Collaborative Research: Developing a Predictive Approach for Relating Landscape Attributes to Carnivore Distributions: A Case Study in the Greater Yellowstone Ecosystem

合作研究：开发一种将景观属性与食肉动物分布联系起来的预测方法：大黄石生态系统的案例研究

• 批准号: 9622789
• 负责人: Peter Kareiva
• 金额: $ 6.09万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-04-01 至 1999-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9622789&HistoricalAwards=false
• 关键词: Collaborative; Research; Developing; Predictive; Approach

This study will link the distribution, pattern of movement, and resource use of martens in different forest habitats using Geographic Information Systems (GIS) in order to determine the marten's response to habitat change at varying spatial scales. We will determine the scale at which marten movement is most sensitive to the arrangement of physical and vegetation structure, characteristics that are frequently altered by human use of forest lands. Most data will be gathered during the winter when martens show strongly selective patterns of movement and resource use. Our methods will combine snowtracking of martens and DNA 'fingerprinting" from fur samples collected by hairsnares to estimate abundance and movement in relation to habitat structure determined from NASA satellite imagery and U. S. Forest Service data. Models will be developed to evaluate how the scale of vegetative and weather variables explicitly influence marten resource use, and how varying levels of forest fragmentation influence movement patterns and population dispersion relative to landscape attributes. The non-intrusive, low-cost methods employed by this study are unique additions to forest ecology and management, and will play an important role as we move to large-scale and long-term management of sustainable ecosystems and regions.

这项研究将利用地理信息系统(GIS)将不同森林生境中貂的分布、运动模式和资源利用联系起来，以确定貂在不同空间尺度上对生境变化的响应。我们将确定貂皮移动对物理和植被结构的安排最敏感的规模，这些特征经常因人类对林地的使用而改变。大部分数据将在冬季收集，此时貂显示出强烈的运动和资源使用模式。我们的方法将结合雪貂的雪迹和毛发夹收集的毛皮样本的DNA指纹，根据NASA卫星图像和美国林业局的数据来估计与栖息地结构相关的丰度和运动。我们将开发模型来评估植被和天气变量的规模如何明确地影响貂资源的使用，以及不同程度的森林破碎化如何影响相对于景观属性的运动模式和种群分散。本研究采用的非侵入性、低成本方法是森林生态和管理的独特补充，在我们转向大规模和长期管理可持续生态系统和地区的过程中将发挥重要作用。