A Bayesian approach to inferring dispersal kernels with incomplete mark-recapture data

Abstract

Dispersal is a fundamental ecological process that links populations, communities and food webs in space. However, dispersal is tremendously difficult to study in the wild because we must track individuals dispersing in a landscape. One conventional method to measure animal dispersal is a mark-recapture technique. Despite its usefulness, this approach has been recurrently criticized because it is virtually impossible to survey all possible ranges of dispersal in nature. Here, I propose a novel Bayesian model to better estimate dispersal parameters from incomplete mark-recapture data. The dispersal-observation coupled model, DOCM, can extract information from both recaptured and unrecaptured individuals, providing less biased estimates of dispersal parameters. Simulations demonstrated the usefulness of DOCM under various sampling designs. I also suggest extensions of the DOCM to accommodate more realistic scenarios. Application of the DOCM may, therefore, provide valuable insights into how individuals disperse in the wild.