Society for Ecological Restoration - Northwest Chapter Conference

Presented by Bruce Marcot, USDA Forest Service

Abstract
A major challenge in conservation is managing species and ecosystems with scant scientific knowledge and great uncertainty. Decision support models (DSMs) can aid this by:

1. evaluating the implications of uncertainty in meeting management goals,
2. combining empirical data with expert judgment, and
3. through sensitivity testing and validation steps, identifying key habitat elements as a basis for prioritizing inventory and monitoring.

DSMs include a wide range of tools and include Bayesian analyses and belief network modeling, data and text mining, decision modeling such as decision tree analysis, expert systems, fuzzy logic and fuzzy set theory models, genetic algorithms, rule and network induction, neural networks, reliability analyses, quantitative (environmental) risk analysis, simulation and scenario modeling, and other approaches. Successful use of DSMs for plant and animal conservation depends largely on the availability of data or experts, and the willingness of decision-makers to articulate their risk attitudes and decision criteria. These are no small hurdles. Many DSMs can aid in merging scientific data with expert knowledge, although no model can replace empirical field studies. Several examples of DSMs are demonstrated to illustrate the 3 objectives listed above.