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

这个项目的目标是弥合生态学研究和自然资源管理人员之间的差距，特别是田野林业管理人员，他们每天决定在这些收获的林分中采伐哪些林木和留下哪些树木。这些管理者平衡了相互冲突的目标：创造收入、保护森林和提供栖息地。该项目旨在确定哪些生态学研究成果和数据最适用于叶树选择问题，并将相关工作提炼成太平洋西北地区田间林农可以使用的形式。最初，该团队将确定最适用的生态学研究结果，并为选择树叶制作原型决策辅助工具。我们将开发森林管理指南和树冠特征的知识表示法。研究小组由来自长青州立学院和波特兰州立大学的计算机科学家和生态学家，以及华盛顿州自然资源部的森林生物鉴定学家、程序员和资源经理组成。这项工作的结果在于数据库和工具，以及开发的文件和软件，这些文件和软件将有助于将最近的研究成果从数据转移到决策和文件处理，以及伴随而来的计算机科学研究，其影响超出了这个问题及其领域。其中包括研究结果和科学制品的适当知识表示、模型、研究结果和科学可视化的认知方面，以及改进决策辅助。其他影响包括华盛顿州自然资源决策的改善以及某一类决策者的需求特征。教育影响包括全日制、为期四分之一的本科生跨学科课程(计算机科学、数学、生态学)中的自然资源管理部分。将为常青树硕士项目设计一门基于证据的自然资源管理课程，并为俄勒冈州立大学生态信息学博士生举办研讨会。

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.