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Evolution of complex density-dependent dispersal strategies

Kalle Parvinen, Anne Seppänen, John D. Nagy, Evolution of complex density-dependent dispersal strategies. BULLETIN OF MATHEMATICAL BIOLOGY 74(11), 2622–2649, 2012.

http://dx.doi.org/10.1007/s11538-012-9770-9

Abstract:

The question of how dispersal behavior is adaptive and how it responds to changes in selection pressure is more relevant than ever, as anthropogenic habitat alteration and climate change accelerate around the world. In metapopulation models where local populations are large, and thus local population size is measured in densities, density-dependent dispersal is expected to evolve to a single-threshold strategy, in which individuals stay in patches with local population density smaller than a threshold value and move immediately away from patches with local population density larger than the threshold. Fragmentation tends to convert continuous populations into metapopulations and also to decrease local population sizes. Therefore we analyze a metapopulation model, where each patch can support only a relatively small local population and thus experience demographic stochasticity. We investigated the evolution of density-dependent dispersal, emigration and immigration, in two scenarios: adult and natal dispersal. We show that density-dependent emigration can also evolve to a nonmonotone, “triple-threshold” strategy. This interesting phenomenon results from an interplay between the direct and indirect benefits of dispersal and the costs of dispersal. We also found that, compared to juveniles, dispersing adults may benefit more from density-dependent vs. density-independent dispersal strategies.

BibTeX entry:

@ARTICLE{jPaSeNa12a,
  title = {Evolution of complex density-dependent dispersal strategies},
  author = {Parvinen, Kalle and Seppänen, Anne and Nagy, John D.},
  journal = {BULLETIN OF MATHEMATICAL BIOLOGY},
  volume = {74},
  number = {11},
  pages = {2622–2649},
  year = {2012},
  keywords = {Demographic stochasticity , Metapopulation},
}

Belongs to TUCS Research Unit(s): Biomathematics Research Unit (BIOMATH)

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