Wingbeat frequency of barn swallows and house martins: a comparison between free flight and wind tunnel experiments
Swiss Ornithological Institute, CH-6204 Sempach, Switzerland
* e-mail: felix.liechti{at}vogelwarte.ch
Accepted 14 May 2002
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Summary |
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Key words: barn swallow, Hirundo rustica, house martin, Delichon urbica, wingbeat pattern, intermittent flight, effective wingbeat frequency, wind tunnel, free flight, flight costs
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Introduction |
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Materials and methods |
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For comparisons of climbing rates between birds flying at different speeds, flight angles were calculated as arctan(vz/va). In contrast to wind tunnel experiments, variables such as air speed, vertical speed and flight angle for birds in free flight were averaged over intervals of 20 s. The flight behaviour of single tracked birds may be quite variable within such intervals. However, 20 s intervals cover flight distances of 200-300 m and give a robust estimate of the resulting flight vector.
Hirundines were tracked on spring migration approaching the coast from the Mediterranean Sea in a northerly flight direction. Birds passing closer than 900 m were identified visually during daylight hours by means of a 12.4x telescope mounted parallel to the radar beam. We recorded 39 flight paths of visually identified barn swallows and 26 flight paths of visually identified house martins; for these birds, wingbeat frequencies could be determined from echo signatures, allowing single wing beats to be analysed. Once a bird had been identified, it was tracked for as long as possible (a maximum of 5 min). To reduce the interdependence of 20 s intervals within individual flight paths, no neighbouring intervals were chosen for analysis. In addition, more than one interval per bird was accepted only if flight angles differed by more than ±2.5 °, representing different types of flight behaviour, e.g. climbing, horizontal flight or descent. As a consequence, any individual contributed at most four intervals to the analysis of wingbeat frequency.
Wingbeat patterns and wingbeat frequencies
If a single bird is tracked, recording the fluctuations in its echo
signature offers the possibility of obtaining its wingbeat pattern
(Eastwood, 1967;
Bruderer, 1969
;
Bruderer et al., 1972
). The
amount of radar energy reflected depends on the size and reflective properties
of the target; it changes with the distance and aspect presented to the radar
(Bruderer and Joss, 1969
;
Bruderer et al., 1995
). The
fluctuations in echo signatures are related to rapid changes in the
circumference and volume of the bird's body
(Bruderer, 1997a
), which are
due to the movements of pectoral muscles during down- and upstrokes. However,
peaks of echo signatures of radar-tracked birds may not be interpreted
unambiguously as single wing beats. Because of phase shifts in radar waves
bent around the target, a single wing beat of a bird of the size of a
hirundine may produce two peaks (Mi-effect, occurring if wavelengths and
target size are of the same order of magnitude; see
Bruderer, 1969
). The relative
size of these two peaks may change within a flight path, depending on the
aspect a bird is presenting to the incident radar waves. In hirundines, great
attention had to be paid to this potential doubling of single wing beats
caused by the Mi-effect (see Fig.
1). Within one 20 s interval, either main or secondary peaks had
to be marked consistently. Before interactively marking single wing beats, the
signal of the raw echo signature was passed through a band-pass filter that
eliminated high-frequency oscillations (>18 Hz) caused by the rotation of
the radar feed and low-frequency oscillations (<4 Hz) caused mainly by
tracking movements of the radar antenna.
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In general, wingbeat frequencies are measured as the number of flapping
cycles per second for a phase of continuous flapping. However, unless in a
steady climb, hirundines rarely flap their wings continuously, nor do they
show regular patterns of flapping and rest phases, as do most other small
passerines (Bruderer et al.,
2001). Single, pulse-like wing beats are often interspersed over
time and, especially in free flight, very few successive flapping cycles are
of exactly equal duration. We therefore calculated the number of wingbeat
cycles per unit time, termed the effective wingbeat frequency, according to
the formula:
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Wind tunnel experiments
Seven juveniles, four barn swallows (Hirundo rustic) and three
house martins (Delichon urbica), were hand-raised and later tested in
a wind tunnel at different flight angles (-10, -5, 0 and 5 °) and air
speeds (5.1, 8.2, 10.2 and 12.5 m s-1) at the University of
Saarbrücken. Flight intervals of 20 s at a constant air speed and flight
angle were recorded on video (50 frames s-1). The movement of the
wingtips relative to the body axis were analysed using the same software as
for the radar data (see above). Mean values of body mass and wing span for the
individuals tested are given in Table
1. A detailed description of the experimental settings and the
analysis are presented in Bruderer et al.
(2001).
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Statistical analyses
Statistical analyses were performed with the software package Genstat 5.0
(1993). To analyse minimum
wingbeat duration, the 5% range per individual was taken instead of the
absolute minimum. Differences were tested using a t-test for
independent samples. The influence of flight variables and species on
effective wingbeat frequency was modelled by multiple linear regression
analysis. To compare wind tunnel results with data for free-flying birds, an
analysis of variance (ANOVA) with the factors flight angle and treatment (free
or wind tunnel) was performed. Differences were tested using Tukey's honest
significant difference (HSD) test for unequal sample sizes.
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Results |
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The frequency distribution of the duration of flapping cycles was skewed in both species, whether in climbing or horizontal flight (Fig. 2). As flight angle decreased, the proportion of longer flapping cycles increased gradually, while the mode remained constant. Some flapping cycles of the barn swallows during climbing were rather long, caused by temporary interruptions to climbing flight by short rest phases. In general, the flapping cycles of barn swallows were slightly longer than those of house martins. Minimum values (see Materials and methods) per individual for the duration of flapping cycles differed significantly between the two species (all angles, one value per individual, BS, N=39, mean 0.1054 s, Feff=9.5Hz; HM N=27, mean 0.0891 s, Feff=11.2Hz; t-test for independent samples, t=5.4, P<0.001).
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The effective wingbeat frequencies (Feff) of barn swallows, including all flight angles and speeds, ranged from 2.5 to 8.4Hz; those of house martins ranged from 3.0 to 8.1Hz (Fig. 3). Mean Feff was 4.4Hz for barn swallows and 5.3Hz for house martins. A large proportion of the tracked hirundines descended during their approach to the coast. Including only horizontal flight paths in the analysis (flight angle ±1.5°) gave somewhat higher mean Feff values of 5.4Hz for barn swallows and 6.0Hz for house martins. Overall, Feff of barn swallows was significantly lower than that of house martins (t-test for independent samples, NBS=65, NHM=51, t=3.8, P<0.001), but Feff values did not differ significantly between species for individuals during horizontal flight (flight angle 0±1.5°, NBS=13, NHM=9, t=1.9, P=0.07).
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A multiple linear regression was performed to examine the variability of Feff for barn swallows and house martins in free flight with respect to flight angle, true air speed and flight altitude (Table 2). Flight angle was by far the most important factor, explaining approximately two-thirds (71.3%) of the variance. In addition, Feff increased with true air speed in house martins but not in barn swallows. Remarkably, true air speed was strongly correlated with flight angle in barn swallows (r=-0.71), but only moderately in house martins (r=-0.44) (Fig. 4). For barn swallows Feff decreased (r=-0.61, P<0.001) with increasing true air speed (Fig. 3A). However, as demonstrated by the multiple regression, this was caused by the general increase in true air speed during descent (Fig. 4). The effective wingbeat frequency of both species increased significantly (BS: r=0.87, P<0.001; HM: r=0.75, P<0.001) with increasing flight angle (Fig. 3B). Flight altitude varied between 50 and 900m above sea level, but had no significant influence on Feff. The second and third powers of the three variables did not explain any significant proportion of the variance.
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In addition, we analysed five individual flight paths with a relatively
long tracking time (100 s) and considerable variability in flight angles
(Fig. 5). Within each
individual track, flight angle accounted for a significant 65.1-93.5% of the
variance (simple linear regression). Including true air speed in a multiple
regression did not add significantly to the amount of variance explained.
However, for one house martin (see Fig.
5, filled circles; Feff,minimum=3.2Hz, six
flight intervals), true air speed explained slightly more of the variance
(t=3.6) than flight angle (t=3.4; simple linear regression).
In this case, the correlation between the two variables was strongly positive
(r=0.91, see above). In contrast to most other tracks, this bird
decreased its true air speed from 16.2 m s-1 during horizontal
flight to 13.0 m s-1 during descent.
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Wind tunnel versus field data
It is not possible to compare wingbeat frequencies for identical flight
conditions, since flight angle and air speed are more variable during free
flight and, thus, their measurement is less exact. The air speeds of
hirundines during free flight were generally somewhat higher than the speeds
testable in the wind tunnel used in the complementary study. Measurements of
Feff recorded at the highest test speeds (12.5 m
s-1, except during climbing, 10.2 m s-1) at a given
angle were compared with field measurements, for which air speeds were
restricted to 10-16 m s-1. The mean air speeds per flight angle for
free-flying barn swallows were between 14.1 m s-1 (-10°) and
11.6 m s-1 (+5°), and those of house martins were between 14.3
m s-1 (-5°) and 11.4 m s-1 (+5°). The flight
angles of radar-tracked hirundines were roughly equivalent to those in the
wind tunnel experiments (Fig.
6). In both species, Feff was significantly
higher in wind tunnel experiments than during free flight (ANOVA, Tukey's test
for unequal sample sizes, P<0.001; see Materials and methods). In
barn swallows, mean Feff differed by 1.8 Hz (from 1.5 to
2.6 Hz within flight angles), corresponding to an increase of 40 %; in house
martins, mean Feff differed by 1.7 Hz (from 0.5 to 2.7 Hz
within flight angles), corresponding to an increase of 32%.
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Discussion |
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Wind tunnel observations have shown that this variability is due mainly to
interruptions of upstrokes during which hirundines commonly perform partial
bounds (Pennycuick et al.,
2000; Bruderer et al.,
2001
). These are characterised by completely flexed primaries and
partially spread arm wings, which most probably act as small aerofoils,
generating residual lift during rest phases. During descending flight, house
martins tend to aggregate their wing beats into an almost `passerine-like'
pattern, with bursts of wing beats and intermittent rest phases
(Fig. 1; HM3). However, the
durations of flapping and rest phases were highly variable and clearly
different from the regular bursts of flappings of other small passerines
(Stark, 1996
).
Hirundines hunt in free flight and are among the small number of diurnal long-distance passerine migrants. We speculate that this highly variable wingbeat pattern might be an adaptation to flying long distances through air that is much more turbulent during the day than at night. Migrating swifts mostly combine several wing beats into bursts of variable length, followed by relatively long gliding phases (Bruderer and Weitnauer, 1992). Provided that the radar echo signatures are of good quality, hirundines as a group can be identified by their characteristic wingbeat pattern alone.
Effective wingbeat frequency
During powered flight, wingbeat frequency is closely related to mechanical
power output, although other factors (amplitude, angle of attack, etc.) may be
of considerable importance (e.g. Tobalske
et al., 1999). To account for the restricted spatial accuracy of
radar tracks, rather long flight intervals of 20 s were selected,
corresponding to flight paths of a few hundred metres (mean flight speed
approximately 14 m s-1). It is obvious that, in this case,
effective wingbeat frequency, rather than wingbeat frequency during short
bursts of flaps, will be related to mechanical power output. A strong positive
correlation between flight angle and Feff was found in
both species, both in the field data and in wind tunnel experiments. The
difference in Feff between the two species is in
accordance with the differences in their wingspan and mass of 10-15 %.
Although hirundines on migration flew mostly at higher air speeds (10-20 m
s-1) than in the wind tunnel experiments (5-12.5 m s-1),
only house martins showed the expected increase in Feff
with air speed. The tail of the expected U-shaped curve for
Feff against air speed at low speeds was not covered by
our field data. It is not surprising that in barn swallows, with their high
interdependence between air speed and flight angle, no independent effect of
air speed was found in the field data. The large scatter due to the
heterogeneity of these data may have masked some of the expected
correspondence.
All birds tested in the wind tunnel were juveniles before their first
migration. They had thus not yet reached adult wingspan, whereas their body
masses were within the upper half of the ranges found in the literature
(Table 1). According to
Pennycuick (1996), wingbeat
frequency (f) is related to body mass (m), wing span
(b) and wing area (S):
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Pennycuick et al. (2000)
estimated the mechanical flight power of barn swallows flying in a wind
tunnel. Their estimates, based on recordings of the movements of the humerus,
were higher than expected from recent theory. This result is in agreement with
our observations and indicates either that hirundines (at least juveniles) may
perform additional flight manoeuvres in the wind tunnel, causing flight costs
to increase, or that their flight muscles might still be smaller than those of
adults and thus produce less thrust and lift during a downstroke. Data
presented by Tobalske et al.
(1997
) on magpies (Pica
pica) also suggested that wingbeat frequency was lower outdoors than in
the wind tunnel. If free-living juveniles also differ from adults in wingspan
but not in body mass, we must assume higher flight costs for juveniles than
for adults, which might be of some importance for the high mortality of barn
swallows after fledging due to aerial predators (L. Schifferli, personal
communication).
It is of great interest whether these differences in mechanical flight
power observed between free-flying hirundines and wind tunnel experiments are
restricted to these two species or whether there are some flight costs caused
by a wind tunnel effect. However, there is theoretical evidence that flight
costs in closed wind tunnels are lower than during free flight, primarily for
low speeds (Rayner, 1994). We
compared speeds clearly above the minimum power speed (>10 m
s-1), so this effect does not have a strong influence on our
results. At present, we cannot exclude the possibility that flight cost
calculations based on wind tunnel experiments overestimate mechanical power
output compared with free flight.
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Acknowledgments |
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