Related Research Areas
Carbon Cycle & Ecosystems, Climate Variability & Change

Climate change is rarely considered when implementing conservation measures to address species-level threats. In particular, the International Union for Conservation of Nature (IUCN) Red List, which is widely used to guide conservation policy and spending, does not explicitly incorporate climate change into its criteria to evaluate extinction risk. We aim to address this shortcoming by developing a modeling framework that links remote sensing of the environment with in situ biological data sets and global climate model predictions. We will couple habitat suitability models (including the Maximum Entropy method, Maxent) with metapopulation simulations (implemented in RAMAS software). These models will link remote sensing products (e.g., land cover classifications, NDVI, EVI, LAI, GPP, NPP, fPAR) with biodiversity data from natural history collections and biodiversity surveys, and future climate scenarios generated for the Intergovernmental Panel on Climate Change (IPCC). Our linked modeling approach will enable extinction risks under climate change to be assessed based on both landscape and demographic properties, therefore providing more reliable assessments, subject to fewer uncertainties, than when applying habitat suitability models alone. The principal objectives of the proposed project are to: 1) test the use of remote sensing products alongside in situ biological data and climate data for building habitat suitability models; 2) link habitat suitability models and metapopulation simulations to assess extinction risk under climate change; 3) investigate the impact that including versus excluding remotely sensed data has on assessments of extinction risk; 4) develop guidelines to ensure that remote sensing products are fully integrated into ongoing efforts to incorporate climate change within the IUCN Red List criteria. We will test our approach using the herpetofaunas of the United States (where in situ datasets are relatively abundant) and Madagascar (where data is much more limited) as case studies. Through collaboration with the IUCN, we will produce guidelines and software tools to advance the use of remote sensing products in environmental forecasting that influences policy and resource management. Our project will therefore expand and accelerate the realization of societal benefits from Earth system science.

PI: Richard Pearson/American Museum of Natural History

Center for Biodiversity and Conservation American Museum of Natural History Central Park West at 79th Street New York, New York 10024

Phone: 212 769 5742  Fax: 212 796 5292

Email: pearson 'at'


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Started: Aug 09, 2010

Last Activity: Dec 07, 2010


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