Framework for Optimizing Selection of Interspecies Correlation Estimation Models to Address Species Diversity and Toxicity Gaps in an Aquatic Database

The Chemical Aquatic Fate and Effects (CAFE) database is a tool that facilitates assessments of accidental chemical releases into aquatic environments. CAFE contains aquatic toxicity data used in the development of species sensitivity distributions (SSDs) and the estimation of hazard concentrations (HCs). For many chemicals, gaps in species diversity and toxicity data limit the development of SSDs, which may be filled with Interspecies Correlation Estimation (ICE) models. Optimization of ICE model selection and integration ICE-predicted values into CAFE required a multistep process that involved the use of different types of data to assess their influence on SSDs and HC estimates. Results from multiple analyses showed that SSDs supplemented with ICE-predicted values generally produced HC5 estimates that were within a 3-fold difference of estimates from measured SSDs (58%–82% of comparisons), but that were often more conservative (63%–76% of comparisons) and had lower uncertainty (90% of comparisons). ICE SSDs did not substantially underpredict toxicity (<10% of comparisons) when compared to estimates from measured SSD. The incorporation of ICE-predicted values into CAFE allowed the development of >800 new SSDs, increased diversity in SSDs by an average of 34 species, and augmented data for priority chemicals involved in accidental chemical releases

Acute Toxicity Prediction to Threatened and Endangered Species Using Interspecies Correlation Estimation (ICE) Models

Evaluating contaminant sensitivity of threatened and endangered (listed) species and protectiveness of chemical regulations often depends on toxicity data for commonly tested surrogate species. The U.S. EPA’s Internet application Web-ICE is a suite of Interspecies Correlation Estimation (ICE) models that can extrapolate species sensitivity to listed taxa using least-squares regressions of the sensitivity of a surrogate species and a predicted taxon (species, genus, or family). Web-ICE was expanded with new models that can predict toxicity to over 250 listed species. A case study was used to assess protectiveness of genus and family model estimates derived from either geometric mean or minimum taxa toxicity values for listed species. Models developed from the most sensitive value for each chemical were generally protective of the most sensitive species within predicted taxa, including listed species, and were more protective than geometric means models. ICE model estimates were compared to HC5 values derived from Species Sensitivity Distributions for the case study chemicals to assess protectiveness of the two approaches. ICE models provide robust toxicity predictions and can generate protective toxicity estimates for assessing contaminant risk to listed species

SEPMdata

Data used to simulate sheepshead minnow exposure to oiled sediment in Barataria Bay, Louisiana following the Deepwater Horizon oil spill. Files include temperature- and concentration-specific population parameters, laboratory data used to derive oiled sediment effects on growth and survival, field data used in habitat suitability model, and temperature and oiling severity data from Barataria Bay