The northeastern United States is a predominately-forested region that, like most of the eastern U.S., has undergone a 400-year history of intense logging, land clearance for agriculture, and natural reforestation. This setting affords the opportunity to address a major ecological question: How similar are today's forests to those existing prior to European colonization? Working throughout a nine-state region spanning Maine to Pennsylvania, we assembled a comprehensive database of archival land-survey records describing the forests at the time of European colonization. We compared these records to modern forest inventory data and described: (1) the magnitude and attributes of forest compositional change, (2) the geography of change, and (3) the relationships between change and environmental factors and historical land use. We found that with few exceptions, notably the American chestnut, the same taxa that made up the pre-colonial forest still comprise the forest today, despite ample opportunities for species invasion and loss. Nonetheless, there have been dramatic shifts in the relative abundance of forest taxa. The magnitude of change is spatially clustered at local scales (<125 km) but exhibits little evidence of regional-scale gradients. Compositional change is most strongly associated with the historical extent of agricultural clearing. Throughout the region, there has been a broad ecological shift away from late successional taxa, such as beech and hemlock, in favor of early- and mid-successional taxa, such as red maple and poplar. Additionally, the modern forest composition is more homogeneous and less coupled to local climatic controls.
Citation: Thompson JR, Carpenter DN, Cogbill CV, Foster DR (2013) Four Centuries of Change in Northeastern United States Forests. PLoS ONE 8(9): e72540. doi:10.1371/journal.pone.0072540
Editor: Ben Bond-Lamberty, DOE Pacific Northwest National Laboratory, United States of America
Received: May 21, 2013; Accepted: July 10, 2013; Published: September 4, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: This research was supported by the US National Science Foundation through its programs in Long Term Ecological Research (DEB 06-20443). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The land use history of the northeastern United States is well documented but its ecological consequences remain poorly understood, especially at a regional scale –. For more than 10,000 years native people cleared modest areas along waterways and seasonal settlements and managed some upland areas through sporadic understory burning . Even so, the region was overwhelmingly forested and chiefly governed by non-anthropogenic disturbances and successional dynamics until around 1650, when two centuries of logging and agricultural clearing were initiated that removed more than half of the forest cover and cut over almost all of the rest. Outside of the far north and rugged mountainous regions, the northeast became a predominantly humanized agrarian landscape. Forest cover reached its nadir in the mid nineteenth century, after whichagricultural expansion to the Midwest and eastern industrialization resulted in widespread farm abandonment, population concentration and, in turn, a century of natural reforestation and forest growth ,. The emerging forest supported new wood-based industries and natural processes including forest succession interrupted by damage from severe storms such as the powerful 1938 Hurricane, which was compounded by subsequent salvage logging on massive scales , , . The modern landscape appears to have recently reached its apex of reforestation and the region is again experiencing a net loss of forest cover. While agricultural land cover continues to decline throughout the region, land cover transitions to developed uses now override reforestation . As of 2010, approximately 80 percent of the region was forested, though less than one-percent of old-growth forest remains intact .
Given the long and tumultuous history of landscape change, an important ecological question emerges: How similar is the composition of today's forests compared to those existing prior to European colonization? This question has relevance far beyond the northeastern U.S. Conversion of natural ecosystems for agriculture and developed uses are a hallmark of human civilization. Indeed, more than one-third of the land surface of the earth is currently under agricultural use and more than half of the human population lives in urban regions . Throughout human history shifts in economic forces have resulted in land abandonment followed by natural reforestation, as happened in the northeastern U.S. , in Central America after the Mayan collapse , in parts of the Ecuadorian Amazon  and western Europe, and elsewhere . Large-scale Reforestation and natural restoration of forest regions are also major goals for modern conservation. Therefore the question looms: how do regional ecosystems recover from such massive scales of anthropogenic disturbance? Are the secondary ecosystems qualitatively different in terms of composition, function, and services? Or, does an inherent resilience and species fidelity to environmental conditions drive regional ecosystems back to their pre-disturbance condition? Because the processes of regional disturbance and recovery occur over such large spatial and long temporal scales, these questions are rarely addressed empirically.
Here we use a unique dataset, singular in its geographic scope, to quantify the Northeast U.S. region's response to long-term, broad-scale shifts in land use. We compare modern forest data to historical vegetation data gleaned from archival, colonial land-survey notes collected over a nine-state area. Colonial surveyors described the details of “witness trees,” which served as semi-permanent monuments of survey corners. Ecologists have been using these inadvertent forest inventories to reconstruct pre-colonial forest composition for almost a century (e.g., ). The records contain a wealth of information about North America's historical ecosystems . But because the records were not designed to record vegetation, they do have several limitations and biases, (see ,  for reviews). In the Northeast, the witness trees demarcated the corners of lots ranging in size from 0.5 to 65 hectares ,. Unlike the more systematic approach used by the General Land Office, concurrent with westward expansion from Ohio to California, witness tree data in the Northeast were collected using a variety of methods, typically do not include tree size, and commonly identify trees only to genera as opposed to species . Despite these comparative shortcomings, ecologists have made great use of town proprietor records and the references to witness trees therein.
Witness tree have been used extensively throughout the northeast to reconstruct historical forest composition, without making comparisons to modern forest conditions –. Far fewer studies have quantified compositional change and these have all focused on relatively small regions. Their localized perspective limits our understanding of the regional variability and the relative importance of land use history and biophysical setting for determining the degree and nature of forest change. A review of these studies shows some consistent changes. For instance, the loss of American chestnut (Castanea dentata) due to an introduced fungal blight (Cryphonectaria parasitica) is evident throughout the region . But looking across these studies shows that changes in composition have not been uniform throughout the Northeast. Of course, there are a number of reasons to expect different patterns in different places related to variation in: land use, edaphic factors, disturbance regimes or position relative to climatic drivers and floristic boundaries. For example, Bürgi et al.  compared witness trees to modern inventory data in two counties in Massachusetts and two in Pennsylvania and found an almost two-fold difference in the degree of overall compositional change between study areas. Similarly, some studies suggest that the modern forest is compositionally more homogeneous than the forests they replaced ,  while others have found the opposite . Trends in the abundance of individual taxa also vary widely. The abundance of oak, for example, has increased in some areas  and decreased in others . Variation in land use is often assumed to be a primary determinant of change and, indeed, agricultural clearing, harvesting bark for tanning, and logging have been correlated with compositional change , , .
From these local-scale studies we know that the forests have changed, but we are unable to put these changes in the context of the massive shifts in land-use that reshaped the regional ecosystem during the past 400 years. By assembling a comprehensive geo-database of witness-tree records in the Northeast, including more than 150,000 tree records that have not been published before, we addressed several significant questions: (1) How does the magnitude of compositional change vary across the region? (2) Do changes in composition reflect the region's land-use history mosaic and environmental gradients? (3) Is the regional forest recovery producing a landscape that is compositionally more or less diverse to what existed prior to colonization? (4) Is the nature of compositional change consistent across the region? (5) What are the patterns of change among individual taxa? By answering these questions we begin to quantify the resilience of regional ecosystems subjected to large-scale shifts in land use.
We examined changes in forest composition within a sample of colonial townships distributed throughout a nine state region of the northeastern U.S. (Figure 1), ranging latitudinally from northern Maine (47°30′ N) to southern New Jersey (39°30′ N) and longitudinally from western Pennsylvania (67° W) to the Atlantic Ocean (80°30′ W). The study area encompasses 4.33×105 km2 and spans nine physiographic provinces, primarily in the Appalachian Highlands Division, but also extending into portions of the Atlantic and Interior Plains. The region's rolling topography is interrupted by several discrete sub-mountain ranges belonging to the greater Appalachian Chain. Several major rivers including the Hudson, Connecticut, Merrimack, Susquehanna, and Penobscot form low-elevation valleys across the study area. Much of the present geology of the region was shaped by the last glaciation (c. 20,000 ybp). The region is influenced by a range of climatic conditions; annual mean temperatures range from 3 to10°C (mean Jan temp = −6°C; mean July temp = 19°C), and average annual precipitation ranges from 79 to 255 cm. The study area includes four USFS designated ecoregions (Figure 1), defined based on a broad set of ecologically-relevant attributes , which we used to stratify our samples.
Colors correspond to U.S. Forest Service designated ecoregions. The inner polygons correspond to the 1280 colonial town where pre-colonial forest data were collected. Of these, 701 contained an adequate sample of witness trees and modern forest data to permit comparative analyses. Town with insufficient data are grayed-out in the map. Inset: The location of the study area within the conterminous United States.
We used the relative abundance of witness tree taxa (i.e., proportion of each taxon) identified in Table 1 within proprietary towns, as our metric of pre-colonial forest composition (c.f. ). Proprietary towns (hereafter “towns”) were granted by the colonies and states to absentee individuals to encourage colonization and “improvement” of the land throughout the period spanning from just after English colonization (1620) to after the creation of the Erie Canal (1825). Towns were usually 6-miles square (≈100 km2) and regularly shaped. Within each town, individual lots were established and surveyed using witness trees (WT) as markers. The original sources of the WT data typically include proprietors' records, field books, manuscripts, maps and published records of town land surveys before colonization . Town lotting surveys are the authoritative source, but when unavailable or inadequate other sources containing contemporary tree data were used. The witness trees are thus a relatively objective sample of forest composition prior to colonization. The land surveyors used English colloquial names to describe the trees and while they were skilled naturalists they often did not discern individual species within a genus. To reduce taxonomic uncertainty and ensure consistency across surveys we classified all trees into widely represented genera, following Cogbill et al. . While this introduces some species ambiguity into the groupings, it is unavoidable, since surveyors of pre-colonial witness trees often did not distinguish species within genera. We assembled available town land survey records within the region, totaling 1280 towns and 325,000 trees. WT data are publicly available from individual town halls and archives throughout the region. Approximately 55 percent of the WT towns had been utilized in previous published studies, each of which focused on a smaller region.
The modern tree data come from the USDA Forest Service Forest Inventory and Analysis (FIA) program. We used the FIA census, spanning 2003–2008. FIA plots were sampled at an intensity of one plot per 2400-ha throughout the study region. Each plot consists of four, 7.3-m fixed-radius subplots (totaling 168-m2), on which all trees >1.3-m in height are identified to species and the dbh recorded. . FIA protocols and data are publically available online (http://apps.fs.fed.us/fiadb-downloads/datamart.html). However, we obtained coordinates for the inventory plots, which allowed us to pair FIA plots with the WT town in which they reside, from the US Forest Service pursuant to a Memorandum of Understanding #09MU11242305123 between the U.S. Forest Service and Harvard University. We excluded FIA plots that were not classed as “Forest” within the FIA Condition table or contained <10 trees >12.5-cm dbh. In the remaining plots we excluded all trees <12.5-cm dbh to reduce the potential for bias against smaller trees within the pre-colonial data , . (Based on the findings of Wang et al , we explored a 20-cm dbh threshold for inclusion. This resulted in a large reduction in number of towns meeting the sample intensity criteria outlined below, with little qualitative change in our findings.) We excluded any towns with <2 qualifying FIA plots. We then binned the tree species into the same 20 taxa used for the WTs (Table 1) and calculated the relative abundance in each plot.
Assessing the sample intensity
The density of WTs and FIA plots within towns varied widely. We used an approach somewhat akin to rarefaction analysis (e.g. ) to estimate the minimum density of FIA plots and WTs at which tree compositional diversity had been adequately sampled – i.e. to determine whether or not each town had sufficient tree data to include in our analyses. Our approach relied on the fact that tree diversity increases asymptotically with the addition of each new WT or FIA plot. By using bootstrap sampling and fitting Michalis-Menton (M-M) functions, we estimated the density of WTs and FIA plots at which the full complement of diversity was represented. This density was used as a minimum threshold for determining whether or not to include a town in the analyses.
More specifically, our procedure for determining adequate FIA plot density was as follows: (1) We stratified the study region by ecoregions (Figure 1). (2) Within each ecoregion, we selected the most plot-dense towns, taking only those towns with ≥5 FIA plots and that were within the upper tercile of plot density (plots/km2). (3) For each town within this subset, we iteratively took 100 sets of bootstrapped samples, with each set consisting of a sample of one FIA plot, a sample of two FIA plots (sampled with replacement), and so on up to the total number of FIA plots in that town. (4) For each bootstrap sample within a set we calculated the mean Sørenson distance (see below) between the sample's relative composition and the relative composition with all plots included. As the number of plots sampled increases, Sørenson similarity tends to initially increase sharply before slowly leveling off as composition stabilizes (Figure 2). (5) Accordingly, for each set of bootstrap samples we fit an M-M curve and recorded the asymptote, Smax Smax is an estimate of the similarity between two random samples of the town's forest when plot density is impossibly large and is typically slightly less than one, or complete similarity, due to variation introduced by bootstrap sampling. (6) We calculated a threshold plot density, Dmin, as the minimum plot density required to reach a given proportion of Smax. Since reaching Smax would require infinite plot density, we decided that the plot density required to reach 90 percent of Smax would be adequate to approximate a town's true forest composition. (7) We averaged Dmin over all 100 sample sets from each town, and then over all towns within each ecoregion. This ecoregion grand mean, Dmin, was taken to be the ecoregion-wide threshold plot density necessary to capture a town's compositional diversity.
Each dot represents the Sørensen similarity between a bootstrap sample of FIA plots or bins of witness trees, and the town's composition with all plots included, for n in one through the total number of plots/bins in the town. The curve is a Michaelis-Menten function fit to the similarity values. Smax is the curve's asymptote and Dmin is the plot or tree density at 0.9Smax. Dmin was averaged over 100 sets of bootstrap samples from each of the most plot-dense towns in an ecoregion to determine the ecoregion's minimum sampling density.
We followed a similar procedure for determining the adequate number of WTs necessary to capture the compositional diversity within a town, except that we iteratively sampled bins of 20 trees, as opposed to FIA plots, before fitting the M-M function. We also used only those towns with at least 100 WTs and that were within the upper tercile of tree density (WTs per km2).
Comparing pre-colonial to modern forest composition
We first compared the relative abundance of each taxon across the study region and within each ecoregion. We used paired Monte Carlo tests (10,000 randomizations of group membership) to compute p-values describing the probability of encountering differences in relative abundance that were at least as large as those observed from chance alone, given the distribution of data .
We calculated the Sørensen's distance measure for several analyses of compositional change, both in time and in space. Sørensen is defined as:Where xij is the abundance of taxon i in town j and xik is the abundance of taxon i in town k. Values of S range from 0, indicating identical composition, to 1 for no overlap in composition. Empirical analyses have shown Sørensen's distance to be a robust measure of compositional dissimilarity in ecological data because it retains sensitivity in more heterogeneous data sets and gives less weight to outliers .
For each town we calculated the Sørensen's distance from its pre-colonial composition to its modern composition to estimate the degree of change over time. We mapped the change in community composition and compared differences between the ecoregions using a permutation test to compute p-values describing the probability of encountering differences in dissimilarity that were at least as large as those observed from chance alone . To understand whether the degree of community change over time (i.e., Sørensen's) was spatially structured, we constructed a Moran's I spatial correlogram. We experimented with several distance classes within the correlogram; based on the distribution of town-to-town distances and the average nearest neighbor distance (≈7.4 km based on town centroids), we settled on 15 km uniform distance classes. The statistical significance of spatial autocorrelation within each distance class was calculated using a permutation test with α = 0.05.
We ordinated the pre-colonial and modern towns in forest community space to visually examine changes in community composition. We constructed a non-metric multidimensional scaling (NMDS) ordination (50 random starts; scaling; centering; PC rotation; half-change scaling ) using the “MetaMDS” function in the Vegan v2.0-5 library in the R statistical language . Wisconsin double standardization was applied and the Sørensen's measure was used. The appropriate number of dimensions (axes) was determined by plotting final stress values against the number of dimensions on a scree plot.
We used several approaches to examine potential evidence of compositional homogenization and changes in the strength of associations between community structure and the environment. First, following Rooney et al. , we calculated the average dissimilarity (i.e., Sørensen's) of each town to all other towns within the study area and within each ecoregion within at each time period. We then compared the mean town-to-town dissimilarity between the pre-colonial and modern data using a permutation test. A reduction in town-to-town dissimilarity over time is evidence that the forests have converged in terms of compositional diversity. To gauge changes in the spatial structure of forest community composition between the time periods, we constructed Mantel correlograms  using the “mantel.correlog” function within the Vegan v 2.0-5Library of the R statistical language . In brief, the Mantel's test of matrix correlation, rM, is calculated at multiple distance classes and plotted across the full range of distances. In those distance classes where rM is positive and significantly different than zero, the multivariate similarity among towns is higher than expected by chance. Conversely, when rM is negative and significantly different than zero, then the towns are more dissimilar than expected by chance. Whether the correlation is significantly different than zero is determine through a permutation test with a Bonferoni corrected α of 0.05. To understand if the strength of associations between climatic and edaphic variables has changed over time, we used Mantel correlation tests between the community distance matrices from both time periods and temperature (GDD), average precipitation, average elevation, and percent sand (as described in Table 2). Note that rM are not equivalent to the more familiar Pearson's r and should not be directly compared .
Finally, to evaluate the relationships between compositional change and the suite of predictor variables identified in Table 2 further, we used regression tree analysis (RTA) with the Sørenson's distance between time periods as the response variable. RTA is a non-parametric technique that recursively partitions a dataset into subsets that are increasingly homogeneous with regard to the response . We used an implementation of RTA, called conditional inference trees using the “ctree” function in the PARTYv 1.0–7 library  within the R statistical language . Conditional inference trees establish partitions based on the lowest statistically significant P-value that is obtainable across all levels of all predictor variables, as determined from a Monte Carlo randomization test. This minimizes bias and prevents over-fitting and the need for pruning .
Of 1280 towns in the study area with witness tree data, 904 contained two or more forested FIA plots (Figure 1). Of these, 761 towns contained sufficient WT density to meet the sampling density threshold. Most towns with insufficient WT density were clustered in northern Maine. Within the 904 towns with sufficient WT data, 756 towns had sufficient FIA plot density. In all, 701 towns met the sampling density threshold for both the WT and FIA data. We used only these 701 towns in all subsequent analyses. The average density of WTs in the final sample was 1.77 trees per km2 (s = 2.52) and 252 trees/town (s = 357). The average density of FIA plots in the final sample was 0.27 plots per km2 (s = 0.10) and 4.26 plots per town (s = 2.44). The average density of FIA trees was 0.93 trees per km2 (s = 0.41) or 156 trees per town (s = 86). Historical and modern tree data and town shapefiles are available at the Harvard Forest data archive (http://harvardforest.fas. harvard.edu/data/archive.html) as data set HF-210.
While most taxa persist, the modern forest is compositionally distinct from the pre-colonial condition (Figure 3). Across the region, beech experienced the largest decline in relative abundance from the pre-colonial to modern era (Figure 4), dropping from an average of 22 percent to seven percent (Table 3). Major changes in beech were clustered in VT, western MA, and northern PA. Beech abundance was relatively stable in the Adirondack Mountains of NY. Oaks also underwent substantial declines in abundance, from 18 percent in the pre-colonial data to 11 percent in the modern data. Oak declines were most pronounced in central MA and southwestern PA. Hemlock declined from 11 to seven at a regional scale. Chestnut trees were extirpated from the modern forests, dropping from three to zero percent region-wide (Figure S1). The loss was most pronounced in the Appalachian Forest ecoregion where its pre-colonial abundance was ten percent. Due mostly to changes in the north, the abundance of spruce declined overall from 7.6 to 4.0 percent while fir increased from 2.0 to 4.5 percent. Maples experienced the highest absolute change in relative abundance, but unlike the previously mentioned taxa, average maple abundance increased throughout the region, from an average of 11 to 31 percent. Cherries also increased in relative abundance; climbing from <0.4 to 4.4 percent (Figure S1).
Pre-colonial (left); Modern taxa (center); change in abundance between the eras (right), A. Beech; B. Oak; C. Maple; D. Hemlock. Other taxa can be found in online supplementary materials.
The degree of overall compositional change from the pre-colonial to modern forests varied widely from town to town (Figure 5). Sørenson's values ranged from 0.13 to 0.94 ( = 0.54, s = 0.131). The Central Appalachian ecoregion had the highest average compositional change ( = 0.558, s = 0.12), followed by the Eastern Broadleaf Forest ( = 0.551, s = 0.15), then the Laurentian Mixed Forest ( = 0.517, s = 0.13) and finally the Adirondack-New England Mixed Forest ( = 0.494, s = 0.12); however, all pair-wise comparisons between ecoregions were statistically insignificant. The Moran's spatial correlogram showed evidence of clustering in the degree of compositional change at short to moderate geographical distance (<125 km) – i.e., towns that incurred a high degree of compositional change tended to be surrounded by towns that also incurred a high degree of change, and vice versa (Figure 5). At distances >125 km there was no evidence of spatial patterning (i.e., autocorrelation).
Higher values indicate greater compositional change over time. Inset: Spatial autocorrelation in compositional change (Sørensen's distance) over time as shown in a Moran's I spatial correlogram with 20 km distance classes. Solid points indicate that spatial autocorrelation in that distance class is significantly different than zero (P<0.01).
In the two-dimensional NDMS ordination (Stress = 9.56) compositional differences between the colonial and modern periods are apparent as directional shifts along Axis 2 (Figure 6). Ninety thee percent (653/701) of the town's pre-colonial data have Axis 2 scores <0. Conversely, 86 percent (604/701) of the town's modern data have Axis 2 scores >0. The overlay of taxa centroids onto the ordination further demonstrated the differences in composition along Axis 2. For example, cherry had the highest Axis 2 values and chestnut had the lowest (Figure 6a). Axis 1of the ordination captured compositional gradients related to climate Average temperature (i.e., growing degree days) and latitude had the highest correlation with Axis 1 (Figure 6b). Climatic influence on forest composition was also evident in the placement of taxa centroids along Axis 1. For example, the centroid for oak – a taxa more common in the southern part of the study area – scored quite high on Axis 1, while spruce and fir – both northern taxa – scored quite low. Chestnut was placed high on Axis 1, corresponding to its greater abundance in the southern townships in our study area, while also scoring low Axis 2 due to its absence from modern forests.
(A) Points represent each town in each time period. Taxa names are position at the centroid of their distributions within the ordination. (b) Environmental parameters (Table 3) were overlaid onto the NMDS ordination diagram as fitted vectors. R-Square describe the correlation between ordination axes and environmental vectors only vectors with significant Pearson correlation (P<0.05.) were plotted.
At the full regional scale, average town-to-town dissimilarity was higher in the pre-colonial forests ( = 0.58, s = 0.24) than in the modern forests ( = 0.55, s = 0.16; Figure 7). In contrast, in each of the ecoregions average town-to-town dissimilarity was higher in the modern forests than in the pre-colonial. Differences were highest in the Laurentian Mixed Forest ecoregion ( = 0.51 v. = 0.56), followed by Adirondack-New England Mixed Forest ecoregion ( = 0.43 v. = 0.48), then the Eastern Broadleaf Forest ecoregion ( = 0.50 v. = 0.52) and finally the Central Appalachian Ecoregion ( = 0.46 v. = 0.49). All comparisons between average pre-colonial and average modern data were significantly different (P<0.001), though, this reflects the high degrees of freedom associated with all pair-wise town-to-town dissimilarities and does not necessarily imply that the differences were biologically significant.
Higher values correspond to higher β diversity. The box represents the inner quartile range of inter-town compositional dissimilarity. The horizontal line indicates the median. The whiskers extend to 1.5 times the inner quartile range.
The Mantel correlogram showed that pre-colonial forest composition had significant positive spatial correlation from 1 to 400 km and significant negative spatial correlation from 400 to 1500 km (with the exception of sites roughly 700 km apart)–i.e., towns <400 km apart were compositionally more similar than would be expected by chance and towns that were >400 km apart were more dissimilar than would be expected by chance (Figure 8). The modern forest composition followed a roughly similar pattern, except that the strength of compositional clustering and dispersion were not as strong. Mantel correlations between community composition and environmental data varied widely (Table 4). Annual temperature (i.e., growing degree days) had the highest correlations with forest composition in both time periods, but the correlation with the pre-colonial composition was much higher (0.60) than with the modern composition (0.34). The rM values for elevation and percent sand were much weaker (<0.2) in both eras. There was no significant correlation between composition and annual precipitation in either time period.
Filled symbols indicate that the correlation is significantly different than zero using a Bonferroni adjusted α of 0.05. Significant positive correlations indicate towns that are separated by the geographic distance indicated on the x-axis are compositionally more similar than would be expected by chance, while significant negative correlations indicate that towns are more dissimilar than would be expected by chance.
The RTA identified five significant partitions using four different predictor variables (Figure 9). The first partition was based on whether the maximum percent of a town historically in agriculture was greater or less than 56 percent. The group with greater extent of agriculture experienced, on average, significantly higher levels of compositional change. The model further partitioned the high-agriculture group based on whether modern forest cover was greater or less than 62 percent; the less forested of these groups had the highest level of compositional change across all terminal nodes in the tree. The towns with lower levels of agriculture was next partitioned based on latitude, with towns north of 44.9° experiencing the lowest level of compositional change across all nodes in the tree. The towns with a lower level of agriculture but south of 44.9° were again partitioned based on whether the town longitude was east or west of −75.3. The more westerly group was split again based on the maximum percent of a town historically in agriculture. Towns where the maximum proportion of land in agriculture was greater than 49 percent had higher compositional change.
Our analyses document a remarkable paradox about the eastern forest after 400 years of land use: it is at once largely unchanged and completely transformed. It is unchanged insomuch as all the major arboreal taxa remain. With few exceptions, the same taxa that made up the forest in the pre-colonial period comprise the forest today, despite ample opportunities for species invasion and loss. In this sense, the regional ecosystem has been quite resilient and the recovery of the eastern forests has been quite real in extent and composition. Yet, at the same time, the forest has been radically transformed. The relative abundance and distribution of most taxa have shifted dramatically; the relationship between forest composition and the environment has been weakened; and variable patterns of land use have imposed a mosaic of impacts whose legacies are evident centuries later. In the discussion that follows we address the nature of these changes in the context of our research questions, starting with the broad-scale compositional changes and working toward specific trends from individual taxa.
The scale, pattern, and correlates of compositional change
Forest composition changed throughout the region – from Maine to Pennsylvania – and no single ecoregion incurred a significantly higher or lower magnitude of change. The degree and ubiquity of compositional change is clear in the ordination (Figure 6), which showed remarkably little overlap in the distribution of modern and pre-colonial towns. It is important to note, however, that this ‘ubiquitous change’ manifested quite variably at sub-regional scales and that the actual changes – i.e., the rise and fall of individual taxa – occurred non-uniformly. Specific compositional shifts were linked to the specific flora and land-use history of individual towns or small clusters of towns. Indeed, we saw strong local-scale (<125-km) clustering in the magnitude of compositional change but almost no evidence of regional-scale gradients in that change (Figure 2).
Why do we see clusters of two to five towns with similar levels of change? The regression tree analysis suggested that local-scale clustering of compositional change may be explained by a hierarchy of land use history and climatic factors (Figure 9). The modern forests arose following land uses of varying types and intensities that had direct impacts on composition–such as forest harvesting , land clearance and agriculture – and indirect impact, through interactions among land use, climate and the biophysical setting. These different effects are apparent in the form of the regression tree. The first partition – and therefore the variable with the greatest explanatory power – is based on the maximum level of agricultural clearing between 1850 and 1997. Towns with less than 56 percent of their area cleared for agriculture experienced significantly less compositional change. The emergence of agricultural land clearing as the most important variable provides strong evidence of the effects of land use on long-term compositional change. The enduring impact of land use on composition has also been documented in some smaller scale witness tree studies , ,  and some detailed field-based studies that document the strong legacy of agriculture versus managed woodlot on modern vegetation , .
Among the towns with higher agricultural clearing, the level of modern forest cover is the next best predictor of change. Towns that have largely reforested (>62%) exhibit less compositional change than those with high levels agriculture of other developed land covers. Among the towns with lower agriculture cover, the next split in the regression tree is based on latitude with the northerly sites – those in northern Maine – having the lowest level of change across the entire region. Towns with lower historical agriculture but south of Maine were next partitioned based on latitude; towns west of Scranton, PA (75.3°) are less changed, perhaps because these towns were colonized later and, therefore, have a shorter history of land use than towns to the east.
An overall homogenization and de-coupling of composition and environment
The modern forest composition is less coupled to its local environment than was the pre-colonial forest, which has coincided with a broad-scale compositional homogenization (sensu ). Among the suite of environmental variables we examined, temperature had, by far, the strongest association with forest composition in both time periods; however, the strength of that association was dramatically lower in the modern era (rM = 0.60 versus rM = 0.34; Table 4). This has manifested as a compositional “smoothing” across the region. This also affected the inter-town compositional dissimilarity within each time period, which we used as a straightforward comparison of β-diversity (Figure 7). At the full regional scale, mean dissimilarity was marginally higher in the pre-colonial forests – i.e., the community composition between any two towns, on average, is slightly more similar in the modern era than it was in the colonial period. In contrast, at the ecoregional scale, towns were compositionally more similar to each other in the pre-colonial era than in the modern era. Perhaps more telling than the averages, though, were the differences in variation. At the regional scale, the variance was more than twice as high in the pre-colonial era, where many town-pairs had Sørensen values close or equal to one (indicating little or no overlap in taxa) and many towns had values close to zero (indicating similar taxa in similar abundances). In contrast, the distribution of modern inter-town dissimilarities was comparatively concentrated around the mean.
The regional biotic homogenization is visually apparent in mapped community composition (Figure 3). In the pre-colonial maps, the towns in the south are dominated by oak and hickory and have little in common with the northern towns dominated by spruce and fir. In contrast, most modern towns have a significant component of maple regardless of their location. We confirmed this empirically and showed that, indeed, pre-colonial forests were compositionally more similar at short distances (i.e., positive spatial autocorrelation in the Mantel correlogram (Figure 5)) and more dissimilar at longer distance (i.e., negative spatial autocorrelation).
Smaller scale studies have arrived at opposing conclusions with regard to homogenization of the modern forest. For example, in western NY, the replacement of a late successional species (beech) with several early successional species resulted in increased compositional heterogeneity . In contrast, Foster et al  observed a pronounced homogenization and the loss of affinity between taxa abundances and the regional climate gradient in central MA. Such divergent conclusions regarding the impact of historical land use underscore the importance of considering the impacts of a regional-scale disturbance regime at the regional scale. From our analysis, it seems clear that the regional ecosystem has not reestablished the mosaic of forest-types and strong climate-driven compositional gradients.
A broad ecological shift from late to early successional taxa
Our data offer snapshot perspectives on the pre-colonial and modern forest composition. From these we can make inferences about the succession and disturbance processes that shaped the forests. For example, that late successional species such as beech and hemlock were common in the pre-colonial data is a testament to the relative stability of these forests and their disturbance regimes for the millennia preceding colonization. Beech and hemlock are archetypically late successional species; they are shade tolerant, slow growing, long-lived, and slow to re-colonize a site after disturbance. Based on their pre-colonial abundance, it is clear that the disturbance regime was long dominated by small canopy gaps.
The abundance of late successional species is significantly lower on the modern landscape, which offers evidence of the legacy of past land use and to changes in the modern disturbance regime. Of all the taxa we examined, beech experienced the most dramatic reduction in relative abundance, declining in 91 percent of the towns in which its pre-colonial abundance was greater than five percent. We found just one pocket of beech stability (or, at least, low change) located in the Adirondack Mountains of New York, which is the region within our study area that has the most old-growth and primary forest and has had the least exposure to human induced disturbances. This suggests that while climate and disease (particularly beech bark disease) may be contributing factors, the primary cause of beech reduction locally and regionally is the disruption of the forest by deforestation, logging and fire.
The overall reduction in hemlock is significant but less stark and more multifaceted than that for beech. In the past 10,000 years, several dramatic declines in hemlock are evident in pollen data , always coinciding with periods of drought and aridity –. Therefore, hemlock would be expected to be under stress during the greater aridity that coincided with the early colonial period and consequently less able to recover from a regime of intense land use and selective harvesting for its tannins throughout the colonial period . Indeed, an initial decline by hemlock in many pollen diagrams just before European settlement is followed by a more rapid decline with intensive land use . In the modern era, hemlock is being decimated by the invasive hemlock wooly adelgid (HWA; Adelges tsugae). We saw the largest reductions of hemlock in the southern portion of our study area, where it frequently declined to zero, which is consistent with the northward spread of HWA from Richmond, Virginia since 1952 . At present, cold winter temperatures appear to be limiting HWA's extension into northern New England .
On the other end of the successional spectrum are species such as red maple (Acer rubrum), black cherry (Prunus serotina), and aspens (Populus spp). Within our analysis maple had, by far, the largest absolute change in relative abundance – a nearly 20 percent rise in average regional abundance. Because the FIA data have species-level resolution, we know that the maple species that dominates the modern landscape is overwhelmingly red maple, a pioneer species with moderate to great shade tolerance and light, wind-dispersed seeds that readily invades open fields after farm abandonment as well as occupy forest gaps following disturbance. It sprouts readily following damage and has great edaphic amplitude ranging from saturated wetland soils to drier uplands. The ecological versatility of red maple allows it to occupy a broad range of edaphic and climatic conditions throughout our study area –. And, indeed, maple increased in 87 percent of the towns we examined, spanning all the environmental gradients within the region, as demonstrated by its central location within the cloud of modern data in the ordination (Figure 6).
Similarly, cherry is positioned squarely in the modern data cloud within the ordination; indeed, it has the highest position along axis two of all taxa (Figure 6). Cherry was absent in most of the pre-settlement surveys, but was present at low abundance throughout much of the modern forests. Thanks again to the resolution of the FIA data, we know that references to cherry in the modern data are mostly (>75%) black cherry. The prevalence of this relatively short-lived, pioneer taxon reflects the greater extent and intensity of forest disturbance in the modern era. It shows that disturbance, e.g., from harvesting and meteorological processes, is ongoing throughout the region and the macro shift to reforestation 150 years ago does not imply that succession has progressed uninterrupted.
In the absence of anthropogenic influences we would expect succession to increase the similarity to the pre-colonial forests. By examining our two snapshots of forest composition coupled with our understanding of past and ongoing disturbance processes, we can infer that this has not yet occurred because: (1) despite more the 150 years of reforestation, these ecosystems are still in the early stages of recovery, and (2) through that 150-year period there has been ongoing harvests and other land uses as well as exogenous disruptions, including pests (e.g., HWA), pathogens (e.g., chestnut blight), climate change, and atmospheric deposition.
Dynamics of other significant taxa
Chestnut suffered a near complete extirpation as a tree-sized individual from the region due to the introduced fugal blight, Endothia parasitica in the early 1900s. Much has been written about the loss of chestnut and its replacement by oak and maple , –. Conventional wisdom holds that chestnut was among the dominant overstory species throughout eastern forests –. However, the average relative abundance of chestnut in the pre-colonial data was only 3.3-percent. Its highest abundance was in the Appalachian ecoregion, and even there it was less than ten percent and just one-quarter that of oak. Indeed, and in contrast to many other southern and northern taxa, no town in our analysis exceeded 25-percent pre-colonial chestnut abundance. These data suggest that the historical dominance of chestnut is currently often overstated, at least in the Northeast. Nevertheless, even with our comparatively modest estimate of pre-colonial abundance, the loss of chestnut initiated a significant change in forest composition.
Like chestnuts, oaks are mid- to late-successional, hard-mast producing canopy tree. There has been growing concern among researchers and managers that oaks are losing their dominance on the landscape , . And, indeed, oaks declined in 75 percent of the towns in which its abundance was greater than five percent. Across the region, the relative abundance of oaks declined by almost seven percent. Declines were largest in the Eastern Broadleaf ecoregion where relative abundance fell from 40 to 18 percent. It is worth noting, however, that there were a few small pockets where the relative abundance of oak increased (e.g., in eastern PA) and there was little change along much of the northern extent of it pre-colonial range. McEwan et al.  recently dissected the myriad potential causes for the decline in oak; they conclude that a “multiple interacting ecosystem drivers hypothesis” is essential to understand long-term oak dynamics. Important factors include a climatic shift toward increased moisture availability, a shift away from Native American burning and a shift first toward low human populations and then to European colonization, plus changes in populations of acorn consuming fauna, including white tailed deer (Odocoileus virginianus).
The regional stability in average pine abundance belies the fact that pine was among the most dynamic taxa we examined (Figure S1). At the regional scale, the relative abundance of pine did not change significantly. However, in the Central Appalachian ecoregion, pines declined by 11 percent, a reduction that was offset by modest increases in pine in the other ecoregions. The relative abundance of pines increased by more than ten percent in 100 towns and decreased by more than ten percent in 99 towns. This high amplitude of change in pine abundance likely speaks to two countervailing characteristics of pine in the region: On one hand, pine is a pioneer taxon that establishes on disturbed sites including post-fire landscapes and abandoned agricultural fields, grows fast, and lives for a long time. On the other hand, pine is vulnerable to natural disturbance (particularly wind) and has always been among the most sought after timber species in the region , .
Limitations of data sources
It is important to acknowledge the limitations of the datasets we employed and discuss how we dealt with them: (1) The WT data were collected over a period of more than 150 years. While the surveys were conducted at a similar timeframe relative to colonization, they still potentially represent different levels of Native American and European influence and different survey methods. We minimized our exposure to this by excluding towns with explicit evidence of colonial influence (e.g., those with apple trees) and by screening out the trees sampled using surveys types known to produce a bias (e.g., road surveys, sensu ). (2) The witness trees were not collected as a random sample. Instead we assembled all witness tree records known to exist with the nine-state region. While we know of no particular bias in the towns with existing data, we also cannot say emphatically that our collection of towns is a statistically robust sample. (3) The sample of WT and modern trees per town is sparse, which is why we put the data through an exhaustive analysis whereby we only retained towns where the WT and FIA data adequately captured the compositional diversity. (4) The FIA protocol measures in clusters whereas the WTs are dispersed throughout the town, which introduce potential for error and bias. We hedged against this by only including towns with at least two FIA plots and then putting the FIA plots through the rarefaction analysis. Having noted these issues, we still believe that historical survey records offer a singular resource for understanding widespread patterns of forest composition at the time of European colonization. This perspective has been borne out through several studies that have shown that these data provide an accurate account of landscape scale vegetation patterns (e.g. , ). In addition, consistency between WT data and contemporary pollen studies ,  further confirm the utility of this resource.
By comparing the modern forest condition to regional database of WT, we have learned much about the changes regional forest composition. While most native taxa remain, the community composition has shifted dramatically. While significant compositional changes were ubiquitous throughout the region, the specific attributes of change varied at local scales. One important pattern throughout was the reduction of late successional species in favor of early successional species. Additionally, the modern forest is more homogeneous and less coupled to local climatic controls.
Among individual taxa, we found that late successional trees such as beech, hemlock, and spruce, were once predominant across the region but are now much less so, save for in some small refugia within isolated mountain regions with little history of land use. Oaks also declined throughout most, but not all, of its range. The 400-year history of land use benefited maple the most and it is now a dominant taxon throughout most of the region. Short-lived, early seral species are also much more common in the modern landscape, which indicates that land clearing disturbance is ongoing and widespread and the abandonment of agriculture 150 years ago did not mark a return to the natural disturbance regime of small gap openings.
The northeast is once again a predominantly forested landscape, but today's forest is not a facsimile of its predecessor. We find this to be at once disheartening and encouraging. On the one hand, the modern expense of forest is diminished in so many of the components and processes that once characterized the regional ecosystem; on the other, given the extent and magnitude of land use it is remarkable that native species predominate and the forests looks in many ways as it has for millennia.
Maps of relative abundance and change for all taxa.
We thank D. Orwig for a helpful review of an earlier version of this manuscript and Evelyn Strombom for editing and help with figures. FIA field plot locations were provided by Brett Butler and Elizabeth LaPoint (both with the US Forest Service) pursuant to a Memorandum of Understanding 09MU11242305123between the US Forest Service and Harvard University.
Conceived and designed the experiments: JRT DNC CC DRF. Performed the experiments: JRT DNC CC DRF. Analyzed the data: JRT DNC. Contributed reagents/materials/analysis tools: CC. Wrote the paper: JRT DNC CC DRF.
- 1. Cronon W (1983) Changes in the land: Indians, colonists and the ecology of New England. New York: Hill and Wang.
- 2. Whitney GG (1994) From coastal wilderness to fruited plain: A history of environmental change in temperate North America from 1500 to the Present. New York: Cambridge University Press.
- 3. Foster DR, Aber JD (2004) Forest in time: Environmental consequences of 1000 years of change in New England. New Haven: Yale University Press.
- 4. Foster DR, Motzkin G, Slater B (1998) Land-use history as long-term broad-scale disturbance: regional forest dynamics in central New England. Ecosystems 1: 96–119. doi: 10.1007/s100219900008
- 5. Chilton ES (2001) The archaeology and ethnohistory of the contact period in the northeastern United States. Reviews in Anthropology 29: 337–360. doi: 10.1080/00988157.2001.9978265
- 6. Irland LC (1999) The Northeast's changing forests. Cambridge, MA: Harvard University Press.
- 7. Donahue B (2007) Another look from Sanderson's farm: a perspective on New England environmental history and conservation. Environmental History 12: 9–34. doi: 10.1093/envhis/12.1.9
- 8. Foster DR, Aber JD, Melillo JM, Bowden RD, Bazzaz FA (1997) Forest response to disturbance and anthropogenic stress. BioScience 47: 437–445. doi: 10.2307/1313059
- 9. Boose ER, Chamberlin KE, Foster DR (2001) Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27–48. doi: 10.2307/3100043
- 10. Drummond MA, Loveland TR (2010) Land-use pressure and a transition to forest-cover loss in the eastern United States. BioScience 60: 286–298. doi: 10.1525/bio.2010.60.4.7
- 11. Foster DR, Donahue B, Kittredge DB, Fallon-Lambert K, Hunter M, et al.. (2010) Wildland and woodlands: A forest vision for New England. Cambridge, MA: Harvard University Press.
- 12. Foley JA, Ramankutty N, Leff B, Gibbs HK (2007) Global land use changes. In: Kling MD, Parkinson CL, Partington KC, Williams RG, editors. Our changing planet: A view from space. New York: Cambridge University Press. 262–265.
- 13. Foster DR (1992) Land-use history (1730–1990 ) and vegetation in central New England, USA dynamics. Journal of Ecology 80: 753–771. doi: 10.2307/2260864
- 14. Turner BL II, Klepeis P, Schneider LC (2002) Three millennia in the southern Yucatan Peninsula: Implications for occupancy, use, and carrying capacity. In: Fedick S, Allen M, Gomez-Pompa A, Jimenez-Osornio J, editors.The lowland Maya area: Three millennia at the human-wildland interface. New York: Haworth Press. 361–387.
- 15. Rudel TK, Bates D, Machinguiashi R (2002) A tropical forest transition? Agricultural change , out-migration , and secondary forests in the Ecuadorian Amazon. Annals of the Association of American Geographers 92: 87–102. doi: 10.1111/1467-8306.00281
- 16. DeFries RS, Foley JA, Asner GP (2004) Land-use choices: balancing human needs and ecosystem function. Frontiers in Ecology and the Environment 2: 249–257. doi: 10.2307/3868265
- 17. Sears PB (1925) Th natural vegetation of Ohio. I, A map of the virgin forest. Ohio Journal of Science 25: 139–149.
- 18. Schulte L, Mladenoff DJ (2001) The original US public land survey records: Their use and limitations in reconstructing presettlement vegeation. Journal of Forestry Oct. 5–10.
- 19. Whitney G, DeCant J (2001) Government land office surveys and other early surveys. The Historical Ecology Handbook. Washington, DC: Island Press. 147–172.
- 20. Liu F, Mladenoff DJ, Keuler NS, Moore LS (2011) Broadscale variability in tree data of the historical Public Land Survey and its consequences for ecological studies. Ecological Monographs 81: 259–275. doi: 10.1890/10-0232.1
- 21. Cogbill C, Burk J, Motzkin G (2002) The forests of presettlement New England, USA: spatial and compositional patterns based on town proprietor surveys. Journal of Biogeography 29: 1279–1304. doi: 10.1046/j.1365-2699.2002.00757.x
- 22. Lutz HJ (1930) Original forest composition in northwestern Pennsylvania as indicated by early land survey notes. Journal of Forestry 28: 1098–1103 (6)..
- 23. McIntosh RP (1962) The forest cover of the Catskill Mountain region, New York, as indicated by land survey records. American Midland Naturalist 68: 409–423. doi: 10.2307/2422746
- 24. Lorimer CG (1977) The presettlement forest and natural disturbance cycle of northeastern Maine. Ecology 58: 139–148. doi: 10.2307/1935115
- 25. Seischab F (1990) Presettlement forests of the Phelps and Gorham purchase in western New York. Bulletin of the Torrey Botanical Club 117: 27–38. doi: 10.2307/2997126
- 26. Black BA, Ruffner CM, Abrams MD (2006) Native American influences on the forest composition of the Allegheny Plateau, northwest Pennsylvania. Canadian Journal of Forest Research 36: 1266–1275. doi: 10.1139/x06-027
- 27. Wang YC (2007) Spatial patterns and vegetation–site relationships of the pre-settlement forests in western New York, USA. Journal of Biogeography 34: 500–513. doi: 10.1111/j.1365-2699.2006.01614.x
- 28. Paillet FL (2002) Chestnut: history and ecology of a transformed species. Journal of Biogeography 29: 1517–1530. doi: 10.1046/j.1365-2699.2002.00767.x
- 29. Bürgi M, Russell EWB, Motzkin G (2000) Effects of postsettlement human activities on forest composition in the north-eastern United States: a comparative approach. Journal of Biogeography 27: 1123–1138. doi: 10.1046/j.1365-2699.2000.00484.x
- 30. Schulte LA, Mladenoff DJ, Crow TR, Merrick LC, Cleland DT (2007) Homogenization of northern U.S. Great Lakes forests due to land use. Landscape Ecology 22: 1089–1103. doi: 10.1007/s10980-007-9095-5
- 31. Wang Y-C, Larsen CPS, Kronenfeld BJ (2009) Effects of clearance and fragmentation on forest compositional change and recovery after 200 years in western New York. Plant Ecology 208: 245–258. doi: 10.1007/s11258-009-9702-4
- 32. Nowacki GJ, Abrams MD (1991) Community, edaphic and historical analysis of mixed oak forests of the Ridge and Valley Province in central Pennsylvania. Canadian Journal of Forest Research 22: 790–800. doi: 10.1139/x92-108
- 33. Abrams MD, Nowacki GJ (1992) Historical variation in fire, oak recruitment, and post-logging accelerated succession in central Pennsylvania. Bulletin of the Torrey Botanical Club 119: 19–28. doi: 10.2307/2996916
- 34. Glitzenstein J, Canham CD, McDonnell MJ, Streng D (1990) Effects of environment and land-use history on upland forests of the Cary Arboretum. Bulletin of the Torrey Botanical Club 117: 106–122. doi: 10.2307/2997050
- 35. Bailey RG (1995) Description of the ecoregions of the United States (2nd ed). Misc. Pub. No. 1391, Map scale 1:7,500,000. USDA Forest Service.
- 36. Cowell C (1998) Historical changes in vegetation and disturbance on the Georgia Piedmont. American Midland Naturalist 140: 78–89. doi: 10.1674/0003-0031(1998)140[0078:hcivad]2.0.co;2
- 37. Wang YC, Kronenfeld BJ, Larson CPS (2009) Spatial distribution of forest landscape change in western New York from pre-settlement to the present. Canadian Journal of Forest Research 39: 76–88. doi: 10.1139/x08-161
- 38. Gotelli NJ, Colwell RK (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters 4: 379–391. doi: 10.1046/j.1461-0248.2001.00230.x
- 39. Gotelli NJ, Ellison AM (2004) A primer of Ecological Statistics. Sunderland, MA: Sinauer Associates.
- 40. McCune B (1992) Components of error in predictions of species compositional change. Journal of Vegetation Science 3: 27–34. doi: 10.2307/3235994
- 41. Oksanen JR, Kindt P, Legendre B, O'Hara GL, Simpson P, et al.. (2008) vegan: Community Ecology Package. Available: http://vegan.r-forge.r-project.org/. Accessed 2013 Aug 13.
- 42. R Development Core Team (2006) R: A language and environment for statistical computing. Available: http://www.r-project.org/. Accessed 2013 Aug 13.
- 43. Rooney TP, Wiegmann SM, Rogers D, Waller DM (2004) Biotic impoverishment and homogenization in unfragmented forest understory communities. Conservation Biology 18: 787–798. doi: 10.1111/j.1523-1739.2004.00515.x
- 44. Borcard D, Legendre P (2012) Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology 93: 1473–1481. doi: 10.1890/11-1737.1
- 45. Dutilleul P, Stockwell J, Frigon D, Legendre P (2000) The Mantel test versus Pearson's correlation analysis: assessment of the differences for biological and environmental studies. Journal of Agricultural, Biological, and Environmental Statistics 5: 131–150. doi: 10.2307/1400528
- 46. De'ath G, Fabricius KE (2000) Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81: 3178–3192. doi: 10.1890/0012-9658(2000)081[3178:cartap]2.0.co;2
- 47. Hothorn T, Hornik K, Strobl C, Zeileis A (2010) party: A Laboratory for Recursive Partitioning. Available: http://cran.r-project.org/web/packages/party/index.html. Accessed 2013 Aug 13.
- 48. Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics 15: 651–674. doi: 10.1198/106186006x133933
- 49. Hanberry BB, Kabrick JM, He HS, Palik BJ (2012) Historical trajectories and restoration strategies for the Mississippi River Alluvial Valley. Forest Ecology and Management 280: 103–111. doi: 10.1016/j.foreco.2012.05.033
- 50. Motzkin G, Foster D, Allen A, Harrod J, Boone R (1996) Controlling site to evaluate history: Vegetation patterns of a New England sand plain. Ecological Monographs 66: 345–365. doi: 10.2307/2963522
- 51. Eberhardt RW, Foster DR, Motzkin G, Hall B (2013) Conservation of changing landscapes: Vegetation and land-use history of Cape Cod National Seashore Ecological. Applications13: 68–84. doi: 10.1890/1051-0761(2003)013[0068:coclva]2.0.co;2
- 52. Olden JD, Rooney TP (2006) On defining and quantifying biotic homogenization. Global Ecology and Biogrography 15: 113–120. doi: 10.1111/j.1466-822x.2006.00214.x
- 53. Shuman B, Newby P, Huang Y, Webb T III (2004) Evidence for the close climatic control of New England vegetation history. Ecology 85: 1297–1310. doi: 10.1890/02-0286
- 54. Foster DR, Orwig DA (2006) Preemptive and salvage harvesting of New England forests: When doing nothing is a viable alternative. Conservation Biology 20: 959–970. doi: 10.1111/j.1523-1739.2006.00495.x
- 55. Faison E, Foster D, Oswald W, Hansen B, Doughty E (2006) Early holocene openlands in southern New England. Ecology 87: 2537–2547. doi: 10.1890/0012-9658(2006)87[2537:ehoisn]2.0.co;2
- 56. Fuller JL, Foster DR, McLachlan JS, Drake N (1998) Impact of human activity on regional forest composition and dynamics in central New England. Ecosystems 1: 76–95. doi: 10.1007/s100219900007
- 57. Orwig D, Foster DR, Mausel DL (2002) Landscape patterns of hemlock decline in New England due to the introduced hemlock woolly adelgid. Journal of Biogeography 29: 1475–1487. doi: 10.1046/j.1365-2699.2002.00765.x
- 58. Orwig DA, Thompson JR, Povak N, Manner M, Niebyl D, et al. (2012) A foundation tree at the precipice: Tsuga canadensis health after the arrival of Adelges tsugae in central New England. Ecosphere 3: 1–16. doi: 10.1890/es11-0277.1
- 59. Walters R, Yawney H (1991) Acer rubrum L. Red maple. In: Burns RM, Honkala BH, tech. coordinators. Silvics of North America. Volume 2. Hardwoods. Forest Service Agric. Handbook 654. Washington DC: USDA Forest Service. 60–69.
- 60. Abrams MD (1998) The red maple paradox. BioScience 48: 355–364. doi: 10.2307/1313374
- 61. Lorimer CG (1984) Development of the red maple understory in northeastern oak forests. Forest Science 30: 3–22.
- 62. Woods FW, Shanks RE (1959) Natural replacement of chestnut by other species in the Great Smoky Mountains National Park. Ecology 40: 349–361. doi: 10.2307/1929751
- 63. Mackey HE, Sivec N (1973) The present composition of a former oak-chestnut forest in the Allegheny Mountains of western Pennsylvania. Ecology 54: 915–919. doi: 10.2307/1935689
- 64. Mkian C, Orwig DA, Abrams MD, Mikan CJ (1994) Age structure and successional dynamics of a presettlement-origin chestnut oak forest in the Pennsylvania Piedmont. Bulletin of the Torrey Botanical Club 121: 13–23. doi: 10.2307/2996880
- 65. Braun EL (1950) Deciduous Forests of Eastern North America. Caldwell, NJ: The Blackburn Press.
- 66. Jacobs DF, Severeid LR (2004) Dominance of interplanted American chestnut (Castanea dentata) in southwestern Wisconsin, USA. Forest ecology and management 191: 111–120. doi: 10.1016/j.foreco.2003.11.015
- 67. Hiremath S, Lehtoma K, Hebard F (2007) Blight-resistant American chestnut trees: selection of progeny from a breeding program. In: Gottschalk, KW, editor. Proceedings, 17th U.S. Department of Agriculture interagency research forum on gypsy moth and other invasive species 2006; Gen. Tech. Rep. NRS-P-10. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station: 54.
- 68. Abrams MD (2003) Where Has All the White Oak Gone? Bioscience 53: 927–939. doi: 10.1641/0006-3568(2003)053[0927:whatwo]2.0.co;2
- 69. McEwan RW, Dyer JM, Pederson N (2011) Multiple interacting ecosystem drivers: toward an encompassing hypothesis of oak forest dynamics across eastern North America. Ecography 34: 244–256. doi: 10.1111/j.1600-0587.2010.06390.x
- 70. McDonald RI, Motzkin G, Bank MS, Kittredge DB, Burk J, et al. (2006) Forest harvesting and land-use conversion over two decades in Massachusetts. Forest Ecology and Management 227: 31–41. doi: 10.1016/j.foreco.2006.02.006
- 71. Bürgi M, Russell EWB (2000) Evaluating accuracy of two types of early land survey records in the northeastern United States. Journal of the Torrey Botanical Society 127: 94–98. doi: 10.2307/3088751
- 72. Russell EWB, Davis RB, Anderson RS, Rhodes TE, Anderson DS (1993) Recent centuries of vegetational change in the glaciated north-eastern United States. Journal of Ecology 81: 647–664. doi: 10.2307/2261663
- 73. PRISM Climate Group (2011) Copyright © 2011. Oregon State University. Available: http://prism.oregonstate.edu. Accessed 2013 Aug 13.
- 74. Waisanen P, Bliss N (2002) Changes in population and agricultural land in conterminous United States counties, 1790 to 1997. Global Biogeochemical Cycles 16: 1137. doi: 10.1029/2001gb001843
- 75. Huang C, Yang B, Wylie B, Homer C (2001) A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. USGS: 5–7.
- 76. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture (2013) U.S. General Soil Map (STATSGO2). Available: http://soildatamart.nrcs.usda.gov. Accessed 2013 Aug 13.