Numerous quantified and unquantified uncertainties exist that propagate from effects and exposure analysis to the characterization of risks. The ecological risk assessment process for wildlife species requires characterizing multiple exposure factors and extrapolating effects from surrogate species to assessment endpoints. The ecological risk assessment process provides a logical and useful framework for examining potential risks from environmental stressors to ecological receptors, but the quantitative approaches for analyzing the data are much more likely to change than the framework itself as analytical methods advance ( Newman, 1998). Application of Bayesian networks in the computation of HQs provides a transparent and quantitative analysis of uncertainty in risks.
Diagnostic inference was used in a high-risk scenario to demonstrate the capabilities of Bayesian networks for examining probable causes for observed effects. Sensitivity analysis of the Bayesian network showed greatest influence on risk estimates from daily ingested dose by panthers and mercury levels in prey, and less influence from toxicity reference values. Mercury HQs and risks to Florida panthers determined from a Bayesian network analysis were nearly identical to those determined using the prior Monte Carlo probabilistic assessment and demonstrated the ability of the Bayesian network to replicate conventional HQ-based approaches. ECOTOX 13:223), as a representative example of the uncertainty and complexity in HQ calculations.
The Bayesian network was parameterized, using exposure data from a previous Monte Carlo-based assessment of Hg risks ( Barron et al., 2004. We demonstrate a Bayesian network approach to quantitatively assess uncertainties in HQs using a retrospective case study of dietary mercury (Hg) risks to Florida panthers ( Puma concolor coryi).
Traditionally hazard quotients (HQs) have been computed for ecological risk assessment, often without quantifying the underlying uncertainties in the risk estimate.
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