Population Genetics and Microbial Communities of the Gopher Tortoise (<I>Gopherus Polyphemus</I>)

The University of Southern Mississippi The Aquila Digital Community

Dissertations

Spring 5-2014

Population Genetics and Microbial Communities of the Gopher Tortoise (Gopherus polyphemus)

Daniel Lyle Gaillard University of Southern Mississippi

Follow this and additional works at: https://aquila.usm.edu/dissertations

Part of the Biology Commons, Ecology and Evolutionary Biology Commons, and the Other Life Sciences Commons

Recommended Citation Gaillard, Daniel Lyle, "Population Genetics and Microbial Communities of the Gopher Tortoise (Gopherus polyphemus)" (2014). Dissertations. 259. https://aquila.usm.edu/dissertations/259

This Dissertation is brought to you for free and open access by The Aquila Digital Community. It has been accepted for inclusion in Dissertations by an authorized administrator of The Aquila Digital Community. For more information, please contact [email protected]. The University of Southern Mississippi

POPULATION GENETICS AND MICROBIAL COMMUNITIES OF THE

GOPHER TORTOISE (GOPHERUS POLYPHEMUS)

Daniel Lyle Gaillard

Abstract of a Dissertation Submitted to the Graduate School of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

May 2014

ABSTRACT

POPULATION GENETICS AND MICROBIAL COMMUNITIES OF THE GOPHER

TORTOISE (GOPHERUS POLYPHEMUS)

by Daniel Lyle Gaillard

May 2014

The gopher tortoise, Gopherus polyphemus, is an endangered species living in the southeastern United States. The recent and drastic decline in tortoise numbers has resulted in a multi-faceted approach to conserve this species. I used a population genetic approach to determine the population structure, genetic diversity and barriers to gene flow at a broad, regional and local scale. Tortoises are divided into five distinct genetic populations at the broad scale, the central populations have the highest levels of genetic diversity and the Tombigbee, Mobile, Apalachicola, Suwannee and St. John’s Rivers appear to be barriers to gene flow. At the regional scale, the Pascagoula River splits tortoises into two populations within the listed range and the Florida Ridge System plays a role in shaping the genetic structure of tortoises within peninsular Florida. Significant but weak genetic structure was detected at the local scale across the Ft. Benning landscape and there did not appear to be any landscape features contributing to population genetic structure. In addition to population genetics, microbial and plant communities of the gopher tortoise were tested for differences and associations between geographic localities, as these might be important factors for the success of translocated tortoises. Microbial communities did not show a correlation between community dissimilarity and geographic distance nor did they change in response to changes in plant

communities at each site. Gut and soil microbial communities differed significantly from each other suggesting that soil microbes may play a small role in gut inoculation.

Considering the genetic structure of gopher tortoises is important when planning management strategies, whereas, microbial communities might not be strongly impacted due to the translocating of tortoises.

iii

DANIEL LYLE GAILLARD

2014

The University of Southern Mississippi

POPULATION GENETICS AND MICROBIAL COMMUNITIES OF THE

GOPHER TORTOISE (GOPHERUS POLYPHEMUS)

Daniel Lyle Gaillard

A Dissertation Submitted to the Graduate School of The University of Southern Mississippi in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Approved: _Brian Kreiser______Director

_Carl Qualls______

_Joshua Ennen______

_Mike Davis______

_Maureen A. Ryan______Dean of the Graduate School

May 2014

ACKNOWLEDGMENTS

The writer would like to thank the director of my committee, Dr. Brian Kreiser, and my other committee members, Dr. Carl Qualls, Dr. Mike Davis and Dr. Joshua

Ennen for their insight, patience and sharing of important life skills, without which I could not have completed this dissertation.

Special thanks need to go to Dr. Kevin Kuehn for his knowledge and the use of his laboratory equipment, to Dr. Shahid Karim for his help with the microbial data and to

Dr. Jake Schaefer for his help with multivariate statistics. A special appreciation goes to

Dr. Chris Flood for his help in microbial DNA extractions and knowledge of microbial ecology. Appreciation also goes to Tom Mann and Keri Landry for their assistance in gopher tortoise location and collecting.

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGMENTS ...... iv

LIST OF TABLES ...... vii

LIST OF ILLUSTRATIONS ...... ix

CHAPTER

I. GOPHERUS POLYPHEMUS CONSERVATION GENETICS AND GUT MICROBIAL ECOLOGY ...... 1

II. BROAD SCALE PATTERNS OF POPULATION GENETIC STRUCTURE IN THE GOPHER TORTOISE ...... 7

Materials and Methods Results Discussion

III. REGIONAL PATTERNS OF POPULATION GENETIC STRUCTURE IN THE GOPHER TORTOISE ...... 34

Materials and Methods Results Discussion

IV. FINE SCALE GENETIC STRUCTURE ACROSS THE FT. BENNING LANDSCAPE ...... 55

Materials and Methods Results Discussion

V. SOIL AND GUT MICROBIAL COMMUNITIES OF THE GOPHER TORTOISE AND THEIR ASSOCIATED PLANT COMMUNITIES ...... 68

Materials and Methods Results Discussion

APPENDIX ...... 92

LITERATURE CITED ...... 140

LIST OF TABLES

Table

1. Sample Sites (site identification abbreviation, state), Number of Individuals Per Site, Observed Heterozygosity (Ho), Expected Heterozygosity (He), Allelic Richness (A) and Source of Samples ...... 9

2. Average Observed Heterozygosity (Ho) for the Five Groups Defined by the STRUCTURE Analysis with Significant Differences Between Groups Denoted by Different Capitalized Letters...... 16

3. Average Expected Heterozygosity (He) for the Five Groups Defined by the STRUCTURE Analysis With Significant Differences Between Groups Denoted by Different Capitalized Letters...... 17

4. Average allelic richness (A) for the five groups defined by the STRUCTURE analysis with significant differences between groups denoted by different capitalized letters ...... 17

5. A, Ho and He from the STRUCTURE Model for K=2 ...... 18

6. Significant P Values for Heterozygosity Excess Under T.P.M. (Without Bonferroni Correction)...... 18

7. Results From the Five AMOVA Models and Three SAMOVA Runs for K=2-5 Showing the Partition of Molecular Variance Partitioned Among Groups, Among Populations Within Groups, and Within Populations. The Studies Cited for Models of Population Structure Were Clostio et al. (2012) and Ennen et al. (2012) ...... 20

8. Population Groups Described by the Program STRUCTURE with Assigned Gopher Tortoise Sites ...... 22

9. Site Names and Abbreviations, Approximate Location of Site, and Sample Size...... 39

10. Genetic Groups Defined in Peninsular Florida on the Basis of the Three Bayesian Analyses. Sites that Show Admixture Among These Groups are Also Listed...... 48

11. Pairwise FST Values for the A Priori Groups are Located Below the Diagonal. Significance of the FST Values is Indicated Above the Diagonal………… ...... 62

vii

12. The Numbers of Each A Priori Group Assigned to a Particular Group by the Assignment Test Performed by GenAlEx. Individuals Correctly Assigned Back to its Group of Origin are Indicated in Bold Along With the Percentage of Individuals in Each Group Correctly Assigning Back to the Group of Origin .... 63

13. The Average Likelihood Scores for the 10 Runs of Each Value of K in the STRUCTURE Analysis. The Standard Deviation (SD) of These Averages is Also Provided...... 64

14. Fecal, Soil and Plant Sample Site Names and Locations with Sample Site Abbreviations ...... 74

15. Genus Diversity of the Gut Microbial Community Comparisons Between the Two Groups ...... 78

16. Genus Richness of the Gut Microbial Community Comparisons Between the Two Groups...... 79

17. Gut Bacterial Community Pairwise Comparisons Between CNF/TR and All Other Sites ...... 80

18. Soil Microbial Pairwise Comparisons of Shannon’s Diversity Index Between Sites...... 81

19. Soil Microbial Pairwise Differences Between All Sites for TR and PD Sites..... 82

20. Results from Pairwise Comparisons for Plant Species Communities Between Sites. R2 Value is Above Diagonal and P Values Below……………...... 85

21. Mean Species Richness by Site.……………………...... 86

viii

LIST OF ILLUSTRATIONS

Figure

1. Isolation-By-Distance Graph for 45 Populations Across the Range of G. polyphemus………………… ...... 21

2. Delta K Analysis for STRUCTURE Runs………..……………….… ...... 22

3. Log Likelihood Analysis of Structure……….…………………...... …..24

4. Bar Plot Showing the Average Proportion of Individual Ancestry to a Group from STRUCTURE, K=5… ...... 24

5. Bar Plot Showing the Average Proportion of Individual Ancestry to a Group from STRUCTURE, K=2…...... 25

6. A) Averaged TESS DIC Scores Plotted Against K from Admixture Run, Showing Support for K=2, No Increase in Clusters After K=2...... 45

7. Population Genetic Groupings for K=2. ………………...... ……..46

8. A) Averaged TESS DIC Scores Plotted Against K for Admixed Model, Showing Support for K=5, No Increase in Clusters after K=5………… ...... ….…49

9. Population Genetic Groupings for K=5...…..………… ...... …...50

10. Satellite View (Google Maps) of a Portion of the Fort Benning Landscape with Upatoi and Pine Knot Creeks Labeled..………… ...... 60

11. Map Generated by GENELAND of the Posterior Probabilities of Each Individual Belonging to One of the Two Groups. …...... …..65

12. Bar Plot Comparing Average Phylum Percent Abundance of Gopher Tortoise Microbial Communities to Previously Studied Reptilian Microbial Communities……… ...... 78

13. MetaNMDS Plot of Gopher Tortoise Gut Microbial Communities from 14 Sites...... 80

14. MetaNMDS Plot for Soil Bacteria Community Dissimilarity Matrices ...... 83

15. MetaNMDS Plot of Soil vs. Gut Microbial Community Dissimilarity Matrices……...... …84

16. MetaNMDS Plot of Plant Species Community Dissimilarity Matrices...... 85

x 1

CHAPTER I

GOPHERUS POLYPHEMUS CONSERVATION GENETICS AND GUT MICROBIAL

ECOLOGY

The gopher tortoise (Gopherus polyphemus) is a medium sized testudine that is restricted to fire-dependent, xeric longleaf pine forests of the southern United States.

Gopher tortoises typically average between 25-30 cm in carapace length as adults and weigh between 4-8 kg. The gopher tortoise is an herbivore (with few reports of scavenging and egg consumption) that chiefly forages on grasses and forbs. They also dig burrows that can reach up to 10 m in length that provide shelter from predators, fire, and weather extremes (Hallinan 1923; Hansen 1963). They breed from June to

September and oviposit in the aprons of burrows from May to the end of June with eggs typically hatching from August to September. Gopher tortoises are keystone species in their habitat as their burrows provide shelter for over 300 different species in the longleaf pine ecosystem (Jackson and Milstrey 1989; Witz, Wilson, and Palmer 1991).

Historically, these long-leaf pine forests were noted for being open and park like, with the most species-rich under story in the temperate United States (Peet and Allard

1993). The reason for the open canopy in these forests was due to frequent low-intensity fires that occurred at intervals of about every 1 to 10 years (Chapman 1932; Christensen

1981). Increased growth, flowering, seed production, seed germination, and seedling establishment are greatly enhanced by burning (Christensen 1981); however, without the presence of regular burning, hardwood shrubs and trees replace the ground cover and the herbaceous diversity decreases along with flowering production (Christensen 1981;

Streng and Harcombe 1982). When these forests are healthy, gopher tortoise populations will thrive in this environment.

However, over the past century due to habitat destruction and degradation (Kelly and Bechtold 1990) gopher tortoise populations have seen an 80% reduction in total numbers (Auffenberg and Franz 1982) with the greatest reduction found west of the

Tombigbee and Mobile Rivers. Recent studies have shown population declines, poor hatching success (Hammond 2006; Noel, Qualls, and Ennen 2012), and low recruitment

(Epperson and Heise 2003) in protected areas within the De Soto National Forest

(DSNF). Hammond (2006) reported a 37.5% decline in burrow densities from 1995-2007 at two sites in the DSNF, with 28.6% of this decline happening during 1995-2002.

Populations in Florida and Georgia have hatching success rates between 67-97% (Butler and Hull 1996; Desmuth 2001) whereas populations in the DSNF and Camp Shelby have a 28.8%-46.6% hatching success rate (Epperson and Heise 2003; Hammond 2006; Noel,

Qualls, and Ennen 2012). Low post-hatching survival rate only adds to the problem of poor recruitment. Epperson and Heise (2003) radio-transmitted 48 hatchlings and only 1 tortoise survived after 736 days of tracking. The dramatic decline in numbers in the western portion of their range has warranted the species to be federally listed as threatened west of the Tombigbee and Mobile Rivers (USFWS 1987). However, the other portion (non-listed) of the species’ range was recently found to warrant listing as threatened under Endangered Species Act (ESA). However, higher priority listing actions precluded this move, and instead this species was added to the candidate list (USFWS

2011).

The reduced hatching success in the western portion of the range could be due to intrinsic and/or extrinsic factors. Noel, Qualls, and Ennen (2012) performed a study where they incubated two eggs per clutch in an incubator and left the remaining eggs from each clutch in the nest with a predator excluder. They found that in the incubator only 60% hatched, and in the nest only 17% hatched. From this she concluded that about

40% of the hatching failures could be attributed to intrinsic factors and 43% due to extrinsic factors. One possible intrinsic factor contributing to poor hatching success could be low genetic diversity that has been found in Mississippi populations (Ennen,

Kreiser, and Qualls 2010). Ennen, Kreiser and Qualls (2010) found that western populations (i.e. west of Mobile River) had significantly lower expected heterozygosity

(HE) and percent polymorphic loci (% poly loci; HE= 0.2; % poly loci=0.6) than did their eastern counterparts (HE=0.5; % poly loci 0.9). Also, western populations had only 1.9 alleles per locus whereas eastern populations had 3.1 alleles per locus. Low genetic diversity has been linked to poor hatching success, slow growth and development rates, low survival and reduced disease resistance in wild populations (Allendorf and Leary

1988; Ralls, Ballou, and Templeton 1988; Mitton 1997; Crnokrak and Roff 1999; Reed and Frankham 2003).

Genetic variation within a species usually has a geographical hierarchy; therefore, understanding the population genetic structure can help to preserve the genetic diversity within a species (Allendorf and Leary 1988). Defining different levels of population structure such as evolutionary significant units (ESUs) can allow conservation managers to protect and preserve unique traits of different populations and thus help to aid in evolutionary potential of the species (Waples 1991; Moritz 1994). Several studies have

4 attempted to define population structure at various spatial scales across the range of the gopher tortoise. Ostenkowski and Lamb (1995) found there were three distinct geographical assemblages using mitochondrial DNA (mtDNA). Gopher tortoises were divided into eastern and western assemblages by the Apalachicola drainage, with a third assemblage in mid-Florida along the Brooksville Ridge. Another study by Schwartz and

Karl (2005) using nuclear DNA found eight genetic assemblages at a smaller scale in tortoise populations from South Georgia down through the Florida peninsula. More recent studies (Ennen et al. 2012; Clostio et al. 2012) using mtDNA have found that the

Apalachicola and the Chattahoochee rivers delineate the phylogeographic break in mtDNA. However, these studies used different mtDNA markers and therefore had slightly different interpretations. Ennen et al. (2012), similar to Osentowski and Lamb

(1995), found there to be genetic variation along eastern peninsular Florida but not any distinction between western Georgia, Florida, and South Carolina. Clostio et al. (2012) did not detect genetic variation within peninsular Florida but did detect a unique haplotype group in western Georgia.

In addition to mtDNA studies, Clostio et al. (2012) also used microsatellite loci to determine population genetic structure across the tortoises range. They found support for

5 distinct populations in the tortoise’s range. However, her sampling outside of the listed portion of the range was limited with over half of the 452 individuals sampled coming from within the listed range. They suggested that more sampling needs to be done in

Alabama, western Georgia, and Florida in order to better support or refute their findings.

More recent fine scale studies by Richter et al. (2011) and Sinclair, Dawes, and Seigel

(2010) found genetic structure to be weak between the putative populations in their studies.

Therefore, it is my purpose to perform broad-scale, regional-scale and fine-scale population genetics on Gopherus polyphemus. The broad scale study will include approximately 933 individuals from 47 sites across the tortoise’s range from Louisiana to

South Carolina. With this more complete sampling, set I hope to better define the populations that exist and to support/refute the geographical barriers postulated by

Clostio et al. (2012). The regional scale study will involve approximately 267 individuals from 13 sites in the listed portion of the range (Louisiana, Mississippi, and

Alabama west of the Mobile River) and 259 individuals from 15 sites in Florida. The purpose of this study is to help define what geographical features, if any, are barriers to gene flow that might result in genetic structure and allow management agencies to better preserve historical population structure. My fine scale study was done at the Fort

Benning Military Installation in Georgia. I randomly sampled 100 individuals, one from each of the randomly generated 100-1 km2 sections across the Ft. Benning landscape. I want to determine how many populations exist and if there are any landscape level features impeding gene flow that might contribute to genetic structure.

In addition to understanding what role the intrinsic factor of genetic diversity and structure might be playing in tortoise decline, I am also interested in evaluating possible extrinsic factors also. One possible extrinsic factor could be poor nutrient acquisition by tortoises due to a depleted/altered gut floral community or due to a depleted/altered soil microbial community. Adult tortoises found in the listed portion of the range can suffer from yellow-spot, which is a softening in the plastron. Also, hatchlings in the listed

6 portion of the range can suffer from wasting, which means they are lethargic, do not dig burrows, don’t grow, and eventually die. The symptoms of these two phenomena correlate with known symptoms of metabolic bone disease (MBD) (Raiti and Haramati

1997). MBD is usually attributed to a calcium (Ca) deficiency or an improper balance in the ration of Ca and phosphorus. Why would some tortoise populations suffer from nutrient deficiencies, while others do not? One reason might be differences in soil microbial communities.

Schlensinger (1991) found that soil microbes, acting as decomposers, are indirectly responsible for supplying the bulk of terrestrial vegetation’s annual nutrient demand. Franklin and Mills (2003) found that bacterial communities are highly structured and vary across the landscape. With the recent fragmentation and degradation of tortoise habitat, could the microbial communities that supply the nutrients to the tortoise forage have been altered? If the microbial communities are not healthy or a shift in types of microbes has occurred, then the availability of certain nutrients might also be affected and result in poor nutrient availability to the tortoises. Therefore, I want to be able to identify what microbes are present in both the soil and in the gut of the gopher tortoise. In addition to finding what microbes are present, I also want to determine if differences in soil and gut microbial communities exist across the range. Diet is also an important factor in determining gut microbial communities (Rueda 2000; Filippo et al.

2010). Therefore, I collected plant community data at each site to tests for differences in potential diets between sites of gopher tortoises.

CHAPTER II

BROAD SCALE PATTERNS OF POPULATION GENETIC STRUCTURE IN THE

GOPHER TORTOISE

Gopher tortoises (Gopherus polyphemus) are a medium sized testudine that is restricted to fire-dependent, xeric long-leaf pine forests of the southern United States.

Gopher tortoises typically average between 25-30 cm in carapace length as adults and weigh between 4-8 kg. They dig burrows that can reach up to 10 m in length that provide shelter from predators, fire, and from weather extremes (Hallinan 1923; Hansen 1963).

Gopher tortoises are considered keystone species in their habitat as their burrows provide shelter for over 300 different species in the long-leaf pine ecosystem (Jackson and

Milstrey 1989; Witz, Wilson, and Palmer 1991). Over the past century due to habitat destruction and degradation (Kelly and Bechtold 1990), gopher tortoise populations have seen an 80% reduction in total numbers (Auffenberg and Franz 1982) with the greatest reduction found west of the Tombigbee and Mobile Rivers (herein referred to as the western population). The dramatic decline in numbers in the western portion of their range led to the federal listing of threatened (USFWS 1987). While the remainder of the species’ range was recently found to warrant listing as threatened, higher priority listing actions precluded this move, and instead this species was added to the candidate list

(USFWS 2011).

The listed and non-listed portions of the gopher tortoise range do appear to reflect ecological and demographic differences. Recent studies of western populations have shown population declines, poor hatching success (Hammond 2006; Noel, Qualls, and

Ennen 2012) and low recruitment (Epperson and Heise 2003), even in protected areas.

Hammond (2006) reported a 37.5% decline in burrow densities from 1995-2007 from protected sites within the De Soto National Forest (DNF). Populations in the DNF and

Camp Shelby have a 28.8%-46.6% hatching success rate compared to 67-97% for populations in Florida and Georgia (Butler and Hull 1996; Desmuth 2001). Ennen,

Kreiser, and Quallls (2010) showed that there was lower genetic diversity in western populations and suggested that there could be a correlation between low genetic diversity and low hatching success.

Many species with large geographic distributions have been shown to possess genetic structure (Avise 2000). Identifying these genetically distinct populations can help to manage a species to prevent the loss of genetic diversity and adaptations to the local environment. Previous studies of the gopher tortoise have examined range-wide genetic structure using both mitochondrial DNA (mtDNA) and microsatellites. Both Ennen et al.

(2012) and Clostio et al. (2012) found two distinct groups of haplotypes that corresponded roughly to an east and west split at the Apalachicola River. Clostio et al.

(2012) also used microsatellite loci to assess population genetic structure and found support for five genetic groups. However, they had somewhat limited sampling outside of the listed portion of the tortoise’s range, and the authors recognized that further sampling was warranted within the eastern portion of the range. Genetic structure can also be found at smaller spatial scales due to the influence of landscape level features

(Lee-Yaw et al. 2009). At a regional level, Schwartz and Karl (2005) found eight genetic assemblages across southeast Georgia and the Florida peninsula. However, both Sinclair,

Dawes, and Seigel (2010) and Ricther et al. (2011) did not find evidence of genetic differentiation across the local spatial scales (~ 56,000-ha) at the sites they studied.

While many aspects of the population structure of gopher tortoises have been examined, there is still a need for a more comprehensive examination of range-wide genetic structure. The goals of this study are to include additional populations from across the listed and non-listed portions of the range to more fully define broad scale and regional patterns of genetic structure. In particular, I would like to identify biogeographic or landscape features that could be potential barriers to gene flow.

Furthermore, I want to determine if additional sampling supports the pattern of lower levels of genetic variation in the western portion of the range.

Materials and Methods

Field Collections

Blood samples for obtained for 933 individuals collected from 47 sites across the range. I either collected samples personally or obtained from them colleagues (See Table

1).

Table 1

Sample Sites (site identification, abbreviation, state), Number of Individuals Per Site,

Observed Heterozygosity (Ho), Expected Heterozygosity (He), Allelic Richness (A), and

Source of Samples.

SAMPLE SITES N Ho He A SOURCE

Aiken Gopher Tortoise Preserve 13 0.743 0.548 2.69 Tracy Tuberville

(AGTP, SC)

Gum Swamp (GS, SC) 14 0.403 0.421 2.54 Tracy Tuberville

Tillman Sandridge (TSR, SC) 26 0.400 0.415 2.4 Tracy Tuberville

Table 1 (continued).

SAMPLE SITES N Ho He A SOURCE

University of North Florida (UNF, FL) 28 0.568 0.611 3.41 Kris Amatuli

Nassau (Nassau, FL) 16 0.476 0.494 2.66 Josh Ennen

Martin County (MCF, FL) 10 0.574 0.548 3.1 Source?

Hernando County, (HERN, FL) 29 0.627 0.614 3.5 Josh Ennen

Hillsborough (HB, FL) 17 0.626 0.607 3.34 Schwartz and Karl

Pinellas (Pin, FL) 25 0.508 0.581 3.28 Schwartz and Karl

Hilland Hammocks (HH, FL) 18 0.542 0.570 3.26 Schwartz and Karl

Jonathan Dickens (JD, FL) 21 0.555 0.613 3.62 Schwartz and Karl

Kennedy Space Center (KSC, FL) 16 0.647 0.645 3.69 Becky Bolt

Lake County (LAKE, FL) 21 0.573 0.575 3.4 Josh Ennen

Volusia (V, FL) 11 0.593 0.594 3.41 Josh Ennen

Alachua (A, FL) 10 0.621 0.651 3.87 Josh Ennen

Putnam (Putnam, FL) 8 0.617 0.586 3.66 Josh Ennen

Cayo Costa (CC, FL) 20 0.387 0.429 2.53 Schwartz and Karl

Gadsden County (Ga, FL) 9 0.650 0.608 3.43 Josh Ennen

Walton County (WA, FL) 8 0.665 0.703 4.33 Josh Ennen

Jones Research Center (JRC, GA) 26 0.580 0.606 3.18 Schwartz and Karl

Wade Tract (WT, GA) 15 0.633 0.604 3.19 Roger Birkhead

Moody Air Force Base (MAFB, GA) 15 0.624 0.638 3.31 Schwartz and Karl

Ft. Benning (FtB, GA) 104 0.650 0.671 3.62 Daniel Gaillard

Andrews Lock and Dam (ALD, GA) 10 0.638 0.653 3.82 Roger Birkhead

Island (Island, GA) 20 0.696 0.674 3.79 Roger Birkhead

Savannah River Site (SRS, GA) 12 0.469 0.417 2.43 Tracy Tuberville

Mopani (Mop, GA) 28 0.529 0.529 2.94 Jessica Goynyer

Reed Bingham (RB, GA) 30 0.610 0.631 3.4 Jessica Goynyer

Table 1 (continued).

SAMPLE SITES N Ho He A SOURCE

Ft. Gordon (Gordon, GA) 13 0.533 0.501 2.89 Jessica Goynyer/ Roger

Birkhead

Clearwater (CW, AL) 12 0.503 0.580 3.53 Daniel Gaillard

Perdido (Pd, AL) 18 0.633 0.689 3.91 Daniel Gaillard

Troy (Tr, AL) 9 0.647 0.671 3.69 Daniel Gaillard

Conecuh National Forest (CNF, AL) 11 0.675 0.651 3.74 Daniel Gaillard

Meeker’s Creek (MC, AL) 52 0.531 0.584 3 Joshua Ennen

International Paper (IP, AL) 18 0.572 0.554 3.02 Joshua Ennen

Water Tower (WTMS, MS) 8 0.547 0.500 2.64 Joshua Ennen

Y-Road (YR, MS) 23 0.534 0.534 2.77 Joshua Ennen

L-Road (LR, MS) 9 0.445 0.443 2.41 Joshua Ennen

Gopher Farm (GF, MS) 15 0.470 0.501 2.63 Joshua Ennen

Theodore Mars (TM, MS) 6 0.388 0.434 NA Daniel Gaillard

Camp Shelby (T44, MS) 41 0.481 0.469 2.45 Joshua Ennen

Ward Bayou (WB, MS) 17 0.533 0.578 3.07 Joshua Ennen

Lynn’s House (LH, MS) 6 0.597 0.543 NA Joshua Ennen

Little Florida (LF, MS) 18 0.479 0.496 2.62 Joshua Ennen

Marion County WMA (M, MS) 18 0.473 0.455 2.36 Joshua Ennen

Southern Natural Gas (SNG, LA) 18 0.493 0.478 2.40 Daniel Gaillard

Ben’s Creek WMA (BC, LA) 20 0.468 0.423 2.23 Daniel Gaillard

For the personally collected samples, I placed a collapsible Tomahawk® Model

18 Live Trap (81.28 × 25.4 × 30.48 cm) covered with shade cloth and pine needles in front of the mouth of the burrow. I checked the trap daily until I captured a tortoise, and then I obtained a 0.5 mL sample of blood from the femoral vein using a new 23G1 gauge

(Becton Dickinson PrecisionGlide) hypodermic needle. Occasionally, if I could not obtain a sample from the femoral artery, I drew blood from the brachial artery. To stop bleeding after I drew the blood, I applied pressure directly to the site of puncture for 15 sec. I stored each blood sample in a 2.0 mL vial with approximately 0.5 mL of SED tissue preservation buffer (Seutin, White, and Boag 1991). After I captured tortoises from a site, I removed the traps and sprayed them with a 10% solution of bleach and allowed to dry. Alcohol was used to sanitize calipers and hands after the handling of each tortoise to help prevent any possible disease transmission between individuals.

DNA Extraction and Amplification

I extracted genomic DNA from each individual using a DNeasy Tissue Kit

(QIAGEN) and genotyped for 20 of the microsatellite loci described by Kreiser et al.

(2013). I performed polymerase chain reactions (PCR) on a Veriti 96 Ill Thermal Cycler

(Applied Biosystems) in 12.5 μL reactions consisting of 50 mM KCl, 10 mM Tris-HCl

(pH 8.3), 0.01% gelatin, 2.0 mM MgCl2, 200 M dNTPs, 0.1875 units of Taq polymerase

(New England Biolabs), 0.3 M of the M13 tailed forward primer (Boutin-Ganache et al.,

2001), 0.3 M of the reverse primer, 0.1 M of the M13 labeled primer (LI-COR), 20-

100 ng of template DNA and water to the final 12.5 volume. PCR cycling conditions consisted of an initial denaturing step of 94 C for 2 min followed by 35 cycles of 30 sec at 94 C, 1 min at 50-60 C and 1 min at 72 C. A final elongation step of 10 min at 72 C ended the cycle. Microsatellite alleles were visualized using a LI-COR 4300 DNA sequencer along with a 50-350 bp size standard (LI-COR) and scored using Gene Image

IR v. 3.55 (LI-COR).

Summary Statistics & Demographic Analyses

I calculated basic summary statistics (allelic richness A, observed heterozygosity

- Ho and expected heterozygosity - He) with FSTAT. I excluded Lynn’s House and

Theodore Mars WMA sites for the allelic richness analysis due to low sample size. I tested Loci for Hardy-Weinburg equilibrium (HWE) and linkage disequilibrium (LD) using GENEPOP on the web (Raymond and Rousset 1995) and a sequential Bonferroni correction was applied to both tests (Rice 1989). I also used Microchecker (Oosterhout et al. 2004) to test for null alleles. I also tested for significant differences in Ho, He and A among the regions defined by STRUCTURE (see below) using JMP v7 software. I analyzed data using a student T’s test (if assumptions for parametric tests were met) or by using a non-parametric Kruskal-Wallace test if assumptions were not met. I set all significance values to P=0.05.

I checked populations for bottlenecks by using the program BOTTLENECK

(Piry, Luikart, and Cornuet, 1999). As population numbers decline, the number of alleles declines at a faster rate than does heterozygosity; thus, an excess of heterozygosity can be a sign of a bottleneck (Cornuet and Luikhart 1996). I used the Two-phased model of mutation (TPM) (as suggested by the authors) with variance for TPM set to default of 30 and proportion of SMM in TPM set to default of 70% and ran for 1000 iterations. I calculated the significance of heterozygosity excess by using the Wilcoxon sign rank test.

Population Genetic Structure Analyses

The use of statistical analysis to infer population structure is an important and necessary tool for population genetics. Two of my analyses use a priori population structure and the other program defines genetic structure by using just the genetic data

14 given. However, different Bayesian statistical analyses can sometimes lead to different conclusions from the same data (Balkenhol, Waits, and Dezzani 2009; Safner et al. 2011;

Blair et al. 2012), so I employed several different analytical approaches to characterize population genetic structure in gopher tortoises. Congruence from the different analyses will give more confidence in the genetic structure produced by my analyses.

Non-Bayesian

I tested five different models of population structure with an analysis of molecular variance (AMOVA; Excoffier, Smouse, and Quattro 1992) as implemented in

ARLEQUIN v. 3.11 (Excoffier, Laval, and Schneider 2005). For this analysis, 1000 random permutations tested for significance of the data. The models analyzed are as follows: 1) the putative barrier (Tombigbee and Mobile Rivers), which divides populations into the listed (western) and non-listed (eastern) portions of the range

(USFWS boundary), 2) the phylogeographic break at the Apalachicola River supported by mtDNA data in Clostio et al. (2012) and Ennen et al. (2012), 3) a four population model with sites between the Tombigbee and Apalachicola Rivers (CNF, CW, PD, TR and WA) combined with the west GA group to form one group along with the west, FL and east GA groups, 4) the five groups described by Clostio et al. (2012) using microsatellite data, and 5) the five groups described by my STRUCTURE analysis. In addition to AMOVA, I also used SAMOVA 1.0 (Dupanloup, Schneider, and Excoffier

2002) to define population genetic structure. SAMOVA is similar to AMOVA in that it uses FST statistics based on populations of individuals. However, genetic groups are not defined ahead of time, but rather the analysis incorporates spatial data to group populations so that they maximize the amount of genetic variance explained by the

15 regional groups. I ran SAMOVA 1.0 for values of K=2-10 with 100 simulated annealing processes and initial conditions set to 1.

Greater genetic distance between populations can sometimes be explained by an increase in geographic distance between populations, a pattern called isolation-by- distance (IBD). Testing for isolation-by-distance requires a genetic distance matrix in combination with a geographic distance matrix. GenAlEx v6.4 (Peakall and Smouse

2006) produced the genetic distance matrix of Fst, values, and I used the Geographic

Distance Matrix Generator (http://biodiversityinformatics.amnh.org/open_source/gdmg.) to produce the geographic distance matrix. I used the Isolation-By-Distance Web

Services program (http://ibdws.sdsu.edu/~ibdws/) available from San Diego State

University to test for correlations between genetic and geographic distance matrices with

1000 randomizations and remaining settings left as default conditions.

Bayesian

The program STRUCTURE uses Bayesian statistics to assign individuals to groups based on allele frequencies without a priori groupings and without locality origin information. Using the program STRUCTURE 2.3.3 (Pritchard, Stephens, and Donnelly

2000), I tested values of K (number of populations) from 1-10 using a model of admixed ancestry and assuming independent allele frequencies between groups without using populations as a prior. For each value of K, I ran 20 simulations with 1,000,000 Markov chain Monte Carlo (MCMC) iterations and with a burn-in of 200,000 generations. I used the Evanno, Regnaut, and Goudet (2005) method to infer the best value of K based on the rate of change in the log probability of the data. I then summarized the 20 runs for best

16 value of K by using CLUMPP (Jakobsson and Rosenberg 2007) to obtain average membership coefficients (q) for each individual.

Results

Summary Statistics

There are no loci consistently found with deviations from HWE and linkage equilibrium nor are any null alleles detected. Allelic richness (A) ranged from 2.23 to

4.33, Ho ranged from 0.387 to 0.743 and He ranged from 0.415 to 0.703. The mean Ho for all populations is 0.558, the mean He for all populations is 0.560 and the mean A for all populations is 3.13 (Table 1). Pairwise Fst values ranged from 0.014 between YR and

GF to 0.309 between BC and CC (Appendix). The summary statistics for populations according to the results of the STRUCTURE analysis (K=5; Tables 2-4) show the populations in FL, western GA, and AL all have significantly higher values of the genetic diversity indices than populations in eastern GA and the west.

Table 2

Average Observed Heterozygosity (Ho) for the Five Groups Defined by the STRUCTURE

Analysis with Significant Differences Between Groups Denoted by Different Capitalized

Letters

Group Mean Standard Deviation Diff of groups

West 0.50 0.06 C

AL 0.62 0.07 AB

West GA 0.64 0.03 A

East GA 0.47 0.06 C

FLA 0.57 0.07 B

Table 3

Average Expected Heterozygosity (He) for the Five Groups Defined by the STRUCTURE

Analysis with Significant Differences Between Groups Denoted by Different Capitalized

Letters

Group Mean Standard Deviation Diff of group

West 0.50 0.05 C

AL 0.66 0.05 A

West GA 0.64 0.03 A

East GA 0.48 0.06 C

FLA 0.58 0.06 B

Table 4

Average Allelic Richness (A) for the Five Groups Defined by the STRUCTURE Analysis with Significant Differences Between Groups Denoted by Different Capitalized Letters

Group Mean Standard Deviation Diff of groups

West 2.64 0.29 C

AL 3.81 0.36 A

West GA 3.45 0.25 B

East GA 2.65 0.36 C

FLA 3.39 0.35 B

Western GA had the highest mean Ho (0.64) while AL had the highest mean values of He

(0.66) and A (3.81). Eastern GA had the lowest mean Ho (0.47) and He (0.48), and the

18 west had the lowest mean A (2.64). For the K=2 population structure, the populations east of the Tombigbee and Mobile Rivers had significantly higher levels A, Ho and He (See

Table 5).

Table 5

A, Ho and He from the STRUCTURE Model for K=2

Group Mean A p Mean Ho p Mean He p

(SD) (SD) (SD)

East 3.31 (0.47) <0.0001 0.58 (0.09) <0.0001 0.58 (0.08) <0.0011

West 2.64 (0.29) 0.50 (0.06) 0.50 (0.05)

Bottleneck

I analyzed the data with and without a sequential Bonferroni correction in order to compare my results with Clostio et al. (2012) and Ennen, Kreiser, and Qualls (2010) as they did not specify if the correction factor was applied to their data. For the 47 sites tested for potential bottlenecks, without Bonferroni correction only 14 had significant heterozygosity excess (Table 6).

Table 6

Significant P Values for Heterozygosity Excess Under T.P.M. (Without Bonferroni

Correction)

SITE P VALUES FOR EXCESS SITE P VALUES FOR EXCESS

BC 0.007 YR 0.000

GF 0.003 LR 0.018

Table 6 (continued).

SITE P VALUES FOR EXCESS SITE P VALUES FOR EXCESS

TM 0.041 MC 0.011

T44 0.027 WTMS 0.004

M 0.033 Island 0.035

SNG 0.000 Tr 0.004

AGTP 0.000 MAFB 0.018

Of the 14 sites showing bottlenecks, 10 of those sites are located in the listed portion of the range (west population) of gopher tortoises, which means 10 out of 14 (71%) sites sampled have undergone a bottleneck. The west GA group has 2 out of 8 (25%) sites showing a bottleneck and the east GA has 1 out of 7 (14%) sites showing a bottleneck.

With Bonferroni correction, only three sites showed a bottleneck: SNG, YR and

AGTP. Two of these are in the listed portion of the range and AGTP is found in South

Carolina. The significant result for AGTP is likely an artifact of this being site potentially being an admixed population of waif tortoises.

AMOVA & SAMOVA

Each model ran for AMOVA explained a significant portion of the molecular variance. While all of the AMOVA models have significant values, the model with the highest proportion of variance among groups was the K=5 run. The K=5 model is based on structure from Clostio et al. (2012) and from the K=5 Structure results from this study.

The results for the SAMOVA runs for K=2 & 5 (Table 7) showed a similar amount of the variation among groups for each of the comparable values of K from the AMOVA.

Table 7

Results From the Five AMOVA Models and Three SAMOVA Runs for K=2-5. Showing the Partition of Molecular Variance Partitioned Among Groups, Among Populations

Within Groups, and Within Populations. The Studies Cited for Models of Population

Structure Were Clostio et al. (2012) and Ennen et al. (2012)

Models % Among Groups % Among Pop % Within

Within Groups Populations

AMOVA

K=5 (this study, 21.15 7.09 71.76

Clostio et al.)

K=4 (AL/East GA 20.67 7.97 71.34 combined)

K=3 (Clostio et al.) 17.62 11.58 70.80

K=2 (Ennen et al.) 16.81 14.64 68.55

K=2 (USFWS) 17.88 14.53 67.59

SAMOVA

2 18.48 15.15 66.36

4 21.85 8.02 70.13

5 22.52 6.76 70.73

All AMOVA and SAMOVA values for K vary by less than 0.5%, however, the groupings for K that SAMOVA found did not always correspond to my a priori

21 groupings for AMOVA. In the west group, SAMOVA included the CW population from east of the Tombigbee and Mobile Rivers. SAMOVA also added FtB, Gadsden, R and

MAFB populations to the AL group and only placed Islands, JRC, WT and ALD in the west GA population. The east GA and FL populations remained the same in SAMOVA as they did for the other two analyses.

The IBD analysis revealed that genetic distance and geographic distance among gopher tortoise populations show a strong positive correlation (r2 = 0.82; p<0.0010;

Figure 1). This is similar to Clostio et al.’s (2012) study that also shows a correlation between geographic and genetic distance between tortoise populations.

Figure 1. Isolation-By-Distance graph for 45 populations across the range of G. polyphemus. Slope=0.679 and r2=0.82.

STRUCTURE

Based on the log likelihood (L(K)) scores (Figure 3), the STRUCTURE analysis supported K=5. A value of K=4 also shows a reasonable interpretation of the data with

22 the difference being that sites in AL, Walton county FL, and west GA grouped together.

Both groupings of K=4 & K=5 had 35 sites where the majority of individuals showed

>90% ancestry in a particular group. I chose K=5 as the pattern to interpret and for the base of some of the other analyses because of the L(K) values (Figure 3), the low frequency of haplotype mixture between these two groups (AL and west GA, with west

GA having a unique haplotype; Ennen et al. 2012; Clostio et al. 2012), and the high proportion of individual ancestry of the sites TR, CW, PD, CNF, and WA to a distinct group in the K=5 model (Figure 4). Also, the STRUCTURE analysis of the K=2 model shows a sharp change in population membership for individuals west of the Tombigbee and those east of the Apalachicola (Figure 5). The central Alabama and Walton county

Florida individuals have mixed ancestry between the two groups. Also, Clostio et al.

(2012) found support for K=5 using the program TESS for their data set.

All individuals sampled from west of the Tombigbee and Mobile Rivers assigned to one group, whereas, individuals from sites east of the Tombigbee and Mobile Rivers assigned into four separate groups (Table 8).

Table 8

Population Groups Described by the Program STRUCTURE with Assigned Gopher

Tortoise Sites

Group Sites found within group

West Meeker’s Creek, International Paper, Water Tower, Y-Road, L-Road,

Gopher Farm, Theodore Mars WMA, Camp Shelby, Ward Bayou, Lynn’s

House, Little Florida, Marion County WMA, Ben’s Creek WMA, and

Southern Natural Gas.

Table 8 (continued).

Group Sites found within group

AL Walton County, Clearwater, Perdido, Conecuh National Forest, Troy

FLA Caya Costa, University of North Florida, Nassau, Martin County,

Hernando County, Hillsborough, Pinellas, Hilland Hammocks, Jonathan

Dickens, Kennedy Space Center, Orange County, Volusia, Alachua, and

Putnam.

West GA Reed Bingham, Moody Air Force Base, Gadsden, Ft. Benning, Andrews

Lock and Dam, Island, Wade Tract.

East GA Aiken Gopher Tortoise Preserve, Gum Swamp, Tillman Sandridge,

Savannah River Site, Mopani and Ft. Gordon.

These other four groups are roughly delimited by corresponding to the Apalachicola,

Suwannee, and St Johns Rivers. The groups are described in the following table and the graphs for the L(K) and delta K values are given below (Figures 2 and 3).

Figure 2. Delta K analysis for Structure

Figure 3. Log likelihood analysis of Structure

Figure 4. Bar plot showing the average proportion of individual ancestry to a group from STRUCTURE, K=5.

Figure 5. Bar plot showing the average proportion of individual ancestry to a group from STRUCTURE, K=2. Discussion

The first purpose to my study is to examine the pattern of range-wide population genetic structure in gopher tortoises, which has relevance in conservation planning for this species. Identifying distinct populations is important for conservation as delineating management units (MUs) and evolutionary significant units (ESUs) can help in prioritizing conservation efforts (Moritz 1994). Identification and conservation of MUs and ESUs can help to preserve the evolutionary heritage that has led to the current genetic structure of the population and can help to preserve the adaptive potential of the local population (Waples 1991; Moritz 1994). Previous studies already documented range-wide population structure in gopher tortoises using mtDNA (Osentoswki and Lamb

1995; Ennen et al. 2012; Clostion et al. 2012) and microsatellites (Scwartz and Karl

2005; Clostio et al. 2012). However, my study fills some important sampling gaps in the central portion of the range.

A broad range population genetic study on gopher tortoises by Clostio et al.

(2012) showed support for five genetic groups: 1) populations in the listed portion of the range, 2) central Alabama (a single site – Solon Dixon), 3) western Georgia, 4) eastern

Georgia, and 5) peninsular Florida. However, the authors cautioned that more sampling needed to be done in Florida, eastern Georgia, South Carolina, and Alabama in order to confirm their observed structure. My study, with more geographically intensive sampling within these regions suggests that there are five genetic groups that mostly coincide with the ones described by Clostio et al. (2012). One difference is that all of my peninsular

Florida sites did not fall into one group. My Nassau site (in close proximity to the

Clostio et al. [2012] Nassau site) assigned to the east GA group and the UNF site from

Duval County (directly south of Nassau) showed large amounts of admixture (Figure 3) between east GA and FL groups. The SAMOVA and STRUCTURE analyses in this study are generally in congruence with only minor differences, which provides some degree of confidence in the patterns of structure detected.

The boundaries between these five groups roughly correspond to the

Tombigbee/Mobile, Apalachicola, and Suwannee Rivers. These rivers are known to be potential barriers to other species (Pounds and Jackson 2006; Soltis 2006) and Pounds and Jackson (2006) found that the larger a river the greater likelihood of the river being a barrier to gene flow. Although Brode (1959) describes seeing a tortoise swim across a narrow canal, he describes it as a very weak swim and more of a directed float, which makes it plausible that wide rivers with a high discharge rate could present themselves as barriers to tortoise crossings. However, river width and flow rates are not solely

27 responsible for defining structure (Gascon et al. 2000), therefore, rivers with large drainage areas, such as the Suwannee, could also be impediments to tortoise gene flow.

Like Ennen et al. (2012), my results also support the USFWS delineation of the

Tombigbee and Mobile Rivers as a boundary between non-listed and listed populations.

Only 20 out of the 290 individuals on the western side of these rivers have ancestry assignments of <90% for the west group. As with K=5, the K=2 STRUCTURE also shows admixture across the central portion of the range (e.g., in the central Alabama and

Walton, Florida populations). Only 12 out of 58 individuals from this region have >90% ancestry assigned to either the west or east group (2 west and 10 east), and the remaining individuals show roughly equal proportions of ancestry assigned to both groups.

Populations east of the Apalachicola have only 21 out of 585 individuals that show <90% of ancestry assigned to the east group.

The overall population structure within gopher tortoises is fairly distinct with the majority of individuals having a >90% assignment to a group. However, there were four sites that showed admixture between larger groupings. Two out of these four groups are found in Georgia along the Chattahoochee River. This river, in addition to the

Apalachicola River, appears to be the boundary between the Alabama group and the western GA group. The admixture between these groups illustrates that rivers are not absolute barriers to gene flow. The UNF site shows admixture between the eastern GA and peninsular Florida group. The large amounts of admixture here likely reflects this region as a point of contact between two groups that were at one point isolated due to multiple occurrences of fluctuating sea levels during the Pleistocene in peninsular Florida

(Webb 1990) and subsequent mesic habitat expansion resulting in islands of xeric scrub

28 habitat during the late Pleistocene (Watts and Hansen 1988). Osentowski and Lamb

(1995) found that gopher tortoises show genetic structuring along the Brookesville ridge in central Florida, and Schwartz and Karl (2005) showed the Atlantic coast ridge could potentially be influencing genetic structure on the eastern coastline of Florida.

Not all admixture among sites is natural. The fourth site showing admixture is the

Aiken Gopher Tortoise Preserve site, which seems to contain both native tortoises and waif tortoises. Tortoises at this site are postulated to represent original individuals from the region (Tuberville October 6, 2012 pers. comm.), but from my analysis, it would appear that some individuals are of admixed ancestry from peninsular Florida, east GA and west GA populations, with one individual assigning completely to the peninsular

Florida group. Other potential waif tortoises (> 80% assignment to group outside sampling group) appear in WB, RB, IP, Ft.G, Ft.B, and GS sites; however, typically there is only one waif per site. Evidence of waif tortoises is not surprising, as relocation of gopher tortoises is common and they are becoming one of the most translocated turtle species in the US (Tuberville et al. 2005). Clostio et al. (2012) found evidence of waif tortoises and suggested microsatellites could be used as a forensics tool. Schwartz and

Karl (2008) tested the accuracy of using microsatellite loci as a forensics tool to identify location of origins for waif tortoises. They were able to accurately assign location of origin for tortoises from a known locality but were unable to accurately identify location of origin for tortoises from unknown localities. The authors postulate their sampled sites did not cover the original locations of these waif tortoises and resulted in their inability to assign them to populations in their database. The data from this study further highlights the usefulness of genetic data to identify the origin of known waifs and to determine the

29 origin of any suspect tortoises. With the increased number of individuals, microsatelitte loci and sites sampled, the data set in this study will have greater ability to detect population of origins than previous studies.

Genetic Diversity

Differences in the genetic diversity of populations across the range were previously observed for the gopher tortoise (Ennen, Kreiser, and Qualls 2010; Clostio et al. 2012) and other Gopherus species (Fuji and Fornster 2010; Hagerty and Tracy 2010).

The second purpose of my study is to find out if the additional sampling supports the previous studies that showed lower genetic diversity for populations west of the

Tombigbee and Mobile Rivers. In my K=2 comparisons of genetic diversity, the west has significantly lower levels of Ho, A and He than did the east population (Table 5).

Therefore, my work provides much more extensive support in terms of the number of sites and loci for the previous findings that populations in the west do have lower levels of genetic diversity than populations in the east.

One possible explanation for the reduction in genetic diversity is that western populations have undergone extensive bottlenecks in population size. Bottlenecks, whether intense or diffuse, can cause loss of genetic diversity (England et al. 2003), increased inbreeding (Frankham, Ballou, and Briscoe 2002) and an increased risk of extinction of the population (Newman and Pilson 1997). In this work, of the 47 populations tested only 14 showed signs of a genetic bottleneck, without Bonferroni correction. However, ten of those fourteen populations are found in the west. The high percentage of genetic bottlenecks in the west could be due to the severe decline in tortoise numbers and the fragmentation of suitable habitat. These results do contrast

30 somewhat with those of Ennen, Kreiser, and Qualls (2010) and Clostio et al. (2012) who reported fewer sites showing potential bottlenecks using heterozygosity excess. Different markers can have different numbers of alleles and different amounts of heterozygosity, which could potentially result in different outcomes as these indices are used to assess bottlenecks within populations. Not all of the same populations were used in these studies and different populations can have different historical events that can influence these analyses. My study has roughly the same population sizes as the other two studies; however, I used twice the number of loci as the previous studies, which could have resulted in a greater ability to detect bottlenecks (Peery et al. 2012). With Bonferroni correction only three sites showed signs of a genetic bottleneck; however, the only site in the non-listed region was AGTP, which shows signs of admixture due to human translocations (Figure 3). With Bonferroni correction the number of sites in the listed portion of the range showing potential bottlenecks is two out of 14 sites, suggesting tortoises in the listed range have undergone recent population declines.

Lumping all four eastern groups found into one unit might mask variation among groups in their levels of genetic diversity. When the groups from the east are analyzed individually along with the west, I found that the western populations have significantly lower levels of genetic diversity (Ho, A and He) than those in Alabama, western Georgia, and Florida. However, the western populations do not have significantly lower levels of

Ho, A and He than those populations found in eastern Georgia, South Carolina and northeast Florida. In fact, the eastern GA populations had the lowest mean Ho (0.47) and

He (0.48) of all populations, while the west still had the lowest A (2.64) for all populations. The loss of heterozygosity occurs at a slower rate than the loss of alleles

(Maruyama and Fuerst 1985); therefore, the low levels of heterozygosity and lack of bottlenecks observed in the eastern GA populations suggest that these populations might suffer from historically small effective population sizes. Thus, it seems that populations along the periphery of the range have significantly lower levels of genetic diversity than those populations in the more central portion of the range, a pattern that has been observed in both plants and animals (Eckert, Samis, and Lougheed 2008). For example,

Osborne et al. (2012) postulated that peripheral populations of the Chihuahua chub have lower genetic diversity due to genetic drift and a small founding population. The abundant center model suggests that populations in the center of a species range will achieve highest abundance with progressively smaller populations toward the edge of the range (Brussard 1984; Lawton 1993). The effective population size and amount of gene flow should be highest at the range center and decrease toward the edges of the range

(Eckert, Samis, and Lougheed 2008) resulting in peripheral populations having potentially lower levels of genetic diversity than the more central populations. Genetic diversity indices from this study follow this pattern with central populations having the highest levels of genetic diversity and peripheral populations having lower levels of genetic diversity (Tables 2-4).

Another possibility is that the higher levels of genetic diversity in the central portion of the range could be a result of this region being a contact zone between populations once separated into glacial refugia. Avise (2000) postulated that post-glacial expansion occurred from both sides of the Apalachicola River from southern refugia.

Donovan, Semlitsch, and Routmane (2000) hypothesize salamanders used postglacial refugia expansion routes between Texas and Florida, and Swenson and Howard (2005)

32 found Alabama to be a hotspot for contact zones. Therefore, it is possible the AL and west GA populations could be in a contact zone between two post glacial refugia populations. Ennen et al. (2012) found 13 unique haplotypes with two main haplotype groups, 1 and 9, and while haplotype 1 was predominately found west of the

Apalachicola and 9 east of it, these two haplotype groups did overlap. The tortoise populations within the regions where these two haplotype groups overlap are also the populations with highest levels of genetic diversity and also show mixed ancestry in microsatellite loci, suggesting that gene flow between these populations has occurred post glaciation and possibly occurs presently at some level.

Conservation

The tortoise populations west of the Tombigbee and Mobile Rivers have been listed as federally threatened due to habitat loss and fragmentation, low population numbers, and poor recruitment. However, the eastern populations have yet to be listed despite continued population declines (McCoy, Mushinsky, and Lindzey 2006), although they are now a candidate species (USFWS 2011). With my expanded sampling I am able to confirm Clostio et al.’s (2012) findings recognizing that populations in the listed range and those in the west GA group should be considered as evolutionary significant units.

Furthermore, my work strongly supports the recognition of three other groups from the eastern, central, and Florida peninsular portions of the range. Western populations clearly show reduced genetic diversity compared to eastern populations; however, I also found that the eastern Georgia group also showed reduced genetic diversity levels.

Tuberville and Dorcas (2001) found that a tortoise population in this region declined on average about 2.33% since the late 1970’s. Recent reductions in tortoise numbers along

33 with low levels of genetic diversity in these populations should make this a region of high conservation priority. I also found that populations between and close to the

Tombigbee/Mobile Rivers and the Chattahoochee/Apalachicola Rivers have the highest levels of genetic diversity and have the highest amounts of admixture, especially those populations found between these rivers. This area of high genetic diversity and admixture could also provide a safe-haven for future evolutionary potential (England et al., 2003) of the gopher tortoise. Thus, these genetically diverse central populations should also be considered as a high priority for conservation efforts. Populations in peninsular Florida show moderate levels of genetic diversity and comprise a single genetic group. Results from this study show that gopher tortoise populations do show distinct genetic groupings other than just along the Tombigbee and Mobile River drainages. The different groups show distinct allele frequency differentiation and varying levels of genetic diversity, and I suggest that populations in the Alabama, east GA, and

Florida group be considered as management units.

CHAPTER III

REGIONAL PATTERNS OF POPULATION GENETIC STRUCTURE IN THE

GOPHER TORTOISE

Gopherus polyphemus is a medium sized testudine that is restricted to fire- dependent, xeric longleaf pine forests of the southern United States. Gopher tortoises typically average between 25-30 cm in carapace length as adults and weigh between 4-8 kg. The gopher tortoise is an herbivore (with few reports of scavenging and egg consumption) that chiefly forages on grasses and forbs. They also dig burrows that can reach up to 10 m in length that provide shelter from predators, fire, and weather extremes

(Hallinan 1923; Hansen 1963). Gopher tortoises are keystone species in their habitat as their burrows provide shelter for over 300 different species in the longleaf pine ecosystem (Jackson and Milstrey 1989; Witz, Wilson, and Palmer 1991). Over the past century due to habitat destruction and degradation (Kelly and Bechtold 1990), gopher tortoise populations have seen an 80% reduction in total numbers (Auffenberg and Franz

1982) with the greatest reduction found west of the Tombigbee and Mobile Rivers

(western population). The dramatic decline in numbers in the western portion of their range has warranted the species to be listed as federally threatened west of the

Tombigbee and Mobile Rivers (USFWS 1987). However, the other portion (non-listed) of the species’ range was recently found to warrant listing as threatened under

Endangered Species Act (ESA). However, higher priority listing actions precluded this move, and instead this species was added to the candidate list (USFWS 2011).

Genetic structure has been found in species with large geographic distributions

(Avise 2000). Conservation of species with a broad geographic range also includes

35 preventing the loss of genetic diversity that may be associated with adaptations to local selective pressures (Hilborn et al., 2003; Luck, Daily, and Ehrlich 2003). Evolutionary significant units (ESUs) are comprised of individuals that form a unique genetic group and these units can be used to prioritize conservation efforts (Moritz 1994). Previous studies on gopher tortoises used different methods to define gopher tortoise genetic structure. Osentowski and Lamb (1995) used mtDNA markers and found three geographic assemblages corresponding to and east/west split along the Apalachicola

River and a third group in central Florida along the Brooksville Ridge. Ennen et al.

(2012) and Clostio et al. (2012) also used mtDNA and found a distinct phylogeographic break at the Apalachicola River. Clostio et al. (2012) also found a third distinct haplotype group located between the Apalachicola River and Suwanee Rivers. Using microsatellite loci, Clostio et al. (2012) and my rangewide study, found support for five genetic populations across the range of the gopher tortoise. These five populations are roughly delimited by the Tombigbee and Mobile Rivers, Apalachicola River, and the

Suwanee River.

While major rivers appear to influence gopher tortoise genetic structure at the broad scale, several studies also found genetic structure at the regional scale. Clostio et al. (2012) show tortoises in the listed portion of the range (west of the Tombigbee and

Mobile Rivers) are represented by two distinct populations, with a break roughly along the Pascagoula River. Schwartz and Karl (2005) found 8 genetic assemblages throughout southern Georgia and peninsular Florida. Within Florida there is an extensive ridge system that was a refuge for different species during the Pleistocene and has resulted in distinct genetic structuring along these ridges (McDonald et al. 1999; Clark et al. 1999).

However, Schwartz and Karl (2005) found only limited evidence for the Atlantic Coast

Ridge contributing to genetic structure of this species. They suggested that historical structure probably existed along these ridges but recent anthropogenic movements resulted in mixing of these populations and caused them to become homogenized. At smaller spatial scales, studies performed on gopher tortoises did not find significant genetic structure. Richter et al. (2011) found limited evidence for genetic structure at sites located in the DeSoto National Forest, while Sinclair, Dawes, and Seigel (2010) also found a lack of genetic structure at Kennedy Space Center. They both concluded that landscape features at the local scale and anthropogenic effects did not contribute significantly to genetic structure in tortoises.

Within the listed portion of the tortoise’s range, there are several major rivers, along with their associated wetland and riparian habitats, and roads that divide up the landscape and could be potential barriers to gene flow. The USFWS (2009) has designated four regions within the listed portion of the range as independent functional units that correspond with major rivers in this region. The four major rivers that divide up the landscape are the Pascagoula, along with its two main tributaries, the Leaf, the

Chickasawhay, and the Pearl River. These four rivers also have many lower order bodies of water that flow into them, making the listed portion of the tortoise’s range highly divided by rivers and creeks. While individual smaller creeks, might not be a strong barrier to gene flow, multiple small creeks between populations could reduce tortoise movement between populations and thus limit gene flow. Genetic differentiation of populations can be greater than expected due to land-use changes or unsuspected barriers that fragment populations (Booth, Montegomery, and Prodohl 2009). If gene flow is

37 limited, then populations could experience genetic drift, and population structure might exist.

Populations in the non-listed portion of the range have similar threats to those in the listed, mainly habitat destruction and degradation. Within Florida, tortoises are translocated at an alarming rate to accommodate the housing and commercial development. The gopher tortoise is the most translocated tortoise in the southeastern

U.S. (Tuberville et al. 2005), and in Florida alone, thousands of gopher tortoises are moved considerable distances and placed within a new population (Dodd and Seigel

1991). This could potentially cause genetic admixture due to tortoises breeding from two distinct genetic populations. If the genetic differences involve important co-adaptive traits, then this could result in the breakdown of those co-adaptive traits and thus a reduction in fitness (e.g. outbreeding depression).

Within Florida, the Apalachicola, Suwannee, and St. John’s Rivers appear to be important features in shaping genetic structure at the broad scale range. From my rangewide analysis, these rivers divide tortoise populations into four distinct genetic groups within Florida: 1) panhandle, 2) Apalachicola region, 3) peninsular and 4) northeast (north of St. John’s River). The largest group is the peninsular group, which comprises the majority of the state. Within this group the Florida Ridge system is important to shaping the genetic structure in other species (Clark, Bowen, and Branch

1999). These central ridges could also be an important factor in shaping the genetic structure of tortoise populations within peninsular Florida (Osentowski and Lamb 1995;

Schwartz and Karl 2005). If structure exists within peninsular Florida, then it could aid

38 management agencies in developing protocols for where translocated tortoises can be moved.

Landscape features, whether anthropogenic or natural can impede or block gene flow. Murphy, Evans, and Storfer (2010) found that roads, canopy cover, and open water were barriers to gene flow in the amphibian Bufo boreas in Yellowstone National Park.

Coyotes found in heterogeneous or patchy landscapes show genetic subdivisions based on what habitat they live in regardless of how close together they are geographically

(Sacks et al. 2008). Not all major landscape features will be absolute barriers to gene flow. Coulon et al. (2006) found that roe deer populations in southwestern France had significant genetic structure due to the combination of several landscape features with low permeability. Maintaining the historical population structure is important for agencies that are managing these tortoise populations because moving tortoises to areas they otherwise would not be found could cause outbreeding depression.

If genetic structure exists in these two regions, then it can have implications for managing these tortoise populations. For instance, the results from Schwartz and Karl

(2005) suggest structure is based on a north/south direction and not an east/west direction within peninsular Florida. Distinction between the two directional structure possibilities is important because there are no regulations on moving tortoises in an east/west direction, but Florida Wildlife Conservation Commission (FWCC 2001) restricted north/south movements to 80km. If there is structure based in an east/west split, then these regulations might need to be reconsidered in order to preserve the historical genetic structure.

The purpose of this study is to determine if genetic structure exists at a regional level in gopher tortoises for the listed portion of the range and peninsular Florida. I used three different genetic analytical programs to determine the genetic structure of tortoises within these two regions. This information can help inform management agencies of possible evolutionarily significant units and managements units, which will give management agencies the ability to protect unique characteristics of the gopher tortoise and promote the persistence of evolutionary potential (Waples 1991; Moritz 1994).

Materials and Methods

Previously collected and extracted tortoise samples from Louisiana, Mississippi and western AL totaling 267 individuals from 13 sites were used in the analysis of the listed portion of the range while 259 tortoises from 15 sites were used in the Florida analysis (Table 9).

Table 9

Site Names and Abbreviations, Approximate Location of Site, and Sample Size

SITE LOCATION N

Listed analysis

Meeker’s Creek (MC) Saraland, AL 52

International Paper (IP) Mobile County, AL 18

Water Tower (WT) Wayne County, MS 8

Long Road (LR) Jones/Wayne County, MS 9

Gopher Farm (GF) Wayne County, MS 15

Theodore Mars WMA (TM) Poplarville, MS 6

Tract 44 (T44) Camp Shelby, MS 61

Table 9 (continued).

SITE LOCATION N

Ward Bayou (WB) Moss Point, MS 17

Lynn’s House (LH) Jackson County, MS 6

Little Florida (LF) Harrison County, MS 18

Marion County WMA (M) Marion County, MS 18

Southern Natural Gas (SNG) Franklinton, LA 19

Ben’s Creek (BC) Bogaloosa, LA 20

Florida analysis

University of North Florida (UNF) Jacksonville, FL 28

Martin County Florida, (MCF) Martin County, Florida 10

Glen Lakes (HERN) Hernando County, FL 29

Cayo Costa Cayo Costa, FL 20

Ecoarea (HB) Hillborough County, FL 17

Boyd Hill (Pin) Pinellas County, FL 25

Highland Hammocks Highland County, FL 18

Jonathan Dickenson State Park (JD) Martin County, Florida 21

Kennedy Space Center (KSC) Kennedy Space Center, FL 16

Lake Louisa (Lake) Lake County, Florida 21

Volusia (Vol) Volusia County, Florida 11

Alachua (Alachua) Alachua County, Florida 10

Putnam (Putnam) Putnam County, Florida 8

Gadsden (Ga) Gadsden County, Florida 9

Table 9 (continued).

SITE LOCATION N

Nassau (Nassau) Nassau County, Florida 16

A collapsible Tomahawk® Model 18 Live Trap (81.28 × 25.4 × 30.48 cm) covered with shade cloth and pine needles was placed in front of the mouth of the burrow. A 0.5 mL sample of blood was taken from the femoral vein using a new 23G1 gauge Becton Dickinson PrecisionGlide hypodermic needle. Occasionally, if the femoral artery was difficult to bleed, blood was drawn from the brachial vein. To stop bleeding after the blood was drawn, pressure was applied directly to the site of puncture for 15 sec.

Each blood sample was stored in a 2.0 mL vial with approximately 0.5 mL of SED tissue preservation buffer (Seutin, White, and Boag 1991). After all measurements were taken, the tortoise was immediately placed back into the burrow. After tortoises were captured from a site, the traps were removed and sprayed with a 10% solution of bleach and allowed to dry. Alcohol was used to sanitize calipers and hands after the handling of each tortoise to help prevent any possible disease transmission between individuals.

Genomic DNA was extracted from each individual using a DNeasy Tissue Kit

(QIAGEN) and PCR cycling conditions consisted of an initial denaturing step of 94 C for

2 min followed by 35 cycles of 30 sec at 94 C, 1 min at 50-60 C, and 1 min at 72 C. A final elongation step of 10 min at 72 C ended the cycle. Microsatellite alleles were visualized using a LI-COR 4300 DNA sequencer along with a 50-350 bp size standard

(LI-COR) and scored using Gene Image IR v. 3.55 (LI-COR). Basic summary statistics

(allelic richness - A, observed heterozygosity - Ho, and expected heterozygosity - He)

42 were calculated using FSTAT. Loci were tested for Hardy-Weinburg equilibrium (HWE) and linkage disequilibrium using GENEPOP for the web (Raymond and Rousset 1995)

Different analytical approaches contain unique ways to process the genetic and spatial data in the attempt to define genetic structure across a geographical region. These programs all contain the ability to detect barriers to gene flow, but some are more robust than others. When trying to detect linear barriers to gene flow, clustering methods work better than edge-detection methods (Safner et al. 2011). Blair et al. (2012) found that

GENELAND is the best method for detecting linear genetic barriers, but they also recommend using more than one method for detecting barriers as the power and false- detection rates of each program differ. In this study, I used three Bayesian approaches

GENELAND (Guillot et al. 2005a), STRUCTURE (Prtichard, Stephens, and Donnelly

2000) and TESS (Chen et al. 2007)). None of these programs uses a priori designations of populations, but instead uses the genetic data (allele frequencies) to assign individuals to populations. However, both GENELAND and TESS explicitly use geographical coordinates in the analysis (Guillot, Mortier, and Estoup 2005b; Chen et al. 2007) while

STRUCTURE does not, although it can use sampling location as a prior in the analysis

(Hubisz et al. 2009).

STRUCTURE 2.3.3 (Pritchard, Stephens, and Donnelly 2000; Hubisz et al. 2009) uses a Bayesian approach to partition individuals into some number of genetically discrete populations that are in Hardy-Weinberg equilibrium and the loci in each population are in linkage equilibrium. I tested values of K (number of populations) from

1-10 using a model of admixed ancestry and assuming correlated allele frequencies between groups. For each value of K, I performed 10 separate simulations with a burn-in

43 of 100,000 generations followed by a subsequent 500,000 generations. I determined the best value of K by examining the probability scores for each value of K, and then I calculated the ΔK values (Evanno, Regnaut, and Goudet, 2005) by using the program

Structure Harvester v 6.92 (Earl and von Holdt, 2012). The 10 runs at the best value of K were averaged using CLUMPP v. 1.1.2 (Jakobsson and Rosenberg 2007), and the results were visualized with DISTRUCT v. 1.1 (Rosenberg 2004).

The second approach implemented by GENELAND version 3.1.4 software

(Guillot, Mortier, and Estoup 2005b), uses multilocus genotype data from georeferenced individuals to infer population groupings of individuals under a Bayesian model framework. The incorporation of geographical data into the analysis allows

GENELAND to detect subtle population structure and infer the probable locations of genetic transition zones. I ran an uncorrelated allele frequency model with a K range of

1-10 for Florida data and 1-5 for Listed data. To determine the Kmax in the correlated model, I used the best uncorrelated K value. In both cases, the parameters included an assumption error in spatial coordinates. I used the assumption error model because we do not have geographical points for individuals from some sites, but rather I had a general geographic point representing all the individuals at a site. Therefore, I assumed a 1 km error rate for individual locality, which seems to be a reasonable approximation of distance among individuals if they all came from the same colony. Runs were set to

100,000 iterations, which will be sampled every 100th observation. During post-run processing, I set the burn-in to 100.

The program TESS (Chen et al. 2007) uses allele frequencies along with geographical coordinates to place individuals into genetic clusters. I set the TESS

MCMC run at 50,000 sweeps with a burn in of 10,000 sweeps. I first ran the program without admixture for a K of 2-10 for the Florida data and K=2-6 for the listed data for 20 independent runs. To determine the best value of K, I chose the value at which no increases in the number of clusters occurred and the change in the deviance information criteria (DIC) began to level off. I then ran the conditional autoregression Gauissian

(CAR) model for values of K from 2-10 with 20 replicates of each, and I determined the best K value by the same criteria as the no admixture runs. I averaged the estimates of ancestry for each individual using CLUMPP and visualized with DISTRUCT.

Results

No loci consistently deviated from HWE nor did any loci consistently show LD for all populations. In Florida populations, A ranged from 2.53 for CC to 3.87 for

Alachua, Ho ranged from 0.387 for CC to 0.650 for GA, and He ranged from 0.429 for

CC to 0.651 for Alachua. For the Listed analysis, A ranged from 2.23 for BC to 3.07 for

WB, Ho ranged from 0.388 for TM to 0.597 for LH, and He ranged from 0.423 for BC to

0.584 for MC.

Population Structure Results

Listed. From the log likelihood and Delta K analysis a K=2 (Figure 6) is found to be the best fit for the data. This model shows that all populations west of the Pascagoula

River assign strongly to one group (a majority with greater than 90% assignment) and populations east of the Pascagoula River have mixed assignments between the two groups with roughly a 50% assignment between groups (Figure 6). The TESS DIC values (Figure 6) from both the no admixture and admixture models support a Kmax=2.

The assignment values were similar to the STRUCTURE output in that sites west of the

Pascagoula assigned strongly to one population and sites east of the Pascagoula showed admixture between the west population and an eastern population. The individuals east of the Pascagoula had roughly 50-60% probability of membership assigned to the west population (Figure 7). GENELAND found a value of K=2 two to be the best fit for the uncorrelated analysis. I used this K=2 value as Kmax in a correlated allele frequency model and GENELAND assigned posterior probabilities of membership to each individual. The correlated allele frequency model showed strong assignment for the K=2 model with all individuals having a >99% assignment to one of the two groups (Figure

7). The two groups are roughly split and show a strong gradient of membership assignment with the boundary being defined by the Pascagoula River.

Figure 6. A) Averaged TESS DIC scores plotted against K from admixture run, showing support for K=2, no increase in clusters after K=2. B) Delta K plot from STRUCTURE output showing support for K=2.

C D

Figure 7. Population genetic groupings for K=2. A) bar plot of averaged membership coefficients from TESS and B) bar plot of proportion of individual ancestry from STRUCTURE , C) a map of group membership from GENELAND and D) a heat map showing probability of membership to cluster 1.

Florida. The STRUCTURE analysis supported a K=5 (Figure 8) to best describe the data. Both Gadsden and Nassau are assigned to unique groups. The rest of peninsular

Florida is broken up into three groups. Cayo Costa (CC), which is an island off of Fort

Myers, Florida, assigned to its own population. Kennedy Space Center, Volusia and

Jonathan Dickens assigned to an Atlantic coast population and Hernando, Hillsborough,

Pinellas, Lake, and Alachua to a Gulf of Mexico population. Martin County showed admixture with CC and Atlantic coast populations and Putnam showed admixture with

UNF and Duval, Atlantic coast, and Gulf of Mexico populations. The TESS DIC scores from the no admixture run supported a K=5, and the admixture run supported a K=5 also

(Figure 8). Once again, Gadsden is placed into its own population as are the Duval county and UNF samples from northeast Florida. The TESS analysis also showed 3 populations in peninsular Florida, which correspond to the groups defined by

STRUCTURE. Cayo Costa showed admixture with central Florida group and its own unique group into Kennedy Space Center, Volusia, and Jonathan Dickens into an Atlantic coast population and Hernando, Hillsborough, Pinellas, Lake, and Alachua into a central

Florida population. Martin County showed admixture with CC, Gulf of Mexico, and

Atlantic coast populations, and Putnam showed admixture with UNF and Duval, Atlantic coast, and Gulf of Mexico populations. The GENELAND analysis also showed support for a K=5 (Figure 9) and the groupings are similar to those for TESS and STRUCTURE except HERN showed mixed ancestry, Alachua added to west GA, MCF and Putnam added to Atlantic coast, and HERN and Alachua removed from the central Florida group.

All analyses recognized five genetic groups in peninsular Florida (Table 10), although the composition of those groups varied somewhat.

Table 10

Genetic Groups Defined in Peninsular Florida on the Basis of the Three Bayesian

Analyses. Sites that Show Admixture Among These Groups are Also Listed.

GROUP STRUCTURE TESS GENELAND

Eastern GA UNF, Duval UNF, Duval UNF, Duval

Western GA Gadsden Gadsden Gadsden, Alachua

Atlantic Coast KSC, JD, Vol KSC, JD, Vol KSC, JD, Vol, MCF,

Putnam

Central Florida Hern, HH, HB, Pin, Hern, HH, HB, Pin, HB, Pin, HH, Lake

Lake, Alachua Lake, Alachua

Cayo Costa CC CC CC

Admixed MCF, Putnam MCF, Putnam Hern

The biggest discrepancy between the GENELAND and TESS/STRUCTURE outputs would be for the HERN and Alachua sites. Alachua assigns with the Gadsden group in

GENENLAND but does not show any admixture from Gadsden in the TESS or

STRUCTURE analysis, although in GENELAND individuals from Alachua show 15% assignment to the central Florida group assigned to them by the other two analyses.

HERN strongly assigns to the central Florida group in both TESS and STRUCTURE but is split roughly 50/50 between central Florida and Gadsden in the Geneland analysis. In the rangewide analysis, Gadsden strongly assigns to the east GA group, and HERN and

Alachua assign strongly to the peninsular Florida group. Based on the TESS and

STRUCTURE analysis for the Florida data and the rangewide analysis assignment, we consider HERN and Alachua to be grouped with the central Florida group. Cayo Costa assigns to its own group for GENELAND and STRUCTURE with >90% probability; however, it shows 50% admixture with the central Florida group in TESS.

Figure 8. A) Averaged TESS DIC scores plotted against K for admixed model, showing support for K=5, no increase in clusters after K=5. B) STRUCTURE log likelihood showing support for K=5.

Figure 9. Population genetic groupings for K=5. A) bar plot of averaged membership coefficients membership from TESS and B) bar plot of proportion of ancestry from STRUCTURE and C) a map of group membership from GENELAND.

Discussion

I detected regional genetic structure in gopher tortoise populations for both the listed region and the peninsular Florida region. This result supports the previous findings of Schwartz and Karl (2005) and Clostio et al. (2012). Genetic variation within a species usually has a geographical hierarchy; therefore, understanding the population genetic structure can help to preserve the genetic diversity within a species (Allendorf and Leary 1988). Defining different levels of population structure such as ESUs and

MUs can allow conservation managers to protect and preserve unique traits of different populations and thus help to aid in evolutionary potential of the species (Waples 1991;

Moritz 1994). I found meaningful genetic differentiation in both regions that can have implications for gopher tortoise management.

The USFWS listed all populations west of the Tombigbee and Mobile Rivers as federally threatened and designated four functionally independent units based on the four major rivers within this region. Clostio et al. (2012) of the USFWS’s designated units in the listed portion of the range showed support for a K=2 with a rather clear break along the Pascagoula and Chickasawhay River. However, they concluded there is not a genetic break based on these rivers. Perhaps this is due to their finding sites with admixture between the two groups on the western side of the Pascagoula. Admittedly, TESS and

STRUCTURE both show populations west of these rivers that are strongly associated with one group and populations east of the rivers show roughly 40-60% admixture with another group. The Ward Bayou site is located west of these two rivers and assigns membership with populations east of the river; however, this site is within several miles of the river, and historical meandering of the Pascagoula could have placed it on the east

52 side. In Clostio et al.’s (2012) study, Leakesville site, in which many individuals showed

40-60% admixture between the two groups, is also in close proximity to the

Chickasawhay and historically could have been on the eastern side too. Also, admixture of populations near the Pascagoula River can be an indication of contact zones between distinct groups from opposite sides of the river as Jackson and Austin (2010) found in

Scincella lateralis. The GENELAND analysis shows a very strong break at these two rivers with all individuals east (including WB) showing >99% assignment to cluster 2 and those west have 100% assignment to cluster 1. GENELAND has been shown to be more capable of detecting linear barriers to gene flow than other analyses (Blair et al.

2012). I conclude that the Pascagoula and the Chickasawhay are a barrier to gene flow, albeit not an impermeable one. Genetic structure exists between sites on either side of the Pascagoula River with limited admixture in sites close to this boundary. Tortoise populations throughout the range have undergone dramatic losses in habitat resulting fragmented and isolated populations.

Within Florida, real estate development and population growth has resulted in an

88% loss of longleaf pine habitat in Florida (Kautz 1993; Noss 1989). Due to real estate development, the FWCC developed a policy that requires tortoise to be translocated from areas being developed and that the tortoises cannot be moved more than 80km in a north and south direction (FWCC 2009). However, this policy does not limit the distance a tortoise can be moved in an east/west direction. Schwartz and Karl (2005) found support for five genetic assemblages within peninsular Florida that were differentiated based on a mainly north/south orientation and only weakly in an east/west orientation.

I used a different set of analytical approaches (TESS, STRUCTURE and

GENELAND) to assess the genetic structure of populations within peninsular Florida.

These three programs roughly describe the same five groups throughout peninsular

Florida (Table 2). These five groups show structure in both a north/south and an east/west direction. My results show support for Schwartz and Karl’s (2005) middle Florida group and the Jonathan Dickson site separates out into an Atlantic coast group including

Volusia and KSC. These three sites all fall along the Atlantic Coast Ridge and make up their own group in TESS and STRUCTURE outputs. In GENELAND, this group has

MCF and Putnam added to it. MCF and Putnam show mixed ancestry from this group in both TESS and STRUCTURE and also fall along the Atlantic coast of Florida. The geographic extent of the Florida groups seems to correspond to geographical features.

The Suwanee River separates Gadsden from the rest of the peninsular Florida. The St.

John’s River and the Atlantic Coast Ridge separate the northeast (Nassau and UNF) sites from those along the Atlantic coast. The Atlantic Coast Ridge also differentiates the

Atlantic coast sites from those in central and western Florida. Cayo Costa is an island population off the western coast of Florida, which forms its own distinct genetic group.

My findings are similar to Schwartz and Karl (2005) as I found support for five populations in peninsular Florida; however, the composition of the groups I describe differs. One potential reason for the difference is that different sites were used. I used more sites along the east coast and fewer sites in the north central part of Florida. I agree that populations of gopher tortoises are structured along a north/south axis; however, my findings show strong support for population structure in an east/west direction as well.

My results indicate that limiting tortoise translocations to 80km in a north/south direction

54 is warranted, and it also suggests that limitations on translocation distances in an east/west direction are warranted also. Clark et al. (1999) also reported this pattern of differentiation from west to east across peninsular Florida for populations of Sceloporus woodi. Management planning for gopher tortoises should consider the potential impacts of translocating individuals outside of their respective genetic group.

Congruence between three different genetic structure analyses in this study supports previous work showing tortoise populations in both the listed portion of the range and those in Florida contain distinct genetic groups. These distinct groupings at the regional scale show that tortoise populations are not panmictic within these regions.

Using genetic markers and the analyses in this study, I did not find support for the

USFWS delineation of four independent functional groups within the listed portion of the range; however, my data does suggests that two MUs do exist. Within Florida, I detected five distinct groups that are structured in both a north/south and an east/west direction.

FWCC currently only restricts movements of tortoises in a north/south direction; however, part of their policy is to prevent mixing of genetically distinct populations.

Restriction of tortoise movements in an east/west direction by FWCC would help them to achieve this conservation goal. The results from my study support previous work on the genetic structure of tortoise populations and can help aid management agencies in their decisions on gopher tortoise conservation.

CHAPTER IV

FINE SCALE GENETIC STRUCTURE ACROSS THE FT. BENNING LANDSCAPE.

One important component of conservation biology is the ability to identify distinct populations and their level of connectivity in order to establish regional level areas for conservation such as evolutionary significant units and management units

(Moritz 1994). While traditional population genetic models often work at larger spatial scales, they can be limited in their ability to assess the impact landscape features have on gene flow between geographically proximate populations (Segelbacher et al. 2010).

Recently natural (rivers, mountains, slopes) and anthropogenic (roads) landscape features that influence genetic structure have been identified (Gerlach and Musolf 2000; Funk et al. 2005; Coulon et al. 2005; Latch et al. 2008; Frantz et al. 2010). Landscape genetics incorporates population genetics, landscape ecology, and spatial statistics to identify the effects of landscape on gene flow (Manel et al. 2003; Storfer et al. 2007; Holdregger and

Wagner 2008). The information gained from understanding the influence of landscape features on movement between populations can aid in managing species by identifying local-level priority areas (Latch et al. 2011).

One inherent difference in traditional population genetic and landscape genetic approaches is reflected in the way individuals are sampled. Traditionally, individuals sampled within a given geographic locale are referred to a priori as a “population.” On the other hand, landscape genetics based approaches sample individuals from across the landscape since this better captures relationships across the appropriate spatial scale.

(Storfer et al. 2007). The use of a spatially explicit sampling scheme can help determine the influence of geographical features on population structure and locate cryptic barriers

56 to gene flow. Fine-scale genetic structure can be assessed by applying all or some of the five major principles of landscape genetics which include 1) the influence of landscape variables and configuration on genetic variation; 2) identifying barriers to gene flow; 3) identifying source-sink dynamics and movement corridors; 4) understanding the spatial and temporal scale of ecological processes influencing population structure; and 5) testing species-specific ecological hypotheses. Traditional population genetic analyses are somewhat limited in how they can test for the influence of landscape and environmental features on gene flow and population structure (e.g., tests of isolation-by- distance). However landscape genetics provides a way to explicitly test for these factors

(Manel et al. 2003; Guillot et al. 2005a; Holderegger and Wagner 2008).

Landscape genetic approaches are being applied across a variety of taxa (Coulon et al. 2006; Moore et al. 2008; Sacks et al. 2008; Spear and Storfer 2008; Murphy, Evans, and Storfer 2010) including tortoises. Richter et al., (2011) concluded that recent anthropogenic changes to the landscape did not have an impact on genetic structure in gopher tortoise populations across a portion of the DeSoto National Forest (DNF, ~45 km2) and tortoise structure. Similarly, Sinclair, Dawes, and Seigel (2010) detected little to weak genetic differentiation of gopher tortoise populations across Kennedy Space

Center (~560 km2) in Florida. However, in the desert tortoise Latch et al. (2011) determined that roads and natural slopes were both significant barriers to gene flow across a 97 km2 site in California.

My goal in this study is to use a spatially explicit sampling scheme and to apply a landscape genetics approach to determine if fine-scale genetic structure exists in gopher tortoises on the Fort Benning Military Installation. Determining the effects landscape

57 features have on tortoise populations across Ft. Benning will aid in the relocation of tortoises to different areas of the installation due to military actions. A Landscape

Equivalency Analysis (LEA; Bruggeman et al. 2005) is currently being performed on the tortoise habitat on Ft. Benning in order to determine the equality of habitat patches involved in habitat trades. The genetic data collected in this study will be used to develop spatially-explicit population models to determine how trades may affect population dynamics. These models will help to reduce the impact of habitat alteration by the military installation and conserve population connectivity.

Materials and Methods

Sampling Site

Fort Benning covers 182,000 acres (737 km2), and the gopher tortoise population there appears to be sizable and healthy. Recent surveys found over 8,100 burrows on

Fort Benning, and it appears that on average there is 1 tortoise for every 4 burrows

(Susan Fischer, October, 2010, pers. comm.). Using this ratio, there would be about

2,025 tortoises on the installation, with the highest density being found in the northeast around Hastings Range. The tortoises on Ft. Benning have been under intense management by the Georgia Wildlife and Fisheries Department with rotational controlled burns, herbicide treatments, and surveys to monitor the tortoise population numbers.

In order to assess gopher tortoise genetic structure within Ft. Benning, I sampled across the Fort Benning landscape according to a 1 km2 grid-based design developed by

Dr. Doug Bruggeman. This sampling plan is designed to effectively cover the breadth of

Fort Benning so that the spatial coverage would reflect potentially important landscape variables. I collected one individual from each of 100 1-km2 cells located across the 737

58 km2 landscape of Fort Benning. I used ArcGis software to retrieve waypoints of previously marked burrows and to provide aerial photos of the 1 km2 sampling plots. I sampled 1 tortoise from each of the 100 randomly generated sampling plots. I re-ran the sampling scheme 5 times due to the fact that some sampling plots had no active burrows.

Seven of the randomly generated sampling plots had clusters of burrows covering the border of the adjacent plots, and the tortoises sampled were found to be in these adjacent plots.

In the field, I used a Garmin GPSmap 60CSx to find the location of previously surveyed burrows. I gave tortoises found in burrows not marked on the survey a new ID number, and I took coordinates with the GPS unit. Once I located burrows, I placed a collapsible Tomahawk® Model 18 Live Trap (81.28 × 25.4 × 30.48 cm) covered with shade cloth and pine needles in front of the mouth of the burrow. On average, I placed 4 traps within each of the sampling plots. I checked traps daily until I captured a tortoise. I took a 0.5 mL sample of blood from the femoral vein using a new 23G1 gauge (Becton

Dickinson PrecisionGlide) hypodermic needle. Occasionally, I would draw blood from the brachial vein, if the femoral artery was difficult to bleed. To stop bleeding after I drew the blood sample, I applied pressure directly to the site of puncture for 15 sec. I stored each blood sample in a 2.0 mL vial with approximately 0.5 mL of SED tissue preservation buffer (Seutin, White, and Boag 1991). After I took all measurements, I placed the tortoise immediately back into the burrow. After I captured one tortoise from a site, I removed the traps and sprayed them with a 10% solution of bleach and allowed to dry. I used alcohol to sanitize calipers and hands after the handling of each tortoise to help prevent any possible disease transmission between individuals.

I extracted Genomic DNA from each individual using a DNeasy Tissue Kit

(QIAGEN), and PCR cycling conditions consisted of an initial denaturing step of 94 C for 2 min followed by 35 cycles of 30 sec at 94 C, 1 min at 50-60 C, and 1 min at 72 C.

A final elongation step of 10 min at 72 C ended the cycle. Alleles were visualized with a

LI-COR 4300 DNA sequencer along with a 50-350 bp size standard (LI-COR) and scored using Gene Image IR v. 3.55 (LI-COR). Basic summary statistics (number of alleles - A, observed heterozygosity - Ho, and expected heterozygosity - He) were calculated using GenAlex 6.41 (Peakall and Smouse 2006). Loci were tested for Hardy-

Weinburg equilibrium (HWE) and linkage disequilibrium using GENEPOP for the web

(Raymond and Rousset 1995).

Traditional analyses of population structure require the a priori delineation of individuals into some set of groups. Gopher tortoises are terrestrial and prefer upland xeric habitats; therefore, the USFWS proposed the Tombigbee and Mobile Rivers as a boundary between an east/west split of gopher tortoise populations at a broad scale

(USFWS 1987), and they base boundaries of independent functional units along rivers at the regional scale within the western group (USFWS 2009). Thus, in the context of the

Fort Benning landscape, I divided samples into three groups separated by Upatoi and

Pine Knot Creeks (Figure 10).

The northwest group represented individuals to the west of Upatoi Creek. The northeastern and southern groups corresponded to individuals east of Upatoi Creek and either north or south of Pine Knot creek, respectively.

Figure 10. Satellite view (Google Maps) of a portion of the Fort Benning landscape with Upatoi and Pine Knot Creeks labeled. The locations of a subset of 17 individuals have been placed on the map. Color of the symbols indicate the probability of group membership as defined by the GENELAND analysis with a K=2: yellow dots indicate a high probability of belonging to group 1, blue dots indicate a high probability of belonging to group 2, and green dots indicate that the probability of group membership didn’t strongly fall into either one or the other of the two groups.

First, I calculated pairwise FST values and their significance values among the three groups using ARLEQUIN v. 3.11 (Excoffier, Laval, and Schneider 2005). I also performed an analysis of molecular variance (AMOVA; Excoffier, Smouse, and Quattro

1992) as implemented by ARLEQUIN v. 3.11 to examine the distribution of molecular variation within and among these groups. For both of these analyses I assessed the significance by 1000 random permutations of the data. Lastly, I performed an assignment test with GenAlEx to determine the proportion of individuals that self classified back to their group of origin.

I also performed two analyses that do not require an a priori definition of populations. STRUCTURE 2.3.3 (Pritchard, Stephens, and Donnelly 2000; Hubisz et al.

2009) uses a Bayesian approach to partition individuals into some number of genetically discrete populations that are in Hardy-Weinberg equilibrium and the loci in each population are in linkage equilibrium. I tested values of K (number of populations) from

1-4 using a model of admixed ancestry and assuming independent allele frequencies between groups. For each value of K, I performed 10 separate simulations with a burn-in of 100,000 generations followed by a subsequent 500,000 generations. In addition to

STRUCTURE to analyze my data, I also used the GENELAND program (Guillot et al.,

2005a). Blair et al. (2012) found that GENELAND had the highest power, when compared to STRUCTURE and non-Bayesian analyses, to detect linear barriers to gene flow. For the second approach I used GENELAND version 3.1.4 software (Guillot,

Mortier, and Estoup 2005b), which uses georeferenced multilocus genotype data to infer population groupings of individuals under a Bayesian model framework. The incorporation of geographical data into the analysis allows Geneland to detect subtle population structure and infer the probable locations of genetic transition zones. The correlated frequency models are more powerful at detecting subtle differentiations.

Guillot and Santos (2009) suggest using the uncorrelated model first, and then secondly using the correlated model to see how the initial results are changed.

I performed 10 independent runs with both uncorrelated and correlated allele frequency models testing values of genetic clusters (K) ranging from 1 to 10. In both cases, the parameters included an assumption of no error in spatial coordinates, and I set runs to 100,000 iterations, with a sample collected every 100th observation. During post- run processing, I set the burn-in to 200. I ran an AMOVA for the results of the Geneland

62 analysis to compare how its groupings partitioned the genetic variation compared to the 3 previously defined a priori groups

Results

Population and Landscape Genetic Analysis Results

The three a priori population groups demonstrated very low levels of genetic differentiation with pairwise FST values as follows: 0.004 between the northwest and south, 0.007 between the northeast and the south, and 0.013 between the northeast and the northwest (Table 11).

Table 11

Pairwise FST Values for the A Priori Groups are Located Below the Diagonal.

Significance of the FST Values is Indicated Above the Diagonal.

northeast northwest south northeast - 0.004 0.014 northwest 0.013 - 0.177 south 0.007 0.004 -

Although the pairwise FST values were low, only the northwest and south comparison was not significantly different from zero. The low-level population structure was also detected by the AMOVA, which indicated that only a very small proportion of the variation (0.64%) was partitioned among groups. However, although little of the genetic variation was partitioned among groups, this value was still significant (p=0.008).

The assignment test found that only 45% of the individuals assigned back to their group of origin (Table 12).

Table 12

The Numbers of Each A Priori Group Assigned to a Particular Group by the Assignment

Test Performed by GenAlEx. Individuals Correctly Assigned Back to its Group of Origin are Indicated in Bold Along With the Percentage of Individuals in Each Group Correctly

Assigning Back to the Group of Origin.

Group Assignment

Group of Origin northeast northwest south Total % Correct northeast 17 8 6 31 54.8% northwest 6 9 11 26 34.6% south 10 14 19 43 44.2%

Total 33 31 36 100 45.0%

This low degree of correct self-assignment is another reflection of the limited amount of genetic differentiation among groups. However, when the results of the assignment test are examined on a group-by-group basis, the northeast group had the highest level of self- assignment (54.8%) compared to the other two groups (34.6% and 44.2% for the northwest and south, respectively).

In addition to my analysis of a priori groups, I also performed Bayesian analyses that do not rely on any particular grouping of individuals. The STRUCTURE analysis results best supported a population of K equals 1 (average log likelihood -8068.8; SD

=0.77; Table 13).

Table 13

The Average Likelihood Scores for the 10 Runs of Each Value of K in the STRUCTURE

Analysis. The Standard Deviation (SD) of These Averages is Also Provided.

K Avg. Ln P(X|K) SD

1 -8068.8 0.77

2 -8156.7 21.32

3 -8189.2 38.53

4 -8366.4 117.49

This result is the similar to our GENELAND analysis using uncorrelated allele frequencies where the modal value of K was 1 (although some runs indicated a value of 2 or 3). The runs with a correlated allele frequency model had a modal value of 5. Given the results of the a priori group analysis, I selected a K of 2 as being most likely to have biological relevance. I performed an additional run with correlated allele frequencies at a

K of 2 in order to place these results into the context of the Fort Benning landscape. This analysis divided samples into two groups with roughly an east-west split (Figure 11) with a fairly strong genetic break. Many individuals typically had high probabilities of belonging to either one group or the other (49 individuals with a probability > 0.9; see appendix). To place the probability distribution map into the context of the Fort Benning landscape, I mapped out the locations of a subset of individuals and indicated their group membership (Figure 10). The genetic break seems to roughly coincide with the location of Upatoi Creek. However, this is obviously not an impermeable barrier to gene flow.

Several of the samples mapped on Figure 10 show intermediate probabilities of assigning

65 to either group. The AMOVA run for this structure of K=2 showed slightly higher among population variation (0.67%) than did our 3 a priori groups. The pairwise Fst value for the Geneland K=2 was 0.007.

Figure 11. Map generated by GENELAND of the posterior probabilities of each individual belonging to one of the two groups. Dark colors represent one group and lighter colors the other. Each dot represents an individual and the x-y coordinates reflect their relative positions across the Fort Benning landscape.

Discussion

The sampling scheme I employed covered a large extent of the Fort Benning landscape, and it provided a sufficiently intensive level of sampling to detect any possible genetic structure. The a priori groups used in the AMOVA analysis are sufficient to detect weak but significant structure roughly corresponding to the Upatoi and Pine Knot

Creeks. The roads across the Ft. Benning landscape did not appear to significantly influence gopher tortoise genetic structure. Of course, additional sampling would also be useful in the context of providing more thorough geographic coverage.

I conducted two different Bayesian analyses in the hope that they would capture patterns of population structure either with or without reference to spatial data from the samples. Structure resulted in a best-supported estimate of K=1. However,

STRUCTURE assumes within group Hardy-Weinberg and linkage equilibrium (HWLE) and does not use geographical coordinates to analyze the population models.

GENELAND uses geographical coordinates and allows for departure from within group

HWLE. This allows GENELAND to assume that individuals are genotyped from groups with correlated, but different, allele frequencies (Guillot and Santos 2009). Therefore,

GENELAND is able to detect the combined influences of clines in allele frequencies and spatially structured population domains (Wasser et al. 2004; Guillot et al. 2005a). These assumptions allow GENELAND to find discrete populations due to undetected barriers to gene flow.

A value K=2 seemed most biologically relevant, albeit weakly supported, for the

Ft. Benning landscape and was used to interpret the data (Figure 11). This also is supported by the analysis of the a priori population groupings. Although I grouped individuals into three regions (northeast, northwest, and south) in those analyses, the strongest signal of population differentiation in the pairwise FST values (Table 10) and assignment tests (Table 11) appeared to come from individuals falling along an east-west

(northeast vs. northwest, and south) split. The genetic break captured by GENLAND seemed to map out in the general location of Upatoi Creek (Figure 11). The AMOVA of this K=2 also had a slightly higher among population variation than did the AMOVA of the a priori K=3 groups. A full GIS-based analysis of multiple landscape variables

(canopy cover, soil type, etc.) would better explain the signal of population structure that we are seeing on Fort Benning.

All of the analyses, to some extent, support the recognition of population structure in the gopher tortoises sampled at Fort Benning. Although the degree of structure is not strong, this is not necessarily surprising given the relatively small spatial scale across which these samples were taken. My results are similar to the studies by Richter et al.

(2010) and Sinclair, Dawes, and Seigel (2010) who showed that gopher tortoise populations at geographically fine spatial scales have little genetic differentiation. Even with the use of a more powerful method for detecting genetic structure and a targeted sampling scheme, I too could only detect a weak level of genetic structure. A more detailed study of the associations between the genetic structuring and landscape and land use practices on Ft. Benning needs to be performed in order to understand any interactions. A better understanding of the relationship between landscape features and gopher tortoise genetic structure could aid in the management of this species on a heavily disturbed area. If one accepts that biologically relevant population structure exists at the landscape scale of Forth Benning, then the next question is how to apply this knowledge in formulating management decisions.

CHAPTER V

SOIL AND GUT MICROBIAL COMMUNITIES OF THE GOPHER TORTOISE AND

THEIR ASSOCIATED PLANT COMMUNITIES

Gopherus polyphemus is a medium-sized testudine that inhabits longleaf pine and oak scrub habitats of the southeastern United States. It is a keystone species in its habitat where the burrows they construct provide refuge for over 300 species of animals (Jackson and Milstrey 1989; Witz, Wilson, and Palmer 1991). Over the past century they have declined in numbers across their range, and some estimate their population to have declined by as much as 80%. This continued loss of tortoises in the western portion of their range (west of the Mobile River drainage) has warranted them to be federally listed as threatened in Mississippi, southeastern Louisiana, and extreme western Alabama

(USFWS 1987). However, even within protected areas tortoise populations continue to decline. For example, Hammond (2006) showed tortoise burrows declined 37.5% from

1995-2007 in the DeSoto National Forest.

The major factor thought to be contributing to their decline is the loss of habitat.

It is estimated that the long-leaf pine savannah ecosystem originally covered over 60 million acres (Croker 1990), but it currently only covers about 3.7 million acres (a 95% reduction) and most of that is secondary and tertiary growth (Kelly and Bechtold 1990).

The other concern that arises from their habitat being lost, is that the remaining habitat is mostly secondary growth that contains soil that has been disturbed by anthropogenic effects.

The loss of habitat, along with the degradation of the current stands of suitable habitat, has caused the present gopher tortoise populations to become fragmented and

69 isolated. When populations of any species become fragmented or isolated the risk of inbreeding depression and influence of genetic drift increases. Ennen, Kreiser, and Qualls

(2010) found that western populations (i.e. west of Mobile River) had significantly lower expected heterozygosity and percent polymorphic loci than did their eastern counterparts.

Low genetic diversity has been linked to poor hatching success, slow growth and development rates, survival and disease resistance in wild populations (Allendorf and

Leary 1986; Ralls, Ballou, and Templeton 1988; Mitton 1997; Crnokrak and Roff 1999;

Reed and Frankham 2003). Inbreeding depression, genetic drift, and low heterozygosity could be one reason for tortoise declines in the western populations.

The reason for tortoise declines might not only be due to intrinsic factors but extrinsic factors as well, in particular the physical characteristics of their habitat. A problem with the management of tortoises is finding suitable habitat in regards to soil type and canopy cover. Tortoises use loose, deep, sandy soil to construct their burrows and dig their nests, and they are not found in heavy clay soils. They also need a forest that has an open canopy. Soils for tortoises have been described as priority, suitable, and marginal. Priority soils are well drained with high sand content, suitable soils are not as well drained and have some clay content, and marginal soils are poorly drained and have higher clay content. Tortoise populations that exist on well-drained upland xeric sandy soils have higher densities than populations in other habitat types (Auffenberg and Franz

1982; Diemer 1986). Higher tortoise densities could be a result of easier excavation of burrows on sandy soils than higher clay soils (Ultsch and Anderson 1986) and lower egg and hatching mortality on upland well-drained soils (Brode 1959; Auffenberg and Franz

1982; Noel, Qualls, and Ennen 2012).

Tortoises are herbivores (that have infrequently been reported to scavenge on carrion and consume eggs), and their main source of nutrients comes from the vegetation they consume. The soil nutrient content and nutrient availability of the soil directly impacts the nutrient content of the forage tortoises eat. If the soil is either low in nutrients, or the nutrients are in a state where they are not bio-available, the plants will be unable absorb the nutrients through their roots and will become low in nutrients

If forage nutrient quality is poor, then tortoises might not be capable of moving to better foraging grounds like more mobile herbivores such as deer. Gopher tortoises are not known for their great distances of dispersal. For example, Diemer (1992) studied home ranges for tortoises in north Florida and found the longest movement of a dispersing sub-adult to be 0.74 km. If tortoises are incapable of successfully finding improved foraging grounds, then they could suffer nutrient deficiencies. Gopher tortoises are oviparous (egg-laying), and the developing embryos depend upon the contents of the yolk and shell, which must be present by the time of oviposition (Vleck and Hoyt 1991;

White 1991), to sustain development (Thompson and Speake 2003). Quality of forage nutrients can impact the health of adult individuals, and in birds, adult females with calcium deficiencies can either skip ovulation altogether or she can ovulate and lay poor quality eggs that have a hatching success half that of non-deficient female eggs

(Graveland and Drent 1997). In fish, poor brood stock nutrition results in poor egg quality which has a negative effect on hatching success, growth, survival, and recruitment

(Izquierdo, Fernandez-Palacios, and Tacon 2001). These results are consistent with what is observed in the western populations of tortoises with low hatching success and poor recruitment.

The role of microbes in biogeochemical cycles is widely recognized but not much is known about the microbial communities that exist in soils found in gopher tortoise habitat and how they may impact tortoise fitness. We do know that soil fauna appear to be the major regulatory agents of soil processes affecting the chemical and physical fertility of soils (Behan-Pellieter and Hill 1983; Lavelle and Pashanasi 1989; Behan

Pellieter 1993), and there is a strong inverse relationship between mass of accumulated organic material on the soil surface and total soil faunal biomass (Schaeffer and

Schauerman 1990). Schlensinger (1991) found that soil microbes, acting as decomposers, are indirectly responsible for supplying the bulk of terrestrial vegetation’s annual nutrient demand. Most soil nutrients are found in different chemical forms, and different enzymes are required for plant access to these nutrients, and soil microorganisms are a major source of these enzymes (Reynolds et al. 2003). Also, it has been shown that land management disturbances, i.e. tillage (Abbott and Parker 1980;

House 1989), can lead to changes in community composition and reduced biodiversity and biomass of soil organisms. Lavelle and Pashanasi (1989) showed clearing of forest by intensive cultivation in humid tropics led to a decrease in biomass to 6%, of diversity to 17%, and taxon richness to 50% of that of primary forest communities. The disturbed soil of tortoise habitat for anthropogenic uses could have reduced soil microbial communities and a reduction in needed enzymes for plants to access soil nutrients.

Microbes not only play a critical role in soil biogeochemical processes, but they also have a large role in the digestive efficiency of vertebrates, especially herbivores.

Because no vertebrate is known to produce cellulase, vertebrate herbivores must rely on the cellulolytic bacteria found in their gut to break down cellulose in the plant cell walls

72 of their foodstuffs. Bjorndal (1987) found that the high pH and relatively low volatile fatty acids (VFA’s) concentration in the feces indicate that gopher tortoises are able to absorb the fermentation waste products–primarily VFA–and thus gain energy from fermentation. Tortoises must inoculate their gut with these cellulolytic bacteria in order to adequately digest their forage. Troyer (1982) found that iguanas, which did not receive proper gut inoculation, did not develop the same hindgut microflora nor did they grow as fast as those in the wild.

Could there be a gradient of soil microbial community and gut microbial community compositions across the range of the gopher tortoise? If changes in soil and gut microbial communities do occur across the range, then these differences might need to be taken into consideration when planning management strategies. There are a number of potential impacts on gopher tortoises of range-wide variation in microbial communities. Franklin and Mills (2003) found that microbial communities are highly structured and fluctuate across soil properties; if soil microbial communities vary across the landscape, then certain areas might be low in microbial diversity and lack the microbes tortoises need to properly inoculate their gut and digest foodstuffs. Moving tortoises to areas with different microbial communities could negatively impact digestion and immunity (Round and Mazmanian 2009). If plant communities are different, then the tortoise gut microbiota might not be as effective in releasing nutrients from the new forage type as it was from the original type. If microbial communities do vary, then managing tortoise translocations to prevent placing tortoises into areas with unfamiliar microbial and plant communities could help improve the success of translocations.

The goals of this project are to 1) describe the gut microbial communities of the gopher tortoise in relation to other herbivorous reptiles, 2) determine if soil and gut microbial communities differ across geographic localities, and 3) determine if plant communities differ across geographic locality.

Materials and Methods

Sampling Sites

The sites sampled for this study (Table 1) varied in habitat types. Conecuh

National Forest and Troy are both longleaf pine forests with deep sandy soils and are regularly maintained by fire. Hillsdale is a longleaf pine oak scrub habitat with deep sandy soils that does not receive regular maintenance (prescribed fire or thinning). St.

Catherine’s Island is well maintained by mowing and fire and is a cleared area dominated by herbaceous plants and has sandy soils. Ben’s Creek is a powerline right-of-way that is maintained by mowing and is dominated by herbaceous plants with a sand/clay mix soil.

Perdido is a large site with multiple habitat types, but I only sampled on a food plot that is maintained by mowing and is grass dominated and has a clay/sand mixed soil.

Microbial Sample Collection

I opportunistically collected fecal samples while trapping tortoises to collect blood samples for other studies at seven sites during summer field seasons (May-July) in

2010, 2011 and 2012. I collected samples from tortoises in the field by placing a 50ml centrifuge tube directly under the cloaca as the tortoise was voiding feces. In order to minimize contamination, I only used samples that were collected directly from the cloaca and had not touched any other surfaces. I immediately placed the samples into an iced cooler until they transported from the field where they were then frozen in a household

74 freezer. I ultimately stored samples in a -20° C freezer at the University of Southern

Mississippi. Time from collection of the sample until storage in the -20° C freezer ranged from 0 days to 14 days.

I collected soil samples from six of the same sites as the fecal samples (Ben’s

Creek, LA, Hillsdale, MS, Troy, Clearwater, Perdido, and Conecuh National Forest in

AL) (Table 14).

Table 14

Fecal, Soil, and Plant Sample Site Names and Locations with Sample Site Abbreviations.

SAMPLE SITE AND LOCATION ABBREVIATION

Ben’s Creek, LA BC

Hillsdale, MS HD

Clearwater, AL CW

Perdido, AL PD

Conecuh National Forest, AL CNF

Troy, AL TR

St. Catherine’s Island, GA (fecal only) SC

I collected soil samples using an AMS one-piece 2 ½ inch open face auger to a depth of about 15 cm from five burrows at each site except Perdido where only two burrows were sampled. From each burrow, I collected four soil samples from different distances and different cardinal directions. I placed soil samples into an iced cooler in the field and later placed into a household freezer until final placement in a -20° C freezer at the

University of Southern Mississippi. Time from collection of the sample until storage in the -20° C freezer ranged from 0 days to 3 days.

Plant Community Sample Collection

I conducted a plant community inventory at the same burrows and sites as where the soil samples were collected. To assess plant communities at each site, I identified all plants species within a 15-meter radius of each of the five burrows at a site either in the field or I brought samples back to the University of Southern Mississippi for identification. I placed collected samples into a plant press with a field ID. When the species could not be identified, I tried to identify it to the level of genus, and if I could not, then I labeled it as an unknown and gave it a specific unknown ID. I represented the plant communities by using a presence/absence data of plant species from the combined data of all five burrows within a site.

Microbial Methods

I extracted total genomic DNA from both soil and fecal samples by using a

PowerSoil® DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA). Quality and measured concentration of DNA using a Nanodrop spectophotometer.

Mr. DNAlab used the 16S rRNA gene V4 variable region PCR primers 515/806

(Caporaso et al 2011) in a single-step 30 cycle PCR using the HotStarTaq Plus Master

Mix Kit (Qiagen, USA) under the following conditions: 94°C for 3 minutes, followed by

28 cycles of 94°C for 30 seconds, 53°C for 40 seconds, and 72°C for 1 minute, after which a final elongation step at 72°C for 5 minutes was performed. Mr. DNAlab performed sequencing by using an Ion Torrent PGM following the manufacturer’s guidelines at MR DNA (Mr. DNA Lab, 2012)) and derived sequence data by using a

76 proprietary pipeline at www.mrdnalab.com. The sequences had both primers and barcodes depleted. Also, short sequences along with ambiguous base calls and homopolymer sequences exceeding 6bp were subsequently removed. Removal of chimeras and denoising of the sequences followed. Mr. DNAlab defined operational taxonomic units based on 97% genetic similarity (Dowd et al. 2008a; Dowd et al. 2008b;

Edgar 2010; Capone et al. 2011; Dowd et al. 2011; Eren et al. 2011; Swanson et al. 2011) and classified these units using BLASTn with a curated GreenGenes database (Desantis et al. 2006). I used the Phylum counts files for the comparison of gopher tortoise to green iguana, Galapagos Island and marine iguanas, and giant tortoise gut microbial communities and for the comparison between soil and gut microbial data. I then placed these database outputs were into an Excel spreadsheet for statistical analysis in JMP v7 and for multivariate analysis in R (R Core Team 2013).

Community Analyses

I examined genus richness for each soil and gut sample and species richness for the plant community data using the diversityresult function in the Biodiversity package of

R (Oksanen 2010). I then averaged these values for each site and performed pairwise comparisons in JMPv.8. I estimated Shannon’s diversity by using the diversityresult function in the Biodiversity package of R (Oksanen 2010) for soil and gut samples. To compare community composition between sites for gut, soil and plant species, I produced distance matrices in R using the vegdist function in the vegan package of R (Oksanen

2010). For the abundance data for soil and gut microbial community analysis, I used a

Bray-Curtis distance matrix and a Steinhaus dissimilarity matrix for the presence absence data for plant species communities. I then compared distance matrices between sites

77 using a permutation multivariate analysis of variance (MANOVA) in the Adonis function in the vegan package of R (Oksanen 2010) with 99 permutations. I collected gut samples during different years, therefore, I tested for differences among years and for a year*group interaction using the Adonis function. To visualize the differences in the gut and soil bacterial abundance matrices I performed a metaNMDS (non-metric multidimensional scaling) ordination using the function metaNMDS in the vegan package of R (Oksanen 2010). To test for associations between community (bacterial and plant) dissimilarities and geographic distance, I used the mantel function, with Kendall’s rank correlation, in the vegan package of R (Oksanen 2010) and set to 999 permutations.

Results

Gut Microbial Communities

Gopher tortoise gut microbial communities are dominated by Firmicutes, bacteria known for their ability to ferment sugars (hemicellulose and cellulose) (Sharmin et al.

2013), which is similar to other reptilian (green iguana, Galapagos land and marine iguana, and Galapagos giant tortoise) microbial communities studied to date (Figure 12).

However, they have the lowest percentage of Firmicutes and have the highest percentage of Bacteroides and Proteobacteria when compared to the previously studied species.

Bacteroides are carbohydrate fermenters and play important roles in preventing bacterial infections in the gut (Hooper, Midtvedt, and Gordon 2002; Backhed et al. 2005), and

Proteobacteria include pathogenic species, chemolithotrophic, and nitrogen fixing species.

Gut Microbe Comparison

1.6 0 0.5 0.8 1.9 100%

90%

80%

70% 59.7 planctomycetes 60% 74 Firmicutes 75.3 81.1 50% 75.3 Bacteroides

40% proteobacteria

30% 15.9 20%

10% 10.1 15.4 1.44.2 0.68.2 3.1 4.42 0% Land Marine Green Gal tort Goph iggy iggy iggy tort

Figure 12. Bar plot comparing average phylum percent abundance of gopher tortoise microbial communities to previously studied reptilian microbial communities.

Genus richness and diversity are both significantly different between the two groups tested. Group one comprises BC, HD, CW, Pd, and SC, sites and group two comprises TR and CNF sites. Group one had significantly higher levels of genus richness (ChiSq=0.01) and genus diversity (ChiSq=0.04). Gut genus richness and diversity results are found in Tables 15 and 16.

Table 15

Genus Diversity of the Gut Microbial Community Comparisons Between the Two Groups.

GROUP N MEAN SD CHI SQ

1 11 2.97 0.18

Table 15 (continued).

GROUP N MEAN SD CHI SQ

2 3 2.46 0.32

Df=1 0.04

Table 16

Genus Richness of the Gut Microbial Community Comparisons Between the Two Groups.

GROUP N MEAN SD CHI SQ

1 11 92.18 11.41

2 3 72.33 4.04

Df=1 0.01

The TR and CNF sites are different from the main group, and in order for statistical analyses to be performed with the TR site, I combined it with the CNF site.

When the microbial communities from seven sites of gopher tortoises are plotted out in ordination space, three distinct groups appear. With one group comprised of samples from CNF, one from TR and all other sites cluster into one group (Figure 13).

There is a significant overall difference between all sites (p=0.01, R2=0.71).

When comparing dissimilarities between sites, CNF and TR were clumped together and were the only sites to show significant differences for pairwise comparisons (Table 17).

The main genera that differed between CNF/TR and the other sites were

Proteobacterium, Firmicutes, and Actinobacterium. Proteobacterium averaged 66% in the CNF and TR samples but only 16% in the rest of the samples, and TR had 31%

80 actinobacterium (cellulolytic bacteria), but all other sites averaged only 0.76%. CNF and

TR had 16% Firmicutes and the other sites had 71%

Figure 13. MetaNMDS plot of gopher tortoise gut microbial communities from 14 sites.

Table 17

Gut Bacterial Community Pairwise Comparisons Between CNF/TR and All Other Sites.

CNF/TR X P VALUE DF R2

CW P=0.01 4 0.62

HD P=0.01 4 0.64

BC P=0.02 5 0.73

SC P=0.01 5 0.64

When the sites east (minus CNF/TR) and west of the Tombigbee and Mobile

Rivers are compared, a borderline significant p value is obtained (p=0.06, DF=10,

R2=0.15). Geographic distance is not correlated to dissimilarity indices, and a slight negative correlation is found (r=-0.14, p=0.87). There is a significant difference among years (p=0.05, DF=13, R2=0.19) and for year*group interaction (p=0.006, DF=13,

R2=0.57); however, when CNF and TR are removed from this analysis, there are no significant differences found (year: p=0.26, DF=10, R2=0.12 and year*group: p=0.26,

DF=10, R2=0.12)

Soil Microbial Communities

Soil microbial diversity and richness varied between sites (Table 18).

Table 18

Soil Microbial Pairwise Comparisons of Shannon’s Diversity Index Between Sites.

SITE N MEAN STD DEV ORDER

PD 2 3.76 3.78 A

BC 5 3.21 0.1 B

HD 5 3.17 0.21 B

CNF 5 3.16 0.06 B

TR 5 3.11 3.11 B

CW 5 2.93 0.15 C

Note. Significance between groups indicated by a change in letter.

The only significant differences found in richness were between PD and CW sites

(p=0.049, DF=1). Soil microbial communities show a significant difference among all

82 sites (p=0.01, DF=26, R2=0.44), and TR and PD sites show significant differences in most pairwise comparisons between sites (Table 19).

Table 19

Soil Microbial Pairwise Differences Between All Sites for TR and PD Sites.

SITES P VALUE DF R2

TRxHD 0.04 9 0.22

TRxPD 0.01 6 0.71

TRxCW 0.47 9 0.11

TRxCNF 0.03 9 0.20

TRxBC 0.03 9 0.22

PDxBC 0.01 6 0.44

PDxHD 0.01 6 0.66

The differences between bacterial communities are visualized in ordination space in

Figure 14. BC, HD, CW, and CNF sites did not show significant differences in pairwise comparisons between each other.

Mantel’s test did not find a significant correlation between dissimilarities and geographic distances of soil microbial communities (r=0.05, p=0.169).

Figure 14. MetaNMDS plot for soil bacteria community dissimilarity matrices.

Gut and Soil

Comparison between gut and soil microbial communities performed at the phylum level show a significant difference between the two groups (p=0.01, DF=40,

R2=0.71). Clear separation between groups is observed in Figure 15 of a metaNMDS plot.

Gut

Soil

Figure 15. MetaNMDS plot of soil vs. gut microbial community dissimilarity matrices.

Plant Communities

Plant communities show an overall significant difference among sites (p=0.01,

DF=26, R2=0.65), and this is visually represented by a metaNMDS plot in Figure 16.

Plant communities were structured based on site, as all site-by-site pairwise comparisons were significant (Table 20).

Figure 16. MetaNMDS plot of plant species community dissimilarity matrices.

Table 20

Results from Pairwise Comparisons for Plant Species Communities Between Sites. R2

Value is Above Diagonal and P Values Below.

BC HD PD CW CNF TR

BC 0.31 0.44 0.34 0.40 0.45

HD 0.01 0.46 0.34 0.38 0.44

PD 0.01 0.01 0.45 0.56 0.61

CW 0.01 0.01 0.03 20.31 0.33

Table 20 (continued).

BC HD PD CW CNF TR

CNF 0.01 0.01 0.01 0.01 0.42

TR 0.01 0.01 0.01 0.01 0.01

The mantel’s test for correlations between geographic distance and species community dissimilarity is significant (r=0.38, p=0.001), suggesting that plant communities around gopher tortoise burrows differ based on geographic proximity.

Pairwise comparisons of species richness revealed significant differences between sites

(Table 21).

Table 21

Mean Species Richness by Site.

SITE N RICHNESS STD DEV ORDER

BC 5 35.8 4.76 A

CNF 5 25.5 3.56 B

CW 5 21 4.67 B

HD 5 25.2 4.32 BC

PD 2 20.5 0.71 BC

TR 5 18.8 2.86 C

Note. Significant differences of richness between sites denoted by a change in letter.

The maintained power line right-of-way with a sand/clay soil mix at BC had the highest species richness, whereas the reclaimed longleaf pine forest with deep sandy soils

87 at TR had the lowest species richness. The habitat at sites between the highest and lowest species richness values ranged from well-maintained, deep sandy soil longleaf pine forest

(CNF) to clay/sand soil mix food plots (PD).

Discussion

Gut microbial communities in the Galapagos Island land iguana show variation based on geographic distance (Lankau, Hong, and Mackie 2012). However, gopher tortoise gut microbial communities, while not homogeneous, are not structured on the basis of geographic distance between sites. CNF and TR sites are the only two that show significant differences with other sites in pairwise comparisons. These two sites partition out distinctly from the other five sites in ordination space (See Figure 2). There is clear distinction between these two sites as well based upon their averaged pairwise differences

(Bray-Curtis averaged dissimilarity=0.753).

One specific difference in community structure that stands out is that

Stenotrophomonas maltophilia averages 12% at CNF but less than 1% at other sites.

This bacterium is known to be pathogenic in humans, especially in immuno- compromised individuals. Craig Guyer found that tortoises at CNF have slower growth rates and attain smaller adult size at CNF when compared to tortoises at other study sites

(October, 2013 pers. comm.). It should also be noted that CNF and TR show significantly lower diversity indices and genus richness values and are depleted in both

Bacteroides and Firmicutes. Several human diseases are associated with changes in gut microbial composition, specifically with a depletion of Bacteroides and Firmicutes

(Lepage et al. 2005; Scanlan et al. 2006; Frank et al. 2007). Could the differences in gut microbial communities suggest that tortoises at CNF and TR suffer from a chronic health

88 disorder? I only have two samples from CNF, so more samples will be needed before I feel comfortable in proposing a possible connection in this regard. Sites other than CNF and TR do not show any significant differences in pairwise comparisons. This finding is unexpected, as geographic locality is known to be one of the strongest factors in determining gut microbial communities (Ley et al. 2008; Benson et al. 2010; Fallani et al.

2010). Tortoises are generalist herbivores that feed on herbaceous vegetation

(MacDonald and Mushinsky 1988), and this generalist diet could result in a specific gut microbiota needed to digest a wide range of foodstuffs. The need for an ability to digest a broad food source could be driving tortoises to maintain a specific gut microbiome as diet, in addition to geographic locality, is an important factor in driving gut microbial communities (Rueda 2000; Filippo et al. 2010).

My analysis of plant communities found a significant difference in plant community composition among (p=0.01, DF=26, R2=0.65) and between all sites (Table

6). Diet is an important factor shaping gut microbial communities; however, the difference in plant community composition between sites did not associate with a difference in gut microbial community between sites, as only CNF and TR are significantly different. Although they are generalist herbivores, they can show active selection for some species (MacDonald and Mushinsky 1988). It is possible that despite the differences in plant communities between sites, tortoises are eating similar species or species from similar Families between sites. The most commonly found species from all sites are those within the Family Febaceae, Poaceae and Asteraceae, which most frequently occur in tortoise diets (MacDonald and Mushinsky 1988; Birkhead et al.

2005). Selecting to consume certain species that are shared among sites could increase

89 homogeneity of diets between sites despite differences in plant communities. If diets are similar across sites, then it would likely be advantageous to have a conserved gut microbiome in order to more efficiently digest specific foodstuffs. Whether tortoises have a varied diet or conserved diet across sites, our analysis suggests that tortoises are preserving a specific gut bacterial community partially independent of geographic locality.

Soil microbial communities tend to be highly structured at many levels from local

(Franklin and Mills 2003) to continental scales (Fierer and Jackson 2006). In this study, I found soil microbial communities to be somewhat structured based on site locality across the southeastern US. Overall, soil microbial communities are significantly differentiated by site (p=0.01, DF=26, R2=0.44); however, only two out of the six sites show significant differences in pairwise comparisons. PD differed significantly from all other sites and

TR differed significantly from all sites except CW. PD also has the highest Shannon’s species diversity index value, which is significantly higher than all other sites (3.76); however, TR had the second to lowest index value (3.11). The TR site is a reclaimed pine stand that contained planted longleaf pine and is similar in structure to CNF and HD and did not differ significantly from those two sites.

The two sites with the highest amount of species diversity and richness came from disturbed areas. The PD samples came from a food plot that is maintained by mowing, tilling, and planting and the BC site had the second highest amount of species diversity, and all samples came from a power line right-of-way that is maintained by mowing. While Fierer and Jackson (2006) found that soil microbial communities are unrelated to the normal factors that predict animal and plant communities, others (Abbott

90 and Parker 1980; House 1989) found that soil disturbances can lower the diversity of soil microbes. However, the two highly disturbed sites in our study showed the highest levels of species diversity and richness (PD=94 & BC=90.2 although these are not significant), whereas, the less disturbed sites showed lower levels of diversity. These two sites did have a more clayey soil compared to the other sites studied, and soil particle size is an important factor contributing to microbial diversity and structure with smaller particle sizes resulting in greater diversity (Ranjard and Richaum 2001). Another factor that could explain the results would be nutrient content. Soils with a high nutrient content harbor different functional groups than soils with low nutrient content (Smith et al. 2001).

There is variation in species diversity and richness between sites suggesting a possible variation in the available nutrients to tortoises, and I suggest further study into the variation in nutrient content between sites is needed.

Hosts potentially obtain their gut-microbial communities from several different pathways, one of which is from the environment around them (Sullom et al. 2012). One purpose of my study is to assess the similarity between gut and soil microbial communities because of the potential of tortoises needing soil microbial communities to properly inoculate their gut. My analysis shows soil and microbial communities are significantly different from each other and that, while tortoises might obtain a portion of their gut microbial communities from the soil, the soil is definitely not the only source of inoculation. Coprophagy can benefit organisms by inoculating their gut (Steinwacsher

1978; Troyer 1982) and by providing a rich food source (Fenolio et al. 2006).

Coprophagy is observed in gopher tortoises and could be a method for hatchling and

91 juvenile tortoises to inoculate their gut with proper microbial communities, as do juvenile green iguanas (Troyer 1982).

My study found that gut microbial community composition is largely independent of soil microbial community composition. I also found these communities are not structured based on geographic proximity and in general show similar communities regardless of geographic locality. In addition, they appear independent of plant community composition as changes in plant communities between sites does not result in changes in bacterial communities. However, there is a strong differentiation between

CNF and TR sites when compared to the main bacterial community. The causes of this differentiation remain unknown, but ecological data from CNF and a similar depletion in

Bacteroides and Firmicutes associated in human diseases suggest it could be linked to health issues. Soil microbial communities did not show structure due to geographic proximity and it appears that anthropogenic affects can cause soil microbial communities to increase in diversity and richness. Although I cannot definitively state causes for the differences I observed between gut and soil microbial communities, I can suggest that microbial communities vary and could have potential health implications, and therefore, I encourage further research in this area to better understand what environmental components are shaping gopher tortoise gut and soil microbial community composition.

APPENDIX

PAIRWISE FST VALUES BETWEEN ALL 47 SITES

Pop1 Pop2 Fst

AGTP GS 0.097

AGTP TSR 0.113

GS TSR 0.033

AGTP SRS 0.130

GS SRS 0.093

TSR SRS 0.072

AGTP Nassau 0.127

GS Nassau 0.116

TSR Nassau 0.116

SRS Nassau 0.106

AGTP Gordon 0.102

GS Gordon 0.050

TSR Gordon 0.036

SRS Gordon 0.068

Nassau Gordon 0.076

AGTP Mop 0.114

GS Mop 0.088

TSR Mop 0.059

SRS Mop 0.082

Nassau Mop 0.084

Gordon Mop 0.053

AGTP UNF 0.074

GS UNF 0.105

TSR UNF 0.113

SRS UNF 0.097

Nassau UNF 0.081

Gordon UNF 0.094

Mop UNF 0.099

AGTP MCF 0.125

GS MCF 0.192

TSR MCF 0.195

SRS MCF 0.196

Nassau MCF 0.168

Gordon MCF 0.163

Mop MCF 0.180

UNF MCF 0.080

AGTP HERN 0.125

GS HERN 0.191

TSR HERN 0.194

SRS HERN 0.214

Nassau HERN 0.199

Gordon HERN 0.191

Mop HERN 0.176

UNF HERN 0.085

MCF HERN 0.077

AGTP CC 0.182

GS CC 0.251

TSR CC 0.260

SRS CC 0.288

Nassau CC 0.268

Gordon CC 0.250

Mop CC 0.253

UNF CC 0.148

MCF CC 0.101

HERN CC 0.090

AGTP Hillsborough 0.121

GS Hillsborough 0.184

TSR Hillsborough 0.180

SRS Hillsborough 0.187

Nassau Hillsborough 0.166

Gordon Hillsborough 0.172

Mop Hillsborough 0.154

UNF Hillsborough 0.082

MCF Hillsborough 0.083

HERN Hillsborough 0.040

CC Hillsborough 0.098

AGTP Pinellas 0.131

GS Pinellas 0.205

TSR Pinellas 0.201

SRS Pinellas 0.208

Nassau Pinellas 0.180

Gordon Pinellas 0.182

Mop Pinellas 0.173

UNF Pinellas 0.087

MCF Pinellas 0.070

HERN Pinellas 0.040

CC Pinellas 0.076

Hillsborough Pinellas 0.028

AGTP Hiland Ham 0.122

GS Hiland Ham 0.191

TSR Hiland Ham 0.187

SRS Hiland Ham 0.200

Nassau Hiland Ham 0.191

Gordon Hiland Ham 0.188

Mop Hiland Ham 0.175

UNF Hiland Ham 0.088

MCF Hiland Ham 0.072

HERN Hiland Ham 0.039

CC Hiland Ham 0.080

Hillsborough Hiland Ham 0.043

Pinellas Hiland Ham 0.045

AGTP Jonathan Dickens 0.111

GS Jonathan Dickens 0.183

TSR Jonathan Dickens 0.192

SRS Jonathan Dickens 0.177

Nassau Jonathan Dickens 0.175

Gordon Jonathan Dickens 0.180

Mop Jonathan Dickens 0.171

UNF Jonathan Dickens 0.077

MCF Jonathan Dickens 0.082

HERN Jonathan Dickens 0.079

CC Jonathan Dickens 0.120

Hillsborough Jonathan Dickens 0.086

Pinellas Jonathan Dickens 0.084

Hiland Ham Jonathan Dickens 0.068

AGTP Kennedy 0.096

GS Kennedy 0.167

TSR Kennedy 0.177

SRS Kennedy 0.163

Nassau Kennedy 0.154

Gordon Kennedy 0.162

Mop Kennedy 0.155

UNF Kennedy 0.061

MCF Kennedy 0.066

HERN Kennedy 0.062

CC Kennedy 0.119

Hillsborough Kennedy 0.060

Pinellas Kennedy 0.066

Hiland Ham Kennedy 0.051

Jonathan Dickens Kennedy 0.031

AGTP Orange 0.117

GS Orange 0.189

TSR Orange 0.195

SRS Orange 0.205

Nassau Orange 0.185

Gordon Orange 0.188

Mop Orange 0.168

UNF Orange 0.075

MCF Orange 0.071

HERN Orange 0.039

CC Orange 0.077

Hillsborough Orange 0.048

Pinellas Orange 0.042

Hiland Ham Orange 0.036

Jonathan Dickens Orange 0.069

Kennedy Orange 0.055

AGTP V 0.109

GS V 0.186

TSR V 0.187

SRS V 0.192

Nassau V 0.156

Gordon V 0.171

Mop V 0.153

UNF V 0.072

MCF V 0.077

HERN V 0.077

CC V 0.130

Hillsborough V 0.078

Pinellas V 0.080

Hiland Ham V 0.070

Jonathan Dickens V 0.067

Kennedy V 0.048

Orange V 0.063

AGTP A 0.095

GS A 0.159

TSR A 0.175

SRS A 0.173

Nassau A 0.166

Gordon A 0.162

Mop A 0.149

UNF A 0.062

MCF A 0.078

HERN A 0.038

CC A 0.116

Hillsborough A 0.057

Pinellas A 0.064

Hiland Ham A 0.060

Jonathan Dickens A 0.065

Kennedy A 0.049

Orange A 0.059

V A 0.051

AGTP Putnam 0.082

GS Putnam 0.108

TSR Putnam 0.117

SRS Putnam 0.120

Nassau Putnam 0.121

Gordon Putnam 0.105

Mop Putnam 0.129

UNF Putnam 0.048

MCF Putnam 0.082

100

HERN Putnam 0.084

CC Putnam 0.144

Hillsborough Putnam 0.090

Pinellas Putnam 0.095

Hiland Ham Putnam 0.086

Jonathan Dickens Putnam 0.069

Kennedy Putnam 0.061

Orange Putnam 0.088

V Putnam 0.083

A Putnam 0.064

AGTP R 0.089

GS R 0.125

TSR R 0.117

SRS R 0.125

Nassau R 0.120

Gordon R 0.084

Mop R 0.068

UNF R 0.102

MCF R 0.158

HERN R 0.146

CC R 0.233

Hillsborough R 0.141

Pinellas R 0.163

101

Hiland Ham R 0.163

Jonathan Dickens R 0.148

Kennedy R 0.134

Orange R 0.158

V R 0.135

A R 0.110

Putnam R 0.120

AGTP JRC 0.123

GS JRC 0.204

TSR JRC 0.211

SRS JRC 0.212

Nassau JRC 0.183

Gordon JRC 0.175

Mop JRC 0.162

UNF JRC 0.148

MCF JRC 0.197

HERN JRC 0.166

CC JRC 0.248

Hillsborough JRC 0.164

Pinellas JRC 0.187

Hiland Ham JRC 0.177

Jonathan Dickens JRC 0.161

Kennedy JRC 0.151

102

Orange JRC 0.179

V JRC 0.174

A JRC 0.130

Putnam JRC 0.167

R JRC 0.079

AGTP WT 0.106

GS WT 0.178

TSR WT 0.170

SRS WT 0.183

Nassau WT 0.152

Gordon WT 0.135

Mop WT 0.123

UNF WT 0.137

MCF WT 0.182

HERN WT 0.160

CC WT 0.233

Hillsborough WT 0.147

Pinellas WT 0.173

Hiland Ham WT 0.166

Jonathan Dickens WT 0.161

Kennedy WT 0.146

Orange WT 0.168

V WT 0.157

103

A WT 0.128

Putnam WT 0.144

R WT 0.057

JRC WT 0.035

AGTP MAFB 0.075

GS MAFB 0.131

TSR MAFB 0.131

SRS MAFB 0.114

Nassau MAFB 0.119

Gordon MAFB 0.099

Mop MAFB 0.085

UNF MAFB 0.079

MCF MAFB 0.133

HERN MAFB 0.127

CC MAFB 0.210

Hillsborough MAFB 0.121

Pinellas MAFB 0.143

Hiland Ham MAFB 0.141

Jonathan Dickens MAFB 0.118

Kennedy MAFB 0.112

Orange MAFB 0.136

V MAFB 0.113

A MAFB 0.089

104

Putnam MAFB 0.108

R MAFB 0.029

JRC MAFB 0.089

WT MAFB 0.073

AGTP benning 0.093

GS benning 0.121

TSR benning 0.113

SRS benning 0.128

Nassau benning 0.118

Gordon benning 0.086

Mop benning 0.062

UNF benning 0.096

MCF benning 0.151

HERN benning 0.134

CC benning 0.220

Hillsborough benning 0.122

Pinellas benning 0.142

Hiland Ham benning 0.155

Jonathan Dickens benning 0.135

Kennedy benning 0.123

Orange benning 0.150

V benning 0.125

A benning 0.095

105

Putnam benning 0.114

R benning 0.021

JRC benning 0.087

WT benning 0.070

MAFB benning 0.038

AGTP Gasden 0.149

GS Gasden 0.235

TSR Gasden 0.225

SRS Gasden 0.229

Nassau Gasden 0.210

Gordon Gasden 0.195

Mop Gasden 0.170

UNF Gasden 0.150

MCF Gasden 0.175

HERN Gasden 0.134

CC Gasden 0.222

Hillsborough Gasden 0.133

Pinellas Gasden 0.153

Hiland Ham Gasden 0.158

Jonathan Dickens Gasden 0.149

Kennedy Gasden 0.138

Orange Gasden 0.176

V Gasden 0.156

106

A Gasden 0.113

Putnam Gasden 0.168

R Gasden 0.089

JRC Gasden 0.085

WT Gasden 0.088

MAFB Gasden 0.087 benning Gasden 0.080

AGTP Andrews 0.125

GS Andrews 0.199

TSR Andrews 0.191

SRS Andrews 0.214

Nassau Andrews 0.166

Gordon Andrews 0.156

Mop Andrews 0.137

UNF Andrews 0.141

MCF Andrews 0.171

HERN Andrews 0.148

CC Andrews 0.219

Hillsborough Andrews 0.143

Pinellas Andrews 0.162

Hiland Ham Andrews 0.157

Jonathan Dickens Andrews 0.158

Kennedy Andrews 0.144

107

Orange Andrews 0.159

V Andrews 0.134

A Andrews 0.118

Putnam Andrews 0.146

R Andrews 0.075

JRC Andrews 0.055

WT Andrews 0.051

MAFB Andrews 0.093 benning Andrews 0.069

Gasden Andrews 0.086

AGTP Island 0.113

GS Island 0.176

TSR Island 0.162

SRS Island 0.178

Nassau Island 0.149

Gordon Island 0.139

Mop Island 0.118

UNF Island 0.124

MCF Island 0.149

HERN Island 0.134

CC Island 0.199

Hillsborough Island 0.120

Pinellas Island 0.142

108

Hiland Ham Island 0.142

Jonathan Dickens Island 0.136

Kennedy Island 0.122

Orange Island 0.147

V Island 0.130

A Island 0.110

Putnam Island 0.128

R Island 0.060

JRC Island 0.060

WT Island 0.054

MAFB Island 0.076 benning Island 0.055

Gasden Island 0.084

Andrews Island 0.039

AGTP Walton 0.098

GS Walton 0.146

TSR Walton 0.142

SRS Walton 0.163

Nassau Walton 0.125

Gordon Walton 0.116

Mop Walton 0.098

UNF Walton 0.103

MCF Walton 0.138

109

HERN Walton 0.118

CC Walton 0.197

Hillsborough Walton 0.115

Pinellas Walton 0.130

Hiland Ham Walton 0.131

Jonathan Dickens Walton 0.114

Kennedy Walton 0.109

Orange Walton 0.138

V Walton 0.106

A Walton 0.088

Putnam Walton 0.112

R Walton 0.064

JRC Walton 0.090

WT Walton 0.088

MAFB Walton 0.075 benning Walton 0.051

Gasden Walton 0.079

Andrews Walton 0.061

Island Walton 0.059

AGTP CW 0.162

GS CW 0.223

TSR CW 0.234

SRS CW 0.256

110

Nassau CW 0.225

Gordon CW 0.205

Mop CW 0.178

UNF CW 0.177

MCF CW 0.198

HERN CW 0.171

CC CW 0.257

Hillsborough CW 0.170

Pinellas CW 0.185

Hiland Ham CW 0.180

Jonathan Dickens CW 0.156

Kennedy CW 0.157

Orange CW 0.187

V CW 0.145

A CW 0.125

Putnam CW 0.178

R CW 0.133

JRC CW 0.158

WT CW 0.162

MAFB CW 0.129 benning CW 0.112

Gasden CW 0.149

Andrews CW 0.106

111

Island CW 0.121

Walton CW 0.070

AGTP Pd 0.116

GS Pd 0.178

TSR Pd 0.186

SRS Pd 0.199

Nassau Pd 0.174

Gordon Pd 0.159

Mop Pd 0.141

UNF Pd 0.128

MCF Pd 0.150

HERN Pd 0.131

CC Pd 0.209

Hillsborough Pd 0.127

Pinellas Pd 0.139

Hiland Ham Pd 0.138

Jonathan Dickens Pd 0.112

Kennedy Pd 0.111

Orange Pd 0.146

V Pd 0.108

A Pd 0.090

Putnam Pd 0.129

R Pd 0.092

112

JRC Pd 0.105

WT Pd 0.112

MAFB Pd 0.093 benning Pd 0.074

Gasden Pd 0.106

Andrews Pd 0.068

Island Pd 0.076

Walton Pd 0.042

CW Pd 0.031

AGTP Tr 0.133

GS Tr 0.202

TSR Tr 0.195

SRS Tr 0.188

Nassau Tr 0.159

Gordon Tr 0.155

Mop Tr 0.146

UNF Tr 0.129

MCF Tr 0.152

HERN Tr 0.148

CC Tr 0.222

Hillsborough Tr 0.143

Pinellas Tr 0.157

Hiland Ham Tr 0.153

113

Jonathan Dickens Tr 0.129

Kennedy Tr 0.128

Orange Tr 0.167

V Tr 0.131

A Tr 0.117

Putnam Tr 0.143

R Tr 0.095

JRC Tr 0.106

WT Tr 0.108

MAFB Tr 0.086 benning Tr 0.082

Gasden Tr 0.089

Andrews Tr 0.071

Island Tr 0.077

Walton Tr 0.054

CW Tr 0.094

Pd Tr 0.056

AGTP CNF 0.146

GS CNF 0.210

TSR CNF 0.217

SRS CNF 0.231

Nassau CNF 0.188

Gordon CNF 0.177

114

Mop CNF 0.177

UNF CNF 0.141

MCF CNF 0.154

HERN CNF 0.145

CC CNF 0.229

Hillsborough CNF 0.159

Pinellas CNF 0.159

Hiland Ham CNF 0.166

Jonathan Dickens CNF 0.144

Kennedy CNF 0.134

Orange CNF 0.168

V CNF 0.140

A CNF 0.119

Putnam CNF 0.143

R CNF 0.105

JRC CNF 0.122

WT CNF 0.138

MAFB CNF 0.115 benning CNF 0.092

Gasden CNF 0.114

Andrews CNF 0.079

Island CNF 0.082

Walton CNF 0.056

115

CW CNF 0.096

Pd CNF 0.058

Tr CNF 0.065

AGTP MC 0.151

GS MC 0.202

TSR MC 0.209

SRS MC 0.217

Nassau MC 0.199

Gordon MC 0.177

Mop MC 0.158

UNF MC 0.154

MCF MC 0.183

HERN MC 0.170

CC MC 0.237

Hillsborough MC 0.159

Pinellas MC 0.169

Hiland Ham MC 0.182

Jonathan Dickens MC 0.162

Kennedy MC 0.153

Orange MC 0.183

V MC 0.150

A MC 0.126

Putnam MC 0.160

116

R MC 0.119

JRC MC 0.150

WT MC 0.145

MAFB MC 0.120 benning MC 0.105

Gasden MC 0.152

Andrews MC 0.109

Island MC 0.103

Walton MC 0.077

CW MC 0.058

Pd MC 0.052

Tr MC 0.098

CNF MC 0.104

AGTP IP 0.161

GS IP 0.210

TSR IP 0.215

SRS IP 0.234

Nassau IP 0.214

Gordon IP 0.182

Mop IP 0.169

UNF IP 0.175

MCF IP 0.195

HERN IP 0.185

117

CC IP 0.251

Hillsborough IP 0.176

Pinellas IP 0.188

Hiland Ham IP 0.199

Jonathan Dickens IP 0.165

Kennedy IP 0.160

Orange IP 0.204

V IP 0.156

A IP 0.132

Putnam IP 0.165

R IP 0.121

JRC IP 0.166

WT IP 0.156

MAFB IP 0.125 benning IP 0.107

Gasden IP 0.152

Andrews IP 0.120

Island IP 0.113

Walton IP 0.083

CW IP 0.060

Pd IP 0.062

Tr IP 0.107

CNF IP 0.116

118

MC IP 0.019

AGTP WTMS 0.192

GS WTMS 0.239

TSR WTMS 0.248

SRS WTMS 0.263

Nassau WTMS 0.245

Gordon WTMS 0.212

Mop WTMS 0.197

UNF WTMS 0.201

MCF WTMS 0.228

HERN WTMS 0.218

CC WTMS 0.287

Hillsborough WTMS 0.210

Pinellas WTMS 0.219

Hiland Ham WTMS 0.232

Jonathan Dickens WTMS 0.196

Kennedy WTMS 0.193

Orange WTMS 0.234

V WTMS 0.188

A WTMS 0.158

Putnam WTMS 0.196

R WTMS 0.150

JRC WTMS 0.191

119

WT WTMS 0.190

MAFB WTMS 0.152 benning WTMS 0.129

Gasden WTMS 0.185

Andrews WTMS 0.145

Island WTMS 0.137

Walton WTMS 0.111

CW WTMS 0.073

Pd WTMS 0.080

Tr WTMS 0.136

CNF WTMS 0.137

MC WTMS 0.028

IP WTMS 0.026

AGTP YR 0.180

GS YR 0.219

TSR YR 0.224

SRS YR 0.242

Nassau YR 0.230

Gordon YR 0.200

Mop YR 0.176

UNF YR 0.189

MCF YR 0.213

HERN YR 0.202

120

CC YR 0.262

Hillsborough YR 0.187

Pinellas YR 0.200

Hiland Ham YR 0.208

Jonathan Dickens YR 0.176

Kennedy YR 0.175

Orange YR 0.215

V YR 0.174

A YR 0.150

Putnam YR 0.181

R YR 0.143

JRC YR 0.182

WT YR 0.178

MAFB YR 0.147 benning YR 0.121

Gasden YR 0.178

Andrews YR 0.133

Island YR 0.129

Walton YR 0.098

CW YR 0.068

Pd YR 0.069

Tr YR 0.126

CNF YR 0.131

121

MC YR 0.022

IP YR 0.026

WTMS YR 0.017

AGTP LR 0.211

GS LR 0.249

TSR LR 0.253

SRS LR 0.276

Nassau LR 0.254

Gordon LR 0.230

Mop LR 0.205

UNF LR 0.220

MCF LR 0.246

HERN LR 0.241

CC LR 0.284

Hillsborough LR 0.224

Pinellas LR 0.243

Hiland Ham LR 0.242

Jonathan Dickens LR 0.211

Kennedy LR 0.212

Orange LR 0.253

V LR 0.201

A LR 0.186

Putnam LR 0.205

122

R LR 0.179

JRC LR 0.224

WT LR 0.214

MAFB LR 0.182 benning LR 0.158

Gasden LR 0.211

Andrews LR 0.170

Island LR 0.166

Walton LR 0.129

CW LR 0.099

Pd LR 0.102

Tr LR 0.156

CNF LR 0.179

MC LR 0.051

IP LR 0.054

WTMS LR 0.043

YR LR 0.025

AGTP GF 0.187

GS GF 0.240

TSR GF 0.245

SRS GF 0.259

Nassau GF 0.246

Gordon GF 0.218

123

Mop GF 0.192

UNF GF 0.200

MCF GF 0.222

HERN GF 0.214

CC GF 0.272

Hillsborough GF 0.197

Pinellas GF 0.211

Hiland Ham GF 0.221

Jonathan Dickens GF 0.190

Kennedy GF 0.191

Orange GF 0.226

V GF 0.183

A GF 0.161

Putnam GF 0.196

R GF 0.157

JRC GF 0.194

WT GF 0.189

MAFB GF 0.159 benning GF 0.136

Gasden GF 0.193

Andrews GF 0.146

Island GF 0.141

Walton GF 0.113

124

CW GF 0.077

Pd GF 0.086

Tr GF 0.138

CNF GF 0.153

MC GF 0.034

IP GF 0.034

WTMS GF 0.024

YR GF 0.014

LR GF 0.032

AGTP TM 0.222

GS TM 0.281

TSR TM 0.279

SRS TM 0.287

Nassau TM 0.270

Gordon TM 0.247

Mop TM 0.210

UNF TM 0.226

MCF TM 0.257

HERN TM 0.239

CC TM 0.302

Hillsborough TM 0.224

Pinellas TM 0.237

Hiland Ham TM 0.247

125

Jonathan Dickens TM 0.223

Kennedy TM 0.221

Orange TM 0.249

V TM 0.201

A TM 0.193

Putnam TM 0.224

R TM 0.184

JRC TM 0.221

WT TM 0.214

MAFB TM 0.185 benning TM 0.167

Gasden TM 0.212

Andrews TM 0.151

Island TM 0.158

Walton TM 0.133

CW TM 0.097

Pd TM 0.108

Tr TM 0.164

CNF TM 0.178

MC TM 0.050

IP TM 0.059

WTMS TM 0.056

YR TM 0.044

126

LR TM 0.060

GF TM 0.048

AGTP T44 0.205

GS T44 0.253

TSR T44 0.261

SRS T44 0.273

Nassau T44 0.257

Gordon T44 0.227

Mop T44 0.203

UNF T44 0.213

MCF T44 0.238

HERN T44 0.224

CC T44 0.287

Hillsborough T44 0.210

Pinellas T44 0.222

Hiland Ham T44 0.241

Jonathan Dickens T44 0.207

Kennedy T44 0.205

Orange T44 0.243

V T44 0.195

A T44 0.168

Putnam T44 0.211

R T44 0.160

127

JRC T44 0.208

WT T44 0.202

MAFB T44 0.161 benning T44 0.140

Gasden T44 0.188

Andrews T44 0.156

Island T44 0.152

Walton T44 0.115

CW T44 0.081

Pd T44 0.093

Tr T44 0.144

CNF T44 0.159

MC T44 0.028

IP T44 0.027

WTMS T44 0.020

YR T44 0.016

LR T44 0.033

GF T44 0.022

TM T44 0.034

AGTP wb 0.151

GS wb 0.194

TSR wb 0.193

SRS wb 0.205

128

Nassau wb 0.198

Gordon wb 0.167

Mop wb 0.147

UNF wb 0.145

MCF wb 0.161

HERN wb 0.151

CC wb 0.212

Hillsborough wb 0.145

Pinellas wb 0.152

Hiland Ham wb 0.161

Jonathan Dickens wb 0.136

Kennedy wb 0.136

Orange wb 0.164

V wb 0.141

A wb 0.113

Putnam wb 0.140

R wb 0.118

JRC wb 0.159

WT wb 0.155

MAFB wb 0.117 benning wb 0.102

Gasden wb 0.147

Andrews wb 0.119

129

Island wb 0.105

Walton wb 0.081

CW wb 0.074

Pd wb 0.069

Tr wb 0.106

CNF wb 0.109

MC wb 0.027

IP wb 0.033

WTMS wb 0.043

YR wb 0.030

LR wb 0.063

GF wb 0.042

TM wb 0.063

T44 wb 0.036

AGTP LH 0.169

GS LH 0.216

TSR LH 0.223

SRS LH 0.220

Nassau LH 0.213

Gordon LH 0.188

Mop LH 0.175

UNF LH 0.164

MCF LH 0.194

130

HERN LH 0.188

CC LH 0.260

Hillsborough LH 0.176

Pinellas LH 0.188

Hiland Ham LH 0.202

Jonathan Dickens LH 0.166

Kennedy LH 0.160

Orange LH 0.203

V LH 0.161

A LH 0.132

Putnam LH 0.165

R LH 0.137

JRC LH 0.186

WT LH 0.178

MAFB LH 0.133 benning LH 0.121

Gasden LH 0.180

Andrews LH 0.145

Island LH 0.134

Walton LH 0.102

CW LH 0.074

Pd LH 0.071

Tr LH 0.116

131

CNF LH 0.124

MC LH 0.035

IP LH 0.038

WTMS LH 0.047

YR LH 0.046

LR LH 0.076

GF LH 0.051

TM LH 0.083

T44 LH 0.047 wb LH 0.037

AGTP LF 0.205

GS LF 0.245

TSR LF 0.247

SRS LF 0.258

Nassau LF 0.238

Gordon LF 0.213

Mop LF 0.192

UNF LF 0.203

MCF LF 0.228

HERN LF 0.214

CC LF 0.275

Hillsborough LF 0.202

Pinellas LF 0.215

132

Hiland Ham LF 0.230

Jonathan Dickens LF 0.197

Kennedy LF 0.193

Orange LF 0.234

V LF 0.192

A LF 0.166

Putnam LF 0.199

R LF 0.157

JRC LF 0.195

WT LF 0.189

MAFB LF 0.161 benning LF 0.137

Gasden LF 0.180

Andrews LF 0.144

Island LF 0.136

Walton LF 0.109

CW LF 0.093

Pd LF 0.093

Tr LF 0.138

CNF LF 0.144

MC LF 0.032

IP LF 0.034

WTMS LF 0.030

133

YR LF 0.022

LR LF 0.038

GF LF 0.033

TM LF 0.036

T44 LF 0.015 wb LF 0.037

LH LF 0.050

AGTP M 0.218

GS M 0.258

TSR M 0.257

SRS M 0.258

Nassau M 0.250

Gordon M 0.218

Mop M 0.202

UNF M 0.212

MCF M 0.238

HERN M 0.235

CC M 0.304

Hillsborough M 0.220

Pinellas M 0.232

Hiland Ham M 0.247

Jonathan Dickens M 0.210

Kennedy M 0.210

134

Orange M 0.253

V M 0.203

A M 0.180

Putnam M 0.208

R M 0.171

JRC M 0.216

WT M 0.207

MAFB M 0.164 benning M 0.153

Gasden M 0.191

Andrews M 0.167

Island M 0.154

Walton M 0.125

CW M 0.094

Pd M 0.105

Tr M 0.139

CNF M 0.166

MC M 0.036

IP M 0.037

WTMS M 0.029

YR M 0.029

LR M 0.039

GF M 0.034

135

TM M 0.044

T44 M 0.018 wb M 0.040

LH M 0.052

LF M 0.023

AGTP SNG 0.206

GS SNG 0.241

TSR SNG 0.247

SRS SNG 0.262

Nassau SNG 0.239

Gordon SNG 0.215

Mop SNG 0.193

UNF SNG 0.202

MCF SNG 0.224

HERN SNG 0.222

CC SNG 0.295

Hillsborough SNG 0.204

Pinellas SNG 0.223

Hiland Ham SNG 0.234

Jonathan Dickens SNG 0.216

Kennedy SNG 0.202

Orange SNG 0.236

V SNG 0.194

136

A SNG 0.169

Putnam SNG 0.201

R SNG 0.165

JRC SNG 0.200

WT SNG 0.195

MAFB SNG 0.169 benning SNG 0.143

Gasden SNG 0.205

Andrews SNG 0.146

Island SNG 0.143

Walton SNG 0.119

CW SNG 0.094

Pd SNG 0.087

Tr SNG 0.148

CNF SNG 0.145

MC SNG 0.034

IP SNG 0.057

WTMS SNG 0.045

YR SNG 0.038

LR SNG 0.066

GF SNG 0.057

TM SNG 0.064

T44 SNG 0.046

137 wb SNG 0.063

LH SNG 0.069

LF SNG 0.036

M SNG 0.055

AGTP BC 0.232

GS BC 0.274

TSR BC 0.278

SRS BC 0.294

Nassau BC 0.271

Gordon BC 0.245

Mop BC 0.218

UNF BC 0.233

MCF BC 0.253

HERN BC 0.248

CC BC 0.309

Hillsborough BC 0.227

Pinellas BC 0.244

Hiland Ham BC 0.256

Jonathan Dickens BC 0.230

Kennedy BC 0.221

Orange BC 0.264

V BC 0.214

A BC 0.191

138

Putnam BC 0.230

R BC 0.186

JRC BC 0.233

WT BC 0.220

MAFB BC 0.186 benning BC 0.162

Gasden BC 0.214

Andrews BC 0.174

Island BC 0.169

Walton BC 0.139

CW BC 0.102

Pd BC 0.106

Tr BC 0.166

CNF BC 0.182

MC BC 0.050

IP BC 0.059

WTMS BC 0.044

YR BC 0.044

LR BC 0.057

GF BC 0.058

TM BC 0.064

T44 BC 0.040 wb BC 0.077

139

LH BC 0.091

LF BC 0.045

M BC 0.055

SNG BC 0.028

140

LITERATURE CITED

Abbott, I., & Parker, C.A. 1980. “The Occurrence of Earthworms in the Wheat Belt of

Western Australia in Relation to Land Use and Rainfall.” Soil Research 18, no.

3: 343-352.

Allendorf, Fred W., and Rob F. Leary. 1988. “Conservation and Distribution of Genetic

Variation in a Polytypic Species: the Cutthroat Trout.” Conservation Biology 2,

no. 2: 170-184.

Auffenberg, Walter, and Richard Franz. 1982. “The Status and Distribution of the

Gopher Tortoise (Gopherus polyphemus).” In North American Tortoises:

Conservation and Ecology, edited by R. B. Bury, 95-126. U.S. Fish and Wildlife

Service, Wildlife Research Report 12. Washington, D.C.

Avise, John C. 2000. Phylogeography: the History and Formation of Species.

Cambridge, MA: Harvard University Press.

Backhed, Fredrik, Ruth E. Ley, Justin L. Sonnenburg, Daniel A. Peterson, and Jeffrey I.

Gordon. 2005. “Host-Bacterial Mutualism in the Human Intestine.” Science

307, no. 5717: 1915-1920.

Balkenhol, Niko, Lisette P. Waits, and Raymond J. Dezzani. 2009. “Statistical

Approaches in Landscape Genetics: An Evaluation for Linking Landscape and

Genetic Data.” Ecography 32, no. 5: 818-830.

Behan-Pelletier, Valerie M., and S.B. Hill. 1983. “Feeding Habits of 16 Species of

Oribatei (Acari) from an Acid Peat Bog, Glenamoy, Ireland.” Review De

Ecologie et de Biologie du Sol 20, no. 2: 221-67.

141

Behan-Pelletier, Valerie M. 1993. “Diversity of Soil Arthropods in Canada: Systematic

and Ecological Problems.” Memoirs of the Entomological Society of Canada 165:

11-50.

Benson, Andrew K., Scott A. Kelly, Ryan Legge, Fangrui Ma, Soo Jen Low, Jaehyoung

Kim, Min Zhang, Phaik L. Oh, Derrick Nehrenberg, Kunjie Hua, Stephen D.

Kachman, Estuko N. Moriyama, Jens Walter, Daniel A. Peterson, and Daniel

Pomp. 2010. “Individuality in gut microbiota composition is a complex

polygenic trait shared by multiple environmental and host genetic factors.”

Proceedings of the National Academy of Sciences 107, no. 44: 18933-18938.

Birkhead, Roger D., Craig Guyer, Sharon M. Hermann, and William K. Michener. 2005.

“Patterns of Folivory and Seed Ingestion by Gopher Tortoises (Gopherus

polyphemus) in a Southeastern Pine Savanna.” American Midland Naturalist 154,

no. 1: 43-151.

Bjorndal, Karen A. 1987. “Digestive Efficiency in a Temperate Herbivorous Reptile,

Gopherus polyphemus.” Copeia 1987, no. 3: 714-720.

Blair, Christopher, Dana E. Weigel, Matthew Balazik, Annika T.H. Keeley, Faith M.

Walker, Erin Landguth, Sam Cushman, Melanie Murphy, Lisette Waits, and Niko

Balkenhol. 2012. “A Simulation-Based Evaluation of Methods for Inferring

linear Barriers to Gene Flow.” Molecular Ecology 12, no. 5: 822-833.

Booth, W., W.I. Montegomery, and P.A. Prodohl. 2009. “Spatial Genetic Structuring in

a Vagile Species, the European Wood Mouse.” Journal of Zoology 279, no. 3:

219-228.

142

Boutin-Ganache Isabell, Marco Raposo, Martine Raymond, and Christian F. Deschepper.

2001. “M-13 Tailed Primers Improve the Readability and Usability of

Microsatellite Analyses Performed with Two Different Allele-Sizes Methods.”

Biotechniques 31, no. 1: 24–28.

Breininger, David R., Paul A. Schmaler, and C. Ross Hinkle. 1991. “Estimating

Occupancy of Gopher Tortoise (Gopherus polyphemus) Burrows in Coastal Scrub

and Slash Pine Flatwoods.” Journal of Herpetology 25, no. 3: 317-321.

Brode, William E. 1959. “Notes on Behavior of Gopherus polyphemus.” Herpetologica:

101-102.

Bruggeman Douglas J., Michael L. Jones, Frank Lupi, Kim T. Scribner. 2005.

“Landscape Equivalency Analysis: Methodology for Estimating Spatially Explicit

Biodiversity Credits.” Environmental Management 36, no. 4: 518-534.

Brussard, Peter F. 1984. “Geographic Patterns and Environmental Gradients: The

Central-Marginal Model in Drosophila Revisited.” Annual Review of Ecology

and Systematics 15: 25-64.

Butler, Joseph A., and Todd W. Hull. 1996. “Reproduction of the Tortoise, Gopherus

polyphemus, in Northeastern Florida.” Journal of Herpetology 30, no. 1: 14-18.

Capone, Kimberly A., Scott E. Dowd, GeorgiosN. Stamatas, and Janeta Nikolovski.

2011. “Diversity of the Human Skin Microbiome Early in Life.” Journal of

Investigative Dermatology 131, no. 10: 2026-2032.

Caporaso, J. Gregory, Christian L. Lauber, William A. Walters, Donna Berg-Lyons,

Catherine A. Lozupone, Peter J. Turnbaugh, Noah Fierer, and Rob Knight. 2011.

"Global Patterns of 16S rRNA Diversity at a Depth of Millions of Sequences per

143

Sample." Proceedings of the National Academy of Sciences 108, no. 1: 4516-

4522.

Chapman, Herman H. 1932. "Is the Longleaf Type a Climax?." Ecology 13, no. 4: 328-

334..

Chen, Chibiao, Eric Durand, Florence Forbes, and Olivier François. 2007. "Bayesian

Clustering Algorithms Ascertaining Spatial Population Structure: A New

Computer Program and a Comparison Study." Molecular Ecology Notes 7, no. 5:

747-756.

Christensen, N.L. 1981. “Fire Regimes in Southeastern Ecosystems.” In Fire Regimes

and Ecosystem Practices, edited by H.A. Mooney, T.M. Bonnicksen, N.L.

Christensen, J.E. Lotan, and W.A. Reinsers, 112-136. USDA Forest Service

General Technical Report WO-26.

Christensen, N.L. 1987. “The Biochemical Consequences of Fire and Their Effects on

the Vegetation of the Coastal Plain of the Southeastern United States.” In The

Role of Fire in Ecological Systems, edited by L. Traband, 1-21. The Hauge : SPB

Academic Publishing.

Clark, A. M., B. W. Bowen, and L. C. Branch. 1999. "Effects of Natural Habitat

Fragmentation on an Endemic Scrub Lizard (Sceloporus woodi): An Historical

Perspective Based on a Mitochondrial DNA Gene Genealogy." Molecular

Ecology 8, no. 7: 1093-1104.

Clostio, R.W., A.M. Martinez, K.E. LeBlanc, and N.M. Anthony. 2012. “Population

Genetic Structure of a Threatened Tortoise Across the South-Eastern United

144

States: Implications for Conservation Management.” Animal Conservation 15,

NO. 6: 613-625.

Cornuet Jean Marie, and Gordon Luikart. 1996. “Description and Power Analysis of

Two Tests for Detecting Recent Population Bottlenecks from Allele Frequency

Data.” Genetics 144, no. 4: 2001-2014.

Coulon A., G. Guillot, J.F. Cosson, J.M.A. Angibault, S. Aulagnier, B. Carneelutti, M.

Galan and A.J.M. Hewison. 2006. “Genetic Structure is Influenced by

Landscape Features: Empirical Evidence from a Roe Deer Population.”

Molecular Ecology 15, no. 6: 1669-1679.

Croker, Thomas C., Jr. 1990. “Longleaf Pine-Myths and Facts.” In Proceedings,

Symposium on the Management of Longleaf Pine, edited by R.M. Farrar, 2-10.

USDA Forrest Service General Technical Report No. SO-75.

Crnokrak, Peter, and Derek A. Roff. 1999. “Inbreeding Depression in the Wild.”

Heredity 83, no. 3: 260-270.

DeSantis, Todd Z., Philip Hugenholtz, Neils Larsen, Mark Rojas, Eoin L. Brodie, Keith

Keller, Thomas Huber, Daniel Dalevi, Ping Hu, and Gary L. Andersen. 2006

"Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench

Compatible with ARB." Applied and Environmental Microbiology 72, no. 7:

5069-5072.

Desmuth, Jeffery P. 2001. “The Effects of Constant and Fluctuating Incubation

Temperatures on Sex Determination, Growth, and Performance of the Gopher

Tortoise, Gopherus polyphemus.” Canadian Journal of Zoology 79, no. 9: 1609-

1620.

145

Diemer, Joan E. 1986. “The Ecology and Management of the Gopher Tortoise in the

Southeastern United States.” Herpetologica 42, no. 1: 125-133.

Diemer, Joan E. 1992. “Demography of the Tortoise Gopherus polyphemus in Northern

Florida.” Journal of Herpetology 26, no. 3: 281-289.

Dodd, C.Kenneth, and Richard A. Seigel. 1991. “Relocation, Repatriation and

Translocation of Amphibians and Reptiles: are they Conservation Strategies that

Work?” Herpetologica 47, no. 3: 36-350.

Donovan, Michelle F., Raymond D. Semlitsch, and Eric J. Routmane. 2000.

“Biogeography of the Southeastern United States: A Comparison of Salamander

Phylogeographic Studies.” Evolution 54, no. 4: 1449-1456.

Dowd, Scot E., Todd R. Callaway, Randall D. Wolcott, Yan Sun, Trevor McKeehan,

Robert G. Hagevoort, and Thomas S. Edrington. 2008a. "Evaluation of the

Bacterial Diversity in the Feces of Cattle using 16S rDNA Bacterial Tag-Encoded

FLX Amplicon Pyrosequencing (bTEFAP)." BMC Microbiology 8, no. 1: 125.

Dowd, Scot E., Yan Sun, Randy D. Wolcott, Alexander Domingo, and Jeffery A. Carroll.

2008B. "Bacterial Tag-Encoded FLX Amplicon Pyrosequencing (bTEFAP) for

Microbiome Studies: Bacterial Diversity in the Ileum of Newly Weaned

Salmonella-Infected Pigs." Foodborne Pathogens and Disease 5, no. 4: 459-472.

Dowd, S. E., J. Delton Hanson, E. Rees, R.D. Wolcott, A.M. Zischau, Y. Sun, J. White,

D.M. Smith, J. Kennedy, and C.E. Jones. 2011. “Survey of Fungi and Yeast in

Polymicrobial Infections in Chronic Wounds.” Journal of Wound Care 20, no. 1:

40-47.

146

Duplanloup, I., S. Schneider, and L. Excoffier. 2002. “A Simulated Annealing

Approach to Define the Genetic Structure of Populations.” Molecular Ecology

11, no. 12: 2571-2581.

Earl, Dent A., and Bridgett M. von Holdt. 2012. “STRUCTURE HARVESTER: A

Website and Program for Visualizing STRUCTURE Output and Implementing

the Evanno Method.” Conservation Genetics Resources 4, no. 2: 359-361.

Eckert, C.G., K.E. Samis, and S.C. Lougheed. 2008. “Genetic Variation Across Species’

Geographical Ranges: The Central-Marginal Hypothesis and Beyond.”

Molecular Ecology 17, no. 5: 1170-1188.

Edgar, Robert C. 2010. “Search and Clustering Orders of Magnitude Faster than

BLAST.” Bioinformatics 26, no. 19: 2460-2461.

England, Phillip R., Graham H.R. Osler, Lynn M. Woodworth, Margaret E. Montgomery,

David A. Briscoe, and Richard Frankham. 2003. "Effects of Intense Versus

Diffuse Population Bottlenecks on Microsatellite Genetic Diversity and

Evolutionary Potential." Conservation Genetics 4, no. 5: 595-604.

Ennen, Joshua R., Brian R. Kreiser, and Carl P. Qualls. 2010. “Low Genetic Diversity in

Several Gopher Tortoise (Gopherus polyphemus) Populations in the De Soto

National Forest, Mississippi.” Herpetologica 66, no. 1: 91-96.

Ennen, Joshua R., Brian R. Kreiser, Carl P. Qualls, Daniel Gaillard, Matthew Aresco,

Roger Birkhead, Tracey D. Tuberville, Earl D. McCoy, Henry Mushinsky,

Thomas W. “Bill” Hentges. And Aaron Schrey. 2012. "Mitochondrial DNA

Assessment of the Phylogeography of the Gopher Tortoise." Journal of Fish and

Wildlife Management 3, no. 1: 110-122.

147

Epperson, Deborah M., and Collen D. Heise. 2003. “Nesting and Hatching Ecology of

Gopher Tortoises (Gopherus polyphemus) in Southern Mississippi.” Journal of

Herpetology 37, no. 2: 315-324.

Eren, A. Murat, Marcela Zozaya, Christopher M. Taylor, Scot E. Dowd, David H.

Martin, and Michael J. Ferris. 2011. "Exploring the Diversity of Gardnerella

vaginalis in the Genitourinary Tract Microbiota of Monogamous Couples

Through Subtle Nucleotide Variation." PLoS One 6, no. 10: e26732.

Ersts, P.J. Internet. “Geographic Distance Matrix Generator (version 1.2.3).” American

Museum of Natural History, Center for Biodiversity and Conservation. Accessed

June 10, 2013. http://biodiversityinformatics.amnh.org/open_source/gdmg.

Evanno, G., S. Regnaut, and J. Goudet. 2005. “Detecting the Number of Clusters of

Individuals using the Software STRUCTURE: A Simulation Study.” Molecular

Ecology 14, no. 8: 2611-2620.

Excoffier Laurent, Peter E. Smouse, and Joseph M. Quattro. 1992. “Analysis of

Molecular Variance Inferred from Metric Distances Among DNA Haplotypes:

Application to Human Mitochondrial DNA Restriction Data.” Genetics 131, no.

2: 479-491.

Excoffier L, G. Laval, and S. Schneider. 2005. “Arlequin ver. 3.0: An integrated

software package for population genetics data analysis.” Evolutionary

Bioinformatics Online 1: 47-50. Accessed May 10, 2011

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2658868/

148

Fallani, Matteo, David Young, Jane Scott, Elisabeth Norin, Sergio Amarri, Rüdiger

Adam, Marga Aguilera, Sheila Khanna, Angel Gil, Christine A. Edwards, Joel

Dore, and Other Members of the INFABIO Team. 2010. "Intestinal Microbiota

of 6-Week-Old Infants Across Europe: Geographic Influence Beyond Delivery

Mode, Breast-Feeding, and Antibiotics." Journal of Pediatric Gastroenterology

and Nutrition 51, no. 1: 77-84.

Fenolio, Dante B., G.O. Graening, Bret A. Collier, and Jim F. Stout. 2006. “Coprophagy

in Cave-Adapted Salamander; the Importance of Bat Guano Examined Through

Nutritional and Stable Isotopes.” Proceedings of the Royal Society B: Biological

Sciences 273, no. 1585: 440-443.

Fierer, Noah, and Robert B. Jackson. 2006. “The Diversity and Biogeography of Soil

Bacterial Communities.” Proceedings of the National Academy of Sciences 103,

no. 3: 626-631.

Filippo, Carlotta, Duccio Cavalieri, Monica Di Paola, Matteo Ramazzotti, Jean Baptiste

Poullet, Sebastien Massart, Silvia Collini, Giuseppe Pieraccini, and Paolo

Lionetti. 2010. "Impact of Diet in Shaping Gut Microbiota Revealed by a

Comparative Study in Children from Europe and Rural Africa." Proceedings of

the National Academy of Sciences 107, no. 33: 14691-14696.

Florida Fish and Wildlife Conservation Commission. 2001. “Available Options to

Address the Presence of Gopher Tortoises on Lands Slated for Development.”

Accessed June 11, 2013.

http://wildflorida.org/permits/permits.htm#gophertortoise.

149

Frank, Daniel N., Allison L. St Amand, Robert A. Feldman, Edgar C. Boedeker, Noam

Harpaz, and Norman R. Pace. 2007. "Molecular-Phylogenetic Characterization

of Microbial Community Imbalances in Human Inflammatory Bowel Diseases."

Proceedings of the National Academy of Sciences 104, no. 34: 13780-13785

Frankham, Richard, Jonathan D. Ballou, and David Anthony Briscoe. 2002.

Introduction to Conservation Genetics. Cambridge: Cambridge University Press.

Franklin, Rima B., and Aaron L. Mills. 2003. “Multi-Scale Variation in Spatial

Heterogeneity for Microbial Community Structure in an Eastern Virginia

Agricultural Field.” FEMS Microbiology Ecology 44, no. 3: 335-346.

Franz, A.C., L.C. Pope, T.R. Etherington, G.J. Wilson, and T. Burke. 2010. “Using

Isolation-by-Distance-Based Approaches to Assess the Barrier Effect of Linear

Landscape Elements on Badger (Meles meles) Dispersal.” Molecular Ecology 19,

no. 8: 1663-1674.

Fujii, Akiko, and Michael R.J. Fornster. 2010. “Genetic Variation and Population

Structure of the Texas Tortoise, Gopherus berlandieri (Testudinidae), with

Implications for Conservation.” Chelonian Conservation and Biology 9, no. 1:

61-69.

Funk W. Chris, Michael S. Blouin, Paul Stephen Corn, Bryce A. Maxell, David S.

Pilliod, Stephen Amish, and Fred W. Allendorf. 2005. “Population Structure of

Columbia Spotted Frog (Rana luteiventris) is Strongly Affected by Landscape.”

Molecular Ecology 14, no. 2: 483-496.

Gascon, Claude, Jay R. Malcolm, James L. Patton, Maria N.F. da Silva, James P. Bogart,

Stephen C. Lougheed, Carlos A. Peres, Selvino Neckel, and Peter T. Boag. 2000.

150

“Riverine Barriers and the Geographic Distribution of Amazonian Species.”

Proceedings of the National Academy of Sciences 97, no. 25: 13672-13677.

Gerlach, Gabriele, and Kerstin Musolf. 2000. “Fragmentation of Landscape as a Cause

for Genetic Subdivision in Bank Voles.” Conservation Biology 14, no. 4: 1066-

1074.

Graveland, J., and R.H. Drent. 1997. “Calcium Availability Limits Breeding Success of

Passerines on Poor Soils.” Journal of Animal Ecology 66, no. 2: 279-288.

Guillot Giles, Arnaud Estoup, Frederic Mortier, and Jean Francois Cosson. 2005a. “A

Spatial Statistical Model for Landscape Genetics.” Genetics 170, no. 3: 1261-

1280.

Guillot, Giles, Frederic Mortier, and Arnaud Estoup. 2005b. “GENELAND: A

Computer Package for Landscape Genetics.” Molecular Ecology Notes 5, no. 3:

712–715.

Guillot Giles, and Filipe Santos. 2009. “A Computer Program to Simulate Multilocus

Genotype Data with Spatially Auto-Correlated Allele Frequencies.” Molecular

Ecology Resources 9, no. 4: 1112-1120.

Hagerty, Bridgette E., and C. Richard Tracy. 2010. “Defining Population Structure for

the Mojave Desert Tortoise.” Conservation Genetics 11, no. 5: 1795-1807.

Hallinan, Thomas. 1923. “Observations Made in Duval County, Northern Florida, on

the Gopher Tortoise (Gopherus polyphemus).” Copeia 1923:11-20.

Hammond, Shea. 2006. “Hatching Success of the Gopher Tortoise, Gopherus

polyphemus, on the Chickasawhay Ranger District, De Soto National Forest,

Mississippi.” Master’s Thesis. The University of Southern Mississippi.

151

Hansen, Keith L. 1963. “The Burrow of the Gopher Tortoise.” Quarterly Journal of the

Florida Academy of Sciences 26:353-360.

Herman Sharon M., Craig Guyer, J. Hardin Waddle, and Greg M. Nelms. 2002.

“Sampling on Private Property to Evaluate Population Status and Effects of Land

use Practices on the Gopher Tortoise, Gopherus polyphemus.” Biological

Conservation 108, no. 3: 289-298.

Holderegger Rolf, and Helene H. Wagner. 2008. “Landscape Genetics.” BioScience 58,

no. 3: 199-207.

Hong, Pei-Ying, Emily Wheeler, Isaac K.O. Cann, and Roderick I. Mackie. 2011.

“Phylogenetic Analysis of Fecal Microbial Communituy in Herbivorous Land and

Marine Iguanas of the Galapagos Islands using 16S rRNA-Based

Pyrosequencing.” The ISME Journal 5, no. 9: 1461-1470.

Hooper, L.ora V., Tore Midtvedt, and Jeffery I. Gordon. 2002. “How Host-Microbial

Interactions Shape the Nutrient Environment of the Mammalian Intestine.”

Annual Review of Nutrition 22, no. 1: 283-307.

House, G.J., Rosario Alzugaray, Maria Del. 1989. “Influence of Cover Cropping and

No-Tillage Practices on Community Composition of Soil Arthropods in a North

Carolina Agroecosystem.” Environmental Entomology 18, no. 2: 302-307.

Hubisz Melissa J., Daniel Falush, Matthew Stephens, and Jonathan K. Pritchard. 2009.

“Inferring Weak Population Structure with the Assistance of Sample Group

Information.” Molecular Ecology Resources 9, no. 5: 1322-1332.

152

Izquierdo, M.S., H. Fernandez-Palacios, and A.G.J. Tacon. 2001. “Effect of Broodstock

Nutrition on Reproductive Performance of Fish.” Aquaculture 197, no. 1: 25-42.

Jackson, D.R., and E.G. Milstrey. 1989. “The Fauna of Gopher Tortoise Burrows.” In

Proceedings of the Gopher Tortoise Relocation Symposium, edited by Diemer

J.E., D.R Jackson, J.L. Landers, J.N. Layne, and D.A. Wood, 86-98. Gopher

Commission, Nongame Wildlife Program Technical Report.

Jackson, Nathan D., and Christopher C. Austin. 2010. “The Combined Effects of Rivers

and Refugia Generate Extreme Cryptic Fragmentation within the Common

Ground Skink (Scincella lateralis).” Evolution 64, no. 2: 409-428.

Jakobsson, M, and N.A. Rosenberg. 2007. “CLUMMP: A Cluster Matching and

Permutation Program for Dealing with Label Switching and Multimodality in

Analysis of Population Structure.” Bioinformatics 23, no. 14: 1801-1806.

Jenson, J.L., A.J. Bohonak, and S.T. Kelley. 2005. “Isolation by distance, web service.”

BMC Genetics 6:13. V.3.23. Accessed June 10, 2013. http://ibdws.sdsu.edu/.

Kautz, Randy S. 1993. “Trends in Florida Wildlife Habitat 1936-1987.” Florida

Scientist 56, no. 1: 7-24.

Kelly, J.F., and W.A. Bechtold. 1990. “The longleaf pine resource.” In Proceedings of

the symposium on the management of longleaf pine. Long Beach, MS. U.S.D.A.

Forest Service. Southern Forest Experiment Station, New Orleans, LA. 11-22,

General Technical Report SO-75.

Kreiser, Brian R., Joshua E. Ennen, Daniel L. Gaillard, and Carl P. Qualls. 2013.

“Isolation and Characterization of Polymorphic Microsatellie Loci for the Gopher

153

Tortoise (Gopherus polyphemus) and Cross-Amplification in other Species of

Gopherus.” Conservation Genetics Resources 5, no. 5: 255-259.

Lankau, Emily W., Pei-Ying Hong, and Roderick I. Mackie. 2012. “Ecological Drift

and Local Exposures Drive Enteric Bacterial Community Differences within

Species of Galapagos Iguanas.” Molecular Ecology 21, no. 7: 1779-1788.

Latch, Emily K., Daniel G. Scognamillo, Jennifer A. Fike, Michael J. Chamberlain, and

Olin E. Rhodes. 2008. "Deciphering Ecological Barriers to North American

River Otter (Lontra canadensis) Gene Flow in the Louisiana Landscape." Journal

of Heredity 99, no. 3: 265-274.

Latch, Emily, William I. Boarman, Andrew Walde, and Robert C. Fleishcer. 2011.

“Fine-Scale Analysis Reveals Cryptic Landscape Genetic Structure in Desert

Tortoises.” PloS one 6, no. 11: e27794.

Lavelle, P., and B. Pashanasi. 1989. “Soil Macro Fauna and Land Management in

Peruvian Amazonia (Yurimaguas, Loreto).” Pedobiology 33, no. 5: 283-91.

Lawton, John H. 1993. “Range, Population Abundance and Conservation.” Trends in

Ecology and Evolution 8, no. 11: 409-413.

Lee-Yaw, Julie A, Andrew Davidson, Brad H. McRae, and David M. Green. 2009. “Do

Landscape Processes Predict Phylogeographic Patterns in the Wood Frog? “

Molecular Ecology 18, no. 9: 1863-1874.

Lepage, Patricia, Philippe Seksik, Malene Sutrend, Marie-France de la Cochetiere,

Raymond Jian, Philippe Marteau, and Joel Dore. 2005. “Biodiversity of the

Mucosa‐ Associated Microbiota is Stable Along the Distal Digestive Tract in

154

Healthy Individuals and Patients with IBD.” Inflammatory Bowel Diseases 11,

no. 5: 473-480.

Ley, Ruth E, Micah Hamady, Catherine Lozupone, Peter J. Turnbaugh, Rob Roy Ramey,

J.Stephen Bircher, Michael L. Schlegel, Tammy A. Tucker, Mark D. Schrenzel,

Rob Knight, and Jeffrey J. Gordon. 2008. “Evolution of Mammals and their Gut

Microbes.” Science 320, no. 5883: 1647-1651.

Luck, Gary W., Gretchen C. Daily, and Paul R. Ehrlich. 2003. “Population Diversity

and Ecosystem Services.” Trends in Ecology and Evolution 18, no. 7: 331-336.

MacDonald, L.A., and Henry R. Mushinsky. 1988. “Foraging Ecology of the Gopher

Tortoise, Gopherus polyphemus, in a Sandhill Habitat.” Herpetologica 44, no. 3:

345-353.

Manel Stephanie, Michael K. Schwartz, Gordon Luikart, and Pierre Taberlet. 2003.

“Landscape Genetics: Combining Landscape Ecology and Population Genetics.”

Trends in Ecology and Evolution 18, no. 4: 189-197.

Maruyama, Takeo, and Paul A. Fuerst. 1985. “Population Bottlenecks and

Nonequilibrium Models in Population Genetics. II. Number of Alleles in a Small

Population that was Formed by a Recent Bottleneck.” Genetics 3, no. 3: 675-689.

McCoy, Earl D., and Henry R. Mushinsky. 1992. “Studying a Species in Decline:

Gopher Tortoises and the Dilemma of ‘Correction Factors’.” Herpetologica 48,

no. 4: 402-407.

McCoy, Earl D., Henry R. Mushinsky, and Jonathan Lindzey. 2006. “Declines of the

Gopher Tortoise on Protected Lands.” Biological Conservation 128, no. 1: 120-

127.

155

McDonald, David B., Wayne K. Potts, John W. Fitzpatrick, and Glen E. Woolfenden.

1999. “Contrasting Genetic Structures in Sister Species of North American

Scrub-Jays.” Proceedings of the Royal Society of London Series B: Biological

Sciences 266, no. 1424: 1117-1125.

Mitton, Jeffry B. 1997. Selection in Natural Populations. New York, NY: Oxford

University Press.

Moore, J.A., H.C. Miller, H. Daugherty, and N.J. Nelson. 2008. “Fine-Scale Genetic

Structure of a Long-Lived Reptile Reflects Recent Habitat Modification.”

Molecular Ecology 17, no. 21: 4630-4661.

Moritz, Craig. 1994. “Defining ‘Evolutionarily Significant Units’ for Conservation.”

Trends in Ecology and Evolution 9, no. 10: 373-375.

Mr. DNA, Molecular Reasearch LP. 2012. “Sequencing Applications.” Accessed

October 2012. http://www.mrdnalab.com.

Murphy, Melanie A., Jeffrey S. Evans, and Andrew Storfer. 2010. “Quantifying Bufo

boreas Connectivity in Yellowstone National Park with Landscape Genetics.”

Ecology 91, no. 1: 252-261.

Mushinsky Henry R., and Laura A. Esman. 1994. “Perceptions of Gopher Tortoise

Burrows Over Time.” Florida Field Naturalist 22, no. 2: 1-7.

Newman, Dara, and Diana Pilson. 1997. “Increased Probability of Extinction Due to

Decreased Genetic Effective Population Size: Experimental Populations of

Clarkia pulchella.” Evolution 51, no. 2: 1-10.

Noel, Krista M., Carl .P. Qualls, and Joshua R. Ennen. 2012. “A Comparison of

Artificial Incubation and Natural Incubation Hatching Success of Gopher Tortoise

156

(Gopherus polyphemus) Eggs in Southern Mississippi.” Herpetologica 68, no. 3:

324-333.

Noss, Reed F. 1989. “Longleaf Pine and Wiregrass: Keystone Components of an

Endangered Ecosystem.” Natural Areas Journal 9, no. 4: 211-213.

Oksanen, Jari. 2010. “Multivariate Analysis of Ecological Communities in R: Vegan

Tutorial.” R Package version 2.10.1.

Oosterhout, Cock V., William F. Hutchison, Derek P.M Wills, and Peter Shipley. 2004.

“ MICRO-CHECKER: Software for Identifying and Correcting Genotype Errors

in Microsatellite Data.” Molecular Ecology Notes 4, no. 3: 535-538.

Osborne, Megan, Alana Sharp, Jerry Monzingo, David L. Propst, and Thomas F. Turner.

2012. “Genetic Analysis Suggests High Conservation Value of Peripheral

Populations of Chihuahau Chub (Gila nigrescens).” Conservation Genetics 13,

no.5: 1317-1328.

Osentowski, M.F., and T. Lamb. 1995. “Intraspecific Phylogeography of the Gopher

Tortoise, Gopherus polyphemus: RFLP Analysis of Amplified mtDNA

Segments.” Molecular Ecology 4, no. 6: 709-718.

Peakall Rod, Smouse Peter E. 2006. “GENALEX 6:Ggenetic Analysis in Excel.

Population Genetic Software for Teaching and Research.” Molecular Ecology

Notes 6, no. 1: 288-295.

Peery, M. Zachariah, Rebecca Kirby, Brendan N. Reid, Ricka Stoelting, Elena Doucet-

Beer, Stacie Robinson, Catalina Vasquez-Carrillo, Jonathan N. Pauli, and Per J.

Palsboll. 2012. “Reliability of Genetic Bottleneck Tests for Detecting Recent

Population Declines.” Molecular Ecology 21, no. 14: 3403-3418.

157

Peet, Robert K., and Dorothy J. Allard. 1993. “Longleaf Pine Vegetation of the

Southern Atlantic and Eastern Gulf Coast Regions: A Preliminary Classification.”

Proceedings of the Tall Timbers Fire Ecololgy Conference 18: 45-81.

Piry S., G. Luikart, and J.M. Cornuet. 1999. “BOTTLENECK: A Computer Program for

Detecting Recent Reductions in the Effective Population Size Using Allele

Frequency Data.” Journal of Heredity 90, no. 4: 502-503.

Pounds, J. Alan, and James F. Jackson. 1981. “Riverine Rarriers to Gene Flow and the

Differentiation of Fence Lizard Populations.” Evolution 35, no. 3: 516-528.

Pritchard Jonathan K., Matthew Stephens, and Peter Donnelly. 2000. “Inference of

Population Structure Using Multilocus Genotype Data.” Genetics 155, no. 2: 945-

959.

R Core Team. 2013. “R: A language and environment for statistical computing.” R

Foundation for Statistical Computing, Vienna, Austria. Accessed January 11,

2012 http://www.R-project.org/.

Raiti, Paul, and Nogah Haramati. 1997. “Magnetic Resonance Imaging and

Computerized Tomography of a Gravid Leopard Tortoise (Echeloned paradise)

with Metabolic Bone Disease.” Journal of Zoo and Wildlife Medicine 28, no. 2:

189-197.

Ralls, Katherine, Jonathan D. Ballou, and Alan Templeton. 1988. “Estimates of Lethal

Equivalents and the Cost of Inbreeding in Mammals.” Conservation Biology 2,

no. 2: 182-193.

Ranjard, Lionel, and Agnes Richaume. 2001. “Quantitative and Qualitative Microscale

Distribution of Bacteria in Soil.” Research in Microbiology 152, no. 8: 707-716

158

Raymond Michael, and Francois Rousset. 1995. “GENEPOP (version 1.2): Population

Genetics Software for Exact Tests and Ecumenicism.” Journal of Heredity 86, no.

3: 248–249.

Reed, David H., and Richard Frankham. 2003. “Correlation Between Fitness and

Genetic Diversity.” Conservation Biology 17, no. 1: 230-237.

Reynolds, Heather L., Alissa Packer, James D. Bever, and Keith Clay. 2003.

“Grassroots Ecology: Plant-Microbe-Soil Interactions as Drivers of Plant

Community Structure and Dynamics.” Ecology 84, no. 9: 2281-2291.

Rice, William R. 1989. “Analyzing Tables of Statistical Tests.” Evolution 43:223-225.

Richter, Stephen C., Jeffrey A. Jackson, Matthew Hinderliter, Deborah Epperson,

Christopher W. Theodorakis, and S. Marshall Adams. 2011." Conservation

Genetics of the Largest Cluster of Federally Threatened Gopher Tortoise

(Gopherus polyphemus) Colonies with Implications for Species Management."

Herpetologica 67, no. 4: 406-419.

Rosenberg, Noah A. 2004. “DISTRUCT: A Program for the Graphical Display of

Population Structure.” Molecular Ecology Notes 4, no. 1: 137-138.

Round, June L., and Sarkis K. Mazmanian. 2009. “The Gut Microbiota shapes Intestinal

Immune Responses During Health and Disease.” Nature Reviews Immunology 9,

no. 5: 313-323.

Rueda, Angel Gil, Ricardo. 2000. “Modulation of Intestinal Microflora by Specific

Dietary Components.” Microbial Ecology in Health and Disease 12, no. 2: 31-

39.

159

Sacks, Benjamin N., Danika L. Bannasch, Bruno B. Chomel, and Holly B. Ernst. 2008.

“Coyotes Demonstrate how Habitat Specialization by Individuals of a Generalist

Species can Diversify Populations in a Heterogeneous Ecoregion.” Molecular

Biology Evolution 25, no. 7: 1384-1394.

Safner, Toni, Mark P. Miller, Brad H. McRae, Marie-Josée Fortin, and Stéphanie Manel.

2011. "Comparison of Bayesian Clustering and Edge Detection Methods for

Inferring Boundaries in Landscape Genetics." International Journal of Molecular

Sciences 12, no. 2: 865-889

Scanlan, Pauline D., Fergus Shanahan, Caitlin O’Mahony, and Julian R. Marchesi. 2006.

“Culture-Independent Analysis of Temporal Variation of the Dominant Fecal

Microbiota and Targeted Bacterial Subgroups in Crohn’s Disease.” Journal of

Clinical Microbiology 44, no. 11: 3980-3988.

Schaeffer, M., and J. Schauermann. 1990. “The Soil Fauna of Beach Forests:

Comparison Between a Mull and a Moder Soil.” Pedobiology 34, no. 5: 299-314.

Schlesinger, W. H. 1991. Biogeochemistry: an analysis of global change. San Diego,

CA: Academic Press.

Schwartz, Tonia S., and Stephen A. Karl. 2005. “Population and Conservation Genetics

of the Gopher Tortoise (Gopherus polyphemus).” Conservation Genetics 6, no. 6:

917-928.

Scwartz, Tonia S., and Stephen A. Karl. 2008. “Population Genetic assignment of

Confiscated gopher tortoises.” The Journal of Wildlife Management 72, no. 1:

254-259.

160

Segelbacher, Gernot, Samuel A. Cushman, Bryan K. Epperson, Marie-Josée Fortin,

Olivier Francois, Olivier J. Hardy, Rolf Holderegger, Pierre Taberlet, Lisette P.

Waits, and Stéphanie Manel. 2010. "Applications of Landscape Genetics in

Conservation Biology: Concepts and Challenges." Conservation Genetics 11, no.

2: 375-385.

Seutin Giles., Bradley N. White, and, Peter T. Boag. 1991. “Preservation of Avian

Blood and Tissue Samples for DNA Analysis.” Canadian Journal of Zoology 69,

no. 1: 82–90.

Sharmin, Farhana, Steve Wakelin, Flavia Huygens, and Megan Hargreaves. 2013.

“Firmicutes Dominate the Bacterial Taxa within Sugar-Cane Processing Plants.”

Scientific Reports 3:1-7.

Sinclair, Collen S., Patrick Dawes, and Richard A. Seigel. 2010. “Genetic Structuring of

Gopher Tortoise (Gopherus polyphemus) Populations on the Kennedy Space

Center, Florida, USA.” Herpetological Conservation and Biology 5, no. 2: 189-

195.

Soltis, Douglas E., Ashley B. Morris, Jason S. McLachlan, Paul S. Manos, and Pamela S.

Soltis. 2006. “Comparative Phylogeography of Unglaciated Eastern North

America.” Molecular Ecology 15, no. 14: 4261-4293.

Smith, Z., A.E. Craig, J.R. Stephen, T.M. Embley, and J.I. Prosser. 2001. “Species

Diversity of Uncultured and Cultured Populations of Soil and Marine Ammonia

Oxidizing Bacteria.” Microbial Ecology 42, no. 3: 228-237.

161

Spear, Stephen F., and Andrew Storfer. 2008. “Landscape Genetic Structure of Coastal

Tailed Frogs (Ascaphus truei) in Protected vs. Managed Forests.” Molecular

Ecology 17, no. 21: 4642-4656.

Spinks Phillip Q., H. Bradley Shaffer. 2005. “Range-Wide Molecular Analysis of the

Western Pond Turtle (Emys marmorata): Cryptic Variation, Isolation by Distance,

and their Conservation Implications.” Molecular Ecology 14, no. 7: 2047-2064.

Steinwascher, Kurt. 1978. “The Effect of Coprophagy on the Growth of Rana

catesbeiana Tadpoles.” Copeia 1978, no. 1:130-134.

Storfer, A., M. A. Murphy, J. S. Evans, C. S. Goldberg, S. Robinson, S. F. Spear, R.

Dezzani, E. Delmelle, L. Vierling, and L. P. Waits. 2007. "Putting the

‘Landscape’ in Landscape Genetics." Heredity 98, no. 3: 128-14.

Streng, D.R., and P.A. Harcombe. 1982. “Why don’t East Texas Savannas Grow Up to

Forest?” The American Midland Naturalist 108, no. 2: 278-294.

Sullam, Karen E., Steven D. Essinger, Catherine A. Lozupone, Michael P. O’Conner,

Gail L. Rosen, R. O. B. Knight, Susan S. Kilham, and Jacob A. Russell. 2012.

"Environmental and Ecological Factors that Shape the Gut Bacterial Communities

of Fish: A Meta‐ Analysis." Molecular Ecology 21, no. 13: 3363-3378.

Swanson, Kelly S., Scot E. Dowd, Jan S. Suchodolski, Ingmar S. Middelbos, Brittany M.

Vester, Kathleen A. Barry, Karen E. Nelson, Manolito Torralba, Bernard

Henrissat, Pedro M. Coutinho, Isaac K.O. Cann, Bryan A. White, and George C.

Fahey Jr. 2011. "Phylogenetic and Gene-Centric Metagenomics of the Canine

Intestinal Microbiome Reveals Similarities with Humans and Mice." The ISME

Journal 5, no. 4: 639-649

162

Swenson, Nathan G., and Daniel T. Howard. 2005. “Clustering of Contact Zones,

Hybrid Zones and Phylogeographic Breaks in North America.” The American

Naturalist 166, no. 5: 581-591.

Thompson, Michael B., and Brian K. Speake. 2003. “Energy and Nutrient Utilization by

Embryonic Reptiles.” Comparative Biochemistry and Physiology Part A

133:529-538.

Troyer, Katherine. 1982. “Transfer of Fermentative Microbes between Generations in

an Herbivorous Lizard.” Science 216, no. 4545: 540-542.

Tuberville, Tracey D., and M.E. Dorcas. 2001. “Winter Survey of a Gopher Tortoise

Population in South Carolina.” Chelonian Conservation and Biology 4, no. 1:

182-186.

Tuberville, Tracey D., Erin E. Clark, Kurt A. Buhlmann, and J.Whitfield Gibbons. 2005.

“Translocation as a Conservation Tool: Site Fidelity and Movement of

Repatriated Gopher Tortoises (Gopherus polyphemus).” Animal Conservation 8,

no. 4: 349-358.

Ultsch, Gordon R., and John F. Anderson. 1986. “The Respiratory Microenvironment

within the Burrow of Gopher Tortoises (Gopherus polyphemus).” Copeia 1986,

no. 3: 787-795.

USFWS (United States Fish and Wildlife Service). 1987. Endangered and Threatened

Wildlife and Plants; Determination of Threatened Status for the Gopher Tortoise

(Gopherus polyphemus). Federal Register 52:25376-25380.

USFWS (United States Fish and Wildlife Service). 1990. Gopher Tortoise Recovery

Plan. Jackson, MS: U.S. Fish and Wildlife Service.

163

USFWS (United States Fish and Wildlife Service). 2009. Guidelines for the

Establishment, Management and Operation of Gopher Tortoise Conservation

Banks. Jackson, MS: U.S. Fish and Wildlife Service.

USFWS (United States Fish and Wildlife Service). 2011. Endangered and Threatened

Wildlife and Plants; 12-Month Finding on a Petition to List the Gopher Tortoise

as Threatened in the Eastern Portion of its Range. Federal Register 76:45130-

45162.

Vleck, C.M., and D.F. Hoyt. 1991. “Metabolism and Energetics of Reptilian and Avian

Embryos.” In Egg Incubation: Its Effects on Embryonic Development in Birds

and Reptiles, edited by Deeming, D.C., M.W.J Ferguson, 285-306. Cambridge:

Cambridge University Press.

Waples, Robin S. 1991. “Pacific Salmon, Oncorhynuchus spp., and the Definition of

‘Species’ Under the Endangered Species Act.” Marine Fisheries Review 53, no.

3: 11-22.

Wasser, Samuel K., Andrew M. Shedlock, Kenine Comstock, Elaine A. Ostrander,

Benezeth Mutayoba, and Matthew Stephens. 2004. "Assigning African Elephant

DNA to Geographic Region of Origin: Applications to the Ivory Trade."

Proceedings of the National Academy of Sciences of the United States of America

101, no. 41: 14847-14852.

Watts, William A., and Barbara C.S. Hansen. 1988. “Environments of Florida in the

Lake Wisconsin and Holocene.” In Wet Site Archeology, edited by Burdy, P.A.,

307-323. Caldwell, NJ: The Telford Press.

164

Webb, S. David. 1990. “Historical Biogeography.” In Ecosystems of Florida, edited by

Myers, R.L., and J.J. Ewel, 70-102. Orlando, FL: University of Central Florida

Press.

White, H.B. III. 1991. “Maternal diet, maternal proteins and egg quality.” In Egg

Incubation: Its Effects on Embryonic Development in Birds and Reptiles,

Deeming, D.C., and Ferguson, M.W.J, 1-15. Cambridge: Cambridge University

Press.

Witz, Brian W., Dawn S. Wilson, and Michael D. Palmer. 1991. “Distribution of

Gopherus polyphemus and its Vertebrate Symbionts in Three Burrow Categories.”

American Midland Naturalist 126, no. 1: 152-158.

165