University of Sheffield

Tony O'Hagan - Academic pages - Abstracts


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

Grist, E. P. M., O'Hagan, A, Crane, M., Sorokin, N., Sims, I. and Whitehouse, P.

CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia, Department of Probability and Statistics, University of Sheffield, Sheffield, UK, Watts & Crane Associates, Faringdon, Oxfordshire, UK, WRc, Henley Road, Medmenham, Buckinghamshire, UK and Environment Agency, Wallingford, Oxfordshire, UK

Publication details: Environmental Science and Technology, 40, 395-401, 2006.


Species Sensitivity Distributions (SSDs) are increasingly used to analyse toxicity data, but have been criticised 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 point-wise 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 which incorporates expert knowledge, and that use of Bayesian methodology permits estimation of an SSD which 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.

Keywords: Species sensitivity distribution, risk assessment, chlorpyrifos, Bayesian statistics, expert judgment, bootstrap regression, time-to-event analysis.

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Updated: 26 Jamuary 2006
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