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Acute toxicity prediction to threatened and endangered species using Interspecies Correlation Estimation (ICE) models Morgan M Willming, Crystal R Lilavois, Mace G. Barron, and Sandy Raimondo Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03009 • Publication Date (Web): 01 Sep 2016 Downloaded from http://pubs.acs.org on September 12, 2016

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Environmental Science & Technology

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Acute toxicity prediction to threatened and endangered species using Interspecies Correlation Estimation (ICE) models

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Morgan M. Willming*†, Crystal R. Lilavois‡, Mace G. Barron‡, Sandy Raimondo‡

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Oak Ridge Institute for Science and Education, U.S. Environmental Protection Agency, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL USA 32561

U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL USA 32561

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*Corresponding author: [email protected]; Phone: 850-934-9297; Fax: 851-934-2406

1 ACS Paragon Plus Environment


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Abstract Evaluating contaminant sensitivity of threatened and endangered (listed) species and

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protectiveness of chemical regulations often depends on toxicity data for commonly tested

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surrogate species. The U.S. EPA’s internet application Web-ICE is a suite of Interspecies

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Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using

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least squares regressions of the sensitivity of a surrogate species and a predicted taxon (species,

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genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250

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listed species. A case study was used to assess protectiveness of genus and family model

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estimates derived from either geometric mean or minimum taxa toxicity values for listed species.

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Models developed from the most sensitive value for each chemical were generally protective of

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the most sensitive species within predicted taxa, including listed species, and were more

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protective than geometric means models. ICE model estimates were compared to HC5 values

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derived from Species Sensitivity Distributions for the case study chemicals to assess

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protectiveness of the two approaches. ICE models provide robust toxicity predictions and can

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generate protective toxicity estimates for assessing contaminant risk to listed species.

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Introduction The U.S. Endangered Species Act (ESA) requires EPA to determine risks of pesticides

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and other chemicals to federally endangered and threatened (listed) species to ensure that

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chemical registration and water quality criteria are protective of listed taxa. A significant

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challenge in this process is determining the sensitivity of the diversity of listed species to

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chemicals that have only been evaluated using common test species or have limited data

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available. Historically, toxicity data for the majority of listed species or closely related

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representatives has been unavailable because of a lack of standardized culture and test methods

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and limited organism availability. However, previous research suggests that listed species are not

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consistently more sensitive than more commonly tested species1, 2. In the absence of species-

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specific toxicity data, conservative approaches such as applying generic safety factors to toxicity

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values of surrogate species have been used to develop hazard levels assumed to be protective of

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listed species. Recently, the U.S. National Research Council3 recommended the use of

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Interspecies Correlation Estimation (ICE) models in pesticide risk assessments as an alternative

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to generic safety factors.

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ICE models estimate acute toxicity to aquatic or terrestrial organisms using the known

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toxicity data of a chemical for a surrogate species4, 5. The models are log-linear least squares

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regressions of the sensitivity of a predicted taxon (species, genus, or family) and a surrogate

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species. They are constructed from existing acute toxicity values determined in each pair of taxa

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across a number of chemicals. ICE models for aquatic species contain acute toxicity values

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(median lethal or median effect concentrations; LC50/EC50) for a minimum of any three

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different chemicals and have been demonstrated to be robust, accurate estimators of toxicity

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when the surrogate and predicted taxa are within the same taxonomic Order4. Species, genus, and



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family level ICE models are publically available on the U.S. EPA internet application Web-ICE

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(www3.epa.gov/webice).

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ICE model predictions are intended to supplement toxicity databases where species of

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concern or a required diversity of species have not been or cannot be adequately tested6. Previous

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studies evaluating the robustness and application of ICE model predictions have focused

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primarily on species level models and their use in either direct toxicity estimation or in

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developing species sensitivity distributions (SSDs)4-8. Less work has explored the protectiveness

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of ICE predictions in risk assessments for listed species or the use of genus and family models.

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Few species-specific ICE models can be developed for listed species due to limited existing

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toxicity data, therefore genus or family models may be required to predict toxicity to the higher

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taxonomic level. Genus and family models have historically been developed using geometric

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means of toxicity values from multiple species, which are useful in generating toxicity estimates

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in cases such as water quality criteria development. However, the protectiveness of these

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predictions across the range of species sensitivity within the predicted taxon has not been

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investigated, especially for listed or sensitive species. Demonstrating protectiveness of model

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estimates is a critical step for their application in risk assessment of listed species.

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The present study evaluates the use of ICE models for listed species and compares the

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protectiveness of genus and family level models developed from either geometric means or

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minimum toxicity values. Models used in this evaluation were developed from an acute toxicity

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database that was substantially expanded from the previous version of Web-ICE (v3.2, release

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April 2013) and contains 314 species and 1501 chemicals, including new toxicity records for

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listed species. Because there are 87 federally listed species of freshwater unionid mussels, which

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are often the most sensitive taxa to some contaminants9, development of models for this taxon


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was a focus for the present study. We compare the development of genus and family models

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using the most sensitive toxicity value for each chemical within the predicted taxon (minimum

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models) to models built with geometric means in order to evaluate an approach for generating

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protective estimates of toxicity. Models developed with minimum toxicity values were expected

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to result in toxicity predictions that are protective of the most sensitive species within a taxon.

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We also revised model selection guidelines to reduce reliance on professional judgement and

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increase reproducibility of their application. Lastly, we demonstrate the application of models in

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a case study of 11 federally listed species exposed to a diversity of contaminants with a range of

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aquatic toxicity modes of action (MOA) and compared ICE predictions to hazard concentrations

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derived from species sensitivity distributions (SSDs).

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Methods

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Database development

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Standardization of toxicity data for inclusion in ICE models followed the approach and

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selection criteria described in Raimondo et al.4 This included confirming chemical and species

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identity; compiling acute toxicity values as 48 hour EC50/LC50 for specific invertebrate taxa

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(e.g., daphnids, fairy shrimp) or 96 hour EC50/LC50 for fish, amphibians and other aquatic

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invertebrates (e.g., insects); and determining whether each data record met standardization

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criteria for life stage, test conditions, and water quality parameters (e.g., temperature, dissolved

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oxygen, salinity)4. The database included records from the previous Web-ICE database appended

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with new records from ECOTOX (www.epa.gov/ecotox/; downloaded September 2014), and

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recently collected primary data on freshwater mussels9 and fairy shrimp (unpublished data).

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Acute toxicity data, which can represent intrinsic species sensitivity, were used because of the

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large diversity of chemicals and taxa available. Data were standardized for life stage by using 5 ACS Paragon Plus Environment


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only juveniles for fish and decapods, immature aquatic life stages for amphibians and insects,

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and juveniles and spat for molluscs. All life stages were included for other taxa groups except

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egg or embryo stages. Specific aspects of the mussel toxicity dataset are detailed in Raimondo et

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al.9. All chemicals in the ICE database were curated using the distributed structure-searchable

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toxicity database (DSSTox; www.epa.gov/ncct/dsstox/). A single name and the confirmed

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chemical abstract services registry number from the source material were checked against

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DSSTox to validate their consistency. Names that were not contained within the DSSTox list of

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synonyms for a particular chemical were manually checked to validate the agreement between

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the chemical identifiers and confirm the chemical-data linkage. The ICE database toxicity value

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was specified as the compound tested, except for some metal salts (recorded as the element or

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hardness normalized element), pentachlorophenol (pH normalized) and ammonia (pH and

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temperature normalized). Only records for chemicals with an active ingredient purity of ≥ 90%

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were accepted. Open ended (e.g., LC50 > 100 mg/L) or unconfirmed toxicity values were not

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used.

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ICE Model Development Models were developed as least squares log-linear regressions by pairing all possible

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surrogate species toxicity values with all possible predicted taxa toxicity values (i.e., a species,

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genus, or family) by common chemical. Each ICE model describes the relationship of sensitivity

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of the two species as Log10(Predicted Toxicity) = a*Log10(Surrogate Toxicity)+b.

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Here, Predicted Toxicity is the EC/LC50 value of the predicted taxa, Surrogate Toxicity is the

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EC/LC50 value of the surrogate species, a is the slope of the regression line and b is the

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intercept. A slope of 1 describes two species whose relative sensitivity is consistent across a wide 6 ACS Paragon Plus Environment

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range of chemicals. A slope of 1 and an intercept of 0 describes two species with identical

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sensitivity. Each ICE model is also described by an R2 and a Mean Square Error (MSE). The R2

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is a measure of goodness of fit of the regression, where a value closer to one represents a model

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where data are tightly fit around the line and variability in the data are well represented by the

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model. Model MSE is an estimate of average error contained within the model. Small MSE

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indicates that the model is robust, or contains high probability of good performance for data

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drawn from a wide range of samples.

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Each model required toxicity values from at least three different chemicals available in

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the database for both the surrogate species and predicted taxon. Therefore, each model data point

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represents a unique chemical. For species level models, when multiple database records occurred

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for the same species and chemical, the geometric mean of toxicity values was used. For genus

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and family level models, two sets of models were developed: geometric mean and minimum

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toxicity models. First, all species available in the database within a genus or family were

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identified by common chemical. For geometric mean models, the species mean acute value

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(SMAV; geometric mean for each chemical) was calculated. For genus geometric mean models,

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the genus mean acute value was then calculated for each chemical as the geometric mean of the

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SMAVs. For family geometric mean models, the family mean acute value was calculated for

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each chemical as the geometric mean of the SMAV. For minimum toxicity models predicting to

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both genus and family, the minimum toxicity value of all species within each predicted taxa was

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used for each chemical. This second approach using the most sensitive toxicity value was

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intended to generate toxicity estimations protective of more species within that genus or family

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compared to geometric mean models. For both sets of genus and family level models, the



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predicted taxon toxicity value was paired with the SMAV of the respective chemical for the

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surrogate species.

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For ICE models with four or more chemicals, model prediction accuracy was determined

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using leave-one-out cross-validation4. In this process, each data point within a model (e.g.,

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chemical pair of acute values for surrogate and predicted taxon) was systematically removed

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from the original model and a new model was rebuilt with the remaining data. The new model

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was used to estimate the toxicity value of the removed predicted taxa from the removed

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surrogate species toxicity value. For each removed data point, the N-fold difference was

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calculated as the greater value of the estimated/actual or actual/estimated for non-transformed

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values. For each model, the number of removed data points predicted within 5-fold of the actual

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value was determined and a cross-validation (prediction) success rate was calculated as the

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percentage of removed values predicted within 5-fold of the actual value 4. The 5-fold range

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represents standard inter-laboratory variation of a toxicity value tested for the same species and

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chemical as demonstrated in previous studies4, 5, 9-11.

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Model Selection Guidelines Model selection guidelines were developed to reduce the amount of professional

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judgement needed to select the best available model when multiple ICE models (i.e., multiple

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surrogate species) are available for predicting to one taxon. Details of guidance development are

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provided in Supplementary Information (SI 001). Briefly, selection guidance was determined

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from the combination of regression model attributes (MSE, R2, and slope) that corresponded to

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the highest percent of cross-validated data points predicted within 5-fold of the measured value

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using an iterative approach. The highest MSE, lowest R2 and lowest slope that corresponded to

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the highest percent accuracy were then applied as the model selection guidelines. 8 ACS Paragon Plus Environment

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Evaluation of Web-ICE for Listed Species A case study was used to assess the protectiveness of genus and family models applied to

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listed species. The 11 selected species represented a diverse taxonomic range including fish,

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amphibians, and fairy shrimp from the Sacramento California Valley12, and freshwater unionid

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mussels from the Ohio River watershed and southeastern United States (Table 1). The 16

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chemicals chosen for the case study represented priority chemicals encompassing a broad range

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of MOAs (primarily pesticides from the Sacramento Valley) and contaminants of concern for

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unionid mussels (Table 1). First, all minimum and geometric mean ICE models available for

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predicting to the genera and families of the case study species were identified. For each of these

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models, toxicity values in the Web-ICE database were extracted for each surrogate species and

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case study chemical. When multiple toxicity records were available for the same surrogate

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species and chemical, the SMAV was used as the model input. Toxicity estimates and 95%

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confidence intervals (CI) were calculated from available surrogate species toxicity and models

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for each chemical and taxa (genus, family). Where the taxa of a listed species could be predicted

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from multiple surrogates, we applied the model selection guidance determined in the methods

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outlined above to identify the best model (Fig. 1). According to the guidance, model selection

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was first based on the taxonomic distance of the surrogate and predicted taxon, because previous

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analyses indicated higher prediction accuracy for models from closely related taxa. Models were

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then chosen with a low MSE (≤ 0.95), and a narrow 95% CI. If models had similar MSE, the

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model with the greater N was selected.

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Protectiveness of the toxicity estimates and their lower 95% CI predicted from the

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selected model for the genus and/or family of each listed species was determined by comparing

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these values to the measured minimum toxicity value for that taxon available in the ICE 9 ACS Paragon Plus Environment


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database. For example, the model prediction to the family Salmonidae for malathion was

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compared to the minimum Salmonidae LC50 (i.e., the most sensitive measured species value for

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all salmonids) for malathion available in the ICE database. If the model prediction was greater

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than the minimum toxicity value in the database, then the lower 95% CI of the prediction was

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compared to the minimum taxa toxicity value. For each listed taxa and chemical, it was

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determined if the prediction was protective, the lower 95% CI was protective, or models were

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not protective at all. This was performed for genus and family models developed using both

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minimum toxicity values and geometric means.

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Species sensitivity distributions (SSDs) were developed for each chemical to compare the

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5th percentile hazard concentrations (HC5) values to ICE generated predictions and sensitive taxa

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values for the case study species. SSDs were developed from freshwater species data available in

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the ICE database for each case study chemical2, 6. Toxicity data selection followed the same

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standardization criteria as ICE database development, using the SMAV for each chemical to

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build the SSD. SSDs were fit using maximum likelihood estimation in R statistical software13 to

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a log-logistic cumulative distribution so that Y = 1/{1 + exp[(α- X)/β]}

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where Y is the cumulative proportion, X is the log-transformed toxicity, α is the intercept, and β

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is the scale parameter. The HC5 values were derived from this distribution for each chemical and

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were compared to the most sensitive taxa values and ICE minimum model predictions to assess

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protectiveness of each approach.

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Results

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Database and model development


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The Web-ICE database (v. 3.3) has an expanded number of toxicity records, species, and

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chemicals, and yielded considerably more models, including those predicting to listed species,

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compared to the previous version (Table 2). This suite of ICE models can predict toxicity values

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to 25 listed species and 20 genera and 20 families containing U.S. federally listed species (Table

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3). Overall, species model prediction accuracy and cross-validation success were consistent with

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previous results for the previous dataset 4. Models developed with predicted and surrogate

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species within the same family estimated toxicity within 5- and 10-fold of the actual value for 92

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and 98% of data points, respectively (Table S1). All models are available on the EPA Web-ICE

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application webpage (www3.epa.gov/webice).

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Model selection & prediction guidance Analysis of model parameters (MSE, R2, and slope) indicated that models with MSE
0.6, and slope > 0.6 will have the highest prediction accuracy (Table S2). For models

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with a taxonomic distance of 6 (same Kingdom) that have N > 10, models with MSE < 0.55 will

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have improved prediction accuracy because of the increased variation in toxicity data associated

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with less closely related taxa (Table S2). Distributions of each model parameter by taxonomic

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distance are available in the Supporting Information (Fig. S1).

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Figure 1 summarizes the procedures for model selection based on the model guidelines

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when models for multiple surrogates are available. Web-ICE users should first select models

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with the closest taxonomic distance and then apply the model parameter guidelines to identify

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the best model. The analysis also indicated selection of models with confidence intervals within

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5-fold of the predicted value, although using 3-fold slightly improves prediction accuracy, but

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not substantially. 11 ACS Paragon Plus Environment


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Case study analysis of model protectiveness For the case study of 16 chemicals and 11 listed species, there were 787 minimum

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toxicity models and 807 geometric mean models from which predictions could be made from

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available surrogate toxicity values and ICE models for the predicted taxa. Applying model

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selection guidance resulted in 50 ICE predictions for the listed species for each set of models

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(Supporting Information SI 002; Table S3). For both minimum and geometric mean models, 25

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of the 50 predicted values were from family models and 25 were genus models. The Supporting

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Information (SI 002) provides all available minimum models predicting to these taxa with the

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best selected model identified. When model predictions were available for both a family and a

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genus within that same family (e.g., Oncorhynchus and Salmonidae), we selected the family

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model if both models had similar parameters (Table S4). Because family models are comprised

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of greater taxa diversity, estimates derived from these models can be considered to be protective

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of more species. In some cases, genus models may generate more conservative estimates, but

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model selection should ultimately be guided by the goal of the particular analysis; if the goal is

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specific to a species and location, a genus level model will more accurately reflect the sensitivity

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of the species, if the goal is comprehensive protection of listed species, the family model will be

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more inclusive.

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The predicted values for genus level minimum models were protective of the most

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sensitive species within the genus in 84% of cases and for 100% of instances when using the

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lower 95% CI (Fig. 2). For family level minimum models, predictions were protective in 72% of

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cases and the lower 95% CI was protective in 88% of instances (Fig. 2). Of the family level

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models where the toxicity of the most sensitive species was less than the lower 95% CI of the


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prediction (12%, N = 3 models), two of these models were at a taxonomic distance of greater

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than 5 because no models with a closer taxonomic distance were available. For models

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developed with geometric means, model predictions were protective of the most sensitive species

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in 28% of cases for both genus and family models (Fig. 2). The lower 95% CI was protective in

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50% of genus models and 48% of family models built with geometric means.

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SSDs were developed for 13 chemicals that had sufficient data. Fipronil, glyphosate, and

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imidacloprid each only had 4 standardized toxicity values, so SSDs were not constructed. The

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number of species in the SSDs ranged from 7 (thiobencarb) to 66 (copper), and HC5 values

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ranged from 0.08 to 1,389,897 µg/L (Table 4). SSDs with large N contained diverse taxa while

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those with small N ( 0.6 and slope > 0.6

Fig. 1. ICE model selection guidance steps based on model parameters used to identify the best model when predictions from multiple surrogates are available. TD = Taxonomic distance of surrogate and predicted taxon; MSE = Mean square error; N = Number of data points in the model (df + 2); CI = Confidence interval.



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Figure 2

Fig. 2. Comparison of model protectiveness for geometric mean and minimum genus and family models used with priority chemicals and taxa containing listed species in the Sacramento Valley and listed mussels. There were 25 genus model and 25 family model predictions for each set of models (geometric mean or minimum models).


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Acute Toxicity Prediction to Threatened and ... - ACS Publications

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