Spatial Capture-Recapture analysis
We first considered all candidate models ignoring habitat heterogeneity. Among the 42 homogeneous models evaluated (Supplementary Table S2), the top model supported session i.e. density varied by year and baseline detection probability varied with sampling occasion, sex and year. The movement parameter σ varied with the interaction of sex and year.
We included habitat covariates for density and detection probability in the top model and evaluated 24 different inhomogeneous candidate models (Supplementary Table S3). The homogeneous null model ranked as the ninth model with an approximately 25 AIC difference relative to the top model that included habitat characteristics (Table 2). According to the most supported inhomogeneous model, white-tailed deer density was dependent on landcover-type (agricultural areas, coniferous forests, mixed forests and transitional woodland) and detection probability varied with landcover-type (coniferous forest, mixed forests, transitional woodland) as well as distance to agricultural areas (Table 2, Table 3). Distance to water bodies or distance to artificial areas were not supported for either density or detection probability (Supplementary Table S3).
White-tailed deer densities were highest in agricultural areas and mixed forest and lowest in coniferous forests and transitional woodlands during both years (Figure 2, Table 3). Density was session-dependent and was higher during the second year compared to the first year (Figure 2, Table 3).
Detection probability i.e. the probability of capturing an individual at its home range center was highest in transitional woodlands, second highest in mixed forests and lowest in coniferous forests (Table 4). Detection probability decreased with distance to agricultural areas. During the first year, detection probability was higher than during the second year, which was expected, as the sampling interval was shorter in the second year. Overall, females had a slightly higher probability of being detected than males, with p0=0.10 for females and p0=0.08 for males. The average detection probability was 0.09 and varied between 0.02 and 0.24 (Table 4).
The spatial scale parameter σ varied with the interaction of sex and year. For females, σ was larger in the first sampling year compared to the second. Male σ did not differ between years. Female σ was higher than that of males in the first year but in the second year σ was similar between the sexes. However, the confidence intervals generally overlap (Figure 3). Because space use by males was smaller than that of females, the estimated sex ratio (ψ) based on SCR under the top model was about equal (0.52) despite the fact that more female than male individuals were identified (Table 1).
Density estimates were higher for the heterogeneous landscape compared with assuming a homogeneous landscape. The homogeneous top model predicted that the overall density of white-tailed deer across the whole state space was 111.7 (109.5-113.8) deer/1000 ha in 2016 and 254.9 (249.8-259.9) deer/1000 ha in 2017. The inhomogeneous top model with habitat covariates predicted that the overall density was 131.0 (126.1-135.9) deer/ 1000 ha in 2016 and 317.0 (304.9-329.0) deer/ 1000 ha in 2017.