Evolutionarily Stable and Convergent Stable Dispersal Strategies
Adrian Lam (Mathematical Biosciences Institute, The Ohio State University)
(November 21, 2013 10:20 AM - 11:15 AM)
Dispersal, which refers to the movement of an organism between two successive areas impacting survival and reproduction, is one of the most studied concepts in ecology and evolutionary biology. How do organisms adopt their dispersal patterns? Is there an “optimal”, or evolutionarily stable, dispersal strategy that emerges from the underlying ecology? In this talk, we consider a reaction-diffusion model of two competing species for the evolution of conditional dispersal in a spatially varying but temporally constant environment. Two species are different only in their dispersal strategies, which are a combination of random dispersal and biased movement upward along the resource gradient. In the absence of biased movement or advection, A. Hastings (1983) showed that dispersal is selected against in spatially varying environments. When there is a small amount of biased movement or advection, we show that there is a positive random dispersal rate that is both locally evolutionarily stable and convergent stable. Our analysis of the model suggests that a balanced combination of random and biased movement might be a better habitat selection strategy for populations. This is joint work with Y. Lou of Ohio State University.