Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections?

Species distribution models (SDMs) are one of the most important GIScience research areas in biogeography and are the primary means by which the potential effects of climate change on species' distributions and ranges are investigated. 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 environment. Dispersal-related movement can be confounded by factors that vary across landscapes and climates, as well as within and among species, and it has therefore remained difficult to parametrise 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 2008â 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). The accuracy of these coupled projections was assessed with the 2008-2011 atlas data (the observed t2 distribution). 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. Ensemble models of the dispersal methods (generated by combining multiple projection outcomes) were created for each species, and a new Ensemble Agreement Index (EAI), which ranges from 0 (no agreement among models) to 1 (full agreement among models) was developed to quantify uncertainty among the projections. EAI values ranged from 0.634 (some areas of disagreement and therefore medium uncertainty among dispersal models) to 0.999 (large areas of agreement and low uncertainty among dispersal models). The results of this research highlight the importance of incorporating dispersal and also illustrate that the method with which dispersal is simulated greatly impacts the projected future distribution. This has important implications for studies aimed at predicting the effects of changing environmental conditions on species' distributions