Species non-exchangeability in probabilistic ecotoxicological risk assessment

• Published in: Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 175; no. 1; pp. 243 - 262
• Main Authors: Craig, Peter S., Hickey, Graeme L., Luttik, Robert, Hart, Andy
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.01.2012; Blackwell Publishing; Blackwell; Royal Statistical Society; Oxford University Press
• Series: Journal of the Royal Statistical Society Series A
• Subjects: Applications; Assessment factors; Calculus of variations and optimal control; Chemicals; Ecological analysis; Ecological risk assessment; Ecology; Ecotoxicology; Environmental assessment; Environmental economics; Exact sciences and technology; Exchangeability; Food safety; General topics; Global analysis, analysis on manifolds; Mathematical analysis; Mathematics; Modelling; Parameter estimation; Parametric models; Probability; Probability and statistics; Risk assessment; Risk management; Sciences and techniques of general use; Species; Species sensitivity; Standard deviation; Statistical models; Statistics; Synecology; Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds; Toxicology; Toxins; Uncertainty; Statistical method; Probabilistic assessment; Ecotoxicology; Statistical model; Risk assessment; Database; Tolerance; Species sensitivity; Assessment factors; Decision rule; Exchangeability
• ISSN: 0964-1998; 1467-985X
• DOI: 10.1111/j.1467-985X.2011.00716.x

Current ecotoxicological risk assessment for chemical substances is based on the assumption that tolerances of all species in a specified ecological community are a priori exchangeable for each new substance. We demonstrate non-exchangeability by using a large database of tolerances to pesticides for fish species and extend the standard statistical model for species tolerances to allow for the presence of a single species which is considered non-exchangeable with others. We show how to estimate parameters and adjust decision rules that are used in ecotoxicological risk management. Effects of parameter uncertainty are explored and our model is compared with a previously published less tractable alternative. We conclude that the model and decision rules that we propose are pragmatic compromises between conflicting needs for more realistic modelling and for straightforwardly applicable decision rules.