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First published online October 5, 2006
Journal of Experimental Biology 209, 4061-4066 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02473
What explains the trot–gallop transition in small mammals?
1 Institute of Ecology and Biodiversity, Departamento de Ciencias
Ecológicas, Facultad de Ciencias, Universidad de Chile, Casilla 653,
Santiago, Chile
2 Department of Ecology and Evolutionary Biology, Box G-W, Brown University,
Providence, RI 02912, USA
3 Center for Advanced Studies in Ecology and Biodiversity, and Departamento
de Ecología, Facultad de Ciencias Biológicas, Pontificia
Universidad Católica de Chile, Santiago 6513677, Chile
* Author for correspondence (e-mail: jose_iriarte{at}brown.edu)
Accepted 8 August 2006
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Summary |
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Key words: gait transition, body mass, energetics, Froude number, loading
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of abbreviationsReferences |
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;
Vilensky et al., 1991;
Hreljac, 1993;
Minetti et al., 1994;
Diedrich and Warren, Jr, 1995;
Kram et al., 1997). Early
studies proposed that gait transition served to reduce the energetic costs of
locomotion (Hoyt and Taylor,
1981; Taylor,
1985; Alexander,
1989). This hypothesis was supported by two observations. First,
animals tend to use only a narrow range of speeds within a particular gait
(Pennycuick, 1975;
Hoyt and Taylor, 1981;
Perry et al., 1988;
Kenagy and Hoyt, 1989). For
horses, these preferred speeds have been shown to minimize metabolic energy
costs within a gait (Hoyt and Taylor,
1981), suggesting that energetic economy might be an important
determinant of terrestrial locomotion. Second, in humans and horses, each gait
displays a particular relationship between metabolic energy expenditure and
speed. These metabolic curves intersect in such a way that it is energetically
less expensive to switch to another gait at speeds beyond the intersection
point than to maintain the same gait
(Margaria et al., 1963;
Hoyt and Taylor, 1981). The
speed at which the energy cost curves intersect has been referred to as the
`energetically optimal transition speed' (EOTS)
(Hreljac, 1993). However, the
experimental support for this `energetic hypothesis' is contradictory: some
studies have shown that the walk-run transition in humans and the
trot–gallop transition (TGT) in horses occurs at the EOTS
(Mercier et al., 1994;
Diedrich and Warren, Jr, 1995;
Wickler et al., 2003), but
others have shown, for the same species, that gait transitions occurred at
significantly lower speeds (Farley and
Taylor, 1991; Hreljac,
1993; Minetti et al.,
1994).
A second hypothesis to explain gait transitions proposes that animals
switch gait as a mechanism to reduce the forces applied to the musculoskeletal
system (Biewener and Taylor,
1986; Farley and Taylor,
1991). In vivo measurements of peak principal strains
showed that peak limb bone strains in horses and goats increased linearly with
speed within each gait, but decreased significantly at the transition of trot
to gallop (Rubin and Lanyon,
1982; Biewener and Taylor,
1986). Despite the differences in body size and absolute speed
between those species, maximum peak bone strains were similar at the TGT,
suggesting that this transition might be determined by a critical value of
peak limb bone stress (Rubin and Lanyon,
1982; Biewener et al.,
1983; Biewener and Taylor,
1986). To test this hypothesis, Farley and Taylor
(Farley and Taylor, 1991)
added weights to horses, thereby increasing the forces on the limbs for any
given velocity. They found that when loaded, horses transitioned to galloping
at significantly lower speeds than when unloaded, but maintained similar peak
musculoskeletal forces at the TGT. This suggested that the TGT is triggered
mechanically when musculoskeletal forces reach a critical level
(Farley and Taylor, 1991).
Support for a mechanical cue for gait transitions, however, remains tenuous.
It was recently shown that the TGT in horses running uphill occurs at lower
speeds than in horses running on level
(Wickler et al., 2003). Given
that uphill running should not increase the total forces applied to the limbs
(e.g. Roberts et al., 1997),
these authors suggested that their results contradict the idea of a mechanical
trigger for the TGT. However, Wickler et al. did not directly measured the
forces applied to the limb bones (Wickler
et al., 2003), and although average forces for all limbs may not
be elevated on an incline, it seems reasonable that there may be a shift in
weight distribution, with more weight on the hind limbs when ascending.
Whether the same determinants apply to small running mammals is not known. There is little information about gait transitions in species other than humans and horses. The objective of this study is to evaluate the effect of experimental manipulations of body mass on the TGT and the energetics of locomotion of a small south American mammal, the degu Octodon degus (Rodentia, Caviomorpha). If the switch from trot to gallop in degus is triggered to reduce locomotory costs, the TGT should occur at the EOTS, even if body mass is experimentally altered. On the other hand, if the TGT is a mechanism to reduce peak musculoskeletal stresses, the speed at which the transition occurs should decrease in animals with increased body mass.
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Materials and methods |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of abbreviationsReferences |
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). All
animals experienced three treatments: (1) `control', no body mass
modification; (2) `overfed', 
20% increase in body mass via
overfeeding; and (3) `injected', 20% increase in body mass through
intraperitoneal injection of 0.9% saline solution
(Jones, 1986). No rodents
seemed to be affected by the injection. In a previous experiment, the same
injection treatment applied to degus did not modify their resting metabolic
rate, suggesting that the procedure does not produce excessive levels of
stress (R.A.V., unpublished results). Body mass remained constant for about
3–4 h after the injection and animals returned to their original body
mass within 24 h. The order and sequence of treatments for each individual
were randomly assigned. The overfed condition was achieved by providing food
ad libitum. Animals reached the desired body mass in about 2–3
weeks. Because of the random assignment of treatments, some animals were first
overfed, then had their body mass reduced to their original state. This
weight-loss protocol consisted of limiting the daily food available to 80% of
the amount necessary to maintain constant body mass. Body mass was measured
daily to ensure continuous and gradual weight loss. Subjects achieved their
original body mass in about 4–6 weeks. Because the increase in body mass
produced by overfeeding the animals might be a result of increments of
metabolically active tissue (i.e. muscle and organs), inactive tissue (body
fat), or a combination of both, body fat content was measured in three control
and three overfed individuals. The subjects were killed and the whole body
(excepting the head) was homogenized and subjected to a Soxhlet extraction
procedure, which entails the removal of lipids using an ether-based solvent
(Van Soest, 1982). A sample
was taken, dried at 100°C and weighed with an analytical balance
(±0.001 g). Lipid content was removed using a petroleum ether solution
and the dry mass was measured again. The difference corresponded to the fat
content of the sample. Fat content is reported as the percentage of body mass
and metabolically active body mass is calculated as body massx(1–%
body fat). The fat content of control individuals was 20.86±3.97%
(N=3) and 30.82±1.24% (N=3) for overfed individuals.
All procedures were reviewed and approved by the Ethics Committee of the
Faculty of Sciences, Universidad de Chile.
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Stride frequency and trot–gallop transition determination
After several weeks of training, stride frequency was measured in 11
individuals while they ran at a constant speed. Stride frequency was
determined by timing a videotaped interval (Canon A1 digital camera, 30 frames
s–1, shutter speed of 1/500 s, with a time code
registration). Because the recording rate of the camera was not high enough to
reliably estimate an instantaneous step frequency, an average value was
calculated by dividing the number of stride cycles (at least 12) by time.
Assuming the worst-case scenario, measurement error for both foot touch down
and take off will be 2/30 s (two frames error). Thus, for a rodent running at
6 strides s–1, a sequence of 12 strides (2 s approximately)
will give an error of 3–4% for the estimation of stride frequency. For
each gait, a least-square linear regression model was fitted to stride
frequency versus running speed of each individual, and the
intersection between the regression lines for trotting and galloping was used
as the TGT speed (Heglund and Taylor,
1988).
Running energetics
During running, metabolic rate (running
O2) was measured
concurrently with video recording using an open-flow respirometry system
(Sable Systems, Nevada, USA) at ambient temperature (19–21°C). Not
all individuals run long enough at a particular speed to give reliable
respirometry measurements and only six animals were used to estimate running
energetics. All subjects were acclimated to the stopped treadmill for about 20
min; after their metabolic rate had stabilized, the belt was brought to the
desired speed. Instantaneous metabolic rate
(Bartholomew et al., 1981) was
recorded for at least 2 min while dried air was drawn at a rate of 4012 ml
min–1. Before and after entering the chamber, air was passed
through carbon dioxide (Baralyme®=Ba(OH)2) and water
(Drierite®=CaSO4) absorbent granules. Oxygen
consumption was monitored by a Datacan V-PC program every 0.5 s with an
applied electrochemistry oxygen analyzer (model S-3A/I; Ametek, Pittsburgh,
Pennsylvania, USA). The metabolic rate
(O2, in ml
O2g–1 h–1) was calculated by
equation 4a of Withers (Withers,
1977) using the metabolically active body mass and correcting
gases by STP. The net cost of transport (COT; in ml
O2g–1 km–1) was obtained by
dividing the metabolic rate by the running speed. Only trials in which degus
ran steadily at a fixed position on the treadmill were included for further
analysis.
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Results |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of abbreviationsReferences |
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).
Rate of oxygen consumption and net cost of transport
The rate of oxygen consumption increased with treadmill speed in all
subjects and the percentage of increase in
O2 in loaded animals
compared to controls was similar across the range of speeds (see Fig. S2 in
supplementary material). Metabolic rate of injected and overfed individuals
increased by 34±5 (mean ± s.e.m. for all speeds) and
27±4%, respectively, above the control rate. Average metabolic rate of
overfed animals was not significantly different from that of injected subjects
(t=1.712, d.f.=100, P>0.05). The COT kept a rather
constant value or did not show a clear pattern as a function of speed
(Fig. 1). The coefficient of
variation of COT was between 2 and 9% for all subjects. As the COT just before
and after the trot–gallop transition speed did not differ within each
treatment (control: T+=15, P=0.345; injected:
T+=17, P=0.173; overfed:
T+=11, P=0.917;
Fig. 1), the COT for trotting
and galloping for each treatment were pooled to estimate the COT at the
transition speed. The COT estimated for the gait transition differed between
treatments (repeated-measures ANOVA, F=29.22, d.f.=2,10,
P<0.001), being lower in control individuals (3.44±0.13 ml
O2g–1 km–1) than in injected and
overfed animals (4.42±0.25 and 4.35±0.24 ml
O2g–1 km–1, respectively;
Holm–Sidak test, for control versus injected: t=6.86,
P<0.0001; for control vs overfed: t=6.35,
P<0.0001).
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Discussion |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of abbreviationsReferences |
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) and
has been shown to increase the metabolic rate proportionately in several
mammal species (Taylor et al.,
1980). When normalized by total mass (i.e. body mass + load), the
addition of 20% weight produced an increment of about 30% in metabolic rate in
our rodents. This increment is higher than that predicted by Taylor et al.
(Taylor et al., 1980), but
their proportionality is not universally observed. Some studies have not
detected any increase in metabolic rate when carrying loads of 5–10% of
body mass (Cooke et al.,
1991), whereas others have shown increments in metabolic rate
higher and lower than those predicted by Taylor et al.'s proportionality (e.g.
Griffin et al., 2003;
Marsh et al., 2006). In our
experiments, total metabolic rate (measured as ml O2
min–1) increased 38% with a 20% increment in body mass. This
17% deviation from proportionality is close to the 18% deviation observed in
humans carrying loads of 30% their body mass
(Griffin et al., 2003). The
increment in metabolic rate during loaded locomotion was similar regardless of
the overweight treatment used. Only one individual (individual 2) showed a
higher metabolic rate when injected than when overfed. This suggests that
artificial loading and increment in adipose tissue have the same effect on the
energetic of locomotion, as has been suggested for humans
(Browning et al., 2006).
The energetic hypothesis predicts that the TGT should occur at speeds that
minimize the energetic cost. For degus, no association was found between gait
switching and energetic economy. Unlike the concave energy expenditure curve
with a clear energetic minimum observed in studies of humans
(Saibene and Minetti, 2003)
and horses (Hoyt and Taylor,
1981), degus showed a linear relationship between rate of oxygen
consumption and running speed for a given gait, as noted in previous studies
(Taylor et al., 1982;
Hoyt and Kenagy, 1988). The
COT remained relatively constant with running speed. Similarly, a study of
locomotion energetics in Spermophilus saturatus, a degu-sized
squirrel, found that COT decreased exponentially with running speed, reaching
a constant value at high speeds instead of the U-shaped curve
(Hoyt and Kenagy, 1988). Degus
may not show an energetic minimum because they could not be trained to run
beyond their normal range of speeds within each gait. It is possible that
there is an EOTS for degus but that it could not be detected by this study.
Even so, it seems unlikely that animals are able to sense their metabolic rate
and/or COT to trigger a change of gait. A more likely scenario would be a
proprioceptive signal that could be correlated to locomotion energetics. Some
indirect evidence, however, suggests that this might not be the case;
Vásquez et al. (Vásquez et
al., 2002) found that natural travel speeds of degus depend on
travel distance and habitat structure, showing that factors other than
energetic economy influence locomotor behaviour.
Unlike metabolic rate, the TGT speed was not affected when degus' body mass
was increased. Hence, we found no evidence indicating that the gait transition
in degus is triggered by mechanical signals from the musculoskeletal system.
Considering that two different procedures (i.e. saline injection and
overfeeding) produced similar results, it seems unlikely that the lack of
effect of loads on locomotion is a product of a particular methodology. It is
possible that the estimated speed for the TGT depends on how the determination
of transition speed was made. Estimates derived from extrapolation of stride
frequency curves, as in our experiment, might be slightly different than more
direct estimates from experiments where treadmill speed is gradually modified
until the gait change is apparent (e.g.
Kram et al., 1997;
Wickler et al., 2003).
Unfortunately, direct estimation of gait transitions was impractical to adopt
in these experiments because of the high stride frequencies of the rodents and
some technical limitations of the experimental setup. Differences between
methodologies might make it difficult to compare TGT speeds from this work
with speeds recorded in other studies where a more direct determination of
gait transition has been made. This study, however, focuses on the individual
responses to different treatments and thus any significant difference observed
is likely to be the effect of the increased body weight treatment rather than
a methodological artefact.
But why did body mass increments not modify TGT as observed in horses? One
possible answer comes from the diverse mechanisms that mammals adopt to cope
with bone stresses as body mass increases. Given that mammal long bones tend
to scale nearly isometrically, large species are expected to experience higher
stresses on their limb bones than small species, and therefore to put bones
under mechanical loads that are closer to their mechanical limit
(Biewener, 1983;
Biewener, 1990). Mammals,
however, use several mechanisms to keep peak bone stresses within safe
functional limits. In small to medium-sized species (i.e. less than 100 kg),
peak bone stresses are kept relatively constant by changes in limb posture
from very crouched in small species to more upright in larger ones
(Biewener, 1983;
Biewener, 1989). Adopting a
more erect posture reduces not only the bending moment on the bones but also
the moments around the joints, reducing the muscular forces needed to support
the animal's weight (Biewener,
1989). In species larger than 100 kg, however, postural changes
become insufficient to maintain low peak bones stresses and additional
mechanisms are implemented. These include more robust limb bones due to
negative allometry of bone length
(McMahon, 1975;
Prothero and Sereno, 1982;
Bertram and Biewener, 1990;
Christiansen, 1999) and strong
reduction of locomotor performance as size increases
(Garland, Jr, 1983;
Iriarte-Díaz, 2002).
Thus, small-sized mammals seem to be less constrained than large species in
compensating for changes in body mass. A rodent, for example, might acquire a
more upright posture than usual to decrease the stresses induced by additional
weight, whereas in large-sized species this mechanism is not longer
available.
Dynamic similarity is a particularly interesting concept that has been
applied to the study of animal locomotion
(Alexander, 2005;
Vaughan and O'Malley, 2005).
If gravitational force is important, two moving objects, despite differences
in speed and size, would move with the same dynamics only if the ratio between
inertial and gravitational forces for the two objects is the same at
corresponding stages of their motions. This ratio is known as the Froude
number (Fr) and is represented by the equation
v2/gL, where v is velocity,
g is gravitational acceleration and L is the limb
length. Alexander and Jayes (Alexander and
Jayes, 1983) showed that mammals of different sizes, running with
equal Fr, tend to move in a dynamically similar way, using equivalent
limb kinematics and exerting similar patterns of forces on the ground. In this
scenario, the change from one gait to another is expected to happen at equal
Fr and, accordingly, several quadrupedal mammals, ranging from small
mice to rhinoceros, transition from trot to gallop at Fr between 2
and 3 (Alexander and Jayes,
1983). Furthermore, since the Fr is mass independent,
experimental alterations of body mass are expected to produce no change on the
Fr of the TGT. We made a rough estimation of Fr at the TGT
for our degus from a calibrated lateral camera view using an average hip
height obtained from two individuals during midstance while running on the
treadmill. Degus switched from trot to gallop at Fr of
2.8±0.1, 2.7±0.1 and 2.8±0.1 for control, injected and
overfed individuals, respectively. These values fall into the normal range of
Fr observed in mammals for the TGT.
The applicability of Fr and dynamic similarity models in animal
locomotion has been further tested experimentally in humans using reduced
gravity conditions. These models have been unable to predict the kinetics and
kinematics of human walking and running but have successfully predicted gait
transitions (Donelan and Kram,
1997; Donelan and Kram,
2000). Humans, independent of the amount of gravity, switch from
walking to running at different absolute speeds but at the same Fr of
approximately 0.5 (Kram et al.,
1997). It has been argued that Fr might not be adequate
to describe the mechanics of running gaits and that additional dimensionless
numbers should be considered to capture the contribution of elastic elements
to locomotion (Alexander,
1989). However, the fact that several mammal species, regardless
of their size, switch between running gaits at the same Fr suggests
that its use may be justified to study the TGT, at least as an initial
heuristic approach. As in the energetic case, it is unclear how Fr
could be sensed to trigger a change of gait. And the fact that several mammals
of diverse body size switch gaits at similar Fr could be the
coincidental result of additional correlated factors. Thus, studies analyzing
the influence of different forces, including gravitational forces, could
provide some insights about gait transitions in small mammals. For example, a
recent study by Gallardo-Santis et al.
(Gallardo-Santis et al., 2005)
found that spontaneous running speed on vertical surfaces (tree climbing in
several species of small mammals) does not differ from horizontal speed,
suggesting that gravitational forces do not affect speed/body size relations
in small mammals.
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of abbreviationsReferences |
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O2
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Acknowledgments |
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Footnotes |
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