Paul Holloway1, Jennifer Miller2
1Department of Geography, University College Cork, Ireland, 2Department of Geography, University of Texas, United States
Species distribution models (SDMs) have become an important and widely applied research framework in biogeography, particularly in the context of investigating the effects of climate change on species’ distributions and ranges. Along with biotic and abiotic factors, movement or the ability to access suitable habitats is one of the three important factors used to predict species distributions. Movement or dispersal is an important ecological process for species responding to changing climates, however, SDMs and their subsequent spatial products
rarely reflect accessibility to any future suitable habitats. Measuring dispersal-related movement can be confounded by factors that vary spatiotemporally across landscapes and climates, as well as within and among species, and it has therefore remained difficult to parametrize in SDMs. Here we compared 20 models that have previously been used (or have the potential to be used) to represent dispersal processes in SDM to predict future range shifts in response to climate change. We assessed the different dispersal models in terms of their accuracy at predicting future distributions, as well as the uncertainty associated with their predictions. Atlas data for 50 bird species from 1988 to 1991 in Great Britain were treated as
base distributions (t1), with the species–environment relationships extrapolated (using three commonly used statistical methods) to 2008–2011 (t2). Dispersal (in the form of the 20 different models) was simulated from the base distribution (t1) to 2008–2011 (t2).
The results were then combined and used to identify locations that were both abiotically suitable (obtained from the statistical methods) and accessible (obtained from the dispersal models). There was substantial variation in the accuracy of the different dispersal models, and in general, the more restrictive dispersal models (e.g. fixed rate dispersal) resulted in lower accuracy for the metrics which reward correct prediction of presences.
The results of this research highlight both the importance of incorporating dispersal as well as the variability its specific implementation introduces on the spatial predictions. This has important implications for studies aimed at predicting the effects of changing environmental conditions on species’ distributions.
Bio to come