Appropriateness of full-, partial- and no-dispersal scenarios in climate change impact modelling
Correspondence: Brooke L. Bateman, Centre for Tropical Biodiversity and Climate Change Research, School of Marine and Tropical Biology, James Cook University, Townsville, Qld 4811, Australia.
Species distribution models (SDMs) generally use correlative relationships between the species location and the associated environment to project the species potential distribution under climate change. While projecting a future suitable climatic space is relatively simple using SDMs, predicting a species ability to occupy that space relies on understanding dispersal capacity; a lack of knowledge about species-specific dispersal ability, varying geographical contexts and technical constraints of simple SDMs has limited the consideration of dispersal in most studies. We review the current treatment of dispersal in SDM studies addressing the effects of climate change and explore how incorporating ‘partial-dispersal’ scenarios could lead to more realistic projections of species distributions into the future.
We consider the implications for projected distributions of incorporating full- and no-dispersal scenarios in SDMs and identify a range of methods and their associated information needs for implementing partial-dispersal scenarios.
While simplistic and easy to implement, full- and no-dispersal scenarios are only realistic in a few situations. Although implementing partial-dispersal scenarios may require information that is lacking for many species, we argue that even relatively simple partial-dispersal models, with fairly basic knowledge needs, improve projections of altered distributions under climate change. More complex models, using more sophisticated modelling approaches, have been tested in a few cases and provide robust projections.
While climate change SDM outputs have proved useful, we highlight that careful selection of dispersal scenarios, relevant to the particular questions being addressed, is necessary for appropriate interpretation of the model outputs when projecting into novel environments (e.g. future climates). A number of methods have been developed for incorporating partial-dispersal scenarios in SDMs; however, the data and computation requirements currently limit their application to large numbers of species, highlighting the need for other techniques and generic user-friendly modelling platforms.