The Functional Morphology of Lizard Locomotion: Integrating Biomechanics

The Functional Morphology of Lizard Locomotion: Integrating Biomechanics,

Kinematics, Morphology, and Behavior

A dissertation presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Eric J. McElroy

August 2008

This dissertation titled

The Functional Morphology of Lizard Locomotion: Integrating Biomechanics,

Kinematics, Morphology, and Behavior

ERIC J. MCELROY

has been approved for

the Department of Biological Sciences

and the College of Arts and Sciences by

Stephen M. Reilly

Professor of Biological Sciences

Benjamin M. Ogles

Dean, College of Arts and Sciences

ABSTRACT

MCELROY, ERIC J., Ph.D., August 2008, Biological Sciences

The Functional Morphology of Lizard Locomotion: Integrating Biomechanics, Kinematics,

Morphology, and Behavior (182 pp.)

Director of Dissertation: Stephen M. Reilly

Lizards have long served as a model system for understanding how locomotor functional morphology meets ecological demands. However, far less is known about how behavior modulates this relationship. Likewise, there is still a need for a comprehensive understanding of lizard locomotor functional morphology. This dissertation presents four studies that focus on how behavior molds locomotion and advances our understanding of lizard locomotor functional morphology.

Chapter one examines variation in locomotor performance, whole-body mechanics and gaits in a phylogenetic array of lizards that use differing foraging modes. Multivariate and phylogenetic comparative analyses show that foraging mode drives the evolution of biomechanics and gaits in lizards. Sit-and-wait species used only fast speeds, trotting gaits, and running mechanics, whereas wide foraging species independently evolved slower locomotion, different walking gaits, and walking mechanics.

Chapter two examines patterns of variation in the hierarchical relationship among morphology, kinematics and force during fast running in seven species of lizards.

Multivariate analyses test for correlations among levels to reveal the morphological basis for kinematics and locomotor forces. The results show that limb length, support duration and float distance directly influence the nature of forces applied to the ground during the support phase of steady state locomotion and suggest that this relationship may affect locomotor energetics and endurance.

Chapter three examines the effects of habitat, behavior, and their interaction on

undisturbed locomotor speeds in Urosaurus ornatus. Detailed video analysis of undisturbed behavior reveals that locomotor speed decreases with increasing perch height, when animals move on woody substrates, and while displaying and capturing prey. No relationship is found between speed and perch diameter or substrate incline. Finally, the interaction between perch diameter and feeding behavior results in a negative relationship between speed and perch diameter during feeding but not during other behaviors. I suggest that the relationship between habitat, behavior, and speed is affected by the level of locomotor performance (maximal vs. sub maximal) that animals routinely use during undisturbed activity.

Finally, chapter four presents a detailed analysis of “foraging” behavior and field locomotor performance in a sit-and-wait (Sceloporus undulatus) and wide foraging

(Aspidoscelis flagellicaudus) species. Both sit-and-wait and wide foraging species clump relatively slow locomotor behaviors just before prey attacks, a finding at odds with hypotheses from the literature. This result, along with previous studies, suggests that selection on maximum speed and endurance is not acting through the filter of foraging behavior. I suggest that “foraging mode” should be examined in light of an existing model of animal foraging (the Webb model) to direct future studies of the ecology and evolution of animal performance in relation to foraging behavior.

Approved: ______

Stephen M. Reilly

Professor of Biological Sciences

ACKNOWLEDGMENTS

I am especially grateful to my wife Julie and daughter Matilda for their love and support throughout this dissertation. You have made completion of this dissertation even more fulfilling than I could have imagined. We are all thankful for the support and encouragement our families have given over the past five years. Steve Reilly taught me how to think deeply and completely as a scientist. Thank you for the many quarters of research support and travel to Australia and for completely opening up your laboratory to my research endeavors. You are a great mentor, colleague, and friend. Audrone

Biknevicius guided my thoughts on locomotor biomechanics and was a calming force during many frustrating hours fixing broken force plates. Audrone, thank you for all of your time, your effort, and your friendship.

Don Miles provided advice and was key in sparking my interest in comparative methods and biostatistics. Thank you for this inspiration. I thank Gar Rothwell for graciously serving as the external committee member for my defense. I thank Duncan

Irschick for providing advice and financial support for much of the field work presented in this dissertation.

Lance McBrayer, Phil Allman, Andre Fernandez, Pat O’Connor, Nancy Stevens,

Angela Horner, Dawn and Casey Holliday, Tobin Hieronomys, Susan Williams, Larry

Witmer, John Bertram, Henry Streby, Duncan Irschick, Mike Jorgenson, Jen Hancock,

Scarlett Tudor, Willem Roosenburg, Molly Morris, Brian Horne, Jay Meyers, Carl

Hoagstrom, Scott Moody, Pete Zani and many others have engaged in many discussions in regards to this dissertation and my development as a scientist. Thank you all for your time and thoughts.

Jeff Willey and Bob Verb are responsible for my pursuit of a Ph. D. at Ohio

University. Thank you both for guiding me to OU and to pursue a career in academics.

Many people helped me accomplish the research in this dissertation. First and foremost several undergraduates helped collect and analyze hours of lizard locomotor data. For this I grateful to: Emily Bevis, Caityln Cato, Alex Fotis, Bailey Miles, Jodi

Mrosko, Grant Beaman, Kris Stover, Kristen Hickey, and Andrew Parchman. Duncan

Irschick, Jay Meyers, and Chloe Marien provided logistical and moral support during many hard hours filming Urosaurus and Aspidoscelis at Wet Beaver Creek. Lance

McBrayer was instrumental in the collection of data from Sceloporus in Georgia.

Andrew Odum and the herpetology staff at the Toledo Zoo allowed me to race Cordylus.

Jens Vindum and Bob Drewes (California Academy of Sciences), Robert Espinosa

(California State University at Northridge), Steve Rodgers (Carnegie Museum), Jim

McGuire (University of California Museum of Vertebrate Zoology), Linda Trueb

(Kansas) and Kevin De Quieroz (Smithsonian) kindly loaned lizard specimens for morphological study. Scott Moody helped me access the Ohio University reptile collection. Joe Eastman taught me how to use the x-ray machine and the finer points of developing films; he also graciously provided films and chemicals. Robbie Wilson and

Mike Bennett facilitated work in Australia on Eulamprus. John Bertram, Kay Earls,

Andy Lammers, and David Lee provided assistance with the force platform and virtual instrument programming. Along with undergraduates, Ohio University Animal Care

(Nelson Frey and Eric Linder) ensured that all lizards received top notch care while in the lab. Arizona Game and Fish quickly processed research permits.

This research was funded by NSF grants (IBN 9727212; IBN 0080158; IOB

0520100), an Ohio University Research Challenge grant, and a Provost undergraduate research fund grant to Steve Reilly. Duncan Irschick’s NSF grant (IOB 0421917) provided partial support for work in Arizona. I was funded by an Ohio University Center for Ecology and Evolution Research Fellowship, a University Doctoral Fellowship, a

Charles Stearns Grant-in-Aid for Herpetological Research from the California Academy of Sciences, a Phi Delta Theta scholarship, an Ohio Board of Regents Scholarship, an

Ohio University Student Enhancement Award, and at Graduate Student Senate research grant.

TABLE OF CONTENTS Page

ABSTRACT ...... 3 ACKNOWLEDGMENTS ...... 5 LIST OF TABLES ...... 10 LIST OF FIGURES ...... 11 GENERAL INTRODUCTION ...... 13 Synopsis of Chapters...... 14 References ...... 17 CHAPTER 1: THE CORRELATED EVOLUTION OF BIOMECHANICS, GAIT AND FORAGING MODE IN LIZARDS ...... 21 Introduction ...... 21 Materials and Methods ...... 23 Study Species ...... 23 Data Collection ...... 24 Quantifying Gait ...... 25 Quantifying Mechanics ...... 26 Statistical Analyses ...... 27 Results ...... 31 Biomechanics and Foraging Mode ...... 31 Gait and Foraging Mode ...... 32 Multivariate Species Differences in Running and Walking Gaits ...... 33 Correlated Evolution of Biomechanics and Gait ...... 35 Discussion ...... 35 Patterns of Running in Lizards ...... 35 The Evolution of Mechanics and Gait with Foraging Mode in Lizards ....36 Why Change Mechanics and Gait When Moving at a Slower Foraging Speed? ...... 38 Variation in Ecological Relevance of Gait among WF Lizards ...... 42 Why Run Slow? ...... 45 Lizards Only Trot ...... 46 Caveats ...... 47 Conclusions and Future Directions ...... 48 References ...... 49 CHAPTER 2: THE RELATIONSHIP BETWEEN LIMB MORPHOLOGY, KINEMATICS AND FORCE DURING RUNNING: SIZE AND SPEED FREE PATTERNS OF LOCOMOTOR DYNAMICS IN LIZARDS...... 72 Introduction ...... 72 Methods...... 74 Study Species ...... 74 Morphology...... 75 Locomotor Function...... 75 Statistical Analyses ...... 78 Results ...... 83 Multivariate Patterns in Locomotor Levels ...... 83

Relationships between Multivariate Levels ...... 87 Discussion ...... 91 Limb Morphology and Stride Kinematics ...... 91 Stride Kinematics and Whole Body Vertical Force ...... 93 Evolutionary Trends in Lizard Locomotor Functional Morphology ...... 97 References ...... 98 CHAPTER 3: DISSECTING THE EFFECTS OF BEHAVIOR AND HABITAT ON THE LOCOMOTION OF THE ORNATE TREE LIZARD (UROSAURUS ORANTUS) ...... 124 Introduction ...... 124 Methods...... 126 Field Site ...... 126 Focal Observations...... 126 Video Analysis ...... 127 Statistics ...... 128 Results ...... 130 Discussion ...... 131 Individual Effects of Habitat and Behavior on Locomotion ...... 132 Interactive Effects of Habitat and Behavior on Locomotion ...... 134 References ...... 135 CHAPTER 4: A PRELIMINARY ANALYSIS OF BEHAVIORAL AND PERFORMANCE DIFFERENCES IN FOOD ACQUISITION MODES IN A SIT-AND- WAIT AND A WIDE FORAGING LIZARD ...... 145 Introduction ...... 145 Methods...... 148 Study Species and Field Site ...... 148 Focal Observations of Field Behavior ...... 148 Video Analysis ...... 149 Quantifying Movements ...... 150 Statistics ...... 150 Results ...... 153 General Description of Behavioral Time Budgets ...... 153 Lag-Sequential Analysis of Behavioral Events Preceding Prey Attack ..154 Speed Differences between SW and WF Preceding Prey Attack ...... 155 Discussion ...... 156 Behavioral Differences between SW and WF in Events Leading to Prey Attacks ...... 156 Webb’s Foraging Cycle ...... 160 Speed ...... 161 Conclusions ...... 164 References ...... 164 CONCLUSIONS AND FUTURE WORK ...... 180 APPENDIX A: MUSUEM CODES ...... 182

LIST OF TABLES

Table Page

1.1 Species means for gait and biomechanical variables…………………………….59

2.1 Sample sizes for morphology, kinematics, force, and ranges for speed………..105

2.2 Correlations between morphology, kinematics, force, body size, and speed...... 106

2.3 Multivariate morphological analysis……………………………………………107

2.4 Multivariate kinematic analysis………………………………………………...108

2.5 Multivariate force analysis……………………………………………………...109

2.6 Multivariate correlations between species position in multivariate space at each

level……………………………………………………………….…………….110

2.7 Regression parameters between stride kinematics and speed…………………..111

3.1 Habitat use and locomotor speed during different behaviors in Urosaurus

ornatus………………………………………………...……………………...... 140

3.2 Sum-of-squares table for best-fit multiple regression model…………………...141

3.3 Partial regression coefficients for the “best” multiple regression model……….142

4.1 Activity budgets for Sceloporus undulatus and Aspidoscelis flagellicaudus…..170

4.2 Behavioral sequences preceding prey attacks in Sceloporus undulatus ……….172

4.3 Lag sequential analysis for Sceloporus undulatus……………………...... 174

4.4 Behavioral sequences preceding prey attacks in Aspidoscelis flagellicaudus….176

4.5 Lag sequential analysis for Aspidoscelis flagellicaudus………………………..177

LIST OF FIGURES

Figure Page

1.1 Forging mode evolution reconstructed on molecular phylogeny………………..61

1.2 Locomotor gaits for lizards that only used running mechanics……….…………62

1.3 Locomotor gaits for lizards that used both running and walking mechanics...... 63

1.4 The evolution of locomotor biomechanics in relation to foraging mode in

lizards………………………………………………………………………….....64

1.5 Hildebrand plots with species means and significant differences……………….66

1.6 Patterns of gait change when shifting from running to walking mechanics……..68

1.7 Evolutionary patterns of foraging locomotor biomechanics, gait, and foraging

mode……………………………………………………………………………...70

2.1 Phylogeny for seven lizard species………………………..……………………112

2.2 Radiograph summarizing 16 morphological measurements….………………...113

2.3 Representative whole body force profiles………………………………………114

2.4 Plots of the first two discriminant axes for morphology, kinematics, and

forces……………………………………………………………………..…….115

2.5 Visualization of the multivariate relationship between morphology, kinematics,

and whole body force…………………………………………………….……..117

2.6 Three dimensional plot of the first discriminant axis at each level of analysis....119

2.7 Sample vertical forces that illustrate how the longest and shortest legged lizards in

this study varied in kinematics and forces.…………………………...…….…..121

2.8 Hypothetical relationship between locomotor function and

energetics/performance…………………………………..……………………..123

3.1 Relationships between habitat / behavioral variables and locomotor speed…....143

4.1 Activity budgets for Sceloporus undulatus and Aspidoscelis flagellicaudus…..179

GENERAL INTRODUCTION

Although this dissertation encompasses ecology, behavior, morphology, function, and evolution, I am always thinking of what my research means for the organism in its natural environment. This lies at the heart of my philosophy as a researcher and has molded many aspects of this dissertation. Harry Greene (1986, 2005) authored two papers that have been particularly important in guiding my thinking about organisms in nature.

Two overarching questions drive both this dissertation research and my general research interests: 1) how do functional systems relate to whole organism performance and behavior and how do these relationships evolve? and 2) what do animals do in nature?

The four studies presented in this dissertation address these questions and advance our understanding of lizard foraging behavior, locomotor functional evolution and animal performance.

Lizards are amazingly diverse behaviorally, morphologically, and ecologically

(Pianka and Vitt 2003). Accordingly, they are a model group for both laboratory and field studies in ecology, evolution, and behavior (Vitt and Pianka 1994; Fox et al. 2003;

Vitt et al. 2003; Reilly et al. 2007a). In particular, foraging behavior is a major evolutionary force that has driven much of the diversity in lizard biology (reviewed in

Reilly et al. 2007a), including locomotor morphology and performance as well as movement ecology (Miles et al. 2007; Verwaijen and Van Damme 2007; Perry 2007).

However, no studies have examined the implications of locomotor functional morphology for foraging behavior, which is the focus of chapter one. Concordantly, previous studies of lizard locomotor functional morphology have quantified details of morphology and kinematics, but not force. Locomotor forces form the direct link

14 between leg kinematics and movements of the center-of-mass; thus, in order to understand the evolution of locomotor performance a basic understanding of how forces move the body forward is necessary, which is the focus of chapter two. A myriad of studies have examined how habitat and behavior affect the way lizards move through their environment (Jayne and Irschick 2000; Irschick and Garland 2001; Husak and Fox

2006). These studies form the cornerstone for research in how animals move in the nature, however, we still do not understand if and how habitat and behavior interact to affect the way animals move, which is the focus of Chapter three. Finally, most studies of lizard foraging have relied on gross measurements of field movement patterns (Perry

2007) and only recently has a clear framework emerged for testing hypotheses about the details of lizard field movements (Butler 2005). Chapter four applies Butler’s (2005) framework to provide the first detailed analysis of how foraging mode affects the behavioral and performance correlates of the foraging cycle (i.e. “predation cycle”, Webb

1986).

Synopsis of Chapters

Chapter one explores the functional basis for differences in foraging locomotion in a phylogenetically diverse sample of 18 lizard species. Previous work had shown sit- and-wait (SW) species are fast with morphological and physiological traits that favor sprinting, whereas, wide-foragers (WF) can move fast but have evolved traits favoring slower, endurance-based locomotion (Miles et al. 2007; Bonine 2007). I quantified gait

(limb sequence), center-of-mass mechanics, and speed to explore if these functional traits predict foraging behavior. The results show that SW species move fast using trotting gaits and running mechanics, whereas, WF species can also move fast (and trot with

15 running mechanics), but have evolved slower speeds using new walking gaits and mechanics. Furthermore, three evolutionary reversals from WF to SW have resulted in the loss of walking gaits and mechanics; these three SW species can only trot with running mechanics. Using phylogenetic comparative statistics I show that these suites of functional traits have undergone correlated evolution with foraging behavior. Overall, this chapter highlights how a major evolutionary transition in behavior is accompanied by the concordant evolution of underlying functional traits.

Chapter two tests a conceptual framework that proposes locomotor function is driven by hierarchical links between morphology, kinematics, force, and center-of-mass mechanics (Reilly et al. 2007b). In a phylogenetically broad sample of seven lizard species, I show that morphological variation drives kinematic variation, which in turn drives force variation. Species with short limbs use a short stride – high frequency strategy when running and to change speed. This link between morphology and kinematics results in relatively small vertical forces during the support phase.

Conversely, species with long limbs use a long stride – low frequency strategy resulting in large vertical forces during the support phase. Based on these findings, I suggest that limb length drives locomotor energetics in lizards, because energetics are largely determined by vertical force and stride frequency (Pontzer 2005). Additionally, I propose that this results in an energetic tradeoff with both long and short limbed species paying the most energy to move while intermediate limbed species move using less energy.

Overall, this chapter provides a foundation for future studies that seek to ascertain functional traits underlying locomotor energetics/endurance in lizards and animals in general.

While chapters one and two are large-scale comparative studies based in the laboratory, chapters three and four focus on how lizards move in nature. Chapter three examines the effects of habitat, behavior, and their interaction on natural locomotion in the wild. These field data are incredibly time consuming and tedious to collect, so I chose to focus on a single species, Urosaurus ornatus. Detailed analysis of undisturbed behavior revealed that this lizard moves slower on taller perches, on woody substrates, and while displaying and attacking prey. Although previous studies found that speed decreases on small diameter perches (Losos and Irschick 1996) and steep inclines (Jayne and Irschick 2000), I found no relationship between speed and perch diameter or incline.

Finally, the interaction between perch diameter and feeding behavior resulted in feeding lizards moving slower on wider perches. This relationship was not found for other behaviors. Based on these results and previous studies of other lizards I suggest that the relationship between habitat, behavior, and speed is affected by the level of locomotor performance (maximal vs. sub maximal) that animals routinely use during undisturbed activity.

Chapter four presents the first detailed analysis of “foraging” behavior and field locomotor performance using Butler’s (2005) framework in a model sit-and-wait

(Sceloporus undulatus) and model wide foraging (Aspidoscelis flagellicaudus) species

(Cooper et al. 2001). Both the sit-and-wait and wide forager clumped locomotor behaviors just before prey attacks, a finding at odds with hypotheses from the literature

(Butler 2005). I suggest that this may be related to prey mobility, as opposed to foraging mode, because both species in this study attacked highly mobile prey. Speeds were not fast during prey attack, which, along with previous studies (Irschick and Losos 1998,

Braña 2003, Husak and Fox 2006), suggests that selection on maximum speed is not acting through the filter (sensu Garland and Losos 1994) of foraging behavior. In addition, the details of the WF species searching movements suggest that locomotor endurance is not used near maximal capacity while foraging. Based on these findings, I suggest that “foraging mode” should be examined in light of an existing model of animal foraging (the Webb model, 1986) to direct future studies of the ecology and evolution of animal performance in relation to behavior.

Each chapter attempts to bring a functional approach to the study of lizard locomotion and behavior. While these studies are broad, integrative and make important conceptual inroads into our understanding of how and why lizards move; I feel that all four studies are starting points rather than conclusions. Hopefully, questions remaining in each of these studies will be addressed through my own future research and the research of other functional morphologists and lizard biologists.

References

Bonine, K. E. 2007. Physiological correlates of foraging mode. In Lizard Ecology: The

Evolutionary Consequences of Foraging Mode (ed. S. M. Reilly, L. B. McBrayer,

and D. B. Miles). pp. 94 - 119. Cambridge, UK: Cambridge University Press.

Braña, F. 2003. Morphological correlates of burst speed and field movement patterns:

the behavioral adjustment of locomotion in wall lizards (Podarcis muralis). Biol.

J. Linn. Soc. 80:135-146.

Butler, M. A. 2005. Foraging mode of the chameleon, Bradypodion pumilum: a challenge

to the sit-and-wait versus active forager paradigm? Biol. J. Linn. Soc. 84:797-808.

Cooper, W. E., L. J. Vitt, J. P. Caldwell, and S. F. Fox. 2001. Foraging modes of some

American lizards: relationships among measurement variable and discreteness of

modes. Herpetologica 57:65-76.

Fox, S. F., J. K. McCoy, and T. A. Baird 2003. Lizard Social Behavior. Baltimore, MD:

Johns Hopkins University Press.

Garland, T. G. and J. B. Losos 1994. Ecological morphology of locomotor performance

in squamate reptiles. In: Ecological Morphology: Integrative Organismal Biology

(ed. P. C. Wainwright and S. M. Reilly) pp. 240-302. Chicago: University of

Chicago Press.

Greene, H. W. 1986. Natural history and evolutionary biology. In Predator-Prey

Relationships: Perspectives and Approaches from the Study of Lower Vertebrates.

(ed. M.E. Feder and G.V. Lauder). pp. 99-108. Chicago, Illinois: University of

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20:23-27.

Husak, J. F. and S. F. Fox. 2006. Field use of maximal sprint speed by collared lizards

(Crotaphytus collaris): compensation and sexual selection. Evolution 60:1888-

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Irschick, D. J. and J. B. Losos 1998. A comparative analysis of the ecological

significance of maximal locomotor performance in Caribbean Anolis lizards.

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adaptation: investigations of locomotor capacity as a model system. Ann. Rev.

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Jayne, B. C. and D. J. Irschick 2000. A field study of incline use and preferred speeds for

the locomotion of lizards. Ecology 81:2969-2983.

Losos J. B. and D. J. Irschick 1996. The effect of perch diameter on escape behavior of

Anolis lizards: laboratory predictions and field tests. Anim. Beh. 51:593-602.

Mattingly, W. B. and B. C. Jayne 2005. The choice of arboreal escape paths and its

consequences for the locomotor behavior of four species of Anolis lizards. Anim.

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Miles, D. B., J. B. Losos, and D. J. Irschick 2007. Morphology, performance, and

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Mode (ed. S. M. Reilly, L. B. McBrayer, and D. B. Miles), pp. 49 - 93.

Cambridge, UK: Cambridge University Press.

Perry, G. 2007. Movement patterns in lizards: measurement, modality, and behavioral

correlates. In Lizard Ecology: The Evolutionary Consequences of Foraging Mode

(ed. S. M. Reilly, L. B. McBrayer, and D. B. Miles), pp. 13 - 48. Cambridge, UK:

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Consequences of Foraging Mode. Cambridge, UK: Cambridge University Press.

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lacertid lizards? J. Evol. Biol. 20:1950-1961.

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Perspectives. Princeton University Press.

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of squamate reptiles. Am. Nat. 162:44–60.

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Chicago Press.

CHAPTER 1: THE CORRELATED EVOLUTION OF BIOMECHANICS, GAIT

AND FORAGING MODE IN LIZARDS

Introduction

Foraging mode is a pervasive evolutionary force in lizards (reviewed in Reilly et al. 2007). Movement patterns have been used to categorize lizards as sit-and-wait or wide foraging (Perry 2007; Pianka 1966) and numerous physiological, morphological, behavioral, life history, dietary and other ecological traits have been shown to covary with foraging mode (Cooper 1994; Reilly et al. 2007; Schwenk 1993). For example, sit- and-wait foragers (SW) rely primarily on vision to ambush prey (and accordingly have poorly developed chemosensory abilities), and use large sticky tongues coupled with short broad skulls to capture and process prey. In wide foraging (WF), which has evolved several times (Fig. 1.1), lizards use an oscillating forked tongue to search for prey (with highly developed chemosensory systems), and use their long narrow jaws to capture and process prey (McBrayer and Corbin 2007; Reilly and McBrayer 2007).

Predator evasion and territorial defense in all lizards involves short bursts of fast locomotion (Husak 2006; Husak and Fox 2006; Irschick 2000a; Irschick 2000b).

However, during foraging, locomotor speeds differ significantly between foraging types

(Anderson 2007; Cooper et al. 2005). During foraging, SW lizards remain motionless most of the time and then use short bursts of fast locomotion (~10% of activity period) to ambush passing prey. In contrast, WF lizards move slowly most of the time (~10 – 90% of activity period) over long distances chemically sampling the environment to locate hidden caches of prey. Accordingly, performance studies have shown a tradeoff between maximum speed and endurance such that SW foragers sprint faster than WF but WF have

22 larger endurance capacities than SW foragers (Miles et al. 2007). Thus, maximum speed and endurance match the locomotor demands of foraging mode. Although there have been many studies that have examined locomotor function when lizards move at their fastest speeds (Irschick and Jayne 1999; Russell and Bels 2001; Vanhooydonck et al.

2002; White and Anderson 1994) far fewer have addressed function at slower speeds.

Two key functional aspects of locomotion that have likely undergone correlated evolution with foraging mode are center-of-mass (COM) biomechanics and gait. A variety of terrestrial animals, including lizards, move their COM using either running or walking mechanics (Biewener 1998; Cavagna et al. 1977; Farley and Ko 1997; Full and

Tu 1991; Full and Weinstein 1992; Heglund et al. 1982; Reilly et al. 2006). Running mechanics involve the kinetic (KE) and gravitational potential (GPE) energies of the

COM cycling in-phase and are usually associated with faster locomotion. Walking mechanics are characterized by out-of-phase oscillations of the KE and GPE of the COM, are usually associated with slow locomotion, and can involve mechanical energy savings via the inverted-pendular mechanism. Many animals, including most lizards studied to date, use a trotting gait with small duty factors while moving fast and shift toward a singlefoot (4-beat) gait with larger duty factors when moving slowly (e.g. Biknevicius and Reilly 2006; Hildebrand 1976; Sukanov 1968; White and Anderson 1994). Based on these patterns I predict that the speed demands of foraging mode should be related to biomechanics and gait. Sit-and-wait species rely on rapid locomotion during prey capture which should involve running mechanics and trotting gaits. On the other hand,

WF lizards predominantly use slower locomotion to locate prey which should involve

23 walking mechanics and a shift toward singlefoot gaits (while retaining faster locomotion with running mechanics and trotting gaits for predator evasion and social interactions).

To test these hypotheses I examined the relationships among gait, mechanics and foraging mode in 15 lizard species with a phylogenetic history marked by numerous transitions in foraging mode (Fig. 1.1). Comparisons across species show a strong evolutionary correlation of gait and biomechanics with foraging mode and that WF lizards have not only evolved several different ways to walk slowly but some have also evolved very slow running.

Materials and Methods

Study Species

Locomotor biomechanics and gait were quantified in the following species representing a phylogenetic sampling of foraging modes in nine families of lizards (Fig.

1.1): Agamidae (Laudakia stellio Linnaeus), Iguanidae (Leiocephalus schreibersi

Gravenhorst, Oplurus cuvieri Gray, Sceloporus malachiticus Cope, Tropidurus torquatus

Wied-neuwied), Eublepharidae (Eublepharis macularius Blyth), Scincidae (Eumeces schneideri Daudin, Eulamprus quoyii Quoy and Gaimard), Cordylidae (Tracheloptychus petersi Peters, Cordylus warreni Boulenger), Xantusiidae (Lepidophyma flavimaculatum

Dumeril), Teiidae (Ameiva ameiva Linnaeus, Tupinambis teguixin Linneaus), Lacertidae

(Acanthodactylus boskianus Daudin), and Varanidae (Varanus exanthematicus Bosc).

Additional data were taken from the literature for three species: Coleonyx variegatus

(Eublepharidae) and Plestiodon skiltonianus (Scincidae) from Farley and Ko (1996) and

Hemidactylus garnoti (Gekkonidae) from Chen et al. (2006). Species foraging modes

(Fig. 1.1) were based on literature accounts (Reilly et al. 2007) or my field data

(Eulamprus quoyii; E. J. McElroy, unpublished data). Species were selected to sample

SW taxa (Iguania), the major evolutionary transitions to WF mode (ground geckos,

Scincomorpha, Lacertoidea, and Varanidae), and three Scincomorphs that have reverted to SW foraging from WF ancestors (Eulamprus, Cordylus and Lepidophyma). All housing and experimental procedures followed approved animal use protocols.

Data Collection

Gait and whole body mechanics were studied as lizards travelled down a racetrack towards a dark hide box. I induced the fastest speeds by gently pressing on the tail or hind limb, medium speeds by tapping the ground near the animal or waving my hands above the animal, and slow speeds by allowing the animal to move down the track without any human stimulation. This procedure captured a wide range of speeds to represent the locomotor scope and foraging speeds for each species. Each individual was induced to move down the racetrack several times (usually three - to - five times) until I noticed signs of fatigue (uncoordinated limb movements, dragging belly, or refusal to move after 3 tail pinches). The total number of trials collected per species was roughly evenly distributed among individuals. Individuals were allowed to rest and recover for

24 hours before subsequent trials. All diurnal species were maintained at ~ 36-40 °C for the duration of each trial, except for the nocturnal Eublepharis and Lepidophyma which were maintained at ~ 26-30°C. Lizards were warmed to these temperatures under heat lamps and temperature was checked via a infra-red thermal laser directed on the belly periodically throughout the experiments.

Ground reaction forces were quantified using a custom-made force platform based on a strain gauge, spring-blade design described in Bertram et al. (1997). Vertical (V), fore-aft (FA), and medio-lateral (ML) ground reaction forces were sampled at 500 Hz using National Instruments data acquisition hardware and a LABVIEW custom designed virtual data sampling instrument following Parchman et al. (2003). The 0.6 m long by

0.2 m wide force platform surface was flush with the racetrack surface and located 3–3.6 meters along its 5.2-meter length. The entire surface of the racetrack and platform was covered with fine grit sandpaper to prevent foot slippage.

Quantifying Gait

During force data collection, lizards were filmed at 120 Hz or 500 Hz (small, fast lizards required higher frame rates) with high-speed video cameras mounted ~ 1 m above the surface of the force platform. Mirrors were mounted on angled walls along each side of the force platform to visualize footfalls. Kinematic analyses were conducted using

APAS (version 1.0). First, I determined whether trials were steady speed by digitizing the tip of the snout as the lizard crossed 7 evenly spaced lines along the surface of the racetrack. Next I calculated average speed across the entire field of view and discarded any trial that had > 20% difference between any interval speed and the average speed. I recorded the timing of touch-down and lift-off for each limb in the steady speed trials.

Lizards always used symmetrical gaits; therefore, I implemented the Hildebrand terminology to describe gait using two parameters, duty factor and limb phase

(Hildebrand 1976; Reilly and Biknevicius 2003). I examined hind limb duty factor which is the amount of time that the reference hind limb contacts the substrate divided by the

26 total stride duration. Limb phase is the amount of time that the footfall of the ipsilateral fore limb follows the reference hind limb divided by the stride duration. Duty factor and limb phase were multiplied by 100 to obtain percentages. A bivariate plot of limb phase versus duty factor (Hildebrand plot) was used to illustrate gaits following gait terminology of Biknevicius and Reilly (2006). Trots were defined as gaits with limb phases between 37.5% and 62.5%. Lateral sequence gaits were those with limb phases less than 50% and diagonal sequence gaits are those with limb phases greater than 50%.

Singlefoots were defined as those gaits on either side of the trot (limb phases 12.5% -

37.5% and 62.5% - 87.5%).

Quantifying Mechanics

Whole-body mechanics were calculated by aligning steady speed steps with ground reaction forces using another custom LABVIEW virtual instrument. Following

Willey et al. (2004) I defined a step as the time from footfall of the first limb in one couplet to the footfall of the first limb in the opposite couplet. Three-dimensional force data were converted into KE and GPE profiles following the methods of Blickhan and

Full (1992), Donelan et al. (2002), and Parchman et al. (2003). The integration constants for vertical and medio-lateral velocity were set as in Donelan et al. (2002); the integration constant for fore-aft velocity was set as the mean forward speed. Phase shift of the KE and GPE profiles was used to distinguish running from walking mechanical energy patterns. Phase shift was defined as the time difference between the minimum values of

KE and GPE relative to step duration, multiplied by 360° (Farley and Ko 1997; Parchman et al. 2003) and normalized to the range of 0–180 °. Phase shifts from 135–180° were

27 defined as walking mechanics and phase shifts of 0–45° were defined as running mechanics (Ahn et al. 2004; Reilly et al. 2006).

I used the ratio of total GPE to total KE over a step as an index of lumbering vs.

cursoriality following Reilly et al. (2006): lumbering species are defined as having GPE

to KE ratios significantly greater than one; cursorial species have ratios less than or equal to one.

Statistical Analyses

Phylogeny

I ran all phylogenetic comparative analyses (phylogenetic ANOVA, maximum likelihood character reconstruction and independent contrasts, each described in the following sections) on two phylogenies for lizards (Estes et al. 1988; Townsend et al.

2004). Branch lengths were based on both fossil and biogeographic estimates (Estes

1983; Evans 2003; Krause et al. 2003; Wells 2003) and the fossil-based methods of Vidal and Hedges (2005). Root age was set to 225 mya (Vidal and Hedges 2005). Branch lengths were not available for the relationships among skinks (Eulamprus, Plestiodon, and Eumeces), geckos (Eublepharis, Coleonyx and Hemidactylus), or tropidurids

(Leiocephalus and Tropidurus); these branches were arbitrarily assigned to 25 million years. I also ran analyses with all branch lengths set to one, which assumes a punctuational model where all change occurs at the nodes. All of the results from comparative analyses were qualitatively similar for both phylogenies and all branch lengths; thus, I report results of all phylogenetic comparative analyses for the Townsend et al. (2004) phylogeny and fossil-estimated branch lengths.

Correlated Evolution of Biomechanics, Gait and Foraging Mode

I employed a phylogenetic ANOVA to test for correlated evolution among foraging mode and biomechanics or gait. I assigned phase shifts, limb phases, and duty factors to each foraging mode while they were moving like they would while foraging.

Values were assigned this way because it allowed me to test the hypothesis that the foraging behavior and locomotor function have undergone correlated evolution. This resulted in values being assigned to SW foragers for running mechanics and to WF for walking mechanics. The phylogenetic ANOVA examines the difference between the F- value obtained from a non-phylogenetic ANOVA and a critical F-value (Fcrit) obtained

from a null distribution of F-values calculated by simulating character evolution on the

phylogeny (Garland et al. 1993). If the non-phylogenetic ANOVA is significant and its

F-value is greater than the 95th percentile of F-values obtained from the null distribution

then it can be concluded that the two traits have undergone correlated evolution. If the

non-phylogenetic ANOVA is not significant then it is concluded that the two traits are

not associated and there is no need for further analysis, i.e. the traits have not undergone

correlated evolution. First, I ran a non-phylogenetic ANOVA with phase shift, limb

phase, and duty factor as response variables and foraging mode as the main effect. Next,

I generated 1000 data sets that simulated the evolution of phase shift, limb phase, and

duty factor using PDSIMUL (Garland et al. 1993). I performed both bounded and

unbounded simulations and used both Brownian motion and Ornstein-Uhlenbeck

evolutionary models; however, these variations did not qualitatively alter the results. The

simulated data sets were analyzed using PDANOVA to create a null distribution of F-

values from which the Fcrit was determined. I concluded foraging mode and

29 biomechanics or gait to have undergone correlated evolution if the non-phylogenetic

th ANOVA was significant and its F-value was greater than Fcrit determined from the 95 percentile of the null distribution of simulated F-values.

Evolutionary History

I employed ancestor character reconstruction to visualize the evolutionary relationships between foraging mode, biomechanics, and gait. Foraging mode reconstruction was based on the larger sample of ~ 110 species (Miles et al. 2007) and interpretation from Reilly and McBrayer (2007). I reconstructed the ancestral character states for biomechanics (phase shift) and gait (limb phase and duty factor) using maximum likelihood in the computer program ANCML (Schluter et al. 1997). This program also outputs standard errors for reconstructed trait values. I used standard errors to generate 95% confidence intervals for each trait at each node. Then I determined significant evolutionary changes in each trait by comparing ancestor nodes to their associated descendent node or tip values. If the 95% confidence intervals overlapped then ancestor-descendent pairs were considered the same and the trait was not evolving; if the confidence intervals did not overlap then the pairs were considered significantly different and the trait was evolving. I used a modification of this approach to assign node values for phase shift. I assigned node values as running mechanics if the node’s 95% confidence interval was within the range of a running phase shifts (0–45º) but outside of the range of a walking phase shifts (135–180º). Likewise, I assigned node values as walking mechanics if the node’s 95% confidence interval was within the range of walking phase shifts (135–180º) but outside of the range of running phase shifts (0– 45º).

If the confidence interval did not overlap (46–134º) or overlapped both (contained values

30 both ≥135º and ≤45º) running and walking phase shifts then the node was assigned as equivocal.

Species Differences in Gait

I found that foraging mode evolution was strongly associated with phase shift and duty factor, however, there was a much weaker relationship with limb phase (see

Results). In addition, the analysis of species differences for gait is inherently multivariate. Thus, to further probe patterns of gait evolution within Hildebrand gait space, I employed a Repeated Measures MANOVA and CART (classification and regression tree). For the RM-MANOVA, species was a fixed effect, limb phase and duty factor were response variables, and repeated trials per individual was the repeated measure. I ran separate analyses on walking and running mechanics because all species could run, but only seven species walked (Table 1.1). I assigned species to post-hoc groupings by comparing their 95% confidence intervals on the first canonical axis output from the RM-MANOVA (Mardia et al. 1980). Species whose 95% confidence intervals overlapped are placed in the same group; species whose confidence intervals did not overlap are placed in different groups. These data violated some of the assumptions of

MANOVA (unequal group sizes and variances; slight deviation from multivariate normality); therefore, I used non-parametric CART (classification and regression tree) analysis to verify species groupings.

I also analyzed how lizards that used both running and walking COM mechanics shifted from running to walking in Hildebrand gait space. I ran separate MANOVAs for each species with COM mechanics as the main effect and duty factor and limb phase as response variables. A significant MANOVA would indicate that a species has shifted its

31 position in gait space, whereas a non-significant finding would indicate no shift in gait space. A sequential Bonferroni correction was applied to account for multiple hypothesis testing. These analyses were performed in JMP 5.0 (SAS Institute Inc., Cary, NC, USA).

Locomotor Integration

Finally, I wanted to assess the degree of evolutionary integration of the locomotor system (Dickinson et al. 2000; Reilly et al. 2007). I tested the hypothesis that gait and biomechanics had undergone correlated evolution by examining the correlations between phylogenetically independent contrasts for phase shift, limb phase, and duty factor. A significant correlation between independent contrasts would indicate that two traits have undergone correlated evolution (Garland et al. 1992). This analysis was performed in the

PDAP module of Mesquite (Maddison and Maddison 2007; Midford et al. 2002). All regressions were computed through the origin and adequate standardization of contrasts was checked using diagnostics tests in the PDAP module of Mesquite (Garland et al.

1992).

Results

Species means for variables describing biomechanics and gait are presented in

Table 1.1 and sample sizes are indicated on Figures 1.2 and 1.3. Eight of the 15 focal species only used running COM mechanics (Fig. 1.2) while the remaining seven used both walking and running COM mechanics (Fig. 1.3).

Biomechanics and Foraging Mode

Phase shifts observed in the focal species are presented in Fig. 1.4B in relation to the foraging mode reconstruction in Fig. 1.4C. The non-phylogenetic ANOVA found

32 that WF and SW species significantly differ in phase shift (F1, 15 = 43.28, p < 0.0001).

When testing for the effects of phylogeny, the PDANOVA on phase shift calculated a

Fcrit = 5.73 much smaller than that of the non-phylogenetic ANOVA, indicating that

phase shift and foraging mode have undergone correlated evolution. This is also clearly

illustrated in Fig. 1.4 by comparing the appearance of walking phase shifts (panel A) to

the appearance of WF mode (panel C).

The reconstructed pattern of locomotor mechanics based on the maximum

likelihood analysis of phase shift (Fig. 1.4A) assigned running mechanics to the ancestral

node of lizards. Reconstructed phase shift values show that walking mechanics

independently evolved four times at the nodes leading to following five groups: 1)

Eublepharis and Coleonyx, 2) the Eumeces-Plestiodon group, 3) Tracheloptychus, 4)

Varanus, and 5) the Lacertoidea (there was a single evolutionary transition to walking

mechanics at the node leading to the Scincomorpha). Walking mechanics was

independently lost three times in the Scincomorpha (Cordylus, Eulamprus,

Lepidophyma).

Gait and Foraging Mode

To test for correlated evolution of gait with foraging mode I examined duty factor

and limb phase separately. For duty factor, the non-phylogenetic ANOVA found that

WF and SW species were significantly different (F1, 15 = 51.01, p < 0.0001). The

PDANOVA on duty factor calculated a Fcrit = 5.46 much smaller than that of the non-

phylogenetic ANOVA, indicating that duty factor and foraging mode have undergone

correlated evolution. Character reconstruction via maximum likelihood analysis on duty

factor assigned values of ~54-49% across most of the ancestral nodes with significant

33 relative increases in duty factor in the branches leading to four taxa (Eublepharis,

Tracheloptychus, Tupinambis and Varanus).

For limb phase, the non-phylogenetic ANOVA was not significant (F1, 15 = 2.99, p

= 0.104) indicating that limb stepping pattern did not vary to a significant extent across these lizards when tip values alone were considered. Based on this analysis I concluded

that foraging mode and limb phase did not undergo correlated evolution. However,

character reconstruction via maximum likelihood analysis revealed some significant

changes in limb phase. Ancestral nodes had values ~50% while the branches leading to

four species had significant changes in limb phase away from ~50% (decrease:

Tracheloptychus, Ameiva, and Varanus; increase: Acanthodactylus). This indicates that

even though foraging mode and limb phase were not evolutionarily correlated, limb

phase has undergone some evolutionary change.

Multivariate Species Differences in Running and Walking Gaits

Observed gaits for all 15 species are plotted in Hildebrand symmetrical gait space

in Figure 1.5 with running mechanics in panel A and walking mechanics in panel B.

Species were significantly different in Hildebrand gait space for both walking and

running mechanics (Repeated Measures MANOVA: running mechanics, F42,472 = 8.8499,

p < 0.0001; walking mechanics, F9,55 = 20.65, p < 0.0001). Based on both the 95%

confidence ellipses on the first canonical axis and the CART analysis, species clustered in

significantly different portions of Hildebrand gait space. Species clustered into two

groups when using running mechanics (Table 1.1; Fig. 1.5A). Tupinambis, Eumeces

sch., and Eublepharis used high duty factor trots; all other species used low duty factor

trots. While walking, species clustered into three groups (Table 1.1; Fig. 1.5B). All

34 species used trotting limb phases. Most of the wide foragers used trots with high duty factors while walking. Two species exhibited significantly different gaits. Ameiva walked using a significantly lower limb phase with relatively low duty factors.

Acanthodactylus walked using a significantly higher limb phase with relatively low duty factors.

To examine how WF species alter the timing of limb contact when shifting from running to walking mechanics I plotted their running and walking gaits on the same

Hildebrand plot (Fig. 1.6) and used MANOVA to test for significant differences in contact kinematics between the mechanically running vs. walking gaits for each species.

This analysis revealed four patterns of gait transition used by WF lizards when they shift from running to walking mechanics:

Gait 1 (G1): a large increase in duty factor shifting the gait toward a slower speed (0.16-

0.29 m/s) and a significantly lower limb phase (i.e. a more lateral sequence trot;

Varanus, F2,79 = 43.15, p < 0.0001; Tracheloptychus, F2,23 = 20.04, p < 0.0001).

Gait 2 (G2): a small increase in duty factor with moderate speed (0.43 m/s) shifting to a

significantly lower limb phase (i.e. a more lateral sequence trot; Ameiva, F2,25 =

6.83, p = 0.004).

Gait 3 (G3): no change in a fast speed (1.02 m/s) and high limb phase gait (i.e. a more

diagonal sequence trot; Acanthodactylus, F2,33 = 2.96, p = 0.06). Although duty

factor exhibits a large increase when switching from running to walking, the

increase is not statistically significant, probably due to the large standard error

produced by the small walking sample (N=2) for this species.

Gait 4 (G4): no change in gait with slow (0.22-0.24 m/s) trotting walks and runs

(Tupinambis, F2,14 = 2.98 p = 0.08; Eumeces sch., F2,16 = 0.25 p = 0.78;

Eublepharis, F2,7 = 0.36, p = 0.71). This pattern is also unique in having a

significantly lower speed and higher duty factor during running (Fig. 1.5A).

Correlated Evolution of Biomechanics and Gait

Finally, the degree of integration between biomechanics and gait was tested using

phylogenetic independent contrasts between biomechanics (phase shift) and gait

(expressed as both duty factor and limb phase). The independent contrasts for phase shift

2 were significantly related to duty factor (r = 0.78, F1,14 = 51.04, p < 0.0001) and limb

2 phase (r = 0.479, F1,16 = 14.68, p = 0.002). By explicitly accounting for phylogenetic

patterns within these traits these results show that gait and biomechanics have undergone

correlated evolution.

Discussion

Patterns of Running in Lizards

In all lizards, rapid locomotion is an essential behavior for predator evasion and

social interactions. I found that the lizards examined in this study used a trotting gait

(limb phase ~ 50%) with running mechanics when moving at fast speeds (Table 1.1; Fig.

1.5A). Animals may use trotting gaits while running because the line of support generated by diagonal couplets is optimally aligned under the COM, thus offering good dynamic stability at high speed (Cartmill et al. 2002; Chen et al. 2006; Hildebrand 1988).

In addition, running mechanics coupled with a trotting gait may enhance maneuverability

(Chen et al. 2006) and provide a simple template for the neural control of fast locomotion

(Full and Koditschek 1999). Given the observation that all species in this study used mechanical runs with a trotting gait it appears that these species achieve the advantages of stability, maneuverability and simplicity of neural control of fast locomotion.

Although all lizards in this study used a trotting gait while running I found that species cluster into two distinct groups in Hildebrand gait space (Fig. 1.5A) separated by a significant difference in duty factor. Most species (all of the SW and most of the WF) consistently used low (50-30%) duty factor trotting gaits (limb phase 40-60%) at high speeds (0.8-1.64 m/s; Table 1.1). This is similar to the high speed symmetrical gaits used by most cursorial animals when moving fast (Hildebrand 1976; Reilly and Biknevicius

2003). However, three WF species (Eublepharis, Eumeces sch, and Tupinambis) shift to high duty factor (67-72%) and low speed (0.16-0.24 m/s) running (Table 1.1). During mechanical runs, these species overlap the gait space used by tuataras, salamanders and frogs (Ahn et. al. 2004; Reilly et al. 2006).

The Evolution of Mechanics and Gait with Foraging Mode in Lizards

A summary of patterns of evolution of locomotor traits in the sample of lizards based on the analyses is presented in Figure 1.7. Ancestral reconstructions of foraging mode (Miles et al. 2007) show that SW is ancestral for all lizards. I suggest that running mechanics are ancestral for lizards based on three pieces of evidence: 1) all lizards examined in this study use running mechanics; 2) ancestral character reconstruction via maximum likelihood, based on the sample of 18 species, shows that running mechanics is ancestral; and 3) additional comparative analyses show a tight evolutionary coupling between foraging and mechanics. Based on this evidence I suggest that the SW ancestor

37 of lizards only used running mechanics at steady speed. However, when comparing species locomotor function when moving at their foraging speed, the most obvious pattern is that COM biomechanics and foraging behavior have undergone correlated evolution. Three lines of evidence support this pattern. First, foraging mode and biomechanics have a one-to-one pattern when mapped onto the phylogeny; all SW species only use running mechanics and every evolutionary transition to WF behavior is accompanied by the appearance of walking mechanics (Figs. 1.4, 1.7). Second, the three examples of evolutionary reversals from WF back to the SW strategy (Eulamprus quoyii,

Lepidophyma flavimaculatum, and Cordylus warreni) have independently lost walking mechanics (Figs. 1.4, 1.7). The interpretation of these three species having lost walking mechanics is based on the evidence that the basal Scincomorph was WF (Miles et al.

2007) and thus probably used walking mechanics. Third, the results were robust to variations in phylogenetic topology, branch lengths, and evolutionary models; I consistently found that foraging mode and biomechanics had undergone correlated evolution.

Another obvious pattern from the results is that gait, too, has undergone correlated evolution with foraging mode (Figs. 1.5, 1.7). Based on the sample, I show that the ancestral lizard gait used a low duty factor trotting gait at high speeds when running (Fig.

1.5A). Each of the WF species used one of three walking gaits that were significantly different from the ancestral low duty factor running region occupied by most lizards

(Figs. 1.5, 1.6). In addition, the three species that have undergone an evolutionary reversal back to SW foraging retained the ancestral high speed low duty factor trot (Figs.

1.5, 1.7). Patterns of gait shift when changing from running to walking also have

38 undergone correlated evolution with foraging mode (Fig. 1.7). In fact, two of the patterns of gait change (G1 and G4) have evolved more than once with the evolution of WF.

Clearly the evolution of WF and walking is accompanied by evolutionary changes in both biomechanics and gait.

Why Change Mechanics and Gait When Moving at a Slower Foraging Speed?

The tight evolutionary correlation between mechanics and foraging mode suggests that walking mechanics may have been a key innovation in the evolution of slower locomotion in WF lizards. One possible benefit of walking mechanics is that they may decrease the total mechanical energy needed to move the COM because of the pendulum-like exchange of KE and GPE (Biewener 2006; Cavagna et al. 1977; Farley and Ko 1997). Pendular savings, measured as % recovery of external mechanical energy, ranged from 18 – 47% in WF species during walking compared to 1-18% during running

(Table 1.1). Thus, walking in WF lizards requires less external mechanical energy than running, suggesting that it is an energetic adaptation for long periods of slow locomotion.

However, while WF lizards reduce the amount of external mechanical energy used during locomotion it does not necessarily follow that this results in a relevant reduction in metabolic energy, for two reasons: First, lizards have body masses that are too small to realize relevant metabolic savings from mechanical energy savings during walking because their actual metabolic costs are two orders of magnitude greater than their total mechanical energy costs (Reilly et al. 2007). Therefore, no matter how much external mechanical energy is recovered by pendulum-like exchange of KE and GPE it is insignificant in relation to the actual metabolic cost of locomotion. Second, the cost of locomotion during walking has been shown to be greater than running, both on a per

39 stride basis and on an absolute basis, because WF actually spend the majority of their activity budget walking slowly (Anderson and Karasov 1981; Reilly et al. 2007). Thus, it is difficult to support the idea that walking mechanics is a key adaptive innovation to reduce the metabolic cost of locomotion in WF. In fact, it has been proposed that walking and running mechanics may actually be spandrels (sensu, Gould and Lewontin

1979) of legged locomotion in small animals (Reilly et al. 2006, 2007). Resolution of the true energetic relevance of mechanical energy savings in small animals awaits future integrative studies.

Another argument for why animals switch mechanics with speed relates to the relationship between centripetal and gravitational forces acting on the COM (Kram et al.

1997). When animals increase speed centripetal force increases until it exceeds the gravitational force (occurring at a Froude number ~ 1) which prevents the animal from walking with an inverted pendulum and necessitates the switch to running mechanics.

This probably explains why many lizards switch from walking to running mechanics with increasing speed (Fig 1.6; G1–G3). However, this argument does not explain the reverse; when slowing down animals are not physically required to switch back to walking at a given speed. This is clearly illustrated by studies showing that animals actually prefer to switch from walking to running mechanics at a Froude number ~ 0.5 (Alexander 1989), well below the Froude number ~ 1 which requires the switch. Thus, animals appear to be capable of running at any speed, but can only walk up to a critical speed corresponding to a Froude number of ~ 1. It follows that when lizards slow down they are not switching from running to walking mechanics due to a physical requirement. The difference in speeding up vs. slowing down is not trivial because lizards have evolved slower

40 locomotor speeds as a necessity for WF behavior. Thus, neither energy savings nor physical constraints explain the evolutionary transition from fast running to slow walking mechanics in WF lizards.

Although there is not a clear energetic or biomechanical benefit of walking mechanics in WF lizards, there may be other benefits associated with evolving slower speeds and new gaits. This study shows that WF species move at slower speeds than SW species when considering speeds that they likely use while foraging (see also Cooper et al. 2005 for the same pattern in field movement speeds). Wide foraging lizards have evolved entire suites of characters related to their shift to derived chemosensory systems

(Cooper 1994; McBrayer and Corbin 2007; Reilly et al. 2007; Schwenk 1993). From the brain to olfactory receptors to forked air sampling tongues, WF lizards exhibit a number of characters that enhance their ability to slowly search for food (Reilly et al, 2007 and references therein). While foraging, slower locomotor movements may enhance wide foraging by allowing the chemosensory apparatus to meticulously sample a complex heterogeneous habitat for prey chemicals (Anderson 2007; Cooper 1994). The prey items for which WF chemically search for reward them with a higher energy payoff (Gasnier et al. 1994). Fast locomotor movements, while foraging, would preclude WF from being able to sample chemicals thoroughly and follow them in the environment. Thus, simply moving slower while foraging is of adaptive value to WF lizards because it affords them the ability to effectively locate and discriminate energy–rich prey. These findings suggest that the convergent evolution of slower foraging locomotion in WF lizards is an important correlate of effective predatory chemosensory behavior.

Wide foraging lizards couple slower speeds while foraging with a change in gait characterized by an increase in duty factor. Evolutionarily, the lizards examined in this study appear to shift from the ancestral high-speed low duty factor trot while running to slower-speed higher duty factor trots while walking (Fig 1.6). Thus, these findings show

that duty factor is the principal functional parameter that changes when lizards evolve

slower speed locomotion and WF behavior. Previous studies of lizard gait have also

shown that duty factor increases with decreasing speed (Hildebrand 1976; Sukanov

1968). The increase in duty factor associated with moving slowly may have two benefits

for a WF lizard. First, larger duty factors, i.e. longer ground contact time, are actually

energetically less expensive because they allow muscle force to be produced at a slower

rate which is more energetically economical (Kram and Taylor 1990; Pontzer 2007).

Thus, WF lizards may realize metabolic energy benefits by increasing duty factor when

moving slowly. However, all animals appear to increase duty factor as they slow down

suggesting that the energetic benefits are not an adaptation for WF per se, but rather a

general feature of slower terrestrial locomotion. Second, moving with larger duty factors

offers greater stability at slower speeds for animals that only trot (Hildebrand 1976).

When trotting animals slow down they move from aerial trots (alternating periods of

support on 2 diagonal feet) at low duty factors to regions in Hildebrand gait space with

periods of support by 2, 3 or 4 feet (Figure 7 in Hildebrand 1976). Thus, simply

increasing duty factor enhances stability at lower speeds. Accordingly, WF lizards may

realize both force-production energetic benefits and stability benefits as simple correlates

of the increase in duty factor when moving at slower foraging speeds.

Variation in Ecological Relevance of Gait among WF Lizards

Demonstration of the evolution of duty factor with foraging mode supports the general view that gait is a dynamic part of the locomotor system that is capable of responding to divergent ecological and behavioral challenges (Stevens 2006). The WF lizards examined in this study exhibit four patterns of gait change when they switch from running to walking (Fig. 1.6) that may be related to details of their foraging ecology.

These four patterns differ in the relative shifts in speed, duty factor limb phase (Fig. 1.6).

In terms of speed, the slowest G1 and G4 (Fig. 1.6) walkers are extreme WF

(Varanus, Tupinambis, Tracheloptychus), have particularly short limbs and are fossorial

(Eumeces sch.), or are cryptic and nocturnal (Eublepharis). The moderate speed G2

(Ameiva) and high speed G3 (Acanthodactylus) walkers both belong to lizard families that exhibit field movement patterns marked by frequent pauses and changes in direction

(Anderson 2007; Verwaijen and Van Damme 2007) and many of the walks I recorded from Ameiva and Acanthodactylus fit this description. Interestingly, these two species used relatively small duty factors compared to the other species (Table 1.1; Fig. 1.6).

Such low duty factor walking may be useful for foraging with frequent pauses because it has been hypothesized to allow numerous opportunities to change direction, thereby increasing maneuverability (Vanhooydonck et al. 2002) which may facilitate chemosensory tracking abilities. Thus, the speed that each lizard species uses while foraging appears to be related to the specific techniques or ecological context they use to forage.

In terms of patterns of gait change, G1 involved significant increases in duty factor (from 41-50% to 69-72%) and a shift to a more lateral sequence trot (limb phase

43 from 50-57% to 40-43%; Fig. 1.6). Both species that evolved G1 (Varanus and

Tracheloptychus) exhibited significant decreases in both duty factor and limb phase from their immediate ancestral nodes. G1 is also associated with a large decrease in speed

(from running at 0.92 to 1.37 m/s to walking at 0.16 to 0.29 m/s). Although little is known about the field behavior of these species, they clearly follow the general tetrapod pattern of shifting towards a singlefoot gait with larger duty factors when moving slowly

(Biknevicius and Reilly 2006; Hildebrand 1976).

The G2 of Ameiva also had a small decrease in duty factor and limb phase but was different from other species in three ways. First, both running and walking limb phases were the lowest observed, and during walking, Ameiva occasionally utilized a singlefoot gait (Fig. 1.3; sensu Biknevicius and Reilly 2006). Second, character reconstruction showed that limb phase was significantly lower in Ameiva than in its immediate WF ancestor. Third, Ameiva utilized moderate walking speeds (mean = 0.43 m/s; Table 1.1).

Ameiva appears to exhibit G2 for a number of reasons. In the wild, Ameiva travels widely and quickly between patches of resources (Magnusson et al. 1985, Anderson

2007). In addition, Ameiva has comparatively longer feet than most lizards (E. J.

McElroy, unpublished). Thus, both ecological and morphological factors may affect mechanics and gait in Ameiva. Clearly more comparative kinematic, morphometric, and behavioral studies are needed to understand why Ameiva has a lower limb phase during walking.

Acanthodactylus boskianus exhibited G3 with an increase in duty factor that was nearly significant (p = 0.06). The G3 pattern had no change in limb phase with walking.

This species employed the highest limb phase (57%) observed during walking in the

44 locomotor sample I collected. Character reconstruction indicated a significantly higher limb phase in A. boskianus relative to that of its immediate ancestor, indicating it had evolved toward a more diagonal-sequence trot. In addition, A. boskianus adopted a strategy of significantly faster speed walking (1.02 m/s) than other lizards in this study. I propose that very fast walking in A. boskianus may be related to WF on hot desert sands that are nearly devoid of vegetation (Belliure and Carrascal 2002; Perry et al. 1990).

Acanthodactylus erythrurus has been shown to heat up more slowly, cool down more quickly, and exhibit higher physiologically optimal and preferred temperatures than do most lizards. All of these thermal traits are posited to be adaptations to the scarcity of cover, and/or high predation risk in the xeric and thermally demanding environments they inhabit (Bauwens et al. 1995; Belliure and Carrascal 2002; Belliure et al. 1996).

Although A. erythrurus is a SW species it seems likely that WF species (such as A. boskianus) would experience even stronger selection on thermal traits because they are presumably more exposed to predators and hot temperatures than are SW species. Thus, rapid walking and low duty factors during foraging in A. boskianus may be an adaptation for seeking prey on extremely hot sandy substrates found in deserts.

Species (Tupinambis, Eublepharis, Eumeces) exhibiting the fourth pattern (G4) adopt the same walking gait (duty factor 64-73%, limb phase 43-46%) and speed range

(0.22-0.24 m/s) as the G1 pattern. However, the G4 species are unique in utilizing running gaits with significantly higher duty factors (Fig. 1.5A). Thus, they have shifted both the walk and run to high duty factors at slow speeds (0.19 to 0.29 m/s). An additional skink (Plestiodon skiltonianus) and gecko (Coleonyx variegatus) appear to occupy a similar region in gait space (Farley and Ko 1997) and likely experienced a

45 similar evolutionary history because they are closely related to two species in this study

(Eumeces schneideri and Eublepharis macularius). The phylogenetic reconstructions

(Fig. 1.7) suggest that these species have independently evolved low-speed locomotion.

Why Run Slow?

The speeds exhibited by Tupinambis match field and lab foraging speeds (Klein et al. 2003) so I am confident that the patterns of gait and mechanics I observed reflect their walking foraging mode. In terms of running, Tupinambis is known to employ the strategy of defensive and aggressive behavior rather than flight both in the lab (pers. obs.;

Klein et al. 2003) and the field (De Lema 1983). However, Tupinambis is capable of moving more rapidly (Urban 1965) and when they do, they exhibit high speed, low duty factor trots (White and Anderson 1994). Thus, they may actually use the G1 pattern; although given the choice they appear to prefer to fight rather than high-speed running as an antipredatory behavior.

The remaining two species that exhibit slow running probably never have to run fast (at least as fast as other lizards). Eumeces schneideri has extremely small limbs and inhabits burrows, rarely venturing into the open (Disi and Amr 1998). Eublepharis is a large, nocturnal, slow moving, WF ground gecko (Cooper 1994) that does not rapidly flee but uses crypsis, posture, tail movement displays and tail autotomy as antipredatory behaviors (Marcellini 1977). All other WF lizards in this sample were long limbed, diurnal, and preferred high-speed running as an antipredatory response (personal observation).

The G4 species exhibited a lack of relationship between speed and mechanics

(Table 1.1). This has been argued to be related to the basal condition of lumbering

46 locomotion in tetrapods and is found in a variety of sprawling animals such as salamanders, tuataras, alligators, and frogs (Ahn et. al. 2004; Reilly et al. 2006; Willey et al. 2004). Lumbering locomotion has been defined on the basis of having GPE fluctuations greater than KE, whereas cursorial locomotion has been defined by KE being equal to or greater than GPE (Reilly et al. 2006). Interestingly, all lizards exhibited

GPE/KE ratios that were either not significantly different from or far less than one (Table

1.1). Thus, G4 lizards are cursorial even though they exhibit the same patterns of gait and mechanics as other sprawling tetrapods. This finding shows that the evolutionary shift to slow running in the G4 species does not include a shift to lumbering locomotor mechanics.

Lizards Only Trot

One interesting observation about the lizard gaits observed in this study is that they do not substantially deviate from trotting limb phase during running or walking. In general, the SW runners I studied exhibited the “cleanest” trots (near 50% limb phase indicating coordination of diagonal limb couplets). During walking, most WF I studied exhibited lower limb phase values (Table 1.1). Although there are a few data points extending well into limb phases diagnostic of the lateral sequence single-foot (Figs. 1.2,

1.3) all species means fell into the limb phase range of 37.5 to 62.5% that describes a trotting gait (sensu Biknevicius and Reilly 2006). The lack of singlefoot gaits was surprising given that lateral and diagonal sequence singlefoots are predicted to improve stability in slow moving animals due to the larger polygons of support associated with these gaits (Cartmill et al. 2002; Hildebrand 1988). Thus, when lizards shift to walking they may experience some enhanced stability afforded by more lateral or diagonal

47 sequence trots but they do not fully move into the areas of singlefoot gait space that take full advantage of hypothesized increases in stability (Cartmill et al. 2002; Hildebrand

1976). This finding suggests the presence of some underlying neural or biomechanical constraint which may limit lizards from routinely using lateral and diagonal sequence gaits that many mammals use when moving slowly. Primates also exhibit a lack of relationship between limb phase and speed or substrate type (Stevens 2007). Thus, limb phase may show less of a response to functional or environmental requirements than previously envisioned.

Caveats

This sample of 18 species is only a fraction of the ~4000 species of lizards and thus, like virtually all other comparative studies, this study suffers from limited taxon sampling. However, given the difficulty of obtaining data on lizard locomotor function

(particularly center-of-mass mechanics of small animals) I feel that this study provides convincing insights into how locomotor function and foraging ecology have undergone correlated evolution and provides an important starting point for future research in this area. Every comparative study has to address the issue of how the choice of species affects its results. I sampled species to maximize the number of evolutionary transitions to increase the power of statistical tests of trait correlated evolution based on an a priori evolutionary pattern of foraging mode evolution. However, sampling this way can provide results that appear at odds with accepted patterns of evolution. One such instance is the phase shift reconstruction at the base of the Scincomorpha (Fig. 1.4).

Based on the focal sampling (focusing on reversals to test trait correlated evolution) the base of the Scincomorpha reconstructs as using running mechanics. In fact, the basal

Scincomorph is known to be WF (Miles et al. 2007) which is the foundation of the interpretation that the basal Scincomorph would walk. Based on this interpretation I concluded that the three independent evolutionary reversals to SW foraging are accompanied by losses of walking mechanics in the Scincomorpha (Fig. 1.7). Clearly the

Scincomorpha is a hot bed of foraging mode evolution and a complete understanding of functional evolution within the group requires additional sampling. However, given the strength of these findings and their robustness to phylogenetic uncertainty, differences in ecology among species, and the phylogenetic breadth of this sample, I feel that these results are robust and provide a general picture of how foraging ecology and locomotor function have undergone correlated evolution both within Scincomorpha and across lizard phylogeny.

Conclusions and Future Directions

The primary observation of this study is that locomotor mechanics and gait have undergone correlated evolution with foraging mode in lizards, at least based on this sample of 18 species. In addition, different locomotor patterns have appeared (and sometimes convergently evolved) with WF strategies and they subsequently disappear when lizards revert to SW foraging. The strong correlation of locomotion and foraging mode would be predicted given the similar pervasive patterns of correlated evolution and convergence in feeding biomechanics, skull and tongue morphology, chemosensory physiology and behavior in lizards (Cooper 1994; McBrayer and Corbin 2007; Reilly and

McBrayer 2007; Schwenk 1993).

Many previous studies have been focused on how animals move rapidly and the evolution of high speed sprinting locomotion, particularly studies of locomotion in lizards.

This study differs in formally showing the functional responses to the divergent speed demands of foraging mode and in particular how animals evolve slower locomotion.

Overall, this study highlights the need to examine the ecological and behavioral relevance of the full spectrum of locomotor scope, both fast and slow.

Although this research supports a tight evolutionary coupling between biomechanics, gait, and foraging behavior, several issues remain unresolved. First, although I show an evolutionary coupling between function and behavior I still have a poor understanding of how and when biomechanics, gait, and speed are used in the field.

Second, the utility of mechanical energy savings particularly in small animals remains unclear. Finally, it remains unknown how limb morphology is related to locomotor function in the context of foraging ecology. Given the pervasive effects of foraging behavior on lizard biology and the renewed and expanding interest in this subject (Reilly et al. 2007) the time seems ripe for additional detailed integrative studies of the functional and ecological basis of foraging locomotion in lizards.

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Table 1.1

Species means ± standard error or (range) for gait (duty factor and limb phase) and biomechanical (phase shift, % recovery, and the ratio of potential to kinetic energy) variables and speed. Superscipts indicated significant groups (letters: walk groups, numbers: run groups).

Duty Factor Limb Phase Phase Shift % R PE/KE Speed (m/s)

Tracheloptychus petersi WalkC 72 ± 1.4 43 ± 1.0 158 ± 3.6 47 ± 3.0 1.48 0.16 (0.15 – 0.20)

Run1 50 ± 4.1 57 ± 3.8 30 ± 4.9 8 ± 3.5 0.11 0.92 (0.24 – 1.41)

Varanus exanthematicus WalkC 69 ± 1.7 40 ± 1.5 158 ± 5.0 35 ± 5.0 1.18 0.29 (0.24 – 0.34)

Run1 41 ± 0.9 50 ± 0.6 18 ± 1.3 5 ± 0.7 0.11 1.37 (0.28 – 2.62)

Ameiva ameiva WalkB 53 ± 1.5 38 ± 1.4 157 ± 5.1 44 ± 4.8 0.84 0.43 (0.29 – 0.49)

Run1 50 ± 1.9 41 ± 1.1 21 ± 3.2 9 ± 1.4 0.22 0.75 (0.41 – 2.11)

Eublepharis macularius WalkC 72 ± 2.1 44 ± 1.1 151 ± 6.6 32 ± 4.3 0.98 0.24 (0.20 – 0.27)

Run2 70 ± 0.7 43 ± 1.8 8 ± 3.2 16 ± 3.9 0.97 0.29 (0.29 – 0.32)

Eumeces schneideri WalkC 73 ± 1.2 50 ± 0.6 156 ± 2.4 38 ± 4.0 1.86 0.24 (0.10 – 0.47)

Run2 71 ± 3.7 50 ± 0.6 23 ± 2.1 18 ± 4.0 0.58 0.19 (0.10 – 0.28)

Tupinambis teguixin WalkC 64 ± 1.6 46 ± 1.1 151 ± 4.4 35 ± 7.2 0.80 0.22 (0.13 – 0.29)

Run2 67 ± 1.5 49 ± 0.9 19 ± 4.7 16 ± 5.6 1.38 0.23 (0.16 – 0.43)

Table 1.1: Continued

Acanthodactylus boskianus WalkA 53 ± 8.0 57 ± 1.0 152 ± 17 18 ± 0.5 0.14 1.02 (0.82 – 1.23)

Run1 37 ±1.5 56 ± 0.7 16 ± 2.5 1 ± 0.4 0.12 1.84 (0.69 – 3.57)

Laudakia stellio Run1 40 ± 1.5 53 ± 1.0 17 ± 3.3 4 ± 0.8 0.20 1.56 (0.82 – 2.40)

Leiocephalus schreibersi Run1 47 ± 0.8 52 ± 0.6 16 ± 1.1 6 ± 0.5 0.54 0.80 (0.48 – 1.48)

Cordylus warreni Run1 44 ± 1.6 52 ± 2.2 20 ± 2.4 7 ± 1.9 0.25 0.86 (0.53 – 1.23)

Eulamprus quoyii Run1 50 ± 1.7 59 ± 1.4 19 ± 2.0 4 ± 0.8 0.62 1.10 (0.29 – 1.92)

Lepidophyma flavimaculatum Run1 41 ± 1.2 53 ± 1.3 18 ± 3.6 6 ± 1.4 0.29 0.57 (0.42 – 0.73)

Oplurus cuvieri Run1 48 ± 0.6 52 ± 0.6 10 ± 1.0 5 ± 0.6 0.50 1.14 (0.59 – 1.84)

Sceloporus malachiticus Run1 45 ± 0.8 51 ± 0.5 20 ± 1.4 4 ± 0.5 0.45 0.81 (0.24 – 1.49)

Tropidurus torquatus Run1 33 ± 1.1 53 ± 1.2 15 ± 2.5 5 ± 0.9 0.25 1.64 (1.13 – 2.88)

Plestiodon skiltonianus Run - 50 20

Walk - 50 180

Coleonyx variegatus Run - 50 10

Walk - 40 170

Hemidactylus garnoti Run 43 48 0

Figure 1.1. Patterns of foraging mode evolution reconstructed on a molecular phylogeny for lizards (Townsend et al. 2004). Black branches (bold text) are sit-and-wait foragers, white branches are wide foragers, and foraging mode for Plestiodon skiltonianus is unknown (grey). Note that WF evolved independently in the ground geckos, at the base of the Scincomorpha, and in the lineages leading to Varanus and the Lacertoidea.

Foraging mode reconstruction (branch shading) is based on a larger sample of 110 species (Miles et al. 2007) and from Reilly and McBrayer (2007).

Figure 1.2. Locomotor gaits for lizards that only used running mechanics. Gait is expressed as limb phase vs. duty factor (Hildebrand 1976). Species and sample size are indicated on each panel. Note that by convention the numerical scale on each axis is reversed (Hildebrand 1976).

Figure 1.3. Locomotor gaits for lizards that used both running and walking mechanics

(species and samples sizes labelled as in Fig. 1.2). Closed symbols are running mechanics; open symbols are walking mechanics.

Figure 1.4. The evolution of locomotor biomechanics in relation to foraging mode in lizards. A. Ancestral character reconstruction of phase shift values (from panel B) are

65 Figure 1.4: Continued

mapped onto the lizard phylogeny from Townsend et al. (2004) in Panel A. Branch colors indicate running (black), walking (white) and the gray branches leading to the base

of the Gekkota and the Scincidae are equivocal for running or walking. B. Raw phase

shift data indicating running (closed symbols, phase shift ≤ 45) and walking (open

symbols, phase shift ≥ 135) mechanics. C. Reconstructed patterns of foraging mode

evolution from Fig. 1.1 (black branches are SW lineages, white branches are WF

lineages, hatched branch is unknown). Note that walking mechanics evolved each time

WF evolved. Species followed by asterisks were taken from the literature.

Figure 1.5 Mean gaits (+ standard errors) and multivariate differences between species

during running (A, for all species) and walking (B, for WF) mechanics. Ellipses

surround species means that are not significantly different. A. When using running

67 Figure 1.5 continued. mechanics Tupinambis, Eumeces and Eublepharis differed in using significantly larger high duty factor trots than the remaining species. B. During walking mechanics species clustered into three statistically distinct gait groups. Ameiva and Acanthodactylus use a fast walking gait (lower duty factors) but diverge toward more lateral (lower limb phase) and diagonal (higher limb phase) sequence gaits, respectively. The remaining species clustered into a single group that uses a higher duty factor trotting gait while walking.

Figure 1.6. Patterns of gait change when shifting from running to walking mechanics in

WF lizards. White symbols are walking mechanics; black symbols are running mechanics. Solid cloud is the ancestral running gait cloud from Fig. 1.5A. When shifting from running to walking mechanics lizard exhibited four ways of changing position in gait space based on MANOVAs comparing running to walking gaits for each species: G1) dashed arrows Varanus and Tracheloptychus switch from the ancestral trotting run to a higher duty factor and lower limb phase trot while walking, G2) solid

69 Figure 1.6: Continued arrow Ameiva exhibits small shifts in limb phase and duty factor in a lateral sequence trot. G3) dotted arrow Acanthodactylus maintained a fast diagonal sequence trot and

G4) stippled cloud Tupinambis, Eumeces sch. and Eublepharis maintained a slow speed high duty factor trot when switching from running to walking.

Figure 1.7. Evolutionary patterns of foraging locomotor biomechanics and gait in relation to foraging mode in lizards (Fig. 1.1). From the ancestral condition of sit-and- wait foraging (black branches) with running mechanics (RUN ) and a trotting gait

(G:TROT) lizards have evolved walking mechanics (WALK) in concert with wide foraging (white branches) several times. Wide foraging species exhibit one of 4 patterns

71 Figure 1.7: Continued of gait shift (G1,2,3, or 4, from Fig. 1.6) involving different shifts in limb phase (Ç or È

LP) and duty factor (Ç or È DF). Note that walking mechanics was lost (WALK X’d out) each time foraging mode underwent an evolutionary reversal to SW.

72 CHAPTER 2: THE RELATIONSHIP BETWEEN LIMB MORPHOLOGY,

KINEMATICS AND FORCE DURING RUNNING: SIZE AND SPEED FREE

PATTERNS OF LOCOMOTOR DYNAMICS IN LIZARDS.

Introduction

When animals run their limbs move in rhythmic cycles to propel them through the terrestrial environment. From an organismal perspective terrestrial locomotion with limbs requires that that variation of motor pattern and limb morphology will lead to variation of stride kinematics which in turn will lead to variation in limb forces which ultimately leads to differences in center-of-mass dynamics and locomotor performance

(Russell and Bels 2001; Reilly et al. 2007). Thus, quantifying morphology, kinematics

and forces in an array of species provides a quantitative framework for understanding

how morphology relates to locomotion, how complex functional systems evolve and the

ecological relevance of morphology (Arnold 1983; Reilly and Wainwright 1994). A

myriad of studies have examined variation at the level of limb morphology (Alexander et

al. 1981; Bertram and Biewener 1990; Miles 1994; Miles et al. 2007), limb kinematics

(Sukhanov 1968; Heglund and Taylor 1988; Strang and Steudel 1990; White and

Anderson 1994; Irschick and Jayne 1999; Fischer et al. 2002; Vanhooydonck et al. 2002)

and force production (Roberts et al. 1998). In addition, some studies have established

that morphology is related to stride kinematics (Strang and Steudel 1990; Irschick and

Jayne 1999; White and Anderson 1994; Vanhooydonck et al. 2002) and that stride

kinematics are related to forces (Heglund et al. 1982; Full 1989; Farley et al. 1993; Farley

and Ko 1997; Reilly et al. 2006; Chen et al. 2006). However, no study has

73 simultaneously examined the interrelationships among these three levels (morphology, kinematics, force) across a morphologically diverse sample. The purpose of this study is to explain patterns of variation in the hierarchical relationship among morphology, kinematics and force generation in a phylogenetic array of species with differing limb morphology.

How should force covary with stride kinematics and limb morphology? It has been established that animals with shorter limbs have to step more often (i.e. higher stride frequency) than longer-limbed animals to attain fast speeds (e.g Vanhooydonck et al.

2002). In addition, ample studies of ground reaction forces show that during steady speed locomotion the sum of vertical force (i.e. vertical impulse) over a full stride divided by stride time must be equal body weight (Biewener 2003). However, general comparisons across speeds show that stride length, stride frequency, and float distance

(kinematic speed-effects) directly influence how force is propagated during the support phase of the stride cycle (Biewener 2003). Thus, species with short limbs should move at high stride frequency and, as a result, will produce less vertical force per ground contact because they have more ground contacts (i.e. higher stride frequency) per unit time. Conversely, species with longer limbs will have fewer support phases over a given time (i.e. low stride frequency) leading to the necessity of more vertical force per support to effectively support body weight. This limb length – kinematic – force hypothesis has not been explicitly examined in a series of animals with differing limb lengths, but related hypotheses have been proposed by inference from kinematic studies. For example, 1) species taking long strides at low frequency should generate more force than species taking shorter strides at higher frequency (Van Damme et al. 1998; Irschick and

74 Jayne 1999; Aerts et al. 2000) and 2) the relative magnitudes of vertical and accelerative

force should be good predictors of the distance traveled during the aerial phase (i.e.

floating distance) of the stride cycle (Irschick and Jayne 1999). In this study these

hypotheses are tested by first quantifying multivariate patterns in limb skeletal

morphology, stride kinematics, and force profiles for seven species of terrestrial lizards.

Forces are quantified as quantified as both impulses integrated over support duration and

peak forces. Then, multivariate statistical analyses test for differences within levels and

correlations among levels to reveal the morphological basis for kinematics and locomotor

forces. The results illustrate how the dynamics between limb length, support duration

and float distance directly influence the nature of forces applied to the ground during the support phase of steady state locomotion.

Methods

Study Species

Locomotor morphology and function were quantified in the following seven species of lizards (Fig. 2.1): Laudakia stellio Linnaeus (Agamidae), Oplurus cuvieri Gray

(Iguanidae), Tropidurus torquatus Wied-neuwied (Iguanidae), Eulamprus quoyii Quoy and Gaimard (Scincidae), Tracheloptychus petersi Peters (Cordylidae), Acanthodactylus boskianus (Daudin) Lacertidae, and Varanus exanthematicus Bosc (Varanidae). These species were chosen for analysis because they were all capable of high-speed running but encompassed a range of limb morphologies. All housing and experimental procedures followed approved animal use protocols.

75 Morphology

To quantify locomotor morphology I took dorsoventral view radiographs of multiple individuals per species (samples sizes in Table 1) and measured the lengths of the following morphological variables to the nearest 0.01 mm using digital calipers: snout-vent, humerus, ulna, carpal, 3rd metacarpal, 3rd finger, pectoral girdle width, femur,

tibia, tarsal, 4th metatarsal, 4th toe, 5th metatarsal, 5th toe, pelvis length, and pelvis width

(Fig. 2.2). These variables were used because previous studies have found that they are related to locomotor function and performance in lizards (Miles 1994; Irschick and Jayne

1999; Miles et al. 2007). All specimens were obtained via museum loans (catalog numbers in Appendix A).

Locomotor Function

Data Collection

Stride kinematics and force were studied as lizards sprinted down a racetrack

towards a dark hide box. Running was induced by gently pressing on the tail or hind

limb. Running trials were recorded over a range of running speeds including near-

maximum sprint speed for each species (Table 1). All trials consisted of lizard using

running (spring-mass) mechanics and a diagonal-couplet gait. To avoid fatigue effects

each individual was run down the racetrack up to 3 times and then allowed to rest and

recover for 24 hours before subsequent trials. All species were maintained at 36-40°C for

the duration of each trial. Temperature was checked during each trial with an infra-red

laser thermometer aimed along the body axis.

76 Stride Kinematics

Lizards were filmed at 120 Hz or 500 Hz (small, fast lizards required higher

frame rates) with high-speed video cameras mounted 1 m above the surface of the force

platform. Mirrors were mounted on angled walls along each side of the force platform to visualize footfalls. Kinematic analyses were conducted using APAS (version 1.0).

Speed during each trial was quantified by digitizing the tip of the snout as the lizard crossed 7 evenly spaced lines along the surface of the racetrack and calculating average speed across the entire field of view. Only trials with < 20% difference between any

interval speed and the average speed were used. The timing of touch-down and lift-off

for each limb were recorded in these steady speed trials. Velocity and the timing of foot

touch-down and lift-off were used to calculate the following kinematic variables: 1)

stride length, the distance traveled by the center-of-mass during one entire hindlimb cycle, calculated by multiplying velocity by the amount of time between ipsilateral

hindlimb touchdowns; 2) stride frequency, hindlimb strides per second, calculated as the

inverse of the amount of time between ipsilateral hindlimb touchdowns (stride duration);

3) step length, the distance traveled by the center-of-mass while a hindlimb-forelimb

couplet contacted the ground, calculated as velocity multiplied by the amount of time a

couplet contacted the ground; and 4) float distance, the distance traveled by the center-of-

mass when no limbs contact the ground, calculated as stride length minus two times step

length. Support duration was defined as the time from the touchdown of the first limb of

a couplet (fore or hindlimb) until the lift-off of the last of that pair of supporting limbs.

77 Whole Body Locomotor Forces

Ground reaction forces were quantified and using a custom-made force platform based on a strain gauge, spring-blade design described in Bertram et al. (1997). Vertical

(V), fore-aft (FA), and medio-lateral (ML) ground reaction forces were sampled at 500

Hz using National Instruments data acquisition hardware and LABVIEW custom designed virtual data sampling and quantification instruments. The 0.6 m long by 0.2 m wide force platform surface was flush with the racetrack surface and located 3–3.6 meters along its 5.2-meter length. The entire surface of the racetrack and platform was covered with fine grit sandpaper to prevent foot slippage.

From the whole body ground reaction forces I quantified 13 force variables.

These variables describe the shapes and amplitudes of force profiles to be quantitatively compared across species in relation to morphology and gait (Fig. 2.3). Peak forces were measured as the maximum force in each direction: peak vertical, braking (negative value), accelerative, and lateral forces (absolute value for lateral to account for left vs. right limb pushes). The remaining 9 time related variables were measured relative to support duration (i.e. limb morphology and kinematics affect locomotor forces applied to the substrate by the limbs). Impulses were measured as the area under the force curve

(the numerical integration of the force curve over limb contact time) for vertical, braking, accelerative, and lateral directions. Time to peak vertical, braking, accelerative, lateral force and time of the braking-accelerative transition point (when the fore-aft force profile switched from negative to positive values) were also measured and scaled to percent of support duration.

78 Statistical Analyses

Prior to statistical analysis some variables were transformed to approximate a normal distribution. All morphological variables, float distance, impulses and peak

forces were log10 transformed. All other variables were untransformed.

Correcting for Size and Speed

To examine the relationship between morphology, stride kinematics, and whole

body forces across species I had to control differences in both body size and running

speeds. Morphology, stride kinematics, and forces are known to covary with body size

(Mullineaux et al. 2006) and they covaried with size in the species in this study (Table 2).

To correct for the effect of body size I regressed morphological variables against snout-

vent length and kinematics and force variables against body mass and used the residuals

from these regression for further analysis.

Kinematics and force are also known to covary with running speed (Riggs et al.

1993; McLaughlin et al. 1996). Speed effects were evident in kinematics and force

variables (Table 2); thus, I corrected for speed by regressing the size corrected residuals

for each variable (from the regression of body mass) against speed and using these

residuals for further analysis. Thus, morphological variables were corrected for body

size, while kinematic and force variables were corrected for both body size and speed.

Finally, many studies attempt to adjust for speed and size differences across

species by comparing effects at a physiologically equivalent or dimensionless speed

(Farley et al. 1993). I compared the results of using speed corrected kinematic and force

variables (as described above) to a parallel analysis using dimensionless speed. This

analysis used the dimensionless speed defined as v g-1/2 l–1/2 (where v was speed, g was

79 gravitation acceleration, and l was leg length) for each trial (Farley et al. 1993). Leg

length as opposed to hip height (Irschick and Jayne 1999) was used because I did not

have detailed lateral videos from which to estimate hip height. Residuals of the body mass corrected kinematic and force variables regressed against dimensionless speed were

used in the parallel analysis. Results from analyses based on speed and dimensionless

speed produced the same statistical outcomes for the multivariate analyses relating

morphology, kinematics, and forces. Thus, I report the results from analyses based on

speed.

Multivariate Species Differences in Morphology, Kinematics and Force

Prior to multivariate analyses all variables were standardized to a mean of zero

and a variance of one. This placed all variables on the same scale and eliminated the

problem of variables on larger scales having larger influence on the results than variables

on smaller scales (Quinn and Keough 2002).

Each of the levels of analysis contained numerous inter-correlated variables which

can be a major problem for multivariate analyses. I addressed this issue in two ways.

For, morphological and kinematic variables I conducted separate principal components

analyses (PCA) to reduce the dimensionality of the data to a few principal components

that describe the most of the variation in morphological or kinematic variables. The PC

axes are by definition orthogonal and uncorrelated, thus removing the effect of

collinearity in the data prior to further multivariate analysis (see below). Principal

components were considered significant if they had eigenvalues > 1 and had percent

variation explained greater than that expected by the broken stick model (Quinn and

80 Keough 2002). Only significant principal component axes were used in subsequent multivariate analyses.

The PCA on the force variables was not useful in addressing collinearity because it generated numerous “significant” that explained small fractions of the variance in the force data. Therefore, I addressed collinearity in the force data by eliminating highly inter-correlated variables from multivariate analysis (Quinn and Keough 2002). Whole body impulses were always highly correlated with whole body peak forces in each direction (r > 0.65) and braking impulses/forces were highly correlated with accelerative impulses/forces (because animals were moving at steady speed). Thus, the reduced data set included only seven variables: vertical, accelerative, and lateral impulses and the relative time to peak vertical, braking, lateral, and braking-accelerative transition. Force variables in this reduced data set had weak correlations (r < 0.40), indicating that collinearity would not be problematic in subsequent multivariate analyses. Thus, the multivariate analyses of species differences used principal component axes for morphology, kinematics and a reduced force data set (7 variables).

Discriminant function analysis (DFA) was used at each level to determine statistical differences among species in multivariate space. DFA extracts axes that are linear combinations of the original variables that maximize the probability of correctly assigning the data to predetermined groups (Quinn and Keough 2002). Separate DFAs were run on morphology, kinematics, and force with species as the predetermined groups.

Standardized discriminant coefficients were used to determine the relationship between discriminant axes and the PC axes (morphology and kinematics) or original variables

(forces). Differences among species on each level were identified by computing

81 Mahalanobis distances (D2) and associated F-statistics and significance tests between species centroids for DF 1 and 2 (SAS 2001). Species that were not significantly different were placed in the same group; whereas species that were significantly different were placed in different groups.

Relationships between Multivariate Levels

After summarizing the differences between species at each level, three approaches were used to examine relationships among species between morphological, kinematic, and force levels. First, a qualitative comparison of relationships among levels was done visually (Fig. 2.5) by comparing relative positions of significantly different groups at each multivariate level (Reilly and Lauder 1992). Second, a Mantel test assessed significant multivariate correlations between each level. The Mantel test compared matrices of Mahalanobis distances between the centroids of each species in discriminant space at one level to those of another level. The computer program zt (Bonnett and Van de Peer 2002) was used to generate 5000 randomizations to compute the correlation between 1) morphology and kinematics, 2) kinematics and force, and 3) morphology and force. A significant Mantel tests indicates correlation between levels, but does not specify which aspects of morphology, kinematics, or force drive the correlation. Third, to identify which variables are correlated across levels separate multiple regressions were computed between the first (and second) discriminant axes across levels. In this test the morphology and kinematics levels were independent variables while force was the dependent variable. Species values for multiple regressions were their mean scores on each discriminant axis. The combination of these three techniques allowed for thorough

82 examination of the relationship between species position in multivariate space at different

levels of the analysis.

Finally, I employed partial least squares to test for relationships between 1)

morphology and kinematics and 2) kinematics and force. Partial least squares is ideal for

this data set because 1) these data violated some of the assumptions of discriminant

analysis (equal group sizes and variances) 2) collinearity can be explicitly handled

without using principal components or eliminating variables allowing for the direct

estimation of the relationship of each variable between levels. Species mean values for

each variable were used as input for the analysis. I used cross-validation to avoid over- fitting the data and to determine the number of factors to keep in the final analysis.

Effect of Phylogeny

The evolutionary relationships among species make them non-independent data points; thus, traditional statistical analyses are clouded by phylogeny (Felsenstein 1985).

To control for the effects of phylogeny I computed independent contrasts (Garland et al.

1992) in the PDAP module of Mesquite (Midford et al. 2002; Maddison and Maddison

2007) between species means on the first discriminant axis for each level. There was no relationship between the absolute values of standardized contrasts and their standard deviation, indicating that the contrasts were adequately standardized (Garland et al.

1992). Thus, I re-ran the multiple regression with the independent contrasts of each of the first discriminant axes. The regression was forced through the origin (Garland et al.

1992). A significant multiple regression would indicate that species scores on the first discriminant axis have undergone correlated evolution. The Townsend et al. (2004) phylogeny was use for all analyses (Fig. 2.3). Branch lengths were unavailable therefore

83 I set all branch lengths to one which does not substantially impact the outcome of phylogenetic comparative analyses (Diaz-Uriarte and Garland 1998) nor the outcome of similar studies of lizard locomotor function (McElroy et al. 2008).

Finally, squared-change parsimony in Mesquite (Maddison and Maddison 2007) and maximum likelihood in ANCML (Schluter et al. 1997) were both implemented to calculate the ancestral character states of species mean scores on DF1 and DF2 for morphology, kinematics, and force. I used both techniques because squared-changed parsimony can calculate ancestral states for phylogenies with polytomies (as in the

Townsend et al. 2004 phylogeny, Fig. 2.1), whereas, maximum likelihood cannot handle polytomies but can calculate standard errors and confidence intervals around nodal estimates. For maximum likelihood, I addressed polytomies by examining all possible resolutions, however, different resolutions did not materially alter the results. Squared change parsimony ancestral states were then plotted in discriminant space at each functional level. Maximum likelihood 95% confidence intervals (CI) were used to infer significant evolutionary shifts (see details in McElroy et al. 2008)

Results

Multivariate Patterns in Locomotor Levels

Morphology

The PCA on size-corrected morphological variables extracted two axes that together explained 80% of the morphological variance (Table 3). PC 1 explained 70% of the variance in morphology and had uniform moderate positive loadings for all morphological variables (except pelvic length) indicating that all limb segmental lengths

84 and pelvic width increased in relative size along PC1. Thus, species with positive values on PC1 had relatively longer fore and hind limbs while species that had negative values

had relatively shorter fore and hind limbs relative to their size. PC 2 explained 10% of

the morphological variance and had a strong positive loading for pelvis length. Thus,

species with positive values on PC2 had relatively long pelves while species with

negative values had relatively short pelves.

The DFA on these two morphological principal components extracted two axes

(Fig. 2.4) explaining 93% of the variation in morphology (Table 3) and indicated that

there are statistically significant differences between species (Wilk’s λ = 0.020, F18,388 =

65.1, p < 0.0001). Discriminant functions 1 and 2 closely approximated principal

components 1 and 2, respectively, because each PC axis variable scored large and

positively on only one discriminant axis (Table 3). Thus, PC loadings for morphological

variables could be interpreted along the DF axes with DF 1 related to relative limb length

(relatively longer limbs had large positive values) and DF 2 related to relative pelvic

length (relatively longer pelves had large positive values). Mahalanobis distances

between species centroids revealed that all of the species were significantly different in

morphospace (all p < 0.0001). Species lined up on DF 1 from the relatively shortest

limbs of the skink (Eulamprus) through relatively average limbs in the Varanid

(Varanus), Lacertid (Acanthodactylus), and the Gerrhosaurid (Tracheloptychus) to the

relatively longest limbs in the Iguanians (Tropidurus, Oplurus and Laudakia).

Tracheloptychus (a Gerrhosaurid) had a relative limb length similar to Acanthodactylus

and Varanus but was significantly different on DF 2 revealing its novel extremely long

pelvis.

Kinematics

The first two principal components of the size- and speed-corrected kinematic variables together explained 96% of the variance in kinematics (Table 4) and were clearly correlated with different variables. Principal component 1 explained 58% of the kinematic variance and had a large positive loading for stride length and a large negative loading for stride frequency. Thus, species with positive values on PC 1 took relatively long strides at low stride frequencies while species with negative values took relatively short strides at high stride frequencies. Principal component 2 explained 38% of the kinematic variance and had a strong positive loading for float distance and a strong negative loading for step length. Thus species with positive values on PC 2 had strides characterized by relatively short steps and long float distances while species with negative values had strides characterized by relatively long steps and short float distances.

The DFA on these two kinematic principal component axes extracted two axes that explained 99% of the kinematic variation (Table 4). Discriminant functions 1 and 2 were highly correlated with PC’s 1 and 2, respectively, based on large and positive loadings (Table 4). Therefore, the DF axes could be related to the original kinematic variables: large scores on DF 1 depict species with relatively long stride length and low stride frequency while small scores represent relatively short strides at high frequency.

Large scores on DF 2 describe species taking relatively short steps with long floats whereas small scores depict species taking relatively long steps with short floats. Species were significantly different in DF (Wilks’ λ = 0.423, F12,124= 5.54, p < 0.0001) with

86 Mahalanobis distances showing that the species clustered into two different groups (p

within groups, range 0.071 – 0.725 [except Tropidurus, see below], p between groups, range 0.001 – 0.047) in kinematic space (Fig. 2.4). Eulamprus, Varanus and

Tracheloptychus clustered together and were characterized by relatively short strides at

high frequency and intermediate step length and float distance. Oplurus,

Acanthodactylus, Laudakia, and Tropidurus clustered in the other group with relatively

long strides at lower frequency with longer steps and shorter float distances, except

Tropidurus that exhibited relatively short steps with large float distances (Mahalanobis

significance, Tropidurus to Laudakia, p = 0.071; to Oplurus, p < 0.001; to

Acanthodactylus, p = 0.0120).

Forces

The DFA on the reduced set of size- and speed-corrected whole body force

variables extracted two axes that explained 74% of the force variance (Table 5).

Discriminant function 1 accounted for 49% of the force variance and had a large positive

score for relative vertical impulse and relatively weak scores for all other variables (Table

5). Because of the significant correlation between relative vertical impulse and relative

peak vertical force (r = 0.709, p < 0.00001), I interpreted this axis as representative of

both of these vertical force variables. Time to peak vertical force was not correlated with vertical impulse (r =-0.182, p =0.132) or peak vertical force (r =-0.054, p =0.658).

Discriminant function 2 accounted for 25% of the force variance and had large positive score for the relative timing of the braking –accelerative transition and accelerative impulse (Table 5). Species were significantly different (Wilks λ = 0.246, F42,271 = 2.25, p

< 0.0001) with Mahalanobis distances showing that species clustered into two different

87 groups in force space (Fig. 2.5). Eulamprus with the smallest relative vertical forces and impulses was significantly different from the rest of the species (Mahalanobis’

significance p range = 0.001 – 0.038) which exhibited relatively larger vertical forces and

impulses. The extremes of this group on DF 1 (Tropidurus and Tracheloptychus) also

had large scores on DF 2. These two species positions approached significance

(Mahalanobis’ significance: Tropidurus to Acanthodactylus p = 0.133, to other species p

< 0.050; Tracheloptychus to Acanthodactylus p = 0.095, to other species p < 0.050)

indicating larger times to the braking-acceleration transition and larger accelerative

impulse compared to the rest of the group.

Relationships between Multivariate Levels

The a priori model of the causal links for terrestrial locomotion predicted that

morphological variation should predict kinematic variation which, in turn, should predict

force variation (Reilly et al. 2007). In order to examine the relationships between levels I

created a three dimensional plot that stacked each of the multivariate levels (Reilly and

Lauder 1992) aligned along the major axes of variation (DF 1 and DF 2). Then, I

examined relative positions of species between morphological and kinematic levels and

then between kinematic and force levels (Fig. 2.5). Specifically, species were related

between levels as follows:

1) Eulamprus was positioned on the left at each level and was significantly different from

all other species on the morphology and force, but not kinematics levels. It was

characterized by relatively short limbs, short stride length, high stride frequency,

and small vertical impulse and peak force.

88 2) Tropidurus was positioned on the right at each level and approached statistically

significanct differences from other species at the kinematic and force levels. It

had relatively long limbs, long stride length, low stride frequency, and large

vertical impulse and peak force. Tropidurus also had that longest float distance

and shortest step length of any species in this study.

3) Varanus and Tracheloptychus were positioned centrally on all three levels with both

having legs of moderate length and intermediate stride length and frequency.

However, Varanus had a short pelvis and 4th toe whereas Tracheloptychus had a

long pelvis and 4th toe and this difference seemed to manifest itself in Varanus

having relatively smaller accelerative impulse and an earlier braking-accelerative

transition than Tracheloptychus.

4) Oplurus and Laudakia were positioned on the right in morphological space but

centrally in kinematic and force spaces. These species had relatively long legs,

long stride length and low stride frequency, and moderate-to-large vertical

impulse and peak force. Although it had relatively shorter limbs,

Acanthodactylus clustered together with these species at both the kinematic and

force levels.

More generally, comparisons across levels suggest a linear spread corresponding to the first discriminant axis on each level. Species position on DF 1 at the

morphological level generally corresponds to their position on DF 1 at the kinematic

level and at the force level. Thus, increasing relative limb length corresponds to

increasing relative stride lengths and lower stride frequencies. Similarly, increasing

relative stride lengths and lower stride frequencies are related to increasing relative

89 vertical forces and impulses. Three statistical tests of these relationships confirm these

integrative patterns.

First, the Mantel tests indicated a significant positive correlation between the

morphological and the kinematic levels (R=0.78 p=0.0120, Table 6) and between the kinematic and force levels (R=0.88, p=0.0004, Table 6). Together, these results show that there is a significant multivariate correlation between species position in morphological, kinematic, and force discriminant spaces.

Second, to uncover which variables were driving the multivariate correlations between levels I ran a multiple regression testing for the effects of morphology and kinematics as a predictor of force. The multiple regression revealed that the morphological and kinematic levels combined to be an excellent predictor of species’

2 centroid position on the force level for DF 1 (R =0.87, F2,4=13.26, p=0.017) but not on

2 the DF 2 (R =0.44, F2,4=1.58 , p=0.38). Thus, there is a statistically significant

relationship between limb length, stride length and frequency and vertical forces and impulses (Fig. 2.6).

The results of the partial least squares analysis were similar to the results of the multiple regressions based on discriminant function axes. There was one factor between morphology and kinematics. This factor accounted for 81% of the morphological variation and explained 14% of the kinematic variation. The model coefficients indicated a positive relationship between limb skeletal element lengths (except pelvis length) and stride length but a negative relationship with stride frequency. Thus, species with longer limbs ran with longer strides at low frequency, whereas, species with shorter limbs ran with shorter strides at high frequency. There was one factor between kinematics and

90 force. This factor accounted for 49% of the kinematic variation and explained 32% of

the force variation. The model coefficients described a positive relationship between

stride length and float distance and all impulses and peak forces and a negative

relationship between stride frequency and all impulses and peak forces. The signs of the

coefficients between force variables and stride length were equal in magnitude and

opposite in sign of those for stride frequency. Thus, species taking longer strides with

longer float distances at low stride frequency exhibited greater magnitude ground

reaction forces, whereas, species with shorter strides and float distances at higher stride

frequency exhibiter smaller ground reaction force.

Finally, to account for the effect of phylogeny on the relationship between

multivariate levels I re-ran the multiple regression on the independent contrasts for

species centroids position on DF 1. The independent contrasts multiple regression

revealed that morphology, kinematics, and force have undergone correlated evolution

2 (R =0.86 , F2,3=9.35 p=0.050). This shows that species that evolve relatively longer

limbs also evolve relatively longer strides at lower frequency and relatively large vertical

forces and impulses. This relationship is plotted in Figure 2.6 with the phylogeny of the

study species superimposed on the 3-dimensional relationships of morphology,

kinematics and force on DF 1. Squared change parsimony ancestral node reconstructions are plotted in multivariate space in Figure 2.4. Confidence intervals from maximum likelihood ancestral character estimates indicated that only the transition to a longer

pelvis and fourth toe in Tracheloptychus from its ancestor was statistically significant.

This significant transition and other non-significant trends suggested by ancestor

character reconstruction are plotted in Figure 2.6.

Discussion

The goal of this study was to examine the multivariate relationships between

morphology, kinematics, and force variation during running in lizards. These analyses

show that these three levels of locomotor function have tight functional and evolutionary relationships (Table 7; Figs. 2.4, 2.5, 2.6) that meet the prediction that morphological variation should predict kinematic variation which, in turn, should predict force variation

(Reilly et al. 2007).

Limb Morphology and Stride Kinematics

A key issue in animal locomotion is how limb length effects stride kinematics

after accounting for differences in size and speed (Strang and Steudel 1990). I addressed

this issue directly by statistically controlling for the effects of speed and body size on the

relationship between limb morphology and stride kinematics. Thus, one of my principal

findings was that lizard limb length has a statistically significant linear relationship with

stride kinematics: as limb length increases, stride length increases and stride frequency

decreases (Figs. 2.5, 2.6). This finding was not surprising given that it is generally

known across a range of animals (Strang and Stuedel 1990) and has been shown within

several lizard families (Teiidae: White and Anderson 1994; Phrynostomatidae and a

single teiid: Irschick and Jayne 1999; and Lacertidae: Vanhooydonck et al. 2002).

However, this study shows that a strong relationship between limb morphology and stride

parameters holds across a greater portion of the morphological diversity of lizards even

after controlling for phylogeny.

92 These data also allow me to examine if tradeoffs exist regarding how differing

morphologies modulate stride length and stride frequency to move quickly. To examine

potential tradeoffs in the relationship between limb length and speed modulation I

calculated the slopes from least-squares linear regressions for the relationships between

running speed and raw stride length or stride frequency (Table 2.7). This analysis shows

that the relatively shortest limbed species (Eulamprus) modulates only stride frequency

while the one of the longest limbed species (Tropidurus) modulates only stride length to

increase speed, while the intermediate limbed species (except Tracheloptychus) modulate

both. Tracheloptychus had average size limbs but relied on a stride length only

modulation strategy. This may be because Tracheloptychus had an inordinately longer

pelvis or because it had the longest relative 4th toe length (Fig. 2.4) and the fourth toe has

been shown to be an important correlate of stride length (Irschick and Jayne 1999).

Oplurus had relatively long limbs but modulated both stride frequency and stride length.

While it is unclear why this species did not use primarily stride length modulation it is

clear that, for its size, Oplurus exhibited relatively slow maximum sprint speed and the

shortest float distance and this may be related to its deviation from the general pattern in

lizards. Overall, these data support previous studies showing the tight correlation

between limb length and stride length/frequency modulation in lizards (White and

Anderson 1994; Vanhooydonck et al. 2002). In addition, it supports the hypothesis that

animals modulate stride frequency, stride length, or both to increase speed (Biewener

2003). However, this is the first time that the extremes in limb length are related to the reliance on modulation of only one parameter to increase speed. While previous studies have suggested a similar relationship between limb length and stride kinematics may

93 exist in mammals (Strang and Steudel 1990; Reilly et al. 2007) we still await a definitive

test. It would be interesting to reexamine the mammalian data while controlling for the

effects of body size on limb length, kinematics, and force. Finally, these data do not

support the hypothesis that the importance of stride length vs. stride frequency is related

to gait (Biewener 2003) because all of these lizards were using the same trotting gait.

Stride Kinematics and Whole Body Vertical Force

It is important to repeat that I am discussing vertical impulses that are integrated over the support phase for a limb pair, as opposed to over the entire stride (Fig. 2.3).

Although it is well known that vertical forces summed (vertical impulse) over the entire

stride divided by stride time must equal body weight (Biewener 2003) and they do over a

stride in all species in this study (see Fig. 2.7), this was not my interest. Rather, I was

interested in how species modulate forces over the support phase of diagonal couplets

during running to effectively support and propel the body in a sample of morphologically

and kinematically diverse lizard species.

The multivariate analyses of locomotor function showed a significant

correspondence between limb length, stride kinematics and whole body forces (Table 6,

Figs. 2.5, 2.6). Overall, species with longer limbs took relatively longer lengths, shorter duration strides and applied relatively larger peak whole body vertical forces and vertical impulses to the ground. This finding supports the prediction of previous studies based on kinematics (Van Damme et al. 1998; Aerts et al. 2000). The differences observed in vertical impulse and peak forces were manifested in the trade-off between stride frequency and stride length across species. For example, among lizards moving at speeds across their locomotor scope, the long limbed Tropidurus had stride frequencies ~ ½ and

94 vertical impulses (summed over support duration) ~ 2x those of short limbed but

equivalently sized Eulamprus. Figure 2.7 presents representative vertical forces from

these two species that clearly illustrate how morphology, kinematics, and force covary.

The other species of lizards fell between these two extremes. As expected, the relationship between stride kinematics and vertical forces exhibited by these lizards follows well known biomechanical principles (Biewener 2003). However, these data on running lizards clearly demonstrates that the relationship between kinematics and vertical force is driven by the underlying limb morphological variation. Overall, short legged species cycle their short legs more often with less force per support whereas long legged species cycle their long legs less often with more force per support all so that each species have their forces sum to body weight over their entire strides (Fig. 2.7).

Energetic Implications

These findings may have important implications for the energetics of lizard locomotion. Van Damme et al. (1998) suggested that short stride - high frequency species should expend a large amount of metabolic energy because they must perform a lot of internal mechanical work to cycle the limbs. However, Van Damme et al. (1998) also point out that long stride – low frequency species will produce a large amount of force which will also increase metabolic expenditure. My data seem to support these suggestions. The Iguanians (Tropidurus, Oplurus, and Laudakia) produce very large forces and the skink (Eulamprus) moves at very high stride frequency (Figs. 2.5, 2.7) suggesting that these species engage in the most costly locomotion among the lizards in this study. Conversely, the species producing intermediate amounts of force and stride kinematics (Acanthodactylus, Varanus, and Tracheloptychus, Fig. 2.5), thus, based on

95 Van Damme et al. (1998) arguments these species may incur relativity less locomotor costs. Overall, these data suggest that locomotor energetics is largely determined by forces, stride kinematics, and underlying morphology. In fact, the cost of locomotion for

running in erect birds and mammals is largely determined by these three levels of locomotor function (Roberts et al. 1998; Pontzer 2005, 2007). Examination of how the cost of locomotion is related to these levels of locomotor function in sprawling animals

(i.e. turtle, lizards, alligators, salamanders) would be a useful.

It is intriguing that, given the above argument, the high cost species are sit-and-

wait foragers (Tropidurus, Oplurus, Laudakia, and Eulamprus) whereas the low cost

species are wide foragers (Acanthodactylus, Varanus, and Tracheloptychus). This

suggests that, like so many other phenotypic traits (reviewed in Reilly et al. 2007),

foraging behavior is driving with locomotor functional morphology and energetics in

lizards. In addition, I propose that this hypothetical difference in energetics may explain

the differences in endurance and distance running capacity between sit-and-wait and wide

foraging lizards (Miles et al. 2007; Garland 1993). Figure 2.8 shows this hypothetical

relationship (explained in the following text). This study clearly demonstrates that

morphology drive kinematics which drives force and previous studies have shown that

force generation and leg swing costs are the primary determinants of the cost of

locomotion (Kram and Taylor 1990; Roberts et al. 1998; Pontzer 2005). Based on this I

suggest that both high frequency and high force species incur the largest energetic

expenditures whereas intermediates species incur lower expenditures. In turn, these

hypothetical differences in energetic expenditure may be manifested in species

differences in distance running. While the above argument applies to running locomotion

96 (solid inverted curve on Fig. 2.8), walking is clearly important during the evolution of

wide foraging behavior (McElroy et al. 2008) and could have important effects on how

morphology is related to kinematics, force, and energetics in walking(see also Pontzer

2005). The expected effect of walking on the hypothetical relationship between function

and cost of locomotion is that it should dramatically increase endurance capacity (plotted

as the broken inverted curve on Fig. 2.8). While these hypotheses seem to fit my data; some WF species are long legged whereas some SW species have relatively short legs

(Miles et al. 2007, unpublished data) which could impact the above energetic hypotheses.

Propulsive Forces

Interestingly, Tracheloptychus and Tropidurus had the largest values on both kinematic and force DF 2 indicating that they had the largest relative float distances, the latest braking/acceleration transition times and the largest accelerative impulses. Thus, these two species are delivering larger, more focused accelerative forces late during the support phase in order to generate longer float distances. Morphologically this may be related to the novel longer pelvis in Tracheloptychus but there is no clear morphological explanation for the same pattern in Tropidurus. In addition, the ability to deliver more focused accelerative forces to produce longer float distances appears to be a correlate of a stride length speed modulation strategy, as Tropidurus and Tracheloptychus were the only two species to rely solely on stride length to increase speed. Aside from these multivariate patterns, longer float distances were correlated with both larger vertical whole body force (Pearson product-moment correlations data pooled across all species: float distance – vertical impulse r = 0.38 p = 0.001; maximum vertical whole body force: r = 0.23 p = 0.05) and larger accelerative whole body forces (accelerative impulse: r =

97 0.33 p = 0.006; maximum accelerative whole body force: r = 0.34 p = 0.004). Species

that incorporate float phases are essentially producing alternating single-leg jumps in

which the COM is propelled ballistically with each step. This suggests that species that

float greater distances produce more vertical and accelerative forces in order to

effectively propel the COM up and forward during this “jump”. In all, these findings

support predictions from previous studies based on kinematics (Irschick and Jayne 1999)

and hint at an interesting potential morphological correlate (pelvis length) of propulsive

forces.

Evolutionary Trends in Lizard Locomotor Functional Morphology

The mapped characters on the inverted phylogeny in Figure 2.6 summarize the

evolutionary trends in limb length, stride kinematics, and ground reaction forces in

lizards. The putative ancestor (node A) had relatively short limbs, short-high frequency

strides, and intermediate vertical forces (Fig. 2.4, 2.6). From these ancestral traits lizards

diverged along two separate evolutionary trajectories, principally based on divergence

along DF1. Tracheloptychus and Eulamprus exhibit an evolutionary trend towards even shorter limbs, shorter strides at higher frequency, and lower vertical forces (Node B and tip values); whereas the nodes (C-E) leading to Varanus, Acanthodactylus and the

Iguanians showed an evolutionary trend towards longer limbs longer-low frequency

strides, and larger vertical force. From its ancestor (node D), the Iguania evolved to the functional extreme with the longest limbs, longest strides at the lowest frequency and largest vertical forces; whereas Eulamprus has evolved to the extreme in the opposite direction. In all these data, show that deep phylogenetic effects (locomotor functional split at node A) are imprinted upon locomotor functional evolution, a finding in

98 agreement with evolutionary transitions in many other lizard traits (e.g. head morphology, McBrayer and Corbin 2007; diet, Vitt and Pianka 2007; feeding kinematics,

Reilly and McBrayer 2007; chemosensory systems, Cooper 2004).

Several cases of evolutionary convergence are evident within the two overarching trends in lizard locomotor evolution. Varanus has converged with Tracheloptychus and

Eulamprus with its short limbs and short-high frequency strides. However, Varanus has not converged in vertical force dynamics and the reason for this is unclear. Oplurus has reverted to having somewhat shorter, faster strides and this may be related to this species being relatively slow for its size. Finally, Tracheloptychus and Tropidurus exhibit convergence along DF2 evolving longer float distances, shorter steps, later braking accelerative transition times, and larger accelerative impulses than their ancestors and while this may be related to longer pelvic and 4th toe morphology in Tracheloptychus the reason in Tropidurus is unclear. Thus, locomotor functional evolution clearly exhibits some flexibility. The evolutionary pressures driving this flexibility have yet to be determined but are likely related to the ecological relevance of locomotion (Reilly et al.

2007). Future studies should explore how evolutionary flexibility in finer details of locomotor function are related to specific aspects of ecology, habitat use, and behavior.

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Table 2.1

Species sample size for morphology (number of individuals) and locomotor function (number of trials). Running speed and dimensionless speed ranges.

Morphology Kinematics and Force Running Speed (m/s) Dimensionless speed

N N Range Range

Acanthodactylus boskianus 30 6 1.88 - 0.82 2.81 – 1.23

Eulamprus quoyii 29 10 1.92 - 0.71 2.87 – 1.06

Laudakia stellio 33 13 2.40 - 0.82 2.77 – 0.95

Oplurus cuvieri 21 17 1.69 - 0.59 1.80 – 0.63

Tracheloptychus petersi 12 6 1.41 - 1.01 1.97 –1.42

Tropidurus torquatus 7 9 2.62 - 1.37 3.15 – 1.65

Varanus exanthematicus 13 11 2.54 - 0.76 2.78 –0.83

106

Table 2.2

Pearson product-moment correlation coefficients between morphological, kinematic, and whole body force variables and body size (snout-vent length or mass) and speed.

Snout-Vent Length Mass Speed

MORPHOLOGY KINEMATICS

Humerus 0.82 Stride Length 0.60 0.67

Ulna 0.82 Stride Frequency -0.61 0.48

Carpals 0.86 Step Length 0.75 0.17

3rd Metacarpal 0.83 Float Distance 0.07 0.71

3rd Toe 0.74 WHOLE BODY FORCE

Pectoral Girdle Width 0.89 Vertical Impulse 0.98 -0.09

Femur 0.75 Accelerative Impulse 0.87 0.01

Tibia 0.65 Braking Impulse 0.88 0.03

Tarsals 0.86 Lateral Impulse 0.62 -0.01

4th Metatarsal 0.66 Peak Vertical Force 0.97 0.14

4th Toe 0.40 Peak Accelerative Force 0.74 0.31

5th Metatarsal 0.80 Peak Braking Force 0.79 0.27

5th Toe 0.27 Peak Lateral Force 0.69 0.24

Pelvic Girdle Width 0.84 Time to Peak Vertical -0.19 0.35

Pelvic Girdle Length 0.89 Time to Peak Accelerative -0.01 0.23

Time to Peak Braking -0.30 0.18

Time to Peak Lateral -0.32 -0.16

Time to Braking-Acc. Trans. -0.25 0.20 107

Table 2.3

Results of the multivariate morphological analysis. Loadings are calculated between each principal component and each kinematic variable. Standardized coefficients are calculated between each discriminant axis and each principal component.

Morphological PCA Morphological DFA PC1 PC2 DF1 DF2 Eigenvalue 10.47 1.49 Eigenvalue 9.42 1.61

% Variance 70% 10% % Variance 79% 14%

Humerus 0.29 -0.02 PC1 9.03 0.01

Ulna 0.28 -0.11 PC2 1.97 9.50

Carpals 0.19 -0.34

3rd M-carpal 0.28 -0.20

3rd Toe 0.25 -0.15

Pect. Width 0.27 0.06

Femur 0.30 0.06

Tibia 0.30 -0.01

Tarsals 0.24 -0.14

4th Metatarsal 0.29 0.06

4th Toe 0.21 0.41

5th Metatarsal 0.27 -0.13

5th Toe 0.28 0.15

Pelvic Width 0.27 0.21

Pelvic Length 0.04 0.73 108

Table 2.4

Results of the multivariate kinematic analysis. Loadings are calculated between each

principal component and each kinematic variable. Standardized coefficients are

calculated between each discriminant axis and each principal component axis.

Kinematics PCA Kinematics DFA

PC1 PC2 DF1 DF2

Eigenvalue 2.32 1.53 Eigenvalue 0.83 0.29

% Variance 58% 38% % Variance 74% 26%

Stride Length 0.64 0.03 PC1 0.85 0.06

Stride Frequency -0.64 -0.04 PC2 -0.04 0.88

Step Length 0.26 -0.73

Float Distance 0.34 0.68

109

Table 2.5

Results of the discriminant function analysis of whole body force data. Standardized coefficients are calculated between each discriminant axis and each whole body force variable.

DF1 DF2

Eigenvalue 0.85 0.45

% Variance 49% 25%

Vertical impulse 1.29 0.05

Accelerative Impulse 0.05 0.57

Lateral Impulse 0.07 0.04

Time Peak Vertical Force 0.22 -0.01

Time Braking-Acc Transition -0.45 1.12

Time Peak Braking Force -0.01 0.02

Time Peak Lateral Force -0.31 -0.05

110

Table 2.6

Results of Mantel tests for the multivariate correlation between species position in discriminant function space at each level of analysis. Statistical significance was determined by a randomization procedure. Correlations were calculated for velocity corrected

and dimensionless speed corrected data (indicated in parentheses).

Kinematics Kinematics Force Force

(Velocity) (Dimensionless) (Velocity) (Dimensionless)

Morphology 0.66** 0.66** 0.84*** 0.60**

Force (Velocity) 0.79*** - - -

Force (Dimensionless) - 0.59** - -

* < 0.05, ** 0.01 *** 0.001 111

Table 2.7

Regression parameters showing the relationship between raw stride kinematics and

speeds for the seven lizard species in this study. Statistical significance (*) of regression

slopes was determined by t-tests.

Species Stride Length Stride Frequency

Slope Intercept Slope Intercept

Acanthodactylus 0.65* -0.60 0.89* 0.66

Eulamprus 0.02 -0.82 1.99* 1.10

Laudakia 0.80* 0.37 0.52* -0.51

Oplurus 0.40* 0.24 0.86* -0.32

Tracheloptychus 1.38* -0.94 -0.17 1.13

Tropidurus 1.27* -0.27 0.23 -0.01

Varanus 0.37* 1.02 0.89* -1.02

* < 0.05

112

Figure 2.1. Phylogeny for the lizards in this study based on Townsend et al. (2004).

Branching nodes are labeled A-E for discussion of phylogenetic patterns of locomotor functional evolution.

113

Figure 2.2. Ventral radiograph of Laudakia stellio summarizing the 16 morphological measurements. Numbering: 1) snout-vent, 2) humerus, 3) ulna, 4) carpal, 5) 3rd metacarpal, 6) 3rd finger, 7) pectoral girdle width, 8) femur, 9) tibia, 10) tarsal, 11) 4th metatarsal, 12) 4th toe, 13) 5th metatarsal, 14) 5th toe, 15) pelvis length, and 16) pelvis

width. Note that when toes were bent (measurements # 6, 12, and 14) I summed the

measures of the length of individual phalanges.

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Figure 2.3. Representative whole body ground reaction force profiles. Impulses are represented by hatched areas; dotted lines correspond to the peak or time of peak force.

Numbering as follows: 1) vertical impulse, 2) braking impulse, 3) accelerative impulse,

4) lateral impulse, 5) peak vertical force, 6) peak braking force, 7) peak accelerative force, 8) peak lateral force, 9) time to peak vertical force, 10) time to peak braking force,

11) time to peak accelerative force, 12) time to peak lateral force, 13) time of braking- accelerative transition, indicated by a star (º).

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Figure 2.4

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Figure 2.4 continued.

Bivariate plot of the first two discriminant axes for morphology, kinematics, and forces.

The percent variation explained is labeled with each axis. The variables with the largest

coefficients on each axis (from Tables 2.3-2.5) are labeled opposite to that axis. Values

for squared change parsimony ancestral reconstructions for nodes (A-E from Fig. 1) are

plotted on each level.

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Figure 2.5

118

Figure 2.5 continued.

Visualization of the multivariate relationship between morphology, kinematics, and whole body force in the seven lizard species in this study plotted using the method of

Reilly and Lauder (1992). Each plane represents the discriminant space for each level of analysis (from Figs. 4-6) with ellipses surrounding groups at each level determined by

Mahalanobis distances and associated F-tests. Lines connect species’ centroids between

levels. Note that the lines are nearly vertical indicating correspondence in species

position on the first discriminant axis on each level.

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Figure 2.6. Three dimensional plot of the first discriminant axis at each level of analysis.

Data points are species’ means on the first discriminant axis for each level. Bold line

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Figure 2.6 continued.

(best fit line from multiple regression) highlights the tight relationship between species position along on the first discriminant axis. Long-legged species take longer strides at lower frequency resulting in large vertical whole body forces and impulses. I plotted an

inverted version of the Townsend et al. (2004) phylogeny whose tips corresponded to

species position in multivariate space and then mapped multivariate patterns of character

evolution onto the phylogeny, see discussion for explanation. Black and gray bars are

evolutionary trends on DF1 and DF2, respectively, that have resulted in convergence. * indicates statistically significant evolutionary transition.

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Figure 2.7. Sample vertical forces that illustrate how the longest and shortest legged lizards in this study varied in kinematics and whole body forces. These two trials were from individuals of similar mass and speed, thus raw data are presented. L and R indicate left or right leg supports, respectively. Panel A: Tropidurus torquatus: speed, 1.99 m/s; mass, 27 grams; SVL, 91 cm; HL length, 70 cm; FL length, 49 cm; stride frequency, 6.2 s-1; stride length, 28 cm; peak vertical force, 0.82 N; vertical impulse, 0.0163 N-s. Panel

B: Eulamprus quoyii: speed, 1.92 m/s; mass, 21 grams; SVL, 98 cm; HL length, 46 cm;

FL length, 31 cm; stride frequency, 14 s-1; stride length, 14 cm; peak vertical force, 0.36

N; vertical impulse, 0.0061 N-s. The ~ doubling of stride frequency in the short limbed

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Figure 2.7: Continued

Eulamprus results in a ~ halving of peak vertical force and vertical impulse when compared to the long limbed Tropidurus.

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Figure 2.8. Hypothetical relationship showing how the links between morphology, kinematics, and force might dictate the cost of locomotion and may ultimately determine endurance and/or maximum running distance. Because wide foraging lizards are intermediate for leg length, stride kinematics, and vertical impulse they are predicted to have lower cost of locomotion when running (inverted solid curve) and because wide foragers can also walk (McElroy et al. 2008) when walking (inverted broken curve).

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CHAPTER 3: DISSECTING THE EFFECTS OF BEHAVIOR AND HABITAT ON

THE LOCOMOTION OF THE ORNATE TREE LIZARD (UROSAURUS

ORANTUS)

Introduction

Organisms use locomotion to accomplish various fitness-related behavioral tasks

such as home range defense, finding mates or food, escaping predators, and dispersal

(Swingland and Greenwood 1983; Dingle and Holyoak 2001). Further, many organisms

live in complex three-dimensional habitats, and must negotiate substrates that vary in

diameter, incline, height and texture, among other features (Turchin 1998). The specific

behavioral task engaged in and the structural habitat are key factors that can alter the

direction, speed, and timing of locomotor movements (Garland and Losos 1994, Irschick and Garland 2001). Organisms may adjust their locomotion to simultaneously address the effects of habitat and behavioral task; therefore, the effects of these factors are likely to be complex and interactive. Studies that explicitly quantify how locomotion changes

with habitat and behavior individually, as well as collectively, are needed.

Prior studies have confirmed the pervasive effects of habitat on locomotion (e.g.

perch diameter and speed in Anolis lizards, Losos and Sinervo 1989; wind speed and

swarm structure in locusts, Dingle 1996; substrate type and speed in lacertid lizards,

Vanhooydonck and Van Damme 2003; incline, diameter and limb kinematics in Anolis

lizards, Spezzano and Jayne 2004; substrate structure and locomotor repertoire in

orangutans, Thorpe and Crompton 2005). Also, many studies have found that behavioral

context affects locomotion (e.g. Podarcis muralis, Braña 2003; Anolis, Irschick and

Losos 1998 and Irschick 2000b; degu, Vásquez et al. 2002; bottlenose dolphin, Bailey

125 and Thompson 2006). These studies shed light on how habitat or behavior alone affect locomotion, but reveal less about interactions among habitat and behavior. Furthermore, prior studies induced locomotion by simulating behavior (e.g. simulated prey capture and predator escape, Irschick 2000b) rather than recording locomotor movements associated with a range of undisturbed, natural behaviors.

Following prior studies, I used an approach that involves quantitative analysis of undisturbed locomotion, thus allowing the measurement of natural variation in movement speed across a range of behaviors and throughout the complexity of the structural habitat

(Hertz et al. 1988; Garland 1993; Irschick and Garland 2001). I studied the ornate tree lizard, Urosaurus ornatus, because this species is active, conspicuous, abundant, and behaviorally diverse. This study had two goals. First, I examined the relative influence of behavior and habitat on locomotor speed. I focused on three fitness-related behaviors

(prey capture [feeding], predator escape, and displaying) and four aspects of the structural habitat (perch height, perch diameter incline, and substrate type) that can influence locomotion. These habitat variables are a subset of those that may affect locomotion. In terms of behavior, locomotor speeds are expected to be fastest during display behavior, intermediate during predator escape, and slowest during prey capture (Braña 2003). In terms of habitat, I predicted smaller perch diameters and steeper inclines to negatively affect speed (Huey and Hertz 1982, Irschick and Jayne 1998, 1999; Jayne and Ellis

1998). There were no predictions for the effects of perch height or substrate type on speed. The second goal of this study was to test the expectation that the interaction between behavior and habitat can alter the effects of individual variables (habitat or behavior alone) on speed.

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Methods

Field Site

I studied a population of Urosaurus ornatus at Wet Beaver Creek, which is a mid-

elevation site (1100 m) in central Arizona. This site (400 m long by 60 m wide) is a

riparian zone consisting of a varied habitat ranging from complex three-dimensional

clusters of woody debris to flat, open areas of loose sandy soil. Urosaurus ornatus was abundant at this site, and males were defending territories and mating during the sampling period (10 June – 16 June 2005) which corresponded to the peak breeding period. This research was conducted under Ohio University approved research protocols

and followed all regulations in Arizona Game and Fish Department permit # SP638616.

Focal Observations

I gathered focal videos of 15 male Urosaurus ornatus. All videos were filmed on

sunny days during hours of peak activity (0800 – 1900) when ambient temperatures were

28-32° C. To gather a focal video, I systematically searched the site to locate lizards.

After locating a lizard, I used a five-minute assessment period to discern whether the

lizard was disturbed by me. A disturbance was defined as a lizard displaying or sprinting

away from me. If either of these criteria were met within the 5 minute assessment period

I immediately began searching for another individual. I filmed lizards with a handheld

JVC – GR DVL 9800 Mini DV camcorder (30 fps). Other studies using similar

techniques have found that lizard behavior is not affected by the observer (Irschick

2000a; 2000b; Irschick et al. 2005; Mattingly and Jayne 2005). I gathered extended

video sequences that ranged from 5.5 minutes to 46.9 minutes (average=21.4 min.).

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After filming, I captured each lizard via a handheld slip-noose and identified individuals by a unique toe-clipping pattern (all individuals were toe-clipped in the population as part

of a larger study, ensuring that each individual was filmed once). After identification,

lizards were released at the point of capture.

Video Analysis

I followed similar methodologies as in previous studies that documented the

behavior and structural habitat associated with locomotion in lizards (Irschick 2000a;

2000b; Braña 2003). In the field, I reviewed videos frame-by-frame to quantify aspects

of the habitat associated with each movement. First, I identified whether the movement was a jump or a run. I included only running locomotion due to biomechanical differences between running and jumping and because jumping was usually associated

with traveling behavior (only 1 prey capture and 2 displays were associated with jumping

locomotion). Next, I quantified movement distance by placing flagging at the point of

each pause (each movement is preceded and followed by a pause) and then measuring the

straight line distance (to the nearest 4 cm) between consecutive substrate landmarks. To

quantify habitat structure, I recorded perch height at the end of each movement, and the

perch diameter, incline, and substrate (wood, rock, ground) on which each movement occurred. Perch height and diameter were recorded with a tape measure to the nearest 1 cm, and incline was recorded with an analog inclinometer to the nearest 5°. Lizards always paused when major changes in substrate occurred (e.g. wood to ground or flat incline to uphill incline); therefore, I did not make any measurement adjustments to deal with habitat heterogeneity within a single movement.

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In the laboratory, I reviewed videos frame-by-frame to quantify movement duration and the behavioral task associated with each locomotor movement (following

Braña 2003). I quantified movement duration as the amount of time associated with each movement to the nearest 0.1 second. I calculated movement velocity as movement distance (recorded in the field) divided by movement duration. Next, I assigned a behavioral task to each locomotor movement, which included: displaying, prey capture, predator escape, and traveling. Male U. ornatus use a combination of head bobbing, push-ups, dorsoventral flattening, and dewlap extension during displays (Purdue and

Carpenter 1972); therefore, I defined a display behavior as any pause after a locomotor movement during which the lizard used at least one of these. Prey capture behaviors were defined as any locomotor movement that attempted (successful or unsuccessful) to approach, capture and consume prey. Predator escape behaviors were defined as any movement that served to escape or deter a potential predator. I observed only one predator escape behavior and thus it was not used in subsequent analyses. Many of the movements I observed were not associated with displays, prey capture, or predator escape per se; rather, they were used to travel between two locations. I refer to these movements as “traveling” behavior.

Statistics

I used JMP 5.0 and SAS 9 for all analyses. I simultaneously tested for differences in locomotor velocity due to behavior, habitat, and their interaction using multiple regression. Speed was the response variable whereas behavior, habitat, and their interaction were predictor variables. Behavioral variables and substrate types were coded

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using dummy variables (for each variable the behavior/substrate was coded as ‘1’ and all

other behaviors/substrates were coded as ‘0’). One potential problem with interpretation

of dummy variables is intercorrelation among variables. I address this issue by

presenting mean values of speeds for lizards on different substrates and during different

behaviors so that the reader can evaluate the biological significance of these factors for

locomotion. Preliminary analyses indicated that interaction terms were strongly

correlated with their components, which is problematic for multiple regression (Quinn and Keough 2003). I addressed this issue by centering all predictor variables (by subtracting the mean from each observation) which eliminates correlations without affecting regression slopes (Aiken and West 1991). After centering, I found no problems with collinearity (variance inflation factors were less than two, Quinn and Keough 2003).

To find the “best” model I estimated all possible regression models given the data and then used the Bayesian Information Criteria (BIC), Akaike Information Criteria (AIC), and Mallow’s Cp to choose the best fitting model (Quinn and Keough 2003; Mac Nally

2000). These criteria select the model that included the smallest number of predictor

variables while still explaining a large amount of variation. I detected no problems with

residual plots, outliers (Mahalanobis distances), or leverage (Cook’s D).

I had multiple observations from the same individuals (mean number of

movements per individual = 33 movements, range = 1- 58 movements) which could lead

to some individuals driving slope estimates in the regression. To address this issue I re-

ran the regression analyses with individual as a predictor variable (coded using dummy

variables). This showed that four individuals ran at slower speeds than the other 11. To

eliminate the possibility that individual differences were driving the findings, I used a

130 jackknife approach to address each individuals influence on slope estimates. I removed each individual, re-ran the multiple regression, and compared each slope in each jackknifed model with the 95% confidence intervals of the slope in the full (all individuals) model. I found no instances of any of the jackknifed slopes falling outside the 95% confidence intervals of the full-model slopes. Thus, I are confident that no single individual was driving the slope estimates in the full model. As advocated by other studies who also employ multiple regression for examining locomotion in lizards

(and thus took repeated measurements of the same individual, e.g., Mattingly and Jayne

2005), I emphasized results with conservative p values (p < 0.005).

Results

The best regression model had the lowest values of three selection criteria:

Mallow’s Cp (3.28), AIC (-433.27), and BIC (-428.56). This model explained a significant amount of the variation in locomotor speed (Table 3.2). Individually, both habitat and behavior had significant effects on locomotor movements (Table 3.3). On the tallest perches (>120 cm), speeds were approximately 10% of the speeds on the shortest perches (< 120 cm, Fig. 3.1, A). On woody substrates, speeds averaged 0.1 m/s which was 50% of speeds on rocky substrates (0.2 m/s) and 25% of speeds on the ground (0.5 m/s). The fastest speeds used during displaying and prey capture were approximately

40% and 55%, respectively, of maximum racetrack speeds (Table 3.1). During displays, speeds averaged 0.09 m/s which was 60% of speeds during prey capture (0.15 m/s), 33 % of speeds during traveling (0.27), and 19% of speeds during predator escape (Table 3.1).

The standardized regression coefficients of behavioral and habitat effects were

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approximately equal in magnitude (Table 3.3) suggesting that these factors have similar

magnitude effects on speed. However, the two habitat variables that I expected to have

significant effects on speed (incline and perch diameter) had no relationship with speed.

In addition, prey capture behavior and perch diameter interacted to affect locomotor

speed (Table 3.3). This interaction is important because the negative relationship between perch diameter and locomotor speed occurs only during prey capture behavior

(Fig. 3.1, Panel C).

Discussion

The study of animal movement is a central paradigm in behavioral ecology

(Swingland and Greenwood 1983; Stamps 1995; Dingle 1996; Turchin 1998). Research

has focused on quantifying the relationship between habitat and locomotion for a single,

easily induced behavior (e.g. predator escape). While useful, these studies have not

recorded the full range of behaviors realized in nature which prevents them from fully

understanding how habitat, behavior, and locomotion integrate (Kenagy and Hoyt 1989;

Stamps 1995; Firle et al. 1998). In this respect this study represents a novel approach to

understanding how animals move. These findings show that habitat and behavior,

individually, are important for undisturbed locomotion. I confirmed that habitat (perch

height, substrate type) and behavior (displaying, feeding) significantly affect undisturbed

locomotor speed (Table 3.1, 3.3, Fig. 3.1 A, B). In addition, I show that behavior and

habitat can interact to affect how animals move (Table 3.3, Fig. 3.1, C).

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Individual Effects of Habitat and Behavior on Locomotion

Research examining the individual effects of habitat or behavior on locomotor performance can be dichotomized into studies that measure maximal performance under idealized laboratory conditions and field studies that measure ecological performance of animals moving through their natural environment (Garland and Losos 1994; Irschick and Garland 2001). Previous studies of locomotor performance in lizards have found significant relationships between habitat (perch diameter and incline) and both maximal locomotor performance (e.g. Losos and Sinervo 1989; Losos and Irschick 1996) and ecological locomotor performance (Jayne and Irschick 2000; Mattingly and Jayne 2004).

Surprisingly, this study found no relationship between incline, perch diameter, and undisturbed speed. In a field study of undisturbed ecological locomotor performance in the fringe-toed lizard (Uma scoparia) Jayne and Irschick (2000) found that undisturbed speed significantly decreases when running on steeper inclines. Uma scoparia lives in an open sand dune desert that has a fundamentally different structural habitat than the riparian Wet Beaver Creek habitat of Urosaurus ornatus, complicating direct comparisons of these species. A more comparable study found that habitat effects were minor in several species of arboreal Anolis lizards (Mattingly and Jayne 2004) whose structural habitat is more akin to that of U. ornatus. One explanation for the disparity

between the findings for Anolis and Urosaurus ornatus versus Uma scoparia may be that

both Anolis and Urosaurus ornatus generally moves at sub-maximal locomotor speeds

(Table 3.1), whereas Uma scoparia routinely moves near its maximum speed during

undisturbed locomotion (Jayne and Irschick 2000). Uma scoparia may typically move

fast because it is especially vulnerable to predators when it moves across relatively open

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sand dunes whereas Urosaurus ornatus and Anolis may typically move slowly because

they are more sheltered from predators within a complex three-dimensional arboreal

habitat. This is supported by the relatively slow speed (0.48 m/s) Urosaurus ornatus used

during the single predator escape behavior I observed (a lizard moving away from a large

insect). Thus, the influence of habitat variables apparent for maximal or near-maximal

field speeds may not be relevant for animals that routinely use sub-maximal field speeds

during undisturbed locomotion (see also Mattingly and Jayne 2005). However, I detected

a significant effect of substrate type and perch height on speed, showing that previously

overlooked habitat variables may be important for affecting undisturbed locomotion.

These data suggest that both behavior (e.g., displaying and prey capture) and habitat

(substrate, perch height) can influence undisturbed ecological performance but many of the traditionally measured habitat variables (e.g. incline, perch diameter) have little influence on undisturbed speeds for animals moving at sub-maximal speeds.

I note that this hypothesis is testable, and could prove general for arboreal or semi-arboreal animals that move on complex surfaces. Future research could test this hypothesis among species in different habitats (e.g. desert dunes, arboreal, saxicolous) as well as within a single species that occurs in different habitats types (Urosaurus ornatus would be ideal for this type of study). I note that variability in maximal speed among individuals can be problematic when comparing undisturbed field speeds to average maximal speed. This could be addressed by gathering maximal speeds and field speeds from the same individuals and examining the effect of individual variation on the relationship between speed, habitat, and behavior. In addition, research focusing on locomotion during predator escape, the effects of other habitat components (e.g. substrate

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texture) and the relevance of additional performance traits (e.g. acceleration) would be beneficial. Finally, I suggest that findings based on maximum laboratory performance

and experimentally manipulated habitat be examined in light of data on undisturbed field

locomotion to discover if and when habitat or behavior affect ecological performance.

A potential problem with comparing laboratory and field performance is that the

distance and duration of movements in the field may limit an animal’s ability to reach

maximum speeds observed in the laboratory. Most laboratory racetracks measure

maximum sprint speed over 0.25 m intervals along a 2 meter racetrack (e.g. Urosaurus

ornatus Meyers et al. 2006, Miles 1994). In addition, laboratory studies show that lizards

approach maximum speeds within ~ 0.2 - 0.4 seconds when running from a stand-still

(Huey and Hertz 1982, Irschick and Jayne 1998). In Urosaurus ornatus, mean movement

distance was 0.17 meters and mean movement duration was 2.1 seconds. These data

show that U. ornatus probably had enough distance and time to generate maximum sprint

speed during most movements, but chose not to do so.

Interactive Effects of Habitat and Behavior on Locomotion

One of the key findings was the interactive effect of prey capture behavior and

perch diameter on locomotor speed. These data show that Urosaurus ornatus experienced

no change in speed when moving across a variety of perch diameters. However, the

interaction between behavior and habitat revealed a different conclusion: U. ornatus

actually experiences a negative effect of perch diameter on speed during feeding behavior

but not during other behaviors (Fig. 3.1, Panel C). This finding highlights the need to

take into account both the behavioral context and habitat when attempting to understand

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variation in speed, direction, and frequency of animal movement. Future research could attempt to unravel how habitat, behavior, and locomotor movements are causally related by experimentally manipulating the structural habitat within semi-natural enclosures (e.g.

Pounds 1988).

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Table 3.1

Patterns of habitat use and locomotor velocity during different behaviors in Urosaurus ornatus. Means ± standard error and ranges (in parentheses) are presented for 15 adult males. Maximum sprint speed used to calculated % maximum speed from Miles (1994) and

Irschick and Meyers (personal communication), ~ 2 meters per second.

Perch Height (cm) Perch Diameter (cm) Incline (degrees) Velocity (m/s) Maximum Speed (%) Distance

Feed 33.78 ± 5.61 31.00 ± 3.70 6.94 ± 6.14 0.15 ± 0.07 5 ± 1 18.37 ± 5.82

(4 – 87) (7 –60) (-35 – 60) (0.01 – 1.1) (1 – 40) (5 – 77)

Display 50.24 ± 4.57 45.21 ± 3.15 8.85 ± 3.33 0.09 ± 0.01 6 ± 3 27.28 ± 4.38

(0 – 200) (3 – 86) (-70 – 80) (0.01 – 0.8) (1 – 55) (5 – 148)

Travel 29.11 ± 2.20 34.15 ± 1.54 -0.88 ± 2.01 0.27 ± 0.02 16 ± 2 16.12 ± 1.11

(0 – 140) (0 – 78) (-90 – 90) (0.02 – 1.5) (1 – 75) (4 – 78)

Escape 0.48 24

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Table 3.2

Sum-of-squares table for the best-fit multiple regression model.

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Table 3.3

Partial regression coefficients from the “best” multiple regression modeling the effects of

habitat, behavior, the interaction between habitat and behavior, and individual on

locomotor speed. Significance of each coefficient determined by t-tests and associated p-

values.

Variable Coefficient Standard Error Standardized Coefficient

Intercept - 0.481*** 0.119 0

Perch Height - 0.115*** 0.038 - 0.205

Display - 0.289*** 0.081 - 0.268

Feed - 0.333*** 0.104 - 0.175

Rock Substrate - 0.241** 0.121 - 0.219

Wood Substrate - 0.431*** 0.150 - 0.366

Perch Diameter x Display - 0.004* 0.002 - 0.120

Perch Diameter x Feed - 0.012** 0.006 - 0.110

Perch Height x Display 0.172* 0.104 - 0.135

Individual 2 - 0.257** 0.103 - 0.176

Individual 10 - 0.393** 0.166 - 0.126

Individual 14 - 0.183*** 0.068 - 0.161

Individual 18 - 0.514** 0.160 - 0.177

* p < 0.10, ** p < 0.05, *** p < 0.005

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Figure 3.1

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Figure 3.1 continued.

Relationship between habitat / behavioral variables and locomotor speed for Urosaurus ornatus during undisturbed field locomotion. Panels A (perch height vs. velocity) and B

(behavior vs. velocity) show plots of significant individual relationships in the multiple regression model. Panel C plots the relationships between perch diameter and velocity

when data are grouped according to behavior (triangles: traveling, squares: feeding, circles, displaying). Regression coefficients and significance tests are reported in Table

3.3.

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CHAPTER 4: A PRELIMINARY ANALYSIS OF BEHAVIORAL AND

PERFORMANCE DIFFERENCES IN FOOD ACQUISITION MODES IN A SIT-

AND-WAIT AND A WIDE FORAGING LIZARD

Introduction

It has long been recognized that the study of whole organism performance should

incorporate quantitative field and behavioral data (Garland and Losos 1994; Irschick and

Garland 2001). Accordingly, over the past decade researchers have successfully gone

into the field and related maximum locomotor performance (what an animal can do) to

field performance (Irschick et al. 2005), habitat use (Losos and Irschick 1996; Jayne and

Irschick 2000; McElroy et al. 2007) and behavior (Irschick and Losos 1998; Irschick

2000; Braña 2003; Husak and Fox 2006; McElroy et al. 2007). These studies have made

important conceptual advances into our understanding of how selection, through the filter

of habitat and behavior, operates on locomotor performance in the wild.

In squamate reptiles, foraging behavior has molded much of the phylogenetic and

phenotypic diversity (Pianka and Vitt 2003; Reilly et al. 2007) including laboratory

measured maximum locomotor performance (Huey et al. 1984; Miles et al. 2007).

Traditionally the way lizards forage has been viewed as dichotomous “modes” with species grouping as either sit-and-wait (SW) or wide foraging (WF). Foraging “mode” has been defined by quantifying the number of locomotor movements per minute of observation (MPM), the duration of locomotor movements as a percentage of the observation period (PTM), and the percentage attacks on prey while moving (PAM) and

146 while stationary (PAS) (Pianka et al. 1979; Huey and Pianka 1981; Cooper et al. 2001;

Cooper et al. 2005; McBrayer et al. 2007). Thus, SW move infrequently and exhibit low

MPM, PTM, PAM and high PAS whereas WF move often and exhibit high MPM, PTM,

PAM and low PAS.

The idea that lizards use only two discrete foraging modes (SW and WF) has recently come into question. Some studies have suggested that SW and WF are behavioral extremes of a continuum rather than distinct modes (Perry 1999; 2007).

However, other analyses have suggested that lizards use three modes (Reilly and

McBrayer 2007), move at least four different ways when foraging (Butler 2005) or that there are least eight behaviorally distinct ways that a lizard could acquire food (Anderson

2007). Clearly, lizard foraging is more complex than a SW-WF categorization can encompass. Thus, moving away from the traditional dichotomous foraging modes and towards more detailed behavioral categorizations should enrich our understanding of the performance correlates of foraging behavior.

When an animal forages it proceeds through a five part cycle of 1) search, 2) stalk, 3) strike, 4) subdue and consume and 5) digest (Webb 1986). Understanding how lizards differ in the five parts of this “foraging cycle” will allow us to generate more precise description of foraging “modes” and make predictions regarding the how selection should mold performance. For example, there are clear differences between

SW and WF in how they search for prey (ambush vs. actively searching) and accordingly locomotor performance in general is correlated with these behavioral differences (e.g. low endurance vs. high endurance, Miles et al. 2007). However, the expectation of behavioral differences in SW and WF for the stalk, strike, and subdue phases of the

147 hunting cycle are less clear. One might expect that SW use burst locomotion to stalk, strike, and subdue prey whereas WF use slower locomotion to stalk and strike their prey items, but certainly WF may encounter prey that must be chased down with fast locomotor bursts. Thus, more detailed analyses of the foraging cycle components are needed to enhance our understanding of how performance has evolved with foraging behavior.

This study has two goals: 1) identify exactly how a typical SW and WF lizard forage and capture prey and 2) determine how field locomotor speed correlates with the details of prey attacks. To address the first goal I apply lag-sequential analysis to behavioral sequences of a model SW (Sceloporus undulatus) and a model WF lizard

(Aspidoscelis flaggelicaudus) as they actually find, capture and consume prey in the field.

Although lag-sequential analysis of lizard foraging behavior has been heralded as an important tool for uncovering the nuances of foraging behavior (McBrayer et al. 2007), to date, it has only been applied to a study of a chameleon (Butler 2005). I will apply lag sequential analysis to compare behavioral sequences in a typical SW and WF species.

This analysis will test the hypothesis that SW lizards should exhibit movement behaviors that are temporally clumped just before a prey attack event whereas WF species should exhibit movement behaviors that are interspersed throughout their foraging period and are not clumped prior to a prey attack event (Butler 2005). In addition, this analysis will identify if other behaviors (e.g. tongue flicks, digging, jumping, etc.) are temporally clumped around prey attacks. To address the second goal I measured locomotor speed for each movement leading up to prey attacks. Comparative studies have shown that SW are faster than WF (Huey et al. 1984, Miles et al. 2007). However, these studies lack the

148 resolution to determine if fast speeds are actually used with regard to prey attacks within the foraging cycle. Thus, I compared the distribution of speeds used during locomotion preceding prey attacks to distribution of speeds used during the overall activity period to test the hypothesis that SW species use fast speeds to capture prey while WF species use typical speeds to capture prey.

Methods

Study Species and Field Site

I studied one population of Sceloporus undulatus, a sit-and-wait forager (SW) and of Aspidoscelis flagellicaudus a wide forager (WF). Sceloporus undulatus was filmed at

George L. Smith State Park near Statesboro, Georgia. This site was in the sandhills of the Atlantic coastal plain, has loose sandy soils dominated by long-leaf pine and turkey oak and is regularly burned by management agencies. Aspidoscelis flagellicaudus was filmed at Wet Beaver Creek, which is a mid-elevation site (1100 m) in central Arizona.

This site is a riparian zone consisting of a varied habitat ranging from complex three- dimensional clusters of woody debris to flat, open areas of loose sandy soil. This research was conducted under Ohio University approved research protocols.

Focal Observations of Field Behavior

All observations were collected on sunny days during hours of peak activity.

Ambient temperatures ranged from 30 – 40˚ C during filming. I filmed using a JVC-

GRL9800 at 30 frames per second. A 5-minute assessment period ensured that individual lizards had not been disturbed. Then, each individual was followed and filmed as it moved through the environment taking care not to disrupt natural behavior. If the lizard was disturbed during the assessment period or at any time during observation filming was

149 immediately stopped. After filming, lizards were captured with a hand-held slip-noose and identified according to a unique toe-clip pattern, ensuring that each individual was filmed only once. Lizards were released at their point of capture.

Video Analysis

I followed methodologies that previous studies used to study behavioral sequences associated with foraging (Butler 2005) and locomotion (McElroy et al. 2007).

The occurrence and duration of every behavior was recorded for the entire video for each individual from each species. Unbroken bouts of behavior were scored as a single occurrence, for example, prey processing consists of a series of iterative chewing cycles and was scored simply as prey processing. The following behaviors were recorded in both species: move, jump, attack, pause, head, posture, tongue flick, and misc. In addition, I recorded the following behaviors that were unique to each species: Sceloporus undulatus: display; Aspidoscelis flagellicaudus: tail/leg move, dig, prey processing.

Move was any locomotor movement greater than 3 cm, movements less than 3 cm were grouped with postural adjustments. Jump was any saltatory locomotor movement.

Attack was the actual behavioral event that the lizard captured prey in its mouth. Pause was any distinct stop in locomotor movements > 0.03 seconds (2 frames). Head was any movement, rotation or side-to-side repositioning of the head. Posture was any adjustment, rotation (e.g. shifting from head down to head up position on a tree trunk), or translation of the body < 3 cm. Tongue flick was protrusion resulting in the tongue touching the substrate or sampling the air. Misc was a category used for rare behaviors and include: mouth wiping on substrate, defecation, or body scratching. For Sceloporus undulatus, display was any stereotyped display consisting of some combination of head

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bobs and/or push-ups. For Aspidoscelis flagellicaudus, tail/leg movement consisted of a

“twitching” or rapid shaking/waving of the tail, the legs (one, two or all four) or both.

Dig was any attempt to move substrate (sand or leaves) to uncover something. Prey processing was chewing and repositions of captured prey.

Quantifying Movements

In the field, I reviewed videos frame-by-frame to identify substrate landmarks from which to quantify the distance of each movement. Movement distance was quantified by placing flagging at the point of each pause (each movement is preceded and followed by a pause) and then measuring the straight line distance (to the nearest 3 cm) between consecutive flags.

In the laboratory, I reviewed videos frame-by-frame to quantify movement duration and the sequence of specific behaviors for each sampling period. Movement duration was quantified as the amount of time associated with each movement to the nearest 0.03 second. Movement speed was movement distance (recorded in the field) divided by movement duration. Finally, I recorded the sequence and duration of all behaviors using the behavioral categories described above.

Statistics

I used SAS version 8 and JMP 5.0 (SAS Institute 1999, 2002) for all analyses.

Lag-Sequential Analysis of Behavioral Sequences

Studies of animal foraging suggest that SW should exhibit locomotor movements that are temporally clumped just before a prey attack event whereas WF should exhibit locomotor movements that are interspersed throughout their foraging period and are not clumped prior to a prey attack event (Butler 2005). Lag sequential analysis allows one to

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assess if a certain behavior in the sequence preceding a focal behavior occurs at a

frequency higher than observed over the entire data set. The sequence of events

preceding prey attacks are referred to as lags such that lag 1 immediately precedes the

focal behavior, lag 2 occurs two events before the focal behavior and so on. The

frequency of each behavior at each lag (1 – 5) was calculated by dividing the number of

times a specific behavior (i.e. movement, pause, tongue flick, etc.) was recorded in a

single lag by the total number of all behaviors observed in that lag. For example, if I

observed 10 prey attacks and in 4 of the 10 sequences associated with these attacks there

was a movement at lag 2, then the frequency of movement at lag 2 would be 4 of 10, 0.40

or 40%. This observed frequency would then be compared to the frequency of movement behavior in the overall data set. To assess behavioral variation in the sequences

preceding prey attacks, I used lag-sequential analysis with prey attack as the focal

behavior and calculated the frequencies of each behavior at each of the five lags

preceding each prey attack in each species. I expected that Sceloporus undulatus would

exhibit a significantly higher frequency of locomotor movements in the behavioral

sequence preceding prey attack, whereas Aspidoscelis flagellicaudus would exhibit

locomotor movements at the same frequency preceding prey attack as it did in the entire

data set. I used the LAGS.SAS macro version 2.1 written for SAS 8 to compute the

observed frequencies of each behavior at each of the 5 lags preceding prey attack

(Friendly 2001).

Aside from focusing on how the temporal distribution of movement behavior

differs between foraging modes I also wanted to explore how other behaviors differed

during the behavioral sequence leading to prey attack. This was also accomplished using

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lag sequential analysis and examining if other non-movement behaviors occurred at a

higher than expected frequency in the 5 lags preceding a prey attack.

A Chi-square test with one degree of freedom (critical χ2 = 3.84, p = 0.05; χ2 =

6.64, p = 0.01; χ2 = 10.83, p = 0.001) tested if the frequency of a specific behavior at each lag was significantly different from the frequency of that same behavior in the entire data set pooled across all individuals within each species. Significance testing with the Chi- square distribution assumes that the calculated Chi-squares values are continuous (Zar,

1999); however, in this data set Chi-square could only take discrete values (due to the relatively small samples of prey attack behaviors in both species). Therefore, I applied the Yates correction for continuity (Zar, 1999) to the calculation of each Chi-square value.

The Relationship between Speed and Behavior

Even if the clumping of movement behaviors differs between SW and WF species it is unknown if species use speeds differently before prey attack. Previous studies show that SW are generally faster than WF (Huey et al. 1984, Miles et al. 2007), suggesting that SW should use faster speeds just before prey attack when compared to WF. To test this hypothesis I statistically compared the distribution of speeds for movements in the 5 lags preceding the prey attacks to the distribution of speeds for the rest of the movements in the data set for each species using a two sample t-test. Velocity was logarithmically transformed so that it conformed to a normal distribution in each sample. A key assumption of the t-test is that both samples have equal variances, which is often violated when the number of observations in each sample is drastically different. Sample sizes were very different in the two samples for each species (Sceloporus undulatus: prey

153 attack moves n = 9, other moves n = 65; Aspidoscelis flagellicaudus: prey attack moves n

= 8, other moves n = 341). Therefore, I tested for equal variances using Levene’s F-test and found that variances were equal in the two samples in each species (Sceloporus undulatus F1,72 = 2.759, p = 0.101; Aspidoscelis flagellicaudus F1,347 = 0.066, p = 0.797).

Hence, I proceeded with the two sample t-test assuming equal variances.

Results

General Description of Behavioral Time Budgets

Twenty Sceloporus undulatus and 15 Aspidoscelis flagellicaudus were filmed.

Three of the individuals from Aspidoscelis flagellicaudus did not move for the entire observation period (these individuals were clearly not exhibiting typical behavior) and an additional three videos were too poor quality to interpret, therefore I analyzed observations from 9 individuals of Aspidoscelis flagellicaudus. Of these 3 Aspidoscelis flagellicaudus and 8 Sceloporus undulatus actually attacked prey.

Table 4.1 and Figure 4.1A summarize the behavioral time budget for Sceloporus undulatus. In term of both duration (% time) and occurrence (rate), pauses were the most frequent behavior (97.4%, 1.44 per minute). All other behaviors involved extremely small percentages of time in the activity budget (all < 1.28%). Of these displays (1.28%) involved approximately twice the amount of time as locomotor movements (0.54%) and eight times prey attacks (0.16%). Head movements (0.84 per minute) and locomotor movements (0.42 per minute) happened less often than pauses but much more often than other behaviors (< 0.12 per minute). Sceloporus undulatus moved at an average speed of

0.34 m/s ± 0.03 S.E. (range: 0.01 - 1.26 m/s).

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Table 4.1 and Figure 4.1B summarize the activity time budget for Aspidoscelis flagellicaudus. In terms of both % time and rate, pauses (67.4%, 6.18 per minute) and locomotor movements (16.7%, 6.12 per minute) made up the majority of the behavioral time budget. Aspidoscelis flagellicaudus spent smaller amounts of time digging (3.44

%), making tail/leg movements (6.34%), or processing prey (3.90%), other behaviors took up very little time (all < 1.07 %). Most behaviors did not occur very often (< 0.60 per minute) however tongue flicks were quite common (3.24 per minute) although very short in duration (0.04%). Aspidoscelis flagellicaudus moved at an average speed of 0.26 m/s ± 0.02 S.E. (range: 0.01 – 2.46 m/s).

Lag-Sequential Analysis of Behavioral Events Preceding Prey Attack

In Sceloporus undulatus movement and/or jumps occurred in 10 of the 15 behavioral sequences leading up to prey attacks (Table 4.2). Table 4.3 presents the result of the lag sequential analysis of the sequences presented in Table 4.2. Movements occurred at higher than expected frequencies in Lags 1, 3 and 5, while jumps occurred at elevated frequencies in Lags 2 and 3 (Table 4.3). Thus, Sceloporus undulatus exhibits temporal clumping of locomotor movement and jumping in the sequences leading up to prey attacks. Locomotor movements occurred at lower than expected frequencies in Lags

2 and 4 (Table 4.3). Postural adjustments occurred at baseline levels in Lags 2, 3, 4, and

5 but were elevated just before (Lag 1) prey attack (Table 4.3).

For non-locomotor behaviors in Sceloporus undulatus, head movements occurred at baseline or slightly elevated frequencies in Lags 2, 3, 4, and 5 but occurred at significantly lower frequency in Lag 1 just before prey attack (Table 4.3). Displays and

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pauses (except Lag 5) occurred at baseline frequencies while tongue flicks and

miscellaneous behaviors were not observed in the events leading up to prey attacks.

In Aspidoscelis flagellicaudus movement and/or jumps occurred in all four of the

behavioral sequences leading up to prey attacks (Table 4.4). Table 4.5 summarizes the

lag sequential analysis the sequences in Table 4.4. Movements occurred at higher than

expected frequencies in Lags 1, 2 and 5, while jumps occurred at elevated frequencies in

Lags 1 and 3 (Table 4.5). Thus, Aspidoscelis flagellicaudus exhibits temporal clumping of locomotor movement and jumping in the sequences leading up to prey attacks.

For non-locomotor behaviors in Aspidoscelis flagellicaudus, tongue flicks occurred at lower than expected frequencies in Lags 1, 2, 3, and 4 (Table 4.5). Digging occurred at higher than expected frequency in Lag 3 while pauses occurred at elevated frequencies in Lags 2 and 4. Head movements, postural adjustments, tail/leg movements, prey processing, and miscellaneous behaviors were not observed in the 5 Lags leading up to prey attacks (Table 4.5).

In Sceloporus undulatus and Aspidoscelis flagellicaudus the frequency of each

behavior in the overall data set was not significantly different from the frequency of each

behavior in the focal data set (Table 4.3, 4.5). Thus, individuals that attacked prey

generally exhibited behavioral frequencies similar to those in all individuals observed in

each species.

Speed Differences between SW and WF Preceding Prey Attack

Sceloporus undulatus moved at 0.46 m/s ± 0.42 1 S.D. in the movements in the 5

lags before prey attack and 0.33 m/s ± 0.27 1 S.D. in the movements in the rest of the

data set. Movements that occurred before prey attacks were not significantly different

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from those in the overall data set (t = 0.75, df = 72, p = 0.94). The fastest five

movements observed were at speeds (1.3, 1.2, 1.1, 1.0, and 1.0 m/s) and only 2 of these

(1.1 and 1.0 m/s) occurred in the 5 lags preceding prey attack whereas the other three

were not clearly associated with any other behavioral or habitat characteristics.

Aspidoscelis flagellicaudus moved at 0.48 m/s ± 0.31 1 S.D. in the movements in the 5 lags before prey attack and 0.27 m/s ± 0.65 1 S.D. in the movements in the rest of the data set. Movements that occurred before prey attacks were not significantly different from those in the overall data set (t = -1.95, df = 347, p = 0.233). The fastest five movements were at speeds (2.5, 2.1, 2.0, 1.5, 1.4 m/s) and only one of these (2 m/s) occurred in the 5 lags preceding prey attack whereas the other four were associated with movements on hot open sandy substrate.

Discussion

Behavioral Differences between SW and WF in Events Leading to Prey Attacks

Previous authors have suggested that SW should exhibit locomotor movements that are temporally clumped just before a prey attack event whereas WF should exhibit movements that are interspersed throughout their activity period and are not clumped prior to a prey attack event (Butler 2005). Based on this assumption Butler (2005) suggested that lag sequential analysis supported placing the chameleon, Bradypodion pumilum, as a WF. However, no study to date had applied this technique to determine if movements are clumped around prey attacks in species that are clearly SW and WF. I studied two species of lizards that can be unequivocally classified as SW (Sceloporus

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undulatus) and WF (Aspidoscelis flagellicaudus) based on both previous studies (Cooper

et al. 2001, Cooper et al. 2005) and the present study (Table 4.1, Figure 4.1).

Applying lag sequential analysis to the behavioral events preceding prey attack in

these two species revealed that both SW and WF temporally clump movement behaviors

before prey attacks (Tables 4.3, 4.5). Furthermore, both species clumped jumping

behavior in the events leading up to prey attacks (Tables 4.3, 4.5). Thus, this study supports the hypothesis the SW foragers clump movements and jumps before prey

attacks; however, it refutes the hypothesis that WF do not clump movements and jumps before prey attacks. Therefore, both SW and WF appear to use locomotion at elevated frequencies just before they attack prey.

Why did this WF species clump locomotor movements before prey attack? First, it may be that Butler (2005) hypothesis is grounded in species differences in search behavior as opposed to differences in prey attacks. In Butler’s scheme both SW and WF could use high frequencies of movements prior to attack prey but because SW move infrequently and WF move constantly during search it will appear as if SW clump movements before prey attacks whereas WF do not. Thus, the test of Butler’s hypothesis is largely dependent upon the overall frequency of movement in the entire data set. In this study the WF Aspidoscelis flagellicaudus exhibited a relatively low frequency of movements in the overall data set (13.4 % and only 16.7 % time moving). Thus, I may have found that this WF species clumps locomotor movements because it moved less often than other WF species.

A second possibility is that movements are critical for successful prey attacks and thus clumping would be expected in both SW and WF. A successful prey attack must

158 involve an effective search, stalk, strike, and subdue phase (Webb 1986). It is well known that searching behavior in WF lizard involves prolonged periods of locomotion, interspersed with pauses and tongue flicks (Evans 1961; Pianka and Vitt 2003, Anderson

2007). Aspidoscelis flagellicaudus used these three behaviors at the highest rates of any behavior in the overall data set (Table 4.1; Fig. 4.1) supporting the evidence that this species is widely foraging. However, after search behavior has located a prey item, locomotion may play an even more important role in stalking, striking, and subduing that prey. This would be particularly true if the prey was highly mobile, which could be true of the sit-and-wait prey that WF are hypothesized to prey upon (Huey and Pianka 1981).

Indeed, of the four prey attack events I observed in Aspidoscelis flagellicaudus three were on mobile prey (very fast spider which it had ousted from a crack between two boulders and then was chased down, a cricket which was excavated from beneath the leaf litter and subsequently jumped, an unidentified insect which was spotted on the ground and subsequently chased down) while the fourth prey attack event was an immobile spider egg sac. Although locomotor movements do not appear to be faster in the events leading up to prey attack, two of the three jumps I observed in Aspidoscelis flagellicaudus (Table

4.4) occurred just before prey attack suggesting that fast locomotion (jumping) may be critical in striking and subduing mobile prey. Overall, these data suggest that prey mobility, and not foraging mode, may play a key role in determining if locomotion occurs more frequently just before prey attack (see also, Anderson 1993, Anderson 2007, Perry

2007). Future work should expand this approach to additional species and could examine the interaction between foraging mode and prey mobility in determining the sequence of behaviors and speeds that occur just before prey attack. Particularly, useful would be

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detailed studies of other classic WF species (e.g. Varanus sp. and Aspidoscelis tigrus) and

WF prey types (i.e. termites). In addition, other performance traits, such as acceleration,

may be even more important than maximum sprint speed for a WF chasing down a

flushed prey that is mobile.

In addition to locomotion other behaviors appear to be important in the events

preceding prey attack. In Sceloporus undulatus, head movements occurred at baseline levels in lags 2-5; however, just before prey attack head movements were not observed

(Table 4.3). This finding suggests that Sceloporus undulatus spends the majority of its time stationary using head movement to visually search the environment and then stops searching just before attack as it focuses in on a prey item. In this regard it is quite interesting that the behaviors associated with searching in both Sceloporus undulatus

(head movements) and Aspidoscelis flagellicaudus (tongue flicks) occur at higher rates

than most other behaviors (Table 4.1). WF are known for their ability to thoroughly search a large area within their environment; however, these results suggest that SW

species may also thoroughly search the environment but over a smaller area than WF.

Postural adjustments were also important in Lag 1 suggesting that Sceloporus undulatus

waited in ambush until prey were within 3 cm then lunged or repositioned itself to strike

and subdue the passing prey (this was almost always the case when ants were preyed upon). Thus, while locomotion is key for prey attack in this SW species other behaviors are also important for focusing in and capturing prey.

Aspidoscelis flagellicaudus also used non-locomotor behaviors at higher than expected frequencies in the events leading up to prey attack. There was a conspicuous absence of tongue flicking in the 5 lags preceding prey attack and an increase in pauses.

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Thus, much like Sceloporus undulatus, Aspidoscelis flagellicaudus appears to cease

searching behaviors and increase its visual focus (via pauses, Avery et al. 1987) as it keys

in on attacking prey. Digging was also observed at elevated frequencies in the events

leading to prey attack highlighting Aspidoscelis proclivity for locating and excavating

hidden prey (Anderson 1993).

Webb’s Foraging Cycle

How do these data fit into Webb’s (1986) foraging cycle scheme of search, stalk,

strike, subdue, and digest. First, it is clear that both species engage in long periods of

search behavior with the SW Sceloporus undulatus using head movements and the WF

Aspidoscelis flagellicaudus using locomotor movements, pauses, and tongue flicks to find prey (Table 4.1). In these two species I never observed anything that could be classified as stalking behavior; although Gambelia and Callisaurus can stalk prey (Anderson 2007) and given the appropriate prey item it seems likely that any species could stalk. Next, the strike phase coincides with the movements and jumps preceding prey capture. This is the phase where locomotor performance is thought to be most critical (Webb 1986). In both species movements were clumped before prey attacks (Tables 4.3, 4.5) suggesting that locomotion (particularly quick jumps) is indeed critical, although the speed of these movements may be less important (see below). This highlights why breaking foraging into its constituent phases is important; clearly, locomotion is key for striking/attacking prey in both species but is important for search behavior only in the WF species. Next, subdue corresponds to prey attacks, in which the prey is captured with the jaws or tongue.

I observed jaw prehension in the WF Aspidoscelis flagellicaudus and tongue prehension in the SW Sceloporus undulatus which has been shown to be an important difference in

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how WF and SW use the feeding apparatus to capture prey (McBrayer and Reilly 2002;

Reilly and McBrayer 2007). Finally, both species used pauses after subduing prey

suggesting that pausing is an important part of the digestive phase digestion.

Speed

Previous studies have found that SW species generally have faster laboratory

measured maximum sprint speeds than WF species (Huey et al. 1984; Miles et al. 2007).

These findings imply that the fastest sprint speeds are in large part attributable to selection on sprint speed through foraging behavior. This should be especially apparent in SW species which, in theory, should use their fastest speeds when ambushing their prey. However, the above line of reasoning would only hold true if animals actually use their maximum speed in nature and if they are used during the appropriate behavioral context (Garland and Losos 1994; Irschick and Garland 2001). To explore this I quantified locomotor speeds in the events leading up to prey attacks and found that speed did not significantly differ from the rest of the data set in either Sceloporus undulatus or

Aspidoscelis flagellicaudus. Nor did speed differ when examining the five fastest movements (only 1 of 5 Aspidoscelis flagellicaudus and 2 of 5 Sceloporus undulatus fastest movements were associated with prey attacks). Few other studies have examined locomotor speeds associated with prey attacks; however, in general, speeds used in natural and induced (via use of a fly lure and fishing rod) prey attacks are not near maximum sprint speed (Urosaurus ornatus, McElroy et al. 2007; Anolis Irschick and

Losos 1998, Irschick 2000; Podarcis muralis [searching behavior including prey attacks]

Brana 2003; Crotaphytus collaris Husak and Fox 2006). My data along with previous studies suggest that selection is not acting on maximum sprint speed through the filter of

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prey attacks/foraging behavior. Thus, the finding the SW and WF species differ in

laboratory measured maximum sprint speed (Huey et al. 1984; Miles et al., 2007) appears

to be unrelated to the dynamics of prey attack. Why then do SW and WF lizards differ in

maximum sprint speed? One possibility is that selection on endurance capacity used in

search behavior by wide foragers has resulted in a reduction of maximum sprint speed,

due to a trade-off in the ability to be fast and have high endurance. While this would

explain why studies have found that WF are slower than SW, evidence for a trade-off in

these two traits is mixed (Vanhooydonck et al. 2001; Miles et al. 2007). Thus, it remains

unclear as to why SW and WF differ in maximum sprint speed.

When do these two species use their fastest locomotor speeds, if not during prey attack? In Aspidoscelis flagellicaudus the other four of the five fastest movements were associated with movement on open, sandy areas where the substrate temperature was high and exposure to predators was great. Generally, Aspidoscelis flagellicaudus foraged

in leaf litter and boulders that were under the cover of tree canopy either in the riparian zone along Wet Beaver creek or under bushes on the drier hillsides near the creek. Thus, most of the activity period was spent in areas sheltered from aerial predators and the hot desert sun. The few observations I made of Aspidoscelis flagellicaudus venturing into the open involved faster locomotor movements (in open 0.454 ± 0.576 1 S.D.; under cover

0.132 ± 0.240 1 S.D), four of which were among the fastest I observed. Other studies

have made similar qualitative observations in the closely related Aspidoscelis tigris

(Anderson 1993, Garland 1993). Thus, predator escape behavior may be a key selective

factor driving the evolution of sprint speed in this WF lizard, as has been suggested by

others (Miles et al. 2007).

163

It should be noted that in both species I did not observe the use of maximum

sprint speed. Sceloporus undulatus can sprint 1.73 m/s (Crowley 1985; Angiletta et al.

2002) and I observed a maximum speed of only 1.3 m/s; while Aspidoscelis

flagellicaudus can sprint 4.16 m/s (J. Meyers personal communication) and I observed a

maximum speed of only 2.5 m/s. Thus, it is possible that I simply did not observe

behaviors associated with top speeds such as predator escape (Hertz et al. 1988) and possibly territorial encounters (Robson and Miles 2000; Husak and Fox 2006).

Additionally, it is likely that top speeds are associated with rare but selectively important events (Gans 1979; Hertz et al. 1988). Thus, the data I present in this study cannot clearly identify the behavioral (i.e. selective) factors molding sprint speed in these two species.

Aspidoscelis flagellicaudus does not appear to use its maximum endurance

capacity while foraging. While there are no data for endurance capacity in this species,

maximum endurance capacity for the family Teiidae is 26.45 minutes (Miles et al. 2007).

In Aspidoscelis flagellicaudus, the movements associated with searching were short

duration (1.65 ± 2.11 1 S.D. sec) because they were interspersed with frequent pauses

(Table 1.1, pause duration 6.88 ± 24.35 1 S.D. sec). In addition, searching locomotion

was generally very slow (0.069 ± 0.127 1 S.D. m/s) being within the range of

Aspidoscelis aerobic scope (0.083 – 0.406 m/s, Garland 1993). This suggests that search

behavior does not attain a level of continuous intense locomotion that would approach

maximum endurance capacity, a finding in agreement with a study of Aspidoscelis tigris

(Garland 1993). Thus, other behaviors (e.g. digging), rare events and/or temporal

164

fluctuation in prey availability may be driving maximum endurance capacity in

Aspidoscelis flagellicaudus.

Conclusions

This study highlights how detailed behavioral observations of undisturbed, natural behavior can be integrated with measurements of performance in the field to understand if and when selection can operate on performance capacity. SW and WF both appear to elevate the frequency of locomotor behaviors just prior to prey attacks. In addition, the idea that selection has molded maximum sprint speed through the filter of foraging behavior appears to be unfounded, at least in this preliminary data set and based on scant data from the literature. Hopefully, future studies will focus on the existing chasm between what animals can do in laboratory and what they actually do in the wild and how behavior modulates this relationship in order to expand our knowledge of how and why performance evolves.

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170

Table 4.1

Activity budgets for Sceloporus undulatus (n = 20 individuals) and Aspidoscelis

flagellicaudus (n =9). Activity budgets are presented as both the percent time allocated

to each behavior (% time) and the number of times each behavior was observed per

minute of observation (rate, number per minute).

Sceloporus undulatus % time rate

Display 1.28 0.12

Head 0.05 0.84

Move 0.54 0.42

Pause 97.7 1.44

Posture 0.03 0.12

Attack 0.16 0.06

Tongue Flick 0.01 0.06

Jump 0.01 0.02

Misc 0.04 0.01

Aspidoscelis flagellicaudus % time rate

Head 0.41 0.30

Move 16.7 6.12

Pause 67.4 6.18

Posture 1.07 0.42

171

Table 4.1: Continued

Attack 0.04 0.02

Tongue Flick 0.04 3.24

Jump 0.02 0.06

Dig 3.44 0.30

Tail/Leg movement 6.34 0.60

Prey Processing 3.90 0.12

Misc 0.64 0.24

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Table 4.2

Sequences preceding prey attacks (n = 15) in Sceloporus undulatus. Parentheses after each move or jump indicate speed (m/s). Speed could not be estimated for individual 21 or one movement in individual 19.

Individual Lag0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5

3 Attack Move (0.25) Pause Head Pause Move (0.01)

11 Attack Move (0.32) Pause Move (1.06) Pause Head

12 Attack Move (0.96) Pause Jump (2.10) Pause Move (0.04)

15 Attack Pause Head Pause Head Pause

15 Attack Pause Display Move (0.05) Pause Attack

16 Attack Pause Posture Pause Head Pause

16 Attack Pause Head Pause Head Pause

16 Attack Move (0.09) Pause Posture Pause Head

16 Attack Pause Head Pause Attack Move (0.09)

17 Attack Posture Pause Head Pause Head

19 Attack Move (0.95) Jump (1.26) Move Pause Posture 173

Table 4.2: Continued

21 Attack Move Pause Attack Posture Move

21 Attack Posture Head Pause Display Pause

21 Attack Move Attack Move Pause Head

21 Attack Move Pause Head Pause Head

174

Table 4.3

Sceloporus undulatus. Percentages of each behavior in the overall data set (Overall column), in the overall sequences from 8 individuals that attacked prey (Focal column), and in the 5 lags that preceded each prey attack (Lag1 – Lag5). There were a total of

624 behavioral events in the overall data set and 342 events in the focal data set. Chi-square tests (with Yates correction) with 1 degree of freedom tested for significant differences between the observed percentages of behaviors in the focal data set and the expected percentages in the overall data set, as well as, the observed percentages at each lag behavior that preceded prey attacks to the expected percentages in the overall data set.

Behavior Overall Focal Lag1 Lag2 Lag3 Lag4 Lag5

Display 3.84 3.59 0 6.67 0 6.67 0

Head 27.0 27.9 0*** 26.7 20.0 20.0 40.0*

Move 13.4 12.7 53.3*** 0*** 26.7*** 0*** 20.0

Pause 46.6 45.9 33.3 46.7 33.3 60.0 26.7**

Posture 3.36 3.04 13.3*** 6.67 6.67 6.67 6.67

Attack 2.40 4.14 0 6.67 6.67 6.67 6.67

Tongue Flick 2.24 1.38 0 0 0 0 0

175

Table 4.3: Continued

Jump 0.80 0.55 0 6.67*** 6.67*** 0 0

Misc 0.32 0.83 0 0 0 0 0

* p < 0.05, ** p < 0.01, *** p < 0.001

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Table 4.4

Behavioral sequences preceding prey attacks (n = 4) in Aspidoscelis flagellicaudus from Arizona.

Parentheses after each move and jump indicate speed in m/s. I could not reliably estimate speed from a jump (Lag3) from individual

12.

Individual Lag0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5

5 Attack Move (2.00) Move (0.15) Pause Move (0.18) Pause

12 Attack Jump (0.38) Pause Jump Pause Tongue Flick

11 Attack Move (0.25) Pause Move (0.48) Pause Move (0.63)

11 Attack Pause Dig Move (0.1) Pause Move (0.04)

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Table 4.5

Aspidoscelis flagellicaudus. Percentages of each behavior in the overall data set (Overall column), in the overall sequences from 3 individuals that attacked prey (Focal column), and in the 5 lags that preceded each prey attack (Lag1 – Lag5). There were a total of

1111 behaviors in the overall data set and 513 behaviors in the focal data set. Chi-square tests (with Yates correction) with 1 degree of freedom tested for significant differences between the observed and expected percentages.

Behavior Overall Focal Lag1 Lag2 Lag3 Lag4 Lag5

Head 2.79 2.14 0 0 0 0 0

Move 34.4 33.9 50.0** 50.0** 25.0 25.0 50.0**

Pause 34.7 32.9 25.0 50.0* 25.0 50.0*** 25.0

Posture 2.43 2.14 0 0 0 0 0

Attack 0.36 0.78 0 0 0 0 0

Tongue Flick 18.3 19.3 0*** 0*** 0*** 0*** 25.0

Jump 0.27 0.78 25.0*** 0 25.0*** 0 0

Dig 1.53 2.73 0 0 25.0*** 0 0

Tail/Leg movement 3.24 2.73 0 0 0 0 0

178

Table 4.5: Continued

Prey Processing 0.63 1.17 0 0 0 0 0

Misc 1.54 1.37 0 0 0 0 0

* p < 0.05, ** p < 0.01, *** p < 0.001

179

Figure 4.1. Activity budgets illustrating the percent time allocated to each behavior in Sceloporus undulatus and Aspidoscelis flagellicaudus. Some behaviors were grouped as Other because they occurred at low percentages making them difficult to visualize in the pie chart. Sceloporus undulatus Other: prey attack, postural adjustment, tongue flick, jump, and miscellaneous. Aspidoscelis flagellicaudus Other: head movement, digging, jump, prey attack, miscellaneous.

180

CONCLUSIONS AND FUTURE WORK

This dissertation addressed two very general questions: 1) do functional systems relate to whole organism performance and behavior and how does this relationship evolve and 2) what do animals do in nature? I show how locomotor functional morphology is integrated into performance (Chapters one and two) and forms the basis of foraging locomotion and behavior (Chapter one). Additionally, I show that expectations based on laboratory studies or theories do not always meet with how lizards actually move in nature (Chapter three and four). Thus, I feel that this dissertation begins to answer these questions and forms a solid foundation for my future research.

Three future areas of research have their roots in this dissertation. First, with inspiration from Harry Greene’s work I plan on continuing to make quantitative observations of lizard natural history. I will further explore the use of lag sequential analysis as a tool to integrate detailed field videos of natural behavior and performance with maximum performance capacity and behavioral theory. Chapter four suggests that maximum performance has not evolved via selection on foraging mode and that prey type and mobility seem to emerge as more important factors than foraging mode for determining the speed lizards use to attack prey. To further explore these hypotheses I plan to collect data on additional species to more fully explore how the “foraging cycle” differs between foraging modes and relates to performance.

Second, chapter 2 suggests a very simple relationship between locomotor function and energetics/endurance that is grounded in limb morphology. I would like to test this hypothesis by expanding this study and quantifying both energetics and endurance 181

capacity. In addition, I would like apply these data to the LiMB model for locomotor

energetics in mammals and birds to determine if lizards fit the model and what

modification the model requires to account for erect vs. sprawling posture. If a model of

lizard energetics could be developed that is based in limb morphology this could open an

avenue for explore how morphological diversification is related to energetic

diversification, which may have profound implications for observed trends in lizard

ecology and evolution.

Finally, slow locomotion is clearly a derived condition in wide foraging lizards

and is critically important for permitting the chemosensory searching necessary to forage

widely. Chapter one identifies a few functional correlates of evolving slower speeds.

However, virtually nothing is known about how morphology, kinematics and forces

integrate and evolve to allow lizards to move slowly. Thus, I am currently devising a

study that is modeled upon chapter 2 to further investigate the functional correlates of

evolving slow locomotion in wide foraging lizards. In addition, SW species appear to be

constrained to using only running mechanics; a result that has not been shown in any

other vertebrate. Future investigations will examine the morphological and/or kinematic

factors that prevent SW species from vaulting their center-of-mass over their limb.

In all, this entire dissertation has been a great learning experience. I feel that the tools and knowledge I gained in the last five years have prepared me for a career as a scientific researcher, a biologist, and a teacher.

182

APPENDIX A: MUSUEM CODES

Museum abbreviations: CAS, California Academy of Sciences; CSUN, California State University at Northridge; CM, Carnegie Museum; UCMVZ, University of California Museum of Vertebrate Zoology.

Acanthodactylus boskianus: CAS (9723, 9709, 9710, 9708, 138659, 138657, 138658, 138655, 138120, 138724, 138722, 138723, 138721, 13871, 138718), CM (56708 56648, 56569, 56662, 56756, 56597, 56566, 56643, 56761, 56760, 56612, 56649, 56567, 56661, 56568)

Eulamprus quoyii: CAS (76873, 76879, 76830, 76822, 76850, 76868, 76827, 76853, 76845, 76826, 76860, 76880, 76843, 76824, 76868, 76867, 76847, 76877, 76825, 76872, 76858, 76862, 76842, 76852, 76835, 76851, 76848, 76818, 76854)

Laudakia stellio: CAS (217951, 217952, 217874, 217875, 217683, 217876, 217685, 217684, 218089, 218090, 217985, 218091, 5007, 2992, 217987, 217984, 217711, 217709, 217807, 217808, 217809, 217710, 217712, 217803, 217802, 217804, 218005, 218092, 217680, 217980); Three uncataloged individuals from Ohio University.

Oplurus cuvieri: CAS (12777, 126358, 135152, 13951, 13957, 13953, 13950); UCMVZ (238792, 128904, 238791, 238793, 117597, 21117, 238794, 247486, 238795, 238790, 238796); Three uncataloged individuals from Ohio University.

Tracheloptychus petersi: CSUN (1213); UCMVZ (238766, 238764, 238765, 238768, 238767); Six uncataloged individuals from Ohio University.

Tropidurus torquatus: CM (136154, 136117, 64888, 943, 4594, 7411); One uncataloged individual from Ohio University.

Varanus exanthematicus: CM (24705, 24700, 15163); CSUN (1417, 2903); UCMVZ (75661); CAS (169935, 139504, 103109, 130091). Three uncataloged individuals from Ohio University.