Spatial capture-recapture (SCR) models allow ecologists and wildlife managers to estimate demographic rates of animal populations, including a measure of animal movement. Recent advances in spatial capture recapture allow researchers to combine multiple data sources to improve estimates of animal movement. For example, individuals within a population that have been marked with a GPS or radio telemetry collar will produce high resolution relocation data that can be used to model how each animal moves about its home range; subsequently, that information can be used to determine capture probability in an SCR model. Currently, SCR models are restrictive in what movement models they allow, with most implementations assuming a circular home range for individuals. Given that animals are not subject to these restrictions, applying these models to real data may have consequences for estimates of abundance, density, and space usage. Specifically, SCR models that incorporate telemetry data assume a bivariate normal model for telemetry data coupled with a half normal detection model for the traps. In this paper, we studied the potential consequences of this restriction. To test the effect of misspecifying the movement model, we implemented a simulation study parameterizing different detection functions with the bivariate normal model of telemetry data.
From the simulation study, we found that mispecifying the movement model greatly affected estimates of abundance; the strength and direction of the effect depended on the movement and detection model used. For example, using the telemetry data to parameterize an exponential detection model lead to a significant underestimation of abundance. Our results suggest that misspecifying the movement model in spatial capture recapture studies can have a dramatic effect on parameter estimates. These results provide an impetus for relaxing the movement assumptions in SCR models by incorporating advances in the movement modeling literature with finer resolution movement data from radio telemetry and GPS collars.