Version 5 of Forecasts; Forecasts of Climate-Associated Shifts in Tree Species

Friday, 19 December 2014
William Walter Hargrove, USDA Forest Service Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Asheville, NC, United States, Jitendra Kumar, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Kevin M Potter, North Carolina State University at Raleigh, Department of Forestry and Environmental Resources, Raleigh, NC, United States and Forrest M Hoffman, University of California Irvine, Department of Earth System Science, Irvine, CA, United States
Version 5 of the ForeCASTS tree range shift atlas (www.geobabble.org/~hnw/global/treeranges5/climate_change/atlas.html) now predicts global shifts in the suitable ranges of 335 tree species (essentially all woody species measured in Forest Inventory Analysis (FIA)) under forecasts from the Parallel Climate Model, and the Hadley Model, each under future climatic scenarios A1 and B1, each at two future dates (2050 and 2100). Version 5 includes more Global Biodiversity Information Facility (GBIF) occurrence points, uses improved heuristics for occurrence training, and recovers occurrence points that fall in water.

A 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. Occurrence of each tree species on FIA plots and in GBIF samples was used to identify a subset of suitable ecoregions from the full set of 30 thousand. This subset of suitable ecoregions was compared to the known current present range of the tree species. Predicted present ranges correspond well with existing ranges for all but a few of the 335 tree species. The subset of suitable ecoregions can then be tracked into the future to determine whether the suitable home range remains the same, moves, grows, shrinks, or disappears under each model/scenario combination.

A quantitative niche breadth analysis allows sorting of the 17 environmental variables from the narrowest, most important, to the broadest, least restrictive environmental factors limiting each tree species. Potential tree richness maps were produced, along with a quantitative potential tree endemism map for present and future CONUS. Using a new empirical imputation method which associates sparse measurements of dependent variables with particular clustered combinations of the environmental driver variables, and then estimates values for unmeasured clusters, we interpolated FIA measurements of productivity into continuous maps showing productivity across each tree's entire present and future ranges.