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<otherCitDet>Details on the models were published in:
Peters et al. (2019) and Iverson et al. (2019).
Iverson, Louis R.; Peters, Matthew P.; Prasad, Anantha M.; Matthews, Stephen N. 2019. Analysis of Climate Change Impacts on Tree Species of the Eastern US: Results of DISTRIB-II Modeling. Forests. 10(4): 302. https://doi.org/10.3390/f10040302.
Peters, Matthew P.; Iverson, Louis R.; Prasad, Anantha M.; Matthews, Stephen N. 2019. Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB II for 135 eastern U.S. trees. Ecology and Evolution . 9(15): 8876-8899. https://doi.org/10.1002/ece3.5445.</otherCitDet>
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<idPurp>Modeled habitat suitability of tree species helps forest managers consider where species might be found, where species could grow under recent climate conditions, and how different climate change scenarios could impact habitat suitability. These models provide a coarse scale, 10 and 20 km (6.2 and 12.4 miles), representation of individual tree species habitat suitability derived from relative abundance and 45 environmental conditions.</idPurp>
<idAbs>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;Modeled habitat suitability for 125 tree species in the eastern United States under current (1981-2010) climate conditions and projected future conditions (2070-2099) created using a statistical modeling approach that correlates mean importance values to 45 environmental data layers. This data set includes 148 tree species represented by a hybrid lattice of 10 and 20 km grids that provides information on relative abundance (i.e., importance values), derived from tree basal area and number of stems, for the species according to USDA Forest Service Forest Inventory and Analysis data (2001-2016), and modeled for 125 species under current conditions and for eight future scenarios.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
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<keyword>Northern Research Station</keyword>
<keyword>USDA</keyword>
<keyword>Forest Service</keyword>
<keyword>eastern United States</keyword>
<keyword>modeled niche</keyword>
<keyword>potential suitable habitat</keyword>
<keyword>tree species</keyword>
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<useLimit>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</useLimit>
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<suppInfo>Random forest regression models (Breiman 2001) were trained with a subset of the hybrid lattice having two or more FIA plots, forest cover greater than five percent, and the mean importance value was less than 1.5 times the inter-quartile range of all cell importance values for the species. Random forest was parameterized to generate 1,001 regression trees, use eight randomly selected predictor variables at each node (i.e., mtry), and grow each regression tree with a minimum of 10 observations. Predicted importance values were performed on the training records (RFpred) and importance values were imputed to all records within the hybrid lattice having complete predictor variable information (RFimp). Predictions were also performed using the future climate scenarios for three general circulation models (GCM) (CCSM4, GFDL CM3, and HadGEM2-ES) under the 4.5 and 8.5 representative concentration pathways (RCP). An average importance value for each RCP was calculated using the three GCMs.
The random forest default is to calculate the mean value among the regression trees within the ensemble, thus at each terminal node the value among 1,001 regression trees are averaged for the final prediction. In many cases this results in acceptable values, however, there are instances when a majority of the regression trees predict a zero and a few trees predict values greater than zero. This produces low values in locations that one may not expect to find suitable habitat for the species, so to limit these potentially erroneous values, we replaced the mean value with the median (i.e., 0) when the median prediction equaled zero and the mean prediction had a coefficient of variation greater than or equal to 2.75.
See Peters et al. (2019) and Iverson et al. (2019) for more details.
Breiman, Leo. 2001. Random Forest. Machine Learning 45(1): 5-32. https://doi.org/10.1023/A:1010933404324
Iverson, Louis R.; Peters, Matthew P.; Prasad, Anantha M.; Matthews, Stephen N. 2019. Analysis of climate change impacts on tree species of the eastern US: Results of DISTRIB-II modeling. Forests 10(4): 302. https://doi.org/10.3390/f10040302
Peters, Matthew P.; Iverson, Louis R.; Prasad, Anantha M.; Matthews, Stephen N. 2019. Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB-II for 135 eastern U.S. trees. Ecology and Evolution. https://doi.org/10.1002/ece3.5445</suppInfo>
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<keyword>Biota; Environment; Geoscientific; habitat; ecosystem; Northern Research Station; USDA; Forest Service; eastern United States; modeled niche; potential suitable habitat; tree species; importance values; climate change; Interdepartmental Imagery Publication Platform; IIPP;</keyword>
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<idCredit>Details on the models were published in:
Peters et al. (2019) and Iverson et al. (2019).
Iverson, Louis R.; Peters, Matthew P.; Prasad, Anantha M.; Matthews, Stephen N. 2019. Analysis of Climate Change Impacts on Tree Species of the Eastern US: Results of DISTRIB-II Modeling. Forests. 10(4): 302. https://doi.org/10.3390/f10040302.
Peters, Matthew P.; Iverson, Louis R.; Prasad, Anantha M.; Matthews, Stephen N. 2019. Utilizing the density of inventory samples to define a hybrid lattice for species distribution models: DISTRIB II for 135 eastern U.S. trees. Ecology and Evolution . 9(15): 8876-8899. https://doi.org/10.1002/ece3.5445.</idCredit>
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