Our data mainly is composed of forest types, water bodies, and human infrastructure (like roads and transmission lines), as well as subpopulation ranges and abundance estimates.

        For our DEM, we utilized Geogratis which, due to the size of the region of study, resulted in combining nine separate packets to make our final DEM, which would later be used for making our extent layer.

        We retrieved our forest types from DataBC, which included all forest types (coniferous, boreal, mixed forest, shrubland, grassland, etc.) as well as areas of human development (agriculture and urban classified areas). All layers were sized for BC as a whole, which we  clipped to the extent of our project layer. Additionally, DataBC was also the source for our transmission line layer.

        We acquired our roads data from Statistics Canada. This accounted for every type of road, paved or not. For our analysis we selected and used only large roads classified as highways and overpasses within the dataset, as those would be large and active enough to dissuade grizzly crossings.

        We included waterbodies such as lakes and rivers into our project due to these waterbodies being important to grizzly movement and habitat. The data we got came from the BC Freshwater Atlas and was clipped to our extent.

        Our grizzly bear subpopulation estimates came from Data BC, and while it covered the entire province, it came in two discrete components: a map consisting of polygons delineating individual subpopulations and their ranges, and a table containing subpopulation estimates for each area. For the majority of areas, there were multiple subpopulation estimates given for a particular subpopulation unit (taken at different dates in 2012). For use in our analysis, we took the average subpopulation estimate for each subpopulation unit.