Ecological Zones in the Southern 

Appalachians: First Approximation

Steve A. Simon, Thomas K. Collins,

Gary L. Kauffman, W. Henry McNab, and

Christopher J. Ulrey

United States

Department of

Agriculture

Forest Service

Southern

Research Station

Research Paper

SRS–41
Steven A. Simon, Ecologist, USDA Forest Service, National Forests in 

North Carolina, Asheville, NC 28802; Thomas K. Collins, Geologist, 

USDA Forest Service, George Washington and Jefferson National Forests, 

Roanoke, VA 24019; Gary L. Kauffman, Botanist, USDA Forest Service,

National Forests in North Carolina, Asheville, NC 28802; W. Henry McNab, 

Research Forester, USDA Forest Service, Southern Research Station, 

Asheville, NC 28806; and Christopher J. Ulrey, Vegetation Specialist,

U.S. Department of the Interior, National Park Service, Blue Ridge Parkway, 

Asheville, NC 28805. 

The Authors

December 2005

Southern Research Station

P.O. Box 2680

Asheville, NC 28802

Ecological zones, regions of similar physical conditions and biological potential, are numerous and varied 

in the Southern Appalachian Mountains and are often typified by plant associations like the red spruce, 

Fraser fir, and northern hardwoods association found on the slopes of Mt. Mitchell (upper photo) and 

characteristic of high-elevation ecosystems in the region.

Sites within ecological zones may be characterized by geologic formation, landform, aspect, and other 

physical variables that combine to form environments of varying temperature, moisture, and fertility, which 

are suitable to support characteristic species and forests, such as this Blue Ridge Parkway forest dominated 

by chestnut oak and pitch pine with an evergreen understory of mountain laurel (lower photo). 

Cover Photos

DISCLAIMER

The use of trade or firm names in this publication is for reader information and does not imply endorsement 

of any product or service by the U.S. Department of Agriculture or other organizations represented here.
i

Ecological Zones in the 

Southern Appalachians:

First Approximation

Steven A. Simon, Thomas K. Collins, Gary L. Kauffman, 

W. Henry McNab, and Christopher J. Ulrey
ii
iii

Contents

Page

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

 Field Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

 Classification of Plant Communities for Ecological Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

 Classification of Geologic Formations for Fertility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

 Vegetation and Environment Relationships . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

 Database Creation and Model Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

 High-Elevation Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

 Low-Elevation Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Summary of Model Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Mapped Ecological Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Preliminary Validation of the Ecological Zone Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Literature Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Appendix C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Appendix D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Appendix E . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
iv
1

Ecological Zones in the Southern

Appalachians: First Approximation

Steven A. Simon, Thomas K. Collins, Gary L. Kauffman,

W. Henry McNab, and Christopher J. Ulrey

Abstract

Forest environments of the Southern Appalachian Mountains and their 

characteristic plant communities are among the most varied in the Eastern 

United States. Considerable data are available on the distribution of plant

communities relative to temperature and moisture regimes, but not much 

information on fertility as an environmental influence has been published; 

nor has anyone presented a map of the major, broad-scale ecosystems of the

region, which could be used for planning and management of biological 

resources on forestlands. Our objectives were to identify predominant 

ecological units, develop a grouping of geologic formations related to 

site fertility, and model and map ecological zones of the Southern Appalachians. We synthesized 11 ecological units from an earlier analysis and 

classification of vegetation, which used an extensive database of over 

2,000 permanent, 0.10-ha, intensively sampled plots. Eight lithologic 

groups were identified by rock mineral composition that upon weathering 

would result in soils of low or high availability of base cations. The presence or absence of ecological zones (large areas of similar environmental 

conditions consisting of temperature, moisture, and fertility, which are 

manifested by characteristic vegetative communities) were modeled as 

multivariate logistic functions of climatic, topographic, and geologic variables. Accuracy of ecozone models ranged from 69- to 95-percent correct 

classification of sample plots; accuracy of most models was > 80 percent. 

The most important model variables were elevation, precipitation amount, 

and lithologic group. A regional map of ecological zones was developed 

by using a geographic information system to apply the models to a 30-m 

digital elevation dataset. Overall map accuracy was refined by adjusting the 

best probability cut levels of the logistic models based on expert knowledge and familiarity of the authors with known ecological zone boundaries 

throughout the study area. Preliminary field validation of an uncommon 

fertility-dependent ecological zone (Rich Cove) indicated a moderate, but 

acceptable level of accuracy. Results of this project suggest that bedrock 

geology is an important factor affecting the distribution of vegetation. The 

developed map is a realistic depiction of ecological zones that can be used 

by resource managers for purposes ranging from broad-scale assessment to 

local-scale project planning. 

Keywords: Classification, ecosystems, fertility, geologic formations, 

logistic regression, moisture, multivariate analysis, ordination, temperature.

Introduction

The Appalachian belt of mountain ranges, which extends 

from Alabama to Labrador, is among the oldest and most 

weathered in Eastern North America. The Southern Appalachian portion, extending from northeast Georgia to central 

Virginia, is a relatively narrow [10 to 100 km (6 to 60 miles)

wide] region of forested, broadly rounded mountain peaks 

separated by wide U-shaped valleys (fig. 1). Altitudinal 

climatic zonation, complex topography, and a humid, 

temperate climate form some of the most diverse natural 

environments in the Eastern United States (Braun 1950, 

Pittillo and others 1998, Schafale and Weakley 1990). Its

varied climate, geology, and soils provide a range of habitats 

suitable for approximately 2,250 species of vascular plants 

(Southern Appalachian Man and the Biosphere 1996). About 

70 percent of this region is forested and 12 percent is in 

Federal ownership as national forests and parks (Southern 

Appalachian Man and the Biosphere 1996). Public lands, 

particularly national forests, long have been managed for

multiple uses, but timber production traditionally has been

emphasized to meet local and regional economic needs. However, the economies of many communities have changed to 

meet increased demands for services from growing urban 

populations and visitors who view the forested landscape 

as more valuable for biological conservation and recreation 

than for timber production. Accordingly, U.S. Department 

of Agriculture Forest Service (Forest Service) policy has 

evolved toward ecosystem management, which requires 

consideration of physical, biological, and cultural components of forested sites and landscapes (Rauscher 1999). 

To assist managers and planners in implementing ecosystem 

management policies, a hierarchical framework of ecological units has been developed (Cleland and others 1997), 

maps of large regional ecosystems (ecoregions) in the 

United States have been delineated (Bailey and others 1994, 

Keys and others 1995), and generalized vegetation of those 

ecosystems has been described (Bailey 1995, McNab and 

Avers 1994). Hierarchical ecological delineations attempt to 

integrate successively smaller, homogeneous combinations 

of climatic, geologic, and biological components, which 

determine the overall biotic potential of an area (Kimmins 

1987). Mapping of ecological units has been done mostly at 

broad national and regional scales using expert knowledge, 

subjective stratification of ecoregions, and qualitative integration of important environmental features (Host and others 

1996). However, identification of units at a landscape scale 

is necessary for project planning (Cleland and others 1997). 

Logically, delineation of small ecosystem units should be 

based on field data that allow quantitative grouping of sites 
2

where temperature, moisture, and fertility attributes form 

environments with similar ecological characteristics. Such 

units could be expected to respond predictably to natural 

disturbance or management activities.

Different Southern Appalachian environments and patterns 

of distinctive vegetation long have been described, but early 

investigations were largely subjective and descriptive (Cain 

1931, Harshberger 1903). However, Davis (1930) did report 

major vegetative associations in the Black Mountains using 

Livingston atmometers to quantify evaporation (McLeod 

1988, p. 150). Later studies were more objective, describing 

the relationship of vegetation to environment using field plot 

data (Whittaker 1956). More recently, multivariate methods 

of classification and ordination have been used to describe 

the mathematical relationships of vegetation and environment (DeLapp 1978, Golden 1974, McLeod 1988, McNab

and others 1999, Patterson 19941). Although many intensive

ecological investigations have been conducted in the Southern

Appalachians, most have used a restricted scope of study, 

such as being confined to a portion of a mountain range 

(McLeod 19881), a watershed (Newell and Peet 19982), or a 

particular vegetation type (DeLapp 1978, White and others 

1984, Wiser and others 1998). One exception was the work 

of Newell and others (1999), in which data from five widely 

separated locations in the Southern Appalachians were combined in a meta-analysis to examine environmental factors 

influencing the regional distribution of vegetative communities. Most small-scale studies concluded that vegetative

community composition primarily was influenced by temperature regimes, then by moisture availability; the large-scale 

study of Newell and others (1999) reported that soil nutrient 

levels are also an important factor affecting the distribution 

of vegetation across a landscape. 

The relatively narrow geographic or ecologic scope of many 

studies fails to consider broader regional questions, such as 

ecosystem distribution and species interactions, which may 

be important when evaluating species rangewide viability 

and when trying to achieve consistency in ecosystem 

Figure 1—Typical low-elevation forested landscape of the Southern Appalachian Mountains south of Asheville in the Pisgah 

National Forest where evergreen shrubs along ridges form a recurring pattern of vegetation associated with landform. The Blue 

Ridge Mountains on the horizon define the escarpment leading down to the Appalachian Piedmont.

1 Ulrey, C.J.; McLeod, D.E. 1992. Preliminary summary of the biodiversity 

study of the vegetation in the Craggy Mountains, Pisgah National Forest, 

Toecane District, North Carolina. 13 p. Unpublished report. On file with: 

U.S. Department of the Interior, Blue Ridge Parkway, 199 Hemphill Knob 

Road, Asheville, NC 28803.

2 U.S. Department of Agriculture Forest Service. Ecological classification, 

mapping, and inventory for the Chattooga River watershed. 500+ p. 

Unpublished draft report. On file with: USDA Forest Service, National 

Forests in North Carolina, P.O. Box 2750, Asheville, NC 28802.
3

management (Host and others 1996). The differing objectives, methods, data collection, and analyses among studies 

do not allow pooling results for a larger, meta-analysis of 

region-wide datasets or objective development of a regional 

map of ecosystems. 

Ecosystems in the Southern Appalachians have been subjectively delineated through successive stratification of regionalscale map units using a hierarchical framework (Keys and

others 1995). Boundaries of these broadly delineated ecosystems lack detail necessary for resource management purposes

other than planning and assessment. Ecological units derived 

through analysis of field data would provide a means of 

refining boundaries of the large units and perhaps allow derivation of smaller units that nest within the hierarchy. 

A subregional, hierarchical vegetation classification developed by Ulrey3

 could provide a basis for stratifying the 

Southern Appalachians on an ecological basis. That classification identifies units of compositionally similar vegetation 

for the purpose of inventory and management. Ulrey3 wrote 

that “Ideally, these compositionally similar vegetation units 

will also be environmentally similar as well, but this report 

does not address this issue.” The classification was made 

using 18 datasets compiled from over 2,000 sample plots, 

which had been installed to determine species composition 

and abundance, and associated environmental attributes. 

Numerical classification and ordination analyses resulted in 

tentative identification of a hierarchy of vegetation units 

consisting of 3 major vegetation groups, 13 ecological 

groups, and 35 ecological subgroups. Use of this classification system for regional ecological stratification is possible 

because easily quantified topographic variables, i.e., elevation and landform, are correlated with two primary environmental factors (temperature and moisture), but similar 

variables are not available for fertility. Subsequently, Newell 

and others (1999) and Ulrey (2002) reported that soil chemical properties were associated with fertility. However, soil 

maps generally do not provide a means of application of 

those findings because soil taxonomic units are based more 

heavily on physical features of the soil profile than on chemical properties. As an alternative to soil maps, Robinson4

suggested that mapped bedrock formations could be used to 

account for the variation in availability of soil cations that 

typically are associated with soil fertility. 

Geology of the Southern Appalachians has been studied 

extensively in an effort to explain the origin, arrangement, 

and current structure of various bedrock formations (Hack 

1982, Hatcher 1988, King and others 1968). Formation 

types are diverse and range from old, highly metamorphosed 

Precambrian Blowing Rock gneiss in the Grandfather Mountain window to younger, relatively little-changed Devonian 

quartz diorite of Whiteside Mountain granite (North Carolina

Geological Survey 1985). Few studies, however, have included

rock units as an ecological component that potentially affects

vegetation composition and distribution. Zobel (1969) found 

that the occurrence of Table Mountain pine (see appendix E 

for scientific names of species) appeared to be associated

more with the physical features of landforms formed by 

weathering of certain geologic formations, than with the 

chemical composition of the rocks. Working in the Pilot 

Mountains of North Carolina, Rohrer (1983) reported that 

vegetation types were related to rock type. In the mountains 

of northeast Georgia, Graves and Monk (1985) found flora 

differed significantly on adjacent gneiss and limestone rock 

types. In a regional study of Southern Appalachian vegetation present on rock outcrops, Wiser and others (1996) found 

that soil nutrients were associated with the underlying rock 

chemistry, and they explained significant variation in the 

species composition of herbaceous and shrub communities. 

In comparison with moisture and temperature-related environmental components, relatively little current information 

allows grouping of rock types for ecological applications, 

such as Whiteside’s (1953) matrix approach for stratifying 

formations by texture and fertility soil properties.

Few ecological investigations have resulted in quantitative models for predicting the occurrence and distribution 

of ecoregions in the Southern Appalachian Mountains. 

McNab (1991) used multiple discriminant analysis to model 

the distribution of four forest types based on topographic 

variables in a small watershed. Fels (1994) used individual 

multiple regressions based on topographic variables to 

model distribution of 27 species and 5 communities in the 

Ellicott Rock Wilderness of northeastern Georgia. In an 

ecological classification of the Chattooga River, multiple 

discriminant analysis was used to model the landscape 

distribution of 17 environment-vegetation units (see footnote 2). Wiser and others (1998) found that multiple logistic 

regression performed well in predicting the occurrence of 

plant communities on rock outcrops. However, such analytical methods do not allow consideration of judgment or 

expert knowledge in the modeling process, which may help 

overcome limitations of imperfect mathematical models 

based on inadequate datasets (Mora and Iverson 2002).

3 Ulrey, C.J. 1999. Classification of the vegetation of the Southern 

Appalachians. Report to the USDA Forest Service, Asheville, NC. 88 p. 

Unpublished report. On file with: Southern Research Station, Bent Creek 

Experimental Forest, 1577 Brevard Road, Asheville, NC 28806. (Available 

on CD-ROM inside the back cover.)

4 Robinson, G.R., Jr. 1997. Portraying chemical properties of bedrock 

for water quality and ecosystem analysis: an approach for New England. 

U.S. Geological Survey Open-File Rep. 97–154. 11 p. On file with: U.S. 

Department of the Interior, U.S. Geological Survey, 903 National Center, 

Reston, VA 20192.
4

The overall purpose of our study was to investigate and 

quantify the composition and distribution of vegetation relative to environments in a portion of the Southern Appalachian Mountains. Our specific objectives were to: (1) adapt 

the vegetation classification developed by Ulrey (see footnote 3) to provide a framework of hypothesized ecological 

units, (2) devise a classification of geologic formations in 

relation to soil fertility, (3) develop mathematical relationships among vegetation groups and their associated environmental attributes to formulate ecological zones, and (4) 

devise a method of applying models of ecological zones 

with a geographic information system (GIS) that allows 

integration of expert knowledge. Our study primarily was an 

exploratory analysis; in it we observed vegetation composition and correlated environmental variables with minimal 

confirmation of results. Therefore, we do not provide coefficients of the prediction models that would allow users to 

develop customized maps of ecosystems. 

We provide definitions of several terms that are important 

in our study. The physical environment of a site inhabited 

by a plant community consists of the inorganic components 

associated with heat, water, and nutrients. Plant community is defined following Schafale and Weakley (1990): 

“a distinct and reoccurring assemblage of . . . plants . . . 

and their physical environment.” This definition of plant 

community is similar to that used in the national vegetation 

classification system (Grossman and others 1998): “Assemblages of [plant] species that co-occur in defined areas at 

certain times . . . .” Ecological zone is defined as a relatively 

large area of generally similar environmental conditions 

of temperature, moisture, fertility, and disturbance. One or 

more types of disturbance, e.g., ice, wind, drought, and fire, 

are typically associated with ecological zones (White 1979); 

but the scope of our study did not allow investigation of this 

ecosystem component. Supplemental information on autecological relationships, which was the basis of our study on 

the distribution of plant species along environmental gradients, can be obtained from forest ecology texts by Kimmins 

(1987), Spurr and Barnes (1973), and other authors.

Methods

Study Area

The study area consists mainly of the mountainous region 

of western North Carolina, an area of about 2.2 million ha 

(5.6 million acres) that extends in a southwest-northeast 

direction from latitude 35° (near Murphy) to 36.5° (near 

Jefferson) and from longitude 81° to 84° (fig. 2). It ranges in 

width from about 80 km (50 miles) in the north to about 160 

km (100 miles) in the south. Its boundary follows the crests 

of several mountain ranges on the west side, and in the east 

grades into the hilly terrain of the Appalachian Piedmont. 

It also includes small areas of the Great Smoky Mountains 

National Park in eastern Tennessee, and the Chattooga 

River Basin in northeastern Georgia and northwestern South 

Carolina. Geologists refer to this region as the southern 

Blue Ridge Mountains (Hack 1982). Braun (1950) includes 

the study area in a larger region she called the Southern 

Appalachians, which extends from Roanoke Gap, VA, to 

Dalton, GA. Small-scale ecoregion mapping by the Forest 

Service places this area in three units: (1) central Blue Ridge 

Mountains, (2) southern Blue Ridge Mountains, and (3) 

metasedimentary mountains subsections of the Blue Ridge 

Mountains section (Keys and others 1995). 

The region’s climate is characterized as modified continental, with warm summers and cool winters. Mean annual 

temperature varies only slightly from north to south, ranging 

from 10.8 °C (51.4 °F) at Jefferson [844 m (2,777 feet) 

elevation; 36°25′ N., 81°26′ W.] to 13.2 °C (55.8 °F) at 

Murphy [500 m (1,645 feet) elevation; 35°07′ N., 84°00′ W.].

Precipitation and temperature generally increase from north 

to south (fig. 3). Within the study area, recorded precipitation ranges from a low of 96.5 cm (38 inches) at Asheville 

[683 m (2,247 feet) elevation] to 231 cm (91 inches) at Lake 

Toxaway [933 m (3,060 feet) elevation] (fig. 4). These two 

locations are only about 64 km (40 miles) apart, but precipitation is strongly influenced by prominent topographic 

features of the Asheville Basin and the Blue Ridge Escarpment. A conspicuous large area of particularly high interpolated precipitation occurs west of Brevard along the crest of 

the Balsam Mountains. Most summer precipitation results 

from thunderstorms associated with maritime weather 

patterns that are influenced by the Gulf of Mexico; winter 

precipitation results from continental weather systems. 

Generally, precipitation is evenly distributed during the year 

with no pronounced dry or wet seasons, although winter 

precipitation tends to be considerably higher in the southern 

part of the study area. 

Relief of the study area is characterized by discrete ranges 

of relatively high mountains with rounded peaks that are 

separated by broad, somewhat hilly intermountain basins 

(fig. 5). Elevation ranges from 500 m (1,640 feet) at Murphy 

to 2038 m (6,684 feet) at Mt. Mitchell, the highest point in 

the Eastern United States. Relief is steep throughout much 

of the study area, averaging more than 50 m (165 feet) in a 

6-km2 (2.3-square mile) area (Hack 1982). Landscape-scale 

landforms of mountain ranges comprise a recurring pattern 

of secondary and tertiary ridges separated by narrow valleys 

that usually contain perennial streams. Large floodplains 

are restricted to low-gradient rivers and large streams of the 

intermountain basins. The varied gently rounded relief of 
5

South Carolina

Georgia

Tennessee

Virginia

North Carolina

Murphy

Jefferson

Asheville

City

SAVD plot

Ginseng plot

NC counties

Modeled area

0 20 40 60 80 km

N

S

W E

Figure 2—Location of sample plots in the Southern Appalachian vegetation dataset (SAVD).

74 72 72

74 76 78

74

68

72 68

70 72

76

74

78

190

190

170

190

150

190

150 170 190

25

25

25

25

30

30 35 40 40 55 50

50

55

60

55

45

60

50

45

65 80 70

(A) (B)

(C) (D)

45

45 50

40

Figure 3—Temperature and precipitation variation in the study area: (A) average July temperature (° F), (B) average 

number of days without killing frost, (C) average annual precipitation (inches), (D) average warm-season precipitation 

(inches). [Adapted from U.S. Department of Agriculture (1941)].
6

the study area is primarily attributable to a combination of 

warm, humid climate and geologic formations of differing 

resistance to erosion, which has been occurring for about 

300 million years during a relatively long period of geologic 

stability with no mountain-building episodes (Hack 1982, 

Pittillo and others 1998). 

Geologic formations of the study area are among the oldest, 

most complexly arranged, and compositionally varied in the 

Eastern United States. Most have undergone one or more 

periods of metamorphosis, during which the original rocks 

were weathered and eroded into components that were 

transformed to other rock types by varying degrees of heat 

and pressure, making accurate age determination doubtful 

(Hatcher 1972). Generally, formations of the Blue Ridge 

Province are primarily metasedimentary types with lesser 

areas of sedimentary and intrusive rocks. They are arranged 

in six relatively distinctive northeast-southwest trending 

belts of varying width, extent, and age (fig. 6) (North Carolina Geological Survey 1991). From east to west, the first 

belt, in the southeastern part of the study area bordering 

the Appalachian Piedmont, consists of intrusive rocks of 

uneven-grained monzonitic to granodiorite gneiss with large, 

exposed outcrops of moderately to weakly foliated granites. 

Next to the west, the narrow and highly linear Brevard fault 

zone is a relatively young, narrow belt of schist, marble, 

Month

Jan. Mar. May July Sept. Nov.

Precipitation (cm)

0

5

10

15

20

25

Temperature (oC)

0

5

10

15

20

25

Precipitation

Murphy

Jefferson

Asheville

Lake Toxaway Temperature

Murphy

Jefferson

Figure 4—Monthly normal (1961–90) precipitation and temperature in 

the northern (Jefferson) and southern (Murphy) parts of the study area 

and precipitation at stations of the lowest (Asheville) and highest (Lake 

Toxaway) annual amounts. 

 ≤ 600

601 – 900

901 – 1,200

 > 1,200

Elevation (m)

M221Dd M221Dc

M221Dd 231Aa

M221Dc 231Aa

Figure 5—Topographic relief of the study area overlaid with subregional ecological units (Keys and others 1995).
7

and phyllonite that marks the last major episode of geologic 

activity. The third belt, which is the largest and most extensive, consists of clastic gneiss, schist, metagraywacke, 

amphibole, and calc-silicate granofels. Occurring within this 

belt are scattered areas of intrusive quartz diorite to granodiorite formations. This belt is discontinuous and is separated about midway by a large area of varied rocks including 

metavolcanic types of the Grandfather Mountain window, 

gneiss basement rocks, and siltstones and shales. The fourth 

belt, also extensive, consists of felsic gneisses derived from 

sedimentary and igneous rocks that are variably interlayered with amphibolite, calc-silicate granofels, and rare 

marble. Occurring next, in the southwest mainly, are clastic 

metasedimentary, metavolcanic, and quartzite with slate, 

metasiltstone, metagraywacke, and calc-silicate granofels. 

Finally, bordering Georgia, the Murphy Belt is a small area 

of carbonate metasedimentary rocks that includes units of 

schist, phyllite, quartzite, marble, slate, and metasiltstone. 

Most geologic formations in the study area weather to form 

soils of acidic reaction. However, localized areas of hornblende gneiss are present throughout, which weathers to 

produce soils of less acidity. Rock formations range in age 

from middle Proterozoic (1 billion years) to Permian (250 

million years), but age is less important than rock mineral 

content and texture in determining soil properties that can 

influence plant species composition. 

Clastic and carbonate metasedimentary

Sedimentary and metamorphic rocks

Late Proterozoic to early Paleozoic age

Clastic metasedimentary and metavolcanic

Clastic metasedimentary rock, and mafic and felsic metavolcanic rock

Middle Proterozoic age

Felsic gneiss derived from sedimentary and igneous rocks in the

northern area, biotite gneiss in the southern area

Intrusive rocks

Metamorphosed granitic rocks

Late Proterozoic to middle Paleozoic age

Sedimentary

Late Proterozoic age

Metamorphosed gabbro and diorite

Figure 6—Generalized geologic formations of the study area (North Carolina Geological Survey 

1991).
8

although areas with high proportions of conifers occur 

throughout (fig. 7). Elevation strongly influences vegetation 

composition and may be grouped into three broad zonal 

bands of altitude: (1) low, < 671 m (2,200 feet); (2) middle, 

from 671 to 1372 m (2,200 to about 4,500 feet); and (3) 

high, over 1372 m (4,500 feet). Low-elevation ecosystems 

include many of the major intermountain basins, such as 

the Asheville Basin, where several hardwood species more 

typical of Piedmont forests occur, e.g., southern red oak, 

including a high proportion of yellow pines. A hardwoodpine mixture is prevalent in the southwest part of the study 

area near Murphy, NC, and along portions of the Blue Ridge 

Escarpment and several other areas, particularly where soils 

are derived from granitic formations. Floodplain forests are 

uncommon and generally are restricted to the low-elevation

intermountain basins, which also contain much of the 

human population and, consequently, are highly disturbed. 

Middle-elevation forests occur on moderate-to-steep mountain slopes. Xeric-to-submesic sites are dominated by five 

oak species, a midstory stratum of shade-tolerant trees, and 

often an understory of mainly evergreen (Ericaceae) shrubs. 

The overstory of valley and cove sites of middle elevations 

is dominated by mesic species, including yellow-poplar 

and occasionally northern red oak. In the high-elevation 

zone, northern red oak dominates warm slopes and ridges 

and nonoak deciduous species common to northern latitudes increase in importance on colder, north-facing slopes. 

Forests above about 1677 m (5,000 feet) become gradually 

dominated by red spruce and above 1830 m (6,000 feet) by 

Fraser fir. Except at the highest elevations, red maple occurs 

throughout. 

With few exceptions, the range of most vegetative species 

sampled extends throughout the study area. Stands of red 

spruce and Fraser fir generally are absent south of the 

Balsam Mountains (35°15′), which may be a result of the 

lack of high-elevation habitats. Bear huckleberry does not 

occur north of the Asheville Basin. Several herbaceous 

species, including common stonecrop and northern bush 

honeysuckle, are absent or rare in the southern part of the 

study area. 

Natural disturbance to forests in the study area occurs 

mainly from drought, ice storms, and occasionally wind 

from remnants of tropical hurricanes. Isolated, usually small 

areas [< 0.4 ha (< 1 acre)] of wind-thrown trees occur from 

downbursts associated with thunderstorms, mainly during 

the summer growing season. Natural fires are uncommon, 

but may occur from lightning strikes during early spring 

or late fall. Other minor sources of disturbance result from 

debris slides associated with steep, unstable geologic formations, and debris avalanches in streams caused by occasional episodes of high-intensity precipitation. Almost all 

Most soils of this region are classified as Ultisols (primarily 

Hapludults) or Inceptisols (mainly Dystrochrepts) (Pittillo 

and others 1998). Entisols are uncommon and seem to be 

found only in sandy, new alluvium of larger streams and 

rivers, and in colluvium of recent landslides. Hapludults 

generally are formed in stable parent material on gentle-tomoderate slopes and typically have little clay (< 15 percent)

in their A horizon, but have high accumulation in their B 

horizon. Productivity of most Hapludults is low due to 

a combination of low base saturation (< 35 percent) and 

organic matter content, high acidity, and clayey subsoils 

on convex land surfaces that can dry quickly during the 

growing season with lack of precipitation and high-evapotranspiration rates. Dystrochrepts typically are present on 

steep slopes, or in colluvium, and have a loamy texture 

(average of 20 percent clay, 30 percent silt, and 50 percent 

sand) throughout their profiles. Productivity is moderate 

for these soils due to generally higher moisture and organic 

matter contents. Alluvial soils are typically Inceptisols 

and vary in productivity depending mainly on texture and 

organic matter content. The temperature regime of soils on 

landscapes below about 1372 m (4,500 feet) is classified as 

mesic; above that elevation soils are generally frigid. The 

moisture regime of upland soils is classified as udic, indicating that plant growth is not limited by lack of moisture 

during most years. Most soils are deep [> 100 cm (> 40 

inches)]. Soil mapping units in the mountainous terrain of 

the study area are highly correlated with altitude, geologic 

substrate, and topography (Pittillo and others 1998). 

Soil pH influences species composition in the Black Mountains and Craggy Mountains of the Southern Appalachians 

by affecting fertility, e.g. nutrient availability (McLeod 

1988). Most upland soils are strongly acid (pH 4.5 to 5.5) 

and low in fertility, except where the parent material consists 

of carbonate or mafic rock formations. Mafic formations 

contain greater amounts of basic minerals, e.g., horneblende 

gneiss, which can form soils with higher pH and greater 

availability of nutrients. Higher fertility levels also can result 

from nutrient enriched subsurface flow of water from upper 

slopes to lower slopes (Pittillo and others 1998). Newell and 

others (1999) found that soil fertility regimes based on levels 

of manganese, instead of other conventional measures, were 

an important environmental component explaining the distribution of forest community classes in a large regional study 

of vegetation.

About 2,250 species of vascular plants occur in the 

Southern Appalachians (Southern Appalachian Man and the 

Biosphere 1996). Of the 140 tree species, most are deciduous hardwoods; only 10 are conifers. Several dozen shrubs 

are present. Forest cover type is predominantly oak-hickory, 
9

forests within the study area were logged during the late 

1800s and early 1900s, and only small areas of old-growth 

forests remain, primarily on inaccessible, steep areas. 

Among the most devastating disturbances to forests of this 

region was introduction of the chestnut blight (Cryphonectria parasitica) during the early 1900s, which caused 

almost complete mortality of American chestnut, a species 

that dominated mountain slopes in the mid-elevation zone. 

Other serious exotic diseases and insects include dogwood 

anthracnose (Discula destructiva) and balsam woolly 

adelgid (Adelges piceae). 

Field Data

Much of the vegetation data originated from the North

Carolina Vegetation Survey (Peet and others 1998). Field data

were obtained also from 20 investigations of vascular vegetation that had been conducted in the Southern Appalachian 

Mountains between 1976 and 1999 (table 1). Vegetation had 

been sampled throughout the entire study area, although 

sampling was clustered in about 10 locations. Several 

conspicuous areas in the region not sampled intensively 

include the low-elevation intermountain basins (highly 

disturbed by anthropogenic activities); the extreme southwest portion near Murphy (a low-elevation area of somewhat droughty soils derived from shaly, metasedimentary 

rocks); and moderate-to-high elevation sites on mountains 

along the North Carolina and Tennessee boundary. In the 

southern part of the study area, on the Nantahala National 

Forest, additional plots were installed where American 

ginseng was known to occur. Data from various studies 

were standardized by taxonomic nomenclature to account 

for variation in season of field sampling and apparent 

errors in species identification. Botanical nomenclature is 

derived from Weakley5 where updates of the taxa have been 

completed, or from Kartesz (1999) for all remaining cases.

Natural stands generally > 75 years of age and not obviously recently disturbed were subjectively and randomly 

selected to represent uniform site conditions, e.g., similar 

aspect, landform, and species composition. Sampling methodologies of recent studies (after 1990) followed the North 

Carolina Vegetation Survey (Peet and others 1998); earlier 

studies used field methods of either Whittaker (1956) or 

Braun-Blanquet (1932). Field plot size was usually 0.1 ha 

(20 m by 50 m). In most plots ground area covered by each 

species was estimated first in 10-m by 10-m subplots using a 

Oak-hickory

Virginia-shortleaf pine

Hemlock-white pine

Hardwood-pine mixture

Spruce-fir

Maple-beech-birch

Forest cover type

Figure 7—Generalized current forest cover types of the study area (North Carolina Forest Service 

1955).

5 Weakley, A.S. 2000. Flora of the Carolinas and Virginia. Unpublished 

draft. 500+ p. On file with: The University of North Carolina Herbarium, 

CB3280, Coker Hall, University of North Carolina, Chapel Hill, NC 27599.
10

standard 10-class system, ranging from a trace to nearly 100 

percent, then combined to determine mean plot cover. Ulrey 

(see footnote 3) provided additional information on the individual vegetative datasets.

Nonvegetative field data included only location of the field 

plot. Plot locations had been determined in the field using 

7.5-minute scale topographic maps or geographic positioning system, which resulted in confidences of plot location of moderate or high, respectively. Although topographic 

data, e.g., elevation, aspect, and gradient, had been collected 

at each plot, these variables were determined from digital 

elevation models (DEMs) at the plot location because the 

derived models would be applied by GIS (Fels 1994). Soil 

nutrient data had been collected from a number of plots, but 

it could not be used in the analysis because lack of soil maps 

over much of the study area precluded application of prediction models. Sample plots were omitted from the analysis if 

careful examination of the data suggested they were outliers, 

which could have resulted, for example, from an erroneous 

plot location obtained from a topographic map.

Classification of Plant Communities for

Ecological Zones

Eleven hypothesized ecological zones (table 2) were synthesized from 19 Southern Appalachian upland forest communities identified by Ulrey (see footnote 3) (appendix A). 

An overview of the classification methods and results are 

presented in appendix B. Using the classification scheme, 

individual plots within the Southern Appalachian vegetation 

dataset and the two supplementary datasets were objectively 

placed into a modified classification scheme of ecological 

zones based upon the experience and knowledge of the 

authors. The classification hierarchy is relatively coarse to 

aide in recognizing units in the field. The field plots were 

classified into groups of similar species composition using 

a sequence of constancy and ordered tables, indicator 

species analysis, followed by quantitative multivariate 

methods that included cluster analysis and indirect ordination. The goal of the classification was to identify units of 

compositionally similar vegetation for the purpose of inventory and assessment. 

Table 1—Characteristics of the Southern Appalachian vegetation dataset

Identification Taxonomic Plot location

number General locationa Plots Species resolution confidence

 - - - - number - - - - 

05 Grandfather-Roan Mountains 74 495 High High

07 Thompson River watershed 150 312 Moderate Moderate

08 High-elevation red oak 61 227 Moderate Moderate

09 Black and Craggy Mountains 156 370 Moderate Moderate

10 Linville Gorge Wilderness area 181 403 High Moderate

11 Shining Rock Wilderness area 160 433 High Moderate

12 Kilmer-Slickrock Wilderness area 185 425 High Moderate

13 Ellicott Rock Wilderness area 57 387 High Moderate

18 Cedar hardwood woodlands 20 322 High Moderate to high

20 Nantahala Mountains 91 724 High High

21 Kelsey tract 18 146 High Moderate

23 Chattooga Basin (intensive plots) 20 475b

 Moderate High

23 Chattooga Basin (survey plots) 532 475b Moderate High

37 Steels Creek watershed 48 178 Moderate High

38 Craggy Mountains 29 260 Moderate High

39 Great Smoky Mountains-uplands 172 450 Moderate Moderate

40 Great Smoky Mountains-Tennessee 

 and North Carolina 190 475 High High

22 Highlands, NC, area 92 875 High High

35 Chimney Rock and Hot Springs, NC 74 784 High High

a Data from two studies (Wine Spring Creek in Macon County and a study of ginseng occurrence) were included in some models.

b Total number of species for both types of plots.
11

Classification of Geologic Formations for Fertility

Based largely on expert knowledge, a classification of 

geologic formations for fertility was developed that included 

eight primary lithologic groups (table 3). Group membership 

was based on rock characteristics that would produce soils 

of likely differing nutrient availability and water-holding 

capacity.6 Rock characteristics considered in the classification included chemical composition, amount of potentially 

exchangeable base minerals, and texture. These formations 

were classified into fertility groups based on the major 

group and compositions of the primary and secondary rocks 

(appendix C). Lithologic group 1, for example, consisted 

of 47 major rock groups but only 35 unique geologic map 

units. The primary source for rock formation locations and 

descriptions was the geologic map of North Carolina (North 

Carolina Geological Survey 1985). Other sources included 

occasional 1:24,000 and 1:100,000 geologic maps; which 

were available for the Chattooga River watershed in northeast Georgia. Most rock groups occur as relatively large 

geographical areas, except for lithologic group 8, which 

tends to occur as small localized mineral bodies7 ranging in 

area from 0.01 ha to about 1000 ha (0.03 acre to about 2,500 

acres) (Stucky and Conrad 1958). 

This classification is a first approximation and is based on

recent classifications of bedrock formations for environmental

Table 2—Linkages among vegetation-based classification units of the upland forests’ major group 

(appendix A) and hypothesized ecological zones that define areas of similar environments

Ecological group Ecological subgroupa Ecological zone

Spruce and fir forest Fir forest Spruce-Fir

Spruce forest Spruce-Fir

Successional vegetation forest Spruce-Fir

Northern hardwood forest Yellow birch-spruce forest Spruce-Fir

Northern hardwood forest Beech gap and slope forest Northern Hardwood

Northern hardwood forest Northern Hardwood

Boulder field forest Northern Hardwood

Northern hardwood forest High-elevation red oak forest High-Elevation Red Oak

Acid mesic forest Acidic cove forest Acidic Coveb

Hemlock forest Acidic Cove

Rich mesic forest Rich cove forest Rich Cove

Dry-mesic forest Mesic montane oak-hickory forest Mesic Oak-Hickory

Dry-mesic forest Oak-hickory forest Dry and Dry-Mesic Oak-Hickory

Xeric forest Chestnut oak forest Chestnut Oak Heath

Xeric forest Shortleaf pine-oak forest Shortleaf Pine-Oak Heath

Xeric forest Table Mountain pine-pitch pine Xeric Pine-Oak Heath and Oak Heath

 forest

Xeric forest Subxeric oak-pine forest White Pine-Oak Heathc

a Excluded are two minor, uncommon subgroups—calcareous dry-mesic forests and Carolina hemlock forests.

b Excluded are calcareous dry-mesic forests.

c Excluded are Carolina hemlock forests.

6 Collins, T.K. Geo-fertility groups in the Southern Appalachians. 

Unpublished document. 2 p. with attachment. On file with: George 

Washington and Jefferson National Forests, 5162 Valleypointe Parkway, 

Roanoke, VA 24019–3050.

7 No field plots were located in lithologic group 8, which occurs rarely in 

the study area. 
12

or ecological analyses (Bricker and Rice 1989, McCartan 

and others 1998, Robinson and others 19998 9 10). It also 

recognizes the relationships between vegetation and 

physical characteristics of rock formations found important 

in previous studies in the Southern Appalachians, such as 

Graves and Monk (1985), Mansberg and Wentworth (1984), 

McLeod (1988), Pittillo and others (1998), and Rohrer 

(1983). Strahler (1978) used similar logic to stratify rock 

types of the Appalachian Piedmont in Maryland into six 

lithologic groups for purposes of studying the distribution of 

vegetation. In a study of vegetation on rock outcrops in the 

Southern Appalachians, Wiser and others (1996) grouped 13 

bedrock types into 3 generalized classes of minerals: mafic, 

felsic, or intermediate.

Vegetation and Environment Relationships 

Critical to our study was an appropriate method of model 

development for ecological classification—a subject that has 

long received considerable attention (Austin 1987, Cairns 

2001, Guisan and others 1999, Mora and Iverson 2002). 

Multiple discriminant analysis seems to be an obvious 

choice for classification because we had used it, apparently successfully, in previous studies [McNab and others 

1999, Odom and McNab 2000 (see footnote 2)]. We did not 

use discriminant analysis in this study, however, primarily 

because we doubted that the underlying assumptions of 

normality of independent variables were satisfied (Press 

and Wilson 1978). The question of normality was particularly relevant in this analysis, which included eight binary 

response variables associated with geologic formations. 

Other reasons for not using discriminant analysis included 

lack of ability to: (1) apply weights to spatially constrain the 

models when applied at landscape scales (Mora and Iverson 

2002), (2) select a subset of significant explanatory variables to achieve parsimonious models (Guisan and others 

1999), and (3) modify predictions of the models in certain 

parts of the study area where we had specific knowledge of 

vegetation-environmental relationships (Cairns 2001). Other 

methods of multivariate analysis are available for classification purposes, such as principal components regression 

(Host and others 1996) and logistic regression (Wiser and 

others 1998).

We selected logistic regression for developing models to 

predict the probability of occurrence of plant communities 

in differing environments. Logistic regression can use both 

categorical and continuous variables and has less stringent assumptions of normality of independent variables 

Table 3—Classification of Southern Appalachian geologic formations that relate to soil fertility

Lithologic Map Base 

group unitsa

 status Predominant bedrock composition 

1b 47 High Mafic formations, e.g. amphibolites

2b 5 High Carbonate formations, e.g. limestones

3 19 Low Formations with local zones of high mafic or high carbonate

4 43 Low Granitics formations

5 27 Low Sedimentary and metamorphic formations

6 47 Low Quartzose with low fines formations

7 14 Low Sulphidic formations

8 14 High Ultramafic formations

a Listed in appendix C.

b Lithologic groups 1 and 2 were combined for analysis because their fertility properties were similar and few map units were 

available in group 2, most of which were associated with the Brevard geologic fault (appendix C).

8 Peper, J.D.; Grosz, A.E.; Kress, T.H. [and others]. 1995. Acid deposition 

sensitivity map of the Southern Appalachian assessment area, Virginia, 

North Carolina, Tennessee, South Carolina, Georgia, and Alabama. U.S. 

Geological Survey On-Line Digital Data Ser. Open-File Rep. 95–810. On 

file with: U.S. Department of the Interior, U.S. Geological Survey, 903 

National Center, Reston, VA 20192. 1: 1,000,000 scale.

9 Peper, J.D.; McCartan, Lucy B.; Horton, J. Wright, Jr.; Reddy, James E. 

2001. Preliminary lithogeochemical map showing near-surface rock types 

in the Chesapeake Bay watershed, Virginia and Maryland. U.S. Geological 

Survey Open-File Rep. 01–187. On file with: U.S. Department of the 

Interior, U.S. Geological Survey, 903 National Center, Reston, VA 20192. 

1: 500,000 scale.

10 Robinson, G.R., Jr. 1997. Portraying chemical properties of bedrock for 

water quality and ecosystem analysis: an approach for New England. U.S. 

Geological Survey Open-File Rep. 97–154. On file with: U.S. Department 

of the Interior, U.S. Geological Survey, 903 National Center, Reston, VA 

20192. 11 p.
13

(Hosmer and Lemeshow 2000, Press and Wilson 1978). It 

is commonly used to examine the importance of multiple 

independent variables on a binary outcome (Hosmer and 

Lemeshow 2000) but also has been used for purposes of 

discrimination and classification (Press and Wilson 1978). 

Logistic regression occasionally has been used to predict 

the probability of occurrence of plant species in response to 

environmental variables (Austin 1987, Margules and Stein 

1989, McNab and Loftis 2002, ter Braak and Looman 1986, 

Wiser and others 1998) and the use of various forest habitats 

by wildlife (Odom and others 2001, van Manen and Pelton 

1997). We also considered polytomous logistic regression, which is useful in classifying three or more possible 

outcomes, e.g., vegetation communities, but dismissed it 

because interpretation of results is difficult with more than 

two groups (Hosmer and Lemeshow 2000).

We used ordinary multiple logistic regression to determine 

environmental variables associated with the presence or 

absence of the 11 communities at field sample plot locations. Both presence and absence data characterized environmental limits of occurrence. For example, if 85 of the 

approximately 2,500 plots were classified as spruce-fir 

composition in the vegetation analysis it was assumed that 

environmental conditions (including other unmeasured 

factors, such as previous disturbance) at those locations 

were suitable to support spruce-fir plant communities, but 

were unsuitable at 2,415 locations where these conditions 

(and therefore these communities) were absent. We used 

a stepwise analysis procedure to develop the most parsimonious estimated logit of the multiple logistic regression 

model given by the generalized equation:

where

Y = the binary coded (0, 1) dependent variable for each of 

the 11 communities 

 = the intercept

 = the coefficient of each independent variable

 = the value of each continuous independent variable 

(appendix D)

 = the binary value of each discrete independent variable 

(eight lithologic groups)

 = residual error

Our procedure was a modification of the forward selection method, where variables are added to models that 

meet a minimum level of statistical significance. Instead of 

continuing to stay in the model, however, with the addition 

of each new significant variable, each previously included 

variable is tested for threshold significance level and retention. We used a minimum significance level of P < 0.05 for 

retaining independent variables. The goal of our analysis 

was correct classification of sample plots into two categories: present or absent. We used BioMedical Data Processing 

statistical software for statistical analysis.11 Using methodology similar to Wiser and others (1998), we developed a 

“stand alone” model for each of the 11 communities, which 

approximated ecological zones because it established a relationship between vegetation and its associated environment.

Model accuracy was evaluated using several standard 

measures of logistic regression performance, which included 

classification tables, receiver operating characteristic 

(ROC) curves, and selection of probability cutpoints using 

sensitivity and specificity. Two-way classification tables 

allowed evaluation of the performance of each model by 

comparison of observed and classified observations at 

specific probability cutpoints. A cutpoint is the level of 

estimated probability selected for the binary classification 

of an observation that represents occurrence or nonoccurrence of a plant community. Incorrect classifications are 

displayed in the two-way table as false occurrence or false 

nonoccurrence. The initial classification cutpoint for each 

model was set at the greatest value of combined sensitivity 

and specificity. Sensitivity is a measure of accuracy for 

predicting an occurrence and specificity is a measure for 

predicting nonoccurrence. Because the rates of change in 

sensitivity and specificity may differ in some models, ROC 

curves provide a graphic means of assessing the accuracy of 

a logistic model. A ROC curve is a plot of sensitivity over 

1 minus specificity with values that range from zero to 1. A 

model with an area under the ROC curve > 0.7 is considered 

to have acceptable discrimination capability; models with 

ROC values > 0.8 are considered to be excellent (Hosmer 

and Lemeshow 2000). Our classification models are likely 

biased because an independent dataset was not used for 

evaluation. Jackknifing was considered as a means of unbiased model testing, but was rejected because our study was 

largely exploratory. Regression coefficients are omitted 

because the ecological zone models have not been tested and 

are considered preliminary.

Database Creation and Model Application 

Application of the environmental variable-based ecological 

zone models required development of a spatial database 

for the study area. Source data were acquired from U.S. 

11 BioMedical Data Processing. Los Angeles, CA. Release 7. Software 

initially developed by University of Southern California, but with limited 

commercially availability as of 2004.
14

Geological Survey 30-m resolution DEMs. Edge matching 

and smoothing procedures were applied to all DEMs using 

the ArcGrid12 GIS to produce a seamless grid of elevations 

for the entire study area. This elevation grid was processed 

using algorithms to produce estimates of derived terrain and 

environmental variables; e.g., aspect, slope gradient, slope 

length.

All vegetation plots were located using a global positioning 

system (GPS) or from 1:24,000-scale topographic map latitude and longitude coordinates. A GIS was used to assign 

each vegetative plot to the appropriate cell in the DEM. 

Environmental variables were determined for each plot by 

merging the location with the 30-m resolution digital elevation grids. In total, 25 grids were merged with each of the 25 

thematic GIS layers. A database was created that included 

the plot number, vegetation classification type, and four 

groups of environmental characterization variables: landscape, landform, site, and geographic. The two landscape 

variables included dormant-season and growing-season rainfall. Eleven landform variables included: (1) landform index, 

(2) weighted landform index, (3) landform shape 8, (4) 

landform shape16, (5) landform index surface interaction, 

(6) weighted landform index surface interaction, (7) length 

of slope, (8) slope position, (9) distance to bottom, (10) 

distance to intermittent stream, and (11) slope direction. Site 

variables included elevation, terrain shape index, surface 

curvature profile, surface curvature planiform, curvature, 

slope steepness, slope steepness and slope position interaction, and geologic fertility group. Four geographic variables included x coordinates, y coordinates, distance from 

Murphy, NC, and distance from the Blue Ridge Escarpment. 

The geographic variables were included in the analysis to 

account for other environmental variation not accounted for, 

such as temperature and evapotranspiration and the effect 

of past climates on current plant community distribution. 

A brief description of these components is presented in 

appendix D.

Each of the 11 logistic ecological zone models was applied 

to the DEMs representing environmental, geologic, and 

landform variables. The resulting 11 map layers represent 

the probability of occurrence, ranging from zero to 1, of 

each ecological zone in each 30-m (98-foot) cell of the 

DEM grid for the 5.6-million-acre study area. The initial 

cutpoint of each model allowed the matrix of probabilities 

predicted to be classified in two groups: presence of the 

ecological zone or absence of the ecological zone. Clusters 

of cells where the ecological zone was classified as present 

represent bands of probabilities, from the cutpoint (where 

we are fairly sure the ecological zone occurs) to near 1.0 

(where we are almost absolutely confident the ecological 

zone occurs). Typically, the centers of areas of highest probabilities were at sample plot locations, where environmental 

data were obtained to generate the ecological zone model. 

This spatial representation of ecological zones made it 

possible to evaluate their distribution based on model sensitivity and specificity. This process is similar to procedures 

used in wildlife habitat modeling using GIS (Clark and van 

Manen 1993, Star and Estes 1990, van Manen and Pelton 

1997). 

Mapping of ecological zones involved combining individual 

models to form a single GIS coverage and establishing a 

boundary in the transition area between adjacent ecological 

zones. The boundaries often are broad and usually support 

more than one community. Factors contributing to model 

errors, e.g., predicted co-occurrence of two or more ecological zones for the same site, were accuracy of the vegetation 

classification, sample size for model development, appropriate independent variables, robustness of the mathematical 

modeling algorithms, initial cutpoints of the classification 

matrix, whether values of the represented environmental 

variables occurred within the range sampled or required 

extrapolation, and other factors. Individual ecological zone 

models were developed independently of other models and 

varied in their predictive capability. 

We used the stacking order feature in ArcGrid to resolve 

classification conflicts in areas where multiple ecological 

zones were predicted. All ecological zones were arranged in 

vertical sequence from highest, on top of the stack, to lowest 

predictive power. Themes in ArcGrid at the top of the stack 

take precedence over those below, so in areas of overlap, 

the upper themes in descending order obstruct the view 

of those below. Using an iterative process, stacking order 

and probability cutpoints were adjusted until the pattern of 

ecological zones appeared reasonable. During this process 

approximately 10 ecological zone maps representing various 

parts of the study area were continuously viewed to evaluate 

the effect of stacking order, probability of occurrence, and 

reasonableness of ecological zone distribution. These areas 

represented the range of environmental conditions from 

lower to upper elevations, from escarpment to mountains, 

and from north to south of the Asheville Basin. Digital 

orthophotoquads were used to evaluate some of the more 

complex areas. A summary of the process used to develop 

the regional ecological zone map is shown in figure 8.

12 ArcGrid is a trademark and commercial product of Environmental 

Systems Research Institute Corporation and consists of a collection of cellbased spatial analysis tools.
15

Results

We identified 11 ecological zones in the Southern Appalachians of North Carolina (table 2). Two ecological zones, 

however, Spruce-Fir and Northern Hardwood, were subdivided into northern and southern districts for development 

of satisfactory models, which results in a total of 13 models. 

To reduce possible confusion, however, we will refer to the 

models collectively numbering 11, 1 for each ecological 

zone. The centrally located, generally east-west oriented 

Asheville Basin provided an arbitrary, but logical place to 

subdivide the study area into north and south districts for the 

Spruce-Fir and Northern Hardwoods ecological zones. 

Statistics associated with development of the models are 

presented in tables 4 and 5. Model performance indicated 

by classification accuracy at various logistic regression 

cutpoints is presented in tables 6 and 7. An example of the 

method used to select the optimum cutpoint is presented 

for the Spruce-Fir (south) model (table 8). The ROC used 

to evaluate the Spruce-Fir model is shown in figure 9. The 

area under the curve equals 0.95, which suggests the model 

has outstanding discrimination capability (Hosmer and 

Lemeshow 2000). The high ROC values of most logistic 

models suggest that plant communities described by Ulrey 

(see footnote 3), some of which were combined for this 

study, are associated with sites having unique environmental 

characteristics. 

For convenience and ease of recognition, ecological zones 

are named for their dominant plant community. The names 

are widely recognized in the literature, although ecological 

Figure 8—Outline of the methods used to develop the ecological zone map (GPS = global positioning system).

Table 4—Number of plots, classification accuracy, and fit statistics of logistic regression models for 

ecological zones in high-elevation environments

Spruce-Fir Northern Hardwood High-elevation

Item South North South North red oak

Plots present (no.) 59 26 71 33 137

Plots absent (no.) 384 118 884 287 1,138

Cutpoint (proportion) 0.46 0.63 0.14 0.19 0.22

Overall accuracy (percent) 93 92 84 81 85

Receiver operator

 characteristics (proportion) 0.95 0.95 0.84 0.85 0.81

1. Install plots; identify all

plants, (GPS field location).

2. Group plots by similar

vegetation; peer review

and refine groups.

3. Obtain physical site

data for each field plot

based on its GPS location.

4. Develop model for each

vegetation group based on

temperature, moisture, and

fertility attributes to form

ecozones.

Data base

of physical

attributes

for each

0.1-ha site.

5. Map each ecozone using

models based on physical

data for each 0.1-ha part of

study area.

6. Stack ecozone maps to

make a composite map and

refine boundaries using expert

knowledge.

7. Peer review and fieldtest

map to determine specific and

overall accuracy of ecozones.

8. Improve accuracy of some

models or identify a new

ecozone; install additional field

plots.

Devise

geologic -

fertility

groups 
16

Table 5—Number of plots, classification accuracy, and fit statistics of logistic regression models for ecological zones in 

low-elevation environments

 Dry and Xeric Pine-Oak Shortleaf

Acidic Rich Mesic Oak- Chestnut Dry-Mesic White Pine- Heath and Pine-Oak

Item Cove Cove Hickory Oak Heath Oak-Hickory Oak Heath Oak Heath Heath

Plots present (no.) 262 601 237 192 308 106 151 121

Plots absent (no.) 2,371 1,874 2,145 2,283 2,167 2,369 2,324 2,354

Cutpoint (proportion) 0.21 0.58 0.11 0.14 0.41 0.10 0.11 0.53

Overall accuracy

 (percent) 82 80 69 77 85 84 80 95

Receiver operator

 characteristics

 (proportion) 0.80 0.83 0.65 0.77 0.88 0.84 0.79 0.95

Table 6—Cutpoints and classification results (percent of plots predicted correctly as present or 

absent) of logistic regression models for ecological zones in high-elevation environments

Spruce-Fir Northern Hardwood High-elevation

Cut- South North South North red oak

point P A P A P A P A P A

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - -

0.1 92 85 92 78 66 83 91 73 75 72

0.2 86 91 85 87 27 95 42 85 52 89

0.3 80 94 77 90 13 97 39 94 29 95

0.4 70 96 69 97 6 99 0 97 13 98

0.5 61 97 65 98 0 99 0 99 8 99

0.6 41 97 65 98 0 99 0 100 2 100

0.7 34 98 57 100 0 100 0 100 0 100

0.8 25 100 42 100 0 100 0 100 0 100

0.9 17 100 34 100 0 100 0 100 0 100

P = present; A = absent.

Table 7—Cutpoints and classification results (percent of plots predicted correctly as present or absent) of logistic 

regression models for ecological zones in low-elevation environments

Dry- White Xeric Shortleaf

Mesic Chestnut Mesic Pine- Pine- PineAcidic Rich Oak- Oak Oak- Oak Oak Oak

Cove Cove Hickory Heath Hickory Heath Heath Heath Cutpoint P A P A P A P A P A P A P A P A

 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - percent - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

0.1 72 73 92 47 52 70 58 82 91 75 44 91 50 87 81 93

0.2 50 88 78 71 3 97 27 95 78 83 15 98 21 96 68 96

0.3 28 95 68 84 0 100 11 98 58 89 6 100 9 99 61 98

0.4 11 98 52 91 0 100 4 99 25 95 3 100 4 100 53 99

0.5 3 100 38 94 0 100 1 100 14 98 1 100 1 100 47 99

0.6 0 100 27 97 0 100 0 100 4 99 0 100 0 100 39 99

0.7 0 100 17 98 0 100 0 100 1 100 0 100 0 100 23 100

0.8 0 100 9 99 0 100 0 100 0 100 0 100 0 100 13 100

0.9 0 100 5 100 0 100 0 100 0 100 0 100 0 100 3 100

P = present; A = absent. 
17

zones could have been named for the prevailing environmental conditions they represent, such as cold, submesic, 

and mesotrophic for Spruce-Fir. Models for the SpruceFir, Northern Hardwood, and Acidic Cove Zones included 

more than one ecological subgroup, which made it difficult 

to separate plant communities using the coarse scale of 

variables in our analysis and highlighted the importance of 

microhabitat influences in these types. The 11 ecological 

zones with unique climatic, topographic, and geologic 

features and important indicator species are presented in the 

following section, grouped by high- and low-elevation environments. 

High-Elevation Environments

Spruce-Fir—This zone includes spruce, fir, and yellow 

birch-spruce forests and high-elevation successional tree, 

shrub, and sedge communities. Eighty-five field plots were 

used to characterize the Spruce-Fir Zone, and they contained 

185 species—22 trees, 34 shrubs, 126 herbs, and 3 vines. 

Indicator species and species with high constancy or abundance included: Fraser fir, red spruce, American mountainash, yellow birch, mountain woodfern, Pennsylvania sedge, 

mountain woodsorrel, hobblebush, fire cherry, and Catawba 

rhododendron.

The relationship between the Spruce-Fir Zone and the 

physical environment was determined with two models. 

South of the Asheville Basin, overall model accuracy is 93 

percent—68 percent for areas predicted to have the SpruceFir Zone present and 96 percent for areas predicted to have 

it absent. In this area, the zone is primarily at high elevations, away from low-base sedimentary and metamorphic 

rock; secondarily, it occurs near streamheads in areas with 

high growing-season rainfall. Predictive model variables are 

presented in table 9. 

North of the Asheville Basin, overall model accuracy is 92 

percent—65 percent in areas predicted to have the SpruceFir Zone present and 97 percent in areas predicted to have it 

absent. In this area, the zone is primarily at high elevations 

to the northeast; secondarily, it occurs well above the heads 

of streams on broad ridges within low-base metamorphic 

Proportion false positives

0.00 0.20 0.40 0.60 0.80 1.00

Proportion true positives

0.00

0.20

0.40

0.60

0.80

1.00

Figure 9—Receiver operating characteristic (ROC) curve for the SpruceFir (south) logistic model. The proportion of area under the ROC curve is 

0.9501.

Table 8—Accuracy of classification for the logistic model describing the Spruce-Fir Zone (south) based on varying 

cutpoints

Plots Percentages

Cut- Correct Incorrect Overall Sensi- Speci- False False

point Event Nonevent Event Nonevent correct tivity ficity positive negative

0.113 54 327 5 57 86.0 91.5 85.2 14.8 8.5

0.213 51 351 8 33 90.7 86.4 91.4 8.6 13.6

0.313 47 361 12 23 92.1 79.7 94.0 6.0 20.3

0.413 41 368 18 16 92.3 69.6 95.8 4.2 30.5

0.463a 41 371 18 13 93.0 69.5 96.6 3.4 30.5

0.512 36 372 23 12 92.1 61.0 96.9 3.1 39.0

0.613 24 372 35 12 89.4 40.7 96.9 3.1 59.3

0.713 20 377 39 7 89.6 33.9 98.2 1.8 66.1

0.813 15 382 44 2 89.6 25.4 99.5 0.5 74.6

0.913 10 383 49 1 88.7 16.9 99.7 0.3 83.1

a Selected as optimum cutpoint.
18

rock having inclusions of high-base rock. Seven environmental and two spatial variables are significant (table 9).

Northern Hardwood—This zone includes beech gaps 

and slopes, boulder fields, and northern hardwood forests. 

One hundred and four field plots were used to characterize 

it and they contained 308 species—36 trees, 35 shrubs, 

232 herbs, and 5 vines. Indicator species and species with 

high constancy or abundance included: mountain holly, 

Allegheny serviceberry, Pennsylvania sedge, yellow birch, 

American beech, sugar maple, northern red oak, Roan 

snakeroot, Canadian woodnettle, and wild leeks or ramps.

Two models were needed to express the relationship of 

the Northern Hardwood Zone with environmental factors. 

South of the Asheville Basin, overall model accuracy is 

Table 9—Environmental variables included in ecological zone models for three high-elevation environments—

two zones, Spruce-Fir and Northern Hardwood, were modeled as occurring either south or north of the 

Asheville Basin

Spruce-Fir Northern Hardwood High-elevation

Environmental variable South North South North red oak

Dormant-season rainfall 8– — — 5– —

Growing-season rainfall 4+ — 6+ — 6+

Landform index — — — — —

Weighted landform index 10+ — 3+ — 1–

Landform shape8 — 9+ — — —

Landform shape16 — 5– — — 9+

Landform index times surface — — — — —

Weighted landform index times surface 5+ — — — —

Length of slope 6+ — — — —

Slope position — — 5– — —

Distance to bottom 3– — — — —

Distance to intermittent stream 9+ 3– — — —

Slope direction-aspect — 8+ — — —

Elevation 1+ 1+ 1+ 3+ 3+

Terrain shape index 7– — — — —

Surface curvature profile — — — — —

Surface curvature planiform — — — — —

Curvature — — — — 5–

Slope steepness — — — — 4+

Slope times slope position — 6– — — —

Geo1and 2: high-base status formations — — — 1+ —

Geo3: low-base status with high inclusions — 7– — 4+ —

Geo4: low-base granitics formations — — — — 8–

Geo5: low-base sedimentary and

 metamorphic formations 2– — — — 2+

Geo6: low-base quartzitic formations — — — — —

Geo7: low-base sulphidic formations — — — — 7+

Geo8: ultramafic formations — — — — —

x coordinates — 2+ 2– — —

y coordinates — 4– — — —

Distance from Murphy, NC — — 4+ 2+ —

Distance from Blue Ridge Escarpment — — — — —

— = Variable not significant in the final regression model.

Numbers in columns indicate the relative level of importance of significant variables in each ecozone model and sign of the coefficient.
19

84 percent—51 percent in areas predicted to have the 

zone present and 87 percent in areas predicted to have the 

zone absent. In this area, the Northern Hardwood Zone is 

primarily at higher elevations on somewhat protected landscapes in the northwestern portion of western North Carolina; secondarily, it occurs on upper slopes in areas of higher 

growing-season rainfall. The logistic model includes six 

significant variables (table 9).

North of the Asheville Basin, the overall accuracy of the 

model is 81 percent—42 percent in areas predicted to 

have the zone present and 85 percent in areas predicted 

not to have it. In this area, the Northern Hardwood Zone is 

primarily on high-base rock at higher elevations well northeast of the southwest corner of North Carolina; secondarily, 

it occurs where there are inclusions of high-base rock within 

a matrix of low-base rock in areas with lower dormantseason rainfall. Five variables had a significant relationship 

in this model (table 9). 

High-Elevation Red Oak—This zone includes forests 

dominated by northern red oak. One hundred and thirtyseven plots were used to characterize it and they contained 

335 species—46 trees, 45 shrubs, 236 herbs, and 8 vines. 

Indicator species and species with high constancy or abundance included: American chestnut, flame azalea, whorled 

yellow loosestrife, northern red oak, Pennsylvania sedge, 

speckled wood-lily, highbush blueberry, mountain laurel, 

and New York fern.

The overall accuracy of the model is 85 percent—52 percent 

in areas predicted to have the High-Elevation Red Oak Zone 

present and 89 percent in areas predicted not to have it. It is 

found primarily on exposed sites on low-base sedimentary 

and metamorphic rock at higher elevations; secondarily on 

steeper, convex slopes in areas with higher growing-season 

rainfall on low-base sulphidic and low-base granitic rock. 

Predictive model variables are presented in table 9.

Low-Elevation Environments

Acidic Cove—This zone includes hemlock and mixed 

mesophytic forests typically dominated by an evergreen 

understory. Two hundred and sixty-two plots were used to 

characterize the Acidic Cove Zone and they contained 387 

species—61 trees, 45 shrubs, 265 herbs, and 16 vines. Indicator species and species with high constancy or abundance 

included: partridgeberry, great laurel, Canada hemlock, black

birch, heartleaf species, mountain doghobble, eastern white 

pine, yellow-poplar, common greenbrier, and red maple.

Overall, accuracy of the model is 82 percent—57 percent in

areas predicted to have the zone present and 85 percent in

areas predicted not to have it. The Acidic Cove Zone is primarily on lower slopes at lower elevations, areas with high 

growing-season rainfall and low dormant-season rainfall, 

and concave land surface shape. Secondarily, it occurs near 

perennial streams on low-base granitic rock or away from 

high-base rock. Eleven variables were significant (table 10).

Rich Cove—This zone includes mixed mesophytic forests 

typically dominated by a diverse herbaceous understory. 

Six hundred and one plots were used to characterize the 

Rich Cove Zone and they contained 636 species—75 trees, 

68 shrubs, 471 herbs, and 22 vines. Indicator species and 

species with high constancy or abundance include: black 

cohosh, American ginseng, blue cohosh, mandarin, bloodroot, northern maidenhair fern, Dutchman’s pipe, rattlesnake 

fern, mountain sweet-cicely, Appalachian basswood, yellow 

buckeye, white ash, yellow-poplar, and northern red oak.

Overall, the accuracy of the model is 80 percent—68 percent 

in areas where the zone is predicted to be present and 84 percent in areas where it is not. The Rich Cove Zone occurs 

primarily in protected landscapes away from the escarpment

in areas with moderate growing-season rainfall on more gentle

slopes; secondarily, it occurs at higher elevations, on long 

slope segments nearer the heads of streams, more southerly 

latitudes, and away from low-base quarzitic or sulphidic 

rock. There is a weak positive correlation to high-base rock. 

The predictive model included 13 variables (table 10).

Mesic Oak-Hickory—This zone includes mesic mixedoak and oak-hickory forests. Two hundred and thirty-seven 

plots were used to characterize the Mesic Oak-Hickory 

Zone, and they contained 416 species—60 trees, 45 shrubs, 

295 herbs, and 16 vines. Indicator species and species with 

high constancy or abundance include: white oak, flowering 

dogwood, northern red oak, Canada richweed, mockernut 

hickory, New York fern, pignut hickory, chestnut oak, 

speckled wood-lily, and rattlesnakeroot.

Overall, the accuracy of the model is 69 percent—52 percent 

in areas predicted to have the zone present and 91 percent 

in areas predicted not to have it. The Mesic Oak-Hickory 

Zone is found primarily at lower and midelevations in areas 

with higher dormant-season rainfall; secondarily, it occurs 

in areas with low-base rock having inclusions of high-base 

rock and away from broad, gentle sloping landscapes. Four 

variables were significant in the prediction model (table 10).

Chestnut Oak Heath—This zone includes xeric to dry 

mixed-oak forests typically dominated by an evergreen 

understory. One hundred and ninety-two plots were used 

to characterize the Chestnut Oak Heath Zone and they 

contained 297 species—56 trees, 45 shrubs, 187 herbs, and 
20Table 10—Environmental variables included in ecological zone models for three low-elevation environments—two zones, Spruce-Fir and Northern 

Hardwood, were modeled as occurring either south or north of the Asheville Basin

 Dry- White Xeric Shortleaf

 Mesic Chestnut Mesic Pine- Pine- PineAcidic Rich Oak- Oak Oak- Oak Oak Oak

Environmental variable Cove Cove Hickory Heath Hickory Heath Heath HeathDormant-season rainfall 4– — 1+ 11– 2+ — 5– —Growing-season rainfall 3+ 3– — 6+ — 3+ 11+ 9–Landform index — — — — — — — —Weighted landform index — 1+ — — 5– 7– — 3–Landform shape8 — — — — — — — 10+Landform shape16 — — 4– — — — 10+ 4–Landform index times surface 7– 6– — — — 11– — —Weighted landform index times surface — 10+ — 9– — 9+ — —Length of slope 6– 7+ — — — — 9– —Slope position 1+ — — 13– — 5– — 8–Distance to bottom — — — — — — 3+ —Distance to intermittent stream 8+ 11– — — — — — —Slope direction-aspect — — — — — 12– — —Elevation 2– 5+ 2– 8– 1– 1– 6– 1–Terrain shape index — — — — 11– — — 5–Surface curvature profile 5+ — — — — — — 6–Surface curvature planiform 11+ — — 10+ — — — —Curvature — — — 12– 9– — 7+ —Slope steepness — 4– — — 12+ — — —Slope times slope position — — — — — 8+ — —Geo1and 2: high-base status formations 9– 13+ — — 8+ — — —Geo3: low-base status with high inclusions — — 3+ — 7+ — — —Geo4: low-base granitics formations 10+ — — — 10+ 10+ 4+ 11+Geo5: low-base sedimentary and

 metamorphic formations — — — — — — 8+ —Geo6: low-base quartzitic formations — 9– — 7+ — — 1+ —Geo7: low-base sulphidic formations — 12– — 1+ — — 2+ —Geo8: ultramafic formations — — — — — — — —x coordinates — — — 5+ 3– — — 7–y coordinates — 8– — 3+ 6+ 4+ — 2–Distance from Murphy, NC — — — 4– — 6– — —Distance from Blue Ridge Escarpment — 2+ — 2– 4– 2– — —

— = Variable not significant in the final regression model.Numbers in columns indicate the relative level of importance of significant variables in each ecozone model and sign of the coefficient.
21

9 vines. Indicator species and species with high constancy 

or abundance include: chestnut oak, northern red oak, great 

laurel, red maple, mountain laurel, Canada hemlock, galax, 

common greenbrier, and sourwood.

Overall, the accuracy of the model is 77 percent—62 percent 

in areas where it is predicted present and 79 percent in areas 

where the zone is predicted not to be. It is found primarily 

in the southwestern portion of the Southern Appalachians 

in North Carolina on low-base sulphidic rock in areas with 

higher growing-season rainfall; secondarily, it occurs on 

low-base quarzitic rock at lower elevations on convex, 

exposed, upper slopes in areas with lower dormant-season 

rainfall. The best predictive model included 13 significant 

variables (table 10).

Dry and Dry-Mesic Oak-Hickory—This zone includes 

dry and dry-mesic mixed oak and oak-hickory forests. Three 

hundred and eight plots were used to characterize this zone 

and they contained 420 species—60 trees, 50 shrubs, 294 

herbs, and 16 vines. Indicator species and species with high 

constancy or abundance include: scarlet oak, sourwood, bear 

huckleberry, mountain laurel, giant cane, white oak, hillside 

blueberry, blackgum, flowering dogwood, and eastern white 

pine.

Overall, the accuracy of the model is 85 percent—58 percent 

in areas predicted to have the zone present and 89 percent 

in areas predicted not to have it. The Dry and Dry-Mesic 

Oak-Hickory Zone is found primarily at lower elevations, 

northwest but near the escarpment in areas with higher 

dormant-season rainfall; secondarily, it occurs on more 

exposed landscapes with a convex land surface and steeper 

slopes within low-base rock with high-base rock inclusions, 

high-base rock, and low-base granitic rock (table 10). 

White Pine-Oak Heath—This zone includes dry mixed 

pine-oak forests typically dominated by eastern white pine. 

It may represent the transition between xeric pine and pineoak, and dry-mesic oak plant communities. One hundred 

and six plots were used to characterize the zone and they 

contained 219 species—42 trees, 35 shrubs, 133 herbs, and 

9 vines. Indicator species and species with high constancy 

or abundance include: eastern white pine, scarlet oak, sourwood, chestnut oak, bear huckleberry, mountain laurel, hillside blueberry, and blackgum.

Overall, the accuracy of the model is 84 percent—55 percent 

in areas predicted to have the zone present and 86 percent 

in areas predicted not to have it. The White Pine-Oak Heath 

Zone is found primarily at lower elevations near the central 

part of the escarpment in areas with higher growing-season 

rainfall; secondarily, it occurs in exposed upper slopes on 

low-base granitic rock with more southerly exposure. The 

predictive model includes 12 significant variables (table 10).

Xeric Pine-Oak Heath and Oak Heath—This zone 

includes xeric pine, pine-oak, and oak forests typically 

dominated by an evergreen understory. One hundred 

and fifty-one plots were used to characterize it and they 

contained 234 species—48 trees, 43 shrubs, 134 herbs, and 

9 vines. Indicator species and species with high constancy or 

abundance include: Table Mountain pine, scarlet oak, pitch 

pine, black huckleberry, chestnut oak, wintergreen, trailing 

arbutus, mountain laurel, hillside blueberry, and maleberry.

Overall, the accuracy of the model is 80 percent—58 percent 

in areas predicted to have the zone present and 82 percent in 

areas predicted not to have it. The Xeric Pine-Oak Heath and 

Oak Heath Zone is found primarily on all low-base rocks 

in upper slopes in areas with low dormant-season rainfall; 

secondarily, it occurs at lower elevations on broad, gentle 

slopes and ridges with a flat-to-convex surface shape. The 

best model included 11 variables (table 10).

Shortleaf Pine-Oak Heath—This zone includes xeric 

pine and pine-oak forests dominated by shortleaf pine. One 

hundred and twenty-one plots were used to characterize it 

and they contained 262 species—46 trees, 42 shrubs, 163 

herbs, and 11 vines. Indicator species and species with high 

constancy or abundance include: shortleaf pine, sourwood, 

sand hickory, scarlet oak, southern red oak, post oak, hillside 

blueberry, American holly, featherbells, and spring iris.

Overall, the accuracy of the model is 95 percent—65 percent 

in areas predicted to have the zone present and 97 percent in 

areas predicted not to have it. The Shortleaf Pine-Oak Heath 

Zone is found primarily at low elevations on broad, exposed 

landforms in the southwestern portion of the Southern Appalachians in North Carolina having convex surface shape; 

secondarily, it occurs on upper slopes in areas with low 

growing-season rainfall and low-base granitic rock. Eleven 

variables were included in the model (table 10).

Summary of Model Components

Elevation was the only variable present in all models and 

usually ranked first or second in importance. Next in importance were geologic group and precipitation, which were 

present in all but one of the models. A measure of landform 

type or slope shape was present in most models. Aspect 

was relatively unimportant in the models, likely because its 

effect was accounted for by weighted landform index. Topographic variables, particularly a measure of landform, were 

more important in the low-elevation models than in the highelevation models. 
22

Mapped Ecological Zones

Distribution of the 11 ecological zones in relation to hypothesized (see footnote 3) midpoints (not ranges) of their 

associated temperature, moisture, and fertility regimes are 

shown in figure 10. Not shown there are ranges of occurrence of each ecological zone relative to the environmental 

components. Application of these relationships in a site-bysite classification of the landscape would result in a map 

of ecological zones. However, direct application of this 

diagram in a site-by-site classification of a barren landscape 

would be difficult because compensating topographic factors 

almost always are present and make it difficult to assess 

moisture regimes. For example, a site on a lower southfacing slope may have soil moisture conditions equivalent 

to an upper, north-facing slope. Variation in precipitation 

would include additional complexity. Mathematical models 

quantify the complex, compensating relationships among 

variables. 

Occurrences of ecological zones across the Southern Appalachian landscape were predicted based on the 11 mathematical models that used DEMs for the primary data source, as 

illustrated for Wayah Bald (fig. 11). Each of the 11 models 

was applied to the approximate 175,000 cells (or sites) in the 

DEM, resulting in assignment of each site to the ecological 

zone of highest predicted probability. Consider, for example, 

the site at the peak of Wine Spring Bald, shown on the DEM 

with elevation of 1658 m (5,440 feet). If the probabilities

predicted by application of the models on that site ranged 

from 0.001 (Dry Oak-Hickory) to 0.985 (High-Elevation Red

Oak), then it is highly likely that environmental conditions

there are most suitable for the latter ecological zone and the

site was classified as such. Polygons of ecological zones were

not subjectively delineated on the DEM, but are formed by 

varying-sized clusters of similarly classified sites, which 

represent a landscape map of recurring vegetative patterns. 

Ten ecological zones are predicted to occur on the landscape 

within the Wayah Bald DEM with High-Elevation Red Oak, 

Dry and Dry-Mesic Oak-Hickory, and Rich Cove being most 

abundant; Spruce-Fir is absent. The models were applied in 

a similar manner to 146 other DEMs of the study area.

The joined quadrangles provide a map of predicted ecological zones on approximately 5.6 million acres in the Southern 

Appalachians (fig. 12). Mesic Oak-Hickory and Acidic Cove 

are the most extensive ecological zones in this area; SpruceFir and Chestnut Oak Heath are the least extensive (table 11).

Except for two types, ecological zones occur in roughly 

the same proportions on the Nantahala and Pisgah National 

Forests as on non-Forest Service land. These are Shortleaf 

Pine-Oak Heath, represented in a much greater proportion 

on non-Forest Service land and Xeric Pine-Oak Heath and 

Oak Heath, represented in a much greater proportion of the 

Nantahala and Pisgah National Forests. These differences 

reflect the location of National Forest System lands at high 

elevations in the Southern Appalachians.

Preliminary Validation of the

Ecological Zone Map

In addition to using ArcGrid and aerial photos to validate 

the models, we also completed an initial field validation of 

the Rich Cove Zone, an uncommon but floristically distinctive type that commonly occurs on sites with above average 

soil fertility (McLeod 1988, Newell and others 1999, Schafale and Weakley 1990). The first test there was part of the 

logistic regression routine. In that test, model accuracy, 

based on plots from which the model was derived, was 80 

percent overall for Rich Cove; 52 percent for areas predicted 

to have Rich Cove present (sensitivity) and 91 percent for 

areas predicted not to have Rich Cove (specificity) (table 

7). In the summer of 2000, over 70 randomly selected plots 

on the Nantahala and Pisgah National Forests were visited 

to begin field validation and refinement of the Rich Cove 

Zone model. For these field plots, we found results similar 

to the first test—55 percent of the predicted Rich Cove plots 

Fertility

Moisture

Temperature

0.0

1.0

2.0

3.0 0

1

2

3

4

5

0.0

1.0

2.0

3.0

NH

MOH

XP SLP

WP

CO

S-F

NRO

DOH

AC

RC

Figure 10—Hypothesized distribution of ecological zones in relation to 

temperature, moisture, and fertility gradients. Temperature regimes range 

from low (0.0) to average (1.5) to high (3.0), moisture ranges from low 

(0.0), to average (3.0) to high (5.0), fertility ranges from low (0.0) to 

average (1.5) to high (3.0). Abbreviations of ecological zones are: Acidic 

Cove (AC), Chestnut Oak-Heath (CO), Dry and Dry-Mesic Oak-Hickory 

(DOH), Mesic Oak-Hickory (MOH), Northern Hardwood (NH), HighElevation Red Oak (NRO), Rich Cove (RC), Shortleaf Pine-Oak Heath 

(SLP), Spruce-Fir (S-F), White Pine-Oak Heath (WP), Xeric Pine-Oak 

Heath and Oak Heath (XP).
23

Figure 11—Predicted ecological zones of the Wayah Bald topographic quadrangle. (Available in color on CD-ROM inside the back cover.)
24

Figure 12—Predicted ecological zones of the Southern Appalachian Mountains study area in North Carolina. (Available in color on CD-ROM inside the back cover.)
25

were correctly classified. More detail was evident from the 

field validation, however; the incorrectly classified plots 

were predominately Acidic Cove (70 percent), a type found 

in similar topographic situations. Only 3 percent were in 

significantly less mesic sites, indicating that the model was 

performing well in this portion of the moisture and temperature gradient, but less well for fertility.

Discussion

Results of this investigation suggest that the 11 hypothesized 

ecological zones based on plant communities developed by 

Ulrey (see footnote 3) are associated with unique sets of 

environmental variables. In comparison, Whittaker (1956) 

described 13 arborescent-dominated vegetation types in 

the western Great Smoky Mountains of Tennessee. Models 

developed for each of the 11 ecological zones generally 

confirmed the patterns of vegetation environment reported 

by earlier investigators in the Southern Appalachians. Elevation, geofertility, and average annual precipitation were the 

most important predictive variables reflecting the primary 

environmental gradients of temperature, fertility, and moisture, respectively. Weighted landform index, a measure of 

site protection that integrates components of temperature 

and moisture, and to some degree fertility, was the next most 

important predictive variable included in the models.

Landscape variables used in modeling, such as elevation 

and precipitation, are surrogates for environmental factors 

such as temperature, moisture, and fertility. The statistical 

significance of variables, however, does not imply causeand-effect relationships. Their correlation often is unclear 

and interpretations are even more complex when interactions of variables occur within an ecological zone. Because 

the formulation of some models may have resulted from 

artifacts of the dataset used for analysis, and therefore were 

possibly overfitted with variables, our results should be 

considered as preliminary until tested with an independent 

dataset. Overfitting is a contributing factor for predictions 

from some models that appear to be biologically illogical. 

Following elevation, lithologic classification was the next 

most important variable in the models. Lithologic variables 

generally were less important at high elevation than at lower 

elevations. Coefficient sign of the lithologic variable was 

logical for most models. For example, geologic formations 

of high base content were negatively related to the Acidic 

Cove Zone, but positively associated with Rich Cove. For 

some ecological zones, Xeric Pine-Oak Heath and Oak 

Heath for example, the positive association with lithologic 

group was likely a better indicator of soil texture and waterholding capacity than an indicator of fertility.

Our study was among the first attempts to quantify the relationship of geologic variables to the occurrence of vegetation, particularly as related to fertility and factors affecting 

soil-moisture relationships. The importance of the lithologic 

group characterized by high-base status was shown to be 

important in the distribution of two ecological zones (Rich 

Cove and Northern Hardwoods), which have been long 

thought associated with sites of higher fertility levels. In 

Table 11—Ecological zones in the Southern Appalachian Mountains

Ecological zone Total area Federal land

no. acres percent no. acres percent

Spruce-Fir 45,500 0.8 12,400 1.2

Northern Hardwood 197,000 3.5 48,800 4.8

High-Elevation Red Oak 142,000 2.5 45,600 4.5

Rich Cove 498,000 8.8 114,000 11.3

Acidic Cove 1,331,000 23.6 199,700 19.8

Mesic Oak-Hickory 1,772,000 31.4 302,300 30.0

Dry and Dry-Mesic Oak-Hickory 125,600 2.2 25,800 2.6

Chestnut Oak Heath 60,600 1.1 11,000 1.1

White Pine-Oak Heath 133,000 2.4 21,300 2.1

Xeric Pine-Oak Heath and Oak Heath 759,000 13.5 191,800 19.0

Shortleaf Pine-Oak Heath 452,000 8.0 22,100 2.2

Not classified 125,000 2.2 14,000 1.4

 Total 5,640,700 1,008,800
26

a similar, large-scale study of vegetation in the Southern 

Appalachians, Newell and others (1999) reported that Rich 

Cove forests were associated with sites of higher nutrient 

availability, as indicated by soil manganese levels. 

A more detailed study of ecological zones would use 

more accurate geologic maps. For example, an ecological 

study made at a watershed scale would use geologic maps 

at least as detailed as 1:24,000 scale. In addition, a more 

detailed study of ecological zones probably would include 

additional geologic map units, such as surficial deposits. 

Those map units could be classified for fertility and, in 

some cases, may result in the addition of a new member 

to the eight fertility groups described in table 3. Surficial 

deposits such as colluvium and alluvium are part of the 

surface geology and may support locally more diverse plant 

communities (Hatcher 1980, 1988; Pittillo and others 1998). 

Hughes (1995) describes a general procedure for integrating 

geology into ecosystem studies, including consideration 

of geologic factors relating to fertility. In some regions of 

steep slope gradients, however, fertility of some sites may 

not be directly associated with the underlying rock formations because the soil probably has moved downhill from its 

parent material. 

A logical explanation is not obvious for the importance 

of variables in some models. For example, both dormantseason and growing-season precipitation were included in 

four ecological zone models, but with different signs of 

coefficients. In each of the four models, the ecological zone 

was positively associated with growing-season precipitation 

but negatively associated with dormant-season precipitation. 

Also, because dormant-season precipitation is a part of total 

precipitation, its increase often is concurrent with a decrease 

in growing-season rainfall, which could explain the inverse 

relationships. Summer precipitation seems more important 

than winter precipitation. Conventional wisdom suggests 

that inclusion of the latter variable in some ecological zone 

models may simply be a spurious relationship. 

The importance of geographical variables in over half of 

the ecological zone models suggests that such models may 

be lacking important environmental variables. For example, 

geographical variables may be acting as surrogates for 

effects of certain temperature regimes, such as length of 

growing season or perhaps a more complex relationship 

related to evapotranspiration. Geographic variable correlations also may be explaining even more complex biogeographic patterns influenced by past climates and plant 

community migrations. In all but one ecological zone model 

where a geographical variable was important, it was the 

second most important variable. Other explanations for the 

importance of geographical variables include past land use 

patterns and climatic influences.

The classification accuracy of individual ecological zone 

models is variable, ranging from 69 to 95 percent. Models 

with the highest accuracy are Shortleaf Pine-Oak Heath (95 

percent) and Spruce-Fir (92 to 93 percent) Zones, which 

occur at opposite ends of the elevation range of the study 

area. The least accurate models are Mesic Oak-Hickory (69 

percent) and Chestnut Oak Heath (77 percent). One reason 

for the low accuracy of the Chestnut Oak Heath Zone is that 

it can occur both on the dry brow of ridges and on moist 

lower slopes. Accuracy levels are moderate for the Xeric 

Pine-Oak Heath and Oak Heath, although this ecological 

zone is rather broadly mapped and does not separate important pine-oak communities from oak communities. Further 

study is needed to differentiate Table Mountain pine-oak and 

pitch pine-oak communities from oak-dominated communities within this zone.

Model accuracy can be affected by several factors: (1) DEM 

reliability, (2) resolution of geologic maps, (3) field plot 

density and landscape representation, (4) accuracy of plot 

location using GPS and especially latitude and longitude 

from topographic maps, and (5) the definition of ecological 

zones and the classification of plots into these zones. 

Increasing the number and distribution of field sample points 

and their representation of the landscape is an efficient 

means of increasing map resolution and accuracy, given the 

current ecological classification framework. One method of 

improving and testing model accuracy would be to supplement the existing dataset with additional observations, 

perhaps from later years when the North Carolina Vegetation Survey is sampled in the study area. Another method 

of accomplishing this objective involves classifying plant 

communities encountered in the field using a standardized 

dichotomous key, such as developed by Ulrey (see footnote 

3), and recording the location using a GPS. The classified 

plant communities at these locations would be merged with 

the database of physical attributes as illustrated in figure 8. 

The new dataset could then be used to create a more robust 

model for ecological zones that would characterize landscape variation at a scale appropriate for smaller watershedand local-project level analyses. Given the relatively low 

resolution and accuracy of available 30-m DEMs, modeling 

at a finer level of ecological zone classification currently 

appears impractical. 

Ecological zones are a broad level of organization of the 

diverse Southern Appalachian landscapes. In addition to 

providing insight regarding environmental factors affecting 

the distribution of vegetation, ecological zones may be 
27

appropriately used for a number of purposes. For example, 

boundaries of ecological units displayed on existing smallscale ecoregion maps might be refined and evaluated. Also, 

ecological zones may provide a consistent and objective 

means of analysis and evaluation of management options 

proposed in periodic planning for national forest lands. 

Our classification models have one obvious limitation—

they define ecological zones for environments only in the 

Southern Appalachians Mountains in North Carolina. 

Although the mountains are present in five Southern States, 

environmental relationships important in North Carolina 

would likely differ elsewhere, particularly at more northern 

and southern latitudes. A less obvious problem in application of the models elsewhere is the lack of data for the lithologic groups used in our analysis. Although uniform DEMs 

are available for all of the Southern Appalachians, geologic 

unit classifications typically do not match in definition or 

detail across State boundaries. Rock units of other States, 

however, could be classified into lithologic groups similar to 

those used in our study (appendix C). 

Conclusions

Results of this preliminary study suggest that distinct 

ecological zones in the Southern Appalachian Mountains can be objectively identified from plant community 

sampling associated with environmental variables using 

multiple logistic regression, and mapped using DEMs 

applied with a GIS. We found that plant communities 

derived from a previous classification have ecological 

meaning because each is associated with a unique set of 

environmental variables. We also found that geological 

formation, which was used as an indication of soil fertility, 

was an important environmental variable affecting the 

distribution of many ecological zones. Evaluation of model 

formulation should continue and additional environmental 

variables, such as temperature and growing-season length, 

should be included. We suggest that the ecological zones 

identified in this study could be used as a basis for subdividing the forested landscape into homogeneous units to 

provide a basis for planning at a range of scales and evaluation of proposed and implemented management activities.

Acknowledgments

This was a cooperative study with members of the North 

Carolina Vegetation Survey. We are indebted to members 

of the vegetation survey for allowing use of the mountain 

dataset for this investigation. We gratefully acknowledge

the contributions of Larry Hayden for initiation and support 

of this project and Ben Dorsey for GIS assistance in conducting this project. The authors thank Michael P. Schafale, 

Thomas R. Wentworth, Robert K. Peet, C. Scott Southworth, 

and Bernard R. Parresol for critical comments on a preliminary draft of this manuscript.

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Appendix A

A hierarchical classification of vegetation in the Southern Appalachian Mountains of North Carolina1 2

Major group Ecological group Ecological subgroup

Montane wetland (63) Three groups3

 (63) Five subgroups3 (63)

Open upland vegetation (134) Four groups3

 (134) Eleven subgroups3 (134)

Upland forests (2,035) Acid mesic forests (287) Acidic cove forests (184) 

Hemlock forests (103)

Dry-mesic forests (769) Calcareous dry-mesic forests (9)

Chestnut oak forests (174)

Oak-hickory forests (366)

Mesic montane oak-hickory forests (220)

Northern hardwood forests (296) Beech gap and slope forests (6)

Northern hardwood forests (112)

Boulder field forests (31)

High-elevation red oak forests (126)

Yellow birch-spruce forests (21)

Rich mesic forests (226) Rich cove forests (226)

Spruce and fir forests (70) Fir forests (13)

Spruce forests (42)

Successional vegetation forests (15)

Xeric forests (387) Carolina hemlock forests (18)

Shortleaf pine-oak forests (78)

Table Mountain pine-pitch pine forests (159)

Subxeric oak-pine forests (132)

1 Ulrey, C.J. 1999. Classification of the vegetation of the Southern Appalachians. Report to the U.S. Department of Agriculture Forest Service, Asheville, NC.

88 p. Unpublished report. On file with: Southern Research Station, Bent Creek Experimental Forest, 1577 Brevard Road, Asheville, NC 28806. (Available on 

CD-ROM inside the back cover.)

2 Number of plots are in parentheses following group and subgroup names.

3 Subdivisions of these groups and subgroups are omitted because they were not used in this study.
31

emphasis on the subgroups of closed-canopy forests, which 

were used in this study.

Results of the vegetation analysis were somewhat inconsistent with the knowledge of experts on how communities 

are organized in the region. A number of groups consisted 

of plots dominated by one or several species, e.g. Fraser fir, 

red spruce, Carolina hemlock, and readily matched widely 

recognized communities. Several groups of plots, however, 

were compositionally homogeneous, but appeared to be 

variants of oak-hickory or pine-oak heath forests and did 

not represent any recognized community. Because the scope 

of the study did not include identification and description 

of new plant communities, a quasi-subjective, knowledgebased classification was devised. The classification adopted 

includes components of widely used systems for North 

Carolina (Schafale and Weakley 1990) and the national 

vegetation classification (Grossman and others 1998). 

Although the lowest level in the devised classification is 

somewhat broader than that of plant community, it is sufficiently detailed to be useful for the original purpose of this 

study, for inventory, and provides a basis for future hypothesis testing. 

The purpose of this project was to develop an objective classification of forest vegetation for the Southern Appalachian 

Mountains in North Carolina based on quantitative analysis 

of plot data. A combination of quantitative, multivariate 

methods was used to detect patterns of species composition.

Methods included cluster analysis, indirect ordination, constancy, ordered tables, and indicator species analysis. The 

objective of this investigation was to group plots by widely 

recognized plant communities, in preparation for subsequent 

study of plant environment, or ecological, relationships.

A total of 2,232 plots were classified into 3 major groups: 

(1) montane wetlands (63 plots established in wet bogs and 

marshes); (2) open upland vegetation (134 plots in areas 

lacking a closed-tree canopy, such as grassy balds and rock

outcrops); and (3) upland forests (2,035 plots with a largely 

closed canopy). The major groups of vegetation were subdivided into 13 smaller ecological groups of somewhat 

similar physiognomy and species composition consisting of 

7 nonforest and 6 forest units. Finally, the ecological groups 

were subdivided into 35 ecological subgroups of relatively 

homogeneous species composition. The three-level classification of vegetation is presented in appendix A, with 

Appendix B

Approach and Methods Used to Develop a Hierarchical Classification of Vegetation

in the Appalachian Mountains of North Carolina1

1 Ulrey, C.J. 1999. Classification of the vegetation of the Southern Appalachians. Report to the U.S. Department of Agriculture Forest Service, Asheville, NC. 

88 p. Unpublished report. On file with: Southern Research Station, Bent Creek Experimental Forest, 1577 Brevard Road, Asheville, NC 28806. (Available on 

CD-ROM inside the back cover.)
32 Appendix C

Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1

Group Map unit Major group Primary rock Secondary rock

1 Yam2 Migmatitic biotite-hornblende gneiss Amphibolite-hornblende Feldspar

1 am Sugarloaf Mountain rock unit Amphibolite and hornblende gneiss Cut by numerous pegmatite dikes 

1 am Northwest of Brevard fault zone Amphibolite —

1 am Northwest of Brevard fault zone Amphibolite and hornblende gneiss —

1 am Brevard fault zone Amphibolite and hornblende gneiss —

1 am Northwest of Brevard fault zone Amphibolite —

1 am Upper Precambrian-Lower Paleozoic Amphibolite and hornblende gneiss —

1 am Blue Ridge Amphibolite —

1 am Davidson River group Amphibolite —

1 am Inner Piedmont Amphibolite —

1 am1 Biotite granitic gneiss Amphibolite —

1 am2 Biotite-hornblende migmatite Amphibolite —

1 bgb Bakersville metagabbro and metadiabase Metagabbro and metadiabase —

1 bgb East of Fork Ridge fault (Bakersville metagabbro) Metagabbroid, original minerals highly altered (Primary = plagioclase and pyroxene in

 (plagioclase and pyroxene in actinoline, etc.) actinoline, etc.)

1 bhg2 Biotite-hornblende migmatite Biotite-hornblende gneiss —

1 bhgb Bakersville hyperstene metagabbro Hyperstene metagabbro —

1 bhgb East of Fork Ridge fault (Bakersville metagabbro) Hyperstene metagabbroid —1 bhm2 Biotite-hornblende migmatite Biotite-hornblende-migmatite Amphibolite, biotite-hornblende-gneiss

1 bogb Bakersville metaolivine gabbro Metaolivine gabbro Grades outward to amphibolite

1 ck Upper Precambrian-Lower Paleozoic Mafic-ultramafic complex of Carroll Knob (Primary = amphibolite and hornblende-

 (amphibolite and hornblende gneiss) gneiss)

1 ckg Upper Precambrian-Lower Paleozoic Metagabbro units at Carroll Knob complex (Primary = labradorite, hornblende)

 (labradorite, hornblende)

1 cs1 Biotite granitic gneiss Calc-silicate rock Biotite granitic, amphibolite

1 cs2 Biotite-hornblende migmatite Calc-silicate rock Biotite hornblende migmatite

1 cs3 Aluminous metasedimentary Calc-silicate rock Diopside lenses

1 csam2 Biotite-hornblende migmatite Amphibolite and calc-silicate rock —

1 d Middle Paleozoic Diorite (dikes w/hornblende, plagioclase, Serpentine, muscovite-sericite, epidote,

 biotite chorite) sphene, etc.”

1 g Paleozoic intrusive rocks Gab. (olivine-pyroxene, pyroxene-hrnbln., —

 ophitic plagiocla) 

1 g Upper Precambrian-Lower Paleozoic Melagabbro and pyroxenite (Augite, plagioclase, chlorite, muscovite,

 sphene, etc.)

1 ga Unmetamorphosed intrusive rocks Gab. - plagioclase, augite, hrnblnd., —

 magnetite, hyperstene 

1 gb Northwest of Brevard fault zone Metagabbro (pyroxene, horneblende, and —

 plagioclase) 

1 hbgg1 Biotite granitic gneiss Hornblende-biotite gneiss —1 hbm1 Biotite granitic gneiss Hornblende-biotite migmatite Biotite granitic gneiss

continued
33

Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

1 hg East of Fork Ridge fault (amphibole gneiss) Hornblende gneiss —1 hgn Metamorphosed rocks Hornblende gneiss —

1 hmg1 Biotite granitic gneiss Hornblende-magnetite gneiss Biotite granitie, amphibolite

1 hybh2 Biotite-hornblende migmatite Hyperstene-biotite hornblende gneiss —

1 pCaa East of Fork Ridge fault (Ashe formation) Amphibolite and amphibole gneiss —1 pCam Blue Ridge Amphibolite and hornblende gneiss —

1 Trd Unmetamorphosed intrusive rocks Mafic dikes-plagioclase and pyroxene —

1 Ybhg2 Migmatitic biotite-hornblende gneiss Biotite hornblende gneiss —

1 Ybhm2 Migmatitic biotite-hornblende gneiss Undifferentiated group —

1 Ycs2 Migmatitic biotite-hornblende gneiss Calc-silicate granofels Amphibolite, hornblende gneiss

1 Ygcs2 Migmatitic biotite-hornblende gneiss Grossular-calc-silicate granofels —

1 Ymg2 Migmatitic biotite-hornblende gneiss Mafic granulite (hornblende) —

1 Zaa Ashe metamorphic suite Layered amphibolite —

1 Zaa Ashe metamorphic suite Layered amphibolite Meta-calcareous sediments

1 Zacs Ashe metamorphic suite Calc-silicate granofels Amphibolite, quartz-clinozoisite

2 m Brevard fault zone Marble —

2 m Brevard zone and southeast of Brevard zone Marble (mostly calcite, in part dolomitic) —2 m East of Fork Ridge fault (Ashe formation) Marble —2 m Inner Piedmont Marble —

2 pCcm Blue Ridge Very siliceous dolomitic marble —

3 bg2 Biotite-hornblende migmatite Biotite gneiss Amphibolite, biotite hornblende

3 bg3 Aluminous metasedimentary Biotite gneiss Amphibolite, quartzite, calc-silicate

3 bgg1 Biotite granitic gneiss Biotite granitic gneiss Amphibolite, hornblende migmatite

3 bgn Upper Precambrian-Lower Paleozoic Biotite granitic gneiss Lenses and bands of amphibolite, pods of 

 pegmatite

3 ccr Coweeta group (Coleman River formation) Metasandstone and quartz-feldspar gneiss Interlayered pelitic schist and calc-silicate 

 quartzite

3 cmy Brevard zone Calcareous mylonite —3 cs East of Fork Ridge fault (Ashe formation) Calc-silicate rock (85 percent quartz and —

 feldspar, 15 percent amphibole) 

3 egg1 Biotite granitic gneiss Epidote-veined granitic gneiss Amphibolite, biotite gran. gneiss3 egg2 Biotite-hornblende migmatite Epidote-veined granitic gneiss Hornblende migmatite and amphibolite3 ggg Great Smoky group (Grassy Branch formation Metasandstone with muscovite schist Many beds with calcareous concretions up to  - lower metamorphic) 1 foot in diameter 3 ggn Sugarloaf Mountain rock unit Biotite-muscovite granitic gneiss Cut by numerous pegmatite dikes, locally 

 hornblende

3 gt Thunderhead formation Metasandstone with calcareous Muscovite schist

 concretions 2 feet in size 

3 hyp1 Biotite granitic gneiss Hyperstene-plagioclase rock Magnetite, hornblende, biotite3 mbg2 Biotite-hornblende migmatite Magnetite-biotite gneiss Biotite hornblende migmatite3 Ybag2 Migmatitic biotite-hornblende gneiss Biotite augen gneiss Migmatitic biotite-hornblende gneiss and 

 amphibolite

continued
34Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

3 Yhyp2 Migmatitic biotite-hornblende gneiss Pyroxene granulite Amphibolite, mafic granulite

3 Zabg Ashe metamorphic suite Biotite gneiss Musc-bio-gneiss, calc-silicate, amp

3 Zaqc Ashe metamorphic suite Quartz-clinozoisite gneiss Calc-silicate grano. and amphibolite

3 Zbgb Intrusivie rocks Bakersville metagabbro Dikes

4 agg1 Biotite granitic gneiss Augen granitic gneiss Biotite granitic gneiss

4 agg1 East of Fork Ridge fault-aegirine granitic gneiss Massive gneiss to protomylonite gneiss —4 ag Southeast of Brevard fault zone Augen gneiss Minor biotite gneiss and schist

4 bag2 Biotite-hornblende migmatite Biotite augen gneiss Biotite hornblende migmatite

4 Cag Cambrian Augen gneiss (quartz monzonite composition) —4 Cag Southeast of Brevard fault zone Augen gneiss —

4 cg Northwest of Brevard fault zone Cataclastic gneiss (granitic and biotite gneiss) Locally includes biotite schist and 

 amphibolite

4 cg Brevard fault zone Cataclastic gneiss (biotite and muscovite gneiss) Biotite muscovite schist4 cpc Coweeta group (Persimmon Creek gneiss) Quartz diorite gneiss Interlayed with metasandstone, quartz-

 feldspar gneiss and peltic schist

4 DSwg Whiteside intrusive suite Foliated muscovite-biotite granitoid —4 DSwg Paleozoic intrusive rocks-Pink Beds gneiss Granodiorite to quartz monzonite of the —

 Whiteside complex 

4 DSwl Paleozoic intrusive rocks-Looking Glass gneiss Quartz diorite to granodiorite —4 gd Metamorphosed rocks Granodiorite —4 gg East of Fork Ridge fault (granitic gneiss) Massive gneiss to protomylonite —4 gg Southeast of Brevard fault zone Granitic gneiss (biotite granitic gneiss) —4 gg1 Biotite granitic gneiss Granitic gneiss Magnetite, sphene, biotite4 hygg2 Biotite-hornblende migmatite Hyperstene granitic gneiss —4 mag East of Fork Ridge fault-aegirine granitic gneiss Mylonite gneiss and protomylonite —4 mChg Inner Piedmont Mylonitic Henderson gneiss (Mylonitic rocks derived from Henderson 

 gneiss)

4 mgm Middle Precambrian migmatic complexes Banded gneiss and migmatite Includes biotite quartz, geldspar gneisses, 

 mica schist, minor quartzite

4 mgn2 Biotite-hornblende migmatite Magnetite grantitc gneiss Biotite hornblende migmatite4 myg1 Biotite granitic gneiss Mylonite (flaser) gneiss Biotite granitic gneiss4 Osgg Ordovician-Silurian Granitic gneiss Interlayed with augen gneiss on eastern 

 contact

4 OSgg Southeast of Brevard fault zone Granitic gneiss —4 pCc Blue Ridge Mylonitic quartz-feldspar gneiss Minor amounts of garnet4 pCtg Unconformity Toxaway gneiss (banded granitic gneiss) —4 pCwg Grandfather Mountain window (Wilson Creek Sheared granitic unit — series) 4 pg Middle Paleozoic Pegmatite (quartz, plagioclase, microcline, Minor amounts of biotite, garnet

 muscovite) 

4 pg Northwest of Brevard fault zone Pegmatite (quartz, plagioclase, microcline, —

 muscovite)

continued
35

Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

4 pg Northwest of Brevard fault zone Pegmatite and aplite bodies (plagioclase, Locally - garnet, tourmaline, aplite

 microcline, quartz 

4 pg Northwest of Brevard fault zone Pegmatite (quartz, plagioclase, microcline, —

 muscovite) 

4 pg Whiteside intrusive suite Pegmatite (microcline, albite-oligoclase, —

 quartzite and muscovite) 

4 pg Northwest of Brevard fault zone Pegmatite bodies (quartz, plagioclase, —

 microcline, muscovite) 

4 pgb Pegmatite and trondhjemite Pegmatite and trondhjemite —

4 Pzp Intrusive rocks Pegmatite Too small to depict at map scale

4 Pzt Intrusive rocks Trondhjemite-granodiorite Mostly dikes

4 qfgf Metamorphosed rocks Quartzo-feldspathic granofels Unmapped layers granitic gneiss, 

 amphibolite, metaquartzite

4 Sogg Inner Piedmont Biotite granitic gneiss —4 t Paleozoic intrusive rocks Porphyritic trondhjemite with olioclase —

 in quartz 

4 t Lower to Middle Paleozoic Trondhjemite (plagioclase, plagioclase-quartz) —4 tg Shining Rock group Feldspathic gneiss and quartzite (ranges from (Primary ranges from metaarkose to 

 metaarkose to orthoquartzite) orthoquartzite)

4 Ymgn2 Migmatitic biotite-hornblende gneiss Magnetite granitic gneiss Migmatitic biotite hornblende gneiss4 Ypgg2 Migmatitic biotite-hornblende gneiss Porphyroclastic granitc gneiss Biotite augen gneiss5 as East of Fork Ridge fault Actinolite schist —5 as Inner Piedmont Schistose to massive actinolite-chlorite talc —

 body 

5 bg Southeast of Brevard fault zone Biotite gneiss Minor amounts of biotite schist, amphibolite, 

 augen gneiss

5 bg Southeast of Brevard fault zone Muscovite-biotite gneiss —5 bg Southeast of Brevard fault zone Biotite-muscovite gneiss Thin layers of muscovite-biotite schist5 bg Inner Piedmont Biotite gneiss Minor interlayers of garnet-mica schist and 

 amphibolite with hornblende gneiss

5 bggs Shining Rock group Biotic gneiss and garnet schist —5 bmg Tallulah Falls formation (graywacke-schist Biotite-muscovite gneiss and schist Minor biotite-muscovite gneiss, mica schist,

 member) amphibolite

5 bmg Davidson River group Feldspathic mica gneiss Thin bands of neosomal pegatite present5 bpg Tallulah Falls formation (graywacke-schist Biotite-plagioclase-quartz gneiss Minor amounts of metasandstone member) 5 bpg Blue Ridge Biotite paragneiss and schist —5 bw Northwest of Brevard fault zone Metasandstone and schist Granitic and pegmatitc lenses, interbeded

 with mudstone, siltstone

5 bw Northwest of Brevard fault zone Biotite metasandstone —5 bw Northwest of Brevard fault zone Metasandstone and schist Grades into metaconglomerate locally

continued
36Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

5 cg Tallulah Falls formation (graywacke-schist Mylonite gneiss and mylonite schist Muscovite member) 5 Ch West of Fork Ridge fault (Hampton formation) — —5 Chg Brevard zone and southeast of Brevard zone Henderson gneiss (augen gneiss) Locally layers of mylonite5 Chg Inner Piedmont Henderson gneiss (biotite augen gneiss) —5 Chg Metamorphosed rocks Henderson gneiss —

5 crp Coweeta group (Ridgepole Mountain formation) Biotite-garnet schist, pelitic schist, —

 metaorthoquartzite, metasand 

5 cs Northwest of Brevard fault zone Muscovite-chlorite schist Minor thin layers of metasandstone

5 cs Metamorphosed rocks Chlorite schist —

5 Cul West of Fork Ridge fault Metaconglomerate: metatuff, greenstone, Interbedded with arkosic metasandstone (Unicoi formation- lower) metamudstone and metasiltstone

5 Cuu West of Fork Ridge fault Conglomeratic metasandstone Interbedded with quartzite and phyllitic 

 (Unicoi formation - upper) metamudstone

5 cw Northwest of Brevard fault zone Metasandstone, metaconglomerate, and —

 biotite-muscovite schist 

5 cw Northwest of Brevard fault zone Metasandstone, metaconglomerate, and —

 biotite-muscovite schist

5 fs Brevard zone Schistose mylonite and phyllonite —5 gam Great Smoky group (Ammons formation) Metasandstone with metasiltstone and Minor calc-silicate granofels and 

 muscovite schist porphyroblastic muscovite schist

5 gbb Great Smoky group-Buck Bald formation Graywacke metaconglomerate and slate and —

 metasiltstone 

5 gbgs Great Smoky group-Boyd Gap formation Feldspathic metagraywacke Rare beds of graywacke metaconglomerate5 gch Great Smoky group-Copperhill formation Metagraywacke Interlayered with graywacke metaconglom-

 erate, garnet muscovite schist

5 gdf Great Smoky group (Dean formation) Metasandstone, porphyroblastic muscovite Minor metaquartzite, metasiltstone muscovite 

 schist schist and calc-silicate

5 ggs Great Smoky group (Grassy Branch formation Porphyroblastic muscovite schist and Minor muscovite schist, calc-silicate  -upper schist) metasandstone granofels5 gmg East of Fork Ridge fault (granitic gneiss) Mylonite gneiss facies (mylonitic gneiss —

 and schist) 

5 gms Sugarloaf Mountain rock unit Garnetiferous muscovite schist —5 gms Northwest of Brevard fault zone Garnetiferous muscovite schist —5 gms Northwest of Brevard fault zone Garnetiferous mica schist Interlayered with minor amounts feldspathic

 metasandstone

5 gms Northwest of Brevard fault zone Garnet-mica schist Interlayers of mica gneiss and feldspathic

 metasandstone

5 gms Tallulah Falls formation (garnet-aluminous Garnetiferous mica schist —  schist member)5 gms Inner Piedmont Garnet-mica schist Kyanite, oligoclase, ilmenite, chlorite

continued
37

Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

5 gms Metamorphosed rocks Garnet-muscovite-biotite schist —

5 gs Northwest of Brevard fault zone Graphite-muscovite schist —

5 Kakgs Ashe metamorphic suite Kyanite-garnet schist Garnet-muscovite-biotite gneiss and 

 metagrawacke

5 kgms Metamorphosed rocks Kyanite-garnet-muscovite-biotite schist —5 lgn Northwest of Brevard fault zone Layered biotite gneiss (biotite-plagioclase- —

 quartz gneiss and biotite-muscovite gneiss, calc-silicate granofels) 

5 mbg3 Aluminous metasedimentary Muscovite-biotite gneiss Biotite gneiss, metasubgraywacke5 mg Northwest of Brevard fault zone Layered muscovite gneiss and schist —5 mg Northwest of Brevard fault zone Layered mica gneiss and schist Interlayered with garnet-biotite-muscovite 

 schist, biotite schist, etc.

5 mgn Northwest of Brevard fault zone Mica gneiss Interlayers include biotite schist, 

 metasandstone, mica schist

5 mmg Southeast of Brevard fault zone Mixed mica gneiss —5 mps Blue Ridge Muscovite-biotite paraschist Grades into biotite schist, quartz biotite 

 schist,

5 ms Northwest of Brevard fault zone Garnetiferous muscovite and muscovite-biotite Minor amounts of metasandstone

 schist

5 ms Northwest of Brevard fault zone Mica schist Interlayered with micaceous feldspathic 

 metasandstone

5 ms Northwest of Brevard fault zone Mica schist Garnet and muscovite interlayered with 

 micaceous metasandstone

5 ms Northwest of Brevard fault zone Mica schist Thin, conformable granitic or quartz-rich 

 pegmatitic layers throughout

5 ms Tallulah Falls formation (graywacke-schist Muscovite schist and biotite-muscovite schist Minor thin layers of biotite-plagioclase- member) quartz gneiss5 ms/cg Rocks of Kings Creek Valley Mica schist and biotite gneiss Includes cataclastic equivalent (cg) projected 

 from Rosman quadrangle

5 mss Upper Precambrian Metasandstone and schist —5 my Brevard fault zone Porphyroclastic mylonite and ultramylonite —5 my Brevard fault zone Porphyroclastic mylonite Grades to cataclastic schist and phyllonite5 my Brevard zone and southeast of Brevard zone Porphyroclastic mylonite and ultramylonite Interlayers of pophyroclastic phyllonite and 

 phyllonitic schist

5 my Inner Piedmont Porphyroclastic mylonite and ultramylonite —5 my Brevard fault zone Porphyroclastic mylonite Grades into cataclastic schist and phyllonite5 pCags East of Fork Ridge fault (Ashe formation) Mica gneiss and schist —5 pCc East of Fork Ridge fault (Cranberry gneiss) Biotite gneiss and schist —5 pCcg East of Fork Ridge fault (Cranberry gneiss) Quartzo-feldspathic gneiss Minor biotite5 pCcm East of Fork Ridge fault (Cranberry gneiss) Mylonite gneiss inlayered with biotite schist —

 and biotite mylonite gneiss 

5 pCcs East of Fork Ridge fault (Cranberry gneiss) Biotite schist (biotite, clinozoisite and quartz) —

continued
38Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

5 pCwmg Grandfather Mountain window (Wilson Creek Mica gneiss to augen gneiss — series) 5 pCwrg West of Fork Ridge fault-granodiorite gneiss Gneiss, numerous quartz veins and aplite —

 dikes present 

5 pCwrm West of Fork Ridge fault-granodiorite gneiss Mylonite gneiss facies (mylonite schist and (Primary = mylonite schist and 

 quartzofeldspathic mylonite gneiss quartzofeldspathic mylonite gneiss)

5 pgc Davidson River group Porphyroclastic mica gneiss —5 pgn Northwest of Brevard fault zone Biotite-plagioclase-quarzt gneiss Interlayered with minor amounts of 

 muscovite-biotite schist

5 pgn Tallulah Falls formation (garnet-aluminous Porphyroblastic biotite-muscovite gneiss — schist member)5 pgw Northwest of Brevard fault zone Paragneiss and metagraywacke Interlayered biotite schist, metasandstone; 

 garnet schist, phyllite

5 pgwb Northwest of Brevard fault zone Biotite metasandstone Locally garnet5 pmy Metamorphosed rocks Porphyroclastic mylonite schist and gneiss —5 qbgn Metamorphosed rocks Quartz-biotite-plagioclase gneiss —5 sbcgl Snowbird group Metaconglomerate alternates with arkosic Metasiltstone, slate, phyllite

 metasandstone

5 sbss Snowbird group Metasandstone inbeded with metasiltstone, —

 slate and phyllite 

5 sbst Snowbird group Metasiltstone Slate and phyllite5 ssg Tallulah Falls formation Sillimanite schist and gneiss —5 tf Tallulah Falls formation Biotite paragneiss and schist Interlayers of pelitic schist, metasandstone

 to metagraywacke

5 tw Northwest of Brevard fault zone Thin-layered metasandstone and schist Schist locally contains graphite and garnet5 Zag Ashe metamorphic suite Muscovite biotite gneiss Kyanite-garnet schist, metagraywacke5 Zagg Ashe metamorphic suite Garnet-muscovite-biotite gneiss —5 Zamy Ashe metamorphic suite Mylonitic muscovite-feldspar-quartz gneiss —5 Zaw Ashe metamorphic suite Metagraywacke Schist, gneiss, calc-silicate metagraywacke6 bmy Brevard zone Blastomylonite (augen of feldspar in mylonitic —

 matrix) 

6 Cc Grandfather Mountain window (Chilowee group) Quartzite (85 percent quartz) —6 Ce West of Fork Ridge fault (Erwin formation) Metasandstone and quartzite Beds of conglomeratic metasandstone6 my Mylonite — —6 my Brevard zone Mylonite —6 ntq Nantahala formation Metaquartzite with thin laminae of schist Schist, metasiltstone6 P Paleozoic intrusive rocks Pegmatite Sills and pods of quartz and microcline6 pCqq Blue Ridge Mylonitized leucocratic quartz monzonite —6 Pcwq Grandfather Mountain window (Wilson Creek Quartz monzonite unit Cataclastic textures range from mortar gneiss  series) to mylonite throughout 6 pg Unmetamorphosed intrusive rocks Pegmatite - quartz, plagioclase, microcline, —

 muscovite, biotite

continued
39

Mapped geologic units (North Carolina Geological Survey 1985) classified by geofertility group1 (continued)

Group Map unit Major group Primary rock Secondary rock

6 q Upper Precambrian Quartzite (massive metaorthoquartzite) Metagraywacke and metaconglomerate

6 q Blue Ridge Quartzite Muscovite and microcline

6 qm Brevard zone Quartz monzonite —

6 qv East of Fork Ridge fault Quartz veins Minor sericite

7 bpu Inner Piedmont Brecciated phyllonite and ultramylonite (Primary = carbonaceous phyllonite, 

 ultramylonite, porphyroclas)

7 fs Brevard fault zone Cataclatic schist, phyllonite, and mylonite Some layers of calcite7 fs Brevard fault zone Cataclastic schist, phyllonite, and mylonite —7 fs Brevard fault zone Phyllonite, cataclastic schist, and mylonite Garnet, chlorite, muscovite locally7 fs Brevard zone and southeast of Brevard zone Porphyroclastic phyllonite and phyllonitic schist —7 fs Inner Piedmont Porphyroclastic phyllonite and phyllonitic schist —7 ga1 Anakeesta formation (lower black schist unit) Muscovite schist, metasandstone —7 ga2 Anakeesta formation (lower metasandstone unit) Metasandstone facies but with schist and —

 muscovite schist 

7 ga3 Anakeesta formation (middle black schist unit) Schist Metasandstone7 ga4 Anakeesta formation (upper metasandstone unit) Metasandstone facies but with schist and —

 muscovite schist 

7 ga5 Anakeesta formation (upper black schist unit) Muscovite schist Metasandstone7 gbg Great Smoky group-Boyd Gap formation Slate and metasiltstone-very heterogeneous (Primary = notably sulfurous and graphitic)

 (notably sulfurous and graphitic) 

7 gf Great Smoky group-Slaty unit Sulfidic phyllite Interlayered with feldspathic metagraywacke 

 highly metamorphosed

7 ghb Great Smoky group (Ammons formation, Sulphidic mica schist and metasiltstone Interbeded with metasandstone, metasiltstone, Horse) muscovite schist7 gw Great Smoky group-Wehutty formation Sulfidic pyllitite and muscovite schist Interlayered with slate and garnet-muscovite 

 schist

7 nt Nantahala formation Sulphidic schist with quartzose metasiltstone Metaquartzite7 sms/ams Davidson River group Sulfidic muscovite schist Thinly interlayered with amphibolite in 

 portions

8 ckum Upper Precambrian-Lower Paleozoic Ultramafic unit at Carroll Knob complex (Primary = dunite, soapstone, serpentinite)

 (dunite, soapstone, serpentinite)

8 du Dunite — —8 um Southeast of Brevard fault zone Altered ultramafic rock —8 um Northwest of Brevard fault zone Altered ultramafic rock —8 um Northwest of Brevard fault zone Altered ultramafic rock —8 um Upper Precambrian-Lower Paleozoic5 Ultramafic rocks —8 um Blue Ridge Altered ultramafic rock —8 um Paleozoic intrusive rocks Ultramafic rock (olivine, peridotite, bronzitite,

 talc schist) —

8 Zud Intrusive rocks Dunite Unaltered = olivine, altered = serpentinite— = no data.1 Collins, T.K. Geo-fertility groups in the Southern Appalachians. Unpublished document. 2 p. with attachments. On file with: George Washington and Jefferson National Forests, 5162 Valleypointe 

Parkway, Roanoke, VA 24019–3050.
40

Site Characterization Variables

Elevation: elevation from 30-m digital elevation model with 

sinks filled (converted to feet).

Terrain shape index: surface shape in 3 by 3 grid of neighboring DEM cells (convex = negative, concave = positive).

Surface curvature profile: curvature of surface in the direction of slope, Environmental Systems Research Institute 

variable calculated from 3 by 3 grid of cells.

Surface curvature planiform: curvature of surface perpendicular to slope, Environmental Systems Research Institute 

variable calculated from 3 by 3 grid of cells.

Curvature: Environmental Systems Research Institute variable

calculated from 3 by 3 grid of cells (like terrain shape index).

Slope steepness: steepness of slope in percent using Environmental Systems Research Institute algorithm.

Slope steepness and slope position interaction: interaction of 

slope steepness and slope position (focal mean in 3 by 3 grid 

of slope times focal mean in 3 by 3 grid of slope position).

Geologic fertility group:1 geology-fertility classes identified 

from 100 bedrock geology or lithology types: (1, 2) = high 

bases mafic and carbonate rock; (3) = low-base dominant 

rocks with inclusions of high-base; (4) = low-base granitic 

rocks; (5) = low-base sedimentary and metamorphic rock; 

(6) = low-base quartzitic rock; (7) = low-base sulphidic rock;

and (8) = ultramafic rock. Geologic formations in the study 

area classified by geofertility group are listed in appendix C.

Geographic Characterization Variables

x geographic coordinants of plot location: distance east or 

west. 

y geographic coordinants of plot location: distance north or 

south.

Distance from Murphy, NC: straight-line distance of plot 

from the extreme southwestern corner of North Carolina.

Distance from the Blue Ridge Escarpment: minimum 

straight-line distance from the escarpment. 

Landscape Characterization Variables

Dormant-season rainfall: October to April average precipitation in inches, based on a 30-year average orographic effects 

model. Cell size was originally 1,000 feet by 1,000 feet.

Growing-season rainfall: May to September average precipitation in inches, based on a 30-year average, orographic effects

model. Cell size was originally 1,000 feet by 1,000 feet.

Landform Characterization Variables

Landform index: index of landform shape (site protection) 

and macroscale landform. 

Weighted landform index: landform index weighted by 

aspect using northeast (45°) as the reference aspect; as 

above but considers direction-sheltering influence (ridges).

Landform shape8: average elevation change in an 8 by 8 grid

of neighboring digital elevation data cells (find maximum 

elevation in a 3 by 3 grid of cells; subtract elevation from 

this maximum; focal mean on the elevation difference in the 

8 by 8 grid).

Landform shape16: average elevation change in a 16 by 16

grid of neighboring digital elevation data cells (find maximum

elevation in a 3 by 3 grid of cells; subtract elevation from 

this maximum; focal mean on the elevation difference in the 

16 by 16 grid).

Landform index surface interaction: interaction between 

landform index and surface curvature quantified by Environmental Systems Research Institute algorithm Procurve 

(landform index multiplied by Procurve). 

Weighted landform index surface interaction: interaction 

between weighted landform index and surface curvature 

(weighted landform index multiplied by Procurve). 

Length of slope: total slope segment length (from ridge to 

valley, Euclidean distance).

Slope position: position along a slope segment (0 = ridge, 

1 = valley).

Distance to bottom: distance to the valley bottom of the 

slope segment.

Distance to intermittent stream: distance to the closest intermittent stream (modeled first-order streams).

Slope direction: aspect (cosine of aspect) of plot calculated 

by Environmental Systems Research Institute algorithm.

Appendix D

Variables in the Southern Appalachian digital elevation database

1 Collins, T.K. Geo-fertility groups in the Southern Appalachians. Unpublished document. 2 p. with attachment. On file with: George Washington 

and Jefferson National Forests, 5162 Valleypointe Parkway, Roanoke, VA 

24019–3050.
41

Appendix E

Common and scientific names of flora referenced in the text

Common name Scientific name

Table Mountain pine Pinus pungens

Oak-hickory Quercus-Carya

Southern red oak Quercus falcata

Yellow pines Pinus spp.

Yellow-poplar Liriodendron tulipifera

Northern red oak Q. rubra

Red spruce Picea rubens

Fraser fir Abies fraseri

Red maple Acer rubrum

Bear huckleberry Gaylussacia ursina

Common stonecrop Sedum ternatum

Northern bush honeysuckle Diervilla lonicera

American chestnut Castanea dentata

American ginseng Panax quinquefolius

Yellow birch Betula alleghaniensis

American mountain-ash Sorbus americana

Mountain woodfern Dryopteris campyloptera

Pennsylvania sedge Carex pensylvanica

Mountain woodsorrel Oxalis montana

Hobblebush Viburnum lantanoides

Mountain holly Ilex montana

Allegheny serviceberry Amelanchier laevis

American beech Fagus grandifolia

Sugar maple Acer saccharum

Canadian woodnettle Laportea canadensis

Wild leeks or ramps Allium tricoccum

Flame azalea Rhododendron calendulaceum

Whorled yellow loosestrife Lysimachia quadrifolia

Highbush blueberry Vaccinium corymbosum

Mountain laurel Kalmia latifolia

New York fern Thelypteris noveboracensis

Partridgeberry Mitchella repens

Great laurel Rhododendron maximum

Heartleaf species Hexastylis spp.

Eastern white pine Pinus strobus

Blue cohosh Caulophyllum thalictroides

Bloodroot Sanguinaria canadensis

Northern maidenhair fern Adiantum pedatum

Rattlesnake fern Botrychium virginianum

Yellow buckeye Aesculus flava

White ash Fraxinus americana

Common name Scientific name

White oak Quercus alba

Flowering dogwood Cornus florida

Canada richweed Collinsonia canadensis

Pignut hickory Carya glabra

Rattlesnakeroot Prenanthes spp.

Sourwood Oxydendrum arboreum

Scarlet oak Quercus coccinea

Giant cane Arundinaria gigantea

Blackgum Nyssa sylvatica

Pitch pine Pinus rigida

Black huckleberry Gaylussacia baccata

Trailing arbutus Epigaea repens

Maleberry Lyonia ligustrina var

 ligustrina

Shortleaf pine Pinus echinata

Sand hickory Carya pallida

Post oak Quercus stellata

American holly Ilex opaca

Fire cherry Prunus pensylvanica

Catawba rhododendron Rhododendron catawbiense

Roan snakeroot Ageratina altissima var. 

roanensis

Speckled wood-lily Clintonia umbellulata

Hemlock Tsuga spp.

Canada hemlock T. canadensis

Black birch Betula lenta

Heartleaf species Hexastylis spp.

Mountain doghobble Leucothoe fontanesiana

Common greenbrier Smilax rotundifolia

Black cohosh Actaea racemosa

Mandarin Prosartes lanuginosa

Dutchman’s pipe Aristolochia macrophylla

Mountain sweet-cicely Osmorhiza claytonii

Appalachian basswood Tilia americana var. 

heterophylla

Chestnut oak Quercus prinus

Galax Galax urceolata

Hillside blueberry Vaccinium pallidum

Wintergreen Gaultheria procumbens

Featherbells Stenanthium gramineum

Spring iris Iris verna

Source: Kartesz (1999).
42
42
Simon, Steven A.; Collins, Thomas K.; Kauffman, Gary L.; McNab, W. Henry; Ulrey, Christopher J. 2005. 

Ecological zones in the Southern Appalachians: first approximation. Res. Pap. SRS-41. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research Station. 41 p.

Abstract—Forest environments of the Southern Appalachian Mountains and their characteristic plant communities 

are among the most varied in the Eastern United States. Considerable data are available on the distribution of plant 

communities relative to temperature and moisture regimes, but not much information on fertility as an environmental 

influence has been published; nor has anyone presented a map of the major, broad-scale ecosystems of the region, 

which could be used for planning and management of biological resources on forestlands. Our objectives were to 

identify predominant ecological units, develop a grouping of geologic formations related to site fertility, and model and 

map ecological zones of the Southern Appalachians. We synthesized 11 ecological units from an earlier analysis and 

classification of vegetation, which used an extensive database of over 2,000 permanent, 0.10-ha, intensively sampled 

plots. Eight lithologic groups were identified by rock mineral composition that upon weathering would result in soils of 

low or high availability of base cations. The presence or absence of ecological zones (large areas of similar environmental 

conditions consisting of temperature, moisture, and fertility, which are manifested by characteristic vegetative 

communities) were modeled as multivariate logistic functions of climatic, topographic, and geologic variables. Accuracy 

of ecozone models ranged from 69- to 95-percent correct classification of sample plots; accuracy of most models was > 80 

percent. The most important model variables were elevation, precipitation amount, and lithologic group. A regional map of 

ecological zones was developed by using a geographic information system to apply the models to a 30-m digital elevation 

dataset. Overall map accuracy was refined by adjusting the best probability cut levels of the logistic models based on expert 

knowledge and familiarity of the authors with known ecological zone boundaries throughout the study area. Preliminary 

field validation of an uncommon fertility-dependent ecological zone (Rich Cove) indicated a moderate, but acceptable 

level of accuracy. Results of this project suggest that bedrock geology is an important factor affecting the distribution 

of vegetation. The developed map is a realistic depiction of ecological zones that can be used by resource managers for 

purposes ranging from broad-scale assessment to local-scale project planning. 

Keywords: Classification, ecosystems, fertility, geologic formations, logistic regression, moisture, multivariate analysis, 

ordination, temperature.
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