The northern hardwood forest type is an important habitat component for the endangered Carolina northern flying squirrel (CNFS;
The Blue Ridge portion of the southern Appalachians, extending from northern Georgia to central Virginia [
In the southern Appalachians, high elevation forest communities above 1200 m have been subjected to varying degrees of disturbance over the last 200 years. Disturbance to these areas began with Native Americans and early European pioneers clearing land for high-elevation pasture and then large-scale timber harvesting to exploit vast forest resources during the industrial logging period around the turn of the 20th century [
Two sciurid species, the endangered CNFS and the common southern flying squirrel (SFS;
However, northern hardwoods, particularly those in close proximity to oak stands with hard mast production, provide the SFS a high-energy food source. This food source has allowed the more austral SFS to overcome an otherwise thermally difficult environment and increase in abundance and local distribution at high elevations. Despite their smaller size, SFS are more aggressive than CNFS in den-site competition when the two species are synoptic, often displacing CNFS locally [
Northern hardwood study areas in Western North Carolina, eastern Tennessee, and southwestern Virginia surveyed in June 2012–January 2013. Mountain ranges or massifs were (1) Grayson Highlands, (2) Elk Knob State Park, (3) Roan Mountain, (4) Unaka Mountain, (5) Big Bald, (6) Black and Craggy Mountains, (7) Great Balsams, (8), Cherokee Reservation, (9) Wayah Bald, (10) the Standing Indian, and (11) Unicoi Mountains. All of these ranges are believed to have populations of Carolina Northern flying squirrel with the exception of the Wayah Bald and the Standing Indian.
Mapping the distribution of deciduous forest types over large areas and in complex terrain is an important yet challenging task. Data from extensive vegetation inventories such as the US Forest Service Forest Inventory and Analysis program [
In forested mountain landscapes, where topography controls or influences many biophysical characteristics, such as microclimate, incident solar radiation, soil moisture, and organic matter accumulation, modeling terrain attributes within a geographic information system (GIS) can be effective for delineating vegetation community patterns [
The objectives of our study were to define the northern hardwood forest type in western North Carolina, adjacent portions of eastern Tennessee, and southwestern Virginia and to determine if the geographic distribution of northern hardwood forest types can be accurately determined from digital terrain modeling in and adjacent to CNFS recovery areas. To meet the objectives, we used a decision tree approach for initial classification based on similar stand composition and species abundance within those stands as used in previous studies [
During the summer of 2012 and January 2013, we sampled vegetation and terrain characteristics at 338 points over 113 three-point belt transects across 11 study areas in western North Carolina, adjacent portions of eastern Tennessee, and southwestern Virginia within the Blue Ridge physiographic province (Figure
Regionally, Simon et al. [
Using a method to validate DEM-based models similar to the approach of Odom and McNab [
The number of points we established per study area was proportional to the total area above 1219.2.2 m in each. Sampling rates ranged between 0.2% for the larger mountain ranges and 3% on the smaller massifs. Our sampling points in 8 of the 11 study areas met the following criteria: elevation >1219.2 m, within a designated CNFS recovery area, and reasonably accessible. The remaining 3 study areas included one area without documented CNFS populations but met our elevation criteria, and northern hardwood forest species were known to be present there. The lower elevation limit of 1219.2 m was based on the lowest recorded elevation of CNFS capture regionally [
We used a mapping grade Trimble GeoXT GPS unit (the use of any trade, product, or firm names does not imply endorsement by the US government) with submeter accuracy to navigate to the randomly generated study points that were generated from the DEMs. At each sampling point, we tallied the total number of stems for each tree species using a 10-factor basal area prism to identify dominant overstory species and made a visual determination of forest type. We noted dominant understory woody species to aid in classifying sample locations as northern hardwood or other forest types. We also looked for any evidence of past forest disturbance such as cut stumps, old logging roads/skidder trails, or abandoned railroad grades which may have influenced stand development. Our forest type categories included northern hardwood (yellow birch, American beech, sugar maple, mountain maple (
We classified each study point as ridgeline, shoulder, side slope, or cove and further characterized each point by its vertical position on the slope (low, medium, or high) to describe each point's location and topographic position in relation to the surrounding landscape [
We downloaded USGS-produced 1/3 arc-second (10 meter) digital elevation models [
We created a series of six overarching
We used correlation analysis to examine multicollinearity and redundancy relationships among variables and eliminated those variables with a correlation coefficient greater than 0.8, proceeding with the variable believed to be most beneficial to analysis [
The 338 sites we sampled encompassed a wide range of terrain conditions (Table
Mean terrain attributes of the three forest types in the areas above 1219.2.2 m in elevation of western North Carolina, eastern Tennessee, and southwest Virginia which composed our study area (
Forest type | ||||
---|---|---|---|---|
Northern hardwood | Northern red oak | Red spruce-Fraser fir | ||
Terrain attribute |
|
179 | 80 | 73 |
Northing (UTM) | 3952592.87 | 3932901.42 | 3954948.38 | |
Elevation (m) | 1528.03 | 1443.59 | 1704.28 | |
Aspect (degrees) | 198.89 | 195.73 | 195.49 | |
Slope (percent) | 35.63 | 35.51 | 34.63 | |
Curvature | 0.23 | 0.86 | 0.44 | |
TEI* | 58.27 | 60.14 | 81.81 |
*Topographic exposure index.
Ourmodel containing Elevation + TEI, the latitude variable, and all interactions between the variables was shown to be the best approximating model for the overall study area (Table
Logistic regression models explaining the influence of terrain variables on the presence or absence of northern hardwoods (NH) in the areas above 1219.2.2 m in elevation of western North Carolina, eastern Tennessee, and southwest Virginia which composed our study area (
Model |
|
AICc |
|
|
---|---|---|---|---|
Elevation + TEI | 8 | 488.30 | 0.00 | 0.449 |
Elevation | 6 | 523.04 | 34.74 | 507.05 |
Elevation + Curvature | 8 | 525.11 | 36.81 | 493.12 |
Elevation + Aspect | 8 | 534.48 | 46.18 | 502.48 |
Elevation + Slope + Aspect | 10 | 554.59 | 66.29 | 490.59 |
Slope + TEI | 6 | 670.78 | 182.48 | 638.78 |
Global | 15 | 701.89 | 213.59 | 652.45 |
ANumber of parameters + I in approximating model.
BDifference between current model and best approximating model (minimum AlCc).
CCox and Snell’s re-scaled
The best approximating logistic model (Elevation + TEI) explaining presence or absence of northern hardwoods in areas above 1219.2.2 m in elevation of western North Carolina, eastern Tennessee, and southwest Virginia which composed our study area (
Parameter (NH versus NRO) | Estimate | Std. error |
---|---|---|
Intercept |
|
|
Northing |
|
|
Elevation |
|
|
TEI |
|
|
(Northing-3948504) * (Northing-3948504) |
|
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(Elevation-1550.4) * (Elevation-1555.4) |
|
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(TEI-47.58) * (TEI-47.58) |
|
|
(Elevation-1550.4) * (TEI-64.4693) |
|
|
(Northing-3948504) * (Elevation-1550.4) |
|
|
(Northing-3948504) * (TEI-64.4693) |
|
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(Northing-3948504) * (Elevation-1550.4) * (TEI-64.4693) |
|
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||
Parameter (NH versus RSFF) | Estimate | Std. error |
|
||
Intercept |
|
|
Northing |
|
|
Elevation |
|
|
TEI |
|
|
(Northing-3948504) * (Northing-3948504) |
|
|
(Elevation-1550.4) * (Elevation-1555.4) |
|
|
(TEI-47.58) * (TEI-47.58) |
|
|
(Elevation-1550.4) * (TEI-64.4693) |
|
|
(Northing-3948504) * (Elevation-1550.4) |
|
|
(Northing-3948504) * (TEI-64.4693) |
|
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(Northing-3948504) * (Elevation-1550.4) * (TEI-64.4693) |
|
|
*Indicates significance at the
Confusion matrix for our best approximating model (Elevation + TEI) showing predicted versus observed forest types for sample points in areas above 1219.2.2 m in elevation of western North Carolina, eastern Tennessee, and southwest Virginia which composed our study area (
Forest types |
|
||||
---|---|---|---|---|---|
Northern hardwood | Northern red oak | Spruce-fir | Total | ||
|
Northern hardwood | 143 | 22 | 14 | 179 |
Northern red oak | 39 | 41 | 0 | 80 | |
Spruce-fir | 34 | 1 | 38 | 73 | |
Total |
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|
|
|
Predictive map for the study area created using the elevation + TEI model showing predicted northern hardwood forest type that was surveyed in June 2012–January 2013. Areas are numbered to correspond with map in Figure
Our use of GIS to delineate northern hardwood forests in the southern Appalachians gives managers in the region a tool to better understand the distribution and composition of high-elevation forests important for the management and recovery of CNFS as well as other species restricted to high elevation forests. Previous studies have indicated that using digital terrain analysis within a GIS, most physiographic regions can be modeled with some degree of accuracy in the southern Appalachians [
Our best model, Elevation + TEI, supports the current assertion that elevation plays a key role, as does the level of “shelteredness” of a point for determining the presence of the northern hardwood forest type [
Our results also show that there was significant overlap in the range of several of the terrain variables we examined between the northern hardwood forest type and the adjacent red spruce-Fraser fir, high elevation northern red oak, and montane pine community. This can be attributed to the northern hardwood forest type having a higher mean elevation than the northern red oak forest type regionally but a lower mean elevation than the red spruce-Fraser fir forest type (Table
The variance in significance among predictors between the models on the overall study area also shows the complexity of the effects of latitude, terrain, site quality, and disturbance patterns on stand composition in the southern Appalachians. Terrain, site quality, and disturbance patterns are directly related to stand development regionally. However, past exploitative logging [
The acreage of the northern hardwood forest type in the southern Appalachians is likely to increase as this forest type continues to reoccupy areas previously occupied by oak communities. Fire suppression that began in the 1940’s has allowed for conditions to favor mesophytic species commonly associated with the northern hardwood forest type such as American beech, yellow birch, and maples to increase in abundance in the advance regeneration pool [
Conversely, both climate change and atmospheric deposition continue to threaten the red spruce-Fraser fir forests of the southern Appalachians likely resulting in the northern hardwood forest type expanding as temperature and environmental conditions shift [
These models could have significant implications in management practices when used in combination with known areas of red spruce and Fraser fir. A more specific habitat management and conservation plan could be developed for CNFS with available resources channeled to a more focused area. It also increases the likelihood that this species may occupy a larger portion of the landscape than previously suspected and raises the possibility of the existence of a larger population of this species and that recovery efforts may be more successful than previously thought. However with the current threats to the high elevation ecosystems, it will be crucial to continue to gather onsite data for much of this area. Further analysis and inclusion of additional variables would be needed to increase the predictive power of the models examined due to the influence of the variability of points occupied by the northern hardwood forest type. Using a combination of existing forestry data and land cover data from satellite imagery analysis, our predictive map of northern hardwoods could be used for a second step of pruning our incorrect classifications. As such, the combination of predicted northern hardwood distribution and existing land cover data will make it possible for managers to identify and put into practice management policies that will help conserve and improve areas such as recovery areas for CNFS as well as other stewardship goals.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Funding for this study was provided by the US Geological Survey, Cooperative Research Unit Program, the Eastern band of Cherokee Indians, and the Department of Geography at Virginia Tech’s Sidman Poole Foundation Trust. Additional logistic support was provided by the US Forest Service Southern Research Station, Bent Creek Experimental Forest. The authors thank M. Duncan for field assistance and H. McNab for assistance with study design and field methodologies.