title: Modeling and analysis of stochastic invasion processes.
abstract: In this paper we derive spatially explicit equations to describe a stochastic invasion process. Parents are assumed to produce a random number of offspring which then disperse according to a spatial redistribution kernel. Equations for population moments, such as expected density and covariance averaged over an ensemble of identical stochastic processes, take the form of deterministic integro-difference equations. These equations describe the spatial spread of population moments as the invasion progresses. We use the second order moments to analyse two basic properties of the invasion. The first property is 'permanence of form' in the correlation structure of the wave. Analysis of the asymptotic form of the invasion wave shows that either (i) the covariance in the leading edge of the wave of invasion asymptotically achieves a permanence of form with a characteristic structure described by an unchanging spatial correlation function, or (ii) the leading edge of the wave has no asymptotic permanence of form with the length scales of spatial correlations continually increasing over time. Which of these two outcomes pertains is governed by a single statistic, phi which depends upon the shape of the dispersal kernel and the net reproductive number. The second property of the invasion is its patchy structure. Patchiness, defined in terms of spatial correlations on separate short (within patch) and long (between patch) spatial scales, is linked to the dispersal kernel. Analysis shows how a leptokurtic dispersal kernel gives rise to patchiness in spread of a population.
authors.affiliation: Department of Mathematics, University of Utah, 150 South 1400 East, JWB 233, Salt Lake City, UT 84112, USA. mlewis@math.utah.edu
authors.forename: M A
authors.initials: MA
authors.lastname: Lewis
authors.forename: S
authors.initials: S
authors.lastname: Pacala
mesh_terms: D000818:Animals; D001517:Coleoptera / Q000254:growth & development; D003198:Computer Simulation; D008954:Models, Biological; D009010:Monte Carlo Method; D009656:North America; D006109:Poaceae / Q000254:growth & development; D011157:Population Dynamics; D020308:Songbirds / Q000254:growth & development; D013269:Stochastic Processes