Evolution of dispersal in metapopulations
The rate of dispersal is a key trait that fragmented populations can adapt to increase their viability and to escape extinction. Yet, the selective pressures governing dispersal evolution are difficult to evaluate and still poorly understood. In particular, predictions of evolutionarily stable dispersal rates have only been derived under a number of simplifying conditions regarding the ecology of the dispersing species. My project aims at predicting the outcome of dispersal evolution in metapopulations based on assumptions that are more likely to be met in the field: (1) population dynamics within patches are density-regulated by realistic growth functions, (2) demographic stochasticity resulting from finite population sizes within patches is accounted for, and (3) the transition of individuals between patches is explicitly modeled by a disperser pool. On this basis, we demonstrate two general patterns of metapopulation adaptation. We show, first, that evolutionarily stable dispersal rates do not necessarily increase with disturbance rates. Second, we describe how demographic stochasticity affects the evolution of dispersal rates: these rates can remain high even when disturbance rates are low. Moreover, high degrees of demographic stochasticity significantly enrich the behavior of adapted dispersal rates: it is shown for the first time that variation of disturbance rates can result in monotonic increases or decreases as well as in intermediate maxima or minima.