P. M. Grist, Eric O'Hagan, Anthony Crane, Mark Sorokin, Neal Sims, Ian Whitehouse, Paul 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. Bayesian approach;lack;pointwise percentile confidence interval;effect concentration;insecticide chlorpyrifos;Bayesian methodology;data inputs;Risk Assessment;toxicity data;Bayesian methodologies;bootstrap regression;SSD estimation;Chemicals Species sensitivity distributions;frequentist SSD estimation;SSD approach;estimate SSDs;LC;data sets;expert knowledge;Bayesian approach addresses concerns 2006-01-01
    https://acs.figshare.com/articles/journal_contribution/Bayesian_and_Time_Independent_Species_Sensitivity_Distributions_for_Risk_Assessment_of_Chemicals/3246442
10.1021/es050871e.s001