A cautionary tale comparing spatial count and partial identity models for estimating densities of threatened and unmarked populations

Population monitoring is critical to wildlife conservation, but density estimation is difficult for wide-ranging, unmarked species inhabiting remote habitats. Furthermore, recent investigations into density estimation with camera trap data has revealed unmarked models to be potentially unreliable, prompting cautious application and continued model development. Two related approaches with increasing appeal include spatial count (SC), which infer latent identities from the spatial pattern of detections, and spatial partial identity models (SPIM), which additionally leverage partial identity covariates (e.g., sex, antler point count, and presence of GPS/radio collar). To assess the performance of unmarked density models, we applied SC and SPIM to camera trap data of threatened boreal caribou in Canada, which are declining but have few rigorous density estimates across their broad distribution to inform conservation efforts. In particular, we focused on two spatially proximate caribou ranges in northern Alberta, Canada that differ in estimated caribou demographic trends, disturbance histories, and abundances of caribou predators and apparent competitors. Estimates of caribou density varied over a 4 year period (2016 – 2019), and were higher in the region with more stable reported growth rates and less anthropogenic disturbance (mode SPIM estimates: 155 – 225/1000 km2 vs. 19 – 96/1000 km2). However, density estimates differed by modeling approach and had low and variable precision, hindering inferences about population status and trajectories. Simulations suggest that SPIM estimates may have been less biased and more precise. SC models likely underestimated density by mistaking detections of neighboring individuals as recaptures of a single individual, although SPIM may also have overestimated density by inflating assignment probabilities of detections to non-existent individuals. Findings highlight the need to explore how grouping dynamics and non-independent movement violate assumptions and reduce the ability to distinguish individuals. We advocate continued investigation into the accuracy of unmarked density estimation approaches, the ecological and sampling conditions appropriate for different unmarked density models, and coordination across sampling efforts and analyses to improve population inferences.

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