by
Florence Chan
A Thesis
Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Natural Resources and Environmental Sciences in the
School of Graduate Studies
Alabama Agricultural and Mechanical University
Normal, Alabama 35762
December 2007 i

CERTIFICATE OF APPROVAL
Submitted by FLORENCE CHAN in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE specializing in NATURAL
RESOURCES AND ENVIRONMENTAL SCIENCES.
Accepted on behalf of the Faculty of the Graduate School by the Thesis
Committee:
_____________________________
_____________________________
_____________________________
_____________________________
_____________________________ Major Advisor
_____________________________ Dean of the Graduate School
_____________________________ Date ii

Copyright by
FLORENCE CHAN
2007 iii

A
DEDICATION
To my parents Frank Chan and Loretta Lui, and my grandparents, for the endless love and support. And to my siblings Aaron and Maggie for keeping it real.
Thank you. iv

BSTRACT AND KEYWORDS
ABSTRACT AND KEYWORDS
AN INVENTORY OF HERPETOFAUNA ON STATE CONSERVATION LANDS
IN THE CUMBERLAND PLATEAU OF NORTHERN ALABAMA
Chan, Florence, M.S., Alabama A&M University, 2007.
Thesis Advisor: Yong Wang
Recent purchases of the Walls of Jericho and Forever Wild tracts of the southern Cumberland Plateau of northern Alabama by the state of Alabama called for the need of a biological inventory for establishing a baseline dataset that could be used for implementing a biodiversity monitoring program and for adaptive biodiversity and conservation management on these properties and adjacent areas. I performed an inventory of herpetofaunal community and investigated the relationship between landscape and habitat features and herpetofaunal species richness and abundance at the James D. Martin Skyline
Wildlife Management Area, the Walls of Jericho, and Forever Wild Land Trust in the Cumberland Plateau of northern Alabama during spring and fall of 2005 and
2006. The study area was stratified based on landform and solar exposure. A total of 176 points were random selected. Line transect surveys, drift fences with pit-fall traps, pond survey, and targeted and opportunistic search were used to quantify herpetofaunal community. Habitat variables were collected by the collaborators from Auburn University and the Land Division of Alabama
Department of Conservation and Natural Resources. Landscape variables at v

each survey point were generated using remote sensing images, digital elevation models, Alabama Gap Analysis Project, and other spatial reference databases.
A total of 2,307 animals were detected during the line transect surveys, including
26 amphibians and 21 reptiles species. Over 84 percent individuals belonged to amphibians. Slimy Salamanders (Plethodon glutinosis) were the most abundant species, making up 75 percent of all amphibian encounters. The species richness and abundance were greater in spring than in fall due to the increased movement, foraging, and mating activity of herpetofauna in spring. The detection probability on the transect line was the highest for stream salamanders (30%) and the lowest for terrestrial and spring amphibians (both 20%). Based on the distance sampling estimations, there were 38,245,000 amphibians (396/km
2
) and
8,426,600 reptiles (87/km
2
) in the study area. Soil pH ranged from 3.6 to 7.4 and was positively correlated with herpetofaunal species richness and abundance.
The amount of canopy cover, soil condition, amount of disturbance, and distance to the stream were the significant predictors of herpetofaunal species richness abundance. The land type composition analysis suggested that with the increase of the amount of deciduous forests, the herpetofaunal species richness and abundance increased, while developed space and agricultural land had a negative impact to herpetofaunal community. Recommendations for the herpetofaunal monitoring program and conservation at the study site and adjacent areas were provided. vi

KEY WORDS: Alabama, Cumberland, herpetofauna, inventory, landscape, habitat, Walls of Jericho, Geographic Information Systems (GIS). vii

TABLE OF CONTENTS
CERTIFICATE OF APPROVAL
..................................................................................ii
DEDICATION
.........................................................................................................iv
ABSTRACT AND KEYWORDS
................................................................................. v
LIST OF TABLES
................................................................................................... x
LIST OF FIGURES
.................................................................................................xi
LIST OF FIGURES
.................................................................................................xi
INTRODUCTION
.................................................................................................... 1
OBJECTIVES
.................................................................................................................. 2
LITERATURE REVIEW
........................................................................................... 4
Herpetofauna Conservation
........................................................................................ 4
Alabama Herpetofauna
................................................................................................ 7
Distance Sampling
........................................................................................................ 8
Predictive Maps Using ArcMap’s Geostatistical Analyst
........................................ 10
Cumberland Plateau
................................................................................................... 11
The Walls of Jericho and Skyline Wildlife Management Area
.............................. 13
MATERIALS AND METHODS
................................................................................ 15
Study Area
................................................................................................................... 15
Selection of Survey Points
......................................................................................... 17 a. Line Transect Survey
............................................................................................. 19 b. Drift Fences Captures
............................................................................................ 23 c. Other Herpetofauna Samplings
............................................................................ 23 d. Focal Species Search
......................................................................................... 26
Habitat and Vegetation Measurement
..................................................................... 26
Soil Moisture and pH
.................................................................................................. 28
Landscape Variables
................................................................................................... 29
Predictive Maps
........................................................................................................... 33
Validation dataset
....................................................................................................... 34
Data Analysis
............................................................................................................... 37
Estimating Detection Rates and Abundance with Program DISTANCE
.............. 38
RESULTS AND DISSCUSION
............................................................................... 41
Species Richness and Abundance
............................................................................ 41
Species Diversity
......................................................................................................... 45 viii

Species Area Curves
............................................................................................... 55
Cluster Analysis
........................................................................................................... 58 a. Spring 2005
......................................................................................................... 61 b. Fall 2005
.............................................................................................................. 61 c. Overall 2005 Results
.......................................................................................... 62 d. Spring 2006
......................................................................................................... 63 e. Fall 2006
.............................................................................................................. 63 f. Overall 2006 Results
........................................................................................... 64
Other Methods
............................................................................................................ 65
Estimations of Detection Rate, Abundance, and Density
..................................... 66
Prediction Maps
........................................................................................................... 70 a. Herpetofaunal distribution and abundance maps and accuracy assessment
................................................................................................................................... 70
Relationships between Herpetofauna and Landscape and Habitat Factors
...... 80
Correlation of Principle Components and Diversity Indices
................................. 83
Prediction Models for Herpetofaunal Species Richness and Abundance Based on Landscape and Habitat Components
................................................................. 85
Relationship between Land Cover Type Composition and Herpetofaunal
Species Richness and Abundance
............................................................................ 87
CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS
................................. 91
APPENDICES
....................................................................................................... 96
Appendix A. Land type classes of Alabama Gap Analysis Program (AL-GAP
2007) and reclassified types for this study. The AL-GAP classes that were not reclassified did not occur at the study area of Jackson County, Alabama.
....... 97
Appendix B. Land type proportions within 150 m radius of each sampling point at the study area of Jackson County, Alabama. See Appendix 1 for land type classes.
....................................................................................................................... 100
BIBLIOGRAPHY
................................................................................................. 105
VITA
.................................................................................................................. 116 ix

LIST OF TABLES
Table 1. Description, abundance (total number), and total percent (%) of total herpetofauna encountered during distance sampling in the Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama............................ 42
Table 2. Mean diversity indices for fall herpetofauna across all strata for Skyline and Walls of Jericho Management Areas in northern Alabama. ......................... 47
Table 3. Mean diversity indices for spring herpetofaunal encounters across all strata in Skyline and Walls of Jericho Management Areas in northern Alabama.
........................................................................................................................... 48
Table 4. Average number of species and individuals and their standard deviations detected in Skyline Wildlife Management Area and Walls of Jericho by survey season and strata. .................................................................................. 53
Table 5. Probability detection models of different herpetofaunal guilds generated in program DISTANCE using line transect data obtained in Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama............................ 69
Table 6. Principle Components Analysis of landscape variables at survey points in Skyline Wildlife Management Area and Walls of Jericho in northern Alabama.
........................................................................................................................... 78
Table 7. Principle Components Analysis of habitat variables at survey points in
Skyline Wildlife Management Area and Walls of Jericho in northern Alabama... 79
Table 8. Correlations Analysis of herpetofaunal species diversity indices and landscape and habitat variables generated for Skyline and Walls of Jericho
Management Areas in northern Alabama. .......................................................... 84
Table 9. Linear regression coefficients and significance of landscape and habitat components in predicting herpetofauna species richness at Skyline and Walls of
Jericho Management Area in northern Alabama, ............................................... 86
Table 10. Linear regression coefficients and significance of landscape and habitat components in predicting herpetofauna abundance in Skyline and Walls of Jericho Management Area in northern Alabama. ........................................... 86
Table 11. Linear regression coefficients and significance of land cover composition within 150 m radius of sampling point in predicting herpetofauna species richness in Skyline and Walls of Jericho Management Area in northern
Alabama. ............................................................................................................ 90
Table 12. Linear regression coefficients and significance of land cover composition within 150 m radius of sampling point in predicting herpetofauna abundance in Skyline and Walls of Jericho Management Area in northern
Alabama. ............................................................................................................ 90 x

LIST OF FIGURES
Figure 1. Herpetofauna regions of Alabama, USA. (Mount, 1975). ..................... 9
Figure 2. Map of James D. Martin Skyline Wildlife Management Area and
Forever Wild Walls of Jericho Management Area in Jackson County, Alabama,
U.S.A. ................................................................................................................. 16
Figure 3. Line transect design for distance sampling of herpetofauna in the
Skyline Management Area and Walls of Jericho in northern Alabama. .............. 21
Figure 4. Locations of survey points in Skyline Management Area and Walls of
Jericho in Jackson County of northern Alabama for 2005 and 2006. ................. 22
Figure 5. Pitfall and drift fence array used to sample small mammals and herpetofauna in Skyline Wildlife Management Area and Walls of Jericho in northern Alabama. .............................................................................................. 24
Figure 6. Map of ponds and tin locations in the Skyline Wildlife Management
Area and Walls of Jericho in northern Alabama.................................................. 25
Figure 7. Reclassified land cover map of sampling points in the Skyline
Management Area and Walls of Jericho area in northern Alabama.................... 31
Figure 8. The Nature Conservancy Sharp-Bingham property in Jackson County,
Alabama, U.S.A. ................................................................................................. 36
Figure 9. Seasonality, stratification, and their interaction on the number of species detected in the Skyline Wildlife Management Area and Walls of Jericho properties in northern Alabama .......................................................................... 49
Figure 10. Seasonality, stratification, and their interaction on the number of individuals detected in the Skyline Wildlife Management Area and Walls of
Jericho properties in northern Alabama. ............................................................. 50
Figure 11. Species dominance curve of herpetofaunal encounters in Skyline and
Walls of Jericho Management Area in northern Alabama................................... 54
Figure 12. Species area curves for strata on upperslopes with low, medium and high exposure in Skyline and Walls of Jericho in northern Alabama. ................. 55
Figure 13. Species area curves for the strata flats with medium and high exposure in Skyline and Walls of Jericho in northern Alabama. ......................... 56
Figure 14. Species area curves for the strata sideslopes with low, medium and high exposure in Skyline and Walls of Jericho Management Area in northern
Alabama. ............................................................................................................ 57
Figure 15. Cluster dendrogram of herpetofaunal species detected in Skyline and
Walls of Jericho Management Areas in northern Alabama. ................................ 60
Figure 16. Species prediction map for Skyline Wildlife Management Area and
Walls of Jericho in northern Alabama using the simple kriging method in
ArcMap’s Geostatistical analyst.......................................................................... 73
Figure 17. Herpetofauna abundance prediction map for Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama using the radial basis functions method in ArcMap’s Geostatistical analyst. ............................... 74
Figure 18. Soil pH prediction map for Skyline Wildlife Management Area and
Walls of Jericho in northern Alabama using ordinary kriging method in ArcMap’s
Geostatistical analyst.......................................................................................... 75 xi

Figure 19. Soil moisture (% / Volume) prediction map for Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama using ordinary kriging method in ArcMap’s Geostatistical analyst.............................................. 76
Figure 20. First and second Canonical Correspondence axes with herpetofaunal guilds and landscape and habitat variables for Skyline and Walls of Jericho in northern Alabama. .............................................................................................. 82
Figure 21. First and third Canonical Correspondence axes with herpetofaunal guilds and landscape and habitat variables for Skyline and Walls of Jericho in northern Alabama. .............................................................................................. 82 xii

ACKNOWLEDGMENTS
I wish to thank my major advisor Dr. Yong Wang for his guidance and support. Sincere thanks are given to my advisory committee members Drs.
Callie Schweitzer, Wubishet Tadesse, William Stone, and Barry Grand. I would also like to extend my thanks to Jeff Crocker, Becky Hardman, and Lee Borzick for their assistance in the field. Special thanks go to Kelvin ‘Burger’ Young,
Chelsea ‘#2’ Scott, Dawn ‘Amazing Kiwi’ Lemke, and Tom ‘Fiber’ Smith for offering encouragement and being my support system. I appreciate the friendship and professional assistance from fellow graduate students and employees at Alabama A&M University, which include Zach Felix, Bill Sutton,
Lisa Gardener, Trey Petty, Heather Howell, Tim Baldwin, Nevia Brown, and
Iranus Tazisong. Thanks to Eric Soehren, Dr. Wayne Barger, Nick Sharp, and
Alan Hitch for assistance throughout the study. This research is a collaborative effort of Alabama A&M University, Auburn University, and the Alabama
Department of Natural Resources and Conservation. The Alabama Department of Natural Resources and Conservation’s State Wildlife Grant program and the
Department of Natural Resources and Environmental Sciences of Alabama A&M
University provided necessary financial and logistical support for this research. xiii

CHAPTER 1
Reptiles and amphibians are integral parts of forest ecosystems because of their positions in the food web and their large biomass relative to other vertebrate groups making them extremely important in nutrient flow (Burton and
Likens, 1975). In some ecosystems salamander biomass alone is equal to that of small mammals or double that of birds (Burton and Likens, 1975).
Herpetofauna perform many ecological roles or “key ecological functions” (Davic and Welsh, 2004). For example, they are critical for seed dispersal, forest litter decomposition, and controlling invertebrate and rodent populations (Fitch, 1949;
Gibbons, 1988). Herpetofauna are also indicators of forest ecosystem health and environmental quality (Gibbons and Stangel, 1999). Both reptile and amphibian populations are highly influenced by microhabitat factors such as soil pH, site moisture, and the presence of leaf litter and coarse woody debris
(Faccio, 2001). Amphibians are particularly sensitive to moisture conditions because of their permeable skin, while reptiles are more sensitive to temperature variations. Many herpetofaunal species such as vernal pool breeding amphibians are migratory and are associated with different habitats during their
1

life cycle and hence, play important roles to link energy and nutrient budgets of different ecosystems such as terrestrial and aquatic (Cederholm et al., 1999).
OBJECTIVES
My research is a part of a larger project funded by the State Wildllife Grant
(SWG) to perform an inventory of the biodiversity of herpetofauna, birds, and small mammals on the lands of the J.D. Martin Skyline Wildlife Management
Area (SWMA), the Walls of Jericho (WJ), and Forever Wild Land Trust in the
Cumberland Plateau of northern Alabama. My collaborators include Auburn
University and employees of the State Lands Division of the Alabama
Department of Conservation and Natural Resources. The overall objectives of my study were (1) to conduct an inventory of the herpetofauna, (2) to examine the relationship between herpetofaunal community and habitat and landscape variables, (3) to develop a database and predictions to assist decisions for wildlife conservation and habitant management at the study sites and adjacent areas. My specific objectives included:
1. Design and implement a herpetofaunal inventory at the study area;
2. Document the herpetofaunal species composition and abundance;
3. Establish a spatially referenced database of landform and vegetative characteristics and herpetofaunal community using ground-surveyed and remotely sensed data with geographic information system (GIS) software;
4. Examine the seasonal and landform effect on the herpetofaunal species richness and abundance;
2

5. Generate predictive models of herpetofaunal species based on landscape and habitat features; and explore the potential use of these models to facilitate future decisions regarding the land acquisition and management of lands for herpetofauna in the region.
My study is part of a collaborative project with Auburn University and
Alabama Department of Conservation and Natural Resources that includes the inventory of small mammal and bird populations in the study area. Small mammal monitoring will be done utilizing pit-fall arrays, funnel traps, and
Sherman traps at half of the points. Bird surveys will involve doing two cycles of point counts in the spring when breeding birds are most active. Incorporating the vegetation and habitat data with the wildlife information will give the state and the
Skyline Manager an adequate estimate of current species diversity, abundance and distribution for the property.
Conservation of amphibian and reptiles is becoming a higher concern for
State agencies and land preservation groups. This study intended to provide valuable data of current status and distribution of herpetofauna in northern
Alabama. These data would help to promote conservation plans in not only the study area but hopefully in other management areas in the state. The data that I and the other partners collected will be used as baseline information and will help to establish a long-term monitoring program of herpetofauna, small mammals, and birds. Metal plates monumenting the point location and number were placed at each survey point to facilitate relocation for future surveys. Thus, species diversity and abundance trends can be drawn for the area.
3

CHAPTER 2
Herpetofauna Conservation
The global declines of some amphibian (Alford and Richards, 1999; Young et al., 2001; Lips et al., 2005; Stuart et al., 2004) and reptile (Gibbons et al.,
2000) populations call for the need to establish long-term monitoring programs to track population trends of these wildlife species (Marsh and Goicochea, 2003) and to correct these trends. It is estimated that 48% of amphibian species and
52% of reptile species in the United States are listed as being of conservation concern by various government agencies (Mitchell et al., 1999).
Commercial forest harvesting and road construction has lead to habitat fragmentation, which has had negative effects on herpetofauna (Enge and
Marion, 1986; Petranka et al., 1993; Ash, 1996; Gibbs, 1998; Martin and
McComb, 2003). However, Rosenzweig (1995) found that greater habitat diversity increased species diversity. In some “pristine” locations where human activity is inconspicuous, researchers have found that amphibian populations are in decline (Vial and Saylor, 1993; Blaustein and Wake, 1995; Stebbins and
Cohen, 1995; Drost and Fellers, 1996). For example, the Golden Toad (Bufo periglenes) and Harlequin Frog (Atelopus varius) have not been seen in Costa
Rica’s Monteverde Cloud Forest Preserve, a virgin forest ecosystem for more
4

than two decades (Crump et al., 1992; Pounds and Crump, 1994). Other species that have disappeared or declined in their ranges include: Boreal toad ( Bufo boreas ), cascade Frog ( Rana cascadae ), Mountain Yellow-legged Frog ( Rana muscosa ), Yosemite Toad ( Bufo canorus ), and other frogs (Bradford, 1991;
Sherman and Morton, 1993; Blaustein et al., 1994; Drost and Fellers, 1996;
Fisher and Shaffer, 1996). There is evidence of similar population declines for salamander species (Blem and Blem, 1989 and 1991; Blaustein and Wake,
1995; Fisher and Shaffer, 1996).
Given that even fairly undisturbed habitats are exhibiting herpetofauna declines, global features such as UV-B radiation and acid rain may be the cause
(Dunson et al., 1992; Blaustein, 1994). The UV-B hypothesis stated that amphibian species declines are associated with elevation and latitude. Davidson et al. (2001) found that decreases in amphibian abundance were correlated to increased elevation, supporting the UV-B hypothesis, but they did not find any correlation with latitude. Research conducted by Lips (1998) found environmental contaminants, both biotic and chemical, or a combination of climate change and environmental contaminants as possible causes of amphibian declines. Amphibians are more tolerant of high acidity than many fishes (Gosner and Black, 1957; Pierce and Mitton, 1982; Andren et al., 1989;
Whiteman et al., 1995); and some salamander species abundances are connected to breeding pond acidity (Pough, 1976; Pough and Wilson, 1977;
Clark, 1986; Karns, 1992; Sadinski and Dunson, 1992). Acidic precipitation may not only affect pond dwelling amphibians, but may affect terrestrial salamanders.
5

Terrestrial salamanders in New York are rare or absent in soils with low pH because the sodium balance is disturbed, possibly leading to lethal conditions
(Wyman and Hawksley-Lescault, 1987; Wyman, 1988; Frisbie and Wyman, 1991 and 1992; Wyman and Jancola, 1992).
Amphibian diseases and pathogens have recently emerged as a possible causes for declines worldwide (Carey, 2000). One fungus that was responsible for amphibian mortality in rain forests of Australia and Central America was chytridiomycete (Berger et al., 1998). This illness affects wild and captive anurans by causing a fungal skin infection in vertebrates in North America,
Central America, Europe, Africa, and Australia (Rollins-Smith and Conlon, 2005).
It is a chytrid fungus that can live in water or soil and invades the surface layers of the frog’s skin and damages the keratin layer (Chytridiomycosis, 2004). The fungus uses the keratin as a nutrient, which would affect water uptake and respiration (Daszak et al, 2000; Chytridiomycosis, 2004). Toxins are then absorbed through the skin, which may cause death (Chytridiomycosis, 2004).
The Ranavirus was discovered in the late 1980s in Britain and has emerged as a possible cause of global amphibian declines. The two main syndromes for this disease are skin ulceration and systemic haemorrhage (Cunningham et al., In press). The virus causes infection in the liver, kidneys, and digestive tract in frogs and toads (Daszak et al., 2000). Tadpoles die within a week of exposure to the virus (Harp and Petranka, 2006). Secondary bacterial infections occur in salamanders exposed to this virus, which lead to death from epidermal and visceral haemorrhaging (Daszak et al., 2000). Additional potential causes of
6

herpetofauna declines include conversion of natural habitat to urban settings, and invasive species (Gibbons and Stangel, 1999). Some researchers have argued that the amphibian declines are simply natural population fluctuations.
Drought, disease, or other natural causes may be the sources of these natural variations, and populations declining will be able to recover (Blaustein et al,
1993; Pechmann and Wilbur, 1994).
Alabama Herpetofauna
Alabama is one of the most diverse states in the United States for its plant and animal life (Mount, 1975). The state’s strategic location in terms of species and subspecies range limits, geologic and physiographic variability, and surface drainage pattern make the region truly unique (Mount, 1975). Amphibian and reptile species are among the most diverse biotic groups in the state with 134 species (sea turtles excluded) (Mount, 1975). There are an additional 34 subspecies of herpetofauna and three or four species and subspecies endemic to Alabama (Mount, 1975). Species ranges coincide with the physical geography of Alabama and Mount (1975) produced a map of herpetofauna regions for the state (Fig.1). The Coastal Plain region and Upland region is separated by the
Fall Line that extends from the northwestern corner of Alabama across the state in a southeast direction (Mount, 1975). Located in the south and southwest of the state, the Coastal Plain varies topographically from flat to almost montane
(Mount, 1975). Soils in the region range from acid sands and sandy loams to calcareous, alkaline types (Mount, 1975). Streams in this region are often
7

sluggish and have sand, silt, or bedrock bottoms (Mount, 1975). The Coastal
Plains contain forty-three species of amphibians and reptiles (Mount, 1975).
The Upland region is the area above the Fall Line, and 11 herpetofaunal species are limited to this region (Mount, 1975). Included in the Upland region is the Appalachian Plateaus, which is where my study area is located. Streams in the area are clear with rock and sand bottoms (Mount, 1975). Several herpetofauna species are found in this area and no where else in Alabama.
Some of these species include: Mountain Dusky Salamander (Desmognathus ochrophaeus), Flattened Musk Turtle (Sternotherus minor depressus), Cave
Salamander (Eurycea lucifuga), and Green Salamander (Aneides aeneus). Two subspecies that influence their species populations in the region are the Brown
Snake (Storeria dekayi dekayi), and the Northern Ringneck Snake (Diadophis punctatus edwardsi) (Mount, 1975).
Distance Sampling
Distance sampling is a simple visual encounter survey technique that has been recommended and used by many herpetologists (Cassey and McArdle,
1999; Dodd, 2003). Line transects have been used for inventories of herpetofuana in many habitats such as deserts (Anderson et al., 2001; Swann et al., 2002), tropics (Doan, 2003; Gillespie et al, 2004; Donnelly et al., 2005;
Gillespie et al., 2005), and forest ecosystems (Lacki et al., 1994; Faccio, 2001;
Ryberg et al, 2004). Crosswhite, Fox, and Thill (1999) found that visual searches were more effective in detecting herpetofaunal species compared to drift fence
8

arrays and stand alone funnel traps after standardizing a common unit effort.
However, the authors’ results showed that more individuals can be captured by drift fence trapping than line transects because of personnel limitations
(Crosswhite et al., 1999). Doan (2003) found that line transects yielded more individuals and species compared to area constrained sampling. More unique species were also encountered through line transects than time constrained sampling (Doan). The visual line transect method is relatively cheap and simple but is labour intensive (Rodel and Ernst, 2004). One disadvantage with this method is that transects may not cross habitats of species with specific habitat requirements (Halliday, 2006).
Figure 1. Herpetofauna regions of Alabama, USA. (Mount, 1975).
9

Predictive Maps Using ArcMap’s Geostatistical Analyst
Geostatistical Analyst is an extension to ESRI’s ArcMap software program. Some research fields that this extension has been used include: agriculture production, archaeology, environmental protection, exploration geology, forestry, health care, hydrology, meteorology, mining, real estate (ESRI,
2007). The Geostatistical Analyst offers two main types of interpolation techniques: deterministic and geostatistical. Deterministic techniques are based on the extent of similar values (Inverse Distance Weighted), or the degree of smoothing (Radial Basis Functions) (ESRI, 2007). Geostatistical methods utilize statistics and are used for more advanced prediction surface modeling (Global
Polynomial Interpolation, Local Polynomial Interpolation, and Kriging) (ESRI,
2007).
Inverse Distance Weighted (IDW) estimates cell values by averaging the values of sample data points the neighbourhood (ESRI, 2007). An assumption with the method is the variable being mapped decreases in weight with distance from the sampled location (ESRI, 2007). The disadvantage with this method is resultant “bull’s eyes” around data locations (ESRI, 2007). Radial basis
Functions (RBF) is more flexible than IDW and this method can create a surface that incorporates global trends and local variation by bending and stretching the shape of the predicted surface ESRI, 2007). This technique makes no assumptions about the data (ESRI, 2007).
10

Global Polynomial Interpolation (GP) produces a smooth surface by utilizing a polynomial to fit an entire surface (ESRI, 2007). This technique makes no assumptions about the data and is best applied to surfaces that change slowly and gradually (ESRI, 2007). Local Polynomial Interpolation (LP) differs from GP by fitting many polynomials into the surface, each with specified overlapping neighborhoods (ESRI, 2007). This method is more flexible than GP, has no assumptions in the data, and resultant surfaces are comparable to kriging (ESRI,
2007). Within the kriging technique, two sub-methods can be used to predict surfaces on a map, and they are: Ordinary Kriging and Simple Kriging. Ordinary
Kriging assumes an unknown constant mean in the statistical model that generates a semivariogram, while Simple Kriging uses a known constant in the model (ESRI, 2007).
Cumberland Plateau
The Cumberland Plateau is part of the westernmost portion of the
Appalachian Plateau and consists of parts of West Virginia, Tennessee,
Kentucky, and northern Alabama (Cumberland Plateau, 2007). It extends for approximately 725 km in a southwestern direction and is 65 to 80 km wide
(Cumberland Plateau, 2007). The rocks in the area were formed from the shallow sea during the Mississippian (360-320 million years ago) and the
Pennsylvanian (320-296 million years ago) geological time periods (National
Park Service, 2006). Horizontal layers of limestone, shale, coal, and sandstone were formed after compression. Erosion and lifting of the area over time has shaped the landscape into steep rugged slopes with numerous creeks and
11

stream systems. In Alabama, elevation ranges from over 500 m on plateau tops to 200 m at creek bottoms. Average summer temperatures reach a high around
30 o
C and winter temperatures can drop to 0 o
C (National Park Service, 2006).
Annual average precipitation ranges from 104 to 195 cm (Department of Natural
Resources, 2006).
Soil types in the area are primarily limestone rockland rough (Lr), Rough stony land, Muskingum soil (RsM), and Hartsells fine sandy loam, rolling phase
(Hfo) (Natural Resources Conservation Service, 2007). The Lr classification has
40 percent of surface area covered with stones and boulders and a very low available water capacity of approximately 5.6 cm (Natural Resources
Conservation Service, 2007). A typical profile of this soil type is stony silty clay from 0 to 45.7 cm (0 to 18 inches) below ground and unweathered bedrock from
45.7 to 203.2 cm (18 to 80 inches) below ground (Natural Resources
Conservation Service, 2007). The RsM soil type has about 5 percent of the surface area covered in stones and boulders, and a very low available water capacity of approximately 4.6 cm (Natural Resources Conservation Service,
2007). Typically, stony sandy loam occupies the soil layer 0 to 10.2 cm (0 to 4 inches), sandy loam from 10.2 to 40.6 cm (4 to 16 inches), and unweathered bedrock between 4.06 to 203.2 cm (16 to 80 inches) (National Resources
Conservation, 2007). This soil type is well drained and has a moderate available water capacity of about 20.8 cm (8.2 inches) (Natural Resources Conservation
Service, 2007). The typical soil profile consists of fine sandy loam at 0 to 22.9 cm (0 to 9 inches) depth, loam between 22.9 to 114.3 cm (9 to 45 inches), sandy
12

clay loam between 114.3 to 127 cm (45 to 50 inches), and weathered bedrock between 127 to 203.2 (45 to 80 inches) depth (Natural Resources Conservation
Service, 2007). Jacob’s Farm, the southeast property in the study area has many row crops fields managed for wildlife and recreational activities. The main soil type in these crop areas tupelo silt loam, level phase (Tuv) (Natural
Resources Conservation Service, 2007). According to the National Resources
Conservation Service (2007) available water capacity is moderate at approximately 22.6 cm (8.9 inches) and is classed as somewhat poorly drained.
The soil profile for this soil type consists of silt loam from 0 to 15.2 cm (0 to 6 inches), silty clay loam from 15.2 to 25.4 cm (6 to 10 inches), and clay 25.4 to
152.4 cm (10 to 60 inches) depth (Natural Resources Conservation Service,
2007).
The Cumberland Plateau of northern Alabama is one of the most highly diverse and abundant areas for herpetofauna in the country (Ricketts et al.,
1999). Based on Mount (1975), approximately 61 species could be potentially found in the area. These species include some species of high conservation concern. For example, the Green Salamander (Aneides aeneus), Tennessee
Cave Salamander (Gyrinophilus palleucus ssp.), Northern Pine Snake (Pituophis m. melanoleucus), and Eastern Box Turtle (Terrapene Carolina ssp.) are listed as protected species by the Alabama Department of Conservation of Natural
Resources (ADCNR) and of high conservation concern by Mirarchi (2004).
The Walls of Jericho and Skyline Wildlife Management Area
13

The State of Alabama recently purchased The Walls of Jericho and adjacent areas in northern Alabama. The tracts add to the existing state land in the area, now totalling 164 km
2
. These public lands provide the opportunity to manage wildlife species such as herpetofauna and to protect ecosystem integrity.
Estimating herpetofauna density and abundance is imperative for initiating management objectives conservation of rare species, especially those that are threatened or endangered (Gelatt and Siniff, 1999). This study was designed to conduct an inventory of the herpetofauna of these lands and provide an assessment of the current herpetofaunal species composition and their habitat and landscape associations. Study goals also included establishing a baseline database for the study area and providing management recommendations for the herpetofauna community.
14

CHAPTER 3
Study Area
The study site was located in the southern extent of the Cumberland
Plateau in Jackson County of northern Alabama (Figure 2), including the Walls of
Jericho and James D Martin Skyline Wildlife Management Area. The Walls of
Jericho tract measures approximately 50.6 km
2
(12,510 acres) and was purchased by The Nature Conservancy from Coastal Lumber in 2003 (Lein,
2005). The State of Alabama bought the property in early 2004 (Lein, 2005).
The James D. Martin Skyline Wildlife Management Area measures approximately
114 km
2
(28,167 acres). The total area of the two properties combined is approximately 164 km
2
(40,677 acres) managed in public stewardship.
Hurricane Creek, the largest creek on the property, is a major tributary of the
Paint Rock River watershed (Lein, 2005). This creek provides habitat for several federally endangered mussels and fish (Lein, 2005). The study area also has an abundance of caves, sinkholes, and rock outcrops. Vegetation on the northern
Alabama plateaus were characterized as oak and oak-hickory forests with mixed mesophytic communities restricted to valleys and coves (Braun, 1950). Hartsell and Brown (2002) classified nearly 80% of Jackson County timberland as composed of oak-hickory species. Other tree species in the management include American beech (Fagus grandifolia Ehrhart ), yellow poplar (Liriodendron
15

tulipifera Linnaeus), sweetgum ( Liquidambar styraciflua L.), and maples ( Acer spp.), with patches of loblolly ( Pinus taeda L.
) and shortleaf pine ( Pinus echinata
Miller ) interspersed in some locations. In addition to forested areas, the study area has grass fields and row crops that are managed for hunting and wildlife.
Figure 2. Map of James D. Martin Skyline Wildlife Management Area and
Forever Wild Walls of Jericho Management Area in Jackson County, Alabama,
U.S.A.
16

Selection of Survey Points
A total of 176 survey points were generated within the study areas for the inventory, as well as for future long-term monitoring of fauna and flora in the area
(Fig. 3). A 250 m grid of points was classified based on solar exposure and landform (slope). The 250 m grid was used because it provided sufficient number of sampling points, and wildlife data from adjacent points were expected to be independent, as recommended by Ralph, Droege, and Sauer (1995).
Solar exposure and landform data were provided by Dr. Barry Grand of the Alabama Cooperative Fisheries and Wildlife Research Unit at Auburn. The solar exposure data were calculated using sun position data collected by the U.S.
Naval Observatory. For every hour during the first day of the month, the sun’s altitude and azimuth was calculated for Huntsville, Alabama for 2004. The
Hillshade function in ArcGIS Spatial Analyst (ver. 8.1, ESRI, Inc.) was applied to obtain an amalgamated measure of solar exposure for the study area. These exposure values were scaled from 1 to 100 and classified into three classes: low
( x
−
STD ), medium ( x
±
STD ), and high exposure ( x
+
STD ). Landforms which were classified into four classes: flats (<8 o slope, low slope position), upperslopes and coves (>8 o
, <25 o
), sideslopes and coves (>25
0
, <35
0
), and cliffs (>35
0
), were used to subdivide the exposure data into 10 survey strata. A maximum of 25 sampling points were randomly selected in each stratum providing a total of 176 survey sampling points. Only nine of these strata occurred at the study area.
These nine stratifications were:
1. Cliffs with high solar exposure,
17

2. side slopes with high solar exposure,
3. side slopes with medium solar exposure,
4. side slopes with low exposure,
5. upper slopes with high exposure,
6. upper slopes with medium exposure,
7. upper slopes with low exposure,
8. flats with high exposure, and
9. flats with medium exposure.
The land cover type classification was generated for a 150 m radius plot centered at each survey point was produced based on the land classification system from the Alabama-Gap Analysis Project (AL-GAP, 2007). A 150 m radius was used because the maximum distance of the transect extended 141 m from each survey point. The AL-GAP used 85 land classes and sub-classes
(Appendix 1). Sixteen of these classes were mappable for the study area
(Appendix 1). Some of the rare classes were combined to simplify the analyses and to facilitate examining the major patterns (Appendix 1). For example, the low intensity developed (SEGAP220) was merged with developed open space
(SEGAP211) to form the developed class; evergreen plantations (SEGAP420) were merged with southern Appalachian low mountain pine forest to form the evergreen class; successional shrub/scrub (clear cut) (SEGAP511) was merged with successional shrub/scrub (utility swath) (SEGAP512) and successional shrub/scrub (other) (SEGAP513) to form the shrub class; clearcutgrassland/herbaceous (SEGAP710) was merged with other herbaceous
18

(SEGAP720), utility swath herbaceous (SEGAP730), pasture/hay (SEGAP810), and row crop (SEGAP820) to form the herbaceous/crop class; and southern ridge and valley dry calcareous forest a (CES202.457a) was merged with southern ridge and valley dry calcareous forest b (CES202.457b) to form the calcareous forest class. The stream class was ignored from further analysis because it occurred only at two points and accounted for 0.10% of the total mapped area. The resultant eight classes included developed space (0.62%, occurred at 8 points), dry oak forest (21.96%, occurred at 91 points), mesophytic forest (20.22%, occurred at 150 points), calcareous forest a (42.04%, occurred at
121 points), shrub (4.71%, occurred at 46 points), evergreen (1.38%, occurred at
18 points), herbaceous/crop (8.67%, occurred at 43 points), and cliff (0.40%, occurred at 3 points) (Appendix 2).
Surveying Herpetofauna a. Line Transect Survey
This study’s primary inventory method was the line transect distance survey with measurements of the distances between observed animals and the transect center line. Given temporal, personnel, and logistical constraints, this technique has been suggested as an effective technique for sampling animal species richness and abundance (Cassey and McArdle, 1999; Dodd, 2003). Line transect surveys can effectively determine: (1) species richness, (2) habitat characteristics, (3) life history information, and (4) an estimation of density
(Crump and Scott, 1994; Welsh and Droege, 2001; Dodd, 2003). This method is
19

also useful in determining interspecific and intraspecific variation in herpetofaunal populations across continuously changing environmental gradients (Jaeger,
1994). The design of the transect surveys was a figure eight, comprising of two squares at 100 m on each side and the center node being the random point.
The total length of active searching was 800 m at each point (Figure 3).
Using a random bearing between 1 to 360 o
, an observer walked the 100 m measuring tape out to establish the transect line before returning to the start point. Two observers walked along each side of the transect line to intensively search the area by turning over natural covers such as logs, branches, rocks, litter, and other objects. Observers stayed close to the line but were able to investigate logs and objects of interest 10 to 15 m away. When an individual animal was found, its life stage (e.g. hatchling, juvenile, sub-adult adult), sex
(only possible for some species), location substrate (e.g. under log, in litter, in the open, under rock), and the perpendicular distance to the transect line was recorded to the nearest 0.1 m. Animals were returned to their original location and cover objects were returned to their original positions to minimize habitat alteration. The start and end time for each square was recorded to ensure search time was consistent across sampling points. Site conditions were documented at each point and included habitat type (e.g. grass field, marsh, cropland, forested at plateau top, forested gulley, forested cove, clear-cut), primary vegetative species (most dominant tree species in general area), ground moisture conditions (e.g. wet, moderate, dry), weather (current weather conditions, and possibly past 24 hours; e.g. rain, sunny, windy, cold, cloudy), and
20

soil temperature. In addition, photos were taken at every point in each cardinal direction to document habitat conditions at the time of sampling. Due to personnel limitations, 50% of the randomly selected points were surveyed in
2005, and the remaining points were surveyed in 2006 (Figure 4).
1.2 m
Worm Snake
Slimy Salamander
2.7 m
3.9 m
Fence Lizard
Random
Point
American Toad
2.2 m
100 m
Figure 3. Line transect design for distance sampling of herpetofauna in the
Skyline Management Area and Walls of Jericho in northern Alabama.
21

Figure 4. Locations of survey points in Skyline Management Area and Walls of
Jericho in Jackson County of northern Alabama for 2005 and 2006.
22

b. Drift Fences Captures
Drift fence arrays were randomly set up at 50% of the 176 sampling points in conjunction with the mammal study conducted by Auburn University. A silt fence 10 m long was placed in a V-shaped formation bent in the middle, along the contours of the slope (Figure 5). Five 3.785 L (1 gallon) tin cans were positioned flush with the ground, one at the bend, and a pair on each fence side prong. A pair of funnel traps was placed at each end of the pitfall array, and another pair at the outer bend. Each point was sampled for five trap nights.
Data from drift fences were only used for compilation of a species list for the area because of the limited sampling effort. c. Other Herpetofauna Samplings
Herpetofauna heard calling along transects were also documented. Night
Road cruising by vehicle following rainstorms provided additional ancillary data.
Flat pieces of tin were placed at random points for use as cover objects for hiding or nesting. These methods provided ancillary data at points where distance surveys were difficult (e.g. clearcuts and field areas). Ponds were surveyed for herpetofaunal species. Ponds were located during travel on the property, getting to and from survey points, examining topographic maps, and searching opportunistically (Fig. 6). Ponds were surveyed through parts of June, July and
August in 2006 and spring 2007 depending on pond depth and size. I also used minnow traps to gather information on pond dwelling amphibians and reptiles that
23

I might not encounter during terrestrial line transects. Traps were checked everyday to ensure that animal mortality were kept at a minimum.
5 m
Funnel Trap
Pitfall
Figure 5. Pitfall and drift fence array used to sample small mammals and herpetofauna in Skyline Wildlife Management Area and Walls of Jericho in northern Alabama.
24

Figure 6. Map of ponds and tin locations in the Skyline Wildlife Management
Area and Walls of Jericho in northern Alabama.
25

d. Focal Species Search
Mirarchi (2004) classified all the herpetofaunal species of Alabama into different conservation priority groups. Based on historical records (Mount 1975),
Mirachi (2004), and the likelihood of occurrence at the study site, I identified 15 species as focal species, which included five Priority 2 (high conservation concern) species: Green Salamander (Aneides aeneus), Tennessee Cave
Salamander ( Gyrinophilus palleucus), Southern Five-lined Skink ( Eumeces inexpectatu s), Prairie King Snake ( Lampropeltis calligaster), and Northern Pine
Snake ( Pituophis melanoleucus); and eleven Priority 3 (moderate conservation concern) species: Smallmouth Salamander ( Ambystoma texanum), Tiger
Salamander ( Ambystoma tigrinum), Ocoee Salamander ( Desmognathus ocoee),
Slender Glass Lizard ( Ophisaurus attenuatus), Six-lined Racerunner
( Cnemidophorus sexlineatus ), Queen Snake (Regina septemvittata), Eastern
Hognose Snake (Heterodon platyrhinos), Corn Snake (Elaphe guttata guttata),
Eastern Milksnake (Lampropeltis triangulum ), and Mole Kingsnake (Lampropeltis calligaster).
Specialized site searches were conducted to document these species that might not be found with other survey methods used in this project.
The areas searched included seeps, rock outcrops, streams, vernal pools, and ponds.
Habitat and Vegetation Measurement
26

A general vegetative and habitat survey was conducted by Dr. Wayne
Barger, a botanist with the Alabama Department of Conservation and Natural
Resources (ADCNR) State Lands Division, during the spring and summer of
2006 on all 176 points in the study area. The survey method used was an unstructured meandering search around the sampling point. The observer walked around the sampling area identifying dominant, co-dominant, common, occasional, and rare plant species. Percent coverage was estimated for plant species in the overstory, understory, and ground cover. A site description included predominant vegetation, major plant and animal presence, aquatic features, unique land forms, and scenic qualities. Aquatic habitats including ponds, ephemeral pools, and intermittent and permanent streams were also recorded. In addition, any evidence of disturbance, either manmade or natural was documented. If applicable, the surrounding land use of the survey point was recorded.
To compliment the general vegetation and habitat data, additional habitat measurements were collected by Auburn University’s School of Forestry and
Wildlife Science’s Ph.D. candidate Alan Hitch in the summer of 2006. To determine tree height and structure of the habitat, the three largest diameter trees closest to the survey point were selected for data collection. An estimate of the angle to the apex, angle to base of the crown, and angle to the base of the tree were taken at the sampling point. Tree species was also recorded. Covers of canopy, ground, mid-story, and understory, and duff layers were recorded at the point. Dead, downed, woody debris was estimated along 25 m transects
27

radiating from the survey point center in the four cardinal directions. Duff layer was also recorded at the end of these transects. Logs or woody material greater than 5 cm in diameter that crossed the transects had the small and large diameter recorded to obtain an estimate of the volume of dead woody debris at each point. Dead woody debris volume was estimated using the standard equation for a geometric cone. The distance from the dead woody debris to the sampling point was also recorded. In addition, the diameter at breast height
(DBH) of any snags around the point was measured and distances to the point were recorded to give an estimate of total DBH of snags in the immediate area.
Soil Moisture and pH
Soil moisture was measured with a ML2x Theta Probe Type 1, with a HH2 data logger (Delta-T Devices, Cambridge, UK) at each point between August 28 and September 19 of 2006. Moisture probes were calibrated based on manufacturer’s specification and inspected by Robert Metzl, a research associate at Alabama A&M University’s Center for Hydrology, Soil Climatology, and
Remote Sensing prior to data collection. Samples were collected within a short period of time to limit variations in the moisture regime through time. Three soil moisture measurements were taken around each point spaced approximately > 1 m apart, around the sampling point. Based on manufacturer’s suggestions, litter was removed from the soil surface, and soil moisture measurements were taken at 60 mm depth. Soil moisture was measured in percent volume and accuracy was to ±1% (Delta-T Devices, Cambridge, UK).
28

I followed the protocol recommended by Peech (1965) and The American
Society of Agronomy and Soil Science Society of America (Page, 1984) to determine soil pH. After removing the litter from the surface, a soil sample up to
15.24 cm (6 inch) deep from the surface (A horizon) was collected at each plot center. Each soil sample was stored in air tight bags and brought back to the
Alabama A&M University’s Soil Microbiology/Microchemistry Laboratory in the
Agriculture Research Center (ARC) in Normal, Alabama, and stored in a 4˚C refrigerator. For the pH analysis, I followed the standard 1:1 soil to distilled water ratio described in part two of the American Society of Agronomy’s Methods of
Soil Analysis (Page, 1984). From each sample, I measured 10 g of soil and placed it in a sterilized container, where 10 ml of deionized water was added.
After stirring the samples for approximately 5 seconds, I waited 30 minutes and restirred the samples another 5 seconds before pH was measured (Page, 1984).
Soil sample pH was then measured with a standardized Thermo Orion model
620 pH meter (Thermo Fisher Scientific, Waltham, Massachusetts). The resultant pH was recorded as soil pH in water.
Landscape Variables
National Land Cover Data (NLCD) from 2001 (Homer et al., 2004) was used to determine land use and land cover in the study areas. This data set was developed from remote sensing (Landsat TM) and ancillary data. The classification scheme of this data set was too detailed for my research purposes.
I reclassified the land cover types into broader categories in a geographical
29

information system (GIS) using Spatial Analyst in ArcMap (ver. 9.2, ESRI, 2007).
I imported the NLCD image file into ArcMap and converted it into a raster map. I reduced the possible twenty-one land cover classification types to five classes which included: deciduous forest, mixed forest, evergreen forest, disturbed land, and water (Figure 7). Disturbed land included anthropogenic influences such as high and low residential areas, industrial and commercial transportation, orchards and vineyards, row crops, pasture and hay fields, and urban and recreation grasses. Using the Reclassify tool in Spatial Analyst, map units were reassigned into these five classes. To calculate distances between the sampling points and the specific land cover types, I converted each land cover type to a shapefile, which stores geometric location and attribute information of geographic features in one file (ESRI, 2007). Using these distances, regression values were extracted using a factor analysis in SPSS ver. 10.0 (SPSS Inc., Chicago, IL,
1999).
30

Figure 7. Reclassified land cover map of sampling points in the Skyline
Management Area and Walls of Jericho area in northern Alabama.
31

A 30 m Digital Elevation Model (DEM) of Jackson County was used to determine elevation, aspect, and slope for the area using Spatial Analyst
ArcMap. A shapefile for each landscape feature was created. Aspect, a circular measurement was transformed into northness and eastness variables.
Northness is the cosine of aspect and eastness is the sine of aspect (Roberts,
1986; Guisan et al., 1999). To obtain distances from each survey point to a feature, I used Zonal Statistics in Spatial Analyst. Road and stream data was obtained from the U.S. Census Bureau’s 2000 Census Tiger/Line Data. Other environmental characteristics that were generated based on the GIS database included distances to the nearest paved road, stream, and disturbance. Areas that were classified as disturbed land included: developed land use types such as residential and commercial buildings, cultivated vegetation including hay fields, row crops, small grains, and urban grasses, and non-natural woody vegetation from orchards. The distance of points to different landscape features was done utilizing the Distance function in Spatial Analyst. Once a shapefile for the variable was created, I used Zonal Statistics to obtain the distances in meters from each point to the landscape feature. These distances were then joined into a single attribute table of landscape variables. The landscape characteristics were overlaid on land cover maps and herpetofaunal species diversity and abundance maps to generate habitat maps for herpetofaunal species at the study site. Areas with high herpetofaunal species diversity and abundances were identified and landscape features correlated to these were then identified.
32

Predictive Maps
Prediction maps for species richness, abundance, and some selected species with sufficient detections were generated in ArcMap. Herpetofauna data was imported and transformed to shapefiles by using the add XY data function in the Tools option. This utility was used to enter tabular data into layer files using the same XY coordinates in the map. After the new file was created, it was exported as a shapefile in order to edit features in the attribute table, edit shapes or points in the file, or perform any analysis in spatial analyst. Using herpetofauna data entered for each survey point in ArcMap, I used geostatistical approaches (Isaaks and Srivasta, 1989) to produce distribution maps for the number of species and number of individuals across the landscape. The
Geostatistical Analyst in ArcGIS was used to estimate the best prediction functions and to create prediction maps. The geostatistical models considered included inverse distance weighting, radial basis functions, kriging, cokriging, local polynomial interpolation, and global polynomial interpolation (ESRI, 2007).
Advantages of employing the kriging method included flexibility and statistical inferences. Although kriging is similar to inverse distance weighting in that known values from locations that are closer to the point being estimated have more influence than points farther away, kriging weights the values based on the spatial functional relationship among data points (ESRI, 2007). The spatial structure of the data was developed first and generated into a semivariogram.
Using this semivariogram, kriging weights were placed on measured locations and their spatial relationship to unknown locations to produce a continuous
33

prediction map. The difference between simple kriging and ordinary kriging is that simple kriging assumed a known constant mean. The Radial Basis Function method is an exact interpolation technique; the predicted values must go through each measured sample value. It fits the measured values into the map by predicting the shape of the surface between the measured points (ESRI, 2007).
I used two methods to determine the best model for predicting species richness and abundance across the study site. I compared prediction errors (root mean squared of errors) associated with each model (ESRI, 2007). I also performed a cross-validation for each method by using the herpetofaunal data collected at an adjacent site owned by the Nature Conservancy in the Paint Rock
Valley of Jackson County as a validation data set to determine which predictive model came closest for estimating detected animal abundance and diversity. A mean prediction error close to zero and a comparably small root-mean-squared prediction error were the basic diagnoses for the model selection.
Validation dataset
I also conducted a similar study at a nearby site owned by the Nature
Conservancy on the Sharp and Bingham Mountains in the Paint Rock Valley on the western edge of Jackson County, Alabama (Fig.8). The property is about 9.3 km
2
with elevation ranging from 200 to 500 m. Sinkholes, caves, and steep slopes are characteristic of the area. The procedures used to generate the survey points were the same as those used for the surveys for Skyline Wildlife
Management Area and Walls of Jericho. Forty points were surveyed using the same transect technique as used for the Skyline and Walls of Jericho survey in
34

fall 2006 and spring 2007. Data collected from these surveys were used to validate the prediction models from Skyline and Walls of Jericho Management
Areas to determine accuracy.
35

Figure 8. The Nature Conservancy Sharp-Bingham property in Jackson County,
Alabama, U.S.A.
36

Data Analysis
I tested the effect of seasonality, strata, and their interaction in overall species richness and the number of individuals detected at each point. General
Linear Models (Hair et al, 1998) were used to test the null hypotheses including:
1) no seasonal effect; 2) no stratum effect; and 3) no interaction between season and stratum on species richness and abundance. Tukey multiple range test was used to identify differences occurring between the strata. Principle components analysis (PCA) (Hair et al, 1998) was used to examine the relationships between habitat and landscape variables and to reduce the variables for additional analyses. Detrended correspondence analysis (DCA) (Jongman et al., 1995) was used to examine the community structure or species associations of herpetofauna. Cluster analysis was also used to analyze group membership among the herpetofauna species (Jongman et al., 1995). To evaluate the relationship between herpetofauna species and their association with habitat, landscape, and climatic variables, canonical correspondence analysis (CCA) was used (Jongman et al. 1995). I predicted that certain groups (i.e. salamanders, snakes) would show similar responses to specific habitat features. The advantage of using this approach was that a reasonable number of groups can be defined and used for management decisions (McGarigal et al., 2000). I also used ordinary least squared regression (Hair et al. 1998) to generate prediction models for herpetofaunal species richness and abundance based on the components of habitat and landscape variables. Regression models were also generated based on land type compositions at the sampling locations. Land type
37

composition data were arcsine transformed, and herpetofaunal data were log10 transformed, to meet the regression assumptions and to reduce the collinearity among the land composition predictors (Zar, 1999). Species richness and diversity indices were calculated using Shannon-Weaver and Simpson indices
(Magurran 1988). The single point that was generated in the strata cliffs with high solar exposure was combined into upperslopes with high solar exposure for data analysis purposes.
Estimating Detection Rates and Abundance with Program DISTANCE
Using the data collected from line transects, I used program DISTANCE
(Buckland et al., 2004) to estimate detection rates and abundance of herpetofauna in the study area. DISTANCE is a software program that allows users to design and analyze distance sampling surveys of wildlife populations.
Based on the distances of detected animals from the transect line, DISTANCE estimates the detectability (P a
), which is
P a
=
0 w
( x ) dx w (1) where w is the transect width and g ( x ) is the detect function which can be used to estimate the probability of detecting an animal, given that it is at distance x from the line. The detectability was used to estimate the total number of animals (N) in the area and the density (D). For line transect surveys, these two parameters were estimated as:
38

N
= nA
ˆ a
(2)
D
= n
2 wL
ˆ a
(3) where n is the number of animals detected on the transects, A is the size of study area, containing N animals, and L is the total length of transect lines in the survey. DISTANCE analyses were completed by selecting the ‘key’ function and a ‘series expansion’. The ‘key’ functions that were evaluated included: uniform, half-normal, negative exponential, and hazard-rate. A ‘series expansion’ term was selected to adjust the ‘key’ function to improve the fit of the model to the distance data. Models were selected base on the model robustness, shape of the detection curve, and the estimator efficiency. Models with low Akaike
Information Criterion (AIC) and Chi-squared values were selected in the estimation the detection rate (Buckland et al., 2004).
For a reliable estimate of density and population size, a minimum sample size of 60-80 individuals is suggested (Buckland et al., 2004). Because of the sample size limitation, I generated detection functions for herpetofaunal ecological guilds, including all amphibians, all reptiles, amphibian and reptile species in spring and fall separately, terrestrial salamanders, stream salamanders, frogs and toads, snakes, and lizards and skinks. Although distance sampling and estimation can account for the effect of detectability
39

among species and habitats for estimating abundance, there are several assumptions inherent in distance surveys that must be considered including
(Buckland et al., 2004): (1) all animals on or near the line were detected, (2) animals were detected at their initial locations, (3) perpendicular distances were measured accurately (Wilson and Doherty, 2006), (4) all species and individuals have an equal probability of being detected for all times and locations, (5) individuals were recorded only once per survey, and (6) there were no observer biases (Bailey et al., 2004; Muth, 2005). The survey method that I designed for this study maximized the likelihood of meeting these assumptions. However, because of low numbers for most species detected in this study, the detection rate and absolute abundance could only be estimated for those species or group of species with sufficient sample size.
40

CHAPTER 4
RESULTS AND DISSCUSION
Species Richness and Abundance
A total of 2,307 animals were detected during the line transect surveys for
2005 and 2006 combined. Amphibians accounted for 26 species, while reptiles numbered 20 species. One additional species, the Common Snapping Turtle
( Chelydra serpentine) , was found several times during pond surveys and while traveling on the property. Of the species found, 84 percent of individuals detected were amphibians, and Slimy Salamanders (Plethodon glutinosis) were the most abundant species, making up 75 percent of all amphibian encounters.
The remaining five most common species were Eastern Zigzag Salamander
(Plethodon dorsalis), Eastern Worm Snake (Carphophis amoenus), Spotted
Dusky Salamander (Desmognathus fuscus), and Red Salamander (Pseudotriton ruber) (Table 1).
41

Table 1. Description, abundance (total number), and total percent (%) of total herpetofauna encountered during distance sampling in the Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama.
Species
Code Scientific Name Common Name Total Proportion
PLGL
PLDO
CAAM
DEFU
PSRU
DIPU
EULU
Plethodon glutinosis
Plethodon dorsalis
Carphophis amoenus
Desmonathus fuscus conanti
Pseudotriton ruber
Diadophis punctatus
Eurycea lucifuga
Slimy
Salamander 1465 63.50
Eastern Zigzag
Salamander 133 5.76
Eastern Worm
Snake 105 4.55
Spotted Dusky
Salamander 87 3.77
Northern Red
Salamander 67 2.90
Ringneck
Snake 64 2.77
Cave
Salamander 58 2.51
Bufo americanus American Toad 44 1.91 BUAM
SCLA
EUFA
TECA
SCUN
NOVI
THSI
EULA
AGCO
VIVA
GYPO
AMOP
Scincella lateralis Ground Skink 34
Five-Lined
1.47
Eumeces fasciatus
Terrapene carolina
Sceloporus undulates
Notophthalmus viridescens
Eastern Box
Turtle 25 1.08
Eastern Fence
Lizard 24 1.04
Red-spotted
Thamnophis sirtalis
Eumeces laticeps
Agkistrodon contortrix
Virginia valeriae
Gyrinophilus porphyriticus
Ambystoma opacum
Eastern Garter
Snake 14 0.61
Broad-Headed
Skink 13 0.56
Northern
Copperhead 12 0.52
Smooth Earth
Snake 12 0.52
Spring
Salamander 9 0.39
Marbled
Salamander 7 0.30
42

Table 1 (continued)
CRHO
GACA
HYCH/HYVE
Crotalus horridus
Timber
Rattlesnake 7 0.30
Eastern
Narrowmouth Gastrophryne carolinensis
Hyla chrysoscelis/versicolor
Grey
Treefrog/Cope's
Grey Treefrog 6 0.26
ANCA
EUCI
ANAE
ELOB
HESC
PSTR
Anolis carolinensis
Eurycea cirrigera
Aneides aeneus
Elaphe obsoleta
Hemidactyluim scutatum
Pseudacris triseriata feriarum
Green Anole
Southern Two-
5 0.22
Lined
Salamander 5 0.22
Green
Salamander 4 0.17
Black Rat
Snake 4 0.17
Four-toed
Salamander 4
Upland Chorus
0.17
RACL
STOC
Rana clamitans
Storeria occipitomaculata
Green Frog
Northern Red-
Bellied Snake
4
4
0.17
0.17
ACCR Acris crepitans
Northern
Cricket Frog 2 0.09
ACGR
AMMA
Acris gryllus
Ambystoma maculatum
Southern
Cricket Frog 2 0.09
Spotted
Salamander 2 0.09
AQ salamander
COCO Coluber constrictor
Unknown aquatic salamander 2 0.09
Northern Black
Racer 2 0.09
ELGU
PSBR
RACA
Elaphe guttata
Pseudacris brachyphona
Rana catesbeiana
Corn Snake
Mountain
Chorus Frog
2
2
0.09
0.09
Bullfrog 2 0.09
43

RAPA
AMTA
CNSE
EU spp
EULO
HEPL
LANI
NEPL
PSCR
Table 1 (continued)
Rana palustris
Ambystoma talpoideum
Cnemidophorus sexlineatus
Pickerel Frog
Mole
2 0.09
Salamander 1 0.04
Six-Lined
Racerunner 1 0.04
Eurycea longicauda
Heterodon platirhinos
Lampropeltis getula niger
Nerodia sipedon pleuralis
Pseudacris crucifer
1 0.04
Long-Tailed
Salamander 1 0.04
Eastern
Hognose Snake 1 0.04
Black
Kingsnake 1 0.04
Midland Water
Snake 1 0.04
Northern Spring
Peeper 1 0.04
44

Species Diversity
Diversity indices were calculated for all amphibian and reptile encounters in each strata and season (Table 2 and 3). The Shannon-Weaver Diversity Index
(Schemnitz, S.D., 1980), measured a random sample of individuals from populations of all species present, was lowest for the strata flats with medium exposure in the fall. In the fall, points located on sideslopes with low and medium exposure had the highest species richness. Compared to spring evenness, these two strata, in addition to sideslopes with high solar radiation were among the highest in species richness. In fall, points located on flats with high exposure and upperslopes with low exposure had the highest evenness among the strata, while in spring, evenness was the highest at the points located on sideslopes with high and low exposure.
Flats with medium and high exposure had fewer points compared to the other strata because survey points were located in proportion to the availability among the landscape strata. Using point averages, I found that season and strata were significant in affecting the Shannon-Weaver Diversity (F=18.34, df=1 and160, P=0.0001; F=3.28, df=7 and 160, P=0.002, respectively). Simpson’s
Diversity showed the same seasonal and landscape stratification effect (F=17.05, df=1 and160 P=0.0001; F=3.146, df=7 and 160, P=0.003, respectively).
Season and strata had a significant effect on the number of species detected (F=18.24, df=1 and 160 P=0.001; F=5.52, df=7 and 160, P=0.001, respectively, Fig. 9). The number of individuals observed also varied significantly by season and landscape stratification (F=6.46, df=1 and 160, P=
45

0.01; F=3.79, df=7 and 160, P=0.01, respectively, Fig.10). The number of species and number of individuals encountered was greater in the spring season than in the fall. This may be related to increased movement, foraging, and mating activity of herpetofauna at this time. Most amphibians and reptiles in this area are spring breeders, with the exception of Marbled Salamanders
( Ambystoma opacum), which breed in the fall (Mount, 1975). Breeding time of herpetofauna can vary depending on environmental factors such as rainfall and temperature (Gauthreaux, 1980, Sexton et al, 1990), which might also contribute to the low number of individuals found in fall. Temperature averages for northern Alabama in 2005 and 2006 were above annual averages (National
Weather Service, 2007) and may have potentially affecting herpetofauna migration patterns. In addition, precipitation averages for 2005 and 2006 were below historical annual averages (National Weather Service, 2007) which may have affected herpetofauna activities further.
46

Table 2. Mean diversity indices for fall herpetofauna across all strata for Skyline and Walls of Jericho Management Areas in northern Alabama.
Total for each strata Average per sampling point
Sampling
Points Richness Evenness
Shannon-
Weaver
Simpson's
Diversity
Flat_med 53 6 19 0.6 0.087 0.079 0.0454
US_high 77 18 26 1.5 0.303 0.335 0.1909
Abbreviations:
SS_low = Sideslopes with low solar radiation
Flat_med = Flats with medium solar radiation
SS_med = Sideslopes with medium solar radiation
US_med = Upper Slopes with medium solar radiation
SS_high = Sideslopes with high solar radiation
US_high = Upper Slopes with high solar radiation
US_low = Upper Slopes with low solar radiation
Flat_high = Flats with high solar radiation
47

Table 3. Mean diversity indices for spring herpetofaunal encounters across all strata in Skyline and Walls of Jericho Management Areas in northern Alabama.
Total for each strata
Sampling
Points Richness
Average per sampling point
Evenness
Shannon
Weaver
Diversity
Simpson's
Diversity
US_med 78 21 25 1.6 0.413 0.422 0.2546
Abbreviations:
SS_low = Sideslopes with low solar radiation
Flat_med = Flats with medium solar radiation
SS_med = Sideslopes with medium solar radiation
US_med = Upper Slopes with medium solar radiation
SS_high = Sideslopes with high solar radiation
US_high = Upper Slopes with high solar radiation
US_low = Upper Slopes with low solar radiation
Flat_high = Flats with high solar radiation
48

4.0
3.5
3.0
2.5
2.0
1.5
1.0
SEASON
.5
0.0
Flat s_ hi gh
Fla ts
_me d
SS
_h igh
SS
_lo w
SS
_m ed
US
_h igh
US
_lo w fall
U
S_ me d spring
STRATA
Abbreviations:
Flats_high = Flats with high solar radiation
Flats_med = Flats with medium solar radiation
SS_high = Sideslopes with high solar radiation
US_high = Upper Slopes with high solar radiation
SS_low = Sideslopes with low solar radiation
US_low = Upper Slopes with low solar radiation
SS_med = Sideslopes with medium solar radiation
US_med = Upper Slopes with medium solar radiation
Figure 9. Seasonality, stratification, and their interaction on the number of species detected in the Skyline Wildlife Management Area and Walls of Jericho properties in northern Alabama
49

16
14
12
10
8
6
SEASON
4
2
Flat s_ high
Fl ats
_m ed
SS
_h ig h
SS
_low
SS
_m ed
US
_h ig h
US
_low fall
US
_me d spring
STRATA
Abbreviations:
Flats_high = Flats with high solar radiation
Flats_med = Flats with medium solar radiation
SS_high = Sideslopes with high solar radiation
US_high = Upper Slopes with high solar radiation
SS_low = Sideslopes with low solar radiation
US_low = Upper Slopes with low solar radiation
SS_med = Sideslopes with medium solar radiation
US_med = Upper Slopes with medium solar radiation
Figure 10. Seasonality, stratification, and their interaction on the number of individuals detected in the Skyline Wildlife Management Area and Walls of
Jericho properties in northern Alabama.
50

In fall, the sideslopes with medium and low exposure stratum had the highest average number of species detected (Table 4), while the strata flats with medium exposure and upperslopes with low exposure had the lowest average number of species detected. In spring, the sideslope with high and medium exposure was the stratum with the highest average number of species detected, and the upperslopes with medium and high exposure stratum had the lowest average richness. The average species richness of both seasons combined was the highest for points located in the stratum sideslopes with high and medium exposure. In fall, the average number of individuals detected at each survey point was the highest in the strata sideslopes with medium and low exposure, and was the lowest in the strata upperslopes with high and medium exposure. In spring and for both season combined, the average number of individuals detected at each point was the highest in the strata flats with high solar radiation and sideslopes with high solar radiation. The strata upperslopes with high and medium exposure had the lowest average abundance for spring, fall, and both seasons combined. These areas may have had low number of species and individuals detected because of a lack of moisture retention at these steep locations. Water is more likely to travel to lowest elevations or flatter areas in valleys. In addition, it was noticeably drier on the tops of the plateau, and even when surveys were conducted after rainstorms, the ground dried quickly.
Amphibians may have some difficulty fining areas that meet their moisture requirements in these drier habitats.
51

The spring had higher average species and individuals detections across all strata than fall. A reason for the higher species richness and abundance might be related to the higher level of activity exhibited by herpetofauna during the spring. Mating and foraging activities may bring out a substantial number of animals that would normally be below ground or in their hibernation sites.
Species were ranked by the total number of encounters across all strata and the resultant species dominance-diversity curve (Fig. 11) is horizontal to the right of the first ranked species, Slimy Salamander, PLGL. This curve is an indicator of a community with moderate species diversity or evenness (Hunter,
1990).
The number of species detected in each stratum levels off and shows that even with increased sampling effort, there is not a large increase in species detections based on the species area curves for each stratum (Fig. 12-14). This levelling off suggests that sampling effort was sufficient. Although the stratum of flats with high solar exposure had the fewest points, the flattened out species area curve suggested that the sample size sufficient.
52

Table 4. Average number of species and individuals and their standard deviations detected in Skyline Wildlife
Management Area and Walls of Jericho by survey season and strata.
Average
Number of
Species
Fall
Strata
Strata
Flats_high SS_high Averages
2.33 ± 0.58
1.57 ±
0.79
Spring
Spring and Fall
3 ± 1.0
Combined 2.67 ± 0.82
2.6 ± 1.26
2.18 ±
1.19
2.22 ±
1.40
3.70 ±
1.82
3.05 ±
1.78
2.11 ±
1.49
2.57 ±
1.53
2.37 ±
1.51
2.35 ±
1.50
3.32 ±
1.99
2.85 ±
1.82
1.89 ±
0.94
2.95 ±
1.68
2.42 ±
1.46
2.47 ±
1.39
3.35 ±
1.77
2.95 ±
1.65
2.21 ±
1.51
2.29 ±
1.31
2.25 ±
2.18 ±
1.34
3.03 ±
1.71
1.40
Average
Number of
Individuals
Fall
Spring
Spring and Fall
Combined
8.33 ± 3.06
16.33 ±
11.93
12.33 ±
8.94
7.57 ±
6.11
11.8 ±
13.61
10.06 ±
11.08
8.28 ±
9.38
15.52 ±
15.64
12.34 ±
13.61
4.28 ±
3.20
5.70 ±
8.03
5.07 ±
6.35
9.70 ±
6.98
9.28 ±
8.64
9.48 ±
7.81
6.0 ±
6.35
10.05 ±
7.40
8.03 ±
7.10
10.11 ±
12.81
9.43 ±
9.58
9.74 ±
11.01
5.26 ±
7.70
4.59 ±
6.25
4.94 ±
7.40 ±
8.15
9.60 ±
10.60
6.96
Abbreviations:
Flats_high = Flats with high solar radiation
Flats_med = Flats with medium solar radiation
SS_high = Sideslopes with high solar radiation
US_high = Upper Slopes with high solar radiation
SS_low = Sideslopes with low solar radiation
US_low = Upper Slopes with low solar radiation
SS_med = Sideslopes with medium solar radiation
US_med = Upper Slopes with medium solar radiation
53

10000
1000
100
10
1
PL
GL
P CA
AM
DE
FU
PSR
U
DI
PU
EU
B
LU
UA
M
SC
LA FA C
EU TE
A
SC
N
U
NO
VI SI
TH
AG
LA
CO
V
A
GY
P
O
AM
O
P
CRHO
C
A
/H
YVE
AN
A
C
EU
C
I
EL
E
O
B
S
C
C
L
HE RA S
TO
C
PS A
FE
AC
R
G
R
M
AM CO
A
CO
H
YC
Species Rank
U R
C
A
P
A
RA AM
TA
E
LO
HE
P
L
LA
NI
NE
P
P
L
R
TR
Figure 11. Species dominance curve of herpetofaunal encounters in Skyline and Walls of Jericho Management Area in northern Alabama.
54

Figure 12. Species area curves for strata on upperslopes with low, medium and high exposure in Skyline and Walls of Jericho in northern Alabama.
55

Figure 13. Species area curves for the strata flats with medium and high exposure in Skyline and Walls of Jericho in northern Alabama.
56

Figure 14. Species area curves for the strata sideslopes with low, medium and high exposure in Skyline and Walls of Jericho Management Area in northern
Alabama.
57

Cluster Analysis
I performed cluster analysis for all herpetofaunal species detected using the Euclidean distance and Ward’s grouping method (Hair et al., 1998, SPSS
Inc., 1999) which minimized within and maximize between cluster variations.
One of the two main clusters, located at the bottom of the dendrogram, showed
Eastern Garter Snake ( Thamnophis sirtalis) , Northern Red Salamander, Slimy
Salamander, Ringneck Snake ( Ringneck Snake ), American Toad ( Bufo americanus) , and Eastern Worm Snake as a cluster of related species (Fig. 15).
Other than the Eastern Garter Snake and American Toad, the species in this cluster were some of the most abundant species found. The other main cluster divides into two groups, one of which has a small cluster of three species,
Eastern Box Turtle (Terrapene carolina) (TECA), Gray Treefrog (Hyla chrysoscelis/versicolor) (HYCH/HYVE), and Timber Rattlesnake (Crotalus horridus) (CRHO). The other group, branches off into 2 clusters, and the smaller cluster is made up of reptile species which include: Northern Red-bellied Snake
(Storeria occipitomaculata) STOC, Green Anole (Anolis carolinensis) ANCA,
Ground Skink (Scincella lateralis) SCLA, Broad-headed Skink (Eumesces laticeps) EULA, and Five-lined Skink (Eumesces fasciatus) EUFA. A large cluster of rare species is grouped towards the top of the figure. All species in that cluster had fewer than 25 encounters, with Red-spotted Newt
(Notophthalmus viridescens) NOVI as the most abundant in the cluster with 23 detections. One cluster of species Long-Tailed Salamander (Eurycea longicauda) EULO , Southern Cricket Frog ( Acris gryllus ) ACGR, and Southern
58

Two-Lined Salamander ( Eurycea cirrigera ) EUCI are very closely associated with each other due to the distances with the group. A similar cluster is make up of
Green Frog ( Rana clamitans), Upland Chorus Frog ( Pseudacris triseriata feriarum), and Bullfrog ( Rana catesbeiana) did not join the analysis until the late stages, which may indicate that these species were outliers. The distances between the species in each of these clusters were small, suggesting the similarity among the species. These six species were some of the rarer herpetofauna species found on transects and four of the six species were pond dwelling anurans.
59

100 75 50 25 0
VIVA
DEFU
Unknown
RAPA
PSTR
PSCR
NEPL
LANI
HEPL
GYPO
EU spp
CNSE
AQ?
AMTA
AMOP
ANAE
PSBR
ELGU
COCO
ACCR
NOVI
EULU
AMMA
EULO
ACGR
EUCI
PLDO
AGCO
RACL
PSFE
RACA
HESC
SCUN
ELOB
GACA
STOC
ANCA
SCLA
EULA
EUFA
TECA
HYCH/HYV
CRHO
THSI
PSRU
PLGL
DIPU
BUAM
CAAM
Figure 15. Cluster dendrogram of herpetofaunal species detected in Skyline and
Walls of Jericho Management Areas in northern Alabama.
60

a. Spring 2005
A total of 680 individuals consisting of 36 amphibian and reptile species were found in spring 2005. Transects were completed between April 8 through
July 30. Twenty-eight species were found on line transects and an additional eight species were heard, seen, or trapped. The most abundant herpetofaunal species encountered was the slimy salamander with 416 individuals and or 61% of all individuals found. The other four most abundant species were Eastern
Worm Snake with 58 individuals (8.5%), Cave Salamander ( Eurycea lucifuga ) with 37 individuals (5.4%), Ringneck Snake with 36 individuals (5.2%), and
American Toad with 21 individuals (3%). Several special interest areas were located while traveling to and from points, conducting line surveys, or conducting random habitat searches. These habitats included caves, seeps, permanent and ephemeral ponds, and rock outcrops. The Green Salamander ( Aneides aeneus), a priority 2 species, was found in a seep during a special areas site search. A
Corn Snake ( Elaphe guttata) , a priority 3 species, was found underneath litter and a log while surveying point 66. The total line distance surveyed was 70,400 m (70.4 km), giving a detection average of approximately 9.7 individuals per kilometer. An average of 7.7 animals was detected at each point. b. Fall 2005
A total of 32 species of amphibians and reptiles were found during the fall sampling period, with 30 species seen during transects and two species trapped.
Surveys were conducted from September 19 to November 5. On average, 7.9
61

individuals were found per kilometer, and 6.3 animals encountered per survey point.
Several new species were found during the fall surveys that were not found during the spring. These included three salamanders and two snake species.
The species were: Marbled Salamander ( Ambystoma opacum ), Mole
Salamander ( Ambystoma talpoideum), Southern Two-lined , Green Anole ( Anolis carolinensis), and Black Kingsnake ( Lampropeltis getula niger). Of the 556 individuals documented, the most abundant species was the Slimy Salamander with 400 individuals (71% of animals encountered), followed by Red Salamander with 25 individuals (4.5%), Spotted Dusky Salamander with 22 individuals (4%),
Eastern Worm Snake with 20 individuals (3.6%), and Ground Skink ( Scincella lateralis ) with 12 individuals (2.2%).
One individual of the Green Salamander was encountered during a line transect and its GPS location was marked so that future searches could be performed in the area. It was found underneath loose bark of a downed pine log during the survey of point 62. c. Overall 2005 Results
A total of 44 amphibian and reptile species were seen or heard in the study area, with 1,236 animals encountered during line transect surveys.
Amphibian species accounted for 24 species or 54% of the species detected, and 20 reptile species were found. The most abundant species detected was the
Slimy Salamander making up 66% of detections, followed by Eastern Worm
Snake with 78 individuals (6.3%), Ringneck Snake with 44 individuals (3.6%),
62

Cave Salamander with 38 individuals (3%), and Red Salamander with 36 individuals (2.9%). d. Spring 2006
Twenty-nine species were encountered on transects, with 15 amphibian and 14 reptile species. An average of 7.9 animals was found at each point and 9.8 animals per kilometer surveyed. Transects began on March 21 and ended on
June 10. Out of the 693 detections, Slimy Salamanders made up more than 66% of encounters with 460 individuals found. The remaining most abundant species were Spotted Dusky Salamander with 48 individuals (6.9%), Eastern Worm
Snake with 26 individuals (3.8%), Cave Salamander with 19 individuals (2.7%), and Eastern Zigzag Salamander and Ringneck Snake with 18 individuals each.
Three Green Salamander hatchlings were found on a transect underneath bark on a downed tree. They were found underneath different locations on the log, which was on a slope below a rock outcrop approximately 150 meters from point
122. The outcrop was thoroughly searched with headlamps to find additional individuals but the search did not find any other animals. In addition, an adult
Corn Snake, a priority 3 species was found on a transect underneath rocks and litter during the survey of point 173. e. Fall 2006
A total of 16 amphibian species and 12 reptile species were found during transect surveys between September 19 and November 5. On average 4.3
63

individuals were encountered at each point, and an average of 5.4 animals were detected per kilometer. A total of 378 animals were encountered with Slimy
Salamanders making up half the detections. The other species that had relatively high detections included Zigzag Salamanders with 112 individuals
(29.6%), Red Salamanders with 16 individuals (4.2%), Spotted Dusky
Salamanders with 13 individuals (3.4%), and Eastern Box Turtles ( Terrapene
Carolina ) and Five-lined Skinks ( Eumeces fasciatus) each with 6 individuals
(1.6%). Species that were found in the fall that were not detected in the previous spring were: the Southern Cricket Frog, Six-lined Racerunner ( Cnemidophorus sexlineatus) , Northern Black Racer ( Coluber constrictor ), Four-toed Salamander
(Hemidactyluim scutatum ), Northern Spring Peeper ( Pseudacris crucifer) , Upland
Chorus Frog, Bullfrog, and Northern Red-bellied Snake ( Storeria occipitomaculata). f. Overall 2006 Results
For the year, 38 species were identified comprised of 21 amphibian and 18 reptile species. Amphibian species accounted for a large portion of encounters with 88.6 percent, and reptile species at 11.4 percent. The most abundant species found were the Slimy Salamander with 649 encounters (60.6%), Zigzag
Salamander with 130 encounters (12.1%), Spotted Dusky Salamander with 61 encounters (5.6%), Red Salamander with 33 encounters (3.1%), and Eastern
Worm Snake with 27 encounters (2.5%).
64

Other Methods
I used pond surveys to collect data on pool-habituating herpetofauna not readily surveyed by terrestrial line transects. Salamander species found during the pool surveys included: Spotted (Ambystoma maculatum), Marbled
(Ambystoma opacum) , Four-toed , Mole (Ambystoma talpoideum), Red-spotted
Newt in the adult phase (Notophthalmus viridescens), and Zigzag. Frog and toad species encountered during pond surveys included: Northern Leopard Frog
( Rana pipiens ), Gray Treefrog , Green Frog , Bullfrog , Northern Spring Peeper ,
Mountain Chorus Frog (Pseudacris brachyphona), and American Toad .
Two reptile species were found during pond surveys: the Midland Water Snake
(Nerodia sipedon) and the Common Snapping Turtle .
All herpetofaunal species found during pond surveys were also found on terrestrial line transects and did not add to the species richness list generated based on transect survey.
Artificial coverboards (tin pieces) were checked throughout the summer of
2006 whenever pond surveys were conducted. This method did not yield any herpetofaunal individuals. Capture rates of animals may be low or depressed in the first year of monitoring of artificial coverboards because animals might need time to locate such refuges (Grant et al., 1992; Droege et al., 1997). Tin pieces were used as an inventory method because several Black Rat Snakes ranging from juveniles to adults were found under a piece of discarded tin off Tate Cove
Road within the Skyline Management Area in the spring of 2005. The artificial object was probably there for an extended amount of time which allowed for these snakes to find this cover and utilize it.
65

Opportunistic searches on the property at seeps, caves, and rock outcrops found some of the most abundant species encountered during the study such as
Slimy Salamanders (Plethodon glutinosis) , Red Salamanders (Pseudotriton ruber), and Dusky Salamanders (Desmognathus fuscus spp.) . A Green
Salamander (Aneides aeneus) was found along an outcrop. In addition, one
Green Salamander (Aneides aeneus) individual was also found crossing the road during an evening rainstorm.
Estimations of Detection Rate, Abundance, and Density
Because I had low detection numbers for most species, I grouped species into ecological guilds to estimate the detectability, density, and abundance in the study area. These guilds included all amphibians, all reptiles, amphibian and reptile species in spring and fall separately, terrestrial salamanders, stream salamanders, frogs and toads, snakes, and lizards and skinks (Table 5). All detection models were truncated at 8 m to eliminate outliers and improve fit
(Buckland et al., 2004). One of the assumptions of distance sampling is that objects on the line are detected perfectly, whereas the actual detection probability of encountering an animal using transect data is less than 1. Thus, the underestimation of animals along the line was quantified to provide an error term or variance in the detection function and abundance estimates.
The detection probability on the transect line (distance = 0) was the highest for stream salamanders (30%) and the lowest for terrestrial and spring amphibians (both 20%). There was an inverse relationship between effective
66

search width and detection probability on transect lines. When the search width was wider, the probability of finding a herpetofauna individual was lower since the observers searched a larger strip of land. The detection probability of finding an animal varied among guilds due to the variation in the effective search width
(ESW), the distance at which the number of herpetofauna missed within that distance is equal to the number of herpetofauna detected beyond that point
(Wilson and Doherty, 2006). The ESW could be affected by behavioral patterns and habitat associations among species. These results suggest that stream salamanders were more likely to be detected closer to the line compared to other groups. The streams were searched with the same effort as standard terrestrial surveys if they were within 10 m or crossed a transect line. The detection probability in the study area, the likelihood of encountering an individual on the property, was the highest for terrestrial salamanders and spring amphibians, and the lowest for stream salamanders. This is pattern was supported by detection data that showed slimy salamander (a member of both the terrestrial salamander guild and the amphibian guild) as the most often detected animals among all species found during this study. Amphibians and terrestrial amphibians had the highest average number of encounters per kilometer. This was unsurprising since Slimy Salamanders and Zigzag Salamanders were the two most abundant species and were both terrestrial. Based on the distance sampling estimations, there were 38,245,000 amphibians (396/km
2
) and 8,426,600 reptiles (87/km
2
) in the study area. The estimations for density and relative abundance for the
67

groups were obtained by determining the total length of transects and the total area of study.
The variance in estimating density was divided into two components: the variance of observations between sampling points (detection probability) and the variance of individuals within the sampling points (encounter rate). Reptiles had the highest detection variation (33.5%) and the lowest variation in encounter rate
(66.5%), while terrestrial salamanders had the lowest detection variation (4.6%) and the highest variation in encounter rate (95.4%). These results suggested that the number of reptiles was more variable among sampling points and less variable within the sampling point compared to other groups. The general low number of reptiles and high number and ubiquity across landscape of Slimy
Salamanders contributed to the variation in estimating the density of herpetofauna.
68

Table 5. Probability detection models of different herpetofaunal guilds generated in program DISTANCE using line transect data obtained in Skyline Wildlife Management Area and Walls of Jericho in northern Alabama.
Ecological
Guild
Key
Function
Series
Expansion
Detection
Probability on
Transect
Line
Detection
Probability in Study
Area
Effective
Search
Width
(m)
(ESW)
Average No. of
Encounters per km
Estimate of
Density of
Animals
(n/km
2
)
Estimated
No. of
Animals in
Study
Area
Percentages of
Variance (D)
Detection
Probability
Encounter
Rate
Amphibians
Spring
Amphibians
Fall
Amphibians
Terrestrial
Salamanders
Stream
Salamanders
Frogs and toads
Reptiles
Spring
Reptiles
Hazardrate
Hazardrate
Halfnormal
Hazardrate
Halfnormal
Halfnormal
Halfnormal
Halfnormal
Simple
Polynomial
Simple
Polynomial
Hermite
Polynomial
Simple
Polynomial
0.20
0.20
0.21
0.20
0.62
0.63
0.59
0.63
4.96
5.07
4.70
5.08
0.00491
0.0.00278
0.00212
0.00437
396
219
180
344
3.8 x 10
7
5.6 94.4
2.1 x 10
7
7.6 92.4
1.7 x 10
7
19.2 80.8
3.3 x 10
7
4.6 95.4
Cosine 0.30 0.41 3.32 0.000254 30 3.0 x 10
6
6.7 93.3
Simple
Polynomial 0.24 0.52 4.19 0.000194 18 1.8x
Simple
Polynomial
Simple
Polynomial
0.24
0.23
0.52
0.55
4.16
4.40
0.000907
0.006502
87
59
8.4 x 10
5.7 x 10
6
6
33.5
33.2
66.5
66.8
Fall Reptiles Uniform Cosine 0.25 0.50 4.00 0.000257
0.000564
Snakes Uniform Cosine 0.23 0.54 4.34
Lizards and
Skinks
Halfnormal
Simple
Polynomial 0.25 0.49 3.95 0.000273
25
53
27
2.5 x 10
6
23.9 76.1
5.1 x 10
6
12.4 87.6
2.7 x 10
6
28.8 71.2
69

Prediction Maps a. Herpetofaunal distribution and abundance maps and accuracy assessment
For the species prediction map, the simple kriging method produced the lowest prediction errors among all the models run (Fig. 16). Several areas of high species richness (> 4 species) or ‘hot spots’ were illustrated on the property with several areas having clusters of survey points. One hot spot was located by
Tate Cove and Jack Gap. This area includes points 33, 128,172, 122, 144, 148,
120, 37, 152, and 131. Another species richness hot spot was in the southwest part of the Skyline Management Area with points 96, 87, 100, 78, 35, 129, and
10. The best model for predicting species abundance was radial basis functions
(Fig. 17). Hot spots for herpetofaunal abundances were located on the southeast part of the study area (Jacob’s Farm), which consisted of points 93 and 112. The main property of Skyline and Walls of Jericho had four large areas of high abundance (> 27 individuals). Two of the hot spots comprised of large clusters of points, whereas the two remaining hot spots had only a single survey point. One of the two large hot spots was also identified above as a species riches hot spot, the Tate Cove and Jack Gap cluster, while the other group consisted of points
23, 50, 67, 69, 103, 109, and 166 located off Letson Point. The two high abundance hot spots with single points were points 9 and 165 in the Summer’s
Top area.
The cross validation function in ArcMap was used to produce a table of prediction values and errors for each of the survey points by the different models
70

on the Nature Conservancy property. I then compared the prediction errors for the species richness and abundance produced by each prediction model. For the number of species, the most accurate prediction models that had the lowest prediction errors were simple kriging and ordinary kriging. This was consistent with the validation results based on the same points used to generate the prediction function from the Nature Conservancy property. For predicting the abundance of herpetofauna, the cross validation result was also consistent with the models based on the data from the Nature Conservancy with radial basis functions as the best model. Based on validation and cross-validation using the
Nature Conservancy data, prediction maps for soil pH and moisture were generated for the study area. Ordinary kriging was the best predictive model for both soil pH (Fig. 18) and moisture (Fig. 19).
The average soil pH was 5.25 + 0.91 (range 3.61-7.40), and the average soil moisture was 9.26 + 5.51%/volume (range 0.90-30.5%). Soil pH and soil moisture were correlated (r = 0.31, n = 176, p <0.01). Soil pH was also correlated with the herpetofaunal species richness (r = 0.19, n = 176, p < 0.05) and relative abundance (r = 0.31, n = 176, p < 0.01). Soil moisture had no relationship with herpetofaunal richness and abundance. Sugalski and Claussen
(1997) also found that soil pH was more influential than soil moisture in determine amphibian distributions. By overlaying soil pH or soil moisture maps on the herpetofaunal species richness and abundance maps, I noticed two hot spots of points with high species richness that showed different soil pH. The hot spot in the southwest Skyline Management Area had relatively low pH levels
71

ranging 3.70 to 4.63, while the Tate’s Cove points had a relatively higher soil pH ranging 5.18 to 6.29. Wyman (1988) showed that soil acidity and moisture greatly influenced the distribution of amphibians. Forest soils in the eastern
United States are usually acidic (Krug and Frink, 1983) and can be lethal or can inhibit growth of some species (Wyman, 1988). Wyman (1990) found that most amphibian species in the northeast Unites States have soil pH tolerances greater than 3.7. Although soil pH differed between these two locations, the pH levels of these two locations were still above the tolerance level. The similar species richness and composition between these two locations of different pH level may suggest that within the soil pH tolerance levels, other habitat or landscape factors are important in determining herpetofaunal species richness and composition.
72

Figure 16. Species prediction map for Skyline Wildlife Management Area and
Walls of Jericho in northern Alabama using the simple kriging method in
ArcMap’s Geostatistical analyst.
73

Figure 17. Herpetofauna abundance prediction map for Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama using the radial basis functions method in ArcMap’s Geostatistical analyst.
74

Figure 18. Soil pH prediction map for Skyline Wildlife Management Area and
Walls of Jericho in northern Alabama using ordinary kriging method in ArcMap’s
Geostatistical analyst.
75

Figure 19. Soil moisture (% / Volume) prediction map for Skyline Wildlife
Management Area and Walls of Jericho in northern Alabama using ordinary kriging method in ArcMap’s Geostatistical analyst.
76

Principle Components and Canonical Correspondence Analysis
Principles Components Analysis (PCA) (PC-ORD for Windows) was used to reduce the dimensions and redundancy among variables and to estimate relationships among variables. For the landscape variables, the first component
(PC1) had high positive loading on the distances to mixed forest and evergreen forest (Table 5). This component was defined as “distance to forest edge”.
Component two (PC2) had high positive loadings for slope, distance to disturbance, distance to road, and distance to stream, and negative loading on distance to deciduous forest; this component was referred as the “disturbance” component. Component three (PC3) was positively associated with distance to stream and related to elevation, this component was referred to as the “stream” component. Component four (PC4) had high loading for aspect variables:
(northness and eastness) and was referred as the “aspect” term. Cumulative variance for the listed components was approximately 70 percent.
For the habitat variables, component one (PC1) contained positive associations for percent canopy cover, average duff layer, and total DBH, and negative associations for soil moisture (Table 7). This component was referred to as the “canopy, duff, and DBH” component. The second component (PC2) contained positive associations with understory percent cover and mid-story percent cover and both had strong positive values, and was referred to as the
“under and mid-story” component. Component three (PC3) was referred as the
“soil” component because it was strongly positive in soil pH and soil moisture.
The last component was comprised of downed woody debris (CWD) volume and
77

percent ground cover and was referred to as the “CWD and ground” component.
Cumulative percent variation for the vegetation components was approximately
65 percent.
Table 6. Principle Components Analysis of landscape variables at survey points in Skyline Wildlife Management Area and Walls of Jericho in northern Alabama.
Eigenvalues and eigenvectors PC1 PC2 PC3 PC4
% Variation 30.21 15.84 12.39 10.88
Cumulative % variation 30.21 46.04 58.43 69.31
Eigenvectors*
Distance to Mixed For.
Distance to Evergreen For.
0.868
0.856
0.0362 0.0827 0.109
0.219 0.134 0.061
Slope
Distance to Disturbance
Distance to Road
Distance to Stream
-0.168
0.351
0.811
0.714
0.374 0.671
-0.00075 -0.206
-0.0123 0.234
0.139 -0.135
-0.157 -0.146
0.821 0.0713
Elevation
Distance to Deciduous
Forest.
Northness
0.531 0.116 0.702 0.0363
-0.0948 -0.506 -0.654 0.16
-0.0393 0.00255 0.0921 0.784
Eastness 0.158 -0.0205 -0.0742 0.649
* Varimax rotation with Kaiser Normalization used.
78

Table 7. Principle Components Analysis of habitat variables at survey points in
Skyline Wildlife Management Area and Walls of Jericho in northern Alabama.
Eigenvalues and Eigenvectors
Eigenvalue
% Variation
Cumulative % Variation
PC1
1.934
21.491
21.491
PC2
1.539
17.098
38.59
PC3
1.368
PC4
1.085
15.203 12.061
53.793 65.854
Eigenvectors*
Percent Canopy Cover
Average Duff Layer
Total DBH
Understory Percent Cover
0.834
0.815 0.07982 0.0339 -0.152
0.458 -0.39 -0.139 0.321
0.0287 0.831 0.003734 0.292
Mid-story Percent Cover
Soil pH
0.00685
0.716 -0.0574 -0.249
0.146 0.008846 0.871 -0.00181
Soil Moisture -0.501 -0.17
Downed Woody Debris Volume -0.0548 -0.058
Ground Percent Cover 0.07531
0.364
* Varimax rotation with Kaiser Normalization used.
0.63
-0.0625
0.444
-0.158
0.784
0.469
79

Relationships between Herpetofauna and Landscape and Habitat Factors
Canonical Correspondence Analysis (CCA) (PCORD for Windows 2006) was utilized to determine relationships between eight components (four for landscape labeled LAN1 through LAN4 and four for habitat variables, labeled
HAB1 through HAB4) and the herpetofaunal community (Fig. 20). The herpetofaunal guilds were plotted at their optimal habitat or environmental conditions. Their proximity and location on the figure in relation to landscape and habitat axes were the indications of most favorable conditions for the species.
Pond salamanders and turtles were highly associated with the stream component
(Fig. 21), whereas stream and terrestrial salamanders were related to both stream and soil components. These results were consistent with the life history requirement and general distribution pattern of these species (Mount 1975).
Snakes were positively related to distance to forest edges. It supports earlier findings that snakes tended to be associated with forest edges, probably due to higher mammalian prey availability in these areas (Carfagno et al., 2006).
Anuran species appeared to be associated with the aspect component, which might be related to light intensity, moisture gradients, and location of ephemeral pools. Terrestrial and stream salamanders were also affected by the disturbance component. Semlitsch et al. (2007) showed that terrestrial salamanders were affected by disturbances, negatively associated with roads; and salamander abundance was lower in habitats near roads. Forest dependent salamander species, such as Plethodontid salamanders, were found negatively related to old abandoned forest roads (Semlitsch et al., 2007). The road system in the Skyline
80

and Walls of Jericho Management area is extensive, and main roads are maintained for recreational users of all-terrain vehicles (ATV). Line transects would occasionally cross or closely parallel ATV roads, and on these transects, herpetofauna would rarely be detected probably because of the drier conditions close to roads and lack of cover objects. As mentioned earlier soil conditions affected the herpetofaunal community structure and the soil component from
CCA affected not just pond breeding salamanders, but also affected the existence and abundance of terrestrial salamanders. The importance of the canopy, duff, and DBH component affected terrestrial salamanders, which is supported by previous research that suggested that the amount of canopy cover affected salamander presence and abundance (Gibbs, 1998). Gibbs (1998) found that spotted salamanders were absent in forest areas with a forest cover threshold of 30% and red-spotted newts had a limit of about 50%.
81

Figure 20. First and second Canonical Correspondence axes with herpetofaunal guilds and landscape and habitat variables for Skyline and Walls of Jericho in northern Alabama.
Figure 21. First and third Canonical Correspondence axes with herpetofaunal guilds and landscape and habitat variables for Skyline and Walls of Jericho in northern Alabama.
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Correlation of Principle Components and Diversity Indices
Shannon-Weaver and Simpson Diversity Indices had the highest correlation with the canopy, duff, and DHB (HAB1) component (Table 8). Although the relationships (0.37 and 0.37, respectively) were significant at the 0.01 alpha level, it is not a strong correlation. Factor loadings greater then ±0.30 are considered to meet the minimal level of significance, and loadings of ±0.40 are considered more important (Hair et al., 1998). Of the landscape and habitat components, canopy, duff, and DBH (HAB1) was the only component that showed significance at the 0.01 alpha level for all species and diversity factors. It was also the only component having factor loadings greater than ± 0.30 for four of the five factors. The canopy, duff, and DBH component appeared to be an important part in affecting the forest floor microhabitat. Canopy cover shades the forest floor from direct sunlight, and moderates forest floor temperatures and soil moisture (Felix 2007; Brooks and Kyker-Snowman, In Press). Leaf litter provides important cover for forest herpetofauna, especially amphibians, which prefer shaded, cool, and moist forest floors. Disturbance (LAN2) was the other component correlated with the species and diversity indices. Several landscape and habitat components did not show any relationship with the species richness and diversity indices. These components included: aspect (LAN4), understory and midstory (HAB2), and ground cover and CWD (HAB4).
83

Table 8. Correlations Analysis of herpetofaunal species diversity indices and landscape and habitat variables generated for Skyline and Walls of Jericho Management Areas in northern Alabama.
Correlations
NUM_IND
RICHNESS
EVENNESS
Shannon Weaver
SIMPSON
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
Pearson Correlation
Sig. (2-tailed)
N
LAN1
-.234**
.002
176
-.100
.186
176
.132
.081
176
.038
.619
176
.083
.275
176
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
LAN2
.186*
.013
176
.272**
.000
176
.242**
.001
176
.225**
.003
176
.223**
.003
176
LAN3
-.027
.720
176
.136
.072
176
.275**
.000
176
.206**
.006
176
.244**
.001
176
LAN4
-.113
.137
176
-.022
.767
176
-.001
.993
176
.014
.849
176
.002
.975
176
HAB1
.276**
.000
176
.371**
.000
176
.357**
.000
176
.371**
.000
176
.372**
.000
176
HAB2
-.112
.138
176
-.116
.126
176
-.038
.619
176
-.071
.352
176
-.058
.442
176
HAB3
.305**
.000
176
.222**
.003
176
.063
.405
176
.149*
.048
176
.105
.165
176
HAB4
-.015
.845
176
.067
.377
176
.116
.125
176
.137
.070
176
.141
.062
176
Abbreviations for Principle Components:
Lan1 = distance to forest edge
Lan2 = disturbance
Hab1 = canopy, duff, DBH
Hab2 = under and mid story
Lan4 = aspect Hab4 = ground cover, CWD
84

Prediction Models for Herpetofaunal Species Richness and Abundance
Based on Landscape and Habitat Components
The Ordinary Least Squared (OLS) regression model based on eight habitat and landscape variables was significant in predicting herpetofaunal species richness (F = 9.21, df = 8 and 167, R
2
= 0.31, p < 0.001,) and abundance
(F = 9.79, df = 8 and 167, R
2
= 0.32, p < 0.001). Habitat components of canopy cover (HAB1) and soil condition (HAB3) and landscape components of disturbance (LAN2) and stream (LAN3) were the significant predictors of herpetofaunal species richness (Table 9). The standardized beta coefficients suggested that the two habitat components had larger unique contributions in predicting herpetofaunal species richness (Table 9). For the prediction of herpetofaunal abundance, habitat components of canopy cover (HAB1) and soil condition (HAB3) and landscape component of disturbance (LAN2) were the significant predictors (Table 10). The standardized beta coefficients suggested again that the two habitat components had larger unique contributions in predicting herpetofaunal species richness. These models agreed with the patterns observed and discussed above based on the canonical correspondence analysis. It appeared that the amount of canopy cover and soil conditions, particularly pH, are the most important factors in determining the herpetofaunal species richness and abundance. With the region of soil pH and canopy cover conditions found at the study area, the species richness and abundance increased as the values of these variables increased.
85

Table 9. Linear regression coefficients and significance of landscape and habitat components in predicting herpetofauna species richness at Skyline and Walls of
Jericho Management Area in northern Alabama,
Unstandardized
Coefficients
Standardized
Coefficients
Variable
Constant
LAN1
LAN2
LAN3
LAN4
HAB1
HAB2
HAB3
HAB4
B
.570
-.010
.053
.045
-.015
.082
-.018
.061
.029
Std. Error
.017
.019
.020
.020
.018
.021
.018
.019
.018
Beta
-.036
.193
.167
-.056
.303
-.067
.225
.106 t
32.598
-.515
2.601
2.302
-.865
3.928
-1.003
3.230
1.580
P
.000
.607
.010
.023
.388
.000
.317
.001
.116
Table 10. Linear regression coefficients and significance of landscape and habitat components in predicting herpetofauna abundance in Skyline and Walls of Jericho Management Area in northern Alabama.
Unstandardized
Coefficients
Standardized
Coefficients
Variable
Constant
LAN1
LAN2
LAN3
LAN4
HAB1
HAB2
HAB3
HAB4
B
.899
-.067
.081
.024
-.055
.155
-.043
.147
.029
Std. Error
.032
.035
.037
.036
.032
.039
.034
.035
.034
Beta
-.133
.160
.047
-.108
.306
-.085
.291
.058 t
27.889
-1.946
2.182
.649
-1.687
4.011
-1.285
4.230
.872 p
.000
.053
.031
.517
.094
.000
.200
.000
.384
86

Relationship between Land Cover Type Composition and Herpetofaunal
Species Richness and Abundance
The analyses of land cover compositions based on AL-GAP (2007) data within 150 m radius of each sampling point showed eight major classes in the study area, including developed space (0.62%, occurred at 8 points),
Cumberland dry oak forest and woodland (21.96%, occurred at 91 points), southcentral interior mesophytic forest (20.22%, occurred at 150 points), southern ridge and valley dry calcareous forest (42.04%, occurred at 121 points), shrub
(4.71%, occurred at 46 points), pine (1.38%, occurred at 18 points), herbaceous/crop (8.67%, occurred at 43 points), and cliff (0.40%, occurred at 3 points) (Appendix 2).
The OLS regression models for predicting herpetofaunal species richness and abundance based on the land cover compositions at survey points were significant (F = 12,74, df = 8 and 167, R
2
= 0.35, P < 0.001 and F = 18.67, df = 8 and 167, R
2
= 0.47, P < 0.001, respectively). The amount of oak forest, mesophytic forest, and calcareous forest were the significant predictors of herpetofaunal species richness (Table 11). With the increase of these forest types, herpetofaunal species numbers tended to increase. Although not significant, developed space, pine forest, shrub land, herbaceous and crop land cover all had negative slopes in this regression model (Table 11). It suggested that with increases of these landscape covers, the herpetofaunal species richness decreased. Similar to the prediction model of species richness, herpetofaunal abundance also increased with the increase of oak forest,
87

mesophytic forest, and calcareous forest (Table 12). Oak forest had the highest impact on the herpetofaunal abundance. Again, nonforest landcover types all had negative slopes with shrub and herbaceous/crop landcover with the highest negative slope.
Amphibian species populations have been declining, and in some cases disappearing (Alford and Richards, 1999; Marks, 2006). Some documented declines of amphibian populations have been attributed to forest habitat fragmentation or conversion to other habitats (Beebee, 1996; Marks, 2006). For example, Petranka et al. (1993) revealed a significant difference in the species richness and abundance of salamanders in clearcut areas and mature forests in the Appalachian Mountains. Logging operations in North America threatened both the rare larch mountain salamander ( Plethodon larselli ) in Washington
State, and the red hills salamander ( Phaeognathus hubrichti ) in Alabama, probably by changing the soil conditions (Aubry et al., 1987; Dodd, 1991).
Alabama has the second largest commercial forest in the nation (Alabama
Forestry Commission, 2003). Northern Alabama landscape is dominated by deciduous hardwood forest. These forests are largely owned by non-industrial private forest (NIPF) landowners. Approximately 85% of the land area in forest cover belongs to this group, while commercial and public interests combined make up 15% (Hartsell and Vissage, 2001). Some of the largest and least fragmented tracts of mature upland forest in the region are in a mixed matrix of state, industrial, and NIPF ownership and are very important for sustaining herpetofaunal communities. Large areas of forested land under private
88

ownership, as is the case in northern Alabama, should be instrumental to the conservation of forest herpetofauna.
89

Table 11. Linear regression coefficients and significance of land cover composition within 150 m radius of sampling point in predicting herpetofauna species richness in Skyline and Walls of Jericho Management Area in northern
Alabama.
Unstandardized
Coefficients
Standardized
Coefficients t Sig.
Constant
Developed
Cliff
Dry oak forest
Mesophytic forest
Pine forest
Shrub
Herb/crop
Calcareous forest
B
.287
-.001
.006
.005
.005
-.003
-.002
-.003
.004
Std. Error
.126
.004
.005
.002
.002
.003
.002
.002
.002
Beta
-.011
.074
.386
.257
-.061
-.081
-.164
.347
B
2.277
-.171
1.183
2.631
3.016
-.949
-1.063
-1.530
2.200
Std. Error
.024
.865
.239
.009
.003
.344
.289
.128
.029
Table 12. Linear regression coefficients and significance of land cover composition within 150 m radius of sampling point in predicting herpetofauna abundance in Skyline and Walls of Jericho Management Area in northern
Alabama.
Variable
Unstandardized
Coefficients
Standardized
Coefficients
Constant
Developed
Cliff
Dry oak forest
B
.401
-.002
-.004
.012
Std. Error
.217
.007
.008
.003
Beta
-.017
-.027
.468
T
1.852
-.289
-.462
3.456 p
.066
.773
.645
.001
Mesophytic forest
Pine forest
Shrub
Herb/crop
Calcareous forest
.010
-.001
-.006
-.004
.005
.003
.005
.003
.003
.003
.284
-.013
-.141
-.112
.217
3.613
-.215
-1.990
-1.136
1.489
.000
.830
.048
.258
.138
90

Chapter 5
CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS
During this study, the intensive surveys detected a total of 46 species including 26 amphibian and 20 reptile species. Amphibians dominated the herpetofaunal community measured by the abundance (84% total detections).
By using different GIS applications to estimate animal population numbers, the state and other organizations will be able to identify areas of high animal abundance and diversity for the management of state owned lands and areas of possible land acquisition.
While Slimy Salamander, Eastern Zigzag Salamander , Eastern Worm
Snake, Spotted Dusky Salamander, and Red Salamander accounted for 81% of the total detections, most species were rare. The secretive nature of these species could be the factor contributing to this pattern. Although the survey effort was very intensive, the distance sampling estimation suggested that the detection rate was below 50% even for the most abundant species and guilds.
The species and sampling effort curve suggested that the number of species detected had levelled off or the number of new species that could be found by additional sampling effort was at a minimum. These patterns suggest that the sampling effort was sufficient, and the low detection rate of these species were mostly due low abundance and/or the secretive nature of herpetofauna.
91

Species richness and abundance varied by season. Both species richness and abundance in spring were double compared to what was detected in fall. This pattern reflected the more active foraging and breeding activities of herpetofauna in spring. Most salamanders and frogs breed in spring and early summer, and only a few species such as Marbled Salamanders initiate breeding activities in fall. Mating and foraging activities may bring out a substantial number of animals that would normally be below ground or in their hibernation sites. Climate conditions may be contributing factors for the lower detections of species and abundance. In northern Alabama, precipitation has been below average for the past two years, which may have limited the activities of some herpetofauna that are sensitive to drier conditions.
Habitat and landscape components were significant in the prediction of herpetofaunal species abundance and diversity. Canopy, duff, and DBH, soil, and distance to forest edge were components most significant in predictions of herpetofauna species diversity and abundance. Components significantly correlated to species abundance and diversity indices were canopy, duff, and
DBH and disturbance. Species and individuals were not distributed randomly across the landscape. There appeared to be a relationship between richness and abundance among the strata. The sideslopes with medium solar exposure had the highest averages for species detected in both seasons and overall encounters. Sideslopes with high exposure had the highest averages for the number of individuals and species encountered overall detections. This pattern could be related to the vegetation structure and microclimate conditions
92

associated with this stratum. On upperslopes, light and moisture conditions might impact amphibian distribution. Most salamanders react negatively to light
(Test, 1946; Ray, 1970). Sugalski and Claussen (1997) found that salamanders were less likely to migrate away from light if they were in locations of suitable soil pH or moisture levels, but high light levels are usually associated with higher temperatures and decreased moisture conditions (Heatwole, 1962; Roth, 1987).
On the sideslopes, the habitat conditions were probably more diverse with intermediate light and moisture conditions. The Tate Cove and Jack Gap area, which had high species diversity and abundance, also had moderate soil pH levels. Four other areas showed high abundance: Letson Point, northern location of Jacob’s Farm, the southern tip of the southwestern Skyline property, and Summer’s Top. These patterns were consistent with strata-related species and abundance variations. Land cover composition in the sampling area affected herpetofaunal species richness and abundance. With increased deciduous forest cover, herpetofaunal species richness increased. This pattern was likely due to the decline of amphibian species richness and abundance associated with nonforested habitat.
Based on the results from this study, I would like to make the following recommendations:
1. A long term monitoring program for herpetofauna at the study site is recommended. My study was conducted both in spring and fall within a two year period and each sampling point was surveyed for one spring and one fall season. Herpetofaunal population and community structure may
93

vary annually depending on climate conditions. To have a more complete database and better understanding of population dynamics and community structure of herpetofauna, we recommend establishing a multiyear and long-term inventory and monitoring program at the site.
2. Because herpetofaunal species richness and abundance were not randomly distributed across the landscape, I recommend that areas with high species richness and abundance should be further examined and monitored.
3. Green Salamander is a conservation priority species and classified as cliffs and rock-face habitat specialist (Gordon, 1952). Individuals occupy crevices in rock outcrops and cliffs in mature mixed mesophytic and mixed oak forests. During the breeding season females deposit their eggs in crevices and under bark of rotten logs (Petranka 1998). This study detected the species in a forest habitat that is not typical for the species.
A more careful examination of this species’ population status and its specific habitat at the study site and its adjacent area is recommended.
4. GIS was used to predict the distribution of species diversity and abundance. However, because of the low abundance for most species in this study, I performed analyses for ecological guilds of adequate sample size. I recommend that the state use this technique in the future to predict and manage herpetofauna species, particularly after additional data is accumulated.
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5. The amount of deciduous forests was significant positive predictors of herpetofaunal species richness and abundance. Given that more than
85% of the forested land in northern Alabama are currently privately owned, and these lands are constantly under the pressure for development and logging, the acquisition of these lands by the state or other government agencies and conservation organizations is a high priority (Beebee 1996) for the conservation of these prestigious natural resources including the herpetofaunal community.
95

APPENDICES
96

Appendix A. Land type classes of Alabama Gap Analysis Program (AL-GAP
2007) and reclassified types for this study. The AL-GAP classes that were not reclassified did not occur at the study area of Jackson County, Alabama.
ID Code
0 Unclassified
1 SEGAP111 Open (Fresh)
2 SEGAP112 Open (Brackish/Salt)
3 SEGAP113 Open (Aquaculture)
4 SEGAP211 Developed Open Space
5 SEGAP220 Low Intensity Developed
6 SEGAP230 Medium Intensity Developed
7 SEGAP240
12 CES203.266
17 SEGAP312
18 SEGAP313
28 CES202.309
29 CES202.356
30 CES202.386
32 CES203.492
High Intensity Developed
Florida Panhandle Beach Vegetation
Bare
AL-GAP Ecological Land Type
Quarry/Strip Mine/Gravel Pit
Southern Interior Acid Cliff
Southern Interior Calcareous Cliff
Southern Piedmont Cliff
East Gulf Coastal Plain Dry Chalk Bluff
Reclassified
Developed
Developed
Cliff
35 SEGAP321 Unconsolidated Shore (Lake/River/Pond)
36 SEGAP322 Unconsolidated Shore (Beach/Dune)
38 CES202.359b Allegheny-Cumberland Dry Oak Forest and
Woodland – Hardwood
East Gulf Coastal Plain Interior Shortleaf Pine-Oak
44 CES203.506b Forest - Hardwood Modifier
45 CES203.502 East Gulf Coastal Plain Limestone Forest
East Gulf Coastal Plain Northern Dry Upland
46 CES203.483 Hardwood Forest
47 CES203.481 East Gulf Coastal Plain Northern Loess Bluff Forest
Dry oak forest
48 CES203.482a
49 CES203.477
East Gulf Coastal Plain Northern Loess Plain Oak-
Hickory Upland - Hardwood Modifier
East Gulf Coastal Plain Northern Mesic Hardwood
Forest
50 CES203.556
51 CES203.476
East Gulf Coastal Plain Southern Loess Bluff
Forest
East Gulf Coastal Plain Southern Mesic Slope
Forest
53 CES202.898 South-Central Interior Highlands Dry Oak Forest
Mesophytic forest
55 CES202.373 Southern and Central Appalachian Cove Forest
56 CES202.886 Southern and Central Appalachian Oak Forest
60 CES202.457b Southern Ridge and Valley Dry Calcareous Forest Calcareous Forest
East Gulf Coastal Plain Interior Upland Longleaf
62 CES203.496d Pine Woodland - Offsite Hardwood Modifier
Southern Piedmont Dry Oak-(Pine) Forest -
66 CES202.339a Hardwood Modifier
East Gulf Coastal Plain Black Belt Calcareous
69 CES203.478b Prairie and Woodland - Woodland Modifier
97

Appendix A (continued)
ID Code Ecological Land Type
71 SEGAP420 Evergreen Plantations
79 CES203.503 East Gulf Coastal Plain Maritime Forest
Reclassified
Pine forest
East Gulf Coastal Plain Northern Loess Plain Oak-
80 CES203.482b Hickory Upland - Juniper Modifier
85 CES202.332 Southern Appalachian Low Mountain Pine Forest Pine forest
Southern Piedmont Dry Oak-(Pine) Forest -
86 CES202.339b Loblolly Pine Modifier
East Gulf Coastal Plain Interior Upland Longleaf
94 CES203.496c Pine Woodland - Loblolly Modifier
East Gulf Coastal Plain Interior Upland Longleaf
95 CES203.496a Pine Woodland - Open Understory Modifier
100 CES202.319 Southern Piedmont Longleaf Pine Woodland
East Gulf Coastal Plain Northern Dry Upland
101 CES203.483c Hardwood Forest - Offsite Pine Modifier
Allegheny-Cumberland Dry Oak Forest and
102 CES202.359a Woodland - Pine Modifier
103 CES202.457a Southern Ridge and Valley Dry Calcareous Forest Calcareous Forest
East Gulf Coastal Plain Interior Shortleaf Pine-Oak
106 CES203.506a Forest - Mixed Modifier
Northeastern Interior Dry Oak Forest - Mixed
107 CES202.592c Modifier
117 CES202.334 Nashville Basin Limestone Glade
Ridge and Valley Calcareous Valley Bottom Glade
118 CES202.024 and Woodland
125 SEGAP511 Successional Shrub/Scrub (Clear Cut) Shrub
126 SEGAP512 Successional Shrub/Scrub (Utility Swath)
127 SEGAP513 Successional (Other)
Shrub
Shrub
132 CES203.478a
134 CES203.555
East Gulf Coastal Plain Black Belt Calcareous
Prairie and Woodland - Herbaceous Modifier
East Gulf Coastal Plain Jackson Prairie and
Woodland
Eastern Highland Rim Prairie and Barrens - Dry
135 CES202.354a Modifier
143 CES203.500
East Gulf Coastal Plain Dune and Coastal
Grassland
145 SEGAP710 Clearcut - Grassland/Herbaceous Herbaceoups/crop
146 SEGAP720 Other Herbaceous Herbaceoups/crop
147 SEGAP730 Utility Swath - Herbaceous
148 SEGAP810 Pasture/Hay Herbaceoups/crop
149 SEGAP820 Row
East Gulf Coastal Plain Large River Floodplain
157 CES203.489a Forest - Forest Modifier
158 CES203.559
East Gulf Coastal Plain Small Stream and River
Floodplain Forest
Mississippi River Low Floodplain (Bottomland)
Herbaceoups/crop
159 CES203.195 Forest
Lower Mississippi River Bottomland Depressions -
160 CES203.490a Forest Modifier
98

Appendix A (continued)
ID Code Ecological Land Type
South-Central Interior Large Floodplain - Forest
161 CES202.705a Modifier
Reclassified
162 CES202.706 South-Central Interior Small Stream and Riparian Not used
Southern Coastal Plain Blackwater River
163 CES203.493 Floodplain Forest
Southern Piedmont Large Floodplain Forest -
164 CES202.324a Forest Modifier
165 CES202.323
Southern Piedmont Small Floodplain and Riparian
Forest
166 CES203.190 Mississippi River Riparian Forest
179 CES203.384 Southern Coastal Plain Nonriverine Basin Swamp
182 CES202.336
Southern Piedmont/Ridge and Valley Upland
Depression Swamp
East Gulf Coastal Plain Near-Coast Pine Flatwoods
186 CES203.375c - Offsite Hardwood Modifier
East Gulf Coastal Plain Near-Coast Pine Flatwoods
187 CES203.375a - Open Understory Modifier
189 CES203.557
192 CES203.480
East Gulf Coastal Plain Southern Loblolly-
Hardwood Flatwoods
South-Central Interior/Upper Coastal Plain Wet
Flatwoods
195 CES203.251 Southern Coastal Plain Nonriverine Cypress Dome
206 CES203.299 East Gulf Coastal Plain Tidal Wooded Swamp
233 CES203.192
East Gulf Coastal Plain Treeless Savanna and Wet
Prairie
East Gulf Coastal Plain Large River Floodplain
238 CES203.489b Forest - Herbaceous Modifier
250 CES203.303 Mississippi Sound Salt and Brackish Tidal Marsh
99

Appendix B. Land type proportions within 150 m radius of each sampling point at the study area of Jackson County, Alabama. See Appendix 1 for land type classes.
Point ID Developed Cliff
Oak forest
Mesophytic forest
Pine forest Shrub
Herbacious and crop
Calcareous forest
SWG1 0.00 36.25
SWG10 0.00 26.09
0.00
0.00
0.00
SWG103 0.00 43.59
SWG104 0.00 12.68
SWG105 0.00 13.75
0.00
SWG109 0.00 28.57
1.28
SWG110 0.00 73.24
0.00
1.28
0.00
SWG114 0.00 39.24
SWG115 0.00 88.75
SWG116 0.00 57.69
SWG117 0.00 87.34
1.25
0.00
SWG12 0.00 12.82
SWG120 0.00 30.99
SWG121 0.00 73.08
SWG122 0.00 89.74
SWG123 0.00 82.05
0.00
SWG125 0.00 10.26
SWG126 0.00 95.83
SWG127 0.00 19.48
SWG128 0.00 40.00
0.00
SWG13 0.00 90.00
0.00
0.00
0.00
63.75
0.00
0.00
39.13
34.78
0.00
12.82
0.00
0.00
2.82
0.00
0.00
14.10
26.92
32.05
23.08
0.00
0.00
5.63
0.00
80.28
0.00
33.75
0.00
21.62
0.00
0.00
58.44
0.00
0.00
5.13
0.00
0.00
33.33
26.76
0.00
0.00
16.67
0.00
0.00
34.62
0.00
0.00
2.53
0.00
0.00
24.05
60.76
0.00
0.00
5.00
0.00
0.00
39.74
0.00
0.00
12.66
0.00
0.00
28.75
0.00
0.00
40.51
0.00
0.00
53.85
0.00
17.95
15.38
16.90
0.00
0.00
19.23
0.00
0.00
10.26
0.00
0.00
16.67
0.00
0.00
13.92
0.00
7.59
71.79
3.85
1.28
12.82
4.17
0.00
0.00
10.39
0.00
0.00
33.75
0.00
0.00
2.67
0.00
14.67
10.00
0.00
0.00
1.28
0.00
0.00
33.75
0.00
0.00
2.50
3.75
1.25
31.25
100

Appendix B (continued)
Point ID Developed Cliff
Oak forest
0.00
0.00
0.00
SWG136 0.00 15.00
0.00
0.00
0.00
0.00
2.90
0.00
SWG142 0.00 23.19
SWG143 0.00 21.13
SWG144 0.00 16.46
SWG145 0.00 32.50
0.00
SWG147 0.00 48.75
SWG148 0.00 53.16
0.00
SWG15 0.00 49.30
SWG150 8.97 30.77
SWG151 0.00 33.75
0.00
0.00
0.00
0.00
SWG156 0.00 42.50
SWG159 0.00 35.00
SWG16 0.00 22.22
3.75
SWG161 0.00 70.51
7.50
SWG163 0.00 16.67
0.00
2.56
0.00
SWG167 0.00 20.00
0.00
0.00
0.00
Mesophytic forest
Pine forest Shrub
Herbacious and crop
Calcareous forest
0.00
0.00
8.57
91.43
0.00
0.00
12.68
76.06
28.99
0.00
0.00
1.25
0.00
15.00
68.75
5.13
0.00
0.00
0.00
0.00
0.00
100.00
2.67
0.00
0.00
0.00
0.00
18.31
11.27
14.49
0.00
0.00
0.00
0.00
26.76
73.24
27.54
0.00
0.00
54.93
0.00
0.00
70.89
12.66
0.00
67.50
0.00
0.00
12.66
1.27
0.00
18.75
0.00
0.00
39.24
0.00
0.00
0.00
68.75
0.00
28.75
50.70
0.00
0.00
37.18
14.10
0.00
61.25
5.00
0.00
8.45
0.00
0.00
6.33
0.00
0.00
0.00
0.00
27.54
59.42
0.00
0.00
12.66
87.34
42.50
0.00
6.25
22.50
1.25
6.25
32.50
2.78
0.00
73.61
30.00
0.00
0.00
23.08
6.41
0.00
18.75
0.00
0.00
7.69
0.00
1.28
1.27
0.00
0.00
19.23
0.00
0.00
2.63
0.00
0.00
23.75
0.00
0.00
20.25
0.00
0.00
7.50
0.00
0.00
1.28
0.00
0.00
101

Appendix B (continued)
Point ID Developed Cliff
Oak forest
0.00
0.00
SWG172 0.00 85.19
0.00
1.27
0.00
SWG176 0.00 35.06
SWG18 0.00 65.82
8.97
SWG2 0.00 62.34
0.00
0.00
SWG22 0.00 26.32
SWG23 0.00 16.88
SWG24 0.00 48.57
8.86
SWG26 0.00 91.03
SWG27 0.00 36.25
0.00
2.56
0.00
SWG30 0.00 62.03
SWG308 0.00 38.96
SWG31 0.00 68.42
SWG317 0.00 81.82
SWG319 0.00 48.05
SWG33 0.00 37.50
0.00
SWG338 0.00 15.38
6.17
0.00
0.00
SWG36 0.00 61.97
0.00
SWG38 0.00 28.57
SWG4 0.00 50.00
SWG40 0.00 85.90
SWG41 0.00 71.60
0.00
Mesophytic forest
Pine forest Shrub
10.13
0.00
0.00
2.70
0.00
0.00
14.81
0.00
0.00
26.58
0.00
0.00
10.13
0.00
8.86
7.50
0.00
0.00
2.60
0.00
0.00
34.18
0.00
0.00
65.38
0.00
0.00
37.66
0.00
0.00
11.54
0.00
46.15
0.00
0.00
0.00
43.42
0.00
0.00
Herbacious and crop
Calcareous forest
53.25
0.00
0.00
45.71
0.00
0.00
54.43
0.00
0.00
8.97
0.00
0.00
63.75
0.00
0.00
20.99
0.00
27.16
1.28
0.00
5.13
91.03
7.69
0.00
1.28
20.51
27.85
0.00
0.00
61.04
0.00
0.00
30.26
0.00
0.00
18.18
0.00
0.00
46.75
0.00
5.19
27.50
0.00
0.00
0.00
0.00
29.11
70.89
12.82
0.00
12.82
53.85
33.33
0.00
0.00
0.00
0.00
5.00
95.00
1.27
0.00
55.70
12.68
0.00
0.00
0.00
0.00
0.00
71.43
0.00
0.00
50.00
0.00
0.00
11.54
0.00
0.00
28.40
0.00
0.00
41.67
0.00
0.00
102

Appendix B (continued)
Point ID Developed Cliff
Oak forest
0.00
SWG44 0.00 91.14
0.00
0.00
0.00
SWG48 0.00 80.52
0.00
0.00
SWG50 0.00 93.67
0.00
SWG52 0.00 76.62
SWG53 0.00 10.00
0.00
0.00
0.00
SWG57 5.13 10.26
0.00
0.00
SWG6 0.00 49.38
SWG60 0.00 61.54
0.00
SWG62 0.00 25.00
0.00
0.00
0.00
0.00
5.00
0.00
SWG69 0.00 82.28
0.00
SWG70 0.00 16.67
0.00
0.00
0.00
SWG74 0.00 65.82
0.00
0.00
0.00
0.00
Mesophytic forest
Pine forest Shrub
9.09
0.00
20.78
Herbacious and crop
Calcareous forest
8.86
0.00
0.00
0.00
0.00
0.00
11.39
6.25
0.00
0.00
12.35
0.00
0.00
11.11
19.48
0.00
0.00
9.33
0.00
0.00
18.18
0.00
0.00
0.00
0.00
0.00
7.50
0.00
6.25
23.38
0.00
0.00
66.25
0.00
0.00
0.00
0.00
0.00
1.27
0.00
18.99
15.19
35.90
0.00
0.00
0.00
0.00
7.69
2.53
0.00
12.66
13.33
0.00
0.00
19.75
0.00
0.00
34.62
3.85
0.00
0.00
0.00
0.00
47.50
0.00
0.00
1.33
0.00
17.33
0.00
0.00
33.33
66.67
0.00
0.00
0.00
28.21
0.00
0.00
15.00
0.00
0.00
0.00
0.00
0.00
17.72
0.00
0.00
26.25
0.00
0.00
76.92
0.00
0.00
0.00
0.00
1.27
86.08
24.36
0.00
0.00
8.75
0.00
8.75
15.00
7.59
3.80
0.00
17.11
0.00
0.00
3.85
0.00
15.38
6.58
0.00
0.00
0.00
0.00
20.51
103

Appendix B (continued)
Point ID Developed Cliff
Oak forest
SWG79 0.00 72.15
0.00
SWG80 0.00 48.10
0.00
0.00
0.00
SWG84 0.00 78.21
0.00
0.00
0.00
2.67
0.00
SWG9 0.00 76.92
0.00
0.00
0.00
SWG93 0.00 48.72
0.00
0.00
0.00
SWG97 0.00 57.50
SWG98 0.00 65.00
0.00
0.62 0.40
21.96
Total %
Number of points occurred 8 3 91
Mesophytic forest
Pine forest Shrub
20.25
7.59
0.00
Herbacious and crop
Calcareous forest
0.00
0.00
15.79
51.32
51.90
0.00
0.00
17.95
0.00
3.85
43.75
0.00
0.00
0.00
0.00
0.00
21.79
0.00
0.00
1.27
8.86
6.33
43.04
8.75
0.00
0.00
5.00
0.00
0.00
69.33
0.00
0.00
11.69
0.00
18.18
23.38
17.95
5.13
0.00
12.66
0.00
3.80
51.90
8.75
0.00
1.25
8.57
0.00
0.00
47.44
1.28
0.00
0.00
0.00
0.00
2.56
0.00
0.00
11.25
0.00
0.00
42.50
0.00
0.00
35.00
0.00
0.00
6.33
0.00
56.96
20.22
1.38
4.71
8.67 42.04
150 18 46 43 121
104

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VITA
Florence Chan, daughter of Frank Chan and Loretta Lui, was born February 24,
1979, in Vancouver Canada. She graduated with a Bachelor of Science degree from the University of British Columbia, Canada in May 2002. She enrolled in the
Graduate School at Alabama A&M University, Normal, AL, in January 2005 with the intent of earning the degree of Master of Science in the Department of
Natural Resources and Environmental Sciences.
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