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First published online December 14, 2007
Journal of Experimental Biology 211, 58-65 (2008)
Published by The Company of Biologists 2008
doi: 10.1242/jeb.009811
A new method to quantify prey acquisition in diving seabirds using wing stroke frequency
1 International Coastal Research Center, Ocean Research Institute, The
University of Tokyo, 2-106-1 Akahama, Otsuchi, Iwate 028-1102, Japan
2 Centre for Ecology and Hydrology, Banchory, Aberdeenshire AB31 4BW, UK
3 Graduate School of Fisheries Sciences, Hokkaido University, Minato-cho 3-1-1,
Hakodate 041-8611, Japan
4 National Institute of Polar Research, 1-9-10 Kaga, Itabashi, Tokyo 173-8515,
Japan
* Author for correspondence (e-mail: katsu{at}ori.u-tokyo.ac.jp)
Accepted 19 October 2007
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Summary |
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 TOP Summary INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Key words: acceleration, data logger, flight, foraging, seabird, Phalacrocorax aristotelis
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INTRODUCTION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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). Although
devices can now provide detailed recordings of many behaviours, important
details such as determining when a free-ranging individual encounters prey and
the amount of prey consumed remain difficult to estimate. Various methods have
been tried, including animal-borne cameras to record temporal and spatial
patterns of prey encounter (Davis et al.,
1999; Grémillet et al.,
2006; Hooker et al.,
2002; Mitani et al.,
2004; Ponganis et al.,
2000; Watanabe et al.,
2003; Watanuki et al.,
2007), acoustic loggers to identify prey encounters of
echolocating cetaceans (Akamatsu et al.,
2005; Miller et al.,
2004a; Watwood et al.,
2006) and loggers to measure beak opening
(Plötz et al., 2001;
Takahashi et al., 2004;
Wilson, 2003;
Wilson et al., 2002). To
identify the precise timing of prey capture and mass of prey ingested, changes
in oesophageal and stomach temperatures
(Charrassin et al., 2001;
Ropert-Coudert and Kato, 2006;
Ropert-Coudert et al., 2001;
Wilson et al., 1995) have been
used, while nest balances have been employed to estimate the total amount of
prey caught during a foraging trip
(Grémillet et al.,
1996). However, all these methods have some limitations and
sources of error, thus hindering progress in understanding the foraging
ecology of diving species, many of which are of major conservation or economic
importance.
Alternative techniques for quantifying underwater foraging success are
therefore being explored. One promising approach has developed from the
observation that swimming movements of diving animals are strongly influenced
by the total density of their bodies
(Miller et al., 2004b;
Nowacek et al., 2001;
Sato et al., 2002;
Sato et al., 2003;
Skrovan et al., 1999;
Watanabe et al., 2006;
Williams et al., 2000). For
example, time-depth profiles of deep-diving seals (grey seal, Halichoerus
grypus, and northern elephant seal, Mirounga angustirostris),
where the buoyancy of an individual is determined by the ratio of lighter
lipid to heavier lean tissue, are characterized by a drift phase
(Beck et al., 2000;
Crocker et al., 1997;
Webb et al., 1998). Biuw et
al. monitored temporal changes in drift rates of newly weaned pups of southern
elephant seals (Mirounga leoni) during their first foraging trips at
sea (Biuw et al., 2003). All
seals showed a similar trend: drift rates were initially positive but
decreased over the first 30–50 days after departure. Over the following
100 days, drift rates again increased gradually, while during the last
20–45 days drift rates remained constant or decreased slightly.
These temporal changes were considered to correspond to relative changes in
foraging success over the course of the trip
(Biuw et al., 2003).
Unfortunately, the drift rate method is not applicable to diving seabirds
because total body density is mainly affected by the volume of air kept in the
body (Lovvorn et al., 1991;
Sato et al., 2002;
Wilson et al., 1992). However,
the approaches being developed for marine mammals did stimulate us to think
about other indirect methods that might be appropriate for investigating
foraging success in marine birds. Many seabirds make foraging trips in which
they fly between a series of locations at each of which they make a series of
dives (a dive bout). During these flights, birds stroke their wings in order
to provide lift. Within a species it is likely that wing size and shape are
similar and thus heavier birds will need to stroke at a higher frequency than
lighter individuals (Rayner,
1987). According to some comparative studies, body mass of many
species of flying bird changes seasonally, and within a species wing stroke
frequencies show corresponding fluctuations
(Gudmundsson et al., 1995;
Pennycuick, 1996). However, to
our knowledge, no previous study has taken advantage of this relationship and
attempted to use wing stroke frequency to estimate foraging success of
individual seabirds.
Typically, video recording has been used to measure stroke frequency in
flying birds (Pennycuick,
1990; Pennycuick,
1996). However, this method is unsuitable for longitudinal
measurements of a single individual over prolonged periods such as during a
foraging trip. Animal-borne video cameras can record flipper movements of
marine mammals (Davis et al.,
2001; Skrovan et al.,
1999; Williams et al.,
2000) and emperor penguins, Aptenodytes forsteri
(van Dam et al., 2002), but
the devices are too large to be deployed on free-ranging flying birds.
Animal-borne accelerometers have previously enabled us to obtain dominant
stroke frequencies for aerial flight during foraging periods of a range of
species, including razorbill (Alca torda), common guillemot (Uria
aalge), Brünnich's guillemot (U. lomvia), rhinoceros auklet
(Cerorhincha monocerata), European shag (Phalacrocorax
aristotelis), South Georgian shag (P. georgianus), black-browed
albatross (Diomedea melanophris) and streaked shearwater
(Calonectris leucomelas) (Sato et
al., 2007). Accelerometers can be used to record not only the
dominant stroke frequency for each individual but also modulation of the
stroke frequency throughout a certain period. Wilson et al. used
accelerometers on cormorants to estimate activity-specific metabolic rate that
indicated that returning birds worked harder than those flying out from the
colony (Wilson et al., 2006).
The modulation of the wing stroke frequency of a flying bird is expected to
correspond to fluctuations in body mass during a foraging trip. Thus,
animal-borne accelerometers have the potential to provide detailed information
on the amount of prey caught by an individual during a dive bout.
The European shag [Phalacrocorax aristotelis (Linnaeus)] is a
medium-sized seabird that flies with continuous wing stroking. At the breeding
colony on the Isle of May, off the coast of southeast Scotland, UK, shags
typically make 1–4 foraging trips per day during chick rearing
(Wanless and Harris, 1992).
They feed benthically on small fish such as lesser sandeel (Ammodytes
marinus) and butterfish (Pholis gunnellus), and food for the
brood is transported back to the colony in the parent's stomach
(Wanless et al., 1991a;
Wanless et al., 1993a;
Wanless et al., 1993b;
Watanuki et al., 2007). In an
earlier study using VHF telemetry and water-offloading to determine the mass
of food loads brought back to the colony, load size was shown to be extremely
variable, ranging from 8 to 208 g with a mean load mass of 106 g
(Wanless et al., 1993b). These
foraging characteristics, plus the tameness and accessibility of birds on the
Isle of May, thus made this an ideal study system in which to carry out a
pilot study to investigate whether foraging success of a diving seabird can be
estimated from stroking frequency during aerial flight.
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MATERIALS AND METHODS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Instruments and data analysis
Acceleration data loggers (M190L-D2GT; Little Leonardo Ltd, Tokyo, Japan)
were used to obtain detailed information on foraging activity over a 24 h
period (maximum 28.2 h because of memory capacity). Each logger was 15 mm in
diameter, 53 mm in length, had a mass of 18 g in air and recorded depth (1
Hz), two-dimensional acceleration (64 Hz, respectively) and temperature (not
used in this study). Although the mass of the data logger in air was only
1% of adult body mass, it is possible that energy expenditure during a
trip might still be increased. However, the mass of a load of fish typically
brought back after a foraging trip is much higher (mean 106 g)
(Wanless et al., 1993b). Since
there was no evidence of any obvious disruption to attendance behaviour of the
instrumented birds, we therefore assumed that the data collected would be
representative of normal flight behaviour. Furthermore, the aim of our study
was to investigate relative changes in wing stroke frequency in relation to
foraging success.
Loggers were positioned so as to detect longitudinal and dorso-ventral
accelerations. Values recorded by the accelerometers were converted into
acceleration (m s–2) with linear regression equations. To
obtain the calibration equations, values recorded by each logger at 90°
and –90° from the horizontal were regressed against the
corresponding acceleration (9.8 m s–2 and –9.8 m
s–2, respectively). Loggers measured both dynamic
acceleration (such as wing stroking activities) and static acceleration (such
as gravity). Low-frequency components (<1.5 Hz) of the fluctuation in
longitudinal acceleration, along the long axis of the body, were used to
calculate the pitch angle of the animal
(Sato et al., 2003).
The instrumented birds made between two and six foraging trips over their respective deployment periods (Table 1). Acceleration and depth data were used to categorize the birds' behaviour into time on land, in the air (aerial flight) and in a dive bout, including dive and surface time on the water. Dives were defined as bird movements to depths greater than 1 m. Pitch angles were used to discriminate time at the surface (angle nearly 0°), on land (angle >30°) and in aerial flight. Aerial flight was further characterized by a stable inclination of the body angle (7–21°) and periodic fluctuations in dorso-ventral acceleration.
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Trip duration was defined as the interval between departure from and arrival back on land. Most trips started with a period of flight but in a few cases a bird initially went onto the water. Dive bouts were defined as a single or series of dives separated from the next bout by a period in flight, with bout duration estimated from the start of the first dive to the end of the last dive and including all surface intervals between dives.
Power spectral density (PSD) was calculated from entire acceleration, along the dorso-ventral axis, of each bird using a Fast Fourier Transformation with computer program package IGOR PRO (WaveMetrics, Inc., Lake Oswego, OR, USA). Sub-samples for each flying period were used to determine the dominant stroke cycle frequency for each period. The minimum length of flight needed to calculate PSD with 0.03125 Hz resolution of frequency (equal to 18 g resolution of body mass) was 1 min. To investigate modulation of the stroke frequency throughout flying periods and obtain the maximum frequency at taking-off, a spectrogram of the dorso-ventral acceleration was calculated with IGOR PRO. When calculating the spectrogram, the Gabor spectrogram was selected. Frequency resolution was 0.125 Hz and generated a spectrum every 64 points (1 s). Calculated linear amplitude was expressed by colour accordingly (Fig. 1A).
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), a simple argument for the scaling of flight power and speed
was first given by von Helmholtz (von
Helmholtz, 1874). In steady level flight, lift (L) and
weight (mg) must be in equilibrium:
 | (1) |
 | (2) |
is the density of the air (kg m–3), S is
area of the wing (m2), CL is the lift
coefficient and U is speed of the wing section (m
s–1). Substituting L in
Eqn 1 by
Eqn 2, we obtain the following
relationship:
 | (3) |
 | (4) |
, CL) remain unchanged,
the expected relationship is F
m
,
as indicated by Pennycuick (Pennycuick,
1996). The wing stroking amplitude (A) might increase
with load. However, the stroking amplitude was not measured in the present
study. Further work is needed to measure amplitude directly in shags. However,
a payload experiment conducted on cockatiels (Nyphicus hollandicus)
indicated that wing stroking amplitude was unaffected by the increase in
loading (Hambly et al., 2004).
Therefore, we assumed that A was constant and independent of any load
carried.
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 | (5) |
Statistics
For each bout, we examined the effect of dive bout duration and total
underwater time on load size using Residual Maximum Likelihood Analysis (REML)
(Patterson and Thompson, 1971)
with individual as a random effect. For each trip, we analyzed the effect of
cumulative duration of dive bouts, total underwater time, total flight
duration and return flight time on load size during trips using REMLs with
individual as random effect. We also analysed the effect of mass gain at sea
and time on land on mass loss on land using REMLs with individual as a random
effect.
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RESULTS |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Assuming that the main factor affecting these short-term changes in body mass was prey capture, we can thus use this approach to start to explore relationships between foraging success of European shags and other components of the foraging trip. When we analysed the effect of individual effort on load size at the bout level, we found strong positive relationships between load size and dive bout duration (Fig. 3A) (REML: W=15.22, P<0.001) and cumulative dive duration within a bout (Fig. 3B) (REML: W=15.46, P<0.001).
To investigate the total amount of prey caught during a foraging trip, we estimated the net gain in body mass by comparing stroke frequencies on the outward and inward flights. Values varied from –30 to 260 g (Table 1). In contrast to the effect of efforts at the dive bout level, load sizes were unrelated to cumulative dive bout duration (the sum of all dive bouts during the trip) (Fig. 4A) (REML: W=0.77, P=0.38) and total underwater time (Fig. 4B) (REML: W=3.40, P=0.07). There was a highly significant positive relationship between load size and both cumulative flight time (Fig. 4C) (REML: W=27.44, P<0.001) and return flight time (Fig. 4D) (REML=29.73, P<0.001). Thus, heavier loads were brought back to the colony after more distant foraging trips.
By comparing the wing stroke frequencies between return from one trip and departure on the next we could also estimate the net loss in body mass on land. Estimated decreases in mass varied from –17 to 250 g and the amount of mass loss was independent of time on land, which varied between 15.1 and 205.8 min (Fig. 5A) (REML: W=0.00, P=1.00). However, mass loss ashore was positively related to net gain in the preceding foraging trip (Fig. 5B) (REML: W=64.75, P<0.001).
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DISCUSSION |
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TOPSummaryINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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; Wanless et al.,
1993b). As an additional check, where possible we compared the
estimated body mass during the final flight back to the colony with the direct
measurement of body mass on recapture
(Table 1). In two cases,
measured weights were 150 g less than those estimated using the wing stroke
method, almost certainly because the birds had already fed their chicks before
they were recaptured. Comparisons of the remaining values were all within a
range of ±60 g (Table
1), and this close correspondence further increases our confidence
in the wing stroke technique.
The wing stroke method has several important advantages compared with other
techniques. First, it is considerably less invasive than water-offloading or
the deployment of internal devices such as stomach or oesophageal temperature
loggers. Second, it can provide information at a finer temporal scale than
either water-offloading or nest balances, where prey capture is integrated
over the whole trip and success at the individual bout level cannot be
investigated. Third, it is suitable for species such as shags that feed on
relatively small prey items, such as lesser sandeels [mean mass of individual
fish taken by shags was 3.2 g (Wanless et
al., 1993b)], that are difficult to monitor using stomach
temperature loggers, which perform best when large items are ingested.
However, the accelerometer wing-stroke method does have some limitations
that must be kept in mind. First, memory capacity of the loggers currently
limits data collection to 24 h, making the technique unsuitable for
pelagic, wide-ranging species. Second, the estimation of stroke frequency is
only possible if a bird flies for more than 1 min. This interval is the
minimum length needed to calculate PSD with a high resolution of frequency
(0.03125 Hz) using a 64 Hz sampling interval. Most seabird species, even
inshore feeders, fly for longer than this at the start and end of a trip so
that the total amount of prey caught can be estimated in most cases. However,
prey capture during dive bouts preceded or followed by flights shorter than
this interval cannot be quantified. Third, changes in stroke frequency may not
be only affected by changes in body mass. Wing stroking can potentially be
affected by several other factors, for example a bird's flight behaviour
(steady level flight and wing amplitude) and whether it is searching for prey
or travelling, and the wind direction and speed relative to the bird's
direction of flight. To evaluate the effect of body mass changes on stroke
frequency, it is essential to do some further research, such as
weight-dropping experiments, which have been conducted on sea turtles,
Caretta caretta (Hays et al.,
2004; Minamikawa et al.,
2000), and Baikal seals (Phoca sibirica)
(Watanabe et al., 2006).
Finally, a key assumption underpinning the wing stroke method is that
increases in a bird's body mass are primarily due to prey being ingested.
However, changes in body mass can also be due to feathers becoming waterlogged
over the course of a dive bout.
This latter effect could be particularly problematic in species such as
cormorants and shags that do not have a fully waterproof plumage. Ribak et al.
demonstrated that, on average, water retention in the feathers of great
cormorants (Phalacrocorax carbo) was 3% of body mass and the amount
of water increased as a function of time in the water
(Ribak et al., 2005). The
positive relationship between mass gain and dive bout duration
(Fig. 3A) might be caused by
water trapped in plumage. However, mass gain in a trip was not related to
cumulative dive bout duration in a trip
(Fig. 4A), which contradicts
the possibility that the gain in body mass was mainly caused by water in the
plumage. Better plumage insulation has been suggested as a reason for lower
energetic costs of diving in European shags compared with great cormorants
(Enstipp et al., 2005) and,
hence, amounts of water retained in the plumage are likely to be less in the
former. Furthermore, European shags invariably flap their wings very
vigorously before taking off from the sea, which is likely to get rid of some
of the water trapped in the feathers at the end of the dive bout and more
water may be expelled during take off. We are currently unable to partition
changes in wing stroke frequency among these various contributing factors.
Although we assume that the main factor affecting these short term gains in
body mass was prey capture, some amount of body mass may be lost due to
defecation during dives (Wilson et al.,
2004). Thus, our estimate of total amount of prey captured during
a dive bout is likely to be conservative. According to a previous study
(Wanless et al., 1997), in
which masses of material defecated during a feeding trip were estimated by
comparing food load for the trip estimated by stomach flushing and from nest
balances, the mean mass of excreta was 7.2 g per trip. Thus, under the
foraging regime typical of chick rearing at this study site, relatively little
of the food was likely to have been lost as faeces by the time a parent
returned to its nest.
The main aim of this study was to evaluate the wing stroke method as a
means of estimating prey capture rates in diving seabirds. However, the trials
also provided some useful insights into the foraging behaviour of shags during
chick rearing. Previous work on the Isle of May found a highly significant
positive correlation between load size and foraging range
(Wanless et al., 1993b), and
this relationship was also shown using the wing stroke method. The wing stroke
method allowed us to develop this approach further and investigate prey
ingestion during individual bouts. Shags on the Isle of May use a variety of
foraging habitats (Wanless et al.,
1991b), in some cases changing habitat within a trip
(Watanuki et al., in press).
We found a highly significant relationship between the amount of prey ingested
and bout duration. Whilst we did not know precisely where birds were diving,
further work that combined accelerometers with locational loggers, such as
GPS, PTT or VHF radio tags, would enable us to build up a detailed picture of
spatial variability in prey availability and start to determine foraging rules
for this species.
It was clear that shags sometimes encountered very favourable feeding
conditions during which they caught a large amount of prey in a short period.
For example, female 03F2 caught 260 g of food in 17 dives during a bout
lasting only 38.9 min and then returned to the colony. Many foraging trips
lasted much longer than this (Fig.
4A,B), raising the question of why the female did not prolong her
trip and increase her load still further. Part of the reason may be that the
maximum stomach capacity of shags is around 220–250 g of food (S.W.,
personal observation) and thus the bird would have had to stop feeding and
digest some of the load. However, biomechanical constraints may also be
important. According to theory, stroke frequency should be proportional to
m (Pennycuick,
1996; Rayner,
1987). Thus, as body mass increases, birds should increase wing
stroke frequency in aerial flight. Shags stroked at very high frequencies when
taking off (114–129% of the total dominant frequencies)
(Table 1). However, these
elevated levels did not last for more than 10 s and stroking frequency then
equilibrated to lower values in cruising flight
(Table 1,
Fig. 1A). The dominant stroke
frequencies associated with cruising flight varied around the total dominant
frequency (95–106% in Table
1) for each bird. The observed range of sustainable stroke
frequencies for an individual might therefore be within a range of economical
frequencies for aerial flight. Thus, in the case of a bird encountering a good
prey patch and having the option to prolong its dive bout to capture more prey
items, if the increased load required an abnormally higher stroke frequency,
this might be impossible to sustain to allow the bird to return to its nest.
This may be a reason why they appeared to terminate their foraging trips
prematurely when they encountered a good prey patch.
Although our main interest was in determining the amount of prey caught at
sea, the difference in wing stroke frequency between the end of one trip and
the start of the next provided an estimate of the decrease in body mass while
the bird was in the colony. Mass losses occurred over a relatively short
period, a pattern consistent with the decrease being primarily due to the bird
feeding its brood rather than utilizing the prey for itself. Furthermore, mass
loss was positively and significantly related to mass gained during the
preceding trip, supporting the earlier suggestion that the bulk of the prey
caught during a trip was fed to the young
(Wanless et al., 1993b).
In conclusion, recently developed small accelerometers enabled us to monitor stroke frequencies of flying birds throughout their foraging trips, and a new method of analysis enabled us to relate changes in stroke frequency to changes in body mass. The technique would appear to have great potential for providing detailed information on foraging effort and success in a wide range of diving birds.
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Acknowledgments |
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