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Endangered species toxicity extrapolation using ICE models
Citation:
Barron, M., CrystalR Jackson, AND Sandy Raimondo. Endangered species toxicity extrapolation using ICE models. SETAC North America 35th Annual Meeting, Vancouver, BC, CANADA, November 09 - 13, 2014.
Impact/Purpose:
This presentation will summarize how ICE models and Web-ICE can be used to extrapolate toxicity to endangered species.
Description:
The National Research Council’s (NRC) report on assessing pesticide risks to threatened and endangered species (T&E) included the recommendation of using interspecies correlation models (ICE) as an alternative to general safety factors for extrapolating across species. ICE models are log-linear least squares regressions that predict acute toxicity to untested taxa from known toxicity of a single surrogate species. The U.S. EPA houses three ICE databases: aquatic animals, algae, and wildlife. Approximately 2400 models have been developed and made available through the Web-ICE internet tool (http://epa.gov/ceampubl/fchain/webice/), which also includes modules for endangered species extrapolation and generation of species sensitivity distributions (SSDs). ICE models have been developed and validated for predicting the toxicity to over 200 U.S. federally listed species at the species, genus or family level from one of 68 surrogate test species. Mollusc models have been expanded to allow genus level predictions to 14 T&E unionid mussel species. Genus and family level models are used because species-specific models are only available for a limited number of listed species, and our previous research has shown high accuracy of ICE estimates in closely related taxa. The SSD modules within Web-ICE provide an additional approach to developing protective toxicity values for T&E species, based on research showing that hazard concentrations derived from SSDs are generally protective of listed species. This presentation will demonstrate approaches to extrapolation using ICE models for T&E species that are consistent with NRC report recommendations.