Document Type
Publication
Publication Date
Winter 2021
Keywords
toxicology, IETC Climate Change
Abstract
In 2012, a regional risk assessment was published that applied Bayesian networks (BN) to the structure of the relative risk model. The original structure of the relative risk model (RRM) was published in the late 1990s and developed during the next decade. The RRM coupled with a Monte Carlo analysis was applied to calculating risk to a number of sites and a variety of questions. The sites included watersheds, terrestrial systems, and marine environments and included stressors such as nonindigenous species, effluents, pesticides, nutrients, and management options. However, it became apparent that there were limits to the original approach. In 2009, the relative risk model was transitioned into the structure of a BN. Bayesian networks had several clear advantages. First, BNs innately incorporated categories and, as in the case of the relative risk model, ranks to describe systems. Second, interactions between multiple stressors can be combined using several pathways and the conditional probability tables (CPT) to calculate outcomes. Entropy analysis was the method used to document model sensitivity. As with the RRM, the method has now been applied to a wide series of sites and questions, from forestry management, to invasive species, to disease, the interaction of ecological and human health endpoints, the flows of large rivers, and now the efficacy and risks of synthetic biology. The application of both methods have pointed to the incompleteness of the fields of environmental chemistry, toxicology, and risk assessment. The low frequency of exposure‐ response experiments and proper analysis have limited the available outputs for building appropriate CPTs. Interactions between multiple chemicals, landscape characteristics, population dynamics and community structure have been poorly characterized even for critical environments. A better strategy might have been to first look at the requirements of modern risk assessment approaches and then set research priorities. Integr Environ Assess Manag 2021;17:79–94. © 2020 SETAC
Publication Title
Integr Environ Assess Manag
Volume
2021
Issue
17
First Page
79
Last Page
94
DOI
10.1002/ieam.4351
Recommended Citation
Landis, Wayne, "The Origin, Development, Application, Lessons Learned, and Future Regarding the Bayesian Network Relative Risk Model for Ecological Risk Assessment" (2021). Institute of Environmental Toxicology & Chemistry Publications. 3.
https://cedar.wwu.edu/ietc_allpublications/3
Subjects - Topical (LCSH)
Toxicological chemistry; Biochemical toxicology; Bayesian statistical decision theory; Systems biology--Methodology; Bioinformatics
Genre/Form
articles
Type
Text
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf