Combining data from 43 standardized surveys to estimate densities of female American black bears by spatially explicit capture-recapture

Spatially explicit capture-recapture (SECR) models are gaining popularity for estimating densities of mammalian carnivores. They use spatially explicit encounter histories of individual animals to estimate a detection probability function described by two parameters: magnitude (g (0)), and spatial scale (sigma). Carnivores exhibit heterogeneous detection probabilities and home range sizes, and exist at low densities, so g (0) and sigma likely vary, but field surveys often yield inadequate data to detect and model the variation. We sampled American black bears (Ursus americanus) on 43 study areas in ON, Canada, 2006-2009. We detected 713 animals 1810 times; however, study area-specific samples were sometimes small (6-34 individuals detected 13-93 times). We compared AIC (c) values from SECR models fit to the complete data set to evaluate support for various forms of variation in g (0) and sigma, and to identify a parsimonious model for aggregating data among study areas to estimate detection parameters more precisely. Models that aggregated data within broad habitat classes and years were supported over those with study area-specific g (0) and sigma (Delta AIC (c) a parts per thousand yen 30), and precision was enhanced. Several other forms of variation in g (0) and sigma, including individual heterogeneity, were also supported and affected density estimates. If study design cannot eliminate detection heterogeneity, it should ensure that samples are sufficient to detect and model it. Where this is not feasible, combing sparse data across multiple surveys could allow for improved inference.