1

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 Ecological Criteria Division 

Office of Water 

Prepared By: 

Dale Hoff 

Mid-Continent Ecology Division 

Office of Research and Development 

12/20/2011 
2

1.0 Background on Interspecies Correlation Estimation (ICE)

Interspecies correlation estimation (ICE) is a method used to estimate the toxicity 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 equation can be used to estimate the toxicity of 

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

the species on the x-axis of the 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 LC50 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 decades, it was first recommended for use in a 

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

Act (TSCA) was passed in October of 1976, the USEPA was given the authority to require premanufacture testing of chemicals substances for indications of human and environmental 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 potential toxicity. Kenaga stated 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, Maki (1979) published a paper using ICE with 

Daphnia magna and fathead minnow (Pimephales promelas). Maki cited TSCA data 
3

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

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

chronic NOECs with 21 day chronic fathead minnow 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 findings of Kenaga and Maki, Doherty (1983) correlated acute 

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

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

correlated (R2 ranging from 0.78 – 0.94) species combinations 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 highly 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” and 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 material” may be identified and to continue to 

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

conducted various interspecies comparisons 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 toxicological 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 sources. There was strong correlation (R2=0.93, p ≤ 0.01) 

between the warm water fish (bluegill) 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 (bluegill 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 taxa would improve the relationship, 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 toxicity of the two centrarchids was highly correlated 

(R2=0.94, p ≤ 0.01), whereas the bluegill and catfish were much less so (R2=0.46, p ≤ 0.05). 

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

reported R2

 of 0.04. The author concluded that taxonomic relatedness highly influenced 

comparative toxicity for all chemical categories. He also concluded that pesticides are 

engineered to be toxic to specific 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. 1987) on ICE was submitted to the Ecological Risk 

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

Research and Development’s Environmental 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 including both freshwater and 

saltwater species. The report’s general conclusions supported those of the previous authors 

(Kenaga 1978, LeBlanc 1984) that correlations of organisms within the same family are much 
4

improved over those much further related. Also, 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 and supplementing it with several other datasets, 

Asfaw et al. (2003) released software (ICE v 1.0) that would allow users 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 other 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 used to calculate HC5s (hazard concentration of the 5% percentile of 

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

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

dodecyl linear alkybenzene sulfonate (LAS), nonylphenol, fenvalerate, atrazine, and copper from 

published sources. The effect on SSDs using all species 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 influence the rainbow trout 

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

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

the predicted distributions was similar regardless of which species was used as a surrogate. ICEpredicted SSDs, for the most part, were statistically different than the distributions generated 

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

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

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

value based on measured data....” It was also noted that none of the 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 data set of acute values from Ambient Water 

Quality Criteria and reference documents from the 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 models were better when fish only predicted other fish, than when 

used to predict both fish and invertebrates. Improved correlation statistics were not found when 

D. magna was used as the surrogate for invertebrates 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 the 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 chemicals 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 minnow, rainbow trout, or 

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

surrogate fathead minnow value; 4.) data derived 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 Evaluation of Risk) fathead minnow value (Russom 

1991, 1997). The four SSDs were using predicted data were compared to the SSD with 
5

experimentally determined data. Overall, predictability of LC50s among fish species was good, 

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

remaining 3 observations were only slightly greater 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 5th percentile was obtained 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 determined 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 authors noted that phylogeny was again important, as 

the best estimates of toxicity came from more closely 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 species relatedness and chemical 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 available and is described in more detail in the next 

section of this report. Predictions from the ICE 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 determinant 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, respectively. 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 models 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 toxicity 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 regression needs to have a significant slope, 

preferably positive. The regression should have a high R2

 value with tight 95th percentile 

confidence limits. 

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

chemicals of high purity. 
6

3.) Taxonomic relatedness has been repeatedly shown to influence the precision of ICE 

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

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

sensitivities between the phyla are extremely different. 

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

from which to predict toxicity and improve correlation statistics, 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 empirical data for four chemicals, including: 

diazinon (an organophosphate insecticide), chlorpyrifos (an organophosphate insecticide), 

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

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

2.0 Description of Web-ICE

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

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

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

additional modules and improved functionality updates through 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 values 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 toxicity 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 concentrations 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 time of this report, Web-ICE updates are in the 

process of being developed. These updates include: 

1.) Modification to the SSD module 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 default 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 judgement. 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 available 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. 2010a

) and may help to obtain all seven 
7

MDRs. While MOA-specific models may improve toxicity predictions, fewer species pairs will 

be available using the smaller 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, invertebrates, and vertebrates 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 used 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 experimentally determined acute values. 

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

pesticides and one heavy metal: diazinon, chlorpyrifos, 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, because it has AWQC and its associated documentation 

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

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

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

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

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

) provides recommendations for evaluating 

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

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

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

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

following attributes: 

1. Relatively low mean square error (MSE) (<0.22) 

2. Close taxonomic distance (≤ 3) 

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

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

5. High R2

 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 MDRs 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 
8

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 order of insect or in a fourth phylum 

Since the calculation of FAVs are completed using 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 and 3 respectively. An arbitrary surrogate 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 SSDs and HC5 estimates. 

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

the 7th 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 preliminary 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 models 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. Cursory reviews of several criteria 

documents revealed that this MDR is most often 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, glochidia). To ensure that Web-ICE 

relationships minimized extraneous sources of variation, mollusk data were standardized to 

include only juvenile 96-hr EC/LC50 values, which 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 several recent tests that will be processed and added 

to the module (Raimondo, personal communication). 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 available in future updates. With these updates, 

obtaining the 7th 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 species pair regressions. 

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

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

modeling. Conversely, the most useful and precise 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 rainbow trout, bluegill, fathead minnow and D. magna. For 

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

not be needed. In spite of these potential limits, there are 780 models comparing 77 species to 77 

species in Web-ICE and if employed with rational scientific judgement (including the “rules of 
9

thumb” provided in the user’s manual), robust 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 related (within the same genus or family), more 

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

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

random variation seen in laboratory 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 registration documents and entered into the Web-ICE 

SSD module. The output for diazinon, chlorpyrifos, 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 regression statistics include degrees of freedom (df 

(N-2), R2, p-value, mean square error (MSE), taxonomic distance (same genus =1; family = 2; 

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

points that were predicted within 5 fold of the actual value). Tables 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, experimentally determined toxicity data from 

criteria documents were compared to model estimates 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 predicted), total number of comparisons 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 the four chemicals. Not surprisingly, for all four chemicals the 

taxonomic distance is the smallest for categories < 5 and <=2-fold difference (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 cadmium, cross-validation 

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

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

difference (0.2 – 0.4). There were no obvious trends for R2

 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 total 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 comparisons 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. 
10

Precision of individual models did not necessarily 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

Diazinona 1.048 425.8 459.6 12.05 0.3397 + 35.5 

Chlorpyrifosb 1.7 8.485 10 0.906 0.1669 +5.4 

Acroleinc 57 16 27.19 1.75 5.92 -3.4 

Cadmiumd 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 calculated 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 input values lower than regression 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 influence the shape of the SSD curve. In contrast, 

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

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

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

diazinon. Furthermore, only 1 of the 40 models 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) that argued that ICE models are 

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

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

compared with non-target species like fish. Chlorpyrifos is more toxic to the non-target fish 

species and this allows for ICE models to predict acute values in the lower 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 values outside model bounds. ICE models for 

acrolein were the most precise and the HC5 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 and 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 estimates 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 HC5s 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 values 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 could 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, taxonomic distance, cross-validation success, df, R2

- 

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 deselected 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 models in the SSD module the user needs to 

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

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

“rules of thumb” were rigidly applied, only 6 models 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. Likewise for cadmium, there were 51 total models 

in the SSD and after rigidly applying the rules, only 6 species remained: Brown trout, Coho 

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

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

the SSD module of the Web-ICE application should ensure a variety 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 surrogate data available for rainbow trout, bluegill 

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

2. The frequency of precise models for individual species in Web-ICE 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 predictions is better for chemicals that 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 the Web-ICE user’s manual is a good place to start 

when evaluating models. However, applying them 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 organisms 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 environmental concentrations. Environ. 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 interspecies correlation models to 

distributions used to derive water quality criteria. 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 animal species. SAB review documents. 

Kenaga, E.E. 1978. Test organism and methods useful 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 several classes of test substances. J. Fish. Res. Board Can. 

36. 411 – 421. 

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

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

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

Bridges. 2008. Endangered aquatic vertebrates: comparative and probabilistic-based 

toxicology. EPA/600/R-08/045 

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

. 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. 2010b

. Web-based Interspecies 

Correlation Estimation (Web-ICE) for Acute Toxicity: User Manual. Version 3.1. 
13

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

Protection Agency. Gulf Breeze, FL. 

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

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

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

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

http://water.epa.gov/scitech/swguidance/standards/criteria/aqlife/pollutants/acrolein/upload/aqfinal.pdf

 
Table 1. Species correlation statistics and results for available genera using Daphnia magna as the surrogate species. All pred

confidence intervals were calculated using a surrogate toxicity value of 50 ug/L.

ExtrapolatedGenus

Common Name MDR

R2 Df

Cross‐Val.

Success %

Predicted

Acute Valu

(ug/L)

Oncorhynchus Rainbow Trout 1. the family Salmonidae in the class 

Osteichthyes 0.50 263 52.8 274.1 

Lepomis Sunfish/Bluegill 2. a second family in the class Osteichthyes,

preferably acommercially orrecreationallyimportant warmwater

species (e.g., bluegill,channel catfish, etc.)

0.54 236 54.2 352.1 

Cyprinodon Killfish 3. a third family in the phylum Chordata (may 

be in the class Osteichthyes or may be an amphibian, etc.) 

0.45 71 67.1 484.9 

Ceriodaphnia Crustacean 4. a planktonic

crustacean (e.g.,cladoceran, copepod,etc.)

0.79 7 33.3 79.3 

Gammarus Amphipod 5. a benthic crustacean (e.g., ostracod, isopod, 

amphipod, crayfish, etc.) 

0.48 41 51.2 137.2 

Chironomous Midge 6. an insect (e.g., mayfly, dragonfly, 

damselfly, stonefly, caddisfly, mosquito, midge, etc.) 

0.36 17 36.8 232.4 

Palaemonetes Shrimp 8. a family in any order of insect or any phylum 

not already represented. 0.99 2 100 74.5 

Paratanytarsus European Midge 6 0.75 8 40 3760.0 Rana Frogs 3, 8 0.57 6 27.5 279.8 Simocephalus Crustacean 4 0.67 11 69.2 24.2 Caecidota Crustacean 4 0.39 14 43.8 679.3 
Table 2. Species correlation statistics and results for available genera using Bluegill (Lepomis macrochirus) as the surrogate species. A

95% confidence intervals were calculated using a surrogate toxicity value of 50 ug/L.

Extrapolated

Genus

Common Name MDR

R2 Df

Cross‐Val.

Success %

Predict

Acute V

(ug/L

Oncorhynchus Pacific basin Trout 1. the family Salmonidae in the class Osteichthyes 0.88 310 90 48.1Ameiurus Catfish

2. a second family in the class

Osteichthyes, preferably acommercially orrecreationally importantwarmwater species (e.g.,

bluegill, channel catfish, etc.)

0.67 11 62 168.

Cyprinodon Killfish 3. a third family in the phylum Chordata (may be in 

the class Osteichthyes or may be an amphibian, etc.) 

0.72 72 86

127.

Ceriodaphnia Daphnia 4. a planktonic crustacean (e.g., cladoceran, copepod,

etc.) 0.79 4 17

5.3 

Gammarus Amphipod 5. a benthic crustacean (e.g., ostracod, isopod, amphipod, 

crayfish, etc.) 0.51 53 38

19.8 

Chironomous Midge 6. an insect (e.g., mayfly, dragonfly, damselfly, 

stonefly, caddisfly, mosquito, midge, etc.) 

0.44 22 38 50.8

Catostomus Suckers 8. a family in any order of 

insect or any phylum not already represented. 0.98 2 100

18.9 

Caecidota Crustacean 4 0.64 15 47 55.2 Daphnia Crustacean 4 0.55 244 51 23.1 Etheostoma Darter 2, 3 or 8 0.95 2 75 13.4 Menidia Silverside 2, 3 or 8 0.91 6 75 64.8 Micropterus Bass 2, 3 or 8 0.93 30 97 40.7 Notropis Shiners 2, 3 or 8 0.80 3 60 18.9 Paratanytarsus European Midge 6 0.72 5 42 702.Pimephales Bluntnose Minnow 2, 3 or 8 0.79 66 75 122.Salmo European Trout 2, 3 or 8 0.95 17 100 24.0 Salvelinus Salmon Char 2, 3 or 8 0.77 33 82.25 38.9 Rana Frogs 3, 8 0.86 5 71.42 53.6 

*

‐This value is outside the x‐axis range for this model
Table 3. Species correlation statistics and results for available genera using Rainbow trout (Onchorynchus mykiss) as the surrogate sp

values and 95% confidence intervals were calculated using a surrogate toxicity value of 50 ug/L.

Extrapolated

Genus

Common Name MDR

R2 Df

Cross‐Val.

Success %

Predict

Acute V

(ug/L

Salmo European Trout 1. the family Salmonidae in the class Osteichthyes 0.96 23 96 50.6 Ameiurus Catfish

2. a second family in the class

Osteichthyes, preferably acommercially orrecreationally importantwarmwater species (e.g.,

bluegill, channel catfish, etc.)

0.94 11 53.8 222.

Rana Frogs 3. a third family in the phylum Chordata (may be in 

the class Osteichthyes or may be an amphibian, etc.) 

0.98 6 100.0 108 

Caecidota Crustacean 4. a planktonic crustacean 

(e.g., cladoceran, copepod, etc.) 0.63 17 47.4 72.4 

Gammarus Amphipod 5. a benthic crustacean (e.g., ostracod, isopod, amphipod, 

crayfish, etc.) 0.44 53 30.9 20.1

Chironomous Midge 6. an insect (e.g., mayfly, dragonfly, damselfly, 

stonefly, caddisfly, mosquito, midge, etc.) 

0.53 19 38.1 35.7 

Cyprinodon Killfish 8. a family in any order of insect or any phylum not 

already represented. 0.67 73 80.0 153.9

Daphnia Crustacian 4 0.51 269 52.4 42.6 Etheostoma Darter 2, 3 or 8 0.97 3 100.0 53.8 Acipenser Sturgeons 2, 3 or 8 0.94 3 100.0 31.5Micropterus Bass 2, 3 or 8 0.86 31 90.9 39.0 Notropis Shiners 2, 3 or 8 0.99 4 100.0 56.0 Paratanytarsus European Midge 6 0.82 8 70.0 5110Pimephales Bluntnose Minnow 2, 3 or 8 0.83 79 81.5 147.Lepomis Sunfish/Bluegill 2, 3 or 8 0.88 308 90.32 75.9 Salvelinus Salmon Char 2, 3 or 8 0.89 37 92.3 60.5 
Table 4. Web‐ICE output from the species sensitivity distribution model for Diazinon. Surrogate species were D. magna, Rain

with Input toxicity values of 1.048, 425.8 and 459.6 respectively (Diazinon Criteria document). Web‐ICE HC5 was calculated to 

39.23). FAV from criteria document was calculated to be 0.3397 ug/L.

Estimated Measured X‐Fold

Cros‐

Val

Surrogate Toxicity* 95% Toxicity Diff Success 

Common Name Scientific Name Species ug/L C.I. ug/L % R2 

Amphipod Hyalella azteca D. magna 0.059* 0.000 ‐ 91.16 6.51

‐

110.34 14.28 0.63 

Daphnid Simocephalus serrulatus D. magna 1.7 0.621 ‐ 4.65 1.59 1.07 66.66 0.63 Daphnid Ceriodaphnia dubia D. magna 1.9* 0.040 ‐ 90.05 0.38 5.00 37.50 0.8 Daphnid Daphnia pulex D. magna 2.29 0.234 ‐ 22.46 0.78 2.94 42.85 0.3 Starfish Asterias forbesi D. magna 3.58* 2.30 ‐ 5.58 na 0.99 Midge Paratanytarsus parthenogeneticus D. magna 3.67* 0.061 ‐219.09 100.00 0.98 Stonefly Claassenia sabulosa Bluegill 4.54 0.969 ‐ 21.25 71.42 0.58 Midge Chironomus tentans D. magna 4.8* 0.058 ‐392.49 10.7 ‐2.23 75.00 0.98 Northern river crangonyctid Crangonyx pseudogracilis D. magna 5.05* 0.258 ‐ 98.70 45.45 0.71 Amphipod Allorchestes compressa D. magna 15.47* 1.56 ‐ 153.03 100.00 0.92 Amphipod Gammarus lacustris Bluegill 18.72 4.10 ‐ 85.48 30.76 0.35 Stonefly Pteronarcella badia

Rainbow

trout 20.21 6.15 ‐ 66.42 60.00 0.48 

Stonefly Pteronarcys californica

Rainbow

trout 23.05 5.62 ‐ 94.46 25 ‐1.08 46.15 0.39 

Atlantic silverside Menidia menidia

Rainbow

trout 33.34* 6.44 ‐ 172.65 na 0.99 

Mysid Americamysis bahia Bluegill 64.6 44.38 ‐ 94.03 4.82 13.40 60.90 0.68 Pinfish Lagodon rhomboides

Rainbow

trout 71.63 2.26 ‐2263.40 40.00 0.8 

Amphipod Gammarus pseudolimnaeus Bluegill 74.79 26.62 ‐210.09 16.82 4.45 64.70 0.83 Isopod Asellus aquaticus D. magna 151.42* 7.11 ‐3224.68 50.00 0.82 Midge Chironomus plumosus

Rainbow

trout 185.32 61.22 ‐560.95 35.00 0.53 

Amphipod Gammarus fasciatus Bluegill 199.16 76.13 ‐521.00 2.04 97.63 47.22 0.37 Atlantic salmon Salmo salar Bluegill 219.76 135.25 ‐357.06 100.00 0.96 Brook trout Salvelinus fontinalis Bluegill 227.14 134.58 ‐383.35 723 ‐3.18 88.88 0.89 
Walleye Sander vitreus

Rainbow

trout 239.59 98.24 ‐584.32 60.00 0.68 

White sucker Catostomus commersonii Bluegill 255.02 92.79 ‐700.86 100.00 0.97 Northern pike Esox lucius

Rainbow

trout 279.31 50.67 ‐1539.66 85.71 0.8 

Isopod Caecidotea brevicauda

Rainbow

trout 282.89 112.40 ‐711.97 64.70 0.68 

Pink shrimp Farfantepenaeus duorarum Bluegill 293.15 110.39 ‐778.43 21 13.96 72.72 0.94 Eastern oyster Crassostrea virginica Bluegill 299.92 203.45 ‐442.14 71.57 0.55 Shortnose sturgeon Acipenser brevirostrum

Rainbow

trout 307.36 121.70 ‐776.28 100.00 0.98 

Apache trout Oncorhynchus gilae

Rainbow

trout 349.64 241.45 ‐506.30 100.00 0.99 

Yellow perch Perca flavescens Bluegill 359.1 235.72 ‐547.06 94.44 0.93 Largemouth bass Micropterus salmoides Bluegill 391.34 291.93 ‐524.60 96.87 0.93 Brown trout Salmo trutta

Rainbow

trout 392.9 280.42 ‐550.52 94.73 0.96 

Lake trout Salvelinus namaycush

Rainbow

trout 395.98 301.47 ‐520.11 602 ‐1.52 96.15 0.94 

Cutthroat trout Oncorhynchus clarkii

Rainbow

trout 457.9 367.38 ‐570.71 2166 ‐4.73 97.05 0.94 

Fountain darter Etheostoma fonticola

Rainbow

trout 462.67 154.77 ‐1383.06 na 0.99 

Gila topminnow Poeciliopsis occidentalis

Rainbow

trout 477.45* 95.51 ‐2386.81 100.00 0.96 

Black crappie Pomoxis nigromaculatus

Rainbow

trout 533.31 165.12 ‐1722.54 na 0.99 

Spotfin chub Erimonax monachus

Rainbow

trout 541.96 140.99 ‐2083.23 100.00 0.95 

Greenthroat darter Etheostoma lepidum

Rainbow

trout 556.98 195.86 ‐1583.91 100.00 0.96 

Chinook salmon Oncorhynchustshawytscha

Rainbow

trout 587.88 354.48 ‐974.97 100.00 0.98 

Leon springspupfish Cyprinodon bovinus

Rainbow

trout 618.04 80.01 ‐4774.15 75.00 0.91 

Coho salmon Oncorhynchus kisutch

Rainbow

trout 624.47 516.05 ‐755.66 100.00 0.99 

Cape fear shiner Notropis mekistocholas

Rainbow

trout 723.02 406.83 ‐1284.93 100.00 0.99 

Green sunfish Lepomis cyanellus

Rainbow

trout 749.09 443.94 ‐1263.96 92.30 0.91 

Sheepsheadminnow Cyprinodon variegatus Bluegill 785.65 555.19 ‐1111.76 1400 ‐1.78 86.11 0.71 Channel catfish Ictalurus punctatus

Rainbow

trout 848.94 615.36 ‐1171.18 77.64 0.75 

Razorback sucker Xyrauchen texanus

Rainbow

trout 916.52 474.33 ‐1770.94 100.00 0.97 

Common carp Cyprinus carpio

Rainbow

trout 1004.01 548.03 ‐1839.37 86.36 0.83 

Fathead minnow Pimephales promelas

Rainbow

trout 1011.64 736.71 ‐1389.18 7804  ‐7.71 82.71 0.83 
Southern leopard frog Rana sphenocephala

Rainbow

trout 1075.27 270.23 ‐4278.52 50 21.51 60.00 0.93 

Inland silverside Menidia beryllina Bluegill 1212.2 235.67 ‐6234.99 1170 1.04 100.00 0.97 Snipefly Atherix variegata

Rainbow

trout 1282.53* 60.71 ‐27093.09 100.00 0.94 

Threespine stickleback Gasterosteus aculeatus Bluegill 1343.44* 216.92 ‐8320.04 100.00 0.96 Goldfish Carassius auratus

Rainbow

trout 1454.88 772.55 ‐2739.84 9000  ‐6.19 87.50 0.82 

Black bullhead Ameiurus melas Bluegill 1471.09 233.83 ‐9254.87 63.63 0.66 Guppy Poecilia reticulata

Rainbow

trout 2438.71 1282.22 ‐4638.31 800 3.05 80.00 0.92 

Mosquitofish Gambusia affinis D. magna 12365.1 263.63 ‐579946.51 na 0.99 Midge Paratanytarsus dissimilis

Rainbow

trout 13503.38 5731.14 ‐31815.90 75.00 0.87 

N 19 AVG AVG 

* Surrogate value outside of model boundary, not used in HC5 estimate

N < 10

fold 14 75.09 0.78 

N<5 fold 12 76.69 0.77 N>5 fold 7 57.85 0.75 

N<2 5 79.01 0.73 
Table 5. Web‐ICE output from the species sensitivity distribution module for Chlorpyrifos. Surrogate species were D. magna, Rainbow 

Input toxicity values of 1.7, 8.485 and 10.0 respectively (Chlorpyrifos Criteria document for RBT and BG; DM = June 2000 Chlorpyrifos 

package). Web‐ICE HC5 was calculated to be 0.906 ug/L (0.212 – 1.89). FAV from criteria document was calculated to be 0.1669 ug/L

Estimated Measured

X‐

Fold

Cros‐

Val

Surrogate Toxicity 95% Toxicity* Diff Success 

Common Name Scientific Name Species ug/L C.I. ug/L % R2 

Atlantic silverside Menidia menidia

Rainbow

Trout 0.063* 0.002 ‐ 1.77 1.229

‐

19.5 na 0.9930 

Amphipod Hyalella azteca D. magna 0.094* 0.000 ‐100.06 14.28 0.6290 Stonefly Claassenia sabulosa Bluegill 1.12 0.402 ‐ 3.13 0.57 2.0 71.42 0.5848 Amphipod Gammarus pseudolimnaeus Bluegill 1.21 0.293 ‐ 4.99 0.18 6.7 64.70 0.8292 Pinfish Lagodon rhomboides

Rainbow

Trout 1.53 0.013 ‐177.99 40.00 0.7966 

Amphipod Gammarus lacustris Bluegill 1.68 0.148 ‐19.23 0.11 15.3 30.76 0.3519 Mysid Americamysis bahia D. magna 1.87 1.02 ‐ 3.44 64.96 0.6890 Daphnid Simocephalus serrulatus D. magna 2.43 0.902 ‐ 6.55 66.66 0.6340 Shortnose sturgeon Acipenser brevirostrum

Rainbow

Trout 2.61* 0.559 ‐12.18 100.00 0.9837 

Stonefly Pteronarcys californica

Rainbow

Trout 2.66 0.336 ‐21.00 10 ‐3.8 46.15 0.3897 

Pink shrimp Farfantepenaeus duorarum Bluegill 2.98 0.747 ‐11.94 72.72 0.9385 White sucker Catostomus commersonii Bluegill 2.99* 0.288 ‐31.00 100.00 0.9720 Stonefly Pteronarcella badia Bluegill 3.05 0.534 ‐17.49 0.38 8.0 69.23 0.3345 Daphnid Ceriodaphnia dubia D. magna 3.14* 0.080 ‐122.61 37.50 0.8043 Daphnid Daphnia pulex D. magna 3.44 0.400 ‐29.65 42.85 0.8267 Brook trout Salvelinus fontinalis Bluegill 4.01 1.49 ‐ 10.83 88.88 0.8853 Apache trout Oncorhynchus gilae

Rainbow

Trout 4.32 2.88 ‐ 6.49 100.00 0.9954 

Spotfin chub Erimonax monachus

Rainbow

Trout 4.7 1.07 ‐ 20.62 100.00 0.9502 

Northern pike Esox lucius

Rainbow

Trout 5.19 1.03 ‐ 26.02 85.71 0.7998 

Black crappie Pomoxis nigromaculatus

Rainbow

Trout 5.72 2.06 ‐ 15.87 na 0.9995 

Midge Paratanytarsus parthenogeneticus D. magna 5.9* 0.117 ‐297.24 100.00 0.9845 Fountain darter Etheostoma fonticola

Rainbow

Trout 6.38 2.10 ‐ 19.41 na 0.9994 

Cape fearshiner Notropis mekistocholas

Rainbow

Trout 6.83 3.63 ‐ 12.84 100.00 0.9902 

Greenthroat darter Etheostoma lepidum Rainbow 7.83 2.48 ‐ 24.67 100.00 0.9624 
Trout

Yellow perch Perca flavescens

Rainbow

Trout 7.83 4.59 ‐ 13.38 90.47 0.9320 

Midge Chironomus tentans D. magna 7.86* 0.116 ‐529.67 75.00 0.9816 Atlantic salmon Salmo salar

Rainbow

Trout 7.87 3.84 ‐ 16.13 84.61 0.9541 

Largemouthbass Micropterus salmoides Bluegill 7.9 5.33 ‐ 11.68 96.87 0.9298 Northern river crangonyctid Crangonyx pseudogracilis D. magna 8.51* 0.541 ‐133.83 45.45 0.7126 Starfish Asterias forbesi D. magna 8.56* 5.78 ‐ 12.69 na 0.9999 Brown trout Salmo trutta

Rainbow

Trout 8.78 5.86 ‐ 13.15 94.73 0.9642 

Midge Chironomus plumosus

Rainbow

Trout 9.18 1.35 ‐ 62.20 35.00 0.5311 

Coho salmon Oncorhynchus kisutch

Rainbow

Trout 10.29 8.00 ‐ 13.25 100.00 0.9886 

Amphipod Gammarus fasciatus Bluegill 10.64 2.31 ‐ 48.99 0.32 33.3 47.22 0.3651 Cutthroat trout Oncorhynchus clarkii

Rainbow

Trout 10.74 7.06 ‐ 16.34 18 ‐1.7 97.05 0.9357 

Razorback sucker Xyrauchen texanus

Rainbow

Trout 11.89 5.23 ‐ 26.99 100.00 0.9696 

Lake trout Salvelinus namaycush

Rainbow

Trout 12.69 7.88 ‐ 20.42 98  ‐7.7 96.15 0.9354 

Isopod Caecidotea brevicauda

Rainbow

Trout 13.42 3.44 ‐ 52.35 64.70 0.6753 

Threespine stickleback Gasterosteus aculeatus Bluegill 14.24 5.38 ‐ 37.69 100.00 0.9615 Chinook salmon Oncorhynchus tshawytscha

Rainbow

Trout 14.26 6.89 ‐ 29.49 100.00 0.9793 

Gila topminnow Poeciliopsis occidentalis

Rainbow

Trout 16.66 5.83 ‐ 47.63 100.00 0.9559 

Southern leopard frog Rana sphenocephala

Rainbow

Trout 16.7 3.66 ‐ 76.04 60.00 0.9332 

Green sunfish Lepomis cyanellus Bluegill 23.74 11.57 ‐48.74 92.85 0.9072 Eastern oyster Crassostrea virginica

Rainbow

Trout 24.16 11.99 ‐48.64 1991

‐

82.4 71.13 0.5241 

Amphipod Allorchestes compressa D. magna 25.41* 3.31 ‐195.01 100.00 0.9188 Leon springspupfish Cyprinodon bovinus

Rainbow

Trout 27.17 2.55 ‐288.74 75.00 0.9124 

Fathead minnow Pimephales promelas

Rainbow

Trout 31.23 18.34 ‐53.17 331.7

‐

10.6 82.71 0.8260 

Sheepshead minnow Cyprinodon variegatus Bluegill 36.27 19.01 ‐69.18 86.11 0.7112 Snipefly Atherix variegata

Rainbow

Trout 36.67 11.02 ‐121.98 100.00 0.9402 

Common carp Cyprinus carpio

Rainbow

Trout 37.24 15.36 ‐90.26 86.36 0.8285 

Guppy Poecilia reticulata

Rainbow

Trout 38.42 16.07 ‐91.83 80.00 0.9175 

Inland silverside Menidia beryllina Bluegill 42.7* 2.62 ‐694.97 100.00 0.9714 Walleye Sander vitreus Bluegill 42.82 9.94 ‐184.46 70.00 0.5367 Channel catfish Ictalurus punctatus Bluegill 46.85 27.36 ‐80.24 806

‐

17.2 76.71 0.7561 

Black bullhead Ameiurus melas Rainbow 48.51 9.13 ‐257.67 50.00 0.6385 
Trout

Goldfish Carassius auratus

Rainbow

Trout 54.8 20.85 ‐144.01 806

‐

14.7 87.50 0.8198 

Isopod Asellus aquaticus D. magna 225.45* 13.24 ‐3837.70 50.00 0.8227 Midge Paratanytarsus dissimilis

Rainbow

Trout 1833.58* 445.54 ‐7545.81 75.00 0.8697 

Mosquitofish Gambusia affinis D. magna 15284.8 364.23 ‐641409.48 na 0.9970 

Tot. N 13 AVG AVG 

N <10 fold 6 70.00 0.66 

* Surrogate value outside of

model boundary, not used inHC5 estimate N < 5 fold 3 71.54 0.64 

N > 5 fold 10 69.57 0.7072 N <= 2fold 2 84.24 0.76 
Table 6. Web‐ICE output from the species sensitivity distribution module for Acrolein. Surrogate species were D. magna, Rainbow tro

Input toxicity values of 57.0, 16.0 and 27.9 respectively (Acrolein Criteria document). Web‐ICE HC5 was calculated to be 1.75 ug/L (0.

criteria document was calculated to be 5.920 ug/L.

Estimated Measured X‐Fold

Cros‐

Val

Surrogate Toxicity* 95% Toxicity Diff Success 

Common Name Scientific Name Species ug/L C.I. ug/L % R

Atlantic silverside Menidia menidia Rainbow trout 0.174* 0.008 ‐ 3.60 na 0.

Stonefly Claassenia sabulosa Bluegill 1.61 0.581 ‐ 4.50 71.42 0.Amphipod Hyalella azteca Daphnia magna 2.75 0.031 ‐ 242.07 14.28 0.Pinfish Lagodon rhomboides Rainbow trout 2.86 0.036 ‐ 227.15 40 0Amphipod Gammarus lacustris Bluegill 3.16 0.421 ‐ 23.79 30.76 0.Amphipod Gammarus pseudolimnaeus Bluegill 3.55 1.02 ‐ 12.38 64.7 0.Stonefly Pteronarcys californica Rainbow trout 3.77 0.586 ‐ 24.27 46.15 0.Stonefly Pteronarcella badia Bluegill 4.69 1.05 ‐ 20.95 69.23 0.Shortnose sturgeon Acipenser brevirostrum Rainbow trout 5.65* 1.56 ‐ 20.46 100 0.Apache trout Oncorhynchus gilae Rainbow trout 8.8 6.23 ‐ 12.45 100 0.White sucker Catostomus commersonii Bluegill 9.55* 1.55 ‐ 58.59 14 1.5 100 0.Pink shrimp Farfante penaeusduorarum Bluegill 9.9 2.89 ‐ 33.93 72.72 0.Northern pike Esox lucius Rainbow trout 9.9 2.46 ‐ 39.80 85.71 0Spotfin chub Erimonax monachus Rainbow trout 10.15 2.88 ‐ 35.72 100 0.Brook trout Salvelinus fontinalis Bluegill 11.53 5.04 ‐ 26.36 88.88 0.Black crappie Pomoxis nigromaculatus Rainbow trout 11.94 4.71 ‐ 30.23 na 0.Fountain darter Etheostoma fonticola Rainbow trout 12.78 4.77 ‐ 34.23 na 0.Cape fear shiner Notropis mekistocholas Rainbow trout 14.54 8.49 ‐ 24.88 100 0.Atlantic salmon Salmo salar Rainbow trout 14.71 7.63 ‐ 28.38 84.61 0.Greenthroat darter Etheostoma lepidum Rainbow trout 15.63 5.88 ‐ 41.51 100 0.Brown trout Salmo trutta Rainbow trout 16.25 11.24 ‐ 23.51 94.73 0.Cutthroat trout Oncorhynchus clarkii Rainbow trout 19.73 13.57 ‐ 28.67 97.05 0.
Coho salmon Oncorhynchus kisutch Rainbow trout 20.02 15.97 ‐ 25.10 68 3.4 100 0.Yellow perch Perca flavescens Bluegill 20.91 13.09 ‐ 33.41 94.44 0.Midge Chironomus plumosus Bluegill 21.39 4.11 ‐ 111.25 25 0.Largemouth bass Micropterus salmoides Bluegill 21.9 15.78 ‐ 30.40 160 7.3 96.87 0.Isopod Caecidotea brevicauda Rainbow trout 21.99 6.44 ‐ 75.05 64.7 0.Lake trout Salvelinus namaycush Rainbow trout 22.16 14.46 ‐ 33.94 96.15 0.Razorback sucker Xyrauchen texanus Rainbow trout 24.03 12.03 ‐ 48.03 100 0.Chinook salmon Oncorhynchus tshawytscha Rainbow trout 26.05 13.37 ‐ 50.73 100 0.Gila topminnow Poeciliopsis occidentalis Rainbow trout 28.69 12.50 ‐ 65.88 100 0.Mysid Americamysis bahia Daphnia magna 32.15 21.77 ‐ 47.48 500 15.6 64.96 0.Daphnid Simocephalus serrulatus Daphnia magna 32.6 5.83 ‐ 182.23 66.66 0.Southern leopard frog Rana sphenocephala Rainbow trout 32.79 9.02 ‐ 119.19 60 0.Leon springspupfish Cyprinodon bovinus Rainbow trout 45.07 5.83 ‐ 348.17 75 0.Threespine stickleback Gasterosteus aculeatus Bluegill 46.73 16.94 ‐ 128.88 100 0.Fathead minnow Pimephales promelas Rainbow trout 54.86 33.72 ‐ 89.25 28.77 ‐1.9 82.71 0.Green sunfish Lepomis cyanellus Bluegill 57.34 31.19 ‐ 105.41 92.85 0.Common carp Cyprinus carpio Rainbow trout 63.49 28.35 ‐ 142.19 86.36 0.Snipefly Atherix variegata Rainbow trout 65.23 17.65 ‐ 241.03 100 0.Daphnid Daphnia pulex Daphnia magna 65.73 10.84 ‐ 398.38 42.85 0.Walleye Sander vitreus Bluegill 74.1 22.39 ‐ 245.27 70 0.Guppy Poecilia reticulata Rainbow trout 75.26 34.07 ‐ 166.21 80 0.Sheepshead minnow Cyprinodon variegatus Bluegill 81.02 46.90 ‐ 139.95 428 5.3 86.11 0.Black bullhead Ameiurus melas Rainbow trout 89.66 19.41 ‐ 414.23 50 0.Inland silverside Menidia beryllina Bluegill 102.36 9.09 ‐ 1152.21 100 0.Channel catfish Ictalurus punctatus Bluegill 103.96 65.37 ‐ 165.33 76.71 0.Daphnid Ceriodaphnia dubia Daphnia magna 118.33 9.01 ‐ 1553.94 37.5 0Goldfish Carassius auratus Bluegill 127.72 52.95 ‐ 308.04 86.95 0Midge Paratanytarsus parthenogeneticus Daphnia magna 183.21* 11.74 ‐2857.12 100 0.Eastern oyster Crassostrea virginica Daphnia magna 232.13 128.59 ‐419.03 106 ‐2.2 54.63 0.Amphipod Gammarus fasciatus Daphnia magna 265.88 77.47 ‐ 912.51 55.55 0.Midge Chironomus tentans Daphnia magna 283.33* 16.36 ‐4904.16 75 0.Northern river crangonyctid Crangonyx pseudogracilis Daphnia magna 373.9 81.37 ‐1717.92 45.45 0.Amphipod Allorchestes compressa Daphnia magna 933.03 436.64 ‐1993.74 100 0.Midge Paratanytarsus dissimilis Rainbow trout 2533.76* 683.87 ‐9387.67 151  ‐16.8 75 0.
Isopod Asellus aquaticus Daphnia magna 4057.05 863.11 ‐19070.13 50 0.Starfish Asterias forbesi Daphnia magna 4765.6 4000.86 ‐5676.53 na 0.Mosquitofish Gambusia affinis Daphnia magna 71238.19 3462.99 ‐1465460.16 na 0.

N 8 AVG A

* Surrogate value outside of model boundary, not used in HC5 estimate N<10 fold 6 81.47 0.

n<5 fold 4 84.335 0.7

N>5 fold 4 80.735 0N<2 fold 2 91.4 0
Table 7. Web‐ICE output from the species sensitivity distribution module for Cadmium. Surrogate species were D. magna, Rainbow t

Input toxicity values of 13.41, 2.108 and 6028 respectively (Cadmium Criteria document). Web‐ICE HC5 was calculated to be 0.709 ug

from criteria document was calculated to be 5.920 ug/L (final FAV was lowered to 2.108 to protect Rainbow trout).

Estimated Measured X‐Fold Cros‐Val

Surrogate Toxicity* 95% Toxicity Diff Success 

Common Name Scientific Name Species ug/L C.I. ug/L % 

Atlantic silverside Menidia menidia

Rainbowtrout 0.006* 0.000 ‐0.380 779.8

‐

129966.7 na 

Pinfish Lagodon rhomboides

Rainbowtrout 0.391* 0.001 ‐ 117.30 40.00 

Shortnose sturgeon Acipenser brevirostrum

Rainbowtrout 0.479 0.056 ‐ 4.08 100.00 

Amphipod Hyalella azteca D.magna 0.685* 0.002 ‐ 158.58 14.28 Spotfin chub Erimonax monachus

Rainbowtrout 0.869 0.111 ‐ 6.81 100.00 

Apache trout Oncorhynchus gilae

Rainbowtrout 0.906* 0.514 ‐ 1.59 100.00 

Black crappie Pomoxis nigromaculatus

Rainbowtrout 1.14* 0.306 ‐ 4.26 na 

Stonefly Pteronarcys californica

Rainbowtrout 1.23* 0.093 ‐ 16.21 46.15 

Northern pike Esox lucius

Rainbowtrout 1.25 0.133 ‐ 11.82 2221 1776.8 85.71 

Cape fearshiner Notropis mekistocholas

Rainbowtrout 1.3* 0.540 ‐ 3.13 100.00 

Fountain darter Etheostoma fonticola

Rainbowtrout 1.39* 0.319 ‐ 6.07 na 

Greenthroat darter Etheostoma lepidum

Rainbowtrout 1.71* 0.348 ‐ 8.49 100.00 

Brown trout Salmo trutta

Rainbowtrout 2.27 1.38 ‐ 3.74 1.613 ‐1.4 94.73 

Threespine stickleback Gasterosteus aculeatus

Rainbowtrout 2.3 0.350 ‐ 15.14 75.00 

Coho salmon Oncorhynchus kisutch

Rainbowtrout 2.39 1.73 ‐ 3.29 6.221 2.6 100.00 

Razorback sucker Xyrauchen texanus

Rainbowtrout 2.53* 0.808 ‐ 7.95 36.62 14.5 100.00 

Cutthroat trout Oncorhynchus clarkii

Rainbowtrout 2.82* 1.67 ‐ 4.79 97.05 

Lake trout Salvelinus namaycush

Rainbowtrout 3.73 2.06 ‐ 6.74 96.15 

Southern leopard frog Rana sphenocephala

Rainbowtrout 3.79* 0.459 ‐ 31.35 60.00 

Chinook salmon Oncorhynchus tshawytscha

Rainbowtrout 3.8 1.58 ‐ 9.11 4.305 1.1 100.00 

Gila topminnow Poeciliopsisoccidentalis

Rainbowtrout 5.05* 0.922 ‐ 27.69 100.00 

Leon springspupfish Cyprinodon bovinus

Rainbowtrout 8.94* 0.362 ‐ 220.45 75.00 

Mysid Americamysis bahia D.magna 9.98 6.27 ‐ 15.87 41.29 4.1 64.96 
Snipefly Atherix variegata

Rainbowtrout 10.36 2.32 ‐ 46.17 100.00 

Daphnid Simocephalus serrulatus D.magna 11.19 3.06 ‐ 40.87 30.21 2.7 66.66 Stonefly Claassenia sabulosa Bluegill 11.61 1.15 ‐ 116.93 71.42 Black bullhead Ameiurus melas

Rainbowtrout 12.59 1.50 ‐ 105.38 50.00 

Daphnid Daphnia pulex D.magna 19.51 3.23 ‐ 117.83 46.36 2.4 42.85 Daphnid Ceriodaphnia dubia D.magna 26.54 1.40 ‐ 503.05 31.37 1.2 37.50 Midge Paratanytarsus parthenogeneticus D.magna 44.49* 1.78 ‐ 1110.16 100.00 Stonefly Pteronarcella badia Bluegill 47.27 7.37 ‐ 303.02 69.23 Midge Chironomus tentans D.magna 64.72* 2.16 ‐ 1934.18 75.00 Northern river crangonyctid Crangonyx pseudogracilis D.magna 78.7 11.49 ‐ 538.67 1700 21.6 45.45 Amphipod Gammarus lacustris Bluegill 94.45 9.75 ‐ 914.83 30.76 Amphipod Gammarus fasciatus D.magna 102.7 28.64 ‐ 368.23 55.55 Amphipod Allorchestes compressa D.magna 211.45 73.66 ‐ 606.98 100.00 Isopod Caecidotea brevicauda D.magna 346.27 91.18 ‐ 1315.04 41.66 Starfish Asterias forbesi D.magna 352.49 285.62 ‐ 435.02 2413 6.8 na Midge Paratanytarsus dissimilis

Rainbowtrout 901.38* 172.22 ‐4717.54 75.00 

Amphipod Gammarus pseudolimnaeus Bluegill 1197.02 334.30 ‐4286.09 78.69  ‐15.2 64.70 Isopod Asellus aquaticus D.magna 1233.43 172.12 ‐8838.95 50.00 Midge Chironomus plumosus Bluegill 1257.96 317.05 ‐4991.12 25.00 Walleye Sander vitreus Bluegill 1431.12* 213.72 ‐9582.76 70.00 Eastern oyster Crassostrea virginica Bluegill 2024.98 1279.78 ‐3204.12 3800 1.9 71.57 Brook trout Salvelinus fontinalis Bluegill 3423.42 1733.01 ‐6762.67 2.152  ‐1590.8 88.88 Atlantic salmon Salmo salar Bluegill 3935.53 2254.21 ‐6870.87 100.00 Yellow perch Perca flavescens Bluegill 4777.46 2489.78 ‐9167.14 94.44 White sucker Catostomus commersonii Bluegill 5066.95* 691.06 ‐37151.55 3136 ‐1.6 100.00 Largemouth bass Micropterus salmoides Bluegill 5398.05 3428.37 ‐8499.34 96.87 Sheepshead minnow Cyprinodon variegatus Bluegill 6213.19 4173.24 ‐9250.29 50000 8.0 86.11 Pink shrimp Farfantepenaeus duorarum Bluegill 6403.07 2155.59 ‐19020.03 310.5  ‐20.6 72.72 Green sunfish Lepomis cyanellus Bluegill 6692.78 2957.47 ‐15145.83 2965 ‐2.3 92.85 Fathead minnow Pimephales promelas Bluegill 7374.27 4949.93 ‐10985.97 29.21  ‐252.5 75.00 Channel catfish Ictalurus punctatus Bluegill 7689.91 4931.89 ‐11990.26 76.71 Common carp Cyprinus carpio Bluegill 8262.16 3362.97 ‐20298.50 84.21 Guppy Poecilia reticulata Bluegill 8447.94 3263.74 ‐21866.86 2462 ‐3.4 76.47 Goldfish Carassius auratus Bluegill 10602.15 4943.75 ‐22736.92 844  ‐12.6 86.95 Inland silverside Menidia beryllina Bluegill 11499.42 2234.62 ‐59176.31 100.00 Mosquitofish Gambusia affinis D.magna 37785.32 1401.84 ‐1018467.44 na 

Tot. N 22 AVG 

* Surrogate value outside of model boundary, not used in HC5 estimate N <10 fold 13 77.81 

N < 5 fold 11 77.05 N > 5, fold 10 82.32 N <= 2fold 5 80.76 

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