Migration potential is simulated by computing colonization likelihoods (CL) modeled using current species relative abundance, historical migration rates, current habitat fragmentation, and a search distance function to simulate long distance migration (Prasad et al., 2013). End of century migration potential is achieved both inside and beyond the current range by matching future projections of habitat suitability from the DISTRIB-II model based on the generation time (length of time until reproductive maturity) of individual tree species. An optimistic migration rate of 50 km/century (~ 31 mi/century), which is within the range of historical migration, is used within a search window of 500 km (~ 311 mi) to simulate rare long-distance dispersals.

We assume that the colonized species overcomes competitive exclusion and generates propagules for the next generation – this assumption is necessary since the data and computation required to test establishment and reproduction are beyond the scope of our model. We also assume there is no climatic or dispersal (both wind and animal dispersals are treated the same) constraints to migration. The latter assumption is justified since there were no systematic differences in historical migration between wind and animal dispersed trees (Davis 1981; Portnoy and Willson, 1993). The 2011 National Land Cover Database was used to determine percent forest cover within the 1 km2 (0.6 mi2) grids and information for individual species’ generation times were obtained from the USDA Forest Service Silvics Manual (Burns and Honkala 1990a, 1990b).

Modeled habitat suitability for 125 eastern United States trees species under 1981-2010 climate conditions and projected future conditions (2070-2099) were created using a statistical modeling approach that correlates mean importance values (i.e., relative abundance) to environmental data. 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 kilometer (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. (2019) for more details.

End of century colonization likelihoods were intersected with future projections of DISTRIB-II habitat suitability models under RCP 4.5 and 8.5 using the average habitat quality (HQ) among the three GCMs scenarios. This data for the eastern United States provides an estimate of individual species migration potential by the end of the century based on modeled habitat suitability.

An important caveat when interpreting these models is that they are predicting potential suitable habitat and colonization likelihoods by year 2100 – not where the species will be found. See Iverson et al. (2019) and Peters et al. (2019) for more details. Additional information and products available at https://doi.org/10.2737/Climate-Change-Tree-Atlas-v4

Colonization likelihoods were modeled under RCP 4.5 and 8.5 using the average habitat suitability among the three GCMs scenarios. This data for the eastern United States provides an estimate of individual species migration potential by the end of the century based on modeled habitat suitability.

References:

Breiman, L. (2001). Random Forest. Machine Learning 45(1): 5-32.

Burns, R.M., Honkala, B.H. (1990a). Silvics of North America: 1. Conifers. Agric. Handb. 654 Washington, DC, U.S. Department of Agriculture, Forest Service

Burns, R.M., Honkala, B.H. (1990b). Silvics of North America: 2. Hardwoods. Agric. Handb. 654 Washington, DC, U.S. Department of Agriculture, Forest Service

Davis, M.B. (1981). Quaternary history and the stability of forest communities. In: West, D.C., Shugart, H.H., Botkin, D.B. Forest Succession. Springer, New York, NY. doi: 10.1007/978-1-4612-5950-3_10

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: 10.1175/2011jcli3955.1

Gent, 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: 10.1175/2011jcli4083.1

Iverson, 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/f10040302

Jones, 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-4-543-2011

Moss, 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 (2019). 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 9(15): 8876-8899. doi: 10.1002/ece3.5445

Portnoy, S., Willson, M.F. (1993). Seed dispersal curves: Behavior of the tail of the distribution. Evolutionary Ecology 7, 25-44. doi: 10.1007/BF01237733

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

Migration potential is simulated by computing colonization likelihoods (CL) modeled using current species relative abundance, historical migration rates, current habitat fragmentation, and a search distance function to simulate long distance migration (Prasad et al., 2013). End of century migration potential is achieved both inside and beyond the current range by matching future projections of habitat suitability from the DISTRIB-II model based on the generation time (length of time until reproductive maturity) of individual tree species. An optimistic migration rate of 50 km/century (~ 31 mi/century), which is within the range of historical migration, is used within a search window of 500 km (~ 311 mi) to simulate rare long-distance dispersals.

We assume that the colonized species overcomes competitive exclusion and generates propagules for the next generation – this assumption is necessary since the data and computation required to test establishment and reproduction are beyond the scope of our model. We also assume there is no climatic or dispersal (both wind and animal dispersals are treated the same) constraints to migration. The latter assumption is justified since there were no systematic differences in historical migration between wind and animal dispersed trees (Davis 1981; Portnoy and Willson, 1993). The 2011 National Land Cover Database was used to determine percent forest cover within the 1 km2 (0.6 mi2) grids and information for individual species’ generation times were obtained from the USDA Forest Service Silvics Manual (Burns and Honkala 1990a, 1990b).

Modeled habitat suitability for 125 eastern United States trees species under 1981-2010 climate conditions and projected future conditions (2070-2099) were created using a statistical modeling approach that correlates mean importance values (i.e., relative abundance) to environmental data. 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 kilometer (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. (2019) for more details.

End of century colonization likelihoods were intersected with future projections of DISTRIB-II habitat suitability models under RCP 4.5 and 8.5 using the average habitat quality (HQ) among the three GCMs scenarios. This data for the eastern United States provides an estimate of individual species migration potential by the end of the century based on modeled habitat suitability.

An important caveat when interpreting these models is that they are predicting potential suitable habitat and colonization likelihoods by year 2100 – not where the species will be found. See Iverson et al. (2019) and Peters et al. (2019) for more details. Additional information and products available at https://doi.org/10.2737/Climate-Change-Tree-Atlas-v4

Colonization likelihoods were modeled under RCP 4.5 and 8.5 using the average habitat suitability among the three GCMs scenarios. This data for the eastern United States provides an estimate of individual species migration potential by the end of the century based on modeled habitat suitability.

References:

Breiman, L. (2001). Random Forest. Machine Learning 45(1): 5-32.

Burns, R.M., Honkala, B.H. (1990a). Silvics of North America: 1. Conifers. Agric. Handb. 654 Washington, DC, U.S. Department of Agriculture, Forest Service

Burns, R.M., Honkala, B.H. (1990b). Silvics of North America: 2. Hardwoods. Agric. Handb. 654 Washington, DC, U.S. Department of Agriculture, Forest Service

Davis, M.B. (1981). Quaternary history and the stability of forest communities. In: West, D.C., Shugart, H.H., Botkin, D.B. Forest Succession. Springer, New York, NY. doi: 10.1007/978-1-4612-5950-3_10

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: 10.1175/2011jcli3955.1

Gent, 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: 10.1175/2011jcli4083.1

Iverson, 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/f10040302

Jones, 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-4-543-2011

Moss, 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 (2019). 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 9(15): 8876-8899. doi: 10.1002/ece3.5445

Portnoy, S., Willson, M.F. (1993). Seed dispersal curves: Behavior of the tail of the distribution. Evolutionary Ecology 7, 25-44. doi: 10.1007/BF01237733

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