The bottleneck often results from working with issues so complex that it is not feasible for single persons to even conceptualize, much less sort through and analyze alternative decisions. Therefore, few implementations of adaptive management are currently feasible for most wildlife, fisheries, park, and forest managers. What is needed are decision support systems and tools that allow them to capture relevant knowledge about specific habitat and population issues. Decision support systems and tools for fish and wildlife management activities require planning in multiple spatial and temporal scales. Such tools must not only apply to local situations, but also have the capability to integrate the actual process of such plans on flyway, regional, national, ecosystem, watershed, and other ecological bases.
Adaptive management recognizes that knowledge about natural resource systems is uncertain.
Therefore, some management actions are best conducted as experiments in a continuing attempt to reduce the risk arising from that uncertainty. This is particularly difficult to accomplish in an ecological domain, because of the underlying complexity and cross-scale issues. However, technologies successfully being used in the business, defense, and space sectors hold promise. This project will explore computer technologies designed to tackle highly complex, distributed problems, e.g., influence diagrams, Bayesian networks, intelligent agents, and planning algorithms as the heart for generic adaptive management applications. We will provide user-friendly, artificial intelligence based, web applications for field implementation of adaptive management.