Deer resource selection analysis
We defined “used” locations as those where we received a successful location fix from any of our collared deer across the study period, for a total of 3,924 used locations. Available locations were randomly generated across the District of Oak Bay, bounded by the area for which we searched and captured deer. We generated available points in a 3:1 ratio, the number needed to achieve an asymptotic distribution of variable means (Gerber and Northrup 2020, Northrup, Hooten, Anderson Jr and Wittemyer 2013). For each used and available location, we calculated mean vegetation greenness (NDVI) and the percent area of tree cover within a 50-m radius buffer, a size selected to minimize error associated with GIS data resolution while also representing small-scale resource choice. We also calculated the percent area of three small, medium, and large residential lots, as well as road density (km/km2). Parks and golf courses were poorly represented inside buffer areas, so we measured proximity to these features.
We evaluated β coefficients from a single global model containing all selected landscape features. We chose not to do model selection as our goal was not to find the most parsimonious (reduced) model with a small subset of component variables, but rather to ascertain the selection strength of multiple variables (Burnham and Anderson 2002). We used a logistic regression in a generalized linear model (GLM with binomial errors and a logit link) with used locations (1’s) and randomly selected available locations (0’s) regressed against landscape covariates. We examined the second order of selection (Johnson 1980) which examines use within a group of animals: in this case the population of Oak Bay. Third-order selection – selection of resources by individuals within their home-range – is a useful analysis when considering individual-specific behaviours or when resources are not available to all members of a population (Manly, McDonald, Thomas, McDonald and Erickson 2007), but given our small study area and the movement of individuals across that area (making all resources accessible), the second-order analysis was of interest here. We used k-fold cross-validation (Roberts, Bahn, Ciuti, Boyce, Elith, Guillera-Arroita, Hauenstein, Lahoz-Monfort, Schröder, Thuiller, Warton, Wintle, Hartig and Dormann 2017) to examine model fit and calculated odds ratios (OR) from β coefficients as eβ.