Moving beyond static species distribution models in support of conservation biogeography
Article first published online: 12 FEB 2010
© 2010 Blackwell Publishing Ltd
Diversity and Distributions
Special Issue: Special Issue: Conservation biogeography - foundations, concepts and challenges
Volume 16, Issue 3, pages 321–330, May 2010
How to Cite
Franklin, J. (2010), Moving beyond static species distribution models in support of conservation biogeography. Diversity and Distributions, 16: 321–330. doi: 10.1111/j.1472-4642.2010.00641.x
- Issue published online: 13 APR 2010
- Article first published online: 12 FEB 2010
- Climate change;
- landscape dynamics;
- metapopulation model;
- species distribution model;
- species migration
Aim To demonstrate that multi-modelling methods have effectively been used to combine static species distribution models (SDM), predicting the geographical pattern of suitable habitat, with dynamic landscape and population models to forecast the impacts of environmental change on species’ status, an important goal of conservation biogeography.
Methods Three approaches were considered: (1) incorporating models of species migration to understand the ability of a species to occupy suitable habitat in new locations; (2) linking models of landscape disturbance and succession to models of habitat suitability; and (3) fully linking models of habitat suitability, habitat dynamics and spatially explicit population dynamics.
Results Linking species–environment relationships, landscape dynamics and population dynamics in a multi-modelling framework allows the combined impacts of climate change (affecting species distribution and vital rates) and land cover dynamics (land use change, altered disturbance regimes) on species to be predicted. This approach is only feasible if the life history parameters and habitat requirements of the species are well understood.
Main conclusions Forecasts of the impacts of global change on species may be improved by considering multiple causes. A range of methods are available to address the interactions of changing habitat suitability, habitat dynamics and population response that vary in their complexity, realism and data requirements.