Dispersal and the distributions of mammals: moving towards improved predictions

Abstract

Climate change is predicted to become a major cause of species loss in the coming century. Shifts in distribution as a response to changing conditions have already been observed for many terrestrial organisms. A species’ capacity to respond to climate change will depend greatly on its ability to track suitable conditions; those unable to track optimum conditions will be under increased threat of local extinction. There is, therefore, a need to include dispersal parameters in models that forecast the impact of climate change on species distributions, but this is limited by a paucity of dispersal data for many species. In this thesis I develop predictive models of dispersal ability to improve estimates of both distance and rate of dispersal in mammals. Chapter 2 presents a database of empirically derived dispersal distances for mammals and an analysis of the probability distribution of those distances, aimed at describing the ‘tail’ of the kernel, important in understanding long distance dispersal. Chapter 3 assesses the explanatory power of species’ life history and ecology, within a phylogenetic framework, to predict dispersal distances. Chapter 4 examines the roles of dispersal and colonisation ability in mediating the extent to which a species can fill its potential environmental niche and quantifies the effects of model accuracy and projection extent on this approach. Chapter 5 utilises a new technique for identifying patterns of geographic and phylogenetic constraint to examine the dual roles of evolutionary history and environment in determining a species’ ability to fill its potential environmental niche. This thesis helps to clarify controls on range limits and to incorporate such controls into species distribution models. By providing more accurate predictions of the impacts of climate change on species range size and location, this work helps us to better understand the threat to species diversity from global change.