Characterizing Species at Risk II: Using Bayesian Belief Networks as Decision Support Tools to Determine Species Conservation Categories Under the Northwest Forest Plan


"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."



Bayesian learning, decision making, modeling, risk, conservation