Bayesian and time-independent species sensitivity distributions for risk assessment of chemicals.

Species sensitivity distributions (SSDs) are increasingly used to analyze toxicity data but have been criticized for a lack of consistency in data inputs, lack of relevance to the real environment, and a lack of transparency in implementation. This paper shows how the Bayesian approach addresses concerns arising from frequentist SSD estimation. Bayesian methodologies are used to estimate SSDs and compare results obtained with time-dependent (LC50) and time-independent (predicted no observed effect concentration) endpoints for the insecticide chlorpyrifos. Uncertainty in the estimation of each SSD is obtained either in the form of a pointwise percentile confidence interval computed by bootstrap regression or an associated credible interval. We demonstrate that uncertainty in SSD estimation can be reduced by applying a Bayesian approach that incorporates expert knowledge and that use of Bayesian methodology permits estimation of an SSD that is more robust to variations in data. The results suggest that even with sparse data sets theoretical criticisms of the SSD approach can be overcome.

Freshwater to saltwater toxicity extrapolation using species sensitivity distributions.

There is generally a lack of saltwater ecotoxicity data for risk assessment purposes, leaving an unknown margin of uncertainty in saltwater assessments that utilize surrogate freshwater data. Consequently, a need for sound scientific advice on the suitability of using freshwater data to extrapolate to saltwater effects exists. Here we use species sensitivity distributions to determine if freshwater datasets are adequately protective of saltwater species assemblages for 21 chemical substances. For ammonia and the metal compounds among these data, freshwater data were generally protective because freshwater organisms tended to be more sensitive. In contrast, for pesticide and narcotic compounds, saltwater species tended to be more sensitive and a suitable uncertainty factor would need to be applied to surrogate freshwater data. Biological and physicochemical factors contribute to such differences in freshwater and saltwater species sensitivities, but the species compositions of datasets used are also important.