ii

THE SOUTH CAROLINA GAP ANALYSIS PROJECT

FINAL REPORT

2001

Dr. Elise Vernon Schmidt, Coordinator

Dr. David Otis, Principal Investigator

Cindy Aulbach, Botanical Services

Dr. Frank Tian, GIS Analyst

James D. Scurry, GIS Manager

Yvette Alger, Vegetation Ecologist

Dr. Francois G. F. Smith, Remote Sensor

Daniel Fairey, Protected Lands

Dr. David Gordon, Wildlife Biologist

Submitted by:

Dr. Elise Vernon Schmidt, Coordinator

Dr. Craig Allen, Principal Investigator

Research Performed Under:

Cooperative Agreement No. 1434-HQ-97-RU-01569

Research Work Order No. 30

Contract Administration Through:

South Carolina Cooperative Fish and Wildlife Research Unit

Clemson University

Clemson, South Carolina 29236

U.S. Geological Survey

Biological Resources Discipline

Gap Analysis Program
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TABLE OF CONTENTS

Chapter 1. Introduction.....................................................................................................1

How This Report is Organized .........................................................................................1

The Gap Analysis Program Mission.................................................................................1

The Gap Analysis Concept ...............................................................................................2

General Limitations ..........................................................................................................3

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

Chapter 2. Land Cover Classification and Mapping.....................................................9

Introduction.......................................................................................................................9

Land Cover Classification...............................................................................................10

Methods...........................................................................................................................12

Results.............................................................................................................................33

Accuracy Assessment .....................................................................................................37

Limitations and Discussion.............................................................................................55

Chapter 3. Distributions and Species Richness............................................................56

Introduction.....................................................................................................................56

Methods...........................................................................................................................57

Results.............................................................................................................................63

Species Richness.............................................................................................................64

Accuracy Assessment .....................................................................................................70

Limitations and Discussion.............................................................................................75

Chapter 4. Land Stewardship........................................................................................76

Introduction.....................................................................................................................76

Mapping Standard...........................................................................................................77

Methods...........................................................................................................................78

Results.............................................................................................................................84

Limitations and Discussion.............................................................................................85

Chapter 5. Analysis Based on Stewardship and Management Status........................86

Introduction.....................................................................................................................86

Methods...........................................................................................................................87

Results.............................................................................................................................87

Land Cover Analysis.......................................................................................................87

Limitations and Discussion for Land Cover Analysis....................................................90

Predicted Animal Species Distributions Analysis ..........................................................90

Limitations and Discussion.............................................................................................94

Executive Summary ........................................................................................................ viii

Dedication ...................................................................................................................... vii

Acknowledgements............................................................................................................ x
iv

Appendix 5. A crosswalk of the detailed land cover classes to the final vertebrate

Appendix 6. A crosswalk of the detailed land cover to other land covers used in

Appendix 8. List of species used in vertebrate modeling for SC-GAP including species

Appendix 10. Vertebrate species protection status by land owner and protection

Appendix 11. All management areas identified by SC-GAP as management

status 1 or 2 along with the manager and source of the information used to

Appendix 2. NWI classes used in land cover development............................................A-52

Appendix 3. Soil types used in land cover development................................................A-53

Appendix 4. Final rules for land cover modeling ...........................................................A-54

habitat land cover......................................................................................................A-63

modeling vertebrates or accuracy assessment...........................................................A-65

Appendix 7. State taxonomic experts .............................................................................A-68

name, scientific name and ITIS Code .......................................................................A-69

Appendix 9. Vertebrate habitat land cover types by protection type and manager. ......A-80

type............................................................................................................................A-84

categorize the area...................................................................................................A-137

Appendix 1. A detailed description of SC-GAP land cover classes and a listing of 

appropriate TNC Alliances within each class.............................................................A-1

Appendices

Chapter 7. Product Use and Availability.........................................................................97

Chapter 6. Conclusions and Management Implications ................................................95

How to Obtain Products.....................................................................................................97

Metadata.............................................................................................................................98

Disclaimer..........................................................................................................................97

Appropriate and Inappropriate Use of These Data ............................................................98

Literature Cited ................................................................................................................101

Glossary .............................................................................................................................106

Glossary of Acronyms.......................................................................................................111

Appendix 12. List of example GAP applications .......................................................A-141
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LIST OF FIGURES AND MAPS

Figure 1. A general map of South Carolina ...................................................................5

Figure 2. Physiographic regions of South Carolina .......................................................6

Figure 3. Layout of TM scenes used in SC-GAP land cover mapping........................14

Figure 4. 11-digit HUCs used to subset TM imagery for South Carolina. Color

groupings represent the parent 8-digit HUCs ............................................16

Figure 5. Elevation data used in modeling the Blue Ridge Province montane

vegetation...................................................................................................24

Figure 6. Depiction of general soil classification used in modeling detailed

land cover. This map is for Georgetown County, one of eight coastal

Figure 7. General NWI classes used in modeling detailed land cover. This example

is from Georgetown County, one of eight coastal counties.......................28

Figure 8. A detailed land cover of Georgetown County..............................................31

Figure 9. The 27-class general land cover used for modeling vertebrate species........34

Figure 10. The detailed land cover consisting of 54 classes..........................................35

Figure 11. Flightlines used to obtain aerial videography for accuracy assessment

of SC-GAP land cover ...............................................................................41

Figure 12. Predicted distribution for two species illustrating a distribution

including habitat information and the binary presence/absence map used

in the richness modeling process ...............................................................62

Figure 13. Overall species richness ...............................................................................65

Figure 14. Species richness of amphibians....................................................................66

Figure 15. Species richness of reptiles...........................................................................67

Figure 16. Species richness of birds ..............................................................................68

Figure 17. Species richness of mammals.......................................................................69

Figure 18. Sites used in the accuracy assessment of predicted vertebrate

distributions................................................................................................71

counties .............................................................................27

Figure 19. Land Ownership in South Carolina ................................................................83
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LIST OF TABLES

Table 1. Original land cover classes with working description........................................11

Table 2. Thematic Mapper imagery acquired for production of the SC-GAP land

cover.................................................................................................................13

Table 3. SC-GAP vegetation mapping quality assurance spot-checking results by

watershed. ........................................................................................................21

Table 4. The land cover types mapped, their area in square kilometers (multiply by

100 for hectares or 270 for acres), and the percent of the state's total area

represented by the mapped type.......................................................................36

Table 5. Number of ant habitat sampling replicates in the mountain, piedmont, coastal

 plains and sandhills regions of South Carolina, partitioned by land cover

class, 1999-2000. ............................................................................................39

Table 6. The relative amount and accuracy of small vs. large polygons used in the

accuracy assessment.........................................................................................44

Table 7. Error matrix for the 27 class habitat land cover, total accuracy is 33.42% and

error is 1.48%...................................................................................................48

Table 8. Anderson level II codes and percent cover of each mapped type.......................49

Table 9. Anderson Level II error matrix. Estimated user's accuracies and standard

errors are presented in the last two columns, and producer's accuracies with

standard errors are in the last two rows. Overall weighted accuracy is

51.40%, with a standard error of 1.57%. See codes in Table 8. .....................53

Table 10. Anderson Level I error matrix. ..........................................................................54

Table 11. Areas with species lists or surveys that were used to assess the accuracy of

the predicted distribution of vertebrate species in South Carolina. .................72

Table 12. Results of SC-GAP vertebrate accuracy assessment for areas where species

lists were available. Sites are listed in decreasing order of size. ....................74

Table 13. Management Status Categorization; bold characters indicate where SC-GAP

differed from the handbook. ............................................................................81

Table 14. This table indicates the area in square kilometers (km sq) (multiply by 100

for hectares and by 247 for acres) for the following measurements: 1) area

of the land stewardship category in each management status and total for the

state, 2) the steward’s percent of total area in each management category and

of the state area, and 3) total area of each stewardship category in the state

and its percent of state area..............................................................................84

Table 15. Vertebrate habitat classes and their relative protection in GAP status 1 and

Table 16. Some South Carolina species of concern and their percent distribution in

the different protection categories. .................................................................91

2 lands. ..............................................................................................................88
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DEDICATION

We dedicate this report to the naturalists, both professional and amateur, who have

dedicated their lives to the study of the natural diversity of the state and who have shared

that knowledge with others.
viii

EXECUTIVE SUMMARY

The SC-GAP project began in 1996 as an effort of the South Carolina Cooperative Fish

and Wildlife Research Unit. Partners in the project from the beginning included the

South Carolina Department of Natural Resources and the US Fish and Wildlife Services

Coastal Ecosystems Project. The objectives of the project were to 1) map the vegetation

of South Carolina in as much detail as possible with the goal being the production of a

land cover map easily cross-walked to the TNC alliance level, which is dominant species,

2) produce a database of vertebrate ranges and habitat affinities for use in predicting

vertebrate distributions within the state, 3) produce a database of protected lands within

the state including both public and private agencies with associated ownership data and to

classify them by protection status, 4) analyse the relative protection of the state’s

biodiversity through a gap analysis, 5) build partners during the development and

implementation stages of the SC-GAP projects and 6) provide scientific data on the state

of South Carolina’s biodiversity to the managers and decision-makers of the state of

South Carolina.

The SC-GAP land cover maps the state’s natural and man-made vegetation types to two

classifications, a general 27-class habitat map that was used in modeling vertebrate

distributions. We also produced a more detailed 54-class map in accordance with the

National GAP guidelines of mapping to the alliance level where possible. The initial data

used in developing the map was remotely-sensed satellite data that was pre-classified to

28 classes. We used data from detailed soil surveys, National Wetlands Inventory

surveys, and elevation maps to improve this classification and develop our 54-class land

cover. This was aggregated into the habitat map for use in producing vertebrate

distributions. The accuracy of the map was determined through a combination of aerial

photography and ground assessment points that were compared with the general land

cover. Overall map accuracy of the general land cover, Anderson Level II and Anderson

Level I classifications were 33%, 50% and 71%, respectively.

The vertebrate database was developed through consultation with current literature, with

acknowledged state experts and with general taxonomic guides. This produced a

database with ranges and habitat affinities for a total of 455 vertebrate species that

regularly occur in South Carolina. After the database was completed, experts were again

consulted to assist in resolving conflicts in the data and quality checking the database. A

total of 65 amphibians, 73 reptiles, 249 birds and 68 mammals were included in the final

database and predictive modeling. Modeling took place by attributing each species to the

counties where it is known to occur and to the habitat types it uses within those counties.

These occurrence maps were overlain to produce coverages of overall species richness

and richness for each taxonomic group. The accuracy of the predicted vertebrate

distributions was assessed by comparing a list of species that SC-GAP predicts would be

present at a site to a list of species known to occur at a site. We used species lists from

five areas within the state to check our accuracy. The accuracy ranged from 57% to 85%

depending on the site. For the largest site with the longest period of study the accuracy

was 85%.
ix

The third database component of the project was the production of a database on land

ownership within the state. This was accomplished through a partnership with the US

Fish and Wildlife Service who assisted in mapping stewardship lands for the eight coastal

counties. If all GAP status lands (1 through 4) are considered, there is a total of 6580

acres of publicly and privately owned land within the state, comprising about 8.1% of the

total. If the lands of highest protection, GAP status 1 and 2, only are counted, this

amounts to 1801 acres or about 2% of South Carolina. Clearly there is a need for more

activity in land protection within the state.

The GAP analysis phase of the project indicates the need for further action to protect our

natural habitats and our rural landscapes. While many of our forests receive at least some

level of protection, very little of our scrub/shrub, grassland or cultivated land is protected

according to GAP status. The very large majority of our vertebrate species are protected

on less than 10% of their habitat. For some species of concern, we have reached or

surpassed our conservation goals, especially for those with specific habitat needs. For

species such as bats, however, where habitat requirements are less well defined, more

work needs to be done in ensuring their ongoing protection.

The outreach portion of the project has been very successful through partnerships

developed during the accomplishment of our goals. These partnerships will continue

through activities such as finer scale mapping of state natural land covers, through

scientific studies of invertebrate diversity and better habitat modeling and through

ongoing land cover and change detection projects. For the end-users of all kinds within

the state, a close-out reception for the SC-GAP project was held in December 2001 and

was well attended and well received. Posters of GAP products were displayed, and

information was disseminated about future directions. Requests are already coming for

the data and to have a chance to see the displays from the meeting. There are many

committed and excellent land managers, planners, agency personnel and conservation

groups that are interested in the data, and it is our hope that they will take it, use it, and

make it better.
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ACKNOWLEDGMENTS

Thanks to Amos Eno and the staff of the National Fish and Wildlife Foundation, who

funded the early development of the GAP concept and to the originators including J.

Michael Scott, Blair Csuti, and Jack Estes and the pioneering scientists who forged the

way. Thanks to John Mosesso and Doyle Frederick of the US Geological Survey

Biological Resource Division (BRD) Office of Inventory and Monitoring, for their

support of the national Gap Analysis Program, especially during its transition from the

U.S. Fish and Wildlife Service to the National Biological Service and then to the U.S.

Geological Survey BRD. Thanks to Reid Goforth and the staff at the USGS BRD

Cooperative Research Units for administering Gap's research and development phase

from headquarters. Without those mentioned above, there could not have been a Gap

Analysis Program. Thanks also to the staffs of the National Gap Analysis Program,

Center for Biological Informatics, and Biological Resources Division headquarters.

We acknowledge contributions to this report by Chris Cogan, Patrick Crist, Blair Csuti,

Tom Edwards, Michael Jennings, and J. Michael Scott; as well as the previous GAP

projects.

This project could not have been completed without the assistance of the South Carolina

Cooperative Fish and Wildlife Research Unit at Clemson personnel. Thanks to Carolyn

Wakefield who handled budget and personnel details with unfailing cheerfulness and

professionalism. Stacey West, George Eason, and Leslie Parrish provided essential

components of the database and did so with enthusiasm and innovation.

We would like to thank the South Carolina Department of Natural Resources (SCDNR)

for extensive assistance through expertise, in-kind support, and logistical support. This

project would not have been completed without the many professional employees of

SCDNR. Special thanks go to the Natural Resources Information Management and

Analysis staff: Richard Lacy, John Foster, John McDougald, Dave Barthel, Steve

Warner, Malynn Fields, and George Sullivan. These staff spent significant amounts of

time developing and formatting ancillary and tabular data, compiling protected lands

boundary files and providing vertebrate modeling and cartographic design support.

Michael Criss with the Land and Water Conservation Program helped us prepare to get

our message out to local governments. Tom Kohlsaat, Steve Bennett, John Cely, Mary

Bunch, Lex Glover, Bert Pittman, Kathy Boyle and Tom Murphy of the Wildlife

Diversity Section also provided support for many aspects of the project.

The U.S. Fish and Wildlife Service was a significant partner from the beginning in the

SC-GAP project. B.J. Richardson, and Roger Banks provided technical guidance,

database assistance, and in kind support to the project.

We would also like to thank our taxonomic experts from Clemson University, College of

Charleston, University of South Carolina and University of Georgia Savannah River

Ecology Lab for looking at countless maps and participating in long meetings: Sydney
xi

Gauthreaux, Susan Loeb, Edward Pivorun, Richard Montinucci, John Fauth, Julian

Harrison, Arch McCallum and his Conservation Biology Class, Wally Dawson, Whit

Gibbons, Tracey Tuberville, and John Peles.

Other people and agencies provided significant expertise and support without which we

would not have completed the project. We would like to thank Dana Slaymaker, Leonard

Pearlstine, Milo Pyne, the Southeast GAP group and the National GAP staff.
1

Chapter 1

INTRODUCTION

How This Report is Organized

This report is a summation of a scientific project. While we endeavor to make it

understandable for as general an audience as practicable, it reflects the complexity of the

project it describes. A glossary of terms is provided to aid the reader's understanding, and

for those seeking a detailed understanding of the subjects, the cited literature should be

helpful. The organization of this report follows the general chronology of project

development, beginning with the production of the individual data layers and concluding

with analysis of the data. It diverges from standard scientific reporting by embedding

results and discussion sections within individual chapters. This was done to allow the

individual data products to stand on their own as testable hypotheses and provide data

users with a concise and complete report for each data and analysis product.

We begin with an overview of the Gap Analysis Program mission, concept, and

limitations. We then present a synopsis of how the current biodiversity condition of the

project area came to be, followed by land cover mapping, animal species distribution

prediction, species richness, and land stewardship mapping and categorization. Data

development leads to the Analysis section, which reports on the status of the elements of

biodiversity (natural community alliances and animal species), for South Carolina.

Finally, we describe the management implications of the analysis results and provide

information on how to acquire and use the data.

The Gap Analysis Program Mission

The mission of the Gap Analysis Program is to prevent conservation crises by providing

conservation assessments of biotic elements (plant communities and native animal

species) and to facilitate the application of this information to land management

activities. This is accomplished through the following five objectives:

1) map actual land cover as closely as possible to the alliance level (FGDC 1997).

2) map the predicted distribution of those terrestrial vertebrates and selected other taxa

that spend any important part of their life history in the project area and for which

adequate distributional habitats, associations, and mapped habitat variables are

available.

3) document the representation of natural vegetation communities and animal species in

areas managed for the long-term maintenance of biodiversity.

4) make all GAP project information available to the public and those charged with land

use research, policy, planning, and management.

5) build institutional cooperation in the application of this information to state and

regional management activities.
2

To meet these objectives, it is necessary that GAP be operated at the state or regional

level but maintain consistency with national standards. Within the state, participation by a

wide variety of cooperators is necessary and desirable to ensure understanding and

acceptance of the data and forge relationships that will lead to cooperative conservation

planning.

The Gap Analysis Concept

The Gap Analysis Program (GAP) brings together the problem-solving capabilities of

federal, state, and private scientists to tackle the difficult issues of land cover mapping,

animal habitat characterization, and biodiversity conservation assessment at the state,

regional, and national levels. The program seeks to facilitate cooperative development

and use of information. Throughout this report we use the terms "GAP" to describe the

national program, "GAP Project" to refer to an individual state or regional project, and

"gap analysis" to refer to the gap analysis process or methodology.

Much of the following discussion was taken verbatim from Edwards et al. 1995, Scott et

al. 1993, and Davis et al. 1995. The gap analysis process provides an overview of the

distribution and conservation status of several components of biodiversity. It uses the

distribution of actual vegetation and predicted distribution of terrestrial vertebrates and,

when available, invertebrate taxa. Digital map overlays in a GIS are used to identify

individual species, species-rich areas, and vegetation types that are unrepresented or

underrepresented in existing management areas. It functions as a preliminary step to the

more detailed studies needed to establish actual boundaries for planning and management

of biological resources on the ground. These data and results are then made available to

the public so that institutions as well as individual landowners and managers may become

more effective stewards through more complete knowledge of the management status of

these elements of biodiversity. GAP, by focusing on higher levels of biological

organization, is likely to be both cheaper and more likely to succeed than conservation

programs focused on single species or populations (Scott et al.1993).

Biodiversity inventories can be visualized as "filters" designed to capture elements of

biodiversity at various levels of organization. The filter concept has been applied by The

Nature Conservancy, which established Natural Heritage Programs in all 50 states. The

Nature Conservancy employs a fine filter of rare species inventory and protection and a

coarse filter of community inventory and protection (Jenkins 1985, Noss 1987). It is

postulated that 85-90% of species can be protected by the coarse filter without having to

inventory or plan reserves for those species individually. A fine filter is then applied to

the remaining 15-10% of species to ensure their protection. Gap analysis is a coarse-filter

method because it can be used to quickly and cheaply assess the other 85-90% of species.

GAP is not designed to identify and aid protection of elements that are rare or of very

restricted distribution; rather it is designed to help "keep common species common" by

identifying risk far in advance of actual population decline. These concepts are further

developed below.

The intuitively appealing idea of conserving most biodiversity by maintaining examples

of all natural community types has never been applied, although numerous approaches to
3

the spatial identification of biodiversity have been described (Kirkpatrick 1983, Margules

and Nicholls 1988, Pressey and Nicholls 1989, Nicholls and Margules 1993).

Furthermore, the spatial scale at which organisms use the environment differs

tremendously among species and depends on body size, food habits, mobility, and other

factors. Hence, no coarse filter will be a complete assessment of biodiversity protection

status and needs. However, species that fall through the pores of the coarse filter, such as

narrow endemics and wide-ranging mammals, can be captured by the safety net of the

fine filter. Community-level (coarse-filter) protection is a complement to, not a substitute

for, protection of individual rare species.

Gap analysis is essentially an expanded coarse-filter approach (Noss 1987) to

biodiversity protection. The land cover types mapped in GAP serve directly as a coarse

filter, the goal being to assure adequate representation of all native vegetation community

types in biodiversity management areas. Landscapes with great vegetation diversity often

are those with high edaphic variety or topographic relief. When elevational diversity is

very great, a nearly complete spectrum of vegetation types known from a biological

region may occur within a relatively small area. Such areas provide habitat for many

species, including those that depend on multiple habitat types to meet life history needs

(Diamond 1986, Noss 1987). By using landscape-sized samples (Forman and Godron

1986) as an expanded coarse filter, gap analysis searches for and identifies biological

regions where unprotected or underrepresented vegetation types and animal species

occur.

More detailed analyses were not part of this project, but are areas of research that GAP as

a national program is pursuing. For example, a second filter could combine species

distribution information to identify a set of areas in which all, or nearly all, mapped

species are represented. There is a major difference between identifying the richest areas

in a region (many of which are likely to be neighbors and share essentially the same list

of species) and identifying areas in which all species are represented. The latter task is

most efficiently accomplished by selecting areas whose species lists are most different or

complementary. Areas with different environments tend to also have the most different

species lists for a variety of taxa. As a result, a set of areas with complementary sets of

species for one higher taxon (e.g., mammals) often will also do a good job representing

most species of other higher taxa (e.g., trees, butterflies). Species with large home ranges,

such as large carnivores, or species with very local distributions may require individual

attention. Additional data layers can be used for a more holistic conservation evaluation.

These include indicators of stress or risk (e.g., human population growth, road density,

rate of habitat fragmentation, distribution of pollutants) and the locations of habitat

corridors between wildlands that allow for natural movement of wide-ranging animals

and the migration of species in response to climate change.

General Limitations

Limitations must be recognized so that additional studies can be implemented to

supplement GAP. The following are general project limitations; specific limitations for

the data are described in the respective sections:
4

1. GAP data are derived from remote sensing and modeling to make general

assessments about conservation status. Any decisions based on the data must be

supported by ground-truthing and more detailed analyses.

2. GAP is not a substitute for threatened and endangered species listing and recovery

efforts. A primary argument in favor of gap analysis is that it is proactive: it seeks to

recognize and manage sites of high biodiversity value for the long-term maintenance

of populations of native species and communities before they become critically rare.

Thus, it should help to reduce the rate at which species require listing as threatened or

endangered. Those species that are already greatly imperiled, however, still require

individual efforts to assure their recovery.

3. GAP data products and assessments represent a snapshot in time generally

representing the date of the satellite imagery. Updates are planned on a 5-10 year

cycle, but users of the data must be aware of the static nature of the products.

4. GAP is not a substitute for a thorough national biological inventory. As a response to

rapid habitat loss, gap analysis provides a quick assessment of the distribution of

vegetation and associated species before they are lost, and provides focus and

direction for local, regional, and national efforts to maintain biodiversity. The process

of improving knowledge in systematics, taxonomy, and species distributions is

lengthy and expensive. That process must be continued and expedited, however, in

order to provide the detailed information needed for a comprehensive assessment of

our nation's biodiversity. Vegetation and species distribution maps developed for

GAP can be used to make such surveys more cost-effective by stratifying sampling

areas according to expected variation in biological attributes.

The Study Area

A Brief Description of South Carolina

biologically richest areas of North America. It is comprised of three or four

physiographic provinces: the mountains of the Blue Ridge Province, the rolling hills of

the Piedmont, the Sandhills (sometimes included with the Coastal Plain) and the coastal

yellow indicates the coastal plain which includes the shore region, orange depicts the

piedmont, and brown represents the mountains or Blue Ridge Province). Precambrian

granite and gneiss make up the base rock of the Blue Ridge mountain region with a thick

series of late Precambrian sedimentary rocks on top. The Blue Ridge Province

experiences some of the highest rainfall on the east coast and possesses some spectacular

valleys and coves. The flora is very rich and diverse in this area, especially in the

mountain coves.

South Carolina (Figure 1) is located in the southeastern United States, one of the

plain (Figure 2, note that light green depicts shore habitat used in vertebrate modeling,
5

Figure 1. A general map of South Carolina.

Originally, the forests that dominated the mountain region were in the oak-chestnut

association until chestnut trees were decimated by the chestnut blight in the early 1900s.

Generally the forests are mixed mesophytic forests consisting mainly of oak types. The

rich cove forests and seepage communities provide habitat for a diversity of plants,

including many rare species. The mountains are separated from the Piedmont by the

Brevard Zone or fault.

The Piedmont region extends from the foothills of the mountains to the fall line near the

center of the state. The fall line represents a transition from the igneous and metamorphic

rocks of the Piedmont to the unconsolidated segments of the coastal plain. A significant

formation of the Piedmont is the slate belt that comprises about 20 percent of the
6

Figure 2. Physiographic regions of South Carolina.

province and, because of its susceptibility to erosion, forms slightly lower areas with

wide valleys. The Piedmont region has been highly altered by agricultural practices that

contributed to erosion and depletion of the formerly rich soils of the region. The

topography of the Piedmont consists of rolling hills with fairly steep river valleys.

Forests are often oak-hickory associations with a high diversity of plant life along the

rivers.

The Sandhills are often included as part of the coastal plain and form a drier, more hilly

portion of the state with an often unique vegetation including xeric pine/oak forests and

richly vegetated seeps. Variation in vegetation types, from xeric to very mesic is

influenced by soil drainage, topography, and the action of fire. Longleaf pine and turkey

oak can be found on drier uplands with a variety of oaks and hickories found on the
7

slopes of the sandhills. Mesic communities are also found in the sandhills in areas of poor

drainage; these include seepage communities and Atlantic white cedar swamps. The

topography is sculpted by streams passing from the harder bedrock of the Piedmont

through the soft sands of the coastal plain resulting in hills and valleys with occasional

waterfalls, bluffs and shoals.

The Coastal Plain extends from the fall line to the coast and was formed by sediments

laid down by the sea. Ancient seashores experienced periods of shifting for considerable

distances across the coastal plain due to tilting or warping of the landscape and

fluctuations of sea level. The coastal plain is very flat with wandering rivers and streams.

The action of waves and formation of sandbars is responsible for very interesting

formations of ancient ridges and low-lying areas in the coastal areas of the state.

Dominant tree species are often coniferous with hardwoods dominating only in the

maritime forests and river bottoms. Extensive riverine swamps are separated by uplands

with more xeric forests, pine flatwoods, Carolina Bays and isolated freshwater wetlands.

Carolina Bays are a formation unique to the southeastern US and range in occurrence

from Delaware to Florida but are concentrated in the Carolinas and Georgia. They are

elliptical depressions oriented in a northwest to southeast direction with a raised sand

ridge on the southeastern edge. There are varying hydrologic characteristics, ranging

from permanently- to seasonally-flooded. Typical vegetation includes black gum, sweet

gum, magnolia, bald cypress and maple. Shrubs such as sumac, buttonbush, gallberry

and red bay also occur along with other plants such as water lilies, sedges and various

grasses. Carolina Bays provide significant wildlife habitat as well as water quality and

erosion control.

South Carolina has a mild, humid climate ranging from highest temperatures in the center

of the state to cooler temperatures in the mountains and along the coast. Higher rainfall

averages can be found in the mountains with the lowest averages in the upper Coastal

Plain and Sandhills.

Tropical storms or hurricanes occur in the state at a frequency of about one every two

years, often doing little damage and bringing much needed rains. The greatest effect

from hurricanes is on the outer coast, and they generally lose power and strength as they

move inland. High rainfall and storm surge cause damage as do the high winds often

associated with hurricanes. In addition to wind damage and destruction, saltwater

incursion also kills trees in coastal forests. The high winds associated with Hurricane

Hugo in 1989 impacted extensive coastal forests and felled trees inland in areas such as

the Congaree Swamp as it moved up the center of the state. Hurricane Floyd in 1999 was

accompanied by high rainfall and flooding events; much of the damage was to property in

the northeastern portion of the state.

The first Europeans to arrive in South Carolina were Spanish and French explorers who

arrived in the 16th century. The land at that time was inhabitated by many small tribes of

Native Americans, including Cherokees and Catawbas. It wasn’t until 1670 that a

permanent European settlement was established near Charleston. Other European settlers
8

arrived and began growing crops such as rice and indigo. Large numbers of African

slaves were brought into the colony to work on plantations. Cotton became a major crop

in the upcountry after the invention of the cotton gin.

On May 23, 1788, South Carolina became the eighth state to enter the union. However,

by the early 1800s, South Carolinians were dissatisfied with the federal government and

by 1860 became the first of the southern states to secede from the Union. General

Sherman marched through South Carolina burning plantations and most of the city of

Columbia. As with many southern states, the Reconstruction period that followed was a

time of economic, social and political upheaval. The economy suffered from low cotton

prices and the demise of the plantation system.

By the beginning of the 20th Century, economic recovery was beginning with the

development of a textile industry and the arrival of other manufacturers. Tourism has

developed as a major industry, centered around the state’s beaches and mountains.

Hurricane Hugo struck the coast in 1989 and caused tremendous damage to homes,

businesses and natural areas. Most areas have recovered from the damage but remnants

of the hurricane’s damage persist in many formerly intact forests.

South Carolina grows more peaches than any other state except California; it ranks fifth

in overall tobacco production. Other top agricultural commodities include nursery and

greenhouse products, watermelons, peanuts, broilers and turkeys, and cattle and calves.

The only commercial tea plantation in America is 20 miles south of Charleston on

Wadmalaw Island.
9

CHAPTER 2

LAND COVER CLASSIFICATION AND

MAPPING

Introduction

Mapping natural land cover requires a higher level of effort than the development of data

for animal species, agency ownership, or land management, yet it is no more important

for gap analysis than any other data layer. Generally, the mapping of land cover is done

by adopting or developing a land cover classification system, delineating areas of relative

homogeneity (basic cartographic "objects"), then labeling these areas using categories

defined by the classification system. More detailed attributes of the individual areas are

added as more information becomes available, and a process of validating both spatial

pattern and labels is applied for editing and revising the map. This is done in an iterative

fashion, with the results from one step causing re-evaluation of results from another step.

Finally, an assessment of the overall accuracy of the data is conducted. The final

assessment of accuracy will show where improvements should be made in the next

update (Stoms et al.1994).

In its "coarse filter" approach to conservation biology (e.g., Jenkins 1985, Noss 1987),

gap analysis relies on maps of dominant natural land cover types as the most fundamental

spatial component of the analysis (Scott et al. 1993) for terrestrial environments. For the

purposes of GAP, most of the land surface of interest (natural) can be characterized by its

dominant vegetation.

Vegetation patterns are an integrated reflection of the physical and chemical factors that

shape the environment of a given land area (Whittaker 1965). They also are determinants

for overall biological diversity patterns (Franklin 1993, Levin 1981, Noss 1990), and they

can be used as a currency for habitat types in conservation evaluations (Specht 1975,

Austin 1991). As such, dominant vegetation types need to be recognized over their entire

ranges of distribution (Bourgeron et al. 1994) for beta-scale analysis (sensu Whittaker

1960, 1977). These patterns cannot be acceptably mapped from any single source of

remotely sensed imagery, therefore, ancillary data, previous maps, and field surveys are

used. The central concept is that the physiognomic and floristic characteristics of

vegetation (and, in the absence of vegetation, other physical structures) across the land

surface can be used to define biologically meaningful biogeographic patterns. There may

be considerable variation in the floristics of subcanopy vegetation layers (community

association) that are not resolved when mapping at the level of dominant canopy

vegetation types (alliance), and there is a need to address this part of the diversity of

nature. As information accumulates from field studies on patterns of variation in

understory layers, it can be attributed to the mapped units of alliances.
10

Land Cover Classification

Land cover classifications must rely on specified attributes, such as the structural features

of plants, their floristic composition, or environmental conditions, to consistently

differentiate categories (Küchler and Zonneveld 1988). The criteria for a land cover

classification system for GAP are:

• an ability to distinguish areas of different actual dominant vegetation;

• a utility for modeling animal species habitats;

• a suitability for use within and among biogeographic regions;

• an applicability to Landsat Thematic Mapper (TM) imagery for both rendering a

base map and from which to extract basic patterns (GAP relies on a wide array of

information sources, TM offers a convenient meso-scale base map in addition to

being one source of actual land cover information);

• a framework that can interface with classification systems used by other

organizations and nations to the greatest extent possible; and

• a capability to fit, both categorically and spatially, with classifications of other

themes such as agricultural and built environments.

For GAP, the system that fits best is referred to as the National Vegetation Classification

System (NVCS) (FGDC 1997). The origin of this system was referred to as the

UNESCO/TNC system (Lins and Kleckner 1996) because it is based on the structural

characteristics of vegetation derived by Mueller-Dombois and Ellenberg (1974), adopted

by the United Nations Educational, Scientific, and Cultural Organization (UNESCO

1973) and later modified for application to the United States by Driscoll et al. (1983,

1984). The Nature Conservancy and the Natural Heritage Network (Grossman et al.

1994) have been improving upon this system in recent years with partial funding supplied

by GAP. The basic assumptions and definitions for this system have been described by

Jennings (1993).

The classification of vegetation from remotely sensed data for SC-GAP proceeded in two

steps, the production of an initial classified image using 28 general classes and the

refinement of that classification using ancillary data to provide a 54-class map that

consists of aggregated alliances. The initial classification scheme for SC-GAP was based

on the Land Manager’s Guide for Birds (Hamel 1992) and the Land Manager’s Guide for

detail would be readily achieved and would provide the basis for further refinement of the

map using ancillary data. This simplified classification scheme was used because it was

not possible to produce the initial version of the map at the originally intended level for

several reasons. The original methodology called for mapping to the TNC alliance level

classification scheme. This includes classifications such as Sand Live Oak Forest

Alliance, the description of which includes a context of beach shores and a scrub/shrub

comprised of certain species. Soil is also an important part of the classification. These

types of classes are difficult to determine from simply evaluating spectral characteristics.

In other words, these classes are environmental or habitat descriptions that are not

necessarily discernible by simple statistical categorizing of image brightness values.

Reptiles and Amphibians (Wilson, 1995) (see Table 1). It was felt that this level of
11

The general vertebrate habitat land cover provides the basis for producing predicted

vertebrate distributions, and taxonomic experts used the 28 classes of this version to

produce the habitat association database. The vertebrate habitat map can easily be

crosswalked to an Anderson Level III classification (Anderson et al. 1976) as well as

further refined for our final land cover map.

The classification for the final land cover consists of 54 classes. Although the

classification is crosswalked to the NVCS, it doesn’t fit in a neatly hierarchical sense. In

attempting to create a land cover that reflects vegetational patterns on the ground and that

will be appropriate for a variety of users with a range of botanical knowledge, we

simplified the classification from a potential of 231 alliances to a more useable set of

habitat descriptions. A detailed description of the 54 map classes with a crosswalk to

more than once in association with a map class due to the nature of our classification.

Table 1. Original land cover classes with working description

Class Name Class Description

1 - fresh water ponds, rivers, any inland water

2 - marine water salt or estuarine water

3 – marsh high and low marsh, non-forested wetland

4 - bays and pocosins Carolina bays, also called pocosins, wetland ovular

depressions

5 - stream margins 30 meter buffer around rivers and streams

6 - swamps/bottomland Cypress tupelo swamps

7 – bottomland hardwood river floodplain hardwoods, wet depressions

8 - wet soil areas of bare wet soil, little or no canopy, sand bars and

dredge spoil

9 - wet scrub/shrub thicket short bushy vegetation, transitional, over wet soil

10 - dry scrub/shrub thicket short bushy vegetation, transitional, over dry soil

11 - sandy bare/friable soil sand, beaches, sandhill exposed soil

12 - open canopy, recently cleared

forest

transitional areas, mainly bare soil and pioneering grassy

vegetation, fallen trees

13 - rock outcrops caves, bare exposed rocks

14 - aquatic vegetation algae, macrophytes, seagrass, not marsh

15 - upland pine/closed canopy

forest

mainly pine plantations, tight canopies, very little soil or

scrub vegetation shows through

16 - upland pine forest/deciduous

understory

pine with mainly scrub oak understory, canopy is more

spread out but is still fairly close

TNC/UNESCO classification is provided in Appendix 1. Some alliances may appear
12

17 - upland pine forest/grass

understory

pine with natural grasses underneath, canopy is very

spread out

18 - upland deciduous forest deciduous arboreal vegetation in dry soil

19 - mesic deciduous forest deciduous arboreal vegetation in wet soil, not in flood

plain or bottomland depression

20 - upland mixed forest forest which is mixed with evergreen and deciduous so

that each 30 meter squared area is relatively evenly

mixed

21 - mesic mixed forest mixed forests in marginal bottomland floodplain areas

22 - grasslands/pasture native grasses, fallow fields, pastureland

23 - cultivated land agriculture, lawns, golf courses

24 - urban development - high

intensity

industrial development, central business district, large

highways

25 - urban development - low

intensity

residential development

26 - ecotone boundary/edge

shadows

boundary between cultivated and natural classes, based

mainly on shadows from native vegetation on to

cultivated land

27 - mesic evergreen evergreen in wet areas, not bottomland floodplain,

maritime evergreen

28 – maritime forest Live oak, palmetto forests

Methods

Mapping Standards and Data Sources

For SC-GAP we maintained the original 30 x 30 meter pixel resolution of the TM data

throughout the classification process. All scenes were rectified with less than 1 pixel

error. A target accuracy of 80% was set for classification of major vegetation and land

cover types.

Characteristics Consortium (MRLC). This consortium established a partnership with

USGS Earth Resources Observation Systems (EROS) Data Center. The MRLC offers

imagery, which is distributed by EROS Data Center, at a drastically reduced price to its

members. The purpose of MRLC is to generate land cover data of the entire United States

based on standardized satellite image classification procedures and protocols. One means

to do this is to facilitate mapping by offering inexpensive imagery to mapping agencies.

The list of scenes available for South Carolina was found on the MRLC homepage

The TM imagery (see Figure 3) was acquired from the Multi-Resolution Land

(http://www.epa.gov/mrlc). The scenes acquired for this project are as follows:
13

Table 2. Thematic Mapper imagery acquired for production of the SC-GAP land

cover.

path/row date recorded

15/36 10-18-91

16/36 3-1-92

17/36 3-24-92

18/36 10-28-93

15/37 10-18-91

16/37 11-26-91

17/37 12-19-91

16/38 3-1-92

Aerial videography was not obtained for use in developing the land cover. NAPP color

photographs were used to assist in the initial classification of the imagery.

It should be noted that the land cover map produced by these methods is limited to the

time that the imagery was taken. Thus our product reflects land cover in the state as it

existed in 1991 through 1993. The most obvious time-related errors will be those in

transitional classes such as clearcuts, scrub/shrub, closed canopy pine (mainly pine

plantations), urban and other classes impacted by development/land use decisions.
14

Figure 3. Layout of TM scenes used in SC-GAP land cover mapping.
15

Imagery and Ancillary Data

 The imagery was imported into the image processing software format from its native

binary format. The software used in this project was Earth Resources Data Analysis

Systems (ERDAS) Imagine 8.2 and 8.3. The imagery was loaded into the Import tool as a

band sequential file. The rows and columns were determined for each image by viewing

the header information. This is listed in the .dda file and can be accessed using Data View

option in the Import tool. It is necessary to know the number of bands while importing.

MRLC data consists of six bands because the thermal infrared was not distributed to the

state given the applications for which the data is used.

The MRLC imagery came already rectified but unfortunately it was not rectified to UTM

zone 17. In order to correct this problem it was necessary to re-rectify the data. This was

done by first displaying the image so that the rectification attributes could be displayed as

well. This can be done in Imagine 8.2 by selecting Image Info. In this tool the map

information and projection information are displayed. These attributes were deleted and

replaced with new values. Under map info, 30 x 30 meter pixel size was selected, and

UTM was selected for the projection.

The file was originally projected in UTM zone 18, and South Carolina is completely in

zone 17, but the image is consistent within itself. The problem is that coordinate values

are incorrect. This means a full rectification is not necessary, because it is simply

projected to the wrong zone. This can be corrected without collecting GCPs (Ground

Control Points). The scene was instead resampled, UTM zone 17 was selected as the

projection, and Clark 1886 was selected as the spheroid. The scene then had to be refined

by setting coordinates over a known point on the scenes. This was done in Imagine by

using Set Drop Point. Once the point was selected, in this case a road intersection, the

same point was selected from a reference layer. This layer was created by rectifying a

Digital Line Graph (DLG) roads layer using a statewide set of GPS road intersection

data. The drop point corresponded to one of the GPS road intersections. The coordinates

of the GPS point were typed into the drop point dialog box and thus the imagery was

calibrated spatially with this dataset.

The first step in sub-setting the image data was acquiring the 11-digit hydrologic unit

boundary data from South Carolina Department of Health and Environmental Control.

Hydrologic units are small watershed areas based on the digitizing of ridges in the

landscape interpreted from 1:24,000 and 1:62,500 USGS topographic maps. The

topographic maps of South Carolina used to delineate these watersheds were based on

aerial photography flown from the 1930's through the 1950's. The work on the hydrologic

unit dataset for South Carolina was done by Claude Lowry Jr. at the USDA Natural

Resources Conservation Service, USDA, in Columbia S.C. The work was begun in 1966.

The maps were then published at the National Cartographic Office of the USDA in Ft.

Worth, Texas in September 1981. Elevation-based drainage was the only criterion for

watershed boundary delineation. Each watershed boundary makes up a hydrologic unit

and there are a total of 280 of these 11-digit watersheds. There are several levels of

hydrologic units categorized in this way. They are distinguished by the level of watershed

they delineate. The layer used in this project was an 11-digit layer. This refers to the
16

hydrologic unit code (HUC). This number is hierarchical and tells the scale of the

watershed by how many digits the code contains. Presently there are eight-digit (more

general units) and fourteen-digit (more specific units) layers available as well. The 11-

digit layer was selected because 280 units was an appropriate number of units for

HUCs and their parent 8-digit units that have been grouped by color. More units would

have caused the classification to take too long, and fewer units would mean that there was

not the level of stratification necessary to accurately classify the many classes of features.

Figure 4. 11-digit HUCs used to subset TM imagery for South Carolina.

Color groupings represent the parent 8-digit HUCs.

sufficient subdivision of the TM data. Figure 4 shows the boundaries of the 11-digit
17

The hydrologic units were utilized as boundaries for subsetting the imagery. Generally,

the smaller the target classification area, the fewer the number of potential classes. This

is thought to make the classification more accurate. For example, a classification

completed for an area of one square mile is likely to be more accurate than a

classification of a 50 square mile area using the same classification scheme. This is

because it is not as inaccurate to interpolate over a small area than a large area from

samples used to label the classes. Also, if a photo-interpretation error is made during

labeling, the error only affects that area within the unit being classified. Breaking the

imagery up into physically homogeneous areas, or physiographic regions, is standard

practice in large area classification (Jensen 1996) and is called ‘brute-force’ classification

(Yuill 1997).

Each image was subset by the hydrologic units until the state was covered by 280 subset

TM images. This was done in Imagine 8.2 by creating AOIs (Areas of Interest) from the

hydrologic boundaries. Then these were used in the Subset tool. During the subset

process the image was displayed with the AOI displayed over it. Then the Inquire Box

was also displayed, and Fit to AOI option was selected. At this point it is necessary to

extend the box past the AOI slightly. The box is used to chop the image off beyond the

borders of this box. If it is not used then the resulting subset image has zero values to the

extent of the original image. If the box is not extended, then it is possible for slivers to be

produced in the data when the classified units are eventually mosaicked. Unfortunately

this was not realized until after the project was in the mosaicking stage. Slivers of no

value do not occur every time the units are subset, but often enough to be a problem.

Another problem is that hydrologic units sometimes extend beyond their primary scene’s

boundaries. In these cases, the unit boundaries are only partially “filled with data” and

thus a significant hole is left during the mosaicking process. This was subsequently

realized and dealt with by finding the areas that were left empty of data, screen-digitizing

AOIs around them, and subsetting them from the image scenes. In other words, the

hydrologic units were simply broken into parts whereby each part would be contained

within a scene, i.e. not crossing scene boundaries. Then these new subsets were

mosaicked back “into” the statewide dataset. This is done so that parts of two different

images do not have to be classified together since they have different ranges of

reflectance values. This only requires analysis of one image at a time.

During the subset process, it is necessary to select the bands to be used in the analysis. It

is not necessary to use all six bands of the MRLC TM imagery for classification. In fact,

too much spectral information can create confusion in pixel values, thus leading to a “salt

and pepper” look as well as hard-drive space depletion. Each band is the same file size,

therefore, if two bands are deleted the image requires one-third less storage space. Bands

two, three, four, and five were selected for this project. These bands are standard for

general vegetation classification. Band one, which corresponds to the visual blue

wavelengths, is often too influenced by atmospheric scattering to be very helpful in

vegetative studies. Band six is a middle infrared band that is not necessary when middle

infrared (MIR) band five is used since they have a similar spectral curve. Vegetation

reflects a similar amount of band five-wavelength energy as band six wavelengths so that

only one band was used. When looking at the chlorophyll-producing vegetation spectral
18

curve, it is evident that the other four bands must be used. The contrast of red and nearinfrared is the greatest, so these two bands must be used. A principal components analysis

indicates which bands give the greatest contrast in pixel values in an image, and thus

helps to select the bands that provide the most information when used together. This

analysis was done by Florida Gap (see FL-GAP report) as well as other studies. It is

usually the case that bands 2,3,4, and 5 are the four most important bands for vegetation

classification using Landsat TM imagery.

The subset process produced 280 images that were then clustered using the Iterative SelfOrganizing Data Analysis Technique (ISODATA) clustering algorithm. ISODATA

performs an entire statistical classification of the pixel values repeatedly with the number

of times to be defined by the user. It uses a minimum spectral distance method to

determine which pixels form a cluster. The number of clusters is determined by the user.

The first iteration of the ISODATA process creates arbitrary clusters that are accumulated

in the later iterations (ERDAS Field Guide, 1997).

In Imagine 8.2, the defaults were used. Maximum iterations remained six, and the

convergence threshold was maintained at 0.950. The number of classes was set to fifty.

Fifty classes were chosen because this would give the most labeling options (before

diminishing returns) by breaking up the data statistically to such a point that the

interpreter would have more freedom in naming features. This means more detail is

possible in the classification because the features are broken into classes of relatively few

pixels. Also, each image covers a small area, so although this requires more work from

the interpreter, it also means there is more reliance on the knowledge of the interpreter.

This is ideal if the interpreter has botanical knowledge. Otherwise the interpreter is forced

to rely on a fairly general classification scheme as was used in this study.

Digital National Wetlands Inventory (NWI) wetlands information is available for most of

the state. The National Wetlands Inventory began in 1954 as a national inventory of

wetland habitat, primarily related to the presence of valuable waterfowl habitat. The

classification had several shortcomings and was revised in 1974 when further surveys

were initiated. The classification is widely used and applicable to all types of wetlands

(Cowardin et al. 1979). The purpose of the revision was to make the data useful to all

kinds of resource managers and provide a uniform classification. The South Carolina

NWI dataset is complete for all regions of the state except for the Upper Santee drainage

and the Upper Savannah drainage in the Piedmont and most of the Blue Ridge Province.

These data are developed through joint funding agreements between the SC DNR and the

U.S. Fish and Wildlife Service, National Wetlands Inventory (NWI). They are delineated

from 1:40,000-scale NAPP color infrared photography and correspond to the USFWS

Cowardin classification system for wetlands and deepwater habitats of the United States.

The wetlands habitats are mapped to a one-acre minimum map unit but smaller units

often are identified. The upland categories are delineated simultaneously using the

Anderson, Level II system developed by the U.S. Geological Survey with a ten-acre

minimum resolution. Polygons were dissolved for each county to derive 32 classes (see

Appendix 2) of land use and were converted to raster format.
19

Statewide soil information was also available in digital format. Soils data are derived

from existing county surveys available from the Natural Resources Conservation Service

(formerly the Soil Conservation Service). Under a DNR contract, the map sheets are

zoom-transferred (ZTS) to topographic quadrangle base maps and redrafted to stable base

mylar to remove the distortion from the non-rectified photographic base on which they

are delineated. The NRCS reviews the draft product to correct any ZTS problems, and the

data are digitized. After digitization, the data are quality reviewed to ensure edgematching and attributing compliance and linked to the STATSGO tabular attribute files

that characterize each soil type for a series of composition, productivity, and suitability

variables. The resolution of the data is 5 acres, and spatial accuracy conforms to USGS

mapping standards. Polygons were dissolved for each county according to 24 soil type

Classification of Imagery

The clusters generated from the ISODATA algorithm applied to the 1991-1993 TM

images were labeled by interpreting National Aerial Photography Program (NAPP) aerial

photography. A 1994 statewide coverage of NAPP air photos exists for South Carolina.

This photography is in the form of hard-copy color-infrared positive transparencies. The

infrared spectral information of the film enables the interpreter to easily distinguish

between deciduous and evergreen vegetative features. Identification of alliances or

species of vegetation requires extensive botanical knowledge and experience. Also,

water features were easily detected using this source.

Each watershed image subset had a fifty-cluster layer displayed over it in order to

determine where in the image each cluster of values was located. There were some

problems in the clustering, however. For example, often water pixels were extensively

confused with pine plantation pixels. This problem can be remedied using ancillary data

such as soil classification. When this problem occurred at this stage, the clusters with

near equal overlap were assigned to the pine plantation class because the water could be

later determined from other data. There were other problems with trying to classify

according to the classification scheme used, but all of these classes could also be

distinguished from others by using ancillary datasets.

Once the clusters are labeled it is necessary to organize the clusters in terms of the

classification scheme. This was done by recoding the clusters with numbers that

correspond to classes in the scheme. When recoding, a dialog box appears asking what

each cluster should be named. Each cluster was placed in the class that best defines it

according to the labels given to each. The result is 280 classified hydrologic units that are

ready to be mosaicked. The names and colors selected for each class are stored in a

‘working’ hydrologic unit classification that was eventually copied to a final recoded

watershed unit. It is necessary that all the classifications are consistent so that the mosaic

works.

An independent and experienced photo-interpreter checked the accuracy of the initial

classification based on a subjective visual analysis of the data using NAPP photographs

and a knowledge of South Carolina vegetation patterns. Through this approach, obvious

classes (see Appendix 3) and converted to raster format.
20

illogical errors were caught and remedied immediately. For example, in this project an

entire bottomland swamp area, the Congaree National Monument, was mis-labeled as

upland deciduous by the interpreter. This area was easily subset and recoded to

bottomland swamp. These errors may not necessarily be resolved by ancillary data, but

tend to be straightforward to fix. An error matrix was developed for this assessment to

help the interpreter decide what classes were the least accurate and thus needed either

simple recoding changes, cluster-busting, or more fundamental changes using a decision

rule applied to ancillary data. The error matrix helped to make decisions to correct the

The true test of how accurately each subset classification works is how well the subsets

mosaic. When the classifications are mosaicked together, it is easy to tell which subsets

are misclassified because they do not fit in the same pattern with the other units but rather

look like a patchwork layer. Some of this patchiness can be resolved by applying

ancillary information; but some units must be re-classified, or cluster-busted. In Imagine

there is a mosaic tool, but it contains histogram and contrast matching algorithms that

should not be used. Last Overlay is the only appropriate mosaic algorithm to use in this

tool given that classified data is being mosaicked. Sometimes Imagine cannot handle

such large numbers of input files for mosaicking. This depends on system constraints. In

this project, several parts of the state were mosaicked in an initial run. The results of this

process were subsequently mosaicked together to produce a statewide dataset.

The resulting mosaicked statewide dataset was analyzed for errors that this new smaller

scale made possible to detect. At this point, some of the noticeable errors were fixed

using the AOI screen digitizing technique, where a boundary is digitized around the

problem area so that it can be subset, recoded, and mosaicked back into the statewide

dataset.

initial classification (see Table 3).
21

Table 3. SC-GAP vegetation mapping quality assurance spot-checking results by

watershed

Unit Class Name QA/QC Comments

1 9.dry scrub/shrub frequent conflict w/23.urban & 24.low urban

1 15.pine/decid under moderate conflict with 19.mixed

1 17.upland decid frequent conflict w/14.pine/clocan&15.pine/decid

under&19.mixed

1 22.cultivated moderate conflict w/23.urban & 24.low urban

2 17.upland decid moderate conflict w/18.mesic decid

2 22.cultivated moderate conflict w/23.urban & 24.low urban

4 9.dry scrub/shrub moderate conflict w/young16.pine/grass under

4 14.pine/clo can rare conflict w/18.mesic decid (& shadows)

4 17.upland decid rare conflict w/18.mesic decid

4 22.cultivated rare conflict w/23.urban & 24.low urban

5 22.cultivated frequent conflict w/1.water & 24.low urban

6 1.water frequent conflict w/17.upland decid (shadows)

6 15.pine/decid under frequent conflict w/young 14.pine/clo can & 17.upland

decid

6 22.cultivated slight conflict w/12.rock outcrop & 17.upland decid

7 17.upland decid moderate conflict w/11.clearcut & 20.mesic mixed

8 9.dry scrub/shrub frequent conflict w/17.upland decid

8 14.pine/clo can frequent conflict w/1.water & 20.mesic mixed

8 17.upland decid moderate conflict w/18.mesic decid & 19.mixed

8 22.cultivated moderate conflict w/23.urban

9 9.dry scrub/shrub frequent conflict w/11.clearcut & 17.upland decid

9 17.upland decid moderate conflict w/18.mesic decid & 19.mixed

9 22.cultivated slight conflict w/23.urban

10 9.dry scrub/shrub frequent conflict w/17.upland decid

10 14.pine/clo can frequent conflict

w/1.water&15/pine/decidund&16.pine/grassund

10 17.upland decid moderate conflict w/19.mixed

10 22.cultivated moderate conflict w/24.low urban

11 9.dry scrub/shrub frequent conflict w/17.upland decid

11 17.upland decid moderate conflict w/15.young pine/decid under &

18.mesic decid

11 22.cultivated moderate conflict w/9.dry s/s & 23.urban & 24.low urban

11 24.low urban slight conflict w/class-edge transition pixels

14 17.upland decid frequent conflict w/18.mesic decid&19.mixed&20.mesic

mixed
22

Identification of land cover types from remotely-sensed data is only accurate to a certain

degree because it bases the classification totally on spectral reflectance of features on the

surfaced of the earth. This is an accurate approach if the classification scheme is general.

However, to map to the level required for the final product, it was necessary to use

datasets that could further refine the unsupervised classification. In the development of

the initial land cover, vegetation types on soils ranging from moist to very wet were

purposely underestimated with the plan of using soil and wetlands information to

improve the classification of mesic and wetland types. These were the four primary

sources of ancillary data for the classification of South Carolina vegetation:

1. NWI: We used all five types of wetlands (Marine, Estuarine, Riverine, Lacustrine,

and Palustrine) to varying degrees. Lacustrine aquatic beds and riverine aquatic beds

were used to discriminate aquatic vegetation only. The marine unconsolidated shore

class was used to develop a shore category only. Estuarine and palustrine classes were

extensively used to improve wetlands and shore classification. See appendices for

NWI classes such as estuarine vs fresh marsh and salt marsh, fresh and salt water were

used to improve our coastal map classification as well as helping to discriminate salt

flat/salt shrub habitat; the sparse vegetation of salt flat/salt shrub is difficult to distinguish

from satellite imagery. Palustrine forested and scrub/shrub classes were grouped into

deciduous and evergreen forested wetland types and deciduous and evergreen scrub/shrub

wetlands. These were used to improve classification of swamp, forested bottomland, wet

scrub shrub and pocosin land cover types. Some NWI urban classes were helpful in

mapping maritime forests associated with resort residential areas and golf courses, and

NWI upland forest types assisted in the discrimination of natural evergreen forests from

planted pine. Few final SC map classes were derived solely from NWI classification;

most decisions were based on the initial general classification plus a wetlands or soil or

both wetland and soil classification. Final map classes derived solely from NWI ancillary

data include beaches, salt flat/salt shrub, salt vs fresh water, mud flats and spoil

a description of how each class was derived from ancillary data. 

2. Soils: These formed a significant source of data for the mapping procedure, especially

in refining mesic forest and shrub types from original classes and in developing a more

accurate bottomland classification. Efforts to map a seepage shrub class based on soil

slope and drainage proved unsatisfactory as did efforts to use soil complexes in our

mapping effort. Alfisols (circum-neutral soils) were of limited assistance in the coastal

plain, they are usually wet soils but didn’t have consistently unique forest types. In the

Piedmont, alfisols were used to model four new map classes, basic dry and basic mesic

deciduous forests, and basic dry and basic mesic mixed forests. After the analysis, it was

discovered that there was essentially no dry basic deciduous forest modeled (about 81

pixels). In the Piedmont, where it was difficult to discern narrow riparian habitat from

satellite data, soils were very valuable in producing our Piedmont bottomland, narrow

stream valley forests and floodplain forests. Additionally, soil types were used to

produce a rocky shoals class (of very limited extent), rock outcrops, and to include water

where not originally classified. Soil classes were also used with TRMI modeling (see

impoundments in the coastal plain and fresh water in the mountains. See Appendix 1 for

specific rules (see Appendix 4) and for NWI classes used in the rules (Appendix 5).
23

next section) in the mountains. In the coastal plain, specific soils were recognized as

supporting maritime forests and were thus used to assist in mapping this particular land

cover type.

3: Topographic Relative Moisture Index (TRMI) and other elevational data: A different

set of ancillary data was undertaken for modeling in the Blue Ridge Province. We used

slope position, elevation and slope configuration. Using methods outlined in Halpin

(1999) the above-mentioned variables were incorporated into an index referred to as

TRMI. TRMI ranks each pixel according to its relative moisture. Values range from 0 –

60 with lower values indicating drier sites and higher values indicating more mesic sites

(Parker 1982). TRMI, relative slope position, slope, slope configuration, elevation and

soils were used as separate variables to model mountain communities. Predictive land

cover distribution models were built based on patterns observed in ground-truthing

points and published literature. Soils and TRMI were used to distinguish dry vs. mesic

mixed and deciduous forest. Elevation, relative slope position and soils were used to

model ridgetop/upper slope evergreen woodlands. Comparison of mapped landcover

with ground-truthing data revealed that the original 29-class map often mapped steep

upper-slopes as cultivated land or grassland/pasture, when in fact these areas were

covered with dry deciduous forest. Decision rules were created to correct this problem

by changing agricultural map classes on steep slopes (slope > 30 degrees) with upland

forest soils to dry deciduous forest.

NWI wetlands data were not available for the entire region so were not used in

modeling the Blue Ridge Province of South Carolina. This modeling process was also

iterative, as described above. Shadows were a slight problem in the mountains. It was

determined that they were generally found on north facing slopes that were probably

mesic in nature. We made the assumption based on knowledge of the area that they

consisted primarily of deciduous forests so if the slope was greater than about 3%, we

converted the shadows to mesic deciduous forests. In a few cases, soil rules mistakenly

converted new reservoirs to an inappropriate land cover. This occurred where the soil

data pre-dated the creation of a lake. These were fixed by digitizing an AOI and

manually converting the land cover back to water. There was interest in using geological

information to classify cove forests, however, the digital geological data were too coarse

and would have inconsistently mapped cove forests. We therefore used slope

configuration, relative slope position and soil to produce our Deciduous Rich/Acidic

elevation for mountains, soil types for sandhills/Piedmont fall line and NWI wetland

information for salt water/fresh water. Elevation is generally accepted to be a good way

to delineate the Blue Ridge mountain province. Although the Brevard Belt is considered

to be the geologic boundary for the Blue Ridge, we decided that because montane

vegetation continues southward we would use topography to define the mountain

boundary. In developing the boundary we followed the edge of the Blue Ridge

escarpment, south of which the elevation marked drops off into the piedmont. This line

Cove Forest. See Appendix 4 for rules incorporating TRMI values. 

digital elevation models (see Figure 5) to develop measures of slope, aspect, relative

4. Ecoregion Delineation (see Figure 2): Ecoregion delineation was performed using
24

Figure 5. Elevation data used in modeling the Blue Ridge Province montane

vegetation.
25

began with an elevation of 1000 feet at the western edge of the state and rose to 1200 feet

at the eastern edge of Greenville county. We manually adjusted the boundary line to

include specific regions generally accepted to be montane, such as the Keowee-Toxaway

gorge and affiliated parks. Disjunct topographic ridge such as piedmont monadnocks

occurring south of the escarpment were excluded from the mountain province. The

sandhills region lies at the upper extent of the coastal plain and forms the boundary with

the Piedmont. The soils undergo a fairly noticeable change from sandhills to Piedmont

and were used to develop the Piedmont boundary. In some cases where they extended up

a riparian corridor or traversed a heavily urbanized area, the boundary was simplified.

For shore habitat, we used NWI as our primary source to discriminate between estuarine

and palustrine land cover types but a boundary consisting of US17 was used to delineate

habitat used by vertebrates that are strongly tied to marine waters and beaches.

Modeling of the Detailed Land Cover

Once the initial classification of the imagery was complete and the ancillary data

prepared, we were ready to model the final, detailed map. This resulted in a 54-class

detailed land cover. Because we improved many of the general classes used in the habitat

map, we then merged the final 54 classes back into the more general 28-class habitat map

for use in modeling. The initial land cover was, therefore, only used to produce a

background on which to model the final two products. As we discuss the land covers that

were produced by SC-GAP, we will refer to them as the initial map (classified from the

TM imagery), the detailed map (54 classes produced with ancillary data) and the habitat

map (the 28 class land cover used in the vertebrate modeling and all gap analyses).

The modeling of the vegetation using ancillary data was a highly iterative process. We

used expert botanical knowledge, existing vegetation maps, and NAPP color aerial

photographs in addition to digital data on soils and wetlands. Initially, four soil wetness

classes were developed that were merged from detailed county soil digital data (see

soils, water, complex soil types, and eventually specific regional soils were added.

County. Classes from the National Wetlands Inventory were also merged and used to

improve classification of both wetland and upland land cover types. We began by simply

using soil wetness categories to convert upland deciduous forest to mesic deciduous and

similar cases.

We also did a general conversion of many forest types to forested wetlands using both

soil and NWI Palustrine Forested (PFO) classes; this provided a set of general ‘swamp’

generalized NWI classes for Georgetown County. Although it proved impossible to

distinguish the composition of the different ‘swamp’ classes derived from original map

classes such as upland deciduous or mesic mixed, we maintained them as separate map

classes to indicate their origin. This led to a series of land cover types with anomalous

Figure 6 is an example of the map derived from generalized soil classes for Georgetown

forest types. Figure 7 shows the distribution of wetlands and uplands derived from our

Appendix 5). Subsequently classes delineating circum-neutral soils, seep producing
26

names that reflected the original name such as swamp/upland pine deciduous oak

understory or swamp/deciduous forest. For the gap analysis, these were eventually

merged into a general bottomland forest type comprised of primarily deciduous classes,

and others were incorporated into the wet evergreen map class. It is our hope that

maintaining the origin of these bottomland types (for example Swamp/Bottomland Type

14 was originally land cover type 14 on very wet soil) may enable someone to eventually

refine this classification.
27

Figure 6. Depiction of general soil classification used in modeling detailed land

cover. This map is for Georgetown County, one of eight coastal counties.
28

Figure 7. General NWI classes used in modeling detailed land cover. This example

is from Georgetown County, one of the coastal counties.
29

We used both sets of ancillary data to remove common errors such as mistaking water for

planted pine, mistaking clearcuts for cultivated land and to improve the classification of

salt and fresh water, as well as the classification of marshes. We would examine the land

cover for patterns and accuracy after each iteration of applying the decision rules and

then would change the rules accordingly. We used botanical knowledge of the state,

NAPP color infrared photos and site visits to assess the results of each modeling iteration.

In addition to checking our accuracy, this process helped us to understand the relationship

of NWI and soil types to actual land cover and to add new rules and change the priorities.

application. Some areas of the Piedmont and mountains were missing NWI information;

for these areas in the Piedmont, soil data alone were used in the modeling. In the

mountains, soils were used in conjunction with TRMI.

During the development of the land cover we made site visits to problem areas, consulted

color aerial NAPP photos, consulted with public land managers, and used available

smaller area maps to improve our modeling procedure. As we reached the end, we did a

cross-tabulation of original land cover, NWI wetland type, soil type and resulting

classification to analyze problems and potential changes to the rules. This resulted in a

few changes to the decision rules and an understanding of some common misclassifications that are understandable and relatively minor.

When we began the classification of TM data into our original 28-class map, we made the

effort to delineate some potentially useful classes such as ecotone boundary and stream

margins. Additionally, a dataset of Carolina Bays had been developed by the SCDNR

Heritage Trust program (Bennett and Nelson 1991), this was digitized into the land cover.

The Ecotone Boundary class was mapped when the classifier could discriminate shadows

falling from forested habitat onto cleared land, either cultivated or pasture. At the end,

however, it was clear that this class was unevenly discriminated and of little extent. This

made it inappropriate to use in a statewide map.

The Stream Margin class was produced by buffering classified pixels identified as water

for a width of 30 meters (one pixel) and re-classifying this as ‘stream margin.’ This

proved to be un-useable because the buffer was placed around all pixels identified as

fresh water and thus we had a Stream Margin around small bodies of water such as farm

ponds as well as very large lakes. In addition, original habitat type was lost, so there was

no indication whether the stream margin consisted of cleared land, deciduous forest,

evergreen woodland, or any other class. It was decided to remove this class as well, and

return to the original classification and attempt to buffer streams at a later date.

The Carolina Bays were hand-digitized onto the land cover using center points from the

report (SCDNR 1999) and the original TM data to recognize the characteristic oval

shape. These clusters were then labeled as ‘Carolina Bay.’ Unfortunately, the

accompanying report had little vegetation information attached to each bay, and the land

cover now lacked any information as to the diversity of land cover types within the bay.

It was determined to return to the original classification for these areas as well and retain

the Carolina Bay data as a separate coverage.

Eventually we ended up with the set of rules found in Appendix 4 arranged by priority of
30

Maritime forest was delineated in an early version of the map in an incorrect manner; this

was also remedied in the following manner. It was necessary to replace the pixels that

were changed to maritime forest, Carolina Bays and stream margin with an earlier

classification that had the original land cover types. To do this, an earlier version of the

land cover was mosaicked with the latest version for the subset of pixels that were

incorrectly changed. This earlier version was the modified according to the decision

rules. To re-classify ecotone boundary pixels, a nearest neighbor analysis was used since

the class Ecotone Boundary was described in the intial classification. The maritime class

in the vertebrate habitat map was derived from the detailed 54-class land cover.

The vegetation modeling was carried out on a county level using the four ancillary

datasets. This resulted in a set of 46 land cover maps for all counties that were

process resulted in a number of slivers that were removed using a nearest neighbor

analysis. Map units seem to match well across county boundaries and so little correction

was required for this.

subsequently mosaicked together (see Figure 8 for an example county land cover). This
30 
32

As the map was finalized and visually checked, it became apparent that certain AOIs had

been classified differently in the original clustering and identification than the

surrounding AOIs. This resulted in areas of markedly different composition that matched

HUC boundaries. In one case, the urban map class was greatly overestimated, whereas

the surrounding HUCs were predominantly cultivated land; in another, closed canopy

pine predominated in an area of mixed forested classes. Another case involved an

overestimation of mixed forest. We attempted to correct some of these errors with

decision rules that would work statewide to correct similar errors. For example, we

removed urban classes in the land cover by using NWI polygons that were mapped as

cultivated land; this greatly improved the one misclassified AOI. In most cases, since

these problems were errors resulting from the original classification of TM imagery, there

was little we could do, and we decided not to reclassify to remove these errors. We feel

that these errors indicate a trend for a particular AOI that was overemphasized but is not

completely wrong. For example, an AOI that covers a large part of the mountain region

is classified as ‘Closed-canopy Evergreen’ with little other forest or woodland classes,

and the boundary of the AOI is visually obvious. However, a comparison with other data

sources indicated that this particular area is dominated by evergreen forests. Although it

is clear that we underestimated hardwoods in that particular area, we feel the error was

within reason and reflected the nature of the particular area. In general, the AOI-related

errors may emphasize real patterns that differ with surrounding habitat. The problem is

that distinct boundaries were produced rather than a soft edge as would naturally occur

between different land cover types in transition. The errors do not, however, necessarily

misrepresent the predominant cover type.

We used NWI salt and fresh marsh to differentiate between these two types and also to

convert original land cover types to salt or fresh marsh. When NWI indicated

impoundment, we were unable to convert fresh to salt marsh and vice versa. This led to a

mixed pixel effect where fresh marsh shows up in salt marsh and salt marsh within fresh.

Rather than digitize changes to the surrounding marsh type, we left the mixed pixels to

indicate human alteration of the landscape. Nearly all impoundments within the area of

salt water are brackish in nature.

After the land cover modeling was complete, a final round of hand-digitizing was used to

fix significant errors in well-known areas of the coastal plain. Due to mosaicking county

coverages, mismatch of soil polygons with land cover types, and mismatch of NWI and

soils, some pixels were not modeled, such as salt and fresh marsh. These were adjusted

by hand where appropriate and where the omission was obvious. Sometimes pixels were

missing – these were replaced by nearest neighbor analysis.

Special Feature Mapping

 In order to map maritime forests, we depended greatly on soil type and on distribution in

the coastal area. Several types of soils were distinguished: 7MF soils were originally

designated as the barrier island soils and 51MF were the outer coastal, but non-barrier

island soils. The 7MF soils are complexes and no wetness class was assigned because

they are a usually a mix of excessively well drained soils and very poorly drained soils

occurring on the old dune ridges and swales of barrier islands.
33

However, even with the 7MF soil decision rules, we determined that maritime forests

were still underrepresented. We found other soils that were definitely/strongly outer

coastal soils that typically support maritime forests, and we developed classes 7MF1 and

7MF3 to reflect the wetness classes of these soils. The designation of 7MF was

maintained because it was the code for maritime soils. These were then used to map the

four classes of maritime land cover types. Please refer to the decision rules and class

But, it still didn't help with the outer coastal forests that were quite maritime in nature,

formerly called Inland Maritime Forests by the TNC Southeast Regional Classification

(precursor to the TNC Global Classification). They were referred to as “inland maritime

forests” because they didn't occur on barrier islands. This concept was based on North

Carolina coastal forests. Our work in SC showed that in the lower coastal areas, the

“inland maritime forest” concept applies to forests well inland from the direct influence

of salt spray. They occur as far inland as US Highway 17. Therefore, to avoid the issue

of inland versus barrier island "maritime," we named this forest type "Coastal Upland

Mixed Forest." Soils east of US 17 that support mostly evergreen forests of a maritime

nature were classified as 51MF. Since these soils aren’t complexes, wetness classes

could be appended and utilized in the decision rules.

Results

The SC-GAP land cover consists of two versions, a general 27-class land cover (see

the general land cover to the derived detailed classes. The general difference between the

two land covers is a more detailed treatment of wetlands, coastal habitat and maritime

forests. For example, there are 15 wetland map types in the detailed map, including

marsh and swamp/bottomland, as compared with 6 in the general map. There are 4 kinds

of maritime forests in the detailed vs. 1 in the general map and 6 coastal habitats such as

intertidal beach, salt flat/salt shrub, vs 2 (marine water and beach) in the general land

cover. In the detailed 54-class map, we felt that we were taking remotely sensed

mapping as far as it could go and this was only possible due to the availability of

excellent ancillary data. While the accuracy assessment doesn’t address the accuracy of

the detailed land cover, it gives a general idea of the usefulness of the data.

In meeting with land managers and botanists within South Carolina, we find that the map

has been perceived as being very useful in describing patterns of vegetation within

certain areas. Known patches of wet evergreen, for example, have been mapped on our

land cover and can be found in relation to surrounding areas. The purpose for the gap

analysis process is to provide large area maps on which to base decisions for these areas

and to provide a basis for further mapping efforts. We have come to realize the

importance of our land cover and our ancillary data sets for visualizing patterns on the

landscape. These patterns indicate areas for potential future studies and fieldwork.

Table 4 and Figure 9) based on vertebrate habitat affinities and a detailed land cover

consisting of 54 classes (see Figure 10). Appendix 5 contains a crosswalk from classes in

descriptions (see Appendices 1 and 4) for more information.
34

Figure 9. The 27-class general land cover used for modeling vertebrate species.
35

Figure 10. The detailed land cover consisting of 54 classes.
36

Table 4. The land cover types mapped, their area in square kilometers (multiply by

100 for hectares or 270 for acres), and the percent of the state's total area

represented by the mapped type.

Mapped Type Total Area Percent of State

Fresh Water 2361 2.9

Marine Water 1032 1.3

Marsh 2073 2.6

Pocosin 365 0

Swamp 3075 3.8

Bottomland 8185 10.1

Wet Soil 225 0.3

Wet Scrub/Shrub 2387 3.0

Dry Scrub/Shrub 5273 6.5

Sandy Bare Soil 63 0

Recently Cleared 5960 7.4

Rock Outcrop 16 0

Aquatic Vegetation 34 0

Closed Canopy Evergreen 10928 13.5

Evergreen Mixed 7937 9.8

Pine Woodland 713 1.0

Dry Deciduous 695 1.0

Mesic Deciduous 7909 9.8

Dry Mixed 291 0.3

Mesic Mixed 892 1.1

Grassland/Pasture 2197 2.7

Cultivated Land 12310 15.2

Urban Development 1308 1.6

Urban Residential 2446 3.0

Wet Evergreen 1455 1.8

Maritime Forest 655 0.8

Beach 30 0
37

Accuracy Assessment

Introduction

GAP land cover maps are primarily compiled to answer the fundamental question in gap

analysis: what is the current distribution and management status of the nation's major

natural land cover types and wildlife habitats? Besides giving a measure of overall

reliability of the land cover map for Gap Analysis, the assessment also identifies which

general classes or which regions of the map do not meet the accuracy objectives for the

Gap Analysis Program. Thus the assessment identifies where additional effort will be

required when the map is updated. We report the results of the accuracy assessment,

believing that the map is the best map currently available for the project area.

The purpose of accuracy assessment is to allow a potential user to determine the map's

"fitness for use" for their application. It is impossible for the original cartographer to

anticipate all future applications of a land cover map, so the assessment should provide

enough information for the user to evaluate fitness for their unique purpose. This can be

described as the degree to which the data quality characteristics collectively suit an

intended application. The information reported includes details on the database's spatial,

thematic, and temporal characteristics and their accuracy.

Assessment data are valuable for purposes beyond their immediate application to

estimating accuracy of a land cover map. The reference data is therefore made available

to other agencies and organizations for use in their own land cover characterization and

will also serve as an important training data source for later updates.

Even though we have reached an endpoint in the mapping process where products are

made available to others, the gap analysis process should be considered dynamic. We

envision that maps will be refined and updated on a regular schedule. The assessment

data will be used to refine GAP maps iteratively by identifying where the land cover map

is inaccurate and where more effort is required to bring the maps up to accuracy

standards. In addition, the field sampling may identify new classes that were not

identified at all during the initial mapping process.

Methods

The accuracy assessment will inform users of the land cover map’s suitability for their

particular purpose. The goal of accuracy assessment was to assess thematic accuracy of

the South Carolina 27-class vertebrate land cover map. Accuracy assessment is a major

undertaking, requiring large sample sizes of reference data against which to compare

classified map categories. South Carolina Gap accuracy assessment methodology was

designed so that sample size could be maximized, while minimizing cost and time.

Utilization of remote sensing imagery, such as 1994 NAPP photography, 1994 digital

ortho photo quarter-quadrangles (DOQQ’s), and 2000 aerial videography, allowed for

more efficient classification of sites where feasible. Additionally, sample size was

increased by incorporating a separate dataset of ground-truthed vegetation survey points

contributed by Leslie Parris, as a part of her Master’s thesis work on fire ant invasion

map accuracy assessments (see Data Availability for access information). The data set
38

patterns. The methodologies employed for both sets of data, ant survey work and gap

survey work, are presented separately below.

Ant Habitat Survey Dataset

Statewide ant habitat sampling in South Carolina was stratified by four physiogeographic

regions: mountain, piedmont, sandhills, and coastal plain (Barry 1980 and SC-GAP) (see

county and national forest boundaries. The SC-GAP sandhills coverage was used for

verifying the delineation of the sandhills region. The Nature Conservancy’s North

American Ecoregion Classification Map (ABI 2001) was used to verify the mountain,

piedmont, and coastal plains regions delineation.

In addition to region, ant habitat sampling was further stratified by land cover type. SCGAP provided the original spectrally-derived 29-class land cover map in raster format.

Using this land cover, all vegetative land cover classes and an urban land cover class

were selected for ant habitat sampling. Not all land cover classes were represented in

each region.

Vegetative land covers included: freshwater and saltwater marsh (MARSH), Carolina bay

(BAY), swamp/bottomland hardwood forest (SWPBHW), cleared forest (CLEAR),

upland pine forest (UPLDPN), pine woodland (LLPINE), upland (UPLDDEC) and mesic

(MESDEC) deciduous forest, upland (UPLDMX) and mesic (MESMX) mixed forest,

grassland (GRASS), cultivated land (CULT), beach (BEACH) and maritime forest

(MARIT).

Statewide ant habitat sampling was conducted at 364 locations with sampling of 16

completed in the mountain region from May to August. Nine distinct land cover types

were sampled in the mountain region with an average of 10 replicates per land cover

type, totaling 86 sampling locations. Piedmont region ant habitat sampling was

completed during the late summer and early fall of 1999. Sampling was conducted from

August to October at 89 locations. Ten land covers were sampled in the piedmont region,

with approximately ten replicates of each land cover type. During the summer of 2000,

sampling was completed in the coastal plains region from May to August at 111

locations. Eleven land cover types, with ten replicates of each, were sampled in the

coastal plains. Sandhills region was completed in the late summer and fall of 2000.

Eight distinct land cover types were sampled, and the number of replicates of each land

cover ranged from 8 to 11, totaling 78 sampling locations. Variation in the number of

replicates sampled is attributed to the availability of each land cover type and

accessibility to the land cover.

SC GAP's original land cover, roads coverage and public-property boundary coverage

were used in the site-selection process. Land cover grid and road coverage were

overlayed and used to determine the quantity and accessibility of the different habitat

types.

distinct land cover types (see Table 5). During the summer of 1999, sampling was

Figure 2). The regions were delineated on the ground by topographic features and state,
39

Table 5. Number of ant habitat sampling replicates in the mountain, piedmont,

coastal plains and sandhills regions of South Carolina, partitioned by land cover

class, 1999-2000. (RECAREA = recreation area, MARSH = freshwater and

saltwater marsh, BAY = Carolina bay, SWPBHW = swamp/bottomland hardwood

forest, BEACH = beach, CLEAR = cleared forest, UPLDPN = upland pine forest,

LLPINE = pine woodland, UPLDDEC = upland deciduous forest, MESDEC = mesic

deciduous forest, UPLDMX = upland mixed forest, MESMX = mesic mixed forest,

GRASS = grassland/pasture, CULT = cultivated land, MARIT = maritime forest.)

 Region 

Land cover class Mountain Piedmont Coastal plains Sandhills

RECAREA 9 10 10 10

MARSH 0 0 11 0

BAY 0 0 0 8

SWPBHW 0 10 10 10

BEACH 0 0 10 0

CLEAR 5 10 10 11

UPLDPN 8 10 10 10

LLPINE 0 0 11 0

UPLDDEC 10 10 0 10

MESDEC 10 9 10 0

UPLDMX 27 10 0 0

MESMX 10 9 0 0

GRASS 3 10 10 11

CULT 4 1 10 8

MARIT 0 0 9 0

Total 86 89 111 78

Once available land covers were identified in each of the four regions, ant habitat

sampling sites were selected with the aid of the coverages and a systematic evaluation of

land cover types on the ground. Potential ant habitat sampling locations were identified

on the SC-GAP land cover map, based on land cover class, and then visited by vehicle to

evaluate sampling suitability. Potential sampling sites were surveyed at a distance of

50m from the road, to determine if the land cover was contiguous at least 30m in all

directions. Habitat patches, of a single contiguous land cover type, >60m x 80m,

qualified as suitable and were then selected for ant habitat sampling.

Ants were sampled on public lands managed by the U.S. Fish and Wildlife Service, U.S.

Forest Service, U. S. National Park Service, South Carolina Department of

Parks, Recreation and Tourism, South Carolina Department Natural Resources, Duke

Power, Baruch Marine Institute, Fort Jackson Military Reserve, and private land owners,

such as Nemours Foundation. The distance driven between sampling locations was

standardized based the size of the property. Sampling locations were separated by one
40

mile within relatively small tracts of land (<# ha), such as state parks and heritage

preserves, and by two miles within larger tracts of land, including national forests and

national wildlife refuges.

Data collected at each sampling site included fire ant presence/absence, fire ant index of

abundance, region, county, date, temperature, estimated maximum vegetation height,

aspect and degree of slope, road type, percent canopy cover, dominant canopy species,

percent understory cover, dominant understory species, location description, property

ownership, transect orientation and land cover type corresponding to the SC-GAPer land

cover class descriptions. These data were used as part of the accuracy assessment of the

general land cover. A Trimble Geoexplorer II GPS unit (Trimble, Sunnyvale, CA) was

used to collect geographic positions on roadsides corresponding to each transect location.

The data were downloaded, differentially corrected, and exported with Pathfinder 2.1

software (Trimble, Sunnyvale, CA).

At each site, GPS coordinates were recorded roadside at the beginning of the transect.

Vegetation survey data was documented for a 30x30m area at a distance of 50m along the

transect. To adjust for the spatial discrepancy between GPS location and vegetation

survey location, GPS points were subsequently offset by a distance of 50m along the

orientation of the transect. Once the final 27-class vertebrate land cover map had been

completed, the offset GPS points were overlaid onto the vertebrate map, and the

classified land cover at each survey point was recorded. Only 299 of the total 364 ant

survey points were utilized for SC-GAP accuracy assessment. Excluded points were

those that lacked transect orientation and/or land cover interpretation. The 29-class land

cover map used by the ant survey differed slightly from the final SC Gap 27-class land

cover map. The ant survey project leader, who had visited most sites in the field,

crosswalked reference sample points from the old to new classification. For most land

cover classes, the crosswalk simply involved a change in numeric land cover code.

However, four land cover classes required some reinterpretation: pocosins, beaches,

swamps, and bottomlands. The original map lumped all forms of Carolina bay vegetation

including marshes, pocosins, and swamps into a single class. Beaches were originally

included in the sandy bare soil class. Swamps and bottomlands were grouped into a

single class in the original map. To reclassify survey points falling into one of the abovementioned reference classes, field notes and digital photographs were reviewed and in

some cases NAPP photography was examined.

Gap Survey Dataset

Aerial videography has been employed as a standard methodology by a number of GAP

projects to aid in discernment of reference land cover classes. Although videography

requires an initial investment in equipment and training of interpreters, it can

significantly reduce the time and cost of reference data collection (Gap Analysis Program

2000). For SC-GAP land cover accuracy assessment, the US Fish and Wildlife Service

provided necessary equipment for interpretation of videography. In the fall of 2000, SCGAP, in conjunction with the GA-GAP, hired Dana Slaymaker to fly aerial video

transects across both states. Transects were set up so that each physiographic province

would be covered (see Figure 11).
41

Figure 11. Flightlines used to obtain aerial videography for accuracy assessment of SCGAPland cover
42

During the course of aerial videography flights, two digital video cameras -- one set to

12x zoom and the other to wide-angle -- recorded land cover on the ground below. A

global positioning system (GPS) recorded the airplane’s coordinates and matched each

set of coordinates with a time-code. Positional accuracy of coordinates were improved

from +/- 70 meters to +/- 5 meters by adjusting for variability in elevation above ground

and tip/tilt of the plane using a gyroscope and laser imaging of the topography.

The data produced by aerial videography flights were a series of wide and zoom angle

digital videotapes, each with time-codes recorded in the audio tracks. Time-codes were

matched to ground coordinates so that video frames could be aligned with the correct

positions on the ground.

During the course of aerial video interpretation, the interpreter played back the wide and

zoom images through digital video recorders onto a pair of monitors. Clips of video

crossing sample polygons were saved onto a PC using MotoDV Version 1.4 software

(Digital Origin, Inc., Mountain View, CA, 1999). Individual frames were extracted from

the video clips using VisFrameX1_1.exe software supplied by Dana Slaymaker. For each

interpreted videography sample site, the movie clip usually along with one or more

representative video frames were archived on CD-ROM.

The sample units chosen for assessment were contiguous groups of like-classified pixels.

Clusters, not pixels, were chosen as the sample unit because typically adjacent pixels are

not classified independently of one another. Clusters of pixels were converted into

polygons, so that each polygon represents a sample unit.

The goal was to classify all points to the level of the 54-class detailed map where

possible. Although we did not collect sufficient samples to adequately present the

accuracy assessment results at the 54-class level, it is hoped that the more detailed

reference data will be of use to investigators in the future.

In order to ensure adequate sample sizes for all classes, including rare types, sampling

was stratified by vertebrate land cover class. The necessary sample size was arrived at

using the equations below:

n = Np(1-p)

 Ns2 + p(1-p)

where N is the total number of polygons.

As the number of map polygons (N) becomes large, the finite population correction factor

is close to 1, so the previous equation becomes:

n = p(1-p)

 s2

Using this last sample size formula with a conservative presumed accuracy of 50%

(p=0.5) results in a required sample size of no more than 40 samples per land cover type
43

to meet the National GAP criterion of a standard error of 8% (Crist and Deitner 2000).

Due to time constraints, we used a slightly lower required sample size of 30 polygons per

class. As stated above, incorporation of the ant survey data increased sample sizes, but

some classes still had less than 30 samples. Sample sizes, relative acreages, and standard

errors of each class are provided in the results.

To minimize the influence of spatial accuracy on thematic accuracy, polygon edges were

avoided by leaving a one-pixel buffer of like-classified pixels around each sampled

polygon. Clusters of dimension three-by-three pixels or larger were selected, and

polygon boundaries were drawn inside of the one-pixel buffer. This was accomplished

by using the ArcView 3.2 neighborhood analysis program to create a grid of “variety”

values based on the 58-class detailed land cover. The neighborhood was defined as a 3x3

pixel area; a value was assigned to each pixel in the land cover, based on the number of

differently classified pixels within the neighborhood surrounding the pixel. For example,

a pixel of marsh surrounded on all sides by other marsh pixels would be given a variety

of one. A pixel of marsh surrounded by five pixels of marsh and three pixels of fresh

water would be given a value of two. Pixels with a value of one are those that are

surrounded on all sides by like-classified pixels within a minimum of a 3x3 pixel area.

Once the variety grid had been created, it was converted to a polygon coverage and was

used to clip the land cover into polygons. The resultant polygons varied in size from one

to many pixels, but each polygon was surrounded on all sides by a one-pixel buffer area.

These polygons are hereafter referred to as being of dimensions 3x3 pixels or larger,

because the total size of the pixel cluster was larger than 3x3. This approach did not

provide for a sufficient number of sample sites in certain classes. For these classes,

polygons smaller than 3x3 pixels in dimension were accepted, and the one-pixel buffer

was not added. The relative allocation of sampling to small and large polygons is shown

National GAP recognizes that in order to maximize efficiency, it is often helpful to subset

the entire set of polygons so that the entire state is not considered the sample universe

(Crist et al. 2000). In South Carolina, we subset polygons by intersection with roads

and/or videography flight paths. For map classes having >30 polygons along the flight

path, an initial set of approximately five video training points was randomly chosen from

the population of polygons intersecting with both roads and flight paths. These training

sites were visited on the ground in order to familiarize the interpreter with the

representation of different land cover types on aerial videography. Training sites were

included in the accuracy assessment. Once training sites had been selected, additional

accuracy assessment points were randomly chosen from the population of polygons

intersecting with the videography flight path. For those classes with fewer than 30

polygons along the flight path, such as rock outcrops, aquatic vegetation, and beaches,

additional sample sites were randomly chosen from the entire population of polygons for

accuracy assessment using other methods.

along with the accuracy for each map class in Table 6.
44

Table 6. The relative amount and accuracy of small vs. large polygons used in the

accuracy assessment

Small Polygons Large Polygons

Mapped Class n Accuracy

Standard

error n Accuracy

Standard

error

1 0 33 84.85% 6.24%

2 17 47.06% 12.11% 13 84.62% 10.01%

3 0 30 90.00% 5.48%

4 15 0.00% 0.00% 13 15.38% 10.01%

6 0 17 70.59% 11.05%

7 20 60.00% 10.95% 8 37.50% 17.12%

8 20 15.00% 7.98% 10 50.00% 15.81%

9 23 4.35% 4.25% 5 0.00% 0.00%

10 3 0.00% 0.00% 24 20.83% 8.29%

11 28 10.71% 5.85% 1 0.00% 0.00%

12 0 24 37.50% 9.88%

13 20 5.00% 4.87% 11 18.18% 11.63%

14 26 3.85% 3.77% 3 0.00% 0.00%

15 1 0.00% 0.00% 29 58.62% 9.15%

16 0 11 0.00% 0.00%

17 21 14.29% 7.64% 2 50.00% 35.36%

18 17 17.65% 9.25% 0 

19 0 14 14.29% 9.35%

20 25 24.00% 8.54% 0 

21 31 6.45% 4.41% 0 

22 23 13.04% 7.02% 3 0.00% 0.00%

23 0 31 64.52% 8.59%

24 9 11.11% 10.48% 19 42.11% 11.33%

25 12 33.33% 13.61% 17 64.71% 11.59%

27 6 16.67% 15.21% 19 42.11% 11.33%

28 0 31 74.19% 7.86%

29 12 41.67% 14.23% 13 84.62% 10.01%

Overall

Unweighted

Accuracy: 329 17.33% 2.09% 381 53.81% 2.55%
45

Video Interpretation

Once samples had been chosen, video training and interpretation began. The interpreter

attempted to classify each polygon to the level of the 54-class detailed map. Where

unable to classify at this level, sites were classified to either the vertebrate map level or

Individual canopy species can often be distinguished by interpretation of aerial

videography (see VA-GAP for an example of training methodology). However, species

recognition requires some initial training. At the beginning of the accuracy assessment

process in South Carolina, the interpreter visited a selection of accuracy assessment

points in the field and also viewed their corresponding aerial video imagery. An attempt

was made to learn the recognition of diagnostic canopy species for each map class

(Slaymaker et al 1996). After training, the interpreter used aerial video to classify only

those sites where he/she felt comfortable identifying the dominant or diagnostic species.

One class for which aerial videography was heavily utilized was maritime forests, where

live oaks, juniper, and palmetto trees stand out on the video. For many classes, even with

some training, the interpreter did not feel comfortable classifying sites based on aerial

videography. These sites were then visited in the field where possible; where field visits

were not possible, the sites were excluded from the accuracy assessment sample points.

Sites were classified using one or more of three methods – 1994 DOQQ/NAPP, 2000

aerial videography, or field visit. Regardless of which interpretation method was utilized,

there were some general guidelines for interpretation:

• When dealing with mixed polygons, interpretation was based on the dominant

land cover. If no clear dominant was observed, the polygon was thrown out.

• If the polygon could not be interpreted based on DOQQ/NAPP imagery, and a

field visit or aerial videography revealed that the polygon had been altered from a

prior land cover, an attempt was made to classify the site as it had been at the time

of the satellite imagery. For example, often a portion of a site would be clearcut,

but the rest of the site would remain in its original condition. These sites were

classified according to their original condition rather than their current status.

Where it was impossible or questionable to interpret the prior condition of an

altered site, the polygon was not interpreted.

• Sites that could not be interpreted for one of the above-mentioned reasons or for

other reasons, were not included in the accuracy assessment.

• To ensure consistency in interpretation, a key to the detail map classes was

created. For certain detailed map classes, species overlap was such that

interpretation was limited to the habitat level. Affected classes were basic versus

non-basic forest types, which could not usually be distinguished either on the

ground or through remotely sensed imagery. Swamp/bottomland classes, which

were separated in the habitat map but collapsed in the detail map, were always

classified at the habitat level.

• Notes were made describing general characteristics of each polygon, including

descriptions of secondary land cover classes present within the polygon.

Anderson Level II. Appendix 6 provides a crosswalk among the map class levels.
46

For each site, the interpreter overlaid the sample polygon onto the appropriate 1994

DOQQ in ArcView, and also examined the corresponding 1994 color infra-red NAPP

photo. If possible, the interpreter classified the land cover based on this imagery. The

centroid of the interpreted polygon was recorded as the accuracy assessment point. Map

classes such as urban residential, urban development, water, sandy bare soil, spoil

deposits/impoundments, beaches, pine plantations, cultivated land, and other sparsely

vegetated or non-natural classes could usually be interpreted from DOQQ/NAPP

imagery.

If unable to classify the site without more detailed imagery, aerial videography was

utilized for sites where it was available. The interpreter viewed both the wide angle and

zoom imagery for the clip of video intersecting the polygon, and saved this clip as a

movie (.mov file) using MotoDV Version 1.4 software (Digital Origin, Inc., Mountain

View, CA, 1999). Individual frames within the movie clip were extracted using

Mosaiker software supplied by Dana Slaymaker (VisFrameX1_1.exesoftware). If the

interpreter was able to classify the land cover based on the video, the GPS coordinates of

the most representative video frame were recorded as the sample point. For each

interpreted videography sample site, the movie clip and sometimes also one or more

representative frames were archived on CD-ROM.

If the interpreter was unable to classify a site using aerial videography or DOQQ/NAPP

imagery, a field visit was attempted. When surveying sites in the field, the interpreter

navigated to a pre-selected waypoint within the dominant land cover of the polygon. To

avoid property access issues, waypoints were typically placed along roadsides. At the

waypoint, the surveyor noted dominant and/or diagnostic species; recorded general site

characteristics; took a digital photo showing overall stand structure; and assigned the

appropriate land cover class at either the detail or vertebrate map level. To prevent field

surveys from straying outside of the polygon or being biased by small-patch inclusions of

non-dominant land cover types, the surveyor brought a hardcopy image of the DOQQ

with the site’s boundaries overlaid. The area covered by the field survey was greater than

the minimum mapping unit (MMU = 1 pixel = 30x30 meters). Sites that could not be

feasibly reached in the field were left uninterpreted.

Overall, a total of 710 points were classified to the detailed or vertebrate map class. Of

these, 371 points were interpreted using DOQQ/NAPP imagery; 215 points were

interpreted using videography; and 124 points were interpreted by field survey. An

additional 48 points were interpreted to the appropriate Anderson Level I class.

A total of 1009 samples were photo-interpreted or ground truthed at either the 54-class

detailed map level or the 27-class vertebrate map level, including 299 ant survey samples

and 710 gap survey samples. An additional 48 gap survey samples were classified

according to Anderson Level II (Anderson et al., 1976). Samples that had been classified

at the 54-class detailed map level were crosswalked to the appropriate land cover class in

the 27-class vertebrate map without deleting the original detailed classification label (see

crosswalk table, Appendix 6).
47

Results

Results of the vertebrate map accuracy assessment are presented in the form of an error

the table, rows represent the classified data, columns refer to the reference data, and

entries along the major diagonal represent correct classifications. Two different measures

of accuracy can be calculated from an error matrix. The first, user’s accuracy, deals with

errors of commission, and estimates the probability that a sample from a particular

classified land cover type actually represents that land cover type on the ground. User’s

accuracy is calculated by dividing the number of correctly classified samples of land

cover X by the total number of samples classified as land cover X (row total). Producer’s

accuracy deals with errors of omission, and estimates the probability that a sample from a

ground truthed or photo-interpreted land cover type was correctly mapped as that land

cover type. Producer’s accuracy is calculated by dividing the number of correctly

classified samples of land cover X by the total number of reference samples of land cover

X (column total) (Story and Congalton, 1986). The accuracy measure of most interest to

potential map users is the user’s accuracy. Per class user’s and producer’s accuracy

3% for wet shrublands; producer’s accuracies ranged from 100% for pocosins and rock

outcrops to 8% for needle-leaved evergreen mixed woodlands. Sample sizes for

producer’s accuracies were more variable than those of user’s accuracies because

collection of reference samples was stratified by classified land cover classes, not

reference land cover classes. Weighted overall accuracy was calculated by weighting the

accuracy of each land cover class by the relative acreage of that land cover class in the

The overall weighted accuracy for the 27-class vertebrate map is 33.4%. Although this is

a low accuracy, the individual class accuracies reveal that for certain classes the map is a

reliable tool. Following is a discussion of factors affecting the accuracy of map

classification and assessment of map accuracy.

Possible Sources of Error

The temporally dynamic nature of land cover makes accuracy assessment difficult unless

conducted at or near the time of image acquisition. In the SC Gap map, certain highly

dynamic classes such as aquatic vegetation, wet/dry shrublands, wet soil, open

canopy/recently cleared forest, grassland/pasture, cultivated land, urban residential, and

urban development had low to moderate accuracy. Aquatic vegetation coverage varies

from year to year in part due to management of impounded wetlands. Crop rotation

results in conversion of land between cultivation and grassland/pasture. Wet and dry

shrublands are generally transitional in nature, and are therefore often confused with

several other classes. Tidal variations in water level affect the visibility of mud/sand flats.

Fresh and salt/brackish water classes had high to moderate accuracies, but were often

confused with one another and with marshes. Often the line between fresh and brackish

varies from year to year depending on water flow along tidal rivers and management of

impounded wetlands. It is difficult to discern the correct boundary between fresh and

truthed or photo-interpreted reference data (see Table 8 for Anderson Level II). Within

matrix or contingency (see Table 7), in which classified data are compared to ground

measures are provided in Table 7. User’s accuracies ranged from 87.2% for marshes to

map and summing the weighted per class accuracies (see Table 7).
48

Table 7. Error matrix for the 27 class habitat land cover, total accuracy is 33.42% and error is 1.48%.

Mapped Vert

Class 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 27 28 29

Row

Total

User's

Acc

Std

Error Wtd Acc

Water 1 28 3 2 1 1 0 35 80.00% 6.76% 0.0234

Salt Water 2 3 19 9 0 0 1 32 59.38% 8.68% 0.0076

Marsh 3 34 1 2 1 1 39 87.18% 5.35% 0.0224

Pocosin 4 0 2 0 4 1 2 9 1 8 1 28 7.14% 4.87% 0.0003

Swamp 6 2 17 10 1 2 0 32 53.13% 8.82% 0.0202

Bottomland 7 0 3 18 3 2 1 10 1 1 2 5 46 39.13% 7.20% 0.0396

 Wet Soil 8 1 3 3 0 2 8 1 1 1 3 1 1 1 1 2 0 1 30 26.67% 8.07% 0.0007

Wet Sc/Sh 9 0 5 6 1 2 1 1 2 1 5 1 5 2 1 33 3.03% 2.98% 0.0009

Dry Sc/Sh 10 2 0 2 1 1 5 1 4 4 1 1 4 2 7 1 1 2 39 12.82% 5.35% 0.0084

 Sandy soil 11 0 0 1 1 3 2 2 1 1 1 9 2 6 1 0 30 10.00% 5.48% 0.0001

Clearcut 12 0 0 1 1 13 6 2 9 1 1 4 1 1 0 40 32.50% 7.41% 0.0240

Rock 13 9 0 0 1 1 3 2 3 2 1 4 2 2 1 0 31 9.68% 5.31% 0.0000

Aq veg 14 4 2 15 0 1 2 2 1 1 1 29 3.45% 3.39% 0.0000

Upl pine 15 0 0 3 16 31 3 1 7 13 20 11 1 0 106 29.25% 4.42% 0.0395

Pine/dec16 0 0 2 7 12 2 2 1 2 2 1 1 2 1 2 1 38 5.26% 3.62% 0.0052

 Pine wood17 0 0 1 1 7 2 5 1 1 1 4 1 5 29 17.24% 7.01% 0.0015

Dry dec 18 0 0 1 1 4 3 7 5 4 2 1 1 1 0 30 23.33% 7.72% 0.0020

 Wet dec 19 0 0 1 5 7 9 9 9 7 3 1 2 0 53 16.98% 5.16% 0.0166

Dry mix 20 0 0 1 1 5 6 2 2 7 1 2 0 27 25.93% 8.43% 0.0009

Wet mix 21 0 0 1 1 12 2 3 4 3 2 1 4 0 33 9.09% 5.00% 0.0010

 Grass 22 1 0 2 1 1 1 1 6 13 2 2 1 31 19.35% 7.10% 0.0053

Cult 23 1 0 1 1 2 1 22 35 1 0 64 54.69% 6.22% 0.0833

 Urban 24 1 0 0 1 4 1 1 2 2 9 9 0 30 30.00% 8.37% 0.0049

Urb/res 25 0 0 1 1 1 1 1 5 4 15 2 1 32 46.88% 8.82% 0.0142

Wet eve 27 0 0 4 1 2 2 5 9 5 28 32.14% 8.83% 0.0058

 Maritime 28 0 0 1 2 1 4 25 33 75.76% 7.46% 0.0061

Beach 29 1 1 0 1 1 2 1 1 1 22 31 70.97% 8.15% 0.0003

Column Total 45 29 70 2 27 58 12 10 19 10 59 3 4 117 25 27 36 57 50 32 48 89 22 58 20 53 27 1009

Producer's

Accuracy

62.2

2%

65.5

2%

48.5

7%

100.

00%

62.9

6%

31.0

3%

66.6

7%

10.0

0%

26.3

2%

30.0

0%

22.0

3%

100.

00%

25.0

0%

26.5

0%

8.00

%

18.5

2%

19.4

4%

15.7

9%

14.0

0%

9.38

%

12.5

0%

39.3

3%

40.9

1%

25.8

6%

45.0

0%

47.1

7%

81.4

8%

Standard Error

7.23

%

8.83

%

5.97

%

0.00

%

9.29

%

6.07

%

13.6

1%

9.49

%

10.1

0%

14.4

9%

5.40

%

0.00

%

21.6

5%

4.08

%

5.43

%

7.48

%

6.60

%

4.83

%

4.91

%

5.15

%

4.77

%

5.18

%

10.4

8%

5.75

%

11.1

2%

6.86

%

7.48

%
49

Code Label

%Total

Acreage

1 Bare Exposed Rock 0.02%

2 Beaches 0.04%

3 Cropland and Pasture 17.95%

4 Deciduous Forest 10.65%

5 Evergreen Forest 21.78%

6 Forested Wetland 19.14%

7 Mixed Forest 12.09%

8 Nonforested Wetland 2.84%

9 Non-residential Urban 1.62%

10 Residential Urban 3.03%

11 Sandy areas other than beaches 0.08%

12 Shrub and Brush Rangeland 6.52%

13 Water 4.24%

Table 8. Anderson Level II codes and percent cover of each mapped type.
50

brackish water without measuring the salinity on the ground, because often the vegetation in

adjacent marshes may not be reflective of current salinity levels. The mapping of fresh and

salt/brackish water was based entirely on 1989-1994 NWI data. Although this time period

overlaps with that of image acquisition (1991-1993), accuracy may have been improved by

basing the map on salinity measurements taken along tidal rivers concurrently with TM

image acquisition. However, such a task was not feasible for our project. Future mapping

efforts should take into account the temporal variability of potential map classes; final

mapped classes should be those that generally change at a slower rate than the rate of map

creation/assessment.

Another factor influencing accuracy assessment is polygon size. Where feasible, the gap

survey selected samples from the population of polygons greater than or equal to three-bythree pixels in dimension. Polygons were clipped so that each had a one pixel buffer of likeclassified pixels on all sides. The buffer was intended to minimize the influence of edge

effects and spatial accuracy on the assessment of thematic accuracy. In classes where the

number of polygons of dimensions 3x3 or larger was insufficient, smaller polygons were

considered as potential samples. For land cover classes where the sample sizes of large (3x3

pixels or greater) and small (less than 3x3 pixels) polygons were similar, the accuracy of

The overall accuracy of all sampled small polygons was 17.3%, compared to an overall

accuracy of 53.8% for large polygons. Several land cover classes including salt/brackish

water, pocosins, wet soil, wet/dry shrublands, sandy bare soil, rock outcrops, aquatic

vegetation, pine woodlands, dry deciduous forests, mesic/dry mixed forests,

grassland/pasture, urban residential, and beaches were sampled heavily from small polygons

Edge effects are caused by a variety of factors, including spatial offsetting among data layers

used in map creation; conversion from polygon to raster coverages; and the attribution of

categorical labels to environmental gradients. These edge effects were most pronounced in

smaller polygons.

Often, polygons were composed of patches of differing land cover classes. The two sources

of accuracy assessment data, the ant survey and the gap survey, dealt with mixed polygons in

differing manners. The ant survey, conducted at an early phase of map creation, did not have

the final map polygons to assess. Therefore, interpreters were unable to choose the dominant

land cover type and focus ground truthing within patches of dominant land cover. The gap

survey was conducted following completion of the final land cover map, and each polygon

was photo-interpreted to determine the dominant land cover. Where the dominant land cover

could not be discerned from photo-interpretation, the surveyer navigated in the field to a

predetermined waypoint within the dominant spectral signature of the polygon to assess the

land cover. Where a dominant class could not be reliably interpreted, the sample was

excluded from analyses. Although the gap survey’s methodology of designating a dominant

land cover provided for a more comprehensive survey of the polygon as a whole, even this

methodology is not without bias. The designation of a dominant land cover will be affected

by the differences in viewpoint between the mapper and interpreter. For example, areas with

clusters of dense pine trees, interspersed with cleared treeless areas may be classified by the

and had accuracies of less than 50% (see Table 6).

smaller polygons was approximately half the accuracy of the larger polygons (see Table 6).
51

interpreter as two different land cover types – pine forest and grassland/pasture. However,

the mapper may view this same polygon as a pine woodland with a grassy understory.

Another example would be a polygon dominated by forest, but also including small areas of

rock outcrop. To the interpreter, the dominant land cover would be forest. However, the

mapper may pick up more on the rock outcrop and label the polygon as such. These types of

errors will lead to confusion of spatially associated land cover types, such as marsh and

water, bottomlands and adjacent mesic deciduous forest, and pond pine woodlands and

pocosins. Some of these mixed polygon issues have been dealt with in the SC-GAP

classes are described as complexes.

Spectrally similar classes such as urban residential and urban development, are prone to low

accuracy. Zhu et al. (2000) recognize that it is very difficult to unambiguously separate high

intensity residential areas from industrial areas using TM imagery or NAPP photography. In

the South Carolina Gap project, aerial videography was very helpful in distinguishing

between these two land cover classes for the accuracy assessment. However, the TM data

used in modeling of land cover is of much lower resolution than aerial videography and was

often unable to accurately distinguish these types. Likewise, the spectral similarity of aquatic

vegetion to marshes and water affect the map accuracy and photo-interpretation of accuracy

assessment points. Aerial videography, where available, did provide better resolution

imagery, but submerged aquatic vegetation was often underestimated.

Overlap among map class definitions led to lowered accuracies for certain classes. For

instance, physiognomic categories such as grassland, shrubland, woodland, and forest require

the interpreter to judge the relative percentages of different strata. The cut-off between

categories, even when clearly defined in class descriptions, can often be interpreted

differently among observers. Examples of classes which can and are often confused include

evergreen and mixed forests, deciduous and mixed forests, mesic and dry forests, mesic

forests and temporarily flooded bottomlands, bottomlands and swamps, and pocosins and wet

evergreen woodlands. To determine the extent of intepreter variability in map class

assessment, the gap survey sample points were compared to those of the ant survey. For

classes where the two datasets had comparable sample sizes, the per class accuracy was often

quite different between datasets. For example, the ant survey data showed an accuracy of

33% (n=15) for the swamp class, while the gap survey data gave an accuracy of 71% (n=17)

for the same class. Dry deciduous forests had accuracies of 31% (n=13) and 18% (n=17)

according to the ant and gap surveys, respectively. The earlier ant survey work was based on

very sparse map class definitions, while the later gap survey had more detailed class

descriptions to aid in discriminating map classes. As maps become more detailed, the

amount of overlap between classes increases. In order to minimize interpreter bias, specific

descriptions of map classes should be drawn up, and ideally, more than one observer should

visit sample sites.

Often, confused land cover classes are ecologically very similar. For example, the open

canopy afforded by a woodland and a sparsely wooded shrubland may offer comparable

habitat to vertebrate species. In these cases, it may be useful for the map user to merge

Vertebrate Class Descriptions (see Appendix 5), wherein spatially associated land cover
52

classes. Accuracy assessments themselves often include this type of “fuzzy” accuracy

assessment, wherein confusion between ecologically similar classes is considered less

problematic than confusion between ecologically dissimilar classes (Gopal and Woodcock,

1994). The disadvantage of fuzzy accuracy assessment is that the author presumes to know

the significance of interclass confusions to all readers. Rather than making inferences

regarding the significance of particular class confusions to the reader, we suggest that map

users take it upon themselves to merge those classes that they deem to be ecologically similar

for the purposes of their specific studies. By merging ecologically similar classes, the

accuracy of the map can be greatly improved without losing necessary detail.

As indicated in the methods section, for several classes the minimal recommended sample

size could not be achieved in the time allotted. The standard errors of per class accuracies,

while generally at or below the recommended maximum of 8%, are sometimes greater than

recommended (Crist et al., 2000). Classes with standard errors greater than 8% require

additional sampling to precisely measure class accuracy.

Anderson Level I and II Classification

To facilitate comparison among maps created in other states, the 27-class vertebrate land

cover map was collapsed into a standardized Anderson classification system with fewer land

cover classes (Anderson et al., 1976). When classes were merged into an Anderson Level II

At this classification level, the different upland forested classes, which cumulatively account

for 44.5% of the state’s total area, are still segregated. When these classes are merged in the

Anderson Level I classification, the overall weighted accuracy increases to 71.9% and the

and cultivated land into a single class, the resultant class accuracy becomes 80.0%. The

resultant accuracies are comparable to results achieved by Gap projects in other states.

classification, the overall weighted accuracy increased from 33.4% to 51.4% (see Table 9).

accuracy of the forest class becomes 85.6% (see Table 10). By merging grassland/pasture
53

Table 9. Anderson Level II error matrix. Estimated user's accuracies and standard

errors are presented in the last two columns, and producer's accuracies with standard

errors are in the last two rows. Overall weighted accuracy is 51.40%, with a standard

Reference Data

Land

Cover 1 2 3 4 5 6 7 8 9 10 11 12 13

Row

Total PU%

1 3 1 5 3 1 6 1 11 31 9.68

2 22 3 1 2 1 1 1 31 70.97

3 76 1 5 2 2 3 2 1 3 95 80.00

4 6 30 16 2 25 3 1 83 36.14

5 11 22 89 6 45 1 1 175 50.86

6 1 6 16 27 88 15 2 1 7 1 3 167 52.69

7 1 4 12 43 5 51 2 11 2 131 38.93

8 1 2 1 4 5 2 45 1 2 1 1 4 69 65.22

9 2 3 4 1 9 9 1 1 30 30.00

10 1 6 1 1 3 4 15 1 32 46.88

11 10 1 4 2 1 1 2 6 3 30 10.00

12 9 1 8 4 7 3 1 1 5 39 12.82

13 1 1 3 1 27 2 61 96 63.54

Column

Total 3 27 137 93 203 117 160 82 22 58 10 19 78 1009

PA %

100.

00

81.4 55.4

7

32.2

6

43.8

4

75.2

1

31.8

8

54.8

8

40.9

1

25.8

6

30.0

0

26.3

2

78.2

1

error of 1.57%. See codes in Table 8.
54

Table 10. Anderson Level I error matrix.

Reference Data

Classified

Data Ag

Barren

Land Forest

Range

land

Urban or

Built-up Water Wetland

Row

Total

User's

Accuracy

Standard

Error

%Tota

l

Agriculture 76 1 8 3 5 2 95 80.00% 4.10%

17.95

%

Barren

Land 14 29 21 1 9 12 6 92 31.52% 4.84% 0.14%

Forest 21 3 333 2 17 13 389 85.60% 1.78%

44.52

%

Range

Land 9 1 16 5 1 7 39 12.82% 5.35% 6.52%

Urban or

Built-up

Land 8 1 12 2 37 1 1 62 59.68% 6.23% 4.65%

Water 1 1 1 2 61 30 96 63.54% 4.91% 4.24%

Wetland 8 4 65 4 11 4 140 236 59.32% 3.20%

21.98

%

Column

Total 137 40 456 19 80 78 199 1009

Producer's

Accuracy

55.47

%

72.50

%

73.03

%

26.32

% 46.25%

78.21

% 70.35%

Standard

Error 4.25% 7.06% 2.08%

10.10

% 5.57% 4.67% 3.24%

Overall Weighted Accuracy and

Standard Error: 71.86% 1.42%
55

Limitations and Discussion

The SC-GAP land cover is a satellite imagery derived map of generally coarse nature; it is

not intended to be used at scales finer than approximately 1: 100,000. The land cover

provides significant information about vegetational patterns on the ground and the

relationship of land cover types to one another. The accuracy of the mapped classes varies

by class, and the user should consult specific class accuracy for those they are interested in.

In a general sense, while different forest types were confused with one another, forests were

consistently mapped with high accuracy. Bottomlands and swamps, when grouped together,

were fairly accurate. Grouping cultivated land and pastureland provided a good accuracy for

that class. The maritime forest class is a class of good accuracy, both because of the detailed

soil information used and because of the ease of discerning it on the aerial photography.
56

CHAPTER 3

PREDICTED ANIMAL SPECIES

DISTRIBUTIONS AND SPECIES

RICHNESS

Introduction

All species range maps are predictions about the occurrence of those species within a

particular area (Csuti 1994). Traditionally, the predicted occurrences of most species

begin with samples from collections made at individual point locations. Most species

range maps are small-scale (e.g., >1:10,000,000) and derived primarily from point data to

construct field guides that are suitable, at best, for approximating distribution at the

regional level or counties for example. The purpose of the GAP vertebrate species maps

is to provide more precise information about the current predicted distribution of

individual native species according to actual habitat characteristics within their general

ranges and to allow calculation of predicted area of distributions and associations to

specific habitat characteristics.

GAP maps are produced at a nominal scale of 1:100,000 or better and are intended for

applications at the landscape or "gamma" scale (heterogeneous areas generally covering

1,000 to 1,000,000 hectares and made up of more than one kind of natural community).

Applications of these data to site- or stand-level analyses (site--a microhabitat, generally

10 to 100 square meters; stand--a single habitat type, generally 0.1 to 1,000 ha; Whittaker

1977, see also Stoms and Estes 1993) will likely reveal the limitations of this process to

incorporate differences in habitat quality (e.g., understory condition) or necessary

microhabitat features such as standing dead trees.

Gap analysis uses the predicted distributions of animal species to evaluate their

conservation status relative to existing land management (Scott et al. 1993). However, the

maps of species distributions may be used to answer a wide variety of management,

planning, and research questions relating to individual species or groups of species. In

addition to the maps, great utility may be found in the consolidated specimen collection

records and literature that are assembled into databases used to produce the maps.

Perhaps most importantly, as a first effort in developing such detailed distributions, they

should be viewed as testable hypotheses to be confirmed or refuted in the field. We

encourage biologists and naturalists to conduct such tests and report their findings in the

appropriate literature and to the Gap Analysis Program such that new data may improve

future iterations.

Previous to this effort there were no maps available, digital or otherwise, showing the

likely present-day distribution of species by habitat type across their ranges. Because of

this, ordinary species (i.e., those not threatened with extinction or not managed as game
57

animals) are generally not given sufficient consideration in land-use decisions in the

context of large geographic regions or in relation to their actual habitats. Their decline,

because of incremental habitat loss can, and does, result in one threatened or endangered

species "surprise" after another. Frequently, the records that do exist for an ordinary

species are truncated by state boundaries. Simply creating a consistent spatial framework

for storing, retrieving, manipulating, analyzing, and updating the totality of our

knowledge about the status of each animal species is one of the most necessary and basic

elements for preventing further erosion of biological resources.

Methods

Mapping Standards and Data Sources

The SC-GAP project used scientific literature and expert opinion to develop a list of

vertebrate species in South Carolina and model their potential spatial distribution. An

initial list of 592 species (397 birds, 134 reptiles and amphibians, and 61 mammals) was

created using Thompson 1982, Post and Gauthreaux 1989, Martof et al. 1980, and Golly

1966. The only criterion used in creating the list was the species’ presence in South

Carolina based on the conclusions of these four bibliographic sources. Various experts

evaluated each species for inclusion in the South Carolina database.

Several rules were developed for inclusion/exclusion in the final SC-GAP Species List.

1) Include species that breed, winter, or migrate through South Carolina.

2) Exclude species considered as escapees and accidentals, and species not easily

tied to the vegetation layer, including pelagic birds.

3) Expert opinion could include or exclude species from the final list.

Questionable species, i.e. species occurring irregularly, were compared with

other sources to determine inclusion or exclusion. All questionable species

were given to experts to determine inclusion or exclusion from the final list.

Following these criteria, the list of vertebrate species was reduced to 488.

The minimum mapping unit (MMU) for the vertebrate mapping is the 30 x 30 meter pixel

used in the development of the land cover. For the final product, each vertebrate

distribution was aggregated up to a 90 x 90 meter pixel MMU to meet National GAP

standards. All other modeling was based on 30 x 30 meter pixels. A thematic accuracy of

80% was established as our goal for mapping vertebrate species.

Mapping Range Extent

In order to model the species distributions, the general extent of each species was

required. This information provided the spatial control on each species’ potential

distribution as predicted by its habitat affiliation. The range extent was mapped for each

species using counties as the basis of its general occurrence. It was determined to use

counties because of the familiarity of most taxonomic specialists with similar

distributional studies. Many of them have collected species for local and regional

museums and participated in county-based surveys. Additionally, most of South

Carolina’s counties follow physiographic features to some extent; for example, many of
58

the county boundaries follow major rivers, and some physiographic provinces are

generally recognized by their counties. The range extent was refined for physiographic

addition of a ‘shore’ category specifically for those birds and reptiles, shorebirds and

marine turtles that are tied to coastal beach, scrub/shrub and marine habitats.

Species range maps were created using range descriptions and maps from several

publications. Reptiles and amphibian species ranges followed those identified by Wilson

(1991), Conant and Collins (1991), Petranka (1998), and Ernst and Barbour (1989).

Birds and mammals were derived from Hamel (1992) and Whitaker and Hamilton

(1998), respectively. Additional range information was provided by Martof et al. (1980),

Thompson (1982), Post and Gauthreaux (1989), and Golly (1966).

Range maps included in these source documents generally did not contain information

regarding county-specific distributions. As a result, county associations were determined

by integrating range maps and county boundaries. General species range maps and

county boundary maps were scanned and saved as TIFF files and imported into

WordPerfect8. These maps were rescaled, coregistered and compared graphically to

identify which counties were included in each species’ range map. A data form, which

included a state map with county boundaries for inputting the species ranges by county,

was developed in MS Access. Species presence was coded P for Permanent Residence, S

for Summer Residence, W for Winter Residence, M for Migrant, or left blank for absent.

Accidental range extensions for species occurring regularly within the state were not

included in the county range database. Species whose only occurrence was accidental

according to Hamel (for example Northern Saw-whet Owl) were included. It was

eventually determined to not include migratory species because of the difficulty in

defining habitat – many migratory species tend to be more generalist in their habitat

affinities while in migration. We included a number of introduced species such as the

house sparrow, European starling, Norway rat, black rat, and house mouse.

A prototype data entry project was conducted to enable the GAP team to analyze the

procedure for entering range data to ensure timely and efficient completion of this part of

the vertebrate layer, as well as to ensure relative ease in transferring data into the GIS.

Several species were selected which addressed a multitude of potential data entry

problems or possibilities. Microsoft Access was used to create a relational database with

a form for data entry. Pull-down lists for county names and physiographic regions were

included to eliminate potential mistakes in data input.

Museum data from the Clemson University Museum as well as data from the S.C.

Natural Heritage Database were imported into the Access database. In addition, data

from the Charleston Museum was digitized with assistance from students of the College

of Charleston, Conservation Biology class (Dr. Arch McCallum, Professor). Neither of

these data sources was used to develop species range maps. In general, we tried to avoid

reliance on museum data to build the range maps for several reasons. First, the

collections were largely comprised of data that was greater than 20 years old, and there

has been considerable landscape change since then. Second, museum collections often

regions using the same criteria used in land cover mapping (see chapter 2) with the
59

are concentrated in the immediate area of the institution; this would lead to an

overestimation of vertebrate diversity in a few geographical areas. Third, many

collections are specialized and focus on specific taxa while minimizing distributional data

for other species. For example, the Natural Heritage database was created primarily for

tracking species of concern and thus consists predominantly of records for a small

percentage of the vertebrate diversity in the State. All of the museum data, however, was

used informally as a quality review control to assess the accuracy of the range maps

generated by the SC-GAP project.

The range database was imported into ArcView where a graphical county range map was

generated for each species, based on presence within the county. For avian species,

permanent, summer, and winter ranges were delineated. A layout template was

developed which allowed for simultaneous review of the range map and the habitat

matrix for each species. All layouts were developed to allow for easy photocopying for

Wildlife habitat relationships

Essential to modeling the vertebrate species distribution patterns was the development of

a habitat affinities database. These data provided information regarding each species use

of specific vegetative communities or habitats that was linked to the spatial land cover

data. The habitat information used by the SC-GAP project was compiled using the

scientific literature, field guides, and the Florida GAP database. Based on these records,

a habitat matrix was created for each species using the 28 habitat classes from the initial

vegetation map. This matrix was reviewed by our experts prior to implementation of the

modeling.

During the development of the land cover, it became clear that two land cover classes

were inadequately mapped. The ecotone boundary classification was derived from

shadows cast from forested areas onto agricultural or cleared fields. While this is a

valuable habitat class, it was inconsistently mapped due to the difficulty of discriminating

shadows where varying sun angles and habitat boundary orientation confuse the

landscape. Further, other habitat affinities were considered adequate surrogates to model

species that were identified as using this ecotonal habitat.

Stream margins were also mapped during the initial effort but were removed because the

process of mapping eliminated specific vegetation types found along the streams. Also,

ponds, lakes, streams and rivers were designated as water in the land cover classification

with no differentiation between features based on their structure or function. As a result,

streams could not be isolated in the database for development of a riparian classification.

process. Ecotones and stream margins were dropped from the final land cover of

vertebrate habitat classes. Although these two classes were not used, the information is

maintained in the database for those species using these habitats. A beach class was

added to the vertebrate habitat land cover before the final modeling based on

recommendations of the reviewers. A total of 27 classes were used for the development

of final vertebrate models.

Again, see the Land Cover chapter for further description of the land cover development

distribution to our experts (see Appendix 7).
60

Data review process

Experts were given two opportunities to review the vertebrate database. First, maps for

each species were distributed to state experts for review of both range extent and habitat

affinity. Experts relied on personal knowledge, research on species distribution and

consultation with personal records for assessment of the accuracy of the data.

Comments from all reviewers were compiled and distributed to each of the experts. At

this point, the database was updated and an interface with the Oracle database was

developed for transactional editing and quality review by SC GAP staff.

Second, a joint meeting of taxonomic experts was held for each group – birds, mammals,

reptiles and amphibians. The range extent and habitat affinity data were displayed on a

computer projector and evaluated by meeting participants. The SC GAP coordinator and

vertebrate species technical staff were present to answer any questions regarding the

classification and composition of land cover types. Species distributions and habitat

affinities were discussed until there was general consensus, and the data were modified

accordingly. Some of the 488 species were removed from the list because they are

migratory or, if present during the summer, are not known to breed, have been extirpated,

or are introduced/domestic animals. After final review and debate, the list that was used

to develop vertebrate species models contained 455 species of birds, mammals, reptiles,

As species were entered into the final database, information was also recorded as to their

distribution by physiographic region to incorporate into the final modeling procedures.

Of the 455 species occurring in South Carolina, 210 were of statewide distribution, 65

were found in coastal plain and shore habitats, 1 species was confined to the piedmont,

and 29 were found only in the mountains. The remaining 150 had idiosyncratic

Distribution Modeling

Three primary vertebrate modeling products were generated for the SC GAP project. The

first was a predicted distribution map for each of the 455 species identified by the experts

as relevant to biodiversity studies in South Carolina. These models combined habitat

affinities and land cover data to create distribution maps that highlighted areas whose

vegetation was capable of supporting individual vertebrate species. In the development

of the habitat database, each vertebrate species was only coded as to presence or absence

for particular habitats and county occurrence. No attempt was made to indicate

suitability or preference for habitat by vertebrate species. Experts considered whether the

habitat in question was necessary for maintenance of the species within the state; if so,

that habitat was included in the database.

In order to implement the models, species were grouped into categories based on

statewide or physiographic distribution. An Arc/Info script was developed to attribute

each species to the appropriate counties and habitat. This resulted in a map with 30 x 30

meter pixel resolution attributing each species to a predicted habitat within its

distributions. See the land cover methodology for development of physiographic regions.

and amphibians (see Appendix 8 for a list).
61

habitat type). At this point, information was retained as to particular habitat types.

Predicted habitat maps for all species were subsequently resampled to produce binary

files were produced from the presence/absence maps and saved in Joint Photographic

Expert Group (JPEG) format. After the models were generated, all binary maps were resampled to a 90 x 90 meter pixel resolution for delivery of the final product because of

the volume of data generated at the higher resolution.

documented range (see Figure 12 for an example of a predicted distribution map by

presence/absence maps to comply with National GAP standards (see Figure 12). Graphic
62

Figure 12. Predicted distribution for two species illustrating a distribution

including habitat information and the binary presence/absence map used in the

richness modeling process
63

No ancillary data were used directly to model the vertebrate distributions. NWI wetland,

soil and elevation information was used in the development of the land cover data, and its

influence on species distributions is reflected in the relationship between the land cover

types and habitat affinities. However, use of general habitat affinities resulted in the

overestimation of potential distributions for several species that are limited by proximity

to other significant natural resources or topographic features. Therefore, in order to

refine several of the vertebrate distribution models, we attempted to map riparian species

(those species, primarily birds and amphibians, that are always found within 300 meters

of water bodies) using a buffer for distance from water on the land cover. This was

unsatisfactory in the coastal plain because much of the water in the area is under tree

canopy and so was not mapped in the land cover. This process worked better in the

piedmont, where water is more clearly visible, but still missed some bottomland forests

where water flows at least part of the year and was therefore not used.

Statewide NWI data is not available for all of South Carolina, and an exploratory attempt

at using NWI freshwater classes to improve distributions in the coastal plain alone was

also unsatisfactory. It resulted in clearly different predictions for statewide species

between the coastal plain and the piedmont. Distributions were realistic in the coastal

plain where freshwater buffers limited predicted distributions but were overestimated in

the piedmont where distribution was not limited. A combination of buffering freshwater

using NWI data in the coast and buffering mapped freshwater plus bottomland forests in

the piedmont and mountains might provide a more accurate predicted distribution for

riparian species but was not attempted due to time constraints.

The second vertebrate species product was a general distribution database corresponding

to the U.S. Environmental Protection Agency’s EMAP hexagon structure. In order to

attribute the EMAP hexagon coverage, the national hexagon database was overlain with

the species distribution data by county, and each hexagon was populated based on the

presence or absence of each species within any county overlapping a hexagon. If any

pixels indicated presence within a hexagon, this resulted in the entire hexagon being

attributed for that species. This will result in an overestimation of range extent for many

of the species.

The third vertebrate product was a hyper-distribution. Development of a hyperdistribution was carried out based on the hexagon presence/absence database. This will

allow users to query individual hexagons to determine whether a particular species is

predicted to be found in the area or to get a list of species for each hexagon. The

resolution is in 30 x 30 meter pixels, so an individual pixel could be queried but since the

data is built based on the hexagon distribution that provides the finest scale of resolution.

Results

There were several land cover or habitat types found in South Carolina that affect species

distributions both positively and negatively. Due to the spatial and spectral resolution of

Landsat Thematic Mapper data, many of these could not be adequately identified. For
64

example, rock outcrops provide significant habitat for many species but are not

consistently mapped at the scale of the SC-GAP land cover. Golf courses were also often

mentioned as habitat, especially for some reptiles and amphibians and many species were

associated with the sand rims or other particular aspects of Carolina Bays. Although these

habitats were not specifically mapped, they were noted in the habitat affinity database for

future identification as satellite-based sensors achieve higher resolution. Conversely,

some special habitats that were delineated with NWI include mud flats, beaches, salt

flat/salt shrub.

Mammals

Bats were particularly difficult because of very specific habitat requirements such as

caves for nesting colonies, bridges, or occurrence near buildings. Red wolf is bred in

captivity and was not included. Mountain lions or cougars are sighted from time to time

but are probably escapees from captivity, these were also not included.

Birds

Bald Eagles were problematic, our habitat database included land cover types in which

they would nest. We did not limit distribution to areas surrounding large bodies of water,

a requirement for bald eagle nesting. Farm ponds are important habitat for many species,

especially migratory and wintering waterfowl. While these are apparent as small bodies

of water in a matrix of cultivated land, they were not identified as such. The Cliff

Swallow is limited to land cover types associated with bridges and therefore was difficult

to map. Presence of the Saw Whet Owl in South Carolina is very speculative.

Amphibians

As indicated in the methodology, it was difficult to map amphibians with close ties to

water because of the lack of statewide NWI for buffering water bodies. Many

amphibians are not able to coexist with fish, a problem that might be addressed with an

aquatic gap project for the state. The gray treefrog (Hyla versicolor) and Cope’s gray

treefrog (H. chrysoscelis) are poorly understood and sometimes included as the same by

many projects. Also slimy salamanders, of which SC-GAP included four species, have

have spotty distributions that are not adequately understood or represented by the

mapping process. These are indicated in the database and provide a source of future

projects. Borrow pits are often important larval habitat for many amphibians but weren’t

mapped.

Species Richness

GAP has often been associated with the mapping of species-rich areas or "hotspots."

Richness maps identify where the same numbers of elements co-occur in the same

geographic locations. (In the case of our data, where numbers of animal species are

mapped for the same grid cells.) These are color-coded or shaded in intensity from the

highest numbers of co-occurrence (richness) to the lowest. While we continue to perform

this useful pattern analysis, it is only one of many that may be conducted using the data.

Richest areas may or may not indicate best conservation opportunities. They may occur

in already protected areas or may represent mostly already protected species or those not

recently undergone a taxonomic split (see species list, Appendix 8). Many amphibians
65

Figure 13. Overall species richness
66

Figure 14. Species richness of amphibians

at risk. Still, they are often a useful starting point to examine conservation opportunities

in combination with other analyses described in this report's Introduction and in the

Analysis section. We also feel they may be useful for other rewarding applications such

as identifying places of interest for wildlife observation and study. Richness is depicted

for all mapped taxa (see Figure 13), and by taxonomic groups (see Figures 14-17).
67

Figure 15. Species richness of reptiles
68

Figure 16. Species richness of birds
69

Figure 17. Species richness of mammals
70

Accuracy Assessment

Assessing the accuracy of the predicted vertebrate distributions is subject to many of the

same problems as assessing land cover maps, as well as a host of more serious challenges

related to both the behavioral aspects of species and the logistics of detecting them. These

are described further in the Background section of the GAP Handbook on the national

GAP home page. It is, however, necessary to provide some measure of confidence in the

results of the gap analysis for species collectively, if not individually or by taxonomic

group (comparison to stewardship and management status), and to allow users to judge

the suitability of the distribution maps for their own uses. We, therefore, feel it is

important to provide users with a statement about the accuracy of GAP-predicted

vertebrate distributions within the limitations of available resources and practicalities of

such an endeavor. We acknowledge that distribution maps are never finished products but

are continually updated as new information is gathered. This reflects not only an

improvement over the modeling process, but also the opportunity to map true changes in

species distributions over time. However, we feel that assessing the accuracy of the

current maps provides useful information about their reliability to potential users.

Our goal was to produce maps that predict distribution of terrestrial vertebrates and from

that, total species richness and species content with an accuracy of 80% or higher. Failure

to achieve this accuracy indicates the need to refine the data sets and models used for

predicting distribution. There is a conscious effort in the GAP process, however, to err on

are not. There are two primary reasons for doing so: first, few species have systematic,

unbiased known ranges and we believe science is best served by identifying a greater

potential for sampling and investigation than a conservative approach that may miss such

additional conservation attention that is then refuted by further investigation rather than

identifying a species as sufficiently protected that is discovered not to be by its

subsequent loss. The methods for validating and assessing the accuracy of the vertebrate

distribution maps are presented below along with the results.

Methods

To test the accuracy of the vertebrate species predictions, we used a subset of the state

consisting of public lands that have either been intensively studied for biodiversity

of species accumulated through time and a combination of fieldwork, individual sightings

and research. Surveys, as we define them, include areas for which systematic surveys,

either visual or trapping, have been undertaken. Scientific research involves intensive

study of a particular area, habitat or taxon. A list of contact information for each area

may be obtained upon request from the authors. The numbers of years for each area

varies from less than ten to 50.

assessment or that, because of public use, have well-developed species lists (see Table

opportunities; second, in conducting the analysis of conservation representation (see the

Analysis section), we believe it most appropriate to identify a species that may need

physiographic regions present in the state (see Figure 18). Species lists are generally lists

11). These areas were distributed throughout the state and covered the three

the side of commission: in other words, to attribute species as possibly present when they
71

Figure 18. Sites used in the accuracy assessment of predicted vertebrate

distributions
72

The species lists are compared with predicted distributions for each area, based on county

of occurrence and available habitat, from SC-GAP to provide an assessment of errors of

omission and commission. Species that are accidentals or escapees were not included;

these include nine warbler species from the Mt. Bridge Wilderness Area that do not

regularly visit South Carolina. Other accidentals that were documented in species lists

included the Northern Goshawk (Accipiter gentilis) and Golden Eagle (Aquila

chrysaetos) in Mt. Bridge Wilderness Area. The Cougar (Felis concolor) was

documented in three of the study sites but is considered to be an escapee and was not part

of the SC-GAP species list. We determined to include the four new species of slimy

salamander in the SC-GAP database while recognizing that most species lists and records

would just record slimy salamander. During the accuracy assessment, if a species of

slimy salamander was recorded by a site, it counted as a single match for one or many

species of predicted slimy salamander species. The gray treefrog also counted as one if

both species were recorded under gray treefrog. Sites recorded Bewick’s wren, a species

that has not been seen in the state in the past ten years or so; this was not included in the

SC-GAP database and so was counted as an omission.

Omission error (percentage of species present on a site that were not predicted),

commission error (percentage of species predicted but not present on a site), and overall

accuracy (%) were calculated for each taxonomic class, as well as all classes, within each

test site.

Table 11. Areas with species lists or surveys that were used to assess the accuracy of

the predicted distribution of vertebrate species in South Carolina.

Property Name Animals surveyed Data Source

York County All taxa Species lists, surveys, museum

(1,103 km2) records

Francis Marion National Forest All taxa Species lists, surveys and scientific

(1017 km2) studies

Savannah River Site All taxa Species lists, surveys and scientific

(803 km2) studies

Ft Jackson Army Base All taxa Species lists and surveys

(212 km2)

Mt Bridge Wilderness Area

(43.7 km2) All taxa Species lists and surveys
73

Results

The overall accuracy of the predicted vertebrate distributions ranged from 57% at Ft.

the most extensive study, Savannah River Site and Francis Marion National Forest, had

the highest accuracy. Total omission errors ranged from 4.9 at Ft. Jackson to 11.3 at

Francis Marion NF. Total commission errors ranged from 9.5 at the Savannah River Site

to 66.8 at Ft. Jackson. While sample sizes are too small to make any statistical

comparisons, some general statements can be made. Commission errors were much

higher overall than omission errors, except at Francis Marion National Forest and

Savannah River Site. At Savannah River Site, commission errors were still higher but

not by a large amount. For amphibians, reptiles and mammals, omission error was

highest at the Francis Marion National Forest except in the birds. There was no clear

pattern by taxon for omission errors, and birds had a higher commission error at four of

the five sites. Mt. Bridge Wilderness Area had a very high commission error for reptiles.

There was also no real pattern in differences of accuracy except that mammals and

reptiles were generally better predicted than birds and amphibians.

The highest accuracy was achieved at SRS, a site of intermediate area but with a history

of long-term (50 years) surveys and scholarly work through the Savannah River Ecology

Lab. Through the work of on-site scientists, contractors, government personnel and

visiting scientists, thousands of scientific papers on the flora and fauna of the site have

been published. Francis Marion National Forest, by virtue of being near a major

population center, a museum, and a university, has also been the site of numerous

scientific studies. York County is in the piedmont, an area of the state that is relatively

unknown; we feel our data point strongly to the need for more study in this particular

area.

Jackson to 85.5% at the Savannah River Site (see Table 12). Two of the larger sites with
74

Managed Area List GAP Omission Error Commission Error Overall

And taxa Count Count Count Percent Count Percent Accuracy 

(%)

York County Museum Survey

Amphibians 16 25 2 12.5 7 28.0 64.0

Reptiles 27 42 3 7.0 12 28.5 64.3

Mammals 32 43 1 2.0 10 23.3 74.4

Birds 102 175 12 6.9 61 58.2 58.3

Total 176 286 19 6.6 90 31.5 61.5

Francis Marion National Forest

Amphibians 43 53 8 15.1 2 3.8 81.1

Reptiles 57 66 8 12.1 1 1.5 86.4

Mammals 43 61 16 26.2 2 3.3 70.4

Birds 167 206 4 1.9 36 17.5 81.1

Total 310 386 35 11.3 41 13.2 80.3

Savannah River Site

Amphibians 40 44 1 2.3 3 6.8 90.9

Reptiles 60 64 1 1.6 3 4.7 93.8

Mammals 50 52 2 3.8 0 0.0 96.2

Birds 156 198 14 7.0 28 14.1 78.8

Total 306 358 18 5.0 34 9.5 85.5

Fort Jackson

Amphibians 25 45 1 2.2 19 42.2 55.5

Reptiles 33 53 7 13.2 13 24.5 62.3

Mammals 26 46 2 4.3 18 39.1 56.5

Birds 103 184 6 3.2 75 40.7 56.0

Total 187 328 16 4.9 125 66.8 57.0

Mt. Bridge Wilderness Area

Amphibians 22 32 2 6.3 8 25.0 68.8

Reptiles 16 38 1 2.6 22 57.9 42.1

Mammals 39 59 1 1.7 19 32.2 66.1

Birds 122 166 22 13.3 22 13.3 73.5

Total 199 295 26 8.8 71 24.1 67.5

Table 12. Results of SC-GAP vertebrate accuracy assessment for areas where

species lists were available. Sites are listed in order of decreasing size.
75

Limitations and Discussion

In general, SC-GAP met our goal of 80% accuracy at two sites and fell below at three

additional sites. The fact that our data had the highest accuracy at the two most-studied,

long-term sites leads us to feel that we achieved our goal of predicting vertebrate

distributions with good accuracy. As with many GAP products, errors of commission

were sometimes high; we hope this leads to more study throughout the state to further

develop our knowledge of the state’s vertebrate species and their distribution.

These data are intended for use at a landscape scale or above. As was clear from our data

analysis, we predicted vertebrate species much better in large areas than in small. We

also predict the potential for a species to occur in a certain area but cannot say for certain

that they will be there. We hope our data provides many opportunities to investigate

species habitat relationships and to better understand the distribution of our state species.

This is particularly true in areas such as the piedmont, which has been poorly studied, and

in the mountains where many species occur at the edges of their ranges. It would also be

our recommendation to study those species that occur in lands used by humans such as

cultivated lands and urban/suburban backyards. This is conceivably very significant

habitat and is well worth including in our network of ‘conservation’ lands.
76

CHAPTER 4

LAND STEWARDSHIP

Introduction

To fulfill the analytical mission of GAP, it is necessary to compare the mapped

distribution of elements of biodiversity with their representation in different categories of

land ownership and management. As will be explained in the Analysis section, these

comparisons do not measure viability, but are a start to assessing the likelihood of future

threat to a biotic element through habitat conversion--the primary cause of biodiversity

decline. We use the term "stewardship" in place of "ownership" in recognition that legal

ownership does not necessarily equate to the entity charged with management of the

resource, and that the mix of ownership and managing entities is a complex and rapidly

changing condition not suitably mapped by GAP. At the same time, it is necessary to

distinguish between stewardship and management status in that a single category of land

stewardship such as a national forest may contain several degrees of management for

biodiversity.

The purpose of comparing biotic distribution with stewardship is to provide a method by

which land stewards can assess their relative amount of responsibility for the

management of a species or plant community and identify other stewards sharing that

responsibility. This information can reveal opportunities for cooperative management of

that resource, which directly supports the primary mission of GAP to provide objective,

scientific information to decision makers and managers to make informed decisions

regarding biodiversity. It also is not unlikely that a steward that has previously borne the

major responsibility for managing a species may, through such analyses, identify a more

equitable distribution of that responsibility. We emphasize, however, that GAP only

identifies private land as a homogeneous category and does not differentiate individual

tracts or owners, unless the information was provided voluntarily to recognize a longterm commitment to biodiversity maintenance.

After comparison to stewardship, it is also necessary to compare biotic occurrence to

categories of management status. The purpose of this comparison is to identify the need

for change in management status for the distribution of individual elements or areas

containing high degrees of diversity. Such changes can be accomplished in many ways

that do not affect the stewardship status. While it will eventually be desirable to identify

specific management practices for each tract, and whether they are beneficial or harmful

to each element, GAP currently uses a scale of 1 to 4 to denote relative degree of

maintenance of biodiversity for each tract. A status of "1" denotes the highest, most

permanent level of maintenance, and "4" represents the lowest level of biodiversity

management, or unknown status. This is a highly subjective area, and we recognize a

variety of limitations in our approach, although we maintain certain principles in

assigning the status level. Our first principle is that land ownership is not the primary

determinant in assigning status. The second principle is that while data are imperfect, and
77

all land is subject to changes in ownership and management, we can use the intent of a

land steward as evidenced by legal and institutional factors to assign status. In other

words, if a land steward institutes a program backed by legal and institutional

arrangements that are intended for permanent biodiversity maintenance, we use that as

the guide for assigning status.

The characteristics used to determine status are as follows:

l Permanence of protection from conversion of natural land cover to unnatural (humaninduced barren, exotic-dominated, arrested succession).

l Relative amount of the tract managed for natural cover.

l Inclusiveness of the management, i.e., single feature or species versus all biota.

l Type of management and degree that it is mandated through legal and institutional

arrangements.

The four status categories can generally be defined as follows (after Scott et al. 1993,

Edwards et al. 1995, Crist et al. 1995):

Status 1: An area having permanent protection from conversion of natural land cover and

a mandated management plan in operation to maintain a natural state within which

disturbance events (of natural type, frequency, and intensity) are allowed to proceed

without interference or are mimicked through management.

Status 2: An area having permanent protection from conversion of natural land cover and

a mandated management plan in operation to maintain a primarily natural state, but which

may receive use or management practices that degrade the quality of existing natural

communities.

Status 3: An area having permanent protection from conversion of natural land cover for

the majority of the area, but subject to extractive uses of either a broad, low-intensity type

or localized intense type. It also confers protection to federally listed endangered and

threatened species throughout the area.

Status 4: Lack of irrevocable easement or mandate to prevent conversion of natural

habitat types to anthropogenic habitat types. Allows for intensive use throughout the

tract. Also includes those tracts for which the existence of such restrictions or sufficient

information to establish a higher status is unknown.

Mapping Standard

Due to the variable nature of the existing source data, it was not possible to map

stewardship data to an established accuracy standard. Many of the spatial property files

were extracted from field maps, plat and tax maps and Digital Line Graph (DLG) where

the input data scale ranged from 1:250,000 to 1:4,800. Where multiple boundaries

existed, higher resolution data were used in this database. Coordinate geometry (COGO)

functions of the resident GIS were used to input legal description boundary definitions
78

where available. In addition, all property features were compared to various road,

hydrography and other data layers to ensure that property boundaries conformed to

various natural and cultural features. Data entered from multiple map sources were

manually edgematched to ensure cartographic integrity across quadrangle or other map

boundaries.

Attribute values, record formats and naming conventions conformed to national GAP

management status and ownership protocols. Various input screens were developed in

Oracle to provide pulldown menu choices to ensure spelling and case sensitivity

consistency.

Methods

Dates of Inventory

The primary inventory of public lands in S.C. for GAP began in February 1997 and ran

through March 1999. Updates and corrections continue to be made as received from

agencies.

Method of Collection/Items Collected

A survey of agencies was begun with a letter of request to agency heads initiated on

February 26th, 1997. The letter included a brief description of the GAP project and a

short form listing items of information needed and designation of a contact person

knowledgeable of the agencies’ land property.

A simple data entry form was developed to record information about each property and

provide a paper trail to information collected. A Microsoft Access / Oracle interface and

data entry forms were set up so that attribute data for each property could be recorded. A

unique number was assigned to each property record that could be tied to a map of

property boundaries.

The survey proceeded by making contact and follow-ups with each assigned agency

person or property manager and collecting property information and boundary maps,

usually consisting of plats, tax maps, or boundary maps drawn on topographic or other

base maps. The property was assigned a number, and attribute data was entered into the

Access database system. The boundary map with an attached entry form and/or assigned

number was delivered to the SC DNR NRIMA Section for digitizing. A GAP

management status code and selected metadata descriptors were added to the database

after the property record information was collected.

Existing Sources of Data

At the start of the project, some public lands data existed. Each of these databases was

evaluated for relevance to SC-GAP. The S.C. General Services Office currently is

developing a tabular inventory of state-owned lands based on county assessor office

records. This inventory was not used because it would not be complete within the SCGAP project period and because past experience of working with these records indicated

they were incomplete and outdated. Similarly, the U.S. General Services Administration
79

has regularly published an inventory of U.S. civilian and military real property in each

state. The last inventory was published in September 1997. This inventory is based on

questionnaires to federal agencies, and although it is known to contain some errors, it was

useful as a check on data collected from federal agencies or as a source when no local

contact for a federal agency could be obtained.

Four major databases had been developed over the past decade that provided a

framework for the GAP project. The SCDNR Land Resources Division completed a

tabular inventory of state and federal lands in S.C. in 1986. This inventory contained a

variety of attributes such as owner, manager, and acreage for each property. As a result,

this tabular database was used as the base for the GAP managed lands inventory with the

addition of management status and other attributes. The Land Division also had

completed a digital spatial inventory of DNR-owned lands. Property boundaries for

DNR lands were cartographically transferred from plats and field maps to 1:100,000-

scale topographic base maps and digitized into the Arc/Info geographic information

system. These data provided the initial spatial data for the land stewardship database.

Individual polygon/parcel boundaries were replaced with more accurate data as they

became available.

The second primary data source was the Digital Line Graph (DLG) data developed by the

Water Resources Division (WRD) of the SCDNR. Between 1989 and 1995, the WRD

worked with the U.S. Geological Survey through Joint Funding Agreements (JFAs) to

develop boundary, hydrography, hypsography, and transportation layers for the 566

1:24,000-scale topographic maps for South Carolina. The boundary layer of the DLGs

included state and national park, refuge and forest properties, wildlife management areas,

reservations and other public-owned lands. Because the DLGs had better spatial

resolution and accuracy, these data were used to replace the Land Resources Division

data where appropriate. Parcel identification numbers were assigned to link the higher

resolution spatial data to the tabular inventory database.

The third major contributor in the effort to produce a protected lands layer was the U.S.

Fish and Wildlife Service’s (USFWS) Coastal Ecosystem Program. As a part of this

program, the Charleston Ecological Services office maintains a tabular and digital map

inventory of public and private conservation lands in the eight coastal counties of the

state. This inventory is based on county deeds, plats, tax maps, conservation easement

agreements and other documents available from the local land records office. The results

of this inventory, particularly the private lands data, were incorporated into the overall

protected lands layer for the Gap Analysis.

The fourth database consisted of the forest tract inventory data from the U.S. Forest

Service for the Francis Marion and Sumter National Forest units in South Carolina.

These data are comprised of polygonal vector-based property boundaries including

outholdings and special use areas. Special use areas included natural and recreational

areas, botanical and zoological reserves, wildlife areas, scenic river corridors and roadless

management areas.
80

Scope and Rationale of Inventory

The SC-GAP Managed Lands team discussed the scope of the inventory of public and

private lands prior and during the data collection phase. Group members discussed the

managed lands inventory with representatives from the Southeast including Florida,

North Carolina and Tennessee. All these representatives emphasized the need to collect

the larger parcels for a GIS analysis that are consistent with the large, generally-defined

polygons of other data layers such as vertebrate species ranges. Others in the SC group

pointed out that some of the smaller properties might be important from a special species

habitat or from an ecological representative point. Also considered was the time frame

for the project to reasonably collect the data needed. Another factor was the original

National GAP standard of using 1:100,000 scale base map and the minimum mapping

unit of 100 hectares. The SC GAP group considered this to be too gross a scale for the

parcels of the Southeast.

It was decided to use a two-tiered approach to collecting managed lands. First, all land

100 acres or larger would be automatically included in the inventory and classified into

one of the four standard management classes. In addition, all other public lands of any

size would be included that were designated or considered to be largely natural in nature

or were protected or managed for plant or animal wildlife. Private or institutional land

holdings protecting biodiversity would be brought in from the U.S. FWS-Coastal

Ecosystem Program’s conservation lands inventory or other sources.

The SCDNR-NRIMA Section integrated the three existing source data files into a

statewide land stewardship database of property boundaries and associated attributes. For

each property parcel, the highest resolution spatial data was retained in the database.

Similarly, information updates were requested from property owners and spatial and

tabular attributes were modified. Coordinate Geometry (COGO) functions were used to

input survey coordinates from property plats into the GIS. Other modifications were

obtained from tax maps, site plans or other base maps.

Assignment of GAP Classes/Other Contacts

The managed lands committee of the SC GAP decided to use the four basic classes of the

National GAP program, but to modify the definition of the classes slightly to fit the

conditions in South Carolina. The managed lands committee met with the property

manager of the S.C. Heritage Trust Program and the wildlife manager of the SC DNR

Wildlife & Freshwater Fisheries Division and consulted with GAP coordinators in

An initial assignment of GAP classes was made for each property based on basic

knowledge of the type of property and its assigned use. General guidance was provided

by Heritage Trust, Wildlife Lakes and Fish Hatchery managers of the SC DNR and by

Parks managers of the SC PRT Department. Specific assistance was obtained by

contacting the individual property manager of selected properties (in addition to the

assigned agency contact). Generally, the majority use of the property was considered in

making a decision about what GAP management status class to assign the property.

adjoining states about the definition of the GAP classes in South Carolina (see Table 13).
81

Properties that had a variety of uses, were largely developed, had no program mandate or

no other assurance of remaining unchanged were placed in the unprotected class 4.

Table 13. Management Status Categorization; bold characters indicate where SCGAP differed from the handbook.

Status 1: An area having permanent protection from conversion of natural land cover and

a mandated management plan in operation to restore and/or maintain a natural state

within which disturbance events (of natural type, frequency, intensity, and legacy) are

allowed to proceed without interference or are mimicked through management. This

classification may include hunting activities if hunting is not actively managed for.

Status 2: An area having permanent protection from conversion of natural land cover and

a mandated management plan in operation to maintain a primarily natural state, but that

may receive uses or management practices that degrade the quality of existing natural

communities, including suppression of natural disturbance. This status includes lands

that may be managed for hunting.

Status 3: An area having permanent protection from conversion of natural land cover for

the majority of the area, but subject to extractive uses of either a broad, low-intensity type

(e.g., logging) or localized intense type (e.g., mining). It also confers protection to

federally listed endangered and threatened species throughout the area.

Status 4: There are no known public or private institutional mandates or legally

recognized easements or deed restrictions held by the managing entity to prevent

conversion of natural habitat types to anthropogenic habitat types. The area generally

allows conversion to unnatural land cover throughout

Stewardship Mapping

The 1:24,000 scale coverage incorporated in the South Carolina Gap Analysis Project

was created using the Arc/Info geographic information system software. As indicated

previously, source data from the U.S. Fish and Wildlife Service, the U.S. Forest Service

and the S.C. Dept. of Natural Resources were the initial components of this data set.

Each of these data was constructed using the Arc/Info GIS and was easily transferred

between agencies. All features in each data set were inspected for resolution, positional

accuracy and quality. Redundant or antiquated data were eliminated from the spatial data

set. The remaining features were combined with the Arc/Info GIS to create a single

Once all databases were integrated, additional stewardship properties were incorporated

into the database. Additions and modifications to existing properties were submitted to

the DNR based on the stewardship questionnaire and inventory form mailed to all state

and federal agencies and land management organizations in the state. Revised property

boundaries were returned on a variety of media including topographic maps, survey plats

and legal descriptions, tax maps and digital survey data. Several methods including onscreen and tablet-based digitizing and Coordinate Geometry (COGO) were used to input

additional property boundaries and to edit existing properties.

statewide data set (Figure 19).
82

Use of the COGO module in Arc/Info was the most accurate method of data input

because it operated on survey data and legal descriptions of the property. An AML (Arc

Macro Language program) that contained bearing and traverse information was

generated, which allowed for a high accuracy of property location, or georeferencing. In

many cases survey source maps were not available from the property manager. As a

result, on-screen manual digitizing was used to input the data. This process required the

use of ancillary data sources, such as Digital Raster Graphics (DRGs), Digital Line

Graphs (DLGs) and Digital Orthophoto Quarter-Quadrangles (DOQQs), that aid in

determining the correct location of a property. It is standard practice to reference roads,

utility or hydrography data maps when creating properties using the on-screen digitizing

method. Property boundary conflicts between the data sources were resolved by manual

editing techniques.

Each property in the spatial data file was assigned a unique identification number that

was used to link the spatial feature to the tabular inventory information stored in the

Oracle relational database management system. The database attributes included

property name, owner, manager/contact, address, contact phone number, GAP class

agency code, GAP management status, surveyed acreage and other relevant stewardship

information.
83

 Figure 19. Major land ownership in South Carolina
84

Results

stewardship and management categories in the state. We begin by comparing

stewardship and vice-versa, so that land stewards can see to what degree their lands

areas identified by SC-GAP as management status 1 or 2 along with the manager and

source of the information used to categorize the area. Analyses of representation (see

page_) were based on these identified places, therefore places not identified here (or

changes in management plans that would influence the assigned status) will affect the

analyses.

Table 14. This table indicates the area in square kilometers (km sq) (multiply by 100

for hectares and by 247 for acres) for the following measurements: 1) area of the

land stewardship category in each management status and total for the state, 2) the

steward’s percent of total area in each management category and of the state area,

and 3) total area of each stewardship category in the state and its percent of state

area.

Land Owner Gap Status 1 Gap Status 2 Gap Status 3 Gap Status 4 Total Area

USFWS 454

0.6

0 0 0 454

0.6

USFS 131

0.2

14

0.02

2472

3.1

0 2617

3.2

Other Federal 90

0.1

0 1065

1.3

372

0.5

1437

1.9

SCDNR 332

0.4

372

0.5

3

0

0 704

0.9

SCPRT 0 98

0.1

167

0.2

0 265

0.3

Other State 0

0

82

0.1

414

0.5

338

0.4

834

1.0

Private

Conservation

149

0.2

0

0

14

0.02

0

0

163

0.2

Other 1

0

78

0.1

27

0.03

0

0

106

0.13

Total 1157

1.4

644

0.8

4162

5.2

710

0.9

6580

8.1

The following table (see Table 14) presents summary statistics of area representation of

14 provides information on the proportional make-up of management status categories by

representation of various stewardship categories in management status categories. Table

generally contribute to biodiversity maintenance. Appendix 9 identifies all management
85

Limitations and Discussion

This map is a compilation of ownership maps provided by a variety of sources that are

individually responsible for their accuracy. It was created solely for the purpose of

conducting the analyses described in this report and is not suitable for locating

boundaries on the ground or determining precise area measurements of individual tracts.

The information is as complete as possible with the exception of some small tracts and

many private parcels, especially those under the management of local land trusts.
86

CHAPTER 5

ANALYSIS BASED ON STEWARDSHIP

AND MANAGEMENT STATUS

Introduction

This chapter describes the method and results of the gap analysis as used by the Gap

Analysis Program. As described in the general introduction to this report, the primary

objective of GAP is to provide information on the distribution and status of several

elements of biological diversity. Although GAP "seeks to identify habitat types and

species not adequately represented in the current network of biodiversity management

areas" (GAP Handbook, Preface, Version 1, p. I), it is unrealistic to create a standard

definition of "adequate representation" for either land cover types or individual species

(Noss et al. 1995). A practical solution to this problem is to report both percentages and

absolute area of each element in biodiversity management areas and allow the user to

determine which types are adequately represented in natural areas. There are many other

factors that should be considered in such determinations such as:

ß historic loss or gain in distribution,

ß nature of the spatial distribution,

ß immediate versus long term risk, and

ß degree of local adaptation among populations of the biotic elements that are

worthy of individual conservation consideration.

ß Such analyses are beyond the scope of this project, but we encourage their

application coupled with field confirmation of the mapped distributions.

Currently, land cover types and terrestrial vertebrates are the primary focus of Gap's

mapping efforts, however, other components of biodiversity, such as aquatic organisms

or selected groups of invertebrates may be incorporated into GAP distributional data sets.

Where appropriate, GAP data may also be analyzed to identify the location of a set of

areas in which most or all land cover types or species are predicted to be represented. The

use of "complementarity" analysis, that is, an approach that additively identifies a

selection of locations that may represent biodiversity rather than "hot spots of species

richness" may prove most effective for guiding biodiversity maintenance efforts. Several

quantitative techniques have been developed recently that facilitate this process (see

Pressey et al. 1993, Williams et al. 1996, Csuti et al. 1997, for details). These areas

become candidates for field validation and may be incorporated into a system of areas

managed for the long-term maintenance of biological diversity.

The network of Conservation Data Centers (CDCs) and Natural Heritage Programs

(NHPs) established cooperatively by The Nature Conservancy and various state agencies

maintain detailed databases on the locations of rare elements of biodiversity. GAP

cooperatively uses these data to develop predicted distributions of potentially suitable

habitat for these elements, which may be valuable for identifying research needs and
87

preliminary considerations for restoration or reintroduction. Conservation of such

elements, however, is best accomplished through the fine-filter approach of the above

organizations as described in the introduction. It is not the role of GAP to duplicate or

disseminate Heritage Program or CDC Element Occurrence Records. Users interested in

more specific information about the location, status, and ecology of populations of such

species are directed to their state Heritage Program or CDC.

Methods

summarize the area and percent of total mapped distribution of each element in different

land stewardship and management categories.

Results

The data are provided in a format that allows users to carry out inquiries about the

representation of each element in different land stewardship and management categories

as appropriate to their own management objectives. This forms the basis of Gap's mission

to provide land owners and managers with the information necessary to conduct informed

policy development, planning, and management for biodiversity maintenance.

As a coarse indicator of the status of the elements, we provide a breakdown along five

levels of representation (0-<1%, 1-<10%, 10-<20%, and 20-<50%; >=50%). The <1%

level indicates those elements with essentially none of their distribution in a protected

status while levels of 10%, 20%, and 50% have been recommended in the literature as

necessary amounts of conservation (Noss and Cooperrider 1994; Noss 1991; Odum and

Odum 1972; Specht et al. 1974; Ride 1975; Miller 1994).

Land Cover Analysis

247 for acres) of each types' mapped distribution by management status and land steward

for status 1 and 2 lands, and the percent of the types' total distribution in each category.

For example, bottomland/floodplain forest comprises a total of 8185 km 2 of the state’s

area. Of that, 2.23% occurs on GAP status 1 or 2 lands and 5.35% on GAP status 3.

The gap analysis is accomplished by first producing: maps of land cover (see Figures 8,

A summary of the analysis according to the thresholds described above is shown in Table

15 below. This table provides the area in square kilometers (multiply by 100 for hectares,

9, and 10), predicted distributions for selected animal species (see Figure 12), and land

and management map with the distribution of the elements results in tables that

stewardship and management status (see Figure 18). Intersecting the land stewardship
88

Table 15. Vertebrate habitat classes and their relative protection in GAP status 1

and 2 lands.

Land Cover # Vert. Spp. Total Area Area Protected % Protected

Fresh Water 168 2361 67 2.83

Marine Water 51 1032 150 14.54

Marsh 179 2073 428 20.64

Pocosin 160 365 26 7.13

Swamp 208 3075 225 7.32

Bottomland 212 8185 183 2.23

Wet Soil 47 225 13 5.77

Wet Scrub/Shrub 69 2387 44 1.84

Dry Scrub/Shrub 96 5273 27 0.51

Sandy Bare Soil 92 63 1 1.59

Recently Cleared 76 5960 45 0.76

Rock Outcrop 33 16 1 6.25

Aquatic Vegetation 72 34 11 32.35

Closed Canopy

Evergreen

56 10928

196 1.80

Evergreen Mixed 185 7937 140 1.76

Pine Woodland 188 713 21 2.94

Dry Deciduous 180 695 99 14.24

Mesic Deciduous 195 7909 130 1.65

Dry Mixed 165 291 7 2.41

Mesic Mixed 212 892 8 0.89

Grassland/Pasture 159 2197 8 0.37

Cultivated Land 144 12310 16 0.13

Urban Development 37 1308 1 0.08

Urban Residential 150 2446 0 0.00

Wet Evergreen 56 1455 44 3.03

Maritime Forest 136 655 39 5.95

Beach 56 30 6 20.00

As explained above, we provide results according to thresholds of representation

advocated in the literature to conserve biodiversity. The values in the table will allow

users to set any desirable threshold to identify elements requiring more protection

according to their criteria. The following summaries highlight potential gaps and

land cover types by land steward and management status.

Land Cover with 0-<1% representation in GAP status 1 and 2: This group included dry

scrub/shrub thicket and open canopy/recently cleared forest. The single forest type with

this low level of protection was the mesic deciduous forest/woodland. The low level of

protection afforded the grassland, pasture, dry scrub/shrub group is consistent with low

conservation needs; Appendix 9 provides figures of stewardship representation for all
89

levels of protection afforded grasslands and shrub habitats throughout the United States

(cite). It is disturbing that mesic deciduous woodland is also poorly protected in that it

forms a significant component of vertebrate habitat within the state, containing the fourth

highest level of diversity.

Land Cover with 1-<10% representation in GAP status 1 and 2: 19, 28. These land

cover types include fresh water, barren habitats including wet soil, sandy bare soil, and

rock outcrops. Bottomland/wetland habitat such as pocosins, swamps, bottomlands, and

wet evergreen are also in this category. Mesic deciduous woodland is also poorly

protected. Four map classes including evergreen are included here: closed canopy pine,

evergreen mixed forest, pine woodland, and dry mixed forest/woodland. Maritime forest

is also poorly protected by GAP status 1 and 2 lands.

Land Cover with l0%-<20% representation in GAP status 1 and 2: This includes only

two land cover types, dry deciduous forest and marine water.

Land Cover with 20% -<50% representation in GAP status 1 and 2: Land cover types

with this level of protection include beach habitat, marsh, and aquatic emergent

vegetation. These are all low diversity vertebrate habitats.

Land Cover with at least 50% representation in GAP status 1 and 2: There are no land

cover types with greater than 50% protection according to GAP status 1 and 2.

Although it has long been generally recognized that swamps and bottomland hardwoods,

along with mesic deciduous hardwoods provide significant habitat and corridors for

wildlife, the level of protection does not reflect the significance of this habitat.

Deciduous forests in general and bottomlands, specifically, have been a target for land

protection within the state prior to GAP. There is significant protection for some of the

deciduous forests, especially in the mountains, through many efforts including significant

recent acquisitions.

It has been recently recognized that several species of concern within the state utilize the

grassland, pastures and successional land that is disappearing to urbanization and to pine

plantations. A recent working group for Partners in Flight recognized within the Atlantic

Coastal Plain that grasslands/savannas/pastures, and early successional shrub-shrub

habitat are among the significant habitat planning units (South Atlantic Coastal Plain

Working Group Executive Summary, 2000). Additionally they recognized

urban/suburban backyard habitats. All of these habitats have been low priority for

acquisition and protection, it is hoped that will change.
90

Limitations and Discussion for Land Cover Analysis

Assessing the conservation status of natural land cover is limited by several confounding

factors:

GAP has typically found the accuracy of the mapped distributions of natural communities

at the floristic (e.g., alliance) level to be substantially lower and more variable than

that of animal distributions;

any aggregation of biotic units (e.g., above species) is a surrogate for species or lower

levels of biotic organization and will underrepresent conservation need (Pressey and

Logan 1995); and

for the most part we cannot distinguish the degree of natural condition or value of the

mapped units due to management manipulation, exotic invasion, or spatial

configuration. Considering an aggregation of species such as we have mapped to be

sufficiently represented in existing conservation areas cannot be determined solely by

the percentage of the community represented because the aggregation has unmapped

variation in species composition that we could not measure. Until individual plant

species distributions can be mapped, it is not possible to assure that the full range of

vegetation biodiversity is represented, and surrogates must be used.

The mapping of successional habitats is very difficult and tends to have a low accuracy.

Because our TM imagery dates were in the early 1990s and our accuracy assessment took

place nearly nine years later, we have a poor idea of the accuracy and condition of those

lands. Urban change is also taking place at a fast rate within the state, in some cases land

is converted to impervious surfaces at a rate 3 to 5 times that of population growth.

Predicted Animal Species Distributions Analysis

A summary table is not provided due to the large number of species analyzed, but some

generalizations and examples of species results by the various thresholds are provided

below. The accompanying database provides this information for further analysis. An

vertebrate species protection, see Appendix 10.

analysis of some species of concern is included in Table 16. For a full analysis of
91

Table 16. Some South Carolina species of concern and their percent distribution in the different protection categories. SP =

Species, TH = total habitat, GAP = GAP Status, PA = total protected habitat, PP = percent total protected, FA = federal

protected habitat, FP = percent federal protected, SA = state protected habitat, SP = percent state protected, OA = other

protected habitat, OP = percent other protected. Area is in km2

.

ITIS

CODE

AREA STATUS STATUS

AREA

STATUS

PCT

FWS

AREA

FS

AREA

OF

AREA

DNR

AREA

PRT

AREA

OS

AREA

PC

AREA

OC

AREA

Flatwoods Salamander

Bog Turtle

173773 52 1 4 7.69 0 0 0 0 0 0 0 4

173773 52 2 0 0.00 0 0 0 0 0 0 0 0

173773 52 3 0 0.00 0 0 0 0 0 0 0 0

173773 52 4 48 92.31 0 0 0 0 0 0 0 0

Gopher Tortoise

173858 375 1 0 0.00 0 0 0 0 0 0 0 0

173858 375 2 9 2.40 0 0 0 9 0 0 0 0

173596 1068 1 34 3.18 24 4 0 6 0 0 0 0

173596 1068 2 12 1.12 0 0 0 5 0 3 4 0

173596 1068 3 122 11.42 0 86 10 0 0 0 26 0

173596 1068 4 900 84.27 0 0 0 0 0 315 0 0

173858 375 3 1 0.27 0 0 0 0 0 0 1 0

173858 375 4 365 97.33 0 0 0 0 0 1 0 0
93

Wood Stork

174897 1740 1 261 15.00 154 0 0 99 0 0 8 0

174897 1740 2 161 9.25 0 0 0 127 0 0 34 0

174897 1740 3 165 9.48 0 3 0 0 15 4 143 0

174897 1740 4 1153 66.26 0 0 19 0 0 8 6 0

Bald Eagle

175420 26583 1 925 3.48 525 50 56 229 0 0 59 6

175420 26583 2 336 1.26 0 0 0 192 26 61 57 0

175420 26583 3 1812 6.82 0 805 439 0 69 133 365 1

175420 26583 4 23510 88.44 0 0 106 1 0 719 14 0

Piping Plover

176507 1766 1 266 15.06 158 0 0 100 0 0 8 0

176507 1766 2 150 8.49 0 0 0 114 0 0 36 0

176507 1766 3 161 9.12 0 2 0 0 16 3 140 0

176507 1766 4 1189 67.33 0 0 20 0 0 10 8 0

Least Tern

176923 2805 1 404 14.40 281 0 0 114 0 0 9 0

176923 2805 2 164 5.85 0 0 0 126 0 0 38 0

176923 2805 3 176 6.27 0 3 0 0 18 4 151 0

176923 2805 4 2061 73.48 0 0 31 0 0 13 9 0

176926 2807 1 411 14.64 284 0 0 118 0 0 9 0
94

Red-cockaded Woodpecker

178257 8744 1 154 1.76 106 9 0 38 0 0 1 0

178257 8744 2 45 0.51 0 0 0 21 4 6 14 0

178257 8744 3 777 8.89 0 312 258 0 26 100 81 0

178257 8744 4 7768 88.84 0 0 7 0 0 53 0 0

Southeastern Myotis

179993 30358 1 498 1.64 214 55 56 125 0 0 48 0

179993 30358 2 170 0.56 0 0 0 51 36 61 22 0

179993 30358 3 1831 6.03 0 994 480 0 67 161 129 0

179993 30358 4 27859 91.77 0 0 74 0 0 700 0 0

Rafinesque’s Big-eared Bat

555664 32562 1 417 1.28 108 57 55 114 0 0 51 32

555664 32562 2 201 0.62 0 5 0 89 36 59 12 0

555664 32562 3 1219 3.74 0 517 383 0 43 104 171 1

555664 32562 4 30725 94.36 0 0 79 1 0 224 6 0
94

Limitations and Discussion

When applying the results of our analyses, it is critical that the following limitations are

considered: 1) the limitations described for each of the component parts (land cover

mapping, animal species mapping, stewardship mapping) of the analyses, 2) the spatial

and thematic map accuracy of the components, and 3) the suitability of the results for the

The land cover used in the vertebrate modeling was a 27-class habitat map, the accuracy

at an Anderson Level II is about 51%, Anderson level I was about 71%. The classes that

were of lowest accuracy included pocosins, aquatic vegetation other than marsh, and

some of the transitional classes such as clearcut and grassland/pasture. When forest types

are merged, they are accurate; sub-classes such as upland deciduous or mesic mixed

should be used with caution. However, the patterns of mixed forest types, or forest types

and clearcut, seem to be very representational of the landscape of South Carolina and can

be used in field studies and in general planning activities.

The vertebrate predicted distributions are overall quite accurate and can be used with

some confidence in areas of the size of a county or portion of a county. These predicted

distributions, however, cannot be used to assess relative abundance or predict a species or

individual occurrence at a particular site. The user should pay special attention to species

with low confidence such as bats.

The stewardship mapping is accurate for the time of the data collection and within the

accuracy standards described in the methods. Again, this analysis is intended for broadscale analyses to determine patterns of the landscape and overall distribution of species

diversity in relation to landscape features and land ownership.

intended application (see Appropriate and Inappropriate Use below).
95

CHAPTER 6

CONCLUSIONS AND MANAGEMENT

IMPLICATIONS

The South Carolina GAP Project has been a success in its effort to map state biodiversity,

to predict distributions of vertebrate species, and to document the stewardship of these

elements through land protection efforts. But in addition to this, connections were made

through teamwork and cooperative projects. It is hoped that the combination of scientific

information with the enhanced communication made possible through our common

history with SC-GAP will facilitate future conservation activities in the state.

The South Carolina land cover represents a significant step as part of an ongoing set of

projects in the state concerning land use change. Urban change detection and projections

of future growth are projects that the SCDNR and the Strom Thurmond Institute at

Clemson University have been involved in. The critical step in any urban change project

is to understand what habitat is being lost, and this can be accomplished through the use

of the SC-GAP land cover. Urban growth and its accompanying sprawl are impacting

some of the least protected areas of the state, our grassland and shrublands.

The process of deriving a land cover and the accuracy assessment process have led us to

understand the difficulty of mapping from remotely sensed information the complex

landscape that exists in the southeast. Pocosins and wet evergreen woodlands are

examples of land cover types that are subtly different in characteristics such as percent

canopy cover. Pine flatwoods are a highly endangered and necessary part of the

landscape; these were also difficult to map because of the variation in percent canopy

cover. Our beaches, coastlines and outer coastal areas are a complex of different soil

types and their accompanying vegetation. Old sand dunes produce ridges of upland soil

separated by wetland depressions, each supporting unique vegetation. These land cover

types will benefit from finer-scale mapping efforts and more intense ground level

assessment.

Another challenge was posed by the conversion of land cover classes from one

transitional state to another. Planted pine (our closed canopy evergreen), scrub/shrub

thickets, recently cleared land and cultivated land may all be present on the same parcel

of land at different times as a result of planting and harvest of pine plantations. This

transition is temporal and may be assessed through examining land covers produced at

different time intervals. The SCDNR proposes mapping the land cover of South Carolina

at approximately five-year intervals, beginning before the 2001 SC-GAP project and

continuing five years after. Analysis of the pine plantation landscape using the potential

transitional classes listed above will greatly assist in mapping this significant part of the

South Carolina landscape.
96

We benefited greatly from the association with and assistance from various taxonomic

experts within South Carolina. It is clear they are well able to predict the distribution and

habitat affinities of the 455 species that occur within the state. Some species clearly

require further study of both habitat use and distribution. Additionally, many features of

the landscape are not mapped in this project and are significant components of species

habitats. These include things such as borrow pits, ecotonal areas, urban roosting sites

for bats and chimney swallows, and environmentally friendly golf courses. GAP projects

explicitly do not deal with abundances, and this information is essential to understanding

the biodiversity of the state. A comprehensive monitoring and inventory program for the

state that includes all species, rare or common, specialists or generalists, is essential in

documenting critical habitat.

South Carolina has protected just over 8% of its habitat. This protection is not distributed

evenly throughout habitat types or physiographic regions. Some of the best-protected

habitats are not rich in terrestrial vertebrates, these include salt water, marsh and aquatic

vegetation. However, this does provide some promise for the analysis of aquatic species

in the future. Cultivated and pasture lands, backyard habitats and transitional vegetation

types are some of the least well protected. These are often privately owned, and the state

would benefit hugely from an emphasis on farmland conservation and protection

programs as well as those addressing backyard habitat. Many species such as the painted

bunting, which has become a species of special concern in the state, are closely tied to

these habitats that are disappearing under spreading urbanization.

The good news is that swamps and bottomlands, while not well protected, enjoy a higher

level of protection than many other habitat types. These are shown by our analysis to be

significant habitats for a large diversity of animals and should certainly continue to be

targets for protection.

Two of the pine classes and our mixed mesic forest are significant vertebrate habitats and

do not occur frequently on protected lands. In many cases these may be associated with

timbering activities. A program of education and cooperation with landowners for these

habitats could result in significant habitat protection such as that seen with the Safe

Harbor program for Red-cockaded woodpeckers. Private landowners and corporations

bear the responsibility for a very large portion of the diversity of South Carolina. They

can act as partners in protecting both habitat and species diversity. Educational and

outreach programs should be developed and disseminated throughout the state to assist

this group in maintaining the natural beauty and diversity of South Carolina.
97

CHAPTER 7

PRODUCT USE AND AVAILABILITY

How to Obtain the Products

It is the goal of the Gap Analysis Program and the USGS Biological Resources Division

(BRD) to make the data and associated information as widely available as possible. Use

of the data requires specialized software called geographic information systems (GIS)

and substantial computing power. Additional information on how to use the data or

obtain GIS services is provided below and on the GAP home page (URL below). While a

CD-ROM of the data will be the most convenient way to obtain the data, it may also be

downloaded via the Internet from the national GAP home page at:

The home page will also provide, over the long term, the status of our state's project,

future updates, data availability, and contacts. Within a few months of this project's

completion, CD-ROMs of the final report and data should be available at a nominal cost--

the above home page will provide ordering information. To find information on this state

GAP project's status and data, follow the links to "project information" and then to the

particular state of interest.

GIS products including state specific coverages and data will be available through the

Minimum GIS Required for Data Use

To use the data available from the clearinghouse, user must have ArcView with Spatial

Analyst, ArcInfo or a compatible ESRI subsystem. The data will be available using

ArcIMS software which can then be viewed using basic internet capabilities beginning in

October 2002.

Disclaimer

Following is the official Biological Resources Division (BRD) disclaimer as of 29

January 1996, followed by additional disclaimers from GAP. Prior to using the data, you

disclaimer.

Although these data have been processed successfully on a computer system at the BRD,

no warranty expressed or implied is made regarding the accuracy or utility of the data on

any other system or for general or scientific purposes, nor shall the act of distribution

constitute any such warranty. This disclaimer applies both to individual use of the data

and aggregate use with other data. It is strongly recommended that these data are directly

acquired from a BRD server [see above for approved data providers] and not indirectly

through other sources which may have changed the data in some way. It is also strongly

recommended that careful attention be paid to the content of the metadata file associated

http://www.gap.uidaho.edu/gap

SCDNR GIS clearinghouse at http://www.dnr.state.sc.us/water/nrima/gisdata/

should consult the GAP home page (see How to Obtain the Data, above) for the current
98

with these data. The Biological Resources Division shall not be held liable for improper

or incorrect use of the data described and/or contained herein.

These data were compiled with regard to the following standards. Please be aware of the

limitations of the data. These data are meant to be used at a scale of 1:100,000 or smaller

(such as 1:250,000 or 1:500,000) for the purpose of assessing the conservation status of

animals and vegetation types over large geographic regions. The data may or may not

have been assessed for statistical accuracy. Data evaluation and improvement may be

ongoing. The Biological Resources Division makes no claim as to the data's suitability

for other purposes. This is writable data which may have been altered from the original

product if not obtained from a designated data distributor identified above.

Metadata

Proper documentation of information sources and processes used to assemble GAP data

layers is central to the successful application of GAP data. Metadata documents the

legacy of the data for new users. The Federal Geographic Data Committee (FGDC 1994,

updated those standards to include biological profiles. Executive Order 12906 requires

that any spatial data sets generated with federal dollars will have FGDC-compliant

metadata.

Each spatial data layer submitted must be accompanied by its metadata (*.html file) in

the same directory. You must also include an additional directory (called "meta_master')

which will include each metadata file in three forms (*.txt, *.html, and *.sgml). These are

The redundancy in format is to provide one file for error checking (*.txt), one for

presentation on the Internet (*.html), and one for indexing elements for the spatial data

clearinghouse (*.sgml). Remember, metadata describes the development of the spatial

data set being documented. If there are companion files to the GIS data, use metadata to

reference (reports, spreadsheet, another GIS layer).

USGS personnel conduct metadata training to meet FGDC standards and to include

for more information.

Appropriate and Inappropriate Use of These Data

All information is created with a specific end use or uses in mind. This is especially true

for GIS data, which is expensive to produce and must be directed to meet the immediate

program needs. For GAP, minimum standards were set (see A Handbook for Gap

Analysis, Scott et al. 1993) to meet program objectives. These standards include: scale or

resolution (1:100,000 or 100 hectare minimum mapping unit), accuracy (80% accurate at

95% confidence), and format (ARC/INFO coverage tiled to the 30' x 60' USGS

quadrangle).

biological data. See the Internet site, <http://www.nbii.gov/metadata/training/index.html>

1995) has published standards for metadata and NBII (<http://www.nbii.gov>) has

readily created in MetaMaker (<http://www.nbii.gov/about/factsheet/factsheet5.html>).
99

Recognizing, however, that GAP would be the first, and for many years likely the only,

source of statewide biological GIS maps, the data were created with the expectation that

they would be used for other applications. Therefore, we list below both appropriate and

inappropriate uses. This list is in no way exhaustive but should serve as a guide to assess

whether a proposed use can or cannot be supported by GAP data. For most uses, it is

unlikely that GAP will provide the only data needed, and for uses with a regulatory

outcome, field surveys should verify the result. In the end, it will be the responsibility of

each data user to determine if GAP data can answer the question being asked, and if they

are the best tool to answer that question.

Scale

First we must address the issue of appropriate scale to which these data may be applied.

The data were produced with an intended application at the ecoregion level, that is,

geographic areas from several hundred thousand to millions of hectares in size. The data

provide a coarse-filter approach to analysis, meaning that not every occurrence of every

plant community or animal species habitat is mapped, only larger, more generalized

distributions. The data are also based on the USGS 1:100,000 scale of mapping in both

detail and precision. When determining whether to apply GAP data to a particular use,

there are two primary questions: do you want to use the data as a map for the particular

geographic area, or do you wish to use the data to provide context for a particular area?

The distinction can be made with the following example: You could use GAP land cover

to determine the approximate amount of oak woodland occurring in a county, or you

could map oak woodland with aerial photography to determine the exact amount. You

then could use GAP data to determine the approximate percentage of all oak woodland in

the region or state that occurs in the county, and thus gain a sense of how important the

county's distribution is to maintaining that plant community.

Appropriate Uses

The above example illustrates two appropriate uses of the data: as a coarse map for a

large area such as a county, and to provide context for finer-level maps. Specific caseapplications:

• Statewide biodiversity planning

• Regional (Councils of Government) planning

• Regional habitat conservation planning

• County comprehensive planning

• Large-area resource management planning

• Coarse-filter evaluation of potential impacts or benefits of major projects or plan

initiatives on biodiversity, such as utility or transportation corridors, wilderness

proposals, regional open space and recreation proposals, etc.

• Determining relative amounts of management responsibility for specific biological

resources among land stewards to facilitate cooperative management and planning.

• Basic research on regional distributions of plants and animals and to help target both

specific species and geographic areas for needed research.

• Environmental impact assessment for large projects or military activities.

study examples are provided in Appendix 10, but following is a general list of
100

• Estimation of potential economic impacts from loss of biological resource-based

activities.

• Education at all levels and for both students and citizens.

Inappropriate Uses

It is far easier to identify appropriate uses than inappropriate ones, however, there is a

"fuzzy line" that is eventually crossed when the differences in resolution of the data, size

of geographic area being analyzed, and precision of the answer required for the question

are no longer compatible. Examples include:

• Using the data to map small areas (less than thousands of hectares), typically

requiring mapping resolution at 1:24,000 scale and using aerial photographs or

ground surveys.

• Combining GAP data with other data finer than 1:100,000 scale to produce new

hybrid maps or answer queries.

• Generating specific areal measurements from the data finer than the nearest thousand

hectares (minimum mapping unit size and accuracy affect this precision).

• Establishing exact boundaries for regulation or acquisition.

• Establishing definite occurrence or non-occurrence of any feature for an exact

geographic area (for land cover, the percent accuracy will provide a measure of

probability).

• Determining abundance, health, or condition of any feature.

• Establishing a measure of accuracy of any other data by comparison with GAP data.

• Altering the data in any way and redistributing them as a GAP data product.

• Using the data without acquiring and reviewing the metadata and this report.
101

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Whittaker, R.H. 1965. Dominance and diversity in land plant communities. Science 147:

250-259.

Whittaker, R.H. 1977. Species diversity in land communities. Evolutionary Biology 10:1-

67.

Wilson, L.A. 1995. Land manager's guide to the amphibians and reptiles of the south.

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106

GLOSSARY

aerial videography - video images of the land surface taken from an airplane

algorithm - a procedure to solve a problem or model a solution (In GAP typically refers

to a GIS procedure used to model animal distributions.)

alliance level - a land unit made up of an "alliance" of natural communities that have the

same dominant or co-dominant plant species or, in the absence of vegetation, by the

dominant land cover typically described according to the Anderson land cover

classification (see "Natural Community Alliance" in Grossman et al. 1995)

alpha diversity - a single within-habitat measure of species diversity regardless of internal

pattern, generally over an area of 0.1 to 1,000 hectares (see Whittaker 1960, 1977) -

Anderson Level II - the second hierarchical level in the Anderson land cover

classification system (see Anderson et al. 1976)

anthropogenic - caused by man

assemblages - a group of ecologically interrelated plant and animal species

band, spectral - a segment of the electromagnetic spectrum defined by a range of

wavelengths (e.g. blue, green, red, near infrared, far infrared) that comprise the Landsat

TM imagery

beta diversity - the change in species diversity among different natural communities of a

landscape; an index of between-habitat diversity (see Whittaker 1960, 1977)

biodiversity - generally, the variety of life and its interrelated processes

biogeographic - relating to the geographical distribution of plants and animals

biological diversity - see biodiversity

cartographic - pertaining to the art or technique of making maps or charts

classify - to assign objects, features, or areas on an image to spectral classes based upon

their appearance as opposed to ‘classification’ referring to a scheme for describing the

hierarchies of vegetation or animal species for an area

coarse filter - the general conservation activities that conserve the common elements of

the landscape matrix, as opposed to the "fine filter" conservation activities that are aimed

at special cases such as rare elements (see Jenkins 1985)

community - a group of interacting plants and animals

cover type - a non-technical higher-level floristic and structural description of vegetation

cover
107

cross-walking - matching equivalent land cover categories between two or more

classification systems

delineate - identifying the boundaries between more or less homogenous areas on

remotely sensed images as visible from differences in tone and texture

delta diversity - the change in species diversity between landscapes along major climatic

or physiographic gradients (see Whittaker 1977)

digitization - entering spatial data digitally into a Geographic Information System

ecoregion - a large region, usually spanning several million hectares, characterized by

having similar biota, climate, and physiography (topography, hydrology, etc).

ecosystem - a biological community (ranging in scale from a single cave to millions of

hectares), its physical environment, and the processes through which matter and energy

are transferred among the components

edge-matching - the process of connecting polygons at the boundary between two

independently created maps, either between TM scenes or between state GAP data sets

element - a plant community or animal species mapped by GAP. May also be referred to

as "element of biodiversity".

error of commission - the occurrence of a species (or other map category) is erroneously

predicted in an area where it is in fact absent

error of omission - when a model fails to predict the occurrence of a species that is

actually present in an area

exact set coverage - a basic optimization problem to determine the best method for

identifying general areas that, when selected sequentially, would have the greatest

positive cumulative impact on attaining adequate representation of any or all biotic

elements of interest

extinction - disappearance of a species throughout its entire range

extirpation - disappearance of a species from part of its range

fine filter - see "coarse filter"

floristic - pertaining to the plant species that make up the vegetation of a given area.

formation level - the level of land cover categorization between Group and Alliance

describing the structural attributes of a land unit, for example, "Evergreen Coniferous

Woodlands with Rounded Crowns" (see Jennings 1993b)

gamma diversity - the species diversity of a landscape, generally covering 1,000 to

1,000,000 hectares, made up of more than one kind of natural community (see Whittaker

1977)
108

gap analysis - a comparison of the distribution of elements of biodiversity with that of

areas managed for their long-term viability to identify elements with inadequate

representation

geographic information systems - computer hardware and software for storing, retrieving,

manipulating, and analyzing spatial data

Global Positioning System (GPS) - an instrument that utilizes satellite signals to pinpoint

its location on the earth's surface

greedy heuristic - an algorithm for exact set cover analysis (see Kiester et al., in press)

ground truthing - verifying maps by checking the actual occurrence of plant and animal

species in the field at representative sample locations

habitat - the physical structure, vegetational composition, and physiognomy of an area,

the characteristics of which determine its suitability for particular animal or plant species

hectare - a metric unit of area of 10,000 square meters and equal to 2.47 acres

hex/hexagon - typically refers to the EPA EMAP hexagonal grid of 635 square kilometer

units

hyperclustering - a efficient, interactive method for accurately analyzing and classifying

remotely-sensed data that reduces data size and computational requirements while

retaining the integrity of the original data

lotic - flowing, e.g., water in a stream or river

metadata - information about data, e.g., their source, lineage, content, structure, and

availability

minimum mapping unit - the smallest area that is depicted on a map

neotropics - the zoo-geographic region stretching southward from the tropic of Cancer

and including southern Mexico, Central and South America, and the West Indies

phenology - the study of periodic biological phenomena, such as flowering, breeding, and

migration, especially as related to climate

phenotype - the environmentally and genetically determined observable appearance of an

organism, especially as considered with respect to all possible genetically influenced

expressions of one specific character

physiognomic - based on physical features

physiographic province - a region having a pattern of relief features or land forms that

differ significantly from that of adjacent regions

pixel - the smallest spatial unit in a raster data structure
109

polygon - an area enclosed by lines in a vector-based Geographic Information System

data layer or a region of contiguous homogeneous pixels in a raster system

preprocessing - those operations that prepare data for subsequent analysis, usually by

attempts to correct or compensate for systematic, radiometric, and geometric errors

pro-active - acting in anticipation of an event as opposed to reacting after the fact

range - the geographic limit of the species

range unit - a spatial, geographic unit to record and display species geographic range.

reach - a stream or river segment between inflowing tributaries

registration, spatial - matching different images to each other by finding points on the

images that can be matched to known points on the ground

remote sensing - deriving information about the earth's surface from images acquired at a

distance, usually relying on measurement of electromagnetic radiation reflected or

emitted from the feature of interest

resolution - the ability of a remote sensing system to record and display fine detail in a

distinguishable manner or: the smallest feature that can be distinguished or resolved on a

map or image, such as a TM pixel

scale, map - the ratio of distance on a map to distance in the real word, expressed as a

fraction; the smaller the denominator, the larger the scale, e.g. 1:24,000 is larger than

1:100,000

sensitivity analysis - the consideration of a number of factors involved in the

mathematical modeling of an ecosystem and its components. These include feedback and

control, and the stability and sensitivity of the system as a whole to changes in some part

of the system. Predictions can be made from the analysis.

simulated annealing - an algorithm used for set coverage analysis (see Kiester et al., in

press)

species richness - the number of species of a particular interest group found in a given

area

spectral cluster - a group of adjacent pixels that are uniform with respect to their

brightness values

supervised classification - the process of classifying TM pixels of unknown identity by

using samples of known identity (i.e., pixels already assigned to informational classes by

ground truthing or registration with known land cover) as training data

synoptic - constituting a brief statement or outline of a subject; presenting a summary

tessellation - the division of a map into areas of equal and uniform shape such as the

EPA- EMAP hexagon
110

Thematic Mapper - a sensor on LANDSAT 4 and 5 satellites that records information in

seven spectral bands, has a spatial resolution of about 30 m x 30 m, and represents digital

values in 256 levels of brightness per band

transect - a transversely cut line along which physical and biological observations are

made

trophic structure - the various levels in a food chain, such as producers (plants), primary

consumers (herbivores), and secondary consumers (carnivores)

Universal Transverse Mercator - one of several map projections or systems of

transformations that enables locations on the spherical earth to be represented

systematically on a flat map

Universal Transverse Mercator grid - a geographic reference system used as the basis for

worldwide locational coding of information in a GIS or on a map

unsupervised classification - the definition, identification, labeling, and mapping of

natural groups, or classes, of spectral values within a scene. These spectral classes are

reasonably uniform in brightness in several spectral channels.

vector format - a data structure that uses polygons, arcs (lines), and points as fundamental

units for analysis and manipulation in a Geographic Information System

virtual reality - a computer-generated simulation of reality with which users can interact

using specialized peripherals such as data gloves and head-mounted computer graphic

displays

wildlife habitat relationship model - a method of linking patterns of known habitat use by

animal species with maps of existing vegetation, thereby identifying the spatial extent of

important habitat features for use in conservation and management.
111

GLOSSARY OF ACRONYMS

Acronyms

ACSM American Congress on Surveying and Mapping

ADAMAS Aquatic Database Management System

ADEM Alabama Department of Environmental Management

AML ARC/INFO Macro Language

ASPRS American Society for Photogrammetry & Remote Sensing

AVHRR Advanced Very High Resolution Radiometer (satellite system)

BEST Biomonitoring of Environmental Status and Trends

BLM Bureau of Land Management

CAFF Conservation of Arctic Flora and Fauna

C-CAP Coastwatch Change Analysis Program (NOAA)

CDC Conservation Data Center

CEC Council on Environmental Cooperation

CENR Committee on Environment and Natural Resources

CERES California Environmental Resources Evaluation System

CIESIN Consortium for Internat'l Earth Science Information Network

CODA Conservation Options and Decision Analysis (software)

CRMP Coordinated Resource Management Plan

CRT Cathode ray tube (?)

CRUC Cooperative Research Unit Center

DLG-E Digital line graph - enhanced

DOI Department of the Interior

EDC EROS Data Center

ECOMAP The National Hierarchical Framework of Ecological Units mapping project of

the USDA Forest Service

EMAP Environmental Monitoring & Assessment Program

EMAP-LC EMAP-Landscape Characterization (USEPA)

EMSL Environmental Monitoring & Systems Laboratory (USEPA)

EMTC Environmental Management Technical Center (NBS)

EOS Earth Observing System

EOSAT Earth Observation Satellite Company (the commercial operator of the Landsat

satellite system)

EOSDIS EOS Data & Information System

ERL Environmental Research Laboratory, Corvallis (USEPA)

EROS Earth Resources Observation Systems (USGS)

ESRI Environmental Systems Research Institute

ETM+ Enhanced Thematic Mapper plus

FGDC Federal Geographic Data Committee

FTP file transfer protocol

FY Fiscal Year

GAO General Accounting Office (Congress)

GAP Gap Analysis Program

GCDIS Global Change Data and Information System

GLIS Global Land Information System (USGS)

GLOBE Global Learning and Observations to Benefit the Environment
112

GPS Global Positioning System

GRASS Geographic Resources Analysis Support System

GRIS Geographic Resource Information Systems

HRMSI High Resolution Multispectral Stereo Imager

IALE International Association of Landscape Ecology

IDRISI A GIS developed by Clark University

LAPS Land Acquisition Priority System

LC/LU Land Cover/Land Use (USGS)

MIPS Map and Image Processing System

MOU Memorandum of Understanding

MMU Minimum mapping unit

MRLC Multi-Resolution Land Characteristics Consortium

MSS Multi-Spectral Scanner

MTPE Mission to Planet Earth

NAFTA North American Free Trade Agreement

NALC North American Landscape Characterization (USEPA, USGS)

NAWQA National Water Quality Assessment (USGS)

NBII National Biological Information Infrastructure

NBS National Biological Service

NCCP Natural Communities Conservation Planning program (in CA)

NDCDB National Digital Cartographic Data Base

NERC National Ecology Research Center (Ft. Collins, CO)

NMD National Mapping Division

NPS National Park Service

NSDI National Spatial Data Infrastructure

NSTC National Science and Technology Council

NWI National Wetlands Inventory (USFWS)

OMB Office of Management and Budget (Administration)

OSIS Oregon Species Information System

PARC Public Access Resource Center

PI Principal Investigator

SAB Science Advisory Board (USEPA)

SCICOLL Scientific Collections Permit Database

SDTS Spatial Data Transfer Standard

SGID State Geographic Information Database

SNEP Sierra Nevada Ecosystem Project

SOFIA Southern Forest Inventory and Analysis

SPOT Système Pour l'Observation de la Terre

RMSE Root mean square error

TIGER Topologically Integrated Geographic Encoding and Referencing system (used for

U.S. census)

TM Thematic Mapper

TNC The Nature Conservancy

UNESCO United Nations Educational, Scientific, and Cultural Organization

URISA Urban and Regional Information Systems Association.

URL Universal Resource Locator
113

USFS US Forest Service

USFWS US Fish & Wildlife Service

UTM Universal Transverse Mercator

UVM University of Vermont

WHRM Wildlife/habitat relationship model

WISCLAND Wisconsin Initiative for Statewide Cooperation on Landscape Analysis and

Data
1

APPENDICES

Appendix 1. A detailed description of South Carolina GAP Land Cover classes and

for the more general habitat map and the detailed land cover

Map Class 1 – Fresh Water

Fresh water includes ponds, lakes, rivers, streams, flooded borrow pits and beaver ponds.

It may also include rocky shoals and wet sand/mud flats, where patches are too small to

map separately.

The aerial extent of this class was expanded using NWI class fresh water and soil class

water; in quadrangles where neither of these ancillary data sources are available, fresh

water pixels may be underestimated.

Map Class 2 – Marine water

Marine water includes salt and brackish water along the coast in the Atlantic Ocean and

in estuaries.

In the eight coastal counties, NWI class salt water was used to change water pixels to

marine water. NWI data were available for all coastal counties.

Map Class 3 – Fresh Water Marsh/Emergent Wetland

This class includes all freshwater wetlands dominated by herbaceous vegetation. It

encompasses several of Nelson’s natural communities to include Depression Meadow,

Tidal Freshwater Marsh, Upland Bog, and Hillside Herb Bog (Nelson, 1986). The GAP

concept of interdune ponds is that of a fresh water marsh; however, Nelson’s description

of interdune ponds is of water-filled ponds supporting aquatic vegetation, which should

be found mapped as map class 12, aquatic vegetation.

Decision rules, especially utilizing NWI, were critical in distinguishing fresh water

marshes from salt marshes.

Several ecological complexes and compositional groups are found in this freshwater

marsh and emergent wetland class. These include the Depression Meadow

Compositional Group, Coastal Island Pond Compositional Group and the Tidal

Freshwater Complex.

Depression Meadow Compositional Group

This group includes flatwoods depression meadows and limestone sinkhole

ponds, inner coastal plain high ponds, and coastal island ponds. If they’re large

a listing of appropriate TNC alliances within each class. A description is provided
Appendices

2

enough to be mapped separately, they are found as class 3. Otherwise, they’re

inclusions within other coastal plain map classes, especially 13, 15, 25 and 44.

V.A.5.N.k.18 Panicum hemitomon Seasonally Flooded Temperate

Herbaceous Alliance

V.A.5.N.k.28 Spartina bakeri Seasonally Flooded Herbaceous Alliance

V.A.5.N.k.65 Carex striata Seasonally Flooded Herbaceous Alliance

V.B.2.N.h.3 Woodwardia virginica Seasonally Flooded Herbaceous Alliance

V.B.2.N.h.100 Polygonum spp. (section Persicaria) Seasonally Flooded

Herbaceous Alliance

Coastal Island Pond Compositional Group

Many of the barrier island and marsh island ponds will be found as inclusions

within the Coastal Upland Mixed Forest, Class 44 or Lowland Maritime Forest,

Class 26. If they’re large enough to be mapped separately, they should be

mapped as Class 3. The Coastal Island Pond Compositional Group includes the

following alliances:

V.A.5.N.k.31 Typha domingensis Seasonally Flooded Temperate Herbaceous

Alliance

V.A.5.N.k.28 Spartina bakeri Seasonally Flooded Herbaceous Alliance

V.A.5.N.k.5 Carex hyalinolepis Seasonally Flooded Herbaceous Alliance

V.B.2.N.h.100 Polygonum spp. (section Persicaria) Seasonally Flooded

Herbaceous Alliance

Tidal Freshwater Ecological Complex

This complex is found in major estuaries and rivers that are under tidal influence

but are fresh water. It includes most of the rice field vegetation where dikes have been

broken and natural hydrology has been restored. Where tidal freshwater marshes can be

distinguished, they are referred to as the Tidal Freshwater Marsh Compositional Group

since all the alliances are named for herbaceous perennials. Where Tidal Freshwater

Forests can be distinguished, they are separated out as the Tidal Freshwater Forest

Compositional Group; however, if they are distinguishable at the mapping level, they will

be mapped as Class 5, Swamps/Bottomland Hardwoods. Tidal Freshwater Shrublands

will be found mapped as Class 7, Wet Scrub/Shrub Thickets where they’re large enough

to be distinguished. Where tidal forests and/or tidal shrublands are present but cannot be

mapped separately from marshes, they are considered to be inclusions in the Tidal

Freshwater Ecological Complex.

Tidal Freshwater Marsh Compositional Group

Tidal Freshwater Marsh

V.A.5.N.n.10 Spartina cynosuroides Tidal Herbaceous Alliance

V.A.5.N.n.2 Typha (angustifolia, domingensis) Tidal Herbaceous Alliance

V.A.5.N.n.14 Zizania aquatica Tidal Herbaceous Alliance

V.A.5.N.n.15 Zizaniopsis miliacea Tidal Herbaceous Alliance

Tidal Freshwater Forest Compositional Group
Appendices

3

Freshwater Tidal Bald Cypress - Tupelo Swamp

Tidal swamp forests usually found mapped as Class 5, Swamps/Bottomland

Hardwoods. These are inclusions in Freshwater Marsh where they’re too

small to map separately.

I.B.2.N.h.2 Nyssa biflora-(Nyssa aquatica, Taxodium distichum) Tidal

Forest Alliance

II.B.2.N.f.2 Taxodium distichum Tidal Woodland Alliance

Tidal Freshwater Shrubland Compositional Group

Tidal River Edge Shrub Wetland – tidally flooded shrublands, occurring in

association with freshwater tidal marshes and tidally flooded forests. Where

large enough, will be mapped at wet scrub/shrub, class 7, but they are usually

found as fringes along tidal creeks and are an inclusion within class 3,

freshwater marsh.

III.A.2.N.l.1 Myrica cerifera-Rosa palustris Tidal Shrubland Alliance

III.B.2.N.h.2 Alnus (incana, serrulata) Tidal Shrubland Alliance

The following alliances are attributed to the freshwater marsh/emergent wetland class.

I.B.2.N.h.2 Nyssa biflora-(Nyssa aquatica, Taxodium distichum) Tidal Forest

Alliance

Tidal swamps occurring as inclusions within tidal freshwater marsh where

they’re too small to map separately.

I.B.2.N.f.2 Taxodium distichum Tidal Woodland Alliance

Tidal swamps occurring as inclusions within tidal freshwater marsh where

they’re too small to map separately.

II.B.2.N.c.4 Taxodium ascendens Seasonally Flooded Woodland Alliance

Coastal plain pond-cypress woodlands and savannas in relatively flat basins,

especially Carolina Bays, remnants of bays, or isolated ponds within a

flatwoods matrix.

III.A.2.N.l.1 Myrica cerifera-Rosa palustris Tidal Shrubland Alliance

Tidal shrublands occurring as inclusions within tidal freshwater marsh where

they’re too small to map separately.

III.B.2.N.h.2 Alnus (incana, serrulata) Tidal Shrubland Alliance

Tidal shrublands occurring as inclusions within tidal freshwater marsh where

they’re too small to map separately.

V.A.5.N.j.2 Andropogon glomeratus Temporarily Flooded Herbaceous

AllianceOccurs in disturbed wetland sites, such as old fields; should map as

fresh water marsh (3).
Appendices

4

V.A.5.N.j.4 Carex torta Temporarily Flooded Herbaceous AllianceAlluvial

wetlands on sand, gravel, and rock bars in the mountains. Probably mapped

as rock outcrop (11), wet soil (6), or fresh water marsh (3).

V.A.5.N.k.3 Aristida palustris - Andropogon (callipes, glaucopsis) –

Rhynchospora spp. Seasonally Flooded Herbaceous Alliance

Occurs in relatively shallow to deep seasonally flooded portions of coastal

plain ponds and lakes of the coastal plain, including limesink ponds.

V.A.5.N.k.5 Carex hyalinolepis Seasonally Flooded Herbaceous Alliance

Seasonally flooded depressional wetlands, occurring in the coastal plain.

V.A.5.N.k.9 Dichanthelium (erectifolium, wrightianum) - Rhynchospora filifolia

Seasonally Flooded Herbaceous Alliance

Coastal plain ponds, including some limestone sinkhole ponds.

V.A.5.N.k.14 Juncus effusus Seasonally Flooded Herbaceous Alliance

Widely variable in terms of geographic location.

V.A.5.N.k.16 Leptochloa fascicularis var. maritima Seasonally Flooded

Herbaceous Alliance

Brackish, non-tidal wetland; often occurring as interdune wetland; also

occurring in waterfowl impoundments.

V.A.5.N.k.18 Panicum hemitomon Seasonally Flooded Temperate Herbaceous

Alliance

Occurs in ponds, lakes, depression meadows, flatwoods ponds, interdune

swales, etc.

V.A.5.N.k.22 Rhynchospora (careyana, inundata) Seasonally Flooded

Herbaceous Alliance

Coastal plain upland depressions.

V.A.5.N.k.23 Rhynchospora spp. - Panicum (rigidulum, verrucosum) – Rhexia

virginica Seasonally Flooded Herbaceous Alliance

Occurs on seasonally flooded pond shores and lakeshores in the coastal plain.

V.A.5.N.k.25 Scirpus cyperinus Seasonally Flooded Herbaceous Alliance

Occurs in seasonally flooded marshes or emergent zones of upland depression

ponds.

V.A.5.N.k.28 Spartina bakeri Seasonally Flooded Herbaceous Alliance

Interdune ponds; most are too small to be mapped. In SC, known only from

the South Atlantic coastal plain ecoregion, these circular depressions are

found on marsh islands.
Appendices

5

V.A.5.N.k.29 Spartina patens Seasonally Flooded Herbaceous Alliance

Maritime Wet Grasslands, interdune ponds.

V.A.5.N.k.31 Typha domingensis Seasonally Flooded Temperate Herbaceous

Alliance

Occurs in interdune ponds, and other coastal non-tidal wetlands.

V.A.5.N.k.34 Zizaniopsis miliacea Seasonally Flooded Temperate Herbaceous

Alliance

Occurs in a variety of situations, from rocky beds of slow-moving rivers to

stagnant coastal plain waters.

V.A.5.N.k.65 Carex striata Seasonally Flooded Herbaceous Alliance

Coastal plain depression meadow.

V.A.5.N.l.1 Eleocharis (elongata, equisetoides) – Rhynchospora tracyi

Semipermanently Flooded Herbaceous Alliance

Deeper coastal plain depressional wetlands, including limestone sinks and

interdune ponds.

V.A.5.N.l.4 Phragmites australis Semipermanently Flooded Herbaceous Alliance

Occurs in depressions, spoil impoundments and along rivers.

V.A.5.N.l.9 Typha (angustifolia, latifolia) - (Scirpus spp.) Semipermanently

Flooded Herbaceous Alliance

Most commonly occurs along lake margins and in shallow basins, and

occasionally in river backwaters. Also occurs in borrow pits and rice field

impoundments.

V.A.5.N.m.17 Rhynchospora oligantha – Sarracenia spp. - (Aristida

beyrichiana, Ctenium aromaticum) Saturated Herbaceous Alliance

Coastal plain herbaceous bog.

V.A.5.N.m.5 Carex crinita – Osmunda spp. / Sphagnum spp. Saturated

Herbaceous Alliance

Fens, often small enough that surrounding trees cast shade and otherwise

affect the microenvironment.

V.A.5.N.n.10 Spartina cynosuroides Tidal Herbaceous Alliance

Narrow stands along tidal creeks and sloughs or on levees of oligohaline tidal

marshes. Should map as fresh water marsh (3) and as salt and brackish

marshes (32).

V.A.5.N.n.14 Zizania aquatica Tidal Herbaceous Alliance

Tidal freshwater marsh.
Appendices

6

V.A.5.N.n.15 Zizaniopsis miliacea Tidal Herbaceous Alliance

Tidal freshwater marsh.

V.A.5.N.n.2 Typha (angustifolia, domingensis) Tidal Herbaceous Alliance

Tidal freshwater to brackish marsh.

V.A.5.N.n.3 Cladium mariscus ssp. jamaicense Tidal Temperate Herbaceous

AllianceTidal brackish marsh.

V.A.5.N.n.7 Phragmites australis Tidal Herbaceous Alliance

Tidal marsh occurring in fresh to brackish salinities.

V.B.2.N.d.2 Justicia americana Temporarily Flooded Herbaceous Alliance

Occurs on rocky river shoals in the piedmont. Should map as rocky shoals

(50), fresh water marsh (3), or fresh water (1). It may also map as urban

development (22) due to the presence of rocks.

V.B.2.N.e.1 Pontederia cordata – Peltandra virginica Semipermanently Flooded

Herbaceous Alliance

Very wet or partially submerged forb vegetation of rivershores and

lakeshores, and sometimes of artificial ponds, lakes, and impoundments.

V.B.2.N.f.12 Senecio tomentosus – Croton willdenowii Saturated Herbaceous

Alliance

Granitic flatrock community occurring on thin soils over granitic rocks.

Where they are large enough to map, these sites should map as rock outcrop

(11) or fresh water marsh (3).

V.B.2.N.f.14 Talinum teretifolium – Portulaca smallii Saturated Herbaceous

Alliance

Granitic flatrock community. Where they are large enough to map, these sites

should map as rock outcrop (11) or fresh water marsh (3).

V.B.2.N.f.15 Vittaria appalachiana – Heuchera parviflora Saturated Herbaceous

Alliance

Herbaceous vegetation on rock substrates associated with waterfalls; soils are

shallow and constantly saturated. Where they are large enough to map, these

sites should map as rock outcrop (11) or fresh water marsh (3).

V.B.2.N.f.7 Diphylleia cymosa – Saxifraga micranthidifolia Saturated

Herbaceous Alliance

Moderate- to high-elevation forested (shaded) seeps in the mountains.

Occurrences are typically very small, but can be extensive (to more than a

hectare in size).
Appendices

7

V.B.2.N.f.9 Impatiens (capensis, pallida) – Monarda didyma Saturated

Herbaceous Alliance

High elevation, open small seeps in the mountains. Marshy – seepy areas

dominated by forbs instead of graminoids may, if large enough, show as wet

soil, not as marsh.

V.B.2.N.h.100 Polygonum spp. (Section Persicaria) Seasonally Flooded

Herbaceous Alliance

Occurs in wet depressions, lakes, and ponds.

V.B.2.N.h.3 Woodwardia virginica Seasonally Flooded Herbaceous Alliance

Occurs in seasonally flooded wetland depressions in the coastal plain.

V.D.2.N.g.1 Eragrostis hypnoides - Lipocarpha micrantha – Micranthemum

umbrosum Seasonally Flooded Herbaceous Alliance

Occurs on seasonally flooded riverbanks and other sandy shores in the coastal

plain. Where large enough to map, should class as fresh water marsh (3) or

wet soil (6).

V.D.2.N.h.1 Amphianthus pusillus – Isoetes spp. Seasonally Flooded Herbaceous

Alliance

Occurs in seasonally flooded pools on granitic flatrocks. May be an inclusion

within rock outcrop (11).

V.D.2.N.i.1 Diamorpha smallii Saturated Herbaceous Alliance

Occurs in ephemeral pools on large granite outcrops in the piedmont. May be

an inclusion within rock outcrop (11).

VI.A.1.N.b.1 Sphagnum cuspidatum Seasonally Flooded Nonvascular Alliance

Occurs in coastal plain depressions.

Map Class 4 – Pocosin

This class includes all freshwater wetlands dominated by evergreen shrubs in the coastal

plain. It encompasses two of Nelson’s natural communities to include Pocosin and Swale

Pocosin (Nelson, 1986). It doesn’t include Nelson’s Streamhead Pocosin which should

be found under wet evergreen, class 25, due to the tree canopy. It also excludes Nelson’s

Seepage Pocosin which also has a tree canopy. However, where the canopy is open

enough that it’s undetected in the mapping process, the occurrences of Seepage Pocosins

in the fall-line sandhills are likely too small to be mapped separately from xeric sandhill

scrub forests. Also, although they support evergreen wetland shrub species, the soils on

which seepage pocosins are found are not mapped by NRCS as wetland soils so they

cannot be identified by decision rules. Pocosins naturally grade into class 25, wet

evergreen. Many pocosins support scattered trees; where the tree density is great enough,

they’ll be mapped as wet evergreen.
Appendices

8

Decision rules using NWI and soil information helped greatly in delineating this class.

Pocosins occur on saturated soils of GAP soil wetness class 3 or 4 and NWI palustrine

evergreen scrub/shrub with or without a forested component.

Pocosins are found in the fall-line sandhills and in the coastal plain. In the coastal plain,

they’re often found in Carolina Bays within a mosaic of various bay communities. The

Carolina Bay Pocosin Ecological Complex takes into account this mosaic of evergreen

wetland communities where they’re too small to be mapped separately.

Carolina Bay Pocosin Ecological Complex – many Carolina Bays are comprised of a

mosaic of pocosins and wet evergreen forests, especially in the Northeastern

coastal plain. Where they cannot be distinguished, this complex should be used.

Where the occurrences are large enough, bay forests and pond pine woodlands

are mapped as class 25, wet evergreen forests.

Low Pocosin

III.C.2.N.e.1 Zenobia pulverulenta-Lyonia lucida-Ilex (coriacea, glabra)

Saturated Shrubland Alliance

High Pocosin

III.A.2.N.j.2 Lyonia lucida-Ilex glabra Saturated Wooded Shrubland

Alliance

III.C.2.N.f.1 Zenobia pulverulenta-Cyrilla racemiflora-Lyonia lucida

Wooded Shrubland

Pond Pine Woodland – see class 25, wet evergreen

II.A.4.N.f.9 Pinus serotina Saturated Woodland Alliance

Bay Forest – see class 25, wet evergreen

I.A.4.N.g.2 Gordonia lasianthus Saturated Forest Alliance

I.A.4.N.g.3 Magnolia virginiana - Persea palustris Saturated Forest

Alliance

The following alliances are attributed to the pocosin class:

III.A.2.N.i.2 Arundinaria gigantea Saturated Shrubland Alliance

III.A.2.N.i.3 Cyrilla racemiflora – Ilex coriacea - (Cliftonia monophylla)

Saturated Shrubland Alliance

Coastal plain and sandhill evergreen shrublands (pocosins) occurring in

poorly drained flats, along lake or pond shores, and on hillside seeps.

III.A.2.N.j.1 Arundinaria gigantea Saturated Wooded Shrubland Alliance

Occurs on saturated peaty soils; pond pine and pocosin shrubs are present

along with the cane.

III.A.2.N.j.2 Lyonia lucida – Ilex glabra Saturated Wooded Shrubland Alliance

High pocosins on saturated mineral or organic soils. May also be found

mapped as class 25, wet evergreen, depending on the amount of Pinus

serotina in the overstory.
Appendices

9

III.B.2.N.e.7 Vaccinium formosum – Vaccinium fuscatum Seasonally Flooded

Shrubland Alliance

Depressional wetlands in the coastal plain and lower piedmont. This should

map as pocosin (4) or wet scrub/shrub (8) in the coastal plain and as wet

scrub/shrub (8) in the piedmont.

III.C.2.N.e.1 Zenobia pulverulenta - Lyonia lucida - Ilex (coriacea, glabra)

Saturated Shrubland Alliance

Low pocosins of coastal plain peat domes.

III.C.2.N.f.1 Zenobia pulverulenta – Cyrilla racemiflora Saturated Wooded

Shrubland Alliance

High pocosins of the coastal plain.

Map Class 5 – Swamp/Bottomland Hardwood

This class is comprised of deep swamps supporting cypress and tupelo and bottomland

hardwood forests of varying hydrologic regimes. Overall, these two primary types of

deciduous forested wetlands were indistinguishable in the mapping process, even with

NWI and soils data. They are separated conceptually into the Swamp Forest Ecological

Complex and the Bottomland Hardwood Forest Ecological Complex.

Several natural plant communities as described by Nelson (1986) are included in this

concept including Bald Cypress – Tupelo Gum Swamp, Bottomland Hardwoods, Marl

Forest, Non-Alluvial Swamp Forest, Piedmont Seepage Forest, Pond Cypress Pond,

Small Stream Forest, Swamp Tupelo Pond, Tidal Bald Cypress – Tupelo Gum Swamp,

and Upland Depression Swamp Forest. Although Marl Forest is considered by Nelson to

be a terrestrial community, it is included in our swamp/bottomland hardwood class. Marl

Forests are found on temporarily flooded, poorly to very poorly drained alfisols in the

coastal plain of South Carolina and they support bottomland hardwood plant species.

We were unable to reliably distinguish forested isolated freshwater ponds from riverine

systems; therefore, alliances representing both types of wetlands are listed below.

Soils and NWI were used to refine the classification of swamps and bottomland

hardwoods. Many of the deeper swamp forests were originally placed in this class in the

original vegetation map; however, many of the bottomland hardwood forests in particular

were originally classified as various upland forest classes. Using GAP soil wetness

classes 3, 4 and 6 (in the coastal plain) and NWI deciduous palustrine forested wetlands,

either combined or separately, swamps and bottomland hardwood forests were

distinguished from upland forests.

Because many of the bottomland hardwood forests were derived from 6 different upland

forest classes, they were reclassified as six separate new classes with a reference to the

original class from which they were derived. Although alliances couldn’t be assigned to
Appendices

10

a particular new class with certainty, there were some general correlations. For example,

swamp type 15 very often supports laurel oak-dominated bottomlands whereas swamp

type 18 often supports more deeply flooded swamp forests dominated by water tupelo.

These new classes have been lumped into the swamp/bottomland hardwood class but we

have retained the ability to separate them out again in the hope that it will be useful in

future studies. Some of the differences are due to differences in originally assigning class

values to the original 280 hydrologic units.

Swamp Forest Ecological Complex

This complex supports the deeper seasonally to semipermanently flooded swamps that

are often characterized by the dominance of cypress and/or tupelo. It includes the

following TNC alliances:

I.B.2.N.e.21 Taxodium ascendens Seasonally Flooded Forest Alliance

Occurs along streams in the Pee Dee River System or in depressions and ponds

throughout the coastal plain.

I.B.2.N.e.22 Taxodium distichum - Nyssa (aquatica, biflora, ogeche) Seasonally

Flooded Forest Alliance

Occurs in floodplains throughout the coastal plain.

I.B.2.N.e.8 Nyssa (aquatica, biflora, ogeche) Floodplain Seasonally Flooded Forest

Alliance

Occurs in floodplains, sloughs, and backswamps throughout the coastal plain.

I.B.2.N.e.9 Nyssa (aquatica, biflora, ogeche) Pond Seasonally Flooded Forest

Alliance

Swamps occurring as isolated ponds within an upland matrix in the coastal plain,

especially within pine flatwoods.

I.B.2.N.f.2 Nyssa aquatica - (Taxodium distichum) Semipermanently Flooded Forest

Alliance

Occurs in backwater sloughs, low wet flats, swales and backswamps, and along

blackwater streams throughout the coastal plain.

I.B.2.N.f.3 Taxodium distichum Semipermanently Flooded Forest Alliance

Occurs throughout the coastal plain.

I.B.2.N.h.2 Nyssa biflora - (Nyssa aquatica, Taxodium distichum) Tidal Forest

Alliance

Tidally flooded forests occurring along rivers in the coastal plain. Should be

mapped as swamp/bottomland hardwoods (5) or as an inclusion in tidal

freshwater marsh (3).

I.B.2.N.f.2 Taxodium distichum Tidal Woodland Alliance
Appendices

11

II.B.2.N.c.4 Taxodium ascendens Seasonally Flooded Woodland Alliance

Coastal plain pond-cypress woodlands and savannas in relatively flat basins,

especially Carolina Bays, remnants of bays, or isolated ponds within a flatwoods

matrix.

Bottomland Hardwood Forest Ecological Complex

This complex includes river bottoms and low-lying floodplains where cypress and tupelo

are usually not dominants. This complex encompasses the concept of map classes 33

through 38 and is discussed further under those classes. Floodplain and levee forests of

class 28 may be found as inclusions within this complex but are listed and described

under class 28.

I.B.2.N.d.11 Fraxinus pennsylvanica - Ulmus americana - Celtis (occidentalis,

laevigata) Temporarily Flooded Forest Alliance

Occurs on base-rich alluvial sites throughout the state. This alliance is mapped as

class 5 in the Coastal plain and sandhills, and as piedmont bottomland (45) in the

piedmont and mountains.

I.B.2.N.d.12 Liquidambar styraciflua - (Liriodendron tulipifera, Acer rubrum)

Temporarily Flooded Forest Alliance

Bottomland community of moderately wet floodplains in piedmont and coastal

plain. In the coastal plain and sandhills, this alliance is mapped as class 5; in the

piedmont it should be found mapped as 43, narrow stream valley forest or as 45,

piedmont bottomland forest.

I.B.2.N.d.16 Quercus (michauxii, pagoda, shumardii) - Liquidambar styraciflua

Temporarily Flooded Forest Alliance

Occurs primarily in brownwater situations (but also sometimes in blackwater) in

the piedmont and coastal plain. This alliance is probably mapped as

swamp/bottomland hardwood forest (5) in the coastal plain and sandhills and as

floodplain (28), piedmont bottomland (45) or narrow stream valley forest (43) in

the piedmont and mountains.

I.B.2.N.d.17 Quercus (phellos, nigra, laurifolia) Temporarily Flooded Forest

Alliance

Occurs along blackwater rivers in the coastal plain. Should be mapped as

swamp/bottomland (5). Swamp forests dominated by Q. laurifolia were most

often found mapped as class 33, swamp type 15.

I.B.2.N.d.4 Acer saccharinum Temporarily Flooded Forest Alliance

Floodplain forest of major rivers. This alliance is probably mapped as floodplain

forest (28) or bottomland hardwood (5) in the coastal plain and sandhills, and as

floodplain forest (28), narrow stream valley forest (43), or piedmont bottomlands

(45) in the piedmont and mountains.
Appendices

12

I.B.2.N.d.9 Fagus grandifolia Temporarily Flooded Forest Alliance

Occurs along small streams in the piedmont and possibly other parts of the state.

This alliance could potentially be mapped as floodplain forest (28), bottomland

hardwood (5), piedmont bottomland (45), or narrow stream valley forest (43).

I.B.2.N.e.1 Acer rubrum - Fraxinus pennsylvanica Seasonally Flooded Forest

Alliance

Occurs in floodplains of rivers and perennial streams. This alliance could

potentially be mapped as swamp/bottomland hardwood forest (5), narrow stream

valley forest (43), or piedmont bottomland (45).

I.B.2.N.e.11 Planera aquatica Seasonally Flooded Forest Alliance

Occurs along coastal plain rivers and backswamps.

I.B.2.N.e.12 Quercus laurifolia Seasonally Flooded Forest Alliance

Seasonally flooded, mostly blackwater forests occurring in the coastal plain.

Swamp forests dominated by Q. laurifolia were most often found mapped as class

33, swamp type 15.

I.B.2.N.e.13 Quercus lyrata - (Carya aquatica) Seasonally Flooded Forest Alliance

Occurs in floodplains, sloughs, and depressions throughout the coastal plain.

I.B.2.N.e.15 Quercus phellos Seasonally Flooded Forest Alliance

Occurs primarily in upland depressions in the piedmont and inner coastal plain.

Should map as piedmont bottomland (45) or swamp/bottomland (5).

I.B.2.N.e.19 Salix nigra Seasonally Flooded Forest Alliance

Occurs in coastal plain floodplains or non-riverine swamps, usually following

disturbance. This alliance will probably be mapped as either floodplain forest

(28) or swamp/bottomland hardwood forest (5).

I.B.2.N.e.20 Sapium sebiferum Seasonally Flooded Forest Alliance

Occurs in seasonally flooded forests and isolated ponds throughout the coastal

plain, especially the outer coastal plain.

I.B.2.N.e.6 Liquidambar styraciflua - (Acer rubrum) Seasonally Flooded Forest

Alliance

Occurs in seasonally flooded depressions (not floodplains) throughout the coastal

plain.

I.B.2.N.g.2 Acer rubrum - Nyssa sylvatica Saturated Forest Alliance

Occurs in seepage areas on hillsides, streamheads, floodplains, and other poorly

drained depressions. No known examples, but could be mapped as

swamp/bottomland hardwood (5), narrow stream valley forest (43), piedmont
Appendices

13

bottomland (45), or floodplain forest (28). It’s likely that this is a piedmont

community in South Carolina.

I.B.2.N.g.4 Liquidambar styraciflua Saturated Forest Alliance

Occurs in seepage areas with peaty soils throughout the coastal plain.

I.B.2.N.g.5 Nyssa biflora - Acer rubrum - (Liriodendron tulipifera) Saturated Forest

Alliance

Occurs on extensive peat flats in the coastal plain, in seepage-fed edges of

floodplains or on slopes wet by nearly constant seepage. Also occurs on seepagefed edges of small streams in the piedmont. In general, this alliance is probably

mapped as swamp/bottomland hardwood (5) or piedmont bottomland (45)

I.B.2.N.g.6 Quercus laurifolia - Nyssa biflora Saturated Forest Alliance

Occurs in the coastal plain on poorly drained interstream flats or on upland flats

with saturated soils. Bottomland forests dominated by Q. laurifolia were most

often found mapped as class 33, swamp type 15

I.B.2.N.g.7 Quercus michauxii - Quercus pagoda Saturated Forest Alliance

Occurs on non-riverine flats. In general, this alliance should be mapped as

swamp/bottomland hardwood (5) in the coastal plain or piedmont bottomland (45)

in the piedmont.

III.A.2.N.g.1 Arundinaria gigantea Temporarily Flooded Shrubland Alliance

Rare community occurring on streamside flats or bottomlands. Probably only

occurs as small-patch inclusions within floodplain forests (28), piedmont

bottomlands (45), swamp/bottomland (5), and narrow stream valley forests (43).

Map Class 6 – Wet Soil

Map Class 7 – Wet Scrub / Shrub Thicket

This class includes shrublands that are saturated or temporarily, seasonally, or

semipermanently flooded. In the coastal plain, these shrublands, as classified here, are

dominated by deciduous shrubs; whereas, in the piedmont, they can support both

deciduous and evergreen shrubs. Shrublands dominated by evergreen shrubs in the

coastal plain are placed in map class 4, pocosins. Many of these shrublands are a result

of disturbance, particularly clearing, and may be found mapped as class 10, open

canopy/recently cleared forest.

Soils that were in wetness class 3, 4 or 6, were used to refine the wet scrub/shrub class.

The NWI deciduous scrub/shrub wetland class was also used to distinguish this class.

There is some overlap between our wet scrub/shrub thicket class and open canopy,

recently cleared forests, map class 10.
Appendices

14

Nelson (1986) doesn’t treat any communities that would fall under our concept of wet

scrub/shrub thickets. The following are TNC alliances that are included in this class:

III.A.2.N.g.2 Ligustrum sinense Temporarily Flooded Shrubland Alliance

Wetland bottomlands heavily infested with Ligustrum. May also occur as an

inclusion within floodplain forest (28), piedmont bottomland (45), narrow stream

valley forest (43), and swamp/bottomland hardwood (5).

III.B.2.N.d.5 Salix caroliniana Temporarily Flooded Shrubland Alliance

Shrub thickets occurring on riverbanks, sand bars, and other wet sites. Occurs

mostly in the coastal plain, rarely in the piedmont and mountains. This alliance

will probably be mapped as a narrow band inclusion within floodplain forest (28),

swamp/bottomland hardwood forest (5), narrow stream valley forest (43), or

piedmont bottomland (45).

III.B.2.N.d.7 Salix nigra Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers or lakeshores of the piedmont and coastal plain. This

alliance will probably be mapped as a narrow band inclusion within floodplain

forest (28), swamp/bottomland hardwood forest (5), narrow stream valley forest

(43), or piedmont bottomland (45).

III.B.2.N.e.8 Vitis rotundifolia - Ampelopsis arborea - Campsis radicans Seasonally

Flooded Vine-Shrubland Alliance

This alliance includes wetland vegetation that commonly develops following

disturbance in coastal plain bottomlands. This alliance is also present as a smallpatch inclusion within the swamps/bottomland hardwood forests (5) and

floodplain forests (28).

III.A.2.N.h.100 Cyrilla racemiflora Seasonally Flooded Shrubland Alliance

Seasonally flooded coastal plain ponds, often occurring in matrix of pine

flatwoods. May also be found mapped as pocosin, class 4, if the occurrence is

large enough.

III.A.2.N.i.100 Myrica cerifera Saturated Shrubland Alliance

Wetland dune swales, flats, and interdune swales, usually situated between dune

grasslands and maritime forest.

III.A.2.N.l.1 Myrica cerifera – Rosa palustris Tidal Shrubland Alliance

Tidally flooded shrublands, occurring in association with freshwater tidal marshes

and tidally flooded forests. Occurrences can exceed 50 acres in size, according to

TNC. Should fall under wet scrub/shrub (7), or could be small-patch inclusion

within fresh water marsh (3).

III.A.4.N.d.2 Hypericum fasciculatum Seasonally Flooded Shrubland Alliance
Appendices

15

Shrubby wet prairies and depressional wetlands of the coastal plain; possibly also

in old borrow pits. Classified as wet scrub/shrub (7) or as an inclusion within pine

flatwoods.

III.B.2.N.e.5 Decodon verticillatus Seasonally Flooded Shrubland Alliance

Occurs in coastal plain depression ponds, floodplain pools, waterfowl

impoundments, and lakeshores. It is mapped as wet scrub/shrub (7) or it could be

a small-patch inclusion within freshwater marsh (3).

III.B.2.N.e.7 Vaccinium formosum – Vaccinium fuscatum Seasonally Flooded

Shrubland Alliance

Depressional wetlands in the coastal plain and lower piedmont. It should map as

pocosin (4) or wet scrub/shrub (7) in the coastal plain and as wet scrub/shrub (7)

in the piedmont.

III.B.2.N.f.1 Cephalanthus occidentalis Semipermanently Flooded Shrubland

Alliance

Occurring in shallow water depressions, oxbow ponds, sinkhole ponds, and

backwater sloughs of stream and river floodplains. This alliance is probably

either mapped as wet scrub/shrub (7) or as a small-patch inclusion within

swamp/bottomland hardwood (5), piedmont bottomland (45), narrow stream

valley forest (43), or floodplain forest (28).

III.B.2.N.g.1 Alnus serrulata Saturated Shrubland Alliance

Non-alluvial, palustrine vegetation of the piedmont.

III.B.2.N.h.2 Alnus (incana, serrulata) Tidal Shrubland Alliance

Occurs along the tidal freshwater reaches of rivers. May occur as a fringing

shrubland, zonal between wild rice tidal marshes and tidal cypress-gum forests.

Less commonly, it occupies large patches in freshwater marshes. Probably occurs

either as a small-patch inclusion within fresh water marsh (3) or as wet

scrub/shrub (7).

Map Class 8 – Dry Scrub / Shrub Thicket

Dry scrub/shrub thickets often characterize recently cleared or other disturbed areas in

uplands. Since they occur on moderately well drained to excessively drained soils, soil

decision rules were used to distinguish dry scrub/shrub thickets from wet scrub/shrub

thickets.

The only dry scrub/shrub thicket communities that were recognized by Nelson (1996)

were the maritime shrub thickets, placed in map class 27, and the salt shrub thickets,

placed in map class 30. There are few alliances recognized at this time by TNC in this

map class.

Dry scrub/shrub thickets can include the following TNC alliances:
Appendices

16

III.A.2.N.a.1 Ligustrum sinense Shrubland Alliance

Upland areas infested with Ligustrum.

III.A.2.N.b.5 Rhododendron maximum Shrubland Alliance

Evergreen shrublands restricted to the Blue Ridge province.

III.A.4.N.a.5 Ceratiola ericoides Shrubland Alliance

Xeric shrublands occurring on deep sands. Occurs in sandhills and the outer

coastal plain as small-patches within turkey oak barrens and dry sandhill scrub.

Most occurrences are probably too small to map.

III.B.2.N.a.11 Pueraria montana Vine-Shrubland Alliance

Kudzu vine-shrubland occupying a variety of disturbed sites throughout the state.

IV.A.1.N.a.3 Chrysoma pauciflosculosa Dwarf-Shrubland Alliance

Evergreen shrublands of fall line sandhills. They’re probably inclusions in dry

sandhill scrub forests as most occurrences are small.

Map Class 9 – Sandy Bare Soil

Map Class 10 – Open Canopy/Recently Cleared Forest

Map Class 11 – Rock Outcrop

Caves, crevices, rocky outcrops. Includes granitic flatrocks of the piedmont and granitic

domes of the mountains. Rock outcrops were often either too small to map or were

confused with urban land cover types by the mapper, so the rock outcrop class is

underestimated on the Gap map.

In the Blue Ridge sections of Greenville, Pickens, and Oconee counties, GAP soil class

18, rock, was used to map rock outcrops.

Nelson (1986) classifies rock outcrops as Granitic Domes and Granitic Flatrocks. TNC

alliances found on rock outcrops include:

V.A.5.N.e.4 Carex biltmoreana Herbaceous Alliance

Occurs on steeply sloping outcrops of felsic to mafic rock in the mountains.

V.A.5.N.j.4 Carex torta Temporarily Flooded Herbaceous Alliance

Alluvial wetlands on sand, gravel, and rock bars in the mountains.

V.A.5.N.m.100 Scirpus cespitosus Saturated Herbaceous Alliance

Restricted to saturated or ponded areas at the base of sheer, north-facing granitic

dome cliffs.
Appendices

17

V.B.2.N.b.10 Saxifraga michauxii Herbaceous Alliance

Occurs on acidic rocky summits in the mountains.

V.B.2.N.b.11 Selaginella (tortipila, rupestris) Herbaceous Alliance

Granitic dome community, occurring on smooth rock substrates or rock with few

crevices or fractures in the mountains and piedmont.

V.B.2.N.f.12 Senecio tomentosus – Croton willdenowii Saturated Herbaceous

Alliance

Granitic flatrock community occurring on thin soils over granitic rocks.

V.B.2.N.f.14 Talinum teretifolium – Portulaca smallii Saturated Herbaceous

Alliance

Granitic flatrock community.

V.B.2.N.f.15 Vittaria appalachiana – Heuchera parviflora Saturated Herbaceous

Alliance

Herbaceous vegetation on rock substrates associated with waterfalls; soils are

shallow and constantly saturated.

V.D.2.N.h.1 Amphianthus pusillus – Isoetes spp. Seasonally Flooded Herbaceous

Alliance

Occurs in seasonally flooded pools on granitic flatrocks.

V.D.2.N.i.1 Diamorpha smallii Saturated Herbaceous Alliance

Occurs in ephemeral pools on large granite outcrops in the piedmont.

VI.B.1.N.b.3 Umbilicaria mammulata Nonvascular Alliance

Occurs on relatively moist, shaded rock outcrops.

VII.A.1.N.a.1 Asplenium montanum Sparsely Vegetated Alliance

Occurs on near-vertical cliffs.

VII.A.1.N.a.2 Asplenium ruta-muraria – Pellaea atropurpurea Sparsely Vegetated

Alliance

Occurs on limestone and dolomite outcrops, usually shaded by trees rooted in

adjacent forest.

Map Class 12 – Aquatic Vegetation

Vegetation of semi-permanent or temporary lakes, ponds or pools. Abandoned rice fields

supporting emergent vegetation are mapped as class 3, fresh water marsh.

In fresh water areas, NWI lacustrine, riverine, or palustrine aquatic plant beds can be

used to define this class.
Appendices

18

The following list of alliances are those attributed to South Carolina by TNC. However,

the knowledge of aquatic plant distribution hasn’t kept up with the dissemination of that

knowledge and several other alliances are known to occur in the state but have not yet

been submitted to TNC for inclusion. These include alliances dominated by Najas spp.,

Brasenia schreberi, Potamogeton spp., Pistia, Eichhornia, and Ceratophyllum.

V.B.2.N.e.100 Ludwigia peploides Semipermanently Flooded Herbaceous Alliance

Floating or stranded mats occurring in shallow water of flats in slow-moving

streams, shallow lakes, natural and artificial impoundments.

V.B.2.N.e.2 Ludwigia uruguayensis Semipermanently Flooded Herbaceous Alliance

Floating or stranded mats, often covering large numbers of acres in lakes and

reservoirs. This alliance is probably referring to Ludwigia hexapetala.

V.C.1.N.b.3 Halodule beaudettei Permanently Flooded - Tidal Herbaceous Alliance

Estuarine seagrass bed attributed by TNC to South Carolina. Should map as

aquatic vegetation (13).

V.C.2.N.a.2 Brasenia schreberi Permanently Flooded Herbaceous Alliance

V.C.2.N.a.100 Nelumbo lutea Permanently Flooded Temperate Herbaceous Alliance

Occurs in artificial impoundments, natural oxbow lakes and ponds.

V.C.2.N.a.101 Orontium aquaticum – (Scirpus subterminalis) Permanently Flooded

Herbaceous Alliance

Aquatic vegetation of acidic, oligotrophic waters of the coastal plain. Usually

found in blackwater sandhills streams.

V.C.2.N.a.102 Nymphaea odorata - Nuphar spp. Permanently Flooded Temperate

Herbaceous Alliance

Occurs in a variety of water bodies.

V.C.2.N.a.103 Myriophyllum heterophyllum Permanently Flooded Herbaceous

Alliance

V.C.2.N.a.11 Nymphoides aquatica Permanently Flooded Herbaceous Alliance

Occurs in the aquatic zones of coastal plain ponds.

V.C.2.N.a.12 Podostemon ceratophyllum Permanently Flooded Herbaceous Alliance

Occurs on shoals in rocky streambeds and riverbeds.

V.C.2.N.a.14. Potamogeton spp. - Ceratophyllum spp. - Elodea spp. Permanently

Flooded Herbaceous Alliance

 V.C.2.N.a.5 Hydrilla verticillata Permanently Flooded Herbaceous Alliance

Occurs in natural and man-made lakes.
Appendices

19

V.C.2.N.b.4 Ruppia maritima Permanently Flooded - Tidal Temperate Herbaceous

Alliance

Tidal brackish seagrass beds. Occurs in managed impoundments in the outer

coastal plain.

Map Class 13 – Closed Canopy Evergreen Forest

This class includes closed canopy sites dominated by pines, cedars, hemlocks, and

evergreen oaks. Most forests dominated by evergreen oaks were moved to other classes

using decision rules. Mixed forests will map out here if the density of pine is great

enough. This class can occur on any soil wetness class ranging from dry to wet and it

occurs throughout the state. Most pine plantations are found in this class.

This class was overestimated in the original vegetation map. NWI and soil decision rules

were used to remove maritime forests, wet evergreen forests and bottomlands from this

class and assign them to their appropriate mapping classes.

Decision rules that were useful to identify coastal upland forests and lowland maritime

forests involved using upland NWI classes that excluded planted pines and a specific set

of coastal and maritime soils. If the GAP soil class was class 3 or 4, wetland soils, and

NWI was palustrine evergreen forest, then the land cover class was changed to 25, wet

evergreen forest. For GAP soil classes of 3 or 4 and NWI palustrine evergreen

scrub/shrub, then the land cover class was changed to pocosin in the coastal plain. The

combination of GAP wetland soil classes and NWI upland evergreen forest, excluding

planted pines was used to define wet pinewoods, class 31. Bottomland forest types were

moved from this class by utilizing wetland soils either with NWI palustrine forested

wetlands or other non-planted pine classes.

Nelson (1986) has no natural communities that would be included in this land cover

class. See also Class 53, Montane Evergreen Forest, for evergreen forest alliances

restricted to the mountains. The following TNC alliances are found in this class:

I.A.8.C.x.4 Pinus echinata Planted Forest Alliance

Planted shortleaf pine forests in the piedmont.

I.A.8.C.x.5 Pinus elliottii Planted Forest Alliance

Planted slash pine forests primarily in the coastal plain but also found in the

piedmont.

I.A.8.C.x.6 Pinus palustris Planted Forest Alliance

Planted longleaf pine forests in the coastal plain.

I.A.8.C.x.9 Pinus taeda Planted Forest Alliance

Planted loblolly pine forests. This is the most common alliance in this land

cover class.
Appendices

20

I.A.8.N.b.14 Pinus strobus Forest Alliance

Only occurs in the piedmont and mountains.

I.A.8.N.b.15 Pinus taeda - Pinus echinata Forest Alliance

Successional forests.

I.A.8.N.b.16 Pinus taeda Forest Alliance

Very common succession forest type.

I.A.8.N.b.9 Pinus palustris - (Pinus elliottii) Forest Alliance

Coastal plain successional forests on disturbed sites.

I.A.8.N.c.2 Juniperus virginiana Forest Alliance

Successional forests occurring on old fields, successional cleared land, and

other disturbed areas, especially on calcareous bedrock.

I.A.8.N.c.9 Tsuga caroliniana Forest Alliance

Occurs in the mountains and upper piedmont, mostly on cliffs, rocky slopes,

and ridges, less commonly on more gentle slopes and flat areas in valleys.

Occupies higher and drier sites than Tsuga canadensis.

Map Class 14 – Needle-leaved Evergreen Mixed Woodland

Upland pine stands with a relatively open canopy and an understory of deciduous scrub

oaks. Typical evergreen overstory components are longleaf pine, Virginia pine, loblolly

pine, and shortleaf pine. This class includes the Piedmont, Sandhills, and Coastal Plain

Scrub Complexes. Small inclusions of pond pine seeps, longleaf pine seeps, seepage

shrub slopes, small depression pocosins, streamhead pocosins, depression meadows, or

hillside herb bogs may occur within this map class. Mixed forests also map out here, or

these woodlands may be found mapped as dry mixed forests or as dry deciduous forests.

Where the canopy is very sparse, these woodlands may be mapped as dry scrub/shrub (8),

open canopy/recently cleared forest (10), grassland/pasture (20), or cultivated land (21).

This class occurs on soil wetness class 1. Because of the relatively low density of pines,

most of these woodlands are found mapped as dry deciduous forests (16) or dry mixed

forests (18), especially in the fall-line sandhills.

In the sandhills and coastal plain, these woodlands typically occupy the deepest, driest

sands, especially on ridge tops. However, they occur in a mosaic of other sandhills

communities that may be inclusions in this mapping unit. This mosaic of communities is

referred to as the Sandhill Scrub Ecological Complex as described below.

Natural communities defined by Nelson (1986) that typify this class are Pine – Scrub Oak

Woodland and Xeric Sandhill Scrub. These are the classic sandhill scrub communities

occurring on the driest, deepest sands in the fall-line sandhills and coastal plain. Small
Appendices

21

mapping inclusions in this class would include Nelson’s Bay Forest, Hillside Herb Bog,

Pond Pine Woodland, Seepage Pocosin, Swale Pocosin, Streamhead Pocosin, and Upland

Pine – Wiregrass Woodland.

II.A.4.N.a.1 Pinus palustris / Quercus spp. Woodland Alliance

Xeric to dry woodlands of the sandhills and coastal plain. This alliance should

fall under this needle-leaved evergreen mixed woodland class (14), but may

instead be mapped as closed canopy evergreen forest (13) if the canopy is

relatively closed or as a variety of open canopy classes (8, 10, 20, 21) if the

canopy is too sparse.

II.C.3.N.a.11 Pinus echinata - Quercus (alba, falcata, stellata, velutina) Woodland

Alliance

Xeric to dry woodlands of the piedmont. This alliance may be mapped as closed

canopy evergreen forest (13) if the canopy is dominated by pines or as dry mixed

forest (18). It may be mapped as dry deciduous forest (16) if pines are relatively

few in number.

Sandhill Scrub Ecological Complex

II.A.4.N.a.1 Pinus palustris / Quercus spp. Woodland Alliance

Southeastern Coastal Plain Xeric Sandhill Scrub

Southeastern Coastal Plain Subxeric Pine – Scrub Oak Sandhill

Inclusions:

Atlantic Coastal Plain Mesic Longleaf Pine Forest/Upland Pine – Wiregrass

Woodland

II.A.4.N.a.22 Pinus palustris Woodland Alliance

Southeastern Coastal Plain Turkey Oak Barrens

II.B.2.N.a.17 Quercus laevis Woodland Alliance

Pond Pine Seep

II.A.4.N.f.9 Pinus serotina Saturated Woodland Alliance

Longleaf Pine Seep/Seepage Pocosin

II.A.4.N.f.6 Pinus palustris - Pinus (elliottii, serotina) Saturated Woodland

Alliance

Swale Pocosin/ Small Depression Pocosin

III.A.2.N.j.020 Lyonia lucida - Ilex glabra Saturated Wooded Shrubland

Alliance

Streamhead Pocosin/Bay Forest/ Coastal Plain Hillside Herbaceous Seepage Bog

I.A.4.N.g.2 Gordonia lasianthus Saturated Forest Alliance

II.A.4.N.f.9 Pinus serotina Saturated Woodland Alliance

Map Class 15 – Pine Woodland

This class consists of upland pine woodlands with a grassy understory. Flatwoods with longleaf

pine and/or slash pine are included in this class. Examples of small wetland inclusions within

this map class are pine savannas, depression meadows, tupelo or pond cypress pond forests, wet
Appendices

22

pine flatwoods, pond pine woodlands, pocosins, and pitcher plant flats. Small changes in soil

and/or elevation result in a mosaic of these communities in coastal plain interstream flats and in

more mesic sandhills areas. Where the canopy is very sparse, pine woodlands may be mapped as

dry scrub/shrub (8), open canopy/recently cleared forest (10), grassland/pasture (20), or

cultivated land (21).

Pine woodlands are distinguished from wet pine flatwoods (wet pinewoods, class 31) by the

predominance of upland plant species and by the absence of pond pine.

Pine woodlands occur on soils in wetness classes 1 and 2. This class was originally

overestimated on the first vegetation map. Most decision rules dealing with this class were

written to remove erroneously assigned forests from this class.

Nelson’s (1986) concept of pine woodlands includes Upland Pine – Wiregrass Woodlands and

Pine Flatwoods. TNC alliances that encompass the pine woodlands concept include:

II.A.4.N.a.19 Pinus elliottii Woodland Alliance

Upland woodlands occurring on barrier islands and other near-coastal areas.

II.A.4.N.a.22 Pinus palustris Woodland Alliance

Mesic to dry-mesic upland woodlands and savannas, occurring in the coastal plain,

sandhills, and possibly also piedmont.

II.A.4.N.a.28 Pinus taeda Woodland Alliance

Coastal plain woodlands often occurring in maritime situations.

TNC alliances that are associated with pine woodlands in the landscape include:

II.A.4.N.f.9 Pinus serotina Saturated Woodland Alliance

II.A.4.N.f.6 Pinus palustris - Pinus (elliottii, serotina) Saturated Woodland Alliance

III.A.2.N.j.020 Lyonia lucida - Ilex glabra Saturated Wooded Shrubland Alliance

II.A.4.N.a.1 Pinus palustris/Quercus spp. Woodland Alliance

II.B.2.N.c.4 Taxodium ascendens Seasonally Flooded Woodland Alliance

V.A.5.N.k.18 Panicum hemitomon Seasonally Flooded Temperate Herbaceous

Alliance

V.A.5.N.k.28 Spartina bakeri Seasonally Flooded Herbaceous Alliance

V.A.5.N.k.65 Carex striata Seasonally Flooded Herbaceous Alliance

V.B.2.N.h.3 Woodwardia virginica Seasonally Flooded Herbaceous Alliance

V.B.2.N.h.100 Polygonum spp. (section Persicaria) Seasonally Flooded Herbaceous

Alliance

Map Class 16 – Dry Deciduous Forest / Woodland

This class includes oak-hickory xeric upland forests and woodlands. Pine - scrub oak

woodlands may also map here where pines are at very low density. Dry woodlands with

a very open canopy may be mapped as dry scrub/shrub (8), open canopy/recently cleared
Appendices

23

(10), grassland/pasture (20), or cultivated land (21).

This class occurs on soil wetness class 1 and is distinguished from mesic deciduous

forests/woodlands by soil wetness.

Nelson (1986) described several natural plant communities that would be included in this

class. These include Chestnut Oak Forest, Pine – Oak Heath, and Oak – Hickory Forest

I.B.2.N.a.22 Liquidambar styraciflua Forest Alliance

Probably will map out as both 17 and 18, depending on soils.

I.B.2.N.a.27 Quercus alba - (Quercus rubra, Carya spp.) Forest Alliance

Described as dry-mesic forest, sometimes occurring on calcareous substrate.

I.B.2.N.a.29 Quercus alba - Quercus (falcata, stellata) Forest Alliance

Described as dry or dry-mesic deciduous forests, often occupying transitional

zones between mesic and xeric communities.

I.B.2.N.a.31 Quercus falcata Forest Alliance

Described as dry deciduous forests.

 I.B.2.N.a.36 Quercus prinus - (Quercus coccinea, Quercus velutina) Forest

Alliance

Xeric oak forest occurring on extreme sites, such as monadnocks in the piedmont

or ridgetops in the mountains.

I.B.2.N.a.37 Quercus prinus - Quercus (alba, falcata, rubra, velutina) Forest

Alliance

Dry-mesic to mesic forests occurring on moderately sheltered low ridges, flats,

and valleys at low elevations in the mountains and on upper to middle dry-mesic

slopes in the piedmont. May also include forests overlying calcareous bedrock.

I.B.2.N.a.38 Quercus prinus - Quercus rubra Forest Alliance

Dry-mesic oak forests of the mountains and upper piedmont.

I.B.2.N.a.41 Quercus stellata - Quercus marilandica Forest Alliance

Dry oak forest, sometimes occurring on calcareous substrates. This forest type is

found at the Rock Hill Blackjack Heritage Preserve. This could also be mapped

under class 46, dry basic deciduous forest.

II.B.2.N.a.17 Quercus laevis Woodland Alliance

Turkey oak barrens occurring on sandy ridges or high dunes in the sandhills and

coastal plain. This alliance may also be found mapped as dry scrub/shrub (8).

II.B.2.N.a.23 Quercus prinus - Quercus marilandica Woodland Alliance

Woodlands occurring on south- to west-facing slopes in the piedmont.
Appendices

24

Map Class 17 – Mesic Deciduous Forest

Southern mixed hardwood forests and mesic mixed hardwoods forests with a diversity of

deciduous hardwood trees. Where the canopy is very sparse, deciduous woodlands may

be mapped as dry scrub/shrub (8), open canopy, recently cleared (10), grassland/pasture

(20), or cultivated land (21).

This class occurs on soil wetness class 2 and is distinguished from dry deciduous

forests/woodlands by soil decision rules.

Nelson (1986) would place Mesic Mixed Hardwood Forest, Southern Mixed Hardwood

Forest, Beech-Magnolia Hammock and Oak-Hickory Forest in this class. BeechMagnolia is included in the Gap class 29, Mesic Mixed Forest due to the presence of the

evergreen Magnolia grandiflora. If Magnolia is underrepresented, then it would be

mapped in this class. Oak-Hickory Forests would be found in both dry deciduous forest

and mesic deciduous forest depending on the soils.

I.B.2.N.a.16 Fagus grandifolia - Quercus alba Forest Alliance

Dry-mesic to mesic forests occurring on mesic slopes and small stream bottoms in

the coastal plain and adjacent physiographic provinces. Occurrences may be too

narrow to map separately.

I.B.2.N.a.100 Quercus velutina - Quercus alba - (Quercus coccinea) Forest Alliance

Dry-mesic forests that occur in the piedmont and mountains only.

I.B.2.N.a.101 Quercus muehlenbergii - (Acer saccharum) Forest Alliance

Basic forest which may be found in the mountains. Coastal plain occurrences

contain Acer barbatum, instead of Acer saccharum. Gap doesn't map basic

forests separately in the CP. Therefore, coastal plain occurrences of this alliance

will be mapped under class 17, mesic deciduous forests while piedmont and

mountain occurrences will be mapped as class 47, mesic basic deciduous forest.

I.B.2.N.a.103 Quercus alba Montane Forest Alliance

I.B.2.N.a.108 Quercus rubra Montane Forest Alliance

I.B.2.N.a.11 Carya glabra – Tilia americana var. caroliniana – Celtis laevigata

Forest Alliance

Mesic Shell Midden Forest. Shell midden EO's documented by NHP are

evergreen types. This alliance is most likely found as an inclusion within coastal

upland mixed forests, class 44.

I.B.2.N.a.15 Fagus grandifolia - Acer saccharum - (Liriodendron tulipifera) Forest

Alliance
Appendices

25

Rich, mesic forests occurring throughout the state.

I.B.2.N.a.2 Acer barbatum - Fraxinus americana - Juglans nigra Forest Alliance

Mesic calcareous forests. Gap doesn't map basic forests separately in the coastal

plain. Therefore, coastal plain occurrences will be mapped under mesic

deciduous class 17, while piedmont occurrences will be mapped as mesic basic

deciduous, class 47. In the coastal plain, this alliance is most likely found as an

inclusion within coastal upland mixed forests, class 44.

I.B.2.N.a.22 Liquidambar styraciflua Forest Alliance

I.B.2.N.a.24 Liriodendron tulipifera Forest Alliance

I.B.2.N.a.26 Quercus alba - (Quercus nigra) Forest Alliance

Mesic and dry-mesic forests of the coastal plain.

I.B.2.N.a.27 Quercus alba - (Quercus rubra, Carya spp.) Forest Alliance

Described as dry-mesic forest, sometimes occurring on calcareous substrate.

I.B.2.N.a.35 Quercus nigra Forest Alliance

Described as upland mesic or dry-mesic stand, sometimes with 10-60% or more

Pinus taeda in canopy.

I.B.2.N.a.37 Quercus prinus - Quercus (alba, falcata, rubra, velutina) Forest

Alliance

Dry-mesic to mesic forests occurring on moderately sheltered low ridges, flats,

and valleys at low elevations in the mountains and on upper to middle dry-mesic

slopes in the piedmont. May also include forests overlying calcareous bedrock.

I.B.2.N.a.38 Quercus prinus - Quercus rubra Forest Alliance

Dry-mesic oak forests of the mountains and upper piedmont.

Map Class 18 – Dry Mixed Forest / Woodland

These are xeric mixed evergreen and deciduous forests and woodlands. They are

distinguished from Dry Deciduous Forest, class 16, by having a mixed canopy of

evergreen and deciduous trees. This class is underestimated on the land cover map, as

many mixed forests have been mapped as upland evergreen forests (13 and 14. Dry

mixed woodlands with very open canopies may be mapped as dry scrub/shrub (8), open

canopy/recently cleared forest (10), grassland/pasture (20), or cultivated land (21).

This class occurs on soil wetness class 1 and is distinguished from mesic mixed

forests/woodlands by soil wetness decision rules.

Nelson (1986) doesn’t separate mixed evergreen/deciduous forests of this type. The

following TNC alliances may be found in this class:
Appendices

26

I.C.2.N.a.5 Quercus hemisphaerica - Carya glabra Forest Alliance

Dry-mesic to sub-xeric sandy forests occurring in the coastal plain.

I.C.3.N.a.14 Pinus echinata - Quercus (alba, falcata, stellata, velutina) Forest

Alliance

Dry-mesic forests occurring primarily on dry hilltops, upper slopes, and ridges on

acidic soils throughout the state.

I.C.3.N.a.15 Pinus echinata – Quercus (coccinea, falcata, prinus) Forest Alliance

Dry mixed pine-oak forests of the low mountains and upper piedmont.

I.C.3.N.a.16 Pinus echinata - Quercus stellata - Quercus marilandica Forest

Alliance

Dry and xeric mixed forest occurring in the piedmont, often on monadnocks, and

sandhills.

I.C.3.N.a.22 Pinus strobus - Quercus (coccinea, prinus) Forest Alliance

Dry mixed forests occurring in the mountains.

I.C.3.N.a.28 Pinus virginiana - Quercus (coccinea, prinus) Forest Alliance

Xeric mixed forests of the mountains and piedmont.

II.C.3.N.a.11 Pinus echinata - Quercus (alba, falcata, stellata, velutina) Woodland

Alliance

Xeric woodlands occurring on edaphically extreme sites.

Map Class 19 – Mesic Mixed Forest/Woodland

Mesic mixed evergreen and deciduous forests and woodlands. This class is

underestimated on the land cover map, as many mixed forests have been mapped as

closed canopy evergreen forests (13) or as mesic deciduous forests (17). Mesic mixed

woodlands with very sparse canopies may be mapped as dry scrub/shrub (8), open

canopy/recently cleared forest (10), grassland/pasture (20), or cultivated land (21).

This class occurs on soil wetness class 2 and is distinguished from dry mixed

forests/woodlands by soil wetness.

Nelson (1986) doesn’t separate mixed evergreen/deciduous forests of this type. The

following TNC alliances may be found in this class:

I.B.2.N.a.35 Quercus nigra Forest Alliance

Described as upland mesic or dry-mesic stand, sometimes with 10-60% or more Pinus

taeda in canopy.

I.C.2.N.a.2 Fagus grandifolia - Magnolia grandiflora Forest Alliance
Appendices

27

Mesic to dry-mesic forest, occurring throughout the coastal plain. These forests

often occur between bottomland and upland forests along streams and rivers.

They’re often too narrow to be found mapped separately.

I.C.2.N.a.8 Quercus virginiana - Quercus pagoda Forest Alliance

Wet-mesic forests of the outer coastal plain.

I.C.3.N.a.21 Pinus strobus - Quercus (alba, rubra, velutina) Forest Alliance

Dry-mesic to mesic mixed forests occurring in the mountains.

I.C.3.N.a.23 Pinus taeda - (Liquidambar styraciflua, Liriodendron tulipifera) Forest

Alliance

Occurs on successional sites in the piedmont and coastal plain.

I.C.3.N.a.24 Pinus taeda - Quercus (alba, falcata, stellata) Forest Alliance

Mesic mixed forests of the piedmont and coastal plain.

I.C.3.N.a.26 Pinus taeda - Quercus nigra Forest Alliance

Dry to dry-mesic mixed forests of the coastal plain, often occurring as maritime

forests.

Map Class 20 – Grassland/Pasture

Old fields, meadows, wet meadows, grassy fields, livestock pastures. This also includes

low-density rural – low density housing with cleared land, trees, outbuildings, etc.

Woodlands with very sparse canopies may be mapped as this class.

Because this class is not considered to be natural vegetation, few TNC alliances have

been described that include grasslands and pastures as they occur in South Carolina.

V.A.5.N.c.3 Andropogon virginicus Herbaceous Alliance

V.A.5.N.c.8 Festuca (arundinacea, pratensis) Herbaceous Alliance

V.A.5.N.e.6 Cynodon dactylon Herbaceous Alliance

Map Class 21 – Cultivated Land

Agricultural fields, lawns, golf courses. This also includes low-density rural – low

density housing with cleared land, trees, outbuildings, etc. Woodlands with very sparse

canopies may be mapped as this class.

Map Class 22 – Urban Development

Industrial, CBD, cities, etc. Rocky shoals, rock outcrops, and bare sand are sometimes

mapped as this class, where reflectance confounded the mapper. This class also shows up

in agricultural areas, especially in areas of open bare sand.
Appendices

28

Map Class 23 – Residential Urban

High intensity residential development associated with cities. Rocky shoals, rock

outcrops, and bare sand are sometimes labeled as this class, where reflectance

confounded the mapper.

Map Class 24 – Density Residential

Residential with interspersed patches of forest and urban.

Map Class 25 – Wet Evergreen

Wet evergreen includes pond pine woodlands, bay forests, wet longleaf/pond pine

flatwoods, blackwater streamside forests, and Atlantic white cedar swamps.

Blackwater streamside forests are found in the fall-line sandhills and in blackwater

systems of the coastal plain. They’re often referred to as evergreen streamside forests or

streamside pocosins but fall within the pond pine woodland definition. White cedar

swamps, pond pine woodlands/streamhead-streamside pocosin, and bay forests occur in a

mosaic in the saturated areas along blackwater streams and rivers. The complex of

communities that comprise the wetlands of blackwater streams has been referred to as the

Sandhills Palustrine Ecological Complex.

Pond pine woodlands and wet flatwoods often occur in a mosaic on interstream flats in

the coastal plain in association with various swamp/bottomland communities. Wet

flatwoods are those that usually support a mixture of longleaf pines and pond pines with

evergreen shrubs dominating in the understory/shrub layer. Flatwoods without a pond

pine component are placed in the Wet Pinewoods class (31).

Soils and NWI are crucial to mapping wet evergreen. Wet evergreen occurs on soil

classes 3 and 4 in association with NWI palustrine forested evergreen wetlands. All

upland forest classes from the original vegetation map that met the soil and NWI criteria

were changed to wet evergreen.

This class encompasses Nelson’s (1986) concepts of Streamhead Pocosin, Swale Pocosin,

Bay Forest, Atlantic White Cedar Swamp and Pond Pine Woodland

I.A.4.N.g.2 Gordonia lasianthus Saturated Forest Alliance

Loblolly bay forest, found on saturated soils along blackwater streams and in

peat-based Carolina bays.

I.A.4.N.g.3 Magnolia virginiana - Persea palustris Saturated Forest Alliance

Sweet bay-red bay forests.

I.A.8.N.g.2 Chamaecyparis thyoides Saturated Forest Alliance
Appendices

29

Occurs on saturated soils along blackwater streams in the sandhills and coastal

plain. In South Carolina, these forests are rarely found associated with Carolina

Bays.

II.A.4.N.f.2 Pinus elliottii Saturated Temperate Woodland Alliance

Coastal plain wet flatwoods; restricted to southern South Carolina.

III.A.2.N.j.2 Lyonia lucida – Ilex glabra Saturated Wooded Shrubland Alliance

High pocosins on saturated mineral or organic soils. This alliance is mapped as

pocosin (4) or wet evergreen depending on the tree canopy present.

II.A.4.N.f.6 Pinus palustris - Pinus (elliottii, serotina) Saturated Woodland Alliance

Wet pine flatwoods of the coastal plain and sandhills.

II.A.4.N.f.9 Pinus serotina Saturated Woodland Alliance

Pond pine woodland or pocosin, occurring in Carolina bays throughout the coastal

plain and on saturated soils along blackwater streams in the sandhills and coastal

plain.

Map Class 26 – Lowland Maritime Forest

Wet to wet-mesic evergreen forests of the outer coastal plain; characterized by the

presence of live oak and palmetto. This forest type is referred to as hydric hammocks in

Florida and is now ascribed to South Carolina. With additional research, it’s likely that

more alliances will be described for South Carolina similar to those attributed to the Gulf

Coast.

These forests occur on inland maritime soils with wetness class 3 where the vegetation is

characterized by NWI as upland evergreen forests, upland mixed forests or evergreen

palustrine forested wetland. They also occur on outer maritime soils where NWI

characterizes the forests as evergreen palustrine wetlands.

These forests fall under the general community of Maritime Forest (Nelson, 1986). The

following alliance is the only TNC alliance documented at this time in South Carolina>

I.A.4.N.e.1 Quercus virginiana Temporarily Flooded Forest Alliance

Wetland maritime forests occurring in the southeast coastal plain of South Carolina.

Map Class 27 – Maritime Shrub Complex

This complex consists of evergreen shrubs occupying dunes and dune swales along the

coast. Occurrences are usually linear and are found between the beaches and the upland

forests. Because they’re usually quite narrow, they are most often found as inclusions

within the coastal upland mixed forest (44).

Scrub/shrub and open, non-beach areas on the original vegetation map (classes 6, 7, 8,
Appendices

30

20, 21) combined with NWI upland evergreen forest and palustrine evergreen forests and

shrublands occurring on outer maritime soils were reclassified to the maritime shrub

complex.

This complex includes John Nelson’s 1986 Maritime Grassland and Maritime Shrub

Thicket natural communities.

Maritime dune systems are comprised of grasslands and shrublands on dunes and in

interdune swales. Where they cannot be reliably distinguished, they should be classified

as the Maritime Dune Complex which consists of the Maritime Shrubland Ecological

Complex and the Maritime Grassland Ecological Complex as described below.

Maritime Shrubland Ecological Complex

This complex consists of all communities called Maritime Dune Shrub

Thickets.

III.A.2.N.c.32 Quercus virginiana-Ilex vomitoria-(Myrica cerifera)

Shrubland Alliance

Upland maritime dune shrubland.

III.A.2.N.i.100 Myrica cerifera Saturated Shrubland Alliance

Maritime Grassland Ecological Complex

This complex consists of herbaceous dune and swale vegetation. These are inclusions

within the Maritime Shrub Complex but are often too small or narrow to map separately.

They are primarily included in the beach class (40).

V.A.5.N.a.9 Uniola paniculata Temperate Herbaceous Alliance – Maritime

Grassland

V.A.5.N.c.12 Muhlenbergia filipes Herbaceous Alliance – Maritime Wet

Grassland

V.A.5.N.j.2 Andropogon glomeratus Temporarily Flooded Herbaceous

Alliance – Maritime Grassland

VII.C.2.N.a.2 Cakile edentula Sparse Vegetation Alliance – Maritime

Grassland

Map Class 28 – Floodplain Forest

Floodplain forests are temporarily flooded communities occurring on levees and in

floodplains throughout the state. In piedmont occurrences of this class, the community

composition may be similar to that of narrow stream valley forests (class 43). Where

they are too narrow to map separately, adjacent communities such as seasonally to

semipermanently flooded bottomlands will be included in this map class.

Soils were the primary feature distinguishing these forests. These soils include

Buncombe, Congaree, Chewacla, Shellbluff, Riverview, Toccoa and mixed alluvial land.
Appendices

31

The soils are classified by NRCS as being well drained, non-wetland soils, although the

forests are comprised of wetland vegetation.

Decision rules were used to identify this map class. All upland forests on the original

vegetation map that occurred on soil class 5, levee soils, were placed in floodplain forest.

Where NWI was available, planted pines were excluded by a decision rule.

Nelson (1986) refers to these forests as Levee, which is essentially the concept utilized

here. The following TNC alliances may be found in this class:

I.B.2.N.d.13 Platanus occidentalis - (Fraxinus pennsylvanica, Celtis laevigata, Acer

saccharinum) Temporarily Flooded Forest Alliance

Occurs on fronts, terraces, and levees of medium to large rivers in the coastal

plain and lower piedmont.

I.B.2.N.d.15 Populus deltoides Temporarily Flooded Forest Alliance

Riverfront floodplain forest occurring throughout the state.

I.B.2.N.d.21 Salix caroliniana Temporarily Flooded Forest Alliance

Occurs on riverfronts and lakeshores in the coastal plain.

I.B.2.N.d.22 Salix nigra Temporarily Flooded Forest Alliance

Occurs along rivers and streams throughout the state.

I.B.2.N.d.3 Acer negundo Temporarily Flooded Forest Alliance

Early successional forests along large river floodplains and sandbars.

I.B.2.N.d.5 Betula nigra - (Platanus occidentalis) Temporarily Flooded Forest

Alliance

Occurs on riverfronts, especially on levees, throughout the state.

I.A.8.N.e.2 Pinus taeda Temporarily Flooded Forest Alliance

Occurs on higher ridges in floodplains.

I.C.3.N.b.2 Pinus glabra - Quercus (laurifolia, michauxii, nigra) Temporarily

Flooded Forest Alliance

Occurs on natural levees and floodplains of coastal plain streams. This class

should be mapped as floodplain forest (30) or wet pine - mixed hardwood forest

(35).

III.A.2.N.g.1 Arundinaria gigantea Temporarily Flooded Shrubland Alliance

Rare community occurring on streamside flats or bottomlands. Occurs as smallpatch inclusions within floodplain forests (28), piedmont bottomlands (45),

swamp/bottomland (5), and narrow stream valley forests (43).

III.A.2.N.g.2 Ligustrum sinense Temporarily Flooded Shrubland Alliance
Appendices

32

Wetland bottomlands heavily infested with Ligustrum. Probably occurs as

inclusion within floodplain forest (28) and other bottomlands. May also occur as

wet scrub/shrub (7) if large enough.

III.B.2.N.d.5 Salix caroliniana Temporarily Flooded Shrubland Alliance

Shrub thickets occurring on riverbanks, sand bars, and other wet sites. Occurs

mostly in the coastal plain, rarely in the piedmont and mountains. This alliance is

probably an inclusion within floodplain forest.

III.B.2.N.d.7 Salix nigra Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers or lakeshores of the piedmont and coastal plain. This

alliance is probably an inclusion within floodplain forest.

III.B.2.N.e.8 Vitis rotundifolia – Ampelopsis arborea – Campsis radicans Seasonally

Flooded Vine-Shrubland Alliance

Wetland vegetation that develops following disturbance in the coastal plain. This

alliance is probably present as a small-patch inclusion within forested wetlands.

Map Class 29 – Wet Maritime Forest

This class consists of wet-mesic to wet areas characterized by Sabal palmetto, Quercus

virginiana and/or Q. laurifolia. It includes floodplain forests and sloughs and may be

restricted to the South Atlantic coastal plain of South Carolina. This class encompasses

the concept of hydric hammock as it is applied in Florida and South Georgia.

Wet maritime forests were derived from upland forests on the original Gap map by using

a specific set of very poorly drained coastal soils combined with NWI evergreen forests.

They were also derived by using NWI estuarine forested wetlands.

I.A.4.N.e.1 Quercus virginiana Temporarily Flooded Forest Alliance

Floodplain or levee forests in the outer coastal plain.

I.A.4.N.g.4 Sabal palmetto - Quercus virginiana Saturated Forest Alliance

Palmetto sloughs, hydric hammocks.

1.C.2.N.d.3 Sabal palmetto – Quercus laurifolia – Quercus virginiana – Magnolia

virginiana – Ulmus americana Saturated Forest Alliance

Hydric hammocks, more diverse than the Sabal palmetto – Quercus virginiana Saturated

Forest Alliance.

II.A.2.N.c.3 Sabal palmetto Saturated Woodland Alliance

Wet woodlands often occurring in old maritime dune swales and flats. It also includes

marsh edge Sabal palmetto communities with marsh grass understories.

Map Class 30 – Estuarine Salt Flat/Salt Shrub Thicket
Appendices

33

Salt flats and salt shrub thickets tend to occur in a matrix and are not differentiable at the

Gap mapping level. Often patches of salt marsh will also occur as inclusions within this

class. In some areas salt flats and/or salt shrub thickets map out as sandy bare soil.

On NAPP photos, they appear as bright, reflective sandy areas (salt flats) or grayish green

areas within salt marshes (salt shrub thickets). NWI and soils provided excellent tools to

distinguish this class. If the land cover class was salt marsh and NWI classified it as a sandy

area, it was placed in estuarine salt flat/salt shrub thicket. NWI alone can be used if it’s

classified as emergent intertidal scrub-shrub or as estuarine irregularly flooded

unconsolidated sediments. For occurrences along the margins of marsh and barrier islands, if

the land cover class was wet or dry scrub/shrub, NWI was palustrine deciduous scrub-shrub

and soils were outer maritime, then it was reclassified as estuarine salt flat/salt shrub thicket.

Nelson (1986) uses the same classification for these natural communities: Salt Flat and Salt

Shrub Thicket. The following TNC alliances are found in this class:

III.A.4.N.e.100 Tamarix spp. Tidal Shrubland Alliance

Thickets in upper tidal zones of salt or brackish marshes.

III.B.2.N.h.1 Baccharis halimifolia – Iva frutescens Tidal Shrubland Alliance

Maritime shrub community, growing in association with salt marshes. Where

occurrences are too small to map, they will be found as inclusions within salt and

brackish marshes (32).

III.B.2.N.h.4 Borrichia frutescens Tidal Shrubland Alliance

Occupies upper marsh edges and flats; patch size ranges from narrow bands to

flats in excess of 500 ha. Where patches were large enough, they were mapped

usually as salt flat/salt shrub thicket (30)or less commonly as wet scrub/shrub (7).

Narrow linear patches were not differentiated from adjacent upland or salt marsh

(32).

IV.A.2.N.c.5 Sarcocornia perennis - (Distichlis spicata, Spartina alternifolia) Tidal

Dwarf-Shrubland Alliance

Tidally flooded hypersaline flats or very shallow depressions. They are mapped

as salt flat/salt shrub thicket (30) where large enough, or as surrounding salt

marsh (32) where too small to map.

VII.C.2.N.d.3 Sesuvium spp. – Atriplex spp. – Suaeda spp. Tidal Sparsely Vegetated

Alliance

Occurs on the backside of the ends of barrier islands. Tends to appear as scattered

mound-like clumps of vegetation in a wet sand flat. May be found mapped as

mud/sand flat (39).

Map Class 31 – Wet Pinewoods
Appendices

34

The concept of this coastal wetland class was originally based on lowland pine - oak

forests and spruce pine - mixed hardwood forests. However, wet-mesic to wet longleaf

flatwoods without a pond pine component are also found mapped here. Planted pines on

wet soils may also map out here.

Decision rules are necessary to delineate this class. From the original vegetation map, all

areas in the outer coastal plain originally mapped as upland forest types that are classified as

NWI forested wetlands on alfisols were placed in wet pinewoods.

These forests correspond to Nelson’s (1986) Spruce Pine – Mixed Hardwood Swamp, Pine

Flatwoods, in part, Pine Savannah, and Pond Cypress Savannah. TNC alliances included in

this class are:

I.C.2.N.d.2 Quercus virginiana - Quercus nigra Saturated Forest Alliance

Occurs on low moist to wet flats in the coastal plain.

I.C.3.N.b.2 Pinus glabra - Quercus (laurifolia, michauxii, nigra) Temporarily

Flooded Forest Alliance

Occurs on natural levees and floodplains of coastal plain streams and rivers.

Covers broad flats in Charleston, Dorchester and Colleton Counties in particular.

I.C.3.N.b.4 Pinus taeda - Liquidambar styraciflua - Nyssa biflora Temporarily

Flooded Forest Alliance

Mixed forests on slightly elevated ridges within coastal plain floodplains.

I.C.3.N.b.5 Pinus taeda - Liriodendron tulipifera Temporarily Flooded Forest

Alliance

Occurs in floodplains of the piedmont and coastal plain following disturbance.

I.C.3.N.b.8 Pinus taeda - Quercus (phellos, nigra, laurifolia) Temporarily Flooded

Forest Alliance

Mixed forests of blackwater coastal plain rivers.

I.C.3.N.d.5 Pinus taeda - Liquidambar styraciflua - Acer rubrum Saturated Forest

Alliance

Successional forests occurring on coastal plain nonriverine flats.

II.A.4.N.f.6 Pinus palustris – Pinus (elliottii, serotina) Saturated Woodland Alliance

Several associations in this alliance are considered the classic longleaf pine grasssedge savanna. Due to the very low density of trees, these savannas may be

mapped as freshwater marshes. However, NWI decision rules should have placed

most pine savannas in class 31, wet pine woods.

II.C.3.N.c.1 Pinus elliottii - Taxodium ascendens Saturated Woodland Alliance

Coastal plain wet flatwoods and cypress strands.
Appendices

35

Map Class 32 – Salt and Brackish Marsh

This class includes all salt and brackish marshes. They often have small patch inclusions

of salt shrub thickets, salt flats, mud and sand flats, and shell midden woodlands.

NWI class salt marsh was used to define salt and brackish marshes and to separate them

from freshwater marshes.

This class corresponds to Nelson’s (1996) Salt Marsh class.

Salt Marsh Complex

The Salt Marsh Ecological Complex is comprised of two or more of the following

alliances where salt marshes, brackish marshes and salt flats cannot be mapped

separately and/or where shrublands dominated by Baccharis, Borrichia or Iva cannot

be distinguished from salt or brackish marshes. Small occurrences of Shell Midden

Woodland within a large matrix of marsh may not be mapped from satellite imagery

because of their size. Therefore, this alliance is included in the salt marsh complex.

Emergent Herbaceous Estuarine Wetlands

Salt Marsh

V.A.5.N.n.1 Spartina alterniflora Tidal Herbaceous Alliance

Other Tidal and Brackish Marshes

V.A.5.N.n.3 Cladium mariscus ssp. jamaicense Tidal Herbaceous Alliance

V.A.5.N.n.5 Juncus roemerianus Tidal Herbaceous Alliance

V.A.5.N.n.7 Phragmites australis Tidal Herbaceous Alliance

V.A.5.N.n.9 Spartina bakeri – (Spartina patens) Tidal Herbaceous Alliance

V.A.5.N.n.10 Spartina cynosuroides Tidal Herbaceous Alliance

V.A.5.N.n.11 Spartina patens-(Distichlis spicata) Tidal Herbaceous Alliance

Salt Flats – these are inclusions in salt marshes, see class 30

IV.A.2.N.c.5 Sarcocornia perennis - (Distichlis spicata, Spartina alterniflora)

Tidal Dwarf Shrubland Alliance

VII.C.2.N.d.3 Sesuvium spp.-Atriplex spp.-Suaeda spp. Tidal Sparsely

Vegetated Alliance

Barrier Island/Marsh Shrublands

Salt Shrub Thickets – these are inclusions in salt marshes, see class 30, estuarine

salt flat/salt shrub thicket

III.B.2.N.h.1 Baccharis halimifolia-Iva frutescens Tidal Shrubland Alliance

III.B.2.N.h.4 Borrichia frutescens Tidal Shrubland Alliance

Marsh shrublands – transition from salt marsh to marsh islands

III.A.2.N.l.1 Myrica cerifera – Rosa palustris Tidal Shrubland Alliance

Woodlands

Shell Midden Woodland – these are inclusions in salt marshes

II.A.2.N.a.3 Quercus virginiana-Juniperus virginiana-(Sabal palmetto)

Woodland Alliance
Appendices

36

Map Classes 33-38 – Swamp/Bottomland Types 14-19

These classes have been merged with map class 5, Swamp/Bottomland Hardwood Forest.

They represent a diversity of bottomland and swamp communities that range from

temporarily flooded to seasonally flooded to saturated. The original vegetation map erred

on the upland side of wetness values. Therefore, to derive these swamp classes, forests

originally classified as vegetation classes 14 through 19 that occur on soils of wetness

classes 3 or 4 were reclassified as these map classes.

We have been unable to definitively assign specific alliances to each of these

swamp/bottomland types. However, there are some generalizations that have become

apparent. For example, the swamp/bottomland type 18 often supports more deeply

flooded swamp forests dominated by water tupelo. Also, swamp/bottomland type 15

very often supports laurel oak-dominated bottomlands. This is because since laurel oaks

are tardily deciduous, they often provide an evergreen infrared signature, thus originally

this map class was classified as an upland evergreen forest/woodland class.

Swamp/bottomland type 15 is sometimes strongly correlated with wet evergreen (25),

especially in the Francis Marion Forest and Georgetown County. In this case, merging

this class with swamp/bottomland hardwood results in a misleading map. We were

unable to resolve this problem.

These new classes have been lumped into the swamp/bottomland hardwood class but we

have retained the ability to separate them out again in the hope that it will be useful in

future studies. Some of the differences are due to differences in originally assigning class

values to the original 280 hydrologic units and are AOI problems and some of the

differences are indeed real.

Bottomland Hardwood Forest Ecological Complex

This complex includes river bottoms and low-lying floodplains where cypress and tupelo

are usually not dominants. This complex encompasses the concept of map classes 33

through 38 and is discussed further under those classes. Floodplain and levee forests of

class 28 may be found as inclusions within this complex but are listed and described

under class 28. The following TNC alliances have been assigned to this complex and are

attributed to map classes 33 through 38.

I.B.2.N.d.11 Fraxinus pennsylvanica - Ulmus americana - Celtis (occidentalis,

laevigata) Temporarily Flooded Forest Alliance

Occurs on base-rich alluvial sites throughout the state.

I.B.2.N.d.12 Liquidambar styraciflua - (Liriodendron tulipifera, Acer rubrum)

Temporarily Flooded Forest Alliance

Bottomland community of moderately wet floodplains in piedmont and coastal

plain.
Appendices

37

I.B.2.N.d.16 Quercus (michauxii, pagoda, shumardii) - Liquidambar styraciflua

Temporarily Flooded Forest Alliance

Occurs primarily in brownwater situations (but also sometimes in blackwater) in

the piedmont and coastal plain.

I.B.2.N.d.17 Quercus (phellos, nigra, laurifolia) Temporarily Flooded Forest

Alliance

Occurs along blackwater rivers in the coastal plain. Swamp forests dominated by

Q. laurifolia were most often found mapped as class 33, swamp type 15.

I.B.2.N.d.4 Acer saccharinum Temporarily Flooded Forest Alliance

Floodplain forest of major rivers.

I.B.2.N.e.1 Acer rubrum - Fraxinus pennsylvanica Seasonally Flooded Forest

Alliance

Occurs in floodplains of rivers and perennial streams.

I.B.2.N.e.11 Planera aquatica Seasonally Flooded Forest Alliance

Occurs along coastal plain rivers and backswamps.

I.B.2.N.e.12 Quercus laurifolia Seasonally Flooded Forest Alliance

Seasonally flooded, mostly blackwater forests occurring in the coastal plain.

Swamp forests dominated by Q. laurifolia were most often found mapped as class

33, swamp type 15.

I.B.2.N.e.13 Quercus lyrata - (Carya aquatica) Seasonally Flooded Forest Alliance

Occurs in floodplains, sloughs, and depressions throughout the coastal plain.

I.B.2.N.e.15 Quercus phellos Seasonally Flooded Forest Alliance

Occurs primarily in upland depressions in the inner coastal plain.

I.B.2.N.e.19 Salix nigra Seasonally Flooded Forest Alliance

Occurs in coastal plain floodplains or non-riverine swamps, usually following

disturbance.

I.B.2.N.e.20 Sapium sebiferum Seasonally Flooded Forest Alliance

Occurs in seasonally flooded forests and isolated ponds throughout the coastal

plain, especially the outer coastal plain.

I.B.2.N.e.6 Liquidambar styraciflua - (Acer rubrum) Seasonally Flooded Forest

Alliance

Occurs in seasonally flooded depressions (not floodplains) throughout the coastal

plain.

I.B.2.N.g.4 Liquidambar styraciflua Saturated Forest Alliance

Occurs in seepage areas with peaty soils throughout the coastal plain.
Appendices

38

I.B.2.N.g.5 Nyssa biflora - Acer rubrum - (Liriodendron tulipifera) Saturated Forest

Alliance

Occurs on extensive peat flats in the coastal plain, in seepage-fed edges of

floodplains or on slopes wet by nearly constant seepage.

I.B.2.N.g.6 Quercus laurifolia - Nyssa biflora Saturated Forest Alliance

Occurs in the coastal plain on poorly drained interstream flats or on upland flats

with saturated soils. Bottomland forests dominated by Q. laurifolia were most

often found mapped as class 33, swamp type 15

I.B.2.N.g.7 Quercus michauxii - Quercus pagoda Saturated Forest Alliance

Occurs on non-riverine flats.

Map Class 39 – Mud/Sand Flat

These are open, unvegetated to sparsely vegetated flats of sand or mud within estuarine

systems. They are usually infrequently or irregularly flooded by salt water.

These are mapped by using NWI in conjunction with salt marshes. Mud and sand flats

are mapped by NWI as E2USM or E2USN.

Nelson (1986) refers to these areas as Intertidal Mud/Sand Flats. They are usually

without vegetation; however, inclusions of the following TNC alliances may be found in

association with mud/sand flats. These alliances are attributed to class 30, estuarine salt

flat/salt shrub thicket.

IV.A.2.N.c.5 Sarcocornia perennis - (Distichlis spicata, Spartina alterniflora) Tidal

Dwarf Shrubland Alliance

VII.C.2.N.d.3 Sesuvium spp.-Atriplex spp.-Suaeda spp. Tidal Sparsely Vegetated

Alliance

Map Class 40 – Beach

Derived entirely from NWI and soils, this class includes marine beaches along the coast

that are above intertidal beaches (42) and are rarely to irregularly flooded.

GAP/NWI class beach (2) was used to define beaches. Areas on soils labeled coastal

beaches were reclassified as beach.

This class includes Nelson’s Maritime Grassland (1986). Because soil wetness cannot be

readily determined, this alliance includes dry grasslands as well as wetland swales.
Appendices

39

Maritime Grassland Ecological Complex

This complex consists of herbaceous dune and swale vegetation. These dune

grasslands are often too small or narrow to map separately and are usually found

as inclusions within the Maritime Shrub Complex.

V.A.5.N.a.9 Uniola paniculata Temperate Herbaceous Alliance

V.A.5.N.c.12 Muhlenbergia filipes Herbaceous Alliance – Maritime Wet

Grassland

V.A.5.N.j.2 Andropogon glomeratus Temporarily Flooded Herbaceous Alliance –

VII.C.2.N.a.2. Cakile edentula Sparse Vegetation Alliance

Map Class 41 – Spoil Deposit/Impoundment

Spoil deposits and spoil impoundments are found primarily along the Intracoastal

Waterway in the outer coastal plain. Where spoil deposits are new or impounded, they

typically have either bare soil or herbaceous vegetation. Older, unimpounded spoil

deposits usually support evergreen forests and will be found mapped as coastal upland

mixed forests, class 44.

NWI was used to determine this class – the modifiers ‘s’ for spoil and ‘sh’ for spoil

impoundment determined this class.

The only TNC alliance specifically attributed to spoil areas is the following:

V.A.5.N.a. Phragmites australis Herbaceous Vegetation

Map Class 42 – Intertidal Beach

This class was primarily identified in Charleston County as low and regularly flooded

beaches.

NWI class Marine, unconsolidated shores was used to define this class. Nelson (1986)

also refers to this class as Intertidal Beach. It is a non-vegetated community.

Map Class 43 – Narrow Stream Valley Forest

Narrow stream valley forests are temporarily flooded forests bordering small first and

second order streams in the piedmont. The vegetation of these forests may be similar to

that of floodplain forests (28). Where they are too small to map separately, seasonally to

semipermanently flooded bottomlands may be included in this class.

Forests in this class are defined as occurring on specific soils to include Worsham, local

alluvial land and mixed or mixed wet alluvial land. Worsham soils are in soil wetness

class 3 and they are typically found at the headwaters of small streams. However,

downstream from the Worsham soils, the drainageway typically supports soils referred to
Appendices

40

as alluvial land which have a soil wetness class of 2. Therefore, these soils were used to

define this class to provide complete mapping of these small streams.

This class is included in Nelson’s (1986) Bottomland Hardwoods.

I.B.2.N.d.11 Fraxinus pennsylvanica - Ulmus americana - Celtis (occidentalis,

laevigata) Temporarily Flooded Forest Alliance

Occurs on base-rich alluvial sites throughout the state.

I.B.2.N.d.12 Liquidambar styraciflua - (Liriodendron tulipifera, Acer rubrum)

Temporarily Flooded Forest Alliance

Bottomland community of moderately wet floodplains in piedmont and coastal

plain.

I.B.2.N.d.14 Platanus occidentalis - (Liquidambar styraciflua, Liriodendron

tulipifera) Temporarily Flooded Forest Alliance

Occurs on rocky streambeds and alluvial deposits on relatively high-gradient

rivers in the upper piedmont and mountains.

I.B.2.N.d.16 Quercus (michauxii, pagoda, shumardii) - Liquidambar styraciflua

Temporarily Flooded Forest Alliance

Occurs primarily in brownwater situations (but also sometimes in blackwater) in

the piedmont and coastal plain.

I.B.2.N.d.22 Salix nigra Temporarily Flooded Forest Alliance

Occurs along rivers and streams throughout the state.

I.B.2.N.d.9 Fagus grandifolia Temporarily Flooded Forest Alliance

Occurs along small streams in the piedmont and possibly other parts of the state.

I.B.2.N.g.2 Acer rubrum - Nyssa sylvatica Saturated Forest Alliance

Occurs in seepage areas on hillsides, streamheads, floodplains, and other poorly

drained depressions

I.B.2.N.g.5 Nyssa biflora - Acer rubrum - (Liriodendron tulipifera) Saturated Forest

Alliance

Occurs on seepage-fed edges of small streams in the piedmont. Also occurs in the

coastal plain.

I.C.3.N.b.5 Pinus taeda - Liriodendron tulipifera Temporarily Flooded Forest

Alliance

Occurs in floodplains of the piedmont and coastal plain following disturbance.

III.A.2.N.g.1 Arundinaria gigantea Temporarily Flooded Shrubland Alliance

Rare community occurring on streamside flats or bottomlands.
Appendices

41

III.A.2.N.g.2 Ligustrum sinense Temporarily Flooded Shrubland Alliance

Wetland bottomlands heavily infested with Ligustrum.

III.B.2.N.d.2 Alnus serrulata Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers and streams on rocky shoals, gravel, and bars in the

mountains.

III.B.2.N.d.7 Salix nigra Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers or lakeshores of the piedmont and coastal plain.

III.B.2.N.f.1 Cephalanthus occidentalis Semipermanently Flooded Shrubland

Alliance

Occurring in shallow water depressions, oxbow ponds, sinkhole ponds, and

backwater sloughs of stream and river floodplains.

Map Class 44 – Coastal Upland Mixed Forest

This class consists of upland coastal and maritime forests with Quercus virginiana, Pinus

spp. Ilex vomitoria, Myrica cerifera and Sabal palmetto. It includes upland forests of

barrier islands as well as those forests considered to be inland maritime forests. Adjacent

to and within maritime forests are small patch and narrow linear maritime communities

which often cannot be distinguished at the Gap mapping level. Examples of these

communities include: dune grasslands, dune shrublands, interdune ponds, palmetto

sloughs, tidal shrublands, shell midden woodlands, and calcareous coastal fringe forests.

Most coastal upland forests were originally classified as closed canopy evergreen forests

due to the presence of evergreen oaks and pines. NWI and soils were used extensively to

refine the classification. Outer coastal soils known to support maritime forests, such as

Newhan, Crevasse, and Fripp, and other coastal soils of soil wetness classes 1 and 2 were

utilized in conjunction with NWI. NWI upland evergreen, upland mixed, and residential

resort forests on the aforementioned soils work well to define maritime forests.

Nelson (1986) refers all coastal evergreen forests to the natural community, Maritime

Forest.

Maritime Forest Ecological Complex

This complex includes all three of the forest alliances as well as the woodland alliance

listed below. Generally, on barrier and marsh islands, the Quercus virginiana-(Sabal

palmetto) Forest Alliance has spatial dominance. Embedded within these live oak

forests, interdune ponds occur that are below our mapping level as well as palmettodominated sloughs that may be too narrow for mapping. Normally, the maritime forests

are bordered by tidal shrublands that also are too narrow to map. In the case of marsh

islands, the tidal shrublands completely encircle the islands. The Carya-Tilia-Celtis

Forest Alliance usually occurs as small patches within maritime forests. The Quercus

virginiana- Juniperus virginiana -(Sabal palmetto) Woodland Alliance may also be

found on the margins of marsh islands or separately within the salt marsh where they may
Appendices

42

not be distinguishable from the surrounding marsh at our mapping level. On barrier

islands, the transition from maritime forest to the live-oak dominated dune shrub thicket

may not be distinguished so it is also included here. Therefore, this complex can include:

Palmetto Sloughs – see wet maritime forest, class 29

I.A.4.N.g.4 Sabal palmetto-Quercus virginiana Saturated Forest Alliance

Calcareous Coastal Fringe Forest

I.B.2.N.a.11 Carya glabra-Tilia americana var. caroliniana-Celtis laevigata

Forest Alliance

Shell Midden Woodlands

II.A.2.N.a.3 Quercus virginiana- Juniperus virginiana -(Sabal palmetto)

Woodland Alliance

Maritime Dune Shrub Thicket – see maritime shrub complex, class 27

III.A.2.N.c.32 Quercus virginiana-Ilex vomitoria-(Myrica cerifera) Shrubland

Alliance

Salt Shrub Thicket – see estuarine salt flat/salt shrub thicket, class 30

III.B.2.N.h.1 Baccharis halimifolia-Iva frutescens Tidal Shrubland Alliance

III.B.2.N.h.4 Borrichia frutescens Tidal Shrubland Alliance

Interdune Pond – see freshwater marsh/emergent wetland, class 3

V.A.5.N.k.29 Typha domingensis Seasonally Flooded Temperate Herbaceous

Alliance

V.A.5.N.k.28 Spartina bakeri Seasonally Flooded Herbaceous Alliance

In SC, known only from the South Atlantic coastal plain ecoregion, these

circular depressions are found on marsh islands, not barrier islands.

V.A.5.N.k.5 Carex hyalinolepis Seasonally Flooded Herbaceous Alliance

V.B.2.N.h.100 Polygonum spp. (section Persicaria) Seasonally Flooded

Herbaceous Alliance

The following TNC alliances are attributed to the Coastal Upland Mixed Forest class:

I.A.4.N.a.2 Quercus hemisphaerica Forest Alliance

Dry-mesic to mesic forests of the coastal plain.

I.A.4.N.a.4 Quercus virginiana - (Sabal palmetto) Forest Alliance

Maritime forest and xeric marsh hammock.

I.C.2.N.a.8 Quercus virginiana - Quercus pagoda Forest Alliance

Wet-mesic forests of the outer coastal plain.

I.C.3.N.a.26 Pinus taeda - Quercus nigra Forest Alliance

Dry to dry-mesic mixed forests of the coastal plain.

II.A.2.N.a.2 Sabal palmetto Temperate Woodland Alliance
Appendices

43

II.A.2.N.a.3 Quercus virginiana- Juniperus virginiana -(Sabal palmetto) Woodland

Alliance

Shell midden woodlands.

II.A.4.N.a.19 Pinus elliottii Woodland Alliance

Upland woodlands occurring on barrier islands and other near-coastal areas.

II.A.4.N.a.28 Pinus taeda Woodland Alliance

Coastal plain woodlands often occurring in maritime situations.

Map Class 45 – Piedmont Bottomland Forest

These are seasonally to semipermanently flooded bottomland forests of the piedmont,

which occur on poorly drained soils along streams and rivers. The vegetation of this

class is similar to that of swamp/bottomland hardwood forests (5). Where they are too

small or narrow to map separately, temporarily flooded forests may be included in this

class.

Piedmont bottomland forests were defined as those occurring in the piedmont

physiographic province on soil wetness classes 3 or 4. They were also determined by

using the NWI palustrine forested wetland class.

Nelson (1986) includes this class in a general Bottomland Hardwood class. The

following TNC alliances may be represented in piedmont bottomland forests:

I.B.2.N.d.11 Fraxinus pennsylvanica - Ulmus americana - Celtis (occidentalis,

laevigata) Temporarily Flooded Forest Alliance

Occurs on base-rich alluvial sites throughout the state.

I.B.2.N.d.12 Liquidambar styraciflua - (Liriodendron tulipifera, Acer rubrum)

Temporarily Flooded Forest Alliance

Bottomland community of moderately wet floodplains in the piedmont and

coastal plain..

I.B.2.N.d.13 Platanus occidentalis - (Fraxinus pennsylvanica, Celtis laevigata, Acer

saccharinum) Temporarily Flooded Forest Alliance

Occurs on fronts, terraces, and levees of medium to large rivers in the CP and

lower piedmont.

I.B.2.N.d.14 Platanus occidentalis - (Liquidambar styraciflua, Liriodendron

tulipifera) Temporarily Flooded Forest Alliance

Occurs on rocky streambeds and alluvial deposits on relatively high-gradient

rivers in the upper piedmont and mountains.

I.B.2.N.d.15 Populus deltoides Temporarily Flooded Forest Alliance

Riverfront floodplain forest occurring throughout the state.
Appendices

44

I.B.2.N.d.16 Quercus (michauxii, pagoda, shumardii) - Liquidambar styraciflua

Temporarily Flooded Forest Alliance

Occurs primarily in brownwater situations (but also sometimes in blackwater) in

the piedmont and coastal plain.

I.B.2.N.d.22 Salix nigra Temporarily Flooded Forest Alliance

Occurs along rivers and streams throughout the state.

I.B.2.N.d.3 Acer negundo Temporarily Flooded Forest Alliance

Early successional forests along large river floodplains and sandbars.

I.B.2.N.d.4 Acer saccharinum Temporarily Flooded Forest Alliance

Floodplain forest of major rivers.

I.B.2.N.d.5 Betula nigra - (Platanus occidentalis) Temporarily Flooded Forest

Alliance

Occurs on riverfronts, especially on levees, throughout the state. Corresponds to

Forest Svc stand type #72.

I.B.2.N.d.9 Fagus grandifolia Temporarily Flooded Forest Alliance

Occurs along small streams in the piedmont and possibly other parts of the state..

I.B.2.N.e.1 Acer rubrum - Fraxinus pennsylvanica Seasonally Flooded Forest

Alliance

Occurs in floodplains of rivers and perennial streams.

I.B.2.N.e.15 Quercus phellos Seasonally Flooded Forest Alliance

Occurs primarily in upland depressions (not floodplains) throughout the coastal

plain and piedmont.

I.B.2.N.g.2 Acer rubrum - Nyssa sylvatica Saturated Forest Alliance

Occurs in seepage areas on hillsides, streamheads, floodplains, and other poorly

drained depressions.

I.B.2.N.g.5 Nyssa biflora - Acer rubrum - (Liriodendron tulipifera) Saturated Forest

Alliance

Occurs on extensive peat flats in the coastal plain, in seepage-fed edges of

floodplains or on slopes wet by nearly constant seepage. Also occurs on seepagefed edges of small streams in the piedmont.

I.B.2.N.g.7 Quercus michauxii - Quercus pagoda Saturated Forest Alliance

Occurs on non-riverine flats.

I.C.3.N.b.5 Pinus taeda - Liriodendron tulipifera Temporarily Flooded Forest

Alliance
Appendices

45

Occurs in floodplains of the piedmont and coastal plain following disturbance.

III.A.2.N.g.1 Arundinaria gigantea Temporarily Flooded Shrubland Alliance

Rare community occurring on streamside flats or bottomlands.

III.A.2.N.g.2 Ligustrum sinense Temporarily Flooded Shrubland Alliance

Wetland bottomlands heavily infested with Ligustrum. This alliance will

probably be mapped as a small inclusion within bottomlands or it may also occur

as wet scrub/shrub (7) if patch is large enough.

III.B.2.N.d.2 Alnus serrulata Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers and streams on rocky shoals, gravel, and bars in the

mountains. Probably this alliance will usually just show up as a narrow band

inclusion within adjacent forest types or it may show up as rocky shoals (50),

when situated on a rocky shoal.

III.B.2.N.d.7 Salix nigra Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers or lakeshores of the piedmont and coastal plain. This

alliance will probably be mapped as a narrow band inclusion within

swamp/bottomland hardwood (5), piedmont bottomland (45), narrow stream

valley forest (43), or floodplain forest (28).

III.B.2.N.f.1 Cephalanthus occidentalis Semipermanently Flooded Shrubland

Alliance

Occurring in shallow water depressions, oxbow ponds, sinkhole ponds, and

backwater sloughs of stream and river floodplains. This alliance is found mapped

as wet scrub/shrub (7) or as a small-patch inclusion within swamp/bottomland

hardwood (5), piedmont bottomland (45), narrow stream valley forest (43), or

floodplain forest (28).

Map Class 46 – Dry Basic Deciduous Forest

Xeric forests or woodlands overlying basic or circumneutral soil. Soils were used to

distinguish this class from classes 17, 18, and 47. Disturbed deciduous sites overlying

basic soils will be mapped as class 46, but may not contain typical basic forest species.

Woodlands with very sparse canopies may be mapped as dry scrub/shrub (8), open

canopy/recently cleared forest (10), grassland/pasture (20), or cultivated land (21).

Deciduous forests were placed in this class if they occurred on alfisols with a soil wetness

class of 1. The original vegetation map occasionally erroneously placed deciduous and

mixed upland forests in class 5, swamps and bottomland hardwoods. A combination of

NWI deciduous upland forest class on alfisols with soil wetness class 1 corrected this

error.

This class is equivalent to Nelson’s (1986) Montmorillonite Forest. The following TNC

alliance comprises this class:
Appendices

46

I.B.2.N.a.27 Quercus alba - (Quercus rubra, Carya spp.) Forest Alliance

Described as dry-mesic forest, sometimes occurring on calcareous substrate.

I.B.2.N.a.37 Quercus prinus - Quercus (alba, falcata, rubra, velutina) Forest

Alliance

Dry-mesic to mesic forests occurring on moderately sheltered low ridges, flats,

and valleys at low elevations in the mountains and on upper to middle dry-mesic

slopes in the piedmont. May also include forests overlying calcareous bedrock.

I.B.2.N.a.41 Quercus stellata - Quercus marilandica Forest Alliance

Dry oak forest, sometimes occurring on calcareous substrates.

Map Class 47 – Mesic Basic Deciduous Forest

Mesic forests or woodlands overlying basic or circumneutral soil. Soils were used to

distinguish this class from classes 16, 17, and 46. Disturbed deciduous sites overlying

basic soils will be mapped as class 47 but may not contain typical basic forest species.

Woodlands with very sparse canopies may be mapped as dry scrub/shrub (8), open

canopy/recently cleared forest (10), grassland/pasture (20), or cultivated land (21).

Deciduous forests were placed in this class if they occurred on alfisols with a soil wetness

class of 2. The original vegetation map occasionally erroneously placed deciduous and

mixed upland forests in class 5, swamps and bottomland hardwoods. A combination of

NWI deciduous upland forest class on alfisols with soil wetness class 2 corrected this

error.

This class is equivalent to Nelson’s Basic Forest (1986) which he attributes to the

piedmont, especially in association with diabase dikes. The concept here is also of

piedmont forests. Mesic basic deciduous forests in the coastal plain weren’t able to be

distinguished by soil rules because the alfisols of the coastal plain fall into wetness class

3.

I.B.2.N.a.16 Fagus grandifolia - Quercus alba Forest Alliance

Dry-mesic to mesic forests with Quercus alba and Fagus grandifolia that

typically occur on mesic slopes and small stream bottoms.

I.B.2.N.a.101 Quercus muehlenbergii - (Acer saccharum) Forest Alliance

Basic forest which may be found in the mountains.

I.B.2.N.a.2 Acer barbatum - Fraxinus americana - Juglans nigra Forest Alliance

Mesic calcareous forests. Gap doesn't map basic forests separately in the coastal

plain. Therefore, coastal plain occurrences of this alliance will be mapped under

mesic deciduous class 18, while piedmont occurrences will be mapped as 47.

I.B.2.N.a.27 Quercus alba - (Quercus rubra, Carya spp.) Forest Alliance
Appendices

47

Described as dry-mesic forest, sometimes occurring on calcareous substrate.

I.B.2.N.a.37 Quercus prinus - Quercus (alba, falcata, rubra, velutina) Forest

Alliance

Dry-mesic to mesic forests occurring on moderately sheltered low ridges, flats,

and valleys at low elevations in the mountains and on upper to middle dry-mesic

slopes in the piedmont. May also include forests overlying calcareous bedrock.

II.B.2.N.a.25 Quercus stellata - Quercus marilandica Woodland Alliance

Post oak - blackjack oak barrens occurring on montmorillonitic clays or

limestone-derived soils in the piedmont and coastal plain.

Map Class 48 – Mesic Basic Mixed Forest/Woodland

Mesic forests or woodlands with mixed evergreen and deciduous canopy species,

overlying basic or circumneutral soil. These forests are often mapped as evergreen

forests or woodlands, classes 13 and 14. Soils were used to distinguish this class from

classes 18, 19, and 20. Disturbed mixed sites overlying basic soils will be mapped as

class 48, but may not contain typical basic forest species. Woodlands with very sparse

canopies may be mapped as dry scrub/shrub (8), open canopy/recently cleared forest (10),

grassland/pasture (20), or cultivated land (21).

I.C.3.N.a.3 Juniperus virginiana – Quercus (stellata, velutina, marilandica) Forest

Alliance

Mixed dry-mesic forests of the piedmont and possibly elsewhere in the state.

Map Class 49 – Ridgetop/Upper Slope Evergreen Woodland

Ridgetops and upper slopes dominated by Pinus rigida, P. pungens, P. virginiana, and/or

Tsuga caroliniana. Oaks, especially Quercus prinus and Q. coccinea are typically also

present and may be co-dominant with the evergreen species. The canopy ranges from

open woodlands to closed canopy forests. Sites on shallow rocky soils often have open,

stunted canopies. Typical shrubs include Vaccinium pallidum, Kalmia latifolia, and V.

stamineum.

Digital elevation models and soils were used to devise decision rules to map this class.

This class is defined as occurring on ridgetops above 2000 feet in elevation where the

soils were classed as type 1 or 2.

I.A.8.N.b.5 Pinus echinata Forest Alliance

I.A.8.N.b.17 Pinus virginiana Forest Alliance

I.A.8.N.c.9 Tsuga caroliniana Forest Alliance

I.C.3.N.a.15 Pinus echinata – Quercus (coccinea, falcata, prinus) Forest Alliance
Appendices

48

II.A.4.N.a.23 Pinus pungens – (Pinus rigida) Woodland Alliance

Woodlands occurring in the mountains on xeric ridges and exposed, steep sideslopes.

Map Class 50 – Rocky Shoals

These are regularly flooded rocky shoals in streams and rivers in the mountains and piedmont.

Rocky shoals were initially underestimated for several reasons. If the water levels were high,

they mapped as water. If water levels were low, they often mapped as urban development, class

22 due to the similarities of rock and concrete to the mapper. If mapped as urban development,

rocky shoals could be identified in those areas where the original vegetation map indicated urban

and the soil class was water.

Nelson (1986) described this class as Shoal and Stream Bar. His definition includes

sandy shoals in the coastal plain whereas the GAP concept is strictly of rocky shoals.

Nelson’s sandy shoals will be found mapped as fresh water marsh (3) or wet soil (6). The

following TNC alliances typify this land cover class:

III.B.2.N.d.2 Alnus serrulata Temporarily Flooded Shrubland Alliance

This alliance includes shrub thickets dominated by Alnus serrulata along rivers

and streams on rocky shoals and gravel and bars.

V.A.5.N.j.4 Carex torta Temporarily Flooded Herbaceous Alliance

Alluvial wetlands on sand, gravel, and rock bars in the mountains.

V.B.2.N.d.2 Justicia americana Temporarily Flooded Herbaceous Alliance

Occurs on rocky river shoals in the mountains and piedmont. Hymenocallis coronaria is

found in association with this alliance in piedmont streams and rivers.

V.C.2.N.a.12 Podostemum ceratophyllum Permanently Flooded Herbaceous Alliance

Occurs on shoals in rocky streambeds and riverbeds.

Map Class 51 – Montane Riparian Forest

Temporarily flooded to saturated forests along streams and rivers in the mountains. This

class includes several forest types including bogs, seeps, bottomlands, and floodplains.

Riparian shrublands, which are typically too narrow to map separately, have been listed

as inclusions within this class. Typical canopy species within the forests include: Acer

rubrum, Platanus occidentalis, Liquidambar styraciflua, Betula lenta, B. nigra,

Liriodendron tulipifera, Tsuga canadensis, Pinus strobus, and Magnolia fraseri.

This class was created exclusively by using soil decision rules. If the land cover class in

the original vegetation map was an upland forest type to include evergreen, mixed and

deciduous forests, and the soil type was wetness class 3, 4, 5, or 11, then it was placed in
Appendices

49

montane riparian forest.

This class falls within the Small Steam Forest natural community of Nelson (1986). The

following TNC alliances should be found in this class:

I.A.8.N.e.3 Tsuga canadensis – (Pinus strobus) Temporarily Flooded Forest Alliance

Occurs on floodplains and terraces in the mountains.

I.B.2.N.d.14 Platanus occidentalis - (Liquidambar styraciflua, Liriodendron

tulipifera) Temporarily Flooded Forest Alliance

Occurs on rocky streambeds and alluvial deposits on relatively high-gradient

rivers in the upper piedmont and mountains. Should be found mapped as narrow

stream valley forest (43) or floodplain forest (28) in the piedmont and as montane

riparian forest (69) in the mountains.

I.B.2.N.e.19 Salix nigra Seasonally Flooded Forest Alliance

Occurs along rivers and streams throughout the state. This alliance will also be

mapped as floodplain forest (28) and narrow stream valley (43).

I.B.2.N.d.5 Betula nigra - (Platanus occidentalis) Temporarily Flooded Forest

Alliance

Occurs on riverfronts, especially on levees, throughout the state. This alliance is

also mapped as floodplain forest (28), narrow stream valley forest (43), and

piedmont bottomland (45).

I.C.3.N.d.7 Tsuga canadensis – Acer rubrum Saturated Forest Alliance

Hemlock - hardwood forests occurring on poorly drained bottomlands in the

mountains.

III.A.2.N.g.1 Arundinaria gigantea Temporarily Flooded Shrubland Alliance

Rare community occurring on streamside flats or bottomlands in the mountains

and piedmont. When patches are large enough to distinguish, this alliance they

should be mapped as wet shrub/scrub (7). It probably occurs mostly as smallpatch inclusions within floodplain forests (28), narrow stream valley forests (43),

or montane riparian forests (51).

III.B.2.N.d.2 Alnus serrulata Temporarily Flooded Shrubland Alliance

Shrub thickets along rivers and streams on rocky shoals, gravel, and bars in the

mountains. When patches are large enough to distinguish, this alliance is mapped

as wet shrub/scrub (7). This alliance likely occurs as a narrow band inclusion

within montane riparian forest (51). Alternatively, this alliance may also be found

on rocky shoals (50).

Map Class 52 – Deciduous Rich/Acidic Cove Forest
Appendices

50

These are mesic deciduous forests occurring in protected coves and ravines. In rich cove

communities, the canopy is usually a diverse mix of mesic hardwoods such as

Liriodendron tulipifera, Tilia americana var. heterophylla, T. americana var.

caroliniana, Aesculus flava, Magnolia fraseri, and Halesia carolina. The shrub layer is

dominated by non-ericaceous species such as Lindera benzoin, Hydrangea arborescens,

and Calycanthus floridus var. glaucus. The herbaceous layer is typically very lush and

diverse. Acidic coves typically have lower species richness and a shrub layer dominated

by ericaceous species, especially Rhododendron maximum.

Digital elevation models and soils were used to define this class. Deciduous forests

occurring on concave valley bottoms where the soils were class 1 or 2, were mapped as

cove forests.

Nelson (1986) places these forests in Cove Forest. The following TNC alliance typifies

rich coves:

I.B.2.N.a.23 Liriodendron tulipifera - Tilia americana var. heterophylla -

Aesculus flava - Acer saccharum Forest Alliance

Rich cove forests, occurring in the mountains only.

The following TNC alliance typifies deciduous acidic coves:

I.B.2.N.a.24 Liriodendron tulipifera Forest Alliance

Map Class 53 – Montane Evergreen Forest

These are closed canopy, evergreen-dominated forests, including planted pine stands.

Canopy dominants include: Pinus strobus, Pinus virginiana, Pinus echinata, Pinus

taeda.

This class is restricted to the mountains and is derived entirely by decision rules. All

areas located within the mountain province, originally classified as land cover classes 13,

Closed Canopy Evergreen Forest, or 14, Needle-leaved Evergreen Mixed

Forest/Woodland, were placed in Montane Evergreen Forest.

Nelson (1986) has no natural communities that would be included in this land cover

class.

I.A.8.C.x.8 Pinus strobus Planted Forest Alliance

Planted white pine forests in the mountains.

I.A.8.C.x.10 Pinus virginiana Planted Forest Alliance

Planted Virginia pine forests, possibly in the mountains.

I.A.8.N.b.14 Pinus strobus Forest Alliance

Only occurs in the piedmont and mountains.
Appendices

51

I.A.8.N.b.5 Pinus echinata Forest Alliance

Dry, evergreen forests occurring in the mountains and piedmont. Soils are acidic

and are derived from sandstone, chert, or granitic rock. These communities are

situated on ravines, ridges, and steep, often S- to W-facing slopes below 2400 ft.

I.A.8.N.b.17 Pinus virginiana Forest Alliance

Occurs on lower slopes and ridges in the Southern Blue Ridge, often in

association with Pinus rigida. Also occurs in the piedmont.

I.A.8.N.c.2 Juniperus virginiana Forest Alliance

Successional forests occurring on old fields, successional cleared land, and other

disturbed areas, especially on calcareous bedrock.

I.A.8.N.c.9 Tsuga caroliniana Forest Alliance

Occurs in the mountains and upper piedmont, mostly on cliffs, rocky slopes, and

ridges, less commonly on more gentle slopes and flat areas in valleys. Occupies

higher and drier sites than Tsuga canadensis.

Map Class 54 – Eastern Hemlock Ravine Forest

These are evergreen to mixed forests that occur on protected lower slopes in mountain

coves and ravines. The canopy is dominated or co-dominated by Tsuga canadensis, often

mixed with Pinus strobus and mesic hardwoods such as Liriodendron tulipifera,

Magnolia fraseri, Acer rubrum, Fagus grandifolia, Quercus rubra, Betula lenta, and

Tilia americana var. heterophylla. The understory is characterized by dense thickets of

Rhododendron maximum, sometimes mixed with Ilex opaca, Kalmia latifolia, Leucothoe

fontanesiana, Clethra acuminata, Hydrangea arborescens, and/or Hamamelis virginiana.

Digital elevation models and soils were used to define this class. Evergreen or mixed

forests occurring in valley bottoms on soil class 1 or 2 were mapped as hemlock ravines.

This class corresponds to Nelson’s (1986) Hemlock Forest. The following TNC alliances

are considered to be typical of this class:

I.A.8.N.b.13 Pinus strobus - Tsuga canadensis Forest Alliance

Occurs on alluvial terraces and steep, protected gorge slopes in the mountains.

I.A.8.N.c.8 Tsuga canadensis Forest Alliance

Occurs in mountains on valley flats, narrow ravines, and north- to east-facing

slopes from 1800-3500 ft.

I.C.3.N.a.33 Tsuga canadensis - Liriodendron tulipifera Forest Alliance

Acidic cove forests or mesic successional forests occurring in the mountains.
Appendices

52

Appendix 2. NWI classes used in land cover development.

1. Agriculture

2. Beach

3. Deciduous forest

4. Evergreen forest

5. Forested wetland – not included in category 25, 26, 28

6. Fresh marsh

7. Fresh water

8. Fresh/salt water

9. LAB

10. Mixed forest

11. Other

12. PAB

13. Planted pine

14. Salt marsh

15. Salt water

16. Shore-M2US

17. SS Wetland – not included in category 27, 29

18. Urban

19. Shore – E2USN

20. Shore – E2USP

21. Shore – E2USM\

22. Impoundment

23. RAB

24. Shore – E2US

25. Evergreen PFO

26. Evergreen PFO/PSS

27. Evergreen PSS

28. Deciduous PFO

29. Deciduous PSS

30. Salt flat E2SS*

31. PFO1/SS3B*- include in inclusive evergreen PFO

32. Combined U11, U12, and U17h

* any combination within this class
Appendices

53

Appendix 3. Soil types used in land cover development

1. excessively and somewhat excessively drained

2. well and moderately-well drained

3. somewhat poorly and poorly drained

4. very poorly drained

5. levee or temporarily flooded soils

6. alfisols

7. complexes

8. water

9. no drainage

10. seep-producing soils – NOT USED

11. piedmont and mountain special soils, Worsham, Local Alluvial, Elbert wetness 1 or 2

12. no code

13. no code

14. no code

15. Rutledge and Paxville soils in Dillon and Marlboro counties

16. piedmont soils – same as 6a – class 1 alfisols

17. piedmont soils – same as 6b – class 2 alfisols

18. rock outcrops – Greenville, Oconee, Pickens

19. 7MF

20. 7MF1

21. 7MF3

22. 4SM

23. CO – Coastal Beaches

24. 51MF1

25. 51MF2

26. 51MF3

27. 51MF4
Appendices

54

Appendix 4. Final rules for land cover modeling

Priority Rules

1. If NWI class E2USN or E2USM, put in veg class 39, mud/sand flat

2. If NWI contains the modifiers hs or sh, put in veg class 41, spoil deposit/impoundment

3. If NWI class beach, put in veg class 40, beach

4. If NWI class M2US, put in veg class 42, intertidal beach

5. If NWI class E2USP or E2SS*, put in veg class 30, estuarine salt flat/salt shrub thicket

6. If NWI class fresh water, or fresh/salt water, specifically palustrine or lacustrine

unconsolidated bottom, put in veg class 1, fresh water

7. If NWI class fresh/salt water, specifically marine and estuarine unconsolidated bottom, put in

veg class 2, marine water

8. If veg class 13, 14, 15, 21, or 22 and NWI class fresh marsh, put in veg class 3, fresh water

marsh/emergent wetland

9. If veg classes 33 or 34 and NWI class PFO1/PSS3B, put in veg class 4, pocosin

10. If NWI class salt marsh, and soil type 22, put in veg class 32, salt and brackish marsh

Statewide Rules

11. If veg class 26 (from original gapmo 17) and NWI class deciduous PSS, put in veg class 7

12. If veg class 26 (from original gapmo17) and NWI class deciduous PFO put in veg class 5,

swamp/bottomland hardwood forest

13. If veg class 26 (from original gapmo 17) and NWI U42 or U43 and soil type 3, 4, or 6, put in

veg class 31, wet pinewoods

14. If veg class 26 (from original gapmo 17), put in veg class 13, closed canopy evergreen

forest/woodland

15. If veg class 1 and NWI class LAB, RAB or PAB, put in veg class 12, aquatic vegetation.

16. If veg class 3 and NWI class agriculture, put in veg class 21, cultivated land

17. If veg class 8 and NWI class planted pine, put in veg class 10, open canopy/recently cleared

forest

18. If veg class 20 or 21 and NWI class planted pine, put in veg class 10/open canopy, recently

cleared forest
Appendices

55

19. If veg class 3 and NWI class planted pine, put in veg class 10, open canopy/recently cleared

forest

20. If veg class 7 and NWI class planted pine, put in veg class 10, open canopy/recently cleared

forest

21. If veg class 16 and soil type 2, put in veg class 17, mesic deciduous forest/woodland

22. If veg class 17 and soil type 1, put in veg class 16, dry deciduous forest/woodland

23. If veg class 18 and soil type 2, put in veg class 19, mesic mixed forest/woodland

24. If veg class 19 and soil type 1, put in veg class 18, dry mixed forest/woodland

25. If veg class 5 and NWI class deciduous PSS, put in veg class 7, wet scrub/shrub thicket

26. If veg class 8 and soil type 3, 4, 5, 6, 11, 21, 22, 26 or 27, put in veg class 7, wet scrub/shrub

thicket

27. If veg class 7 and soil type 1, 2, 20, 24 or 25, put in veg class 8, dry scrub/shrub thicket

28. If veg class 22 and NWI class agriculture, put in veg class 21, cultivated land

29. If veg class 13 or 14 and soil type 3 or 4 and NWI class deciduous PSS, put in veg class 7,

wet scrub/shrub thicket

30. If veg class 13 through 19 and NWI class ‘not planted pine’ and soil type 5 with soil wetness

class of 1 or 2, put in veg class 28, floodplain forest

31. For counties without NWI: If veg class 14 through 19 and soil type 5 with soil wetness class

of 1 or 2, put in veg class 28, floodplain forest.

Coastal Plain and Sandhills

32. If veg class 22 and soil type 3, 4 or 6 and NWI forested wetland classes (5, 25, 26,

28), put in veg class 5, swamp/bottomland hardwood forest

33. If veg class 13, 14, or 15 and NWI class evergreen PSS and soil type 3 or 4, put in

veg class 4, pocosin

34. If veg class 13, 14, or 15 and NWI class evergreen PFO and soil type 3 or 4, put in

veg class 25, wet evergreen

35. If veg class 13, 14, or 15 and NWI class evergreen PFO/PSS and soil type 3 or 4, put

in veg class 4, pocosin

36. If veg class 13 or14 and NWI class deciduous PFO, and soil type not 20, 21, 22, 23, 25, 26, or

27, put in veg class 33, swamp/bottomland type 14
Appendices

56

37. If veg class 15 and NWI class deciduous PFO, and soil type not 20, 21, 22 23, 25, 26, 27, put

in veg class 34, swamp/bottomland type 15

38. If veg class 13, 14, or 15 and NWI U42 and soil type 3, 4, or 6, put in veg class 31 wet

pinewoods

39. If veg class 13 or 14 and soil type 3, 4, or 6 and NWI class deciduous forest, forested

wetland, mixed forest, or SS wetland, put in veg class 33, swamp/bottomland type 14

40. If veg class 15 and soil type 3, 4, or 6 and NWI class deciduous forest, forested wetland,

mixed forest, or SS wetland, put in veg class 34, swamp/bottomland type 15

41. If veg class 16, 17, 25 or 44 and NWI class fresh marsh, put in veg class 3, freshwater

marsh/emergent wetland

42. If veg class 16 and NWI class deciduous PFO, and soil type not 20, 21, 22, 23, 25, 26, 27, put

in veg class 35, swamp/bottomland type 16

43. If veg class 16 and NWI class evergreen PFO or evergreen PFO/PSS, and soil type 3 or 4, put

in veg class 25, wet evergreen.

44. If veg class 16 and soil type 3, 4 or 6, put in veg class 35, swamp/bottomland type 16

45. If veg class 17 and NWI class deciduous PFO, and soil type not 20, 21, 22, 23, 25, 26, or 27,

put in veg class 36, swamp/bottomland type 17

46. If veg class 17 and NWI class evergreen PFO or evergreen PFO/PSS, and soil type 3 or 4, put

in veg class 25, wet evergreen.

47. If veg class 17 and soil type 3, 4 or 6, put in veg class 36, swamp/bottomland type 17

48. If veg class 18 and NWI class deciduous PFO, and soil type not 20, 21, 22, 23, 25, 26, or 27

put in veg class 37, swamp/bottomland type 18

49. If veg class 18 and NWI class evergreen PFO or evergreen PFO/PSS, and soil type 3 or 4, put

in veg class 25, wet evergreen

50. If veg class 18 and soil type 3, 4 or 6, put in veg class 37, swamp/bottomland type 18

51. If veg class 19 and NWI class deciduous PFO, and soil type not 20, 21, 22, 23, 25, 26, or 27,

put in veg class 38, swamp/bottomland type 19

52. If veg class 19 and NWI class evergreen PFO or evergreen PFO/PSS, and soil type 3 or 4, put

in veg class 25, wet evergreen.

53. If veg class 19 and soil type 3, 4 or 6, put in veg class 38, swamp/bottomland type 19

54. If veg class 20 or 21 and NWI class deciduous PFO, deciduous PFO/PSS, deciduous PSS,

forested wetland, or SS wetland, put in veg class 10, open canopy/recently cleared forest.
Appendices

57

55. If veg class 20 or 21 and NWI class evergreen PFO, evergreen PFO/PSS, or evergreen PSS

and soil type not 20, 21, 22, 23, 25, 26, or 27, put in veg class 10, open canopy/recently

cleared forest

Eight Coastal Counties

56. If veg class 3 and NWI class planted pine, put in veg class 13, closed canopy evergreen

57. If veg class 5 and NWI class fresh marsh, put in veg class 3, fresh water marsh/emergent

wetland

58. If veg class 7 and NWI class fresh marsh, put in veg class 3, fresh water marsh/emergent

wetland

59. If veg class 8 and NWI class fresh marsh, put in veg class 3, fresh water marsh/emergent

wetland

60. If veg class 6, 7, 8, 20 or 21, and NWI U42, or NWI class evergreen PFO, evergreen

PFO/PSS or evergreen PSS and soil type 20 or21, put in veg class 27, maritime shrub

complex

61. If veg class 7 or 8 and NWI class deciduous PSS and soil type 20 or 21, put in veg class 30,

estuarine salt flat/salt shrub thicket

62. If veg class 20 and NWI class fresh marsh, put in veg class 3, fresh water marsh/emergent

wetland

63. If veg class 21 and NWI class fresh marsh, put in veg class 3, fresh water marsh/emergent

wetland

64. If veg class 6, 7, 14, 15, 16, 17, 18, 19, 25, 33 through 38 and NWI class fresh marsh, put in

veg class 3, fresh water marsh/emergent wetland

65. If veg class 3 or 13 and NWI class salt marsh put in veg class 32, salt and brackish marsh

66. If veg class 5 and NWI class salt marsh, put in veg class 32, salt and brackish marsh

67. If veg class 6, 7, or 8 and NWI class salt marsh, put in veg class 32, salt and brackish marsh

68. If veg class 20 and NWI class salt marsh, put in veg class 32, salt and brackish marsh.

69. If veg class 14 through 19, 21, 25, or 33 through 38, and NWI class salt marsh, put in veg

class 32, salt and brackish marsh

70. If veg class 3, 5, 13 through 19 or 25 and NWI class U11, U12, or U17 and soil type 25 or 26,

put in veg class 44, coastal upland mixed forest

71. If veg class 3, 5, 13 through 19 or 25 and NWI U42, U43 U11, U12, or U17 and soil type 20

put in veg class 44, coastal upland mixed forest. Only east of US17
Appendices

58

72. If veg class 5, 13 through 19 or 25 and NWI U11, U12, U17, U42, or U43 and soil type 21,

put in veg class 26, lowland maritime forest

73. If veg class 13, 14 or 15 and NWI class deciduous PFO, deciduous PFO/PSS, or deciduous

PSS and soil type 20, 21, 25, 26, or 27, put in veg class 35, swamp/bottomland type 16

74. If veg class 16 through 19 and NWI class deciduous PFO, deciduous PFO/PSS, or deciduous

PSS and soil type 20, 21, 25, 26, or 27, put in veg class 36, swamp/bottomland type 17

75. If veg class 3, 5, 13 through 19 and NWI class evergreen PFO, evergreen PFO/PSS,

evergreen PSS, or PFO1/SS3B* and soil type 20 or 21 put in veg class 26, lowland maritime

forest

76. If veg class 3, 5, 13 through 19 and NWI class evergreen PFO, evergreen PFO/PSS,

evergreen PSS, or PFO1/SS3B* and soil type 26, put in veg class 26, lowland maritime forest

77. If veg class 3, 5, 13, 14, 15, or 25 and NWI U11, U12, U17, U42, or U43, and soil type 22,

put in class 44, coastal upland mixed forest

78. If veg class 5, 6, 7, 16 through 19 and soil type 22, put in veg class 32, salt and brackish

marsh

79. If veg class 3, 13 through 19 and NWI U42, or U43, and soil type 25, put in veg class 44,

coastal upland mixed forest

80. If veg class 3, 13 through 19 and NWI U42, or U43, and soil type 26, put in veg class 26,

lowland maritime forest

81. If veg class 3, 13 through 19 and NWI U42, U43, evergreen PFO, evergreen PFO/PSS,

evergreen PSS, or PFO1/SS3B* and soil type 27, put in veg class 29, wet maritime forest

82. Special Georgetown county rule: If veg class 13 through 19 and NWI U11, U42, U43, or

unknown and soil name Levy, put in veg class 44, coastal upland mixed forest

83. If veg class 3, 6, 9, 20 or 21 and soil type 23, put in veg class 40, beach

84. If veg class 7 or 8 and soil type 23, put in veg class 27, maritime shrub complex

85. If veg class 13 through 19 and soil type 23, put in veg class 44, coastal upland mixed forest

86. If veg class 3 and soil type 22, put in veg class 32, salt and brackish marsh

87. If veg class 22 and soil type 22, put in veg class 32, salt and brackish marsh

88. If veg class 22 and NWI class salt marsh, put in veg class 32, salt and brackish marsh

89. If veg class 13 through 19 or 25 and NWI class E2FO*, put in veg class 29, wet maritime

forest

90. If veg class 3 and NWI E2FO*, put in veg class 32, salt and brackish marsh
Appendices

59

91. If veg class 13, 14, 15, or 25 and soil type 22, put in veg class 36, salt and brackish marsh

92. If veg class 3, 13 through 19, or 25 and NWI U11, U12, U17, or U76 and soil type 27, put in

veg class 44, coastal upland mixed forest

93. If veg class 3, 13, 14, or 15 and NWI U11, U12, U17, or U76 and soil type 8, put in veg class

44, coastal upland mixed forest

Piedmont

94. If veg class 22 and soil type 8, put in veg class 50, rocky shoals

95. If any veg class and soil type 8, put in veg class 1, water

96. If veg class 4 or 25 put in veg class 13, closed canopy evergreen forest/woodland

97. If soil type 18, put in veg class 11, rock outcrop

98. If veg class 14, 15, 16, 17, 18, or 19 and soil type 11 put in veg class 43, narrow stream valley

forest

99. If veg class 14 and NWI class deciduous PFO, put in veg class 45, piedmont bottomland

forest

100. If veg class 14 and soil type 3 or 4, put in veg class 45, piedmont bottomland forest

101. If veg class 15 and NWI class deciduous PFO, put in veg class 45, piedmont bottomland

forest

102. If veg class 15 and soil type 3 or 4, put in veg class 45, piedmont bottomland forest

103. If veg class 16 and NWI class deciduous PFO, put in veg class 45, piedmont bottomland

forest

104. If veg class 16 and soil type 3 or 4, put in veg class 45, piedmont bottomland forest

105. If veg class 17 and NWI class deciduous PFO, put in veg class 45, piedmont bottomland

forest

106. If veg class 17 and soil type 3 or 4, put in veg class 45, piedmont bottomland forest

107. If veg class 18 and NWI class deciduous PFO, put in veg class 45, piedmont bottomland

forest

108. If veg class 18 and soil type 3 or 4, put in veg class 45, piedmont bottomland forest

109. If veg class 19 and NWI class deciduous PFO, put in veg class 45, piedmont bottomland

forest
Appendices

60

110. If veg class 19 and soil type 3 or 4, put in veg class 45, piedmont bottomland forest

111. If veg class 22 and soil type 3 or 4, and NWI class forested wetland, put in veg class 45,

piedmont bottomland forest

112. If veg class 16 or 17 and soil type 16, put in veg class 46, dry basic deciduous

forest/woodland

113. If veg class 16 or 17 and soil type 17, put in veg class 47, mesic basic deciduous

forest/woodland

114. If veg class 18 or 19 and soil type 16, put in veg class dry basic mixed forest/woodland

(there were no pixels of this class in the final map, therefore no number)

115. If veg class 18 or 19 and soil type 17, put in veg class 48, mesic basic mixed

forest/woodland

116. If veg class 5 and soil type 1 or 2 and NWI class planted pine, put in veg class 13, closed

canopy evergreen forest/woodland

117. If veg class 5 and soil type 1 and NWI class deciduous forest, put in veg class 16, dry

deciduous forest/woodland

118. If veg class 5 and soil type 2 and NWI class deciduous forest, put in veg class 17, mesic

deciduous forest/woodland

119. If veg class 5 and soil type 16 and NWI class deciduous forest, put in veg class 46, dry

basic deciduous forest/woodland

120. If veg class 5 and soil type 17 and NWI class deciduous forest, put in veg class 47, mesic

basic deciduous forest/woodland

121. If veg class 5 and soil type 1 and NWI class mixed forest, put in veg class 18, dry mixed

forest/woodland

122. If veg class 5 and soil type 2 and NWI class mixed forest, put in veg class 19, mesic

mixed forest/woodland

123. If veg class 5 and soil type 16 and NWI class mixed forest, put in veg class dry basic

mixed forest

124. If veg class 5 and soil type 17 and NWI class mixed forest, put in veg class 48, mesic

basic mixed forest/woodland

125. If veg class 5 and soil type 1, 2, 16, or 17 and NWI class agriculture, put in veg class 21

cultivated land

126. If veg class 5 and soil type 1, 2, 16 or 17 and NWI class planted pine, put in veg class 13,

closed canopy evergreen forest/woodland

127. If veg class 5, change to veg class 45, piedmont bottomland forest
Appendices

61

Mountains

Module 1 (highest priority – should override any other conflicting modules in Mountain

Rules)

128. If soil type 18 and any veg class, put in veg class 11, rock outcrop

129. If soil type 8 and any veg class, put in veg class 1, fresh water

Module 2 – RSP_Config (should override modules 3-6 in Mountain Rules)

130. If RSP_reclass2 = 20 (valley bottom) and Config = 10 (concave) and soil type 1 or 2 and

veg class 16 or 17, put in veg class 52, deciduous rich/acidic cove forest.

131. If veg class 13, 14, 18, or 19 and RSP_reclass2 = 20 and soil type 1 or 2, put in veg class

54, eastern hemlock ravine forest

Module 3 - Slope (should override modules 4-6 in Mountain Rules)

132. If slope_reclass < 10 (slope >3 degrees) and soil <> water and veg class 1, put in veg

class 17, mesic deciduous forest/woodland

133. If (slope_reclass = 0 (slope >30 degrees)) and veg class 20 or 21 and soil type 1 or 2, put

in veg class 16, dry deciduous forest/woodland

Module 4 – Elev_top (should override modules 5 and 6 in Mountain Rules)

134. If (elevation > 2000 ft and Top = 1 (ridgetop)) and veg class 20 or 21 and soil type 1 or 2,

put in veg class 16, dry deciduous forest/woodland

135. If top = 1 and elevation > 2000 ft and veg class 13, 14, 18, or 19 and soil type 1 or 2, put

in veg class 49, ridgetop/upper slope evergreen woodland

Module 5 – TRMI (should override module 6)

136. If veg class 16 and soil type 1 or 2 and TRMI>=25, put in veg class 17, mesic deciduous

forest/woodland

137. If veg class 17 and soil type 1 or 2 and TRMI<25, put in veg class 16, dry deciduous

forest/woodland

138. If veg class 18 and soil type 1 or 2 and TRMI>=25, put in veg class 19, mesic mixed

forest/woodland

139. If veg class 19 and soil type 1 or 2 and TRMI<25, put in veg class 18, dry mixed

forest/woodland
Appendices

62

Module 6 (should not override any other modules)

140. If veg class 13, 14, 15, 16, 17, 18, or 19 and soil type 3, 4, 5, or 11 put in veg class 51,

montane riparian forest

141. If veg class 13 or 14, put in veg class 53, montane evergreen forest

142. If veg class 28, put in veg class 51, montane riparian forest

Piedmont soil rules:

1. If Worsham, Elbert, Mixed alluvial, or Local alluvial soil and soil wetness class 3, put in soil

type 3

2. If Mixed alluvial or Local alluvial soil and it is modified by the word 'wet' and crosses soil

types 2 and 3, put in soil type 3

3. If Worsham, Elbert, Mixed alluvial, or Local alluvial soil and soil wetness class 2 or 2/3, put

in soil type 11

4. If alfisol and soil wetness class 1, put in soil class 16 (6a), put in dry basic forest types

5. If alfisol and soil wetness class 2, put in soil class 17 (6b), put in mesic basic forest types

6. If levee soil types have wetness class 1 or 2, put in soil type 5

7. If levee soil types have mixed wetness classes, i.e. 2/3, put in soil type 11

8. If levee soil types are wetness class 3, put in soil type 3
Appendices

63

Appendix 5. A Crosswalk of the detailed land cover classes to the final vertebrate

habitat land cover

Number Animal Habitat Class Detailed Class

1 Fresh Water Fresh Water

2 Marine Water Marine Water

3 Marsh/Emergent Wetland Fresh Water Marsh/Emergent

Wetland

Salt and Brackish Marsh

4 Pocosin Pocosin

5 Not used

6 Swamp Swamp/Bottomland Hardwood

Forest

7 Bottomland/Floodplain Forest Floodplain Forest;

Swamp/Bottomland Type 14;

Swamp/Bottomland Type 15;

Swamp/Bottomland Type 16;

Swamp/Bottomland Type17;

Swamp/Bottomland Type 18;

Swamp/Bottomland Type 19;

Narrow Stream Valley Forest;

Piedmont Bottomland Forest;

Montane Riparian Forest

8 Wet Soil Wet Soil;

Mud/Sand Flat;

Spoil Deposit/ Impoundment

9 Wet Scrub/Shrub Thicket Wet Scrub/Shrub Thicket;

Estuarine Salt Flat/Salt Shrub

Thicket

10 Dry Scrub/Shrub Thicket Dry Scrub/Shrub Thicket;

Maritime Shrub Complex

11 Sandy Bare Soil Sandy Bare Soil

12 Open Canopy/Recently Cleared

Forest

Open Canopy/Recently Cleared

Forest

13 Rock Outcrop Rock Outcrop;

Rocky Shoals

14 Aquatic Vegetation Aquatic Vegetation

15 Closed Canopy Evergreen

Forest/Woodland

Closed Canopy Evergreen

Forest/Woodland;

Ridgetop/Upper Slope Evergreen

Woodland;

Montane Evergreen Forest;

Eastern Hemlock Ravine Forest

16 Needle-leaved Evergreen Mixed

Forest/Woodland

Needle-leaved Evergreen Mixed

Forest/Woodland;

17 Pine Woodland Pine Woodland
Appendices

64

18 Dry Deciduous Forest/Woodland Dry Deciduous Forest/Woodland;

Dry Basic Deciduous Forest/

Woodland

19 Mesic Deciduous Forest/Woodland Mesic Deciduous Forest/Woodland;

Mesic Basic Deciduous Forest/

Woodland;

Deciduous Rich/Acidic Cove Forest

20 Dry Mixed Forest/Woodland Dry Mixed Forest/Woodland

21 Mesic Mixed Forest/Woodland Mesic Mixed Forest/Woodland;

Mesic Basic Mixed Forest/

Woodland

22 Grassland/Pasture Grassland/Pasture

23 Cultivated Land Cultivated Land

24 Urban Development Urban Development

25 Urban Residential Urban Residential;

Low Density Residential

26 Not used

27 Wet Evergreen Wet Evergreen;

Wet Pinewoods

28 Maritime Forest Lowland Maritime Forest;

Wet Maritime Forest;

Coastal Upland Mixed Forest

29 Beach Beach;

Intertidal Beach
Appendices

65

Appendix 6. A crosswalk of the detailed land cover to other land covers used in

modeling vertebrates or accuracy assessment

Anderson

Level I

Anderson Level

II Vertebrate Class Label Detail Map Class Label

Water Water Fresh water Fresh water

Water Water Marine water Marine water

Wetland

Nonforested

Wetland Marsh/emergent wetland

Freshwater

Marsh/Emergent Wetland

Wetland

Nonforested

Wetland Marsh/emergent wetland Salt and Brackish Marsh

Wetland

Forested

Wetland Pocosin Pocosin

Wetland

Forested

Wetland Swamp

Swamp/Bottomland

Hardwood Forest

Wetland

Forested

Wetland Bottomland/floodplain forest Floodplain Forest

Wetland

Forested

Wetland Bottomland/floodplain forest Montane Riparian Forest

Wetland

Forested

Wetland Bottomland/floodplain forest

Narrow Stream Valley

Forest

Wetland

Forested

Wetland Bottomland/floodplain forest

Piedmont Bottomland

Forest

Wetland

Forested

Wetland Bottomland/floodplain forest

Swamp/Bottomland Type

14

Wetland

Forested

Wetland Bottomland/floodplain forest

Swamp/Bottomland Type

15

Wetland

Forested

Wetland Bottomland/floodplain forest

Swamp/Bottomland Type

16

Wetland

Forested

Wetland Bottomland/floodplain forest

Swamp/Bottomland Type

17

Wetland

Forested

Wetland Bottomland/floodplain forest

Swamp/Bottomland Type

18

Wetland

Forested

Wetland Bottomland/floodplain forest

Swamp/Bottomland Type

19

Wetland

Nonforested

Wetland Wet soil Mud/Sand Flat

Wetland

Nonforested

Wetland Wet soil

Spoil

Deposit/Impoundment

Wetland

Nonforested

Wetland Wet soil Wet soil

Wetland

Forested

Wetland Wet scrub/shrub thicket

Estuarine Salt Flat/Salt

Shrub Thicket

Wetland

Forested

Wetland Wet scrub/shrub thicket Wet Scrub/Shrub Thicket
Appendices

66

Rangeland

Shrub and Brush

Rangeland Dry scrub/shrub thicket Dry Scrub/Shrub Thicket

Rangeland

Shrub and Brush

Rangeland Dry scrub/shrub thicket Maritime Shrub Complex

Barren Land

Sandy areas

other than

beaches Sandy bare soil Sandy Bare Soil

Forest Evergreen Forest

Open canopy/recently

cleared forest

Open Canopy, Recently

Cleared Forest

Barren Land

Bare Exposed

Rock Rock outcrop Rock Outcrop

Barren Land

Bare Exposed

Rock Rock outcrop Rocky Shoal

Water Water Aquatic vegetation Aquatic Vegetation

Forest Evergreen Forest

Closed canopy evergreen

forest/woodland

Closed Canopy Evergreen

Forest

Forest Evergreen Forest

Closed canopy evergreen

forest/woodland

Eastern Hemlock Ravine

Forest

Forest Evergreen Forest

Closed canopy evergreen

forest/woodland Montane Evergreen Forest

Forest Evergreen Forest

Closed canopy evergreen

forest/woodland

Ridgetop/Upper Slope

Evergreen Woodland

Forest Mixed Forest

Needle-leaved evergreen

mixed forest/woodland

Needle-leaved Evergreen

Mixed Woodland

Forest Evergreen Forest Pine woodland Pine Woodland

Forest

Deciduous

Forest

Dry deciduous

forest/woodland

Dry Basic Deciduous

Forest

Forest

Deciduous

Forest

Dry deciduous

forest/woodland

Dry Deciduous

Forest/Woodland

Forest

Deciduous

Forest

Mesic deciduous

forest/woodland

Deciduous Rich/Acidic

Cove Forest

Forest

Deciduous

Forest

Mesic deciduous

forest/woodland

Mesic Basic Deciduous

Forest

Forest

Deciduous

Forest

Mesic deciduous

forest/woodland

Mesic Deciduous

Forest/Woodland

Forest Mixed Forest Dry mixed forest/woodland

Dry Mixed

Forest/Woodland

Forest Mixed Forest

Mesic mixed

forest/woodland Mesic Basic Mixed Forest

Forest Mixed Forest

Mesic mixed

forest/woodland

Mesic Mixed

Forest/Woodland

Agriculture

Cropland and

Pasture Grassland/pasture Grasslands/Pasture

Agriculture

Cropland and

Pasture Cultivated land Cultivated Land
Appendices

67

Urban or

Built-up Land

Non-residential

Urban Urban development Urban Development

Urban or

Built-up Land

Residential

Urban Urban residential Low Density Residential

Urban or

Built-up Land

Residential

Urban Urban residential Residential Urban

Wetland

Forested

Wetland Wet evergreen Wet Evergreen

Wetland

Forested

Wetland Wet evergreen Wet Pinewoods

Forest Mixed Forest Maritime forest

Coastal Upland Mixed

Forest

Forest Mixed Forest Maritime forest Lowland Maritime Forest

Forest Mixed Forest Maritime forest Wet Maritime Forest

Barren Land Beaches Beach Beach

Barren Land Beaches Beach Intertidal Beach
Appendices

68

Appendix 7. State taxonomic experts

Name Affiliation Taxonomic

Group

Task

Steve Bennett SCDNR Herptiles Both

John Cely SCDNR Birds Both

Sid Gauthreaux Clemson University Birds Both

Richard Montinucci Clemson University Herptiles Both

John Fauth College of Charleston Herptiles Both

Julian Harrison College of Charleston Herptiles Both

Lex Glover SCDNR Birds Both

Wally Dawson University of South Carolina Mammals Both

Mary Bunch SCDNR Mammals Both

Susan Loeb US Forest Service Mammals Both

Ed Pivorun Clemson University Mammals Both

Arch McCallum College of Charleston All taxa Range

Tom Murphy SCDNR Birds Range
Appendices

69

Appendix 8. List of species used in vertebrate modeling for SC-GAP including

species name, scientific name and ITIS Code

lesser siren Siren intermedia 173736

greater siren Siren lacertina 173735

eastern newt Notophthalmus viridescens 173615

two-toed amphiuma Amphiuma means 173609

green salamander Aneides aeneus 173699

flatwoods salamander Ambystoma cingulatum 173596

Mabee's salamander Ambystoma mabeei 173600

spotted salamander Ambystoma maculatum 173590

marbled salamander Ambystoma opacum 173591

mole salamander Ambystoma talpoideum 173604

tiger salamander Ambystoma tigrinum 173592

seepage salamander Desmognathus aeneus 173636

southern dusky salamander Desmognathus auriculatus 173637

dusky salamander Desmognathus fuscus 173633

seal salamander Desmognathus monticola 173640

mountain dusky salamander Desmognathus ochrophaeus 173641

blackbelly salamander Desmognathus quadramaculatus 173642

southern two-lined salamander Eurycea cirrigera 550246

three-lined salamander Eurycea longicauda 173687

dwarf salamander Eurycea quadridigitata 173695

spring salamander Gyrinophilus porphyriticus 173715

four-toed salamander Hemidactylium scutatum 173678

shovelnose salamander Leurognathus marmoratus 173723

Atlantic coast slimy salamander Plethodon chlorobryonis 208282

white-spotted slimy salamander Plethodon cylindraceus 208283

Jordan's salamander Plethodon jordani 173660

South Carolina slimy salamander Plethodon variolatus 208295

webster's salamander Plethodon websteri 173673

southern Appalachian salamanderPlethodon teyahalee 208294

mud salamander Pseudotriton montanus 173682

red salamander Pseudotriton ruber 173680

many-lined salamander Stereochilus marginatus 173647

dwarf waterdog Necturus punctatus 173625

dwarf siren Pseudobranchus striatus 173738

eastern spadefoot Scaphiopus holbrooki 173426

American toad Bufo americanus 173473

Woodhouse's toad Bufo woodhousii 173476

oak toad Bufo quercicus 173479

southern toad Bufo terrestris 173475
Appendices

70

southern cricket frog Acris gryllus 173518

northern cricket frog Acris crepitans 173520

pine barrens treefrog Hyla andersonii 173509

bird-voiced treefrog Hyla avivoca 173511

gray treefrog Hyla versicolor 173503

Cope's gray treefrog Hyla chrysoscelis 173502

green treefrog Hyla cinerea 173505

pine woods treefrog Hyla femoralis 173499

barking treefrog Hyla gratiosa 173508

squirrel treefrog Hyla squirella 173504

spring peeper Pseudacris crucifer 207303

southern chorus frog Pseudacris nigrita 173530

little grass frog Pseudacris ocularis 207286

Brimley's chorus frog Pseudacris brimleyi 173524

upland chorus frog Pseudacris triseriata 173525

ornate chorus frog Pseudacris ornata 173531

gopher frog Rana capito 207016

bullfrog Rana catesbeiana 173441

green frog Rana clamitans 173438

pig frog Rana grylio 173442

southern leopard frog Rana sphenocephala 173436

carpenter frog Rana virgatipes 173437

river frog Rana heckscheri 173453

pickerel frog Rana palustris 173435

wood frog Rana sylvatica 173440

eastern narrowmouth toad Gastrophryne carolinensis 173467

common musk turtle Sternotherus odoratus 173758

eastern mud turtle Kinosternon subrubrum 173763

loggerhead Caretta caretta 173830

green turtle Chelonia mydas 173833

hawksbill Eretmochelys imbricata 173836

Atlantic ridley Lepidochelys kempii 551770

snapping turtle Chelydra serpentina 173752

leatherback Dermochelys coriacea 173843

painted turtle Chrysemys picta 173783

spotted turtle Clemmys guttata 173771

bog turtle Clemmys muhlenbergii 173773

chicken turtle Deirochelys reticularia 173786

diamondback terrapin Malaclemys terrapin 173780

river cooter Pseudemys concinna 173805

Florida cooter Pseudemys floridana 173809

eastern box turtle Terrapene carolina 173776

yellowbelly slider Trachemys scripta 173819

striped mud turtle Kinosternon baurii 173765

gopher tortoise Gopherus polyphemus 173858
Appendices

71

Florida softshell Apalone ferox 208675

spiny softshell Apalone spinifera 208680

American alligator Alligator mississippiensis 551771

slender glass lizard Ophisaurus attenuatus 174106

island glass lizard Ophisaurus compressus 174109

eastern glass lizard Ophisaurus ventralis 174110

mimic glass lizard Ophisaurus mimicus 209006

green anole Anolis carolinensis 173885

Texas horned lizard Phrynosoma cornutum 173938

fence lizard Sceloporus undulatus 173865

coal skink Eumeces anthracinus 173962

five-lined skink Eumeces fasciatus 173959

southeastern five-lined skink Eumeces inexpectatus 173960

broadhead skink Eumeces laticeps 173961

ground skink Scincella lateralis 174008

six-lined racerunner Cnemidophorus sexlineatus 174014

worm snake Carphophis amoenus 174161

scarlet snake Cemophora coccinea 174195

black racer Coluber constrictor 174169

ringneck snake Diadophis punctatus 174158

corn snake Elaphe guttata 174175

rat snake Elaphe obsoleta 174177

mud snake Farancia abacura 174164

rainbow snake Farancia erytrogramma 174166

eastern hognose snake Heterodon platirhinos 563935

southern hognose snake Heterodon simus 174156

mole kingsnake Lampropeltis calligaster 174185

eastern kingsnake Lampropeltis getula 209247

scarlet kingsnake (milk) Lampropeltis triangulum 174187

coachwhip Masticophis flagellum 174238

redbelly water snake Nerodia erythrogaster 174244

banded water snake Nerodia fasciata 174248

brown water snake Nerodia taxispilota 174255

northern water snake Nerodia sipedon 174251

Florida green water snake Nerodia floridana 209382

rough green snake Opheodrys aestivus 174172

pine snake Pituophis melanoleucus 174263

glossy crayfish snake Regina rigida 174123

queen snake Regina septemvittata 174125

pine woods snake Rhadinaea flavilata 174265

black swamp snake Seminatrix pygaea 174273

brown snake Storeria dekayi 174129

redbelly snake Storeria occipitomaculata 174131

southeastern crowned snake Tantilla coronata 174280

eastern ribbon snake Thamnophis sauritus 174134
Appendices

72

eastern garter snake Thamnophis sirtalis 174136

rough earth snake Virginia striatula 174150

smooth earth snake Virginia valeriae 174151

eastern coral snake Micrurus fulvius 174354

copperhead Agkistrodon contortrix 174296

cottonmouth Agkistrodon piscivorus 174299

eastern diamondback rattlesnake Crotalus adamanteus 174309

timber rattlesnake Crotalus horridus 174306

pygmy rattlesnake Sistrurus miliarius 174302

American black duck Anas rubripes 175068

acadian flycatacher Empidonax virescens 178339

American avocet Recurvirostra americana 176721

american bittern Botaurus lentiginosus 174856

American coot Fulica americana 176292

American crow Corvus brachyrhynchos 179731

American goldfinch Carduelis tristis 179236

American kestrel Falco sparverius 175622

American oystercatcher Haematopus palliatus 176472

American redstart Setophaga ruticilla 178979

American robin Turdus migratorius 179759

American wigeon Anas americana 175094

American woodcock Scolopax minor 176580

anhinga Anhinga anhinga 174755

American swallow-tailed kite Elanoides forficatus 175289

bachman's sparrow Aimophila aestivalis 179386

bald eagle Haliaeetus leucocephalus 175420

barn swallow Hirundo rustica 178448

black-and-white warbler Mniotilta varia 178844

black-bellied plover Pluvialis squatarola 176567

black-crowned night-heron Nycticorax nycticorax 174832

barred owl Strix varia 177921

belted kingfisher Ceryle alcyon 178119

blue-gray gnatcatcher Polioptila caerulea 179853

brown-headed cowbird Molothrus ater 179112

brown-headed nuthatch Sitta pusilla 178785

blackburnian warbler Dendroica fusca 178904

blue grosbeak Guiraca caerulea 179145

blue jay Cyanocitta cristata 179680

black scoter Melanitta nigra 175171

black skimmer Rynchops niger 554447

black vulture Coragyps atratus 175272

barn owl Tyto alba 177851

black-necked stilt Himantopus mexicanus 176726

bonaparte's gull Larus philadelphia 176839

brant Branta bernicla 175011
Appendices

73

brewer's blackbird Euphagus cyanocephalus 179094

brown creeper Certhia americana 178803

brown pelican Pelecanus occidentalis 174685

brown thrasher Toxostoma rufum 178627

black-throated blue warbler Dendroica caerulescens 178888

boat-tailed grackle Quiscalus major 179108

black-throated green warbler Dendroica virens 178898

bufflehead Bucephala albeola 175145

broad-winged hawk Buteo platypterus 175365

blue-winged teal Anas discors 175086

blue-winged warbler Vermivora pinus 178853

Carolina chickadee Parus carolinensis 178709

cattle egret Bubulcus ibis 174803

Canada goose Branta canadensis 174999

canvasback Aythya valisineria 175129

Carolina wren Thryothorus ludovicianus 178581

caspian tern Sterna caspia 176924

Canada warbler Wilsonia canadensis 178977

cedar waxwing Bombycilla cedrorum 178532

chipping sparrow Spizella passerina 179435

chimney swift Chaetura pelagica 178001

clapper rail Rallus longirostris 176209

cliff swallow Hirundo pyrrhonota 178453

common ground-dove Columbina passerina 177152

common goldeneye Bucephala clangula 175141

common grackle Quiscalus quiscula 179104

cooper's hawk Accipiter cooperii 175309

common loon Gavia immer 174469

common merganser Mergus merganser 175185

common moorhen Gallinula chloropus 176284

common nighthawk Chordeiles minor 177979

common raven Corvus corax 179725

common snipe Gallinago gallinago 176700

common tern Sterna hirundo 176888

common yellowthroat Geothlypis trichas 178944

chestnut-sided warbler Dendroica pensylvanica 178911

chuck-will's-widow Caprimulgus carolinensis 177960

double-crested cormorant Phalacrocorax auritus 174717

dark-eyed junco Junco hyemalis 179410

dickcissel Spiza americana 179165

downy woodpecker Picoides pubescens 178259

dunlin Calidris alpina 176661

eastern bluebird Sialia sialis 179801

eastern kingbird Tyrannus tyrannus 178279

eastern meadowlark Sturnella magna 179034
Appendices

74

eastern phoebe Sayornis phoebe 178329

eastern screech-owl Otus asio 177856

eastern towhee Pipilo erythrophthalmus 179276

eastern wood-pewee Contopus virens 178359

European starling Sturnus vulgaris 179637

evening grosbeak Coccothraustes vespertinus 179173

fish crow Corvus ossifragus 179737

field sparrow Spizella pusilla 179443

fox sparrow Passerella iliaca 179464

Forster's tern Sterna forsteri 176887

gadwall Anas strepera 175073

great black-backed gull Larus marinus 176815

great blue heron Ardea herodias 174773

gull-billed tern Sterna nilotica 176926

great crested flycatcher Myiarchus crinitus 178309

golden-crowned kinglet Regulus satrapa 179865

great horned owl Bubo virginianus 177884

glossy ibis Plegadis falcinellus 174924

gray catbird Dumetella carolinensis 178625

great egret Casmerodius albus 174810

green heron Butorides virescens 174793

greater scaup Aythya marila 175130

grasshopper sparrow Ammodramus savannarum 179333

greater yellowlegs Tringa melanoleuca 176619

green-winged teal Anas crecca 175081

hairy woodpecker Picoides villosus 178262

herring gull Larus argentatus 176824

Henslow's sparrow Ammodramus henslowii 179340

hermit thrush Catharus guttatus 179779

house finch Carpodacus mexicanus 179191

horned grebe Podiceps auritus 174482

horned lark Eremophila alpestris 554256

hooded merganser Lophodytes cucullatus 175183

house sparrow Passer domesticus 179628

hooded warbler Wilsonia citrina 178972

house wren Troglodytes aedon 178541

indigo bunting Passerina cyanea 179150

kentucky warbler Oporornis formosus 178937

killdeer Charadrius vociferus 176520

king rail Rallus elegans 176207

laughing gull Larus atricilla 176837

lapland longspur Calcarius lapponicus 179526

long-billed curlew Numenius americanus 176593

long-billed dowitcher Limnodromus scolopaceus 176679

little blue heron Egretta caerulea 174827
Appendices

75

le conte's sparrow Ammodramus leconteii 179345

least bittern Ixobrychus exilis 174846

least sandpiper Calidris minutilla 176656

lesser scaup Aythya affinis 175134

least tern Sterna antillarum 176923

lesser yellowlegs Tringa flavipes 176620

lincoln's sparrow Melospiza lincolnii 179484

loggerhead shrike Lanius ludovicianus 178515

louisiana waterthrush Seiurus motacilla 178935

marbled godwit Limosa fedoa 176686

mallard Anas platyrhynchos 175063

marsh wren Cistothorus palustris 178608

merlin Falco columbarius 175613

Mississippi kite Ictinia mississippiensis 554268

mourning dove Zenaida macroura 177125

mottled duck Anas fulvigula 175070

northern bobwhite Colinus virginianus 175863

northern cardinal Cardinalis cardinalis 179124

northern flicker Colaptes auratus 178154

northern harrier Circus cyaneus 175430

northern mockingbird Mimus polyglottos 178620

baltimore oriole Icterus galbula 179083

northern parula Parula americana 178868

northern pintail Anas acuta 175074

northern rough-winged swallow Stelgidopteryx serripennis 178443

northern shoveler Anas clypeata 175096

northern saw-whet owl Aegolius acadicus 177942

orange-crowned warbler Vermivora celata 178856

oldsquaw Clangula hyemalis 175147

orchard oriole Icterus spurius 179064

osprey Pandion haliaetus 175590

ovenbird Seiurus aurocapillus 178927

painted bunting Passerina ciris 179156

palm warbler Dendroica palmarum 178921

pied-billed grebe Podilymbus podiceps 174505

peregrine falcon Falco peregrinus 175604

Piping plover Charadrius melodus 176507

pine siskin Carduelis pinus 179233

pine warbler Dendroica pinus 178914

pileated woodpecker Dryocopus pileatus 178166

Prairie warbler Dendroica discolor 178918

prothonotary warbler Protonotaria citrea 178846

Purple finch Carpodacus purpureus 179186

Purple gallinule Porphyrula martinica 176280

Purple martin Progne subis 178464
Appendices

76

Purple sandpiper Calidris maritima 176646

ring-billed gull Larus delawarensis 176830

red-breasted merganser Mergus serrator 175187

red-breasted nuthatch Sitta canadensis 178784

red-bellied woodpecker Melanerpes carolinus 178195

ruby-crowned kinglet Regulus calendula 179870

red-cockaded woodpecker Picoides borealis 178257

red crossbill Loxia curvirostra 179259

redhead Aythya americana 175125

red knot Calidris canutus 176642

red-eyed vireo Vireo olivaceus 179021

red-headed woodpecker Melanerpes erythrocephalus 178186

ring-necked duck Aythya collaris 175128

rock dove Columba livia 177071

royal tern Sterna maxima 176922

red-shouldered hawk Buteo lineatus 175359

red-tailed hawk Buteo jamaicensis 175350

ruby-throated hummingbird Archilochus colubris 178032

rusty blackbird Euphagus carolinus 179091

ruddy duck Oxyura jamaicensis 175175

Ruffed grouse Bonasa umbellus 175790

Ruddy turnstone Arenaria interpres 176571

red-winged blackbird Agelaius phoeniceus 179045

sanderling Calidris alba 176669

sandwich tern Sterna sandvicensis 176927

savannah sparrow Passerculus sandwichensis 179314

short-billed dowitcher Limnodromus griseus 176675

scarlet tanager Piranga olivacea 179883

short-eared owl Asio flammeus 177935

semipalmated plover Charadrius semipalmatus 176506

seaside sparrow Ammodramus maritimus 179346

sedge wren Cistothorus platensis 178605

Snow bunting Plectrophenax nivalis 179532

snowy egret Egretta thula 174813

Snow goose Chen caerulescens 175038

sora Porzana carolina 176242

song sparrow Melospiza melodia 179492

Sooty tern Sterna fuscata 176894

blue-headed vireo Vireo solitarius 179010

spotted sandpiper Actitis macularia 176612

Sharp-shinned hawk Accipiter striatus 175304

surf scoter Melanitta perspicillata 175170

summer tanager Piranga rubra 179888

swamp sparrow Melospiza georgiana 179488

Swainson's warbler Limnothlypis swainsonii 178848
Appendices

77

tree swallow Tachycineta bicolor 178431

tricolored heron Egretta tricolor 174826

tundra swan Cygnus columbianus 174987

Tufted titmouse Parus bicolor 178738

turkey vulture Cathartes aura 175265

vesper sparrow Pooecetes gramineus 179366

virginia rail Rallus limicola 176221

Water pipit Anthus spinoletta 178489

White-breasted nuthatch Sitta carolinensis 178775

White-crowned sparrow Zonotrichia leucophrys 179455

White-eyed vireo Vireo griseus 178991

Worm-eating warbler Helmitheros vermivorus 178850

White ibis Eudocimus albus 174930

whimbrel Numenius phaeopus 176599

willet Catoptrophorus semipalmatus 176638

Wilson's plover Charadrius wilsonia 176517

wild turkey Meleagris gallopavo 176136

winter wren Troglodytes troglodytes 178547

Wood duck Aix sponsa 175122

Wood stork Mycteria americana 174897

Wood thrush Hylocichla mustelina 179777

whip-poor-will Caprimulgus vociferus 177961

White-throated sparrow Zonotrichia albicollis 179462

White-winged scoter Melanitta fusca 175163

Yellow-breasted chat Icteria virens 178964

Yellow-billed cuckoo Coccyzus americanus 177831

Yellow-bellied sapsucker Sphyrapicus varius 178202

Yellow-crowned night-heron Nyctanassa violacea 174842

Yellow rail Coturnicops noveboracensis 176259

Yellow-rumped warbler Dendroica coronata 178891

Yellow-throated vireo Vireo flavifrons 179009

Yellow-throated warbler Dendroica dominica 178905

Yellow warbler Dendroica petechia 178878

virginia opossum Didelphis virginiana 179921

masked shrew Sorex cinereus 179929

southeastern shrew Sorex longirostris 179936

smoky shrew Sorex fumeus 179943

pygmy shrew Sorex hoyi 179946

northern short-tailed shrew Blarina brevicauda 179967

Carolina short-tailed shrew Blarina carolinensis 179968

least shrew Cryptotis parva 179971

hairy-tailed mole Parascalops breweri 179977

eastern mole Scalopus aquaticus 179979

star-nosed mole Condylura cristata 179964

little brown myotis Myotis lucifugus 179988
Appendices

78

southeastern myotis Myotis austroriparius 179993

eastern small-footed myotis Myotis leibii 179999

northern long-eared myotis Myotis septentrionalis 180000

Silver-haired bat Lasionycteris noctivagans 180014

eastern pipistrelle Pipistrellus subflavus 180025

big brown bat Eptesicus fuscus 180008

eastern red bat Lasiurus borealis 180016

Seminole bat Lasiurus seminolus 180020

Hoary bat Lasiurus cinereus 180017

northern yellow bat Lasiurus intermedius 180019

evening bat Nycticeius humeralis 180022

Rafinesque's big-eared bat Corynorhinus rafinesquii 555664

Brazilian free-tailed bat Tadarida brasiliensis 180088

nine-banded armadillo Dasypus novemcinctus 180103

marsh rabbit Sylvilagus palustris 180120

eastern cottontail Sylvilagus floridanus 180124

new england cottontail Sylvilagus transitionalis 180127

swamp rabbit Sylvilagus aquaticus 180121

eastern chipmunk Tamias striatus 180207

woodchuck Marmota monax 180137

eastern gray squirrel Sciurus carolinensis 180175

eastern fox squirrel Sciurus niger 180172

red squirrel Tamiasciurus hudsonicus 180166

southern flying squirrel Glaucomys volans 180170

American beaver Castor canadensis 180212

Marsh rice rat Oryzomys palustris 180336

eastern harvest mouse Reithrodontomys humulis 180342

deer mouse Peromyscus maniculatus 180276

oldfield mouse Peromyscus polionotus 180290

white-footed mouse Peromyscus leucopus 180278

Cotton mouse Peromyscus gossypinus 180279

golden mouse Ochrotomys nuttalli 180379

Hispid cotton rat Sigmodon hispidus 180349

eastern woodrat Neotoma floridana 180372

southern red-backed vole Clethrionomys gapperi 180294

meadow vole Microtus pennsylvanicus 180297

woodland vole Microtus pinetorum 180314

muskrat Ondatra zibethicus 180318

black rat Rattus rattus 180362

norway rat Rattus norvegicus 180363

House mouse Mus musculus 180366

meadow jumping mouse Zapus hudsonius 180386

woodland jumping mouse Napaeozapus insignis 180390

coyote Canis latrans 180599

red fox Vulpes vulpes 180604
Appendices

79

common gray fox Urocyon cinereoargenteus 180609

black bear Ursus americanus 180544

common raccoon Procyon lotor 180575

long-tailed weasel Mustela frenata 180556

mink Mustela vison 180553

eastern spotted skunk Spilogale putorius 180570

striped skunk Mephitis mephitis 180562

northern river otter Lutra canadensis 180572

bobcat Lynx rufus 180582

feral pig Sus scrofa 180722

white-tailed deer Odocoileus virginianus 180699
Appendix 9. Vertebrate habitat land cover types by protection type and manager. FWS Area = Fish and Wildlife Service

owned property, FS Area = Forest Service owned property, OF Area = Other federally-owned property, DNR Area = SC

Department of Natural Resources owned property, PRT = SC Parks, Recreation and Tourism owned property, OS = Other

state owned property, PC = Protected Conservation property and OC = Other conservation property.

COVER

TYPE

AREA STATUS STATUS

AREA

STATUS

PCT

FWS

AREA

FS

AREA

OF

AREA

DNR

AREA

PRT

AREA

OS

AREA

PC

AREA

OC

AREA

1 2361 1 59 2.58 48 0 0 7 0 0 0 4

1 2361 2 4 0.25 0 0 0 3 1 0 0 0

1 2361 3 56 2.41 0 21 14 0 5 7 9 0

1 2361 4 2242 94.96 0 0 54 0 0 511 0 0

2 1032 1 135 13.28 123 0 0 12 0 0 0 0

2 1032 2 12 1.26 0 0 0 11 0 0 1 0

2 1032 3 13 1.36 0 0 0 0 2 0 11 0

2 1032 4 872 84.50 0 0 4 0 0 2 1 0

3 2073 1 275 13.31 172 0 0 95 0 0 8 0

3 2073 2 151 7.33 0 0 0 116 0 2 33 0

3 2073 3 203 9.94 0 8 0 0 15 4 176 0

3 2073 4 1444 69.66 0 0 26 0 0 23 6 0

4 365 1 20 6.03 0 3 0 17 0 0 0 0

4 365 2 2 1.10 0 0 0 0 0 0 2 0

4 365 3 43 12.33 0 35 4 0 0 0 4 0

4 365 4 300 82.19 0 0 0 0 0 0 0 0

6 3075 1 165 5.43 45 19 42 33 0 0 26 0

6 3075 2 56 1.89 0 0 0 5 8 43 0 0

6 3075 3 200 6.60 0 90 81 0 3 7 19 0

6 3075 4 2654 86.31 0 0 1 0 0 38 0 0

7 8185 1 130 1.62 28 27 13 42 0 0 19 1

7 8185 2 48 0.61 0 0 0 25 7 8 8 0

7 8185 3 435 5.35 0 264 88 0 10 20 53 0

7 8185 4 7572 92.51 0 0 3 0 0 91 0 0

8 225 1 9 4.44 7 0 0 2 0 0 0 0

8 225 2 2 1.33 0 0 0 0 0 0 2 0

8 225 3 5 2.67 0 1 0 0 0 0 4 0
8 225

4 209 92.8900400600

9 23871 33 1.42 1163 120010

9 238729 0.4200060210

9 23873 94 4.060 639002 200

9 23874 2251 94.3000400 1400

10 52731 13 0.3031060012

10 52732 10 0.2100073000

10 52733 209 3.980 101 78099 120

10 52734 5041 95.6000700 1000

11 6310 1.5900000000

11 6320 0.0000000000

11 6330 3.1700000000

11 634 63 100.0000100300

12 59601 25 0.42 1020 130000

12 59602 19 0.34000 100630

12 59603 310 5.220 241 1304 36 160

12 59604 5606 94.0600100 2300

13 1610 0.0000000000

13 1620 6.2500000000

13 1630 0.0000000000

13 164 16 100.0000000000

14 3410 0.0000000000

14 342 11 32.35000 110000

14 3433 8.8200000030

14 344 20 58.8200000100

15 109281 119 1.12 50 370 210029

15 109282 72 0.68050 47 11360

15 109283 1268 11.630 731 3820 33 73 490

15 109284 9469 86.6500 1000 2100

16 79371 107 1.37 8720 170010

16 79372 30 0.39000 1334 100

16 79373 722 9.120 371 2140 22 79 360

16 79374 7078 89.1800700 4300

17 7131 14 2.10 111020000

17 71325 0.8400020210
17 713

3 101 14.450 48 1702 21 130

17 7134 593 83.1700000600

18 6951 52 7.778807001 28

18 6952 44 6.47020 29 13000

18 6953 58 8.630 28 2104410

18 6954 541 77.8400000100

19 79091 58 0.754 120 13002 27

19 79092 69 0.90050 32 30200

19 79093 366 4.680 241 600 25 3640

19 79094 7416 93.7700600 1600

20 29112 0.6900020000

20 29125 1.7200032000

20 29139 3.4401602000

20 2914 275 94.5000000000

21 89211 0.2200010000

21 89225 0.6700023000

21 8923 32 3.8106 1603700

21 8924 854 95.7400000000

22 219716 0.3240020000

22 219721 0.0500010000

22 21973 48 2.2302 3700540

22 21974 2142 97.5000200600

23 123101 10 0.0940030003

23 1231024 0.0400040000

23 123103 102 0.850 11 4002 21 280

23 123104 12194 99.0600 2500 4310

24 130810 0.0800000000

24 130820 0.0000000000

24 13083 30 2.4503 2500110

24 13084 1278 97.7100 2300 1300

25 244610 0.0000000000

25 244620 0.0000000000

25 24463 17 0.8201 1401100

25 24464 2429 99.3000 1400 1600

27 14551 32 2.27860 180000
27 1455

29 0.7600052020

27 14553 98 6.870 535045 310

27 14554 1316 90.4500000500

28 6551 22 3.51700 140010

28 6552 15 2.44000 140010

28 6553 49 7.79000060 430

28 6554 569 86.8700800340

29 3014 16.6720020000

29 3021 3.3300010000

29 3030 3.3300000000

29 304 25 83.3300000000
Appendix 10. Vertebrate species protection status by land owner and protection type. FWS Area = Fish and Wildlife Service

owned property, FS Area = Forest Service owned property, OF Area = Other federally-owned property, DNR Area = SC

Department of Natural Resources owned property, PRT = SC Parks, Recreation and Tourism owned property, OS = Other

state owned property, PC = Protected Conservation property and OC = Other conservation property.

ITIS

CODE

AREA STATUS STATUS

AREA

STATUS

PCT

FWS

AREA

FS

AREA

OF

AREA

DNR

AREA

PRT

AREA

OS

AREA

PC

AREA

OC

AREA

173426 63482 1 834 1.31 317 120 60 204 0 0 58 75

173426 63482 2 407 0.64 0 13 0 206 85 70 33 0

173426 63482 3 3656 5.76 0 1925 1013 0 128 292 297 1

173426 63482 4 58585 92.29 0 0 142 2 0 821 7 0

173441 49663 1 935 1.88 419 76 56 257 0 0 62 65

173441 49663 2 467 0.94 0 8 0 263 70 67 59 0

173441 49663 3 2424 4.88 0 1129 578 0 104 216 396 1

173441 49663 4 45837 92.30 0 0 150 2 0 799 13 0

173503 39383 1 443 1.12 137 60 56 104 0 0 49 37

173503 39383 2 201 0.51 0 6 0 76 52 57 10 0

173503 39383 3 1275 3.24 0 638 355 0 53 107 122 0

173503 39383 4 37464 95.13 0 0 110 1 0 724 2 0

173509 3856 1 104 2.70 98 0 4 2 0 0 0 0

173509 3856 2 1 0.03 0 0 0 1 0 0 0 0

173509 3856 3 277 7.18 0 87 90 0 16 83 1 0

173509 3856 4 3474 90.09 0 0 1 0 0 2 0 0

173615 25182 1 540 2.14 154 73 56 143 0 0 52 62

173615 25182 2 281 1.12 0 8 0 131 68 60 14 0

173615 25182 3 1375 5.46 0 737 311 0 65 107 154 1

173615 25182 4 22986 91.28 0 0 76 0 0 673 5 0

173752 27101 1 729 2.69 305 63 56 214 0 0 58 33

173752 27101 2 364 1.34 0 6 0 201 52 60 45 0

173752 27101 3 1405 5.18 0 668 304 0 65 90 277 1

173752 27101 4 24603 90.78 0 0 94 0 0 691 7 0

173776 49870 1 457 0.92 162 57 13 132 0 0 29 64

173776 49870 2 255 0.51 0 7 0 137 66 21 24 0

173776 49870 3 2223 4.46 0 1114 603 0 92 210 203 1

173776 49870 4 46935 94.11 0 0 78 1 0 240 7 0
173858 375

10 0.0000000000

173858 37529 2.4000090000

173858 37531 0.2700000010

173858 3754 365 97.3300000100

173885 478101 664 1.39 219 85 59 18400 56 61

173885 478102 332 0.69070 150 73 72 300

173885 478103 2703 5.650 1497 6480 97 232 2281

173885 478104 44111 92.2600 8010 28060

173961 224951 476 2.12 106 72 55 13400 51 58

173961 224952 257 1.14070 113 64 60 130

173961 224953 1301 5.780 714 2910 57 99 1400

173961 224954 20461 90.9600 2100 15950

174106 164761 150 0.91 10750 320033

174106 164762 54 0.33000 2796 120

174106 164763 1102 6.690 526 3550 38 116 670

174106 164764 15170 92.0700 1900 7000

174134 361001 736 2.04 324 57 59 22800 599

174134 361002 318 0.88010 190 20 60 470

174134 361003 1279 3.540 501 2940 47 71 3651

174134 361004 33767 93.5400 14020 750 130

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4 18022 91.3200 1400 14800

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3 379 2.140 38 560 25 33 2270

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2 372 0.800 130 179 82 67 310

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1 292 0.76 127 260 69008 62

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4 1887 72.2400 2400980

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3 199 6.680300 184 1740

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2 239 2.97000 1480 49 420

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4 1191 67.4000 2000 1080

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4 5046 95.4600910 2270

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3 1847 3.990 1026 4400 64 163 1540

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2 254 1.91000 145 13 54 420

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1 686 1.81 254 82 59 17400 55 62

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4 14857 88.2700 3300 67670

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173699 2701 44 16.300907001 27

173699 2702 49 18.15040 29 16000
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3 34 12.590 32002000

173699 2704 143 52.9600000000

173723 3411 49 14.370908001 31

173723 3412 53 15.54050 30 18000

173723 3413 37 10.850 33004000

173723 3414 202 59.2400000000

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173738 42172 114 2.70000 1130010

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173763 330583 1129 3.420 435 2840 39 69 3011

173763 330584 30959 93.6500 12810 73380

173765 46431 131 2.82 7000 530080

173765 46432 86 1.85000 850100

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173809 137033 849 6.200 330 1880 30 34 2661

173809 137034 11931 87.0700 3000 66570

173830 26111 393 15.05 27400 1100090
173830 2611

2 161 6.17000 12500 360

173830 26113 171 6.550200 184 1470

173830 26114 1886 72.2300 2400980

173836 26951 397 14.73 27600 1120090

173836 26952 163 6.05000 12700 360

173836 26953 182 6.750400 184 1560

173836 26954 1953 72.4700 2500980

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173960 224774 20581 91.5600 2900 9350

173962 58011 0.1700010000

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174110 217371 91 0.42 2950 530040

174110 217372 88 0.40000 5549 200

174110 217373 1227 5.640 347 6300 31 41 1780

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174125 60682 20 0.330103 16000

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1 171 1.29 12340 410030

174156 132302 55 0.42000 3426 130

174156 132303 897 6.780 273 3780 32 103 1110

174156 132304 12107 91.5100 3900 8150

174166 137031 648 4.73 295 48 56 19400 550

174166 137032 275 2.01000 160 15 56 440

174166 137033 849 6.200 330 1880 30 34 2661

174166 137034 11931 87.0700 3000 66570

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174238 364671 295 0.81 178 510 620040

174238 364672 103 0.28070 6449 190

174238 364673 2393 6.560 1150 7870 67 199 1900

174238 364674 33676 92.3500 6510 13260

174248 138581 596 4.30 300 480 19300 550

174248 138582 276 1.99000 162 14 56 440

174248 138583 832 6.000 349 1440 29 40 2691

174248 138584 12154 87.7000 3000 67170

174251 97431 67 0.690 140 18002 33

174251 97432 83 0.85060 36 38300

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174251 97434 9146 93.8700 5900900

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174273 69453 484 6.970 136 990 22 15 2120
174273 6945

4 5709 82.2000 2700 57560

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174482 33952 18 0.53000 151020

174482 33953 72 2.120 21 15077 220

174482 33954 3108 91.5500 6000 51410

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174505 32532 156 4.80000 11903 340

174505 32533 227 6.980 11 110 174 1840

174505 32534 2542 78.1400 2600 50660

174717 45111 470 10.42 34500 1160090

174717 45112 169 3.75000 13103 350

174717 45113 250 5.540 12 140 207 1970

174717 45114 3622 80.2900 3100 53780

174826 26161 304 11.62 19400 1020080

174826 26162 156 5.96000 11903 340

174826 26163 215 8.220 1100 164 1840

174826 26164 1941 74.2000 2600 27360

174832 65461 500 7.64 265 20 43 13600 360

174832 65462 214 3.27000 1259 46 340

174832 65463 438 6.690 102 950 22 15 2040

174832 65464 5394 82.4000 2700 57260

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174897 17404 1153 66.2600 1900860

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174930 66183 478 7.220 137 1000 19 10 2120

174930 66184 5392 81.4700 2900 57860

174987 99961 336 3.36 22400 1030090

174987 99962 160 1.60000 12303 340
174987 9996

3 300 3.000 15 370 19 17 2120

174987 99964 9200 92.0400 4110 52880

175011 9951 139 13.97 12400 150000

175011 9952 15 1.51000 130020

175011 9953 14 1.41000020 120

175011 9954 827 83.1200400120

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175063 294483 1076 3.650 431 2320 40 63 3091

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175070 15642 16 1.02000 140020

175070 15643 28 1.79040020 220

175070 15644 1353 86.5100500 25810

175074 59731 446 7.47 32000 1170090

175074 59732 172 2.88000 13403 350

175074 59733 263 4.400 1500 184 2260

175074 59734 5092 85.2500 4410 29190

175094 167461 347 2.07 22510 1050097

175094 167462 164 0.98000 12523 340

175094 167463 367 2.190 41 560 23 33 2140

175094 167464 15868 94.7600 10610 57870

175129 54691 473 8.65 34500 1150094

175129 54692 172 3.14000 13213 360

175129 54693 277 5.060 30 150 23 11 1980

175129 54694 4547 83.1400 8500 53880

175130 38021 442 11.63 31800 1150090

175130 38022 169 4.45000 13103 350

175130 38023 233 6.130 1200 186 1970

175130 38024 2958 77.8000 3100 27680

175141 17901 59 3.30 480070004

175141 179024 0.2200031000

175141 17903 47 2.630 16 11046 100

175141 17904 1680 93.8500100 48600

175147 9671 135 13.96 12200 130000
175147 967

2 12 1.24000 110010

175147 9673 13 1.34000020 110

175147 9674 807 83.4500400010

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175350 388701 302 0.78 132 270 72009 62

175350 388702 199 0.51070 110 57 11 140

175350 388703 1717 4.420 812 4940 80 187 1440

175350 388704 36652 94.2900 6010 13260

175365 173581 283 1.63 39 22 55 9400 16 57

175365 173582 178 1.03070 67 65 3810

175365 173583 730 4.210 324 2750 48 7670

175365 173584 16167 93.1400800 11600

175622 152201 32 0.21 201080003

175622 152202 12 0.0800081210

175622 152203 256 1.680 62 9605 47 460

175622 152204 14920 98.0300 2800 5610

176207 21091 275 13.04 17200 950080

176207 21092 163 7.73000 12802 330

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176207 21094 1465 69.4600 2500 2560

176209 16011 251 15.68 14900 940080

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176259 118662 155 1.31000 12002 330

176259 118663 335 2.820 13 770 17 20 2080

176259 118664 11089 93.4500 5110 6170
176292 4471

1 335 7.49 22000 1020094

176292 44712 170 3.80000 13213 340

176292 44713 264 5.900 29 150 20 11 1890

176292 44714 3702 82.8000 8000 53660

176472 27341 402 14.70 28000 1130090

176472 27342 163 5.96000 12500 380

176472 27343 175 6.400200 184 1510

176472 27344 1994 72.9300 2500 1190

176507 17661 266 15.06 15800 1000080

176507 17662 150 8.49000 11400 360

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176507 17664 1189 67.3300 2000 1080

176567 17661 266 15.06 15800 1000080

176567 17662 150 8.49000 11400 360

176567 17663 161 9.120200 163 1400

176567 17664 1189 67.3300 2000 1080

176593 17661 266 15.06 15800 1000080

176593 17662 150 8.49000 11400 360

176593 17663 161 9.120200 163 1400

176593 17664 1189 67.3300 2000 1080

176612 47521 351 7.39 23000 1080094

176612 47522 163 3.43000 12213 370

176612 47523 272 5.720 32 160 22 11 1910

176612 47524 3966 83.4600 8600 54580

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176619 22542 155 6.88000 11800 370

176619 22543 194 8.610300 174 1700

176619 22544 1628 72.2300 3010 1390

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176638 18382 153 8.32000 11600 370

176638 18383 166 9.030200 164 1440

176638 18384 1246 67.7900 2000 1180

176646 4215 11.9020030000

176646 4221 2.3800010000

176646 4230 0.0000000000
176646 42

4 36 85.7100100000

176661 17661 266 15.06 15800 1000080

176661 17662 150 8.49000 11400 360

176661 17663 161 9.120200 163 1400

176661 17664 1189 67.3300 2000 1080

176675 17661 266 15.06 15800 1000080

176675 17662 150 8.49000 11400 360

176675 17663 161 9.120200 163 1400

176675 17664 1189 67.3300 2000 1080

176686 17661 266 15.06 15800 1000080

176686 17662 150 8.49000 11400 360

176686 17663 161 9.120200 163 1400

176686 17664 1189 67.3300 2000 1080

176721 17961 268 14.92 15900 1010080

176721 17962 150 8.35000 11400 360

176721 17963 165 9.190200 154 1440

176721 17964 1213 67.5400 1900 1170

176815 31401 405 12.90 28200 1140090

176815 31402 166 5.29000 12800 380

176815 31403 200 6.370400 184 1740

176815 31404 2369 75.4500 4110 15 100

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176837 30992 169 5.45000 12902 380

176837 30993 185 5.97050024 1740

176837 30994 2391 77.1500710 23 100

176888 27341 402 14.70 28000 1130090

176888 27342 163 5.96000 12500 380

176888 27343 175 6.400200 184 1510

176888 27344 1994 72.9300 2500 1190

176922 28061 410 14.61 28400 1170090

176922 28062 166 5.92000 12800 380

176922 28063 181 6.450300 184 1560

176922 28064 2049 73.0200 2500 1290

176924 27341 402 14.70 28000 1130090

176924 27342 163 5.96000 12500 380
176924 2734

3 175 6.400200 184 1510

176924 27344 1994 72.9300 2500 1190

176927 27341 402 14.70 28000 1130090

176927 27342 163 5.96000 12500 380

176927 27343 175 6.400200 184 1510

176927 27344 1994 72.9300 2500 1190

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177831 238022 250 1.05070 105 67 59 120

177831 238023 1257 5.280 731 2890 52 80 1050

177831 238024 21822 91.6800 1600 16310

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177884 183203 328 1.790 65 1110 13 49 900

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177935 17251 259 15.01 15200 990080

177935 17252 147 8.52000 11300 340

177935 17253 160 9.280200 163 1390

177935 17254 1159 67.1900 2000870

177961 88511 85 0.96 12 120 30004 27

177961 88512 92 1.04050 51 32310

177961 88513 426 4.810 242 650 35 37 470

177961 88514 8248 93.1900 1500 1940

178032 283611 534 1.88 115 84 59 16500 53 58

178032 283612 278 0.98070 125 69 61 160

178032 283613 1428 5.040 787 3090 64 87 1801

178032 283614 26121 92.1000 4010 18850

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178166 346303 2162 6.240 1144 5320 88 188 2091

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178202 405873 2471 6.090 1384 5450 92 224 2251

178202 405874 37141 91.5100 4610 24750

178279 271041 98 0.36 4150 420046
178279 27104

2 60 0.22000 405960

178279 271043 826 3.050 403 1870 26 93 1170

178279 271044 26120 96.3700 4710 9360

178359 205401 523 2.55 285 250 14300 14 56

178359 205402 321 1.56070 202 53 12 470

178359 205403 1507 7.340 705 3370 77 156 2320

178359 205404 18189 88.5500 4100 9270

178431 69471 346 4.98 20960 1220090

178431 69472 170 2.45000 13004 360

178431 69473 349 5.020 7800 174 2500

178431 69474 6082 87.5500 5110 30380

178464 169511 18 0.11 100050003

178464 1695127 0.0400061000

178464 169513 173 1.020 16 9403 27 330

178464 169514 16753 98.8300 4200 6510

178532 494001 714 1.45 248 115 56 17400 54 67

178532 494002 373 0.760 120 174 82 72 330

178532 494003 3594 7.280 2063 9220 116 292 2010

178532 494004 44719 90.5200 4710 26010

178605 126421 346 2.74 20283 11900 113

178605 126422 182 1.44000 13447 370

178605 126423 663 5.240 223 1430 29 42 2260

178605 126424 11451 90.5800 4000 6170

178709 508291 762 1.50 257 121 56 20100 57 70

178709 508292 391 0.770 130 190 87 68 330

178709 508293 3647 7.180 1978 9930 132 271 2721

178709 508294 46029 90.5600 6210 25560

178784 246671 283 1.15 158 480 640049

178784 246672 134 0.54050 75 22 11 210

178784 246673 2269 9.200 1219 6570 70 190 1330

178784 246674 21981 89.1100 3410 9200

178844 220711 464 2.10 95 71 55 13300 52 58

178844 220712 256 1.16070 113 67 57 120

178844 220713 1209 5.480 667 2790 58 78 1270

178844 220714 20142 91.2600 2000 15350
178853 346

1 51 14.74080 10002 31

178853 3462 57 16.47020 35 20000

178853 3463 41 11.850 34007000

178853 3464 197 56.9400000000

178878610 0.0000000000

178878620 0.0000000000

178878630 0.0000000000

178878646 100.0000000000

178898 70841 222 3.13 22 750 3000 38 57

178898 70842 166 2.34070 81 33 26 190

178898 70843 843 11.900 66800 20 35 1191

178898 70844 5853 82.6200500 5000

178905 314711 606 1.93 190 80 56 16900 53 58

178905 314712 298 0.95070 132 72 62 250

178905 314713 2038 6.480 1093 5000 84 165 1951

178905 314714 28529 90.6500 2910 20150

178911 5931 96 16.190 170 17003 59

178911 5932 105 17.71070 63 35000

178911 5933 75 12.650 6500 10000

178911 5934 317 53.4600000000

178927 89241 163 1.83 95 110 27002 28

178927 89242 80 0.90020 45 194 100

178927 89243 797 8.930 402 2430 29 86 370

178927 89244 7884 88.3500700 4400

178935 65201 120 1.84 212 55 3000 111

178935 65202 23 0.350002 16500

178935 65203 284 4.360 71 1690 12 2750

178935 65204 6093 93.4500100 9700

178964 315451 546 1.73 197 67 59 16600 525

178964 315452 192 0.61000 78 27 61 260

178964 315453 1923 6.100 984 5210 59 132 2261

178964 315454 28884 91.5600 3510 21260

178979 149711 266 1.78 76 13 55 86007 29

178979 149712 167 1.12050 55 45 5570

178979 149713 549 3.670 245 1660 37 65 360
178979 14971

4 13989 93.4400900 9600

179010 431111 744 1.73 253 119 56 19400 55 67

179010 431112 379 0.880 120 183 83 68 330

179010 431113 3416 7.920 1876 8990 121 260 2591

179010 431114 38572 89.4700 3910 22860

179045 345051 685 1.99 276 63 59 22500 575

179045 345052 294 0.85000 166 21 59 480

179045 345053 1265 3.670 532 2840 39 69 3392

179045 345054 32261 93.5000 7720 23880

179104 610111 742 1.22 262 116 56 18200 56 70

179104 610112 384 0.630 130 189 84 67 310

179104 610113 3578 5.860 1860 10140 125 289 2891

179104 610114 56307 92.2900 10620 30770

179108 54431 314 5.77 18800 11600 100

179108 54432 174 3.20000 13403 370

179108 54433 302 5.550 1700 244 2570

179108 54434 4653 85.4900 8110 60 130

179145 318621 292 0.92 130 280 68007 59

179145 318622 198 0.62070 102 57 16 160

179145 318623 1872 5.880 1040 4660 76 202 880

179145 318624 29500 92.5900 2700 10900

179165 164301 17 0.1090050003

179165 1643026 0.0400060000

179165 164303 166 1.010 12 9303 24 340

179165 164304 16241 98.8500 4200 6310

179186 508291 762 1.50 257 121 56 20100 57 70

179186 508292 391 0.770 130 190 87 68 330

179186 508293 3647 7.180 1978 9930 132 271 2721

179186 508294 46029 90.5600 6210 25560

179236 546821 636 1.16 211 83 59 16400 55 64

179236 546822 307 0.56070 134 75 66 250

179236 546823 2474 4.520 1268 6940 91 220 2010

179236 546824 51265 93.7500 7510 28920

179259 394715 0.1300050000

179259 39472 14 0.3500067100
179259 3947

3 340 8.610 169 1210 15 2960

179259 39474 3588 90.9000300900

179333 308918 0.2640040000

179333 308926 0.1900033000

179333 30893 83 2.6909 5404 1240

179333 30894 2992 96.8600300700

179340 83031 45 0.54 2630 160000

179340 83032 26 0.31000 130850

179340 83033 456 5.490 289 6707 60 330

179340 83034 7776 93.6500300 3600

179346 16011 251 15.68 14900 940080

179346 16012 144 8.99000 11100 330

179346 16013 155 9.680200 153 1350

179346 16014 1051 65.6500 1900860

179435 265901 199 0.75 116 120 36003 32

179435 265902 94 0.35020 54 206 120

179435 265903 1074 4.040 466 3540 35 135 840

179435 265904 25223 94.8600 5110 11710

179464 286231 184 0.64 118 113 420046

179464 286232 63 0.22000 3389 130

179464 286233 1241 4.340 597 3610 37 135 1110

179464 286234 27135 94.8000 4510 11710

179628 182641 19 0.10 100060003

179628 1826427 0.0400061000

179628 182643 206 1.130 19 11904 29 350

179628 182644 18032 98.7300 6610 7910

179725 5701 96 16.840 170 17003 59

179725 5702 105 18.42070 63 35000

179725 5703 64 11.230 55009000

179725 5704 305 53.5100000000

179731 571671 944 1.65 394 84 56 28600 63 61

179731 571672 486 0.85070 268 74 73 640

179731 571673 2833 4.960 1406 6250 111 255 4342

179731 571674 52904 92.5400 10020 320 130

179779 398431 672 1.69 216 89 59 19100 56 61
179779 39843

2 327 0.82070 148 76 67 290

179779 398433 2450 6.150 1307 6050 97 198 2421

179779 398434 36394 91.3400 4110 23260

179865 331721 589 1.78 194 75 55 15400 53 58

179865 331722 295 0.89070 130 70 64 240

179865 331723 2062 6.220 1090 5270 84 182 1781

179865 331724 30226 91.1200 4300 21950

179883 68621 104 1.520 210 23003 57

179883 68622 126 1.84070 66 50300

179883 68623 299 4.360 215 120 34 3800

179883 68624 6333 92.2900500700

179921 758501 1117 1.47 460 129 59 32800 68 73

179921 758502 595 0.780 130 341 90 79 720

179921 758503 4423 5.830 2306 10960 156 341 5222

179921 758504 69715 91.9100 12330 371 141

179943 9611 142 14.780 520 22003 65

179943 9612 134 13.940 120 84 38000

179943 9613 234 24.350 21800 14020

179943 9614 451 46.9300000000

179964 110921 551 4.97 279 460 16500 556

179964 110922 255 2.30010 1534 55 420

179964 110923 748 6.740 303 1510 25 11 2571

179964 110924 9538 85.9900 3000 55970

179971 424791 499 1.17 305 143 15700 146

179971 424792 252 0.59000 168 15 18 510

179971 424793 1872 4.410 858 4500 65 190 3090

179971 424794 39856 93.8300 9120 18780

179977 5401 94 17.410 170 15003 59

179977 5402 98 18.15070 60 31000

179977 5403 62 11.480 55007000

179977 5404 286 52.9600000000

180000 9731 144 14.800 530 21003 67

180000 9732 133 13.670 120 85 36000

180000 9733 237 24.360 22300 12020

180000 9734 459 47.1700000000
180014 32834

1 534 1.63 215 62 56 11700 50 34

180014 328342 227 0.69060 85 55 61 200

180014 328343 1849 5.630 997 4910 73 162 1260

180014 328344 30224 92.0500 8900 71810

180025 233901 301 1.29 160 250 50005 61

180025 233902 163 0.70070 83 50 10 130

180025 233903 1369 5.850 716 3690 63 154 670

180025 233904 21557 92.1600 10800 60900

180088 386314 0.1020020000

180088 386321 0.0300010000

180088 38633 93 2.4106 7801260

180088 38634 3765 97.4600 3800 2900

180120 184321 527 2.86 256 27 45 16100 380

180120 184322 258 1.40000 1609 51 380

180120 184323 782 4.240 205 1850 31 51 3091

180120 184324 16865 91.5000 6610 126 120

180127 10411 148 14.220 530 23004 68

180127 10412 138 13.260 120 86 40000

180127 10413 250 24.020 23300 15020

180127 10414 505 48.5100000000

180166 9371 141 15.050 520 21003 65

180166 9372 129 13.770 120 82 35000

180166 9373 232 24.760 21800 12020

180166 9374 435 46.4200000000

180207 134621 163 1.210 570 33005 68

180207 134622 170 1.260 120 91 63400

180207 134623 857 6.370 619 1150 54 6720

180207 134624 12272 91.1600 1100 1900

180276 9451 141 14.920 520 21003 65

180276 9452 131 13.860 120 82 37000

180276 9453 232 24.550 21800 12020

180276 9454 441 46.6700000000

180279 373361 790 2.12 376 77 59 22000 580

180279 373362 392 1.05070 243 19 67 560

180279 373363 2418 6.480 1185 6150 75 199 3431
180279 37336

4 33736 90.3600 6110 25180

180294 9671 142 14.680 520 22003 65

180294 9672 136 14.060 120 85 39000

180294 9673 234 24.200 21800 14020

180294 9674 455 47.0500000000

180342 222171 332 1.49 18650 12500 106

180342 222172 174 0.78000 13053 360

180342 222173 615 2.770 158 1630 28 41 2250

180342 222174 21096 94.9500 6110 8480

180363 160671 11 0.0750030003

180363 1606725 0.0300041000

180363 160673 156 0.970 17 8104 24 300

180363 160674 15895 98.9300 6310 7310

180372 200831 422 2.10 77 71 59 10500 52 58

180372 200832 237 1.18070 111 41 64 140

180372 200833 1092 5.440 602 2990 29 46 1160

180372 200834 18332 91.2800 2600 19200

180390 9611 142 14.780 520 22003 65

180390 9612 134 13.940 120 84 38000

180390 9613 234 24.350 21800 14020

180390 9614 451 46.9300000000

180553 420321 779 1.85 355 85 56 20700 58 18

180553 420322 356 0.85060 208 33 60 490

180553 420323 2368 5.630 1139 6630 75 147 3431

180553 420324 38529 91.6700 12420 73680

180570 8015 6.2500030002

180570 802 12 15.0000075000

180570 8038 10.0004004000

180570 804 55 68.7500000000

180572 97481 510 5.23 270 20 43 13800 354

180572 97482 229 2.35000 139 10 46 340

180572 97483 519 5.320 122 1340 25 24 2140

180572 97484 8490 87.0900 8400 58260

180599 657331 559 0.85 215 93 13 13500 30 73

180599 657332 330 0.500 130 181 77 28 310
180599 65733

3 3713 5.650 2053 9940 124 319 2230

180599 657334 61131 93.0000 8120 28020

180699 743301 1083 1.46 452 123 59 30800 68 73

180699 743302 584 0.790 130 336 87 78 700

180699 743303 4319 5.810 2251 10900 152 335 4901

180699 743304 68344 91.9500 12130 362 141

207286 277641 698 2.51 303 57 59 22200 570

207286 277642 307 1.11000 1846 64 530

207286 277643 1492 5.370 645 3910 38 48 3691

207286 277644 25267 91.0100 6710 76590

208283 187691 278 1.48 1565 13 32005 67

208283 187692 161 0.86000 93 64400

208283 187693 1130 6.020 676 1790 73 19930

208283 187694 17200 91.6400 1200 1800

208295 86061 86 1.00 23 320 1600 150

208295 86062 81 0.94000 810000

208295 86063 889 10.330 69490 152 1681

208295 86064 7550 87.7300 3200 7850

208680 126611 470 3.71 249 28 14 14500 286

208680 126612 222 1.75010 1589 12 420

208680 126613 705 5.570 294 1040 31 32 2431

208680 126614 11264 88.9700 8300 62770

209006 31021 42 1.35930 280020

209006 31022 43 1.39000 280690

209006 31023 360 11.610 25700 101 920

209006 31024 2657 85.6500 1500 3150

550246 223971 459 2.05 197 540 11500 31 62

550246 223972 173 0.77000 42 52 57 220

550246 223973 1417 6.330 816 3680 32 92 1090

550246 223974 20348 90.8500 1500 59800

551771 147951 771 5.21 404 48 56 20800 550

551771 147952 289 1.95000 172 15 56 460

551771 147953 847 5.720 337 1880 27 15 2791

551771 147954 12888 87.1100 3500 66790

554447 27341 402 14.70 28000 1130090
554447 2734

2 163 5.96000 12500 380

554447 27343 175 6.400200 184 1510

554447 27344 1994 72.9300 2500 1190

563935 240801 346 1.44 148 39 13 9200 24 30

563935 240802 162 0.67020 91 30 17 220

563935 240803 1497 6.220 728 4190 54 142 1540

563935 240804 22075 91.6700 3900 17350
137

Appendix 11. All management areas identified by SC-GAP as management status 1

or 2 along with the manager and source of the information used to categorize the

area

Manager Name Acres Status Source

CU Clemson Experimental Forest-Causey 798.94 2 1

CU Clemson Experimental Forest-Liberty 1 135.45 2 1

CU Clemson Experimental Forest-Liberty 2 225.56 2 1

CU Clemson Experimental Forest-Liberty 3 207.14 2 1

NAS Francis Beidler Forest 11019.62 1 2

NPS Congaree Swamp National Monument 22200.00 1 3

SCDNR Aiken Gopher Tortoise Heritage Preserve 1394.90 1 4

SCDNR Ashmore Heritage Preserve 1039.52 1 4

SCDNR Bald Rock Heritage Preserve 163.02 1 4

SCDNR Bay Point Shoal Heritage Preserve 1.00 1 4

SCDNR Bear Branch Heritage Preserve 172.25 1 4

SCDNR Belvue Springs Heritage Preserve 28.41 1 4

SCDNR Bennett's Bay Heritage Preserve 679.83 1 4

SCDNR Bird Key-Stono Heritage Preserve 20.00 1 4

SCDNR Bird Mountain Tract 43.70 1 4

SCDNR Blackwell Bunched Arrowhead Heritage

Preserve

15.80 1 4

SCDNR Brasstown Creek Heritage Preserve 461.30 1 4

SCDNR Bunched Arrowhead Heritage Preserve 158.66 1 4

SCDNR Buzzard Roost Heritage Preserve 501.00 1 4

SCDNR Buzzard's Island Heritage Preserve 0.74 1 4

SCDNR Capers Island Heritage Preserve 1203.00 1 4

SCDNR Cartwheel Bay Heritage Preserve 568.00 1 4

SCDNR Cathedral Bay Heritage Preserve 58.00 1 4

SCDNR Chandler Heritage Preserve 252.68 1 4

SCDNR Chestnut Ridge Heritage Preserve 1669.23 1 4

SCDNR Clear Creek Heritage Preserve 18.83 1 4

SCDNR Congaree Bluffs Heritage Preserve 201.07 1 4

SCDNR Congaree Creek Heritage Preserve 627.00 1 4

SCDNR Crab Bank Heritage Preserve 22.00 1 4

SCDNR Craig Pond Tract 264.49 1 4

SCDNR Crosby Oxypolis Heritage Preserve 32.00 1 4

SCDNR Crowfield Plantation 11.78 1 4

SCDNR Daw's Island Heritage Preserve 1866.00 1 4

SCDNR Deveaux Bank Heritage Preserve 15.00 1 4

SCDNR Dungannon Plantation Heritage Preserve 643.13 1 4

SCDNR Eastatoe Creek Heritage Preserve 373.55 1 4

SCDNR Fort Frederick Heritage Preserve 3.04 1 4

SCDNR Fort Lamar Heritage Preserve 14.00 1 4

SCDNR Forty Acre Rock Heritage Preserve 1540.87 1 4

SCDNR Glassy Mountain Heritage Preserve 64.88 1 4
Appendices

138

SCDNR Gopher Branch Heritage Preserve 10.09 1 4

SCDNR Great Pee Dee River Heritage Preserve 2725.00 1 4

SCDNR Green's Shell Enclosure Heritage

Preserve

3.00 1 4

SCDNR Henderson Heritage Preserve 417.07 1 4

SCDNR Janet Harrison Highpond Heritage

Preserve

30.00 1 4

SCDNR Joiner Bank Heritage Preserve 1.00 1 4

SCDNR Laurel Fork Heritage Preserve 1000.00 1 4

SCDNR Lewis Ocean Bay Heritage Preserve 9392.63 1 4

SCDNR Little Pee Dee River Heritage Preserve 10025.47 1 4

SCDNR Little Pee Dee State Park Bay Heritage

Preserve

301.00 1 4

SCDNR Long Branch Bay Heritage Preserve 50.90 1 4

SCDNR Longleaf Pine Heritage Preserve 842.75 1 4

SCDNR Lynchburg Savanna Heritage Preserve 290.82 1 4

SCDNR Nipper Creek Heritage Preserve 90.00 1 4

SCDNR North Santee Bar Heritage Preserve 1.00 1 4

SCDNR Old Island Heritage Preserve 400.00 1 4

SCDNR Otter Island 2094.12 1 4

SCDNR Pacolet River Heritage Preserve 277.57 1 4

SCDNR Peter's Creek Heritage Preserve 160.51 1 4

SCDNR Rock Hill Blackjacks Heritage Preserve 289.78 1 4

SCDNR Savage Bay Heritage Preserve 110.33 1 4

SCDNR Savannah River Bluffs Heritage Preserve 83.84 1 4

SCDNR Segars-McKinnon Heritage Preserve 795.20 1 4

SCDNR Shealy's Pond Heritage Preserve 62.00 1 4

SCDNR Snee Farm Heritage Preserve 12.00 1 4

SCDNR St. Helena Sound Heritage Preserve 10302.00 1 4

SCDNR Stevens Creek Heritage Preserve 433.77 1 4

SCDNR Stoney Creek Battery Heritage Preserve 0.50 1 4

SCDNR Tillman Sand Ridge Heritage Preserve 963.06 1 4

SCDNR Tom Yawkey Wildlife Center Heritage

Preserve

18085.50 1 4

SCDNR Victoria Bluff Heritage Preserve 1111.00 1 4

SCDNR Waccamaw River Heritage Preserve 5346.65 1 4

SCDNR Watson-Cooper Heritage Preserve 1707.41 1 4

SCDNR Woods Bay State Park Heritage Preserve 470.00 1 4

SCDNR Angelus Tract 52.89 2 5

SCDNR Bagwell Property/Wildlife Habitat 85.65 2 5

SCDNR Bear Island Wildlife Management Area 12031.83 2 5

SCDNR Cohen Campbell Fish Hatchery/Styx

Compound

297.98 2 5

SCDNR Crossroads Tract 358.91 2 5

SCDNR Donnelley Wildlife Management Area 331.84 2 5

SCDNR Draper Tract/Wildlife Management Area 806.40 2 5
Appendices

139

SCDNR Gray Court Wildlife Management Area 61.00 2 5

SCDNR Hitchcock Woods 1934.03 2 5

SCDNR James Ross Wildlife Preserve 304.61 2 5

SCDNR James W. Webb Wildlife Center 5863.00 2 5

SCDNR Jocassee Gorges 31903.28 2 5

SCDNR Lake George Warren 1159.00 2 5

SCDNR Lake John D. Long/Wildlife Lake 216.20 2 5

SCDNR Mason Wildlife Preserve 1994.74 2 5

SCDNR McBee Wildlife Management Area 778.27 2 5

SCDNR McConnells Tract/Wildlife Management

(Turkey Creek)

249.15 2 5

SCDNR Palachucola Wildlife Management Area 7104.11 2 5

SCDNR Samworth Wildlife Management Area 1586.26 2 5

SCDNR Santee Coastal Reserve 23499.00 2 5

SCDNR Santee Delta Wildlife Management Area 1791.50 2 5

SCDNR Trask Natural Area 84.00 2 5

SCDNR Turtle Island Waterfowl Management Area 1667.00 2 5

SCDNR Victoria Bluff Natural Area 156.92 2 5

SCDNR Waddell Mariculture Center 144.00 2 5

SCPRT Aiken State Natural Area 1067.00 2 6

SCPRT Croft State Natural Area 7053.76 2 6

SCPRT Keowee-Toxaway State Natural Area 1000.00 2 6

SCPRT Lee State Natural Area 2839.00 2 6

SCPRT Mountain Bridge Wilderness Area

(Caesars Head & Jones Gap)

10813.00 2 6

SCPRT Woods Bay State Natural Area 1541.00 2 6

SCDOT Black River Mitigation Site 1711.51 2 7

SCDOT Bucksport & Oliver Mitigation Site 8029.00 2 7

SCDOT Huspah Creek Mitigation Site 370.29 2 7

SCDOT Sandy Island Mitigation Site 9164.74 2 7

SCDOT Vacant-Wetlands Mitigation (Rd. S-114) 10.60 2 7

TNC Bailey Island Preserve 417.10 1 8

TNC Bamberg County Cobane 58.49 1 8

TNC Black River Swamp 1311.39 1 8

TNC Campbell and Lockhart Tracts (Blue Wall

Preserve)

137.00 1 8

TNC Cliffs At Glassy Preserve 602.00 1 8

TNC Greenville Watershed 19000.00 1 8

TNC Johns Island Maritime Forest 96.63 1 8

TNC Landrum Watershed 585.00 1 8

TNC Peachtree Rock Preserve 305.00 1 8

TNC Penell Tract (Blue Wall Preserve) 452.00 1 8

TNC Red Bluff Marsh 2198.79 1 8

TNC Tryon Watershed 640.00 1 8

USFWS ACE Basin NWR Combahee Unit 326.00 1 9

USFWS ACE Basin NWR Combahee Unit 988.00 1 9
Appendices

140

USFWS ACE Basin NWR Combahee Unit 6677.22 1 9

USFWS ACE National Wildlife Refuge-Bonnie 1421.00 1 9

USFWS ACE National Wildlife Refuge-Combahee 1819.00 1 9

USFWS ACE National Wildlife Refuge-Grove 1955.00 1 9

USFWS ACE National Wildlife Refuge-Jehossee 4500.00 1 9

USFWS Cape Romain National Wildlife Refuge 34044.94 1 9

USFWS Carolina Sandhills National Wildlife

Refuge

45348.43 1 9

USFWS Pickney Island National Wildlife Refuge 4052.70 1 9

USFWS Santee National Wildlife Refuge 4413.28 1 9

USFWS Savannah National Wildlife Refuge 14334.47 1 9

USFWS Tybee NWR 129.57 1 9

USFWS Waccamaw National Wildlife Refuge 134.00 1 9

USFS FMNF Botanical and Zoological Area 1 10

USFS FMNF Roadless Area 1 10

USFS FMNF Wilderness Area 1 10

USFS SNF Andrew Pickens Ranger District

Botanical and Zoological Area

1 10

USFS SNF Andrew Pickens Ranger District

Roadless Area

1 10

USFS SNF Andrew Pickens Ranger District

Wilderness Area

1 10

USFS SNF Enoree Ranger District Botanical and

Zoological Area

1 10

USFS SNF Long Cane Ranger District Botanical

and Zoological Area

1 10

USFS SNF Wild and Scenic Corridor along the

Chattooga River

2 10

1. CU - Clemson University, Contact Person, Stassen Thompson, (864) 656-1287

2. NAS – National Audubon Society, Contact Person, Norman Brunswick, (843)

462-2150

3. NPS – National Park Service, National GAP Handbook

4. SCDNR – South Carolina Department of Natural Resources, Heritage Trust Fund

Act, South Carolina Statutes, Contact Johnny Stowe, (803) 734-4037

5. SCDNR- Wildlife Management Areas, Contact Tim Ivey, (803) 734-4116

6. SCPRT – South Carolina Parks, Recreation and Tourism, State Parks, A New

Vision for the 21st Century, SCPRT, 1998.

7. SCDOT – South Carolina Department of Transportation, Contact Person, Blanche

Sproul, (803) 737-1395

8. TNC – The Nature Conservancy, South Carolina Chapter, (803) 254-9049

9. USFWS – U.S. Fish and Wildlife Service, David Gordon, (843) 727-4707

USFS – U.S. Forest Service Strategic Plan, 2000
Appendices

141

Businesses and Non-government Organizations:

The following are some examples of applications of GAP data by the private sector:

• The Wyoming Natural Heritage Program (a private non-government organization)

transformed the endangered and sensitive species database into a spatially referenced

digital geographic information system using the GAP digital base map and other GAP

spatial data.

• Hughes Corp. is experimenting with the Utah and Nevada GAP digital base maps,

simulating images to aid the development of new space-based remote sensing

devices.

• The Nature Conservancy used the Wyoming GAP data to develop a map of

ecoregions of Wyoming.

• Weyerhaeuser Corp. is using the Arkansas GAP data in managing their lands in

Arkansas.

• IBM Corp. is funding a project at the University of California-Santa Barbara that, in

part, uses GAP data in the development of visualization software.

• NM-GAP vegetation data is being used for an environmental assessment of a

proposed spaceport, a state/private venture.

County and City Planning:

Some other examples of the use of GAP by local governments are:

• CA-GAP biological data were combined with the Southern California Association of

Governments (SCAG) land ownership data to show which ownerships and

jurisdictions were needed for joint conservation planning and management of a

particular natural community or species, maximizing efficiency and minimizing the

potential for yet another conservation crisis.

• In California, county and city planners of several jurisdictions, wildlife agencies,

developers of the 4S Ranch property, and the state Natural Communities

Conservation Planning program used the GAP regional data, as well as more detailed

information, to conserve 1,640 acres of habitat within a 2,900-acre planned

development.

• Day-to-day county planning operations in Piute, Grande, and Washington counties,

Utah.

• County planners in Piute County, Utah, used GAP data to optimize the siting of a

proposed sawmill for aspen with respect to the distribution of aspen stands.

• Missoula County, Montana, used the GAP land cover map of the area as a base map

for its comprehensive long-range plan.

• Snohomish County, Washington, used the GAP land cover map in meeting state

requirements for a growth management plan.

• The City of Bainbridge Island, Washington, used GAP data to assist them in

development of a watershed planning project.

State Uses:

The following are some examples of uses of GAP data by state agencies.

Appendix 12. List of example GAP applications
Appendices

142

• The GAP database of species habitats was used by the Tennessee Wildlife Resources

Agency (TWRA) to update its book "Species in Need of Management."

• Images of land cover derived from GAP TM data are used by TWRA for locating

particular habitat types. Information on the locations of these habitat types is provided

by TWRA to the public for a wide variety of public service functions, from education

to cooperative resource management.

• Early GAP data developed by TWRA were used to help identify an extremely

important area of the state with high biodiversity that was subsequently purchased by

the state for conservation.

• Preliminary findings from GAP were used by TWRA to develop three resource

management initiatives.

• The Tennessee GAP project, which is being carried out primarily by TWRA, is the

foundation of a multi-agency, long-term biodiversity program for Tennessee.

• GAP data have been used by the Tennessee Forestry Stewardship Program to help

develop a district program for nine conservation planning districts, outlining Best

Management Practices (BMPs) for biological conservation on private lands.

• GAP data are being used extensively by TWRA in the preparation of project

proposals to the North American Waterfowl Conservation Program. These proposals

require that biodiversity issues are addressed in specific detail. The use of GAP data

on occurrence of land cover types and terrestrial vertebrates has made this possible.

• The Wyoming Department of Fish and Game used GAP data to assist them in

transforming the Wildlife Observation System database into a spatially referenced

geographic information system.

• The Utah Division of Wildlife Resources and the Bear River Water Conservancy

District used the Utah GAP land cover map in a resource management assessment for

mitigating conflicts between a proposed groundwater withdrawal project and the

maintenance of an elk calving area in the Uinta Mountains.

• The Utah Division of Wildlife Resources, the Rocky Mountain Elk Foundation, and

Sheik Safari International used the Utah GAP land cover map to identify critical elk

habitat. The environmental profile of these areas was then used to identify other

similar areas for elk habitat enhancement.

• The Utah Division of Wildlife Resources used the Utah GAP land cover map for a

rapid ecological assessment of the Echo Henefer Wildlife Management Area.

• The Washington Department of Fish and Wildlife used GAP data to develop a

breeding bird atlas and an atlas of mammals of Washington State.

• The Washington Department of Fish and Wildlife uses GAP data to operate an

integrated landscape management program.

• The Washington Department of Fish and Wildlife uses GAP data from Eastern

Washington to assist with an innovative program that brings the forest products

industry, state agency biologists, non-government organizations, and tribal biologists

together in the field to jointly determine the appropriate management practices for

any particular site of concern (Timber, Fish & Wildlife Program).

• The Idaho Department of Fish and Game used GAP data to evaluate the impact from

expanded military training activities on public lands in Southern Idaho.

• The Idaho Department of Fish and Game uses GAP data for regional planning efforts

on a regular basis.
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Statewide Planning:

Biodiversity planning programs or projects are now under way in Arizona, California,

Colorado, Maine, Missouri, Nevada, Oregon, and Tennessee. It is likely that similar

efforts will develop in other states. These activities were the subject of the State

Biodiversity Programs meeting discussed on page _ in this report. In some cases, these

efforts grew out of the state GAP project, however, in most cases, the GAP data are being

used to meet a previously defined need. In all cases, GAP data are central to their

development and operations. The goals of each of these programs or projects are

presented briefly below.

Federal Agency Applications:

Some examples of applications of GAP data by federal agencies follow:

• GAP data are being supplied to all military installations in the Great Basin ecoregion

for integrated management of the natural resources. These installations constitute a

very large amount of land area. Much of it is of high value for native species.

• The Ouachita National Forest used the Arkansas GAP data to help them develop an

ecosystem management plan.

• The Wyoming GAP data were used by NASA to calibrate a model that predicts

vegetation types based on climate and soil variables.

• The potential contributions to biodiversity conservation of four different options

proposed for new wilderness designation in Idaho were quantified by the Idaho

Cooperative Fish and Wildlife Research Unit in cooperation with the Park Studies

Unit.

• The potential contributions to biodiversity conservation of four different options

proposed for new national park designation in Idaho were quantified by the Idaho

Cooperative Park Studies Unit.

• The U.S. Forest Service in Booneville, Arkansas, used the Arkansas GAP data land

cover maps in a 3-dimensional presentation to provide the public with a visual

representation of the region and to enhance the public's involvement with the

National Forest planning process.

• The U.S. Fish and Wildlife Service regularly uses the GAP data for Southern

California for habitat evaluation and management.

• The U.S. Forest Service, Bureau of Land Management, and National Park Service are

using the GAP data for a wide variety of natural resource management operations in

Utah. For example, the entire Utah GAP database is directly linked with existing

National Park Service databases for use by National Parks.

• The Bureau of Land Management uses the Wyoming GAP data for managing the

Buffalo Resource Area.

• The U.S. Forest Service used the Utah GAP data to help assist them in evaluating

human-induced impacts to forested lands surrounding ski resorts in central Utah.

• The U.S. Fish and Wildlife Service in Delaware used GAP data to help identify

potential habitat for the federally endangered Delmarva fox squirrel. These maps

were displayed and served as a catalyst for bringing together people with a stake in

the issue.
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144

• The U.S. Fish and Wildlife Service used the Indiana GAP data as part of a biological

assessment for the base closure of the Jefferson Proving Grounds and its conversion

to a National Wildlife Refuge. This 58,000-acre installation has restricted human

access due to unexploded ordinance and contains some of the highest-quality natural

habitat in Indiana.

• The U.S. Fish and Wildlife Service in Louisiana used GAP data to avoid conflict over

the designation of critical habitat of the federally endangered Louisiana black bear.

• The NOAA Coastal Marine Sanctuary in Washington State uses GAP data for an

educational display.

• In Washington and New Mexico, digital land cover maps have been distributed to all

National Forests.

• The U.S. Natural Resources Conservation Service (NRCS) in New Mexico is

using a GAP clustered imagery as a base for their land cover mapping activities.

• The Department of Defense is funding the development of an electronic

environmental information system for the Mojave ecoregion, which would use GAP

data as a foundation or base layer of information. The system will link 29 DoD

installations to a common source of environmental information.