Кл.слова (ненормированные):
Data reduction -- Ecology -- Insecticides -- Toxicity -- Data inputs -- Species sensitivity distributions (SSD) -- Time-independent species -- Sensitivity analysis -- chlorpyrifos -- organophosphate insecticide -- risk assessment -- toxicity test -- aquatic environment -- article -- Bayes theorem -- confidence interval -- controlled study -- LC 50 -- linear regression analysis -- nonhuman -- risk assessment -- species sensitivity distribution -- time -- toxicity testing -- United Kingdom -- Animals -- Chlorpyrifos -- Data Interpretation, Statistical -- Fishes -- Insecticides -- No-Observed-Adverse-Effect Level -- Regression Analysis -- Risk Assessment -- Sensitivity and Specificity -- Species Specificity -- Water Pollutants
Аннотация: 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. В© 2006 American Chemical Society.
Scopus
Держатели документа:
CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, Tasmania 7001, Australia
Department of Probability and Statistics, University of Sheffield, Hicks Building, Sheffield S3 7RH, United Kingdom
Watts and Crane Associates, Faringdon, Oxfordshire SN7 7AG, United Kingdom
WRc, Henley Road, Marlow, Buckinghamshire SL7 2HD, United Kingdom
Environment Agency, Wallingford, Oxfordshire, OX10 8BD, United Kingdom
Доп.точки доступа:
Grist, E.P.M.; O'Hagan, A.; Crane, M.; Sorokin, N.; Sims, I.; Whitehouse, P.