Dispersal success on spatially structured landscapes: when do spatial pattern and dispersal behavior really matter?

Abstract

Dispersal is a fundamental component of many spatial population models. Concerns over the need to incorporate detailed information on dispersal behavior in spatially explicit population models (SEPMs) motivated us to undertake a simulation study in which we explored (1) the conditions under which landscape structure affects dispersal success and (2) the dependency of dispersal success on the choice of dispersal algorithm. We simulated individual dispersal as a random process (the mean-field approximation), a percolation process (PD) or a nearest-neighbor process (NND) on random and fractal neutral landscapes across gradients of habitat fragmentation and abundance (0.1–90%). Both landscape structure and dispersal behavior affected dispersal success in landscapes with <30–40% habitat. Landscape structure, in the form of contagious habitat, was always important for predicting the success of weak dispersers constrained to move within a local neighborhood, unless habitat was abundant (≥80%). Dispersers generally attained highest success on landscapes in which habitat had high spatial contagion. Habitat clumping may thus mitigate the negative effects of habitat loss on dispersal success. Spatial pattern is generally not important for predicting dispersal success when habitat abundance exceeds 40% and the mean-field approximation (random dispersal) adequately describes dispersal success in these landscapes. Because species of conservation concern generally occur in landscapes with <20% habitat, modeling dispersal as a random process may not be warranted for these species. In these cases, the required interaction between spatial structure and dispersal may be captured adequately by a simple local dispersal algorithm such that detailed movement rules may not be needed.