Transcending scale dependence in identifying habitat with resource selection functions
Transcending scale dependence in identifying habitat with resource selection functions
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DOI：
10.1890/11-1610.1
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发表时间：
2012-06-01
影响因子：
5
通讯作者：
Musiani, Marco
中科院分区：
文献类型：
--
作者：
DeCesare, Nicholas J.;Hebblewhite, Mark;Musiani, Marco
多尺度资源选择模型被用来识别限制物种在空间和时间尺度上分布的因素。栖息地适宜性的这种多尺度性质使将推论翻译成物种保护所需的栖息地的单一空间分布变得复杂。我们在三个尺度上估计了一种受威胁的有蹄类动物、林地驯鹿的资源选择函数（RSFs）（Rangifer tarandus caribou），有两个目标：（1）推断两种人为干扰形式的相对影响（林业和线性特征）在林地驯鹿分布在多个尺度和（2）估计尺度综合资源选择函数（SRSFs）综合各尺度的结果，以管理为导向的生境适宜性制图。我们发现了一个以前没有记录的尺度特定的开关林地驯鹿响应两种形式的人为干扰。驯鹿避免森林砍伐块在广泛的规模，根据第一和第二阶RSFs和避免线性功能，在精细的尺度，根据第三阶RSFs，证实预测开发的每种干扰类型的捕食者介导的影响。此外，一个单一的SRSF验证，以及每三个单尺度RSFs在三个不同的空间尺度的预测估计栖息地适宜性。我们表明，一个单一的SRSF可以应用于预测相对栖息地适宜性在当地和景观尺度上的关键栖息地识别和物种恢复的支持。
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.