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On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

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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.

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Authors and Affiliations

  1. Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA, 93106-9610, USA

    Masami Fujiwara & Kurt E. Anderson

  2. Biology Department, MS #34 Woods Hole Oceanographic Institution, Woods Hole, MA, 02543, USA

    Michael G. Neubert & Hal Caswell

Authors
  1. Masami Fujiwara
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  2. Kurt E. Anderson
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  3. Michael G. Neubert
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  4. Hal Caswell
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Corresponding author

Correspondence to Masami Fujiwara.

Additional information

Received: January 2004 / Revised: July 2005

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Fujiwara, M., Anderson, K.E., Neubert, M.G. et al. On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data. Environ Ecol Stat 13, 183–197 (2006). https://doi.org/10.1007/s10651-005-0007-z

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  • DOI: https://doi.org/10.1007/s10651-005-0007-z

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