University of Florida Thesis Or Dissertation Formatting

THE POTENTIAL FOR SPREAD OF A NOVEL INVADER, THE TROPICAL CLAWED FROG (Xenopus tropicalis), IN FLORIDA

COLIN M. GOODMAN

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2020

To my family

ACKNOWLEDGMENTS

I would like to thank my family and friends for all of the support during this process. I would also like to thank my spouse, Olivia, for all of her support and assistance in editing this thesis. I would like to thank my advisor, Dr. Christina

Romagosa, for helping me to develop this project, as well as the tremendous amount of support she has provided me. I would like to thank my committee members, Drs. Jeffrey

Hill, Steve Johnson, and Miguel Acevedo, for their time, material support, and their invaluable input on my thesis. Another thanks to Dr. Quenton Tuckett for his guidance on the analyses. Thanks to Dr. Anthony Herrel for his input on locomotor performance methodology. I am extremely grateful to the Florida Fish and Wildlife Conservation

Commission for funding; without this funding, this project would not have materialized.

Further, thanks to Daniel Quinn, Clinton Cunningham, and Tyson Dallas for providing preliminary data. I would like to thank Craig Watson, Amy Wood, Micah Alo, and all others at the University of Florida Aquaculture Laboratory. They offered material supplies, as well as a venue for the conduction of all laboratory research associated with this research. I would also like to thank Dr. Bob Reed and the United States

Geological Survey for the material support. Thanks to Katie Buckman, Lauren Lapham, and Dr. Allison Durland, for their assistance with field work and animal husbandry.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 9

ABSTRACT ...... 111

CHAPTER

1 BACKGROUND ON THE EXTANT INVASION OF THE TROPICAL CLAWED FROG IN WEST-CENTRAL FLORIDA ...... 13

General Introduction ...... 13 Invasion History ...... 14 The Current Invasion ...... 16 Research Objectives ...... 17

2 DISPERSAL, MORPHOLOGY, AND LOCOMOTOR PERFORMANCE OF TROPICAL CLAWED FROGS IN WEST-CENTRAL FLORIDA ...... 21

Synopsis ...... 21 Background ...... 22 Study Area ...... 24 Methods ...... 25 Field Sampling ...... 25 Marking...... 26 Performance Assays ...... 28 Maximal exertion capacity ...... 28 Jumping performance ...... 29 Morphology ...... 30 Data Analysis ...... 31 Results ...... 32 Discussion ...... 36

3 CLIMATIC AND HABITAT SUITABILITY FOR INVASION OF THE TROPICAL CLAWED IN FLORIDA ...... 57

Synopsis ...... 57 Background ...... 58 Methods ...... 60

Compiling Occurrence Records ...... 60 Variable Selection and Manipulation ...... 61 Sampling Bias ...... 64 Model Construction and Tuning ...... 66 Model Projection and Constraining ...... 69 Results ...... 71 Model Evaluation ...... 71 Model Projection and Transfer ...... 72 Model Constraining ...... 73 Discussion ...... 74

4 CONCLUSION ...... 91

LIST OF REFERENCES ...... 966

BIOGRAPHICAL SKETCH ...... 118

LIST OF TABLES

Table page

2-1 Pairwise correlations between each caliper-based measurement and its pictorial extracted analogue...... 50

2-2 Mean and standard deviation of morphometric measurements used in models, split up by sex and dispersal status ...... 51

2-3 Mean and standard deviation of performance trait values, split up between sex and dispersal status ...... 51

2-4 Mean and standard deviation of jumping trial values, split up between sex and dispersal status ...... 51

2-5 Rankings of all candidate logistic regression models using only frogs that had dispersed or remained at the same site for an entire dry season (N=92) ...... 52

2-6 Summary from the top model from the candidate set in Table 2-5 ...... 52

2-7 Rankings of all candidate logistic regression models linking maximal exertion capacity to dispersal status (N=84) ...... 54

2-8 Summary of the conditional results from the subset of top-performing models Table 2-7 ...... 54

2-9 Rankings of all candidate logistic regression models from the jumping performance trials (N=90) ...... 55

3-1 Descriptions of all variables used in ENMs, and the reasoning for their inclusion ...... 80

3-2 Legend of all the categories inclued in the GlobCover dataset (from Bicheron et al., 2013)...... 82

3-3 Summary of thermal buffer data gathered from temperature logger data...... 84

3-4 Table showing performance metrics of all models evaluated...... 84

3-5 All terms used in the top-performing model...... 85

3-6 Relative suitability scores across all sites within the invaded range where X. tropicalis has been detected ...... 90

LIST OF FIGURES

Figure page

1-1 Dorsal view of the skeleton (Field ID CMG-111) based on a CT scan...... 19

1-2 Map of detections and non-detections of X. tropicalis in Riverview, Florida ...... 20

2-1 Collage showing the variation in sites within the study area ...... 44

2-2 Map of all sites where X. tropicalis was detected ...... 45

2-3 Map showing the sites used for the capture-mark-recapture study...... 46

2-4 Images of the different types of traps used for the surveys in this study...... 47

2-5 Schematic of the morphometric measurements obtained by all frogs used for performance trials...... 48

2-6 Exemplar of the photographs from which all morphometric measurements were extracted, excepting dimensions of ilia ...... 49

2-7 A histogram showing the number of dispersers captured at varying distances, split up between two sampling seasons...... 49

2-8 Number of dispersal events broken down by each month during the study period ...... 50

2-9 Differences in size-relative morphometric traits between dispersers and residents, as well as males and females ...... 53

2-10 Differences in maximal exertion capacity between males and females, and dispersers and residents ...... 55

2-11 Size-relative differences in jumping performance between males and females, and dispersers and residents ...... 56

3-1 Map showing all background and occurrence localities used in the models...... 83

3-2 Response curves for the top-performing ENM...... 86

3-3 Relative suitability map of the accessible area of X. tropicalis ...... 87

3-4 Relative suitability map based on the top-performing ENM ...... 88

3-5 Map of relative suitability using various thresholds ...... 89

4-1 Photographs of three different species observed depredating X. tropicalis individuals...... 95

LIST OF ABBREVIATIONS

AICC Akaike’s Information Criterion for smaller sample sizes

AUC Area under the curve

AUCtest Area under the curve for testing data

AUCtrain Area under the curve for training data

C Celsius

CPUE Catch per unit effort

CLmax Chronic lethal maximum

CLmin Chronic lethal minimum cm Centimeter

CTmax Critical thermal maximum

CTmin Critical thermal minimum

E East eDNA Environmental Deoxyribonucleic Acid

ENM Ecological niche model

FWC Florida Fish and Wildlife Conservation Commission g Grams

GBIF Global Biodiversity Information Facility

Ha Hectare

IUCN International Union for Conservation of Nature

Km Kilometer

L Liter

LP Linear + Product

LQ Linear + Quadratic

LQP Linear + Quadratic + Product

LSTmin Minimum monthly Land Surface Temperature m Meter mm Millimeter

Max Maximum

MERIS Medium Resolution Imaging Spectrometer

MESS Multivariate Environmental Similarity Surface

Micro-CT Micro computed tomography

MODIS Moderate Resolution Imaging Spectroradiometer

MS222 Tricaine methane sulfonate

N North

PIT Passive Integrated Transponder

RM Regularization multiplier

ROC Receiver Operating Characteristic s Second

SSBL Surrogate species bias layer

SSSmax Maximum sum of specificity and sensitivity

SVL Snout vent length

VIE Visible Implant Elastomer

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

THE POTENTIAL FOR SPREAD OF A NOVEL INVADER, THE TROPICAL CLAWED FROG (Xenopus tropicalis), IN FLORIDA

Colin Goodman

August 2020

Chair: Christina Romagosa Major: Wildlife Ecology and Conservation

Non-native species are a major hazard to global biodiversity, and present a fiscal burden of $120 billion annually in the United States alone. Given these high costs, effective management is key, and this often entails a taxon-specific approach. Recently, a high-density breeding population of the tropical clawed frog was discovered in west- central Florida. Tropical clawed frogs have since been observed at a total of 24 sites. I conducted a series of observational, experimental, and simulation studies, to determine how likely the tropical clawed frog is to continue to spread throughout Florida. In the field, I measured the frequency with which individually-marked animals dispersed between sites. Using an experimental design, I tested individual frog locomotor performance. Lastly, I used a suite of habitat and bioclimatic variables to generate projections of relative suitability across the state of Florida. I found that despite being predominantly aquatic, individuals were quite adept at overland dispersal. I also found that dispersers had longer hindlimbs and wider ilia than did residents, female dispersers had greater size-relative stamina, and jumped with greater size-relative force than did residents. However, neither overall jumping performance nor maximal exertion capacity predicted dispersal status. In spite of the cooler climate in Florida relative to the native

range, I found that most of peninsular Florida appears suitable for invasion. However, thermal tolerance data suggest that most of northern Florida may be too cold for the tropical clawed frog. Our results suggest a high potential for spread if containment measures are not undertaken.

CHAPTER 1 BACKGROUND ON THE EXTANT INVASION OF THE TROPICAL CLAWED FROG IN WEST-CENTRAL FLORIDA

General Introduction

Non-native species present a major hazard to global biodiversity (Mack et al.,

2000) and are one of the largest drivers of contemporary extinction (Bellard et al.,

2016). Further, non-native species threaten human and ecosystem health (Juliano &

Lounibos, 2005; Pejchar & Mooney, 2009), and cost the United States alone approximately $120 billion annually (Pimentel et al., 2005). Given these large costs, prevention of establishment and containment of extant invasions are key to preserving biodiversity (Molnar et al., 2008). However, the management of invasive species can be quite challenging, as there is often a tradeoff between the resources and time necessary to manage the population effectively and the increase in cost and severity over time (Baxter & Possingham, 2010). Simberloff (2003) recommends a brute force removal method, but this presumes that the invasion is localized, and can be easily eradicated. Later stage invasions often require relevant biological knowledge. This knowledge can then be integrated into best-management practices, allowing managers to determine both when and where to efficiently and effectively employ their limited resources (Lennox et al., 2015).

These issues are particularly salient in Florida. As of 2007, there were an estimated 123 established non-native wildlife and marine species throughout the state

(Hardin, 2007). Owing in large part to the steady propagule pressure provided by the pet trade, coupled with Florida’s subtropical climate, more recent estimates are a at minimum of 63 established non-native herpetofaunal species (Krysko et al., 2016;

Lockwood et al., 2019).Given the high number of invaders, managers may be forced to

prioritize finite resources based on the potential impacts of the species or its likelihood to spread. Thus, the ability to predict this likelihood is key.

In recent years, there has been a growing interest in the study of dispersal— defined as the permanent movement of an organism from one habitat patch to another

(Clobert et al., 2009)—and how it can drive evolutionary change (Snell et al., 2019).

One prediction born out of dispersal theory is that the hostility of the space between habitats (the matrix) will predict the extent to which a species adopts strategies for lowering the cost of dispersal (Schtickzelle et al., 2007). The current invasion of the tropical clawed frog (Xenopus tropicalis) in Florida represents a great opportunity to explore this question. Almost fully aquatic, and occupying discrete water bodies, X. tropicalis typically only leaves water in order to disperse from one water body to another

(Measey, 2017). This means that the area between suitable patches—the matrix—is relatively hostile. Given this fact, dispersal in this species may be predicted by functional traits.

Invasion History

The initial record of a clawed frog (Xenopus sp.) in Hillsborough County dates back to the 1970s (Tinsley & McCoid, 1996). The individual was collected near an animal import facility, close to, or within, Riverview, FL (Steve Godley, personal communication 2019), and was deposited into scientific collections at the University of

South Florida (Roy McDiarmid, personal communication 2019). This specimen was identified as an African clawed frog (Xenopus laevis) due to the species’ ubiquity in global invasions rather than any formal identification (Roy McDiarmid, personal communication 2019), and the specimen was destroyed during an extended renovation of the University of South Florida’s Science Center (Henry Mushinsky, personal

communication 2019). With at least 10 extant populations spanning four continents

(Measey et al. 2012; Measey et al., 2020), X. laevis is one of the most invasive anuran species, and has been linked to reduced reproduction in native anurans (Lillo et al.

2011), altered macroinvertebrate composition (Courant et al., 2018a), reduced species richness (Courant et al., 2018b), and pathogen spread (Weldon et al., 2004).

It is unclear whether this initial individual found in Hillsborough County represented a lone escapee or a breeding population, but no further verified reports occurred until 2013 (Hill et al., 2017). In 2013, a homeowner reported having found an individual Xenopus sp. on her property, which was subsequently identified as X. laevis.

Surveys were conducted at abandoned aquaculture facilities nearby, but no Xenopus individuals were observed (Hill et al., 2017). This same person found another individual

Xenopus sp. in 2016. It was after this that Hill et al. (2017) discovered the first Xenopus breeding population in Riverview, FL.

Despite the initial identification, further anatomical and genetic analyses have subsequently determined that the Riverview population is actually a cryptic invasion of the tropical clawed frog (Xenopus tropicalis; Goodman et al., unpublished data), a congener to X. laevis. I used high resolution microcomputed tomography scans (micro-

CT) to taxonomically validate the subgeneric identity of the Riverview population (Figure

1-1). Members of the subgenus Silurana—of which X. tropicalis is a part—can be distinguished from members of the subgenus Xenopus—of which X. laevis is a part— with the presence of unfused nasal bones, absence of vomer bones, and fusion of the first two presacral vertebrae (Cannatella et al., 1988). I used molecular data to generate

a maximum likelihood phylogeny (Goodman et al., unpublished data), confirming the species identity to be X. tropicalis.

The Current Invasion

While the impacts of X. laevis invasions are well-documented, it remains unclear what impacts the current invasion of X. tropicalis is having on native species. However, there are parallels between these two species that may be cause for concern. Both X. laevis and X. tropicalis are present in the pet trade and are used in biomedical research

(Beck & Slack, 2001; Measey, 2017). However, because X. tropicalis is diploid

(opposed to tetraploid X. laevis; Kobel et al. 1996), it has become increasingly popular in genomics research (O’Rourke, 2007) and may possess a similar capacity for global release as X. laevis. Additionally, both X. laevis and X. tropicalis are generalist aquatic predators, predominantly feeding on macroinvertebrates (Imasuen & Aisien, 2016;

Measey, 1998; Courant et al., 2017a). The larval stages of both species are filter feeders, and adults are cannibalistic (Tinsley & McCoid, 1996), a strategy that effectively allows these species the ability to capture energy from primary production.

X. laevis and X. tropicalis have distinct maturation rates, with X. tropicalis having a much shorter generation time (Hirsch et al., 2002). Generation time is an important life-history characteristic and is linked to an invasive species’ ability to colonize new areas (Sakai et al., 2001). Additionally, Allen et al. (2017) found that within amphibians and reptiles, species with increased maturation rates and higher fecundity were more likely to successfully establish novel populations as well as to spread. X. laevis is known to travel large distances over land between habitat sites (De Villiers & Measey, 2017). If

X. tropicalis possesses a similar capacity, this could indicate the potential for rapid spread, given the high density of water bodies within Hillsborough County (~6.6/km2;

ArcGIS REST Services Directory, 2019). Since the first verified report in 2013, X. tropicalis has been observed at 26 different sites, representing a total occupied area of about 16.3 km2 (Goodman et al., unpublished data; Figure 1-2). Although X. tropicalis is associated with tropical areas in the Guinean Rainforest belt in Sub-Saharan Africa

(Rödder et al., 2017), experimental evidence from individuals in the native range suggests that the species may be relatively cold-tolerant (Herrel & Bonneaud, 2012a), and thus may be able to persist throughout much of Florida. Taken together, these traits underscore the importance of quantifying the capacity of X. tropicalis to continue to colonize new areas outside of the known invaded range in Florida.

Research Objectives

The primary aim of my research was to evaluate the potential of X. tropicalis to spread throughout the state in Florida. I accomplished this this by using a combination of field study, experimental physiology, and correlative modeling. In Chapter 2, I used a capture-mark-recapture design to determine the frequency with which free-ranging individuals moved between water bodies. I then used experimental performance assays to determine the maximal exertion capacity and jumping performance of free-ranging X. tropicalis individuals. I coupled these performance assays with a description of individual morphology to determine whether these traits could predict an individual’s likelihood to travel between sites.

In Chapter 3, I used a mixture of climate and habitat data from within both the native range and Florida to predict the relative suitability of Florida for further invasion of

X. tropicalis. Last, I used both ecologically relevant temperature data collected from within the invaded range, along with experimental thermal tolerance data gathered from individuals within the Florida X. tropicalis population, to determine how much of Florida

may experience thermal regimes suitable for X. tropicalis These data will both add to our understanding of the ecology of this non-native species and provide information to help guide management of this population.

All research was approved by the University of Florida Institutional Animal Care and Use Committee, protocol number 201810274. This protocol covered all relevant data collection, including the marking, releasing, and subsequent recapture of free- ranging tropical clawed frogs. This protocol also covered all of the physiological assays described in this study. Additionally, all research was conducted with the approval of the

Florida Fish and Wildlife Conservation Commission (FWC), contract numbers 13416-

A3035 and 13416-A3041.

Figure 1-1. Dorsal view of the skeleton based on a micro-CT scan. CT scan illustrates three osteological traits that distinguish members of the subgenus Silurana from members of the subgenus Xenopus. 1) unfused nasals, 2) absence of vomer bones, 3) fusion of first two presacral vertebrae (Cannatella et al. 1988)

Figure 1-2. Map of detections and non-detections of X. tropicalis in Riverview, Florida. Red symbols represent sites where X. tropicalis of any life stage were detected; yellow symbols represent sites where no X. tropicalis were detected; triangles represent sites where X. tropicalis larvae were observed; circles represent sites where no X. tropicalis larvae were observed. Open objects represent ponds; cross-hatched objects represent residences. All data come from verified reports, unpublished FWC data, and from this study. Map made in ArcGIS 10.7.1 (ESRI 2019, Redlands, CA).

CHAPTER 2 DISPERSAL, MORPHOLOGY, AND LOCOMOTOR PERFORMANCE OF TROPICAL CLAWED FROGS IN WEST-CENTRAL FLORIDA

Synopsis

Dispersal is a key driver of population dynamics, and dispersal ability and propensity often differ among populations and individuals. In periods of range expansion, these differences can lead to non-equilibrium dynamics, whereby more dispersive phenotypes arrange themselves spatially. This can be even more pronounced when dispersal success is nonrandom with respect to a heritable trait, thus acting as an agent of selection. This process—dubbed spatial sorting—can be particularly pronounced in non-native species, often hastening the speed of invasion spread. I tested whether dispersal status was nonrandom with respect to morphology and physiology in a recently discovered, high-density breeding population of Xenopus tropicalis in west-central Florida. Specifically, I predicted that individuals that successfully dispersed would have increased jumping performance, increased maximal exertion capacity, longer hindlimbs, and larger ilia than those that did not disperse. To test this, I compared the morphology, maximal exertion capacity, and jumping performance of dispersers and residents. To test maximal exertion capacity, frogs were encouraged to jump around a circular track until exhaustion. Jumping performance was tested in a large, drained aquarium. I found that after controlling for residency time, individuals that dispersed had increased maximal exertion capacity, increased jumping performance, longer hindlimbs, and wider ilia. However, sex was generally observed as a significant interaction effect, suggesting that any impact of these measured traits on dispersal may be heterogeneous between males and females.

Background

Dispersal is defined as the permanent movement of an organism from one habitat patch to another (Clobert et al., 2009) As a key component of metapopulation and community dynamics (Hanski and Gilpin, 1991; Holt, 2003; Ronen et al., 2018), dispersal can impact a species’ distributional range (Penner & Rödel, 2019), and serves a wide array of functions. For example, dispersal can act as the mechanism by which individuals can access more favorable habitats (Stamps, 2001; Cornelius et al., 2017;

Nafus et al., 2017). Additionally, where resources may be scarce, dispersal can serve to alleviate the impacts of inbreeding (Hamilton & May, 1977) and competition (both intra- and interspecific; Grabowska et al., 2019; Denno & Roderick, 1992).

Despite its importance at the species and population levels, dispersal can be quite costly—even fatal—to individuals (Lucas et al., 1994). Thus, dispersal is often context-dependent, and there are frequently biological tradeoffs associated with dispersal at the individual and population levels (Yawata et al., 2014; de Waal et al.,

2015; Weigang & Kisdi, 2015). The selection pressures related to dispersal can be viewed as acting upon two broad categories: propensity and ability to disperse (Bowler

& Benton, 2009). Propensity to disperse refers to a group or an individual’s ‘willingness’ to disperse, and is generally thought of as being behavioral. Indeed, realized dispersal propensity has been linked to myriad behavioral traits, such as boldness (Rehage & Sih,

2004), willingness to explore (Botero-Delgadillo et al., 2019), and aggression

(Duckworth & Badyaev, 2007). Ability to disperse refers to an individual’s ability to survive a given dispersal attempt. This success rate can be influenced by a multitude of traits, including behavior, physiology (Hillman et al., 2014), and morphology (Andersen,

1993). While there may be tradeoffs between some of these traits in some species

(Zollner & Lima, 2004), often they are not mutually exclusive. There is also a growing body of literature recognizing the existence of dispersal syndromes, whereby the presence or absence of a particular dispersal-related trait is correlated to other dispersal-related traits (Ronce & Clobert, 2012).

Within the context of an invasion, dispersal is one of two primary determinants of the invading population’s ability to colonize novel areas (Bass et al., 2006). This process of range expansion can lead to non-equilibrium dynamics (Phillips et al., 2008), whereby traits are selected heterogeneously across space (Phillips et al., 2008), a process that has been dubbed spatial sorting (Shine et al., 2011). Insofar as the propensity and/or ability to disperse is nonrandom with respect to any trait or suite of traits, individuals with those traits will find themselves along the range front with similar individuals. If any of these traits are heritable, this spatial sorting can compound in an iterative and inter- generational process, ultimately increasing the overall rate and stochasticity of range expansion (Perkins et al., 2013; Ochocki & Miller, 2017). Empirical evidence of spatial sorting abounds across a diverse array of taxa, from plants (Williams et al., 2016) to invertebrates (Berggren et al., 2012) to vertebrates (Berthouly-Salazar et al., 2012).

This array also includes a congener to X. tropicalis, X. laevis. Since its initial introduction in the 1980s (Measey & Foquet, 2006), the French population of X. laevis has exhibited spatial selection for reproductive investment (Courant et al., 2017b), limb morphology, and locomotor performance (Louppe et al. 2017). The Florida population of

X. tropicalis offers a novel opportunity to examine whether some of these same traits are undergoing spatial selection in a similar species, but in a different environmental context.

Despite being almost fully aquatic, members of the genus Xenopus often rely on overland dispersal to move between habitat patches, and Xenopus spp. have been observed dispersing relatively long distances, upwards of 2.4 km (De Villiers & Measey,

2017). Presumably, overland dispersal is an important aspect of patch connectivity within the Florida population of X. tropicalis as well. My study aimed to determine whether dispersal status and ability are nonrandom with respect to a suite of morphological and physiological traits in the non-native X. tropicalis population in

Riverview, Florida. Due to the high uncertainty of introduction point as well as invaded range boundary, I was unable to explore trait differences in an explicitly spatial context.

Instead, I was specifically interested in whether there were signs of selection at the individual level, given a successful dispersal event. In anurans, dispersal has been linked to sex (Wang et al., 2012), overall size (Chelgren et al., 2008), hindlimb and forelimb length (Bredeweg et al., 2019; Clarke et al., 2019), dimensions of the ilia

(Louppe et al., 2017), jumping ability (Bredeweg et al., 2019), and physiological endurance capacity (Maes et al., 2013). Relating to sex, I predicted that males would be more likely to disperse, and to disperse greater distances. Regarding morphology, I predicted that dispersal status and distance would be positively associated with relative hindlimb length, relative ilium width, and relative ilium length. Lastly, relating to physiology, I predicted that dispersal status and distance would be positively related to endurance capacity and jumping ability.

Study Area

I conducted this study within the ~1630 ha known invaded range of X. tropicalis, in Riverview, Florida (Figure 1-2). The area has a human population density of about

594/km2 (U.S. Census Bureau, 2011), and is largely dominated by housing

developments. Most of the water bodies in the area are used for stormwater runoff, draining into various tributaries of the Alafia River, which drains into Tampa Bay. The sites used in this study were highly variable in size, amount of canopy cover, and length of hydroperiod (Figure 2-1).

Methods

Field Sampling

I selected a total of four water bodies to serve as marking sites over two sampling seasons, 29 May-7 December 2018 and 5 January-13 November 2019. These sites were selected based upon their relatively high catch per unit effort (CPUE; Figures

2-2, 2-3). To quantify CPUE, I divided the total number of X. tropicalis individuals captured at each site by the total number of traps deployed, such that the final estimate was comprised of the average number of frogs captured in a trap over a given night.

Each of these sites vary in their hydro-period, but all typically experience some portion of the year with no standing water. In addition to marking sites, I sampled in the accessible water bodies in proximity to the marking sites in order to maximize the number of dispersal events captured. Sampling was accomplished through the use of various trap types (Figure 2-4). Traps were baited with raw chicken liver, and were placed in water bodies between 15:00 and 23:30, and then retrieved the following morning. During each trapping occasion, I recorded the location of the trap using a

Garmin Etrex GPS (3m accuracy). All captured frogs were inspected for markings

(methods described in the next section). Unmarked frogs were either marked in the field and then released or brought back to the lab to be marked. All marked frogs—excepting frogs being used for performance assays—were immediately released back at the site of capture.

For the purposes of this study, a dispersal event was defined as any individual frog’s movement from one water body to another; thus movements within water bodies were not considered. All frogs captured after having dispersed were brought back to the

Tropical Aquaculture Laboratory (TAL) for performance assays and subsequent euthanasia. Dispersal distances were calculated as the Euclidean distance from the initial site of capture to the final site of capture using ArcGIS 10.7.3 (ESRI, Redlands,

CA).

In addition to dispersers, I also collected a subset of marked individuals that did not disperse from two of the marking sites for performance assays. These frogs had remained at the same site for a minimum duration of one dry season, and were considered as the resident group. I interpreted this as a way of maximizing the probability that these individuals were true residents.

Marking

Because of the relatively high summer temperatures, coupled with the fact that most ponds sampled were on private lands, I had to transport captured frogs to TAL

(~26 km away) prior to marking them. Starting in May 2018, frogs were marked using a combination of visible implant elastomer (VIE, Northwest Marine Technology,

Anacortes, WA) and clipping the distal portion of two toes (Hoffman et al., 2008;

Doherty-Bone et al., 2013). VIE comes in a variety of colors, and fluoresces under ultra- violet light. The colors used in this study were orange, red, green, and blue; and the color of elastomer used corresponded to the water body of initial capture. To minimize tag loss, VIE was injected subcutaneously near the inner thigh (Sapsford et al., 2015).

Prior to marking, frogs were anesthetized in order to minimize distress and increase processing rate. Frogs were anesthetized through immersion in a 0.5g/L

solution of tricaine methane sulfonate (MS222) and water (Torreilles et al., 2009) buffered with sodium bicarbonate to a solution pH of 7.0-7.5 (Professional Plus multiparameter instrument, YSI, Yellow Springs, OH). Frogs were left in the immersion bath until they ceased attempting to right themselves after being placed on their back.

Frogs were then marked as described above, and the sex of each frog was noted. Sex was determined by the presence of nuptial pads in males and the presence of a protruding ovipositor in females (Measey & Tinsley, 1998). Any frog for which sex could not be unambiguously determined was denoted as being a juvenile. After processing, each frog was then placed into a recovery chamber with just enough water to avoid desiccation. After recovery, each frog was then released back at the site of initial capture.

In May 2019, I discontinued batch marking individuals and began giving individuals unique identifiers through the use of Passive Integrated Transponder (PIT) tags. The PIT tag dimensions were 8 mm x 1.4 mm (Oregon RFID, Portland, OR). PIT tags were injected into each frog’s dorsal lymph sac following Donnelly et al. (1994).

Prior to marking, frogs were transported back to the lab as described above. However, in order to determine what—if any—impact transportation had upon capture probability, a subset of frogs from two sites were marked within 15 m of the site of capture and then immediately released at the site of capture. To minimize the impact of PIT tags, only frogs greater than 3.5 cm SVL were marked using this method (De Villiers & Measey,

2017). On each sampling occasion, I visually inspected each frog for the presence of a

PIT tag or VIE mark for a minimum of 30 seconds.

Performance Assays

Maximal exertion capacity

To test for maximal exertion capacity, I constructed a (3.35 m) circular track, lined with cork to allow for better grip (Herrel & Bonneaud, 2012b). To measure maximal exertion capacity, individual frogs were introduced to the track, and then were chased around, using gentle taps to the urostyle and either side of the hindlimbs. A trial was considered over when the frog ceased responding to taps, and could no longer right itself after being flipped on its back (Herrel & Bonneaud, 2012b). The variables selected to measure maximal exertion capacity were both the distance and time travelled before reaching exhaustion. I recorded all trials with a Nikon D3100 DSLR camera (Nikon,

Tokyo, Japan), using a frame rate of either 30 or 60 frames per second. The camera was placed in the center of the track, at an elevated height (~2 m), such that the entire track was in frame for the duration of each trial. Distances were measured after the completion of trials using the computer software ImageJ2 (Schindelin et al., 2012), such that the observer was blind to dispersal status. All trials were performed in a climate- controlled area, close to the optimal temperature of X. tropicalis (~26° C, Herrel &

Bonneaud, 2012a). Prior to each trial, the cork substrate was moistened in order to avoid desiccation of the frog. Each frog performed this assay a total of three times, and only the greatest value for each measurement (distance and time travelled prior to exhaustion) was retained in the analysis. After cessation of each trial, frogs were patted dry, weighed using an Ohaus Scout Pro scale (0.01 g accuracy, Parsippany, New

Jersey), and then placed back into their aquaria. Frogs were given at least 24 hours to recover between each trial. All maximal exertion trials were conducted between 19 May

2019-16 October 2019.

Jumping performance

Jumping performance was determined using a drained, 50-gallon aquarium

(90x30x30 cm), lined with a cork substrate. Frogs were introduced to the aquarium, and then gently poked on the urostyle to illicit a jumping response. This was done repeatedly for 1.5 minutes. After cessation of the trial, each frog was weighed as above and then placed back into its aquaria. Every frog performed this assay twice, with a minimum of one-hour rest between each trial. I recorded each trial with the use of two cameras, one positioned directly above, such that the entire aquarium was in focus (Nikon D5300, 60 frames per second), and one positioned anterior to the aquarium (Nikon D3100, 30 frames per second). The dorsal view was used to determine jumping distance (to the nearest cm), and the anterior view jump height (to the nearest cm). All distances were measured using ImageJ2 (Schindelin et al., 2012), such that the observer was blind to dispersal status. For each individual frog, I used only the jump with the greatest length

(De Villiers, 2016), and then measured this jump’s maximum height. I then calculated the resultant force from the following equation (James et al., 2007):

퐿 2 푠푖푛2훼 푑 = (푊 × ) ⁄3 × 푚푏 푔 (2-1)

Here, d represents the distance jumped in the x-y plane, W is the resultant force, L is the distance from the end of the hindlimb to the frog’s center of mass (De Villiers, 2016), m is the mass of the frog at the time of the trial, g is the acceleration due to gravity, and alpha is the jumping angle, calculated by taking the tangent of jump height, divided by half the total jump distance.

Morphology

Within two days after the completion of performance trials, frogs were humanely euthanized via the same method as described above, with an increased concentration of MS222 (~5 g/L) and concomitant increase in dosage of sodium bicarbonate. Frogs remained in the immersion for a minimum of 15 minutes and were then checked for signs of respiration and heartbeat. If signs of respiration or heartbeat persisted, an intra- cardiac injection of the aforementioned solution was administered. I then waited five minutes, and then checked again for signs of respiration or heartbeat. If no signs were present, I waited another five minutes prior to declaring individuals deceased. Note that no individuals had signs of heartbeat or respiration after the intra-cardiac injection was administered. After death was confirmed, I took two different pictures using a Nikon

D5300 camera in order to quantify morphometrics. The first picture was taken of the dorsal side of the frog (Figures 2-5, 2-6), and the second the ventral side of the frog.

Using ImagJ2, I then extracted the following measurements: Snout-vent-length, forearm length, femur length, tibia length, foot length, and longest toe length. There were also a series of measurements that I was unable to extract from pictures: ilium width, ilium length, head width, and head length. As such, I used calipers (iGaging Store, Los

Angeles, CA; 0.02 mm accuracy) to take these measurements on the euthanized frogs.

All measurements were taken in such a way that I was blind to dispersal status. In order to estimate disparities between the two different measurement methods, I also took the first set of measurements (hindlimbs and forelimbs)—on a subset of individuals

(N=31)—using calipers. There is generally thought to be high intra- and inter-observer measurement error, even with high-precision instruments (Hayek et al., 2001); to compensate for this, all measurements were recorded at the highest level of precision,

and then rounded to the nearest mm. However, the highest level of precision was retained for the inter-method comparison.

Data Analysis

To test the prediction that males are more likely than females to disperse, I used a chi-squared test to compare the proportion of males and females that were marked to the proportion of males and females that were captured after having dispersed. To determine the concordance between different morphometric measurement methods, I examined pairwise Pearson correlation coefficients for each measurement extracted from pictures and those measurement’s caliper-measured analogue. I then refined this list of variables using those with greater inter-method correlations. I used logistic regression, where dispersal status was classed as a binary variable, with zero representing a resident and one representing a disperser to determine whether dispersal status was predicted by morphology and locomotor performance. Explanatory variables were the various performance and morphological metrics (Tables 2-2, 2-3, 2-

4). I used three different sets of models, one for morphology, one for maximal exertion capacity, and one for jumping performance. Within each model set, I developed different combinations of variables based on our a priori predictions and previous scientific findings.

Model evaluation was performed using corrected Akaike’s Information Criterion

(AICc; Burnham & Anderson, 2003), which attempts to maximize model parsimony.

Within each model set, the best model was said to be the model with the lowest AICc score. However, any model within two AICc points was considered a competing model.

When there existed multiple competing models, I used model averaging—based on AIC weights (Burnham & Anderson, 2003)—to calculate a weighted average of each model

term used in the top model set. I also calculated the area under the receiver operating characteristic curve (AUC; Henley & McNeil, 1982) in order to determine how well each model actually fit the data. To determine whether the traits of interest differed among dispersers in residents disproportionately to size, I used the Mann Whitney U test to compare the ratio of each respective measure—excepting maximal time prior to exhaustion—to SVL. Given that strong sex differences have been observed in congeners to X. tropicalis (Herrel et al., 2012; De Villiers & Measey, 2017; Louppe et al.,

2017), I used separate tests for males and females.

Results

Between 29 May 2018 to 13 November 2019, I marked a total of 1998 individual frogs from the four selected marking sites (Figure 2-3). This includes all of the batch marked individuals (N=1321), as well as individually marked, PIT-tagged individuals

(N=677). The vast majority (N=1866) of the individuals marked were from two of these four sites. During this same time period, I recaptured a total of 72 individual frogs after having dispersed from one site to another. The majority of these individuals (N=70) originated from the same two sites where the majority of the marking took place (Figure

2-3). Because of the initial method of batch marking, it is not possible to determine how many unique individuals were recaptured during the 2018 season. In the 2019 season, a total of 168 unique PIT tagged individuals were recaptured (24.8%), and 16 (9.5%) of these individuals were recaptured after having dispersed. There was an apparent male- bias in dispersal, with 1.77 male dispersers captured for every female dispersal.

However, there was also a strong male-bias in capture rate and marking rate, with 1.57 males being marked for every female. There was no significant difference between the

percentage of marked males that dispersed (7.13%) and the percentage of marked females that dispersed (6.34%; X2 = 0.138, p = 0.355).

Mean dispersal distance was relatively consistent across both sampling seasons

(2018: 222±61.8m; 2019: 213±69.9m; Figure 2-7). Breaking the dispersal events down by month, the greatest number of dispersal events across both seasons occurred in

August (Figure 2-11), which is typically the month with the highest precipitation for the area. Overall, the majority of dispersal events occurred during the summer months, with few events occurring in the spring and fall, and none during the winter.

Concordance between different measurement types was highly variable, with the greatest correlation being between measurements of the hindlimb (r(29) = 0.95, p

<0.001)—which consisted of the aggregate of femur, tibia, foot, and longest toe measurements—and the lowest inter-method correlation being the humerus measurement (r(29) = 0.04, p = 0.815). In spite of the relatively low correlations between each constitutive portion of the forelimb, the entirety of the forelimb was still relatively consistent across both measurement methods (r(29) = 0.71, p < 0.001). With the exception of the humerus measurements, all correlations were statistically significant (Table 2-1). Given that the inter-method measurement correlations were greater for the entire hindlimb and forelimb relative to their constitutive parts, only these measurements—alongside SVL, ilium width, and ilium length—were retained for further analysis.

I used dispersers and residents collected in the 2019 field season in morphological comparisons and performance assays (Table 2-2). Beginning with morphology (dispersers n = 43, residents n = 49), the best performing model was the

one that included sex, SVL, hindlimb length, and ilium width (Table 2-5). This model shows individuals that dispersed had both longer hindlimbs and wider ilia but lower SVL

(Table 2-6). There were no additional models within 2 AICc points, and the singular top model had roughly 60% of the AICc weight. Examining differences in size-relative morphological traits, female dispersers had significantly longer size-relative hindlimbs than did residents (Figure 2-9; Z = -2.459, p = 0.007), but males did not (Figure 2-9; Z =

-1.565, p = 0.0588). Conversely, male dispersers had longer size-relative forelimbs than did male residents (Z = -2.424, p = 0.007), but this trend was not significant in females

(Z = 0, p = 0.5). Male dispersers also had greater size-relative ilium widths than did residents (Z = -1.795, p = 0.036), but the difference among females was not significant

(Z = -1.193, p = 0.116). Size-relative ilium lengths were greater in female dispersers than in residents (Z = -1.880, p = 0.030), but not in male dispersers relative to residents

(Z = 0.736, p = 0.769). There were significant sex differences across all traits excepting size-relative ilium length (Z = 1.392, p = 0.918), with males having longer size-relative hindlimbs (Z = -0.376, p < 0.001), longer size-relative forelimbs (Z = -7.429, p < 0.001), and wider size-relative ilia (Z = -2.939, p = 0.002).

I had issues with maximal exertion assays; the first iteration of the track did not function properly, which caused large intra- and inter-individual inconsistencies across trials. The analysis of maximal exertion assays thus only includes individuals tested during the track’s second iteration (dispersers n = 35; residents n = 49; Table 2-3). The best performing model for maximal exertion capacity was the one that included pond ID, maximal distance travelled until exhaustion, sex, and their interaction as explanatory variables (Table 2-7). However, there were a total of six models that were within two

ΔAICc, including the intercept-only model. Nonetheless, the intercept-only model received little support, with only 10% of the AICc weight. The five remaining models received substantial support, with 81% of the AICc weight. The results from model averaging indicate that dispersers travel longer distances and for longer intervals prior to reaching exhaustion (Tables 2-8). Examining size-relative variation in maximal exertion capacity, female dispersers travelled significantly greater size-relative distances prior to exhaustion than did residents (Figure 2-10; Z = -1.7, p = 0.045). This trend was not significant in male (Z = -1.020, p = 0.154). Males travelled significantly greater size-relative distances prior to exhaustion than did females (Z = -1.757, p =

0.039).

During two jumping trials, one of the cameras did not record, which resulted in two frogs not being used in the analysis(dispersers n = 42; residents n = 48).The best model relating to jumping performance the model that included maximum jump length, sex, and their interaction as terms (Table 2-9). There were two competing models within two ΔAICc, including the intercept-only model. Further, the intercept-only model received a substantial amount of support, with 28% of the AICc weight. The two remaining competing models received limited support, with only 48% of the AICc weight.

Thus, I concluded that there was not strong evidence that jumping performance predicted dispersal status. Examining size-relative variation in jumping performance, neither male nor female dispersers jumped greater size-relative lengths (Figure 2-11; males: Z = -0.237, p = 0.406; females: Z = -0.95, p = 0.171) or heights (males: Z = -

0.071, p = 0.472; females: Z = 0.209, p = 0.583). Female dispersers did jump with significantly greater force relative to SVL (Z = -2.089, p = 0.018) than did female

residents; however, trends among males were not significant (Z = 0.521, p = 0.7). I observed significant sex differences in jumping performance, with males jumping greater lengths (Z = -4.316, p < 0.001) and heights (Z = -2.557, p = 0.005) relative to

SVL. Conversely, females jumped with marginally greater resultant force relative to

SVL, but this difference was not significant (Z = 0.202, p = 0.42).

Discussion

Our results indicate that X. tropicalis is capable of dispersing relatively large distances, with the greatest observed dispersal distance being over 400 meters. With the exception of two dispersal events, all observed dispersal presumably occurred over land. While there is a stream that flows in proximity to many of the sites within the study area, the vast majority of the dispersal directions were either perpendicular to, or running opposite to the direction of the stream. However, this stream, as well as others within the area, may in fact serve as corridors, allowing this species to access suitable areas farther than it otherwise would be able via overland dispersal. Within its native range, X. tropicalis is known to utilize riverine habitats, particularly during the dry season (Rödel, 2000). Additionally, lotic systems within the area may also allow this species to disperse through what might otherwise be highly resistant landscapes.

Most of the dispersal events occurred during the summer months, with the peak number of captures being in August, during both seasons (Figure 2-3). While June, July, and August are typically cooler months within the native range of X. tropicalis, they nonetheless coincide with the rainy seasons in both the native and invaded ranges.

Further, this is also the period where peak breeding appears to occur across the invaded and native range (Rödel, 2000; pers. obs.). Additionally, there were numerous instances where a trapping session resulted in no captured dispersers, and then a rain

event was followed by numerous captured dispersers (pers. obs.) at the same site.

Given that this species—and members of the genus Xenopus more broadly—can desiccate and die without access to water or moist substrate, this peak in dispersal may be an adaptive characteristic. Examples of rain prompting movements in anurans abound (Dimmitt & Ruibal, 1980; Fisher & Shaffer, 1996; Baumberger et al., 2019), including the congener, X. laevis (Measey, 2016). However, there is also evidence in both X. laevis and X. gilli that the desiccation of seasonal water bodies may prompt dispersal (De Villiers, 2016). Presumably this would be in order to seek out a permanent water source. It is difficult to say whether or not this is the case with X. tropicalis, but there is some preliminary evidence from this study that suggests this type of dispersal.

Of all PIT tagged frogs that were captured after dispersing (N=16), 13 of these individuals came from the smallest marking site. This site also had the shortest hydroperiod, which is consistent with the hypothesis that dispersal might also be prompted by pond desiccation.

The total number of dispersers captured across both sampling periods was relatively low compared to the total number of marked individuals (3.6%), and while the number of PIT tagged dispersers was also low (N=16), it represents approximately 9.5% of all PIT tagged frogs that were recaptured. Although this dispersal rate is comparable to 5% movement rate of congeners X. laevis and X. gilli in their respective native ranges in Southern Africa (X. laevis, 5.17%, X. gilli, 3%, De Villiers, 2016), it is below the observed dispersal estimates for introduced populations of X. laevis (11.3%; Courant et al., 2019; 21% Measey & Tinsley, 1998). It is important to note, however, that all of these studies were conducted over a longer period of time than was this study (this

study, 19 months; De Villiers, 2016, 21 months; Courant et al., 2019, three years;

Measey & Tinsley, 1998, 14 years), thus confounding comparisons of dispersal rate.

The dispersal rate observed in this study represents the minimum rate, and does not account for detection probability, which can impact estimators (Kellner & Swihart, 2014).

However, the total number of captured dispersers as a proportion to total daily captures was as high as 6.8%. Assuming uniform detection between marked and unmarked individuals, this would suggest a relatively large proportion of frogs from some ponds being comprised of individuals that had successfully dispersed.

Additionally, because of the nature of the capture-mark-recapture study, sampling effort was focused primarily at marking sites. As such, efforts were more limited at adjacent sites, and thus probably underestimated the amount of dispersal to these sites relative to the marking sites. Indeed, spatial variation in sampling effort has been shown to bias dispersal estimates (Van Noordwijk, 1995). This study was also limited by the areas that could be accessed. Many of the ponds within the invaded range—and within the range of measured dispersal distance—were on land where I could not obtain permission to sample. Lastly, sampling restrictions caused by the

COVID-19 pandemic made it such that I was unable to sample in spring 2020. Because dispersal kernels are often fat-tailed, with rare—but present—long-distance dispersal events, additional sampling may have allowed for better characterization of the distribution’s tail (Pielaat et al., 2006). Despite these caveats, the results of this study still suggest a high capacity for overland movement in this species as seen with its congeners X. laevis and X. gilli.

I predicted that dispersers would be more likely to be male, would have longer hindlimbs, longer and wider ilia, increased maximal exertion capacity, and increased jumping performance, relative to residents. Evidence of sex-biased dispersal in amphibians is mixed, with some taxa exhibiting female-biased, and others exhibiting male-biased dispersal (Trochet et al., 2016). I found no evidence of male sex-bias in dispersal after correcting for the male sex-bias in capture rate. These results are similar to those reported by De Villiers and Measey (2017), where researchers found no evidence of sexual bias in dispersal in X. laevis. It may be that within the genus

Xenopus, dispersal is not related to mate access, but rather to access of more suitable habitat.

In relation to morphology, other studies have attempted to directly or indirectly link dispersal status to morphology in anurans. Phillips et al. (2006) found that in their invaded range in Australia, cane toads (Rhinella marina) with longer hindlimbs move more frequently and are more likely to be in newly invaded areas (i.e. along the invasion front). In the same R. marina invasion, Hudson et al. (2016) found variation in body dimensions (e.g. wider forelimbs, narrower hindlimbs, more compact skulls), such that individuals along the invasion front were morphologically adapted to larger movement bouts than were individuals in the invasion core. The same was also found in the

French X. laevis invasion, with individuals along the invasion periphery having longer hindlimbs (Louppe et al., 2017). I found, after controlling for residency time, dispersers were smaller (as measured by SVL), but possessed longer hindlimbs and wider ilia

(Table 2-6). This model exhibited a moderate goodness of fit (AUC = 0.710), suggesting that the impacts of variables on dispersal status may be significant. Further, I found that

female dispersers had significantly longer size-relative hindlimbs and ilia, and male dispersers had significantly wider size-relative ilia (Figure 2-6). This suggests that the differences between dispersers and residents are not fully explained by overall differences in size.

There are a number of possible explanations for why SVL might be negatively related to dispersal. It may be that the act of dispersal selects for individuals with lower

SVL. There is evidence of this occurring in congener X. laevis. Louppe et al. (2017) found that in the French invasion of X. laevis, individuals along the range front have lower SVLs than do individuals in the range core. This finding implies that overall size is being spatially selected during the process of range expansion, via dispersal.

Alternatively, it may be that dispersal is age-biased within the Florida invasion of X. tropicalis. Dispersal can serve to ameliorate competition with kin (Cote & Clobert, 2010;

Kubisch et al., 2013), and a number of studies have found juvenile-biased dispersal in anuran species (Bulger et al., 2003; Funk et al., 2005). Although all the frogs used in performance assays exhibited secondary sex characteristics (i.e. appeared to be adults), it remains a possibility that younger adult frogs may be more likely to disperse.

Another study tested this more directly, where researchers experimentally constructed ponds at varying distances from natural sites currently occupied by green frogs (Rana clamitans; Searcy et al., 2018). The researchers then measured the body condition, SVL, and hindlimb length of all R. clamitans individuals that dispersed to one of the experimental patches, as well as a subset of those that did not disperse.

However, contrary to the results of my study, Searcy et al. (2018) found no relationship between dispersal status and hindlimb length, but did find dispersers were larger than

were residents. R. clamitans is less aquatic than is X. laevis (Searcy et al., 2018), and presumably relies on jumping ability more than does X. laevis. Thus, it may be that the hindlimbs of R. clamitans are already adapted for the locomotion necessary for dispersal.

Aligning with my predictions, individuals that dispersed travelled farther and for longer intervals before reaching exhaustion, relative to residents. Also, similar to morphology, these disparities among dispersers and residents were more pronounced in females than in males (Table 2-3). Despite females travelling marginally greater distances than males, males travelled significantly greater distances relative to SVL

(Figure 2-10), which is consistent with my findings in morphology. Individual differences in maximal exertion capacity are often thought to be at least in part a consequence of individual differences in morphology (Herrel et al., 2012). However, I did not find evidence of size-dependence on either the maximum distance travelled or on the time travelled until exhaustion. Results from a simple linear regression indicate that endurance is unrelated to SVL in X. tropicalis (maximum distance: F1,116 = 1.485, p =

0.225; maximum time: F1,116 = 0.313, p = 0.577). These results contradict what has been shown in congener X. laevis within its native range (De Villiers, 2016). This could be due to the relatively restricted size variation in the frogs used in my study, or due to disparities in average size between X. tropicalis and X. laevis. It may also be due to the fact that measurements in my study were rounded to a lower level of precision that what is reported in similar studies. Alternatively, these results may indicate that endurance physiology is decoupled from size, and is instead dependent upon some other factors.

Anuran endurance capacity has been shown to be dependent on both the proportion of

muscle-fiber types present (Wilson et al., 2002), as well as the rate of oxygen transport throughout the body (Seebacher & Franklin, 2011).

My prediction of increased jumping performance in dispersers relative to residents received little support. The intercept-only model had a relatively large amount of AICc weight. Additionally, the remaining two top models suggest no relationship between dispersal status and maximum jump height and a negative relationship with maximum jump force. Despite this, female dispersers exerted significantly greater size- relative force than did residents. While males jumped proportionally higher than did females, size-relative jumping heights did not differ between dispersers and residents.

Given that X. tropicalis (as well as all members of Pipidae) is characterized by a dorso- ventrally flat body plan, with individuals spending most of their time foraging in pond substrates (Tinsley & McCoid, 1996), it is not surprising that vertical jumping ability is not selected via dispersal. However, it is unclear why dispersers would jump with weaker resultant force relative to residents. Some studies have suggested that interspecific variation in endurance may be related to jumping ability (Reilly et al., 2015).

Additionally, Herrel et al. (2014) found a trade-off between jump force and the time travelled prior to exhaustion in a Xenopus sp. If this finding is true of X. tropicalis, it may be that dispersal is selecting for stamina at the expense of jumping force. However, this same study also found a positive correlation between jump force and distance travelled until exhaustion. Further, this trade-off would still be insufficient to explain why maximum jump length was greater among dispersers in this study. Indeed, since dispersers in this study had greater size-relative endurance, one should expect any

jumping ability trade-off to manifest itself into both reduced maximum jump force and reduced maximum jump length.

A more plausible explanation may be that resultant force was calculated, rather than measured. Thus, it may be the case that compounding measurement error led to an apparent statistical relationship where no biological relationship exists. This explanation is supported by the fact that only the competing model included maximum jump force as a term (Table 2-9).

These results indicate that even amongst relatively isolated patches surrounded by resistant, urbanized matrices, X. tropicalis demonstrates a clear ability to disperse over land. Further, these results from morphometric and locomotor performance analyses suggest that dispersal is nonrandom with respect to these traits. Although these data are not sufficient to demonstrate the existence of spatial sorting, the observed individual differences in dispersal-related traits may lead to spatial sorting if any of these differences are heritable. There is evidence for the heritability of locomotor performance in X. tropicalis. Using transcriptomics, Richards et al. (2016) identified 42 different genes associated with variation in endurance in X. tropicalis individuals. This study, coupled with the results of my research, suggest that dispersal-related traits in X. tropicalis may compound across space, leading to a concomitant increase in the rate of invasion spread over time. This underscores the importance of developing methods of control while the invasion may still be somewhat isolated.

Figure 2-1. Collage showing the variation in sites within the study area.

Figure 2-2. Map of all sites where X. tropicalis was detected. The size of the yellow dots represents the catch per unit effort. Numbers represent the maximum CPUE in each category.

Figure 2-3. Map showing the sites used for the capture-mark-recapture study. The size of circles corresponds to the total number of individuals captured after having dispersed from each respective site. The color corresponds to the total number of individuals marked from each site over the course of the study. Coordinates of marking sites: (A) 27.859664° N, -82.307414° E; (B); 27.853722° N, -82.320114° E (C) 27.853318° N, -82.318078° E; (D) 27.850352° N, -82.319040° E. All coordinates are in decimal degrees (WGS84 Datum).

Figure 2-4. Images of the different types of traps used for the surveys in this study.

Figure 2-5. Schematic of the morphometric measurements obtained by all frogs used for performance trials. Note that the actual measurements were based on external measurements extracted from pictures (Figure 2-6).

Figure 2-6. Exemplar of the photographs from which all morphometric measurements were extracted, excepting dimensions of ilia. Ilia measurements were made using calipers.

Figure 2-7. A histogram showing the number of dispersers captured at varying distances, split up between two sampling seasons. Distances represent the Euclidean distance from the initial and subsequent sites of capture.

Figure 2-8. Number of dispersal events broken down by each month during the study period.

Table 2-1. Pairwise correlations between each caliper-based measurement and its pictorial extracted analogue. Hindlimb measurements represent the aggregate of the femur, tibia, foot, and longest toe measurements. Forelimb measurements represent the aggregate of the humerus, radius, hand, and longest finger measurements. Note that I was unable to extract ilium dimensions from pictures. Variable r Lower CI Upper CI p SVL 0.935 0.869 0.969 <0.001† Femur 0.927 0.853 0.964 <0.001† Tibia 0.942 0.883 0.972 <0.001† Foot 0.757 0.551 0.877 <0.001† Longest toe 0.848 0.706 0.925 <0.001† Hindlimb 0.954 0.901 0.978 <0.001† Humerus 0.044 -0.315 0.392 0.8152 Radius 0.555 0.25 0.76 0.0012† Hand 0.401 0.055 0.661 0.0253† Longest finger 0.676 0.423 0.831 <0.001† Forelimb 0.709 0.473 0.85 <0.001† † denotes statistical significance at the 0.05 alpha level.

Table 2-2. Mean and standard deviation of morphometric measurements used in models, split up by sex and dispersal status. Note that these values are not rounded, and represent the mean of all individuals. Female Male

Resident Disperser Resident Disperser n=15 n=18 n=34 n=25 SVL (mm) 45.40±6.32 46.77±4.97 40.32±2.89 39.67±2.84 Mean trial weight (g) 9.75±4.06 10.56±3.04 7.2±1.57 7.12±1.31 Ilium width (mm) 10.33±1.35 10.94±0.94 9.68±0.84 9.84±0.85 Ilium length (mm) 19.07±2.63 20.17±2.07 17.03±1.34 16.52±1.19 Hind-limb length 64.87±8.81 69.33±7.11 61.06±4.95 60.84±4.48 (mm) Forelimb length (mm) 17.60±2.44 18.06±2.13 18.09±1.16 18.44±1.26

Table 2-3. Mean and standard deviation of performance trait values, split up between sex and dispersal status. Female Male

Resident Disperser Resident Disperser n=15 n=16 n=34 n=19 Maximal distance (m) 27.25±5.84 31.69±5.49 28.12±7.81 28.90±5.71 Maximal time (s) 99.07±27.99 125.20±49.25 116.82±47.47 132.32±55.46

Table 2-4. Mean and standard deviation of jumping trial values, split up between sex and dispersal status. Female Male

Resident Disperser Resident Disperser n=14 n=18 n=34 n=24 Maximal jump length (cm) 38.71±5.12 42.56±4.59 41.12±5.27 40.58±4.37 Maximal jump height (cm) 8.71±1.14 9.06±1.21 8.56±1.08 8.58±1.47 Maximal resultant force (N) 1.35±0.66 1.68±0.52 1.29±0.39 1.21±0.30

Table 2-5. Rankings of all candidate logistic regression models using only frogs that had dispersed or remained at the same site for an entire dry season (N=92). Model terms AUC K AICc ΔAICc AICc Wt SVL + sex + hindlimb length + ilium 0.710 5 123.7377 0.0000 0.6007 width SVL + hindlimb length + ilium width + 0.688 5 127.5257 3.7880 0.0904 ilium length SVL + sex + hindlimb length + ilium 0.718 7 127.8934 4.1557 0.0752 width + ilium length + source pond SVL * source pond + sex + hindlimb 0.716 7 128.0416 4.3039 0.0698 length + ilium width Intercept-only 0.681 5 128.1963 4.4586 0.0646 SVL + hindlimb length + sex + source NA 1 129.1919 5.4542 0.0393 pond Hindlimb length + ilium width 0.626 3 129.6109 5.8731 0.0319 SVL + hindlimb length + ilium width + 0.734 9 130.6885 6.9508 0.0186 ilium length + weight + forelimb length + hindlimb length + ilium width SVL + hindlimb length + ilium width + 0.684 7 132.0476 8.3099 0.0094 ilium length + weight + forelimb length

Table 2-6. Summary from the top model from the candidate set in Table 2-5. Covariates Estimate Std. error z-value p-value Intercept -2.7801 2.8626 -0.9712 0.3315 Sex (male) -1.2231 0.6489 -1.8848 0.0595 SVL -0.5169 0.1767 -2.9257 0.0034† Hindlimb length 0.2849 0.1075 2.6502 0.0080† Ilium width 0.7180 0.3401 2.1111 0.0348† † denotes statistical significance at the 0.05 alpha level.

Figure 2-9. Differences in size-relative morphometric traits between dispersers and residents, as well as males and females. Dispersers are denoted by green circles, and residents by blue triangles. Whiskers represent 95% confidence intervals. Asterisks represent significant differences at the 0.05 alpha level, based on individual Mann Whitney U tests.

Table 2-7. Rankings of all candidate logistic regression models linking maximal exertion capacity to dispersal status. (N=84). Model selection based on minimizing AICc. Model terms AUC K AICc ΔAICc AICc Wt Max time + sex 0.634 3 114.8 0.00 0.201 Max distance * sex + max time + 0.697 6 114.9 0.02 0.199 source pond Max distance * sex + source pond 0.679 5 115.1 0.23 0.179 Max distance 0.609 2 115.7 0.89 0.129 Intercept-only NA 1 116.2 1.31 0.104 Max distance * sex + max time 0.664 5 116.3 1.45 0.097 Max distance + max time 0.622 3 117.1 2.28 0.064 Max distance * SVL + source pond 0.631 5 119.9 5.04 0.016 Max distance + max time + SVL + 0.656 6 120.5 5.65 0.012 sex + source pond

Table 2-8. Summary of the conditional results from the subset of top-performing models Table 2-7. Values were calculated from model averaging, based on AIC weight. Covariates Estimate Std. error z-value p-value Intercept -3.655 2.585 1.402 0.161 Max distance 0.119 0.077 1.511 0.131 Sex 3.403 3.395 0.995 0.320 Max time 0.012 0.007 1.687 0.092 Source Pond (site 2) -0.799 0.541 1.455 0.146 Max distance * Sex (male) -0.188 0.097 1.915 0.055

Figure 2-10. Differences in maximal exertion capacity between males and females, and dispersers and residents. Dispersers are denoted by green circles, and residents by blue triangles. Whiskers represent 95% confidence intervals. Asterisks represent significant differences at the 0.05 alpha level, based on individual Mann Whitney U tests. Note that while maximal distance travelled is size-relative, maximal time is not.

Table 2-9. Rankings of all candidate logistic regression models from the jumping performance trials (N=90). Model terms AUC K AICc ΔAICc AICc Wt Max jump length * sex 0.693 4 125.9815 0.0000 0.3438 Intercept-only NA 1 126.4117 0.4301 0.2773 Max jump length * sex + max jump force 0.677 5 127.7955 1.8139 0.1388 Max jump length * sex + SVL 0.686 5 128.1653 2.1838 0.1154 Max jump length * sex + source pond 0.683 5 128.2176 2.2360 0.1124 Max length + max jump force + max height 0.572 5 132.9154 6.9338 0.0107 + SVL Max length + max jump force + max height 0.576 7 136.6396 10.6580 0.0017 + SVL + sex + source pond

Figure 2-11. Size-relative differences in jumping performance between males and females, and dispersers and residents. Dispersers are denoted by green circles, and residents by blue triangles. Whiskers represent 95% confidence intervals. Asterisks represent significant differences at the 0.05 alpha level, based on individual Mann Whitney U tests.

CHAPTER 3 CLIMATIC AND HABITAT SUITABILITY FOR INVASION OF THE TROPICAL CLAWED IN FLORIDA

Synopsis

The management of non-native species relies—in part—on the ability to allocate resources effectively and efficiently. This, in turn, relies on the ability to prioritize areas of special concern. Thus, the use of forecasting can help both predict the possible course of an invasion, as well as allow managers to focus their efforts. There is a growing interest in using Ecological Niche Models (ENMs) for this purpose. ENMs seek to estimate a species’ available fundamental niche, in order to predict where that species is most likely to invade—free of dispersal barriers—across a given area of interest. I use this framework to predict the areas of Florida which are most suitable for further invasion of Xenopus tropicalis. To do so, I used publicly available climate and habitat data, along with occurrence records gathered from the native range in Africa.

Additionally, I constrained the final predictions using a combination of experimental thermal physiological data, as well as data from temperature loggers deployed in Florida water bodies during the winter. I then used three different thresholds, varying in their balancing of omission and commission, to generate a presence/ absence map. I found much of northern Florida to be unsuitable due to current thermal constraints. However, I found the majority of peninsular Florida to be suitable given the two lower omission thresholds, and much of southern Florida suitable given the higher omission threshold.

Additionally, all occurrence points collected from the invaded range were predicted as presences at the mid-level threshold, suggesting the model has predictive validity.

Background

The distribution of a species is the product of both biotic factors—including competition (MacArthur, 1972) and predation (Holt et al., 2011)—and abiotic factors— such as habitat (Brum et al., 2013; Platts et al., 2019) and climatic conditions

(Rodrigues & Lima-Ribeiro, 2018). The overlap of these constraints in geographical space represents the area that is actually occupied by the species, and is generally called the realized niche (Hutchinson, 1957). However, more recently there has been a push to incorporate dispersal constraints into this definition (Soberón, 2007; Colwell &

Rangel, 2009), which can also be key to explaining species range boundaries (Evans et al., 2011; Algar et al., 2013). Moreover, dispersal is often one of the limiting factors in the establishment and spread of non-native species (Blackburn et al., 2011). However, as earth’s human population continues to globalize and modify the landscape, the dispersal barriers for many species are increasingly diminished via large, human- mediated jump dispersal events, leading to the widespread proliferation of non-native species (Crooks & Suarez, 2006; Hulme, 2009; Everman et al., 2013). If this trend continues, these abiotic and biotic factors may become more influential in explaining more broadscale trends.

One specific tool growing in popularity due to its application in invasion biology is the Ecological Niche Model (ENM). ENMs seek to estimate the geographically available fundamental niche—defined as the environmental conditions that support population persistence (Hutchinson, 1957; Holt, 2009)—over some area of interest, often in the absence of dispersal constraints. ENMs typically rely on the use of non-consumable variables, such as habitat and climate, and are typically premised on the assumption that abiotic factors are more salient at broader spatial scales (Pearson & Dawson, 2003;

Soberón, 2007). However, this assumption is in debate, and biotic interactions may still play a role in shaping distributions (Anderson, 2016; Simões & Peterson, 2017).

Nonetheless, ENMs can offer invaluable insight into the environmental conditions needed to support species persistence in particular areas.

One limitation of correlative niche models that focus on data gathered from the native range is that they assume the niche is conserved across geographical space.

There are many instances of confirmed niche conservatism (Peterson, 2011), and thus

ENMs have proven reliable at predicting the invaded ranges of non-native species

(Areias-Guerreiro, 2016; Barbet-Massin et al., 2018). However, exceptions to niche conservatism are also plentiful, including in anuran species such as the cane toad

(Tingley et al., 2014), American bullfrog (López et al., 2017), and Xenopus laevis

(Rödder et al., 2017). One way to counteract this is to use data from within the invaded range, including physiological constraints (Rougier et al., 2015). The constraints may be directly integrated into the models themselves, or may be used to constrain predictions outside of experimentally-derived limits (Evans et al., 2015a).

There are a litany of correlative model types used to predict the likelihood of invasion by a species into novel environments. Although there is no consensus for an algorithm that works best in all possible cases, BIOCLIM and Maxent have both been shown in simulations to have the greatest average model performance, relative to other algorithms (Qiao et al., 2015). BIOCLIM is a relatively simple type of envelope model that relies on environmental variables gathered from known occurrence locations (Nix,

1986; Booth et al., 2013). These variables are then used to generate a multi- dimensional environmental space where the range of values at known occurrence

points represents the abiotic tolerances of the target species (Araújo & Peterson, 2012).

Maxent is a more recently developed machine learning algorithm, which seeks to generate a relative probability distribution based on a comparison between known covariate values at known occurrence locations and at a set of reference points (Phillips et al., 2006). Maxent also has added flexibility, allowing for the incorporation of categorical predictors, as well as more complex response curves.

The aim of this study was to determine the suitability of Florida for further invasion by X. tropicalis. To accomplish this, I used a combination of correlative niche models and experimentally derived physiological constraints. Niche models were comprised of a set of bioclimatic and habitat variables, and physiological constraints were the upper and lower temperatures at which X. tropicalis experiences mortality. I used a suite of model evaluation metrics, and then projected the top-performing model over Florida.

Methods

Compiling Occurrence Records

To compile presence points to use in niche models, I began by downloading all occurrences available in the Global Biodiversity Information Facility (GBIF). Due to changes in taxonomy, I used queries for both “Silurana tropicalis” and “Xenopus tropicalis”. These queries resulted in a total of 2,158 and 2,335 records, respectively.

Records were then filtered so as to exclude any fossil records. Further, only points that were both georeferenced and had no known coordinate issues were retained, resulting in a total of 87 occurrences. In this study, I follow the eastern range boundary of X. tropicalis set by Loumont (1983), which concludes that the Sanaga River in Cameroon separates X. tropicalis and Xenopus epitropicalis. The remainder of the range boundary

was set using the International Union for the Conservation of Nature’s (IUCN; IUCN

SSC Amphibian Specialist Group, 2019) range of extent. Thus, all records that fell outside of this extent were discarded.

Using only records from GBIF resulted in large disparities between IUCN’s distribution and the range implied by occurrence records. To compensate for this, I also used all available locality records from primary literature, using a combination of two different databases (Google Scholar and Web of Science), as well as a bibliography compiled by Manfred Beier (http://pddb.org/index.html). Only points with unambiguous locality information were used. I then aggregated the points from GBIF and those compiled from primary sources, discarding all duplicate localities. After aggregating and discarding duplicated points, a total of 141 occurrence records remained.

Variable Selection and Manipulation

Variables used in niche models were selected based on a priori predictions of biological relevance, coupled with existing literature on similar species (Table 3-1). The primary variables used were gathered from WorldClim 2.1 (Fick & Hijmans, 2017). This consists of a suite of temperature and precipitation-related variables averaged from

1970-2000 (Fick & Hijmans, 2017). The highest available resolution is 30 arc-seconds

(~1 km at the equator). However, the resolution of these rasters had to be reduced to match the resolution of other variables used in the analysis. I accomplished this by resampling all rasters using bilinear interpolation in ArcGIS 10.7.1 (ESRI 2019,

Redlands, CA). The resulting raster resolution was 1.37 km at the equator. The variables from WorldClim 2.1 are derived from weather stations around the globe, and then interpolated across areas without coverage (Hijmans et al., 2005).

The temperature-related variables of WorldClim are based upon air temperatures, generally taken at 1.5m above the ground (Faye et al., 2014). This can result in insufficient capture of important variation (or lack thereof) in surface temperatures (Mildrexer et al., 2011). Water has relatively high specific heat capacity, and thus water bodies may experience reduced variation in temperatures than the surrounding air and other landcover classes (Deng et al., 2018). This fact will likely be more pronounced at higher latitudes, which are subject to larger temperature fluctuations. Therefore, I also generated a dataset analogous to BIO 6 (Mean minimum temperature of coldest month), using the land surface temperature available from the

Moderate Resolution Spectroradiometer (MODIS; MODIS 11A2, version 006; Wan et al., 2015). The MODIS products use satellite imagery to generate estimates of land surface temperatures at 1 km resolution. MODIS11A2 offers minimum nightly land surface temperatures as 8-day running averages, which precluded me from generating perfect monthly composites. I chose the coldest month across both the native and invaded range (invaded range: January; native range: August), and then created a composite of four 8-day averages. For Florida, this resulted in a single average minimum land surface temperature for the period of 1 January to 1 February. For the native range, this resulted in a single average minimum land surface temperature for the period of 5 August to 6 September. I repeated this for all available years (2001-2018) and then created a composite based upon the mean value across all years. All raster processing was performed in ArcGIS 10.7.1 (ESRI 2019, Redlands, CA).

The only habitat variable selected was forest cover. I chose this variable because of the association between X. tropicalis and forested areas throughout its native range

(Rödel, 2000). Data for forest cover were gathered at the global scale from GlobCover at 10 arc-second resolution (Bicheron et al., 2013). The GlobCover data set is a land cover map derived from the Medium Resolution Imaging Spectrometer satellite

(MERIS). This map consists of categories relating to broad vegetation types, artificial, and water-related land cover types (Table 3-2). Using these classifications, I generated a binary raster based on whether or not the primary land class of each grid was forest

(Table 3-2). Lastly, I reduced the resolution of the resultant raster to match that of the land surface temperature raster. I accomplished by using the nearest neighbor interpolation method in ArcGIS 10.7.1 (ESRI 2019, Redlands, CA).

ENMs, like other statistical models, can be negatively impacted by collinearity among variables, leading to over-fitting and increased model uncertainty (Júnior &

Nóbrega, 2018). Machine-learning algorithms such as Maxent have generally been shown to be robust to collinearity, given their ability to increase or decrease variable weight based on variable redundancy (Elith et al., 2011; Feng et al., 2019).

Nonetheless, multicollinearity can still impact the transferability of ENMs to novel environments (Feng et al., 2019), making it potentially problematic for the study of non- native species. Because all the bioclimatic variables offered in WorldClim are measurements of similar processes, the respective WorldClim variables may be particularly prone to being highly correlated with one another. To address this, I examined the correlation structure of all variables across the entire accessible area (i.e. the native range). Using a pairwise approach, I retained only those variables with both a hypothesized biological significance and a Pearson’s correlation coefficient less than

0.866 (Ihlow et al., 2016). This resulted in a final set of seven variables (Table 1).

Sampling Bias

Because occurrence data are often collected via convenience sampling, the distribution of records often reflects the results of human effort rather than strictly biological processes (Boakes et al., 2010; Kramer‐Schadt et al., 2013). This tendency, coupled with the fact that environmental and habitat variables are typically spatially autocorrelated (Dormann et al., 2007), can result in some variables being erroneously weighted in models, leading to biases in predictions (Segurado et al., 2006). Indeed, the occurrence records for X. tropicalis appear densely clustered in many areas, and there is no way of knowing a priori whether this is the result of increased population density

(i.e. resulting from more suitable environmental conditions) or increased sampling effort.

To compensate for this issue, I used the “Spatially rarefy occurrence data” tool in the

SDMToolbox (Rosaur et al., 2015), implemented in ArcGIS 10.7.1(ESRI 2019,

Redlands, CA). This tool allows the user to filter the number of occurrence points using a combination of Euclidean distance and habitat heterogeneity distance thresholds. For this study, the minimum Euclidean distance was set to 5 km, and the maximum set to

10 km (Aiello-Lammens et al., 2015). Prior to using this tool, I removed all points that fell on raster grids with no data for any one of the predictor variables. The resulting records were then used in models (N=111).

Another form of bias occurs when dealing with presence-only models. Without true absence data, SDMs and ENMs rely on comparing the values of predictor variables at known presence locations to the values of predictors at a set of background locations

(Merow et al., 2013). These background data are typically comprised of a series of points within the hypothesized accessible area (Barve et al., 2011). Thus, in addition to being areas that are unsuitable for the species of interest (but are accessible),

background points may also represent suitable areas where no sampling has occurred.

One way to compensate for this bias is to preferentially sample from areas closer to occurrence points. However, doing so effectively reduces the accessible area, and thus may lead to decreased model performance (Barve et al., 2011). Another method is to use a surrogate species bias layer (SSBL; Fithian et al., 2014; Molloy et al., 2017).

SSBLs rely on the use of occurrence data of species similar to the species of interest.

Based on the premise that surveys of surrogate species would likely lead to detections of the target species if they were present, SSBLs allow for the generation of background points from areas that appear more well-sampled. I addressed this uncertainty related to

X. tropicalis by selecting background localities from an SSBL.

The Guinean Forests of West Africa are considered a global biodiversity hotspot

(Critical Ecosystem Partnership Fund, 2015), and a large proportion of research in this area has focused on simply cataloguing the species present throughout the region

(Rödel, 2000; Rödel & Branch, 2003; Rödel & Bangoura, 2004; Veith et al., 2004; Rödel et al., 2005). Because of this, I used the occurrences of all anurans to serve as surrogate species. This choice was predicated on the assumption that if biologists are surveying anuran diversity in a particular area, they are likely to observe X. tropicalis if it is present. Thus, areas with a greater density of surrogate species occurrences (all anurans), but with no X. tropicalis occurrences, are more likely to be areas where X. tropicalis is absent. Species’ occurrence records were downloaded from GBIF and filtered to only include georeferenced points. As above, all occurrences derived from fossil specimens were discarded. Additionally, any records that occurred at the same coordinates of any X. tropicalis record were removed. This resulted in a final set of 1025

occurrence records. To create the bias layer, I used the R package “ntbox” (Osorio-

Olvera et al., 2020), which uses the coordinates of all occurrence points to generate a two-dimensional Gaussian kernel density estimate. This allows background points to be preferentially drawn from areas with higher relative densities of surrogate species occurrences. The resolution of the resultant raster was specified as the same as all environmental variables used (1.37 km at the equator). In total, I selected 5,000 points from the resultant raster to serve as background points in the models (Valavi et al.,

2019; Figure 1). For this study, the accessible area was defined by creating a 250-km buffer around all X. tropicalis occurrence localities. The importance of an explicitly defined accessible area is well-documented (VanDerWal et al., 2009; Barve et al.,

2011), and this specific method of point buffering has achieved marked success in predicting invasive populations of congener X. laevis (Measey et al., 2012; Ihlow et al.,

2016).

Model Construction and Tuning

One of the primary concerns with machine-learning algorithms is model overfitting (Rodda et al., 2011; Radosavljevic & Anderson, 2013). Overfitting can lead to decreased model accuracy (Heikkinen et al., 2012), which may be of particular concern when transferring models to new spatial regions (Townsend et al., 2007), as is typically done when examining non-native species. The method most commonly used to counteract this is setting aside some proportion of data to train the model (i.e. training data), and then testing the model on data not used in the training process (i.e. test data;

Phillips et al., 2004). The R package ENMeval offers a variety of different methods for splitting data into training and testing data (Muscarella et al., 2014). ENMeval depends on the R package Dismo (Hijmans et al., 2017), which runs the Maxent software

(Phillips et al., 2006) through Program R. I chose the checkerboard2 method, which partitions all of the background localities (N=5000) and occurrence localities (N=111) into four distinct spatial bins (Figure 1; Muscarella et al., 2014). This method has been shown to be more robust against issues of spatial heterogeneity in model performance than simple random k-fold cross validation (Valavi et al., 2019). The checkerboard2 method splits the study area into equal portions spatially; however, this means that occurrence and background localities are not necessarily equally distributed within each spatial bin. As such, I chose the bin size such that occurrence localities would be as close to evenly distributed as possible (aggregation factor of 5).

To evaluate model performance, I used the area under the curve (AUC), estimated from the receiver operating characteristic (ROC) curve. For ENMs, the ROC curve plots the proportion of true positives (i.e. correctly predicted presences) against the proportion of false positives (i.e. presences predicted at background points; Fielding

& Bell, 1997). An AUC of 1.0 represents a perfect prediction of all occurrences, whereas an AUC of 0.5 represents predictions no better than random. One symptom of model overfitting is large variation between the AUC of training data and testing data (Warren

& Seifert, 2011). Specifically, for overfit models, AUCtrain will be much greater than

AUCtest. To evaluate this, I compared the mean AUCtrain and AUCtest of each model.

The relationships between environmental conditions and species occurrence are often complex (Duncan et al., 2009; Rushing et al., 2019), and linear models often fail to capture such complexity. In Maxent, users can customize the types of relationships allowed between predictors and response thereby increasing or decreasing model

complexity. Here, I used the following feature class combinations: linear-only (L), linear- product (LP), linear-quadratic (LQ), linear-quadratic-product (LQP; Ihlow et al., 2016).

Another related issue is model transferability. Applying ENMs to non-native species generally relies on projecting models into new areas (i.e. spatial extrapolation).

If climatic conditions are drastically different from what is observed throughout the native range (i.e. environmental extrapolation), model accuracy may suffer (Qiao et al.,

2018). This uncertainty often increases with model complexity as well as with increasing environmental disparity between training and transfer regions (Moreno-Amat et al.,

2015; Elith et al., 2010). I addressed this issue in two ways. First, I created a

Multivariate Environmental Similarity Surface (MESS) map to measure the amount of extrapolation across all continuous variables of interest. Here negative values represent areas of extrapolation in at least one predictor variable, and positive values represent areas of interpolation for all predictor variables (Elith et al., 2010). The second way I addressed the problem of model transferability is through clamping. Clamping holds the response values of each predictor variable constant when extrapolating outside of the range of that predictor variable within the training region (i.e. the native range; Phillips &

Dudik, 2008).

Using complex feature classes can lead to model over-fitting, as the model itself may be capturing more statistical noise than actual signal (Babyak, 2004). To counteract this, I used varying regularization multipliers, which act to give higher penalties to more complex models via a smoothing approach (Phillips & Dudik, 2008).

Here, I chose regularization multipliers of 0.5, 1, and 2, with 2 penalizing complex models the most, and 0.5 the least. This resulted in the creating of a total of 12 separate

models, one for each regularization parameter and feature class combination. After all models were run, I retained only the model with the lowest AICc for further processing

(Porfirio et al., 2014).

Model Projection and Constraining

Once the model selection process was completed, I used only the top performing model to project onto Florida. Using the results from the top model, I also generated a map of predicted presences and absences based on three differing thresholds. The first and most stringent threshold I used is the maximum sum of specificity and sensitivity

(SSSmax), which sets the presence/absence suitability threshold to a value which maximizes the sum of specificity (proportion of accurately predicted presences) and sensitivity (proportion of accurately predicted background points; Liu et al., 2016).

SSSmax has proven to be a reliable threshold in presence-only models (Liu et al., 2013), but can result in relatively high omission rates (Norris, 2014). Although model accuracy is of high importance, managers and stakeholders concerned with the potential proliferation of a novel invader may be more concerned with omission rather than commission errors. I remedied this by incorporating two additional thresholds, a 5% and

10% training threshold (Radosavljevic & Anderson, 2014; Pearson et al., 2007). These thresholds simply calculate the respective 5 and 10% quantiles of relative suitability from the entire set of occurrence localities. Lastly, I extracted the relative suitability values at each site within the invaded range where X. tropicalis has been detected. If the model has predictive validity, and assuming niche conservatism, the occurrences throughout the native range should be classed as presences above the minimum omission rate threshold.

To incorporate experimental physiological data into my analysis, I used data on thermal tolerance gathered from the X. tropicalis population in Florida. These data used were from a study where Hill et al. (unpublished data) measured both the chronic lethal minimum and maximum (CLmin, CLmax) of adult individuals. This was achieved by subjecting frogs to gradual increases or decreases in temperature (1°C day-1) until mortality occurred. Thus, the estimates of CLmin (9.73°C ± 0.87) and CLmax (36.68°C ±

1.21) represent the minimum and maximum temperatures at which individuals can survive. However, X. tropicalis (and members of Xenopus more broadly) is known to aestivate in substrate during unfavorable conditions (Tinsley & McCoid, 1996). The substrate may offer thermal buffering during periods of temperature extremes, and may render land surface temperature an unreliable proxy of a frog’s internal temperature. I addressed this discrepancy by deploying temperature loggers (HOBO UA-001-08;

Onset Computer Corp., Bourne, MA; 0.47°C accuracy) into nine water bodies from 8

August 2019 to 8 February 2020 (Table 3-3). At each site, I placed an individual temperature logger 10-15 cm beneath the surface substrate. Another temperature logger was placed at the same point, only above the substrate surface. The loggers captured temperature at 30-minute intervals. Inspection of monthly maximum temperatures (BIO 5) in Florida revealed no areas above this species’ CLmax, so I only estimated the winter thermal buffer.

I estimated the magnitude of thermal buffering by subtracting the surface temperature from the substrate temperature at the same location. I used only nighttime and early morning temperatures, from 21:00 to 7:00. Lastly, I calculated the average across all sites, and used this to represent the thermal buffer offered by substrate

aestivation. Research restrictions related to COVID-19 precluded me from retrieving the temperature loggers at the end of this study, and so I was only able to calculate the thermal buffering for 1 January 2020 to 8 February 2020. Additionally, one of the temperature loggers malfunctioned and only logged data from 1 January to 28 January

2020. The thermal buffer from this site was calculated from this reduced time interval.

The estimates of thermal buffering were then subtracted from the values of CLmin, and the resulting values represented the surface temperatures at which a frog would likely experience mortality, even in substrate. I then used these values to transform the minimum monthly land surface temperature (LSTmin) rasters into binary rasters, representing values above or below the aforementioned thresholds. Finally, I used the resulting binary raster to constrain the predicted presences throughout the state of

Florida, such that no predicted presences could occur in areas that experienced monthly minimum temperatures below the estimated minimum, defined as the mean thermal buffer, subtracted from the mean CLmin.

Results

Model Evaluation

Using the seven selected variables (Table 3-1), I evaluated 12 different models.

All models performed similarly with respect to training AUC (Table 3-4), with values ranging from 0.698 to 0.732. As expected, mean test AUC was consistently lower across all models, but generally appeared not to indicate model over-fitting. The top- performing model according to AICc was the model which utilized linear and product features only, and had a regularization multiplier value of 1. However, there was a single competing model within two ∆AICc; this model utilized linear, quadratic, and

product features, and had a regularization multiplier value of two. The remainder of these results will focus on the top-performing model.

Of the seven variables used in the model, only three were used as linear features with lambda values greater or less than zero (Table 3-5). LSTmin had a moderately negative effect on relative suitability, as predicted (Figure 3-2). Similarly, there was a positive relationship between suitability and the amount of precipitation in the driest quarter (BIO 17), although the magnitude of this relationship was greater than expected.

Relative suitability was also positively related to precipitation of the wettest quarter (BIO

16) and precipitation seasonality (BIO 15). However, contrary to my predictions, there was a strong positive relationship between predicted suitability and diurnal temperature range (BIO 2), and there appeared to be no relationship between predicted suitability and forest cover.

Model Projection and Transfer

Despite the lack of relationship captured within the top model, the areas of highest suitability within the native range accessible area occurred along the coast, and appeared to coincide with the Guinea Forest area (Figure 3-3). Traveling inland and into higher latitudes, suitability decreases, and there is a concomitant decrease in the other anurans, as evidenced by the SSBL (Figure 3-1). In general, occurrence localities were found in areas of relatively high suitability, with a median value of 0.624. Further, the

SSSmax was 0.482, and the 5 and 10% training thresholds were 0.241 and 0.266, respectively.

Projecting the model throughout Florida revealed similar results as in the native range, with the areas of highest suitability appearing to follow the coast, and overall suitability declining with increasing latitude (Figure 3-4). In spite of this trend, using the

three thresholds shows a large proportion of Florida as being suitable for invasion of X. tropicalis. Most of the area predicted by the SSSmax occurs in more southern Florida, with smaller inland pockets scattered throughout the state (Figure 3-5). However, only a very small region of northeast Florida—near the Georgia border—fell below the 5% and

10% training presence thresholds. All known occurrence points from within the invaded range were below the SSSmax threshold, but above the 10% training presence threshold, suggesting that the model used has predictive validity (Table 3-6).

Model Constraining

MESS analysis demonstrated that the majority of Florida was within the range of all climatic variables used, and thus fit for projecting (Figure 3-4). I performed a subsequent analysis on each variable individually, and only diurnal temperature range

(BIO 2) and precipitation seasonality (BIO 15) were extrapolated across the Florida panhandle. Specifically, both diurnal temperature range and precipitation seasonality are reduced throughout much of the Florida panhandle, relative to the native range of X. tropicalis.

I deployed temperature loggers in nine different water bodies, and the mean difference between substrate temperature and surface temperature was positive at all but one site (Table 3-3). This finding coincides with the prediction that substrate can offer thermal buffering during periods of temperature extremes. The magnitude of the thermal buffer varied from -0.13°C (±1.22) to 7.15°C (±1.68), with a mean value across all sampled sites of 1.58°C (±2.05). This resulted in a final corrected CLmin threshold of

8.15°C. Constraining the final suitability maps in Florida with this threshold did impact the predicted invadable area. Specifically, much of northern Florida reaches temperatures that would be lethal even after applying a correction for thermal buffering.

Discussion

I found strong evidence that much of Florida is climatically suitable for invasion of

X. tropicalis. Although the SSSmax threshold predicts limited areas in south Florida as being suitable, both the 5 and 10% training thresholds predict most of Florida as being suitable for X. tropicalis. Further, the two latter thresholds both predicted X. tropicalis presence across the known invaded range, suggesting these thresholds may be more appropriate for this species. Although the models I used do not explicitly incorporate dispersal, Florida has more than 4 million ha of freshwater wetlands (Dahl, 2005). This, coupled with the fact that the average water body in Hillsborough County is roughly 121 m from its nearest neighbor (ArcGIS REST Services Directory, 2019)—well within the dispersal capacity of X. tropicalis—suggests a relatively high level of habitat connectivity. Further, Florida is home to the highest density of aquaculture facilities

(Tuckett et al., 2016) and at least one Xenopus wholesaler (http://www.xenopus.com/).

This suggests that even with dispersal constraints, there is a potential for high propagule pressure throughout the state.

Overall model performance was moderate, with the top-model having an AUCtrain value of 0.72, suggesting only an intermediate level of discriminatory power. However, this is not unexpected, given my use of a SSBL. SSBLs tend to reduce the total accessible area, which tends to lead to reductions in model performance as measured by AUC (Barve et al., 2011). Another potential driver could be the relatively high uncertainty regarding the delineation of the distribution of X. tropicalis. The current distribution accepted by the IUCN separates the ranges of X. tropicalis and X. epitropicalis to the east by the Sanaga River (IUCN SSC Amphibian Specialist Group,

2019). However, this delineation is based on a single study (Loumont, 1983).

Interestingly, the model I used predicted high relative suitability to the south and east of the Sanaga River. This suggests that this area is climatically suitable for X. tropicalis, and that there may be biotic factors or dispersal barriers precluding X. tropicalis from inhabiting this region. Alternatively, it may be the case that the current range delineation is incorrect, such that X. tropicalis is found south of the Sanaga River.

There has recently been taxonomic revision within the subgenus Silurana, where

X. fraseri—which occurs in sympatry with X. tropicalis—has been given species status

(Evans et al., 2015b). This taxonomic and distributional uncertainty may help explain the moderate model performance. It may also be that the climatic and habitat variables used did not fully capture the realized niche of X. tropicalis. Although there is not much research on the influence of biotic factors in native X. tropicalis populations, these factors may in fact drive some of the current distribution within the native range.

One method that has been used to measure the predictive validity of a model is to compare specific predictor variable response curves to their analogous and experimentally derived critical thermal limits (CTmin, CTmax, Mothes et al., 2019). CTmin and CTmax are broadly defined as the temperatures at which an organism loses its ability to locomote. The response curve for LSTmin in the top model mirrors well the experimentally derived measure of critical thermal minimum (CTmin), taken from X. tropicalis individuals captured from within their native range (Herrel & Bonneaud, 2012a;

12°C). However, the same is not true for the response curve of BIO 5, where the lowest values of predicted suitability are not reached until well beyond the estimate of CTmax

(34°C).

Contrary to my predictions, I found a strong positive relationship between mean diurnal range in temperature (BIO 2) and relative suitability. If this is underpinned by a biological reality within the species, this relationship may be threshold dependent. Much of the native range of X. tropicalis has a relatively high mean annual temperature, so any diurnal variation in temperature may not preclude X. tropicalis from being able to remain active at night. Arrighi et al. (2013) found that extreme temperature variation did adversely impact morphology and development in Korean fire-bellied toads (Bombina orientalis). Interestingly, the impact was most pronounced when the disparity was both at its maximum, and when the upper and lower temperature bounds were near the thermal limits of the species. This seems to suggest that any positive relationship between X. tropicalis occurrence and diurnal temperature range may decrease as average temperatures become more extreme.

I also found no supporting evidence for greater probability of X. tropicalis occurrence in forested areas versus non-forested areas. There are two possible explanations for this result. First, the response curves in Figure 2 are marginal response curves, illustrating the impact of each environmental variable, holding all other variables constant. It may simply be the case that while forests are positively associated with X. tropicalis occurrence, they do not add any marginal predictive power over all other variables used. The second possibility is that it is an artefact of sampling bias. More heavily human-populated areas—where sampling is often concentrated—may be less likely to occur in more densely forested areas. Indeed, there are heavily forested areas in the western portion of the range of X. tropicalis, with a dearth of all anuran occurrences. This suggests a lack of sampling effort in more heavily forested areas,

which may mask the relationship between the presence of these forests and the probability of X. tropicalis occurrence.

This study focused solely on using environmental data to examine the climatic and habitat suitability of Florida for X. tropicalis invasion. Although much of Florida may be abiotically suitable, there may be biotic constraints that preclude invasion into some areas. Negative Interactions such as competition and predation have both been shown to impact the distribution of species (Wisz et al., 2013). Predator-prey experiments have shown that various native and non-native fish species will depredate X. tropicalis larvae

(Hill et al., unpublished data). This is anecdotally corroborated by the fact that I observed no signs of successful X. tropicalis breeding at sites where fish were present

(pers. obs.). Thus, predatory fish may hinder X. tropicalis from inhabiting otherwise suitable patches. However, Florida has a large diversity of anurans that rely on ephemeral ponds, so this is unlikely to impact the potential distribution of X. tropicalis at broader scales.

There is also evidence that suggests competition may not limit the potential distribution of X. tropicalis in Florida. Unlike most anurans, X. tropicalis larvae are filter- feeders (Tinsley & McCoid, 1996), and as such, can take advantage of resources not available to other species (but see narrow-mouth toads; Hopkins et al., 2005).

Additionally, X. tropicalis is almost fully aquatic, and adapted to actively foraging in water (Tinsley & McCoid, 1996). Although there are many native anurans in Florida that occupy ephemeral ponds, few utilize the aquatic environment to the same extent that X. tropicalis does. These disparities in ecology may reduce the potential for competitive interactions.

It is important to note some limitations of the model constraints that I used. To begin, the bounds using LSTmin, thresholded by my estimate of thermal buffering of substrate, should be interpreted with caution. The estimated grid area for each raster used in our models about 188 ha. However, the average area across all water bodies sampled was 0.39 ha. Given the fact that water is likely to have a higher specific heat capacity than the surrounding areas, land surface temperatures at the lower resolutions are likely to underestimate temperature relative to any water bodies. These results may underestimate the northward extent to which X. tropicalis may be able to survive. It is also important to note that the land surface temperatures used represent the average daily low temperatures of the coldest month, from 2001-2018. The values therefore represent a measure of the lowest daily temperatures an organism is likely to experience regularly throughout the winter. In reality, there may be unseasonably cold years that could lead to increased mortality even within the areas that fall above the

LSTmin threshold used. Such events are not uncommon, and led to the extirpation of at least one invasive population of X. laevis (Tinsley et al., 2015).

My estimates of thermal buffering did not explicitly account for variation in pond substrate, depth, or area. These variables will likely affect the level of thermal buffering, and this heterogeneity may impact where X. tropicalis can survive in Florida. I did, however, select ponds in an effort to capture as much variation in substrate type as was possible within the study area (Table 3-3).

Lastly, my estimates of minimum temperature are based on the temperature at which X. tropicalis individuals experienced mortality. Although this threshold may serve as an absolute limit, it may be unrealistic to infer that this is the lowest temperature that

will support positive population growth within X. tropicalis. Indeed, there is evidence that lethal limits can be poor predictors of distributions in other species (Sará et al., 2011;

Parratt et al., 2020). Further, there is a growing body of evidence that suggests that more ecologically relevant measures, such as locomotor performance curves, may more reliably be incorporated into ENMs (Evans et al., 2015b).

Taken in their totality, our results suggest an urgent need to contain the current

X. tropicalis population in central Florida. Although it is unlikely for this population to spread into northern Florida given the current thermal physiology of the population, rapid adaptation to novel climate regimes is not unheard of in non-native species, including in Florida invader the Burmese python (Python bivittatus, Card et al., 2018), as well as in congener X. laevis (Araspin et al., 2020). Experimental evidence gathered from within the native range suggests that there may be relatively large individual variation in thermal tolerance in X. tropicalis (Goodman et al., 2019). If heritable, directional selection could result in shifting thermal tolerance over time, increasing the likelihood of X. tropicalis persisting in areas that may be at the margins of thermal suitability. Even using a conservative threshold with a relatively high rate of omission

(SSSmax), there still remains a relatively large amount of area in southern Florida that appears suitable for invasion of X. tropicalis. Given both the high fecundity and the capacity for overland dispersal, the current X. tropicalis invasion will likely continue to spread without mitigating management efforts.

Table 3-1. Descriptions of all variables used in ENMs, and the reasoning for their inclusion. Arrows represent the direction of the predicted relationship between each respective variable and relative suitability, such that arrows facing upward represent a positive relationship, and arrows facing downward a negative relationship. Predicted Variable Description Reasoning relationship Mean difference of Xenopus spp. are predominantly monthly minimum and nocturnal (Archard, 2013), so maximum increasing day/night temperature temperatures in disparities may limit available BIO 2 (diurnal range; activity time, particular in cooler ↓ averaged from 1970- climates. 2000; Fick & Hijmans, 2017) Mean maximum X. tropicalis has been temperature of the experimentally shown to exhibit warmest month relatively poor locomotor BIO 5 (averaged from 1970- performance at high temperatures ↓ 200; Fick & Hijmans, (Herrel & Bonneaud, 2012a). 2017) Measure of the annual Various predatory fish species variation in monthly have been shown to depredate precipitation (averaged Xenopus spp. (Hill et al., from 1970-200; Fick & unpublished data; lobos, 2020), so BIO 15 Hijmans, 2017) increasing variation in precipitation ↑ may increase the number of ephemeral pools, which less reliably support predatory fish species. Measure of Large bouts of precipitation are precipitation in the associated with breeding in many wettest quarter anuran species (Ulloa et al., BIO 16 (averaged from 1970- 2019), and are known to and are ↑ 200; Fick & Hijmans, known to prompt dispersal events 2017) in congener X. laevis (Measey, 2016) Measure of X. tropicalis is nearly fully aquatic, precipitation in the and nominal amounts of rain driest quarter during the dry season may prevent BIO 17 (averaged from 1970- desiccation and ultimately death. ↑ 200; Fick & Hijmans, 2017)

Table 3-1. Continued. Variable Description Reasoning Predicted relationship Mean minimum land Tropical anuran species are surface temperature of predicted to have relatively lower the coldest month cold tolerance (Snyder & (averaged from 2001- Weathers, 1975), and this pattern LSTmin 2018; Wan et al., has been shown in comparative 2015) locomotor performance between ↑ Xenopus spp. from different latitudes (Padilla et al., 2019). Binary classification X. tropicalis is closely associated denoting whether the with forests throughout its native Forest dominant landcover range (Rödel et al., 2000). cover ↑ type is forest (Bicheron et al., 2013)

Table 3-2. Legend of all the categories inclued in the GlobCover dataset (from Bicheron et al., 2013). All categories were converted into binary values, representing either forested, or unforested areas. Binary classification Landcover class 0 Post-flooding or irrigated croplands (or aquatic) 0 Rainfed croplands Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) 0 (20-50%) Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland 1 (20-50%) * Closed to open (>15%) broadleaved evergreen or semi-deciduous 1 forest (>5m) * 1 Closed (>40%) broadleaved deciduous forest (>5m) * 1 Open (15-40%) broadleaved deciduous forest/woodland (>5m) * 1 Closed (>40%) needleleaved evergreen forest (>5m) * 1 Open (15-40%) needleleaved deciduous or evergreen forest (>5m) * Closed to open (>15%) mixed broadleaved and needleleaved forest 1 (>5m) * 1 Mosaic forest or shrubland (50-70%) / grassland (20-50%) * 0 Mosaic grassland (50-70%) / forest or shrubland (20-50%) Closed to open (>15%) (broadleaved or needleleaved, evergreen or 0 deciduous) shrubland (<5m) Closed to open (>15%) herbaceous vegetation (grassland, savannas 0 or lichens/mosses) 0 Sparse (<15%) vegetation Closed to open (>15%) broadleaved forest regularly flooded (semi- 1 permanently or temporarily) - Fresh or brackish water * Closed (>40%) broadleaved forest or shrubland permanently flooded 1 - Saline or brackish water * Closed to open (>15%) grassland or woody vegetation on regularly 0 flooded or waterlogged soil - Fresh, brackish or saline water 0 Artificial surfaces and associated areas (Urban areas >50%) 0 Bare areas 0 Water bodies 0 Permanent snow and ice 0 No data (burnt areas, clouds,…) * Denotes landcover classes that were considered forested areas in the final binary raster. All other areas were categorized as not forested.

Figure 3-1. Map showing all background and occurrence localities used in the models. Small circles denote background localities (N=5,000) and triangles represent occurrence localities (N=111). The colors represent the groupings (k=4) used for separating training and testing data. Note that the heterogenous density of background localities as the result of the SSBL. Areas with a higher density of background localities are areas with a higher density of anuran occurrences.

Table 3-3. Summary of thermal buffer data gathered from temperature logger data. Thermal buffers are a measure of the average difference between substrate and surface temperatures. Temperatures were compiled for approximately one month, at 30-minute intervals. Substrate Mean thermal Site ID Latitude Longitude type buffer ± SD UF-ACF01-17 27.853809 -82.320348 Leaf litter 1.31 ± 1.69 UF-ACF03-17 27.843965 -82.315583 Silt -0.13 ± 1.22 UF-ACF04-17 27.844167 -82.317026 Sand 0.84 ± 1.40 UF-ACF06-17 27.853149 -82.318045 Leaf litter 0.45 ± 0.45 UF-ACF06-17 27.853364 -82.318209 Leaf litter 1.00 ± 2.22 UF-ACF09-17 27.859630 -82.307353 Silt 1.81 ± 2.13 UF-ACF11-17* 27.855507 -82.316273 Clay 7.15 ± 1.68 UF-Pond_10 27.852885 -82.319912 Leaf litter 0.60 ± 1.32 UF-Pond_24 27.856323 -82.320301 Leaf litter 0.79 ± 1.43 UF-Pond_34 27.842815 -82.314926 Silt 1.95 ± 2.23 * Denotes the site where a temperature logger malfunctioned.

Table 3-4. Table showing performance metrics of all models evaluated. Mean Mean Features RM AUCtrain AUCtest ∆AUC AICc ∆AICc Parameters L 0.5 0.698 0.678 0.043 3072.9 22.6 8 LQ 0.5 0.725 0.670 0.069 3059.5 9.2 11 LP 0.5 0.725 0.689 0.050 3063.3 13.0 17 LQP 0.5 0.732 0.689 0.054 3064.5 14.2 20 L 1 0.698 0.678 0.043 3070.7 20.4 7 LQ 1 0.711 0.675 0.057 3060.0 9.7 9 LP 1 0.723 0.695 0.044 3050.3 0.0 10 LQP 1 0.727 0.693 0.049 3056.6 6.3 14 L 2 0.700 0.679 0.042 3073.3 23.1 8 LQ 2 0.708 0.685 0.043 3057.1 6.8 7 LP 2 0.721 0.694 0.045 3054.8 4.5 9 LQP 2 0.725 0.694 0.045 3052.0 1.7 10 RM denotes the regularization multiplier used.

Table 3-5. All terms used in the top-performing model. Only linear and product features were used in this model. Note that λ values of 0 mean that that specific variable was not used in the model. Note that the direction of the relationship between each variable and suitability cannot be inferred by the sign of λ. Feature λ Min Max Type Forest cover 0 0 1 categorical BIO 2 5.298 5.945 16.999 linear BIO 5 0 21.316 39.955 linear BIO 15 0 41.355 156.218 linear BIO 16 0 398.812 2629.152 linear BIO 17 -2.010 0.000 458.518 linear LSTmin 4.547 8.479 24.576 linear BIO 2 * BIO 5 -3.182 154.488 639.071 product BIO 2 * BIO 17 3.086 0 4184.038 product BIO 5 * BIO 15 -3.651 1089.439 5794.268 product BIO 5 * BIO 16 -2.317 13707.444 97337.026 product BIO 15 * BIO 16 7.686 23216.105 350291.685 product BIO 15 * BIO 17 8.133 0 23931.852 product BIO 16 * BIO 17 -5.635 0 595689.106 product λ represents the coefficient used for each term in the model

Figure 3-2. Response curves for the top-performing ENM. Each panel represents a separate variable used in the top model. Within each panel, the x-axis represents values of the variable, and the y-axis represents the predicted suitability. Note that each curve represents the effect of a given variable, holding all other variables at their median value.

Figure 3-3. Relative suitability map of the accessible area of X. tropicalis. Red areas represent areas of higher relative suitability, and blue areas lower. Grey triangles are all occurrence localities (N=111) used in models.

Figure 3-4. Relative suitability map based on the top-performing ENM. Red areas represent areas of high relative suitability, and blue areas low relative suitability. White areas represent areas where the average minimum monthly land surface temperature is at or below the lethal threshold (less the estimated substrate thermal buffer; 8.15°C). Cross-hatched areas are those with at least one predictor variable with values in the projected area outside of the range of that variable within the accessible area (i.e. areas with MESS values less than 0).

Figure 3-5. Map of relative suitability using various thresholds. Thresholds used were maximum sum of specificity and sensitivity (SSSmax), 10% training presence, and 5% training presence. Blue areas represent areas with suitability scores under all three thresholds (i.e. areas of predicted absence). Note that the 5% and 10% thresholds were very similar, so the former adds very little area beyond the latter

Table 3-6. Relative suitability scores across all sites within the invaded range where X. tropicalis has been detected. Note that because of the low resolution (1.37 km at the equator), many of these locations fall within the same grids. Site ID Latitude Longitude Relative suitability UF-ACF01-17 27.85367 -82.32007 0.431 UF-ACF02-17 27.85085 -82.32104 0.419 UF-ACF03-17 27.84402 -82.31580 0.419 UF-ACF04-17 27.84406 -82.31699 0.419 UF-ACF05-17 27.84338 -82.31498 0.412 UF-ACF06-17 27.85331 -82.31808 0.419 UF-ACF07-17 27.83909 -82.30583 0.417 UF-ACF08-17 27.85038 -82.31905 0.419 UF-ACF09-17 27.85969 -82.30738 0.415 UF-ACF10-17 27.86054 -82.30967 0.415 UF-ACF11-17 27.85558 -82.31631 0.431 UF-ACF12-17 27.83326 -82.34072 0.393 UF-Pond_10 27.85202 -82.31995 0.419 UF-Pond_11 27.85322 -82.32053 0.419 UF-Pond_12 27.85437 -82.32051 0.431 UF-Pond_24 27.85631 -82.32023 0.431 UF-Pond_25 27.85463 -82.32520 0.431 UF-Pond_27 27.85399 -82.32521 0.431 UF-Pond_28 27.85682 -82.32061 0.431 UF-Pond_30 27.85633 -82.31670 0.431 UF-Pond_39 27.85424 -82.31995 0.431 UF-Pond_40 27.83909 -82.30322 0.417 UF-Pond_42 27.88894 -82.32655 0.432

CHAPTER 4 CONCLUSION

The broad goal of my research was to determine the likelihood of continued spread of the current tropical clawed frog invasion. I demonstrated that this species is capable of frequently dispersing relatively long distances over land. I found that dispersers had longer hindlimbs and wider ilia than did residents. I also found evidence that dispersers had increased stamina, and travelled longer size-relative distances prior to reaching exhaustion. Although female dispersers jumped with greater size-relative force than residents, this trend was not held across sexes and for jump length or jump height.

My results have both theoretical and applied implications. There is a growing body of research demonstrating that dispersal-related traits can assemble themselves in a spatially heterogeneous way, and these results indicate that this process may be detectable at the individual level. Further, these results suggest that the process of invasion is not static. These individual differences, if selected spatially, may lead to an increased rate of invasion spread. Future research should investigate whether or not these trends are resulting in the spatial sorting of these traits across populations within the invaded range.

Using bioclimatic and habitat variables from within the native range, I modeled the relative likelihood of invasion across Florida, in the absence of dispersal constraints.

Even when using a high threshold (SSSmax), my results indicate that a large proportion of peninsular Florida may be climatically suitable for invasion. However, my use of constraints based on thermal physiology suggests that, without cold-adaptation, much of the panhandle and northern peninsular Florida may be unsuitable under the current

climate regimes. I was unable to incorporate more fine-scale microhabitat variables into my models. Given the aquatic lifestyle of this species, the characteristics of particular water bodies may also be important to determining the future range, and I suggest that future research consider these characteristics. Additionally, future research should focus on integrating dispersal data into models, which may enable a more explicit prediction of the potential rate of spread of X. tropicalis beyond its current range in

Florida.

Another important area for future research is to determine the current extent of the tropical clawed frog invasion. While I was able to repeatedly sample 43 different water bodies, there were many more in the surrounding areas which I could not access due to time and supply constraints. Future researchers should also look to the development of environmental DNA (eDNA) primers of X. tropicalis, given that the entire species genome has been sequenced (Hellsten et al., 2010). This method has proven effective in detection of congener X. laevis (Secondi et al., 2016), and may allow researchers to more exhaustively sample throughout the surrounding areas in

Riverview, without requiring hundreds of baited traps and person-hours of sampling.

Delineating the extent of the invasion may allow researchers greater opportunity to contain and/or eradicate the X. tropicalis population in west-central Florida. The use of chemicals has seen some success in eradicating local X. laevis populations

(Zachuto, 1975; Boone, 2017). Using this method of chemical control might be effective with X. tropicalis. However, the results from our study on dispersal suggest that without knowledge of all occupied sites, recolonization from unknown sites to treated sites may occur. Further, any use of chemical control must account for this species’ burrowing

ability. Toxicants that cannot penetrate substrate may be insufficient to eradicate individuals that burrow in response. Further, governmental approvals of toxicants for anuran control may be difficult to obtain, and thus not always feasible.

Another potential strategy for management is the use of biocontrol. The eastern mosquito fish (Gambusia holbrooki) is quite common in ponds throughout the invaded range (Tuckett et al., 2017), and can tolerate very low dissolved oxygen levels in water

(Lewis, 1970). Additionally, G. holbrooki was linked to breeding collapse in an X. laevis population in Chile (Lobos, 2020), and has experimentally been shown to depredate X. tropicalis larvae (Hill et al., unpublished data). Future research should seek to determine whether transplanting G. holbrooki to occupied sites might have a similar impact on X. tropicalis breeding. However, it must also be considered that any impacts G. holbrooki has on X. tropicalis breeding may spill over to native species as well.

Lastly, and given the well-documented impacts of congeneric invader X. laevis, future research should seek to determine what effects the X. tropicalis invasion is having on native species. During this research, I observed that the ponds with the greatest X. tropicalis CPUE also appeared to have the lowest abundance of larval-stage native anurans. Future research should examine this more systematically. The native anurans of Florida are already at risk due to habitat degradation (Delis et al. 1996) and the presence of other invaders (Guzy et al. 2006; Smith 2005). In addition to predation by X. tropicalis, predation of X. tropicalis may be a concern. Throughout this study, I observed numerous instances of predation by native bird species on X. tropicalis

(Figure 4-1). It is unclear what the impacts of this predation are; however, there is evidence of the skin secretions of X. tropicalis being toxic to some predators

(Barthalmus & Zielinski, 1988; Tinsley et al., 1996). Although there is still much unknown about the tropical clawed frog’s invasion ecology, our results indicate that if left unmanaged, the species will likely continue to spread. Managers should seek to control the current invasion while it still appears localized.

Figure 4-1. Photographs of three different species observed depredating X. tropicalis individuals. 1) Barred owl (Strix varia) consuming a juvenile X. tropicalis, 2) Little blue heron (Egretta caerulea) consuming an adult X. tropicalis, 3) Little green heron (Butorides virescens) consuming a larval-stage X. tropicalis. All photographs courtesy of the author (C. Goodman).

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BIOGRAPHICAL SKETCH

Colin graduated from the University of Maryland in 2016 with a Bachelor of

Science in environmental science and policy. During his time there, he worked as a field assistant for the Clark Lab and San Diego State University, helping graduate students on various projects related to the behavioral ecology of rattlesnakes and their mammalian prey. Prior to starting at the University of Florida, Colin interned with the

United States Geological Survey, working on various projects related to invasive species management in and around Everglades National Park. For his master’s thesis,

Colin studied the invasive ecology of the tropical clawed frog, and graduated with a

Master of Science in wildlife ecology and conservation in August 2020.

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