EPA/600/R-07/071
August 2007
Web-based Interspecies Correlation Estimation
(Web-ICE) for Acute Toxicity: User Manual
Version 2.0
\
http://www.epa.gov/ceampubl/fchain/webice/
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. Barren. 2007. Web-based Interspecies
Correlation Estimation (Web-ICE) for Acute Toxicity: User Manual. Version 2.0.
EPA/600/R-07/071. 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 2
Introduction 3
Methods 4
I. Database Development 4
Aquatic (Fish and Invertebrates) 4
Wildlife (Birds and Mammals) 4
II. Model Development 5
III. Model Validation 5
Using the Web-ICE Program 7
I. Working with Web-ICE Aquatic or Web-ICE Wildlife 8
Selecting Model Taxa 8
Estimating Toxicity 9
II. The Species Sensitivity Distribution (SSD) Module 10
Generating an SSD: 12
III. Accessing Model Data 13
Guidance for Model Selection and Use 14
I. Statistical Definitions 14
II. Selecting a Model with Low Uncertainty 15
Rules of Thumb 15
Surrogate Species Selection: An Example 16
III. Evaluating Model Predictions 16
IV. Selecting Predicted Toxicity Values for SSDs 17
V. Applying Web-ICE in Ecological Risk Assessment (ERA) 17
Acknowledgements 18
References 18
Appendix 20
I. List of Species in Aquatic Database 20
II. List of Species in Wildlife Database 24
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Abstract
Predictive toxicological models are integral to environmental risk assessment
where 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 includes a total
2081 models for aquatic taxa and 852 models for wildlife taxa. For aquatic species
within the same order, Web-ICE models predict within 5-fold and 10-fold of the actual
value with 90% and 95% 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 decreases with increasing
taxonomic distance.
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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) software package (version
1.0) 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) expands the fundamental ground work
of the original ICE program (Asfaw et al. 2003) as an internet application to include
additional chemical and species toxicity data, providing an increased number of
interspecies correlations.
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; ng/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 value for
a specific chemical is available for a selected surrogate species or can be estimated
(e.g., QSAR), and there is an existing ICE model between the 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.
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Methods
I. Database Development
Aquatic (Fish and Invertebrates)
The aquatic database is in development and is currently composed of 4706
LC50/EC50 values for 217 species and 695 chemicals. The data were compiled from
the published peer reviewed literature and databases compiled by the U.S. Geological
Survey (Mayer and Ellersieck 1986) and the US EPA, including Mayer (1987),
ECOTOX/AQUIRE (US EPA 2006), and the Office of Pesticide Programs (OPP)
registrant database. All confidential data were censored before inclusion in the public
domain database.
Data were used only for tests that adhered to standard acute toxicity test
condition requirements of the American Society for Testing and Materials (ASTM 2002,
and earlier editions). Data were standardized to similar test conditions and organism life
stage to reduce variability. Selection criteria for aquatic test data were as follows:
- 96-hr LC50 data for fish and most invertebrates;
- 96-hr EC50 for most molluscs
- 48-hr EC50 data for daphnids;
- Technical chemicals or formulations > 90% active ingredient; and
- Water Quality in accordance with ASTM standards (ASTM 2002).
Open-ended toxicity values (i.e. > 100 ng/L or < 100|jg/L), toxicity values for
larval fish and shrimp, adult ("mature") fish, oysters, shrimp, and blue crabs, and
duplicate records among multiple sources were not included in model development.
When there was more than one toxicity value 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 metals (e.g. copper) and pentachlorophenol were adjusted to 50
mg/L hardness and pH 6.8, respectively (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 I).
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
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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 II), 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 Logi0(predicted toxicity) = a + b*Logio(surrogate toxicity), where a and bare
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
models were only developed between two aquatic taxa or two wildlife taxa; there are no
models to predict toxicity to an 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: 1074 models comparing 105 species to 105 species;
2) Aquatic genera: 481 models comparing 96 species to 33 genera;
3) Aquatic family: 526 models comparing 97 species to 32 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
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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, interlaboratory 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.
For wildlife species, cross-validation of models showed predicted toxicity values
within 5-fold and 10-fold of the actual values with 85% and 95% certainty, respectively.
There was a strong relationship between taxonomic distance and cross-validation
success rate, with uncertainty increasing with larger taxonomic distance (Raimondo et
al., 2007). 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).
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Using the Web-ICE Program
The primary component of Web-ICE (Web-ICE Modules) 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). A secondary component, the Species Sensitivity
Distribution Module is available for aquatic and wildlife species (Figure 1). 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.
U.S. Environmental Protection Agsney
Interspecies Correlation Estimation
Search: <~ All EPA <* This Area
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Models
Aquatic Species
Aquatic Family
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Wildlife Family
Distributions
Aquatic
Basic Information
User Manual
Download Model Data
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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, P!ease refer
to the User^Manual for detailed instructions on using Web-ICE
Web-ICE
ICE Aquatic
Aquatic vertebrates / invertebrates
* Species
* Genus
• Family
Modules
ICE Wildlife
Terrestrial Birds / Mammals
* Species
* Family
Species Sensitivity Distribution Module
Please address all comments and questions to the webmaster
Office of Research and Development | National Health and Environmental Effects Research Laboratory | Gulf Ecology Diuisic
Figure 1. Home page of Web-ICE program
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I. Working with Web-ICE Aquatic or Web-ICE Wildlife
Selecting Model 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 maybe sorted by surrogate
species or predicted taxa to identify available models.
U.S. EAV/ronmentaJ Protecfi'on Agnmcy
Wildlife Species - Taxa Selection Page
Figure 2. Taxa selection page
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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 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 (dots), 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%.
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, either select the BACK button on the browser or
select the link to the desired module in the blue navigation bar on left side of the
page.
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Wildlife Species
Surrogate Speacsi Japanese quail (Coturnix japonica}
Predicted Species: Northern bob*hit* (Co-iinus vircilnianus)
Figure 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
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
estimated toxicity values one or more known toxicity values for 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 multiple 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.
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
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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
F(C) = 1/(1 +exp((a-C)/
(2002):
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 3 is defined as VS/rc * o, where o is the standard deviation of the log™
-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.
U.S. ENVIRONMENTAL PROTECTION AGENCY
Interspecies Correlation Estimation
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Figure 4. SSD taxa selection page.
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U.S. ENVIRONMENTAL PROTgCTfQN AGENCY
Search; * All EPA & This
Species Sensitivity Distributions - Aquatic
Surrogate Species; Blue crab (Califneetes sapjdus). Channel catftsh (Ictatufus punctatus), Rambow
trout (Onco-'hynchus mykiss)
Input Toxicity: 150,200,250
30.S8 ug/L 9S% Confidence Interval; ?,4i - S7.S1
Show Data:
Farfantepenaeus
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Figure 5. SSD output page.
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 maybe 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.
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III. Accessing Model Data
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. The raw data used to develop the ICE models is not available due to proprietary
rights of some information. A list of chemicals in the aquatic and wildlife databases with
the number of species present for each chemical is available for download using the
Chemicals in Aquatic and Chemicals in Wildlife links.
Using model data provided, users may calculate toxicity as:
Predicted toxicity = 10A(intercept + slope*Logio(surrogate toxicity)
And confidence intervals as:
Lower bound = 10A(log(predicted) - t1.a*V[MSE*(1/n + (log(x) - x.ave)A2/Sxx) ]
Upper bound = 10A(log(predicted) + t^cNlMSE'XI/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%).
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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.
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 in
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 in
parentheses is the log-transformed value.
Mean Square Error (MSB) - An unbiased estimator of the variance of the
regression line.
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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.
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. high R2 value (> 0.6)
2. low p-values(< 0.01)
3. high degrees of freedom ( df > 8, N > 10)
4. close taxonomic distance (< 3)
5. high cross-validation success rate (> 85%)
6. Relatively low mean square error (MSE) (<0.22)
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 are recommended to reduce model
uncertainty. A priori power analysis determined that linear models with df > 8 have
enough statistical power (1-R, > 0.8) to sufficiently increase the chance of finding
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 toxicity estimation.
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.
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Surrogate Species Selection: An Example
In 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
good 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 maybe evaluated by reviewing the confidence intervals calculated
with the predicted value. Narrow confidence intervals represent higher confidence that
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.
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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 (ERA)
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 (e.g., endangered species) or a larger number of taxa (species, genera,
family) with known uncertainty. Potential applications of Web-ICE generated acute
toxicity values 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 intended to reduce the reliance on 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
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.
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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 and Sarah Kell. 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
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conducting acute toxicity tests on test materials with fishes, macroinvertebrates,
and amphibians. In Annual Book of ASTM Standards. E729-96. American Society
for Testing and Materials, West Conshohocken, 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. Barren.2008. Development of Species Sensitivity
Distributions for wildlife using interspecies toxicity correlation models. Environmental
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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.
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Barren. 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.
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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.Barren. 2007. Estimation of chemical toxicity in
wildlife species using interspecies correlation models. Environ. Sci. Technol.
5888-5894.
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). 2005. TREX: Terrestrial Residue
Exposure model. Office of Pesticide Programs. U.S. Environmental Protection
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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Appendix
I. List of Species in Aquatic Database
The following species were used to develop Web-ICE aquatic species, genus, or family-
level models.
Invertebrates
Haplotaxida
Tubificidae
Scolecida
Capitellidae
Aciculata
Nereididae
Diptera
Athericidae
Chironomidae
Plecoptera
Perlidae
Pteronarcyidae
Amphipoda
Gammaridae
Hyalellidae
Isopoda
Asellidae
Diplostraca
Daphniidae
Decapoda
Cambaridae
Canceridae
Annelida
Varichaetadrilus pacificus
Capitella capitata
Neanthes arenaceodentata
Neanthes virens
Insecta
Atherix variegata
Chironomus plumosus
Paratanytarsus dissimilis
Claassenia sabulosa
Pteronarcella badia
Pteronarcella californica
Crustacea
Gammarus fasciatus
Gammarus lacustris
Gammarus pseudolimnaeus
Hyalella azteca
Caecidotea brevicauda
Ceriodaphnia reticulata
Daphnia magna
Daphnia pulex
Simocephalus serrulatus
Simocephalus vetulus
Orconectes nais
Cancer magister
Oligochaete
Polychaete
Polychaete
Polychaete
Short-horned flies
Midge
Midge
Stonefly
Stonefly
Stonefly
Amphipod
Amphipod
Amphipod
Amphipod
Isopod
Daphnid
Daphnid
Daphnid
Daphnid
Daphnid
Crayfish
Dungeness crab
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Nephropidae
Ocypodidae
Palaemonidae
Penaeidae
Portunidae
Mysida
Mysidae
Calanoida
Acartiidae
Temoridae
Myioda
Myidae
Mytiloida
Mytilidae
Ostreoida
Ostreidae
Pectinidae
Unionoida
Unionidae
Veneroida
Mactridae
Basommatophora
Physidae
Neogastropoda
Nassariidae
Forcipulatida
Asteriidae
Plumatellida
Lophopodidae
Pectinatellidae
Homarus americanus
Uca pigulator
Palaemonetes kadiakensis
Palaemonetes pugio
Farfantepenaeus duorarum
Litopenaeus setiferus
Callinectes sapidus
Carcinus maenas
Americamysis bahia
Acartia clausi
Acartia tonsa
Eurytemora affinis
Mollusca
Mya arenaria
Mytilus edulis
Crassostrea virginica
Argopecten irradians
Actinonaias pectorosa
Lampsilis straminea
Lampsilis feres
Utterbackia imbecillis
Villosa iris
Villosa vibex
Villosa villosa
Rangia cuneata
Aplexa hypnorum
Physella gyrina
Nassarius obsoletus
Miscellaneous
Aster/as forbesi
Lophopodella carter!
Pectinate/la magnified
American lobster
Fiddler crab
Mississippi grass shrimp
Daggerblade grass shrimp
Pink shrimp
White shrimp
Blue crab
Green crab
Mysid
Copepod
Copepod
Copepod
Softshell clam
Blue mussel
Eastern oyster
Bay scallop
Pheasantshell
Southern fatmucket
Yellow sandshell
Paper pondshell
Rainbow mussel
Southern rainbow
Downy rainbow
Atlantic rangia
Snail
Tadpole physa snail
Eastern mud snail
Starfish
Bryozoan
Bryozoan
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Vertebrates
Acipenseriformes
Acipenseridae
Anguilliformes
Anguillidae
Atheriniformes
Atherinopsidae
Cypriniformes
Catastomidae
Cyprinidae
Cyprinodontiformes
Cyprinodontidae
Fundulidae
Poeciliidae
Esociformes
Esocidae
Mugiliformes
Mugilidae
Perciformes
Centrarchidae
Cichlidae
Moronidae
Percidae
Pisces
A cipenser bre virostrum
Anguilla rostrata
Leuresthes ten uis
Menidia beryllina
Menidia menidia
Menidia peninsulae
Catostomus commersonii
Xyrauchen texanus
Carassius auratus
Cyprinus carpio
Erimonax monachus
Gila elegans
Notropis mekistocholas
Pimephales promelas
Ptychocheilus lucius
Ptychocheilus oregonensis
Cyprinodon bovinus
Cyprinodon variegatus
Jordanella floridae
Fundulus diaphanus
Fundulus heteroclitus
Gambusia affinis
Poecilia reticulata
Poeciliopsis occidentalis
Esox lucius
Mugil cephalus
Lepomis cyan el I us
Lepomis gibbosus
Lepomis macrochirus
Lepomis microlophus
Micropterus dolomieu
Micropterus salmoides
Pomoxis nigromaculatus
Oreochromis mossambicus
Morone americana
Morone saxatilis
Etheostoma fonticola
Shortnose sturgeon
American eel
California grunion
Inland silverside
Atlantic silverside
Tidewater silverside
White sucker
Razorback sucker
Goldfish
Common carp
Spotfin chub
Bonytail chub
Cape fear shiner
Fathead minnow
Colorado pikeminnow
Northern pikeminnow
Leon springs pupfish
Sheepshead minnow
Flagfish
Banded killifish
Mummichog
Mosquitofish
Guppy
Gila topminnow
Northern pikeminnow
Striped mullet
Green sunfish
Pumpkinseed sunfish
Bluegill
Redear sunfish
Smallmouth bass
Largemouth bass
Black crappie
Mozambique tilapia
White perch
Striped bass
Fountain darter
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Sciaenidae
Salmoniformes
Salmonidae
Siluriformes
Ictaluridae
Anura
Bufonidae
Hylidae
Ranidae
Etheostoma lepidum
Perca flavescens
Sander vitreus
Leiostomus xanthurus
Oncorhynchus clarkii
Oncorhynchus gilae
Oncorhynchus kisutch
Oncorhynchus mykiss
Oncorhynchus tshawytscha
Salmo salar
Salmo trutta
Salvelinus fontinalis
Salvelinus namaycush
Ameiurus me/as
Ictalurus punctatus
Amphibia
Bufo boreas
Bufo fowleri
Pseudacris triseriata
Rana catesbeiana
Ran a pipiens
Rana sphenocephala
Greenthroat darter
Yellow perch
Walleye
Spot
Cutthroat trout
Apache trout
Coho salmon
Rainbow trout
Chinook salmon
Atlantic salmon
Brown trout
Brook trout
Lake trout
Black bullhead
Channel catfish
Western toad
Fowlers toad
Western chorus frog
Bullfrog
Northern leopard frog
Southern leopard frog
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II. List of Species in Wildlife Database
The following species were used to develop Web-ICE wildlife species or family-level
models.
Aves
Anseriformes
Anatidae
Columbiformes
Columbidae
Anas discors
Anas domestica
Anas platyrhynchos
Anas superciliosa
Anassp.
Anassp.
Branta canadensis
Dendrocygna bicolor
Columba livia
Columba oenas
Columbina inca
Columbin a passerina
Geopelia cuneata
Geopelia humeralis
Leptotila verreauxi
Streptopelia risoria
Streptopelia senegalensis
Zenaida asiatica
Zenaida auriculata
Zenaida macroura
Falconiformes
Accipitridae
Falconidae
Galliformes
Odontophoridae
Callipepla californica
Callipepla gambelii
Colinus virginianus
Phasianidae Alectoris chukar
Alectoris rufa
Centrocercus urophasianus
Coturnix japonica
Gal I us gal I us
Meleagris gallopa vo
Perdixperdix
Phasianus colchicus
Tympanuchus phasianellus
Aquila chrysaetos
Falco sparverius
Gruiformes
Gruidae
Grus canadensis
Bluewinged teal
Peking duck
Mallard
Pacific black duck
Pintail
Widgeon
Canada goose
Fulvous whistling duck
Rock dove
Stock dove
Inca dove
Common ground-dove
Diamond dove
Bar-shouldered dove
White-fronted dove
Ringed turtledove
Laughing dove
White-winged dove
Eared dove
Mourning dove
Golden eagle
American kestrel
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
Emberizidae
Fringillidae
Icteridae
Passeridae
Ploceidae
Sturnidae
Turdidae
Psittaciformes
Psittacidae
Strigiformes
Aphelocoma sp.
Corcorax melanorhamphos
Corvus bennetti
Corvus brachyrhynchos
Corvus corax
Corvus coronoides
Corvus frugileg us
Corvus mellori
Cyanocorax yncas
Pica hudsonia
Pica nuttalli
Junco hyemalis
Spizella pa IIid a
Volatinia jacarina
Zonotrichia atricapilla
Zonotrichia leucophrys
Carpodacus mexicanus
Serinus sp.
Agelaius phoeniceus
Agelaius tricolor
Euphagus cyanocephalus
Molothrus aeneus
Molothrus ater
Quiscalus major
Quiscalus quiscula
Xanthocephalus xanthocephalus
Neochmia temporalis
Passer domesticus
Passer luteus
Taeniopygia guttata
Euplectes orix
Ploceus cucullatus
Ploceus taeniopterus
Quelea quelea
Sturnus vulgaris
Turdus migratorius
Aratinga canicularis
Aratinga pertinax
Calyptorhynchus funereus
Melopsittacus undulatus
Myiopsitta monachus
Platycercus elegans
Platycercus eximius
Psephotus haematonotus
Scrub jay
White-winged chough
Little Crow
American crow
Common raven
Australian raven
Rook
Little raven
Green jay
Black-billed magpie
Yellowbilled magpie
Darkeyed junco
Clay-colored sparrow
Blue back grassquit
Golden-crowned sparrow
White-crowned sparrow
House finch
Canary
Red-winged blackbird
Tricolored blackbird
Brewer's blackbird
Bronzed cowbird
Brown-headed cowbird
Boat-tailed grackle
Common grackle
Yellow headed blackbird
Red-browed firetail
House sparrow
Golden sparrow
Zebra finch
Red bishop
Village weaver
Northern masked weaver
Red billed quelea
Starling
American robin
Orange fronted conure
Brown-throated conure
Yellow tailed black cockatoo
Budgerigar
Monk parakeet
Crimson rosella
Eastern rosella
Red-rumped parrot
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Strigidae Megascops asio
Eastern screech owl
Artiodactyla
Bovidae
Cervidae
Carnivora
Canidae
Lagomorpha
Leporidae
Rodentia
Caviidae
Echimyidae
Muridae
Sciuridae
Mammalia
Capra hircus
Ovis aries
Odocoileus hem/onus
Can is familiaris
Canis latrans
Lepus californicus
Oryctolagus cuniculus
Ca viars poreel I us
Myocastor coy pus
Gerb///ussp.
Microtus californicus
Microtus pineforum
M/crofussp.
Miscrotus pennsylvanicus
Mus musculus
Oryzomys palustris
Peromyscus maniculatus
Rattus argent/venter
Rattus exulans
Rattus norvegicus
Rattus rattus
Sigmodon hispidus
Cynomys ludovicianus
Spermophilus beecheyi
Spermophilus lateral is
Spermophilus richardsonii
Domestic goat
Domestic sheep
Mule deer
Dog
Coyote
Blacktailed jackrabbit
Rabbit
Guinea pig
Nutria
Gerbil
Meadow mouse
Pine mouse
Vole
Meadow vole
Mouse
Rice rat
Deer mouse
Ricefield rat
Polynesian rat
Norway rat
Roof rat
Cotton rat
Blacktailed prairie dog
California ground squirrel
Goldenmantled ground squirrel
Richardsons ground squirrel
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