Analysis of Climate Change Impacts on Tree Species of the Eastern US: Results of DISTRIB-II Modeling

Forests across the globe are faced with a rapidly changing climate and an enhanced understanding of how these changing conditions may impact these vital resources is needed. Our approach is to use DISTRIB-II, an updated version of the Random Forest DISTRIB model, to model 125 tree species individually from the eastern United States to quantify potential current and future habitat responses under two Representative Concentration Pathways (RCP 8.5 -high emissions which is our current trajectory and RCP 4.5 -lower emissions by implementing energy conservation) and three climate models. Climate change could have large impacts on suitable habitat for tree species in the eastern United States, especially under a high emissions trajectory. On average, of the 125 species, approximately 88 species would gain and 26 species would lose at least 10% of their suitable habitat. The projected change in the center of gravity for each species distribution (i.e., mean center) between current and future habitat moves generally northeast, with 81 species habitat centers potentially moving over 100 km under RCP 8.5. Collectively, our results suggest that many species will experience less pressure in tracking their suitable habitats under a path of lower greenhouse gas emissions.


Introduction
The climate is changing, globally becoming warmer almost every year in recent decades. Risks associated with this warming are high, sometimes manifesting into multiple, broad threats to humanity [1] and the economy [2]. The recent Intergovernmental Panel on Climate Change (IPCC) report on the impacts of global warming of 1.5 • C above pre-industrial levels, and in comparison to impacts of 2.0 • C, describes many 'Reasons for Concern' related to efforts to strengthen the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [3]. Even so, with current pledges in the Paris Agreement on Climate Change,~2.6-3.2 • C of warming is projected by 2100, though the Agreement aims to limit global warming "well below 2 • C" and to "pursue efforts" to limit temperatures above pre-industrial levels to 1.5 • C [4]. The biodiversity implications of these various levels of warming are huge, as outlined in Warren, et al. [5], where climatically determined geographic range losses exceeding 50% were projected for 44%, 16%, and 8% of plants by 2100, corresponding to warming of 3.2, 2.0, and 1.5 • C, respectively. Even though climatically determined range losses do not equate with actual distributions of plants because trees live a long time while harboring great genetic diversity, the potential effects of climate change on the biota    Tree Data. As done in the earlier effort [42], we used U.S. Forest Service Forest Inventory and Analysis (FIA, www.fia.fs.fed.us) data to derive individual tree species importance values (IV) for each of 84,204 FIA plots. All plots were included with no filtering. The assumption was if the species already grows there, it can grow there. The relative number of stems and relative basal area for each species were weighted equally to calculate IV for each plot. Thus, some species with large numbers of smaller stems (e.g., Ulmus, Acer, Fraxinus spp.) may be calculated as more important than species with fewer, but larger stems (e.g., some Quercus). All 84,204 annualized FIA records sampled during the period 2000-2016 were processed, and aggregated to cells with native resolutions of either 10 × 10 km or 20 × 20 km to represent the mean IV within the grid cell. We strove to increase spatial resolution, over that of our previous effort, where the FIA data would support it; to that end, a hybrid lattice was generated through an iterative algorithm to determine whether resolution could be increased to 10 × 10 km (four cells within each 20 × 20 km cell), or maintained at 20 × 20 km. To do so, a 10-km was accepted if ≥50% of the four 10-km cells within a 20-km cell contained two or more FIA plots, otherwise the focal 20-km cell was retained. The resulting hybrid lattice for the eastern U.S. had 29,357 cells, 84.7% of which were comprised of 10 × 10 km cells, and accounting for 2.49 million km 2 , or 58% of the eastern U.S. (Figure 2). The 20 × 20 km cells occupied 1.79 million km 2 , or 42% of the area, and were mostly confined to highly agricultural areas, predominantly in the western portion of the eastern U.S. (Figure 2). To minimize species that have too few samples to build a respectable model, species were only included if they had at least 60 grid cells with at least two FIA plots per cell. This filter resulted in a total of 125 species in the analysis.

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or 58% of the eastern U.S. (Figure 2). The 20 × 20 km cells occupied 1.79 million km 2 , or 42% of the area, and were mostly confined to highly agricultural areas, predominantly in the western portion of the eastern U.S. (Figure 2). To minimize species that have too few samples to build a respectable model, species were only included if they had at least 60 grid cells with at least two FIA plots per cell. Environmental Data. A suite of 45 environmental variables was used to predict IV, for 125 species across the entire eastern US. We used seven climate-related variables, seven elevation-related variables, a solar-related variation of day length variable, nine soil taxonomic orders, and 21 variables related to soil properties to derive the Random Forest models [41] predicting current species IV (Table   180 2). These data were acquired from various sources, with most soils information from gSURRGO [52], elevation data from the shuttle radar topography mission [63], a model of solar radiation via latitude 182 [64], and a model of soil productivity based on soil taxonomy [65]. We then swapped the seven climate-related variables with future (2070-2099) projections of the same variables according to each of the six GCM/RCP combinations (see above), and Random Forest predicted future IVs for each 185 species. It is important to note that we are not using elevation variables as a proxy for climate -we 186 use them to discriminate among species that prefer lower elevation habitats (for example along the 187 coastal plains or swamps) from those that prefer more elevated habitats with rugged terrain. Also, in Environmental Data. A suite of 45 environmental variables was used to predict IV, for 125 species across the entire eastern US. We used seven climate-related variables, seven elevation-related variables, a solar-related variation of day length variable, nine soil taxonomic orders, and 21 variables related to soil properties to derive the Random Forest models [41] predicting current species IV (Table 2). These data were acquired from various sources, with most soils information from gSURRGO [52], elevation data from the shuttle radar topography mission [63], a model of solar radiation via latitude [64], and a model of soil productivity based on soil taxonomy [65]. We then swapped the seven climate-related variables with future (2070-2099) projections of the same variables according to each of the six GCM/RCP combinations (see above), and Random Forest predicted future IVs for each species. It is important to note that we are not using elevation variables as a proxy for climate-we use them to discriminate among species that prefer lower elevation habitats (for example along the coastal plains or swamps) from those that prefer more elevated habitats with rugged terrain. Also, in addition to improving model fit, the numerous soil variables help restrain the models' response under future climates and distinguish among species that are mostly climate driven vs. those that are less so. The susceptibility of a soil to sheet and rill erosion by water estimated by the percentage of silt, sand, and organic matter and on soil structure and saturated hydraulic conductivity

Modeling
Individual tree species IV were modeled using the randomForest library [67] in R version 3.1.1 [68] (hereafter RF), in which 1001 regression trees were trained with eight randomly selected environmental variables evaluated at each node, and grown to include a minimum of 10 observations. To train the models, only grid cells within the hybrid lattice (10 × 10 or 20 × 20 km) were used that had (1) two or more FIA plots (to ensure representation within each cell), (2) ≥5% forest cover defined by the 2006 NLCD [69] (classes 41, 42, 43, and 90, to exclude very highly agricultural regions), and (3) a mean IV ≤ 1.5 times the inter-quartile range of IVs across all cells (to exclude outliers because they were unlikely to represent the full 100 or 400 km 2 ). Each of the 1001 regression trees built by RF provides information about the predicted IV, and the default is to report the mean prediction. However, the random resampling of only eight of 45 variables at each node can result in spurious outcomes due to, for example, omission of an entire class of variables (e.g., climate); while these spurious trees rarely influence overall prediction [70], outliers can influence prediction distributions at a given cell [71]. Therefore, we compared the mean predicted value to the median for each cell; if the median = 0 and among all 1001 predicted values the coefficient of variation ≥2.75, then 0 was used as the predicted IV rather than the mean; which was 0 < IVmean < 8 among all species. This "mean-median" combination is a modification to the approach suggested by Roy and Larocque [71] which limits the influence on outlier predictions, minimizing the area of modeled low suitability, due to a few outliers within the 1001 regression trees for each species.
Once the RF model was trained, predictions of IV were made to all 29,357 cells irrespective of cell size within the hybrid lattice, whether or not at least 2 FIA plots were present, or whether percent forest cover was less or more than five percent.

Model Reliability
We created a model reliability (ModRel) score from a series of five metrics obtained from the performance statistics of each of 125 species. These included (1) a pseudo R 2 obtained from the RF model (RF R 2 ); (2) a Fuzzy Kappa (FK) metric which compares outputs of the imputed RF-predicted map to the FIA-derived map [72]; (3) the deviance of the CV (CVdev) among 30 regression trees via bagging [41]; and (4) the stability of the top five variables (Top5) from 30 regression trees, and (5) a true skill statistic (TSS) of the imputed RF. The first four were used previously, described in Iverson et al. [42]. The five variables were normalized to a 0-1 scale and weighted as follows to arrive at a final ModRel score: 0.33 × RF R 2 + 0.33 × FK + 0.11 × CVdev + 0.11 × Top5 + 0.11 × TSS which gives more weighting to RF R 2 and FK, a primary performance metric and a comparison of predicted to observed values, respectively. Then, ModRel scores were assigned to one of four classes: High (ModRel ≥ 0.7), medium (0.7 > ModRel > 0.54), low (0.55 > ModRel ≥ 0.14), and unreliable and excluded from further modeling (ModRel < 0.14).

Variable Importance
Each of the 45 predictor variables was scored for all species cumulatively according to a variable importance index, which was the average of three normalized (0-100) scores. First, the variable importance, as calculated within the RF function (percent increase in MSE based original and permuted predictors of the out-of-bag data-see the help for "importance" in randomForest library in R), for each of 125 species was summed. Second, the sum of the reciprocal of ranked predictor importance across all species was calculated; the reciprocal produced higher scores for top ranked variables. Third, the frequency, or count, of the number of times a predictor ranks in the top 10 across all species was tabulated. These metrics allowed comparison among the 45 variables for their value in creating the tree species models. Importantly, these metrics are based on all species across the entire eastern U.S. so that species that have specific requirements will not garner much support with these indicator metrics.

Area-Weighted Importance Values
To incorporate both the area and the relative abundance of each species, we calculated area-weighted importance values for each species. We use area-weighted importance values as a surrogate for the strength of suitable habitat across a species' distribution. The higher the IV score, the higher the tendency for that species to occupy that cell, and the higher the possible basal area of that species within the cell. This measure of suitable habitat is not a probability of occurrence (though likely similar for many species) but rather an indication of the potential of the cell to host the species. Any value above 0 can be considered suitable habitat, though the strength of that habitat varies according to the area-weighted IV score. These values thus provide an estimate of each species' importance based on the IV modeled for each cell (or partial cell), multiplied by the area the cell represents. Because of the variation of grid sizes (100 km 2 or 400 km 2 ), due to the hybrid grid structure, and the partial cells especially along coasts, the area-weighted values are truer to their actual and projected future suitable habitat. The ratio of future to present modeled condition represents the potential change of suitable habitat in the future, where values >1 indicate an increase in area-weighted importance and values <1 indicate a decrease.

Changes in Mean Center of Spatial Data
Within ArcGIS 10.3 (ESRI, Redlands, CA, USA), the Mean Center and Directional Distribution functions were used to calculate the current and future 'center of gravity' and directional ellipse within 1 standard deviation, respectively, of species ranges generated by our models. No weighting was applied to the IV, but only cells modeled to have an IV > 0 were considered in the calculation of the mean centers and directional ellipses. The coordinates of the mean center were used to calculate distance and direction of potential movement of the suitable habitat for each species and were visualized using polar graphs to evaluate potential changes among all species for each scenario of climate change.

Analysis of Dominants, Gainers, and Losers
The area-weighted IV allowed comparison of species prominence and potential change according to the climate scenarios, by spatial domain. These provide valuable supplements to FIA data and state reports (www.fia.fs.fed.us) on the current situation for tree species, as the IVs are based on both density (number of stems) and dominance (basal area) simultaneously. We provide this information for each of 37 states and the District of Columbia, and for five regions within the eastern US. Notably, for the six states split by the 100th meridian (our boundary of the eastern U.S.), some forest patches will be missed but the area in those states west of the 100th meridian is dominated by nonforest or western species (not modeled), with the exception of the Black Hills of South Dakota. We ranked each species according to the modeled current IV and selected the top three for each spatial unit and then calculated the potential changes in area-weighted IV, as ratios of future to current IVs, among the various scenarios of climate change.

Comparison to Earlier DISTRIB Models
We have been modeling tree species suitable habitat within the eastern U.S. since 1998 [17,39,40,42,43], and there have been changes in many dimensions throughout this period. First, we modeled 80 species at the county level of resolution, then 134 species at 20 × 20 km resolution, and most recently 125 species at a hybrid of 10 × 10 and 20 × 20 km resolution, depending on the density of FIA plots (~forest cover). Throughout the period, there has also been a remarkable improvement in environmental data, especially climate and soils data. And, the modeling improvement from regression tree analysis to random forests [41] was particularly dramatic in enhancing model performance. As expected when using multiple models, updated data sets, or variations in modelling technique, model outcomes will differ between iterations; this is true in this case too.

Scope and Limitations
The models depicted here represent changes in potentially suitable habitat according to scenarios of climate change; they do not depict projections of actual future distributions by 2100. Earlier work has shown that natural migration proceeds at a much slower pace than change in habitat, especially for long-lived trees [17,[73][74][75]. Therefore, our projections of an increase in the range are likely to overestimate the actual distributions by century's end, unless humans get seriously involved in moving species.
Though Random Forest has been shown to be a robust modeling tool, highly resistant to overfitting, we sometimes are making predictions into novel parameter space through extrapolation; nonetheless, the resistance to overfitting of Random Forest predictions gives us confidence that the extrapolations are suitably constrained and are not exaggerated projections [41]. Obviously, not all 125 species models are created equal, and we calculate several metrics to assess model reliability for each species [42].
When we model potential changes in suitable habitat, one would normally expect the greatest impacts to be experienced by young plants at the point of regeneration, when seedlings or saplings are more susceptible to the increased extreme weather events and other ramifications of the changing climate. However, mature forests are certainly also susceptible, either directly via droughts, especially 'hot' droughts [76], or indirectly via pests and pathogens [77]. Because the models are based on FIA inventories of trees >2.5 cm dbh, the regeneration component is not well represented in the model formulations. We are modeling the potential niche space that may be available to species under future climates, which may not be the realized niche because disturbances and extreme events will be operating within the suitable habitats. Though the FIA data, in effect, integrates past disturbances by documenting those species that have survived past events, we cannot anticipate future disturbances (like an exotic pest invasion) that will influence actual future distribution and abundance. Further, we cannot assume that all species are in equilibrium with their current climate or other environmental variables.

Model Reliability and Variable Importance
Of the 125 species in this assessment, we scored 29 species with high model reliability, 47 with medium, and 49 with lower model reliability. These model reliability classes are presented for each species in Table A1. Admittedly, the cut off values presented in the methods section are arbitrary and adjusting the cut offs would change the proportion of each model reliability class. We chose to stay conservative in assigning the cut offs, leading to a loading of species at the lower end of reliability.
When we scored each of the 45 predictor variables according to a variable importance index, we found the climate variables dominated in importance. In fact, seven of the top nine variables were the climate variables. Of course, several of these variables are correlated with each other across the entire eastern U.S. but will be important locally for particular species. The first and second ranked variables were summer (30-year mean of the warmest month) and winter (30-year mean of the coldest month) temperatures; these indicate some species are limited by cold, some are linked to warm temperatures, and some may be driven by both together. Because these metrics are based on all species across the entire eastern U.S., the wide ranging, generalist species will tend to be correlated with wide ranging temperature or precipitation patterns as well. The day length coefficient of variation among months (based on latitude) was the most influential non-climate variable, followed by soil variables pH, texture (soil fraction passing a sieve with a 2 mm square opening), soil productivity (based on soil taxonomic family), and permeability (saturated hydraulic conductivity). The lower ranked variables, though not rated high for all species together, will rank high for individual species in particular habitats, etc. Though space prevents discussion of individual species and their variables of importance, these will be presented in upcoming updates to our Climate Change Tree Atlas (www.fs.fed.us/nrs/atlas).

Potential Changes in Species Area-weighted Importance Values
For the 125 species with acceptable models, Table 3 provides an indication of the quantity of species that may lose (Future: Current ratios < 0.9) or gain (ratios > 1.1) suitable habitat by 2100, as well as those projected to remain somewhat stable (0.9 < ratios < 1.1). Averaged across all scenarios, 88 species showed at least a 10% increase in area-weighted IV, and 26 species showed at least a 10% decrease, with 12 species having little or no change (Table 3). For those 88 species inclined to have increasing habitat, the RCP8.5 scenario showed more species at least doubling habitat (55 species) than under the RCP4.5 scenario (42 species) of lower emissions. Notably, there was not much difference between RCPs for those species losing habitat (Table 3) (Table A1). Table 3. Potential species changes in area-weighted importance value for habitat suitability for 125 species in the eastern United States. Allowing for a 10% buffer around the future:current ratio of 1.0 (i.e., no change), values below 0.9 indicates a loss, while values above 1.1 indicate a gain in suitable habitat. Scenarios refer to model (CCSM, GFDL, Had, and mean of all three GCM models) and emission level (RCP 4.5 and 8.5). The data do show that for many of the species gaining in excess of 10% in habitat, they are often from less reliably modeled species than those species losing habitat. For example, only 12 of 88 species (14%) which show at least 10% increase in habitat had highly reliable models, but 12 of 26 species (46%) showing a decrease of at least 10% had highly reliable models (Table A1). For those more common species (arbitrarily selected as those with the sum of IV > 15,000), those ratios are 11 of 34 (32%) for gainers compared to 8 of 8 (100%) for the losers. The large gainers fall into three categories: First, the species is currently common in a region that is now quite warm and fairly dry, that being the southwestern portion of the eastern U.S. (e.g., Texas, Oklahoma, southern Missouri). These species, like Quercus stellata (post oak), Quercus marilandica Muenchh. (blackjack oak), Carya texana Buckley (1861) (black hickory), ashe juniper (Juniperus ashei J. Buchholz) and Juniperus virginiana L. (eastern red cedar), are primarily temperature driven, and expand greatly in suitable habitat when provided much warmer temperatures as projected under climate change. Second, the species is currently quite rare or sparse according to current FIA plot data, and the models project the species to 'fill in' some additional territory with suitable habitat. Species in this category include Diospyros virginiana L. (common persimmon), Ilex opaca Aiton (American holly), and Ostrya virginiana (Mill.) K.Koch (eastern hophornbeam). Third, the species is an important southern species now but is expected to substantially expand its suitable habitat northward by end of the century. These species include Quercus falcata (southern red oak), Quercus nigra L. (water oak), Pinus echinata (shortleaf pine), and P. palustris (longleaf pine) (Table A1).

Changes in Mean Center of Spatial Data
The potential changes in mean centers of suitable habitat under various scenarios of climate change indicate that roughly 3-4 times as many species show habitat movement in a northerly direction as compared to a southerly direction (Table 4, Figure 3). As many as 81 species (RCP8.5 mean) could have mean center movement at least 100 km northward. The data also clearly show that those northward-moving species will likely have their mean habitat centers move greater distances under the hotter (RCP8.5) scenarios as compared to the RCP4.5 scenarios. Some of the species modeled to move habitats long distances northward include Carya texana (black hickory), Quercus virginiana (live oak), and Ulmus crassifolia (winged elm) (Figure 3). The scenario with the least change in temperature, CCSM45, also shows less northward movement of mean centers, but this scenario still has 54 species moving habitats at least 100 km by the end of the century. Some of the species moving habitats southward include Acer pensylvanicum L. (striped maple), Prunus pensylvanica (pin cherry), and Sorbus americana (American mountain ash) ( Figure 3); these species models, however, had lower model reliability and are complicated by the geographic influence of the spine of the Appalachian Mountains. Example maps showing the mean centers and their ellipses around current and potential future habitat distributions for two southern species, Liquidambar styraciflua L. and Pinus echinata, are shown in Figure 4. Fei et al. [78], in an analysis of FIA data across three decades for 86 species/groups in the eastern U.S., found that 62% of species show evidence for a northward shift and that 73% of species show evidence for a westward shift. This westward trend was associated with changes in moisture availability (more moisture now westward) and successional trends (afforestation farther west), though the much sparser FIA data westward into the highly agricultural Midwestern Corn Belt can also contribute to the differences in results with ours. Of the species we have in common (n = 78) with the Fei et al. study, our results from the GCM85 scenario show much more potential for northward (87% N, 13% S) over westward (31% W, 69% E) migration of climatically suitable habitat. In future, the GCMs do show a lot more warming northward as compared to the previous 30 years [78,79], and when coupled with a probable constraint of the increased moisture westward [79], these two studies are not incongruent. previous 30 years [78,79], and when coupled with a probable constraint of the increased moisture 411 westward [79], these two studies are not incongruent.

Analysis of Dominants, Gainers, and Losers by State and Region
habitat, with 1 meaning no change, <1 meaning a loss in habitat, and >1 meaning a gain in habitat 434 (Table 5). Over the entire eastern U.S., the top three species currently are loblolly pine (Pinus taeda),  to a changing climate [80,81] increases the probability that many of these species, even the losers, may

Analysis of Dominants, Gainers, and Losers by State and Region
In this analysis we identified, for the entire East, five regions, and 37 states plus the District of Columbia, the dominant three species now and what their overall changes are projected for suitable habitat, with 1 meaning no change, <1 meaning a loss in habitat, and >1 meaning a gain in habitat (Table 5). Over the entire eastern U.S., the top three species currently are loblolly pine (Pinus taeda), red maple (Acer rubrum), and sweetgum (Liquidambar stryraciflua L.). Of the 36 unique species ranked among the top three positions, those that most frequently scored among the dominant three species are red maple (for 21 of 44 states or regions), loblolly pine (15 of 44), sugar maple (Acer saccharum, 11 of 44), and sweetgum (10 of 44) (Table 5). By genus, the three Acer species were found among the top three species 33 times, the five Pinus species 25 times, and the nine Quercus species 23 times.
For the top three species within the 38 state (and District of Columbia) rankings (n = 114), 50 (44%) are expected to lose >10% of their suitable habitat, while 41 (36%) species are projected to gain >10% of habitat by 2100 for the RCP 4.5 scenario; comparable numbers under RCP 8.5 are 58 (51%) losers and 40 (35%) gainers. So, although more of these dominant species are expected to lose habitat suitability in the changed climate, the fact that they are abundant presently and often very adaptable to a changing climate [80,81] increases the probability that many of these species, even the losers, may still be plentiful in their respective states by 2100.
Contrary to the data for the entire suite of 125 species, where 88 species were modeled to gain at least 10% habitat (Table 3), the analysis of only the top three species by state or region shows that a larger number of species are projected to lose habitat as compared to gain habitat (Table 5). Of the 132 iterations of species listed on Table 5 under regions or states, 56 species lost >10% habitat and 51 gained >10% habitat. Primary losers were Acer rubrum, A. saccharum, Liriodendron tulipifera L., and Populus tremuloides, while primary gainers were Pinus taeda and Liquidambar styraciflua.

Species-Level Maps
Though putting 10 maps on a page masks fine-scale visualization, it does provide a way to assess the current distribution according to the relatively sparse FIA plots, the modeled current distribution, and the potential future distributions according to each of the model/RCP scenarios, as well as the mean RCP 4.5 and 8.5. For example, Liriodendron tulipifera (yellow poplar), a species important for wood products throughout its range ( Figure 5) shows a general northward expansion of suitable habitat under warming, with more expansion under RCP8.5 vs. RCP4. The least expansion is with the relatively cool CCSM4 model as compared to the more equivalent Hadley and GFDL models. However, with the driest Hadley model (either RCP4.5 or RCP8.5), habitat for yellow poplar noticeably contracts in the southern third of its current distribution ( Figure 5). These maps are available in Figures A1-A3 for three other species, Pinus echinata (shortleaf pine) P. teada (loblolly pine), and Liquidambar styraciflua (sweetgum); all species will eventually be on our atlas website www.fs.fed.us/nrs/atlas.

Comparison to Earlier DISTRIB Models
As expected, there are differences between DISTRIB-II outputs described here from those presented in our earlier work [15]. In our current effort, differences mainly arise from using a hybrid lattice approach, and also from: (1) Newer FIA records, (2) recent 30-year climate normals, (3) a newer set of predictor variables, (4) removal of outlier training data, and (5) modifying predicted IVs with the mean-median combination. The recent FIA records provide information about disturbances and changes in species demographics, while the 30-year climate normals attempt to match conditions experienced by the trees inventoried by FIA. The newer set of environmental predictors incorporates finer scale information (e.g., gSSURGO soils), as well as additional variables not previously used. The removal of outliers from training datasets aims to limit the influence of unrepresentative cells that might result from plantations or severe disturbance events, while the modification of predicted values with the mean-median combination reduced the influence of spurious predictions among the prediction set.
Attributing the differences in results between DISTRIB and DISTRIB-II to any single or combination of the listed factors would be very difficult to quantify and of little practical value to forest managers. Suffice it to say that the newer results are an improvement over the earlier ones. However, as with our earlier results, models with low reliability should be interpreted with caution. As always with modeling studies, 'all models are wrong' [82]; we strive to make them useful by making them available to use in concert with any other information and experience available to the decision makers.

Conclusions
The forests of the eastern United States are characterized by a diverse assemblage of tree species. Climate change has the potential to shift and influence species patterns, thereby creating novel communities, with the greatest disruption and change clearly linked to the emissions pathways that unfold over the course of this century. By quantifying potential habitat changes across a wide array of species over a broad geographic extent, we can consider several dimensions of potential habitat change that lend to understanding individual species responses, as well as focused regional quantification that lend to informed on-the-ground adaptation planning. The trend of increasing general habitat conditions for a large portion of the species is a function of spreading at the range margin extents with, in many cases, a decline in core habitat suitability. Those species projecting losses, while fewer in number, generally show a contraction in the range of suitable habitat under either RCP, but especially so under RCP 8.5. In many cases, a finer extent of regions or state level evaluations reveals the potential impact of shifting habitats via ranked species importance and summary of winners and losers by states or regions. In these more discrete extents, the gradual fading in or out for species presents useful information for planners. Many of the current top species projected to decline in the region (even though their range-wide ratio may be increasing) epitomize the conditions where macro level pressures of climate change can have local level implications. In the end, these results show the high potential for a reshuffling of where suitable habitats for species will occur across the eastern United States, and it is clear that these will reshape competitive pressures and ultimate final outcomes that are beyond any modeling approach. Therefore, we intend these models to be one piece of a package of information that practitioners use for decisions related to adapting to the changing climate we now face. Working together, adaptations in silviculture and ecological management should improve the potential for eastern U.S. forests to continue to thrive in the coming decades.
Author Contributions: All four authors cooperated on conceptualization, methodology, validation, formal analysis, investigation, data curation, visualization, review and editing, and preparing the proposal for funding. L.R.I. prepared the original draft, and supervised and administered the overall project. Acknowledgments: Climate scenarios used were from the NEX-DCP30 dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). We appreciate reviews by Bryce Adams, Patricia Leopold, and Chris Swanston, along with the editors and reviewers of this Forests issue.

Conflicts of Interest:
The authors declare no conflict of interest The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Appendix A Table A1. Importance value × area scores (FIAsumIV) and their future:current ratios showing potential gains (light green 1.1-2.0, dark green > 2.0-fold increase) or losses (orange 0.5-0.9, red < 0.5 times decrease) or no change (black 0.9-1.1) under three scenarios of climate change: CCSM4.5, GFDL8.5, Had8.5 and mean of the three models for each RCP (GCM45 and GCM85). Also shown is overall average score (GCMave), and the model reliability for each species.