A Practical Probabilistic Graphical Modeling Tool for Weighing Ecological Risk-Based Evidence. Issue 4 (18th May 2016)

• Record Type: Journal Article
• Authors: Carriger, John F.; Barron, Mace G.
• Is Part Of: Soil & sediment contamination. Volume 25:Issue 4(2016)
• Page Start: 476
• Page End: 487
• Publication Date: 2016-05-18
• Subjects: Bayesian belief networks -- ecological risk assessment -- probabilistic graphical models -- sediment quality criteria -- sediment quality triad -- weight-of-evidence; Soil pollution -- Periodicals; Soil protection -- Periodicals; 628.5505
• DOI: 10.1080/15320383.2016.1171293
• Languages: English
• ISSNs: 1532-0383

Abstract

ABSTRACT: Past weight-of-evidence frameworks for adverse ecological effects have provided soft-scoring procedures for judgments based on the quality and measured attributes of evidence. Here, we provide a flexible probabilistic structure for weighing and integrating lines of evidence for ecological risk determinations. Probabilistic approaches can provide both a quantitative weighing of lines of evidence and methods for evaluating risk and uncertainty. The current modeling structure was developed for propagating uncertainties in measured endpoints and their influence on the plausibility of adverse effects. To illustrate the approach, we apply the model framework to the sediment quality triad using example lines of evidence for sediment chemistry measurements, bioassay results, and in situ infauna diversity of benthic communities using a simplified hypothetical case study. We then combine the three lines evidence and evaluate sensitivity to the input parameters, and show how uncertainties are propagated and how additional information can be incorporated to rapidly update the probability of impacts. The developed network model can be expanded to accommodate additional lines of evidence, variables and states of importance, and different types of uncertainties in the lines of evidence including spatial and temporal as well as measurement errors.