A novel approach to assessing natural resource injury with Bayesian networks.

Quantifying the effects of environmental stressors on natural resources is problematic due to complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aims to restore natural resources injured by oil spills and hazardous substance releases into the environment; exploration of alternative statistical methods to evaluate effects could be beneficial to addressing NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury to a more traditional approach using toxicity thresholds for water and sediment chemistry. BNs offered several distinct advantages compared to traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application is needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many U.S. Federal, State, and Tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants.