Science Inventory

Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments

Citation:

Moe, S., John F Carriger, AND M. Glendell. Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, 17(1):53-61, (2021). https://doi.org/10.1002/ieam.4369

Impact/Purpose:

Introduce a special series on Bayesian networks in environmental risk assessment for the journal Integrated Environmental Assessment and Management.

Description:

Environmental or ecological risk assessment (ERA) is defined as the process for evaluating how likely it is that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on non-probabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modelling, increasingly used in many fields of environmental science. BNs are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last two decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of SETAC North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modelling in relation to water quality. This special series contains ten articles based on presentations in these sessions, reflecting a range of BN applications to systems ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics including climate and management scenarios, ecological and socio-economic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual framework for ERA, such as Adverse Outcome Pathways and the Relative Risk Model. The contributions from EGU demonstrate recent advancements in areas such as spatial (GIS-based) and temporal (dynamic) BN modelling. The special series supports that statement that increased use of Bayesian network models will improve ecological risk assessments (Hart & Pollino 2008. Human and Ecological Risk Assessment 14:815-853).