Habitat Model
To control for repeated captures of animals (e.g., a single moose being photographed repeatedly), we first binned moose detections daily, where each site was assigned a positive detection if any moose were captured that day or a negative detection (absence) if no moose were detected that day. We then binned data monthly to estimate moose site use, controlling for the number of days each camera was functional. The number of days moose were present and absent at each site was combined to generate the response variable. In this approach, we assumed that if a moose was not detected at a site within the years of sampling, we could reliably state it did not occur there – rather than assuming false absence as in an occupancy framework ( MacKenzie et al., 2003).
Moose habitat use was examined using binomial family generalized linear models (GLM). Our predictor variables for each model included the 30 land cover, human features, and burn area variables detailed above. We summarize the area of each response variable by generating twenty buffers for each camera site, ranging from 250 m to 5000 m in diameter at 250-m intervals. Our GLM models were run across all sites using bidirectional stepAIC model selection ( Zhang, 2016). A top model and scale were then determined for all data and for each age or sex category examined. After determining a top model, we examined predictor variance inflation factors (VIFs; ensuring each was < 4), component plus residual plots (to check for missing polynomial relationships( Fox et al., 2012)), and residual versus leverage plots for top models. The estimate and standard error were then assessed for each variable in each model.