es050871e_si_001.pdf (24.57 kB)
Bayesian and Time-Independent Species Sensitivity Distributions for Risk Assessment of Chemicals
journal contribution
posted on 2006-01-01, 00:00 authored by Eric P. M. Grist, Anthony O'Hagan, Mark Crane, Neal Sorokin, Ian Sims, Paul WhitehouseSpecies 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.
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Bayesian approachlackpointwise percentile confidence intervaleffect concentrationinsecticide chlorpyrifosBayesian methodologydata inputsRisk Assessmenttoxicity dataBayesian methodologiesbootstrap regressionSSD estimationChemicals Species sensitivity distributionsfrequentist SSD estimationSSD approachestimate SSDsLCdata setsexpert knowledgeBayesian approach addresses concerns
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