Spatial Capture-Recapture
We used Spatial Capture-Recapture with the R package oSCR (Sutherland et al. 2016) in RStudio (RStudio Team 2018) to examine white-tailed deer habitat selection i.e. how habitat affects white-tailed deer densities (D) and capture probabilities (p0). We first chose the top homogeneous model without habitat covariates by AIC and continued fitting habitat covariates into the parameters of that model. This simplified the model selection procedure by reducing possible combinations of covariates (Efford and Fewster 2013, Brazeal et al. 2017). We constructed 42 different homogeneous models where we let D, p0 and σ (the spatial scale parameter) vary by year, sampling occasion, and sex. Supplementary Table S3 shows the combinations of fitted inhomogeneous models where D and p0 vary by different habitat covariates. All inhomogeneous models are modifications of the most supported homogeneous model. We also compared the overall predicted density estimates of the top homogeneous model with the top inhomogeneous SCR model including habitat covariates. We used multi-session SCR models with sampling year as a “session”. The state space was defined by a grid with resolution 120 m, which is about 0.5 x σ based on the estimate of σ from this study (see Results). A state space buffer of 1000m around the traps was used.
Habitat covariates were defined using the open-source Corine Land Cover data (2012) provided by Finnish Environment Institute. For water bodies, we used the vector data of waterways provided by National Land Survey of Finland (2018). We considered three different habitat covariates for density. First was a categorical habitat class variable with four different levels: agricultural areas (fields), coniferous forests, mixed forests and transitional woodland/shrub. Other density covariates were distance to artificial areas (e.g. buildings, roads and other artificially surfaced areas) and distance to water. These covariates were assigned to the state space by extracting them from the Corine Land Cover raster data with a function extr.rast() (oSCR package) using the habitat which was the most frequent when summarizing the Corine Land Cover raster values (resolution 20m x 20m) around the central coordinates of the state space pixel on the same resolution as the state space is defined (here 120m). Artificial areas would have covered only 2% of the state space and those pixels were assigned to the habitat that was the second most frequent. Artificial areas and water bodies were included in the analysis by calculating the nearest distance from the state space pixel central coordinates to artificial area or water.
We included four different trap-level covariates to study how landcover type affects capture probability (p0). The first covariate was a categorical landcover class variable with three different levels: coniferous forest, mixed forests, transitional woodland/shrub (hereafter: transitional woodland). Other three covariates for capture probability were distance to agricultural areas, distance to artificial areas, and distance to water bodies. To define the landcover class for each plot, the central coordinates of the sampling plots (“traps”) were buffered by 30m buffer and the proportion of each landcover type was calculated for the buffered area. The landcover type with the largest proportion in the buffered area was assigned as the landcover class for that plot. If two or more landcover types existed in exactly same proportions, then the class was defined using field notes on the landcover type of the central coordinate of the sampling plot. Only 3% of the 92 plots would have been assigned to deciduous forests. Because of this small proportion, this landcover class as trap-level covariate was changed to the class that covered the second largest proportion of the buffer area. To include agricultural areas, artificial areas and water bodies as covariates on capture probability, we calculated the nearest distance between the central coordinate of the plots and these landscape features.