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Competing populations on fragmented landscapes with spatially structured heterogeneities: improved landscape generation and mixed dispersal strategies

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Abstract

Interactions between two species competing for space were studied using stochastic spatially explicit lattice-based simulations as well as pair approximations. The two species differed only in their dispersal strategies, which were characterized by the proportion of reproductive effort allocated to long-distance (far) dispersal versus short-distance (near) dispersal to adjacent sites. All population dynamics took place on landscapes with spatially clustered distributions of suitable habitat, described by two parameters specifying the amount and the local spatial autocorrelation of suitable habitat. Whereas previous results indicated that coexistence between pure near and far dispersers was very rare, taking place over only a very small region of the landscape parameter space, when mixed strategies are allowed, multiple strategies can coexist over a much wider variety of landscapes. On such spatially structured landscapes, the populations can partition the habitat according to local conditions, with one species using pure near dispersal to exploit large contiguous patches of suitable habitat, and another species using mixed dispersal to colonize isolated smaller patches (via far dispersal) and then rapidly exploit those patches (via near dispersal). An improved mean-field approximation which incorporates the spatially clustered habitat distribution is developed for modeling a single species on these landscapes, along with an improved Monte Carlo algorithm for generating spatially clustered habitat distributions.

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  1. Department of Mathematics and Statistics, University of Maine, 333 Neville Hall, Orono, ME, 04469-5752, USA

    David E. Hiebeler

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  1. David E. Hiebeler
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Correspondence to David E. Hiebeler.

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Hiebeler, D.E. Competing populations on fragmented landscapes with spatially structured heterogeneities: improved landscape generation and mixed dispersal strategies. J. Math. Biol. 54, 337–356 (2007). https://doi.org/10.1007/s00285-006-0054-6

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