Transcending scale dependence in identifying habitat with resource selection functions

• Main Authors: DeCesare, Nicholas J., Hebblewhite, Mark, Schmiegelow, Fiona, Hervieux, David, McDermid, Gregory J., Lalenia Neufeld, Bradley, Mark, Whittington, Jesse, Smith, Kirby G., Morgantini, Luigi E., Wheatley, Matthew, Musiani, Marco
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Figshare 2016
• Subjects: Environmental Science; Ecology; FOS Biological sciences; Rangifer tarandus
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.3295427.v1; https://figshare.com/collections/Transcending_scale_dependence_in_identifying_habitat_with_resource_selection_functions/3295427/1

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.