November 2009
Web-based Interspecies Correlation Estimation
(Web-ICE) for Acute Toxicity: User Manual
Version 3.0
http: //www. epa. g o v/cea m p u b l/fcha i n/web ice/
Sandy Raimondo, Deborah N. Vivian, and Mace G. Barron
U.S Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Gulf Ecology Division
Gulf Breeze, FL 32561
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Reference Web-ICE as:
Raimondo, S., D.N. Vivian, and M.G. Barron. 2009. Web-based Interspecies
Correlation Estimation (Web-ICE) for Acute Toxicity: User Manual. Version 3.0. Office of
Research and Development, U. S. Environmental Protection Agency. Gulf Breeze, FL.
Disclaimer:
The information in this document has been reviewed in accordance with U.S.
Environmental Protection Agency policy and approved for publication. Approval does
not signify that the content reflects the views of the Agency, nor does mention of trade
names or products constitute endorsement or recommendation for use.
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Contents
Abstract 3
Introduction 4
Methods 5
I. Database Development 5
Aquatic (Fish and Invertebrates) 5
Wildlife (Birds and Mammals) 6
II. Model Development 6
III. Model Validation 7
Using the Web-ICE Program 8
I. Working with Web-ICE Aquatic or Web-ICE Wildlife Modules 9
Selecting Model Taxa 9
Estimating Toxicity 10
II. The Species Sensitivity Distribution (SSD) Module 11
Generating an SSD: 14
III. The Endangered Species Module 14
Producing an Endangered Species Toxicity Report 14
IV. Accessing Model Data 16
Guidance for Model Selection and Use 17
I. Statistical Definitions 17
II. Selecting a Model with Low Uncertainty 18
Rules of Thumb 18
Surrogate Species Selection: An Example 19
III. Evaluating Model Predictions 19
IV. Selecting Predicted Toxicity Values for SSDs 20
V. Applying Web-ICE in Ecological Risk Assessment (ERA) 20
Acknowledgements 22
References 22
Appendices 25
1
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Appendix I. Summary of acceptance requirements for data included in ICE
models 25
Appendix II. List of Species in Aquatic Database 27
III. List of Species in Wildlife Database 31
2
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Abstract
Predictive toxicological models are integral to ecological risk assessment
because data for most species are limited. Web-based Interspecies Correlation
Estimation (Web-ICE) models are least square regressions that predict acute toxicity
(LC50/LD50) of a chemical to a species, genus, or family based on estimates of relative
sensitivity between the taxa of interest and that of a surrogate species. Web-ICE 3.0
includes a total 1440 models for aquatic taxa and 852 models for wildlife taxa. For
aquatic species within the same family, Web-ICE models predict within 5-fold and 10-
fold of the actual value with 91 % and 96% certainty, respectively. For two species within
the same order, aquatic models predict within 5-fold and 10-fold of the actual value with
86% and 96% certainty, respectively. Overall for wildlife species, Web-ICE predicts
toxicity within 5-fold of the actual value with 85% certainty and within 10-fold of the
actual value with 95% certainty. Models predict within 5-fold and 10-fold of the actual
value with 90 and 97% certainty for wildlife surrogate and predicted taxa within the same
order. For both aquatic and wildlife taxa, model certainty increases with decreasing
taxonomic distance. Web-ICE 3.0 improves on earlier versions with the inclusion of an
endangered species module, improved functionality of the SSD module, and more
rigorous standardization of toxicity data.
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Introduction
Information on the acute toxicity to multiple species is needed for the assessment
of the risks to, and the protection of, individuals, populations, and ecological
communities. However, toxicity data are limited for the majority of species, while
standard test species are generally data rich. To address data gaps in species
sensitivity, the Interspecies Correlation Estimations (ICE) application was developed by
the U.S. Environmental Protection Agency (US EPA) and collaborators to extrapolate
acute toxicity to taxa with little or no acute toxicity data, including threatened and
endangered species (Asfaw et al. 2003). Web-based Interspecies Correlation
Estimations (Web-ICE) provides interspecies extrapolation models for acute toxicity in a
user-friendly internet platform.
ICE models estimate the acute toxicity (LC50/LD50) of a chemical to a species,
genus, or family with no test data (the predicted taxon) from the known toxicity of the
chemical to a species with test data (the surrogate species). ICE models are least
square regressions of the relationship between surrogate and predicted taxon based on
a database of acute toxicity values: median lethal water concentrations for aquatic
species (LC50; |ig/L) and median lethal oral doses for wildlife species (LD50; mg/kg
bodyweight). ICE models can be used to estimate acute toxicity when a toxicity is known
for a surrogate species or it can be estimated (e.g., QSAR), and there is an existing ICE
model between the surrogate and taxa of interest (e.g., species-species; species-genus;
species-family).
In addition to direct toxicity estimation from a surrogate species to predicted taxa,
Web-ICE contains a Species Sensitivity Distribution (SSD) module that estimates the
toxicity of all predicted species available for a common surrogate. Acute toxicity values
generated by Web-ICE are expressed as a logistic cumulative probability distribution
function in the SSD module to estimate an associated Hazardous Concentration (HC) or
Hazardous Dose (HD) (Dyer et al. 2006). For example, the HC5 corresponds to the 5th
percentile of the log-logistic species sensitivity distribution and is assumed to be
protective of 95% of tested species. ICE-generated SSD hazard levels have been
shown to be within an order of magnitude of measured HC5s (Dyer et al. 2006, Dyer et
al. 2008) and HD5s (Awkerman et al. 2008) and provide additional information for
ecological risk assessment.
This manual provides step-by-step instructions for using Web-ICE, as well as
information on the expanded databases, model development, model validation, and
user guidance on model selection and interpretation. User guidelines outlined in the
Guidance for Model Selection and Use section of this manual should be followed to
ensure high confidence and low uncertainty in model predictions used in risk
assessment. Web-ICE 3.0 improves on earlier versions with the inclusion of an
endangered species module, improved functionality of the SSD module, and more
rigorous standardization of toxicity data.
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Methods
I. Database Development
Aquatic (Fish and Invertebrates)
The database of acute toxicity used in development of ICE models included 5501
EC/LC50 values of 180 species and 1266 chemicals. The database was compiled from
the following EPA1 and public domain sources:
• US EPA ECOTOX (http://cfpub.epa.gov/ecotox/; accessed February 2009),
• US EPA Office of Pesticide Programs excotoxicity database (accessed January
2007)
• US EPA Office of Water Ambient Water Quality Criteria
• US EPA OPPT PreManufacture Notification (PMN)
• US EPA OPPT High Production Volume (HPV) Challenge Program
• US EPA Office of Research and Development data sources
• Mayer and Ellersieck 1986
• Open literature (for list of references, see Raimondo et al. 2008, 2009)
Data used in model development adhered to standard acute toxicity test condition
requirements of the American Society for Testing and Materials (ASTM 2007, and
earlier editions) and the US EPA Office of Prevention, Pesticides, and Toxic Substances
(US EPA 1996). Data were standardized for test conditions and organism life stage to
reduce variability (Appendix I). In short, selection criteria for aquatic test data were as
follows:
• Reported chemical name or structure with chemical active ingredient > 90%
• Open-ended toxicity values (i.e. > 100 mg/kg or <100 mg/kg) were excluded
• Endpoint was death (LC50) or immobilization (EC50)
• 48h EC/LC50 for daphnids, midges and mosquitoes; 96h EC/LC50 for fish and all
other invertebrates
• Juvenile only for fish, amphibians, insects, molluscs, decapods; all life stages for
other groups (Raimondo et al. 2009)
• Water quality parameters reported for test condition (e.g. temperature, salinity) or
confirmation that test conditions met appropriate guideline conditions (e.g. GLP,
previously reviewed OPP ecotoxicity data)
• Water quality parameters provided for normalization of metals, ammonia and
pentachlorophenol as directed by Ambient Water Quality Criteria (AWQC; US
EPA 1986)
1 All confidential business information (CBI) and data have been censored.
5
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When there was more than one toxicity value reported from multiple sources for a
species and chemical, the geometric mean of the values were used. In cases where the
range of minimum and maximum values for a chemical and species were greater than
10-fold, all data records for that chemical were removed for that species due to their
high variability. Toxicity test values for specific compounds were normalized according
to Ambient Water Quality Criteria procedures (e.g., specific metals adjusted to 50 mg/L
hardness; reported on element basis; pentachlorophenol and ammonia were
temperature and pH normalized; US EPA 1986). The resulting aquatic database was
used to develop models to predict toxicity to a species, genus, or family from a surrogate
species (see Appendix II).
Wildlife (Birds and Mammals)
The wildlife database was comprised of 4329 acute, single oral dose LD50 values
(mg/kg body weight) for 156 species and 951 chemicals. The data were collected from
the open literature (Hudson et al. 1984; Shafer and Bowles 1985, 2004; Shafer et al.
1983; Smith 1987) and from datasets compiled by governmental agencies of the United
States (US EPA) and Canada (Environment Canada) (Baril et al. 1994; Mineau et al.
2001). Data were standardized by using only data for adult animals and data for
chemicals of technical grade or formulations with > 90% active ingredient. Open-ended
toxicity values (i.e. > 100 mg/kg or <100 mg/kg) and duplicate records among multiple
sources were not included in model development. When data were reported as a range
(ie. 100-200 mg/kg; Hudson et al. 1984) or data were collected from multiple sources for
a species and chemical, the geometric mean of the values was used. In cases where
the range of minimum and maximum values for a chemical and species were greater
than 10-fold, all data records for that chemical were removed for that species due to
their high variability. Models derived from this wildlife database may be used to predict
toxicity to a species or family from a surrogate species. Genus level models were not
developed from the wildlife database because there were limited genera that had two or
more species (See Appendix III), which is requirement for development of higher taxa
models.
II. Model Development
Models were developed using least squares methodology in which both variables
are independent and subject to measurement error (Asfaw et al. 2003). For species-
level models developed from aquatic and wildlife databases, an algorithm was written in
S-plus (Insightful 2001) to pair every species with every other species by common
chemical. Three or more common chemicals per pair were required for inclusion in the
analysis. For each species pair, a linear model was used to calculate the regression
equation Log-io(predicted toxicity) = a + b*l_og-io(surrogate toxicity), where a and &are
the intercept and slope of the line, respectively. Genus (aquatic only) and family-level
models were similarly developed by pairing each surrogate species with each genus or
family by common chemical. Predicted genera and families required unique toxicity
values for two or more species within the taxon. Toxicity values for the surrogate
species were removed in cases where it was compared to its own genus or family. ICE
6
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models were only developed between two aquatic taxa or two wildlife taxa; there are no
models to predict toxicity to aquatic taxa from a wildlife species, or vice versa.
Only models that had a significant relationship (p-value < 0.05) are included in
Web-ICE. The following summarizes the number of significant models developed from
the aquatic and wildlife databases for different taxonomic levels:
1) Aquatic species: 782 models comparing 77 species to 77 species;
2) Aquatic genera: 287 models comparing 63 species to 28 genera;
3) Aquatic family: 371 models comparing 69 species to 27 families;
4) Wildlife species: 560 models comparing 49 species to 49 species;
5) Wildlife family: 292 models comparing 49 species to 16 families.
III. Model Validation
The uncertainty of each model was assessed using leave-one-out cross-
validation (Insightful 2001). In this method, each pair of acute toxicity values for
surrogate and predicted taxa were systematically removed from the original model. The
remaining data were used to rebuild a model and estimate the toxicity value of the
removed predicted taxa toxicity value from the respective surrogate species toxicity
value. This method could only be used for models with degrees of freedom equal or
greater than 2 (N > 4). To maintain uniformity among the large number of models
contained within Web-ICE, the "N-fold" difference among each estimated and actual
value was calculated and used to determine the fitness of the estimated toxicity value.
For aquatic species, inter-laboratory variation of acute toxicity test data for a given
species and chemical can be as great as a 5-fold difference (Fairbrother 2008). For
wildlife species, the average range of multiple toxicity measurements for a specific
chemical and species was determined to be between 4.0 and 6.4 (Raimondo et al.
2007). Thus, a 5-fold difference was deemed a good fit in the validation analysis of both
aquatic and wildlife models.
The cross-validation success rate was calculated for each model as the
proportion of removed data points that were predicted within 5-fold of the actual value
from models that were statistically significant. In cases where the removal of a xy data
pair resulted in the development of a model that was not significant at the p < 0.05 level,
these replicates were not included in the cross-validation success rate. This is because
models that are not significant at the p<0.05 level have a greater risk of Type I error.
This was only the case for models with low degrees of freedom (<8) and a p-value
between 0.01 and 0.05 in the original model.
There is a strong relationship between taxonomic distance and cross-validation
success rate, with uncertainty increasing with larger taxonomic distance (Raimondo et
al., 2007). In aquatic species, models predict within 5-fold and 10-fold of the actual value
with 91 and 96% certainty for surrogate and predicted taxa within the same family, and
for 86 and 96% within the same order. In wildlife species, models predict within 5-fold
and 10-fold of the actual value with 90 and 97% certainty for surrogate and predicted
taxa within the same order. Model certainty decreases with increasing taxonomic
distance. A more detailed account of model uncertainty as it relates chemical mode of
action/class is discussed in Raimondo et al. (2007).
7
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Using the Web-ICE Program
The Web-ICE platform contains separate modules that predict acute toxicity to
aquatic (vertebrates and invertebrates) species, genera, or families (ICE Aquatic) and
wildlife (terrestrial birds and mammals) species or families (ICE Wildlife) (Figure 1). The
Species Sensitivity Distribution Module is available for aquatic and wildlife species and
batch processes species level toxicity from all entered surrogates. The Endangered
Species Module, also available for aquatic and wildlife taxa, predicts toxicity to listed
species from all available species, genus, or family level models for the entered
surrogates. Each module is accessible from either the home page or from the blue
navigation bar along the left side of the page. Before working with a Web-ICE module,
you must first decide if you are going to work with aquatic or wildlife taxa, the program
does not contain models that estimate wildlife toxicity from an aquatic surrogate, or vice
versa.
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The Web-based Interspecies Correlation Estimation (Web-ICE) application estimates acute
toxicity to aquatic and terrestrial organisms for use in risk assessment. Please refer to the
User Manual for detailed instructions on using Web-ICE
Web-ICE Modules
ICE Aquatic
Aquatic vertebrates invertebrates
Species
Genus
Family
ICE Wildlife
TerrestriaJ Birds Mammals
Species
Family
Species Sensitivity Distribution Module
ICE Aquatic
ICE Wildlife
Endangered Species Module
ICE Aquatic
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Figure 1. Home page of Web-ICE program
8
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I. Working with Web-ICE Aquatic or Web-ICE Wildlife Modules
Selecting Mode! Taxa
1. From either the home page or the blue navigation bar, click the link for the
module with which you will be working (Aquatic species, genus, or family; Wildlife
species or family).
2. You will then be directed to a Taxa Selection Page (Figure 2) which will allow you
to select your surrogate and predicted taxa for the model.
3. You may search for your surrogate and predicted taxa by either common name or
scientific name by selecting the appropriate option in the Sort by: drop down
menu. The default is set to common name.
4. From the drop down menus, select the surrogate species and predicted taxon. It
does not matter which you select first; however, the second choice is limited to
the models available for the taxon chosen first.
5. To change any of your selections, press Reset and start again.
6. Click Continue to be directed to the calculator page for toxicity estimation.
If there is not a model for your predicted species of interest, you will need to use
a genus or family-level model to predict toxicity. The available models may be
determined by browsing through the genus (aquatics only) and family level modules, or
by searching through the spreadsheets of model information available through the
Download Model Data option on the blue navigation bar. The downloadable Microsoft
Excel® spreadsheets provided for each Web-ICE module may be sorted by surrogate
species or predicted taxa to identify available models.
9
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Aquatic Species - Taxa Selection Page
Surrogate:
| Rainbow trout (Oncorhynchus mykiss)
Sort By | Common Name
Reset |
"Hi
irown trout (balmo trutt
Continue
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Figure 2, Taxa selection page
Estimating Toxicity
The surrogate and predicted species selected from the previous page are listed
at the top of a calculator page (Figure 3). This page is divided into four parts: input,
calculated results, model statistics, and model graphic . The known toxicity for the
surrogate species is entered under Surrogate Acute Toxicity, below which the desired
confidence limits can be selected (Figure 3A). Predicted toxicity estimates and
confidence intervals are displayed under Predicted Acute Toxicity (Figure 3B). The
bottom left side of the page contains the model statistics (Figure 3C). Please refer to
the Statistical Definitions section of this manual for more specific information. The graph
shows the data (LC50/LD50 values) used to develop the model, the regression line
(straight inner line), and 95% confidence intervals (curved outer lines) (Figure 3D). The
surrogate and predicted taxa are labeled on the X and Y axes, respectively. Both the
model statistics and the graph are unique for each model and will change for each
surrogate species and predicted taxon.
1. Enter the acute toxicity value in the box located under Surrogate Acute Toxicity
(Figure 3A).
2. Select your desired confidence interval (90, 95, or 99%) from the drop down
menu located under Select Confidence Interval (Figure 3A). The default for the
confidence intervals is 95%.
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3. Press Calculate
4. The calculated values will appear in the three boxes labeled Predicted Acute
Toxicity, Lower Limit and Upper limit (Figure 3B).
5. Log-transformed values of the surrogate and predicted toxicity values appear in
parentheses next to the values.
6. If the entered surrogate toxicity value is outside the range of toxicity values used
to develop the model, a pop-up with the warning "This value is outside the x-axis
range for this model. Continue?" will appear. The user may select "OK" to
proceed to calculate the toxicity value or hit cancel to enter another value.
7. To select a different model, select the link to the desired module in the blue
navigation bar on left side of the page.
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Calculator - Aquatic Species
Surrogate Species: Rainbow trout (Oticorhynchus mykiss)
Predicted Species: Brown trout (Salmo trutta)
Surrogate Acute Toxicity (log value)
Predicted Acute Toxicity (log value)
|150 uq, L (2.17}
142.71 pg,L(2.15)
Select Confidence Interval:
A
Lower Limit
Upper Limit
195%
104.10
Mg.'L || 195.65 pg/L
B
Calculate
Model Information
Intercept:
Slope: Z"1
Degrees of Freedom (N-2):
R2:
p-value:
Average value of surrogate (log value):
0.042271
0.970642
17
0.964248
0.000000
119.80 (2.07)
Minimum value of surrogate (log value): 0.163864 (-0.785515)
Maximum value of surrogate (log value): 1 7808.08 (4.25)
Mean Square Error (MSE): 0.079728
Sum of Squares (Sxx): 38.80
Cross-validation Success (%): 94.73
Taxonomic Distance: 2
Oncorhynchus mykiss
(Log LC50)
-igure 3, Calculator Page
II. The Species Sensitivity Distribution fSSD) Module
Species Sensitivity Distributions (SSDs) are probabilistic models that describe
the sensitivity of biological species to a chemical. SSDs generated in Web-ICE are log-
logistic cumulative distribution functions of toxicity values for multiple species (de Zwart
2002) and are used to estimate a hazard level (hazardous concentration (HC) or
hazardous dose (HD)) that is protective of most test species (e.g. 95%) by estimating
11
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the concentration or dose at a corresponding percentile (e.g. 5th) of the distribution (Dyer
et al. 2006).
The SSD modules for aquatic and wildlife species generate SSDs from Web-ICE
toxicity values estimated from one or more surrogate species. Toxicity values for one or
more surrogate species are used to simultaneously estimate toxicity to all possible
predicted species with existing Web-ICE models. The SSD is then generated using all
estimated toxicity values and the entered toxicity of the surrogate species. Toxicity
values for up to 25 surrogate species may be entered (Figure 4). If more than one
surrogate species estimates toxicity to the same predicted species, Web-ICE selects the
toxicity value with the smallest confidence intervals. If multiple surrogates are used and
a predicted value is estimated for one of the surrogate species, Web-ICE uses the
entered value for that species and excludes the predicted value(s) from the SSD.
An HC/HD level is automatically calculated from the distribution. The user can
deselect toxicity values for predicted species that they wish to exclude from the SSD by
clicking on the box to the left of the predicted species (Figure 5), and the associated
HC/HD value is automatically recalculated. An HC/HD drop down menu on the output
page allows the user to specify the hazard level to calculate. HC1/HD1 corresponds to
the 1st percentile, HC5/HD5 corresponds to the 5th percentile, and HC10/HD10
corresponds to the 10th percentile. The default is set to HC5 for aquatic species and
HD5 for wildlife species.
Web-ICE uses the SSD described by the logistic distribution function of de Zwart
(2002):
F(C)= 1/(1 + exp ((a-C)/p))
The logio-transformed environmental concentration (or dose) of the evaluated chemical
is represented by C, the parameter, a, is the sample mean of the log™ -transformed
toxicity values and p is defined as V3/71 * o, where o is the standard deviation of the log-10
-transformed toxicity values (de Zwart 2002). The HC/HD level is determined as the
percentile of interest (e.g. 5th) of the described distribution. Corresponding SSDs are
also developed from the upper and lower confidence limits of the predicted toxicity
values and are used to calculate the upper and lower bounds of the HC/HD value at a
given percentile. For example, the lower bound of the HC5 is calculated as the 5th
percentile of the SSD developed from the estimated lower confidence limit of each
predicted toxicity value. Similarly, the upper bound of an HC5 is calculated as the 5th
percentile of the SSD developed from the estimated upper limit of each predicted toxicity
value.
12
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Species Sensitivity Distributions - Aquatic Species
Multiple Surrogate SSD
Surrogate:
r
Sort By | Common Name H
Species
Fathead minnow (Pimephales promelas)
Bluegill (Lepomis macrochirus)
Daphnid (Daphnia magna)
Sheepshead minnow (Cyprinodon variegatus)
Calculate SSD I
Toxicity'
|150
Remove Species ]
|125
Remove Species
|75
Remove Species
1100
Remove Species
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News by Email ' • ) Widgets
Figure 4. SSD taxa selection page.
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Species Sensitivity Distributions - Aquatic
Surrogate Species: Fathead minnow (Pimephales promelas), Bluegill (Lepomis
macrochirus), Daphnid (Daphnia magna), Sheepshead minnow (Cyprinodon variegatus)
Input Toxicity: 1 50, 125, 75, 100 ug L
|HC5 8.44 ug, L 95% Confidence Interval: 1.44 - 21.62
Common Name Scientific
Sort Sort
W Stonefly
P Amphipod
W' Stonefly
P Daphnid
Sort 1
Claassenia sabulosa 1.38
Hyalella azteca 3.58
Pteronarcella badia 3.66
Ceriodaphnia dubia 8.20
Estimated 95% Show Data:
Toxicity Confidence | Surrogate
W Amphipod Gammarus lacustris 8.26
P Chinook salmon Oncorhynchus 13.41
tshawytscha
P Amphipod Gammarus 18.39
pseudolimnaeus
17 Shortnose Acipenser 32.82
sturgeon brevirostrum
P Mvsid Americamvsis bahia 39.30
Figure 5. SSD output page.
Intervals
Sort
0.460 - 4.1.3 Fathead minnow (Pimephales
promelas)
0.048 - Daphnid (Daphnia magna)
266.24
2.15 - 6.21 Fathead minnow (Pimephales
promelas)
0.769 - Fathead minnow (Pimephales
87.56 promelas)
1.71 - 39.90 Bluegill (Lepomis macrochirus)
1.63 - Bluegill (Lepomis macrochirus)
110.04
6.29 - 53.71 Bluegill (Lepomis macrochirus)
8.99 - Fathead minnow (Pimephales
119.81 promelas)
26.84 - Daohnid (Daohnia maana)
"3
13
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Generating an SSD:
1. Under the SSD module, select either Aquatic or Wildlife.
2. On the SSD taxa selection page, select your surrogate species from the drop
down menu and click Add to add the species as a surrogate.
3. If desired, select additional surrogate species from the drop down menu and click
Add. A maximum of 25 species can be selected.
4. To remove a surrogate species from the list after it is added, click Remove next to
the species name.
5. Enter the known toxicity for the surrogate species, click Calculate SSD.
6. On the SSD output page, the HC/HD level may be changed from drop down box.
The hazard level is automatically recalculated if the level is changed. The default
is the HC/HD5.
7. The warning "Input toxicity is greater (less) than model maximum (minimum)"
indicates if a predicted value was generated from a surrogate species toxicity
value that was outside the range of toxicity values used to generate that model.
8. The user can unmark the box to the left of a predicted species to exclude it from
the SSD, which is automatically recalculated. (NOTE: See Selecting Predicted
Toxicity Values for SSDs in the Guidance for Model Selection and Use section
below for guidance on removing estimated toxicity values).
9. The drop down menu in the Show Data column provides additional model
information (surrogate, taxonomic distance, cross-validation success rate,
degrees of freedom, R2, p-value, or mean square error) for the user to view.
10. The user may sort the ICE-estimated toxicity values by each column by selecting
the sort tab below the column heading.
III. The Endangered Species Module
The Endangered Species Module batch processes toxicity values for endangered
species from all species, genus, and family level models available for the entered
surrogates. The list of threatened and endangered species was obtained from the US
Fish and Wildlife Service Threatened and Endangered Species module of
Environmental Conservation Online System (http://ecos.fws.gov/tess_public; Accessed
August 2007), which was linked to Web-ICE species, genus, and family model
databases for aquatic organisms and wildlife. Users may predict to all available
endangered species within a broad taxonomic groups (e.g. Fishes) or a particular
species (e.g. Atlantic Salmon, Salmo saia/) using up to 25 surrogates.
Producing an Endangered Species Toxicity Report
1. Under the Endangered Species module, select either Aquatic or Wildlife.
14
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2. On the Endangered Species taxa selection page, select either the broad taxa of
interest (e.g. Fishes) or a particular species of interest from the drop down menu
(Figure 6).
3. Select your surrogate species from the drop down menu and click Add to add the
species as a surrogate. A maximum of 25 species can be selected.
4. To remove a surrogate species from the list after it is added, click Remove next to
the species name.
5. Enter the known toxicity for the surrogate species, click Calculate.
6. The Endangered species output page provides the estimated toxicity for each
predicted taxa, the model level (e.g. species), surrogate, and model information
(Figure 7).
7. The user may sort the ICE-estimated toxicity values by each column by selecting
the sort tab below the column heading.
Interspecies Correlation Estimation HContact us Share
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Aquatic Species
Aquatic Genus
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Wildlife Species
Wildlife Family
Species Sensitivity
Distributions
Endangered Species
Basic Information
Download Model Data
Bibliography
Endangered Species Module - Aquatic Species
Step 1: select Taxa of Interest
f All Species Fishes f Amphibians C' Crustaceans Molluscs
Species:
I 3
Sort By: | Common Mame ^ 1
Step 2: select Surrogate(s)
Surrogate(s):
Sort By
| Common Mame ~ |
Species
Bluegill (Lepomis macrochirus)
Fathead minnow (Pimephales promelas) [
Daphnid (Daphnia magna)
Toxicity (pg L)
Remove Species
Remove Species
Remove Species
Calculate
Reset this Form
Please address all comments and questions to the webmaster
:igure 6. Taxa selection page of Endangered Species module.
15
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You are her
I ruldn*.
Predtctecf Taxa
Sort ;
Cficorh^richus
Ontarrv.tichus
Gncofhynchiis
Cricorhyrichus
SsJ'-ncnidc*
¦¦ielasj, Sli;egi:l (Leporrns macrocfrirtiS). Rainbow trout (Onrorhy?ich"js
» Exposure 4-sessnem Foc-d C "/• "• .» >» - " ,-aiic Sweats »Resu!ts
Species - Aquatic
Surrogate Spec
;ryk)$s-, Sheepshj
Input Toxicity: 500:400; 200, 300 yg i
Estimated 95% Degrees of R2 p- value Mean Cross- l axonornic
Toxicity Confidence Freedom (N- So rr Sort Square Error validation Distance
Success m Soil
Model Surrogate
level Sort
Soil
Sott
so^ces Eli^egill {Lesomis
macrochirus)
ss>eces Ra-nac.v :rcut 286.82
(Oiicofhynchus ny-ciss?
geiL-s Fathead 2~j ' Ba
fpimeotiaies orcmetas)
Intervals
Soft
' 3.27 -
308 87
4 78 99
2)
Sort
gent-s
¦¦ ¦ "lyiuss.'
minnow 243 <
rami:,' 1 ..1 1 ¦; .
family
iamlk' Ra:nbow trout
t.elas'-
(MSE)
Sort
0.9039 O.OC35 0 2064
>.9793 0.9COO 0 0644
5 832S 0-1000 03636
J.S840 O.OCOO 022:5
5 9572 0 0000 0 072:
0 603-2 0 0000 0 :
0.8555 O.OCOO 0 3022
J.SS52 0.0000 0.2 ¦ ?
0 955 5 0 0000 0 0683
Sort
83.33
zigure 7. Endangered species predicted toxicity report
IV. Accessing Model Data
A list of chemicals in the aquatic and wildlife databases is available for download
using the Chemicals in Aquatic and Chemicals in Wildlife links. In the Chemicals in
Aquatic file the chemical CAS number and associated toxicity values used in each
model are provided. The Chemicals in Wildlife file contains the number of species
present for each chemical. The acute data used to develop the ICE models for wildlife
are not available due to proprietary rights of some information.
Models for all Web-ICE aquatic and wildlife modules are available as a
downloadable Microsoft Excel® spreadsheet under the Download Model Data option on
the blue navigation bar. The data spreadsheets include model parameters (R2, p-value,
df, intercept, slope, standard error of the slope, Sxx, and MSE), general model
information (taxonomic distance, cross-validation success rate), descriptive statistics
(average, minimum, and maximum values of the surrogate species), and critical t-values
used to calculate 90, 95, and 99% confidence intervals (t90, t95, t99). These
spreadsheets provide all of the information that is needed to generate Web-ICE toxicity
estimates and confidence intervals, as well as facilitate the selection of the most robust
models.
Using model data provided, users may calculate toxicity as:
Predicted toxicity = 10A(intercept + slope*Logio(surrogate toxicity))
16
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And confidence intervals as:
Lower bound = 10A(log(predicted) - ti.a*V[MSE*(1/n + (log(x) - x.ave)A2/Sxx) ])
Upper bound = 10A(log(predicted) + ti_a*V[MSE*(1/n + (log(x) - x.ave)A2/Sxx) ])
Where x is the untransformed value of surrogate toxicity, x.ave is the average value of
log-transformed surrogate toxicity values, Sxx is the sum of squared deviations of the
surrogate, MSE is the mean square error, and ti_a is the value of the t distribution
corresponding to the desired level of confidence (ie. 90%, 95%, 99%).
Guidance for Model Selection and Use
I. Statistical Definitions
Several statistics are provided with each model and may be used to evaluate the
accuracy and precision of the estimated value. These statistics are shown to the left of
the graph on the calculator page (Figure 3C) and are provided in the spreadsheet of
model information available in the Download Model Data option. The following provides
a basic interpretation of model statistics to help guide users in model selection:
Intercept - The log™ value of the predicted taxon toxicity when the log™ of the
surrogate species toxicity is 0.
Slope - The regression coefficient, represents the change in log™ value of the
predicted taxon toxicity for every change in log™ value of the surrogate species
toxicity.
Degrees of Freedom (df, N - 2) - Reflects the number of data points used to build
the model. Degrees of freedom are related to statistical power; in general, the
higher the degrees of freedom, the more robust the model.
R2 - The proportion of the data variability that is explained by the model. The
greater the R2 value and the closer it is to one, the more robust the model is in
describing the relationship between the predicted and surrogate taxa.
p-value - The significance level of the linear association and the probability that
the linear association was a result of random data. Models with lower p-values
are more robust. Model p-values of < 0.00001 are reported as 0.00000.
Average value of the surrogate - The average of toxicity values for the surrogate
species used in the model. The first number is the actual value and the number in
parentheses is the log-transformed value.
17
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Minimum value of the surrogate - The lowest toxicity value for the surrogate
species used in the model. The first number is the actual value and the number
parentheses is the log-transformed value.
Maximum value of the surrogate - The largest toxicity value for the surrogate
species used in the model. The first number is the actual value and the number
parentheses is the log-transformed value.
Mean Square Error (MSE) - An unbiased estimator of the variance of the
regression line.
Sum of Squares (Sxx) - Sum of squared deviations of the surrogate.
Cross-validation Success - The percentage of removed data points that were
predicted within 5-fold of the actual value. Models with a Cross-validation
Success of "na" are those that either had df = 1 or where no significant models
were developed when data points were removed.
Taxonomic Distance - Describes the taxonomic relationship between the
surrogate and predicted taxa. Two taxa within the same genus have taxonomic
distance of 1; within the same family = 2; within the same order = 3; within the
same class = 4; within the same phylum = 5; within the same kingdom = 6.
II. Selecting a Model with Low Uncertainty
Rules of Thumb
Model attributes, such as taxonomic distance of the predicted and surrogate
species, model parameters (listed below) and cross-validation success rate, should be
used to select models with low uncertainty. For best estimates, models should be
selected that possess the following:
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
The best estimations generally occur for surrogate and predicted taxa that are
within the same genus, family, or order and for models with R2 > 0.6 (Raimondo et al.
2007). In general, models with more degrees of freedom (df) have greater statistical
power and choosing a model with df greater than 8 is recommended to reduce model
18
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uncertainty. A priori power analysis determined that linear models with df > 8 have
enough statistical power (1-I3> > 0.8) to sufficiently increase the chance of finding a
significant relationship within the data. It is also recommended to choose models with p-
values < 0.01 to further reduce the chance of Type I errors in the toxicity estimations.
Cross-validation success rate is a conservative estimate of model uncertainty and
should not be interpreted as an exact estimate of model error. Cross-validation removes
data from the original model, potentially causing a large change in the model for small
datasets. Due to changes in a model (i.e. reduced df, altered slope/intercept) during this
validation process, cross-validation success rate should be considered only an estimate
of generalization error. Particularly for models built from small datasets, actual error can
be expected to be lower than cross-validation error.
Surrogate Species Selection: An Example
In an example of how to select a suitable model, Raimondo et al. (2007) outlined
a selection procedure to find an appropriate surrogate species to estimate the toxicity of
a chemical to red-winged blackbird. In the example, toxicity data for the chemical of
interest was available for northern bobwhite, mallard, Japanese quail, fulvous whistling
duck, common grackle, and house sparrow, making them all potential surrogates. The
common grackle and house sparrow have the closest taxonomic distance (2, same
family; 3, same order); the other potential surrogates in this example have a taxonomic
distance of 4 (same class). Of the grackle and house sparrow, both have similar MSE
(~0.13), however house sparrow has a higher model R (0.84), higher cross-validation
success rate (95), and greater degrees of freedom (107), and is the best surrogate for
red-winged blackbird in this example. The grackle would also provide good surrogacy,
with high R2 (0.65), high cross-validation success rate (93), and good degrees of
freedom (54). If neither of these species were available surrogates, Japanese quail (R2
= 0.79, MSE = 0.15, df = 135, cross-validation success rate = 91) would be the next best
surrogate, followed by northern bobwhite (R2 = 0.63, MSE = 0.23, df = 45, cross-
validation success rate = 85) and mallard (R2 = 0.48, MSE = 0.34, df = 80, cross-
validation success rate = 79). Although fulvous whistling duck has the highest model R2,
low degrees of freedom (df = 2) and comparatively higher MSE (0.30) do not make it as
suitable of a surrogate as the other species.
III. Evaluating Model Predictions
Uncertainty of model predictions may be evaluated by assessing (1) the
characteristics of the model used in the predictions, and (2) the value of the input data
relative to the data used to generate the model. The former was discussed in the
previous section and the Rules of Thumb should be followed to ensure high confidence
in model selection. Even for robust models, however, model uncertainty increases
outside the range of surrogate species toxicity values that were used to develop the
model.
Uncertainty may be evaluated by reviewing the confidence intervals calculated
with the predicted value. Narrow confidence intervals represent higher confidence that
19
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the model fits through the range of datapoints for the entered surrogate species toxicity.
If the surrogate toxicity value entered into an ICE model is outside the range of
surrogate toxicity data used to generate the model, the warning "This value is outside
the x-axis range for this model. Continue?" will appear to alert the user. This warning
alone does not indicate low confidence in the model estimate, but should be used in
conjunction with the calculated confidence intervals to evaluate the model prediction.
For example, if the upper and lower bounds of the confidence interval are several orders
of magnitude from the predicted value, caution should be used in applying the ICE
estimate in risk assessment.
IV. Selecting Predicted Toxicity Values for SSDs
The SSD modules of Web-ICE automatically predict toxicity values from all
available models for the selected surrogate species simultaneously. The user has the
discretion to remove predicted toxicity values from the SSD to either customize the SSD
for a particular taxa (e.g. birds only, fish only), or to remove predicted toxicity values with
large confidence intervals. If an estimated toxicity value was derived from an input value
that was outside of the range of surrogate species data used to generate the model from
which it was predicted, a warning appears next to the value indicating the maximum or
minimum value of the model. This warning alone does not indicate low confidence in the
model estimate, but should be used in conjunction with the calculated confidence
intervals to evaluate the model prediction.
Users should also use the confidence intervals around the HC/HD level to guide
the selection of toxicity values to exclude from the SSD. Cases in which the upper
bound of the SSD is less than the HC/HD level occur when predicted toxicity values with
extremely large confidence intervals are included in the SSD; removal of predicted
toxicity with such confidence intervals results in HC/HD values with adequate
confidence. Users may also refer to the model information provided by the Show Data
dropdown menu when selecting data to include in SSDs.
V. Applying Web-ICE in Ecological Risk Assessment fERA)
Web-ICE was developed to support both chemical hazard assessment and
ecological risk assessment (ERA) by providing a method to estimate acute toxicity to
specific taxa, such as endangered species, or to a larger number of taxa (species,
genera, family) with known uncertainty. Potential applications of acute toxicity values
generated by Web-ICE include the problem formulation phase of an ERA to screen for
contaminants of potential concern and in the analysis phase to characterize effects to a
larger number of species. The estimation of species-specific toxicity values using Web-
ICE is recommended as an alternative to safety factors typically applied when
extrapolating toxicity or risks to taxa without chemical and species-specific toxicity data.
Another potential application of the chemical and taxon-specific acute toxicity estimates
generated from ICE models include input into existing exposure and risk models (e.g.
TREX; EPA, 2005). Web-ICE generated toxicity values may also be used in the analysis
20
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of uncertainty and variability in toxicity to ecological receptors in both screening level
and baseline or Tier II ERAs.
In the absence of taxa-specific ICE models, Web-ICE can be used to generate
SSDs and estimated 1st, 5th or 10th percentile values of the cumulative distribution of
species-specific toxicity values. These percentile values, expressed as the hazard
concentration (e.g. HC5) or hazardous dose (e.g. HD5), provide an estimate of toxicity
at a prescribed level of species protection with known uncertainty. Hazard
concentrations could be used in ERA in place of species-specific toxicity values or as a
component of the uncertainty analysis.
21
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Acknowledgements
For database development, the authors would like to thank Sonny Mayer (US EPA,
retired), Thomas Steeger and Brian Montague (US EPA, Office of Pesticide Programs),
Don Rodier (US EPA, Office of Pollution Prevention and Toxics), Pierre Mineau, Alain
Baril and Brian Collins (National Wildlife Research Centre, Environment Canada), Chris
Russom and Teresa Norberg-King (US EPA, Mid-Continent Ecology Division), and
Christopher Ingersoll and Ning Wang (Columbia Environmental Research Center, U.S.
Geological Survey). Special thanks to Wally Schwab and Derek Lane (Computer
Sciences Corporation) for constructing the website, and to Carl Litzinger (US EPA, Gulf
Ecology Division) and David Owens (Computer Sciences Corporation) for their
facilitation of website development. Also, thanks to our support personnel: Marion
Marchetto, Anthony DiGirolamo, Brandon Jarvis, Christel Chancy, Nathan Lemoine,
Nicole Allard, Laura Dobbins, Cheryl McGill, Sarah Kell, and Crystal Jackson. Peer
review and beta testing of the website were contributed by Larry Goodman, Michael
Murrell, Raymond Wilhour, and Susan Yee (US EPA, Gulf Ecology Division), Rick
Bennet (US EPA, Mid-Continent Ecology Division), Glen Thursby (US EPA, Atlantic
Ecology Division), and Anne Fairbrother (US EPA, Western Ecology Division).
References
American Society for Testing and Materials (ASTM). 2007. Standard guide for
conducting acute toxicity tests with fishes, macroinvertebrates, and amphibians. E
729-96(2007). Philadelphia PA..
Asfaw, A., M. R. Ellersieck, and F. L. Mayer. 2003. Interspecies Correlation Estimations
(ICE) for acute toxicity to aquatic organisms and wildlife. II. User Manual and
Software. EPA/600/R-03/106. U.S. Environmental Protection Agency, National
health and Environmental Effects Research Laboratory, Gulf Ecology Division, Gulf
Breeze, FL. 14 p.
Awkerman, J., S. Raimondo, and M.G. Barron.2008. Development of Species Sensitivity
Distributions for wildlife using interspecies toxicity correlation models. Environmental
Science and Technology. 42 (9): 3447-3452.
Baril, A., B. Jobin, P. Mineau, and B. T. Collins. 1994. A consideration of inter-species
variability in the use of the median lethal dose (LD50) in avian risk assessment.
Technical Report No. 216. Canada Wildlife Service, Headquarters.
De Zwart, D. 2002. Observed regularities in species sensitivity distributions for aquatic
species. In Species Sensitivity Distributions in Ecotoxicology, L. Posthuma, G.W.
Suter, T.P.Traas, Eds. Lewis Publishers, Boca Raton, FL. pp133-154.
Dyer, S. D., D. J. Versteeg, S. E. Belanger, J. G. Chaney, and F. L. Mayer. 2006.
Interspecies correlation estimates predict protective environmental concentrations.
Environ. Sci. Technol.. 40: 3102-3111.
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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. 42: 3076-3083.
Fairbrother, A. 2008. Risk Management Safety Factor. In. Encyclopedia of Ecology, vol.
4. S. E. Jorgensen and B. D. Fath (eds.). Elsevier publishing, pp. 3062-3068.
Hudson, R. H., R. K. Tucker, and M. A. Haegele. 1984. Handbook of toxicity of
pesticides to wildlife. U.S. Fish and Wildlife Service, Resource Publ. 153,
Washington D.C. 90 p.
Insightful. 2001. S-plus 6 Guide to Statistics. Volume 1. Insightful Corporation, Seattle,
WA.
Mayer, F. L. 1987. Acute toxicity handbook of chemicals to estuarine organisms.
EPA/600/X-97/332. U.S. Environmental Protection Agency, National health and
Environmental Effects Research Laboratory, Gulf Ecology Division, Gulf Breeze, FL.
274 p.
Mayer, F. L. and M. R. Ellersieck. 1986. Manual of acute toxicity: Interpretation and data
base for 410 chemicals and 66 species of freshwater animals. US Fish and Wildlife
Service Resource Publication 160. Washington DC. 579 p.
Mineau, P., A. Baril, B. T. Collins, J. Duffe, G. Joerman, and R. Luttik. 2001. Pesticide
acute toxicity reference values for birds. Rev. Environ. Contam. Toxicol 170: 13-74.
Raimondo, S., P. Mineau, and M. G.Barron. 2007. Estimation of chemical toxicity in
wildlife species using interspecies correlation models. Environ. Sci. Technol. 5888-
5894.
Raimondo, S., D.N. Vivian, C. Delos, M.G. Barron. 2008. Protectiveness of Species
Sensitivity Distribution Hazard Concentrations for Acute Toxicity Used in
Endangered Species Risk Assessment. Environ Toxicol Chem. 27 (12): 2599-2607.
Raimondo, S., D.N. Vivian, and M.G. Barron. 2009. Standardizing acute toxicity data for
use in ecotoxicological models: influence of test type, life stage, and concentration
reporting. Ecotoxicology. 18:918-928.
Shafer, E. W. Jr. and W. A. Bowles Jr. 1985. Acute oral toxicity and repellency of 933
chemicals to house and deer mice. Arch. Environ. Contam Toxicol. 14: 111-129.
Shafer, E. W. Jr. and W. A. Bowles Jr. 2004. Toxicity, repellency or phototoxicity of 979
chemicals to birds, mammals and plants. Research Report No. 04-01. United States
Department of Agriculture, Fort Collins, CO. 118 p.
Shafer, E. W. Jr., W. A. Bowles Jr. and J. Hurlbut,. 1983. The acute oral toxicity,
repellency and hazard potential of 998 chemicals to one or more species of wild and
domestic birds. Arch. Environ. Contam Toxicol. 12: 355-382.
Smith, G. J. 1987. Pesticide use and toxicology in relation to wildlife: organophosphorus
and carbamate compounds. Resource Publication 170. United States Department of
the Interior, Washington, DC. 171 p.
US Environmental Protection Agency (EPA). 1986. Ambient water quality criteria for
pentachlorophenol. EPA 440/5-86-009.
US Environmental Protection Agency (EPA). 1996. Ecological Effects Test Guidelines.
OPPTS 850.1075 Fish Acute Toxicity Test, Freshwater and Marine. EPA 712-C-96-
118. Washington DC.
US Environmental Protection Agency (EPA). 2005. TREX: Terrestrial Residue
Exposure model. Office of Pesticide Programs. U.S. Environmental Protection
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Agency.
http://www.epa.gOv/oppefed1/models/terrestrial/trex_usersguide.htm#content4
US Environmental Protection Agency (EPA). 2006. ECOTOX Ecotoxicology Database.
http://cfpub.epa.gov/ecotox. Duluth MN.
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Appendices
Appendix I. Summary of acceptance requirements for data included in
ICE models
Component
Information required
Acceptance requirements
Test organism
Aquatic taxa tested
fish, aquatic invertebrates,
amphibians
species level model: identifiable
to genus and species
genus or family level model:
identifiable to genus or family
Life stage1
juvenile only: fish, amphibians,
insects, mollusks, decapods
all life stages: all other species
Salinity requirements
identifiable as freshwater (FW)
or saltwater (SW; estuarine or
marine) organism
Test chemical
Test chemical identity
reported CAS, chemical name
or structure
confirmed name and CAS
Test chemical purity
>90% or analytical/reagent
grade or equivalent
Single compound tested
CAS corresponds to single
compound or element
mixtures excluded except for
chemical salts and specific
congener mixtures
Test conditions
Aqueous exposure
no sediment, dietary or mixed
dose exposures
no phototoxicity results
Test duration
48 hr: daphnids, midges,
mosquitoes
96 hr: all other species
Test type
static, flow-through or static
renewal
Temperature'*
species specific (+ 3C)
Dissolved oxygen3
Test type specific4
Salinity3
<1 ppt: FW species5
l-5ppt: Cyprinodon bovinus
>15 ppt: SW species6
25
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Component
Information required
Acceptance requirements
pH or hardness (FW only: required for
specific chemical normalizations)
pH: ammonia,
pentachlorophenol (PCP)
Hardness: Ag, Cu, Cd, Cr(III),
Pb, Ni, Zn
Reported toxicity
value
Acute toxicity endpoint: death (LC50) or
immobilization (EC50)
48 hrEC50/LC50: daphnids,
midges, mosquitoes
96 hr EC50/LC50: all other
invertebrates
96 hrLC50: fish, amphibians
Concentration units
mass/volume or molar units
Toxicity value
standardization
Concentration units
conversion to ug/L
Chemical specific normalizations'
PCP: pH 6.5
ammonia: pH 8; temperature
dependent
Ag, Cu, Cd, Cr(III), Pb, Ni, Zn:
hardness 50 mg/L
Element specific normalization
Ag, Al, Cu, Cd, Co, Cr(III),
Cr(VI), Hg, NH4, Ni, Pb, Zn
1. if life stage not reported, must be determined through reported age/size
2. only tests of single compounds; included metal and other chemical salts, and specific
congener mixtures (e.g., standard Aroclors, toxaphene)
3. meets ASTM or equivalent test guidelines for test species.
4. Test type specific dissolved oxygen saturation. Static: <48 hr 60-100%; >48 hr 40-100%.
Static renewal or flow-through: 60-100%.
5. FW: test water source identifiable as freshwater, reported salinity <1 ppt, or test species is
a stenohaline freshwater species; only FW salmonid tests.
6. SW: test water identifiable as saltwater, salinity reported to be > 15 ppt, or test species is a
stenohaline saltwater species; only SW striped bass tests were included.
7. normalized according to AWQC
26
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Appendix II. List of Species in Aquatic Database
The following species were used to develop Web-ICE aquatic species, genus, or family-
level models.
Invertebrates
Tricladida
Planariidae Dugesia tigrina
Platyhelminthes
Annelida
Aciculata
Nereididae Neanthes virens
Lumbriculida
Lumbriculidae
Lumbricu/us variegatus
Flatworm
Polychaete
Polychaete
Insecta
Diptera
Athericidae Atherix variegata
Chironomidae
Chironomus p/umosus
Chironomus tentans
Paratanytarsus c/issimi/is
Paratanytarsus parthenogeneticus Midge
Odonata
Coenagrionidae
/schnura verticaiis
Plecoptera
Perlidae Claassenia sabu/osa
Pteronarcyidae
Pteronarcella badia
Pteronarcys californica
Short-horned flies
Midge
Midge
Midge
Eastern forktail
Stonefly
Stonefly
Stonefly
Crustacea
Diplostraca
Daphniidae
Ceriodaphnia dubia
Daphnia magna
Daphnia pulex
Simocepha/us serru/atus
Podocopida
Cyprididae
Amphipoda
Crangonyctidae
Crang onyx pseudograci/is
Cypris subg/obosa
Daphnid
Daphnid
Daphnid
Daphnid
Ostracod
Amphipod
27
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Gammaridae Gammarus fasciatus
Hyalellidae
Decapoda
Camba ridae
Penaeidae
Isopoda
Asellidae
Mysida
Gammarus /acustris
Gammarus pseudo/imnaeus
Allorchestes compressa
Hyalella azteca
Orconectes nais
Farfantepenaeus duorarum
Metapenaeus dobsoni
Aseiius aquaticus
Caecidotea brevicauda
Caecidotea intermedia
Mysidae Americamysis bahia
Amphipod
Amphipod
Amphipod
Amphipod
Amphipod
Crayfish
Pink shrimp
Kadal shrimp
Isopod
Isopod
Isopod
Mysid
Forcipulatida
Asteriidae
Echinodermata
Asterias forbesi
Ostreoida
Ostreidae
Basommatophora
Planorbidae Pianorbeiia trivoivis
Mollusca
Crassostrea virginica
Starfish
Eastern oyster
Snail
Vertebrates
Pisces
Acipenseriformes
Acipenseridae
A cipenser bre virostrum
Atheriniformes
Atherinopsidae
Menidia beryl Una
Menidia menidia
Cypriniformes
Catastomidae
Catostomus commersonii
Xyrauchen texanus
Cyprinidae Carassius auratus
Cyprinus carpio
Erimonax monachus
Gila eiegans
Notropis meklstocho/as
Pimephaies promeias
Shortnose sturgeon
Inland silverside
Atlantic silverside
White sucker
Razorback sucker
Goldfish
Common carp
Spotfin chub
Bonytail chub
Cape fear shiner
Fathead minnow
28
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Ptychocheilus lucius
Cyprinodontiformes
Cyprinodontidae
Cyprinodon bovinus
Cyprinodon variegatus
Poeciliidae Gambusia affinis
Poecilia reticulata
Poeciliopsis occidenta/is
Esociformes
Esocidae Esox lucius
Gasterosteiformes
Gasterosteidae
Gasterosteus acuieatus
Mugiliformes
M u g i I i d a e Cheion iabrosus
Perciformes
Centrarchidae
Lepomis cyaneiius
Lepomis macrochirus
Lepomis micro/ophus
Micropterus doiomieu
Micropterus sa/moides
Pomoxis nigromacuiatus
Channidae Channa maruiius
Colorado pikeminnow
Leon springs pupfish
Sheepshead minnow
Mosquitofish
Guppy
Gila topminnow
Northern pikeminnow
Threespine stickleback
Thicklip mullet
Green sunfish
Bluegill
Redear sunfish
Smallmouth bass
Largemouth bass
Black crappie
Bullseye snakehead
Cichlidae
Percidae
Sparidae
Salmoniformes
Salmonidae
Siluriformes
Ictaluridae
Oreochromis mossambicus
Etheostoma fonticoia
Etheostoma iepidum
Perca fiavescens
Sander vitreus
Lagodon rhomboides
Oncorhynchus ciarkii
Oncorhynchus giiae
Oncorhynchus kisutch
Oncorhynchus mykiss
Oncorhynchus tshawytscha
Sa/mo saiar
Sa/mo trutta
Salvelinus conf/uentus
Salvelinus fontinaiis
Salvelinus namaycush
Ameiurus me/as
Icta/urus punctatus
Mozambique tilapia
Fountain darter
Greenthroat darter
Yellow perch
Walleye
Pinfish
Cutthroat trout
Apache trout
Coho salmon
Rainbow trout
Chinook salmon
Atlantic salmon
Brown trout
Bull trout
Brook trout
Lake trout
Black bullhead
Channel catfish
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Amphibia
Anura
Bufonidae Bufo boreas Western toad
Ranidae Rana sphenocepha/a Southern leopard frog
30
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III. List of Species in Wildlife Database
The following species were used to develop Web-
models.
ICE wildlife species or family-level
Aves
Anseriformes
Anatidae
Columbiformes
Columbidae
Anas discors
Bluewinged teal
Anas domestica
Peking duck
Anas piatyrhynchos
Mallard
Anas superciiiosa
Pacific black duck
Anas sp.
Pintail
Anas sp.
Widgeon
Branta canadensis
Canada goose
Dendrocygna bicoior
Fulvous whistling duck
Coiumba iivia
Rock dove
Coiumba oenas
Stock dove
Coiumbina inca
Inca dove
Coiumbina passerina
Common ground-dove
Geopeiia cuneata
Diamond dove
Geopeiia humeraiis
Bar-shouldered dove
Leptotiia verreauxi
White-fronted dove
Streptopeiia risoria
Ringed turtledove
Streptopeiia senegaiensis
Laughing dove
Zenaida asiatica
White-winged dove
Zenaida auricuiata
Eared dove
Zenaida macroura
Mourning dove
Aquiia chrysaetos
Golden eagle
Faico sparverius
American kestrel
Falconiformes
Accipitridae
Falconidae
Galliformes
Odontophoridae
Callipepla caiifornica
Callipepla gambeiii
Co/inus virginianus
Phasianidae Aiectoris chukar
Aiectoris rufa
Centrocercus urophasianus
Cotumix japonica
Gaiius ga/ius
Meieagris gaiiopavo
Perdix perdix
Phasianus co/chicus
Tympanuchus phasianeiius
Gruiformes
Gruidae
Grus canadensis
California quail
Gambel's quail
Northern bobwhite
Chukar
Red partridge
Sage grouse
Japanese quail
Chicken
Turkey
Gray partridge
Ring-necked pheasant
Sharp-tailed grouse
Sandhill crane
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Passeriformes
Corvidae
Aphelocoma sp.
Scrub jay
Corcorax melanorhamphos
White-winged chough
Corvus bennetti
Little Crow
Corvus brachyrhynchos
American crow
Corvus co rax
Common raven
Corvus coronoides
Australian raven
Corvus frugi/egus
Rook
Corvus met tori
Little raven
Cyanocorax yncas
Green jay
Pica hudsonia
Black-billed magpie
Pica nuttaiii
Yellowbilled magpie
Emberizidae Junco hyemalis
Darkeyed junco
Spizeiia pallida
Clay-colored sparrow
Volatinia jacarina
Blue back grassquit
Zonotrichia atricapilla
Golden-crowned sparrow
Zonotrichia leucophrys
White-crowned sparrow
Fringillidae
Carpodacus mexicanus
House finch
Serinus sp.
Canary
Icteridae
Age/a ius phoenice us
Red-winged blackbird
Ageiaius tricolor
Tricolored blackbird
Euphagus cyanocephalus
Brewer's blackbird
Molothrus aeneus
Bronzed cowbird
Molothrus ater
Brown-headed cowbird
Quisca/us major
Boat-tailed grackle
Quisca/us quiscuia
Common grackle
Xanthocephaius xanthocephaius Yellow headed blackbird
Passeridae
Neochmia temporalis
Red-browed firetail
Passer domesticus
House sparrow
Passer luteus
Golden sparrow
Taeniopygia guttata
Zebra finch
Ploceidae
Eup/ectes orix
Red bishop
P/oceus cucuiiatus
Village weaver
Pioceus taeniopterus
Northern masked weaver
Que tea que tea
Red billed quelea
Sturnidae
Sturnus vulgaris
Starling
Turdidae
Turdus migratorius
American robin
Psittaciformes
Psittacidae
Aratinga canicular is
Orange fronted conure
Aratinga pertinax
Brown-throated conure
Calyptorhynchus funereus
Yellow tailed black cockatoo
Melopsittacus unduiatus
Budgerigar
Myiopsitta monachus
Monk parakeet
Piatycercus eiegans
Crimson rosella
Piatycercus eximius
Eastern rosella
Psephotus haematonotus
Red-rumped parrot
Strigiformes
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Strigidae
Megascops asio
Eastern screech owl
Mammalia
Artiodactyla
Bovidae
Capra hi reus
Domestic goat
Ovis aries
Domestic sheep
Cervidae
Odocoiieus hem/onus
Mule deer
Carnivora
Canidae
Can is famiiiaris
Dog
Canis iatrans
Coyote
Lagomorpha
Leporidae
Lepus caiifornicus
Blacktailed jackrabbit
Oryctoiagus cunicuius
Rabbit
Rodentia
Caviidae
Caviars porceiius
Guinea pig
Echimyidae
Myocastor coypus
Nutria
Muridae
Gerbiiius sp.
Gerbil
Microtus caiifornicus
Meadow mouse
Microtus pinetorum
Pine mouse
Microtus sp.
Vole
Miscrotus pennsyivanicus
Meadow vole
Mus muscuius
Mouse
Oryzomys paiustris
Rice rat
Peromyscus manicuiatus
Deer mouse
Rattus argentiventer
Ricefield rat
Rattus exuians
Polynesian rat
Rattus norvegicus
Norway rat
Rattus rattus
Roof rat
Sigmodon hispidus
Cotton rat
Sciuridae
Cynomys iudovicianus
Blacktailed prairie dog
Spermoph/ius beecheyi
California ground squirrel
Spermoph/ius lateralis
Goldenmantled ground squirrel
Spermoph/ius richardsonii
Richardsons ground squirrel
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