期刊
ECOLOGICAL APPLICATIONS
卷 22, 期 4, 页码 1068-1083出版社
ECOLOGICAL SOC AMER
DOI: 10.1890/11-1610.1
关键词
critical habitat; scale-integrated resource selection function, SRSF; species distribution model; woodland caribou
资金
- Alberta Conservation Association
- Government of Alberta Department of Sustainable Resource Development
- British Columbia Ministry of the Environment
- Canadian Association of Petroleum Producers
- Canadian Forest Products
- Foothills Research Institute
- Montana Institute on Ecosystems
- NSF EPSCoR [EPS-1101342]
- NSERC
- Petroleum Technology Alliance of Canada
- Parks Canada
- Royal Dutch Shell Canada
- University of Alberta
- University of Calgary
- University of Montana
- West Central Alberta Caribou Committee
- Weyerhaeuser Company
- World Wildlife Fund
Multi-scale resource selection modeling is used to identify factors that limit species distributions across scales of space and time. This multi-scale nature of habitat suitability complicates the translation of inferences to single, spatial depictions of habitat required for conservation of species. We estimated resource selection functions (RSFs) across three scales for a threatened ungulate, woodland caribou (Rangifer tarandus caribou), with two objectives: (1) to infer the relative effects of two forms of anthropogenic disturbance (forestry and linear features) on woodland caribou distributions at multiple scales and (2) to estimate scale-integrated resource selection functions (SRSFs) that synthesize results across scales for management-oriented habitat suitability mapping. We found a previously undocumented scale-specific switch in woodland caribou response to two forms of anthropogenic disturbance. Caribou avoided forestry cut-blocks at broad scales according to first-and second-order RSFs and avoided linear features at fine scales according to third-order RSFs, corroborating predictions developed according to predator-mediated effects of each disturbance type. Additionally, a single SRSF validated as well as each of three single-scale RSFs when estimating habitat suitability across three different spatial scales of prediction. We demonstrate that a single SRSF can be applied to predict relative habitat suitability at both local and landscape scales in support of critical habitat identification and species recovery.
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