SGER: From Measurement to Management: Evidence-Based Practice in Natural Resource Management

该项目的目标是弥合生态学研究与自然资源管理者之间的差距，特别是实地林业工作者，他们每天都在决定哪些林分应该采伐，哪些树应该留在这些采伐的林分中。这些管理者平衡相互冲突的目标：创造收入，保护森林，提供栖息地。 该项目旨在确定哪些生态学研究结果和数据最适用于叶树选择问题，并将相关工作提炼成可供太平洋西北地区实地林业工作者使用的形式。 最初，该团队将确定最适用的生态学研究结果，并为选择叶树制作原型决策辅助工具。我们将开发一个知识表示森林管理指南和树冠特征。 该研究小组由来自万年青州立大学和波特兰州立大学的计算机科学家和生态学家组成;以及来自华盛顿州自然资源部的森林生物统计学家，程序员和资源经理。 这项工作的结果在于数据库和工具，以及论文和软件开发，这将有助于将最近的研究结果从数据转移到决策和论文处理，以及随之而来的计算机科学研究，其影响超出了这个问题及其领域。 这些措施包括适当的知识表示的研究结果和科学文物，认知方面的模型，研究结果，科学可视化，并提高决策辅助。其他影响包括改善华盛顿州的自然资源决策，以及确定某类决策者的需求。教育影响包括在一个全职的，四分之一长的本科跨学科课程（计算机科学，数学，生态学）的自然资源管理组件。将为万年青硕士课程设计一门循证自然资源管理课程，并为俄勒冈州州立大学生态信息学博士举办一个讲习班。学生将被引导。

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.