the influence of habitat structure on squamate …

THE INFLUENCE OF HABITAT STRUCTURE ON SQUAMATE SPECIES RICHNESS IN

THE COASTAL PLAIN OF GEORGIA

by

ELIZABETH MARIE SCHLIMM

(Under the Direction of Steven Castleberry)

ABSTRACT

Squamate communities are a diverse and important group in the longleaf pine ecosystem.

I examined the efficacy of a box trap array sampling method to sample squamate communities

and used the method to examine the influence of habitat structure on squamate species richness

at two spatial scales in the Coastal Plain of Georgia. I also developed a habitat suitability model

for the southern hognose snake (Heterodon simus) for the region. My results suggest that

squamate behavior, specifically habit (i.e., arboreal, fossorial, and terrestrial) has the potential to

bias captures by the box trap sampling method. Species richness models documented the

importance of wetland and forested habitat structure at a local and landscape scale to squamate

communities. The southern hognose snake habitat suitability model identified areas of potentially

suitable habitat throughout the Coastal Plain where targeted surveys and research can be focused.

INDEX WORDS: squamate, snake, lizard, detection probability, capture rate, box trap,

modeling, species richness, Heterodon simus, maximum entropy



by


BS, Stevenson University, 2008

A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial Fulfillment

of the Requirements for the Degree

MASTER OF SCIENCE

ATHENS, GEORGIA

2013

© 2013

Elizabeth Marie Schlimm

All Rights Reserved



by


Major Professor: Steven Castleberry

Committee: Lora Smith

Jeff Hepinstall-Cymerman

Electronic Version Approved:

Maureen Grasso

Dean of the Graduate School

The University of Georgia

December 2013

iv

DEDICATION

I would like to dedicate this thesis to my grandfather, Albert “Big Strong Pop” Schlimm.

Some of my earliest and most fond childhood memories include hiking and searching for

salamanders with my grandfather. I could always count on him to help me climb a tree or make

my way across a stream. I credit him for instilling in me a lifelong love, appreciation, and

respect for nature and wildlife. I hope that the work I do today makes him proud.

v

ACKNOWLEDGEMENTS

I would like to thank my committee members, Drs. Steven Castleberry, Lora Smith and

Jeff Hepinstall-Cymerman, for their guidance throughout my graduate school experience. I also

thank Jean Brock and James Deemy for their GIS expertise and Javan Bauder for his help with

my data analysis.

I’d like to thank the following people for their help with installing traps and wrangling

animals: Jen Howze, Michelina Dziadzio, Tom Baldvins, Anthony Vicente, Will McGuire, Brad

O’Hanlon, and Rich Adams. I also need to thank the following people and organizations for

giving me permission to conduct research on their properties as well as for providing logistical

support: Georgia Department of Natural Resources, David Mixon, John Adams, John Denton,

The Nature Conservancy, Chuck Martin, The Orianne Society, Chris Jenkins, Wayne Taylor,

The Joseph W. Jones Ecological Research Center, Lindsay Boring, Warren Bicknell, and Everett

Barlow. I would also like to thank the Georgia Department of Natural Resources, the Jones

Ecological Research Center, and the Gopher Tortoise Council for providing funding for my

graduate research.

Lastly, I would like to thank my family and my boyfriend, Kevin Stohlgren, for their

encouragement and support throughout my graduate school experience. I wouldn’t have made it

through this endeavor without you. I also want to thank my two adorable, block-headed pitbulls,

Molly and Duncan, whose wiggly butts and kisses put a smile on my face even on the most

frustrating days.

vi

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS .............................................................................................................v

LIST OF TABLES ....................................................................................................................... viii

LIST OF FIGURES ..................................................................................................................... xiii

CHAPTER

1 INTRODUCTION AND LITERATURE REVIEW .....................................................1

INTRODUCTION ...................................................................................................1

LITERATURE REVIEW ........................................................................................2

LITERATURE CITED ..........................................................................................12

2 AN EVALUATION OF THE EFFICACY AND BIAS OF THE BOX TRAP

SAMPLING TECHNIQUE FOR SNAKES AND LIZARDS ....................................23

INTRODUCTION .................................................................................................23

METHODS ............................................................................................................26

RESULTS ..............................................................................................................30

DISCUSSION ........................................................................................................31


3 EVALUATING THE INFLUENCE OF HABITAT STRUCTURE AT MULTIPLE

SCALES ON SQUAMATE SPECIES RICHNESS IN THE COASTAL PLAIN OF

GEORGIA....................................................................................................................56

INTRODUCTION .................................................................................................56

vii

METHODS ............................................................................................................60

RESULTS ..............................................................................................................65

DISCUSSION ........................................................................................................66


4 A MAXIMUM ENTROPY APPROACH TO HABITAT SUITABILITY

MODELING FOR THE SOUTHERN HOGNOSE SNAKE (HETERODON SIMUS)93

INTRODUCTION .................................................................................................93

METHODS ............................................................................................................97

RESULTS ............................................................................................................101

DISCUSSION ......................................................................................................102

LITERATURE CITED ........................................................................................106

5 CONCLUSION AND MANAGEMENT IMPLICATIONS .....................................123

LITERATURE CITED ........................................................................................128

APPENDICES

A APPENDIX 1 .............................................................................................................130

viii

LIST OF TABLES

Page

Table 2.1: Names and counties of sites, land ownership types, and size of upland habitat for

study sites sampled for upland snake and lizard species in the in the Coastal Plain of

Georgia, 2012 and 2013. ....................................................................................................44

Table 2.2: Foraging and habit guild assignments for snake and lizard species captured on seven

sites in the Coastal Plain of Georgia, 2012 and 2013. Habit refers to whether the species

is arboreal (n = 5), fossorial (n = 8), or terrestrial (n = 13). Foraging identifies species as

active foragers (n = 21) or ambush predators (n = 5).. ......................................................45

Table 2.3: Description of a priori models used to model snake and lizard detection probabilities

based on captures on seven sites in the Coastal Plain of Georgia, 2012 and 2013.

Variables included TEMP (average daily temperature during the sampling period),

PRECIP (total precipitation accumulated over the sampling period), and DAY (first day

of the sampling period where January 1=1). ......................................................................46

Table 2.4: Numbers of snakes and lizards captured on seven study sites in the Coastal Plain of

Georgia in box trap arrays. All sites were trapped for eight sampling periods

(approximately 240 trap nights each) during 2012 and 2013. Refer to Table 2.1 for site

name codes. ........................................................................................................................47

Table 2.5: Number of snakes and lizards captured in box traps, pitfalls, and along drift fences at

passive trap arrays on seven sites in the Coastal Plain of Georgia, 2012 and 2013. All

sites were trapped for eight sampling periods (approximately 240 trap nights each) .......49

ix

Table 2.6: Total captures and capture rates (# captures/trap night) for squamate species caught in

box trap arrays on seven sites in the Coastal Plain of Georgia, 2012 and 2013 ................50

Table 2.7: Number of detections (defined as a capture of a species in any of the three traps on a

site during a sample period), mean detection probabilities, and associated standard errors

across seven sites sampled in the Coastal Plain of Georgia, 2012 and 2013. Detection

probabilities were not estimated for species with fewer than six detections .....................51

Table 2.8: Detection probability model selection results including -2log likelihood, number of

parameters in model (K), AICc value for each model, ΔAICc from top model for each

species, and model weight (w) for snake and lizard species (with >5 detections) captured

on seven sites in the Coastal Plain of Georgia, 2012 and 2013. ........................................52

Table 2.9: Mean ranks of capture rates and detection probabilities from Wilcoxon Rank Sum and

Kruskal-Wallis tests for squamate guilds (i.e., foraging mode and habit) sampled on

seven sites in the Coastal Plain of Georgia, 2012 and 2013. .............................................53

Table 3.1: Names and counties of sites, land ownership types, and size of upland habitat for

seven sites sampled for upland snake and lizard species in the in the Coastal Plain of

Georgia, 2012 and 2013. ...................................................................................................78

Table 3.2: Names of sites sampled for upland snake and lizard species in the Coastal Plain of

Georgia (during 2012 and 2013) and total area within site (300-m) and landscape buffers

(1000-m) surrounding box trap sampling arrays... ............................................................79

Table 3.3: Aggregated land use class, class abbreviations, and Georgia Land Use Trends (GLUT)

class used to examine associations with squamate species richness sampled on seven sites

in the Coastal Plain of Georgia, 2012 and 2013 ................................................................80

x

Table 3.4: Twelve site-level and their associated hypotheses developed to examine factors

associated with squamate species richness sampled on seven sites in the Coastal Plain of

Georgia, 2012 and 2013 .....................................................................................................81

Table 3.5: Twelve landscape-level models and their associated hypotheses developed to examine

factors associated with squamate species richness sampled on seven sites in the Coastal

Plain of Georgia, 2012 and 2013. ......................................................................................82

Table 3.6: Snake and lizard species captured on seven sites in the Coastal Plain of Georgia

during 2012 and 2013 in box trap arrays (open for approximately 240 trap nights each).

Refer to Table 3.1 for site names. ......................................................................................83

Table 3.7: Measured (total species captured in box trap arrays during the study) and EstimateS

estimated species richness on seven sites sampled for upland squamates in the Coastal

Plain of Georgia, 2012 and 2013 .......................................................................................84

Table 3.8: Bray-Curtis species similarity values for seven sites sampled for upland squamates in

the Coastal Plain of Georgia, 2012 and 2013. ...................................................................85

Table 3.9: Mean, standard error (SE), and range of site and landscape variables measured on

seven sites surveyed for squamate species richness in the Coastal Plain of Georgia, 2012

and 2013. ............................................................................................................................86

Table 3.10: Site-level species richness model selection results including the number of

parameters (K), Akaike’s Information Criterion for small sample sizes (AICc), ΔAICc,

model weight (w), and cumulative model weight for squamate species captured on seven

sites in the Coastal Plain of Georgia, 2012 and 2013. Models were ranked by AICc. ......87

xi

Table 3.11: Landscape-level species richness model selection results including the number of

parameters (K), Akaike’s Information Criterion for small sample sizes (AICc), ΔAICc,

model weight (w), and cumulative model weight for squamate species captured on seven

sites in the Coastal Plain of Georgia, 2012 and 2013. Models were ranked by AICc .......88

Table 3.12: Variables, model-averaged parameter estimates, and 95% confidence intervals (CI)

for variables in the top 0.95 cumulative weight of site and landscape-level models and the

composite model to examine associations with squamate species richness sampled on

seven sites in the Coastal Plain of Georgia, 2012 and 2013 ..............................................89

Table 3.13: Estimated squamate species richness, predicted squamate species richness from the

top site-level model, landscape-level model, and composite model, their standard errors

(SE), and the difference between estimated and predicted squamate species richness from

seven sites in the Coastal Plain of Georgia, 2012 and 2013. Refer to Table 3.1for site

name codes. ........................................................................................................................90

Table 3.14: Correlation matrix for site and landscape variables used to examine associations with

squamate species richness sampled on seven sites in the Coastal Plain of Georgia, 2012

and 2013. Variables were considered to be correlated if r ≥ 0.70. ..................................130

Table 4.1: List of predictor variable categories, predictor variables, resolution/map scale of

variables, and associated data sources included in the habitat suitability model for H.

simus in the Coastal Plain of Georgia. .............................................................................111

Table 4.2: Categories of habitat suitability probabilities, area of each category within the Coastal

Plain of Georgia, percentage of total Coastal Plain landscape, area of each category

within Coastal Plain conservation lands, and percentage of conservation lands within

each category. Probabilities and areas were obtained from a continuous MaxEnt habitat

xii

suitability model output predicting the distribution of potentially suitable habitat for H.

simus in Georgia. .............................................................................................................112

xiii

LIST OF FIGURES

Page

Figure 2.1: Locations of sites trapped for upland snakes and lizards in the Coastal Plain of

Georgia from May-October, 2012 and March-April, 2013. ..............................................54

Figure 2.2: Diagram of box trap array design with drift fences and pitfalls (Burgdorf et al. 2005,

Steen et al. 2010b) used for sampling snake and lizard communities on seven sites in the

Coastal Plain of Georgia, 2012 and 2013. Diagram not drawn to scale ............................55

Figure 3.1: Locations of sites trapped for upland snakes and lizards in the Coastal Plain of

Georgia from May-October, 2012 and March-April, 2013. ..............................................91

Figure 3.2: Diagram of box trap array design with drift fences and pitfalls (Burgdorf et al. 2005,

Steen et al. 2012) used for sampling squamate communities on seven sites in the Coastal

Plain of Georgia, 2012 and 2013. Diagram not drawn to scale.. .......................................92

Figure 4.1: Occurrence data (i.e., incidental observations, museum specimens, and trap captures)

documented from 1941 through 2012 used to develop a habitat suitability model for H.

simus in the Coastal Plain of Georgia… ..........................................................................113

Figure 4.2: Map of fire frequency in the Coastal Plain of Georgia developed from satellite data

(Remote Sensing Applications Center, U.S. Forest Service) for 2001-2012 based on heat

anomalies indicative of prescribed fire or wildfire and used to create a habitat suitability

model for H. simus ...........................................................................................................114

xiv

Figure 4.3: Georgia GAP Analysis distribution model (Kramer et al. 2003) of suitable habitat for

H. simus in the Coastal Plain of Georgia. The binary output predicts suitable habitat in

green and unsuitable habitat in black. ..............................................................................115

Figure 4.4: Receiver operating characteristic plot (sensitivity and 1- specificity) and the

associated area under the curve (AUC) value ± 0.18 for the MaxEnt habitat suitability

model developed for H. simus in the Coastal Plain of Georgia. ......................................116

Figure 4.5: Results of the jackknife test to evaluate importance of predictor variables included in

the MaxEnt habitat suitability model developed for H. simus in the Coastal Plain of

Georgia. The graph shows the training gain of each variable if the model was run in

isolation (blue), when the model was run without that variable (green), and when the

model was run with all five variables (red). ....................................................................117

Figure 4.6: Results of the jackknife test of fire frequency parameter (values ranging from 0-12)

importance in predicting the probability distribution of habitat suitability for H. simus in

Georgia. The graph shows the probability of presence if the parameter value was run in

isolation (blue), model was run without that parameter value (green), and when the model

was run with all parameter values (red). ..........................................................................118

Figure 4.7: Results of the jackknife test of land use parameter importance in predicting the

probability distribution of habitat suitability for H. simus in Georgia. The graph shows

the probability of presence if the parameter value was run in isolation (blue) and when

the model was run with all parameter values (red). .........................................................119

Figure 4.8: A continuous MaxEnt model output showing habitat suitability probability from low

to high with H. simus occurrence data across the Coastal Plain of Georgia ....................120

xv

Figure 4.9: Comparison of A) the MaxEnt habitat suitability model and B) a Georgia GAP

Analysis model (Kramer et al. 2003) predicting the distribution of suitable habitat for H.

simus in the Coastal Plain of Georgia. .............................................................................121

Figure 4.10: A continuous MaxEnt model output showing habitat suitability probability for H.

simus from low to high relative to conservation lands (categorized as private, federal, or

state/local; The University of Georgia Natural Resources Spatial Analysis Lab and the

Georgia Department of Natural Resources) in the Coastal Plain of Georgia. .................122

1

CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

Introduction

The Coastal Plain of Georgia has one of the highest levels of snake and lizard (Order

Squamata) species richness in the United States and supports a number of rare and endemic

species, including several species of conservation concern (Vitt 1987, Guyer and Bailey 1993,

Georgia Department of Natural Resources 2005). Squamates are important components of the

longleaf pine ecosystem as they are predators of small mammals, birds, amphibians, and

invertebrates (Hamilton and Pollack 1961, Vitt and Cooper 1986, Weatherhead et al. 2003,

Halstead et al. 2008, Stevenson et al. 2010). As ectothermic vertebrates, squamates convert food

into growth and reproduction with high efficiency and provide an important source of biomass

for higher level predators (Crump 2011). Despite their importance to the longleaf ecosystem, the

basic ecology and natural history of many squamate species have been understudied as the

cryptic behavior, tendency to occur in difficult-to-survey habitats (e.g., arboreal and fossorial

habitats), and extensive periods of inactivity pose a challenge for field studies (Parker and

Plummer 1987, Gibbons and Semlitsch 2001).

Reports of worldwide declines in reptile populations have garnered increasing attention

over the last several decades (Gibbons et al. 2000). This trend has been documented in the

southeastern United States as declines in populations of several snake species have been reported

(Martin and Means 2000, Tuberville et al. 2000, Winne et al. 2007, Stapleton et al. 2008).

2

Extensive habitat loss, fragmentation and degradation have been implicated as the primary

causes of declines in snake abundance and richness (Dodd 1987, Gibbons et al. 2000).

Additional suggested causes of declines include environmental pollution, road mortality, over

collection, invasive species, such as the red-imported fire ant (Solenopsis invicta), and disease

(Dodd 1987, Bernadino and Dalrymple 1992, Gibbons et al. 2000, Clark et al. 2010). Though

lizards are susceptible to the same threats as snake populations, reliable population status

information is lacking for many lizard species in the Southeast. The Georgia Department of

Natural Resources recognizes several high priority lizard species due to rarity (Georgia

Department of Natural Resources 2005). Further monitoring is needed for many squamate

species to determine the conservation status of populations and to distinguish between natural

fluctuations and declines.

In an effort to fill information gaps on the habitat needs of squamate communities, I

sampled snakes and lizards on seven sites in the Coastal Plain of Georgia during 2012 and 2013.

The primary objectives of this thesis were to: 1) provide baseline data on squamate communities

in the Coastal Plain of Georgia, 2) use capture rates and detection probabilities to examine biases

of the box trap array sampling technique, 3) determine site and landscape variables that influence

squamate species richness, and 4) develop a spatially explicit habitat suitability model for the

southern hognose snake.

Literature Review

Southeastern Coastal Plain and Squamate Communities

The longleaf pine (Pinus palustris)- wiregrass (Aristida stricta) ecosystem of the

southeastern Coastal Plain is characterized by highly diverse and specialized plant and animal

3

communities (Guyer and Bailey 1993). Longleaf pine forests historically dominated the

landscape of the southeastern United States extending from southeastern Virginia into eastern

Texas (Landers et al. 1995, Frost 2006). Today, this imperiled habitat has been displaced from

more than 97% of its pre-settlement range (Landers et al. 1995). Degradation and loss of the

longleaf pine ecosystem is primarily due to agricultural development, forestry practices, and

urbanization (Landers et al. 1995). As this ecosystem relies on frequent fire to maintain open

canopy structure and a diverse groundcover, fire suppression is also a cause of degradation (Frost

1993). The decline of longleaf pine forests in the Southeast also has threatened populations of a

suite of endemic squamate species including the eastern diamondback rattlesnake (Crotalus

adamanteus), eastern indigo snake (Drymarchon couperi), Southern hognose snake (Heterodon

simus), and Florida pine snake (Pituophis melanoleucus).

The Coastal Plain is the largest ecoregion in Georgia, covering over 9 million hectares

(Edwards et al. 2013). It is separated from the Piedmont ecoregion to the north by the Fall Line

which is a 32 km wide zone named for the dramatic fall of rivers flowing from the Piedmont into

the Coastal Plain. The Coastal Plain in Georgia is broken down into an upper and lower region.

The upper Coastal Plain is characterized by rolling terrain with higher elevations than the lower

Coastal Plain which has a relatively flat, low elevation landscape. Successive inundations by the

Atlantic Ocean and the Gulf of Mexico since the Cretaceous period deposited sand and clay

across the Coastal Plain upon receding (Edwards et al. 2013). The Coastal Plain is characterized

by species-rich vegetative communities including the longleaf pine ecosystem and its many

endemic species (Drew et al. 1998, Walker 1998, Edwards et al. 2013). An abundance of

wetlands, ephemeral and permanent, are also scattered throughout the landscape further

contributing to the diverse flora and fauna (Edwards et al. 2013).

4

The Coastal Plain is the most herpetologically diverse ecoregion in Georgia with 15

lizard (13 native and 2 non-native) and 42 snake species (41 native and 1 non-native; Jensen et

al. 2008). The mild winters and extended summers provide an ideal climate for squamates.

Additionally, the wide array of unique habitats significantly contributes to squamate diversity

(Edwards et al. 2013). The squamate communities of the Coastal Plain include generalist species

(e.g., Anolis carolinensis and Coluber constrictor) as well as habitat and dietary specialists (e.g.,

Heterodon simus and Plestiodon egregius; Smith 1946, Ernst and Ernst 2003c). The long and

diverse list of Coastal Plain squamates includes the federally threatened eastern indigo snake as

well as several species that are state listed as rare or threatened (i.e., Ophisaurus mimicus,

Farancia erytrogramma, and H. simus; Georgia Department of Natural Resources 2005).

Habitat Associations

Research into the spatial ecology and habitat needs of squamates has focused primarily

on the requirements of individual species (Baxley and Qualls 2009, Hoss et al. 2010, Steen et al.

2010a, Blevins and With 2011, Klug et al. 2011, Frost and Bergmann 2012). Structural

components of habitat are likely more important to snakes and lizards than plant species

(Heatwole 1966, Kiester et al. 1975, Vitt 1987, Reinert 1993, Waldron et al. 2008). Previous

research has demonstrated a positive association between snake and lizard species and forested

habitat structure which provides a suite of refugia types (e.g., tree holes, stump holes, fallen

trees; Guyer and Bailey 1993, Parker 1994, Means 2005, Steen et al. 2010a, Sutton et al. 2010,

Miller et al. 2012, Steen et al. 2012). In particular, forests with a low basal area have been shown

to promote reptile species richness (Loehle et al. 2005). Spatial heterogeneity has been shown to

have a positive impact on individual squamate species as well as squamate diversity (Pianka

1967, Gillespie et al. 2005, Hoss et al. 2010, Steen et al. 2012).

5

Removal of natural habitat structure through agricultural development and urbanization

has had detrimental effects on squamate populations. Structural disturbance from clearing land

for crops, pastures, and silvicultural practices has been shown to negatively impact snake

populations and reduce reptile species richness (Brown 1993, Ribeiro et al. 2009). Agriculture

and timber harvest activities remove forest structure and thus, suitable refugia. Forest structural

features, particularly stumpholes, are important sources of refugia for squamate species (Means

2005, Steen et al. 2010c). The stumps of longleaf pines provide ideal subterranean refugia as

they persist for many years as fire erodes the stump base to open up root channels (Heyward

1933, Wahlenberg 1946). Other pine species, planted as part of silvicultural activities, provide

less suitable refugia as they have less extensive root systems and do not persist as long as

longleaf pine stumps. Stump harvest for oleoresin extraction further reduces the availability of

stumphole refugia (Wahlenberg 1946). Urbanization reduces natural habitat by increasing roads

which have been documented to negatively impact squamates through direct mortality for

individuals that attempt to cross and by their linear structure which fragments habitat (Bernadino

and Dalrymple 1992, Rudolph and Burgdorf 1997, Bonnet et al. 1999, Brehme et al. 2013).

Additionally, urbanization brings people into contact with snakes and lizards which often results

in wanton killing (Seigel 1986, Brown 1993, Martin and Means 2000).

Squamate Ecology

Snakes and lizards are globally distributed with the exception of high latitudes where

suitable temperatures are limiting for ectotherms (Vitt et al. 2003). Squamates are a highly

diverse group in terms of behavior, natural history, and habitat use (Fitch 1987).

There are two widely recognized foraging modes, active foraging and ambush predation,

among carnivorous squamates (Pianka and Vitt 2003, Vitt and Caldwell 2009). Depending on

6

foraging mode, squamates use a combination of visual and chemical cues in foraging

(Mushinsky 1987, Cooper 1994). Active foraging requires frequent movements through the

landscape searching for or tracking prey (Paulissen 1987, Secor 1995, McElroy et al. 2012).

Species that use active foraging behavior rely heavily on chemical cues and tongue flicking

behavior to locate prey (Cooper 1990, Saviola et al. 2012). In contrast, ambush (sit-and-wait)

predators exhibit a more sedentary technique whereby an animal will wait in one location for an

encounter with prey (Waldron et al. 2006, McElroy et al. 2012, Wittenberg 2012). This

technique is characterized by multiple capture attempts initiated from a sedentary position

(Cooper 1994). Snakes that use an ambush predation technique, typically vipers, rely on

chemical cues to select ambush sites and use visual and thermal cues to capture prey (Klauber

1982, Clark 2004). Ambush predatory lizards use visual cues to capture prey (Cooper 1989).

Habit, defined herein as a propensity to use terrestrial, arboreal, or fossorial

environments, varies among snake and lizard species. Terrestrial species are defined as those that

move through the landscape on the ground’s surface. While surface active, terrestrial species

may take refuge in dense vegetation, coarse woody debris, leaf litter, or beneath cover objects

such as logs or rocks (Fitch and von Achen 1977, Steen et al. 2010a). Many terrestrial species

exhibit a temporal shift to a fossorial habit during the inactive season (November through

February in the Coastal Plain of Georgia) when they seek overwintering refugia below ground

(Fitch and von Achen 1977, Diemer and Speake 1983, Martin and Means 2000). Arboreal

species are those that spend large amounts of time in trees and/or shrubs. Though arboreal

species may seek refuge and hunt above ground, they tend to move through the landscape on the

ground as opposed to moving through the canopy (Lillywhite and Henderson 2001, Carfagno and

Weatherhead 2008, Jennifer Howze, Joseph W. Jones Ecological Research Center, personal

7

communication). Arboreal snakes typically have a slender body form and have elongated muscle

segments that allow them to support parts of the body without branch support (Lillywhite and

Henderson 2001). Arboreal lizards use sharply curved claws and specialized toe pads in climbing

(Ernst and Ruibal 1967, Tulli et al. 2009). Fossorial species are those that spend much of their

lives underground. Fossorial snakes often have an enlarged or upturned rostral scale which aids

in burrowing (Ernst and Ernst 2003b, Tuberville and Mason 2008). Fossorial lizards are often

characterized by a countersunk jaw, reduced limbs, or lack limbs entirely (Vitt and Pianka 2003).

Fossorial species exhibit some degree of surface or terrestrial activity which for many species

occurs in unimodal (single peak during the annual activity season) or bimodal (two distinct peaks

during the annual activity season) periods, typically associated with mate searching and

emergence of young (Trauth 1984, Gibbons and Semlitsch 2001, Enge and Wood 2003, Willson

and Dorcas 2004). In the Coastal Plain of Georgia, these peaks typically occur in the spring and

fall.

Squamate sampling

Squamates are notoriously difficult to survey and large-scale field studies are often

hindered by the inability of traditional field techniques to detect all species. The difficulty in

capturing squamates is due in large part to their cryptic behavior, tendency to inhabit difficult-to-

survey habitats (e.g., arboreal and fossorial habitats), and extensive periods of inactivity (Parker

and Plummer 1987, Gibbons and Semlitsch 2001). However, there are numerous standard

methods, varying in taxa effectively captured and data resolution, that have been developed for

sampling squamates and other reptiles (Foster 2012).

Active sampling techniques for snake and lizard species include quadrat and visual

encounter surveys, distance sampling, and road surveys (Fitch 1987, Buckland et al. 2001, Doan

8

2003, Foster 2012). Active sampling techniques require significant time and effort and are often

temporally limited to periods of highest activity to detect a species or group. Passive capture

techniques involve setting traps that animals encounter during normal activity. Though passive

traps may require significant time and cost to install and maintain, they have the benefit of

sampling a wide array of species while limiting observer bias associated with active survey

techniques (Fitch 1987). Additionally, components of the traps (e.g., pitfalls, funnel traps, box

traps, drift fence material) can be modified to increase the probability of capture for a desired

species or taxonomic group. The ability to leave traps for long periods of time in the natural

habitat of reptiles makes passive trapping one of the most effective techniques at capturing rare

and cryptic species that have a low probability of being detected by active surveys (Steen 2010).

A commonly used passive method to survey squamate communities is a box trap array

with drift fences and pitfalls or funnel traps (Burgdorf et al. 2005, Steen et al. 2010b). Box trap

arrays are comprised of a central box trap with a funnel entrance on each of the four sides and

associated drift fences and pitfalls or funnel traps. Drift fences (generally composed of hardware

cloth, aluminum flashing or silt fencing) extend from each funnel entrance and terminate at the

mouth of a buried pitfall or a mesh funnel trap. The box trap was designed to capture large-

bodied upland snake species (Burgdorf et al. 2005, Hyslop et al. 2009, Steen et al. 2012). The

pitfall or funnel trap components typically capture small snake species (Todd et al. 2007, Todd

and Andrews 2008). Though the box trap array was not designed to capture lizards, they are

often captured by the box trap array (Smith et al. 2006).

Detection

The secretive nature and episodic activity of snakes and lizards makes detection by

traditional trapping and survey techniques a difficult and time consuming task (Parker and

9

Plummer 1987, Gibbons and Semlitsch 2001). While a detection indicates presence, a non-

detection is not equivalent to absence (MacKenzie et al. 2002). Non-detection of a species can

arise from two situations: (1) the species was present but was not detected by survey or trapping

methods or (2) the species was truly absent from a site (MacKenzie et al. 2002). The effort

required to determine presence of a rare or elusive species may be substantially greater than that

required to establish absence of species with higher detection probability (K ry 2002). Detection

probabilities of squamates are likely to be influenced by temperature and precipitation as well as

life history characteristics such as foraging mode, habits (i.e., arboreal, fossorial, terrestrial),

breeding season, activity range size, and abundance. Studies that do not account for detection

probability assume that species are either detected perfectly or that detection is constant for a

given species throughout the study, which is rarely the case (Mazerolle et al. 2007). Numerous

studies show that the likelihood of maintaining a constant detection probability over the course

of reptile surveys is low (Harvey 2005, Roughton and Seddon 2006, Durso et al. 2011, Rodda

2012). To improve estimation of state variables (e.g., occupancy, abundance, species richness)

for cryptic species, it is important to account for imperfect detection (Mazerolle et al. 2007).

Conclusions drawn from analyses that fail to incorporate detection probabilities risk inaccurate

results and misinterpretation.

Habitat Suitability Modeling

Habitat suitability modeling (HSM) is a widely used technique for delineating potential

distributions of wildlife populations based on an understanding of a species’ habitat requirements

and species occurrence records (Phillips et al. 2009, Cianfrani et al. 2010). In recent decades,

several multivariate modeling techniques (i.e., maximum entropy, ecological-niche factor

analysis) have been developed to predict suitable habitat for a species of interest (Hirzel et al.

10

2002, Phillips et al. 2006). Recent advances in technology including remote sensing, geographic

information systems (GISs), and global positioning systems (GPSs) have vastly improved our

ability to model species distributions and suitable habitat at a landscape and even regional scale

(Jenkins et al. 2009). HSMs have proven useful even when occurrence data are scarce, thus

demonstrating their application to conservation efforts for rare or elusive species with limited

occurrence data (Pearson et al. 2007, Rebelo and Jones 2010). Objective modeling of suitable

habitat is crucial to provide targeted application of survey and conservation efforts (Anderson

and Martinez-Meyer 2004). Potential applications of habitat suitability models include:

survey/inventory planning, habitat monitoring, conservation status assessments, threat

assessments, natural resource management, and identification of new populations of rare species

(Kramer et al. 2003, Sattler et al. 2007, Martínez-Freiría et al. 2008, Santos et al. 2009, Bombi et

al. 2011). However, caution should be exercised when interpreting habitat suitability models.

Regions of suitable habitat, interpreted most conservatively, indicate areas with comparable

conditions to where populations are currently documented. HSM outputs should not be

interpreted as defining the actual range of a species; field validation of HSMs is essential (Gentil

and Blake 1981, Fleming and Shoemaker 1992, Pearson et al. 2007).

Heterodon simus

The southern hognose (Heterodon simus) is a rare species endemic to the Coastal Plain of

the southeastern United States that is experiencing declines throughout its range. The historic

range of the southern hognose extended from Mississippi to Florida and north into South

Carolina and North Carolina (Tuberville et al. 2000). Currently, it is thought to be extirpated

from Alabama and Mississippi (Tuberville et al. 2000, Ernst and Ernst 2003a). In the eastern

portion of the range, the southern hognose snake appears to be rare and uncommon though it is

11

thought to be locally common in the central Panhandle of Florida (Tuberville et al. 2000, Enge

and Wood 2003). Declines in southern hognose populations have been attributed to habitat

degradation and loss, road mortality, and invasion by the red imported fire ant, Solenopsis invicta

(Guyer and Bailey 1993; Mount 1981; Tuberville et al. 2000). In Georgia, H. simus is designated

a threatened species (Georgia Department of Natural Resources 2005). The Comprehensive

Wildlife Conservation Strategy places the southern hognose in Species Conservation Emphasis

Category D which acknowledges that “evidence of endemism or rarity exists, but significant

questions remain as to current range, population status, habitat needs and/or threats” (Georgia

Department of Natural Resources 2005).

Despite the status of the southern hognose snake as a species of conservation concern,

relatively little is known about the natural history and population status in Georgia. It is a highly

fossorial species with a prominent rostral scale that enables it to excavate burrows (Mount 1975,

Ernst and Ernst 2003a). The southern hognose is a dietary specialist feeding primarily on anurans

(Goin 1947, Ernst and Ernst 2003a). While the southern hognose spends large portions of the

year below ground, this species is surface active in May-June and in October-November

(Gibbons and Semlitsch 2001). The southern hognose is typically found in xeric habitats with

well-drained soils including: oak-pine forest, oak-scrub, dry river floodplains and sandhills

(Ernst and Ernst 2003a, Tuberville and Jensen 2008). The cryptic, fossorial nature of this species

makes it difficult to employ traditional survey and capture techniques to fill in information gaps

on natural history and habitat selection.

12

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specificity of a declining North American reptile: A remnant of former landscapes.

Biological Conservation 141:2477-2482.

22

Walker, J. 1998. Ground layer vegetation in longleaf pine landscapes: An overview for

restoration management. In Proceedings of the Longleaf Pine Ecosystem Restoration

Symposium. Longleaf Alliance Report no. 3, pp. 2-13.

Weatherhead, P. J., G. Blouin-Demers, and K. M. Cavey. 2003. Seasonal and prey-size dietary

patterns of black ratsnakes (Elaphe obsoleta obsoleta). American Midland Naturalist

150:275-281.

Willson, J. D., and M. E. Dorcas. 2004. Aspects of the ecology of small fossorial snakes in the

western piedmont of North Carolina. Southeastern Naturalist 3:1-12.

Winne, C. T., J. D. Willson, B. D. Todd, K. M. Andrews, and J. W. Gibbons. 2007. Enigmatic

decline of a protected population of eastern kingsnakes, Lampropeltis getula, in South

Carolina. Copeia 2007:507-519.

Wittenberg, R. D. 2012. Foraging ecology of the timber rattlesnake (Crotalus horridus) in a

fragmented agricultural landscape. Herpetological Conservation and Biology 7:449-461.

23

CHAPTER 2

AN EVALUATION OF THE EFFICACY AND BIAS OF THE BOX TRAP SAMPLING

TECHNIQUE FOR SNAKES AND LIZARDS

Introduction

Reports of worldwide declines in reptile populations have garnered increasing attention

over the last several decades. Habitat degradation and loss, overharvesting, disease, pollution,

and invasive species have all been suggested as causes of decline (Dodd 1987, Gibbons et al.

2000, Ara o et al. 2006, Whitfield et al. 2007, Clark et al. 2010, Chessman 2011). As a result,

there is an urgent need for research on the basic ecology and natural history of many reptile

species as well as baseline information about reptile communities. Reptiles are grossly

understudied when compared with other vertebrate groups (e.g., mammals and birds; Garner et

al. 2010). Information gaps have made determining conservation status for numerous species

difficult. As of 2013, 20% of reptile and lizard species evaluated by the International Union for

Conservation of Nature (IUCN) have yet to be assigned a conservation status due to deficient

data (IUCN 2013). In the face of documented declines, targeted studies are needed to determine

the scope and magnitude of declines and establish effective conservation efforts.

Reptiles are notoriously difficult to survey and large-scale field studies are often hindered

by the inability of traditional field techniques to adequately capture representative species within

reptile communities (Parker and Plummer 1987, Steen 2010). The difficulty in capturing reptiles

is due in large part to their cryptic behavior, tendency to inhabit difficult-to-survey habitats (i.e.,

24

arboreal and subterranean), and infrequent activity (Parker and Plummer 1987, Gibbons and

Semlitsch 2001). A variety of standard methods have been established for sampling reptile

communities (Foster 2012). Often, conducting a community level inventory is the primary

objective for an area interest. Common survey techniques employed for community level studies

include quadrat surveys, visual encounter surveys, distance sampling, road surveys, and drift

fence arrays associated with pitfall or funnel traps (Fitch 1987, Buckland et al. 2001, Doan 2003,

Burgdorf et al. 2005, Foster 2012). The diversity in size, life-style, and habitat preference of

reptiles, even within the same communities, makes it difficult to identify a single technique that

will sample even the majority of species present (Fitch 1987, Fisher and Foster 2012). Because

standard sampling methods are often used to sample a wide array of species simultaneously, it is

important to identify and quantify their limitations and biases. Evaluations and comparisons of

standard methodologies reveal advantages and disadvantages to each technique and provide

insight into the most appropriate application of trapping methods (Ryan et al. 2002, Doan 2003,

Sung et al. 2011).

Passive capture techniques are considered the most efficient and effective tools for reptile

sampling (Steen 2010). Though passive traps may require significant time and cost to install and

maintain, they have the benefit of sampling a wide array of species while limiting observer bias

associated with active survey techniques (Fitch 1987). Additionally, components of the traps

(e.g., pitfalls, funnel traps, box traps, drift fence material) can be modified to increase the

probability of capture for a desired species or taxonomic group. While active techniques require

observers to conduct surveys during periods of highest activity to detect a species or group,

passive techniques can be left to capture individuals over long periods of time. Passive traps can

25

be left in place in natural habitats to capture rare and cryptic species with low detection

probabilities.

A commonly used passive method to survey reptile communities is a box trap array with

drift fences and pitfalls or funnel traps (Burgdorf et al. 2005, Steen et al. 2010). Box trap arrays

are composed of a central box-shaped trap with a funnel entrance on each side and associated

drift fences and pitfalls or funnel traps. Drift fences (generally composed of hardware cloth,

aluminum flashing, or silt fencing) extend from each funnel entrance and terminate at the mouth

of a buried pitfall or a mesh funnel trap. The box trap was designed to capture large-bodied

upland snakes species including Drymarchon couperi and Masticophis flagellum (Burgdorf et al.

2005, Hyslop et al. 2009, Steen et al. 2012b) and pitfalls commonly capture small snake species

(Todd et al. 2007, Todd and Andrews 2008). While the box trap array was not designed to

capture lizards, they are frequently captured in box traps (Smith et al. 2006). The box trap array

is an effective technique for sampling entire squamate communities in that it captures large and

small-bodied snakes and lizards and also captures species that inhabit a variety of environments

(i.e., arboreal, fossorial, and terrestrial; Smith et al. 2006).

Habits and behavioral characteristics of species have the potential to bias capture

probabilities when using a box trap array design to sample squamate communities. More

specifically, foraging method and habits, defined herein as the propensity to use terrestrial,

fossorial, and arboreal environments, could influence a species’ movement through the landscape

and thus, the chance of encountering a trap. Two widely recognized foraging modes, ambush

and active foraging, are vastly different in the movements and energy required to locate and

capture prey (Pianka and Vitt 2003, Vitt and Caldwell 2009). Active foraging requires animals to

move over the landscape searching for or tracking prey (Paulissen 1987, Secor 1995, McElroy et

26

al. 2012). In contrast, ambush predators wait in one location for an encounter with prey (Waldron

et al. 2006b, McElroy et al. 2012, Wittenberg 2012). The differences in length and frequency of

movements associated with these foraging techniques could affect the probability of encounter

with a trap. Habit may also influence capture probability. Box trap arrays are installed at ground

level and require that the animal display some degree of terrestrial activity to be captured.

Therefore, arboreal and fossorial species may not be captured at the same rate as terrestrially

active species.

Although box trap arrays may be an effective method for surveying reptile communities,

life history traits of individual species can bias conclusions. My objective was to evaluate the

influence of foraging mode (i.e., active or ambush) and habit (i.e., arboreal, fossorial or

terrestrial) on capture rates and detection probabilities for a suite of snake and lizard species. I

hypothesized that box trap arrays would be more effective at capturing terrestrial species than

arboreal or fossorial species and that active foragers will be captured at a higher rate than

ambush predators.

Methods

Study Sites

My study was conducted on seven sites in the Coastal Plain of Georgia, U.S.A. (Table

2.1; Figure 2.1). Study sites were private or publically owned properties that had previously been

surveyed for gopher tortoise (Gopherus polyphemus) as burrow density was a variable of interest

to my research questions (Smith et al. 2009). Sites contained ≥ 400 ha (range 418-2,850 ha) of

upland habitat characterized by a suite of pine and hardwood species as well as mixed pine-

hardwood forest. All sites were undergoing some degree of longleaf pine restoration.

27

Squamate Sampling

I sampled snake and lizard communities during the active season (May through October

of 2012 and March through April of 2013) using box trap arrays consisting of a central box trap

with drift fences terminating at the mouth of 19 L pitfalls (Figure 2.2.; Burgdorf et al. 2005,

Steen et al. 2010). With the exception of one site, drift fences were constructed of 15-m lengths

of aluminum flashing standing 50 cm above ground and buried 10 -15 cm in the soil. At one site

(Ichauway), drift fences were 30 m in length and were constructed of 0.64-cm mesh hardware

cloth. I compared capture rates from the 30-m length traps with capture rates from the 15-m

length traps with a Wilcoxon Rank Sum test as I expected longer drift fences would increase

capture rates and influence the results of this study. However, capture rates were significantly

lower for the 30-m length traps (W=583.0, p=0.049) suggesting differences in capture rates were

due to site effects rather than trap design. Because of this result, I included captures from the 30-

m length traps in the analysis.

Three arrays were installed at each site from March through May of 2012. Traps were

installed in upland habitats meeting the following criteria: ≥ 300 m from property boundary, ≥

250 m from wetlands, ≥ 300 m from the nearest trap array, and within contiguous patches of one

forest type ≥ 1 ha. Trap location criteria were relaxed for one site (Ichauway) as 16 arrays had

been installed previously for an ongoing upland snake study; three traps that were closest to

meeting the desired criteria were selected for inclusion in this study. On Ichauway, one trap was

located in a patch ≤ 1 ha and two traps were located ≤ 250 m from wetlands. I attempted to place

one array in each of the following forest types: evergreen, mixed pine-hardwood and hardwood

based on 2008 Georgia Land Use Trends data (GLUT, 30-m pixels). However, patches meeting

all of the above criteria were not available at all sites. Therefore, on three sites, two traps were

28

placed in evergreen forest and one trap was placed in mixed forest. On one site, one trap was

placed in evergreen forest and two traps were placed in a regenerating clear-cut/sparse evergreen

forest stand.

Trap arrays were open for 10 nights each month during the annual activity season

(March-October). A sampling period was defined as a 10 trap-night period. Traps were opened

on rotation (3 or 4 sites; 9-12 traps per sampling period). Traps were opened on day 1, checked

every other day during the sampling period, and closed on day 11. Traps were open for a total of

1666 nights over the course of the study.

All snakes and lizards captured in box traps, pitfalls and found travelling along drift

fences were identified to species. Lizards were immediately released in the general vicinity of

the trapping array without being marked. Each snake was assigned a unique identification by

heat branding or by insertion of a passive integrated transponder (PIT) tag subcutaneously in the

lower third of the body (Weary 1969, Gibbons and Andrews 2004).

I assigned snake and lizard species to guilds based on foraging mode and habit which I

hypothesized to influence their likelihood of encountering a trapping array. Species were

characterized as active foragers or ambush predators (Smith 1946, Ernst and Ernst 2003c, Jensen

et al. 2008). I also assigned species to one of three guilds describing habits based on their

propensity for arboreal, fossorial, or terrestrial activity (Table 2.2).

Statistical Analyses

Capture data were pooled across sites to calculate a capture rate (total number of captures

divided by total number of trap nights) for each species. Capture data also were used to

determine the number of detections, defined as a capture of a species (regardless of the number

of individuals) in any of the three traps on a site during a sample period, and estimate mean

29

detection probability for each species using the single-season model in program PRESENCE

(Donovan and Hines 2007). Detection probability was only estimated for species with greater

than five detections. Four a priori models describing hypothesized influences of sampling

covariates (i.e., temperature, precipitation, day of year, and a null model) on snakes and lizards

were used to estimate detection probability (Table 2.3). Temperature and precipitation data were

obtained from the weather station in closest proximity to each site ( x =20.1 km, range 3.8-37.3

km; Georgia Automated Environmental Monitoring Network, University of Georgia College of

Agricultural and Environmental Sciences, http://www.georgiaweather.net/; Accessed: May 8,

2013). Covariates were standardized prior to analysis so mean=0 and standard deviation=1

(Mackenzie et al. 2006). A null model, assuming constant detection probability, was also

included. Models estimating detection probability were ranked using Akaike’s Information

Criteria adjusted for small sample sizes (AICc; Burnham and Anderson 2002). Models with

ΔAICc values ≤ 2 were considered to have equal support for being the top model (Burnham and

Anderson 2002).

I used capture rates and mean detection probabilities from the top models for each species

to investigate sampling bias between reptile guilds. Wilcoxon rank sum tests were performed to

compare combined capture rates for species in the active foraging guild and those in the ambush

foraging guild. A comparison of detection probabilities could not be made between ambush and

active foragers as four out of five ambush foraging species did not have sufficient detections to

calculate detection probability. I used a Kruskal-Wallis test to compare capture rates and mean

detection probabilities among the arboreal, fossorial, and terrestrial guilds. Significant Kruskal-

Wallis tests were followed up with pairwise post hoc Wilcoxon rank sum tests to determine

where differences occurred among guilds (SAS Institute Inc. 2008).

30

Results

I recorded 149 captures of 16 snake species and 190 captures of 10 lizard species across

all sites (Table 2.4). Box traps accounted for 95% of all snake captures and 50% of all lizard

captures whereas pitfalls accounted for 4% of snake and 33% of lizard captures (Table 2.5). One

percent of snakes and 17% of lizards were captured while travelling along the drift fence. Snakes

captured in box traps were large-bodied species whereas snake species captured in pitfalls were

limited to small fossorial species (i.e., Storeria occipitomaculata and Tantilla coronata) and

neonates of large-bodied species (i.e., Heterodon platirhinos and Thamnophis sirtalis). Large-

bodied lizard species (e.g., Ophisaurus attenuatus, Ophisaurus ventralis, and Plestiodon

laticeps) were captured in both box and pitfall traps while small-bodied species (i.e., Scincella

lateralis and Plestiodon egregius) were captured only in pitfalls or on the drift fence. Snake

species with the highest capture rates included the Black Racer (C. constrictor), Coachwhip (M.

flagellum), Common Garter Snake (T. sirtalis), and Pine Snake (Pituophis melanoleucus; Table

2.6). The lizard species with the highest capture rates were the Eastern Fence Lizard (Sceloporus

undulatus), Six-lined Racerunner (Aspidoscelis sexlineata), Green Anole (Anolis carolinensis),

and Broadhead Skink (Plestiodon laticeps). Five individuals of three snake species (C.

constrictor, M. flagellum, and T. sirtalis) were recaptured during the study.

Numerous species of insects, small mammals and amphibians were caught in the box and

pitfall traps. Amphibians were identified to species and the number of individuals captured was

recorded (data not presented here). We detected 12 anuran and 3 salamander species. The

Southern Toad (Anaxyrus terrestris) and Spadefoot Toad (Scaphiopus holbrookii) were the most

frequently captured amphibian species.

31

Number of detections for snake and lizard species, pooled across sites, ranged from 1 to

30 (Table 2.7). For species with sufficient detections to model detection probabilities, the

detection probabilities ranged from 0.12 to 0.62, with standard errors from 0.07 to 0.10. The null

model of detection probability was the best supported model for all species with ≥ 5 detections

with the exception of A. sexlineata (Table 2.8). The best supported model for A. sexlineata

contained TEMP and had high support (w =0.9783). However, this model failed to converge and

could not be used for inference. The null model had the second highest AIC weight (w=0.0204)

and was therefore considered the top model of A. sexlineata detection probability.

Capture rates did not differ between active foragers and ambush foragers (W= 58.5,

p=0.290; Table 2.9). However, capture rates were different among terrestrial, arboreal, and

fossorial guilds (H=6.49, 2 d.f., p=0.039; Table 2.9). Post hoc comparisons revealed that mean

capture rates of species within the arboreal guild were significantly higher (W=51.5, p=0.009)

than mean capture rates of species within the fossorial guild. There were no differences between

capture rates in the terrestrial guild and arboreal guild (W= 64.0, p=0.058) or terrestrial guild and

fossorial guild (W=69.5, p=0.090). Mean ranks of detection probabilities were not different

among arboreal, fossorial, and terrestrial guilds (H=4.54, 2 d.f., p=0.103).

Discussion

Consistent with other studies employing the box trap array, my results demonstrated that

the box trap array design was an effective method for sampling a wide array of snake and lizard

species (Burgdorf et al. 2005, Hyslop et al. 2009, Steen et al. 2012b). As expected, the box trap

was the most successful component at capturing large-bodied snake species. Pitfalls do not

effectively trap large snakes as they are relatively shallow (37 cm), allowing escape of larger

32

animals. Similar to previous studies, pitfalls were more successful at capturing small-bodied

species as well as neonates/juveniles of large-bodied species (Todd et al. 2007, Todd and

Andrews 2008). Though box trap and pitfall arrays are often employed for snake sampling, the

trap design in this study was also successful at capturing lizard species whose small size makes

them vulnerable to capture by pitfalls. Similar to snakes, the box trap was more effective at

capturing larger lizard species. Leaf litter species (i.e., Scincella lateralis and Plestiodon

egregius) were not detected in box traps as many are small enough escape through the hardware

cloth. Results from my study illustrate that it is important to incorporate both components into a

sampling design for sampling squamate communities.

In addition to snake and lizard species, a suite of non-target animals (i.e., insects,

amphibians, and small mammals) were captured in box and pitfall traps. It is possible that non-

target animals within traps could have served as an attractant to snakes and lizards thus,

influencing capture rates and detection probabilities. Several snakes captured in traps were

observed to contain a prey bolus and may have ingested prey within the trap. Many native lizard

species are insectivorous and could have entered traps while tracking prey (Smith 1946, Jensen

et al. 2008). Dietary preferences among snake species are varied and the presence of amphibians,

mammals, lizards and even other snakes could potentially entice a snake to enter a trap (Ernst

and Ernst 2003c, Jensen et al. 2008).

Detection probability was highly variable among species ranging from 0.12 to 0.62.

These estimates are similar to published detection probabilities for snakes and lizards, including

aquatic snakes in the upper Coastal Plain of South Carolina (0.03-0.46; Durso et al. 2011) and

for tallgrass prairie dwelling lizards in the mid-west (0.22-0.41; Blevins and With 2011).

However, estimates of detection probability for snakes in my study were higher than those

33

presented for a suite of terrestrial and aquatic snakes surveyed in the southeastern Coastal Plain

(0.00-0.17; Steen et al. 2012a). Detection probability in my study was not strongly influenced by

environmental conditions which was contrary to my expectation that environmental variables

(i.e., temperature, precipitation, and time of year) would influence activity and consequently,

detection probability. It is unlikely that detection probability remains constant across surveys as

previous studies have demonstrated the influence of environmental variables (i.e., cloud cover

and air temperature) on squamate detection probabilities (Bauder et al. 2010, Stohlgren 2013).

As the trapping period for my study was restricted to the annual activity season when conditions

were favorable for squamate activity, variation in environmental conditions during this time may

not have affected activity enough to influence detection probability. Additionally, there may be

different factors influencing snake and lizard activity and detection that were not included in

models in this study.

Contrary to my hypotheses, capture rates were similar for active foragers and ambush

predators. I expected movement through the landscape associated with locating and capturing

prey in active foraging species to result in more frequent trap encounters (Paulissen 1987, Secor

1995, Waldron et al. 2006b, McElroy et al. 2012, Wittenberg 2012). However, though ambush

predators may not move across the landscape for foraging, they may move frequently enough or

far enough to be captured at a similar rate to active foragers. Previous studies of the spatial

ecology and activity patterns of several ambush predators documented increased

activity/movement during breeding and neonate dispersal (Timmerman and Martin 2003,

Waldron et al. 2006a, Howze et al. 2012). Long distance movements by ambush predators,

associated with mate location and neonate dispersal, are periodic when compared with frequent

movements of many active foraging species (Fitch and von Achen 1977, Johnson et al. 2007,

34

Carfagno and Weatherhead 2008). Increased captures of ambush predators during breeding and

dispersal could have compensated for low captures during foraging periods when movement is

reduced and resulted in similar capture rates to active foragers. I designated a species as either an

active forager or an ambush forager. In reality, species may exhibit behavior associated with both

foraging techniques (Ernst and Ernst 2003a;b). My study suggests that foraging activity alone

does not result in biased captures by the box trap array sampling design.

I found that capture rates for terrestrial species were similar to those for arboreal and

fossorial species. However, arboreal species capture rates were higher than fossorial species.

Arboreal species, while spending large amounts of time in trees, tend to move through the

landscape on the ground as opposed to moving through the canopy (Lillywhite and Henderson

2001, Carfagno and Weatherhead 2008, Jennifer Howze, JWJERC, pers. comm). Terrestrial

forays throughout the annual activity season in search of food or alternative retreat sites could

have resulted in more frequent trap encounters than fossorial species. Surface activity of many

fossorial species often occurs in unimodal (single peak during the annual activity season) or

bimodal (two distinct peaks during the annual activity season) peaks associated with mate

searching and emergence of young (Trauth 1984, Gibbons and Semlitsch 2001, Enge and Wood

2003, Willson and Dorcas 2004). In the Coastal Plain of Georgia, these peaks occur in the spring

and fall. The probability of capturing fossorial species likely increases at times of peak activity

when animals are moving terrestrially and decreases outside of these activity periods, resulting in

an overall low detection probability and accounting for the observed differences in capture rates.

Though not significantly different, higher capture rates for arboreal species than terrestrial

species could have been influenced by thermoregulatory strategies of snakes and lizards. Lizards

actively thermoregulate through movement toward suitable thermal environments while snakes

35

conserve energy by thermoconforming to their environment (Alford and Lutterschmidt 2012).

The terrestrial guild was largely comprised of snakes (nine out of thirteen species) which could

have resulted in lower overall movement compared to the arboreal guild which contained two

snake species and three lizard species.

Although published range distributions of several species captured in my study did not

encompass all seven sites, I assumed that all species had the potential to occur and be captured

on each site. Specifically, the published range distributions of Drymarchon couperi and Tantilla

coronata did not encompass all survey sites (Jensen et al. 2008). Based on the widespread

distribution of these species in the Coastal Plain and lack of targeted field surveys, I took a

conservative approach to ensure that I did not rule out the possibility that they could have been

present on these sites despite not being included in the published range.

Though I attempted to identify biases of this trapping method, my ability to make

inferences from the analysis was limited by the lack of data on the abundance of snake and lizard

populations. I used capture rates and detection probabilities to comment on the effectiveness of

the field method. However, rate of capture and detection probability of a species are related to

abundance (Royle and Nichols 2003). The box trap array method may capture a particular guild

more effectively for two reasons. First, the behavior or ecology of species within a guild may

render them more vulnerable to capture by the technique than another guild, as I suggested

earlier in this discussion. Alternatively, species within a guild may be more abundant than

species in other guilds and could thus be captured more frequently. I was unable to explore the

relationship between detection probability and population size because I was not able to estimate

abundance due to the short duration of the study and relatively low number of captures for many

species. Consequently, I could not investigate whether differences in capture rates and detection

36

probabilities were due to differences in abundance of species in each guild or due to the behavior

of species in each guild.

Given the low numbers of captures with the box trap sampling method, future research

should focus on modifications to this method or development of novel methods that may

improve capture rates and allow for reliable abundance estimates. The difficulties of developing

accurate abundance estimates for herpetofauna, a cryptic and secretive group, have been well

documented (Parker and Plummer 1987, Bailey et al. 2004, Steen 2010, Steen et al. 2012a).

Numerous indices of relative abundance have been proposed. This list includes: number of

captures per length of drift fence, number of encounters per length of road, and number of

animals observed per person-hour of searching (Clark 1970, Semlitsch et al. 1981, Reynolds

1982). While these indices are informative, they do not provide reliable estimates of population

size because they do not account for detection probability. Mark-recapture methods typically are

employed as an alternative to using count data to estimate abundance. However, the difficult-to-

study nature of most reptiles results in few recaptures (Parker and Plummer 1987). Low

recapture rates were evident in my study in which only five individuals were recaptured in 1666

trap nights. Detection/non-detection data also may be used to estimate abundance if the

assumption of a positive relationship between abundance and detection is made (Royle and

Nichols 2003). Even within this framework, low detection rates can still result in unreliable

abundance estimates (Steen 2010).

Numerous aspects relating to the behavior, natural history, and ecology of a species will

ultimately determine the likelihood of capture by a particular method. My study demonstrated

the utility of this technique in sampling a suite of snake and lizard species and its applicability in

large-scale survey efforts. It also revealed potential biases of the box trap sampling design and

37

established a need for future research into the effectiveness of this methodology. Future

investigation into the efficacy of this method would benefit from long-term datasets (e.g.,

surveying sites over multiple annual activity seasons) which would likely increase sample sizes. I

am not suggesting that short-term studies cannot be informative. Rather, I advocate for

increasing the number of traps deployed or utilizing multiple trapping methods if studies are

restricted in their duration. In the end, it is most important to consider the amount of data

required to answer research questions as well as the amount of time permitted for data collection.

Further research will allow herpetologists to identify biases of the box trap design and

incorporate biases into inference about squamate communities and populations when using

capture data from field studies.

38

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44

Table 2.1. Names and counties of sites, land ownership types, and size of upland habitat for

study sites sampled for upland snake and lizard species in the in the Coastal Plain of Georgia,

2012 and 2013.

Site Name County Ownership

Estimated

Upland

Habitat (ha)

Ichauway (ICH) Baker Private 1,941

Moody Forest Natural Area (MOO) Appling Public/Private 1,076

Orianne Society Indigo Snake Preserve (OISP) Telfair Private 418

River Creek Wildlife Management Area (RC) Thomas Public 452

Silver Lake Wildlife Management Area (SL) Decatur Public 2,850

Barrington Tract (BAR) McIntosh Public 447

Warbick Farms (WAR) Thomas Private 563

45

Table 2.2. Foraging and habit guild assignments for snake and lizard species captured on seven

sites in the Coastal Plain of Georgia, 2012 and 2013. Habit refers to whether the species is

arboreal (n = 5), fossorial (n = 8), or terrestrial (n = 13). Foraging identifies species as active

foragers (n = 21) or ambush predators (n = 5).

Species Habitat Foraging

Copperhead, Agkistrodon contortrix terrestrial ambush

Cottonmouth, Agkistrodon piscivorus terrestrial ambush

Green Anole, Anolis carolinensis arboreal active

Six-lined Racerunner, Aspidoscelis sexlineata terrestrial active

Scarlet Snake, Cemophora coccinea fossorial active

Southern Black Racer, Coluber constrictor terrestrial active

Eastern Diamondback Rattlesnake, Crotalus adamanteus terrestrial ambush

Eastern Indigo Snake, Drymarchon couperi terrestrial active

Eastern Hognose Snake, Heterodon platirhinos fossorial active

Common Kingsnake, Lampropeltis getula terrestrial active

Coachwhip, Masticophis flagellum terrestrial active

Slender Glass Lizard, Ophisaurus attenuatus fossorial active

Eastern Glass Lizard, Ophisaurus ventralis fossorial active

Corn Snake, Pantherophis guttatus arboreal active

Rat Snake, Pantherophis obsoletus arboreal active

Pine Snake, Pituophis melanoleucus fossorial active

Mole Skink, Plestiodon egregius fossorial active

Five-lined Skink, Plestiodon fasciatus terrestrial active

Southeastern Five-lined skink, Plestiodon inexpectatus terrestrial active

Broadhead skink, Plestiodon laticeps arboreal active

Eastern Fence Lizard, Sceloporus undulatus arboreal ambush

Ground Skink, Scincella lateralis terrestrial active

Pygmy Rattlesnake, Sistrurus miliarius terrestrial ambush

Red-bellied Snake, Storeria occipitomaculata fossorial active

Southeastern Crowned Snake, Tantilla coronata fossorial active

Common Garter Snake, Thamnophis sirtalis terrestrial active

46

Table 2.3. Description of a priori models used to model snake and lizard detection probabilities

based on captures on seven sites in the Coastal Plain of Georgia, 2012 and 2013. Variables

included TEMP (average daily temperature during the sampling period), PRECIP (total

precipitation accumulated over the sampling period), and DAY (first day of the sampling period

where January 1=1).

Model Hypothesis

p(DAY) Detection rate increases as the sampling season progresses

p(PRECIP) Detection rate increases with increasing precipitation

p(TEMP) Detection rate increases with increasing temperature

p(.) Detection rate is constant across the sampling season

47

Table 2.4. Numbers of snakes and lizards captured on seven sites in the Coastal Plain of Georgia

in box trap arrays. All sites were trapped for eight sampling periods (approximately 240 trap

nights each) during 2012 and 2013. Refer to Table 2.1 for site name codes.

ICH

MO

O

OIS

P

RC

SL

BA

R

WA

R

Tota

l

Snake Species

Copperhead, Agkistrodon contortrix 1 0 0 0 0 0 0 1

Cottonmouth, Agkistrodon piscivorus 0 0 0 0 0 0 1 1

Scarlet Snake, Cemophora coccinea 0 0 0 1 0 0 0 1

Black Racer, Coluber constrictor 5 10 0 3 17 8 8 51

Eastern Diamondback Rattlesnake, Crotalus

adamanteus 0 2 0 0 0 3 0 5

Eastern Indigo Snake, Drymarchon couperi 0 1 1 0 0 0 0 2

Eastern Hognose Snake, Heterodon platirhinos 1 2 0 0 2 1 0 6

Common Kingsnake, Lampropeltis getula 1 0 0 0 2 0 0 3

Coachwhip, Masticophis flagellum 0 5 12 1 3 6 5 32

Corn Snake, Pantherophis guttatus 1 0 0 1 2 0 1 5

Rat Snake, Pantherophis obsoletus 0 4 0 0 0 3 2 9

Pine Snake, Pituophis melanoleucus 0 1 6 0 2 0 1 10

Pygmy Rattlesnake, Sistrurus miliarius 0 0 0 0 4 0 0 4

Red-bellied Snake, Storeria occipitomaculata 0 1 0 0 0 0 0 1

Southeastern Crowned Snake, Tantilla

coronata 0 1 2 0 0 0 0 3

Common Garter Snake, Thamnophis sirtalis 1 0 0 9 3 0 2 15

Total snake captures 10 27 21 15 35 21 20 149

Snake species richness 6 9 4 5 8 5 7 16

Lizard Species

Green Anole, Anolis carolinensis 10 3 4 6 0 12 3 38

Six-lined Racerunner, Aspidoscelis sexlineata 4 2 6 9 8 10 5 44

Slender Glass Lizard, Ophisaurus attenuatus 4 0 0 0 1 0 0 5

Eastern Glass Lizard, Ophisaurus ventralis 0 0 0 0 1 0 0 1

Mole Skink, Plestiodon egregius 0 0 1 0 0 0 0 1

Five-lined Skink, Plestiodon fasciatus 1 0 0 0 0 0 0 1

Southeastern Five-lined Skink, Plestiodon

inexpectatus 1 0 2 1 1 0 0 5

Broadhead Skink, Plestiodon laticeps 0 5 0 8 4 0 10 27

Eastern Fence Lizard, Sceloporus undulatus 2 12 7 7 1 24 3 56

48

Table 2.4. Continued.

ICH

MO

O

OIS

P

RC

SL

BA

R

WA

R

Tota

l

Ground Skink, Scincella lateralis 0 4 2 1 3 2 0 12

Total lizard captures 22 26 22 32 19 48 21 190

Lizard species richness 6 5 6 6 7 4 4 10

49

Table 2.5. Numbers of snakes and lizards captured in box traps, pitfalls, and along drift fences at

passive trap arrays on seven sites in the Coastal Plain of Georgia, 2012 and 2013. All sites were

trapped for eight sampling periods (approximately 240 trap nights each).

Box Pitfall Drift Fence

Snake Species

Copperhead, Agkistrodon contortrix 1 0 0

Cottonmouth, Agkistrodon piscivorus 1 0 0

Scarlet Snake, Cemophora coccinea 1 0 0

Black Racer, Coluber constrictor 51 0 0

Eastern Diamondback Rattlesnake, Crotalus adamanteus 5 0 0

Eastern Indigo Snake, Drymarchon couperi 2 0 0

Eastern Hognose Snake, Heterodon platirhinos 4 1 1

Common Kingsnake, Lampropeltis getula 3 0 0

Coachwhip, Masticophis flagellum 32 0 0

Corn Snake, Pantherophis guttatus 5 0 0

Rat Snake, Pantherophis obsoletus 9 0 0

Pine Snake, Pituophis melanoleucus 10 0 0

Pygmy Rattlesnake, Sistrurus miliarius 4 0 0

Red-bellied Snake, Storeria occipitomaculata 0 1 0

Southeastern Crowned Snake, Tantilla coronata 0 3 0

Common Garter Snake, Thamnophis sirtalis 14 1 0

Total snake captures 142 6 1

Lizard Species

Green Anole, Anolis carolinensis 20 1 17

Six-lined Racerunner, Aspidoscelis sexlineata 26 17 1

Slender Glass Lizard, Ophisaurus attenuatus 5 0 0

Eastern Glass Lizard, Ophisaurus ventralis 1 0 0

Mole Skink, Plestiodon egregius 0 1 0

Five-lined Skink, Plestiodon fasciatus 1 0 0

Southeastern Five-lined Skink, Plestiodon inexpectatus 5 0 0

Broadhead Skink, Plestiodon laticeps 22 5 0

Eastern Fence Lizard, Sceloporus undulatus 14 31 11

Ground Skink, Scincella lateralis 0 8 4

Total lizard captures 94 63 33

50

Table 2.6. Total captures and capture rates (# captures/trap night) for squamate species caught in

box trap arrays on seven sites in the Coastal Plain of Georgia, 2012 and 2013.

Species Total # captures Capture rate

Copperhead, Agkistrodon contortrix 1 0.0006

Cottonmouth, Agkistrodon piscivorus 1 0.0006

Green Anole, Anolis carolinensis 38 0.0228

Six-lined Racerunner, Aspidoscelis sexlineata 44 0.0264

Scarlet Snake, Cemophora coccinea 1 0.0006

Southern Black Racer, Coluber constrictor 51 0.0306

Eastern Diamondback Rattlesnake, Crotalus adamanteus 5 0.0030

Eastern Indigo Snake, Drymarchon couperi 2 0.0012

Eastern Hognose Snake, Heterodon platirhinos 6 0.0036

Common Kingsnake, Lampropeltis getula 3 0.0018

Coachwhip, Masticophis flagellum 32 0.0192

Slender Glass Lizard, Ophisaurus attenuatus 5 0.0030

Eastern Glass Lizard, Ophisaurus ventralis 1 0.0006

Corn Snake, Pantherophis guttatus 5 0.0030

Rat Snake, Pantherophis obsoletus 9 0.0054

Pine Snake, Pituophis melanoleucus 10 0.0060

Mole Skink, Plestiodon egregius 1 0.0006

Five-lined Skink, Plestiodon fasciatus 1 0.0006

Southeastern Five-lined skink, Plestiodon inexpectatus 5 0.0030

Broadhead skink, Plestiodon laticeps 27 0.0162

Eastern Fence Lizard, Sceloporus undulatus 56 0.0336

Ground Skink, Scincella lateralis 12 0.0072

Pygmy Rattlesnake, Sistrurus miliarius 4 0.0024

Red-bellied Snake, Storeria occipitomaculata 1 0.0006

Southeastern Crowned Snake, Tantilla coronata 3 0.0018

Common Garter Snake, Thamnophis sirtalis 15 0.0090

51

Table 2.7. Number of detections (defined as a capture of a species in any of the three traps on a

site during a sample period), mean detection probabilities, and associated standard errors across

seven sites sampled for squamates in the Coastal Plain of Georgia, 2012 and 2013. Detection

probabilities were not estimated for species with fewer than six detections.

Species

Total #

Detections

Mean

Detection

Probability SE

Black Racer, Coluber constrictor 30 0.62 0.07

Six-lined Racerunner, Aspidoscelis sexlineata 26 0.46 0.07

Eastern Fence Lizard, Sceloporus undulatus 25 0.45 0.07

Green Anole, Anolis carolinensis 22 0.45 0.07

Coachwhip, Masticophis flagellum 19 0.39 0.07

Broadhead Skink, Plestiodon laticeps 16 0.50 0.09

Common Garter Snake, Thamnophis sirtalis 10 0.29 0.09

Ground Skink, Scincella lateralis 9 0.18 0.07

Rat Snake, Pantherophis obsoletus 7 0.27 0.10

Pine Snake, Pituophis melanoleucus 7 0.17 0.08

Eastern Hognose Snake, Heterodon platirhinos 6 0.12 0.07

Corn Snake, Pantherophis guttatus 5 - -

Southeastern Five-lined Skink, Plestiodon inexpectatus 5 - -

Eastern Diamondback Rattlesnake, Crotalus adamanteus 4 - -

Slender Glass Lizard, Ophisaurus attenuatus 4 - -

Southeastern Crowned Snake, Tantilla coronata 3 - -

Eastern Indigo Snake, Drymarchon couperi 2 - -

Common Kingsnake, Lampropeltis getula 2 - -

Pygmy Rattlesnake, Sistrurus miliarius 2 - -

Copperhead, Agkistrodon contortrix 1 - -

Cottonmouth, Agkistrodon piscivorus 1 - -

Scarlet Snake, Cemophora coccinea 1 - -

Eastern Glass Lizard, Ophisaurus ventralis 1 - -

Mole Skink, Plestiodon egregius 1 - -

Five-lined Skink, Plestiodon fasciatus 1 - -

Red-bellied Snake, Storeria occipitomaculata 1 - -

52

Table 2.8. Detection probability model selection results including -2log likelihood, number of

parameters in model (K), AICc value, ΔAICc value from top model for each species, and model

weight (w) for snake and lizard species (with >5 detections) captured on seven sites in the

Coastal Plain of Georgia, 2012 and 2013.

Species

Top

Model -2Log(L) K AICc ∆AICc w

Anolis carolinensis p(.) 71.86 2 78.86 0.00 0.8219

Aspidoscelis sexlineata p(.) 77.35 2 84.35 7.74 0.0204

Coluber constrictor p(.) 69.25 2 76.25 0.00 0.8649

Heterodon platirhinos p(.) 38.09 2 45.09 0.00 0.8919

Masticophis flagellum p(.) 69.96 2 76.96 0.00 0.7862

Pantherophis obsoletus p(.) 38.09 2 45.09 0.00 0.7427

Pituophis melanoleucus p(.) 41.64 2 48.64 0.00 0.8933

Plestiodon laticeps p(.) 53.89 2 59.53 1.36 0.3255

Sceloporus undulatus p(.) 76.99 2 83.99 0.00 0.7483

Scincella lateralis p(.) 49.26 2 56.26 0.00 0.8918

Thamnophis sirtalis p(.) 48.85 2 55.85 0.00 0.8941

53

Table 2.9. Mean ranks of capture rates and detection probabilities from Wilcoxon Rank Sum and

Kruskal-Wallis tests for squamate guilds (i.e., foraging mode and habit) sampled on seven sites

in the Coastal Plain of Georgia, 2012 and 2013.

Capture rate Detection probability

Foraging mode

Active 13.929 -

Ambush 11.700 -

Habit

Arboreal 20.100* 7.250

Fossorial 9.125* 1.500

Terrestrial 13.654 6.800

* Mean ranks were significantly different from each other (p=0.05)

54

Figure 2.1. Locations of sites trapped for upland snakes and lizards in the Coastal Plain of

Georgia from May-October, 2012 and March-April, 2013.

55

Figure 2.2. Diagram of box trap array design with drift fences and pitfalls (Burgdorf et al. 2005,

Steen et al. 2010b) used for sampling snake and lizard communities on seven sites in the Coastal

Plain of Georgia, 2012 and 2013. Diagram not drawn to scale.

56

CHAPTER 3

INFLUENCE OF HABITAT STRUCTURE ON SQUAMATE SPECIES RICHNESS AT

MULTIPLE SCALES IN THE COASTAL PLAIN OF GEORGIA

Introduction

The longleaf pine (Pinus palustris)- wiregrass (Aristida stricta) ecosystem of the

southeastern Coastal Plain is characterized by highly diverse and specialized plant and animal

communities (Guyer and Bailey 1993). Longleaf pine forests historically dominated the

landscape of the southeastern United States extending from southeastern Virginia into eastern

Texas (Landers et al. 1995, Frost 2006). Silvicultural practices, fire suppression, agricultural

development, and urbanization have displaced the longleaf pine ecosystem from more than 97%

of its pre-settlement range (Landers et al. 1995).

The longleaf pine ecosystem has some of the highest numbers of squamate species in the

United States and supports a number of rare and endemic species, including several species of

conservation concern (Vitt 1987, Guyer and Bailey 1993, Georgia Department of Natural

Resources 2005). Squamates are important components of the longleaf pine ecosystem as they

control populations of small mammals, birds, amphibians, and invertebrates (Hamilton and

Pollack 1961, Vitt and Cooper 1986, Weatherhead et al. 2003, Halstead et al. 2008, Stevenson et

al. 2010). Despite their importance, the basic ecology and natural history of many squamate

species have been understudied as the cryptic behavior, tendency to inhabit difficult-to-survey

57

habitats (i.e., arboreal and subterranean), and episodic activity patterns of snakes and lizards pose

a challenge for field studies (Parker and Plummer 1987, Gibbons and Semlitsch 2001).

Several studies have reported declines in snake populations across the Southeast (Martin

and Means 2000, Tuberville et al. 2000, Winne et al. 2007, Stapleton et al. 2008). Extensive

habitat loss, fragmentation and degradation have been implicated as the primary causes of

declines in snake abundance and richness (Dodd 1987). Additional potential causes of declines

include environmental pollution, road mortality, over-collection, invasive species such as the

red-imported fire ant (Solenopsis invicta), and disease (Dodd 1987, Gibbons et al. 2000).

Population status information is generally lacking for lizard species in the Southeast. Further

monitoring is needed for many squamate species to determine the conservation status of

populations and to distinguish between natural fluctuations and declines.

In the face of declining squamate populations and shrinking longleaf pine forests in the

Coastal Plain, it is essential to understand habitat requirements that facilitate diverse squamate

communities. Previous research into the spatial ecology and habitat needs of squamates has

focused primarily on the requirements of individual species (Baxley and Qualls 2009, Hoss et al.

2010, Steen et al. 2010a, Blevins and With 2011, Klug et al. 2011, Frost and Bergmann 2012).

Though species specific habitat and spatial requirements provide valuable information,

developing community-level management and conservation plans is difficult. It is therefore

imperative that research efforts focus on habitat needs of squamate communities as a whole so

that remaining habitat can be effectively managed to maintain diversity.

Remaining habitat in the Coastal Plain is fragmented and disjunct as anthropogenic

disturbance has shaped the landscape through urbanization, agriculture, and forestry practices

(Napton et al. 2010). Landscape and habitat change due to anthropogenic practices has a direct

58

and significant impact on squamate communities as research has demonstrated the importance of

habitat structure for snake and lizard species (Heatwole 1966, Kiester et al. 1975, Vitt 1987,

Reinert 1993, Waldron et al. 2008). Though the presence of specific structural features is

important in habitat selection by individual species, it is important to identify the impacts of

broad scale habitat and landscape structure on squamate communities to properly manage for

appropriate habitat structure.

Previous research has demonstrated a positive association between snake and lizard

species and forested habitat (Parker 1994, Steen et al. 2010a, Sutton et al. 2010, Miller et al.

2012, Steen et al. 2012). This positive association is due in large part to the suite of refugia types

available in forests (i.e., stumpholes, snags, tree holes, fallen trees, and animal burrows; Guyer

and Bailey 1993, Means 2005, Pierce et al. 2008). Gopher tortoise (Gopherus polyphemus)

burrows, often constructed in open canopy pine habitat, are a primary source of refugia for

hundreds of species in the Coastal Plain, including squamates, as overwintering sites and

protection from fire (Jackson and Milstrey). In particular, forests with low basal area have been

shown to promote reptile species richness (Loehle et al. 2005). As opposed to forests with high

basal area creating canopy closure, forest stands with a low basal area and open canopy structure

allow light to penetrate the forest floor supporting diverse ground cover communities when fire

is used to inhibit the growth of mid-story hardwoods (Landers et al. 1995, Kirkman et al. 2007).

Open canopy forests with a ground cover layer can provide favorable thermoregulatory

conditions for squamates and provide vegetative cover from aerial predators.

Agricultural development and urbanization has had detrimental effects on squamate

populations. Structural disturbance from clearing land for crops, pastures, and timber harvest has

been shown to negatively impact snake populations and reduce reptile species richness (Brown

59

1993, Ribeiro et al. 2009). Urbanization introduces a unique set of threats to squamate

populations beyond the direct loss of habitat. With urbanization comes an increase in roads

which negatively impact squamates through direct mortality and by their linear structure which

fragments habitat (Bernadino and Dalrymple 1992, Rudolph and Burgdorf 1997, Bonnet et al.

1999, Brehme et al. 2013). Additionally, urbanization brings people into contact with snakes and

lizards which often results in malicious killing (Seigel 1986, Brown 1993, Martin and Means

2000).

Spatial heterogeneity has been shown to have a positive impact on individual squamate

species as well as overall squamate diversity (Pianka 1967, Gillespie et al. 2005, Hoss et al.

2010, Steen et al. 2012). Complexity in habitat and the landscape can support high diversity as

individual species can inhabit different parts of the overall mosaic (Pianka 1967). Spatial

heterogeneity can further support species rich squamate communities by positively influencing

diversity in prey taxa including amphibians, birds, insects, and small mammals (Farley et al.

1994, Atauri and de Lucio 2001, Williams et al. 2002, Brose 2003).

As little is known about the habitat needs of snakes and lizards at a community level, my

objective was to investigate associations between upland squamate species richness and habitat

and landscape structure across a gradient of forest conditions, from clearcut pine stands to

mature longleaf pine forests. It is important to consider how the influence of habitat may vary at

differing scales as the availability of habitat structure can differ depending on the scale being

examined (Wiens 1989). I hypothesized that squamate species richness would be positively

associated with the proportion of habitat in upland forest and negatively associated with the

proportion of habitat with altered structure (i.e., agricultural land, clearcuts, and development). I

also expected species richness to be positively associated with landscape heterogeneity of non-

60

developed land cover types. The results of this study will improve our understanding of the

relationship between squamate species richness and habitat structure and will help promote

effective management strategies for long-term conservation of squamate communities.

Methods

Study Sites

My study was conducted on seven sites in the Coastal Plain of Georgia, U.S.A. (Table

2.1; Figure 2.1). Study sites were private or publically owned properties that had previously been

surveyed for gopher tortoise (Gopherus polyphemus) as burrow density was a variable of interest

to my research questions (Smith et al. 2009). Sites contained ≥ 400 ha (range 418-2,850 ha) of

upland habitat characterized by a suite of pine and hardwood species as well as mixed pine-

hardwood forest. As the result of differences in land use history and current management

regimes (e.g., forestry and agricultural practices), sites varied in landscape composition in terms

of available forested and non-forested habitat.

Squamate Sampling

I sampled snake and lizard communities during the active season (May-October of 2012

and March-April of 2013) using box trap arrays consisting of a central box trap with drift fences

terminating at the mouth of 19 L pitfalls (Figure 2.2.; Burgdorf et al. 2005, Steen et al. 2010b).

With the exception of one site, drift fences were constructed of 15-m lengths of aluminum

flashing standing 50 cm above ground and buried 10 -15 cm in the soil. At one site (Ichauway),

drift fences were 30 m in length and were constructed of 0.64-cm mesh hardware cloth. I

compared capture rates from the 30-m length traps with capture rates from the 15-m length traps

with a Wilcoxon Rank Sum test as I expected longer drift fences would increase capture rates

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and influence the results of this study. However, capture rates were significantly lower for the

30-m length traps (W=583.0, p=0.049) suggesting differences in capture rates were due to site

effects rather than trap design. Because of this result, I included captures from the 30-m length

traps in the analysis.

Three arrays were installed at each site from March through May of 2012. Traps were

installed in upland habitats meeting the following criteria: ≥ 300 m from property boundary, ≥

250 m from wetlands, ≥ 300 m from the nearest trap array, and within contiguous patches ≥ 1 ha

of one habitat type. Trap location criteria were relaxed for one site (Ichauway) as 16 arrays had

been installed previously for an ongoing upland snake study; three traps that were closest to

meeting the desired criteria were selected for inclusion in this study. On Ichauway, one trap was

located in a patch ≤1 ha and two traps were located less than 250 m from wetlands. I attempted

to place one array in each of the following forest types: evergreen, mixed pine-hardwood and

hardwood based on 2008 Georgia Land Use Trends data (GLUT, 30-m pixels; University of

Georgia Natural Resources Spatial Analysis Lab, http://data.georgiaspatial.org; Accessed

September, 2011). However, patches meeting all of the above criteria were not available at all

sites. Therefore, on three sites, two traps were placed in evergreen forest and one trap was placed

in mixed forest. On one site, one trap was placed in evergreen forest and two traps were placed in

a regenerating clear-cut/sparse forest stand.

Trap arrays were open for 10 nights each month during the annual activity season

(March-October). A sampling period was defined as a 10 trap-night period. Traps were opened

on rotation (3 or 4 sites; 9-12 traps per sampling period). Traps were opened on day 1, checked

every other day during the sampling period, and closed on day 11. Traps were open for a total of

1666 nights over the course of the study.

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All snakes and lizards captured in box traps, pitfalls and found travelling along drift

fences were identified to species. Lizards were immediately released in the general vicinity of

the trapping array without being marked. Each snake was assigned a unique identification by

heat branding or by insertion of a passive integrated transponder (PIT) tag subcutaneously in the

lower third of the body (Weary 1969, Gibbons and Andrews 2004).

Species Richness and Similarity Estimation

Snake and lizard capture data for the three traps on each site were combined to estimate

species richness at the site level. Species richness was estimated for squamates using the Chao 1

estimator for abundance data in program EstimateS (Colwell 2006). Species similarity between

sites was estimated using the Bray-Curtis similarity index for sample pairs based on abundance

data.

Spatial Data

Each site was spatially defined by a 300-m buffer surrounding each of the three snake

trap arrays so that the total area within buffers was considered a site for my spatial analysis. A

300-m buffer size was selected to define a site as this scale encompasses home range habitat for

a number of Coastal Plain squamate species (Plummer and Mills 2000, Hoss et al. 2010, Linehan

et al. 2010). Additionally, this scale is restricted to managed habitat within the seven study sites

as traps were placed a minimum of 300 m from sites boundaries. The landscape surrounding

each site was spatially defined by a 1-km buffer around each of the three snake trap arrays so that

the total area within buffers was considered the landscape for my spatial analysis. The 1-km

buffer size at the landscape scale was selected to encompass habitat outside of the home range of

squamate species. Spatial data were examined within each site and landscape. There was overlap

63

in buffers at both the site and landscape scale resulting in varying total area among the seven

sites (Table 3.2).

Spatial variables were measured using 2008 GLUT dataset in ArcGIS. The GLUT dataset

contained 13 land use and land cover classes which I reclassified into four structural classes (i.e.,

forested upland, non-forested upland, wetland, and development) for analysis (Table 3.3) and a

fifth class which contained land use and land cover classes not present on my sites. These classes

were selected based on hypotheses of how upland squamates respond to habitat structure. Percent

area of the four structural classes was calculated for each site and landscape. Patch density (PD;

patches/100 ha) was calculated within landscape buffers using Fragstats (McGarigal et al. 2012).

Patches in the developed class were not included in the patch density calculation as my sites

were in rural areas where the development class primarily consisted of roads which did not

provide useable habitat for squamates.

I recorded basal area (m2/ha) using a wedge prism at random points, generated with the

‘create random points’ tool in ArcGIS (version 10.0; Environmental Systems Research Institute

2011), across upland habitat for each site (points were not associated with box trap arrays). Basal

area was recorded at 20 points on sites < 1,000 ha and at 30 points on sites > 1,000 ha. The

number of points surveyed within each upland habitat types was proportional to the amount of

the habitat type within upland. Mean basal area of upland habitat was calculated for each site.

Gopher tortoise burrow densities (BD; burrows/ha) were calculated from burrows and lengths of

survey transect falling within site buffers which were obtained from previous Line Transect

Distance Sampling Surveys (Smith et al. 2009, Ballou 2013).

64

Modeling

Twelve models describing the association between upland squamate species richness and

site-level factors were developed from six variables (i.e., forested upland, non-forested upland,

wetland, development, gopher tortoise burrow density, and basal area; Table 3.4). Twelve

landscape models were developed from five variables (i.e., forested upland, non-forested upland,

wetland, development, and patch density; Table 3.5). Models were developed based on

hypothesized relationships between estimated squamate species richness and site and landscape

composition and configuration. Correlated variables (r≥0.70) were not included in the same

model (Table 3.13). Due to constraints by the available degrees of freedom, a traditional global

model was not included in this analysis. I created a multi-scale (composite) model using

variables in top site and landscape models (models within the top 0.95 cumulative weight) with

95% confidence intervals that did not include zero.

I examined the relationships between site and landscape variables using linear regression

analysis with estimated species richness as the response variable in program R (version 3.0.1; R

Core Team 2013). I used Akaike’s Information Criterion corrected for small sample sizes to

identify the top site and landscape model (AICc; Burnham and Anderson 2002). Models within

two AICc units were considered to have equal support. I used model averaging to obtain

parameter estimates for variables in models within the top 0.95 cumulative weights using the

AICc and AICcmodavg functions in the AICcmodavg package (version 3.0.1; R Core Team

2013). I evaluated variable significance by evaluating if the unconditional variance on the

parameter estimate included zero.

I developed model predictions using the top site-level and landscape-level model and the

composite model using the modavgpred function in the AICcmodavgpackage (version 3.0.1; R

65

Core Team 2013) which predicts species richness values using the parameters from the linear

regression models. I compared predicted species richness values from the models with estimated

species richness values for the seven sites to assess model fit.

Results

I captured a total of 16 snake and 10 lizard species across seven sites (Table 3.6). Mean

measured squamate species richness across sites was 11.7 species (range 9-15 species) and mean

estimated squamate species richness across sites was 17.2 species (range 9.0-36.5; Table 3.7).

Species similarity values ranged from 0.306-0.623 (Table 3.8). Squamate species assemblages

were most similar between the Barrington tract and Moody Forest NA. Species assemblages

were least similar between Ichauway and Moody Forest NA.

At the site and landscape scale, percent area of four structural classes varied among the

seven survey sites (Table 3.9). Site level burrow density and basal area as well as landscape level

patch density also varied among the seven survey sites (Table 3.9).

The best supported site model, which included percent area of wetland, had relatively

high support (w = 0.94; Table 3.10). The model-averaged parameter estimate of 7.48 indicated

unequivocally (confidence intervals did not include zero) that species richness was positively

associated with wetland at the site level (Table 3.12).

The best supported landscape model included percent area of forested upland (w = 0.40;

Table 3.11). However, the top three models were within two AICc units indicating that these

models had equal support. There were five variables (i.e., FU, NU, W, D, and PD) in models

within the 0.95 cumulative weight. Forested upland and non-forested upland were the only

variables for which the 95% confidence intervals did not include zero (Table 3.12). Parameter

66

estimates from model averaging revealed a positive association between species richness and

percent area of forested upland and a negative association between species richness and percent

area of non-forested upland.

Based on site and landscape-level model results, I developed a composite model that

included the wetland site variable and forested upland and non-forested upland landscape

variables. The composite model had a higher AICc value (63.11) than the best individual site and

landscape models (47.49 and 60.57, respectively). I compared predicted squamate species

richness from site, landscape and composite models with estimated species richness (Table 3.13).

The sum of differences between predicted species richness and estimated species richness for

site, landscape, and composite models were close to zero.

Discussion

Species richness estimates were close (within two species) to measured species richness

values on sites with the exception of River Creek and Ichauway. The estimate of 36.5 species for

Ichauway is potentially an overestimate for the site and can be explained by the large proportion

(eight of twelve) of species with singletons (single individual captured) and doubletons (two

individuals captured) indicating rarity and resulting in a large species richness estimates (Gotelli

and Colwell 2001). However, a previous long-term study documented 38 squamate species on

Ichauway (Smith et al. 2006) suggesting the estimate of 36.5 was possible.

Percent wetland area was the best explanatory variable for squamate species richness at

the site level. Though box trap arrays were set in upland habitat in an effort to target species

using uplands, the presence of wetlands in proximity to traps appears to increase the pool of

species using a site. Aquatic and semi-aquatic species use upland habitat during terrestrial forays

67

for nesting, for overwintering sites, and to find other wetlands (Riemer 1957, Roe et al. 2003).

Also, having wetland habitat available may influence site use by species that exploit amphibians

as a prey source (i.e., Agkistrodon contortrix juveniles, Coluber constrictor, Heterodon

platirhinos, Heterodon simus, and Thamnophis sirtalis). Wetlands also may influence species

richness by increasing detection of species that use hardwood areas as hardwood species are

common in mesic habitats (i.e., Crotalus horridus, Pantherophis obsoleta, and Plestiodon

laticeps; Steen et al. 2007). This enlarged species pool is illustrated by the capture of A.

contortrix, H. platirhinos, P. laticeps, and T. sirtalis which were not captured on all sites but

were captured on Ichauway and River Creek which had the highest percent area of wetland and

the highest estimated species richness among the seven sites. The significance of wetland area in

the site models suggests that the presence of aquatic habitats influences squamate diversity in

upland habitats, possibly by providing alternative food sources (Rossman et al. 1996, Halstead et

al. 2008) and by acting as an additional source of habitat heterogeneity.

Consistent with my hypotheses, species richness was positively related to forested upland

habitat at the landscape scale demonstrating the benefits of a forested landscape matrix. Though

previous squamate research has documented associations with upland forest at a landscape scale,

studies typically focus on a single species and often discuss associations with specific forest

types (Steen et al. 2010a, Baxley et al. 2011, Miller et al. 2012). My results emphasize the

importance of forest structure, as opposed to the occurrence of specific forest types, for

squamates and suggest that a forested landscape matrix promotes species richness. A positive

association with percent forested upland also concurs with previous studies that demonstrate the

importance of a suite of structural components found in natural forest stands (i.e., snags, stump

68

holes, tree holes, litter layer, coarse woody debris, and animal burrows) as snake and lizard

refugia (Guyer and Bailey 1993, Means 2005, Todd and Andrews 2008).

As hypothesized, species richness was negatively associated with percent area of non-

forested upland habitat. The negative association is consistent with previous research that

suggests some timber harvesting practices and agriculture remove arboreal retreats, coarse

woody debris, and leaf litter important to many snake and lizard species (Greenberg et al. 1994,

DeMaynadier and Hunter 1995). Todd and Andrews (2008) also suggest that non-forested

habitats create an unsuitable thermal environment for some ectothermic vertebrates by having

higher daily temperatures and lower nighttime temperatures as well as reduced humidity levels.

Though my results indicate that squamate species richness was negatively associated with area of

non-forested habitat structure, further research is needed to determine the scale at which non-

forested habitat becomes detrimental to squamate communities. Many of my sites utilize wildlife

openings (food plots) for wildlife habitat and hunting purposes (Smith et al. 2007). Conceivably,

small patches of non-forested habitat such as food plots may introduce heterogeneity into a larger

forested landscape and provide favorable thermoregulatory conditions for squamates as well as

offering suitable habitat for prey species such as small mammals. Several snake habitat use

studies have shown that some species preferentially use forest-field edges for thermoregulation

(Carfagno and Weatherhead 2008). Silvicultural practices can also be conducted in such a way

that they only remove forest structure on a small scale (i.e., small patches as with shelterwood,

single tree, or group selection harvests) potentially minimizing impacts on squamate

communities. The single-tree selection method which has been shown to preserve the overall

forest structure in such a way that impacts on fauna (i.e., birds and insects) are minimal

(Atlegrim and Sjöberg 1996, Leblanc et al. 2011).

69

It was not surprising that patch density was included in the list of top landscape models as

previous studies have shown the importance of habitat heterogeneity for squamates (Pianka

1967, Gillespie et al. 2005, Hoss et al. 2010, Steen et al. 2012). Though the parameter estimate

for patch density was positive, the lower bound of the confidence interval fell on zero indicating

the direction of the effect was ambiguous. Nevertheless, several landscape scale studies have

demonstrated single species associations with heterogeneity at similar scales to this study. Steen

et al. (2012) found heterogeneity in habitat type to influence occupancy in large terrestrial snake

species at a 1000-m scale and Hoss et al. (2010) found heterogeneity in habitat configuration to

influence home range size of C. adamanteus at a 750-m scale.

The sums of the differences between estimated squamate species richness and predicted

species richness were close to zero for site, landscape, and composite models indicating the

models fit the data well and that there was a balance between under and over-prediction of

species richness. Though the composite model had the highest AICc value out of the three

models used to predict species richness, this is likely because the model was penalized for having

three variables while site and landscape models had one variable. A comparison of the

differences between predicted and estimated species richness values suggests that the composite

model is the best fit model to the data as the sum absolute differences was the least. Habitat

structure at multiple scales likely influences species richness of squamate communities as the

composite model containing site and landscape variables was the best fit model to the data. The

importance of multi-scale habitat structure in my study is consistent with previous research

demonstrating the influence of habitat at multiple scales on occupancy, spatial ecology, and

habitat selection of squamates (Hoss et al. 2010, Steen et al. 2010a, Baxley et al. 2011, Miller et

70

al. 2012, Steen et al. 2012). The influence of habitat structure at multiple scales also bolsters the

need to include habitat at local and landscape scales in conservation and management efforts.

At a time when emphasis is being placed on restoration and maintenance of longleaf pine

forests in the Southeast, it is vital to understand how available habitat influences wildlife

populations, including squamate communities. Many private landowners in the Coastal Plain

have taken advantage of federal financial assistance and easement programs (e.g., Wetlands

Reserve Program, Wildlife Habitat Incentive Program and the Environmental Quality Incentives

Program) that provide aid in habitat management and restoration (U. S. Department of

Agriculture 2009). My results have the potential to inform how habitat management and

restoration efforts, as part of federal programs, could influence squamate communities.

Restoration of altered habitats from agriculture or timber harvest to a natural forest structure

could promote diversity in squamate communities. Additionally, maintaining hardwood species

in mesic areas such as riparian zones and wetlands will retain heterogeneity within forest patches

and support squamate species richness. My results can also shed light on how restoration efforts

to transition forest stands back to natural longleaf pine stands will likely improve habitat for

squamates. Selective thinning of densely planted stands or stands of undesirable pine species, as

opposed to clearcutting, can mitigate negative impacts on squamate communities by preserving

structural attributes of forests (Kirkman et al. 2007). A gradual restoration approach likely

requires a longer time period to achieve the desired goal but can help preserve existing squamate

communities over the course of the restoration process.

71

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77




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78

Table 3.1. Names and counties of sites, land ownership types, and size of upland habitat for

seven sites sampled for upland snake and lizard species in the in the Coastal Plain of Georgia,

2012 and 2013.

Site Name County Ownership

Estimated

Upland Habitat

(ha)

Ichauway (ICH) Baker Private 1,941

Moody Forest Natural Area (MOO) Appling Public/Private 1,076

Orianne Society Indigo Snake Preserve (OISP) Telfair Private 418

River Creek WMA (RC) Thomas Public 452

Silver Lake WMA (SL) Decatur Public 2,850

Townsend WMA (Barrington Tract; BAR) McIntosh Public 447

Warbick Farms (WAR) Thomas Private 563

79

Table 3.2. Names of sites sampled for upland snake and lizard species in the Coastal Plain of

Georgia (during 2012 and 2013) and total area within site (300-m) and landscape buffers (1000-

m) surrounding box trap sampling arrays.

Site Site Area (ha) Landscape Area (ha)

Barrington Tract 83.61 733.68

Ichauway 83.34 722.97

Moody Forest NA 85.14 846.00

Orianne Indigo Snake Preserve 77.58 701.55

River Creek WMA 77.04 699.48

Silver Lake WMA 84.96 910.98

Warbick Farms 82.80 557.37

80

Table 3.3. Aggregated land use class, class abbreviations, and Georgia Land Use Trends (GLUT)

class used to examine associations with squamate species richness sampled on seven sites in the

Coastal Plain of Georgia, 2012 and 2013.

Aggregated Land Use Class Abbreviation GLUT Class

Forested Upland FU Deciduous forest

Evergreen forest

Mixed forest

Non-forested Upland NU Row crops and pastures

Clear cuts and sparse vegetation

Development D Low intensity urban

High intensity urban

Wetland W Open water

Forested wetland

Non-forested wetland

81

Table 3.4. Twelve site-level models and their associated hypotheses developed to examine

factors associated with squamate species richness sampled on seven sites in the Coastal Plain of

Georgia, 2012 and 2013.

Model Hypothesis

Site-level

1 Null model

1FU Species richness will be positively correlated with % area of forested upland.

FU+W Species richness will be positively correlated with % area of forested upland and

negatively correlated with % area of wetland.

FU+W+D Species richness will be positively correlated with % area of forested upland and

negatively correlated with % area of wetland and development.

FU+BD Species richness will be positively correlated with % area of forested upland and

gopher tortoise burrow density.

FU+BA Species richness will be positively correlated with % area of forested upland and

inversely related to mean basal area.

FU+BD+BA Species richness will be positively correlated with % area of forested upland and

burrow density and will be inversely related to mean basal area.

NU+BD+BA Species richness will be negatively correlated with % area of non-forested upland,

inversely related to mean basal area, and positively correlated with burrow density.

NU Species richness will be negatively correlated with % area of non-forested upland.

NU+W+D Species richness will be negatively correlated with % area of non-forested upland,

wetland, and development.

W Species richness will be negatively correlated with % area of wetland.

D Species richness will be negatively associated with % area of development.

1FU = % area of forested upland; NU = % area of non-forested upland; W = % area of wetland;

D = % area of development; BD = gopher tortoise burrow density; BA = mean basal area of

upland habitat

82

Table 3.5 Twelve landscape-level models and their associated hypotheses developed to examine

factors associated with squamate species richness sampled on seven sites in the Coastal Plain of

Georgia, 2012 and 2013.

Model Hypothesis

Landscape-level

1 Null Model

FU Species richness will be positively correlated with % area of forested upland.

FU+NU Species richness will be positively correlated with % area of forested upland

and negatively correlated with % area of non-forested upland.

FU+PD Species richness will be positively correlated with % area of forested upland

and patch density.

FU+D Species richness will be positively correlated with % area of forested habitat

and negatively correlated with % area of development.

FU+NU+D

Species richness will be positively correlated with % area of forested habitat

and negatively correlated with % area of non-forested habitat and

development.

NU Species richness will be negatively correlated with % area of non-forested

upland.

NU+W Species richness will be negatively correlated with % area of non-forested

and wetland.

NU+W+D Species richness will be negatively correlated with % area of non-forested

upland, wetland, and development.

W Species richness will be negatively correlated with % area of wetland.

D Species richness will be negatively correlated with % area of development.

PD Species richness will be positively correlated with patch density.

1FU = % area of forested upland; NU = % area of non-forested upland; W = % area of wetland;

D = % area of development; PD = patch density

83

Table 3.6. Snake and lizard species captured on seven sites in the Coastal Plain of Georgia

during 2012 and 2013 in box trap arrays (open for approximately 240 trap nights each). Refer to

Table 3.1 for site name codes.

Snake Species BA

R

ICH

MO

O

OIS

P

RC

SL

WA

R

Copperhead, Agkistrodon contortrix

X

Cottonmouth, Agkistrodon piscivorus

X

Scarlet Snake, Cemophora coccinea

X

Black Racer, Coluber constrictor X X X

X X X 1Eastern Diamondback Rattlesnake, Crotalus adamanteus X

X

Eastern Indigo Snake, Drymarchon couperi

X X

Eastern Hognose Snake, Heterodon platirhinos X X X

X

Common Kingsnake, Lampropeltis getula

X

X

Coachwhip, Masticophis flagellum X

X X X X X

Corn Snake, Pantherophis guttatus

X

X X X

Rat Snake, Pantherophis obsoletus X

X

X

Pine Snake, Pituophis melanoleucus

X X

X X

Pygmy Rattlesnake, Sistrurus miliarius

X

2Red-bellied Snake, Storeria occipitomaculata

X

Southeastern Crowned Snake, Tantilla coronata

X X

Common Garter Snake, Thamnophis sirtalis

X

X X X

Lizard Species

Green Anole, Anolis carolinensis X X X X X

X

Six-lined Racerunner, Aspidoscelis sexlineata X X X X X X X

Slender Glass Lizard, Ophisaurus attenuatus

X

X

3Eastern Glass Lizard, Ophisaurus ventralis

X

Mole Skink, Plestiodon egregius

X

Five-lined Skink, Plestiodon fasciatus

X

Southeastern Five-lined Skink, Plestiodon inexpectatus

X

X X X

Broadhead Skink, Plestiodon laticeps

X

X X X

Eastern Fence Lizard, Sceloporus undulatus X X X X X X X

Ground Skink, Scincella lateralis X

X X X X 1

Incidentally observed on OISP, RC, SL; 2incidentally observed on BAR;

3incidentally observed

on RC.

84

Table 3.7. Measured (total species captured in box trap arrays during the study) and EstimateS

estimated species richness on seven sites sampled for squamates in the Coastal Plain of Georgia,

2012 and 2013.

Site

Measured

Species

Richness

Estimated

Species

Richness SD

95% CI Lower

Bound

95% CI Upper

Bound

Barrington 9 9.0 0.3 9.0 9.1

Ichauway 12 36.5 31.1 15.6 179.2

Moody 14 15.5 2.2 14.2 26.5

OISP 10 10.3 0.7 10.0 14.8

River Creek 11 21.0 10.2 12.9 63.3

Silver Lake 15 16.2 1.8 15.1 25.4

Warbick 11 12.0 1.8 11.1 21.7

85

Table 3.8. Bray-Curtis species similarity values for seven sites sampled for squamates in the

Coastal Plain of Georgia, 2012 and 2013. Refer to Table 3.1 for site name codes.

BA

R

ICH

MO

O

OIS

P

RC

SL

WA

R

BAR - 0.436 0.623 0.446 0.466 0.374 0.473

ICH 0.436 - 0.306 0.293 0.456 0.372 0.438

MOO 0.623 0.306 - 0.458 0.440 0.486 0.617

OISP 0.446 0.293 0.458 - 0.444 0.309 0.405

RC 0.466 0.456 0.440 0.444 - 0.455 0.591

SL 0.374 0.372 0.486 0.309 0.455 - 0.526

WAR 0.473 0.438 0.617 0.405 0.591 0.526 -

86

Table 3.9. Mean, standard error (SE), and range of site and landscape variables measured on

seven sites surveyed for squamate species richness in the Coastal Plain of Georgia, 2012 and

2013.

Variable Mean SE Range

Site-level

1FU 68.2 9.4 18.4-91.1

NU 28.8 9.6 5.5-77.1

W 0.4 0.2 0.0-1.5

D 2.6 0.7 0.0-5.3

BD 3.1 1.2 0.1-8.9

BA 11.2 2.1 4.1-19.1

Landscape-level

FU 62.0 7.3 22.3-79.4

NU 18.9 4.2 4.5-36.1

W 15.7 4.9 1.4-39.3

D 3.4 1.0 1.2-8.6

PD 11.8 1.2 8.3-16.3 1FU = % area of forested upland; NU = % area of non-forested upland; W = % area of wetland;


upland habitat; PD = patch density

87

Table 3.10. Site-level species richness model selection results including the number of

parameters (K), Akaike’s Information Criterion for small sample sizes (AICc), ΔAICc, model

weight (w), and cumulative model weight for squamate species captured on seven sites in the

Coastal Plain of Georgia, 2012 and 2013. Models were ranked by AICc.

Model K AICc ΔAICc w Cumulative w 1W 2 47.49 0.00 0.94 0.94

FU+W 3 53.38 5.89 0.05 0.99

FU 2 57.29 9.8 0.01 0.99

NU 2 57.85 10.36 0.01 1.00

FU+BD 3 60.09 12.6 0.00 1.00

1 1 63.02 15.54 0.00 1.00

FU+BA 3 63.61 16.13 0.00 1.00

D 2 66.99 19.51 0.00 1.00

FU+W+D 4 67.04 19.55 0.00 1.00

NU+W+D 4 67.13 19.64 0.00 1.00

NU+BD+BA 4 69.17 21.68 0.00 1.00

FU+BD+BA 4 69.56 22.07 0.00 1.00 1FU = % area of forested upland; NU = % area of non-forested upland; W = % area of wetland;


upland habitat

88

Table 3.11. Landscape-level species richness model selection results including the number of

parameters (K), Akaike’s Information Criterion for small sample sizes (AICc), ΔAICc, model

weight (w), and cumulative model weight for squamate species on seven sites in the Coastal

Plain of Georgia, 2012 and 2013. Models were ranked by AICc.

Model K AICc ΔAICc w Cumulative w 1FU 2 60.57 0.00 0.40 0.40

NU 2 62.04 1.47 0.19 0.59

FU+PD 3 62.36 1.79 0.16 0.76

1 1 63.02 2.45 0.12 0.87

W 2 64.84 4.26 0.05 0.92

D 2 66.16 5.58 0.02 0.95

PD 2 66.99 6.42 0.02 0.96

FU+NU 3 67.21 6.64 0.01 0.98

FU+D 3 67.51 6.94 0.01 0.99

NU+W 3 67.95 7.38 0.01 1.00

FU+NU+D 4 81.05 20.48 0.00 1.00

NU+W+D 4 81.84 21.27 0.00 1.00 1FU = % area of forested upland; NU = % area of non-forested upland; W = % area of wetland;


upland habitat; PD = patch density

89

Table 3.12. Variables, model-averaged parameter estimates, and 95% confidence intervals (CI)

for variables in the top 0.95 cumulative weight of site and landscape-level models and the

composite model to examine associations with squamate species richness sampled on seven sites

in the Coastal Plain of Georgia, 2012 and 2013.

Variable Estimate SE Lower 95% CI Upper 95% CI

Site-level

1W 7.48 1.71 4.12 10.84

Landscape-level

FU 1.48 0.67 0.17 2.8

NU -1.35 0.69 -2.69 -0.01

PD 0.08 0.04 0 0.16

W -0.73 0.51 -1.73 0.28

D 1.05 1.44 -1.77 3.86

Composite

SW 7.84 1.86 4.2 11.48

LFU 1.68 0.79 0.14 3.23

LNU 1.15 0.96 -0.74 3.03 1W = % area of wetland; FU = % area of forested upland; NU = % area of non-forested upland;

D = % area of development; PD = patch density; SW = site-level % area of wetland; LFU =

landscape-level % area of forested upland; LNU = landscape-level % area of non-forested

upland.

90

Table 3.13. Estimated squamate species richness, predicted squamate species richness from the top site-level model, landscape-level

model, and composite model, their standard errors (SE), and the difference between estimated and predicted squamate species richness

from seven sites in the Coastal Plain of Georgia, 2012 and 2013. Refer to Table 3.1 for site name codes.

Site

Estimated

Species

Richness

Site

Predicted

Species

Richness Difference SE

Landscape

Predicted

Species


Composite

Predicted

Species


BAR 9.0 11.58 2.6 1.63 9.8 0.8 2.56 6.96 -2.0 2.13

ICH 36.5 28.62 -7.9 3.74 19.05 -17.5 1.82 33.42 -3.1 5.16

MOO 15.5 11.58 -3.9 1.63 20.84 5.3 2.33 12.87 -2.6 2.36

OISP 10.3 14.52 4.2 1.56 13.49 3.2 1.96 12.9 2.6 2.28

RC 21.0 28.62 7.6 3.74 17.64 -3.4 1.6 23.42 2.4 4.5

SL 16.2 14.52 -1.7 1.56 18.81 2.6 1.77 17.92 1.7 2.68

WAR 12.0 11.58 -0.4 1.63 21.38 9.4 2.53 13.51 1.5 2.46

Total 0.52 0.51 0.50

Sum absolute difference 28.3

42.2

15.9

91

Figure 3.1. Locations of sites trapped for upland snakes and lizards in the Coastal Plain of

Georgia from May-October, 2012 and March-April, 2013.

92

Figure 3.2. Diagram of box trap array design with drift fences and pitfalls (Burgdorf et al. 2005,

Steen et al. 2010) used for sampling squamate communities on seven sites in the Coastal Plain of

Georgia, 2012 and 2013. Diagram not drawn to scale.

93

CHAPTER 4

A MAXIMUM ENTROPY APPROACH TO HABITAT SUITABILITY MODELING FOR

THE SOUTHERN HOGNOSE SNAKE (HETERODON SIMUS)

Introduction

Widespread declines in reptile populations have garnered considerable attention in recent

decades (Gibbons et al. 2000). Habitat degradation and loss, overharvesting, disease, pollution,

and invasive species have all been proposed as causes of declines (Dodd 1987, Gibbons et al.

2000, Ara o et al. 2006, Whitfield et al. 2007, Clark et al. 2010, Chessman 2011). There is an

urgent need for widespread research into the basic ecology and natural history of many reptile

species as well as baseline information about reptile communities. Reptiles are understudied

when compared with other vertebrate groups (e.g., mammals and birds; Garner et al. 2010).

Information gaps have made it difficult to determine conservation status for numerous species.

As of 2013, approximately 20% of reptile species evaluated by the International Union for

Conservation of Nature (IUCN) have yet to be assigned a conservation status due to deficient

data (IUCN 2013). In the face of perceived declines, emphasis must be placed on targeted studies

to fill in knowledge gaps to determine the scope and magnitude of future declines and establish

effective conservation efforts.

Conservation efforts for declining reptile species are hampered by a lack of basic

knowledge about their distribution and biological requirements (Bohm et al. 2013). The cryptic

behavior of reptiles, their tendency to inhabit difficult-to-survey habitats, and episodic activity

94

patterns result in low detection and are largely to blame for knowledge gaps (Parker and

Plummer 1987, Gibbons and Semlitsch 2001). An additional issue that has hindered reptile

conservation, is that much of the data on the status of reptile populations are anecdotal (Dodd

1987, Dodd 2001). Though anecdotal data can be informative, they do not allow for

quantification of declines in populations or provide a baseline to monitor population trends.

The southern hognose snake (Heterodon simus) is a rare species endemic to the longleaf

pine (Pinus palustris) ecosystem of the Coastal Plain of the southeastern United States. The

historic range of the southern hognose extended from Mississippi to Florida and north into South

Carolina and North Carolina (Tuberville et al. 2000). H. simus is thought to be extirpated from

Alabama and Mississippi but appears to be rare and uncommon through most of the eastern

portion of the range (Tuberville et al. 2000, Ernst and Ernst 2003). In the central Panhandle of

Florida, H. simus appears to be locally common (Enge and Wood 2003). In 1991, the U.S. Fish

and Wildlife Service (USFWS) classified H. simus as a Category 2 status with a declining trend

suggesting that it might warrant protection under the Endangered Species Act (U.S. Fish and

Wildlife Service 1991). However, there was insufficient “persuasive data on biological

vulnerability and threat” to list it as threatened or endangered (U.S. Fish and Wildlife Service

1991;1994). The USFWS no longer recognizes the Category 2 status and H. simus is now

federally considered a species of concern (U.S. Fish and Wildlife Service 1996, Jensen et al.

2008, Adkins Giese et al. 2012). Declines in H. simus populations have been attributed to habitat

degradation and loss, road mortality, and invasion by the red imported fire ant, Solenopsis invicta

(Guyer and Bailey 1993; Mount 1981; Tuberville et al. 2000). In Georgia, H. simus is designated

a threatened species (Georgia Department of Natural Resources 2005). Georgia’s

Comprehensive Wildlife Conservation Strategy places H. simus in Species Conservation

95

mphasis Category D which acknowledges that “evidence of endemism or rarity exists, but

significant questions remain as to current range, population status, habitat needs and/or threats”

(Georgia Department of Natural Resources 2005).

While H. simus is of conservation concern, relatively little is known about the natural

history and population status. It is a highly fossorial species with a prominent rostral scale that

enables it to excavate burrows (Ernst and Ernst 2003). While it spends large portions of the year

below ground, there are several peaks in surface activity during the active season (generally

March through November). These peaks occur in May-June, during the mating season, and in

October-November, when hatchlings emerge from nests (Gibbons and Semlitsch 2001). H. simus

is typically found in xeric habitats with well-drained soils including: oak-pine forest, oak-scrub,

dry river floodplains and sandhills (Ernst and Ernst 2003, Tuberville and Jensen 2008). The

cryptic, fossorial nature of this species makes it difficult to employ traditional survey and capture

techniques to fill in information gaps on population status, natural history and habitat selection.

Predictive modeling can be used to initiate status assessments and conservation efforts for

rare and declining species (Anderson and Martinez-Meyer 2004, Santos et al. 2009). Recent

advances in technology have improved our ability to develop spatially explicit models predicting

the distribution and areas of suitable habitat for poorly understood species. However, the

accuracy of predictive models is influenced by the quality of biological data and sampling effort

(Phillips et al. 2009). Subsequent field studies are essential to validate predictive models (Gentil

and Blake 1981, Fleming and Shoemaker 1992). Potential applications of habitat and distribution

models for wildlife include: survey/inventory development, habitat monitoring, conservation

status assessments, threat assessments, and natural resource management (Kramer et al. 2003,

Martínez-Freiría et al. 2008, Santos et al. 2009, Bombi et al. 2011, Lawing and Polly 2011).

96

Habitat suitability models (HSMs) are used to delineate potential distributions of wildlife

populations based on a species’ habitat requirements and occurrence records (Anderson and

Martinez-Meyer 2004, Phillips et al. 2009, Cianfrani et al. 2010). In recent decades, several

multivariate modeling techniques (i.e., maximum entropy, genetic algorithm for rule-set

prediction, and ecological-niche factor analysis) have been developed to predict suitable habitat

for a species of interest (Stockwell and Noble 1992, Stockwell and Peters 1999, Hirzel et al.

2002, Phillips et al. 2006). Objective modeling of suitable habitat is crucial for targeted

application of surveys and habitat conservation and is a vast improvement over coarse scale

maps that extrapolate ranges outside of known occurrence points (Anderson and Martinez-Meyer

2004). HSMs have proven useful in identifying previously unknown populations of species with

sparse occurrence data, thus demonstrating their application to conservation efforts for rare or

elusive species with limited occurrence data available (Pearson et al. 2007, Rebelo and Jones

2010).

Maximum entropy modeling is a statistical modeling technique that develops spatially

explicit HSMs by estimating a probability distribution for a species by finding the distribution of

maximum entropy or the distribution that is closest to uniform (Phillips et al. 2004, Phillips et al.

2006).This technique offers a desirable approach to habitat suitability modeling as it does not

require absence data which is often unavailable, provides robust results with small sample sizes,

and can incorporate continuous as well as categorical environmental data (Pearson et al. 2007,

Phillips et al 2006). Maximum entropy models are developed from a set of input environmental

variables and geo-referenced occurrence locations. The output is continuous and establishes a

probability of habitat suitability from zero to one. Areas with a high probability of habitat

suitability may indicate areas worth conducting targeted surveys for rare species (Phillips et al.

97

2004, Phillips et al. 2006). A maximum entropy approach was used to build the HSM in this

study as maximum entropy models have been shown to outperform other multivariate models

(i.e., genetic algorithm for rule-set prediction and ecological niche factor analysis; Pearson et al.

2007, Tittensor et al. 2009).

The impetus for developing a HSM for the southern hognose snake was a lack of

knowledge on habitat associations coupled with declining population trends. The primary

objectives of my study were to 1) develop a spatially explicit habitat suitability model for H.

simus in Georgia, 2) identify variables that influence the distribution of suitable habitat for the

southern hognose in Georgia, and 3) compare the distribution of suitable habitat from this model

with a species-habitat association model developed by the Georgia GAP (Geographic Approach

to Planning) Analysis project. The output of this habitat suitability model can be used to direct

future surveys and field studies to assess population trends and to bridge the gap in knowledge

about the ecology of the southern hognose snake.

Methods

Study Area

My study was conducted in the Coastal Plain physiographic region of Georgia. The Coastal

Plain in Georgia comprises approximately 9,233,307 ha (Edwards et al. 2013). The Fall Line is

the upper boundary separating the Coastal Plain from the Piedmont physiographic region. The

Coastal Plain is broken into two regions, the Southeastern Plains (spanning the northern and

southwestern portion of the Coastal Plain) and the Southern Coastal Plain (including the coastal

counties and southeastern portion of the Coastal Plain). The Southeastern Plains region is

characterized by a relatively divided landscape with higher elevations than the Southern Coastal

98

Plain which has low elevation and a flat landscape (Edwards et al. 2013). Although a solitary

record (from the 1950’s) of a southern hognose snake exists for the Piedmont from Butts County,

the Piedmont was excluded from this analysis as my goal was to focus the model on extant

populations in the Georgia Coastal Plain.

Species Occurrence Data

I used 77 documented occurrences of H. simus in the Georgia Coastal Plain to serve as

presence data for building the HSM (Figure 4.1). Occurrence data were obtained from the

Georgia Department of Natural Resources, Fort Gordon Military Installation, Joseph W. Jones

Ecological Research Center, and through personal communication with field biologists and

included museum records, incidental observations, and passive trap captures. Only occurrence

data with associated observation dates were included in the model. Observation dates ranged

from 1941 to 2012.

Land Use/Land Cover

I used Georgia Land Use Trends (GLUT; University of Georgia Natural Resources Spatial

Analysis Lab, http://data.georgiaspatial.org; Accessed September 2011) raster data delineating

13 major land use types as the land cover variable included in this HSM (Table 4.1). To promote

temporal correspondence between occurrence data and land use, I used GLUT data, available for

seven time periods (i.e., 1974, 1985, 1991, 1998, 2001, and 2008), for each occurrence that

corresponded to the time when it was recorded (Anderson and Martinez-Meyer 2004). Due to

limited availability of digital land use data, I used aerial photos, obtained from the Digital

Library of Georgia (http://dlg.galileo.usg.edu; Accessed October, 2012) or from the print

collection at the University of Georgia Map Library, to determine land use for H. simus

occurrence points documented before GLUT data was available (i.e., before 1974). Aerial photos

99

were ortho-rectified in ArcGIS 10.0 (Environmental Systems Research Institute 2011) and land

cover was classified using GLUT land use types. Whenever possible, aerial photos were obtained

for the same year the snake occurrence record was documented; however, most photos available

were within three years of the observation date. To obtain the current distribution of predicted

suitable habitat, 2008 GLUT data (the most recent available) was incorporated into the model as

the land cover variable.

Environmental Variables

Three categories of environmental variables were used in the model including habitat

management, substrate, and topography (Table 4.1). Substrate variables, soil texture and soil

drainage, were derived from the State Soil Geographic dataset (STATSGO; Natural Resources

Conservation Service, U.S. Department of Agriculture,

http://soildatamart.nrcs.usda.gov/USDGSM.aspx; Accessed August, 2012). Substrate variables

were obtained as a vector coverage which was converted to 60-m resolution raster layer using

ArcGIS 10.0. I used a 60-m resolution raster digital elevation model as a topography variable

(National Elevation Dataset, U.S. Geological Survey, http://data.georgiaspatial.org; Accessed

July, 2013).

I developed a fire frequency raster layer using satellite data showing annual fire and

thermal anomalies for Georgia from 2001-2012 (Figure 4.2; U.S. Forest Service, Remote

Sensing Applications Center, http://activefiremaps.fs.fed.us/gisdata.php; Accessed August,

2013). Thermal anomaly data were provided as centroids of fires with a 1-km radius. To create

single year fire rasters, I buffered each thermal anomaly by 1 km and converted the buffer to a

raster with pixels assigned a value of one indicating that area was burned once during the year.

Single year fire rasters (for years 2001-2012) were added with the ‘raster calculator’ tool in

100

ArcGIS 10.0 to obtain an overall fire frequency raster. Values of pixels in the final fire frequency

raster ranged from 0 to 12 indicating an area could have been burned up to12 times within the 12

year period. I clipped all spatial layers to the extent of the Coastal Plain counties (Figure 4.1) and

used the ArcGIS ‘extract multi-values to points’ tool to create an output file assigning spatial

data values (i.e., land cover and four environmental variables) to snake occurrence data.

Modeling

I used Program MaxEnt (version 3.3.3; Phillips et al. 2004, Phillips et al. 2006) to model

potentially suitable habitat of the southern hognose snake in the Coastal Plain of Georgia.

Occurrence data with associated variable values (samples with data) were input into MaxEnt as

presence or training data while random points were generated by MaxEnt to serve as background

(artificial absence) data. The model output was displayed as a continuous raster with cells

assigned a value between zero and one indicating probability of habitat suitability based on the

variables and occurrence data included in the model.

Model Evaluation

I evaluated the importance of five variables used in the model using a jackknife test

which evaluates the importance of each variable at explaining the distribution as well as the

unique information provided by each variable. Since independent test data (additional occurrence

data) were not available to validate the model, I used a 10 replicate bootstrap to resample the

training data with replacement. A receiver operating characteristic plot, which plots sensitivity

(how well model correctly predicted presence) and 1- specificity (how well model correctly

predicted absence), and the associated area under the curve (AUC) value were used to assess

model accuracy (Fielding and Bell 1997, Tittensor et al. 2009).

101

Habitat suitability probabilities were broken into four groups (Table 4.3) to allow for

quantification of habitat in the MaxEnt output for comparison with a species-habitat association

model previously developed by the Georgia GAP Analysis project (Figure 4.3; Kramer et al.

2003). The GAP model was developed using H. simus occurrence data and habitat association

variables including: elevation, water features, road density, habitat patch size, and land cover.

The spatial output of the GAP model consists of a binary raster classifying predicted suitable and

predicted unsuitable habitat. I visually compared the distribution of habitat with a high

probability of suitability from the HSM, developed in MaxEnt, with the GAP model and also

compared the percentage of predicted suitable habitat in each. I also calculated the amount of

predicted suitable habitat from the HSM within conservation lands including wildlife

management areas, military installations, property easements, state/federal forests, and parks

(The University of Georgia Natural Resources Spatial Analysis Lab and the Georgia Department

of Natural Resources, http://data.georgiaspatial.org; Accessed September, 2013). Conservation

lands were classified as private, federal and state or locally (county government) owned and

managed properties.

Results

The HSM had a mean AUC value of 0.879 (±0.018) indicating the model performed

better than a random prediction (Figure 4.4). Fire frequency and land use had the most influence

on habitat suitability probabilities in the model (Figure 4.5). Fire frequency values of four, six,

seven, eight, and nine years had a high probability of explaining the HSM distribution (Figure

4.6). Due to the discrepancy in probabilities when using year nine in isolation versus without it,

year nine is likely to be the most important fire frequency value. Clearcut/early successional and

102

low intensity urban land use types had the best probability of explaining the HSM distribution

(Figure 4.7). Soil drainage, elevation, and soil texture were similar in their contribution to the

model (Figure 4.5).

Habitat suitability probabilities ranged from 0.00 to 0.99 (Figure 4.8). Habitat with the

highest probability of suitability (i.e., 0.75-1.0) comprised 0.6% of the Coastal Plain landscape

(Table 4.2). Habitat with a probability of suitability from 0.5-0.75 comprised 3.2% of the Coastal

Plain landscape and habitat with a probability of < 0.5 comprises 96.2% (Table 4.2). Habitat with

the highest probability of suitability (Figure 4.8) was concentrated along the western Fall Line

Sandhills, the Dougherty Plain, Atlantic Southern Loam Plains, and in the Tallahassee Hills

region along the Georgia-Florida border though small patches also appear on several military

installations in the eastern portion of the Coastal Plain (i.e., Ft. Gordon, and Ft. Stewart). H.

simus occurrence data used to build the model were located in habitats with a wide range of

suitability probabilities (Figure 4.8).

A visual comparison showed habitat with the highest probability of suitability in the

HSM overlaps the same general areas predicted as suitable in the GAP model (Figure 4.9)

though the predicted suitable habitat in the GAP model covered a larger portion of the Coastal

Plain landscape (i.e., 17%). Conservation lands contained 33% of habitat with the highest

suitability probabilities and 20% of habitat with suitability probabilities between 0.5 and 0.75

(Table 4.2; Figure 4.10).

Discussion

The HSM identified the Dougherty Plain, Tallahassee Hills, Atlantic Southern Loam

Plains, and the Fall Line Sandhills (Edwards et al. 2013) as the most suitable areas for the

103

southern hognose snake. However, habitat with a high probability of suitability appears to be

restricted even within these regions. With only 0.6% of habitat in the Coastal Plain having a high

probability of suitability and 3.2% having a moderate probability of suitability, there is an urgent

need to confirm whether populations exist in these areas so that remaining habitat can be

effectively managed.

My results suggest that an open canopy structure is important to H. simus habitat

suitability as frequent fire and clearcut/early successional and low intensity (LI) urban land use

types best explained the HSM distribution. Frequent fire promotes an open canopy structure

through suppression of hardwood species in the shrub and mid-story (Landers et al. 1995,

Kirkman et al. 2007). The importance of clearcut/early successional and LI urban land use is

consistent with a previous study which found H. simus in ruderal habitat disproportionately more

than it was available (Enge and Wood 2003). Though the literature suggests H. simus prefer

sandhill and oak-pine habitat (Tuberville et al. 2000, Ernst and Ernst 2003), it is possible that fire

suppression and silvicultural practices have created a close canopy structure in many habitats

and forced snakes into open canopy ruderal habitats. The importance of low intensity urban

habitat could have also been influenced by the high number of H. simus observations

(approximately 14%) on roads.

I found that the H. simus occurrence data used to build the HSM did not coincide with the

current distribution of habitat with a high probability of suitability. Snake occurrence in areas of

a low probability of suitability may result from two scenarios. First, habitat (for historic

occurrence data) may have been suitable at the time the occurrence was documented but the area

may not be suitable under current conditions (i.e., based on the 2008 GLUT land cover). My

MaxEnt output predicted suitable habitat based on 2008 land use/land cover data. Therefore, the

104

presence of older localities in unsuitable habitat indicates a decline in the extent of suitable

habitat for the southern hognose. Second, there could be variables that influence habitat

suitability that were not included in this model which may have resulted in an underestimation of

suitable habitat.

I propose that the maximum entropy approach to habitat suitability modeling is preferable

to modeling approaches that provide a binary output like that of the GAP model. The maximum

entropy approach categorizes habitat using a range of probabilities allowing for identification of

high probability habitats where populations may currently exist (Pearson et al. 2007). When

resources are limited, this approach allows for a targeted concentration of survey efforts.

Nonetheless, areas with a high probability of habitat suitability in the MaxEnt HSM

corresponded closely with predicted suitable habitat in the GAP model. Areas of concordance

between the two models warrant priority for field surveys to document current snake

populations.

Predicted suitable habitat for H. simus was distributed both within and outside

conservation lands in the Coastal Plain with approximately 33% of habitat with the highest

probability of suitability occurring within conservation land boundaries. Conservation lands

(7.7% of the Coastal Plain landscape) already contain a large portion of the small fraction of the

landscape (i.e., 0.6%) where habitat with a high probability of suitability occurs. This

information has the potential to inform and prioritize survey efforts. Populations of H. simus

outside of conservation lands are most vulnerable to anthropogenic threats such as habitat loss

and mortality due to development. Therefore, priority surveys should be conducted outside of

conservation lands in habitat with a high probability of suitability to identify vulnerable

populations in need of protection.

105

Caution should be exercised when interpreting habitat suitability models. Regions with a

high probability of habitat suitability, interpreted most conservatively, indicate areas with

comparable conditions to where populations were documented. HSM outputs should not be

interpreted as defining the actual range of a species (Pearson et al. 2007). The spatial resolution

of this HSM was limited by the available environmental data. The Coastal Plain landscape is

incredibly diverse in terms of habitat, geology, and soils(Edwards et al. 2013). I expect that

analyses using a finer grain size and more detailed soil and land cover data would better identify

patches of suitable habitat for the southern hognose. Future modeling efforts would benefit from

the use of the soil survey geographic database (SSURGO) which provides more detailed soil

classification than the STATSGO dataset used here. The SSURGO dataset was not used in this

model as it has not yet been completed for all counties in the Coastal Plain.

106

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Table 4.1. List of predictor variable categories, predictor variables, resolution/mapped scale, and

associated data sources included in the habitat suitability model for H. simus in the Coastal Plain

of Georgia.

Category

Predictor

Variable

Resolution/

Mapped Scale Data Source

Land Cover Georgia Land Use

Trends Data

30-60 m (depending

on class)

Natural Resources Spatial Analysis

Lab, UGA

Habitat

Management Fire Frequency 60 m

Remote Sensing Applications

Center, U.S. Forest Service

Substrate Soil Texture 1:250,000 Soil Conservation Service, U.S.

Department of Agriculture

Substrate Soil Drainage 1:250,000 Soil Conservation Service, U.S.

Department of Agriculture

Topography Digital Elevation

Model 1:250,000

National Elevation Dataset, U.S.

Geological Survey

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Table 4.2. Categories of habitat suitability probabilities, area of each category within the Coastal

Plain of Georgia, percentage of total Coastal Plain landscape, area of each category within

Coastal Plain conservation lands, and percentage of conservation lands within each category.

Probabilities and areas were obtained from a continuous MaxEnt habitat suitability model output

predicting the distribution of potentially suitable habitat for H. simus in Georgia.

Probability of

habitat

suitability

Area (ha) within

Coastal Plain % of landscape

Area (ha) within

conservation

lands

% of

conservation

lands

0.0-0.25 7,970,804 81.5% 533,275 70.8%

0.25-0.50 1,435,961 14.7% 139,156 18.5%

0.50-0.75 315,475 3.2% 61,648 8.2%

0.75-1.0 58,016 0.6% 19,399 2.6%

Totals 9,780,255 100% 753,478 100%

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Figure 4.1. Occurrence data (i.e., incidental observations, museum specimens, and trap captures)

documented from 1941 through 2012 used to develop a habitat suitability model for H. simus in

the Coastal Plain of Georgia.

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Figure 4.2. Map of fire frequency in the Coastal Plain of Georgia developed from satellite data

(Remote Sensing Applications Center, U.S. Forest Service) for 2001-2012 based on heat

anomalies indicative of prescribed fire or wildfire and used to create a habitat suitability model

for H. simus.

115

Figure 4.3. Georgia GAP Analysis distribution model (Kramer et al. 2003) of suitable habitat for

H. simus in the Coastal Plain of Georgia. The binary output predicts suitable habitat in green and

unsuitable habitat in black.

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Figure 4.4. Receiver operating characteristic plot (sensitivity and 1- specificity) and the

associated area under the curve (AUC) value ± 0.18 for the MaxEnt HSM developed for H.

simus in the Coastal Plain of Georgia.

117

Figure 4.5. Results of the jackknife test to evaluate importance of predictor variables included in

the MaxEnt HSM developed for H. simus in the Coastal Plain of Georgia. The graph shows the

training gain of each variable if the model was run in isolation (blue), when the model was run

without that variable (green), and when the model was run with all five variables (red).

118

Figure 4.6. Results of the jackknife test of fire frequency parameter (values ranging from 0-12)

importance in predicting the probability distribution of habitat suitability for H. simus in

Georgia. The graph shows the probability of presence if the parameter value was run in isolation

(blue), model was run without that parameter value (green), and when the model was run with all

parameter values (red).

119

Figure 4.7. Results of the jackknife test of land use parameter importance in predicting the

probability distribution of habitat suitability for H. simus in Georgia. The graph shows the

probability of presence if the parameter value was run in isolation (blue) and when the model

was run with all parameter values (red).

120

Figure 4.8. A continuous MaxEnt model output showing habitat suitability probability from low

to high with H. simus occurrence data across the Coastal Plain of Georgia.

121

Figure 4.9. Comparison of A) the MaxEnt habitat suitability model and B) a Georgia GAP

Analysis model (Kramer et al. 2003)predicting the distribution of suitable habitat for H. simus in

the Coastal Plain of Georgia.

122

Figure 4.10. A continuous MaxEnt habitat suitability model output showing habitat suitability

probability for H. simus from low to high relative to conservation lands (categorized as private,

federal, or state/local; The University of Georgia Natural Resources Spatial Analysis Lab and the

Georgia Department of Natural Resources) in the Coastal Plain of Georgia.

123

CHAPTER 5

CONCLUSIONS AND MANAGEMENT RECOMMENDATIONS

Basic natural history information on squamate species of the southeastern Coastal Plain is

needed to inform habitat management and conservation efforts. The purpose of this study was to

investigate biases of a community-level sampling technique for squamates and to evaluate the

influence of habitat structure on squamate species richness at local and landscape scales.

Additionally, I used spatial modeling to predict suitable habitat and identify environmental

variables important to the southern hognose snake, a cryptic, fossorial species that is not

effectively captured by traditional survey techniques.

Numerous aspects relating to the behavior, natural history, and ecology of a species may

influence the likelihood of capture using the box trap array design (Burgdorf et al. 2005, Steen et

al. 2010) and warrant future investigation. Knowledge of the biases and limitations of a sampling

method is essential when inferring information about a population or community (i.e.,

occupancy, abundance, species richness) from capture data (Mazerolle et al. 2007). I assessed

biases of box traps arrays, a common method for sampling squamates, in upland habitat on seven

sites in the Coastal Plain of Georgia during 2012 and 2013. My results indicated that foraging

mode (i.e., active foragers or ambush predators) did not influence squamate capture rates with

the box trap sampling method. However, capture rates were significantly higher for arboreal than

fossorial species. I did not find differences in detection probabilities among arboreal, fossorial, or

terrestrial species though this comparison included only a subset of the species included with

124

adequate sample sizes in the capture rate comparison. Nonetheless, I detected 16 snake and 10

lizard species, which demonstrated the utility of this technique in sampling a suite of squamate

species as well as its applicability in large-scale survey efforts. My results and previous studies

suggest that pitfalls and box traps capture different species demonstrating the importance of

incorporating both components into the array (Burgdorf et al. 2005, Todd et al. 2007, Todd and

Andrews 2008, Hyslop et al. 2009, Steen et al. 2012). If large-bodied snake species are of

particular interest, funnel traps could be incorporated into the design through placement along

drift fences to potentially increase captures (Enge 2001).

To address the lack of information on community-level habitat needs of squamates, I

investigated the influence of habitat and landscape structure on squamate communities. Species

richness estimates for my seven study sites ranged from 9.0 to 36.5. Using structural

characteristics of habitats at the site and landscape scale, I developed a suite of models to explain

squamate species richness. At the site level, percent area of wetland habitat within a 300-m

buffer was the best explanatory variable and was positively associated with squamate species

richness. The significance of wetland area in the site models suggests that aquatic habitats are

important sources of upland diversity possibly by providing alternative food sources for

squamates and by providing habitat heterogeneity (Steen et al. 2007). Squamate species richness

was positively associated with forested upland habitat at the landscape scale demonstrating the

importance of a forested landscape matrix for squamates. Patch density also was present in the

list of top landscape models and had a positive estimate indicating habitat heterogeneity may be

important to some species. The composite model containing site and landscape-level habitat

structural variables was the best fit model for the data and demonstrates the importance of

examining and managing for wildlife habitat at multiple spatial scales.

125

The results indicate that wetland and forested habitat structure positively influence

squamate species richness and can inform both habitat management and restoration efforts to

maintain and promote squamate species richness. Maintaining hardwood species in mesic areas

such as riparian zones and wetlands will retain heterogeneity within forest patches and support

squamate species richness. Restoration efforts should focus on converting altered habitats (i.e.,

agriculture or clear cuts) to a forested structure to promote diversity in squamate communities.

My results also shed light on how restoration efforts to transition forest stands back to natural

longleaf pine stands will impact squamates. A common technique for restoring stands of

undesirable pine species to longleaf pine in the Southeast is to clearcut stands and replant them in

longleaf pine (Jack et al. 2006). However, selective thinning of stands and replanting in gaps, as

opposed to clearcutting, can mitigate negative impacts on squamate communities by preserving

structural attributes of forests (Kirkman et al. 2007). A gradual restoration approach such as

selective thinning likely requires a longer time period to achieve the desired goal but can help

preserve squamate habitat and communities over the course of the restoration process.

Knowledge gaps in basic natural history information plague many squamate species

(Garner et al. 2010). These gaps are particularly problematic when trying to implement

conservation efforts for declining species. The southern hognose snake (Heterodon simus) is one

such species with a poorly understood ecology and apparently declining populations (Tuberville

et al. 2000). I created a spatially explicit habitat suitability model (HSM) for this species in the

Coastal Plain of Georgia. I used the program MaxEnt (Phillips et al. 2004, Phillips et al. 2006) to

build a habitat suitability model based on historic (1941-1999) and recent (2000-2012) H. simus

localities along with environmental variables with potential to influence occurrence of H. simus

including land use, elevation, soil texture, soil drainage, and fire frequency. Habitat with a high

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probability of suitability (0.75-1.0) was concentrated in the Dougherty Plain, Tallahassee Hills,

Atlantic Southern Loam Plains, and the Fall Line Sandhills (Edwards et al. 2013) and was

restricted even within these regions. Fire frequency and land use were the most important

variables in the HSM and indicated that frequent fire and open canopy habitat may be important

for predicting habitat suitability. The results of my habitat suitability model have the potential to

direct survey efforts for the southern hognose in Georgia. Habitat with a high probability of

suitability for H. simus comprises 0.6% of the Coastal Plain landscape and 33% of that occurs

within conservation lands which includes wildlife management areas, state/national parks and

forests, easement properties, and military installations. The results of this HSM establish an

urgent need to identify populations of H. simus and conduct research to determine specific

habitat needs of so that the little suitable habitat remaining can be effectively managed. Habitat

with a high probability of suitability occurring outside of conservation lands should be

considered priority for survey as this habitat is most vulnerable to anthropogenic threats. Surveys

could also be conducted in habitat with a low probability of suitability to determine if H. simus

populations are persisting in less suitable habitat and can shed light on the degree of habitat

specialization exhibited by this species. Additional occurrence data and new information

regarding life history and habitat requirements obtained from targeted surveys will improve

future HSMs and estimates of suitable habitat.

Future research on squamates should focus on improving the effectiveness of sampling

techniques to allow more precise estimates of species richness and abundance. In addition, fine-

scale data on habitat structure are needed as this information is integral for effective monitoring

and conservation efforts for diversity. My conclusions are based on a short-term survey of

squamate communities. Due to the low detectability and capture rates of squamates, I advocate

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the collection and use of long-term data when studying squamate communities to improve

estimates of state variables. Additionally, future modeling efforts for squamate species richness

should incorporate fine scale data including land cover data delineating detailed forest types,

habitat management regimes, and availability of below ground refugia. It is imperative that

future research focus on community-level requirements of squamates as this information will be

more useful to land managers than individual species needs and will increase the chances of

meeting habitat requirements for a suite of species present on a site.

128

Literature Cited





Enge, K. M. 2001. The pitfalls of pitfall traps. Journal of Herpetology 35:467-478.







Jack, S. B., R. J. Mitchell, and S. D. Pecot. Silvicultural alternatives in a longleaf pine/wiregrass

woodland in southwest Georgia: Understorey hardwood response to harvest-created gaps.

Proceedings of the 13th Biennial Southern Silvicultural Research Conference. 2006.



44:604-614.



Research Unit.

Mazerolle, M. J., L. L. Bailey, W. L. Kendall, J. A. Royle, S. J. Converse, and J. D. Nichols.

2007. Making great leaps forward: Accounting for detectability in herpetological field

studies. Journal of Herpetology 41:672-689.



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Phillips, S. J., M. Dudik, and R. E. Schapire. 2004. A maximum entropy approach to species

distribution modeling. Pages 655-662 in Twenty-First International Conference on

Machine Learning.







Steen, D. A., L. L. Smith, L. M. Conner, J. C. Brock, and S. K. Hoss. 2007. Habitat use of

sympatric rattlesnake species within the Gulf Coastal Plain. Journal of Wildlife










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APPENDIX

Table 3.14. Correlation matrix for site and landscape variables used to examine associations with squamate species richness sampled

on seven sites in the Coastal Plain of Georgia, 2012 and 2013. Variables were considered correlated if r ≥ 0.70.

S_FU S_NU S_W S_D S_BD S_BA L_FU L_NU L_W L_D L_PD

1S_FU 1.000 -0.964 0.397 0.179 -0.214 0.571 0.607 -0.892 -0.357 -0.036 -0.643

S_NU -0.964 1.000 -0.227 -0.321 0.250 -0.464 -0.75 0.821 0.536 -0.179 0.571

S_W 0.397 -0.227 1.000 -0.642 0.34 0.246 -0.189 -0.227 0.076 -0.265 0.113

S_D 0.179 -0.321 -0.643 1.000 -0.214 -0.143 0.429 -0.143 -0.143 0.429 -0.321

S_BD -0.214 0.250 0.34 -0.214 1.000 -0.607 -0.500 0.393 0.464 0.429 0.679

S_BA 0.571 -0.464 0.246 -0.143 -0.607 1.000 0.500 -0.821 -0.214 -0.571 -0.714

L_FU 0.607 -0.75 -0.189 0.429 -0.500 0.500 1.000 -0.607 -0.821 0.357 -0.393

L_NU -0.892 0.821 -0.227 -0.143 0.393 -0.821 -0.607 1.000 0.214 0.286 0.821

L_W -0.357 0.536 0.076 -0.143 0.464 -0.214 -0.821 0.214 1.000 -0.426 0.036

L_D -0.036 -0.179 -0.265 0.429 0.429 -0.571 0.357 0.286 -0.429 1.000 0.536

L_PD -0.643 0.571 0.113 -0.321 0.679 -0.714 -0.393 0.821 0.036 0.536 1.000 1S_FU = site-level % area of forested upland; S_NU = site-level % area of non-forested upland; S_W = site-level % area of wetland

S_D = site-level % area of development; S_BD = gopher tortoise burrow density (burrows/ha); S_BA = mean basal area (m2/ha);

L_FU = landscape-level % area of forested upland; L_NU = landscape-level % area of non-forested upland; L_W = landscape-level

% area of wetland L_D = landscape-level % area of development; L_PD = patch density (# of patches/100 ha).

the influence of habitat structure on squamate …

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