CONTROL ID: 1805568

TITLE: Global Tree Range Shifts Under Forecasts from Two Alternative GCMs Using Two Future Scenarios

AUTHORS (FIRST NAME, LAST NAME): William Walter Hargrove1, Jitendra Kumar2, Kevin M. Potter3, Forrest M. Hoffman2

INSTITUTIONS (ALL): 1. Eastern Threat Center, USDA Forest Service, Asheville, NC, United States.
2. Oak Ridge National Laboratory, Oak Ridge, TN, United States.
3. Dept. Forestry & Env. Resources, North Carolina State University, Research Triangle Park, NC, United States.

ABSTRACT BODY: Global shifts in the environmentally suitable ranges of 215 tree species were predicted under forecasts from two GCMs (the Parallel Climate Model (PCM), and the Hadley Model), each under two IPCC future climatic scenarios (A1 and B1), each at two future dates (2050 and 2100). The analysis considers all global land surface at a resolution of 4 km2. A statistical multivariate clustering procedure was used to quantitatively delineate 30 thousand environmentally homogeneous ecoregions across present and 8 potential future global locations at once, using global maps of 17 environmental characteristics describing temperature, precipitation, soils, topography and solar insolation.

Presence of each tree species on Forest Inventory Analysis (FIA) plots and in Global Biodiversity Information Facility (GBIF) samples was used to select a subset of suitable ecoregions from the full set of 30 thousand. Once identified, this suitable subset of ecoregions was compared to the known current range of the tree species under present conditions. Predicted present ranges correspond well with current understanding for all but a few of the 215 tree species. The subset of suitable ecoregions for each tree species can then be tracked into the future to determine whether the suitable home range for this species remains the same, moves, grows, shrinks, or disappears under each model/scenario combination.

Occurrence and growth performance measurements for various tree species across the U.S. are limited to FIA plots. We present a new, general-purpose empirical imputation method which associates sparse measurements of dependent variables with particular multivariate clustered combinations of the independent variables, and then estimates values for unmeasured clusters, based on directional proximity in multidimensional data space, at both the cluster and map-cell levels of resolution. Using Associative Clustering, we scaled up the FIA point measurements into contonuous maps that show the expected growth and suitability for individual tree species across the continental US.

Maps were generated for each tree species showing the Minimum Required Movement (MRM) straight-line distance from each currently suitable location to the geographically nearest "lifeboat" location having suitable conditions in the future. Locations that are the closest "lifeboats" for many MRM propagules originating from wide surrounding areas may constitute high-priority preservation targets as a refugium against climatic change.

INDEX TERMS: 1630 GLOBAL CHANGE Impacts of global change, 1632 GLOBAL CHANGE Land cover change, 0429 BIOGEOSCIENCES Climate dynamics, 0476 BIOGEOSCIENCES Plant ecology.
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CONTACT (NAME ONLY): William Hargrove