Modeling Species Distributions to Support Assessment of Extinction Risks under Climate Change

H. Resit Akcakaya, Stony Brook University, resit.akcakaya@gmail.com (Presenter)
Richard Pearson, American Museum of Natural History, pearson@amnh.org
Jessica C. Stanton, Stony Brook University, jstanton@life.bio.sunysb.edu
Ned Horning, American Museum of Natural History, horning@amnh.org
Peter Ersts, American Museum of Natural History, ersts@amnh.org

Assessing species extinction risk under climate change requires integrating a diverse set of information. To achieve this, we have been exploring a novel modeling approach, which links downscaled global climate model ensembles, species distribution models, and stochastic metapopulation models with dynamic spatial structure. The ultimate goal is to provide general rules for red-listing species that may be threatened by climate change. An important component of this integrated modeling approach is predicting shifts in species’ ranges due to climate change using species distribution models (SDMs). SDMs use 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 soil type, land cover, and land use, which are often measured using remote sensing. Some of these predictor variables must be treated as static, either because the change during the projection period is negligible, or because reliable projections are not available. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial. Using a set of simulated species, we tested alternative methods such as using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether. 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, even if the variable is, in fact, expected to change in the future. The difference in predictive ability was most pronounced when there is an interaction between the static and dynamic variables. 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 as a result of climate change.

Presentation: 2011_Poster_Akcakaya_107_28.pdf (233k)

Presentation Type:  Poster

Session:  Global Change Impact & Vulnerability   (Tue 11:30 AM)

Associated Project(s): 

• Pearson, Richard: Integrating remotely sensed data and ecological models to assess species extinction risks under climate change ...details

Poster Location ID: 107