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Combining static and dynamic variables in species distribution models under climate change
Article first published online: 28 SEP 2011
© 2011 The Authors. Methods in Ecology and Evolution © 2011 British Ecological Society
Methods in Ecology and Evolution
Volume 3, Issue 2, pages 349–357, April 2012
How to Cite
Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P. and Reşit Akçakaya, H. (2012), Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution, 3: 349–357. doi: 10.1111/j.2041-210X.2011.00157.x
- Issue published online: 4 APR 2012
- Article first published online: 28 SEP 2011
- Received 6 June 2011; accepted 17 August 2011 Handling Editor: Robert Freckleton
- land use;
- niche model;
1. Methods used to predict shifts in species’ ranges because of climate change commonly involve species distribution (niche) modelling using climatic variables, future values of which are predicted for the next several decades by general circulation models. However, species’ distributions also depend on factors other than climate, such as land cover, land use and soil type. Changes in some of these factors, such as soil type, occur over geologic time and are thus imperceptible over the timescale of these types of projections. Other factors, such as land use and land cover, are expected to change over shorter timescales, but reliable projections are not available. Some important predictor variables, therefore, must be treated as unchanging, or static, whether because of the properties of the variable or out of necessity. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial and has been dealt with differently in studies to date. Alternative methods include using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether.
2. Using a set of simulated species, we tested various methods for combining static variables with future climate scenarios. Our results showed that including static variables in the model with the dynamic variables performed better or no worse than either masking or excluding the static variables.
3. The difference in predictive ability was most pronounced when there is an interaction between the static and dynamic variables.
4. For variables such as land use, our results indicate that if such variables affect species distributions, including them in the model is better than excluding them, even though this may mean making the unrealistic assumption that the variable will not change in the future.
5. These results demonstrate the importance of including static and dynamic non-climate variables in addition to climate variables in species distribution models designed to predict future change in a species’ habitat or distribution as a result of climate change.