The goal of this project is to bridge the gap between ecology research and natural resource managers, in particular field foresters who make day-to-day decisions regarding which stands to harvest and which trees to leave in these harvested stands. These managers balance conflicting goals: produce revenue, preserve forests, and provide habitat. The project aims to determine which ecology research results and data are most applicable to the problem of leavetree-selection, and distill relevant work into a form usable by field foresters in the Pacific Northwest. Initially, the team will identify the most applicable ecology research results, and produce prototype decision aids for the selection of leave trees. We will develop a knowledge representation for forest management guidelines and tree crown characterization. The research team is composed of computer scientists and ecologists from The Evergreen State College and Portland State University; and a forest biometrician, programmer, and resource managers from the Washington State Department of Natural Resources. The result of the work lies in databases and tools, as well as papers and software developed that will help move recent research results from data to decisions and paper to process, and in the concomitant computer science research that has implications beyond this problem and its domain. These include appropriate knowledge representations for research results and scientific artifacts, cognitive aspects of models, research results, and scientific visualization, and improving decision making aids. Other impacts include improved natural resource decisions in Washington State and a characterization of needs of a certain class of decision makers. Educational impact includes a natural resource management component in a full time, quarter-long undergraduate interdisciplinary course (computer science, mathematics, ecology). A course in evidence-based natural resource management for the Evergreen Masters' Program will be designed, and a workshop for Oregon State University ecoinformatics Ph.D. students will be conducted.
