Modeling dispersal using capture-recapture data: A comparison of dispersal models

Capture-recapture methods have been a cornerstone of field-based dispersal ecology. However, obtaining unbiased estimates of dispersal parameters from capture-recapture data is challenging because it is impossible to survey all possible range of dispersal in the field. There are several approaches to address this critical issue of capture-recapture methods. Still, a lack of formal comparisons among these modeling approaches has confused about which is the best practice given the available dataset. Here, I compared the performance of three dispersal models using test datasets simulated under various sampling designs. In the first approach, a probability distribution (a dispersal kernel) was simply fitted to the capture-recapture data (the "simple dispersal model"). In the second approach, a truncated probability distribution was used to account for the finite range of observations (the "truncated dispersal model"). Finally, the dispersal and observation processes were coupled to consider the spatial organization of sampling designs (the "dispersal-observation model"). The simulation study provided three important insights. First, the dispersal-observation model provided reliable estimates of dispersal parameters, even under sampling designs with a few recaptures. Second, the truncated dispersal model was also effective, but only when the number of recaptures was large. Finally, the use of the simple dispersal model caused a substantial underestimation of dispersal parameters regardless of sampling designs; this modeling approach should be avoided where possible. The results of this simulation study should help choose a suitable modeling approach.