You are here:
A Bayesian network approach to refining terrestrial risk assessments: Mercury and the Florida panther (Puma concolor coryi)
Citation:
Barron, M. AND J. Carriger. A Bayesian network approach to refining terrestrial risk assessments: Mercury and the Florida panther (Puma concolor coryi). SETAC North America 39th Annual Meeting, Sacramento, CA, November 04 - 08, 2018.
Impact/Purpose:
Bayesian networks are useful tools for calculating uncertainties and ecological risks. This presentation will demonstrate and discuss the potential benefits of using Bayesian networks for probabilistic ecological risk assessments. Bayesian networks were applied to a case study on retrospective mercury risks to Florida panthers. Risk analysts will be interested in this presentation, especially ones looking to better understand how Bayesian networks could be applied to ecological risk assessments.
Description:
Traditionally hazard quotients (HQs) have been computed for ecological risk assessment, often without quantifying the underlying uncertainties and variability in the risk estimate. We demonstrate a Bayesian network approach to quantitatively assess variability and uncertainties in HQs using a retrospective case study of mercury (Hg) risks to Florida panthers (Puma concolor coryi) based on Barron et al. (2004. Ecotox 13:223). The Florida panther is the last remaining wild puma population in the eastern U.S. Mercury exposure has been hypothesized as a contributing factor to population decline and individual panther deaths across the South Florida ecosystem. A tiered approach with Bayesian networks was developed to identify and quantify the causal risk factors, propagate uncertainties in causal relationships, and examine the refinements necessary for predicting the overlap of Hg exposure and effects distributions to calculate HQs. Multiple HQs for the Florida panther and Hg risks were calculated with the Bayesian network to examine potential scenarios based on changes to causal exposure factors. Application of Bayesian networks in the computation of HQs provides a transparent and quantitative analysis of uncertainty in risks.