title: Bayesian Networks Improve Causal Environmental Assessments for Evidence-Based Policy.
abstract: Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on valued ecological resources. These aspects are demonstrated through hypothetical problem scenarios that explore some major benefits of using Bayesian networks for reasoning and making inferences in evidence-based policy.
authors.affiliation: Oak Ridge Institute for Science and Education, U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, Florida 32561, United States.
authors.forename: John F
authors.initials: JF
authors.lastname: Carriger
authors.affiliation: U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, Florida 32561, United States.
authors.forename: Mace G
authors.initials: MG
authors.lastname: Barron
authors.affiliation: College of William & Mary, Virginia Institute of Marine Science, P.O. Box 1346, Route 1208 Greate Road, Gloucester Point, Virginia 23062, United States.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Newman
mesh_terms: D001499:Bayes Theorem; D008962:Models, Theoretical; D018570:Risk Assessment; D012308:Risk Management; D035501:Uncertainty