How cockatiels (Nymphicus hollandicus) modulate pectoralis power output across flight speeds
Tyson L. Hedrick1,*,
Bret W. Tobalske2 and
Andrew A. Biewener1
1 Concord Field Station, Museum of Comparative Zoology, Harvard University,
Old Causeway Road, Bedford, MA 01730, USA
2 Department of Biology, University of Portland, 5000 N. Willamette
Boulevard, Portland, OR 97203, USA
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Fig. 1. (A) A depiction of the pectoralis muscle, its attachment to the humerus at
the deltopectoral crest (DPC) of the humerus, and implanted transducers. A
pair of sonomicrometry crystals was implanted in the proximal portion of the
pectoralis, and an EMG electrode was placed between the two crystals. A
metal-foil strain gauge was attached to the dorsal surface of the DPC of the
humerus. Wires from all these transducers were passed subcutaneously to a
customized miniature plug attached to the bird's back. (B) Sonomicrometry and
electromyogram (EMG) recordings are shown for three successive wingbeats at 7
m s-1. The time course of pectoralis shortening (downstroke) is
shaded in gray. (C) Pectoralis force recorded by the strain gauge and (D)
z-axis (vertical) motion of the wrist and wing-tip obtained from a
3-D reconstruction of digitized markers based on 125 Hz dorsal and lateral
camera views.
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Fig. 2. A timing histogram relating the timing of electromyogram (EMG) activity,
muscle length change, muscle force production and kinematic data for an
individual bird flying at 7 m s-1. The zero time is set to the time
at which maximum force occurred, which corresponds to mid-downstroke. The bars
on each rectangle indicate standard deviation. Additional data on the
variation across speeds in the relationship between EMG onset time, muscle
shortening and force production are given in
Table 2.
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Fig. 3. Whole wingbeat in vivo work loops obtained from an individual
cockatiel flying at (A) 1 m s-1, (B) 7 m s-1 and (C) 13
m s-1. The direction of each loop is counter-clockwise, resulting
in positive work. Pectoralis mass-specific power output, wingbeat duration and
work `shape factor' are noted in the upper left for each loop. The shape
factor quantifies work loop shape by dividing the actual loop area by the
theoretical maximum area for the observed peak force and length change.
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Fig. 4. (A) The relationship between mechanical power output and flight speed
expressed as inter-individual means ± S.D. (N=5 for each
speed; mean and minimum wingbeat sample sizes were 46 and 17, respectively,
across all individuals). Pectoralis force recordings were calibrated using an
aerodynamic power analysis at two intermediate flight speeds (7 m
s-1 and 9 m s-1). The broken lines indicate the possible
range of variation in muscle power output given varying aerodynamic
assumptions. (B) A power curve for an individual cockatiel showing
within-individual means ± S.D. (mean and minimum sample sizes were 50
and 19 wingbeats, respectively, per animal for each speed). The cockatiels
tended to repeatedly gain and lose potential energy while flying in the wind
tunnel, leading to large variation in per-wingbeat power output at all but the
fastest speeds. We restricted our analysis to sequences of wingbeats with no
net change in potential energy but allowed individual wingbeats that resulted
in a change in potential energy.
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Fig. 5. Variation in pectoralis power, pectoralis work and wingbeat frequency
across flight speeds. All values are inter-individual means (N=5 for
each speed) normalized as a percentage of the overall mean for each parameter.
Standard deviations are not shown to improve clarity (these are presented in
Table 2). These data show that
pectoralis power is determined mainly by changes in muscle work per wingbeat
and not by wingbeat frequency.
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Fig. 6. (A) Variation in whole wingbeat, upstroke and downstroke durations across
flight speeds (inter-individual means ± S.D.; N=5 for each
speed) as measured via muscle lengthening and shortening. Consistent
with the modest change in wingbeat frequency versus speed
(Fig. 5), wingbeat duration
varies slightly but significantly (P<0.05;
Table 2) with speed. Changes in
wingbeat duration across speeds is due entirely to changes in upstroke
duration, as downstroke duration does not vary significantly with speed
(P>0.1; Table 2).
(B) Variation in wingbeat duration measured via a 125 Hz,
three-dimensional kinematic reconstruction. Although the general pattern is
similar to that shown in A, the relative durations of upstroke and downstroke
have shifted such that downstroke is shorter than or equal to, rather than
longer than or equal to, upstroke.
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Fig. 7. (A) Variation in peak muscle force and muscle strain rate as a function of
flight speed. (B) Changes in the amplitude of muscle strain with speed
(P<0.01; Table 2).
Values represent interindividual means ± S.D. (N=5 for each
speed). Pectoralis force, strain rate and strain amplitude vary similarly with
speed, and the pattern of variation matches that found for muscle power output
versus speed (Fig.
5).
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Fig. 8. A partial regression component model of the factors underlying pectoralis
power output among individual birds as a function of flight speed with a
least-squares multiple linear regression of power against selected factors.
The arrows indicate the proposed relationships between variables, together
with their partial regression coefficients, which indicate the relative
strength of the relationship. Pectoralis power output as a function of flight
speed is determined by the combination of work per wingbeat and wingbeat
frequency, but work per wingbeat exerts the strongest effect. Pectoralis work
per wingbeat is influenced by several factors, but the two most important are
muscle force (r2=0.77) and muscle length change
(r2=0.45; also see Fig.
6). The double-headed arrow between muscle length change and work
loop shape factor indicates that increases in length change are correlated
with decreases in the shape factor, and vice versa. The correlation
also influences the total effect of the variables; for example, the total
effect of muscle length change is 0.80-(0.63x0.40), or 0.55. There were
no significant correlations between model variables aside from those indicated
in the model via arrows. Statistical tests were conducted based on a
set of individual means across speeds (5 individuals x 7 speeds:
N=35).
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Fig. 9. (A) A least-squares regression of muscle force versus
electromyogram (EMG) amplitude within one individual across speeds. (B) A
least-squares regression of muscle shortening velocity during downstroke
versus EMG amplitude within the same individual across speeds. EMG
amplitude was quantified as the integrated rectified EMG signal, divided by
its duration. We performed the regression against mean values for amplitude,
force and shortening velocity at each speed to give a balanced data set.
However, we plotted all points included in the mean values as small `x'
symbols to show the full range of variation; the mean values used in the
regression analyses are shown as large diamonds.
Table 4 reports additional
analyses of EMG amplitude with respect to muscle force.
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© The Company of Biologists Ltd 2003
