Dispersal, demography and spatial population models for conservation and control management

Spatial population dynamics can seldom be ignored in management aimed at conserving or controlling plant species in a spatial context. Therefore, spatial population models, that bring together knowledge about a species' local demography and dispersal behavior, are of growing applied importance. Here, we survey increasingly complex analytical and simulation models that are being developed to describe both demography and dispersal in applied studies. Local population dynamics can be modeled in an unstructured way, by specifying age- or stage-structure or by modeling each individual. Dispersal is often summarized in population-spread models with descriptive and simple statistical models. Mechanistic models that incorporate the physical or behavioral dynamics of dispersal vectors, however, provide more insight and can more readily be applied to novel situations. Importantly, mechanistic models provide a tool for linking variation in species traits and environments to dispersal and population spread. Spatial population models span a wide range: from diffusion models, metapopulation models, integrodifference equation models, and Neubert-Caswell models, to spatially explicit individual-based models. The complexity (and biological realism) of such models often trades off with tractability: for instance, individual-based simulation models allow for unlimited incorporation of biological detail, but rarely for analytical exploration of the model dynamics. We discuss the advantages and disadvantages of these various spatial population models; the choice of the most appropriate model will depend on the management objective, the biological complexity, available data and the principle of parsimony. We present five case studies of endangered and invasive species for which spatial population models have been developed to inform management, for instance to decrease the spread rate of invasive species or to improve the regional persistence of endangered species. We also anticipate exciting new developments in both spatial analytical and spatial simulation models with increasing demographic, dispersal and spatial sophistication. (c) 2007 R bel Foundation, ETH Zurich. Published by Elsevier GmbH. All rights reserved.