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Integrating spatial and temporal distribution of snow dynamics into mule deer winter range habitat selection

University of British Columbia

Many migratory terrestrial mammal species within North America rely on particular habitat characteristics to provide shelter from snow cover in order to assure inter-annual survivorship. Mule deer (Odocoileus hemionus) in particular, are reported to be in decline across South-central British Columbia, likely due to reductions in adequate winter range habitat, limiting the degree of shelter that can be utilized to avoid snow and low temperatures. This thesis sought to evaluate predictions from multiple step selection functions (SSF) by considering both mule deer responses to the timing and distribution of snow cover as well as forest stand attributes including canopy cover and forest edge. In order to generate such SSFs, increasing spatial and temporal information regarding snow timing and distribution across the landscape was required. Previously however, predictions of fine-scale snow dynamics across the landscape suitable for analysis with hourly telemetry data were limited. Therefore, the first component of this thesis was to utilize the strengths of both medium spatial resolution and high temporal resolution satellite imagery and develop a data fusion algorithm to predict snow cover dynamics at a 30m spatial resolution daily, since 2000 using Landsat data with MODIS (Moderate Resolution Imaging Spectroradiometer) snow map data as inputs. The final fused snow map product (MODSAT-NDSI) achieved an overall accuracy of 90% using 33 validation test sites, which included government snow pillow data and an installed camera network. Environmental covariates from MODSAT-NDSI snow maps and 77 deer’s GPS telemetry data in the mule deer SSFs were used to produce predictions of relative probability of use for population-level estimates of habitat selection patterns. The top-ranked SSF models (based on AIC) indicated that mule deer avoided areas with greater, and more persistent, snow cover, and selected areas closer to forest edge. Key thesis outcomes include generated snow cover maps that can be updated and utilized in further studies, a data fusion algorithm that can be replicated for other remote sensing metrics, and habitat selection models that may help to inform future mule deer habitat management.

Text

2019-03-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/68633

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/