Voluntary running in deer mice: speed, distance, energy costs and temperature effects
Department of Biology, University of California, Riverside, CA 92521, USA
* Author for correspondence (e-mail: chappell{at}citrus.ucr.edu)
Accepted 26 July 2004
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Summary |
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Locomotor behavior was highly variable among individuals but had high
repeatability, at least over short intervals. We found few temperature-related
changes in speed or distance run, but Ta strongly affected
energy costs. Partial substitution of exercise heat for thermogenic heat
occurred at low Ta. This reduced energy expenditure during
low-temperature running by 2337%, but running costs comprised a fairly
minor fraction of the energy budget, so the daily energy savings via
substitution were much smaller. Deer mice did not adjust running speed to
maximize metabolic economy, as they seldom used the high speeds that provide
the lowest cost of transport. The highest voluntary speeds (45 km
h-1) were almost always below the predicted maximal aerobic speed,
and were much less than the species' maximal sprint speed. Maximum voluntarily
attained rates of oxygen consumption
(O2) were
highest at low Ta, but rarely approached maximal
O2 during forced
treadmill exercise. Mean respiratory exchange ratios coincident with maximal
voluntary
O2
increased slightly as Ta declined, but were always below
1.0 (another indication that metabolic rate was less than the aerobic
maximum). Individuals with high running performance (cumulative distance and
running time) had high resting metabolism, which suggests a cost of having
high capacity or propensity for activity.
Key words: aerobic capacity, deer mouse, exercise, locomotion, maximal oxygen consumption, metabolism, Peromyscus maniculatus, wheel-running
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Introduction |
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Another uncertainty about running energetics concerns the manner in which
exercise costs are measured: except for studies on humans, nearly all data on
the metabolic costs of running have been obtained from animals forced to run
on treadmills, sometimes with the added complication of a face-mask for
measurements of gas exchange. It is unclear if animals engaged in voluntary
locomotion experience the same energy costs elicited by forced exercise. For
example, data from horses (Hoyt and
Taylor, 1981), ground squirrels (Hoyt and Kenagy, 1999;
Kenagy and Hoyt, 1989
), and a
few other species (e.g. Pennycuick,
1975
; Perry et al.,
1988
) show that mammals often prefer to travel within narrow speed
ranges, apparently because of gait- and speed-related optima in locomotor
efficiency (i.e. cost of transport, J km-1) or biomechanical
factors such as muscle and tendon stress (e.g. Wickler et al.,
2001
,
2003
). Hence it is possible
that small mammals preferentially use particular speed ranges that confer
lower costs of transport than may be apparent in forced exercise protocols, or
that the kinematics of locomotion or aspects of energy metabolism (such as
substrate utilization) differ between forced and voluntary running, perhaps
because of stress responses or other artifacts of forced exercise.
In this paper we use the North American deer mouse Peromyscus maniculatus to examine voluntary exercise across a range of ambient temperatures (Ta). We developed equipment and methods that provide nearly continuous long-duration records of energy metabolism and wheel-running performance in unrestrained, freely behaving small mammals, with high temporal resolution. To our knowledge, this is the first time such measurements have been accomplished. We used these data to examine relationships between resting metabolism, maximal aerobic capacity, Ta, exercise intensity and preferred speeds, and metabolic power use.
Deer mice are good natural models for studies of exercise physiology. Their
thermal and aerobic physiology have been intensively studied and much is known
about aerobic capacity changes in relation to temperature acclimation and
acclimatization (Hayes and Chappell,
1986,
1990
; Hayes,
1989a
,b
;
Rezende et al., 2004
) and
adaptations to oxygen availability across a wide altitudinal range (below sea
level to above 4000 m; Chappell and Snyder,
1984
; Chappell et al.,
1988
). The species' maximal sprint-running speeds have also been
measured (Djawdan and Garland,
1988
). The primary questions we address here (1) What is
the energetic cost of voluntary locomotion? (2) How does it change with
ambient temperature? (3) Are particular running speeds preferred? (4) How is
voluntary running performance related to aerobic traits such as resting
metabolism and maximal aerobic capacity? are closely relevant to the
ecology of deer mice. Field studies at a high-altitude site
(Hayes and O'Connor, 1999
; J.
P. Hayes, personal communication) show that these mice routinely move across
hundreds of meters of linear distance nightly. Moreover, in many parts of
their extensive geographic range, deer mice seldom encounter thermoneutral
temperatures (approximately 2535°C;
Chappell, 1985
) during their
above-ground nocturnal activity periods. Some populations from high altitudes
must deal with activity temperatures that rarely if ever exceed 10°C, even
in summer (Hayes,
1989a
,b
),
and are often much colder in winter (M. A. Chappell, personal observations).
Recent work shows that maximum metabolic power output during forced exercise
does not increase at low ambient temperatures, even in cold-acclimated deer
mice that have considerably elevated power output during maximal thermogenesis
(Chappell and Hammond, 2003
).
Therefore, unless some substitution of exercise heat for thermogenic heat
production occurs, the sustained locomotor capacity of these mice will be
severely constrained at the low ambient temperatures that they routinely
encounter in nature.
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Materials and methods |
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Gas exchange measurements
For both treadmill tests and voluntary exercise measures, we used
positive-pressure, flow-through respirometry to determine rates of oxygen
consumption
(O2); we also
measured carbon dioxide production
(
CO2) during
voluntary exercise. Oxygen concentration changes during treadmill tests were
measured with an Applied Electrochemistry S-3A (Sunnyvale, CA, USA); for
voluntary exercise measurements we used an `Oxzilla' dual-channel
O2 analyzer (Sable Systems; Henderson, NV, USA) and two Sable
Systems CA-2A CO2 analyzers (one oxygen channel and one
CO2 analyzer for each of two mice measured simultaneously). We
regulated air flow with upstream mass flow controllers [Applied Materials
(Sunnyvale, CA, USA), Tylan (Billerica, MA, USA) or Porter Instruments
(Hatfield, PA, USA)], using flow rates that maintained excurrent O2
concentrations above 20.4%. About 100 ml min-1 of excurrent air was
subsampled and analyzed for O2 and CO2. Data from gas
analyzers and other instruments were recorded on Macintosh computers equipped
with A-D converters (National Instruments, Austin, TX, USA) and `Labhelper'
software (Warthog Systems,
warthog.ucr.edu).
Different conversion equations were used to compute
O2 for treadmill
tests and during voluntary exercise. For treadmill tests, we scrubbed
subsampled air of water vapor and CO2 (Drierite and soda lime,
respectively) prior to gas analysis and calculated
O2 as:
![]() | (1) |
For measurements of
O2 and
CO2 during
voluntary exercise, we dried subsampled air with magnesium perchlorate
(Drierite® interacts with CO2). We did not remove
CO2 as required for Equation
1 (in order to avoid the large volumes of soda lime or frequent
scrubber changes that otherwise would be necessary for these long-duration
tests) and calculated
O2 as:
![]() | (2) |
![]() | (3) |
Respirometry during voluntary exercise
Like many small rodents, deer mice readily use running wheels, so to
measure voluntary exercise performance we enclosed a commercially available
rodent wheel (Lafayette Instruments, Lafayette, IN, USA; stainless steel and
Plexiglas construction; circumference 1.12 m;
Swallow et al., 1998) and a
standard plastic mouse cage within an airtight Lucite® housing
(Fig. 1;
http://www.biology.ucr.edu/people/faculty/Garland/Wheel_Metab_Alone_1.jpg;
http://www.biology.ucr.edu/people/faculty/Garland/Wheel_Metab_Two_2.jpg).
Mice could enter and exit the wheel at will through an access port (diameter
7.7 cm) cut into the side of the mouse cage. Wheel rotation turned a small
generator, producing a voltage proportional to rotation speed and polarized to
the direction of rotation. Paired incurrent and excurrent ports provided for
air flow (2500 ml min-1, ±1%), and an internal fan rapidly
recirculated air within the enclosure to facilitate mixing. The mouse cage
contained bedding (wood shavings), a food hopper and a drinking tube. Food and
water were available ad libitum and mice were left in enclosures for
periods of 4896 h, with data recorded every 1.5 s. Computer-controlled
valves took 2.5 min reference readings every 45 min. Two of the wheel
enclosures were housed in a large incubator that controlled temperature (3,
10, or 25°C, ±0.5°C) and photoperiod (12 L:12 D, dark period =
19:00 h07:00 h, which was approximately the same as the light cycle in
our animal room). Every few days, we tested rotational resistance by spinning
wheels to high speed (
80 r.p.m.) with an electric drill fitted with a
rubber friction disk, and then monitoring the time needed for speed to decay
to zero. No appreciable resistance changes occurred over the course of the
study, nor did resistances differ between the two wheels.
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Most unused volume in the enclosures was filled with plastic inserts (the
four corners of the wheel housing) or high-density foam (the space surrounding
the mouse cage; Fig. 1), and
the remaining internal volume was about 22.6 liters after accounting for
displacements of the wheel, cage, food and bedding. Even with mixing from the
recirculating fan, this large volume resulted in a slow response to changes in
gas concentration. Therefore, we used the `instantaneous' transformation
(Bartholomew et al., 1981) to
provide accurate resolution of short-term metabolic changes. To determine the
effective volume for this transformation, we flowed gas through the system at
the standard rate of 2500 ml min-1, established a stable baseline
concentration, and then instantly switched to a different O2
concentration at the same flow rate (we used air and a mixture of 14%
O2, 86% N2) while recording the response to the step
change. The time lag between gas switches and detection by the gas analyzers
was approximately 20 s for CO2 and 45 s for O2, and
effective volume was estimated as 17.0 liters.
At this combination of flow rate and effective volume, the instantaneous
transformation is sensitive to small fluctuations in O2 data (from
electrical noise, air pressure transients, etc.). We used a very stable
O2 analyzer and each recorded datum was the average of several
hundred readings during the 1.5 s inter-sample interval. Nevertheless,
additional smoothing was necessary to obtain usable
O2 records.
Experimentation with step changes in gas concentrations (described above)
indicated that the best resolution of
O2 was obtained
with 7-point nearest-neighbor smoothing (i.e. the smoothed value of a given
sample was the average of that sample and the three samples on either side)
repeated 20 times, prior to instantaneous calculations. Considerably less
noise was present in CO2 records, but for consistency we applied
the same smoothing protocol. We used `LabAnalyst' software (Warthog Systems)
to perform smoothing, baseline and lag time corrections, replace reference
data by interpolation, compute
O2 and
CO2 with
Equations 2 and
3, and extract the following
values for each 23.5 h recording period (approximately 24:00 h11:30 h
local time):
Numbers (Bouts) and mean durations (Dbout) of running bouts (a `bout' was defined as a period of wheel rotation lasting 3 s or more, at speeds above 0.5 r.p.m. (0.038 km h-1) in either direction of rotation).
All deer mice (N=41) were measured initially at 25°C for at least 48±0.5 h (approximately noon to noon local time); some animals were measured for up to 96 h. At least 1 month after 25°C measurements, most (32) of these mice were also tested for 24 h at 10°C and again at 3°C on consecutive days. The order in which mice experienced 10°C and 3°C was random. For most comparisons, we restricted analyses to the 32 individuals used at all three Ta.
Energy cost of wheel-running
Deer mice ran at a range of speeds, so we were able to explore the
relationship between speed and energy metabolism. An inherent problem in using
multiple values from continuous metabolic data is avoiding autocorrelation:
metabolism does not respond instantly to changes in behavior, so repeated
readings of O2
made at short intervals are not independent of each other. To define the
limits to this problem, we analyzed extended sessions of running (several
hundred to several thousand consecutive 1.5 s samples). Within sessions, we
used the time series periodicity test in LabAnalyst to produce regressions
between samples separated by 1200 sample intervals. This procedure
tests how well a value at sample k predicts the value at sample
k+i, where i is 1200. The inter-sample interval was
1.5 s, providing r2 values for repeated readings over
intervals of 1.5 s to 300 s (Fig.
2). Results from numerous animals showed that
r2 always decayed to very low levels (<0.02) within
150180 s (i.e. values of
O2 separated by
more than 180 s were essentially uncorrelated). Accordingly, we used a
`stepped sampling' algorithm in LabAnalyst that took a series of 60 s averages
of speed and
O2,
with the final sample in each 60 s block separated by 180 s from the start of
the next 60 s block. To obtain stepped samples, we selected an extended
session of running (as defined above) and identified the time of the highest
60 s mean
O2
(Tpeak) within the session. Stepped sampling began at
Tpeak and proceeded forwards and backwards in time to the
beginning and end of the selected session. Thus, a session of wheel-running
lasting 3.0 h would yield 45 stepped samples. To reduce potential problems
associated with electrical noise and activity outside of running wheels, we
discarded data with absolute wheel speeds less than 0.5 r.p.m. (0.034 km
h-1) averaged over the 60 s block.
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Like O2,
wheel rotation did not respond instantly to changes in behavior (because of
the inertia of the wheel). However, the decay time for wheel rotation (defined
as the time necessary for a wheel spinning at
50 r.p.m. to slow to a
stop) was 2030 s, so the 180 s stepped sampling interval developed for
O2 also
eliminated autocorrelation problems in wheel speed data.
The resulting datasets were used to generate regressions of
O2 vs
wheel speed for each mouse (e.g. Fig.
3); when a mouse ran on more than 1 day at a particular
temperature and hence provided up to 4 regressions at that temperature, we
used the regression with the highest maximum speed or, if all maximum speeds
were >1.3 km h-1 (20 r.p.m.), the highest
r2.
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Maximum aerobic capacity in exercise
Maximum O2
during forced exercise
(
O2max) was
obtained by running deer mice in an enclosed motorized treadmill, as described
previously (Chappell, 1984
;
Chappell and Snyder, 1984
;
Hayes and Chappell, 1990
;
Chappell et al., 2003
;
http://biology.ucr.edu/people/faculty/MACpubs/treadmill.html).
In brief, the treadmill's working section (6 cm wide, 7 cm high, 13.5 cm long)
was supplied with air at 2100 ml min-1 STP. Mice were placed in the
working section, allowed a 12 min adjustment period, and then run at
increasing speeds, starting at 0.150.2 m s-1 and raised in step
increments of about 0.1 m s-1 every 3045 s. A test was
terminated when the mouse no longer maintained position and
O2 did not
increase with increasing speed; this typically occurred at 0.50.8 m
s-1 (1.82.9 km h-1). All mice showed behavioral
signs of exhaustion at the end of exercise (loss of coordination, failure to
maintain speed, stable or declining
O2 despite speed
increases) but none were injured. Reference readings of incurrent air were
obtained at the start and end of measurements.
Because of the short duration of treadmill tests (most were completed with
<10 min of exercise), we applied the `instantaneous' transformation
(Bartholomew et al., 1981) to
resolve rapid changes in metabolism. The effective volume of the treadmill,
calculated as described for wheel enclosures, was 903 ml. We calculated
O2 with
Equation 1 and computed
O2max as the
highest instantaneous
O2 averaged over
continuous 1-min intervals, using LabAnalyst.
Treadmill tests were performed at room temperature (2225°C) after 25°C wheel enclosure studies, but prior to wheel tests at 10°C and 3°C. Each individual's wheel and treadmill tests were at least 2 weeks apart.
Statistics
Since most individuals were tested for voluntary exercise under several
experimental conditions (multiple days at 25°C, and 24 h at each of two
other ambient temperatures), we used general linear models (GLM) for repeated
measures to test for differences among variables. Individuals were
experimental units, day (or temperature) was the within-subjects factor, and
sex was included as a fixed factor. Analyses showed that body mass affected
metabolic variables
(O2), but did
not influence behavioral variables (speed, distance and time spent running).
Accordingly, body mass Mb was included as a covariate for
metabolic variables only. Where sphericity assumptions were invalid (Mauchly's
sphericity test), the HuynhFelt degrees of freedom correction was
applied in significance tests. Least-squares regression was used to describe
relationships among metabolic and behavioral variables within test
temperatures. Residuals from univariate ANCOVA (sex and mass as covariates)
were used to assess repeatability between days or across temperatures. Several
behavioral variables (Drun, T, Vmean,
Bouts and Dbout) were log-transformed prior to analysis to
provide normal distributions. The upper and lower limits to measured metabolic
rates were treadmill
O2max [which
scaled to Mb0.789] and RMR at 25°C (which
scaled to Mb0.791). Accordingly, when
presenting mass-adjusted results (e.g. frequency histograms for voluntary
O2), we scaled
data to Mb0.790. We performed most analyses
with the regression and GLM procedures in SPSS for the Macintosh (SPSS Inc.),
or with Statistica/Mac (StatSoft, Inc.). The significance level (P)
was 0.05. For multiple simultaneous tests, we adjusted P using a
sequential Bonferroni correction (Rice,
1989
).
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Results |
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Behavior in wheel enclosures
Judging from direct observations and inferences from
O2 records, deer
mice in wheel enclosures were often active even when not wheel-running. For
example, some animals frequently performed `back-flips' in the mouse cage
portion of the enclosure (Fig.
1). At 25°C, only 72% of the mice had been running in the
wheel in the 4 min preceding the time of their highest 1 min average
O2. A few mice
ran less than 100 m in wheels during 24 h, while one animal (a 25.0 g male
running at 10°C) covered 25.3 km in 24 h (running for a total of 14.3 h
during both night and day). As we measured wheel speed and not running
behavior per se, we do not know the fraction of measured distance
that was attributable to `coasting' (rotation caused by inertia after mice
ceased running). However, for laboratory mice running in identical wheels,
coasting accounted for about one third of total wheel rotations
(Koteja et al., 1999a
; see
also Girard et al., 2001
).
In light of the large behavioral variability it is unsurprising that
variation in daily energy expenditure (DMR) and maximal voluntary
O2 was much less
than the variation in behavioral measures. Among the 32 mice tested at all
three Ta, daily wheel-running distance at 25°C ranged
from 0.232 to 16.5 km (mean 3.005 km) with a coefficient of variation (CV) of
124%. Running time at 25°C ranged from 17.8 to 464 min (mean 126 min, CV
82%) and maximum wheel speed ranged from 1.94 to 4.94 km h-1 (mean
2.93 km h-1, CV 25%). In contrast, CVs were 22% for mass-adjusted
DMR (mean 1.11 ml O2 min-1 for the mean mass of 22.2 g)
and 16% for mass-adjusted maximal
O2 averaged over
1 min (2.97 ml O2 min-1). For the same animals at
3°C, CVs were 112% for distance (mean 5.55 km; range 0.02724.5 km),
94.5% for running time (mean 197 min; range 6707 min), 24.5% for
maximum wheel speed (mean 2.57 km h-1; range 1.444.64 km
h-1, 15% for DMR (mean 1.31 ml O2 min-1;
range 1.833.83 ml O2 min-1), and 11% for 1 min
O2 (mean 4.04 ml
O2 min-1; range 3.184.83 ml O2
min-1).
Conditioning during wheel-running
In adult laboratory mice Mus domesticus, initial access to running
wheels generally elicits successive daily increases in wheel running that last
for up to 3 weeks, followed by a stabilization and eventual decline in daily
wheel-running distance (e.g. Swallow et
al., 2001; Morgan et al.,
2003
; Belter et al.,
2004
). We checked for such changes in deer mice using
repeated-measures procedures to test for changes in 25°C running
performance across days (`day' effect;
Table 1). Forty-one mice
experienced 2 consecutive days of wheel access and some experienced 3 or 4
consecutive days (N=16 and 7, respectively). In contrast, the 32 mice
tested at 10°C and 3°C had only 1 day of wheel access at these
Ta, but all had previously experienced wheels when tested
at 25°C.
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Although within- and between-individual variation in wheel-running at
25°C was considerable, repeated-measures ANCOVA (with body mass as
covariate) found no change in DMR, minimum resting metabolic rates (RMR), or
running behavior across 2, 3 or 4 days of wheel access
(Table 1). However, mass
affected only metabolic variables, and repeated-measures ANOVA found
significant increases in mean running speed (33.1%) and maximum running speed
averaged over 1, 2 and 5 min intervals (16.1%, 21.9% and 25.2%, respectively)
between days 1 and 2. The number of running bouts decreased by 23.0% between
days 1 and 2, but bout duration almost doubled (95.0% increase). There was a
slight but statistically significant decrease (averaging about 1%) in
110 min average maximal
O2 between days
1 and 2. Few changes occurred over days 3 and 4 in the considerably smaller
subset of mice tested for more than 2 days.
Given that all mice tested at 3°C and 10°C had previous exposure to the wheels, we normally used the second day of 25°C data when comparing running performance across temperatures. Exceptions were made for a few mice that ran substantially less on day 2 than on day 1, or ran considerably more on day 3 or 4 than during the first 2 days; for these individuals, we used data from the day with the greatest amount of running.
Relationships among performance variables
Many metabolic and locomotor traits covaried, even after removing the
effects of body mass (Table 2).
The 1, 2, 5 and 10 min maximal voluntary
O2 values were
tightly correlated (r>0.92), as were 1, 2, 5 and 10 min maximal
speeds (r>0.90), so we used only 1 min values in most analyses. At
25°C, RMR was positively correlated to maximal voluntary
O2, to DMR, and
to cumulative distance, run time and maximal running speeds (but not to mean
running speed). Maximal voluntary
O2 was
correlated to cumulative distance and to maximal running speed.
Unsurprisingly, there were positive correlations between cumulative distance,
running time and running speed (both mean speed and short-term maximum
speeds). Both the number of running bouts and mean bout duration
(Dbout) were positively correlated to cumulative distance
and running time; Dbout was also correlated to running
speed in other words, mice ran greater distances by increasing the
duration of running bouts, the number of bouts, and by running faster.
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Relationships among variables were similar at 3°C and 10°C
(Table 2). For the most part,
correlations among behavioral variables at both of the lower
Ta closely resembled those at 25°C: distance, speed
and time were strongly correlated, mice increased cumulative distance by
augmenting both the number and duration of running bouts, and DMR and RMR were
strongly correlated to maximal voluntary
O2. However, in
contrast to 25°C, correlations between metabolic variables (DMR, RMR,
maximal voluntary
O2) and
behavioral variables (cumulative distance, running time, running speed) were
not significant.
The respiratory exchange ratio (RER;
=CO2/
O2)
averaged over the 23.5 h daily measurement period was not correlated to any
variables except mass at 3°C (r=0.377, P=0.0333,
N=32) and DMR at 25°C (r=0.367,
P=0.0388, N=32). Daily RER averages (over about 23.5 h) were
0.895 at 3°C, 0.897 at 10°C, and 0.860 at 25°C; the 25°C RER
was significantly less than RER at lower Ta
(F2,89=6.35, P=0.00265). During exercise (i.e. at
the times of 1, 2, 5 and 10 min maximal voluntary
O2) at the three
test temperatures, the mean RER for 32 mice was always less than 0.9
(Table 3), and in only three
measurements did RER slightly exceed 1.0 (all at 25°C; maximum 1.08).
Repeated-measures ANCOVA (body mass as the covariate) revealed no effects of
mass or sex on RER. However, temperature (F2,88=6.27,
P=0.00284) and averaging interval (F3,88=5.74,
P=0.00228) had small but significant effects: RER was higher at
3°C and 10°C than at 25°C, and also tended to increase as the
measurement interval increased.
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Temperature effects and locomotion energetics
As expected, Ta strongly affected most aspects of
energy metabolism (Tables 4,
5;
Fig. 4A). RMR, DMR and maximal
voluntary O2
were all significantly higher at low Ta than at 25°C.
However, Ta did not affect mean or maximum running speeds.
The highest instantaneously attained speeds (i.e. a single 1.5 s sample) were
4.73, 4.99 and 4.64 km h-1 at 25°C, 10°C and 3°C,
respectively (the three maxima came from different mice), and the
corresponding highest 1 min averages were 4.27, 4.00 and 4.24 km
h-1 (again, each value came from a different individual).
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|
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The relationship between running speed and power output (measured as
O2) was affected
by temperature but not by body mass (Fig.
4B; Table 6). Some
individuals ran poorly in wheels, so to be included in the analysis a mouse's
O2 vs
speed regression had to have (i) a significant positive correlation, (ii) at
least 10 data points at speeds >0.5 r.p.m. (about 0.01 m s-1)
and (iii) a maximum speed >0.2 m s-1 (0.7 km h-1). We
analyzed data in two ways: with univariate ANCOVA (to include all mice that
ran well, N=26 at 25°C, 22 at 10°C, and 17 at 3°C), and
with repeated-measures ANCOVA for the 16 mice that provided data at all three
Ta. Both methods yielded similar results and we present
the repeated-measures statistics here and in
Table 6. At 10°C, the
regression between speed and
O2 had a higher
intercept and lower slope than at 25°C (F1,14=43.4,
P<0.00001 for intercept; F1.14=17.0,
P=0.00078 for slope). Regressions were more similar at 3°C and
10°C (Fig. 4B). The
intercept at 3°C was higher than at 10°C
(F1,14=35.4; P<0.00001), but slopes and
3°C and 10°C did not differ (F1,14=0.102,
P=0.754). Regression coefficients (r2) declined
at low Ta; a repeated-measures ANCOVA including slope as
covariate indicated that this was partly a temperature effect
(F2,60=4.47, P=0.016) but primarily resulted from
the decrease in slope at low Ta
(F1,60=11.6, P=0.00121).
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For the 16 deer mice that had good running performance at all three
Ta, we used each individual's slope, intercept and
treadmill O2max
to estimate its maximum aerobic running speed (MAS, the speed at which
O2max is
attained; Fig. 3) as:
![]() | (4) |
Ambient temperature affected MAS, which averaged 5.45±0.50 km
h-1 at 25°C, 6.53±0.66 km h-1 at 10°C,
and 4.70±0.65 km h-1 at 3°C (repeated-measures ANCOVA,
F2,26=4.07, P=0.029), with an overall mean of
5.56±0.47 km h-1. Sex did not affect MAS, but there was a
marginally significant interaction between Ta and mass
(F2,26=3.76, P=0.037). Using means of
O2max, slope and
intercept (Table 6), the MAS
for a mouse of average mass (22.2 g) is 4.12 km h-1 at 25°C,
5.54 km h-1 at 10°C, and 4.33 km h-1 at 3°C
(overall mean 4.65 km h-1).
Deer mice shifted their preferred running speeds according to
Ta (Fig.
5A). A well-defined peak at low speeds (0.10.3 km
h-1) was seen at all Ta, probably attributable
to the inertia of the wheel leading to slow starting or ending of rotation. At
higher speeds (>0.5 km h-1), preferred running speeds increased
as Ta decreased. At 25°C, the distribution of running
speeds resembled a simple declining function from the low-speed peak, with a
weakly defined second peak at about 0.8 km h-1. However, at
10°C mice running faster than 0.5 km h-1 preferred speeds
between 1 and 2 km h-1, and at 3°C the preferred range was
between 1.8 and 2.6 km h-1.
|
The distance traveled at different speeds also varied with Ta (Fig. 5B; Table 3). Despite the large amount of wheel rotation at <0.5 km h-1 (Fig. 5A), deer mice did not move very far at these speeds. At 25°C, mice used a broad range of speeds (0.53 km h-1) to cover most of the distance they traveled. At lower Ta mice did most of their traveling within narrower and higher speed ranges, with peaks at 1.42 km h-1 at 10°C and about 2.5 km h-1 at 3°C. 75% of total distance run at 3°C was done at speeds of 1.6 km h-1 or higher; the corresponding values were 1.2 km h-1 at 10°C and 1.0 km h-1 at 25°C.
We used stepped sampling (60 s averages separated by 3 min) across the
entire daily sampling period (about 23.5 h) to obtain distributions of
voluntary O2.
Data were adjusted to the average body mass of 22.2 g using a scaling factor
of mass0.79 (see Statistics). At 25°C, the distribution of
voluntary
O2 was
unimodal, with a large peak around 0.6 ml O2 min-1 and a
gradual decline in frequency at higher
O2
(Fig. 6). However, the
distribution was strongly bimodal at 10°C (with peaks at 1.3 and 2.6 ml
O2 min-1) and at 3°C (with peaks at 1.7 and 3.0 ml
O2 min-1). Few voluntary
O2 exceeded
treadmill
O2max
at any Ta, but the fraction of data exceeding
O2max was higher
at 3 and 10°C than at 25°C, in both number of samples
(
2=94.3, d.f.=2, P<0.0001; 1.3% of 9666 samples at
3°C, 0.9% of 10292 samples at 10°C, and 0.1% of 9472 samples at
25°C) and in numbers of individuals with
O2 above
O2max
(
2=9.96, d.f.=2, P<0.01; 10/32 at 3°C, 4/32 at
10°C, and 1/32 at 25°C).
|
Behavioral and metabolic repeatability
In multi-day tests at constant warm temperatures, nearly all behavioral and
metabolic variables were highly repeatable
(Table 1). Repeatability
declined by days 3 and 4, but this was partially a result of small sample size
over those intervals (only 16 and 7 individuals, respectively). The main
exception was the mean length of running bouts, which showed no repeatability
over any interval.
Repeatability was also high across temperatures, but only for tests at 10 and 3°C, which were made on sequential days (Table 5). As for sequential-day comparisons at 25°C, all metabolic variables and most behavioral variables were significantly repeatable between 10 and 3°C. Repeatability between initial measurements at 25°C at measurements at 3 and 10°C (performed at least 1 month apart) was lower. Nevertheless, many traits remained significantly repeatable over the larger interval.
Maximum O2 during forced vs voluntary running
Maximal voluntarily attained
O2 during
wheel-running were substantially higher at low Ta than at
25°C (Tables 4,
5;
Fig. 4). Nevertheless, even at
3°C, voluntarily attained maximal
O2 averaged
significantly less than the
O2max elicited
during forced treadmill exercise. Ratios between maximal 1 min voluntary
O2 and treadmill
O2max (also a 1
min average) declined significantly from 0.933±0.168 at 3°C to
0.869±0.174 at 10°C to 0.716±0.133 at 25°C
(F2,92=16.4, P<0.00001). At all
Ta, 1 min voluntary
O2 was
significantly lower than treadmill
O2max (paired
t-tests: t=2.68, P=0.0118 at 3°C;
t=3.94, P=0.00043 at 10°C, and t=9.27,
P<0.0001 at 25°C, N=32 for all
Ta). Results were qualitatively similar for longer
averaging periods (2, 5 and 10 min maximal voluntary
O2).
A few deer mice did attain maximal voluntary
O2 that exceeded
treadmill
O2max
(e.g. Fig. 3), but these high
O2 values were
not sustained for long periods. The highest ratios of maximal 1 min voluntary
O2/treadmill
O2max in
individual mice were 1.07 at 25°C, 1.27 at 10°C and 1.38 at 3°C.
These three maxima were reached by different animals; the 3°C animal (an
18 g female) had an unusually low treadmill
O2max (her
voluntary
O2/treadmill
O2max ratio
would have been 1.10 had she achieved the predicted
O2max for a deer
mouse of her mass, based on mass regressions for our mice).
![]() |
Discussion |
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Metabolism and temperature
The general metabolic response of deer mice to changing ambient temperature
was as expected for a small endotherm: compared to 25°C (approximately the
lower critical temperature of P. maniculatus;
Chappell and Holsclaw, 1984;
Chappell, 1985
), mice spent
more energy overall as Ta decreased to 10°C and
3°C. Because our mice did not have access to materials that could be
formed into well-insulated nests, minimal resting metabolism (RMR) was also
inversely related to Ta. At 25°C, the RMR we observed
for a deer mouse of the average mass of 22.2 g (0.53 ml O2
min-1; 30 min average) was slightly less than the previously
reported basal metabolism for this population (about 0.60.7 ml
O2 min-1; Chappell
et al., 2003
), but RMR increased 2.5-fold at 10°C and
2.97-fold at 3°C. The change in DMR was less pronounced, with the value at
10°C and 3°C elevated by 1.87-fold and 2.16-fold, respectively, over
the 25°C DMR. For a 22.2 gmouse, the difference between DMR and RMR
presumably the energy spent on activity rose from 0.603 ml
O2 min-1 at 25°C to 0.815 ml O2
min-1 at 10°C and 0.926 ml O2 min-1 at
3°C (F2,89=15.8, P<0.00001).
For our non-reproductive deer mice, DMR largely comprised resting
metabolism and the energy costs of exercise (plus an unknown but probably
minor contribution from energy used for processing food). Therefore, it is
unsurprising that DMR was positively correlated to the amount of wheel-running
activity (distance run and time spent running) at 25°C
(Table 2). However,
wheel-running activity was not significantly correlated to DMR at the two
lower Ta. This may be attributable to the 2.5- to 3-fold
higher RMR at 10 and 3°C and the lower slope of the relationship between
speed and O2 at
low Ta (Fig.
4). The combination of these factors reduces the proportional
difference between RMR and
O2 at the mean
running speed of about 1.3 km h-1 (which did not vary with
Ta; Fig.
5). Also, variance in speed and running time was greater at low
Ta than at 25°C
(Table 4).
Energetics of locomotion
Increased expenditure on activity at low Ta is
consistent with the observation that mice spent more time running and covered
greater distances at 10°C and 3°C than at 25°C (Tables
4 and
5). Taken together, these data
also hint that energy costs of thermoregulation and locomotor exercise are not
completely substitutive (although this argument is tenuous, since many mice
engaged in exercise outside of the running wheels). Regressions of running
speed vs metabolism (Fig.
4, Table 6) clearly
reveal partial substitution of exercise heat for thermogenic heat, as
indicated by significantly lower slopes at 10°C and 3°C than at
25°C (complete additivity would result in all three regressions having the
same slope, and complete substitution would be indicated by slopes of zero at
low Ta, at least for low to moderate speeds).
Interestingly, the so-called `postural cost' of exercise the
difference between resting metabolism and the zero-speed intercept of the
speed vs cost regression (Taylor et al.,
1970,
1982
) did not change
significantly with Ta, averaging 1.25 ml O2
min-1 for a 22.2 g mouse (F2,60=2.45,
P=0.095).
How does the relationship between speed and power output during voluntary
exercise compare to the corresponding relationship for forced exercise? We are
not aware of any published data on treadmill-derived locomotor energetics of
deer mice, but considerable information exists for other small rodents
(particularly wild house mice and laboratory mice, Mus musculus and
M. domesticus). A widely cited early study by Taylor et al.
(1970) yielded an incremental
cost of locomotion (i.e. the slope of the speed vs power
relationship) of 1.4 kJ km-1 (about 70 ml O2
km-1 assuming 20.1 J ml-1 O2) for 21 g house
mice over a fairly narrow speed range (maximum speed <1 km h-1).
Taylor et al. (1982
) provided
an allometry for the incremental cost of terrestrial locomotion in relation to
body mass, based on a number of studies of various birds and mammals. Their
equation (kJ km-1=10.7xmass in kg0.684) predicts a
slope of 0.79 kJ km-1 for a 22.2 g animal. By comparison, the slope
we found for 22.2 g deer mice at 25°C was 0.70 kJ km-1 (34.9 ml
O2 km-1; Table
6), half the value reported by Taylor et al.
(1970
) for mice, but
reasonably close to the allometrically predicted slope.
We emphasize that comparisons between wheel and treadmill data are complex
and should be regarded with caution, for several reasons. First, for all such
studies with small endotherms, temperature may be important in determining
slopes and intercepts (as our results reveal), and thermal conditions are
sometimes unspecified in papers describing locomotor costs. We presume that in
such cases tests were carried out at normal room temperatures
(2022°C), which for many small rodents is below the thermal neutral
zone (however, temperatures within treadmill chambers may have risen to
substantially higher values). Second, in treadmill studies exercise costs are
usually steady-state values obtained during sustained running at constant
speeds, while our mice typically ran in short bouts
(Fig. 2,
Table 4) and speeds were seldom
constant for periods of more than a few seconds. Third, speed data from large
wheels as we used are likely to be biased [from mice `coasting', because
wheels continue to turn from momentum for several seconds after mice cease
running and exit (Koteja et al.,
1999a), and because inertia prevents wheels from accelerating as
fast as an unhindered mouse might]. Fourth, animals in wheels can change
between uphill, level and downhill running, depending on where they position
themselves. Finally, the intermittency of typical voluntary running, with
numerous short running bouts interspersed with brief rest periods (e.g.
Fig. 1;
Girard et al., 2001
), could
conceivably affect the metabolic data because of `excess' post-exercise oxygen
consumption (Baker and Gleeson,
1998
). Despite these caveats, our results suggest fairly close
correspondence between voluntary and forced running costs, possibly with
voluntary costs being slightly lower.
The only comparable study of wheel-running energetics is that of Koteja et
al. (1999b). These authors
used food intake coupled with measures of wheel rotation in a regression model
to estimate energy expenditures of laboratory mice running in the same wheels
as used to construct the present metabolic chambers. They report slopes of
0.76 kJ km-1 in males (scaled from 32.2 g to the deer mouse body
mass of 22.2 g with a mass exponent of 0.75) and 0.39 kJ km-1 in
females (scaled as described for males from a mass of 25.5 g). Their value for
females is considerably less than what they found in males, our findings, and
the allometric predictions of Taylor et al.
(1970
) Koteja et al.
(1999b
) suggest that
behavioral mechanisms accounted for the striking sex differences in their
experiments. In contrast, we found no influence of sex on incremental running
costs for deer mice (Table 6)
and few sex effects on other behavioral parameters
(Table 5).
The cost of running in wheels (excluding postural costs and RMR) was a
small fraction of daily energy expenditures. At 25°C, deer mice spent
about 6.3% of DMR on wheel-running. Mice covered more distance at the two
lower Ta than at 25°C
(Table 4). However, the slope
of speed vs energy cost regressions were lower at low
Ta (presumably because of partial substitution of exercise
heat for thermogenesis; Table
6) and DMR was considerably higher because of thermoregulatory
expenditures. Consequently, the fraction of DMR spent on wheel-running was
lower at 10°C and 3°C (3.7% and 2.7%, respectively) than at 25°C.
For comparison, Koteja et al.
(1999b) estimated that
wheel-running at room temperature consumed 4.4% and 7.5% of the DMR of
laboratory mice running 4.4 and 11.6 km (the two values are for control lines
and lines selected for increased wheel-running activity, respectively).
Given the small fraction of DMR used in running, it is worthwhile to
calculate the energy savings attributable to partial substitution at low
Ta. Because the relationship between speed and power was
linear (e.g. Fig. 3), we
calculated the energy benefits of partial substitution at 10°C and 3°C
as:
![]() | (5) |
Accordingly, mice running at 10°C used about 48% less energy on
locomotion (exclusive of postural costs and RMR) than would have been
necessary without substitution, which is a saving of about 1.9% of DMR at that
Ta. The corresponding values for 3°C are a 50%
reduction in locomotor costs and a 1.9% reduction in DMR by substitution. It
is debatable whether such a small energy savings on a daily basis would have
much selective significance in nature. However, the reduction in power output
while mice are running is quite substantial. At 3 km h-1, the
O2 of a deer
mouse of average mass is 3.42 ml O2 min-1 at 10°C
and 3.80 ml O2 min-1 at 3°C. Without substitution,
metabolism at 3 km h-1 would be 4.26 ml O2
min-1 at 10°C and 4.67 ml O2 min-1 at
3°C. Those are substantial increases in energy costs (37% and 23%,
respectively). Perhaps more significantly, absence of substitution could push
O2 at 3 km
h-1 close to or even above
O2max; hence,
running at this fairly routinely utilized speed
(Fig. 5) would require
anaerobic energy production and more rapid fatigue.
Limits to locomotor performance
Recently, Chappell and Hammond
(2003) found that the maximal
aerobic power output of deer mice undergoing forced treadmill exercise is
constant across a wide temperature range (16 to 20°C), even after
cold acclimation. They suggested that at low Ta (when RMR
is considerably above thermoneutral values), the metabolic power available for
sustained running i.e. the difference between exercise
O2max and RMR
would be reduced unless mice can extensively substitute exercise heat
for thermogenesis. The present study provides additional insight into this
question. We found no indication that mean maximum voluntary speeds declined
at low Ta, as might be predicted if aerobic capacity
constrained exercise
O2 (Figs
4,
5;
Table 5). However, a few deer
mice ran at speeds that engendered
O2 close to (or
even exceeding) their treadmill-elicited
O2max (Figs
3,
6). Also, the 1 min maximal
O2 at 3°C
was within 7% of
O2max
(Fig. 4A), suggesting that
constraints might have been apparent had we tested for voluntary activity at
subzero temperatures (as encountered in winter by many Peromyscus
populations; M. A. Chappell, personal observations;
Wickler, 1980
). If regressions
for speed vs power are extrapolated to the highest voluntarily
attained instantaneous speed of about 5 km h-1, the estimated
O2 are close to
or slightly greater than
O2max at all
Ta (Fig.
4B). Deer mice can run much faster than we recorded in our study,
as their maximal sprint speed in a photocell-timed racetrack is about 13 km
h-1 (Djawdan and Garland,
1988
). Apparently, they do not utilize their capacity for high
sprint speeds when running on wheels, which also true for laboratory house
mice (Girard et al.,
2001
).
Deer mice did not select running speeds strictly on the basis of metabolic
economy. Although high speeds engender the highest rates of energy
utilization, they provide the lowest cost of transport (the energy cost needed
to move a unit of mass a unit of distance, independent of speed;
Taylor et al., 1982).
Free-living golden-mantled ground squirrels Spermophilus saturatus
apparently exploit this by preferentially traveling at speeds close to their
maximal aerobic speed (Kenagy and Hoyt,
1989
). In contrast, deer mice seldom used the high end of their
voluntary speed range at any Ta
(Fig. 5), and speeds
approaching the maximal aerobic speed were very rare. Preferred speed showed a
temperature-related shift (Fig.
5), but the reason is not clear. One explanation is that partial
substitution reduces the incremental cost of exercise at low
Ta, making high-speed running less expensive as a fraction
of DMR. More speculatively, wild deer mice may have experienced selection to
minimize exposure to low Ta by moving more rapidly between
sheltered locations than in warm conditions. Sustained locomotion in very cold
conditions may result in hypothermia in deer mice
(Chappell and Hammond,
2003
).
How do the upper and lower limits of aerobic performance
thermoneutral RMR (i.e. at 25°C) and treadmill
O2max
correlate with behavioral and metabolic indices of running activity? At
25°C we found positive correlations between RMR (which, at this
Ta, is similar to the species' measured basal metabolism;
Chappell and Holsclaw, 1984
)
and two measures of voluntary power output (DMR and maximal voluntary
O2), and with
most indices of running performance (run time and distance, bout duration, and
maximal speed; Table 2). Also,
DMR was strongly positively correlated to maximal voluntary
O2 at all
Ta. One interpretation of these results is that
individuals capable of high aerobic exercise also incur high maintenance
costs. Our current data do not reveal whether the association between RMR and
running performance is attributable to genetic correlations among these traits
or to phenotypic plasticity (e.g. the exercise conditioning resulting from
extensive wheel-running may elevate RMR). Interestingly, 22 generations of
selective breeding for high voluntary wheel running in laboratory house mice
(Swallow et al., 1998
;
Garland, 2003
) did not result
in an increased basal metabolic rate (T. Garland, Jr, unpublished
results).
As shown in Fig. 6,
O2 during wheel
running rarely approached treadmill-elicited
O2max,
regardless of temperature. Therefore, voluntary wheel-running speeds in deer
mice do not appear constrained by aerobic capacity. That conclusion is
consistent with the observation that voluntarily attained wheel-running speeds
(Fig. 5A) are less than the
predicted maximal aerobic speed of
5 km h-1. In contrast, the
wheel running of selectively bred house mice may be limited by
O2max (see fig.
6 in Girard et al., 2001
;
Garland, 2003
).
At the lower temperatures the distribution of Peromyscus wheel
O2 shifted
toward higher values and became distinctly bimodal. The left peak at all
Ta reflects metabolic rates close to RMR (which increases
as Ta decreases; Table
4). The second peak at 3°C and 10°C, and its absence at
25°C, presumably reflects a shift in the distribution of preferred running
speeds from a declining unimodal function at 25°C (with a sharply defined
peak at low speed) to bimodal distributions at lower Ta
(Fig. 5A).
In addition, and somewhat unexpectedly, we found no correlations between
the O2max in
forced exercise and any measure of voluntary
O2 other than
DMR at 3°C. This further suggests that maximal aerobic capacity is not a
determining factor in routine locomotor activity in deer mice at least
at temperatures of 3°C and above. Even more surprisingly, at low
Ta we found negative correlations between
O2max and some
of the behavioral indices of locomotor performance (such as mean or maximal
running speed, run time, and cumulative distance). However, these negative
correlations showed little consistency between low and high
Ta (Table
2), and we are unsure of their importance (see also
Lambert et al., 1996
). The
conclusion that voluntary wheel-running speeds are not constrained by aerobic
capacity (Fig. 6) is also
consistent with respiratory exchange ratios during wheel-running, which were
nearly always below 1.0 (Table
3). In contrast, during maximal forced treadmill exercise the RER
of deer mice is substantially greater than 1.0
(Chappell, 1984
).
Conclusions
In summary, we found strong influences of temperature on running energetics
in deer mice, with partial substitution of exercise heat for thermogenic heat
at low Ta. For free-living deer mice, which routinely
experience low environmental temperatures in many parts of their range,
partial substitution would permit considerable energy savings while running.
That may confer a fitness advantage by permitting a greater range of speeds to
be supported aerobically at low Ta. Our findings were not
consistent with the hypothesis that voluntary running speeds are tightly
constrained by aerobic capacity (although such a constraint might be evident
at sub-zero temperatures), and mice rarely ran at the high speeds that
minimize costs of transport. From a mechanistic perspective, high voluntary
running performance was associated with high resting metabolism, which may be
viewed as a cost of high performance capacity.
To our knowledge, this is the first report for any animal of energy costs of voluntary exercise measured with high temporal resolution over complete daily activity cycles. As discussed above, this approach provides insights into several aspects of locomotor physiology that are inaccessible with traditional forced-exercise protocols. Although somewhat demanding in terms of enclosure design, analyzer resolution and stability, and acquisition and analysis software, we believe the techniques described here should be applicable to a range of studies of locomotor behavior and energetics in small endotherms.
List of symbols and abbreviations
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Acknowledgments |
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References |
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