Metabolic power of European starlings Sturnus vulgaris during flight in a wind tunnel, estimated from heat transfer modelling, doubly labelled water and mask respirometry
1 Aberdeen Centre for Energy Regulation and Obesity, School of Biological
Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK
2 Institüt der Zoologie, Universität des Saarlandes, D-66041
Saarbrücken, Germany
3 School of Biology, L. C. Miall Building, University of Leeds, Leeds, LS2
9JT, UK
4 Rowett Research Institute, Greenburn Road, Bucksburn, Aberdeen, AB21 9SB,
UK
* Author for correspondence at present address: School of Biology, Bute Medical Buildings, University of St Andrews, St Andrews, Fife, KY16 9TS, UK (e-mail: sw29{at}st-andrews.ac.uk)
Accepted 14 September 2004
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Summary |
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Key words: flight, heat transfer, thermal imaging, thermography, doubly labelled water, metabolic power, bird, efficiency, starling, Sturnus vulgaris
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Introduction |
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An alternative approach to estimating the energetic cost of flight is to
determine mechanical power production for flight (Pmech)
from an aerodynamic model (Rayner,
1979a,b
;
Pennycuick, 1989
), direct
measurements of the mechanical work performed by muscles during flight
(Dial et al., 1997
;
Williamson et al., 2001
;
Tobalske et al., 2003
) or from
wake vorticity (Spedding et al.,
2003
). In principle, one can then readily predict
Pmet from Pmech using the efficiency
with which the animal performs the mechanical work required for flight.
However, such calculations could be in substantial error in practice because
Pmech forms a small, but poorly known, proportion of
Pmet (between 7 and 9% with differences between species,
flight speeds and individual birds;
Norberg et al., 1993
;
Masman and Klaassen, 1987
;
Chai and Dudley, 1995
;
Ward et al., 2001
;
Kvist et al., 2001
). Any
inaccuracy in either Pmech or the value of whole animal
efficiency (Ew, defined as
Pmech/Pmet) is therefore magnified in
the estimated Pmet.
We explore the suggestion (Ward et al.,
1999) that it may be possible to measure the energetic cost of
flight using a novel approach: quantification of heat production
(Pheat) by heat transfer modelling. The majority of
Pmet is lost as heat due to the low conversion efficiency
of chemical to kinetic energy in the flight muscles
(Hill, 1938
). Thus any errors
in the assumed value of Ew will have a relatively small
influence on Pmet. Thermal imaging equipment allows
measurement of radiative heat transfer and surface temperature from
unrestricted animals during flight
(Lancaster et al., 1997
;
Speakman and Ward, 1998
;
Ward et al., 1999
). The
metabolic rate of stationary animals has previously been modelled using heat
transfer theory by assuming that an animal is a series of simple geometric
shapes. For example, Williams
(1990
) used surface
temperature measured by infrared thermography and heat transfer rates from
plates and cylinders to calculate that the metabolic rate for an African
elephant Loxodonta africana was only 6% lower than the allometric
prediction based upon the animal's mass. However, relationships used to
calculate heat loss during flight will differ from those that apply to
stationary animals since convective heat transfer during flight occurs by
forced convection, due to the movement of the animal through the air, while
free convection will predominate in animals that are not moving
(Holman, 1986
).
In the present study, we compared estimates of the energetic cost of flight
determined by heat transfer modelling with those obtained by two independent
techniques (DLW and mask respirometry). We collected data using all three
techniques from European starlings Sturnus vulgaris (hereafter
referred to as starlings) that we trained to fly in a wind tunnel at speeds
between 6 and 14 m s1. Previous studies have suggested that
flight cost estimates may be technique-dependent
(Masman and Klaassen, 1987;
Pennycuick, 1989
;
Rayner, 1990
). We examine
whether this is the case when the same individuals fly under the same
conditions, to test whether the apparent discrepancies between previous
studies are due to biological variation in the energetic cost of flight,
rather than being an artefact of the technique used to make the measurement.
This is the first comparison of the results of three independent measurement
techniques used with the same birds when flying under the same conditions. Our
results therefore allow cross-validation of all the measurement techniques as
well as permitting evaluation of heat transfer modelling as a novel method for
measuring Pmet.
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Materials and methods |
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Metabolic power during flight using doubly labelled water and respirometry
We used DLW (Lifson and McClintock,
1966; Nagy, 1983
;
Speakman, 1997
) to measure the
rate of carbon dioxide production
(
CO2) over
5.86±0.05 h (N=30), which included two flights in the wind
tunnel of 1 h duration. The
CO2 during
75±5% (N=30) of the time that the bird was not flying was
measured by open circuit respirometry (Table A1 in Appendix; see supplementary
material). The
CO2 when the
bird was neither in the wind tunnel nor the respirometry chamber was assumed
to be double the mean value while in the respirometry chamber.
We injected the birds intraperitoneally with isotopically enriched water
(0.217±0.001 g of an isotope mix consisting of 54 APE (atom percent
excess) H218O and 33 APE 2H2O (MSD
Isotopes, Quebec, Canada). The birds were returned to the aviary for
60±2 min (N=30) while the isotopes equilibrated before taking
the initial blood sample (100200 µl) from the femoral vein. The
birds were then placed in a darkened respirometry chamber (0.17 mx0.17
mx0.17 m) for 53±1 min at 15±1°C before transfer to
the wind tunnel, where they flew for 1 h. The bird was allowed to drink for 10
min before transfer back into the respirometry chamber for a further
123±1 min. The final blood sample was taken after a second 1 h flight
at the same speed (±0.2 m s1) as the first flight.
The bird was weighed (to ±0.1 g) immediately after taking the initial
blood sample and before and after each flight. A blood sample was also taken
prior to each injection of the isotopes to determine the background levels of
2H and 18O. We measured the enrichment of the labelling
isotopes using gas source isotope ratio mass spectrometers (Optima, Micromass
IRMS, Manchester, UK) following vacuum distillation
(Nagy, 1983), and small sample
equilibration with carbon dioxide for 18O
(Speakman, 1997
) and reduction
to hydrogen gas with LiAlH4 for 2H
(Ward et al., 2000
).
We calculated the 18O and 2H enrichments of the
injectate, the elimination rates of 18O (ko)
and 2H (kd) and the 18O and
2H dilution spaces (No and Nd by the plateau
method) following Speakman
(1997) (Appendix; see
supplementary material). We calculated
CO2 from
equation 36 of Lifson and McClintock
(1966
) using a dedicated
computer program that took into account changes in the volume of the body
water pool associated with changes in mass during experiments
(http://www.abdn.ac.uk/zoology/jrs.htm;
Speakman, 1997
).
We used a paramagnetic oxygen analyser (Taylor Servomex OA184, Crowbourgh,
UK) and an infra-red carbon dioxide analyser (Hartmann and Braun URAS MT,
Frankfurt, Germany) to measure the concentrations of oxygen and carbon dioxide
in excurrent air while the bird was in the respirometry chamber. We used a
customised BASIC program running on a microcomputer to sample gas analyser
output at 30 Hz and stored the mean of 900 observations twice each minute. We
used a wet test gas flow meter (Wrights DM3A, Zeal, London, UK) to measure the
flow rate of gases through the chamber. Gases from the chamber were dried with
silica gel before and after passing through the flow meter. The gas analysers
were calibrated daily by setting the zero points with oxygen-free nitrogen gas
(Messer Griesheim, Krefeld, Germany), the span of the oxygen analyser with
ambient air and the span of the carbon dioxide analyser with a gas mixture of
known carbon dioxide content (1.85%, Messer Griesheim).
CO2 was
calculated from the proportional increase in the carbon dioxide content of the
gases leaving the chamber attributable to the presence of the bird, multiplied
by the flow rate (corrected to STPD). The rate of oxygen
consumption (
O2)
was calculated from equation 3b in Withers
(1977
). The respiratory
quotient (RQ) was calculated from
CO2/
O2.
We estimated metabolic power during the part of the DLW measurement that the
bird spent flying (Pmet,DLW) using a RQ of 0.71
(Torre-Bueno, 1977
).
Wing beat kinematics
We filmed one of the birds (bird 15;
Table 1) during stable flight
to measure wing beat frequency, amplitude and fluctuation in the area of the
wings during the wing beat cycle by stereophotogrammetric resection
(Albertz and Kreiling, 1980).
We obtained lateral and dorsal images taken simultaneously from
near-perpendicular viewing angles during flight at approximately 1 m
s1 increments in speed between 6 and 14 m
s1 (Photo-Sonics Series 2000 16 mm-1Pl cameras, Burbank, CA,
USA; 255 frames per second; shutter speed 1/1500 s; 16 mm Agfa XTR 250/XTS 400
colour negative film) (Möller,
1998
). We used two 16 mm film projectors (NAC Analysis Projector
DF-16C; Stuttgart, Germany) connected with a synchronisation unit (NAC SYNC
Conti Box) to project images on to a digitiser board (Kontron DK 1515 OP,
Munich, Germany) using a silver-plated mirror. Both digitiser boards were
connected to a PC (Intel Pentium II 266 MHz) and data was digitised using a
customised program. We calculated wing beat frequency from the number of
frames required to complete between 34 and 71 complete wing beats during
periods when the birds flapped constantly and maintained station in the flight
chamber. We determined wing beat amplitude from projected dorsoventral
excursions of the wing tip over five consecutive wing beats. We measured
wingspan from the maximum extension of the wings in the dorsal view during the
downstroke. We calculated the fluctuations in wing area during flight at 6, 8,
10 and 13 m s1 and used interpolation and extrapolation to
determine the area during flight at 12 and 14 m s1. Wingbeat
frequency, amplitude and wing span were measured at 6, 8, 10, 12 and 14 m
s1. Details of the calculations are given in Möller
(1998
) and Ward et al.
(1999
,
2001
).
|
Heat transfer modelling
We used an Agema Infrared Systems Thermovision 880 system (FlirSystems,
Portland, OR, USA) with a 20° lens linked to a dedicated thermal imaging
computer (TIC-8000) running CATS E 1.00 software to measure the intensity of
radiation from starlings during wind tunnel flight. We used the software to
calculate the surface temperature (Ts, measured to
±0.1°C) of each section of the surface of the bird assuming an
emissivity of 0.95 (Cossins and Bowler,
1987). The principals by which the thermal imager measures
radiative heat transfer and calculates Ts are explained in
Speakman and Ward (1998
).
We obtained thermal images from the same birds that were used in the DLW
measurements during flights in the wind tunnel at approximately 6, 8, 10, 12
and 14 m s1. Thermal image collection and analysis followed
Ward et al. (1999). We
calculated the convective heat transfer coefficient (h), taking into
account the build up of a thermal boundary layer as air flowed from the head
to the tail of the bird (method 2 in Ward
et al., 1999
). We calculated fluctuations in air speed past the
wings due to flapping by taking into account changes in air speed and wing
area measured for one of the birds (bird 15) for six sections of the wing,
which we divided into 10 strips along the wings at 50 steps in the wing beat
cycle (Ward et al., 1999
). The
value of h for the legs was estimated from equations applicable to
cross flow over isolated cylinders, taking into account the extent to which
the legs were extended into the air stream (method 3 in
Ward et al., 1999
).
We calculated heat loss by evaporation using the relationship between air
temperature and evaporative heat transfer for starlings during flight in a
wind tunnel (Torre-Bueno,
1978). We calculated overall heat transfer
(Pheat) from the sum of heat transfer by radiation,
convection and evaporation. We calculated metabolic power during flight from
heat transfer (Pmet,heat) from
Pheat/(1Ew). We assumed that
Ew was 0.15 (the mean value determined for two of the
birds in a previous study; Ward et al.,
2001
). We also examined the effects of varying
Ew in the range 0.070.19.
Statistics
We examined relationships between metabolic power and flight speed using
linear regression and curves of the form
Pmet=V1+ßV3+
,
where V was flight speed (m s1). The latter curve
describes the approximate powerspeed relationship that is expected from
aerodynamic models in which induced power is proportional to
V1, parasite power (and profile power in some
aerodynamic models) varies with V3, and basal metabolism
(and profile power in some aerodynamic models) is constant (Rayner,
1979a
,b
;
Pennycuick, 1989
;
Ward et al., 2001
). When more
than one form of relationship provided a significant fit to the data, we
present the one with the highest coefficient of determination. We used general
linear models analysis of covariance (GLM ANCOVA), with bird and measurement
technique as factors and flight speed and air temperature as covariates, to
analyse the effects of these variables upon Pmet or upon
heat transfer by radiation or convection. We included interactions between
terms in our initial models and performed stepwise elimination of
non-significant terms till only those that contributed significantly to the
model remained. When the slopes of relationships did not differ significantly
between birds, we used a common slope and common intercept to describe the
relationship. We used Tukey's post-hoc multiple comparisons to test
for differences between factors. We performed our statistical analyses
following Zar (1996
) and Winer
(1971
). Two-tailed tests of
statistical significance were applied to all analyses. Differences where
P<0.05 were regarded as significant. Regression coefficients are
presented ± standard error (S.E.) and means ±
standard deviation (S.D.). Mean values across birds are averages of
the mean values for each of the birds.
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Results |
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Metabolic power and flight speed
Metabolic power calculated from heat transfer modelling
(Pmet,heat) increased linearly with flight speed
(V) from 8.1±0.8 W at 6.0±0.1 m s1 to
12.4±1.2 W at 14.0±0.1 m s1 and did not vary
between birds (GLM ANCOVA with V as a covariate and bird as a factor:
V, F1,19=50.2, P<0.001; bird,
F3,19=0.5, P=0.7, N=20,
Fig. 1). The scatter in the DLW
data and the uncertainty inherent in each measurement meant that although the
best-fit line through the these data was a U-shaped curve with a minimum of
9.4±2.7 W at 10.3±0.8 m s1, both the
coefficients of the relationship and the minimum power speed and
Pmet are only approximate
(Fig. 2).
|
Comparison of metabolic power across measurement techniques
Fig. 3 compares the
estimates of Pmet made using heat transfer modelling with
an Ew of 0.15 (N=4 birds), DLW (N=4
birds) and previously published data obtained by mask respirometry
(Pmet,resp excluding the estimated additional cost of
carrying the respirometry mask and tube, N=2 birds; Ward et al.,
2001,
1998
).
Pmet did not vary systematically between measurement
techniques when we assumed that Ew was 0.15 (ANOVA with
bird and measurement technique as factors and flight speed V as a
covariate: measurement technique, F2,94=1.26,
P=0.289; V, F1,94=10.87, P<0.001;
bird, F3,94=3.43, P=0.020) or when we assumed
that Ew was 0.19 (ANOVA: measurement technique,
F2,94=0.22, P=0.807; V,
F1,94=11.36, P<0.001; bird,
F3,94=3.35, P=0.023;
Fig. 3).
Pmet,heat was significantly lower than
Pmet,resp or Pmet,DLW if we assumed
that Ew was less than 0.11 (ANOVA when
Ew=0.10: measurement technique,
F2,94=3.66, P=0.030; V,
F1,94=10.30, P=0.002; bird,
F3,94=3.52, P=0.018).
|
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Discussion |
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Calculated Pmet,DLW was increased by 7.3±0.9%
(N=30) if
CO2 when the
bird was not flying or inside the respirometry chamber was decreased to the
mean value inside the respirometry chamber. Calculated
Pmet,DLW was decreased by the same amount if
CO2 during
transfers was raised to three times that when the bird was inside the
respirometry chamber. Metabolism during transfers probably did not vary as
much as this from the value used in our calculations, so the change in
Pmet,DLW calculated here is the upper limit of that
introduced by this assumption.
Sensitivity of metabolic power calculated from heat transfer modelling
Most (79.9±2.7%) heat transfer from starlings during flight occurs
by convection, so our calculation of Pmet,heat is most
sensitive to any error in convective heat transfer. An increase of 10% in
convective heat transfer would raise Pmet,heat by
8.0±0.3%. Accordingly, we paid most attention to computation of
convective heat transfer, especially how we expected convection from the wings
to vary during the wing beat cycle. Comparison between the heat transfer
coefficient that we calculated for the wings and those determined empirically
from a heated model of a starling suggested that the assumptions that we used
were realistic (Ward et al.,
1999). A possible source of error is our assumption of laminar
flow over the surface of the wings and body. Turbulent flow would increase
convective heat transfer, especially towards the trailing edge of the wings
and towards the tail, because turbulence prevents the build-up of a thermal
boundary layer (Holman, 1986
).
Maybury and Rayner (2001
) have
shown that turbulent flow occurs towards the tail of taxidermic model
starlings; however, it is not known how flapping wings or any differences in
plumage position between living birds and models may influence the build-up of
turbulence over flying starlings. The primary, secondary and tail feathers
that are found on the trailing edges of the wings and body (where air flow may
be turbulent) are the coolest parts of a flying starling
(Ward et al., 1999
), so there
is less potential for raised heat transfer from these surfaces than would be
the case from hotter parts of the body such as the head
(Ward et al., 1999
) where air
flow is thought to be laminar. Since convective heat transfer represents such
a large proportion of overall heat transfer, the accuracy of the calculated
values of the convective heat transfer coefficient could be checked
empirically in a future study by using a heated flapping model bird at a range
of air speeds. However, the overall agreement between
Pmet,heat, Pmet,DLW and
Pmet,resp suggests that the convective heat transfer used
in our calculation is close to the correct value.
Although evaporative heat transfer may have differed between our birds and
those studied by Torre-Bueno
(1976,
1977
,
1978
;
Torre-Bueno and Larochelle,
1978
), Pmet,heat was relatively insensitive to
changes in evaporative heat transfer because this contributed only
11.6±2.3% to overall heat transfer. A change of 10% in evaporative heat
transfer would alter Pmet,heat by 1.2±0.2%
(N=20). Heat transfer by radiation contributed only 8.6±1.0%
to overall heat transfer. A 10% change in radiative heat transfer would only
alter Pmet,DLW by 0.9±0.1%.
Heat generated during flight could potentially be stored in the body of
exercising animals, and this may account for reductions in metabolic rate
following flight, because heat generated during exercise could substitute for
thermoregulatory heat production (Webster
and Weathers, 1990; Bautista et
al., 1998
; Edwards and
Gleeson, 2001
). Heat storage typically occurs during wind tunnel
flight by birds (Butler et al.,
1977
; Rothe et al.,
1987
), but does not account for an important proportion of heat
production during long flights
(Torre-Bueno, 1976
;
Craig and Larochelle, 1991
;
Butler and Woakes, 2001
).
Cloacal temperature did not vary between measurements made before and after
the flights during which we obtained thermal images of the starlings
(Ward et al., 1999
), so our
assumption that no heat was stored in the body of the birds was unlikely to
introduce a significant error into Pmet,heat.
Most metabolic power during flight is converted to heat
(Pheat) rather than to mechanical work
(Pmech) due to losses in conversion of chemical energy to
kinetic energy (Hill, 1938).
Thus, Pmech is 719% of Pmet
while Pheat forms 8193%
(Kvist et al., 2001
;
Ward et al., 2001
). Efficiency
can vary between individuals, with bird mass and with flight speed
(Kvist et al., 2001
;
Ward et al., 2001
).
Uncertainty in the value of efficiency presents a problem when predicting
Pmet from Pmech, but has a much
smaller effect on Pmet,heat since much less extrapolation
is needed to calculate Pmet from Pheat
than from Pmech. The mean Pheat during
flight by our starlings at 10 m s1 was 9.63 W. The mean
Pmech predicted from Pennycuick's aerodynamic model was
1.55 W (Pennycuick, 1989
).
Changing the assumed value of Ew from 0.19 to 0.07 would
increase Pmet predicted from Pmech
from 8.2 to 22.2 W (a 2.7-fold increase). The same change in
Ew would alter Pmet,heat from 11.9 W
to 10.4 W (a 13% decrease). Heat transfer modelling therefore produces
predictions of Pmet that are much less sensitive to the
variation in the assumed value of efficiency than those that are based on
Pmech.
Comparison of metabolic power determined by different techniques
There was no statistically significant difference between
Pmet estimated by heat transfer modelling, DLW and mask
respirometry when we assumed an efficiency between 0.11 and 0.19. Since
previous data from the same birds suggested that efficiency lies in this range
(Ward et al., 2001), we
concluded that all three techniques provided consistent estimates of
Pmet. The DLW data were more variable than those obtained
by the other techniques, both between birds at the same flight speed and
across increments in flight speed (Figs
2 and
3). Variability in individual
data points is typical of DLW data
(Speakman, 1997
), so the trend
in Pmet,DLW across all birds and speeds rather than
individual data points should be used to evaluate these results (Figs
2 and
3).
Pmet,DLW appeared to show a U-shaped power-speed curve
while the Pmet,heat and Pmet,resp
increased linearly with flight speed. Our results therefore do not enable us
to determine the form of the relationship between metabolic power and speed,
and further experiments are needed to resolve this issue. The differences
between the results may be due to the greater flight time during collection of
DLW data (2 x 1 h flights) than of mask respirometry (12 min) or
thermography data (up to 30 min).
Our measurements of Pmet during flight in the wind
tunnel were similar to those of free-living starlings measured using DLW
(8.412.5 W; Westerterp and Drent,
1985) and those predicted from modelling cardiac output
(1112 W; Bishop 1997
).
These results suggest that flight in wind tunnels does not have a different
energetic cost than free flight (Masman
and Klaassen, 1987
; Rayner,
1990
; Wikelski et al.,
2003
). The somewhat higher values of Pmet
measured in our starlings than in the birds studied by Torre-Bueno and
Larochelle (1978
) (910
W) may be due to the greater mass of our birds (mean mass 82.0 g during doubly
labelled water measurements compared with a mean mass of 72.8 g in
Torre-Bueno's birds).
Heat transfer modelling based on thermal images is a novel technique by which to calculate Pmet, which has an advantage over calculations based on Pmech in that the result is much less sensitive to the assumed value of efficiency. Heat transfer modelling also has an advantage over DLW or mask respirometry since it is non-invasive and the bird can fly without encumbrance from a respirometry mask and tube. Heat transfer modelling could be used to study Pmet during free-flight rather than in a wind tunnel. Our results show that DLW, mask respirometry and heat transfer modelling produced consistent estimates of Pmet. Heat transfer modelling could be used as an additional method by which to measure Pmet, particularly if the potential influence of turbulent air flow on heat transfer could be better modelled.
List of symbols and abbreviations
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Acknowledgments |
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Footnotes |
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