On the estimation of dispersal kernels from individual mark-recapture data uri icon

abstract

  • We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Springer Science+Business Media, LLC 2006.

published proceedings

  • ENVIRONMENTAL AND ECOLOGICAL STATISTICS

altmetric score

  • 1

author list (cited authors)

  • Fujiwara, M., Anderson, K. E., Neubert, M. G., & Caswell, H.

citation count

  • 26

complete list of authors

  • Fujiwara, Masami||Anderson, Kurt E||Neubert, Michael G||Caswell, Hal

publication date

  • June 2006