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Talk Abstract
Migration Models Based on Dispersal Data

Mark A. Lewis
Department of Mathematics
University of Utah

Whether plants can migrate fast enough to track climate change depends on dispersal. Seed rain data collected in forest understories indicate that seed shadows for most species possess `fat tails' describing rare, long-distance dispersal events. Simple diffusion models do a poor job of describing migration rates when these long-distance dispersal events dominate. Models based on spatial dispersal kernels do a better job, but predicted rates are not robust to small changes in the parametric form of the kernel.

I will discuss a non-parametric estimator for migration rates which is based on discrete seed rain data. It is possible to construct confidence intervals for the estimated migration speed---a new development in the context of invasion theory. If time permits I will discuss a version of the model which incorporates stochastic spatial interactions.

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  1998-1999 Mathematics in Biology

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