How should we define fitness in structured metapopulation models? Including an application to the calculation of evolutionarily stable dispersal strategies

TLDR

The authors define a fitness concept for structured metapopulations with infinitely many equally coupled patches and introduce a more easily calculated quantity Rm analogous to R0 in ordinary dynamics. Rm is defined as the average number of newborn dispersers produced by a local colony founded by a newborn disperser, and the authors provide efficient algorithms to compute it. Using these concepts, the authors compute the evolutionarily stable conditional dispersal strategy, showing that below a threshold density ã individuals should stay while above it they should leave.

Abstract

We define a fitness concept applicable to structured metapopulations consisting of infinitely many equally coupled patches. In addition, we introduce a more easily calculated quantity Rm that relates to fitness in the same manner as R0 relates to fitness in ordinary population dynamics: the Rm of a mutant is only defined when the resident population dynamics converges to a point equilibrium and Rm is larger (smaller) than 1 if and only if mutant fitness is positive (negative). Rm corresponds to the average number of newborn dispersers resulting from the (on average less than one) local colony founded by a newborn disperser. Efficient algorithms for calculating its numerical value are provided. As an example of the usefulness of these concepts we calculate the evolutionarily stable conditional dispersal strategy for individuals that can account for the local population density in their dispersal decisions. Below a threshold density ã, at which staying and leaving are equality profitable, everybody should stay and above ã everybody should leave, where profitability is measured as the mean number of dispersers produced through lines of descent consisting of only non–dispersers.