August 2007
Web-based Interspecies Correlation Estimation

   (Web-ICE) for Acute Toxicity:  User Manual

                     Version 2.0
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

                            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.
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.


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

      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.


      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

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

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

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

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.

                       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.
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              Interspecies Correlation Estimation
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  Aquatic Species

  Aquatic Family
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 Basic Information

 User Manual

 Download Model Data
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
ICE Aquatic
Aquatic vertebrates / invertebrates
* Species
* Genus
• Family
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

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

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
   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

             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

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)/
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
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        '-""- """•
               Species Sensitivity Distributions - Aquatic Species
               Multiple Surrogate SSD

               Sort Byi j Common Name j^J
               Blue crab {Cslisnectes sapidus)
               Channel catfish (tctakirus punctatus) |200

               Rainbow trout t'Oncorhynchus rnvksss) |250f
Remove Sptcits
                                  [" Catcul'aie &SD
Figure 4. SSD taxa selection page.

                                                  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:
               jv" Amphfpod

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.

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%).

              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

      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.

      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.

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
      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.

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.


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).

American Society for Testing and Materials (ASTM). 2002. Standard guide for
      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
   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.
Dyer, S. D., D. J. Versteeg, S. E. Belanger, J. G. Chaney, S. Raimondo and M. G.
      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.
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,
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-
Raimondo, S., P. Mineau, and M. G.Barren. 2007. Estimation of chemical toxicity in
      wildlife species using interspecies correlation models. Environ. Sci. Technol.
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
US Environmental Protection Agency (EPA). 2006. ECOTOX Ecotoxicology Database.
      http://cfpub.epa.gov/ecotox. Duluth MN.

I.  List of Species in Aquatic Database
The following species were used to develop Web-ICE aquatic species, genus, or family-
level models.



 Varichaetadrilus pacificus

 Capitella capitata

 Neanthes arenaceodentata
 Neanthes virens


Atherix variegata
 Chironomus plumosus
 Paratanytarsus dissimilis

 Claassenia sabulosa
 Pteronarcella badia
 Pteronarcella californica


 Gammarus fasciatus
 Gammarus lacustris
 Gammarus pseudolimnaeus
 Hyalella azteca

 Caecidotea brevicauda

 Ceriodaphnia reticulata
 Daphnia magna
 Daphnia pulex
 Simocephalus serrulatus
 Simocephalus vetulus

 Orconectes nais
 Cancer magister


Short-horned flies




Dungeness crab






Homarus americanus
Uca pigulator
Palaemonetes kadiakensis
Palaemonetes pugio
Farfantepenaeus duorarum
Litopenaeus setiferus
Callinectes sapidus
Carcinus maenas

Americamysis bahia

Acartia clausi
Acartia tonsa
Eurytemora affinis


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


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


Softshell clam

Blue mussel

Eastern oyster
Bay scallop

Southern fatmucket
Yellow sandshell
Paper pondshell
Rainbow mussel
Southern rainbow
Downy rainbow

Atlantic rangia

Tadpole physa snail

Eastern mud snail







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
Common carp
Spotfin chub
Bonytail chub
Cape fear shiner
Fathead minnow
Colorado pikeminnow
Northern pikeminnow

Leon springs pupfish
Sheepshead minnow
Banded killifish
Gila topminnow

Northern pikeminnow

Striped mullet

Green sunfish
Pumpkinseed sunfish
Redear sunfish
Smallmouth bass
Largemouth bass
Black crappie
Mozambique tilapia
White perch
Striped bass
Fountain darter


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


Bufo boreas
Bufo fowleri
Pseudacris triseriata
Rana catesbeiana
Ran a pipiens
Rana sphenocephala
Greenthroat darter
Yellow perch

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
Northern leopard frog
Southern leopard frog

II.  List of Species in Wildlife Database
The following species were used to develop Web-ICE wildlife species or family-level
                  Anas discors
                  Anas domestica
                  Anas platyrhynchos
                  Anas superciliosa
                  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
                  Callipepla californica
                  Callipepla gambelii
                  Colinus virginianus
      Phasianidae Alectoris chukar
                  Alectoris rufa
                  Centrocercus urophasianus
                  Coturnix japonica
                  Gal I us gal I us
                  Meleagris gallopa vo
                  Phasianus colchicus
                  Tympanuchus phasianellus
                  Aquila chrysaetos
                  Falco sparverius
                  Grus canadensis
Bluewinged teal
Peking duck
Pacific black duck
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
Red partridge
Sage grouse
Japanese quail
Gray partridge
Ring-necked pheasant
Sharp-tailed grouse

Sandhill crane


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
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
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
American robin

Orange fronted conure
Brown-throated conure
Yellow tailed black cockatoo
Monk parakeet
Crimson rosella
Eastern  rosella
Red-rumped parrot

      Strigidae    Megascops asio
                              Eastern screech owl




Capra hircus
Ovis aries
Odocoileus hem/onus

Can is familiaris
Canis latrans

Lepus californicus
Oryctolagus cuniculus

Ca viars poreel I us
Myocastor coy pus
Microtus californicus
Microtus pineforum
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


Blacktailed jackrabbit

Guinea pig
Meadow mouse
Pine mouse
Meadow vole
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