Ecology and Society Ecology and Society
E&S Home > Vol. 11, Iss. 2 > Art. 12 > Abstract Open Access Publishing 
Characterizing Species at Risk II: Using Bayesian Belief Networks as Decision Support Tools to Determine Species Conservation Categories Under the Northwest Forest Plan

Bruce G Marcot, USDA Forest Service
Paul A Hohenlohe, Oregon State University
Steve Morey, USDI Fish and Wildlife Service
Russ Holmes, USDA Forest Service
Randy Molina, USDA Forest Service
Marianne C Turley, USDI Bureau of Land Management
Mark H Huff, USDI Fish and Wildlife Service
John A Laurence, USDA Forest Service


Full Text: HTML   
Download Citation


We developed a set of decision-aiding models as Bayesian belief networks (BBNs) that represented a complex set of evaluation guidelines used to determine the appropriate conservation of hundreds of potentially rare species on federally-administered lands in the Pacific Northwest United States. The models were used in a structured assessment and paneling procedure as part of an adaptive management process that evaluated new scientific information under the Northwest Forest Plan. The models were not prescriptive but helped resource managers and specialists to evaluate complicated and at times conflicting conservation guidelines and to reduce bias and uncertainty in evaluating the scientific data. We concluded that applying the BBN modeling framework to complex and equivocal evaluation guidelines provided a set of clear, intuitive decision-aiding tools that greatly aided the species evaluation and conservation process.

Key words

Bayesian belief networks; decision models; expert panels; risk analysis; Northwest Forest Plan; species conservation.

Copyright © 2006 by the author(s). Published here under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license.

Ecology and Society. ISSN: 1708-3087