Climatic projections and data downscaling across southwestern ecosystems

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Warming climates affect biodiversity by changing the abundance and the spatial distribution of species. These changes occur as animals either track moving habitats, or fail to track them and disappear from parts of their existing range. Overall, some species will be successful in adapting to climate change, while others will not.Currently, the rates and magnitudes of changes in habitat distribution in response to climate change, and the possible existence of barriers to subsequent species movement, are either unknown or predictable only at very coarse scales. This complicates efforts of wildlife professionals to plan and act, and therefore reduces the likelihood of successful species conservation efforts in the future. This project seeks to provide information that facilitates managing for projected changes in biodiversity at scales appropriate to support planning within federal, state and private conservation organizations.The project focuses on the southwestern United States and northern Mexico, areas expected to experience relatively large climatic changes. Previous work used Prism (4 x 4 km) data to calculate yearly min, max, mean and standard deviation of temperature and precipitation from baseline (1930 - 1974) and present (1975 - 2007) categories. From these data we obtained measures describing growing season lengths, frost free days, extreme events. With this information, we determined places across the study area that have been hotter/wetter , hotter/drier, colder/wetter, colder/drier, less growing days, more growing days etc. These are places we should explore for climatic induced ecosystem shifts. We evaluated geographical spatial changes (shifts) and rates of change in these focal areas in two ways: 1) relying on a 30 year Landsat record at ~30 m spatial resolution; and 2) examining vegetation as presented in the ecological systems and macrogroups from the National Vegetation Classification. We cross-walked spectral findings and our knowledge of ecological side-boards defining ecological systems to the abiotic data likely responsible for these changes, identifying causal mechanisms and modeling relationships. We determined which variables, and the relationships between variables best explain the ecological shifts discovered.

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