Abstract
Capture-recapture studies are frequently used to monitor the status and trends of wildlife populations. Detection histories from individual animals are used to estimate probability of detection and abundance or density. The accuracy of abundance and density estimates depends on the ability to model factors affecting detection probability. Non-spatial capture-recapture models have recently evolved into spatial capture-recapture models that directly include the effect of distances between an animal’s home range centre and trap locations on detection probability. Most studies comparing non-spatial and spatial capture-recapture biases focussed on single year models and no studies have compared the accuracy of demographic parameter estimates from open population models. We applied open population non-spatial and spatial capture-recapture models to three years of grizzly bear DNA-based data from Banff National Park and simulated data sets. The two models produced similar estimates of grizzly bear apparent survival, per capita recruitment, and population growth rates but the spatial capture-recapture models had better fit. Simulations showed that spatial capture-recapture models produced more accurate parameter estimates with better credible interval coverage than non-spatial capture-recapture models. Non-spatial capture-recapture models produced negatively biased estimates of apparent survival and positively biased estimates of per capita recruitment. The spatial capture-recapture grizzly bear population growth rates and 95% highest posterior density averaged across the three years were 0.925 (0.786–1.071) for females, 0.844 (0.703–0.975) for males, and 0.882 (0.779–0.981) for females and males combined. The non-spatial capture-recapture population growth rates were 0.894 (0.758–1.024) for females, 0.825 (0.700–0.948) for males, and 0.863 (0.771–0.957) for both sexes. The combination of low densities, low reproductive rates, and predominantly negative population growth rates suggest that Banff National Park’s population of grizzly bears requires continued conservation-oriented management actions.
Highlights
Increasing human activity throughout the world threatens many species and subsequent ecosystem processes [1]
When surveys are conducted across multiple years or sessions, open population capture-recapture models track individual detections over time to estimate demographic parameters such as apparent survival, per capita recruitment, and population growth rates [3,4]
Our simulation results suggest that open population spatial models have fewer biases and better credible interval coverage than non-spatial models, there was little difference between empirical results
Summary
Increasing human activity throughout the world threatens many species and subsequent ecosystem processes [1]. Capture-recapture techniques are commonly used to estimate abundance, density, and demographic parameters such as population growth, apparent survival, and recruitment. Capture-recapture studies use repeated surveys of identifiable individuals to estimate detection probability and variance around density, apparent survival, recruitment, and population growth rates [2]. When surveys are conducted across multiple years or sessions, open population capture-recapture models track individual detections over time to estimate demographic parameters such as apparent survival, per capita recruitment, and population growth rates [3,4]. Capture-recapture models have included the distance between an animal’s home range center and the edge of the study area as a covariate affecting detection probability [5,6,7,8], but this approach assumes a linear relationship between distance to edge and detection probability and does not reflect observation processes. Spatial capture-recapture techniques are a rapidly evolving class of models that directly estimate the effects of distance between an animal’s home range centre and each trap location on probability of detection [9,10,11,12]
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