Accounting for heterogeneous density and detectability in spatially explicit capture-recapture studies of carnivores

Reliable estimates of population density are fundamental for managing and conserving wildlife. Spatially explicit capture-recapture (SECR) models in combination with information-theoretic model selection criteria are frequently used to estimate population density. Variation in density and detectability is inevitable and, when unmodeled, can lead to erroneous estimates. Despite this knowledge, the performance of SECR models and information-theoretic criteria remain relatively untested for populations with realistic levels of variation in density and detectability. We addressed this issue using simulations of American black bear (Ursus americanus) populations with variable density and detectability between sexes and across study areas. We first assessed the reliability of Akaike information criterion adjusted for small sample sizes (AIC(c)) to correctly identify the true data generating model or a good approximating model. We then assessed the bias, accuracy, and precision of density estimates when such a model was selected or not. We demonstrated that unmodeled heterogeneity in detection and, more importantly, density can lead to pronounced bias. However, when a good approximating model is included in the candidate set, models with lower AIC(c )included important forms of variation and yielded accurate estimates. We encourage researchers and practitioners to consider the impact of unmodeled variation in SECR models when making inferences and to strive to include covariates likely to be the most influential based on the species biology and ecology in candidate model sets. Doing so can improve the robustness of wildlife density estimation methods that can be leveraged to make more sound conservation and management decisions.

Automated summary

Reliable estimates of population density are crucial for wildlife management and conservation. Spatially explicit capture-recapture models and information-theoretic model selection criteria are commonly used for density estimation. This study tested the performance of these models and criteria in the presence of realistic levels of density and detectability variation. Simulations of American black bear populations were used to assess the reliability of model selection criteria and the accuracy of density estimates. The study found that unmodeled heterogeneity in density and detectability can lead to biased estimates, but including a good approximating model can yield accurate results.