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Description: Potential suitable habitat was modeled using Random Forest (Breiman 2001), a decision-tree based ensemble approach that correlates mean importance values with environmental data on climate, elevation, and soil. Swapping 30-year mean climate conditions for a baseline period (1981-2010) with projections for the future (2070-2099) results in potential suitable habitat representing a species potential abundance. A hybrid lattice consisting of 10 × 10 and 20 × 20 km cells, defined by the density of forest inventory plots, was used to process environmental data and model potential suitable habitat as individual tree species importance values.Downscaled future climate projections were obtained from the NASA Earth Exchange Downscaled Climate Projections (NEX-DCP30) program (Thrasher et al. 2013). Output from three general circulation models (GCM) were used to explore possible changes in habitat suitability resulting from climate change, and included the NCAR Community Climate System Model (CCSM4, Gent et al. 2011), NOAA Geophysical Fluid Dynamics Laboratory Coupled Model 3 (GFDL CM3, Donner et al. 2011), and Met Office Hadley Global Environment Model 2 - Earth System (HadGEM2 - ES, Jones et al. 2011). Projections under the representative concentration pathways (RCP, Moss et al. 2008) 4.5 and 8.5 were used to encapsulate the range of plausible increases in greenhouse gases during this century.Reported values are based on a statistical process to include mean importance values supplemented by median importance values, when the median predicted values were zero and the mean predicted values were≥ 2.75 times the coefficient of variation for each 10 × 10 or 20 × 20 km cell.An important caveat when interpreting these models is that they are predicting potential suitable habitat by year 2100 – not where the species will be found. See Iverson et al. (2019) and Peters et al. (In Review) for more details. Additional information and products available at https://www.fs.fed.us/nrs/atlasReferences:Breiman, L. (2001). Random Forest. Machine Learning 45(1): 5-32.Donner, L. J., B. L. Wyman, R. S. Hemler, L. W. Horowitz, et al. (2011). The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3. Journal of Climate 24(13): 3484-3519. doi: 10.1175/2011jcli3955.1Gent, P. R., G. Danabasoglu, L. J. Donner, M. M. Holland, et al. (2011). The Community Climate System Model Version 4. Journal of Climate 24(19): 4973-4991. doi: 10.1175/2011jcli4083.1Iverson, L. R., M. P. Peters, A. M. Prasad and S. N. Matthews (2019). Analysis of Climate Change Impacts on Tree Species of the Eastern US: Results of DISTRIB-II Modeling. Forests 10(4): 302. doi: 10.3390/f10040302Jones, C. D., J. K. Hughes, N. Bellouin, S. C. Hardiman, et al. (2011). The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4(3): 543-570. doi: 10.5194/gmd-4-543-2011Moss, R., W. Babiker, S. Brinkman, E. Calvo, et al. (2008). Towards New Scenarios for the Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Technical Summary. Intergovernmental Panel on Climate Change, Geneva, 25 pp.Peters, M. P., L. R. Iverson, A. M. Prasad and S. N. Matthews (In Review). Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB-II for 135 eastern United States trees. Ecology and Evolution.Thrasher, B., J. Xiong, W. Wang, F. Melton, et al. (2013). Downscaled Climate Projections Suitable for Resource Management. Eos, Transactions American Geophysical Union 94(37): 321-323. doi: 10.1002/2013eo370002

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Copyright Text: USDA Forest Service, Northern Research Station, Northern Institute of Applied Climate Science

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