Asymptotically exact analysis of stochastic metapopulation dynamics with explicit spatial structure

We describe a mathematically exact method for the analysis of spatially structured Markov processes. The method is based on a systematic perturbation expansion around the deterministic, non-spatial mean-field theory, using the theory of distributions to account for space and the underlying stochastic differential equations to account for stochasticity. As an example, we consider a spatial version of the Levins metapopulation model, in which the habitat patches are distributed in the dd-dimensional landscape RdRd in a random (but possibly correlated) manner. Assuming that the dispersal kernel is characterized by a length scale LL, we examine how the behavior of the metapopulation deviates from the mean-field model for a finite but large LL. For example, we show that the equilibrium fraction of occupied patches is given by p0+c/Ld+O(L-3d/2)p0+c/Ld+O(L-3d/2), where p0p0 is the equilibrium state of the Levins model and the constant c   depends on p0p0, the dispersal kernel, and the structure of the landscape. We show that patch occupancy can be increased or decreased by spatial structure, but is always decreased by stochasticity. Comparison with simulations show that the analytical results are not only asymptotically exact (as L→∞L→∞), but a good approximation also when LL is relatively small.