1985 Guidelines, i.e .- a species from a family in a phylum other than

Chordata or Arthropoda. No species pairs for any of the 3 surrogate species are currently

available in Web-ICE to meet this MDR. In a pr eliminary analysis (Hoff et al. 2010), a saltwater

species (Eastern oyster, Crassostrea virginica ) was used to fulfill this requirement in lieu of

further evaluation of the application. After a more thorough evaluation, given the requirements

of the other 7 MDRs, there are no available mo dels in the current version of Web-ICE to

populate this MDR. This MDR is often a limitation in developing criteria. These particular taxa

are simply not well represented in the toxicological literature. Curs ory reviews of several criteria

documents revealed that this MDR is most of ten met with a freshwater mollusk, although both

annelid and rotifer species were also used. Historical mollusk data varies widely among test

durations ( e.g . 48, 96 hr) and life stage (juvenile, gl ochidia). To ensure that Web-ICE

relationships minimized extrane ous sources of variation, mollusk data were standardized to

include only juvenile 96-hr EC/LC50 values, whic h are most relatable to the acute values of

other taxa. Through this standardization process, many of the existing mollusk data that were

used to meet past MDRs were excluded from the ICE database from which current models were

developed. Very recently, an ASTM protocol for testing freshwater mollusks has been completed

and Web-ICE authors have the results from severa l recent tests that will be processed and added

to the module (Raimondo, personal communicati on). Additionally, the Web-ICE authors are

engaged in a Regional Methods Development project with USGS where toxicity testing of fairy

shrimp will make models for this species availa ble in future updates. With these updates,

obtaining the 7 th 

# MDR may be possible.

While Web-ICE can predict the toxicity of many species it is important to remember that those

predictions come from experimentally determined toxicity values of speci es pair regressions.

Therefore, the application is still dependent on existing toxicological da ta. Where there is a

paucity of information in the toxicological lite rature, there will continue to be holes in ICE

modeling. Conversely, the most useful and preci se models in Web-ICE are often for species

pairs of which there typically are measured values . For example, some of the best correlations

available are for combinations of rain bow trout, bluegill, fathead minnow and D. magna . For

benchmark development, measured data will like ly exist for these species and predictions will

not be needed. In spite of th ese potential limits, there are 780 m odels comparing 77 species to 77

species in Web-ICE and if employed with rational scientific judgement (i ncluding the “rules of

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# thumb” provided in the user’s manual), robus t predictions for single species with known

uncertainty bounds are feasible.

One especially strong aspect of this application are highlighted when predicting the toxicity of

chemicals in endangered species using closely rela ted (within the same genus or family), more

common, test species. Mayer et al. (2008) demons trated that in these applications, ICE models

can make predictions less than 2-fold different than experimentally derived values, emulating

random variation seen in la boratory toxicity testing.

3.2 Using Web-ICE to populate an SSD and calculate an HC5

Bluegill, rainbow trout and D. magna SMAVs were obtained from diazinon, chlorpyrifos,

acrolein and cadmium AWQC and FIFRA registra tion documents and entered into the Web-ICE

SSD module. The output for diazinon, chlorpyrif os, acrolein and cadmium SSDs are illustrated

in Tables 4 – 7 respectively.

Included in the output of the SSD module are regression statistics that can be used to evaluate

each model in the HC5 estimate. These regressi on statistics include degrees of freedom (df

(N-2), R 2 

# , p-value, mean square error (MSE), taxo nomic distance (same genus =1; family = 2;

order =3; class = 4; phylum = 5; kingdom = 6), and cross validati on (percentage of removed data

points that were predicted within 5 fold of the actual value). Ta bles 4-7 include these statistics

for each of the models in the SSD except for p-value because only significant models are

included in the application. When possible, ex perimentally determined toxicity data from

criteria documents were compared to model esti mates and the X-fold difference calculated. At

the end of each chemical table, average values for correlation summary statistics were calculated

for several categories. These include Tot. N (total number of comparisons between

experimentally determined and pr edicted), total number of compar isons less than 10 fold, greater

than 5 fold and less than 10 fold, and less than or equal to two fold different in an effort to

observe differences.

Several trends appear throughout th e four chemicals. Not surprisingly, for all four chemicals the

taxonomic distance is the smallest for categories < 5 and <=2-fold di fference (2.6 – 4.0). This is

consistent with earlier authors of ICE publications noting that model predictions across phlyla

(taxonomic distance of 5) should be avoided. With the exception of cad mium, cross-validation

percentage was always highest for categories < 5 and <=2-fol d difference (71 – 91 %). Again,

with the exception of cadmium, the lowest MSE values belonged to cate gories < 5 and <=2-fold

difference (0.2 – 0.4). There were no obvious trends for R 2 

# or degrees of freedom among the

chemicals.

The X-fold difference values also seem to vary among the 4 chemicals. For diazinon, 19 total

comparisons were possible and 14 (74%), 12 (63%) and 5 (26%) were less than 10, 5, and 2-fold

different than measured values. By contrast, there were 13 tota l comparisons for chlorpyrifos

and 6 (46%), 3 (23%), and 2 (15 %) were less than 10, 5, and 2-fold different compared to

measured values. For cadmium, 22 total comparis ons were possible and 13 (59%), 11 (50%) and

5 (23%) were less than 10, 5, and 2-fold different than measured values. For acrolein, 8 total

comparisons were possible and 6 (75%), 4 (50% ) and 2 (25%) were less than 10, 5, and 2-fold

different than measured values.

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# Precision of individual models did not necessar ily influence the precision of the HC5 estimate

(see table below).

Chemical D. magna Rainbow

trout Bluegill HC5 FAV

Input

Value

(ug/L) Input Value

(ug/L) Input Value

(ug/L) (ug/L) (ug/L) X-Fold

Difference

Diazinon a 

# 1.048 425.8 459.6 12.05 0.3397 + 35.5

Chlorpyrifos b 

# 1.7 8.485 10 0.906 0.1669 +5.4

Acrolein c 

# 57 16 27.19 1.75 5.92 -3.4

Cadmium d 

# 13.41 2.108 6028 0.709 2.763 -3.9

a- USEPA 2005

b-USEPA 1986

c- USEPA 2009

d-USEPA 2001

Diazinon had the highest precision in terms of the accuracy of predictions of individual species

(74% of estimates were within 10 fold of experimentally determined values), but had the largest

difference when comparing the HC5 with a ca lculated FAV (12.05 vs. 0.3397 respectively).

This is likely due to the fact that D. magna was the surrogate species for 9 out the 10 most

sensitive species in the estimated toxicity range of 0.059 to 15.47 ug/L. All but 3 of these

models had surrogate toxicity i nput values lower than regressi on model parameters. In this

comparison, all models from the SSD whose surrogate toxicity values fell outside of the model

bounds were excluded and therefore did not influen ce the shape of the SSD curve. In contrast,

there are 40 points in the chlo rpyrifos SSD in the same approximate range of 0.063 – 14.26 ug/L

and only 9 of them were removed from HC5 es timate due to model value boundaries. Thus, the

lower end of the distribution was much more re presented for chlorpyrifos as compared to

diazinon. Furthermore, only 1 of the 40 m odels in that toxicity range used D. magna as a

surrogate species and also met model boundaries. It appears there is a paucity of acute values for

D. magna around and below 2 ug/L.

Note the difference in diazinon input toxicity values between D. magna and the fish species ( 1

vs ~ 400 ug/L) and that same comparison of input toxicities for chlorpyrifos (~2 for D. magna

and ~ 10 ug/L for the fish) and relative difference in precision around the HC5 estimate. These

findings are also consistent with earlier authors (LaBlanc, 1984) th at argued that ICE models are

vulnerable to error when manufactured organic pe sticides, like diazinon (an anticholinesterase

MOA) which are engineered to be toxic at low levels to target species (invertebrates) are

compared with non-target species li ke fish. Chlorpyrifos is mo re toxic to the non-target fish

species and this allows for ICE models to predic t acute values in the lo wer portions of the SSD.

In contrast, Acrolein (an herbicide determined by ASTER to have a non-specific reactive MOA),

has modest and comparable toxicity input values and out of the 59 models that are used to

populate the SSD, only 4 of them have input va lues outside model bounds. ICE models for

acrolein were the most precise and the HC 5 was only 3.4-fold lower than the FAV.

Like diazinon, one of the input toxicity values for cadmium was extremely different than the

other two; bluegill = 6028 ug/ L vs. rainbow trout = 2.108 ug/L. In this case, the extreme

difference between the two values had less of an impact on the SSD and HC5 estimate likely

# 11



# because of the huge range of toxicity values an d high number of records associated with these

species. Rainbow trout are surrogate species for 9 out the 10 models with input toxicity values

within model bounds within the range of 0.006 – 10.36 ug/L (Table 7). And conversely, even

though bluegill has such an elevated value of 6,028 ug/L, it still had viable surrogate species for

18 of 20 models in toxicity estim ates ranging from 1,197 – 11,499 ug/L.

The importance of using multiple surrogate species can also be demonstrated using the SSD

module. Depending on what surrogate species is used, very different HC 5s can be generated.

For example, the HC5 estimate for diazinon using only the D. magna acute value is 0.322 ug/L

(0.111 – 1.05). However, using acute surrogate valu es from rainbow trout and bluegill, the HC5

estimates were 67.6 (16.8 – 148.32) and 28.41 (5.01 – 82.31) respectively. If a user had only one

of these 3 measured results, their HC5 estimate c ould range from being virtually identical to the

diazinon FAV (0.3397 ug/L), or be as much as 200 fold higher if only rainbow trout data were

available.

In the SSD module of Web-ICE, MSE, taxonomi c distance, cross-validation success, df, R 2



# - values and p-values can be found for each model. If the user judges any one of these model

statistics undesirable, that model can be de selected from the calculation of HC5 which is

automatically updated to reflect the exclusion. While these “rules of thumb” are agreeable

recommendations, when applying them to the m odels in the SSD module the user needs to

balance the strengths of the regressions with spec ies representation in the SSD. For example, in

the diazinon SSD completed for th is analysis, 59 total models were produced. When all 7 of the

“rules of thumb” were rigidly applied, only 6 mo dels representing 6 predicted species were left

to populate the SSD. These species included: Yellow perch, Largemouth bass, Brown trout,

Lake trout, Cutthroat trout and Coho salmon. Li kewise for cadmium, there were 51 total models

in the SSD and after rigidly applying the rule s, only 6 species rema ined: Brown trout, Coho

salmon, Lake trout, Yellow perch, Largemouth ba ss and Green sunfish. For both chemicals, the

only invertebrate represen ted in the SSD is the D.magna value used as input toxicity. Users of

the SSD module of the Web-ICE a pplication should ensure a variet y of species before estimating

an HC5. To make the SSD module useful, these “rules of thumb” need to be considered general

guidelines and professional judgement with some documented bounds will need to be developed.

4.0 Conclusions

1. The current version of Web-ICE does not have sufficient data to fulfill all of the MDRs of the

1985 Guidelines when there are only acute surroga te data available for rainbow trout, bluegill

and D. magna. This may change with future updates.

2. The frequency of precise models for indivi dual species in Web-IC E does not necessarily

influence the accuracy of an HC5 estimate of an SSD.

3. When using Web-ICE to develop SSDs, it is ill-advised to use only one surrogate species.

4. The precision of Web-ICE predic tions is better for chemicals th at do not target specific taxa

in a distribution of species.

5. The precision of Web-ICE predictions are best for closely related species ( e.g . within the

same family.)

# 12



# 6. Using the “Rules of Thumb” provided in th e Web-ICE user’s manual is a good place to start

when evaluating models. However, applying th em all rigidly can dramatically reduce the

number of models available to develop an SSD.

References:

Asfwa, A. M.R. Ellersieck, F.L. Mayer. 2003. ICE v1.0. Interspecies correlation estimations

(ICE) for acute toxicity to aquatic organism s and wildlife: User manual and software.

EPA/600/R-03/106.

Doherty, F.G. 1983. Interspecies correlations of acute aquatic median lethal concentration for

four standard testing species. Environ. Sci. Technol. 17. 661 – 665.

Dyer, S.D., D. J. Versteeg, S.E. Belanger, J.G. Channey and F. L. Mayer. 2006. Interspecies

correlation estimates predict protective environmen tal concentrations. Envi ron. Sci. technol. 40.

3102 – 3111.

Dyer, S.D., D.J.Versteeg, S.E. Belanger, J.G. Chaney, S. Raimondo and M.G. Barron. 2008.

Comparison of species sensitivity distributions derived from inte rspecies correlation models to

distributions used to derive water quality cr iteria. Environ. Sci. Technol. 43. 3076 – 3083.

Hoff, D., W. Lehmann, A. Pease, S. Raimondo, C. Russom, T. Steeger . 2010. Predicting the

toxicities of chemicals to aquatic an imal species. SAB review documents.

Kenaga, E.E. 1978. Test organism and methods us eful for early assessment of acute toxicity of

chemicals. Environ. Sci. Technol. 12. 12. 1322 -1329.

LeBlanc, G. 1984. Interspecies relationships in acute toxicity of chemicals to aquatic

organisms. Environ. Toxic. Chem. 3. 47 – 60.

Maki, A.W. 1979. Correlations between Daphnia magna and fathead minnow ( Pimephales

promelas ) chronic toxicity values for se veral classes of test substan ces. J. Fish. Res. Board Can.

36. 411 – 421.

Mayer, F.L., C.H. Deans and A.G. Smith. 1987. In ter-taxa correlations for toxicity to aquatic

organisms. EPA/600/X-87/332.

Mayer, F.L., D.R.Buckler, F.J. Dwyer, M.R. E llersieck, L.C. Sappington, J.M. Besser, and C.M.

Bridges. 2008. Endangered aquatic vertebra tes: comparative and probabilistic-based

toxicology. EPA/600/R-08/045

Raimondo, S., C.R. Jackson and M.G. Barron. 2010 a 

# . Influence of taxonomic relatedness and

chemical mode of action in acute interspecies estimation models for aquatic species. Environ.

Sci. Technol. 44. 7711 – 7716.

Raimondo, S., D.N. Vivian, and M.G. Barron. 2010 b 

# . Web-based Interspecies

Correlation Estimation (Web-ICE) for Acut e Toxicity: User Manual. Version 3.1.

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# EPA/600/R-10/004. Office of Research and Development, U. S. Environmental

Protection Agency. Gulf Breeze, FL.

USEPA. 1986. Ambient water quality cr iteria for chlorpyrifos. EPA 440/5-86-005.

USEPA. 2001. Ambient water qualit y criteria for cadmium. EPA-822-R-01-001.

USEPA. 2005 Aquatic life ambient wate r quality criteria for Diazinon. EPA-822-R-05-006.

USEPA. 2009. Ambient aquatic life water quality criteria for acrolein.

http://water.epa.gov/scitech/swgui dance/standards/criteria/aqlife /pollutants/acrolein/upload/aqfinal.pdf14Table1.SpeciescorrelationstatisticsandresultsforavailablegenerausingDaphniamagnaasthesurrogatespecies.Allpredictedvaluesand95%confidenceintervalswerecalculatedusingasurrogatetoxicityvalueof50ug/L.ExtrapolatedGenusCommonNameMDRR2DfCross‐Val.Success%PredictedAcuteValue(ug/L)95%ConfidenceIntervalsOncorhynchusRainbowTrout1. the familySalmonidae in the classOsteichthyes0.5026352.8274.1196.8–381.8LepomisSunfish/Bluegill2.asecondfamilyintheclassOsteichthyes,preferablyacommerciallyorrecreationallyimportantwarmwaterspecies(e.g.,bluegill,channelcatfish,etc.)0.5423654.2352.1258.0–480.6CyprinodonKillfish3. a third family in thephylum Chordata (maybe in the classOsteichthyes or may bean amphibian, etc.)0.457167.1484.9277.5–847.3CeriodaphniaCrustacean4.aplanktoniccrustacean(e.g.,cladoceran,copepod,etc.)0.79733.379.37.7–811.1GammarusAmphipod5. a benthic crustacean(e.g., ostracod, isopod,amphipod, crayfish,etc.)0.484151.2137.255.0–342.5ChironomousMidge6. an insect (e.g.,mayfly, dragonfly,damselfly, stonefly,caddisfly, mosquito,midge, etc.)0.361736.8232.433.6–1605.7PalaemonetesShrimp8. a family in any orderof insect or any phylumnot already represented.0.99210074.544.4–125.1ParatanytarsusEuropeanMidge60.758403760.0644.1‐21949.6RanaFrogs3, 80.57627.5279.812.1–6453.8SimocephalusCrustacean40.671169.224.26.3–92.5CaecidotaCrustacean40.391443.8679.3237.9–1940.115Table2.SpeciescorrelationstatisticsandresultsforavailablegenerausingBluegill(Lepomismacrochirus)asthesurrogatespecies.Allpredictedvaluesand95%confidenceintervalswerecalculatedusingasurrogatetoxicityvalueof50ug/L.ExtrapolatedGenusCommonNameMDRR2DfCross‐Val.Success%PredictedAcuteValue(ug/L)95%ConfidenceIntervalsOncorhynchusPacificbasinTrout1. the family Salmonidae inthe class Osteichthyes0.883109048.1140.7–56.9AmeiurusCatfish2.asecondfamilyintheclassOsteichthyes,preferablyacommerciallyorrecreationallyimportantwarmwaterspecies(e.g.,bluegill,channelcatfish,etc.)0.671162168.442.4–669.3CyprinodonKillfish3. a third family in thephylum Chordata (may be inthe class Osteichthyes or maybe an amphibian, etc.)0.727286127.579.9–203.7CeriodaphniaDaphnia4.aplanktoniccrustacean(e.g.,cladoceran,copepod,etc.)0.794175.30.10–278.3GammarusAmphipod5. a benthic crustacean (e.g.,ostracod, isopod, amphipod,crayfish, etc.)0.51533819.88.5–46.3ChironomousMidge6. an insect (e.g., mayfly,dragonfly, damselfly,stonefly, caddisfly, mosquito,midge, etc.)0.44223850.814.2–180.9CatostomusSuckers8. a family in any order ofinsect or any phylum notalready represented.0.98210018.95.3–66.7CaecidotaCrustacean40.64154755.217.8–171.9DaphniaCrustacean40.552445123.114.7–36.5EtheostomaDarter2,3or80.9527513.41.8–100.8MenidiaSilverside2,3or80.9167564.814.6–288.2MicropterusBass2,3or80.93309740.730.2‐54.9NotropisShiners2,3or80.8036018.92.2–164.3ParatanytarsusEuropeanMidge60.72542702.514.5‐34093.31*PimephalesBluntnoseMinnow2,3or80.796675122.875.1–200.8SalmoEuropeanTrout2,3or80.951710024.015.5–37.2SalvelinusSalmonChar2,3or80.773382.2538.921.6‐70.3RanaFrogs3, 80.86571.4253.66.9–416.8*‐Thisvalueisoutsidethex‐axisrangeforthismodel16Table3.SpeciescorrelationstatisticsandresultsforavailablegenerausingRainbowtrout(Onchorynchusmykiss)asthesurrogatespecies.Allpredictedvaluesand95%confidenceintervalswerecalculatedusingasurrogatetoxicityvalueof50ug/L.ExtrapolatedGenusCommonNameMDRR2DfCross‐Val.Success%PredictedAcuteValue(ug/L)95%ConfidenceIntervalsSalmoEuropeanTrout1. the family Salmonidae inthe class Osteichthyes0.96239650.637.7–67.9AmeiurusCatfish2.asecondfamilyintheclassOsteichthyes,preferablyacommerciallyorrecreationallyimportantwarmwaterspecies(e.g.,bluegill,channelcatfish,etc.)0.941153.8222.756.4–880.2RanaFrogs3. a third family in thephylum Chordata (may be inthe class Osteichthyes or maybe an amphibian, etc.)0.986100.010853.1–219.4CaecidotaCrustacean4. a planktonic crustacean(e.g., cladoceran, copepod,etc.)0.631747.472.424.7–212.1GammarusAmphipod5. a benthic crustacean (e.g.,ostracod, isopod, amphipod,crayfish, etc.)0.445330.920.127.8–51.8ChironomousMidge6. an insect (e.g., mayfly,dragonfly, damselfly,stonefly, caddisfly, mosquito,midge, etc.)0.531938.135.79.0–142.2CyprinodonKillfish8. a family in any order ofinsect or any phylum notalready represented.0.677380.0153.9594.1–251.8DaphniaCrustacian40.5126952.442.627.7–65.5EtheostomaDarter2,3or80.973100.053.825.5–113.5AcipenserSturgeons2,3or80.943100.031.5412.7–78.1MicropterusBass2,3or80.863190.939.025.72–59.24NotropisShiners2,3or80.994100.056.041.7–75.2ParatanytarsusEuropeanMidge60.82870.05110.81180.2–22133.1PimephalesBluntnoseMinnow2,3or80.837981.5147.097.1–222.5LepomisSunfish/Bluegill2,3or80.8830890.3275.964.7–89.2SalvelinusSalmonChar2,3or80.893792.360.542.9–85.417Table4.Web‐ICEoutputfromthespeciessensitivitydistributionmodelforDiazinon.SurrogatespecieswereD.magna,RainbowtroutandBluegillwithInputtoxicityvaluesof1.048,425.8and459.6respectively(DiazinonCriteriadocument).Web‐ICEHC5wascalculatedtobe12.05ug/L(4.05–39.23).FAVfromcriteriadocumentwascalculatedtobe0.3397ug/L.EstimatedMeasuredX‐FoldCros‐ValSurrogateToxicity*95%ToxicityDiffSuccessDeg.ofTaxon.CommonNameScientificNameSpeciesug/LC.I.ug/L%R2MSEFreedomDist.AmphipodHyalellaaztecaD.magna0.059*0.000‐91.166.51‐110.3414.280.631.8355DaphnidSimocephalusserrulatusD.magna1.70.621‐4.651.591.0766.660.630.45102DaphnidCeriodaphniadubiaD.magna1.9*0.040‐90.050.385.0037.500.81.4162DaphnidDaphniapulexD.magna2.290.234‐22.460.782.9442.850.30.6351StarfishAsteriasforbesiD.magna3.58*2.30‐5.58na0.99016MidgeParatanytarsusparthenogeneticusD.magna3.67*0.061‐219.09100.000.980.0925StoneflyClaasseniasabulosaBluegill4.540.969‐21.2571.420.580.256MidgeChironomustentansD.magna4.8*0.058‐392.4910.7‐2.2375.000.980.0825NorthernrivercrangonyctidCrangonyxpseudogracilisD.magna5.05*0.258‐98.7045.450.710.8295AmphipodAllorchestescompressaD.magna15.47*1.56‐153.03100.000.920.0535AmphipodGammaruslacustrisBluegill18.724.10‐85.4830.760.351.13116StoneflyPteronarcellabadiaRainbowtrout20.216.15‐66.4260.000.480.84136StoneflyPteronarcyscalifornicaRainbowtrout23.055.62‐94.4625‐1.0846.150.390.97116AtlanticsilversideMenidiamenidiaRainbowtrout33.34*6.44‐172.65na0.990.0114MysidAmericamysisbahiaBluegill64.644.38‐94.034.8213.4060.900.680.911316PinfishLagodonrhomboidesRainbowtrout71.632.26‐2263.4040.000.81.1134AmphipodGammaruspseudolimnaeusBluegill74.7926.62‐210.0916.824.4564.700.830.75156IsopodAsellusaquaticusD.magna151.42*7.11‐3224.6850.000.820.345MidgeChironomusplumosusRainbowtrout185.3261.22‐560.9535.000.531.02186AmphipodGammarusfasciatusBluegill199.1676.13‐521.002.0497.6347.220.371.61346AtlanticsalmonSalmosalarBluegill219.76135.25‐357.06100.000.960.0994BrooktroutSalvelinusfontinalisBluegill227.14134.58‐383.35723‐3.1888.880.890.216418WalleyeSandervitreusRainbowtrout239.5998.24‐584.3260.000.680.2784WhitesuckerCatostomuscommersoniiBluegill255.0292.79‐700.86100.000.970.0424NorthernpikeEsoxluciusRainbowtrout279.3150.67‐1539.6685.710.80.2954IsopodCaecidoteabrevicaudaRainbowtrout282.89112.40‐711.9764.700.680.59156PinkshrimpFarfantepenaeusduorarumBluegill293.15110.39‐778.432113.9672.720.940.3896EasternoysterCrassostreavirginicaBluegill299.92203.45‐442.1471.570.550.66936ShortnosesturgeonAcipenserbrevirostrumRainbowtrout307.36121.70‐776.28100.000.980.0224ApachetroutOncorhynchusgilaeRainbowtrout349.64241.45‐506.30100.000.990.0131YellowperchPercaflavescensBluegill359.1235.72‐547.0694.440.930.12163LargemouthbassMicropterussalmoidesBluegill391.34291.93‐524.6096.870.930.12302BrowntroutSalmotruttaRainbowtrout392.9280.42‐550.5294.730.960.08172LaketroutSalvelinusnamaycushRainbowtrout395.98301.47‐520.11602‐1.5296.150.940.09242CutthroattroutOncorhynchusclarkiiRainbowtrout457.9367.38‐570.712166‐4.7397.050.940.09321FountaindarterEtheostomafonticolaRainbowtrout462.67154.77‐1383.06na0.99014GilatopminnowPoeciliopsisoccidentalisRainbowtrout477.45*95.51‐2386.81100.000.960.0224BlackcrappiePomoxisnigromaculatusRainbowtrout533.31165.12‐1722.54na0.99014SpotfinchubErimonaxmonachusRainbowtrout541.96140.99‐2083.23100.000.950.0934GreenthroatdarterEtheostomalepidumRainbowtrout556.98195.86‐1583.91100.000.960.0634ChinooksalmonOncorhynchustshawytschaRainbowtrout587.88354.48‐974.97100.000.980.0661LeonspringspupfishCyprinodonbovinusRainbowtrout618.0480.01‐4774.1575.000.910.1124CohosalmonOncorhynchuskisutchRainbowtrout624.47516.05‐755.66100.000.990.02151CapefearshinerNotropismekistocholasRainbowtrout723.02406.83‐1284.93100.000.990.0234GreensunfishLepomiscyanellusRainbowtrout749.09443.94‐1263.9692.300.910.13114SheepsheadminnowCyprinodonvariegatusBluegill785.65555.19‐1111.761400‐1.7886.110.710.39704ChannelcatfishIctaluruspunctatusRainbowtrout848.94615.36‐1171.1877.640.750.43834RazorbacksuckerXyrauchentexanusRainbowtrout916.52474.33‐1770.94100.000.970.0444CommoncarpCyprinuscarpioRainbowtrout1004.01548.03‐1839.3786.360.830.35204FatheadminnowPimephalespromelasRainbowtrout1011.64736.71‐1389.187804‐7.7182.710.830.3779419SouthernleopardfrogRanasphenocephalaRainbowtrout1075.27270.23‐4278.525021.5160.000.930.135InlandsilversideMenidiaberyllinaBluegill1212.2235.67‐6234.9911701.04100.000.970.124SnipeflyAtherixvariegataRainbowtrout1282.53*60.71‐27093.09100.000.940.0626ThreespinesticklebackGasterosteusaculeatusBluegill1343.44*216.92‐8320.04100.000.960.0924GoldfishCarassiusauratusRainbowtrout1454.88772.55‐2739.849000‐6.1987.500.820.42224BlackbullheadAmeiurusmelasBluegill1471.09233.83‐9254.8763.630.660.9994GuppyPoeciliareticulataRainbowtrout2438.711282.22‐4638.318003.0580.000.920.24134MosquitofishGambusiaaffinisD.magna12365.1263.63‐579946.51na0.990.0216MidgeParatanytarsusdissimilisRainbowtrout13503.385731.14‐31815.9075.000.870.1466N19AVGAVGAVGAVGAVG*Surrogatevalueoutsideofmodelboundary,notusedinHC5estimateN<10fold1475.090.780.4421.933.50N<5fold1276.690.770.3618.183.55N>5fold757.850.750.8836.134.75N<2579.010.730.4023.403.6020Table5.Web‐ICEoutputfromthespeciessensitivitydistributionmoduleforChlorpyrifos.SurrogatespecieswereD.magna,RainbowtroutandBluegillwithInputtoxicityvaluesof1.7,8.485and10.0respectively(ChlorpyrifosCriteriadocumentforRBTandBG;DM=June2000Chlorpyrifosreregistrationpackage).Web‐ICEHC5wascalculatedtobe0.906ug/L(0.212–1.89).FAVfromcriteriadocumentwascalculatedtobe0.1669ug/L.EstimatedMeasuredX‐FoldCros‐ValSurrogateToxicity95%Toxicity*DiffSuccessDeg.ofTaxon.CommonNameScientificNameSpeciesug/LC.I.ug/L%R2MSEFreedomDist.AtlanticsilversideMenidiamenidiaRainbowTrout0.063*0.002‐1.771.229‐19.5na0.99300.006014AmphipodHyalellaaztecaD.magna0.094*0.000‐100.0614.280.62901.830055StoneflyClaasseniasabulosaBluegill1.120.402‐3.130.572.071.420.58480.203856AmphipodGammaruspseudolimnaeusBluegill1.210.293‐4.990.186.764.700.82920.7507156PinfishLagodonrhomboidesRainbowTrout1.530.013‐177.9940.000.79661.100034AmphipodGammaruslacustrisBluegill1.680.148‐19.230.1115.330.760.35191.1300116MysidAmericamysisbahiaD.magna1.871.02‐3.4464.960.68900.82221355DaphnidSimocephalusserrulatusD.magna2.430.902‐6.5566.660.63400.4477102ShortnosesturgeonAcipenserbrevirostrumRainbowTrout2.61*0.559‐12.18100.000.98370.023624StoneflyPteronarcyscalifornicaRainbowTrout2.660.336‐21.0010‐3.846.150.38970.9722116PinkshrimpFarfantepenaeusduorarumBluegill2.980.747‐11.9472.720.93850.382496WhitesuckerCatostomuscommersoniiBluegill2.99*0.288‐31.00100.000.97200.040324StoneflyPteronarcellabadiaBluegill3.050.534‐17.490.388.069.230.33450.8088116DaphnidCeriodaphniadubiaD.magna3.14*0.080‐122.6137.500.80431.410062DaphnidDaphniapulexD.magna3.440.400‐29.6542.850.82670.626751BrooktroutSalvelinusfontinalisBluegill4.011.49‐10.8388.880.88530.2026164ApachetroutOncorhynchusgilaeRainbowTrout4.322.88‐6.49100.000.99540.007031SpotfinchubErimonaxmonachusRainbowTrout4.71.07‐20.62100.000.95020.092634NorthernpikeEsoxluciusRainbowTrout5.191.03‐26.0285.710.79980.287054BlackcrappiePomoxisnigromaculatusRainbowTrout5.722.06‐15.87na0.99950.002714MidgeParatanytarsusparthenogeneticusD.magna5.9*0.117‐297.24100.000.98450.087425FountaindarterEtheostomafonticolaRainbowTrout6.382.10‐19.41na0.99940.002614CapefearshinerNotropismekistocholasRainbowTrout6.833.63‐12.84100.000.99020.016934GreenthroatdarterEtheostomalepidumRainbow7.832.48‐24.67100.000.96240.05583421TroutYellowperchPercaflavescensRainbowTrout7.834.59‐13.3890.470.93200.1450194MidgeChironomustentansD.magna7.86*0.116‐529.6775.000.98160.082825AtlanticsalmonSalmosalarRainbowTrout7.873.84‐16.1384.610.95410.1306112LargemouthbassMicropterussalmoidesBluegill7.95.33‐11.6896.870.92980.1180302NorthernrivercrangonyctidCrangonyxpseudogracilisD.magna8.51*0.541‐133.8345.450.71260.821295StarfishAsteriasforbesiD.magna8.56*5.78‐12.69na0.99990.000116BrowntroutSalmotruttaRainbowTrout8.785.86‐13.1594.730.96420.0797172MidgeChironomusplumosusRainbowTrout9.181.35‐62.2035.000.53111.0200186CohosalmonOncorhynchuskisutchRainbowTrout10.298.00‐13.25100.000.98860.0227151AmphipodGammarusfasciatusBluegill10.642.31‐48.990.3233.347.220.36511.6100346CutthroattroutOncorhynchusclarkiiRainbowTrout10.747.06‐16.3418‐1.797.050.93570.0805321RazorbacksuckerXyrauchentexanusRainbowTrout11.895.23‐26.99100.000.96960.037644LaketroutSalvelinusnamaycushRainbowTrout12.697.88‐20.4298‐7.796.150.93540.0855242IsopodCaecidoteabrevicaudaRainbowTrout13.423.44‐52.3564.700.67530.5853156ThreespinesticklebackGasterosteusaculeatusBluegill14.245.38‐37.69100.000.96150.086834ChinooksalmonOncorhynchustshawytschaRainbowTrout14.266.89‐29.49100.000.97930.064461GilatopminnowPoeciliopsisoccidentalisRainbowTrout16.665.83‐47.63100.000.95590.021524SouthernleopardfrogRanasphenocephalaRainbowTrout16.73.66‐76.0460.000.93320.097435GreensunfishLepomiscyanellusBluegill23.7411.57‐48.7492.850.90720.1478121EasternoysterCrassostreavirginicaRainbowTrout24.1611.99‐48.641991‐82.471.130.52410.7305956AmphipodAllorchestescompressaD.magna25.41*3.31‐195.01100.000.91880.053235LeonspringspupfishCyprinodonbovinusRainbowTrout27.172.55‐288.7475.000.91240.108224FatheadminnowPimephalespromelasRainbowTrout31.2318.34‐53.17331.7‐10.682.710.82600.3723794SheepsheadminnowCyprinodonvariegatusBluegill36.2719.01‐69.1886.110.71120.3955704SnipeflyAtherixvariegataRainbowTrout36.6711.02‐121.98100.000.94020.058626CommoncarpCyprinuscarpioRainbowTrout37.2415.36‐90.2686.360.82850.3470204GuppyPoeciliareticulataRainbowTrout38.4216.07‐91.8380.000.91750.2386134InlandsilversideMenidiaberyllinaBluegill42.7*2.62‐694.97100.000.97140.095324WalleyeSandervitreusBluegill42.829.94‐184.4670.000.53670.365283ChannelcatfishIctaluruspunctatusBluegill46.8527.36‐80.24806‐17.276.710.75610.4487714BlackbullheadAmeiurusmelasRainbow48.519.13‐257.6750.000.63850.943310422TroutGoldfishCarassiusauratusRainbowTrout54.820.85‐144.01806‐14.787.500.81980.4188224IsopodAsellusaquaticusD.magna225.45*13.24‐3837.7050.000.82270.301845MidgeParatanytarsusdissimilisRainbowTrout1833.58*445.54‐7545.8175.000.86970.137566MosquitofishGambusiaaffinisD.magna15284.8364.23‐641409.48na0.99700.021816Tot.N13AVGAVGAVGAVGAVGN<10fold670.000.660.5414.334.17*Surrogatevalueoutsideofmodelboundary,notusedinHC5estimateN<5fold371.540.640.4216.004.33N>5fold1069.570.70720.605448.70004.7000N<=2fold284.240.760.1418.503.5023Table6.Web‐ICEoutputfromthespeciessensitivitydistributionmoduleforAcrolein.SurrogatespecieswereD.magna,RainbowtroutandBluegillwithInputtoxicityvaluesof57.0,16.0and27.9respectively(AcroleinCriteriadocument).Web‐ICEHC5wascalculatedtobe1.75ug/L(0.338–4.22).FAVfromcriteriadocumentwascalculatedtobe5.920ug/L.EstimatedMeasuredX‐FoldCros‐ValSurrogateToxicity*95%ToxicityDiffSuccessDeg.ofTaxon.CommonNameScientificNameSpeciesug/LC.I.ug/L%R2MSEFreedomDist.AtlanticsilversideMenidiamenidiaRainbowtrout0.174*0.008‐3.60na0.990.00614StoneflyClaasseniasabulosaBluegill1.610.581‐4.5071.420.580.203856AmphipodHyalellaaztecaDaphniamagna2.750.031‐242.0714.280.631.8355PinfishLagodonrhomboidesRainbowtrout2.860.036‐227.15400.81.134AmphipodGammaruslacustrisBluegill3.160.421‐23.7930.760.351.13116AmphipodGammaruspseudolimnaeusBluegill3.551.02‐12.3864.70.830.7507156StoneflyPteronarcyscalifornicaRainbowtrout3.770.586‐24.2746.150.390.9722116StoneflyPteronarcellabadiaBluegill4.691.05‐20.9569.230.330.8088116ShortnosesturgeonAcipenserbrevirostrumRainbowtrout5.65*1.56‐20.461000.980.023624ApachetroutOncorhynchusgilaeRainbowtrout8.86.23‐12.451000.990.00731WhitesuckerCatostomuscommersoniiBluegill9.55*1.55‐58.59141.51000.970.040324PinkshrimpFarfantepenaeusduorarumBluegill9.92.89‐33.9372.720.940.382496NorthernpikeEsoxluciusRainbowtrout9.92.46‐39.8085.710.80.28754SpotfinchubErimonaxmonachusRainbowtrout10.152.88‐35.721000.950.092634BrooktroutSalvelinusfontinalisBluegill11.535.04‐26.3688.880.890.2026164BlackcrappiePomoxisnigromaculatusRainbowtrout11.944.71‐30.23na0.990.002714FountaindarterEtheostomafonticolaRainbowtrout12.784.77‐34.23na0.990.002614CapefearshinerNotropismekistocholasRainbowtrout14.548.49‐24.881000.990.016934AtlanticsalmonSalmosalarRainbowtrout14.717.63‐28.3884.610.950.1306112GreenthroatdarterEtheostomalepidumRainbowtrout15.635.88‐41.511000.960.055834BrowntroutSalmotruttaRainbowtrout16.2511.24‐23.5194.730.960.0797172CutthroattroutOncorhynchusclarkiiRainbowtrout19.7313.57‐28.6797.050.940.080532124CohosalmonOncorhynchuskisutchRainbowtrout20.0215.97‐25.10683.41000.990.0227151YellowperchPercaflavescensBluegill20.9113.09‐33.4194.440.930.1167163MidgeChironomusplumosusBluegill21.394.11‐111.25250.491.04186LargemouthbassMicropterussalmoidesBluegill21.915.78‐30.401607.396.870.930.118302IsopodCaecidoteabrevicaudaRainbowtrout21.996.44‐75.0564.70.680.5853156LaketroutSalvelinusnamaycushRainbowtrout22.1614.46‐33.9496.150.940.0855242RazorbacksuckerXyrauchentexanusRainbowtrout24.0312.03‐48.031000.970.037644ChinooksalmonOncorhynchustshawytschaRainbowtrout26.0513.37‐50.731000.980.064461GilatopminnowPoeciliopsisoccidentalisRainbowtrout28.6912.50‐65.881000.960.021524MysidAmericamysisbahiaDaphniamagna32.1521.77‐47.4850015.664.960.690.82221355DaphnidSimocephalusserrulatusDaphniamagna32.65.83‐182.2366.660.630.4477102SouthernleopardfrogRanasphenocephalaRainbowtrout32.799.02‐119.19600.930.097435LeonspringspupfishCyprinodonbovinusRainbowtrout45.075.83‐348.17750.910.108224ThreespinesticklebackGasterosteusaculeatusBluegill46.7316.94‐128.881000.960.086834FatheadminnowPimephalespromelasRainbowtrout54.8633.72‐89.2528.77‐1.982.710.830.3723794GreensunfishLepomiscyanellusBluegill57.3431.19‐105.4192.850.910.1478121CommoncarpCyprinuscarpioRainbowtrout63.4928.35‐142.1986.360.830.347204SnipeflyAtherixvariegataRainbowtrout65.2317.65‐241.031000.940.058626DaphnidDaphniapulexDaphniamagna65.7310.84‐398.3842.850.830.626751WalleyeSandervitreusBluegill74.122.39‐245.27700.540.365283GuppyPoeciliareticulataRainbowtrout75.2634.07‐166.21800.920.2386134SheepsheadminnowCyprinodonvariegatusBluegill81.0246.90‐139.954285.386.110.710.3955704BlackbullheadAmeiurusmelasRainbowtrout89.6619.41‐414.23500.640.9433104InlandsilversideMenidiaberyllinaBluegill102.369.09‐1152.211000.970.095324ChannelcatfishIctaluruspunctatusBluegill103.9665.37‐165.3376.710.760.4487714DaphnidCeriodaphniadubiaDaphniamagna118.339.01‐1553.9437.50.81.4162GoldfishCarassiusauratusBluegill127.7252.95‐308.0486.950.80.4231214MidgeParatanytarsusparthenogeneticusDaphniamagna183.21*11.74‐2857.121000.980.087425EasternoysterCrassostreavirginicaDaphniamagna232.13128.59‐419.03106‐2.254.630.271.17956AmphipodGammarusfasciatusDaphniamagna265.8877.47‐912.5155.550.461.81255MidgeChironomustentansDaphniamagna283.33*16.36‐4904.16750.980.082825NorthernrivercrangonyctidCrangonyxpseudogracilisDaphniamagna373.981.37‐1717.9245.450.710.821295AmphipodAllorchestescompressaDaphniamagna933.03436.64‐1993.741000.920.053235MidgeParatanytarsusdissimilisRainbowtrout2533.76*683.87‐9387.67151‐16.8750.870.13756625IsopodAsellusaquaticusDaphniamagna4057.05863.11‐19070.13500.820.301845StarfishAsteriasforbesiDaphniamagna4765.64000.86‐5676.53na0.990.000116MosquitofishGambusiaaffinisDaphniamagna71238.19 3462.99‐1465460.16na0.99 0.02181 6N8AVGAVGAVGAVGAVG*Surrogatevalueoutsideofmodelboundary,notusedinHC5estimateN<10fold681.470.780.390837.144.13n<5fold484.3350.7650.401347.753.75N>5fold480.7350.80.368360.254.25N<2fold291.40.90.240.54.026Table7.Web‐ICEoutputfromthespeciessensitivitydistributionmoduleforCadmium.SurrogatespecieswereD.magna,RainbowtroutandBluegillwithInputtoxicityvaluesof13.41,2.108and6028respectively(CadmiumCriteriadocument).Web‐ICEHC5wascalculatedtobe0.709ug/L(0.0595–5.96).FAVfromcriteriadocumentwascalculatedtobe5.920ug/L(finalFAVwasloweredto2.108toprotectRainbowtrout).EstimatedMeasuredX‐FoldCros‐ValSurrogateToxicity*95%ToxicityDiffSuccessDeg.ofTaxon.CommonNameScientificNameSpeciesug/LC.I.ug/L%R2MSEFreedomDist.AtlanticsilversideMenidiamenidiaRainbowtrout0.006*0.000‐0.380779.8‐129966.7na0.990.006014PinfishLagodonrhomboidesRainbowtrout0.391*0.001‐117.3040.000.791.100034ShortnosesturgeonAcipenserbrevirostrumRainbowtrout0.4790.056‐4.08100.000.980.023624AmphipodHyalellaaztecaD.magna0.685*0.002‐158.5814.280.631.830055SpotfinchubErimonaxmonachusRainbowtrout0.8690.111‐6.81100.000.950.092634ApachetroutOncorhynchusgilaeRainbowtrout0.906*0.514‐1.59100.000.990.007031BlackcrappiePomoxisnigromaculatusRainbowtrout1.14*0.306‐4.26na0.990.002714StoneflyPteronarcyscalifornicaRainbowtrout1.23*0.093‐16.2146.150.380.9722116NorthernpikeEsoxluciusRainbowtrout1.250.133‐11.8222211776.885.710.800.287054CapefearshinerNotropismekistocholasRainbowtrout1.3*0.540‐3.13100.000.990.016934FountaindarterEtheostomafonticolaRainbowtrout1.39*0.319‐6.07na0.990.002614GreenthroatdarterEtheostomalepidumRainbowtrout1.71*0.348‐8.49100.000.960.055834BrowntroutSalmotruttaRainbowtrout2.271.38‐3.741.613‐1.494.730.960.0797172ThreespinesticklebackGasterosteusaculeatusRainbowtrout2.30.350‐15.1475.000.970.090724CohosalmonOncorhynchuskisutchRainbowtrout2.391.73‐3.296.2212.6100.000.990.0227151RazorbacksuckerXyrauchentexanusRainbowtrout2.53*0.808‐7.9536.6214.5100.000.970.037644CutthroattroutOncorhynchusclarkiiRainbowtrout2.82*1.67‐4.7997.050.940.0805321LaketroutSalvelinusnamaycushRainbowtrout3.732.06‐6.7496.150.940.0855242SouthernleopardfrogRanasphenocephalaRainbowtrout3.79*0.459‐31.3560.000.930.097435ChinooksalmonOncorhynchustshawytschaRainbowtrout3.81.58‐9.114.3051.1100.000.980.064461GilatopminnowPoeciliopsisoccidentalisRainbowtrout5.05*0.922‐27.69100.000.960.021524LeonspringspupfishCyprinodonbovinusRainbowtrout8.94*0.362‐220.4575.000.910.108224MysidAmericamysisbahiaD.magna9.98 6.27‐15.8741.294.164.96 0.69 0.8222 135527SnipeflyAtherixvariegataRainbowtrout10.362.32‐46.17100.000.940.058626DaphnidSimocephalusserrulatusD.magna11.193.06‐40.8730.212.766.660.630.4477102StoneflyClaasseniasabulosaBluegill11.611.15‐116.9371.420.580.203856BlackbullheadAmeiurusmelasRainbowtrout12.591.50‐105.3850.000.640.9433104DaphnidDaphniapulexD.magna19.513.23‐117.8346.362.442.850.830.626751DaphnidCeriodaphniadubiaD.magna26.541.40‐503.0531.371.237.500.801.410062MidgeParatanytarsusparthenogeneticusD.magna44.49*1.78‐1110.16100.000.980.087425StoneflyPteronarcellabadiaBluegill47.277.37‐303.0269.230.330.8088116MidgeChironomustentansD.magna64.72*2.16‐1934.1875.000.980.082825NorthernrivercrangonyctidCrangonyxpseudogracilisD.magna78.711.49‐538.67170021.645.450.710.821295AmphipodGammaruslacustrisBluegill94.459.75‐914.8330.760.351.1300116AmphipodGammarusfasciatusD.magna102.728.64‐368.2355.550.461.8100255AmphipodAllorchestescompressaD.magna211.4573.66‐606.98100.000.920.053135IsopodCaecidoteabrevicaudaD.magna346.2791.18‐1315.0441.660.330.7873105StarfishAsteriasforbesiD.magna352.49285.62‐435.0224136.8na0.990.000116MidgeParatanytarsusdissimilisRainbowtrout901.38*172.22‐4717.5475.000.870.137566AmphipodGammaruspseudolimnaeusBluegill1197.02334.30‐4286.0978.69‐15.264.700.830.7507156IsopodAsellusaquaticusD.magna1233.43172.12‐8838.9550.000.820.301845MidgeChironomusplumosusBluegill1257.96317.05‐4991.1225.000.491.0400186WalleyeSandervitreusBluegill1431.12*213.72‐9582.7670.000.540.365283EasternoysterCrassostreavirginicaBluegill2024.981279.78‐3204.1238001.971.570.550.6643936BrooktroutSalvelinusfontinalisBluegill3423.421733.01‐6762.672.152‐1590.888.880.890.2026164AtlanticsalmonSalmosalarBluegill3935.532254.21‐6870.87100.000.960.089694YellowperchPercaflavescensBluegill4777.462489.78‐9167.1494.440.930.1167163WhitesuckerCatostomuscommersoniiBluegill5066.95*691.06‐37151.553136‐1.6100.000.970.040324LargemouthbassMicropterussalmoidesBluegill5398.053428.37‐8499.3496.870.930.1180302SheepsheadminnowCyprinodonvariegatusBluegill6213.194173.24‐9250.29500008.086.110.710.3955704PinkshrimpFarfantepenaeusduorarumBluegill6403.072155.59‐19020.03310.5‐20.672.720.940.382496GreensunfishLepomiscyanellusBluegill6692.782957.47‐15145.832965‐2.392.850.910.1478121FatheadminnowPimephalespromelasBluegill7374.274949.93‐10985.9729.21‐252.575.000.790.4318664ChannelcatfishIctaluruspunctatusBluegill7689.914931.89‐11990.2676.710.760.4487714CommoncarpCyprinuscarpioBluegill8262.163362.97‐20298.5084.210.830.3714174GuppyPoeciliareticulataBluegill8447.943263.74‐21866.862462‐3.476.470.840.4402154GoldfishCarassiusauratusBluegill10602.154943.75‐22736.92844‐12.686.950.800.4231214InlandsilversideMenidiaberyllinaBluegill11499.422234.62‐59176.31100.000.970.095324MosquitofishGambusiaaffinisD.magna37785.321401.84‐1018467.44na0.990.021816Tot.N22AVGAVGAVGAVGAVG*Surrogatevalueoutsideofmodelboundary,notusedinHC5estimateN<10fold1377.810.850.368727.713.2N<5fold1177.050.830.433328.732.6N>5,fold1082.320.870.293717.004.5N<=2fold580.760.850.451724.803.0\