Modelling range dynamics under global change: which framework and why?

To conserve future biodiversity, a better understanding of the likely effects of climate and land-use change on the geographical distributions of species and the persistence of ecological communities is needed. Recent advances have integrated population dynamic processes into species distribution models (SDMs), to reduce potential biases in predictions and to better reflect the demographic nuances of incremental range shifts. However, there is no clear framework for selecting the most appropriate demographic-based model for a given data set or scientific question. We review the computer-based modelling platforms currently used for the development of either population- or individual-based species range dynamics models. We describe the features and requirements of 20 software platforms commonly used to generate simulations of species ranges and abundances. We classify the platforms according to particular capabilities or features that account for user requirements and constraints, such as (i) ability to simulate simple to complex population dynamics, (ii) organism specificity or (iii) their computational capacities. Using this classification, we develop a protocol for choosing the most appropriate framework for modelling species range dynamics based in data availability and research requirements. We find that the main differences between modelling platforms are related to the way in which they simulate population dynamics, the type of organisms they are able to model and the ecological processes they incorporate. We show that some platforms can be used as generic modelling software to investigate a broad range of ecological questions related to the range dynamics of most species, and how these are likely to change in the future in response to forecast climate and land-use change. We argue that model predictions will be improved by reducing usage to a smaller number of highly flexible freeware platforms. Our approach provides ecologists and conservation biologists with a clear method for selecting the most appropriate software platform that meets their needs when developing SDMs coupled with population-dynamic processes. We argue that informed tool choice will translate to better predictions of species responses to climate and land-use change and improved conservation management.