Bayesian and Time-Independent Species Sensitivity Distributions for Risk Assessment of Chemicals

• Published in: Environmental science & technology Vol. 40; no. 1; pp. 395 - 401
• Main Authors: Grist, Eric P. M, O'Hagan, Anthony, Crane, Mark, Sorokin, Neal, Sims, Ian, Whitehouse, Paul
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 01.01.2006
• Subjects: Animal, plant and microbial ecology; Animals; Applied ecology; Bayesian analysis; Biological and medical sciences; Chemicals; Chlorpyrifos - toxicity; Data Interpretation, Statistical; Ecotoxicology, biological effects of pollution; Environmental science; Fishes; Fundamental and applied biological sciences. Psychology; Insecticides - toxicity; No-Observed-Adverse-Effect Level; Regression Analysis; Risk Assessment; Sensitivity and Specificity; Species Specificity; Techniques; Toxicity; Water Pollutants - toxicity; Chlorpyrifos; Evaluation; Insecticide; Toxicity; Interspecific comparison; Pesticides; Risk; Zoobenthos; Freshwater environment; Statistical method; Pollutant; Sensitivity resistance; Investigation method; Organophosphorus compounds; Water pollution; Invertebrata
• ISSN: 0013-936X; 1520-5851
• DOI: 10.1021/es050871e

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.