Posted by: oikosasa | November 12, 2013

Editor’s choice November 3.0

Last week, EiC Chris Lortie presented the editor’s choice papers for the November issue. Below you find the nice figure and table from one of them, “Dispersal and species’ responses to climate change”, by Travis et al. And remember, Editor’s choice papers are freely available online throughout the month!

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Table 1: Effects of climate change on individual dispersal. Climate change is predicted to lead to lower windspeeds (A), higher temperatures (B), increased frequency of storms (C), flooding (D), reduced snow cover (E), and changed rainfall (F) (1, 2). Each of these climatic factors has been shown to affect dispersal in a range of organisms, either through a direct impact on the individual during dispersal, or indirectly by altering the biophysical environment or the state of the dispersing organism. Key empirical examples of these effects are described with the arrow (    decrease;     increase) describing how predicted changes in specific climatic factors would alter the propensity to emigrate or the distance dispersed during transfer.

Figure1_399b

Figure 3: Dispersal will be the heart of a new generation of process-based models developed to predict, and inform the management of, species’ responses to environmental change. By incorporating dispersal together with an explicit representation of population dynamics, models will become much better able to simulate the spatio-temporal dynamics of species under alternative future climate and land-use scenarios. To date, most projections of biodiversity responses to climate change have been made using all or nothing dispersal with fewer examples of nearest-neighbour dispersal or statistical dispersal kernels. While more detailed mechanistic dispersal models have been developed both for animal and plant dispersal, they have yet to be used extensively in the climate change field. In part this is due to the substantial challenges faced with these more sophisticated models, both in terms of the data needs for parameterisation and the greater computation needs of these more complex approaches. We argue that incorporating greater realism in the dispersal process will result in improved predictive capability, particularly when there are likely to be synergistic impacts of climate and land use change.

[Image credits: Corine Land Cover (land cover map); Wordclim (climate map); James Bullock (bustard); María Triviño (observed and predicted maps of bustard distribution)]

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