A Bayesian network approach to refining ecological risk assessments: Mercury and the Florida panther (Puma concolor coryi)

Abstract

Traditionally hazard quotients (HQs) have been computed for ecological risk assessment, often without quantifying the underlying uncertainties in the risk estimate. 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). The Bayesian network was parameterized, using exposure data from a previous Monte Carlo-based assessment of Hg risks (Barron et al., 2004. ECOTOX 13:223), as a representative example of the uncertainty and complexity in HQ calculations. 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. 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. Diagnostic inference was used in a high-risk scenario to demonstrate the capabilities of Bayesian networks for examining probable causes for observed effects. Application of Bayesian networks in the computation of HQs provides a transparent and quantitative analysis of uncertainty in risks.