of the southwest united states a dissertattion …
TRANSCRIPT
THE EVOLUTION AND DESIGN OF THE LOCOMOTOR SYSTEM IN LIZARDS
OF THE SOUTHWEST UNITED STATES
A DISSERTATTION SUBMITTED TO THE GRADUATE DIVISION OF THE
UNIVERSITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
ZOOLOGY
AUGUST 2014
By
Jeffrey A. Scales
Dissertation Committee:
Marguerite A. Butler, Chairperson
David B. Carlon
Kathleen S. Cole
Leonard A. Freed
Marcelo Kobayashi
Keywords: locomotor performance, adaptation, trade-offs, lizards, selective pressures
ii
ACKNOWLEDGEMENTS
I would like to thank all of the volunteers who helped with fieldwork during this
project including, but not limited to M. Altmann, B. DeRoy, R. Grey, K. Mullins, and
M.J. Truini. I also extend thanks to C. Brong for assistance in video analyses, M.A. Pena
for helping with muscle dissections, and J. Rivera for providing the gene sequences used
in this study. K. Bonine, A.K. Lappin, and W. Sherbrooke provided invaluable assistance
and guidance with lizard collection in the field, for which I am deeply grateful. I express
my gratitude to R. Blob for teaching me the muscle dissection and measurement
techniques used in this study. For logistical support in the field, I thank the AMNH
Southwestern Research Station in Portal, AZ, A.K. Lappin, and J. Scales and R. Scales.
Funding for this research was provided by National Science Foundation (NSF) grants
DDIG 0910400 to M. A. Butler and J. Scales, DEB-0515390 to M.A. Butler, and DEB-
0542360 to A.A. King. Additional funding was supplied by The Society of Integrative
and Comparative Biology (SICB) Grants in Aid of Research, and the University of
Hawaii Colleges of Arts and Sciences Student Research Awards to J. Scales. The Arizona
Fish and Game Department, California Department of Fish and Game, and New Mexico
Department of Game and Fish all provided permits and assistance for lizard collection. I
thank Y. Chan, S. Evers, E. Henry, C. Kokami, M.A. Pena, and J. Rivera constructive
comments that greatly improved this manuscript. Finally, I sincerely thank my advisor,
M.A. Butler and committee members, D.B. Carlon, K.S. Cole, L.A. Freed, and M.
Kobayashi for their mentorship and support throughout my graduate career.
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ABSTRACT
Understanding the adaptation and diversification of complex phenotypes is a
central task of evolutionary biology. However, because many phenotypes are complex,
constructed in a in a hierarchy of biological organization that often must perform multiple
functions, studies frequently lack the detail required to fully understand the evolution of
these systems. To gain a deeper understanding regarding the evolution of complex
phenotypes, I integrate evolutionary modeling and functional morphology approaches to
investigate locomotor evolution in lizards of the Southwest United States. I use
evolutionary models based on habitat use, predator escape behavior, and foraging mode
to examine what drives diversity in sprint speed, acceleration, and exertion in these
lizards. I then examine how the locomotor system is designed for specific locomotor
tasks. Because muscle provides the force required for locomotion, I focus on muscle
cross-sectional area (CSA). I first examine the relationship between each performance
and muscle CSA, and then explore how these relationships and CSA distribution vary
with function (flexors vs. extensors) and anatomical location (hip, knee, and ankle).
Finally, because the locomotor system is composed of numerous traits that can vary in
their functional roles, I explore what drives the evolution of each trait individually. I
model the evolution of each trait, and compare the best fitting models across traits to
determine how the locomotor system evolves as a whole. Overall, I demonstrate that
multiple selective pressures act on both locomotor performance and morphology from the
cellular to whole animal level. However, selection is not uniform within the locomotor
system. Performance related selection acts across many locomotor traits, but some are
more constrained or not subject to selection. Behavioral variation is an important driver
of phenotypic diversity as behavioral shifts guide the evolution of performance, muscular
design, and form-function relationships in these lizards. Finally, the complexity of the
locomotor system may promote adaptation and diversification through the mitigation of
potential functional trade-offs and allowing alternative locomotor designs to yield similar
performance capabilities.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS.………………………..………………………………….…ii
ABSTRACT….……………………………………..……………………………...…….iii
LIST OF TABLES….……………………………………..…………………………….viii
LIST OF FIGURES.……...……………………………………………………………....ix
CHAPTER 1. INTRODUCTION…...………………………………………………..…10
Lizards as a study system……………………………………….………..11
CHAPTER 2. ADAPTIVE EVOLUTION IN LOCOMOTOR PERFORMANCE: HOW
SELECTIVE PRESSURES AND FUNCTIONAL RELATIONSHIPS PRODUCE
DIVERSITY.……………………………………………………………………………..15
Abstract.………………………………………………………………………….15
Introduction.………………………………………………………………...……15
Materials and Methods.……………………………………………………..……18
Field-work.………...……………………………………………………..18
Performance trials.……………………………..………………………..18
Phylogeny..……………………………………………………...………..20
Comparative Analyses….…………………………………………..……20
Selective Regimes for OU model…………….…………………………..21
Results and Discussion……….………………………………………………….24
Adaptive evolution of performance.……………………………….......…24
Predator Escape Pressure.……………………………………….24
Foraging strategy.…………………………………………..……25
Habitat use.……………………………………………………....26
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The reality of performance types.………………………………………..27
Effects of body size on performance.…………………………………….27
Trade-offs in performance...……………………………………………...28
Summary....………………………………………………………………30
CHAPTER 3. HINDLIMB MUSCLE CROSS-SECTIONAL AREA REFLECTS
LOCOMOTOR PERFORMANCE AND MICROHABITAT USE IN LIZARDS.……...37
Abstract.…..……………………………………………………..……………….37
Introduction.…..……………………………………………………….…………38
Materials and Methods…..……………………………………………….………40
Fieldwork…………………………………………………….…………..40
Performance Trials…...…………………………………………….……40
Morphological and Muscle measurements..……………….………….…41
Phylogeny………………………………………………………………...42
Statistical Analyses..………………………………………………….….42
Results.…………………………………………………………………………...43
Scaling of hind limb CSA…..………………………………...……...……43
Hind limb CSA and sprint performance…………………….….…...……44
Hind limb CSA and exertion………...…………………………………...44
Variation in CSA across the hind limb…………………………………...45
Discussion.…………………………………………….…………..……………..45
Muscle CSA and Size…………………………………………..….……..45
CSA, acceleration, and speed...………………………………………….46
Distribution of extensor CSA and locomotor performance...……………48
Summary..…………………………………………….………………….50
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CHAPTER 4. RUNNING FOR YOUR LIFE OR RUNNING FOR YOUR DINNER:
WHAT DRIVES FIBER-TYPE EVOLUTION IN LIZARD LOCOMOTOR MUSCLES?
……………………………………………...…………………………………………….60
Abstract..…………………….……………………….…………………………..60
Introduction...…………………………………………………………………….60
Materials & Methods.…………………………..………………………………..63
Muscle fiber-type data and assumptions…………………………………63
Phylogeny.………………………..………………………………………64
Selective Regimes...………………………………………………………65
Analysis...………………………………………………………………...67
Results...…………………………………………….……………………………68
Fast-twitch (FG and FOG)...…………………………………………….68
Slow-twitch oxidative (SO)…………………………...………………….69
Discussion………………………………………...……………………………...69
Selective Hypotheses…………………………………...………………...70
Multiple Evolutionary Models……………………………...……………72
CHAPTER 5. TARGETS OF SELECTION AND POTENTIAL CONSTRAINTS: HOW
THEY SHAPE THE EVOLUTION OF THE LOCOMOTOR SYSTEM IN LIZARDS
……………………………………..…………………….……………………………….78
Abstract……………………………………………….………………………….78
Introduction…………………...………………………………………………….79
Materials and Methods.………………………..…………………………………81
Morphological Data Measurements…………………………...………...81
Morphological traits for evolutionary models………………….………..82
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Comparative Analyses……………………………………...……………83
Selective Regimes for evolutionary models……………………....………84
Results…………………………………………….…………………………...…86
Selection on body shape…………………………………………...……..86
Selection on musculature…………………………………………...……86
Selection at the tissue level…………………………………………...….87
Discussion…………………………………………….…………………...……..87
CHAPTER 6. CONCLUSION…..……………………………………………...………95
APPENDIX….………………...………………………….…………...………………...99
REFERENCES…………………………………………….…………….……………..104
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LIST OF TABLES
Table 2.1. Performance of alternative models for the evolution of performance………..31
Table 2.2. Model parameters estimated for the predator escape model……………….…31
Table 2.3. Model parameters estimated for the foraging mode model…………………..32
Table 2.4. PC analysis loadings for four performance variables………………………...32
Table 2.5. The relationship between size and performance…………………………...…33
Table 3.1. Relationships between body mass and hind limb CSA……………………….51
Table 3.2. Relationship between hind limb CSA and acceleration and sprint speed…….51
Table 3.3. Relationship between CSA at each joint and speed and acceleration...………52
Table 3.4 Relationship between hind limb CSA and exertion………………….………..53
Table 3.5 PCA loadings of CSA at each joint………………………………………...….54
Table 4.1 Fast-twitch fiber type model selection criteria………………………..…….…74
Table 4.2. Parameters of the best fitting fast-twitch fiber type model…………………...74
Table 4.3. Slow-twitch fiber type model selection criteria……………………………....75
Table 4.4. Parameters of the best fitting slow-twitch fiber type models………..………..75
Table 4.5. Model estimates of fiber type composition compared to raw data……...……76
Table 5.1. Model selection results for locomotor traits………………………………….92
Table 5.2. Comparison of evolutionary models…………………………………………93
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LIST OF FIGURES
Figure 2.1. Adaptive evolutionary hypotheses for the evolution of locomotor
performance…………………………………………………...…………………………34
Figure 2.2. Performance phenotypes reflect locomotor demands……………………….35
Figure 2.3. Correlations of PIC between each of the performance variables……………36
Figure 3.1. Phylogenetic relationships of the 20 species of lizards included in this
study……………………………………………………………………………………...55
Figure 3.2. Relationships between log total hind limb and log body mass…………..….56
Figure 3.3. Non-phylogenetic and phylogenetic relationships between hind limb CSA and
performance variables……………………………………………………………………57
Figure 3.4. The proportion of hind limb CSA by function and joint…………………….58
Figure 3.5. PCA of extensor muscle CSA distribution across the hip, knee, and ankle…59
Figure 4.1. Evolutionary hypotheses for the evolution of fiber-type composition in the
iliofibuaris muscle of lizards………………………………………………………..……77
Figure 5.1. Evolutionary hypotheses for the evolution of body shape, musculature, and
individual muscle traits of the locomotor system in lizards……………………………..94
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CHAPTER 1. INTRODUCTION
Understanding phenotypic adaptation and diversification has been a primary goal
in evolutionary biology, ecology, and physiology. Phenotypic adaptations, features
shaped by natural selection that improve the survival and fitness of an organism, have
played a central role in the study of biology not only because they are a key component
of Darwin's theory of natural selection, but also because adaptations elucidate form-
function relationships, can identify abiotic and biotic factors important to the survival or
reproduction of organisms, and can provide insight into mechanisms of speciation and
determinants of species distributions (Darwin 1859, Schluter 2000, Reznik & Travis
1996). Adaptation to different environments or biotic conditions can also lead to
phenotypic diversification, intimately linking the two (Schluter 2000). As a consequence,
the immense diversity we observe today is frequently attributed to adaptation. However,
adaptation and diversity may be limited because phenotypic traits are commonly subject
to multiple selective pressures and frequently serve multiple functions while forced to
follow design rules (Alfaro et al. 2004, Collar et al. 2006). Thus, to understand the
processes contributing to phenotypic adaptation and diversification requires an
integrative approach, but adaptation is most frequently examined with respect to a single
function or single selective pressure. Here, I combine functional morphology and
evolutionary modeling to examine the evolution of the locomotor system in relation to
multiple selective pressures and locomotor traits to gain a more complete understanding
of the evolution of complex phenotypes.
Locomotion and the locomotor system have served as one of the primary models
for the study of phenotypic adaptation and diversification. First, locomotor performance
is important for numerous fitness related tasks such as escaping predators and obtaining
mates suggesting that it is subject to selection (Alexander 2003, Miles 2004, Lailvaux &
Irschick 2006, Langerhans 2009a). Second, many morphological and physiological traits
have been linked to variation in performance (Bonine et al. 1999, Biewener 2003,
Kohlsdorf & Navas 2012), and variation in performance has been linked to fitness
differences (Arnold 1983, Garland & Losos 1994, O’Steen et al. 2010, Miles 2004,
Langerhans 2009a, Husak et al. 2006, Strobbe et al. 2009) providing evidence for
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locomotor adaptation. Finally, locomotor performance and morphology is heritable
(Garland 1990) allowing performance to evolve in response to selection.
While it may seem straightforward to examine adaptation and diversification in
locomotion, it can be difficult as the locomotor system is quite complex. Locomotor
systems are organized in a hierarchy of biological levels including cellular, tissue,
system, and whole-animal so that locomotor performance is determined by numerous
morphological and physiological traits, all of which must be integrated to function
properly (Schmidt-Nielsen 1984, Bauwens et al. 1995, Kohlsdorf & Navas 2012).
Locomotor systems must also accomplish a wide array of functions (ie. running, jumping,
climbing, swimming) with the same machinery. If two functions place different demands
on the locomotor system, the two performances may not be optimized simultaneously
resulting in trade-offs, potentially limiting locomotor adaptation and diversity (Lewontin
1978, Arnold 1992, Reznik & Travis 1996). Furthermore, because the locomotor system
must perform so many ecologically relevant tasks, several forms/agents of selection more
than likely act on locomotor performance (Arnold 1992, Reznik & Travis 1996,
Ghalambor et. al. 2004). Despite this complexity, performance is commonly examined
with respect to a single aspect of locomotion such as sprint speed or at a single level of
biological organization, limiting our knowledge of functional roles and integration (e.g.,
Van Damme & Vanhooydonck 2001). Additionally, locomotor morphology and
performance are most often studied in the context of a single selective pressure (e.g.,
Fulton et al. 2003, Verwaijen & Van Damme 2007, Gifford et al. 2008). However, by
examining multiple selective pressures, aspects of performance, and locomotor traits
simultaneously we can gain important inferences into the processes and mechanisms that
promote and/or limit the adaptation and diversification of the locomotor performance and
morphology.
Lizards as a Study system
Lizards have long served as popular models for physiological, ecological, and
evolutionary studies. This popularity largely stems from their enormous diversity. With
roughly 5,900 (and counting) species, lizards make up the bulk of the order squamata
(which also includes snakes and amphisbaenians) and occur widely on all continents
12
except Antarctica (Pough et al. 2004). While many aspects of lizard biology are
conserved (ectothermy, scaled skin, hemipenes in males, etc.), they vary greatly in
lifestyle and life history (Pough et al. 2004). For example, diets range from herbivores,
to omnivores, insectivores and strict carnivores (Pough et al. 2004). Life history varies
even more as sexual and asexual reproduction, and oviparity and viviparity all occur in
lizards (Pough et al. 2004). These diverse lifestyles and life histories have resulted in an
incredible range of morphologies and physiological adaptations.
Diverse lifestyles coupled with a strong reliance on locomotion for ecologically
relevant tasks have made lizards one of the primary models for studies of locomotor
adaptation (Garland & Losos 1994, Aerts et al. 2000, Irschick & Garland 2001).
Thermoregulation, finding mates, defending home ranges and territories, escaping
predators, and obtaining food all depend on locomotion, and lizards have diversified
immensely to accomplish these tasks (Garland & Losos 1994, Pough et al. 2004). Species
specialize in running, climbing, swimming, gliding, and even limbless locomotion, which
has evolved repeatedly for burrowing or serpentine locomotion (Pough et al. 2004). This
high locomotor diversity is associated with several aspects of lifestyle. For example,
lizards occupy a wide array of habitats including hot, dry deserts, rain forests, grasslands,
mountains, and even a marine species (Pough et al. 2004). Within these habitats, they
further vary in microhabitat use, ranging from fossorial species that rarely surface to
arboreal specialists. Lizards also vary greatly in behavior. Predator escape strategies
range along a continuum from species that rely almost exclusively on crypsis to avoid
detection to those that rely heavily on speed to escape attacks (Vitt & Congdon 1978,
Pough et al. 2004). Foraging mode shows a similar spectrum of diversity. Sit-and-wait
predators spend most of their time motionless with occasional short bursts to catch prey,
while at the other end of the spectrum, active foragers spend the majority of time in
motion searching for prey (Reilly et al. 2007). Differences in microhabitat use, predator
escape behavior, and foraging behavior are all commonly invoked to explain variation in
lizard locomotor performance, and recent studies show that all three factors influence
locomotor morphology and performance (Herrel et al. 2002, Melville & Swain 2003,
Miles 2004, Reilly et al. 2007). Thus, lizards vary widely in both their locomotor
demands and the potential selective pressures that act on performance, providing the
13
opportunity to examine how multiple forms of selection interact to guide the evolution of
the locomotor system.
Specifically, I examine locomotor evolution in lizards of the Southwest United
States. I focus mainly on the family Phrynosomatidae, the major radiation of lizards in
North America (Wiens et al. 2010, Wiens et al. 2013). This family includes several
genera, but I focus on the Sceloporus group (fence and spiny lizards), the sand lizards
(Callisaurus, Cophosaurus, Holbrookia, and Uma), and horned lizards (Phrynosoma).
Sceloporines have a “typical” lizard body shape and are more generalist in most behavior,
but vary widely in microhabitat use including terrestrial species as well as arboreal and
rock specialists (Herrel et al. 2002, Bonine et al. 2001, Stebbins 2003). The sand lizards,
on the other hand are largely terrestrial, often inhabiting open areas. Several of these
species are sprint specialist with slender bodies and long hind limbs that use high speeds
to evade predation (Herrel et al. 2002, Bonine et al. 2001, Stebbins 2003). In contrast,
the horned lizards (sister clade to the sand lizards) are cryptic specialists that use
camouflage and morphology to avoid detection and predatory attacks. These lizards are
robust, heavy species with relatively shorter limbs and tails, and ornate scales and
“horns” (Herrel et al. 2002, Bonine et al. 2001, Stebbins 2003, Bergmann & Irschick
2010). Furthermore, most horned lizards feed mainly on ants, which has resulted in
foraging behavior that differs from other Phrynosomatid lizards.
In addition, I include one species of Crotaphytus, two species from the Teiidae
family and a single species from the Anguidae family. Crotaphytus species are large,
quick, sit-and-wait predators that often feed on other lizards within the community (Vitt
& Congdon 1978, Stebbins 2003). Lizards of the genus Aspidoscelis (Teiidae) are
slender, active foragers that rarely stop moving when active, generally use speed to avoid
predation, and occur in a variety of habitats (Vitt & Congdon 1978, Anderson & Karasov
1981, Stebbins 2003). Anguidae is a diverse family of lizards, but most species in North
America are long, slender lizards with relatively small limbs, are active in leaf litter and
other cluttered habitats, and are generally active foragers (Stebbins 2003, Reilly et al.
2007). Thus, the lizards included in this study vary widely in performance abilities,
morphology, habitat use, and foraging and predator escape strategies providing an
excellent opportunity to examine how these factors influence the evolution of locomotor
14
performance and morphology as well as how lizards are designed for specific tasks. I ask
three questions related to the evolution of performance and locomotor system design in
these lizards. 1) What drives the diversity of locomotor performance in these lizards, is it
habitat use, foraging mode, predator escape behavior, or none of these? 2) How are
lizards designed for specific performance capabilities? Which traits are most important
for performance? Does morphological integration allow for multiple pathways to the
same performance? 3) At what level of biological organization does adaptation occur, is it
uniform, or are some traits constrained while others are influenced by selection?
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CHAPTER 2. ADAPTIVE EVOLUTION IN LOCOMOTOR PERFORMANCE:
HOW SELECTIVE PRESSURES AND FUNCTIONAL RELATIONSHIPS
PRODUCE DIVERSITY
Abstract
The incredible diversity of athletic performances in the animal kingdom has long
fascinated biologists. However, most studies of complex phenotypes take a single
approach, either exploring the role of natural selection in producing sometimes
spectacular athletes or alternatively, focusing on the role of design rules, the material
properties of tissues, or functional interactions in limiting phenotypes. Here we show that
a combination of both approaches is necessary to capture the extent of biodiversity in a
complex phenotype. Adaptation plays a prominent role in explaining the diversity of
locomotor phenotypes in southwestern lizards, but a combination of selective pressures is
required to explain variation in separate aspects of performance. Speed and acceleration
are strongly influenced by selective pressures related to predator escape, whereas exertion
is explained by foraging strategy. The interactions of these different performances also
play a role, resulting in a trade-off between speed and time to exhaustion. Surprisingly,
however, this trade-off is not obligatory and can be overcome by selection for both
performances simultaneously. Therefore, if multiple selective pressures are shaping a
complex phenotype, ignoring the contribution of one will lead to substantial
underestimation of the adaptive component of phenotypic diversification. Alternatively, if
intrinsic and extrinsic factors interact, but intrinsic factors are ignored, selection will
appear inadequate in explaining diversity and we may misinterpret what limits
adaptation.
Introduction
Whether it is for catching dinner, avoiding being someone else's dinner, or
winning contests for mates, an enormous diversity of athletic abilities have evolved
across animals. A variety of selective pressures have been proposed to explain such
16
impressive variation in locomotor capabilities. Behavioral strategies, in particular, play
an important role. For example, predator avoidance is known to select for increases in
speed and acceleration in the fast start of fish (e.g., Langerhans 2009a), swimming speed
of insects (Strobbe et al. 2009), and the sprint speeds of lizards (Miles 2004) and African
herbivores (Bro-Jorgensen 2013). Another commonly involved selective pressure are the
strategies associated with foraging mode. Because the ability to find and acquire prey
determines an animal’s energy budget, foraging strategy can have large fitness
consequences. Interestingly, the different strategies are often quite divergent, with active
foragers relying on frequent but often slow locomotion, whereas ambush predators move
rarely but rapidly (Miles et al. 2007 and references therein), resulting in phenotypes
shaped by selection for energetically efficiency and higher stamina, versus high speed
burst locomotion (Miles et al. 2007 and references therein). Accordingly, variation in
foraging strategy influences locomotor performance and morphology in a wide range of
species (e.g., Norberg and Rayner 1987, Verwaijen & VanDamme 2008, Pruitt 2010).
Furthermore, foraging, predator escape, and other locomotor behaviors all occur in the
context of specific microhabitats, and animals use a wide variety of microhabitats.
Running on narrow branches, for instance, may place a greater premium on stability,
which is not experienced by species that run on flat ground (Losos & Sinervo 1989,
Losos 1990). Therefore, the specific microhabitat context may define the scope of the
functional problem on which selection should act (Losos & Sinervo 1989, Losos 1990,
Fulton et al. 2001, Hodgkison et al. 2004, Gomes et al. 2009).
While natural selection is central in shaping locomotor abilities, phenotypes may
not be able to reach the adaptive peak defined by a single selective pressure. Complex
phenotypes are often limited by functional constraints that place bounds on the action of
adaptive evolution (Arnold 1992, Walker 2007). For example, the same locomotor
machinery is responsible for producing diverse performances such as walking, jumping,
and fighting. As a consequence, if two aspects of performance place conflicting demands
on the same design features, trade-offs can occur so that excellence in one performance
will come at the expense of another. For example, sprinters can achieve the fast running
speeds, but tire quickly, whereas distance runners can run for great lengths of time but
locomote at much lower speeds. The physiological basis for this observed trade-off is
17
thought to occur at the cellular level, as sprinters are enriched with fast-twitch muscle
fibers that produce high power but fatigue quickly, whereas fatigue-resistant muscle
fibers produce lower power outputs (Wilson et al. 2002, Wilson and James 2004). Due to
this negative relationship, we may expect a trade-off between burst speed and sustained
locomotion so that individuals display either high burst speeds coupled with low
endurance or vice versa (Bonine et al. 2005). Therefore, if multiple selective pressures
place antagonistic demands on the locomotor system, the phenotype may reflect a
balance that is suboptimal values for each performance. Alternatively, strong selection an
exclusive strategy may lead to specialization with poor performance for other tasks.
Although numerous empirical studies have documented performance trade-offs in several
functional systems (e.g., Vanhooydonck et al. 2001, Levinton and Allen 2005, Oufiero et
al. 2011), fewer studies have explored to what extent trade-offs may limit adaptation and
diversification (but see Holzman et al. 2011, Vanhooydonck et al. 2014).
While variation in locomotor performances may be governed by ecological
selection and functional interactions, body size can also influence locomotor
performance. Body size has wide-ranging effects on most morphology and physiological
processes, including locomotion (Alexander 1981, Schmidt-Nielsen 1984, Clemente et al.
2012). Thus, locomotor diversity may simply reflect variation in size as opposed to
selection or other design factors.
In this study, we investigate how multiple selective pressures interact to guide the
evolution of locomotor diversity. We focus on the highly diverse lizards of the American
southwest, which include members of the Crotaphytidae, Anguidae and Teiidae as well as
one of the largest radiations of lizards in North America, the Phrynosomatidae (Wiens et
al. 2010). Collectively, these lizards show a broad diversity in locomotor strategies,
differing not only in microhabitat use (Stebbins 2003), but also locomotor behaviors
ranging from extreme cryptic specialists that rely little on speed such as the horned
lizards, to the swift zebra-tailed lizards, who run at impressive speeds, and whiptails that
spend the majority of their time in sustained, slow motion (Anderson & Karasov 1981,
Bonine & Garland 1999). Additionally, their phylogenetic relationships are relatively
well-established (Townsend et al. 2004, Vidal & Hedges 2005, Wiens et al. 2010, Wiens
et al. 2012, Pyron et al. 2013) allowing phylogenetic analyses. Specifically, we test
18
whether selection related to microhabitat use, foraging strategy, and predator escape
behavior act on sprint speed, acceleration, and exertion abilities, and if so, how they
interact to produce the diverse performance types observed in these lizards. We also
examine the relationship between each aspect of performance to determine whether trade-
offs occur as a consequence of multiple locomotor demands, and if so, to what extent
trade-offs limit excellence in multiple aspects of performance. Alternatively, factors
other than selection, such as body size, may explain performance differences. Thus, we
explore the relationship between size and performance as well.
Materials and Methods
Field-work
Three to 10 individuals of 21 species (comprising four families) of lizards were
collected from the wild in California, Arizona and New Mexico during the summers of
2009 and 2010 (Table 1S in Appendix). After capture, lizards were transported to the
Southwestern Research Station or an alternative field lab (a temperature controlled house)
for locomotor trials. Lizards were held overnight for passage of gut contents, with
locomotor performance trials commencing on the day following capture. Lizards were
kept in cloth bags between performance trials with access to water ad libitum. Trials
continued for three days after which time lizards were either euthanized (by sodium
pentobarbital injection) for morphological study, or returned to their site of capture and
released live. All animal procedures followed University of Hawaii IACUC protocol 09-
463.
Performance Trials
Prior to each performance trial, lizards were placed under a heat lamp to achieve
field body temperature (based on previous studies and summarized in Bonine & Garland
1999) for at least an hour. Body temperatures were verified before each trial using a
cloacal thermometer (Miller & Webber Inc.).
For sprint trials, lizards were induced to run down a 3.0 x 0.15m trackway by
clapping or lightly tapping their tail. Each individual was tested five times over a span of
19
two days. Lizards were given a minimum of one hour of rest between each trial and were
run a maximum of three times in one day in order to capture maximum acceleration and
maximum sprint speed. Only runs in which lizards ran straight without jumping, pausing,
or touching the walls were used for analysis.
Sprint speed was measured using a series of infrared photocells placed every
0.25m over the second meter of the trackway. The single fastest time between two
photocells over the five runs was designated as the individual’s maximum sprint speed
under the specified test conditions (Peterson and Husak 2006, Vanhooydonck et al. 2001).
Acceleration was measured by digitally tracking a white marker glued (Elmer’s
glue) dorsally, directly above the pelvis of each lizard. The pelvis is an easily located
landmark near the animal's center of mass and care was taken to minimize handling time
to reduce the lizard’s stress. The first meter of all runs was filmed at 250 frames sec-1
from a dorsal view using a high-speed video camera (Fastec Troubleshooter LE). This
frame rate has shown to be sufficient to accurately measure the speeds and accelerations
observed in these lizards (Walker 1998, Vanhooydonck et al. 2006). The position of the
hip point was tracked and digitized using Kwon 3D (Kwon 1994). The hip position data
was smoothed using a quintic spline in the pspline package (Ramsey & Riley 2010) in the
R computing environment (R development Core Team 2010). Instantaneous acceleration
was calculated by differentiating the smoothed hip position data twice (Walker 1998).
Maximum instantaneous acceleration was considered the maximum acceleration of an
individual over the five runs.
Lizards were given a minimum of 24 hours of rest after their last running trial
before maximal exertion trials. Maximal exertion is defined here as the time and distance
a lizard can run before they reach exhaustion. In maximum exertion trials, lizards were
chased by clapping and tail-tapping around a four meter oval track (marked at 0.25m
intervals) until exhaustion defined here as the point at which an individual refused to run
after 10 consecutive tail taps or loss of righting response (Bennett 1980, Mautz et al.
1992, Vanhooydonck et al. 2014). Maximal exertion was measured in two ways: (i) as
distance covered; and (ii) time to exhaustion (timed with a stopwatch; Bennett 1980,
Mautz et al. 1992, Huyghe et al. 2007, Vanhooydonck et al. 2014).
20
Phylogeny
The desert Southwest lizard species used in our study includes 18 species of
Phrynosomatidae and three additional species. For the 18 species of Phrynosomatidae, we
used the topology and branch lengths from the most comprehensive phylogeny of the
family to date, which included 122 species and used six nuclear and five mitochondrial
genes (Wiens et al. 2010). Retaining branchlengths, we pruned the Wiens topology to
include only the 18 species in this study using the ape package (Paradis et al. 2004) in R.
Our estimate of the relationships of the three species not included in the
Phrynosomatidae phylogeny with respect to the Wiens topology are based on previously
established familial relationships (Townsend et al. 2004, Vidal & Hedges 2005, Wiens et
al. 2012, Pyron et al. 2013). We then combined previously published and new sequences
to estimate branchlengths. Sequences for Elgaria kingii (12s: AY525103.1, 16s: this
study, ND1: AF407538.1, ND2: AF407538.1, ND4: AY605103.1) and A. tigris (BDNF:
EU402619.1, PRLR: HQ130585.1, RAG1: EU402829.1, TRAF6: EU391053.1, 12s:
AY046452.1, 16s: AY046494.1, ND1: HM160771.1, ND2: U71332.1) were downloaded
from Genbank. The 12s, 16s, and ND1 genes for A. uniparens and the 16s sequence for
E. kngii were newly sequenced for the purpose of this study. Sequences for all 21
species were aligned using ClustalX. We used the GTR+ Γ model, selected based on the
Akaike Information Criterion (AIC) using jModelTest, for our analyses. We used
Maximum Likelihood analyses in PAUP 4.0 (Swofford 2003) to estimate the
relationships and branchlengths of all 21 species. We then scaled the branchlengths of
the three additional species so that they were on the same time scale as the Wiens et al.
(2010) tree and grafted these branches to the Wiens et al. (2010) topology and
branchlengths. The resulting tree agrees with previously published phylogenies on the
familial relationships (Townsend et al. 2004, Vidal and Hedges 2005, Wiens et al. 2012,
Pyron et al. 2013) while preserving the topology and branchlengths of Wiens et al.
(2010).
Comparative Analyses
All performance data were log-transformed prior to analysis. Four aspects of
performance, sprint speed, acceleration, and time and distance run to exhaustion were
21
analyzed in a phylogenetic context. The performance data was modeled using Hansen’s
(Hansen 1997) model, an Ornstein-Uhlenbeck (OU) process that models the evolution of
a continuous phenotypic trait subject to the influences of noise and selection (Hansen
1997, Butler & King 2004). The Hansen model describes the change in a trait dX(t), over
time (t), as an increment of a stochastic Brownian motion process (dβ(t)), influenced by
selection (α) toward an optimal trait value (θ) and subject to stochastic changes
proportional to a noise parameter (σ):
dX(t) = α [θ (t) – X(t)] dt + σdβ (t) (1)
Importantly, the Hansen model allows theta to vary along the branches of the phylogeny
to represent shifts in selective regime (or 'adaptive zone' sensu Simpson (1953))
experienced by the evolving lineages. We produced four mappings of selective regimes
on the phylogeny, each representing a distinct evolutionary hypothesis for the evolution
of the trait (sensu Butler & King 2004), see selective regimes below). Each evolutionary
model was fit to the log-transformed data, assuming our phylogeny using the OUCH
software package (Butler & King 2004) in the R statistical computing environment (R
Development Core Team 2010). The fits of each model were compared using the Akaike
information criterion corrected for small sample size (AICc, Butler & King 2004).
Information criteria were used to measure the strength of evidence in support of each
competing model (Burnham & Anderson 2002). Model selection frequencies and model
parameter confidence intervals are based on 2000 bootstrap replicates (Burnham &
Anderson 2002).
Selective Regimes for OU model
We tested four evolutionary scenarios, including Brownian motion and three
selective drivers, for producing the diversity of locomotor abilities observed in extant
species. The Brownian motion (BM) model is a non-adaptive model. This model
assumes that locomotor performance evolves according to random drift, and makes no
assumptions about adaptation with respect to the selective pressures considered here.
22
The first adaptive model is based on microhabitat use and contains five optima:
terrestrial, arboreal, saxicolous, generalist, and litter as defined below (Figure 2.1).
Species were placed into habitat regimes based on published descriptions of microhabitat
use (Table 1S in Appendix). Because most lizards are capable of using multiple
microhabitats, for a species to be placed into a specific regime the literature had to
describe a strong association/preference for a specific microhabitat. Species were
considered terrestrial if the literature described them as spending most of their time on the
ground. Species were placed in the arboreal category if they are strongly associated with
trees or woody bushes, climbing among their branches or trunks. Saxicolous species
were those that showed an association with rocks or vertical rock surfaces. Species were
placed in the clutter category if their microhabitat descriptions specifically included
cluttered areas such as under logs or rocks, or in leaf litter. Finally, species were
considered generalists if the literature didn’t specify a specific preference/association or
the species used multiple habitats.
The second model is based on predator escape behavior and has three optima:
flight, crypsis, and mixed (Figure 2.1; see Table 1S in Appendix for classifications).
Species were classified based on published descriptions of escape behavior (Table 1S in
Appendix). Cryptic escapers (CE) are lizards that rely heavily on crypsis or morphology
to evade predators with little reliance on running. Flight escapers (FE) are species that
rely mainly on bursts of sprinting for escape, whereas mixed escapers (ME) are species
that use a mix of cryptic behavior and short sprints to refugia, or other behaviors (e.g.,
“squirreling”) to avoid predation.
The third adaptive model is based on foraging mode and contains three optima.
Lizard species were classified as sit-and wait predators (SWF), active foragers (AF), or
mixed foragers (MF) based on movement criteria. SWF predators tend to spend long
periods of time motionless, and strike out for prey in very sort and rapid bursts. In
contrast, AF lizards move much more frequently, although at slower speeds (i.e.,
constantly on the go and a moderate pace). Previous authors have demonstrated that
foraging modes can be classified based on percent time moving (PTM) and movements
per minute (MPM) data (Figure 2.1, Table 1S in Appendix), based on the following
criteria: SWF (PTM<10% and MPM<1), MF (MPM>1 and PTM10% < 25%), and AF
23
(MPM >1 and PTM >30%, (Butler 2005, Reilly et al. 2007). Since published PTM and
MPM data were not available for E. kingii, data for the family Anguidae were used,
placing it in the AF category. Most species were readily classified using these criteria,
with some exceptions. The genus Phrynosoma was modeled as MF because its PTM
places it in the AF category, but near the MF category (Reilly et al. 2007), whereas the
MPM designates it as SW (Shaffer & Whitford 1981). Furthermore, the patchy
distribution of Phrynosoma’s food source (largely ants) results in a strategy of actively
searching for food patches followed by a sit-and-wait strategy once a patch is found.
Thus, they exhibit an intermediate foraging strategy between sit-and-wait and active
foraging strategies (Munger 1984, Butler 2005, Perry 2007). Sceloporus graciosus was
modeled as SWF despite an MPM of 1.31, due to its low PTM. Aspidoscelis uniparens
was modeled as an AF despite MPMs less than 1 due to their high MPM (70%<).
The adaptive models of evolution were constructed by assigning adaptive regimes
to individual branches of the phylogeny based on independent ecological data or
descriptions for the extant species (Table 1S in Appendix) with ancestral regimes
reconstructed based on linear parsimony using Mesquite (Maddison & Maddison 2009).
Linear parsimony designates regimes on internal branches that minimize the number of
evolutionary changes across the phylogeny. However, previous study that included more
taxa and specifically more basal species estimated ME to be most basal character state in
this group of lizards (Scales et al. 2009), and so we again used this value (Scales et al.
2009).
We used a principal components analysis (PCA) including all four performance
variables to visualize the performance phenotypes of these lizards in performance space.
We then used linear regression to determine size-performance associations. We also
assessed the correlations between the PICs of each of the four performance variables to
determine whether they display positive correlated evolution, no relationship, or negative
correlated evolution indicative of trade-offs. PIC were performed assuming our
phylogeny and the correlation between each performance trait was determined using
Pearson’s correlation in R. All PIC were performed using the ape package (Paradis et al.
2004) in R.
24
Results and Discussion
Adaptive evolution of performance
We tested four evolutionary hypotheses for the evolution of performance: a
hypothesis of neutral evolution modeled by Brownian motion (BM) and three selective
hypotheses modeled as an Orstein-Uhlenbeck process. These models of adaptation
differed in the source of selective pressure: needs related to predator escape; foraging
strategy; or habitat use (Figure 2.1).
Predator Escape Pressure. We found that sprint speed and acceleration were each
best explained by evolution in response to selection related to predator escape pressures
(Table 2.1). The predator escape model outperformed all others by both AIC.c and SIC
model selection criteria for both sprint speed and acceleration (Table 2.1), with the habitat
use and foraging mode models performing relatively poorly. Bootstrap model selection
frequencies indicate strong support for the predator escape model with regard to speed
and acceleration, as it was selected at least 91% of the time in both cases (Table 2.1).
The evolutionary optima estimated for the predator escape model reveal a trend of
increasing sprint capabilities with greater reliance on speed to escape predators (Table
2.2). Species that use a sprint escape strategy are evolving toward optima for very
effective sprinting, whereas cryptic species evolve toward optima with poor sprinting
capabilities, and generalists are intermediate.
Many studies have established the link between predation intensity and locomotor
performance (as discussed in the introduction), and between sprint performance and
fitness (Watkins 1996, Miles 2004, Husak 2006, Langerhans 2009a) within species.
However, it has rarely been demonstrated that differences in predator escape behavior can
drive performance diversity across a wide range of species (but see Scales et al. 2009).
Our analyses indicate that predation is a strong selective pressure that has resulted
in the diversification of species in terms of speed-related locomotion capabilities. We
tested this hypothesis against strong alternatives. It is not the case that the most complex
model provided the greatest explanatory power, as the habitat model and foraging mode
25
models had as many or more parameters than the predator escape model. Interestingly,
the BM model often outperformed these alternative models (Table 2.1), suggesting that
using the BM model is better than using a misleading ecological model.
Furthermore, an open question is whether the alternative strategies each require
distinct physiological or behavioral adaptations. Interestingly, cryptic and mixed
strategy lizards show substantial overlap in sprint speed and acceleration abilities, while
flight escape species do not (Table 2.2). An ad hoc evolutionary analysis based on a
predator escape model including only two selective regimes, flight specialists and all
other lizards, performed better than our original predator escape model for sprint speed
(ΔAIC.c =-3.30, ΔSIC = -2.84) and acceleration (ΔAIC.c=-1.87, ΔSIC = -1.41). This
suggests that there may be strong selection on sprint specialists above a general minimum
level of performance for all other lizards.
Foraging strategy. While predator escape behavior drives the diversity of speed-
related performance, it does not strongly influence the evolution of maximal exertion.
Instead, the foraging mode hypothesis best explained the evolution of both metrics for
maximal exertion with little support for any other models (Table 2.1). Furthermore, there
was very strong support from bootstrap model selection frequencies as the foraging
model was selected at least 95% of the time according to AIC.c criteria for both exertion
variables (Table 2.1).
The foraging mode model predicts that active foragers should move long
distances and periods of time before exhaustion, whereas sit-and-wait predators should
move shorter distances over a much shorter period of time prior to exhaustion, and mixed
foragers should be intermediate in both (Table 2.3). The estimated optima values are
similar to the measured performance capabilities of lizards in each respective regime
(Table 2.3) and support an evolutionary trend of increased maximal exertion with higher
levels of foraging movement.
Maximal exertion has received less study than speed, but lizards exhibiting
different foraging behaviors are known to differ in energy budgets and expenditure, and
field metabolic rate (Brown & Nagy 2007 and references therein). Furthermore, previous
studies have found that actively foraging species display higher exertion than sit-and-wait
26
species (Bennett 1994 and references therein). Thus, active foragers may require higher
maximal exertion due to high activity levels, whereas sit-and-wait predators may rarely
use their maximal exertion capabilities.
Habitat use. We did not find a strong association between microhabitat use and
locomotor performance. This result is surprising as it is in conflict with previous studies
(e.g., Losos 1990, Melville & Swain 2000, Kholsdorf et al. 2001, Butler and Losos 2002,
Herrel et al. 2002). One potential reason for this difference is that there may be multiple
strategies that are equally effective in a particular microhabitat (e.g., Liolaemus lizards:
Schulte et al. 2004). For example, P. modestum, C. texanus, and A. tigris, share the same
microhabitat and were observed together in a single desert wash during this study, but
they nevertheless exhibit the full range of predator escape and foraging strategies
examined here (Stebbins 2003, J. Scales, pers. obs.). In contrast, the well-studied Anolis
genus shows a strong association between microhabitat and locomotor performance (e.g.,
Losos & Sinervo 1989, Losos 1990). This difference may stem from the fact that Anolis
species are more closely related than the species in this study, and have evolved less
extreme behavioral strategies (e.g., Vanhooydonck et al. 2007, Johnson et al. 2008) such
that shifts in microhabitat use have promoted predictable evolutionary diversification in
locomotion associated morphology and performance (Losos 2009). Here we show that
locomotor performance is not fundamentally linked to microhabitat. Nevertheless,
microhabitat use may still play an indirect role by setting the bounds within which
specialization can occur (Butler, et al. 2000, Mellville & Swain 2003, Collar et al .2010).
In dragon lizards, arboreal specialists showed limited morphological variation while
terrestrial lizards were much more variable (Collar et al. 2010). Other studies have found
that predator escape behavior and locomotor performance were associated with and in
some cases limited by microhabitat use (Melville & Swain, 2003). Thus, some
microhabitats may facilitate behavioral variation whereas other microhabitats may be
more limiting.
27
The reality of performance types
A principal component (PC) analysis showed that variation in performance among
these lizards can be summarized by two principal component axes: a sprint axis (PC1;
acceleration and sprint speed; Table 2.4), and an exertion axis (PC2; distance and time
run to exhaustion; Table 2.4). Therefore, we will hereafter use the term “sprint
parameters” to refer to maximum sprint speed and maximum acceleration collectively,
and “exertion” to refer to time and distance to exhaustion. Three main groups separate in
this performance space: 1) lizards that are slow runners but can run for a long time 2)
lizards with low exertion, but range from slow to fast runners, and 3) lizards that run fast
and can do so for long periods (Figure 2.2). These groupings correspond to a
combination of the two selective pressures favored in our evolutionary analyses above.
These predator avoidance and foraging behaviors are important organizing factors
of locomotor performance. If we map selective regimes onto the PC performance space,
species separate into discrete clusters of exertion abilities according to foraging mode
(Figure 2.2). Similarly, mapping of the predator escape selective regimes reveals
distinctive densities of performance phenotypes by sprint ability, but with some overlap
(Figure 2.2). Interestingly, we find that species can excel along both axes. In conjunction
with the expected fast species with low exertion and slow species with relatively high
exertion, species (e.g., A. tigris) can be active foragers with high exertion and still
accomplish high-speed sprints. Conversely, some species perform poorly in both aspects
showing slow speeds and low exertion. Poor overall performance is an interesting case.
Have these species made behavioral modifications to reduce dependence on locomotion,
or do they live in predator-poor environments?
Effects of body size on performance
Many aspects of the structure, physiology, and function of organisms scale with
size, including locomotor capabilities (e.g., Schmidt-Nielsen 1984, Heglund & Taylor
1988). Surprisingly, we do not observe a strong influence of size on performance here,
with low explanatory power, particularly for sprint speed, acceleration, and time to
exhaustion (r2 values ranging from -0.05 to 0.07 for regression of snout-to-vent length
with performance variables, Table 2.5). Distance to exhaustion showed the strongest
28
relationship with size (r2 = 0.25, P = 0.012). Taken together, these results show the small
and large lizards have similar sprint capabilities, but larger lizards cover greater distances
in the same amount of time before reaching exhaustion. For example, S. graciosus, a
small lizard, and S. clarkii, a relatively large lizard, ran for similar durations, yet S. clarkii
covered almost 10 more meters (Table 2S in Appendix). The scaling of distance to
exhaustion with size may be explained by stride length, which commonly scales with
body size (e.g., Cumming & Cumming 2003, Clemente et al. 2012). Thus, it is possible
that the amount of time animals are able to sprint is governed by physiological limitations
unrelated to size, whereas the distance covered scales with size via limb length and size.
Indeed, time and distance to exhaustion are not correlated (PIC regression: r2= 0.22, p =
0.34, t = 0.97, Figure 2.3).
Trade-offs in performance
Several investigators have proposed a trade-off between speed and acceleration,
either because body size will have opposing effects on the two performance traits (Hill
1950), or because of the force-velocity trade-off predicted by muscle fiber contractile
properties in which increased contraction speed, beneficial for speed, results in low force
production that should be detrimental for acceleration (Vanhooydonck et al. 2006).
However, we observed no such trend. Instead, acceleration and sprint speed show strong
positively correlated evolution (PIC correlation: r2 = 0.84, p < 0.001, t= 6.49, Figure 2.3),
similar to other studies (Huey and Hertz 1984, Vanhooydonck et al. 2006). Since both
traits are important in predator escape, this correlation could be the result of a common
selective pressure (e.g., this study, Huey & Hertz 1984, Walker 2005). Alternatively, it
may be the result of the same design traits improving both aspects of performance.
Vanhooydonck et al. (2006) demonstrated that variation in many of the same
morphological traits were associated with increases in acceleration and sprint speed in
Anolis species. How tight the linkage is between sprint speed and acceleration remains
an open question.
Another commonly invoked trade-off occurs between exertion and sprinting
(Vanhooydonck et al. 2001, Herrel & Bonneaud 2012), and we find evidence for this
trade-off here. However, the relationship is complex and depends on which metric is used
29
for exertion. Faster lizards run farther before exhaustion and show positive correlated
evolution between these traits (PIC correlations speed: r2= 0.53, p= 0.017, t =2.64; and a
marginal correlation with acceleration: r2=0.42, p=0.064, t = 1.97; Figure 2.3). On the
other hand, faster lizards are sooner spent, indicating an evolutionary trade-off between
speed and time to exhaustion (PIC correlations: speed: r2= -0.54, p =0.015, t = -2.69;
acceleration: r2= -0.54, p = 0.014, t=-2.72, Figure 2.3). The physiological characteristics
of muscle fiber types are thought to underlie this trade-off, with fast, powerful muscle
fiber types tiring quickly whereas slower fiber types are much more fatigue resistant (e.g.,
Bonine et al. 2005, Wilson & James 2004). Indeed, we see this performance relationship
in most of the species included in this study. The horned lizards (Phrynosoma) and E.
kingii showed relatively high exertion, but low sprint parameters and have a high
proportion of more fatigue resistant muscle fibers (Bonine et al. 2005). Conversely, the
Sceloporus group and sand lizards have higher sprint parameters and low exertion
accompanied by higher proportions of fast, powerful fibers (Bonine et al. 2005).
Even within this general trend, however, there are interesting exceptions. The
Aspidoscelis species, which have the highest exertion, also exhibit high sprint speeds and
accelerations, especially A. tigris (Figure 2.3). Interestingly, species in the genus
Aspidoscelis are also the only species that experience selection for both performances
according to our model. All other species experience selection for relatively high speeds
coupled with relaxed selection for exertion, or selection for higher exertion accompanied
with relaxed selection for speed. This finding implies that the trade-off is not obligatory
(see Holzman et al. 2011), and selection to perform well at all of these aspects of
locomotion can result in a locomotor design with no apparent trade-off.
How is this trade-off circumvented? It may be that the expected trade-off is not
present because trade-offs at the muscular level do not translate to the whole-organism
level (Wilson et al. 2002, Vanhooydonck et al. 2014). Thus, our expectation for a trade-
off between speed and exertion is unfounded. Additionally, the two performances may be
determined by different traits. Whereas sprint parameters are correlated with traits such
as power output and limb length, exertion may be determined by other physiological
factors such as the circulatory system, blood buffering, or enzymatic activity. In this case
we again would not expect a trade-off. Finally, it may be that there are alternative designs
30
to achieve the same performance ability (many-to-one mapping Wainwright et al. 2005,
Holzman et al. 2011) so that when there is selection for both exertion and speed, the
complexity of the locomotor system can accommodate both (Holzman et al. 2011). How
the Aspidoscelis species accomplish both high speeds and exertion is an interesting
question for future study that may shed light on the limitations of performance.
Summary
Here we demonstrate that multiple approaches are essential not only in
understanding phenotypic diversity, but also for understanding what factors are
responsible for the evolutionary diversification of phenotypes. Our results indicate that
multiple sources of selection are responsible for the locomotor diversity in lizards. Both
the need to flee predators as well as the efficient food acquisition are powerful selective
factors, but they act on different aspects of the locomotor system. Behavioral variation
can therefore play a significant role in shaping phenotypic diversity. Furthermore, the
functional integration of the locomotor system does not necessarily limit the potential
combinations of locomotor performances. We generally observe an expected locomotor
trade-off between speed and exertion, but our analyses show that it is possible to
circumvent this trade-off when selection is strong and acting on multiple performance
axes.
31
Table 2.1. Performance of alternative models for the evolution of locomotor performance.
Model fit statistics for sprint speed, acceleration, time and distance to exhaustion (Ex).
The model with the best fit is in bold and listed first. Bootstrap selection frequencies are
listed in parentheses. The Best model was predator escape for both sprint speed and
acceleration and Foraging Mode for both time and distance to exhaustion
Sprint Speed
Acceleration
Time to Ex
Distance to Ex
Model AIC.c AIC.c AIC.c AIC.c
Predator Escape 0 (92%) 0 (97%) 2.4 (2%) 18.3 (0%)
Brownian Motion 2.8 (1%) 12.5 (0%) 7.7 (1%) 19.8 (0%)
Foraging Mode 4.0 (6%) 6.9 (2%) 0 (95%) 0 (100%)
Habitat 6.8 (1%) 16.3 (0%) 12.4 (2%) 33.9 (0%)
Table 2.2. Model parameters estimated for the predator escape model, the best fitting
sprint speed and acceleration model. Estimated optimal values () for sprint speed (m/s)
and acceleration (m/s2) data given in columns. The predator escape model posits that
lizards are in selective regimes for cryptic escapers (CE), mixed escapers (ME), and
flight escapers (FE). Bootstrap 95% confidence intervals are in parentheses. Measured
data are means of performance data for species grouped by selective regime.
Parameter
Sprint Speed
(m/s)
Measured Sprint
Speed (m/s)
Acceleration
(m/s2)
Measured
Acceleration
(m/s2)
35.4 (13.0,
1284) 56.57 (18.37,
1170)
6.16 (1.80,
201)
3.72 (0.86,
81.5)
CE 1.94 (1.33,
2.92) 2.21 (0.60)
25.96 (20.27,
31.80)
27.92
(6.47)
ME 2.18 (1.76,
2.73) 2.41 (0.79)
30.29 (26.77,
34.25)
30.43
(5.49)
FE 3.78 (2.85,
5.11) 3.69 (0.42)
43.15 (36.53,
51.44)
43.00
(10.04)
32
Table 2.3. Parameters estimated for the foraging mode model, the best fitting maximal
exertion model, and actual measured performance. Estimated optimal values (q) for the
distance (m) and time (s) run to exhaustion data are presented. The foraging mode model
posits that lizards are in selective regimes for active foragers (AF), mixed foragers (MF),
and sit-and-wait foragers (SWF). Bootstrap 95% confidence intervals are in parentheses.
Measured data (standard deviations in parentheses) are means of performance data for
species grouped by selective regime.
Parameter Distance θ (s)
Measured
Distance
(m) Time θ (s)
Measured
Time (s)
α 418.5 (58.07, 612.3) 197.1 (26.32, 637.2)
σ 56.51 (8.09, 92.10) 11.64 (1.16, 44.2)
θAF 38.69 (29.09, 52.54)
40.16
(12.37) 239.41 (199.1, 291.1)
239.72
(14.92)
θMF 29.76 (22.27, 40.28)
30.06
(5.35) 126.70 (104.3, 172.9)
127.12
(12.40)
θSWF 20.52 (18.05, 23.47)
21.30
(6.12) 59.75 (54.81, 65.40)
60.92
(12.16)
Table 2.4. PC analysis loadings and the percent of variation explained by each PC axes
for four performance variables. PC 1 can be interpreted as primarily a linear function of
speed and acceleration, while PC 2 consists of the maximal exertion variables. Together
the two PC’s explain 92% of the variation in performance. Only correlation values greater
than 0.200 are shown.
Performance PC1 PC2
Acceleration 0.662
Sprint Speed 0.669
Distance to Exhaustion 0.309 0.665
Time to Exhaustion 0.737
Variation explained 51% 41%
33
Table 2.5. The relationship between size and performance. Linear regressions of
performance on size (snout-to-vent length) show no influence of size on sprint speed,
acceleration and time to exhaustion. Only distance to exhaustion is significantly related to
size.
Performance Variable r2 F P-value
Sprint Speed 0.01 1.21 0.28
Acceleration 0.07 2.6 0.12
Time to Exhaustion -0.02 0.65 0.43
Distance to Exhaustion 0.25 7.78 0.012
34
Figure 2.1. Adaptive evolutionary hypotheses for the evolution of locomotor
performance abilities. Each selective regime is represented by a different color, and color
codes are located below each hypothesis. Branchlengths are not to scale.
35
Figure 2.2. Performance phenotypes reflect the locomotor demands of foraging mode and predator escape. High scores on PC1
indicate fast sprinters while high scores on PC2 species with high exertion. A) Exertion abilities separate into discrete clusters by
foraging mode (green = sit-and-wait, purple=mixed, active= orange) with active foragers having high exertion and sit-and-wait
predators have low exertion. B) Sprints vary by predator escape behavior with sprint specialists reaching higher speeds (blue= cryptic
and mixed species, red= sprint specialists). Ellipses represent kernel density estimates for 30, 60, and 90% levels of each group.
36
Figure 2.3. Correlations of phylogenetic independent contrasts (PIC) between each of the
performance variables. (A) Sprint speed and acceleration show positive correlated
evolution, as do (B) sprint speed/acceleration and distance run to exhaustion. However,
there is negative correlated evolution between (C) sprint speed/acceleration and time run
to exhaustion, but no relationship between time and (D) distance run to exhaustion. Plots
for acceleration are excluded because they closely resemble those of sprint speed.
37
CHAPTER 3. HINDLIMB MUSCLE CROSS-SECTIONAL AREA REFLECTS
LOCOMOTOR PERFORMANCE AND MICROHABITAT USE IN LIZARDS
Abstract
Locomotor performance is essential to the fitness of many terrestrial vertebrates.
Therefore, determining what defines locomotor performance is an integral part of
understanding the evolution of morphology, physiology, and behavior of vertebrates. The
majority of vertebrate movement is powered by muscle indicating that muscle design
should impact performance abilities. Specifically, the cross-sectional area (CSA) of a
muscle is a significant determinant of force production capabilities, suggesting that CSA
may influence performance abilities. However, few studies examine how muscle CSA
actually impacts performance, especially in a comparative context. Here I examine how
the CSA of muscles of the hind limb are related to acceleration, sprint speed, and exertion
in 21 species of lizards. I find that speed and acceleration, but not exertion increase with
CSA, and the CSA of extensor muscles better predicts speed and acceleration abilities
than flexors. Furthermore, muscle CSA scales isometrically in these lizards, but sprint
specialists tend to have more CSA for their size, while horned lizards have less. PCA
also shows that fast lizards have increased extensor CSA, but the distribution varies.
Fast, terrestrial species have muscle distributed more at the ankle, whereas as climbers
have more muscle at the hip. Interestingly, species with similar speed and acceleration
abilities can have very different muscle distributions, suggesting that there is more than
one way to achieve high speeds and acceleration. I conclude that muscle CSA plays an
important role in defining some, but not all performance capabilities, and that the amount
and distribution of hind limb musculature reflects the locomotor demands of these
lizards.
38
Introduction
Locomotor performance is critical to the survival and reproduction of most
vertebrates and this locomotion is chiefly driven by muscle. In particular, muscle cross-
sectional area (CSA) is likely a key contributor to locomotor performance, as it is tightly
linked with the amount of force a muscle can generate (Alexander 2003). In terrestrial
runners force production is an important determinant of locomotor abilities such as speed
and acceleration (Weyand 2000, Roberts & Scales 2002, Curtin et al. 2005, Weyand
2010). Thus, one may expect the amount of CSA to be a significant determinant of
running performance (e.g., Crook et al. 2008). However, how muscle CSA relates to
performance and is designed for specific tasks is rarely tested, even though relationships
between muscle CSA, body size and how musculature is distributed across the limb
potentially play a significant role in governing locomotor abilities.
Body size is intimately linked to locomotor design and performance (e.g.,
Schmidt-Nielsen 1973, Alexander 2003, Iriarte-Diaz 2012, Clemente et al. 2012),
especially in the muscular system. In fact, the majority of variation in muscle function
observed in vertebrates can be explained by scaling effects (Medler 2002). Thus,
variation in size should influence the amount and distribution of muscle CSA. In animals
that experience similar locomotor demands and have comparable lifestyles, one may
expect muscle CSA to scale isometrically (i.e., in a geometrically similar manner) with
size. However, CSA seldom scales isometrically, but instead, frequently varies with
locomotor mode (Bennett 1996, Kikuchi 2010), posture (Alexander et al. 1981, Kikuchi
2010), behavior (Alexander et al. 1981, Pollock & Shadwick 1994, Bennett 1996) or
some combination thereof, suggesting that the CSA-size relationship plays a prominent
role in locomotion and may be an important design trait dictating the limits of locomotor
performance.
Along with size, the arrangement of muscle CSA should also impact locomotor
performance. Muscles are arranged into functional groups that vary in their role during
locomotion. For example, in running, a stride is composed of the stance phase, when the
limb is contacting the ground providing propulsion, and swing phase, when the limb is
moving through the air to be repositioned (Biewener 2003, Higham et al. 2011).
39
Individual muscles may serve a support and propulsion function, while others function to
swing the limb (Reilly 1995, Higham 2011). Muscle function can further vary by joint so
that musculature across joints has different purposes during locomotion (Alexander et al.
1981, Pollock & Shadwick 1994, Zaaf 1999, Reilly 1995). Accordingly, the distribution
of muscle CSA across joints commonly reflects performance specialties (Pasi & Carrier
2003), mode of locomotion (Kikuchi 2010, Bennett 1996, Michilsens et al. 2009), or
differences in lifestyle (Alexander et al. 1981, Pollock & Shadwick 1994, Myatt et al.
2011).
The divergence in function of the muscular groups suggests that although CSA is
important, not all functional groups may influence performance in the same manner (Zaaf
et al. 1999, Higham et al. 2011). Furthermore, because of the complexity in structure and
function of the locomotor system, it is possible that similar abilities may be achieved with
different designs (Alfaro et al. 2004, Wainwright et al. 2005). This many-to-one mapping
of form and function is thought to be an emergent property of complex systems such as
the locomotor system (Wainwright et al. 2005). However, how muscle CSA of different
functional groups influence performance abilities is rarely explicitly tested leaving gaps
in our understanding of how the locomotor system is constructed. From a functional
perspective, researchers may fail to recognize how overall musculature is designed to
achieve a given performance. From an evolutionary perspective, researchers may miss
out on how the muscle system is adjusted over time to meet the different locomotor needs
of organisms.
Here I examine the relationship between hind limb muscle CSA and locomotor
performance in lizards. Lizards serve as an exceptional model for locomotor studies due
to their tremendous diversity in locomotor behaviors and performance abilities. I
concentrate on lizards of the Southwest United States as they display a wide breadth of
locomotor abilities ranging from slow, cryptic specialists to fast, sprint specialists
(Bonine & Garland 1999, Bonine et al. 2001, Chapter 2). I include only hind limb
musculature as the hind limbs provide the propulsion for running in lizards (Curtin et al.
2005). First I test whether hind limb CSA influences three aspects of performance, sprint
speed, acceleration, and exertion. While acceleration and sprint speed are force related
performances and should improve with increased CSA, exertion is not. Thus, I expect
40
that the relationship between CSA and locomotor abilities will vary with aspect of
performance. Second, I examine whether the relationship between CSA and performance
varies by functional group (stance and swing) or by joint (hip, knee, and ankle). Finally, I
explore how the muscular design of the hind limb varies across species. Specifically, is
CSA a function of body size or is it related to locomotor demands so that variation in
CSA distribution is associated with specific performance abilities or lifestyle?
Materials and Methods
Fieldwork
Twenty species of lizards comprising three families were used in this study.
Three to 10 individuals of each species were collected from the wild in California,
Arizona, and New Mexico from June to August in each of 2009 and 2010 (Table 1S in
Appendix). After capture, all lizards were transported to the Southwestern Research
Station or a house (Sceloporus occidentalis) for locomotor trials. Lizards were kept
overnight to allow gut passage, and locomotor trials were performed on the day following
capture. Lizards were kept in cloth bags between performance trials with access to water
ad libitum. Trials were performed for three sequential days after capture. At the
completion of all performance trials lizards were humanely euthanized for morphological
study, or returned to their site of capture. All animal procedures followed University of
Hawaii IACUC protocol 09-463.
Performance Trials
Lizards were placed under a heat lamp for a minimum of one hour prior to all
performance trials to achieve field body temperature. Body temperatures were verified
before each trial using a cloacal thermometer (Miller & Webber Inc.).
For sprint trials, lizards were induced to run down a 3 x 0.15m wide trackway by
clapping, or lightly tapping the tail. Each individual was raced five times over a span of
two days. Lizards were given a minimum of one hour of rest between each trial and were
run a maximum of three times in one day in order to capture maximum acceleration and
41
maximum sprint speed. Only runs in which the lizard ran straight without jumping or
touching the wall were used for analysis.
Sprint speed was measured using a series of four infrared photocells placed every
0.25 m over the second meter of the trackway. The single fastest time between two
photocells over the five runs was used as an estimate of the individual’s maximum sprint
speed (Huey & Pianka 1981, Miles & Smith 1987, Vanhooydonck et al. 2001).
Acceleration was measured using high-speed video. The first meter of each run
was filmed at 250 frames sec-1 from a dorsal view using a high-speed video camera
(Fastec Troubleshooter LE). A white marker was glued dorsally, directly above the pelvis
of each lizard for digitizing position coordinates. The pelvis was chosen as it is an easily
located landmark near the animal's center of mass. The position of the hip point was
tracked and digitized using Kwon 3D (Kwon 1994). The hip position data was smoothed
using a quintic spline in the pspline package (v. 1.0-14) in the R computing environment
(R Development Core Team 2010). Instantaneous acceleration was calculated by
differentiating the smoothed hip position data twice. Maximum instantaneous
acceleration was calculated from the maximum acceleration value of an individual over
the five runs.
Lizards were given a minimum of 24 hours of rest after their last running trial
before maximal exertion trials. In maximum exertion trials, lizards were chased by
clapping and tail-tapping around a four meter oval track (marked every 0.25m) until
exhaustion, defined here as the point at which an individual refused to run after 10
consecutive tail taps or loss of righting response (Bennett 1980, Mautz et al. 1992,
Huyghe et al. 2005, Vanhooydonck et al. 2014). Maximal exertion was measured in two
ways: (i) as distance covered; and (ii) time to exhaustion (timed with a stopwatch;
Bennett 1980, Mautz et al. 1992, Huyghe et al. 2005).
Morphological and Muscle measurements
Snout-vent length (SVL, distance from the tip of the snout to the cloaca) was
measured using digital calipers (Mitutoyo Digimatic D6-6CX) and body mass was
measured shortly after capture with Pesola scales to the nearest 0.1 g. Three to six
individuals of each species were then used for muscle morphology measurements. The
42
majority of the hind limb muscles were removed from the right hind limb. Each muscle
was blotted dry and measured to the nearest 0.0001g using an Acculab Sartorius ACL-
80.4 scale. The fascicle length (the length of muscle fiber bundles, used here to estimate
fiber length) of a muscle was measured at mid muscle (the midpoint of the muscle length)
under a Zeiss DV4 stemi spot dissecting scope to the nearest 0.01mm using Mitoyo
Digimatic D6-6CX calipers (Biewener & Full 1992). Pennation angle () was estimated
by bisecting the muscle and measuring the angle of the fibers relative the tendon, bone, or
aponeurosis at mid-muscle (Biewener & Full 1992). These methods were applied
uniformly across species, and should provide adequate data for comparison across
species. The cross-sectional area (CSA) of a muscle was estimated as the muscle mass
(g) divided by fascicle length (cm), assuming a muscle density of 1.06g/cm (Pasi &
Carrier 2003). The effective CSA of pennate muscles was then estimated as CSA
multiplied by the cosine of the pennation angle (cos).
Phylogeny
To construct a phylogeny of the 20 species included in this study, I used the same
phylogeny constructed by Scales and Butler (see Chapter 1 Materials and Methods), but
pruned Elgaria kingii from the tree because of its overwhelming influence on analyses.
Thus, I provide only a brief description of its construction here. For the 18 species of
Phrynosomatidae, I used the topology and branchlengths from the Wiens et al. (2010)
phylogeny, which is the most comprehensive phylogeny of Phrynosomatidae to date
including 122 species and is based on 6 nuclear and 5 mitochondrial genes. I then
estimated the phylogenetic relationships of the remaining species to the Wiens et al.
(2010) tree based on previously established familial relationships. The resulting tree
(Figure 3.1) agrees with previously published phylogenies on the familial relationships
(Townsend et al. 2004, Vidal & Hedges 2005) while retaining the topology and
branchlengths of Wiens et al. (2010) for a majority of species included in this study.
Statistical Analyses
Log transformed species means for performance and morphology variables were
used for all analyses. To determine how hind limb scales with body mass, linear
43
regressions were performed on body mass and hind limb CSA. To correct for the effects
of size, I regressed each performance and CSA on body mass. I then used the residuals
from these regressions for further analyses.
Muscle data were analyzed with respect to functional group. Muscles were first
grouped into extensor and flexor muscles. The muscle groups were then further divided
by joint into hip retractors, knee extensors, ankle extensors, hip protractors, knee flexors,
and ankle flexors. The CSA of a functional group was the sum of the CSA of each muscle
included in that functional group.
Pearson’s product moment correlation was used to test CSA correlations between
functional groups. Linear regressions were also used to determine the relationship
between each performance and the muscle CSA of each functional group. The
distribution of extensor musculature was compared across species using principal
components analysis (PCA) on size corrected data.
Phylogenetic independent contrasts (PIC) were used for phylogenetic analyses of
the relationships between each performance variable and CSA (Felsenstein 1985). PICs
were calculated using the ape package in R, assuming our phylogeny (Paradis 2004).
Linear regressions forced through the origin (Garland et al. 1992) were performed on the
PIC of body mass and PICs of hind limb CSA to determine the relationship between
muscle CSA and body mass. To correct for the effects of size, I regressed the PIC of each
performance and CSA on the PIC of body mass. I then regressed the residual PIC of each
performance on the residual PIC of muscle CSA to determine the relationships between
CSA and each performance.
Results
Scaling of hind limb CSA
Hind limb CSA scales isometrically with body mass in these lizards (slope=0.675,
Figure 3.2, Table 3.1). While there is an overall trend of isometry, there are also
intriguing deviations from this trend. Phrynosoma species fall well below the general
trend line (Figure 3.2) suggesting that they have less CSA for their body mass.
Conversely, two sand lizards (C.texanus and C. draconoides), the Aspidoscelis species,
44
and C. collaris tend to have more muscle CSA for their body mass (Figure 3.2). From an
evolutionary perspective, hind limb CSA and body size show correlated evolution (Table
3.1).
Hind limb CSA and sprint performance
Speed and acceleration are positively correlated with total hind limb CSA (Figure
3.3, Table 3.2), such that lizards with relatively large hind limb CSA are fast runners.
However, these relationships become marginal when phylogeny is taken into account
(Table 3.2) and are primarily associated with the evolutionary split of the sand and
horned lizards (Figure 3.3). In fact, the contrast value at the node of the horned-sand
lizard split (3.66) is far more than a standard deviation (sd=1.23) from the mean
standardized contrast (0.22).
The relationship between CSA and the sprint performances varies with functional
group. Extensor and flexor CSA are highly correlated (r=0.93, t=10.92, p<0.001). Yet,
extensor CSA is more strongly related to speed and acceleration than flexor CSA (Table
3.2). Among the joints of the hind limb, ankle CSA is most strongly associated with
speed and acceleration (Table 3.3). Phylogenetic analyses also indicate that flexor and
extensor CSA show correlated evolution (r=0.94, t=11.58, p<0.01), and although the
relationships between muscle CSA and performance are now marginal, extensor CSA
shows a stronger evolutionary association with speed and acceleration than flexor CSA
(Table 3.2). Similar to total hind limb CSA, the evolutionary CSA-performance
relationship is correlated with the evolutionary divergence of the horned and sand lizards
(Figure 3.3).
Hind limb CSA and exertion
In contrast to the sprint performances, distance and time run to exhaustion are
unrelated to hind limb CSA (Figure 3.3, Table 3.4). However, according to phylogenetic
analyses, time run to exhaustion does show marginal negative correlated evolution with
hind limb CSA (Figure 3.3, Table 3.4). This negative relationship remains marginal for
extensor CSA, but is significant for flexor CSA (Table 3.4). Again, these evolutionary
trends are related to variation between the sand and horned lizard clades (Figure 3.3).
45
Horned lizards have relatively low CSA, but still run for a relatively long time, whereas
sand lizards have high CSA, but low times to exhaustion (Figure 3.3). The two
Aspidoscelis species, however, do not follow this trend as they have very long times to
exhaustion and high hind limb CSA (Figure 3.3).
Variation in CSA across the hind limb
Because extensor CSA best explains speed and acceleration performance, I
explore its variation across the hind limb in more depth. Extensor CSA generally makes
up more than half of hind limb CSA (Figure 3.4) and is commonly largest at the ankle,
but distribution varies widely across species (Figure 3.4). A PCA shows that variation
across the hip, knee, and ankle can be summarized by two axes: an axis of general
amount of CSA (PC1; ankle loading most heavily followed by the knee, Table 3.5), and
an axis of distribution between the hip and knee (PC2; hip contrasting with the knee,
Table 3.5). Together, the axes explain 97.5% of the variation in extensor CSA
distribution.
Several Sceloporines and the Urosaurus species have similar CSA distributions
with higher scores on PC2 suggesting that more CSA is at the hip (Figure 3.5 A). On the
other hand, the C. draconoides, C. texanus, and Aspidscelis species score high on PC1,
but relatively low on PC2 and C. collaris, has the lowest score on PC2 suggesting that
these species have high overall CSA but more of it is distributed at the ankle and knee
(Figure 3.5 A). Phrynosoma species score low on PC1 and intermediate to low on PC2
indicating they have low CSA in general (Figure 3.5 A).
Discussion
Muscle CSA and Size
The relationship between muscle CSA and body size can often reflect variation in
locomotor mode, behavior, or posture (Alexander et al. 1981, Pollack & Shadwick 1994,
Bennett 1996, Kikuchi 2009). Given the variety of behaviors and habitats used by the
lizards studied here, it is somewhat surprising that I find hind limb CSA scales
isometrically with body mass. One reason I may observe isometry is that these lizards all
46
use a sprawling posture typical of most lizards. Posture likely plays an important role in
the CSA-size relationship as hind limb CSA scaled close to isometry in birds with similar
postures (Bennett 1996), but scaled allometrically in mammals where posture and
locomotor habits differed (Alexander et al. 1981, Pollock & Shadwick 1994). Thus, the
sprawling posture of lizards may require a common CSA-mass relationship so that there
may be a fundamental “lizard” muscle design across species.
In spite of the overall isometric scaling of CSA and mass, there are several
deviations from this trend that are reflective of differences in lifestyle. Callisaurus
draconoides, Cophosaurus texanus, Crotaphytus collaris and the Aspidoscelis species all
rely heavily on sprinting to escape predators and experience selection for high speeds to
do so (Chapter 2). These lizards also have high hind limb CSA for their size.
Conversely, the horned lizards are cryptic specialists that rely little on locomotion to
escape predators, and experience relaxed selection for sprinting (Chapter 2).
Accordingly, they have reduced CSA for their size. Thus, while there maybe a typical
“lizard design”, the relative amount of hind limb CSA appears to be shifted up or down
according to the locomotor needs of a species.
CSA, acceleration, and speed
Hind limb CSA appears to be an important determinant of sprint performance in
these lizards as it was positively associated with both acceleration and sprint speed. This
correlation is likely because muscular force production is directly related to CSA
(Alexander 2003), and both acceleration and speed are linked to force. Acceleration is a
direct function of force (a= f/m) and runners only accelerate when a foot is in contact
with the ground, while sprint speed has been linked to the amount and rate at which force
is applied to the ground in runners (Weyand 2000, 2010). Extensors are generally active
when the foot is in contact with the ground providing propulsion (Reilly 1995, Nelson &
Jayne 2001) which explains why extensor CSA showed the strongest relationship with the
two sprint performances, a trend observed in at least one other species of lizard
(Sceloporus woodi, Higham et al. 2011). Furthermore, ankle extensors showing the
strongest association with sprint performances reflects that in lizards, propulsion is
47
powered by ankle extension in level running (Aerts et al. 2000, Fieler & Jayne 1998, Zaaf
et al. 1999).
Increases in hind limb CSA, especially in the extensors, are likely required to
meet the elevated force demands of both high speed and accelerations (Curtin et al. 2005)
indicating that the same design traits underlie the two performances. Vanhooydonck et al.
(2006) also observed a partial overlap in traits that determine sprint speed and
acceleration in Anolis lizards. Thus, speed and acceleration appear to be functionally
linked at the muscular level so that fast lizards also accelerate well, a trend frequently
observed in lizards (Huey & Hertz 1982, 1984, Vanhooydonck et al. 2006, Chapter 2).
Flexor CSA was also correlated with speed and acceleration although not as well
as extensor musculature. Given that extensors are important for providing propulsion for
acceleration and sprint speed, why do we see a similar increase in flexor CSA? Flexors
are generally active during running as they swing the hind limb through the air to
reposition the leg for the next ground contact (Snyder 1954, Reilly 1995, Nelson & Jayne
2001). As a result, increases in flexor musculature may be required to effectively swing
the increased extensor musculature of fast runners. Furthermore, faster running requires
increased recruitment of swing-phase muscles (Jayne et al. 1990, Marsh et al. 2004),
which are generally flexors (Snyder 1954, Reilly 1995). Therefore, both flexors and
extensors likely play a role in determining speed related performances. While flexor
CSA may not influence speed as strongly as extensor CSA, or limit performance within
species (Higham et al. 2011), it may partially contribute to the large-scale performance
differences observed between the species studied here.
The increases in hind limb CSA associated with sprint abilities may be especially
effective for improved performance if they are due to fast-glycolytic (FG) fibers. FG
fibers have larger diameters, force production, and speed of contraction, all of which
would improve acceleration and speed (Rome et al. 1990, Bonine 2001, Biewener 2003).
This association may be the case as the larger diameters of FG fibers would contribute to
muscle CSA, and many of the lizards with high CSA in this study also have high
proportions of FG fibers (Bonine et al. 2001, Bonine et al. 2005).
Contrary to the sprint parameters, the relationship between muscle CSA and
exertion is unclear, but CSA does not appear to be an important determinant of exertion
48
capabilities. Instead, distance may be determined by other traits such as limb and stride
length (Christiansen & Bonde 2002, Cumming & Cumming 2003, Irschick & Jayne
2000, White & Anderson 1994, Clemente et al. 2012) while time to exhaustion may be
the result of physiological factors such as blood buffering, heart size, and enzymatic
activities (Bennett et al. 1984, Bennett 1994, Bonine 2007). Thus, exertion and
speed/acceleration may be largely determined by different attributes. Separate design
traits underlying these performances would allow for the independent evolution of speed
and exertion, and may explain the ambiguous relationship between these performances
not only in these lizards, but in other species as well (Chapter 2, Tsuji et al. 1989, Wilson
et al. 2002, Herrell & Bonneaud 2012).
Our analyses indicate that the emergence of the sand and horned lizard clades was
an important event in the diversification of hind limb CSA and performance, a trend also
observed in the evolution of muscle fiber types (Bonine et al. 2001). The horned and
sand lizards occupy similar habitats but have diverged significantly in behavior. Sand
lizards have become sprint specialists that rely on speed for many aspects of life, whereas
horn lizards have become cryptic, feed mainly on ants and rely little on speed for most of
their behaviors (Vitt & Congdon 1978, Stebbins 2003, Bonine et al. 2001, Sherbrooke
2003). Thus, substantial divergence in lifestyle can be accompanied by large phenotypic
divergences, and these events may be times of considerable innovation. Furthermore,
that a single evolutionary event can result in such a significant divergence indicates that
evolution does not necessarily happen in small increments, but may occur in larger
evolutionary steps.
Distribution of extensor CSA and locomotor performance
Our PC analysis suggests that the amount of extensor CSA is likely related to
sprint performance, which corroborates the findings of the regression analyses. Slower
species tend to have lower hind limb CSA overall, indicating a lower reliance on speed
(Figure 3.5 B). Conversely, faster species generally have high overall muscle CSA,
especially at the ankle (Figure 3.5 B). Thus, the variation across this first PC axis likely
reflects a performance gradient.
49
Interestingly, most of the fast sprint specialists are also largely terrestrial
compared to the generalist and climbing species studied here, and have more ankle and
knee extensors relative to hip retractors (Figure 3.5 B, C). In level running, knee
extensors must support the body mass, and propulsion is powered by ankle extension
(Fieler & Jayne 1998, Zaaf et al. 1999, Aerts et al. 2000). Hence, higher extensor CSA at
the knee and ankle compared to the hip may be indicative of a more terrestrial lifestyle
(Figure 3.5 C). Accordingly, Zaaf et al. (1999) found that a terrestrial gecko species
exhibited much higher moments at the knee and ankle compared to an arboreal species.
On the other hand, a few Sceloporus (S. jarovii, S. clarkii, S. poinsettii) and
Urosaurus species tend to be climbers, either arboreal or saxicolous (utilizing vertical
rock faces, Stebbins 2003, Chapter 2 and references therein). These species also have
relatively more muscle CSA at the hip (Figure 3.5 C). S. occidentalis a more generalist
species, but an adept climber, was frequently observed climbing trees and rocks also
showed relatively high hip CSA (Figure 3.5 C). Hip musculature is important for
climbing as it supplies the propulsive forces to overcome inertia and counteract gravity
(Zaaf et al. 1999, Aerts et al. 2000). Zaaf et al. (1999) found higher hip moments in a
specialized climber compared to a terrestrial species of gecko, and Higham and Jayne
(2004) found increases in EMG amplitude at the hip (and knee) with increased inclines in
Chameleo calyptratus both suggesting that musculature at the hip is important for
climbing. Only, C. collaris, a rock dweller, does not show high extensor CSA at the hip.
However, this species often require open ground for running, and are not considered
exceptional climbers (Smith 1995). Thus, the distribution of CSA along the limb likely
reflects mode of locomotion suggesting that microhabitat use also has an important
influence on muscular design.
Our analyses also reveal that all of the performances studied here can be achieved
through multiple muscular designs, a trend called many-to-one mapping. Sprint speed
and acceleration both increase with hind limb CSA, but the muscle can be distributed
very differently along the limb and still achieve relatively high speeds and accelerations
(Figure 3.5 B). For example, C. texanus, which has high ankle CSA, and U. graciosus,
which has high hip CSA show similar accelerations (Figure 3.5 B). Horned lizards and
Aspidoscelis species both display relatively high exertion, but Aspidoscelis have high
50
hind limb CSA and proportions of FG fibers, whereas horned lizards have low CSA and
proportions of FG fibers (this study, Bonine et al. 2005). This many-to-one mapping
suggests that the locomotor system can accommodate multiple demands placed on it by
different tasks. Moreover, finding that many-to-one mapping occurs for the three
performances studied here indicates that it may be widespread in locomotor systems.
Summary
The relative amount and distribution of hind limb CSA is linked to locomotor
performance and reveals important information about locomotor modes and lifestyles.
While CSA increases with body mass, the relative amount shifts up or down according to
the locomotor demands of species. Hind limb CSA plays a role in determining speed and
acceleration, but not exertion, suggesting that different traits underlie these performances.
Large behavioral shifts appear to be especially important in the evolution of these CSA-
performance relationships. With respect to speed and acceleration, extensor CSA best
explains variation in performance, but CSA can be distributed in different ways.
Climbers, such as arboreal and saxicolous species tend to have more CSA at the hip,
while in terrestrial species CSA is largely concentrated at the knee and ankle. Thus, CSA
reflects variation in mode of locomotion and microhabitat use.
51
Table 3.1. Relationships between body mass and hind limb CSA. Results of non-
phylogenetic and PIC regressions of hind limb CSA on body mass. Italics indicate results
are from phylogenetic analyses.
Muscle CSA Slope r2 p F
Total Hind limb 0.665 0.80 p<0.001 78.03
0.73 0.88 p<0.001 146.80
Table 3.2. Relationship between hind limb CSA and acceleration and sprint speed.
Results of non-phylogenetic and PIC regressions of sprint performances on hind limb
CSA. Italics indicate results from phylogenetic analyses.
Muscle CSA r2 p F
Sprint Speed
Total Hind limb 0.42 0.001 14.84
0.12 0.079 3.47
Extensor 0.46 <0.001 17.36
0.12 0.076 3.55
Flexor 0.33 0.005 10.42
0.10 0.096 3.08
Acceleration
Total Hind limb 0.38 0.002 12.51
0.12 0.077 3.52
Extensor 0.41 0.001 14.08
0.13 0.067 3.80
Flexor 0.30 0.007 9.29
0.09 0.109 2.85
52
Table 3.3. Relationship between CSA at each joint and speed and acceleration. Results
multiple regression analyses of sprint performance and hind limb CSA by joint. Overall
models were significant. Speed: F= 5.626, p =0.008, r2= 0.42, Acceleration: F= 4.61, p =
0.017, r2 = 0.36.
Joint CSA Sum Sq Df F p
Sprint speed
Hip 0.00401 1 0.0770 0.7849
Knee 0.01635 1 0.3142 0.5829
Ankle 0.15229 1 2.9259 0.1065
Acceleration
Hip 0.0177 1 0.4484 0.5126
Knee 0.0058 1 0.1470 0.7065
Ankle 0.0674 1 1.7072 0.2098
53
Table 3.4. Relationship between hindlimb CSA and exertion variables. Results of non-
phylogenetic and PIC regressions of exertion performances on hind limb CSA. Italics
indicate results from PIC.
Muscle CSA r2 p F
Distance to exhaustion
Total Hindlimb -0.04 0.623 0.25
-0.05 0.858 0.03
Extensor - - -
- - -
Flexor - - -
- - -
Time to exhaustion
Total Hindlimb -0.01 0.370 0.85
0.14 0.059 4.07
Extensor -0.04 0.584 0.31
0.16 0.082 3.41
Flexor 0.05 0.170 2.05
0.16 0.044 4.70
54
Table 3.5. PCA loadings of CSA at each joint. The two PC axes explain 97.5% of the
variation in hind limb CSA. PC1 explains 81.3% of the variation and is largely related to
amount of ankle CSA, while PC2 explains 16.2% and is a contrast between muscle CSA
at the hip and knee. Only loadings greater than 0.2 are shown.
Joint PC1 PC2
Hip 0.38 -0.88
Knee 0.47 0.43
Ankle 0.80
81.3% 16.2%
55
Figure 3.1. Phylogenetic relationships of the 20 species of lizards included in this study.
The tree is scaled to one and branches are proportional to estimated branchlengths.
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Figure 3.2. Relationship between log total hindlimb CSA and log of body mass for 20
species of lizards. Total hind limb CSA scales isometrically with body mass in these
lizards (slope=0.675, p<0.001, F= 81.35, r2= 0.819). However, several lizards that rely
heavily on speed (Crotaphytus collaris, Callisaurus draconoides, Cophosaurus texanus,
Aspidoscelis tigris, and A uniparens) have more CSA for their size (red), while horned
lizards (Phrynosoma cornutum, P. solare, and P, modestum), cryptic specialists that rely
little on speed, have less CSA for their mass (green).
57
Figure 3.3. Non-phylogenetic and phylogenetic relationships between hind limb CSA and
acceleration and the exertion variables (distance and time run to exhaustion). Sprint
speed is not shown as it shows trends very similar to acceleration. Acceleration and
sprint speed are positively correlated with hind limb CSA, but this relationship is
marginal when phylogeny is taken into account. Distance and time run to exhaustion are
unrelated to CSA, but time to exhaustion exhibits a marginal negative relationship
according to PIC. The CSA-performance relationships are associated with the
evolutionary split between horned and sand lizards.
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Figure 3.4. The proportion of hind limb muscle CSA composed of extensors (A) and the
proportion of extensor CSA comprised of hip (dark gray), knee (light gray), and ankle
(white) muscles (B). Species are ordered from slowest relative sprint speeds
(Phrynosoma cornutum) to fastest (Aspidoscelis tigris). Extensor musculature makes up
at least half of hind limb CSA in all species. CSA at the knee or ankle make up the largest
proportion of extensor CSA, but distribution varies widely.
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Figure 3.5. PCA of extensor muscle CSA distribution across the hip, knee, and ankle. PC1 represents the general amount of CSA, and
the ankle has the highest loading. PC2 reflects a contrast between hip CSA and knee CSA. B) PCA in relation to speed and
acceleration (black- species with fast speed or acceleration, red- species with intermediate speed and acceleration, green – species with
the slowest speed or acceleration). PC1 reflects performance abilities, with fast species generally having high amounts of CSA.
However, the CSA can be distributed largely at the ankle, knee or hip. C) PC2 reflects mode of locomotion related microhabitat use
(Climbing- black, generalist- red, terrestrial – green). Climbing species generally have higher CSA at the hip compared to the knee,
whereas terrestrial species often have more CSA distributed at the ankle or knee.
60
CHAPTER 4. RUNNING FOR YOUR LIFE OR RUNNING FOR YOUR
DINNER: WHAT DRIVES FIBER-TYPE EVOLUTION IN LIZARD
LOCOMOTOR MUSCLES?
Abstract
Despite its role in whole-animal performance, the adaptation of muscle
physiology remains little explored regarding terrestrial locomotion. We tested
evolutionary models based on predator escape and foraging strategies of lizards to test
whether fiber-type composition of a leg muscle is adaptive for behavior. The best-fitting
model for fast-twitch fiber-type evolution was one based on predator escape strategy,
while the foraging-mode model fared poorly (Akaike Information Criterion with small
sample size correction [hereafter, AICc], AICc = 29.7). According to the predator
escape model, lizards relying on sprints to avoid predators are predicted to have higher
relative proportions of fast glycolytic (FG) fibers (70%) while cryptic lizards are
predicted to have higher relative fast oxidative glycolytic (FOG) fiber proportions (77%).
This pattern suggests an evolutionary trend toward greater FG (respectively FOG) fiber
composition among lizards that specialize in sprinting (respectively crypsis). The best-
fitting model for slow-twitch fibers was a single optimum suggesting a common selective
pressure across these lizards. The second-best model explaining slow-twitch fiber-type
evolution was Brownian motion (AICc = 0.80), indicating some support for neutral
evolution. We find evidence suggesting that different fiber types occurring in the same
muscle can evolve under different evolutionary pressures.
Introduction
The morphological evolution of organisms is intimately linked to adaptation via
whole animal performance and behavior (Arnold 1983, Garland & Losos 1994). Lizards
have long served as a model system for locomotor adaptation because they exhibit a wide
variety of locomotor behaviors and abilities. Among ecologically differentiated lizards,
variation in gross morphology, especially external limb length, has evolved in association
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with differences in sprint performance and environmental locomotor needs, thus
providing compelling evidence for adaptation (Losos & Sinervo 1989, Losos 1990,
Garland & Losos 1994, Bonine & Garland 1999, Irschick & Jayne 1999, Irschick 2002).
Two principal rationales are commonly invoked to explain the observed variation
in lizard locomotor behavior: selection associated with foraging strategy or with predator
escape behavior (Snell et al. 1988, Cooper 1994, Garland & Losos 1994, Miles et al.
2007). Lizards are particularly diverse in foraging strategy. Sit-and-wait (or ambush)
foragers remain motionless for much of the time, and rely on short bursts of locomotion
to capture prey that moves within close proximity. Alternatively, active foragers spend
much of the time moving in search of prey. Differences in foraging mode have been
shown to influence the evolution of life-history strategies covering such wide-ranging
aspects as behavior, diet, morphology, and physiology of lizards (Reilly et al. 2007). Sit-
and-wait predators often rely on short bursts of speed to capture prey, but because of their
brief durations of continuous movement, they often do not require high endurance
(Anderson & Karasov 1981). Conversely, the high activity levels of active foragers may
put a premium on locomotor endurance (Anderson & Karasov 1981). These different
demands imply that selection for acceleration and speed for overtaking prey or endurance
for continuous searching may influence the evolution of muscle fiber type in locomotion.
Predator escape behavior is commonly evoked as a selective force acting on the
morphology and performance of lizards (Snell et al. 1988, Garland & Losos 1994,
Vervust et al. 2007). While some species rely on crypsis to avoid detection by predators
(with some remaining extremely still), other species use sprinting to out-run predators.
Thus, lizards that escape by sprinting are presumably under stronger selection for high
accelerations and maximum speed than lizards that use a generalized strategy, while
lizards that rely on crypsis may experience relaxed selection. Relaxed selection can
increase the variation in a locomotor trait, or may increase the value of a given trait, if the
alternative, unused trait has a deleterious effect.
Locomotion is achieved using skeletal muscles. In vertebrates, muscles are
composite structures made up mostly of three common fiber-types: fast-twitch glycolytic
fibers, fast-twitch oxidative-glycolytic fibers, and slow oxidative fibers (Gleeson et al.
1980, Gleeson & Johnston 1987). Each of these fiber types differ in force generated,
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speed of contraction, and fatigue resistance. Fast-twitch glycolytic (FG) fibers have fast
shortening velocities (V), and high maximum shortening velocities (Vmax, Rome et al.
1990). FG fibers can produce the high force and power needed for sprinting, but fatigue
quickly (Peter et al. 1972). Many animals use FG fibers for high-speed burst locomotion
(fish: Rome et al. 1988, frogs: Lutz et al. 1998; savannah monitor lizard, Varanus
exanthematicus: Jayne et al. 1990). By contrast, slow oxidative (SO) fibers have low V
and Vmax values, produce low force and power, but fatigue slowly (Peter et al. 1972,
Gleeson et al. 1980, Gleeson & Johnston 1987, Rome et al. 1990). Finally, fast-twitch
oxidative-glycolytic (FOG) fibers have intermediate V and Vmax values, generally
producing intermediate force and power, and show intermediate fatigue-resistance (Peter
et al. 1972, Gleeson et al. 1980, Gleeson & Johnston 1987). Given that there are limits
on total muscle mass (and hence, the number of muscle fibers), the fiber type proportion
of a muscle should reflect its functional requirements.
Bonine et al. (2001; 2005) found a large variation in muscle fiber type
composition among lizard species, and that lizards generally have a negative relationship
between FG and FOG fibers. In particular, Bonine et al. (2005) found that two small
clades of lizards (sand lizards which includes the genera Uma, Callisaurus, Cophosaurus,
and Holbrookia, and horned lizards which consist of the genus Phrynosoma) have
experienced an unusually large amount of evolutionary change in muscle fiber-type
composition over a short period of time. Based on these finding, the authors
hypothesized that natural selection may shape the iliofibularis fiber type composition of
these lizards. However, their Brownian motion (BM) model could not test whether
changes in fiber-type composition of the iliofibularis are under selection within the
contexts set by the ecology and behavior of the species of lizards studied.
The fiber-type composition of a muscle has been proposed to evolve for a specific
whole muscle function, but to date no study has provided strong evidence for such
adaptation. Here, we use the Bonine et al. (2005) data and the direct modeling approach
of Hansen (1997) and Butler and King (2004) to test whether fiber-type composition of a
locomotor muscle evolves under the influence of drift and selection. We explicitly model
different selective pressures (namely, foraging or predator escape strategies, a single
global optimum for all lizards, or random drift) and compare reasonable alternative
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models that may be responsible for the evolution of fiber type composition in locomotor
muscles.
Materials & Methods
Muscle fiber-type data and assumptions
We obtained iliofibularis muscle fiber-type composition data from Bonine et al.
(2005), the largest interspecific dataset available for any locomotor muscle. The
iliofibularis is a hindlimb muscle that is active during burst locomotion and its muscle
fiber recruitment increases with running speed (Jayne et al. 1990). The iliofibularis is a
swing-phase muscle, and although it is not active during stance phase, it may still
contribute to faster running speed and endurance. At high running speeds, limbs are
cycled quickly, (Fieler & Jayne 1998), and some lizards use stride frequency to modulate
their sprint speed (Vanhooydonck et al. 2002). Additionally, although stance-phase
muscles consume most of the energy during running, a substantial amount of energy is
also used in swing phase muscles (Marsh et al. 2004, Marsh et al. 2006). Consequently,
the contractile properties of the iliofibularis may influence stride frequency, sprint speed,
and fatigue-resistance during continuous locomotion in lizards (Bonine et al. 2005).
Bonine et al. (2005) used independent contrasts and ancestral character state
reconstruction to study fiber type composition of the iliofibularis muscle in lizards. We
analyzed proportional cross sectional areas of each fiber-type (FG, FOG, SO) derived
from proportions reported in Bonine et al. (2005) for the iliofibularis. The evolution of
fiber-type proportions was modeled using the Hansen model, an Ornstein-Uhlenbeck
(OU) process with discrete switches of adaptive regime, applied to the analysis of
phenotypic evolution along phylogenies (Hansen 1997, Butler & King 2004). Hansen’s
(1997) OU process models both stabilizing selection and drift and allows for multiple
trait optima. The OU model (Equation 1) expresses the change in a quantitative
character, dX(t), over time, t, based on stabilizing selection toward an optimal trait value
and random drift.
dX(t) = α [θ (t) – X(t)] dt + σdβ (t) (1)
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In this model, (t) is the optimal trait value, which can vary along any lineage; ,
measures the strength of selection, and the strength of drift. The Butler and King
(2004) approach uses the OU process to test alternative evolutionary hypotheses
concerning the location of different selective regimes (regimes are the combination of
environmental and organismal characters associated with exhibiting a particular behavior,
Baum & Larson 1991) on the phylogeny. Their method also allows the estimation of
optimal trait values for each selective regime. The model assumes that the trait of interest
remains close to a fitness optimum by selection and that the strength of selection can be
interpreted in a similar manner across different regimes (Hansen 1997). This method, like
all other comparative methods, assumes that the phylogeny accurately reflects the
evolutionary history of the organisms.
Phylogeny
We began with the phylogenetic hypothesis published in Bonine et al. (2005),
which provides a topology, but lacks the branch lengths that are required for applying the
OU model sensu Butler and King (2004). The Bonine phylogenetic hypothesis is a
composite of available phylogenies, each of which is well supported (see references in
Bonine et al. 2005). We revised the placement of the scincid and anguid branches of the
Bonine topology in light of the more recent phylogenetic hypothesis of Vidal and Hedges
(2005), which places the Scincidae more basal than Anguidae. We call this revised
topology the BVH topology.
We estimated the branch lengths on the BVH topology based on nucleotide
sequence data collected from the NCBI Entrez nucleotide database. We used
mitochondrial 12s rRNA gene sequences because this locus has data available for the
most species. For Laudakia stellio we substituted the con-generic L. stoliczkana, as these
species are a similar phylogenetic distance from Phrynosomatidae (Macey et al. 1997,
Macey et al. 2000). For two species (D. dorsalis, and P. sicula) suitable sequences could
not be located: we excluded these species from the analysis. All sequences were aligned
using ClustalX, resulting in a sequence dataset of 248 basepairs. The tree topology was
constrained to the BVH topology and the branch lengths were estimated using maximum
likelihood in PAUP. However, the mitochondrial 12s rRNA gene sequences could not
65
resolve the basal branch lengths between the Scincidae (Carlia and Eumeces) and
Lacertidae/Teiidae (Aspidoscelis and Acanthodactylus) split, or between the
Lacertidae/Teiidae (Aspidoscelis and Acanthodactylus) and Anguidae (Elgaria) split. In
these cases we used the branch lengths reported by Vidal and Hedges (2005).
Selective Regimes
We constructed five evolutionary scenarios for the proportion of fiber-types in
lizard iliofibularis muscles by assigning adaptive regimes to particular branches of our
phylogeny. The first adaptive model contains three optima and is based on the foraging
modes of each species. Debate continues as to whether foraging mode is a discrete or
continuous trait (e.g., Perry 1999, Cooper 2007, Reilly et al. 2007). Nevertheless, based
on multivariate movement data, several studies have demonstrated that sit-and-wait and
active foragers form distinct clusters that are validated statistically (Cooper 1994, Butler
2005, Cooper 2005, Cooper 2007). Thus, even if foraging mode is not strictly discrete, it
can usefully be described as modal.
Lizard species are classified as sit-and-wait foragers (SWF), active foragers (AF),
or mixed foragers (MF) based on published data on percent time moving (PTM) and
movements per minute (MPM, Table S3 in Appendix). Several studies have found
support for threshold values of 1 MPM and 10% PTM for sit-and-wait foragers (Butler
2005; Cooper 2007; Miles et al. 2007). Additional studies suggest that active foragers
have PTM greater than 30%, and species that fall between 10% and 30% PTM may be
mixed foragers (Reilly & McBrayer 2007). Miles et al. (2007) additionally found
significant morphological differences among foraging modes. We used these criteria to
classify lizards: SWF, (PTM <10% and MPM <= 1), MF (MPM > 1 and 10% < PTM <
30%), and AF (MPM > 1 and PTM > 30%). If published data on PTM or MPM were not
available for a species we used data for a related species that exhibits similar behavior
(eg. Eumeces laticeps for E. fasciatus), or for the family if only one foraging mode is
known to occur in the family (e.g., Anguidae, Miles et al. 2007). The genus Phrynosoma
was modeled as both SWF and AF because its PTM and MPM are from different studies
and would place them in different categories (Table S3 in Appendix). Sceloporus
graciosus was also modeled as both SWF and AF based on PTM and MPM, while E.
66
fasciatus was only modeled as AF due to the very high value of PTM used for this
species (Table S3 in Appendix). Our results and conclusions based on the different
models were qualitatively the same, so models from only one set of foraging mode
classifications are reported.
The second model also has three optima, but is based on predator escape mode
(flight, crypsis, and mixed). Lizard species were classified according to published
descriptions of escape behavior, because little quantitative data are available (Table S3 in
Appendix). Species were classified as flight escapers (FE) if their main mode of escape
relies heavily on bursts of sprinting, as cryptic escapers (CE) if they rely heavily on
crypsis to remain undetected with lower reliance on running, or mixed escapers (ME) if
they rely on both cryptic behavior, and short sprints to evade predators.
The third model of evolution assumed that both foraging and predator-escape
behaviors were important determinants of muscle fiber-type composition. Under this
model, each unique combination of foraging mode and predator escape behaviors
corresponds to a distinct adaptive regime. For instance, Holbrookia maculata is a sit-
and-wait predator that relies on crypsis so it is in the sit-and-wait/crypsis category
(SWF/CE), while Callisaurus draconoides is a sit-and wait predator that relies mainly on
sprinting from predators so it is placed in the sit-and-wait/flight category (SWF/FE).
Although there are nine logically possible combinations, the lizards studied here exhibit
only six of these combinations (Figure 4.1).
Ancestral regimes were reconstructed using linear parsimony. This algorithm
chooses regimes on internal branches to minimize the number of evolutionary changes
along the phylogeny. Linear parsimony resulted in a few ambiguities. For example, the
branch immediately ancestral to the Phrynosomatidae family could be assigned to either
mixed or flight escape with equal parsimony. In these cases, all possibilities were tested
and results compared (however, these variations did not affect the conclusions).
The final two models make no assumptions about adaptation. The simplest model
is Brownian motion, or random drift. The last model assumes stabilizing selection with a
single global optimum fiber-type composition for all species. It implies that all lizards
share the same adaptive regime, varying little outside of the optimal range of fiber type
67
compositions. This situation would be expected to occur if the functional demands of
locomotion are identical among all lizards irrespective of behavior or ecology.
Analysis
The fiber-type data in Bonine et al. (2005) were proportions that sum to one and
hence only have two degrees of freedom. The proportional nature of the fiber-type data
result in non-independence, and no manipulation will make them truly independent.
However, to minimize some of the problems associated with non-independence in our
analysis, we calculated the proportion of SO amongst the total (SO/(SO+FG+FOG)), and
that of FG only amongst the fast (FG/(FG+FOG). Thus, both of the proportions we
analyzed could range from 0 to 1 within a single lizard species, whereas the traditionally
calculated proportions of SO and FG would be constrained to range between 1-FG and 1
– SO, respectively. Consequently, the fiber type proportions we used allowed us to
capture the essential biological information in this system while reflecting the two
degrees of freedom present in the data. We note that had we modeled all three fiber-type
proportions separately, interpretation would have been more complicated because of
interdependency among the models. We logit transformed relative proportions of the
fiber-types to convert the data to a scale not bounded by zero and one. We used quantile-
quantile plots to diagnose deviations from normality.
We fit each of our evolutionary models to the muscle fiber-type data assuming the
BVH phylogeny. The fits of each of the models were compared using Akaike
Information criterion corrected for small sample size (AICc) and the more conservative
Schwartz information criterion (SIC). We use information criteria to assess the strength
of evidence in favor of the competing models (Burnham & Anderson, 2002). We fit
models of evolution using the OUCH package (version 1.2-4, Butler & King 2004) in the
R statistical computing environment (R Development Core Team 2007). Model selection
frequencies and 95% confidence intervals for model parameters were calculated based on
2000 bootstrap replicates (Burnham & Anderson 2002). All statistical analyses were
conducted in the R statistical computing environment (R Development Core Team 2007).
Once the optima for each model were calculated, these parameters were back-
transformed into proportions to facilitate interpretation.
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Results
Fast-twitch (FG and FOG)
Predator escape mode was the best predictor of fast-twitch fiber type evolution.
In the best fitting model, evolution of fast-twitch (FG and FOG) fiber-type composition is
predicted by adaptive regimes related to predator escape strategy (Table 4.1). The
predator-escape model out-performed all other models by both model selection criteria
and showed both selection and drift to be important (Tables 4.1 & 4.2). Under the
predator-escape model, the fast-twitch optimum indicates that lizards that sprint to escape
should have very high proportions of FG fibers, while lizards that rely on crypsis should
have mostly FOG fibers (Table 4.2 & 4.5). Lastly, lizards that use a mixed strategy
should have an intermediate proportion with slightly more FG than FOG fibers (Tables
4.2 & 4.5).
Bootstrap model selection frequencies demonstrated that there was little support
for the Brownian motion, single-optimum, and combination models, while the foraging
mode model fared the worst (Table 4.1). The predator escape model was selected the
majority of the time using both the AIC and SIC (98.1% and 88.3% respectively). The
poor performance of the foraging mode and combination models indicates that foraging
mode does not substantially contribute to the evolution of fast-twitch fiber types.
Bootstrap values show overlap of 95% CI’s of optimal FG proportions for the flight and
mixed categories, but no overlap between the mixed or flight and the cryptic category
(Table 4.2).
The data presented here support an evolutionary trend toward muscles composed
mostly of one fast-twitch fiber-type, which is associated with increased behavioral
specialization. Two of the three behavioral strategies (flight and crypsis) exhibited
selection for a single fiber type, while the third behavioral strategy (mixed) showed
intermediate values of the two fast fiber types. The sand lizards, which rely heavily on
speed for predator escape (Vitt & Price 1982, Dial 1986, Bulova 1994), have high FG
fiber-type proportions (0.68), while the horned lizards that rely heavily on crypsis
(Sherbrooke 1987; Stebbins 2003), have very low FG fiber-type proportions (0.29). The
Sceloporus group (which consists of the genera Sceloporus, Uta, and Urosaurus), more
69
generalist in their behaviors (Sites et al. 1992; Stebbins 2003), had intermediate FG fiber-
type proportions (0.51). These fiber compositions associate well with sprint
performance, the sand lizards tend to reach the fastest sprint speeds, the Sceloporines
more intermediate, and horned lizards reached slower speeds (see Bonine & Garland
1999).
Slow-twitch oxidative (SO)
A model with a single optimum was the best fitting for the evolution of the
proportion of SO fibers in the iliofibularis, out performing all other models by both
model selection criteria (Table 4.3). The single optimum model predicts that only 8% of
the iliofibularis will be composed of SO fibers (Tables 4.4 & 4.5). However, the
proportion of SO fibers vary little in the lizards examined here (Bonine et al. 2005, SO
proportions do show more variation in other lizards, see Gleeson and Harrison 1986, and
Mutungi, 1990). The single optimum model was selected more frequently than any other
model (72.1% and 66.6%) based on bootstrap model selection frequencies for both model
selection criteria (Table 4.3). Brownian motion showed similar AICc and SIC values to
the single-optimum model as did the predator escape, but the Brownian motion model
was selected at less than half the frequency of the single optimum model and the predator
escape model was very rarely selected (Table 4.3). On the other hand, the foraging mode,
and combination models received little support based on the bootstrap model selection
frequencies (Table 4.3).
Discussion
Predator escape behavior appears to have driven the evolution of fast-twitch fiber-
type composition. Why is predator escape the driving force behind the fast-twitch muscle
fiber-type composition as opposed to foraging mode or a combination of the two?
Selection for survival against predatory attacks is probably stronger than selection for
incremental increases in foraging efficiency (Abrams 1986, Brodie & Brodie 1999).
Dawkins and Krebs (1979) referred to this as the “life-dinner principle”. While failure to
escape a predator ends all reproduction, poor foraging efficiency only marginally reduces
70
fitness by lowering energy intake and reducing mate acquisition, number of offspring, or
offspring survival (Frey-Ross et al. 1995, Morse & Stephens 1996, Kaspi et al. 2000).
Therefore, foraging behavior is likely a relatively weaker selective pressure on locomotor
musculature when compared to the strong selection of predation. Alternatively, selection
may be operating on speed. Lizards use near-maximal speed while fleeing predators, but
use lower speeds in prey capture attempts (these attempts were aimed at artificial lures;
Irschick & Losos 1998, Husak 2006). Furthermore, sit-and-wait lizards use a variety of
foraging speeds. For example, Crotaphytus collaris runs to capture prey, whereas P.
platyrhinos captures ants at slow speeds, yet both are considered sit-and-wait predators
(Vitt & Congdon, 1978). Examining the variation in FG and FOG fiber composition, sit-
and-wait species span the entire range of variation (Table S3 in Appendix).
Selective Hypotheses
What are the possible mechanisms of selection acting within the predator escape
regime? According to the predator escape model, lizards relying on sprint speed to
escape predators should have fast-twitch muscle composed mostly of FG fibers. Several
lines of evidence suggest an association between FG proportions and running
performance. Recruitment of FG fibers for acceleration and speed is found in organisms
such as fish, frogs, and lizards (e.g., Rome et al. 1988; Jayne et al. 1990; Lutz et al.
1998). Jayne et al. (1990) showed that, in the lizard Varanus exanthematicus, as speed
increased, additional white fibers in the iliofibularis were recruited (80% of the white
fibers are FG; Mutungi 1990). Within Phrynosomatid lizards, sprint speed is associated
with higher FG fiber type proportions (Bonine et al. 1999, 2001, 2005). Furthermore,
sprint speed has been linked to higher survival in lizards (Urosaurus ornatus, Miles 2004,
Crotaphytus collaris, Husak 2006; this latter study measured sprint speed at predator
escape). Thus, having high numbers of FG fibers likely helps achieve fast accelerations
and high running speeds, both of which are integral for escaping predators.
Alternatively, cryptic lizards have reduced FG and higher FOG fiber proportions
in comparison to flight escapers. There are multiple plausible mechanisms that may lead
to high FOG fiber proportions. First, there need not be direct selection for this
phenotype. Rather, it may reflect a lack of selection for FG fibers. Second, because
71
cryptic species escape predators largely by avoiding detection they have little need for
rapid accelerations. However, they still require locomotion for other activities (e.g., mate
acquisition, thermoregulation, feeding). The majority of these movements are well below
their maximum capabilities (Irschick & Losos, 1998, Jayne & Irschick 2000, Mattingly &
Jayne 2004, Husak 2006, Cooper 2007), and thus they may experience little selection for
FG fibers. Instead, FOG fibers may serve as the best general-purpose fiber type. FOG
fibers still allow animals to sprint (for catching prey, which often occurs at lower speeds
than predator escape [Irschick & Losos 1998, Husak 2006]), but have good contractile
properties at lower running speeds, and show greater fatigue resistance while running
(Peter et al. 1972; Gleeson et al. 1980; Gleeson & Johnston 1987). Third, if endurance
improves fitness (Barbosa & Moreno, 1999, Irschick, 2003), selection for fatigue
resistance may be acting on fast-twitch fiber-type composition, favoring FOG fibers.
There is very little known about endurance in cryptic species, but one study found that
horned lizards do not have high endurance at relatively fast speed (Garland 1994).
Whether the same is true at slower speeds is unknown. Finally, as there are differences in
the cellular composition of the different fiber types, they may differ in maintenance costs
(as far as we know, this idea has not been studied), which may play a role in selection for
energetically inexpensive muscle maintenance or contractions. Lack of certainty about
the precise selective mechanism does not diminish the significance of our results, which
clearly show an evolutionary pattern in fiber type evolution in which locomotor muscle is
composed largely of FOG fibers in cryptic species.
Further research is needed to determine the precise selective mechanism acting on
muscle fiber-type composition. Data are needed on fiber type involvement at different
speeds, their relationship with endurance, as well as more data on speeds used in nature.
Furthermore, we have examined only one of many muscles involved in lizard locomotion
and different muscles in the limbs differ in fiber type composition (Putnam et al. 1980;
Gleeson 1983). It would be interesting to see whether analyses of muscles involved in
the propulsive phase of running suggest the action of different selective pressures.
Finally, a systems-level consideration of all locomotor muscles may illuminate the
frequently unclear connection between individual muscle performance and whole animal
performance (e.g., Gleeson & Harrison 1988, Wilson et al. 2002). Locomotion is a
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complex behavior involving many muscles. A full understanding of the mechanisms of
selection acting on locomotor muscles will require a more intimate knowledge of
interaction between muscles of the locomotor system, and links between muscle, whole-
animal performance, and fitness.
Multiple Evolutionary Models
Why is there no single best model for both fast and slow twitch fiber type
evolution? The answer may lie in their different functional roles. SO fiber-types provide
high endurance across a wide range of lower-speed activities such as walking/slow
running (Jayne et al. 1990), and are possibly involved in joint stabilizing, body support,
or posturing (Putnam et al. 1980 and references therein); behaviors which are shared
across lizard taxa. Consistent with this hypothesis, SO fiber composition remains
relatively constant in this dataset, whereas FG and FOG vary widely (Bonine et al. 2005).
It is intriguing that phenotypes which are components of the same functional unit
are predicted by very different evolutionary models. Whereas fast-twitch are best
explained by a strongly adaptive model driven by behavioral strategy, slow-twitch is best
explained by a global optimum model, or even a model of pure drift with no selection.
Perhaps this should not be so surprising, since locomotor muscles must perform such a
wide variety of functions throughout the life of a lizard. Functional variety here is
accomplished by different functional subunits (i.e., fiber types) comprising the muscle,
whose proportions can be differentially molded by evolution. Interestingly, one appears to
be highly conserved whereas the others are more free to vary.
Muscle physiology is often assumed to be under selection for whole animal
performance, but our study is one the first to provide direct evidence for an adaptive link
between muscle physiology and the behavior of an organism. Because Bonine et al.
(2005) were limited to the use of Brownian motion (BM) as their underlying evolutionary
model they were unable to test for selection within the contexts set by the ecology and
behavior of the species of lizards. Furthermore, determining whether foraging or
predator escape behavior exerts a stronger selective pressure on the muscle physiology of
these lizards would be unfeasible unless the two alternatives are compared directly. This
73
illustrates the importance of posing our best biologically-informed models and allowing
them to compete for the best explanation of the data.
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Table 4.1. Fast-twitch fiber type model selection criteria. Model fit statistics for fast-
twitch fiber-type composition of the iliofibularis muscle. The Model with the best fit
(AICc = 24.67, SIC = 25.61) is in bold and bootstrap model selection frequencies (%)
based on 2000 replicates are included in parentheses. See the “Selective Regimes”
portion of the Materials and Methods section for a full explanation of each model.
Model AICc SIC
Predator Escape
0.00 (98.10%) 0.00 (88.30%)
Combination
12.96 (1.85%) 6.84 (11.70%)
Brownian Motion
18.13 (0.05%) 18.74 (0.00%)
Single Optima
20.26 (0.00%) 21.25 (0.00%)
Foraging Strategy 29.65 (0.00%) 29.65 (0.00%)
Table 4.2. Parameters of the best fitting fast-twitch fiber type model. Model
parameter estimates (dX(t) = α [(t) - X(t)] dt + dβ(t)) including selection (), drift (),
and optima () of the best -fitting fast twitch fiber-type model, predator escape.
Estimated optima values are cross-sectional proportions of FG relative to fast twitch
fibers (FG/(FG+FOG)). Escape strategy abbreviations are: FE = flight escape, ME =
mixed escape, and CE = cryptic escape. Bootstrap 95% confidence intervals are included
in parentheses.
Predator
Escape
Parameters
2.05 (0.44,12.58)
0.62 (0.36,1.30)
FE
0.76 (0.65,0.98)
ME
0.53 (0.46,0.77)
CE 0.16 (<0.01,0.39)
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Table 4.3. Slow-Twitch fiber type model selection criteria. Model fit statistics for slow-
twitch fiber-type composition of the iliofibularis muscle. The Model with the best fit
(AICc = 56.52, SIC = 58.46) is in bold and bootstrap model selection frequencies (%)
based on 2000 replicates are included in parentheses.
Model AICc SIC
Single Optima
0.00 (72.10%) 0.00 (66.60%)
Brownian Motion
0.80 (25.55%) 0.41 (29.15%)
Predator Escape
1.40 (1.45%) 0.41 (1.90%)
Foraging Strategy
10.12 (0.85%) 9.13 (1.55%)
Combination 12.94 (0.05%) 7.76 (0.80%)
Table 4.4. Parameters of the best fitting slow-twitch fiber type models. Model
parameter estimates (dX(t) = α [(t) - X(t)] dt + dβ(t)) including selection (α), drift (σ),
and optimum (; for Brownian motion indicates the estimated ancestral state) for slow-
twitch fiber-type composition of the iliofibularis muscle. The estimated optimum value is
the cross-sectional proportion of SO fibers relative to all fibers (SO/(FG+FOG+SO)).
Bootstrap 95% confidence intervals are included in parentheses.
Model Brownian
Motion
Single
Optimum
Parameters
2.96 (1.02,50.62)
1.14 (0.78,1.46) 1.87 (1.12,7.01)
0.09 (0.04,0.23) 0.08 (0.05,0.13)
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Table 4.5. Model estimates of fiber type composition compared to raw data. ‘s indicate
the fiber type proportions relative to total muscle volume estimated by the best models
for fast- (predator escape) and slow-twitch (single optimum) fiber types. FG, FOG, and
SO, are mean fiber type proportions of each regime based on the raw data presented in
Bonine et al. (2005). Because SO is 0.08, FG estimates were calculated as 0.92*
(estimated by the predator escape model), and FOG was calculated as 1-(FG+ SO).
See Table 2 for list of abbreviations.
Predator
Escape
Regime
Fiber
Types
FG
FG FOG FOG SO SO
FE
0.70 0.64 0.22 0.30 0.08 0.06
ME
0.53 0.46 0.39 0.42 0.08 0.12
CE 0.15 0.34 0.77 0.58 0.08 0.07
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Figure 4.1: Evolutionary hypotheses for the evolution of fiber-type composition in the
iliofibularis muscle of lizards. Each color represents a different selective regime and
color codes are indicated below each hypothesis. The time scale of the phylogeny is
standardized to 1.0 from the basal node to terminal species.
78
CHAPTER 5. TARGETS OF SELECTION AND POTENTIAL CONSTRAINTS:
HOW THEY SHAPE THE EVOLUTION OF THE LOCOMOTOR SYSTEM IN
LIZARDS
Abstract
Adaptation arises in response to selective pressures acting on organismal
performance. Performance is generally a product of a complex phenotype constructed in
a hierarchy of biological organization from the cellular to whole animal level. Traits
across these levels can serve different functions and are involved in multiple aspects of
performance. Therefore, all phenotypic traits may not evolve in a uniform manner.
Phenotypic traits may be shaped by different selective pressures, result from a
compromise between function, or be limited in their adaptation due to constraints.
However, studies have largely examined the evolution of the many traits composing a
complex phenotype individually. To further our understanding of complex phenotypic
evolution, I test evolutionary models based on the behavior and ecology to assess how
selection on performance influences the evolution of traits from the cellular to the whole
animal level in the locomotor system of lizards. I find that performance based selection
does not act uniformly across the locomotor system. Selection related to both predator
escape and foraging behaviors acts on several traits spanning the cellular to whole-
organism level. However, the evolution of many traits are explained by very different
evolutionary models. A global optimum model best explains the evolution of several
muscular traits. This model indicates selection is similar on musculature across lizards.
However, this finding may also be a consequence of functional constraints in the
muscular system. Interestingly, a Brownian motion best explained femur and tibia
evolution. While this finding suggests portions of the limb may not be subject to
selection, it may also result from compromises between competing functions. Overall, I
find traits show distinct patterns of evolution within a complex phenotype, with some
reflecting selection related to performance, while others are more constrained in their
evolution.
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Introduction
Organismal performance often results from the integration of a sizeable suite of
morphological and physiological traits (e.g., Liem 1973, Kohlsdorf et al. 2012).
Therefore, one may expect selection acting directly upon variation in performance to
translate into selection across the suite of underlying traits (Arnold 1983, Garland &
Losos 1994, Irschick et al. 2008). However, morphological and physiological variation
may not always reach the evolutionary optima set by performance-based selection as
genetic, developmental, or functional trade-offs may constrain their evolutionary
potential (Wake & Larson 1987, Wake 1991, Arnold 1992, Endler 1995, Walker 2007).
Identifying where selection or constraints dominate the evolutionary dynamics of a
complex, integrated phenotype can provide valuable insight into the processes that guide
the evolution of both performance and morphology/physiology, but multilevel selection is
rarely examined.
Locomotion is one of the primary models used for studying evolution in
performance and morphology, largely because it plays a critical role in the survival and
reproduction of many animals (e.g., Irschick and Garland 2001, Irschick et al. 2008). A
plethora of studies have identified functional links between form (morphology or
physiology) and performance as well as demonstrating that variation in performance can
be associated with fitness differences. However, the majority of these functional studies
do not take into account how selection acts across multiple levels of the biological
hierarchy. The locomotor system consists of a suite of traits integrated across a hierarchy
of biological levels, including the cellular (e.g., muscle fiber type), tissue (e.g., muscle
pennation, muscle cross-sectional area), and whole body (e.g., limb length, tail length)
levels. Yet, how variation at these different levels is shaped by evolution remains little
explored. Furthermore, the same musculoskeletal machinery must perform several tasks
including running, climbing, jumping, and walking for extended periods of time
(Vanhooydonck et al. 2001, Russell & Higham 2009, Oufiero et al. 2011, Herrel &
Bonneaud 2012,). However, trait evolution is most commonly examined in isolation with
very few studies looking at how selection for multiple functions influences the evolution
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of all the traits involved. Researchers therefore, know little about how multiple forms of
selection act across complex phenotypes such as the locomotor system.
Lizards have long served as a model for selection and adaptation in locomotor
performance and morphology. Across lizards, studies have successfully linked
morphology and performance (e.g., Bonine & Garland 1999, Losos 1990, Melville &
Swain 2000, Goodman et al. 2008), and performance and fitness (Husak et al. 2006,
Miles 2004, Irschick et al. 2008) providing strong evidence for locomotor adaptation.
Furthermore, variation in performance and morphology has been linked to numerous
agents of selection including habitat use (Losos & Sinervo 1989, Losos 1990, Melville &
Swain 2000, Herrel et al. 2002, Kohlsdorf et al. 2004, Goodman et al. 2008), predator
escape behavior (Melville & Swain 2003, Miles 2004, Husak 2006, Vervust et al. 2007,
Gifford et al. 2008, Scales et al. 2009), and foraging mode (Huey et al. 1984, Reilly et al.
2007, McElroy et al. 2008, Verwaijen & VanDamme 2008) indicating that multiple
selective agents likely act on the locomotor system. Finally, morphological and
physiological traits across multiple levels of biological organization show variation with
performance (limb length: Bonine & Garland 1999, musculature: Zaaf et al. 1999,
Vanhooydonck et al. 2006 fiber type: Gleeson and Harrison 1988, Bonine et al. 2001,
2005, Kohlsdorf et al. 2004) suggesting that selection acts across the system.
Previously I used a group of lizards from the Southwest United States to
demonstrate that selection related to predator escape behavior drives the evolution of
sprint speed and acceleration, while selection related to foraging mode drives the
evolution of maximal exertion. However, as multiple traits across the locomotor system
influence performance in these lizards (Bonine et al. 2001, Herrel et al. 2002, Chapter 3),
how selection acts on this complex system remains unclear: Does performance related
selection act uniformly across the locomotor system or is it concentrated at one or more
levels? Does selection at one level of the biological hierarchy cascade down to lower
mechanistic levels? Can different selective agents target specific traits?
81
Materials and Methods
Here, I assess whether and where performance based selection acts across the
locomotor system by modeling the evolution of morphological traits across multiple
biological levels of the locomotor system. I use the same evolutionary models that were
previously used to model the evolution of performance in these same lizards (Chapter 2).
I then compare the models that best explain the evolution of morphological traits to the
models that best explained the evolution of performance. If selection acts uniformly
across the locomotor system, I expect all morphological traits to evolve according to the
same selection as performance. Alternatively, if selection is concentrated at one
biological level, I expect to see morphological traits at that level experience selection
similar to performance, but not traits at other levels. Finally, selection may occur across
levels of biological organization, but concentrated only at certain traits. If this is the case,
I expect some, but not all traits distributed across levels, to experience the same selection
as performance.
Morphological Data Measurements
To address the question of whether selection that guides the evolution of
locomotor performance also acts across multiple levels of organization of the locomotor
system, I use the performance data and models presented in Chapter 2 and the hind limb
muscle morphology data from Chapter 3. In addition to these data, I also include
morphological data for an individual muscle, the gastrocnemius, and body shape traits
based on gross morphology. Morphological data for the gastrocnemius was collected in
the same manner used in Chapter 3. Therefore, I supply only a limited explanation of
how the data was collected here. The gastrocnemius was removed, blotted dry and
weighed to the nearest 0.0001g using an Acculab Sartorius ACL-80.4 scale. The fascicle
length was measured at mid-muscle under a Zeiss DV4 stemi spot dissecting scope to the
nearest 0.01mm using Mitoyo Digimatic D6-6CX calipers (Biewener & Full 1992).
Pennation angle was estimated by bisecting the muscle and measuring the angle of the
fibers relative to the tendon or aponeurosis at mid-muscle (Biewener & Full 1992).
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Snout-vent length (SVL, distance from the tip of the snout to the cloaca), tail
length (measured from the cloaca to the tip of the tail), fore limb length (measured from
the shoulder to the tip of the longest toe), and pelvic width (distance between the 2 hip
joints) were measured using digital calipers (Mitutoyo Digimatic D6-6CX) on all
individuals. Femur length (the greater trochanter to medial condyle), tibia length (top of
the medial condyle to medial malleolus), and foot length (base of the heel to the tip of the
longest toe) were all measured directly on the bone(s) of interest once muscles were
removed. Bone measurements were made on the same subset of individuals as muscle
measurements (see Chapter 3). Body mass was measured on live lizards with Pesola
scales to the nearest 0.1 g.
Morphological traits for evolutionary models
I modeled the evolution of several morphological traits and compare the best
performing of these models to the models that explain the evolution of performance from
Chapter 2. Traits selected for evolutionary analyses were those that potentially affect
locomotor performance in lizards, including several body shape traits including pelvic
width, tail length, fore and hind limb lengths. Previous studies of locomotor performance
in these lizards show that while hind limb length is important for sprint performance, the
different elements of the hind limb can vary in their relationships with performance (Zaaf
& VanDamme 2001). Thus, I also model the evolution of femur, tibia, and foot lengths
separately.
I similarly modeled the evolution of the musculature of the hind limb. I focus on
musculature involved in propulsion during running, as these muscles are more tightly
associated with running performance in the lizards studied here (Chapter 3, Higham et al.
2011). I model the evolution of stance phase muscle cross-sectional area (CSA) of the
hind limb as a whole, but also independently model functional groups, including the
major hip retractor, knee extensors, and ankle extensors as they can vary in function
during locomotion (Snyder 1954, Vanhooydonck et al. 2006, Zaaf et al. 1999).
Additionally, to examine how selection acts at the tissue level, I modeled the evolution of
muscle mass, fascicle length, and pennation angle in the gastrocnemius, a muscle
important for force production and propulsion running performance (Reilly 1995, Roberts
83
& Scales 2002, Higham et al. 2011). Although I previously modeled performance in
absolute terms, I chose to model size-corrected morphological data for two reasons. 1)
Relative morphology is a strong predictor of relative performance suggesting that relative
traits are still tightly linked to performance abilities. 2) The evolution of body size is
influenced by a wide range of life history and ecological factors (Blackburn et al. 1999,
Shine 2005, Faribairn 1997), however body shape likely reflects the locomotor demands
of an organism (Losos 1990, Vanhooydonck & VanDamme 1999, Melville & Swain
2000, Bergmann & Irschick 2010). I corrected body morphology traits for SVL to
highlight differences in shape. I corrected all muscle traits for body mass because
muscles provide the force for locomotion, and the effectiveness of force production is
directly related to the mass the force must move (a=f/m). For example, if two lizards
have the same muscle CSA, but one has twice the mass, I expect the lighter lizard to
achieve greater acceleration all else being equal.
Comparative Analyses
For our comparative analyses, I used the same phylogeny constructed Chapter 2,
which contains 21 species from the families Phrynosomatidae (17 species, 6 genera),
Teiidae (Aspidoscelis tigris and A. uniparens), Anguidae (Elgaria kingii) and
Crotaphytidae (Crotaphytus collaris).
All morphological data were log-transformed prior to analysis except for the
gastrocnemius pennation angle, which was arcsine transformed. The data were modeled
using Hansen’s (Hansen 1997) model, an Ornstein-Uhlenbeck (OU) process that models
the evolution of a continuous phenotypic trait subject to the influences of stabilizing
selection and noise (Hansen 1997, Butler & King 2004). The Hansen model describes
the change in a trait dX(t), over time (t), as an increment of a stochastic Brownian motion
process (dβ(t)), influenced by selection (α) toward an optimal trait value (θ) and subject
to stochastic changes proportional to a drift parameter (σ):
dX(t) = α [θ (t) – X(t)] dt + σdβ (t) (1)
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Notably, the Hansen model allows optimal trait values to vary along the branches of the
phylogeny to represent shifts in selective regime (or 'adaptive zone' sensu Simpson 1953)
experienced by the evolving lineages. I compared 5 mappings of selective regimes on the
phylogeny, each representing a distinct evolutionary hypothesis for the evolution of the
trait (sensu Butler & King 2004, see selective regimes below). Each evolutionary model
was fit to the transformed data, assuming our phylogeny using the OUCH software
package (Butler & King 2004) in the R statistical computing environment (R
development Core Team 2010). The fits of each model were compared using the Akaike
information criterion corrected for small sample size (AICc). Information criteria were
used to measure the strength of evidence in support of each competing models (Burnham
& Anderson 2002). Model selection frequencies and model parameter confidence
intervals are based on 2000 bootstrap replicates (Burnham and Anderson 2002).
Selective Regimes for evolutionary models
I tested six evolutionary scenarios, Brownian motion and five adaptive models,
for producing variation in the locomotor morphology of these lizards. The Brownian
motion (BM) model is a stochastic model with no terms for selection. This model
assumes that locomotor performance evolves according to random noise or drift, and
makes no assumptions about adaptation with respect to the selective pressures considered
here.
Three of the five adaptive models are those previously used by Scales and Butler
(in review) to model locomotor performance, so they are covered only very briefly here
(see Scales and Butler in review for a detailed construction of evolutionary models). All
adaptive models of evolution were constructed by assigning adaptive regimes to
individual branches of the phylogeny (Butler & King 2004) and ancestral regimes were
reconstructed based on linear parsimony using Mesquite (Maddison & Maddison 2009).
The first of these adaptive models is based on microhabitat use and contains five
optima. Species were classified as terrestrial if they spend the majority of their time on
the ground. Arboreal species are those strongly associated with trees or woody bushes
whereas saxicolous species show a strong association with rocks and their vertical
surfaces. Litter species are those that are found in leaf litter and cluttered areas and
85
species were considered generalist if they don’t have a specific microhabitat associations
or frequent multiple microhabitat types (Figure 5.1). The second adaptive model is based
on foraging mode and contains three optima. Lizard species were classified as sit-and
wait predators (SWF), active foragers (AF), or mixed foragers (MF) based on movement
criteria (Figure 5.1). The third model is based on predator escape behavior and also has
three optima: flight, crypsis, and mixed (Figure 5.1). In this model cryptic escapers (CE)
are lizards that rely heavily on crypsis or morphology to evade predators with little
reliance on running. Flight escapers (FE) are species that rely mainly on bursts of
sprinting for escape, whereas mixed escapers (ME) are species that use a mix of cryptic
behavior and short sprints to refugia, or other behaviors (e.g., “squirreling” the act of
continually running to the opposite side of trees or rocks to avoid predators) to avoid
predation.
I also incorporated two additional models that were not initially included in the
performance study of Chapter 2. The first new model is a second predator escape model
with only two selective regimes. In Chapter 2, a post hoc analysis showed that a predator
escape model in which lizards were separated into sprint specialists versus all other
lizards (Figure 5.1), outperformed the other models in explaining sprint performance, but
was still within 2 AICc units of the 3 regime model. In this two-regime model, species
classified as flight escapers (see classifications of Chapter 2) experience selection on
performance and morphology that differs from all other predator escape behaviors used
by the lizards here (Figure 5.1).
Finally, I also added a global optimum model that assumes stabilizing selection
with a single optimum for a trait value for all species. It implies that all lizards should
share a single adaptive regime for a given trait with little variation out side of the optimal
range. This model was included because some morphological traits may have an optimal
value for functional purposes such as force production.
86
Results
Selection on body shape
The best predictor of body shape evolution varied across traits. Predator escape
behavior provides the top explanation for the evolution of hind limb length (Table 5.1).
While the three-regime predator model showed the lowest AICc score, the two-regime
predator model also received substantial bootstrap support (Table 5.1). The BM model
was also within it 2 AICc units of the predator escape models, but received almost no
bootstrap support. Interestingly, two different models best explain the evolution of the
separate hind limb segments. The BM model best explained the evolution of both femur
and tibia length, but was not strongly supported as there was modest bootstrap support for
multiple other models (Table 5.1). Conversely, the evolution of foot length was best
explained by the three-regime predator escape model, which had relatively strong
bootstrap support with moderate support for the two-regime model (Table 5.1). Thus, the
evolutionary trend observed for the hind limb appears to be driven by selection on the
foot, suggesting that foot length is a strong target of selection for level running. These
results also show that selection on performance for sprinting does not translate into
selection acting uniformly across the hind limb (Table 5.2).
Other body shape traits also show variation in evolutionary trends. For example,
forelimb length evolution is best explained by the BM model, but multiple models also
showed modest bootstrap support (Table 5.1). On the other hand, the evolution of tail
length and pelvic width are both driven by evolution in response to selection related to
foraging mode. The foraging mode model had strong support for both traits, with all
other models receiving almost no bootstrap support (Table 5.1). Overall, performance
based selection does not guide the evolution of all body shape traits (Table 5.2), but the
two selective pressures that drive sprinting and exertion abilities also target traits such as
foot and tail length, and pelvic width (Table 5.2).
Selection on musculature
Selection acting on musculature also did not always match performance related
selection (Table 5.2). The global optimum model best explained the evolution of overall
87
extensor CSA, as well as muscle CSA at the hip and knee (Table 5.1). This model was
well supported in all three cases (Table 5.1). Alternatively, the two-regime predator
escape model best explained the evolution of CSA at the ankle. While the predator
escape model was reasonably well supported, the global optimum model also received
moderate bootstrap support (Table 5.1). These findings indicate that selection drives the
evolution of hind limb musculature (Table 5.2), but only the more distal musculature at
the ankle is the focus of performance related selection, at least for level sprinting (Table
5.2).
Selection at the tissue level
Even the evolution of traits within an individual muscle are explained by different
evolutionary models. Relative muscle mass and fascicle length of the gastrocnemius both
evolve according to selection related to predator escape behavior (Table 5.1).
Interestingly, the three-regime model outperformed all other models for the evolution of
fascicle length, while the two-regime model best explained muscle mass with minimal
support for any other mode (Table 5.1). The evolution of pennation angle is best
explained by the global optimum model, which was strongly supported (Table 5.1).
These findings suggest that even within a given tissue the forms of selection can vary, but
the evolution of at least some traits are matched to that of performance.
Discussion
The locomotor system is organized in a hierarchy of biological levels that must be
integrated to function properly, but selection may not act uniformly across the system. I
tested whether morphological traits experience the same selection as the locomotor
performances they help to determine. By comparing six evolutionary hypotheses, I find
that the selective pressures that act on performance also act on morphological traits
across multiple levels of biological organization, but not in a uniform fashion.
According to Arnold’s paradigm (1983), natural selection acts directly on
organismal performance (or behavior, Garland & Losos 1994) and secondarily on
morphology resulting in the evolution of suites of coadapted morphological and
88
physiological characters. I find strong evidence for such a scenario as traits at the tissue
level (the gastrocnemius), the muscular system, and body shape, all evolve according to
selection associated with predator escape behavior, the same selective pressure that drives
the evolution of sprint performances. Additionally, Scales et al. (2009) showed that
predator escape behavior also drives the evolution of fast-glycolitic (FG) fiber type
proportions in a group of lizards that largely overlap with the species in this study (Table
5.2). Combined, these studies suggest that selection on the locomotor system spans the
cellular to the whole organism levels resulting in a coadpated suite of traits (Table 5.2).
A combination of several possibilities may underlie the non-uniform selection
across the locomotor system. One probable explanation is that traits in the locomotor
system experience differential selection. Differential selection may be a consequence of
varying selection strength among traits, or be the result of separate selective agents
targeting distinct traits. For example, some traits are stronger determinants of locomotor
performance than others (Scales et al. 2009, Kohlsdorf & Navas 2012, Vanhooydonck et
al. 2006, Herrel et al. 2002), and may be the targets of selection. In the lizards studied
here, foot length, ankle extensor CSA, and FG fiber-type proportions show tight
relationships with sprint performance compared to other traits (Bonine et al. 2001, 2005,
Herrel et al. 2002, Chapter 3). These traits, critical for performance, also evolve in
association with the sprint performances, suggesting that the most crucial determinants of
performance are also the strongest targets of selection. On the other hand, traits including
forelimb length and hip musculature are less associated with horizontal sprint
performances (Vanhooydonck et al. 2006), and may experience weak, or no selection
related to sprint and exertion performance.
Specific targets of selection may also explain why so few of the traits included in
this study evolve in association with foraging mode. Many of the traits studied here are
strong determinants of sprint performance, while others are associated with maximal
exertion. Morphological traits varying in their degree of association with a given aspect
of performance is common across many species (Hildebrand 1985, Alexander 2003,
Vanhooydonck et al. 2006, Kohlsdorf & Navas 2012) suggesting that selection targeting
specific traits may be widespread in complex phenotypes.
89
Locomotor traits may also differ in their evolution because different agents of
selection act on them. The evolution of traits such as foot length and ankle musculature is
driven by predator escape behavior due to their association with speed (Zaaf and
VanDamme 2001, Herrel et al. 2002, Vanhooydonck et al. 2006, Chapter 3). In contrast,
tail length and pelvic width evolve in association with foraging mode. A slender body
form can help reduce the cost of transport and improve maneuverability in lizards
(Garland & Losos 1994, Vanhooydonck & VanDamme 1999), both of which are
important for active foragers. The tail can also play an important role in locomotor
performance in lizards (e.g., Jusufi et al. 2008, Bateman & Flemming 2009 and
references therein) and may facilitate movement through some microhabitats (Wiens &
Slingluff 2001). The tail is also used as an anti predator device in many species (e.g.,
Cooper 1998, Bateman & Flemming 2009 and references therein). Active foragers are
likely more exposed to predation due to their increased movements (Lima & Bednekoff
1999, Hawlena 2009) and may benefit from longer tails to improve escape success (Huey
& Pianka 1981, Vitt 1983). Therefore, foraging mode driving the evolution of pelvic
width and tail length follows biomechanical and ecological predictions and provides
evidence that multiple forms of selection can act on distinct traits within a complex
phenotype.
Separate forms of selection acting on distinct traits may explain additional
variation in our results. Musculature at the hip is more essential for climbing, whereas the
knee and ankle musculature play a more significant role in level running (Zaaf et al.
1999, Zaaf & Van Damme 2001). The more arboreal and saxicolous species in this study
tend to have hind limb musculature distributed more towards the hip, whereas horizontal
runners have more musculature concentrated at the ankle (Chapter 3). Accordingly,
selection related to level sprinting acts strongly on ankle musculature, but not on hip
musculature. This situation may be reversed if I examined selection related to climbing
speed, where we can expect selection at the hip, but not the ankle. Again, such a scenario
lends further support to the idea that the many functional roles of the locomotor system
lead to different selective agents acting on specific traits resulting in distinct evolutionary
patterns.
90
An alternative, but by no means mutually exclusive explanation for our findings is
that some traits may be more limited in their evolution than others. Because locomotor
traits must perform so many functional roles, some may be limited in adaptation due to
functional constraints. Functional constraints may lead to compromises or trade-offs so
that traits cannot be simultaneously optimized for two functional roles (Vanhooydonck et
al. 2001, Ghalambor et al. 2003, Levinton & Allen 2005, Langerhans 2009b, Herrel &
Bonneaud 2012). The tibia and femur must perform level running, climbing, and
jumping all while maintaining posture and supporting body mass (Russell & Bels 2001).
Selection to perform all of these tasks may not allow optimization for a single
performance. Thus, while BM may reflect that traits like tibia and femur length are not
targets of selection, it may also be a consequence of selective pressures interacting so that
these traits are not optimized for a single function, but are compromises between several.
Conversely, traits that serve a single function may be easily adjusted to locomotor
demands. FG fibers are good for power production regardless of the function, be it
climbing, jumping, or level running (Biewener 2003, Bonine et al. 2001). Therefore, the
proportion of FG fibers in the hind limb muscles may be readily adjusted by selection for
specific power production needs regardless of the activity.
The evolution of some locomotor traits may be limited by their multiple
functions, but others may also be restricted by the need to perform as single function
well. A prime example may occur within the gastrocnemius muscle. While
gastrocnemius muscle mass and fascicle length evolve according to the same selective
pressure as sprint performances, a single optimum best explained the evolution of
pennation angle. The gastrocnemius plays an important role in terrestrial running,
providing force for propulsion during the stance phase (Zaaf et al. 2001, Roberts &
Scales 2002, Vanhooydonck et al. 2006, Higham et al. 2011). Pennate muscles are
generally capable of higher force production than parallel fibered muscles (Biewener
2003). Therefore, selection for a pennation angle that maximizes force production for
running may guide the evolution of pennation in the gastrocnemius muscle, limiting
variation.
The single optimum model may not reflect selection at all, but instead, pennation
angle may be constrained resulting in conserved muscle architecture across species. The
91
demands of force production for propulsion may functionally constrain pennation angles,
limiting the range of angles possible in the gastrocnemius. The muscle architecture of the
iliofibularis, another hind limb muscle, is also highly conserved (Bonine et al. 2001, Zaaf
et al. 2001). The iliofibularis, a swing phase muscle that may not require high force
production, but high contraction velocity is parallel-fibered in all species studied here
(unpublished data). Thus, muscle function may constrain muscle architecture, so that
within a muscle, pennation angle is constrained, but other traits are free to vary according
to locomotor needs. However, I cannot rule out developmental or genetic constraints as
pennation angles for a given muscle are frequently limited throughout lizards (Snyder
1954, Zaaf et al. 2001, Higham et al. 2011). Regardless of the mechanism, constraints
may inhibit selection on some traits in the locomotor system, while selection acts to fine
tune those traits that are free to vary.
Understanding the evolution and adaptation of complex phenotypes such as the
locomotor system is one of the main goals of evolutionary biology. Here we show that
the selective agents that act on locomotor performance in lizards also act across multiple
levels of organization in the locomotor system, but not in a uniform fashion. Instead,
adaptation of the locomotor system appears to be the result of a complex balance of
selection and constraint. Traits important for performance are likely targeted by
selection, but different agents of selection can act on individual traits. Conversely, the
adaptation of some traits may be functionally constrained due to compromises between
multiple functions or canalization for a specific function, while other traits may not even
experience selection. Thus, the evolution of the locomotor system seems to consist of
selection tuning evolutionarily labile traits to performance needs, while other traits
remain fixed. Using methods such as this one, helps tease apart the targets of selection
from constraints, providing a deeper understanding of how selection results in the
adaptation of complex, integrated phenotypes.
92
Table 5.1. Model selection criteria for locomotor traits. Model fit statistics for the
evolution of morphological traits across multiple levels of biological organization.
Models with the best fit (Akaike Information Criteria corrected for small sample size,
AICc) are listed in bold (with a value of zero), while ∆AICc values are provided for all
other models. Bootstrap model selection frequencies based 2,000 replicates are included
in parentheses. See “Selective Regimes” for explanations of each model.
Models
BM OU1
Predator
3-regime
Predator
2-regime
Foraging
Mode
Habitat
Use Body shape
Hind limb
0.80
(5.50%)
4.6
(0.35%) 0
(54.15%)
1.9
(31.70%)
2.8
(8.05%)
5.9
(0.25%)
Femur 0
(46.80%)
3.9
(11.80%)
6.4
(3.60%)
4.7
(13.40%)
5.8
(18.10%)
3.2
(6.30%)
Tibia
0
(42.15%)
1.40
(13.10%)
1.88
(4.50%)
0.96
(14.95%)
1.65
(18.60%)
8.87
(6.70%)
Foot
6.4
(0.75%)
8.1
(0.00%) 0
(69.45%)
0.2
(25.35%)
4.5
(4.30%)
15.6
(0.10%)
Pelvis
19.1
(0.00%)
19.4
(0.00%)
11.4
(0.30%)
21.1
(0.00%) 0
(99.70%)
29.9
(0.00%)
Forelimb
0
(45.30%)
3.3
(13.30%)
7.5
(3.90%)
5.1
(13.20%)
6.1
(18.30%)
5.6
(6.00%)
Tail
15.7
(0.00%)
14.3
(0.00%)
11.799
(1.30%)
15.099
(0.00%) 0
(98.70%)
21.2
(0.00%)
Musculature
Extensors
7.4
(0.10%) 0
(81.50%)
3.2
(2.70%)
0.1
(9.05%)
5.7
(5.50%)
7.3
(1.15%)
Hip
12.4
(0.35%) 0
(82.05%)
5.9
(2.80%)
2.7
(8.00%)
5.3
(5.35%)
10.6
(1.45%)
Knee
5.0
(0.01%) 0
(78.90%)
4.4
(2.90%)
0.200
(2.90%)
4.9
(6.55%)
6.0
(1.25%)
Ankle
8.0
(0.10%)
1.5
(27.50%)
2.7
(6.10%) 0
(62.85%)
7.40
(2.80%)
9.2
(0.65%)
Gastrocnemius
Gast mass
4.2
(0.20%)
4.3
(1.30%)
0.70
(12.15%) 0
(84.35%)
3.0
(1.80%)
12.5
(0.20%)
Gast length
5.9
(0.90%)
6.2
(0.50%) 0
(70.80%)
0.5
(16.65%)
6.9
(1.80%)
12.4
(0.20%)
Gast pennation
10.8
(0.40%) 0
(82.05%)
4.0
(3.25%)
2.6
(8.00%)
1.8
(5.20%)
13.1
(1.10%)
93
Table 5.2. Comparison of evolutionary models. The best fitting models for the evolution
of locomotor performance and traits within the locomotor system. Models in bold reflect
morphological traits that experience the same form of selection as performance.
Best Model
*Performance
Speed Predator Escape Acceleration Predator Escape
Exertion Foraging Mode
Whole animal
Hind limb Predator Escape Femur BM Tibia BM Foot Predator Escape
Pelvis Foraging Mode Forelimb Global
Tail Foraging Mode
Musculature
Extensors Global Hip Global
Knee Global Ankle Predator Escape
Tissue (Gastrocnemius)
Mass Predator Escape Fascicle length Predator Escape Pennation angle Global
+Cellular
FG fiber type Predator Escape SO fiber type Global
* Data from J. Scales Chapter 2.
+ Data from Scales et al. (2009).
94
Figure 5.1: Evolutionary hypotheses for the evolution of body shape, musculature, and
individual muscle traits of the locomotor system in lizards. Each color represents a
different selective regime and color codes are indicated below each hypotheses. The
global optimum model is not shown, but would be represented by a single color across
the entire phylogeny. The time scale of the phylogeny is standardized to 1.0 from the
basal node to the terminal species.
95
CHAPTER 6. CONCLUSIONS
Understanding the adaptation and diversification of phenotypic traits is one of the
primary goals of evolutionary biology, physiology and ecology. However, this task can
be difficult as many phenotypes are complex, consisting of numerous traits integrated
across multiple levels of biological organization that must perform a variety of functions,
and experience several forms of natural selection. Using the locomotor system as a
model, I have elucidated several principles regarding phenotypic evolution that are likely
broadly applicable across complex phenotypes. First, multiple selective pressures act on
both performance and morphology. At least two selective pressures act on locomotor
performance and morphology in lizards of the Southwest United States. However, I only
examined a few aspects of performance. Had I included more performance types such as
jumping, climbing, or maneuverability, I would undoubtedly discover the action of even
more selective pressures. Even if they do not act directly, selective pressures such as
habitat use may set the bounds in which specialization may occur (Butler et al. 2000,
Collar et al. 2010). Thus, without considering multiple aspects of performance and
selective pressures, we likely underestimate important selective forces as well as
functional diversity.
Second, variation in behavior is important in promoting phenotypic diversity. In
the lizards studied here, behavioral shifts are associated with the diversification of both
locomotor performance and morphology. Variation in foraging behavior leads to
differences in exertion abilities, while variation in predator escape behavior drives
differences in sprint capabilities. Furthermore, behavioral shifts also guide the evolution
of several morphological traits (muscle fiber type, foot length, etc.) and form-function
relationships between traits such as muscle CSA and speed. Although behavior can have
diverse effects on phenotypic evolution (Huey et al. 2003), we find support for the idea
that behavior is an important driver of evolutionary change. This idea has received
increasing support, especially in terms of locomotion, and has led to a modification of
Arnold’s (1983) morphology-performance-fitness paradigm (Garland & Losos 1994).
Third, performance based selection does not act uniformly across complex
phenotypes. Selection acts directly on variation in performance and only indirectly on
96
the underlying morphology (Arnold 1983). However, we find that selection varies across
traits within the locomotor system. Some traits, the most important determinants of
performance, are targets of selection. For example, foot length, ankle CSA, and muscle
fiber type, all evolve according to the same selective pressure as the locomotor
performances they help determine, sprint speed and acceleration. Interestingly, specific
traits can be subject to distinct selective pressures. Thus, traits serving separate
functional roles may experience distinct evolutionary influences. In contrast, other traits
may be unaffected by selection or limited in their evolutionary capacity. Such limitation
may result from strong selection to perform a single function or functional constraints
restricting variation. Therefore, examining multiple traits across a complex phenotype is
important for understanding functional relationships and integration as well as the factors
that promote or limit the evolution and adaptation of complex phenotypes.
Fourth, the actions of selective pressures can result in distinct phenotypes
integrated from the cellular to whole-organism level designed for specific tasks. For
example, the amount of hind limb muscle cross-sectional area (CSA) and proportion of
fast glycolitic fibers are both linked with sprinting behavior related to predator escape
(Chap.3, Bonine et al. 2001). When the data from this study is combined with other
studies, we see that species that rely heavily on sprints to evade predators should have
long hind limbs, especially the foot, with more muscle CSA, especially at the ankle, and
high proportions of FG fibers. On the other hand, species that rely more on crypsis, but
little on sprints should have shorter limbs with less CSA and higher proportions of FOG
fibers. Thus, selection results in a suite of coadapted traits spanning the locomotor
system, a phenomenon observed in other lizards and systems (Holzman et al. 2011,
Kohlsdorf & Navas 2012) suggesting it is a common theme throughout complex
phenotypes.
Finally, many phenotypes are very complex, and this complexity appears to allow
the mitigation of potential constraints and promote diversification. The locomotor system
is composed of numerous traits and must perform many functions. I find that different
traits are important for specific functions. Many traits help determine speed and
acceleration, but only two are linked with foraging strategy and these are not necessarily
the same traits that govern speed. Thus, exertion abilities and sprint abilities are likely
97
determined by different traits, which may allow them to evolve independently so that
lizards can be well suited for both activities (Arnold 1992). Furthermore, although I find
an expected speed-exertion trade-off, this trade-off is linked to the presence or absence of
selection. Selection for only a single performance, speed or exertion, results in a trade-
off. Conversely, when there is selection for both performances, species excelled at both,
suggesting the presence of mechanisms to alleviate this commonly invoked trade-off.
The ability to mitigate potential trade-offs may be an emergent property of complex
phenotypes such as the locomotor system (Alfaro et al. 2005, Wainwright et al. 2005,
Holzman et al. 2011) as high complexity within a system can allow the evolution of
performance combinations that defy theoretical trade-offs (Holzman et al. 2011).
Mechanisms such as many-to-one mapping, the idea that alternative designs can produce
the same performance, may be especially important in overcoming trade-offs (Alfaro et
al. 2005, Wainwright et al. 2005). In fact, I find evidence of many to one mapping with
respect to hind limb muscle CSA and speed. Species with similar sprint performance can
have muscle distributed more towards the ankle or hip (Chap. 2). Thus, the complexity
across the locomotor system may be what allows it to accommodate multiple functions
(Alfaro et al. 2005, Wainwright et al. 2005, Holzman et al. 2011 Strobbe et al. 2009) and
promote functional and morphological diversification. This phenomenon is almost
certainly widespread across complex phenotypes that underlie ecologically important
performances (Wainwright et al. 2005, Holzman et al. 2011).
Overall, the evolution of the locomotor system and locomotor performance is a
complex interaction of selective pressures acting on multiple aspects of performance
along with a suite of traits across several levels of biological organization. The effects of
selection are coupled with traits potentially unaffected by selection or limited in their
evolution due to functional canalization and constraints. The high level of morphological
and functional complexity likely helps mitigate trade-offs so that functional integration
does not limit possible performance combinations, promoting morphological and
performance diversification. Large behavioral shifts provide the fuel for such
diversification. However, this complexity also means understanding the evolution of
complex phenotypes can be very challenging. Here I show that integrating several
approaches is required to adequately describe performance and morphological diversity,
98
and the evolutionary process that underlie them. Applying an integrated approach to
other systems such as feeding or mating should provide further insights into the
adaptation and diversification of complex phenotypes.
99
APPENDIX
Table 1S. Selective Regime Classifications. Foraging mode classifications are based on references in Miles et al. (2007) and Scales et
al. (2009). Foraging mode variables: MPM = movements per minute, PTM = percent time moving. Foraging mode clasifications:
SW = sit & wait, MF = mixed forager, AF = active forager. Predator escape classifications: CE = cryptic escape, ME = mixed escape,
FE = flight escape. Predator escape classifications are based on Pianka & Parker 1975, Vitt & Congdon 1978, and references in Scales
et al. (2009). Predator escape classifications: CE = cryptic escape, ME = mixed escape, FE = flight escape. Habitat use classifications
are based on Stebbins 2003, and Brennan & Holycross 2006.
Species Family (Clade)
Foraging
mode MPM PTM
Predator
Escape Habitat use
Callisaurus
draconoides Phrynosomatidae
(Sand) SW - 0.02 FE
Terrestrial
Cophosaurus
texanus Phrynosomatidae
(Sand) SW 0.46 0.02 FE
Terrestrial
Holbrookia
maculata Phrynosomatidae
(Sand) SW 0.44 0.02 CE
Terrestrial
Phrynosoma
modestum Phrynosomatidae
(Horned) MF* 0.12 - CE
Terrestrial
Phrynosoma
cornutum Phrynosomatidae
(Horned) MF* - - CE
Terrestrial
Phrynosoma solare Phrynosomatidae
(Horned) MF* - - CE
Terrestrial
Sceloporus
graciosus Phrynosomatidae
(Sceloporus) SW 1.31 5.80 ME
Generalist
Sceloporus
magister Phrynosomatidae
(Sceloporus) SW 0.05 0.20 ME
Generalist
Sceloporus
clarkii Phrynosomatidae
(Sceloporus) SW 0.20 0.80 ME
Arboreal
Sceloporus
poinsettii Phrynosomatidae
(Sceloporus) SW 0.08 0.20 ME Rock-
dwelling
100
Sceloporus
occidentalis Phrynosomatidae
(Sceloporus) SW - - ME
Generalist
Sceloporus
tristichus Phrynosomatidae
(Sceloporus) SW† 0.31† 1.00† ME†
Generalist
Sceloporus
virgatus Phrynosomatidae
(Sceloporus) SW 0.40 0.80 ME
Generalist
Sceloporus
jarrovii Phrynosomatidae
(Sceloporus) SW 0.30 0.90 ME Rock-
dwelling
Urosaurus
graciosus Phrynosomatidae
(Sceloporus) SW* - - CE
Arboreal
Urosaurus ornatus Phrynosomatidae
(Sceloporus) SW 0.66 2.30 ME
Arboreal
Uta stansburiana Phrynosomatidae
(Sceloporus) SW 0.20 0.60 ME
Generalist
Crotaphytus
collaris Crotaphytidae SW 0.09 0.00 FE
Rock-
dwelling
Elgaria kingii Anguidae
AF* - - ME Litter
Aspidoscelis
uniparens Teiidae AF 0.79 78.70 FE
Terrestrial
Aspidoscelis
tigris Teiidae AF 1.62 87.00 FE
Terrestrial
* indicates classifications based on data of closely related species or family designation.
† data based on S. undulatus as it was formerly a subspecies of the S. undulatus complex
101
Table 2S. Locomotor performance and snout-vent length for 21 species of lizards. Lizards varied widely in locomotor performance.
Species means and standard deviations are presented for 4 performance variables: sprint speed, the maximum speed over a 0.25m
distance; acceleration, the maximum instantaneous acceleration; distance, the distance moved prior to exhaustion; time, time run prior
to exhaustion; SVL, distance from tip of the snout to the vent.
Species Sprint Speed
(m/s) Acceleration
(m/s2) Distance (m) Time (s) SVL (mm)
E. kingii 0.95 (0.22) 24.22 (8.37) 25.93 (3.97) 230.00 (58.57) 81.84 (5.67)
U. stansburiana 2.32 (0.37) 30.11 (4.63) 17.21 (5.49) 61.78 (22.29) 48.78 (1.70)
S. graciosus 2.18 (0.18) 24.92 (4.65) 17.88 (4.06) 58.33 (14.73) 51.76 (1.80)
S. magister 3.22 (0.48) 33.26 (7.75) 32.91 (10.05) 78.30 (17.20) 104.62 (9.73)
S. virgatus 1.57 (0.22) 22.51 (4.55) 14.44 (4.42) 64.20 (27.20) 52.79 (3.87)
S. tristichus 2.37 (0.30) 29.54 (7.61) 18.93 (3.56) 62.33 (12.25) 59.58 (5.86)
S. occidentalis 2.75 (0.36) 31.87 (8.68) 26.46 (3.46) 55.50 (9.32) 65.78 (4.46)
S. poinsettii 3.37 (0.46) 37.54 (9.66) 18.68 (1.41) 42.80 (6.83) 98.87 (14.31)
S. jarrovii 1.71 (0.13) 29.57 (5.83) 15.98 (2.92) 71.90 (14.19) 81.45 (5.11)
S. clarkii 3.49 (0.35) 40.8 (17.08) 27.61 (1.56) 55.50 (14.77) 99.60 (3.75)
U. ornatus 2.53 (0.48) 30.44 (8.18) 12.50 (2.37) 67.62 (19.32) 49.58 (3.56)
U. graciosus 2.82 (0.28) 37.79 (1.94) 19.00 (3.38) 62.67 (8.96) 54.70 (2.04)
P. solare 1.95 (0.30) 22.00 (7.17) 36.17 (5.18) 134.43 (20.65) 85.84 (7.01)
P. modestum 1.63 (0.20) 25.10 (5.93) 26.26 (5.64) 112.80 (35.96) 53.10 (5.28)
P. cornutum 1.76 (0.37) 23.73 (3.94) 27.75 (3.05) 134.13 (42.21) 82.49 (1.89)
H. maculata 2.87 (0.23) 31.00 (12.87) 19.06 (3.12) 55.90 (7.17) 52.33 (3.78)
C. texanus 3.50 (0.47) 35.56 (4.85) 20.94 (5.94) 37.20 (9.08) 66.26 (4.66)
C. draconoides 3.94 (0.43) 55.85 (17.12) 31.01 (5.81) 52.57 (11.42) 82.13 (3.43)
C. collaris 3.73 (0.84) 40.36 (7.02) 26.24 (4.60) 85.57 (22.88) 88.97 (1.67)
A. uniparens 3.05 (0.39) 32.35 (6.29) 46.19 (12.45) 256.90 (131.28) 66.01 (4.84)
A. tigris 4.15 (1.03) 50.88 (17.55) 48.35 (9.73) 232.25 (59.11) 81.79 (2.90)
102
Table 3S. Selective regime classifications for fiber-type data. Designations for foraging mode are based on references in Miles et al.
(2007) and Scales et al. (2009). Foraging mode variables: MPM = movements per minute, PTM = percent time moving. Foraging
mode classifications: SW = sit & wait, MF = mixed forager, AF = active forager. Predator escape classifications are based on Pianka
& Parker 1975, Vitt & Congdon 1978, and references in Scales et al. (2009). Predator escape classifications: CE = cryptic escape, ME
= mixed escape, FE = flight escape. Fiber-type proportion data for each species are from Bonine et al. (2005). FG/(FOG+FG) values
were calculated for use in analyses.
Species Family FG FOG SO FG/
(FG+FOG) Foraging
mode MPM PTM Predator
Escape Callisaurus
draconoides Phrynosomatidae 0.70 0.29 0.01 0.71 SW - 0.02 FE
Cophosaurus texanus Phrynosomatidae 0.65 0.33 0.03 0.66 SW 0.46 0.02 FE
Holbrookia maculata Phrynosomatidae 0.65 0.32 0.03 0.67 SW 0.44 0.02 CE
Uma notata Phrynosomatidae 0.64 0.25 0.11 0.72 SW* - 2.40 FE
Phrynosoma modestum Phrynosomatidae 0.30 0.64 0.06 0.32 SW*† 0.12 - CE
Phrynosoma cornutum Phrynosomatidae 0.25 0.66 0.08 0.27 SW*† - - CE
Phrynosoma mcallii Phrynosomatidae 0.31 0.56 0.13 0.36 SW*† - 32.00 CE
Phrynosoma platyrhinos Phrynosomatidae 0.23 0.72 0.06 0.24 SW*† - - CE
Sceloporus graciosus Phrynosomatidae 0.49 0.41 0.11 0.54 SW† 1.31 5.80 ME
Sceloporus magister Phrynosomatidae 0.41 0.42 0.17 0.49 SW 0.05 0.20 ME
Sceloporus undulatus Phrynosomatidae 0.43 0.43 0.15 0.50 SW 0.31 0.40 ME
Sceloporus virgatus Phrynosomatidae 0.44 0.45 0.11 0.49 SW 0.40 0.80 ME
Urosaurus ornatus Phrynosomatidae 0.45 0.41 0.14 0.52 SW 0.66 2.30 ME
Uta stansburiana Phrynosomatidae 0.48 0.46 0.07 0.51 SW 0.20 0.60 ME
Crotaphytus collaris Crotaphytidae 0.58 0.37 0.06 0.61 SW 0.09 0.00 FE
103
Gambelia wislizenii Crotaphytidae 0.58 0.38 0.04 0.60 SW* - - FE
Laudakia stellio Agamidae 0.37 0.46 0.17 0.45 SW* - - FE
Elgaria kingii Anguidae 0.43 0.41 0.16 0.51 AF* - - ME
Acanthodactylus
scutellatus Lacertidae 0.60 0.25 0.15 0.71 SW 1.00 7.70 FE
Aspidoscelis tigris Teiidae 0.76 0.22 0.03 0.78 AF 1.62 87.00 FE
Carlia fusca Scincidae 0.54 0.39 0.07 0.58 MF 2.00 17.70 ME*
Eumeces fasciatus Scincidae 0.49 0.38 0.13 0.56 AF* 0.60 72.40 ME
* indicates classifications based on data of closely related species or family designation.
† indicates a species modeled as both AF and SW due to differences in MPM and PTM.
104
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