Climate change is rarely considered when implementing conservation measures to address species-level threats. When it is considered, the methods usually focus only on climatic variables, ignoring the interactions of climate with land-use, species life history and demography, and the spatial structure of its habitat (including the degree of fragmentation). We are working to address this shortcoming by developing a modeling framework that links remotely sensed environmental data with in situ biological data sets and global climate model predictions.

We are coupling habitat suitability models (including the Maximum Entropy method, Maxent) with metapopulation simulations (implemented in RAMAS software). These models link products derived from remotely sensed data (e.g., land cover classifications, NDVI) with biodiversity data from natural history collections and biodiversity surveys, and future climate scenarios generated for the Intergovernmental Panel on Climate Change (IPCC).

Results/Conclusions

Our linked modeling approach enables extinction risks under climate change to be assessed based on both landscape and demographic properties, therefore providing more robust assessments, subject to fewer uncertainties, than when applying habitat suitability models alone. A central thesis of our approach is that effective assessments of extinction risks from climate change should make use of remotely sensing data that describes land cover and vegetation characteristics. This is especially crucial in regions where anthropogenic impact causes available habitat to be highly fragmented. Remote sensing therefore provides a crucial, and yet under-exploited, resource for incorporating habitat fragmentation into climate change assessments. By applying this approach within the context of the IUCN Red List criteria, our study will utilize remote sensing data alongside biodiversity data from natural history collections to inform conservation policy and resource management.