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First published online March 14, 2008
Journal of Experimental Biology 211, 1029-1040 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.015503
The correlated evolution of biomechanics, gait and foraging mode in lizards
Ohio Center for Ecology and Evolutionary Studies and Department of Biological Sciences, Ohio University, Athens, OH 45701, USA
* Author for correspondence (e-mail: em386403{at}ohio.edu)
Accepted 29 January 2008
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: lizards, biomechanics, gait, foraging behavior, locomotor function, correlated evolution
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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). Movement
patterns have been used to categorize lizards as sit-and-wait or wide foraging
(Perry, 2007;
Pianka, 1966) and numerous
physiological, morphological, behavioral, life history, dietary and other
ecological traits have been shown to covary with foraging mode
(Cooper, 1994;
Reilly et al., 2007;
Schwenk, 1993). For example,
sit-and-wait foragers (SW) rely primarily on vision to ambush prey (and
accordingly have poorly developed chemosensory abilities), and use large
sticky tongues coupled with short broad skulls to capture and process prey. In
wide foraging (WF), which has evolved several times
(Fig. 1), lizards use an
oscillating forked tongue to search for prey (with highly developed
chemosensory systems), and use their long narrow jaws to capture and process
prey (McBrayer and Corbin,
2007; Reilly and McBrayer,
2007).
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; Husak and Fox,
2006; Irschick,
2000a; Irschick,
2000b). However, during foraging, locomotor speeds differ
significantly between foraging types
(Anderson, 2007;
Cooper et al., 2005). During
foraging, SW lizards remain motionless most of the time and then use short
bursts of fast locomotion (
10% of activity period) to ambush passing
prey. In contrast, WF lizards move slowly most of the time (10–90%
of activity period) over long distances, chemically sampling the environment
to locate hidden caches of prey. Accordingly, performance studies have shown a
trade-off between maximum speed and endurance such that SW foragers sprint
faster than WF but WF have larger endurance capacities than SW foragers
(Miles et al., 2007). Thus,
maximum speed and endurance match the locomotor demands of foraging mode.
Although there have been many studies that have examined locomotor function
when lizards move at their fastest speeds
(Irschick and Jayne, 1999;
Russell and Bels, 2001;
Vanhooydonck et al., 2002;
White and Anderson, 1994), far
fewer have addressed function at slower speeds.
Two key functional aspects of locomotion that have likely evolved with
foraging mode are center-of-mass (COM) biomechanics and gait. A variety of
terrestrial animals, including lizards, move their COM using either running or
walking mechanics (Biewener,
1998; Cavagna et al.,
1977; Farley and Ko,
1997; Full and Tu,
1991; Full and Weinstein,
1992; Heglund et al.,
1982; Reilly et al.,
2006). Running mechanics involve the kinetic (KE) and
gravitational potential (GPE) energies of the COM cycling in-phase and are
usually associated with faster locomotion. Walking mechanics are characterized
by out-of-phase oscillations of the KE and GPE of the COM, are usually
associated with slow locomotion, and can involve mechanical energy savings
via the inverted-pendular mechanism. Many animals, including lizards
studied to date, use a trotting gait with small duty factors while moving fast
and shift toward a single-foot (4-beat) gait with larger duty factors when
moving slowly (e.g. Biknevicius and
Reilly, 2006; Hildebrand,
1976; Sukanov, 1974; White and
Anderson, 1994). Based on these patterns we predict that the speed
demands of foraging mode should be related to biomechanics and gait.
Sit-and-wait species rely on rapid locomotion during prey capture, which
should involve running mechanics and trotting gaits. On the other hand, WF
lizards predominantly use slower locomotion to locate prey, which should
involve walking mechanics and a shift toward single-foot gaits (while
retaining faster locomotion with running mechanics and trotting gaits for
predator evasion and social interactions).
To test these hypotheses we examined the relationships among gait, mechanics and foraging mode in 15 lizard species with a phylogenetic history marked by numerous transitions in foraging mode (Fig. 1). Comparisons across species show a strong evolutionary correlation of gait and biomechanics with foraging mode and that not only have WF lizards evolved several different ways to walk slowly but some have also evolved very slow running.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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). Species
foraging modes (Fig. 1) were
based on literature accounts (Reilly et
al., 2007) or our field data (for Eulamprus quoyii;
E.J.M. and S.M.R., unpublished). Species were selected to sample SW taxa
(Iguania), the major evolutionary transitions to WF mode (ground geckos,
Scincomorpha, Lacertoidea and Varanidae), and three Scincomorphs that have
reverted to SW foraging from WF ancestors (Eulamprus, Cordylus and
Lepidophyma). All housing and experimental procedures followed
approved animal use protocols.
Data collection
Gait and whole body mechanics were studied as lizards travelled down a
racetrack towards a dark hide box. We induced the fastest speeds by gently
pressing on the tail or hindlimb, medium speeds by tapping the ground near the
animal or waving our hands above the animal, and slow speeds by allowing the
animal to move down the track without any human stimulation. This procedure
captured a wide range of speeds to represent the locomotor scope and foraging
speeds for each species. Each individual was induced to move down the
racetrack several times (usually 3–5 times) until we noticed signs of
fatigue (uncoordinated limb movements, dragging belly, or refusal to move
after 3 tail pinches). The total number of trials collected per species were
roughly evenly distributed among individuals. Individuals were allowed to rest
and recover for 24 h before subsequent trials. All diurnal species were
maintained at 36–40°C for the duration of each trial, except
for the nocturnal Eublepharis and Lepidophyma, which were
maintained at 26–30°C. Lizards were warmed to these
temperatures under heat lamps and temperature was checked via an
infra-red thermal laser directed on the belly periodically throughout the
experiments.
Ground reaction forces were quantified using a custom-made force platform
based on a strain gauge, spring-blade design described in Bertram et al.
(Bertram et al., 1997).
Vertical (V), fore–aft (FA) and medio-lateral (ML) ground reaction
forces were sampled at 500 Hz using National Instruments data acquisition
hardware and a LABVIEW custom designed virtual data sampling instrument
following Parchman et al. (Parchman et
al., 2003). The 0.6 m long by 0.2 m wide force platform surface
was flush with the racetrack surface and located 3–3.6 m along its 5.2-m
length. The entire surface of the racetrack and platform was covered with fine
grit sandpaper to prevent foot slippage.
Quantifying gait
During force data collection, lizards were filmed at 120 Hz or 500 Hz
(small, fast lizards required higher frame rates) with high-speed video
cameras mounted 1 m above the surface of the force platform. Mirrors were
mounted on angled walls along each side of the force platform to visualize
footfalls. Kinematic analyses were conducted using APAS (version 1.0). First,
we determined whether trials were steady speed by digitizing the tip of the
snout as the lizard crossed seven evenly spaced lines along the surface of the
racetrack. Next we calculated average speed across the entire field of view
and discarded any trial that had >20% difference between any interval speed
and the average speed. We recorded the timing of touch-down and lift-off for
each limb in the steady speed trials. Lizards always used symmetrical gaits;
therefore, we implemented the Hildebrand terminology to describe gait using
two parameters, duty factor and limb phase
(Hildebrand, 1976;
Reilly and Biknevicius, 2003).
We examined hindlimb duty factor, which is the amount of time that the
reference hindlimb contacts the substrate divided by the total stride
duration. Limb phase is the amount of time that the footfall of the
ipsilateral forelimb follows the reference hindlimb divided by the stride
duration. Duty factor and limb phase were multiplied by 100 to obtain
percentages. A bivariate plot of limb phase vs duty factor
(Hildebrand plot) was used to illustrate gaits following gait terminology of
Biknevicius and Reilly (Biknevicius and
Reilly, 2006). Trots were defined as gaits with limb phases
between 37.5% and 62.5%. Lateral sequence gaits were those with limb phases
less than 50% and diagonal sequence gaits are those with limb phases greater
than 50%. Single-foots were defined as those gaits on either side of the trot
(limb phases 12.5%–37.5% and 62.5%–87.5%).
Quantifying mechanics
Whole-body mechanics were calculated by aligning steady speed steps with
ground reaction forces using another custom LABVIEW virtual instrument.
Following Willey et al. (Willey et al.,
2004), we defined a step as the time from footfall of the first
limb in one couplet to the footfall of the first limb in the opposite couplet.
Three-dimensional force data were converted into KE and GPE profiles following
published methods (Blickhan and Full,
1992; Donelan et al.,
2002; Parchman et al.,
2003). The integration constants for vertical and medio-lateral
velocity were set as in Donelan et al.
(Donelan et al., 2002); the
integration constant for fore–aft velocity was set as the mean forward
speed. Phase shift of the KE and GPE profiles was used to distinguish running
from walking mechanical energy patterns. Phase shift was defined as the time
difference between the minimum values of KE and GPE relative to step duration,
multiplied by 360° (Farley and Ko,
1997; Parchman et al.,
2003) and normalized to the range of 0–180°. Phase
shifts from 135–180° were defined as walking mechanics and phase
shifts of 0–45° were defined as running mechanics
(Ahn et al., 2004;
Reilly et al., 2006).
We used the ratio of total GPE to total KE over a step as an index of
lumbering vs cursoriality following Reilly et al.
(Reilly et al., 2006).
Lumbering species are defined as having GPE:KE ratios significantly greater
than one; cursorial species have ratios less than or equal to one.
Statistical analyses
Phylogeny
We ran all phylogenetic comparative analyses (phylogenetic ANOVA, maximum
likelihood character reconstruction and independent contrasts, each described
in the following sections) on two phylogenies for lizards
(Estes et al., 1988;
Townsend et al., 2004). Branch
lengths were based on both fossil and biogeographic estimates
(Estes, 1983;
Evans, 2003;
Krause et al., 2003;
Wells, 2003) and the
fossil-based methods (Vidal and Hedges,
2005). Root age was set to 225 mya
(Vidal and Hedges, 2005).
Branch lengths were not available for the relationships among skinks
(Eulamprus, Plestiodon and Eumeces), geckos
(Eublepharis, Coleonyx and Hemidactylus) or tropidurids
(Leiocephalus and Tropidurus); these branches were
arbitrarily assigned to 25 million years. We also ran analyses with all branch
lengths set to one, which assumes a punctuational model where all change
occurs at the nodes. All of the results from comparative analyses were
qualitatively similar for both phylogenies and all branch lengths; thus, we
report results of all phylogenetic comparative analyses for the Townsend et
al. (Townsend et al., 2004)
phylogeny and fossil-estimated branch lengths.
Correlated evolution of biomechanics, gait and foraging mode
We employed a phylogenetic ANOVA to test for correlated evolution among
foraging mode and biomechanics or gait. We assigned phase shifts, limb phases
and duty factors to each foraging mode while they were moving as they would
while foraging. Values were assigned this way because it allowed us to test
the hypothesis that the foraging behaviour and locomotor function have
undergone correlated evolution. This resulted in values being assigned to SW
foragers for running mechanics and to WF for walking mechanics. The
phylogenetic ANOVA examines the difference between the F-value
obtained from a non-phylogenetic ANOVA and a critical F-value
(Fcrit) obtained from a null distribution of
F-values calculated by simulating character evolution on the
phylogeny (Garland et al.,
1993). If the non-phylogenetic ANOVA is significant and its
F-value is greater than the 95th percentile of F-values
obtained from the null distribution then it can be concluded that the two
traits have undergone correlated evolution. If the non-phylogenetic ANOVA is
not significant then it is concluded that the two traits are not associated
and there is no need for further analysis, i.e. the traits have not undergone
correlated evolution. First, we ran a non-phylogenetic ANOVA with phase shift,
limb phase and duty factor as response variables and foraging mode as the main
effect. Next, we generated 1000 data sets that simulated the evolution of
phase shift, limb phase and duty factor using PDSIMUL
(Garland et al., 1993). We
performed both bounded and unbounded simulations and used both Brownian motion
and Ornstein–Uhlenbeck evolutionary models; however, these variations
did not qualitatively alter the results. The simulated data sets were analyzed
using PDANOVA to create a null distribution of F-values from which
the Fcrit was determined. We concluded foraging mode and
biomechanics or gait to have undergone correlated evolution if the
non-phylogenetic ANOVA was significant and its F-value was greater
than Fcrit determined from the 95th percentile of the null
distribution of simulated F-values.
Evolutionary history
We employed ancestor character reconstruction to visualize the evolutionary
relationships between foraging mode, biomechanics and gait. Foraging mode
reconstruction was based on the larger sample of 110 species
(Miles et al., 2007) and
interpretation from Reilly and McBrayer
(Reilly and McBrayer, 2007).
We reconstructed the ancestral character states for biomechanics (phase shift)
and gait (limb phase and duty factor) using maximum likelihood in the computer
program ANCML (Schluter et al.,
1997). This program also outputs standard errors for reconstructed
trait values. We used standard errors to generate 95% confidence intervals
(CI) for each trait at each node. Then we determined significant evolutionary
changes in each trait by comparing ancestor nodes to their associated
descendent node or tip values. If the 95% CI overlapped then
ancestor–descendent pairs were considered the same and the trait was not
evolving; if the CI did not overlap then the pairs were considered
significantly different and the trait was evolving. We used a modification of
this approach to assign node values for phase shift. We assigned node values
as running mechanics if the node's 95% CI was within the range of a running
phase shifts (0–45°) but outside of the range of a walking phase
shifts (135–180°). Likewise, we assigned node values as walking
mechanics if the node's 95% CI was within the range of walking phase shifts
(135–180°) but outside of the range of running phase shifts
(0–45°). If the CI did not overlap (46–134°) or overlapped
both (contained values both 
135° and 
45°) running and
walking phase shifts then the node was assigned as equivocal.
Species differences in gait
We found that foraging mode evolution was strongly associated with phase
shift and duty factor; however, there was a much weaker relationship with limb
phase (see Results). In addition, the analysis of species differences for gait
is inherently multivariate. Thus, to further probe patterns of gait evolution
within Hildebrand gait space, we employed a repeated-measures (RM) MANOVA and
CART (classification and regression tree). For the RM-MANOVA, species was a
fixed effect, limb phase and duty factor were response variables, and repeated
trials per individual was the repeated measure. We ran separate analyses on
walking and running mechanics because all species could run, but only seven
species could walk (Table 1).
We assigned species to post-hoc groupings by comparing their 95% CIs
on the first canonical axis output from the RM-MANOVA
(Mardia et al., 1980). Species
whose 95% CIs overlapped are placed in the same group; species whose CIs did
not overlap are placed in different groups. Our data violated some of the
assumptions of MANOVA (unequal group sizes and variances; slight deviation
from multivariate normality); therefore, we used non-parametric CART analysis
to verify species groupings.
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We also analyzed how lizards that used both running and walking COM mechanics shifted from running to walking in Hildebrand gait space. We ran separate MANOVAs for each species with COM mechanics as the main effect and duty factor and limb phase as response variables. A significant MANOVA would indicate that a species has shifted its position in gait space, whereas a non-significant finding would indicate no shift in gait space. A sequential Bonferroni correction was applied to account for multiple hypothesis testing. These analyses were performed in JMP 5.0 (SAS Institute Inc., Cary, NC, USA).
Locomotor integration
Finally, we wanted to assess the degree of evolutionary integration of the
locomotor system (Dickinson et al.,
2000; Reilly et al.,
2007). We tested the hypothesis that gait and biomechanics have
undergone correlated evolution by examining the correlations between
phylogenetically independent contrasts for phase shift, limb phase, and duty
factor. A significant correlation between independent contrasts would indicate
that two traits have undergone correlated evolution
(Garland et al., 1992). This
analysis was performed in the PDAP module of Mesquite
(Maddison and Maddison, 2007;
Midford et al., 2002). All
regressions were computed through the origin and adequate standardization of
contrasts was checked using diagnostics tests in the PDAP module of Mesquite
(Garland et al., 1992).
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RESULTS |
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Gait and foraging mode
To test for correlated evolution of gait with foraging mode we examined
duty factor and limb phase separately. For duty factor, the non-phylogenetic
ANOVA found that WF and SW species were significantly different
(F1,15=51.01, P<0.0001). The PDANOVA on duty
factor calculated a Fcrit=5.46, much smaller than that of
the non-phylogenetic ANOVA, indicating that duty factor and foraging mode have
undergone correlated evolution. Character reconstruction via maximum
likelihood analysis on duty factor assigned values of 54–49% across
most of the ancestral nodes with significant relative increases in duty factor
in the branches leading to four taxa (Eublepharis, Tracheloptychus,
Tupinambis and Varanus).
For limb phase, the non-phylogenetic ANOVA was not significant
(F1,15=2.99, P=0.104), indicating that
limb-stepping pattern did not vary to a significant extent across these
lizards when tip values alone were considered. Based on this analysis we
concluded that foraging mode and limb phase did not undergo correlated
evolution. However, character reconstruction via maximum likelihood
analysis revealed some significant changes in limb phase. Ancestral nodes had
values 50% while the branches leading to four species had significant
changes in limb phase away from 50% (decrease: Tracheloptychus,
Ameiva and Varanus; increase: Acanthodactylus). This
indicates that even though foraging mode and limb phase have not undergone
correlated evolution, limb phase has undergone some evolutionary change.
Multivariate species differences in running and walking gaits
Observed gaits for all 15 species are plotted in Hildebrand gait space in
Fig. 5 with running mechanics
in Fig. 5A and walking
mechanics in Fig. 5B. Species
were significantly different in Hildebrand gait space for both walking and
running mechanics (RM-MANOVA: running mechanics,
F42,472=8.8499, P<0.0001; walking mechanics,
F9,55=20.65, P<0.0001). Based on both the 95%
confidence ellipses on the first canonical axis and the CART analysis, species
clustered in significantly different portions of Hildebrand gait space.
Species clustered into two groups when using running mechanics
(Table 1;
Fig. 5A). Tupinambis,
Eumeces sch. and Eublepharis used high duty factor trots; all
other species used low duty factor trots. While walking, species clustered
into three groups (Table 1;
Fig. 5B). All species used
trotting limb phases. Most of the wide foragers used trots with high duty
factors while walking. Two species exhibited significantly different gaits.
Ameiva walked using a significantly lower limb phase with relatively
low duty factors. Acanthodactylus walked using a significantly higher
limb phase with relatively low duty factors.
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Gait 2 (G2): a small increase in duty factor with moderate speed (0.43 m s–1), shifting to a significantly lower limb phase (i.e. a more lateral sequence trot; Ameiva, F2,25=6.83, P=0.004).
Gait 3 (G3): no change in a fast speed (1.02 m s–1) and high limb phase gait (i.e. a more diagonal sequence trot; Acanthodactylus, F2,33=2.96, P=0.06). Although duty factor exhibits a large increase when switching from running to walking, the increase is not statistically significant, probably due to the large standard error produced by the small walking sample (N=2) for this species.
Gait 4 (G4): no change in gait with slow (0.22–0.24 m s–1) trotting walks and runs (Tupinambis, F2,14=2.98 P=0.08; Eumeces sch., F2,16=0.25 P=0.78; Eublepharis, F2,7=0.36, P=0.71). This pattern is also unique in having a significantly lower speed and higher duty factor during running (Fig. 5A).
Correlated evolution of biomechanics and gait
Finally, the degree of integration between biomechanics and gait was tested
using phylogenetic independent contrasts between biomechanics (phase shift)
and gait (expressed as both duty factor and limb phase). The independent
contrasts for phase shift were significantly related to duty factor
(r2=0.78, F1,14=51.04,
P<0.0001) and limb phase (r2=0.479,
F1,16=14.68, P=0.002). By explicitly accounting
for phylogenetic patterns within these traits these results show that gait and
biomechanics have undergone correlated evolution.
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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50%) with running mechanics when
moving at fast speeds (Table 1;
Fig. 5A). Animals may use
trotting gaits while running because the line of support generated by diagonal
couplets is optimally aligned under the COM, thus offering good stability at
high speed (Cartmill et al.,
2002; Chen et al.,
2006; Hildebrand,
1988). In addition, running mechanics coupled with a trotting gait
may enhance maneuverability (Chen et al.,
2006) and provide a simple template for the neural control of fast
locomotion (Full and Koditschek,
1999). Given our observation that all species in this study used
mechanical runs with a trotting gait it appears that these species achieve the
advantages of stability, maneuverability and simplicity of neural control of
fast locomotion.
Although all lizards in this study used a trotting gait while running we
found that species cluster into two distinct groups in Hildebrand gait space
(Fig. 5A) separated by a
significant difference in duty factor. Most species (all of the SW and most of
the WF) consistently used low (50–30%) duty factor trotting gaits (limb
phase 40–60%) at high speeds (0.8–1.64 m s–1;
Table 1). This is similar to
the high-speed gaits used by most cursorial animals when moving fast
(Hildebrand, 1976;
Reilly and Biknevicius, 2003).
However, three WF species (Eublepharis, Eumeces sch, and
Tupinambis) shift to high duty factor (67–72%) and low-speed
(0.16–0.24 m s–1) running
(Table 1). During mechanical
runs, these species overlap the gait space used by tuataras, salamanders and
frogs (Ahn et al., 2004;
Reilly et al., 2006).
The evolution of mechanics and gait with foraging mode in lizards
A summary of patterns of evolution of locomotor traits in our sample of
lizards based on our analyses is presented in
Fig. 7. Ancestral
reconstructions of foraging mode (Miles et
al., 2007) show that SW is ancestral for all lizards. We suggest
that running mechanics are ancestral for lizards based on three pieces of
evidence: (1) all lizards examined in this study use running mechanics; (2)
ancestral character reconstruction via maximum likelihood, based on
our sample of 18 species, shows that running mechanics is ancestral; and (3)
additional comparative analyses show a tight evolutionary coupling between
foraging and mechanics. Based on this evidence we suggest that the SW ancestor
of lizards only used running mechanics. However, when we compare species
locomotor function when moving at their foraging speed, the most obvious
pattern is that COM biomechanics and foraging behavior have undergone
correlated evolution. Three lines of evidence support this pattern. First,
foraging mode and biomechanics have a one-to-one pattern when mapped onto the
phylogeny; all SW species only use running mechanics and every evolutionary
transition to WF behavior is accompanied by the appearance of walking
mechanics (Figs 4,
7). Second, the three examples
of evolutionary reversals from WF back to the SW strategy (Eulamprus
quoyii, Lepidophyma flavimaculatum and Cordylus warreni) have
independently lost walking mechanics (Figs
4,
7). Our interpretation of these
three species having lost walking mechanics is based on the evidence that the
basal Scincomorph was WF (Miles et al.,
2007) and thus probably used walking mechanics. Third, our results
were robust to variations in phylogenetic topology, branch lengths and
evolutionary models; we consistently found that foraging mode and biomechanics
have undergone correlated evolution.
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Why change mechanics and gait when moving at a slower foraging speed?
The tight evolutionary correlation between mechanics and foraging mode
suggests that walking mechanics may have been a key innovation in the
evolution of slower locomotion in WF lizards. One possible benefit of walking
mechanics is that they may decrease the total mechanical energy needed to move
the COM because of the pendulum-like exchange of KE and GPE
(Biewener, 2006;
Cavagna et al., 1977;
Farley and Ko, 1997). Pendular
savings, measured as % recovery of external mechanical energy, ranged from
18–47% in WF species during walking compared to 1–18% during
running (Table 1). Thus,
walking in WF lizards requires less external mechanical energy than running,
suggesting that it is an energetic adaptation for long periods of slow
locomotion. However, while WF lizards do reduce the amount of external
mechanical energy used during locomotion it does not necessarily follow that
this results in a relevant reduction in metabolic energy, for two reasons.
First, lizards have body masses that are too small to realize relevant
metabolic savings from mechanical energy savings during walking because their
actual metabolic costs are two orders of magnitude greater than their total
mechanical energy costs (Reilly et al.,
2007). Therefore, no matter how much percent recovery of external
mechanical energy the lizards attain, it is insignificant in relation to the
actual metabolic cost of locomotion. Second, the cost of locomotion during
walking has been shown to be greater than running, both on a per stride basis
and on an absolute basis, because WF actually spend the majority of their
activity budget walking slowly (Anderson
and Karasov, 1981; Reilly et
al., 2007). Thus, it is difficult to support the idea that walking
mechanics is a key adaptive innovation to reduce the metabolic cost of
locomotion in WF. In fact, it has been proposed that walking and running
mechanics may actually be spandrels (sensu
Gould and Lewontin, 1979) of
legged locomotion in small animals (Reilly
et al., 2006; Reilly et al.,
2007). Resolution of the true energetic relevance of mechanical
energy savings in small animals awaits future integrative studies.
Another argument for why animals switch mechanics with speed relates to the
relationship between centripetal and gravitational forces acting on the COM
(Kram et al., 1997). When
animals increase speed centripetal force increases until it exceeds the
gravitational force (occurring at a Froude number 1), which prevents the
animal from walking with an inverted pendulum and necessitates the switch to
running mechanics. This probably explains why many lizards switch from walking
to running mechanics with increasing speed
(Fig. 6; G1–G3). However,
this argument does not explain the reverse; when slowing down, animals are not
physically required to switch back to walking at a given speed. This is
clearly illustrated by studies showing that animals actually prefer to switch
from walking to running mechanics at a Froude number 0.5
(Alexander, 1989), well below
the Froude number 1, which requires the switch. Thus, animals appear to
be capable of running at any speed, but can only walk up to a critical speed
corresponding to a Froude number of 1. It follows that when lizards slow
down they are not switching from running to walking mechanics due to a
physical requirement. The difference in speeding up vs slowing down
is not trivial because lizards have evolved slower locomotor speeds as a
necessity for WF behavior. Thus, neither energy savings nor physical
constraints explain the evolutionary transition from fast running to slow
walking mechanics in WF lizards.
Although there is not a clear energetic or biomechanical benefit of walking
mechanics in WF lizards, there may be other benefits associated with evolving
slower speeds and new gaits. Our study shows that WF species move at slower
speeds than SW species when considering speeds that they likely use while
foraging [see also Cooper et al. (Cooper
et al., 2005) for the same pattern in field movement speeds]. Wide
foraging lizards have evolved entire suites of characters related to their
shift to derived chemosensory systems
(Cooper, 1994;
McBrayer and Corbin, 2007;
Reilly et al., 2007;
Schwenk, 1993). From the brain
to olfactory receptors to forked air sampling tongues, WF lizards exhibit a
number of characters that enhance their ability to slowly search for food
[Reilly et al. (Reilly et al.,
2007) and references therein]. While foraging, slower locomotor
movements may enhance wide foraging by allowing the chemosensory apparatus to
meticulously sample a complex heterogeneous habitat for prey chemicals
(Anderson, 2007;
Cooper, 1994). The prey items
that WF chemically search for reward them with a higher energy pay-off
(Gasnier et al., 1994). Fast
locomotor movements, while foraging, would preclude WF from being able to
sample chemicals thoroughly and follow them in the environment. Thus, simply
moving slower while foraging is of adaptive value to WF lizards because it
affords them the ability to effectively locate and discriminate energy-rich
prey. Our findings suggest that the convergent evolution of slower foraging
locomotion in WF lizards is an important correlate of effective predatory
chemosensory behavior.
Wide foraging lizards couple slower speeds while foraging with a change in
gait characterized by an increase in duty factor. Evolutionarily, the lizards
examined in this study appear to shift from the ancestral high-speed low duty
factor trot while running to slower-speed higher duty factor trots while
walking (Fig. 6). Thus, our
findings show that duty factor is the principal functional parameter that
changes when lizards evolve slower speed locomotion and WF behavior. Previous
studies of lizard gait have also shown that duty factor increases with
decreasing speed (Hildebrand,
1976; Sukanov, 1974). The increase in duty factor associated with
moving slowly may have two benefits for a WF lizard. First, larger duty
factors, i.e. longer ground contact time, are actually energetically less
expensive because they allow muscle force to be produced at a slower rate,
which is more energetically economical
(Kram and Taylor, 1990;
Pontzer, 2007). Thus, WF
lizards may realize metabolic energy benefits by increasing duty factor when
moving slowly. However, all animals appear to increase duty factor as they
slow down, suggesting that the energetic benefits are not an adaptation for WF
per se, but rather a general feature of slower terrestrial
locomotion. Second, moving with larger duty factors offers greater stability
at slower speeds for animals that only trot
(Hildebrand, 1976). When
trotting animals slow down they move from aerial trots (alternating periods of
support on 2 diagonal feet) at low duty factors to regions in Hildebrand gait
space with periods of support by 2, 3 or 4 feet
[fig. 7 in Hildebrand
(Hildebrand, 1976)]. Thus,
simply increasing duty factor enhances stability at lower speeds. Accordingly,
WF lizards may realize both force-production energetic benefits and stability
benefits as simple correlates of the increase in duty factor when moving at
slower foraging speeds.
Variation in ecological relevance of gait among WF lizards
Demonstration of the evolution of duty factor with foraging mode supports
the general view that gait is a dynamic part of the locomotor system that is
capable of responding to divergent ecological and behavioral challenges
(Stevens, 2006). The WF
lizards examined in this study exhibit four patterns of gait change when they
switch from running to walking (Fig.
6) that may be related to details of their foraging ecology. These
four patterns differ in the relative shifts in speed, duty factor limb phase
(Fig. 6).
In terms of speed, the slowest G1 and G4
(Fig. 6) walkers are extreme WF
(Varanus, Tupinambis, Tracheloptychus), have particularly short limbs
and are fossorial (Eumeces sch.), or are cryptic and nocturnal
(Eublepharis). The moderate speed G2 (Ameiva) and high speed
G3 (Acanthodactylus) walkers both belong to lizard families that
exhibit field movement patterns marked by frequent pauses and changes in
direction (Anderson, 2007;
Verwaijen and Van Damme, 2007)
and many of the walks we recorded from Ameiva and
Acanthodactylus fit this description. Interestingly, these two
species used relatively small duty factors compared to the other species
(Table 1;
Fig. 6). Such low duty factor
walking may be useful for foraging with frequent pauses because it has been
hypothesized to allow numerous opportunities to change direction, thereby
increasing maneuverability (Vanhooydonck
et al., 2002), which may facilitate chemosensory tracking
abilities. Thus, the speed that each lizard species uses while foraging
appears to be related to the specific techniques or ecological context they
use to forage.
In terms of patterns of gait change, G1 involved significant increases in
duty factor (from 41–50% to 69–72%) and a shift to a more lateral
sequence trot (limb phase from 50–57% to 40–43%;
Fig. 6). Both species that
evolved G1 (Varanus and Tracheloptychus) exhibited
significant decreases in both duty factor and limb phase from their immediate
ancestral nodes. G1 is also associated with a large decrease in speed (from
running at 0.92–1.37 m s–1 to walking at
0.16–0.29 m s–1). Although little is known about the
field behavior of these species, they clearly follow the general tetrapod
pattern of shifting towards a single-foot gait with larger duty factors when
moving slowly (Biknevicius and Reilly,
2006; Hildebrand,
1976).
The G2 of Ameiva also had a small decrease in duty factor and limb
phase, but was different in three ways. First, both running and walking limb
phases were the lowest observed, and during walking, Ameiva
occasionally utilized a single-foot gait
(Fig. 3) (sensu
Biknevicius and Reilly, 2006).
Second, character reconstruction showed that limb phase was significantly
lower in Ameiva than in its immediate WF ancestor. Third,
Ameiva utilized moderate walking speeds (mean=0.43 m
s–1; Table 1).
Ameiva appears to exhibit G2 for a number of reasons. In the wild,
Ameiva travels widely and quickly between patches of resources
(Magnusson et al., 1985;
Anderson, 2007). In addition,
Ameiva has comparatively longer feet than most lizards (E.J.McE.,
unpublished). Thus, both ecological and morphological factors may affect
mechanics and gait in Ameiva. Clearly more comparative kinematic,
morphometric and behavioral studies are needed to understand why
Ameiva has a lower limb phase during walking.
Acanthodactylus boskianus exhibited G3 with an increase in duty
factor that was nearly significant (P=0.06). The G3 pattern had no
change in limb phase with walking. This species employed the highest limb
phase (57%) observed during walking in the locomotor sample we collected.
Character reconstruction indicated a significantly higher limb phase in A.
boskianus relative to that of its immediate ancestor, indicating it had
evolved toward a more diagonal-sequence trot. In addition, A.
boskianus adopted a strategy of significantly faster speed walking (1.02
m s–1) than other lizards in our study. We propose that very
fast walking in A. boskianus may be related to WF on hot desert sands
that are nearly devoid of vegetation
(Belliure and Carrascal, 2002;
Perry et al., 1990).
Acanthodactylus erythrurus has been shown to heat up more slowly,
cool down more quickly, and exhibit higher physiologically optimal and
preferred temperatures than do most lizards. All of these thermal traits are
posited to be adaptations to the scarcity of cover, and/or high predation risk
in the xeric and thermally demanding environments they inhabit
(Bauwens et al., 1995;
Belliure and Carrascal, 2002;
Belliure et al., 1996).
Although A. erythrurus is a SW species, it seems likely that WF
species (such as A. boskianus) would experience even stronger
selection on thermal traits because they are presumably more exposed to
predators and hot temperatures than are SW species. Thus, rapid walking and
low duty factors during foraging in A. boskianus may be an adaptation
for seeking prey on extremely hot sandy substrates found in deserts.
Species exhibiting the fourth pattern (G4) adopt the same walking gait
(duty factor 64–73%, limb phase 43–46%) and speed range
(0.22–0.24 m s–1) as the G1 pattern. However, the G4
species are unique in utilizing running gaits with significantly higher duty
factors (Fig. 5A). Thus, they
have shifted both the walk and run to high duty factors at slow speeds (0.19
to 0.29 m s–1). An additional skink (Plestiodon
skiltonianus) and gecko (Coleonyx variegatus) appear to occupy a
similar region in gait space (Farley and
Ko, 1997) and likely experienced a similar evolutionary history
because they are closely related to two species in this study (Eumeces
schneideri and Eublepharis macularius). The phylogenetic
reconstructions (Fig. 7)
suggest that these species have independently evolved low-speed
locomotion.
Why run slow?
The speeds exhibited by our Tupinambis match field and lab
foraging speeds (Klein et al.,
2003), so we are confident that the patterns of gait and mechanics
we observed reflect their walking foraging mode. In terms of running,
Tupinambis is known to employ the strategy of defensive and
aggressive behavior rather than flight both in the lab (E.J.M., unpublished)
(Klein et al., 2003) and the
field (De Lema, 1983).
However, Tupinambis are capable of moving more rapidly
(Urban, 1965) and when they
do, they exhibit high speed, low duty factor trots
(White and Anderson, 1994).
Thus, they may actually use the G1 pattern, although given the choice they
appear to prefer to fight rather than high-speed running as an antipredatory
behavior.
The remaining two species that exhibit slow running probably never have to
run fast (at least as fast as other lizards). Eumeces schneideri has
extremely small limbs and inhabits burrows, rarely venturing into the open
(Disi and Amr, 1998).
Eublepharis is a large, nocturnal, slow moving, WF ground gecko
(Cooper, 1994) that does not
rapidly flee but uses crypsis, posture, tail movement displays and tail
autotomy as antipredatory behaviors
(Marcellini, 1977). All other
WF lizards in our sample were long limbed, diurnal, and preferred high-speed
running as an antipredatory response (E.J.M., personal observation).
The G4 species exhibited a lack of relationship between speed and mechanics
(Table 1). It has been argued
that this is related to the basal condition of lumbering locomotion in
tetrapods, and is found in a variety of sprawling animals such as salamanders,
tuataras, alligators and frogs (Ahn et al.,
2004; Reilly et al.,
2006; Willey et al.,
2004). Lumbering locomotion has been defined on the basis of
having GPE greater than KE, whereas cursorial locomotion has been defined by
KE being equal to or greater than GPE
(Reilly et al., 2006).
Interestingly, all lizards exhibited GPE/KE ratios that were either not
significantly different from or far less than one
(Table 1). Thus, G4 lizards are
cursorial even though they exhibit the same patterns of gait and mechanics as
other sprawling tetrapods. This finding shows that the evolutionary shift to
slow running in the G4 species does not include a shift to lumbering locomotor
mechanics.
Lizards only trot
One interesting observation about the lizard gaits observed in this study
is that they do not substantially deviate from trotting limb phase during
running or walking. In general, the SW runners we studied exhibited the
`cleanest' trots (near 50% limb phase indicating coordination of diagonal limb
couplets). During walking, most WF we studied exhibited lower limb phase
values (Table 1). Although
there are a few data points extending well into limb phases diagnostic of the
lateral sequence single-foot (Figs
2,
3), all species means fell into
the limb phase range of 37.5–62.5% that describes a trotting gait
(sensu Biknevicius and Reilly,
2006). The lack of single-foot gaits was surprising given that
lateral and diagonal sequence single-foots are predicted to improve stability
in slow moving animals due to the larger polygons of support associated with
these gaits (Cartmill et al.,
2002; Hildebrand,
1988). Thus, when lizards shift to walking they may experience
some enhanced stability afforded by more lateral or diagonal sequence trots
but they do not fully move into the areas of single-foot gait space that take
full advantage of hypothesized increases in stability
(Cartmill et al., 2002;
Hildebrand, 1976). This
finding suggests the presence of some underlying neural or biomechanical
constraint, which may limit lizards from routinely using lateral and diagonal
sequence gaits that many mammals use when moving slowly. Primates also exhibit
a lack of relationship between limb phase and speed or substrate type
(Stevens, 2007). Thus, limb
phase may show less of a response to functional or environmental requirements
than previously envisaged.
Caveats
Our sample of 18 species is only a fraction of the 4000 species of
lizards and thus, like virtually all other comparative studies, our study
suffers from limited taxon sampling. However, given the difficulty of
obtaining data on lizard locomotor function (particularly center-of-mass
mechanics of small animals), we feel that our study provides convincing
insights into the evolutionary correlation between locomotor function and
foraging ecology and provides an important starting point for future research
in this area. Every comparative study has to address the issue of how the
choice of species affects its results. We sampled species to maximize the
number of evolutionary transitions to increase the power of statistical tests
of trait-correlated evolution, based on an a priori evolutionary
pattern of foraging mode evolution. However, sampling this way can provide
results that appear at odds with accepted patterns of evolution. One such
instance is the phase shift reconstruction at the base of the Scincomorpha
(Fig. 4). Based on our focal
sampling (focusing on reversals to test correlated evolution), the base of the
Scincomorpha reconstructs as using running mechanics. In fact, the basal
Scincomorph is known to be WF (Miles et
al., 2007), which is the foundation of our interpretation that the
basal Scincomorph would walk. Based on this interpretation we concluded that
the three independent evolutionary reversals to SW foraging are accompanied by
losses of walking mechanics in the Scincomorpha
(Fig. 7). Clearly the
Scincomorpha is a hot bed of foraging mode evolution and a complete
understanding of functional evolution within the group requires additional
sampling. However, given the strength of our findings and their robustness to
phylogenetic uncertainty, differences in ecology among species, and the
phylogenetic breadth of our sample, we feel that our results are robust and
provide a general picture of correlated evolution of foraging ecology and
locomotor function both within Scincomorpha and across lizard phylogeny.
Conclusions and future directions
The primary observation of this study is that locomotor mechanics and gait
coevolve with foraging mode in lizards, at least within our sample of 18
species. In addition, different locomotor patterns have appeared (and
sometimes convergently evolved) with WF strategies and subsequently disappear
when lizards revert to SW foraging. The strong correlation of locomotion and
foraging mode would be predicted given the similar pervasive patterns of
correlated evolution and convergence in feeding biomechanics, skull and tongue
morphology, chemosensory physiology and behavior in lizards
(Cooper, 1994;
McBrayer and Corbin, 2007;
Reilly and McBrayer, 2007;
Schwenk, 1993).
Many previous studies have focused on how animals move rapidly and the evolution of high-speed sprinting locomotion, particularly studies of locomotion in lizards. Our study differs, in formally showing the functional responses to the divergent speed demands of foraging mode and, in particular, how animals evolve slower locomotion. Overall, our study highlights the need to examine the ecological and behavioral relevance of the full spectrum of locomotor scope, both fast and slow.
Although our research supports a tight evolutionary coupling between
biomechanics, gait and foraging behavior, several issues remain unresolved.
First, although we show an evolutionary coupling between function and behavior
we still have a poor understanding of how and when biomechanics, gait and
speed are used in the field. Second, the utility of mechanical energy savings,
particularly in small animals, remains unclear. Finally, it remains unknown
how limb morphology is related to locomotor function in the context of
foraging ecology. Given the pervasive effects of foraging behavior on lizard
biology and the renewed and expanding interest in this subject
(Reilly et al., 2007), the
time seems ripe for additional detailed integrative studies of the functional
and ecological basis of foraging locomotion in lizards.
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References |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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