Penguin head movement detected using small accelerometers: A proxy of prey encounter rate

Abstract

Determining temporal and spatial variation in feeding rates is essential for understanding the relationship between habitat features and the foraging behavior of top predators. In this study we examined the utility of head movement as a proxy of prey encounter rates in medium-sized Antarctic penguins, under the presumption that the birds should move their heads actively when they encounter and peck prey. A field study of free-ranging chinstrap and gentoo penguins was conducted at King George Island, Antarctica. Head movement was recorded using small accelerometers attached to the head, with simultaneous monitoring for prey encounter or body angle. The main prey was Antarctic krill (>99% in wet mass) for both species. Penguin head movement coincided with a slow change in body angle during dives. Active head movements were extracted using a high-pass filter (5 Hz acceleration signals) and the remaining acceleration peaks (higher than a threshold acceleration of 1.0 g) were counted. The timing of head movements coincided well with images of prey taken from the back-mounted cameras: head movement was recorded within ±2.5 s of a prey image on 89.1±16.1% (N=7 trips) of images. The number of head movements varied largely among dive bouts, suggesting large temporal variations in prey encounter rates. Our results show that head movement is an effective proxy of prey encounter, and we suggest that the method will be widely applicable for a variety of predators.

Full text

3760
INTRODUCTION
Determining when and where predators capture prey is one of the
most critical issues for studying mobile foraging animals (Perry and
Pianka, 1997). Understanding the timing and rate of prey encounter
and/or capture enables us to investigate how animals optimize their
foraging behavior in the context of energy expenditure or habitat
use (Stephens and Krebs, 1986). Furthermore, because foraging
provides the link between the environment or lower trophic level
and predators, quantifying top predator foraging behavior is
necessary to understand their roles in both terrestrial and marine
ecosystems (Spiller and Schoener, 1994; Boyd et al., 2006).
Detecting the prey encounter and consequent feeding events of top
predators at a fine scale is important for characterizing the habitat
features associated with high foraging efficiency. This is particularly
relevant for marine ecosystem given their high variability and poor
demarcation (Hindell et al., 2010). Animal-borne data loggers,
developed over the past 30years, have provided great insight into
the previously invisible underwater behavior. Nevertheless, precise
detection of prey encounter rate is still challenging, especially for
free-ranging small predators.
Some indicators of prey encounter and consequent feeding events
have been explored, and each has advantages and disadvantages.
The number of depth wiggles during a dive is a popular indicator
of the number of prey encounter and/or foraging events (Simeone
and Wilson, 2003; Bost et al., 2007), although the accuracy of this
method is lower than that of other direct methods. More direct
indicators such as drops in stomach or oesophageal temperature
(Wilson et al., 1995; Ropert-Coudert et al., 2000; Hanuise et al.,
2010) and beak opening events (Wilson et al., 2002; Takahashi et
al., 2004; Hanuise et al., 2010) have high accuracy. However, the
disadvantages of these methods are the complex procedures for
deployment, and the frequent failure to record because of
regurgitation or dropout of the devices. An alternative method for
quantifying the characteristics of aquatic animal behavior is the use
of accelerometers (Yoda et al., 2001; Wilson et al., 2006). In
particular, a new approach that uses mandible or head acceleration
as a proxy for prey capture attempts has been developed in recent
years (Naito, 2007). The utility of this approach was determined
from experiments firstly with pinnipeds in aquariums (Suzuki et al.,
2009; Skinner et al., 2009; Viviant et al., 2010), and secondly with
free-ranging pinnipeds (Naito et al., 2010; Iwata et al., 2011).
Furthermore, this method appears to be applicable to smaller free-
ranging marine predators, as the size of the accelerometers has
reduced in the last few years.
The medium-sized Antarctic penguins, such as Adélie, chinstrap,
gentoo and macaroni penguins, are some of the major top predators
in the Southern Ocean ecosystem, along with whales and pinnipeds
(Brooke, 2004; Balance et al., 2006; Boyd, 2009). Measuring their
at-sea foraging behavior is increasingly important in terms of
understanding interspecific competition with whales and seals for
the common resource Antarctic krill (Euphausia superba) (Ainley
et al., 2010), or the effects of environmental change (Fraser and
Hoffmann, 2003; Forcada et al., 2006) on their populations. Because
penguins open their beak at frequent intervals when feeding in krill
patches (Takahashi et al., 2004), we can assume that they move
their heads to encounter, pursue and peck prey, as recorded in marine
The Journal of Experimental Biology 214, 3760-3767
© 2011. Published by The Company of Biologists Ltd
doi:10.1242/jeb.058263
RESEARCH ARTICLE
Penguin head movement detected using small accelerometers: a proxy of prey
encounter rate
Nobuo Kokubun1,*, Jeong-Hoon Kim2, Hyoung-Chul Shin2, Yasuhiko Naito1and Akinori Takahashi1
1National Institute of Polar Research, 10-3 Midori-cho, Tachikawa, Tokyo 190-8518, Japan and 2Korea Polar Research Institute,
Songdo Techno Park, 7-50 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea
*Author for correspondence (nobuo.kokubun@aad.gov.au)
Accepted 15 August 2011
SUMMARY
Determining temporal and spatial variation in feeding rates is essential for understanding the relationship between habitat
features and the foraging behavior of top predators. In this study we examined the utility of head movement as a proxy of prey
encounter rates in medium-sized Antarctic penguins, under the presumption that the birds should move their heads actively when
they encounter and peck prey. A field study of free-ranging chinstrap and gentoo penguins was conducted at King George Island,
Antarctica. Head movement was recorded using small accelerometers attached to the head, with simultaneous monitoring for
prey encounter or body angle. The main prey was Antarctic krill (>99% in wet mass) for both species. Penguin head movement
coincided with a slow change in body angle during dives. Active head movements were extracted using a high-pass filter (5Hz
acceleration signals) and the remaining acceleration peaks (higher than a threshold acceleration of 1.0
g
) were counted. The
timing of head movements coincided well with images of prey taken from the back-mounted cameras: head movement was
recorded within ±2.5s of a prey image on 89.1±16.1% (N7 trips) of images. The number of head movements varied largely among
dive bouts, suggesting large temporal variations in prey encounter rates. Our results show that head movement is an effective
proxy of prey encounter, and we suggest that the method will be widely applicable for a variety of predators.
Key words: accelerometry, chinstrap penguin, gentoo penguin, Antarctic krill, foraging effort, patch.
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3761Penguin head movement and prey encounter
mammals (Suzuki et al., 2009; Skinner et al., 2009). Although
making the connection between head movement and
successful/unsuccessful foraging events remains difficult, active
head movement could act as a simple proxy of prey encounter. In
this study we aimed to detect prey encounter rate of free-ranging
penguins precisely, by monitoring their head movement using a
recently developed small accelerometer, with simultaneous
monitoring of underwater images using small cameras.
MATERIALS AND METHODS
Study site
The field study was conducted on Barton Peninsula, King George
Island, South Shetland Islands, where chinstrap [Pygoscelis
antarcticus (Forster 1781)] and gentoo penguins [Pygoscelis papua
(Forster 1781)] breed sympatrically (Antarctic Specially Protected
Area no. 171: Nare˛bski Point). In the 2009–2010 austral summer
season, 2278 breeding pairs of chinstrap penguins and 1759 breeding
pairs of gentoo penguins were counted at the study colony. The
study was conducted from 28 December 2009 to 2 February 2010,
which covered the chick-guarding period of chinstrap and gentoo
penguins.
Deployment of devices
Head movement and dive data were collected from 12 chinstrap
and 12 gentoo penguins, using small accelerometers attached to the
head (ORI-380 D3GT, housed in a pressure-resident cylindrical
container: 12mm diameter, 45mm length, mass 10g including
batteries; Little Leonardo, Tokyo, Japan). Three axes of acceleration
data (heave, surge and sway) were recorded at a frequency of 32Hz,
and dive depth data were recorded every second. Loggers were
attached to the medial portion of the head using Tesa®tape and
cyanoacrylate glue (Loctite®401) to secure the end of the tape
(Fig.1). For eight chinstrap and six gentoo penguins, one more
accelerometer was attached to the lower medial portion of the back
to monitor the body angle of the individual (Fig.1). Recording rates
were the same as accelerometers attached to the head. Camera
loggers (DSL-380, housed in a pressure-resident cylindrical
container: 22mm diameter, 133mm length, mass 82g including
batteries; Little Leonardo) were attached to the back of three
chinstrap and five gentoo penguins to visually monitor prey
encounter in the sea. Still images were taken every 5s and dive
depth data were recorded every second. All loggers were attached
to the birds just before their departure for a foraging trip. Mean
handling time was 26.5±5.4min per bird. The body mass of the
birds was also measured to the nearest 50g using a Pesola®spring
balance. Tagged birds were recaptured after they returned from their
foraging trip, and the loggers were removed. The data were
downloaded from the loggers to a laptop computer.
Effect of accelerometer deployments on penguin behavior
Accelerometers attached to the head of the penguins potentially
affect their at-sea behavior, because of sensitivity of the birds or
hydrodynamic drag caused by the loggers (Ponganis et al., 2000).
The at-sea behavior of penguins without loggers was not observed
in the present study. Instead, to evaluate the effect of accelerometer
deployments, trip duration was compared between birds with and
without head accelerometers, where the birds without head
accelerometers had GPS depth loggers attached on their backs
(Kokubun et al., 2010). No significant effect of the GPS depth
loggers on the behavior of either chinstrap or gentoo penguins has
been recorded (Kokubun et al., 2010). The GPS depth loggers were
attached on the lower medial portion of nine chinstrap and 10 gentoo
penguins, during the period in which the accelerometers were
deployed (28 December 2009 to 23 January 2010).
Analysis of head and body acceleration
Depth profiles were analyzed by examining dive depth, dive duration,
diving bottom duration (the period between the start and end of the
time when birds showed a depth change of 0m) and the number of
depth wiggles (number of changes in symbol of differential depth
every second) for each dive. A dive was considered to occur when
dive depth exceeded 1.0m (Takahashi et al., 2003). Only data that
covered a whole trip were used for subsequent analyses.
Acceleration data were analyzed with Ethographer (Sakamoto
et al., 2009), with the analysis software Igor Pro version 6.0 (Wave
Metrics Inc., Lake Oswego, OR, USA). Dominant frequencies
and amplitudes of the three axes of acceleration from both head
and back during each dive phase (descent, diving bottom and
ascent) were examined visually on the spectrogram (Fig.2). The
minimum frequency resolution was set to 0.01Hz. The calculated
amplitude was expressed by color graphs. The spectrograms from
both the head and back were compared and subsequently a high-
pass filter of 5Hz was applied on the three axes of head
acceleration such that active head movements were highlighted
(Fig.2). Peaks in the filtered acceleration exceeding a threshold
amplitude were counted within a 1.0s time window. The minimum
time interval of the peak was set to 0.2s (i.e. the maximum peak
number was set to five times per second). The number of the
peaks was assumed to be the number of active head movements
of the penguins.
Time series data for static acceleration were extracted using a
low-pass filter of 1Hz on surge acceleration from the back (Sato et
al., 2004). Static acceleration was converted to body angle. Depth
profiles and acceleration were analyzed using Igor Pro.
Fig.1. Attachment of the accelerometers on (A) the head and (B) the back
of a chinstrap penguin. The direction of the three axes recorded by the
accelerometers is also shown.
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3762
Analysis of still images
Still images taken by camera loggers were sorted visually. First, all
images were classified as ‘light’ or ‘dark’ according to their light
level. Dark images were unable to be used to determine prey
presence or absence. Subsequently, all dives were classified as ‘light’
or ‘dark’ because darker images reflected deeper or nighttime dives,
and the high proportion of dark images during a dive makes it
difficult to estimate actual prey encounter rate. Dives were classified
as ‘light’ when the proportion of light images was more than 50%
of the images taken at depths greater than half of the maximum
dive depth. Only data from the light dives were used for subsequent
analyses. The number of images with prey in each dive was assumed
to be the number of prey encounter events of the dive.
Correspondence between head movement and prey
encounter
Active penguin head movement, detected using accelerometers, was
compared with the prey encounter of individuals, as observed by
camera loggers, using a regression model. The number of head
movements varied according to the threshold detection amplitude.
Therefore, a model was fitted for the number of head movements
detected at various threshold amplitudes, ranging from 0.5 to 2.0g
(g9.8ms–2) in 0.1gsteps. The model was fitted for results from three
axes of acceleration. In addition, the number of depth wiggles, which
is a commonly used indicator of foraging effort (Simeone and Wilson,
2003; Bost et al., 2007), was also compared with the number of prey
encounters using the same model. The statistical fit of the models
was assessed using log likelihood values. Dive duration had a
N. Kokubun and others
significant effect on the number of head movements, prey encounter
and depth wiggles. Thus the number of head movements per dive
duration, the proportion of images with prey from all images during
a dive, and the number of depth wiggles per dive duration were used
in the model. A generalized linear mixed model (GLMM) with quasi-
Poisson error distribution and logarithm link function was fitted:
ya+bx+random effect, where yis the proportion of images with prey
from all images during a dive and xis either the number of head
movements or the number of depth wiggles per dive duration. Bird
identity was set as the random effect. The effect of species could not
be included in the model because sample size for the chinstrap
penguins was too low (only two birds). Regression analyses were
performed with R 2.9 (R Development Core Team, 2009).
In addition, temporal concordance of both the images with prey
and the head movement was examined. The two parameters were
assumed ‘concordant’ if a head movement occurred within 2.5s (i.e.
time interval to previous/next image) of the time that the image with
prey was taken.
Stomach contents
Stomach contents from six chinstrap and seven gentoo penguins
were collected to examine the main prey species of the penguins,
using the standard stomach-flushing method (CCAMLR, 1997). The
samples were obtained from individuals with GPS depth loggers
(see Effect of accelerometer deployments on penguin behavior). The
wet mass of each stomach content sample was measured to the
nearest 1g using an electronic balance in the laboratory. Samples
were then sorted and classified to taxonomic group. Fish species
Fig.2. Time series of diving
depth, the three axes of head
acceleration (heave, surge and
sway), spectrogram of the three
axes of head acceleration, body
acceleration (surge) and the
spectrogram of the body
acceleration (surge) for two
gentoo penguin dives (bird
G054, 21 January 2010). The
dashed lines in the
spectrograms show a 0.2s
cycle (i.e. 5Hz). Note that the
amplitude of the high-frequency
(>5Hz) acceleration during the
dives is larger for data from the
head than those from the body.
The high-frequency head
acceleration during the dives
was clearly observed in the
second dive, but not in the first.
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3763Penguin head movement and prey encounter
were identified based on the shape and/or size of the otolith or bones
(Gon and Heemstra, 1990).
RESULTS
Device and data recovery
Eleven chinstrap and 12 gentoo penguins were recaptured in the 1
to 7days after release, and the loggers were retrieved (Table1). One
chinstrap penguin with an accelerometer on the head and a camera
logger on the back was not recaptured. Among the recaptured birds,
one chinstrap penguin with accelerometers on the head and the back
did not depart for a trip (Table1). In addition, one accelerometer
attached on the back of one chinstrap penguin and another
accelerometer on the head of another chinstrap penguin did not work
because of power supply problems (Table1). Overall, records of 12
trips from nine chinstrap penguins (seven trips with a combination
of head and back acceleration data, two trips with a combination
of head acceleration and still image data, and three trips with only
head acceleration data) and 13 trips from 12 gentoo penguins (seven
trips with a combination of head and back acceleration data, five
trips with a combination of head acceleration and still image data,
and one trip with only head acceleration data) were available for
the subsequent analyses (Table1). The recording duration of the
accelerometers on the head covered the entire periods of the trips.
In 11 cases among these, the records continued until the memory
capacity was full. The maximum mean recording duration was
33.9±0.2h (N11 recorders). According to a full-logging test for
the same type of loggers at a sampling rate of 16Hz, mean recording
duration should be 51.8±1.2h (N7 recorders).
Effect of accelerometer deployments on penguin behavior
Trip duration did not differ between the birds with the accelerometer
on the head (N12 and 13 trips for chinstrap and gentoo penguins,
respectively) and the birds with GPS depth loggers on the back
(N11 and 14 trips for chinstrap and gentoo penguins, respectively)
(chinstrap: accelerometer, 7.5±2.8h; GPS depth logger, 10.2±5.2h;
ANOVA, P0.13; gentoo: accelerometer, 8.2±2.1h; GPS depth
logger, 9.8±3.8h; ANOVA, P0.19). Thus we believe that the
accelerometer attached to the head had no substantial negative effects
on penguin behavior, and the effects were comparable to those of
back-mounted GPS depth loggers.
Head and body accelerations
Simultaneous acceleration data from the head and the back of the
penguins were compared with the spectrograms (Fig.2). Active head
movements along the surge, heave and sway axes at a frequency of
>5Hz (i.e. at a cycle of <0.2s) occurred mainly at the bottom of the
dive and, to a lesser extent, during the ascent phases of some dives
(Fig.2). The amplitude of active movement was greater for
acceleration data from the head than for that from the back (Fig.2).
Active head movements (>5Hz) were coincident with changes in body
angle at a cycle of approximately 3s at the bottom of the dive (Fig.3).
Still images
A total of 19,648 underwater images were taken from two trips of
chinstrap penguins and five trips of gentoo penguins (Table1). Of
these, 15,437 images had enough light to determine the
presence/absence of prey. Some form of prey was observed in 998
light images. The data included an outlier from a chinstrap penguin
for which only two images had prey from a total of 1802 light
images. All identified prey species in the images were Antarctic
krill (Fig.4). The proportion of the ‘light’ dives (see Materials and
methods) was 77.3% from a total of 1633 dives. The images showed
that the penguins encountered krill patches for a mean of 23.1±16.1%
(N7 trips) of the light dives.
Table1. Sample size of each deployment type for chinstrap and gentoo penguins
Chinstrap Gentoo
Deployment type Deployments Retrieves Available trips Deployments Retrieves Available trips
Accelerometer on the head 113111
Accelerometer on the head and another on the back 8 8a7667
Accelerometer on the head and a camera on the back 322555
Total 12 11 12 12 12 13
GPSb9 9 11 10 10 14
aAmong the eight individuals, one bird did not depart for a trip. In addition, one back accelerometer and one head accelerometer did not work because of power
supply problems (see Results).
bThe behavior of individuals with GPS loggers was compared with that of individuals with head accelerometers, in the context of assessing impacts of the
accelerometer attachment (see Materials and methods).
–3
0
3
Time (h)
16:24 16:26 16:2816:30 16:32
Surge (g)
100
50
0
Depth (m)
–90
–45
0
45
90
Body angle (deg)
Fig.3. Time series of diving depth, body angle and
the high-frequency (>5Hz) component of surge
acceleration of the head for two gentoo penguin
dives (bird G054, 21 January 2010). The time frame
is the same as that shown in Fig.2. The open circles
indicate the timing of active head movements
(amplitude >1.0g). Note that the active head
movements were observed many times in the
second dive, but only a few times in the first.
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3764
Correspondence between head movement and prey
encounter
Overall, the GLMM model showed that the number of head
movements (determined by surge, heave and sway accelerations) was
a better indicator than the number of depth wiggles in explaining the
number of prey encounter events (Fig.5). The statistical fit was highest
using a threshold amplitude of 1.0gfor surge acceleration (Fig.5). If
heave acceleration was used, the statistical fit was lower than when
the other two axes were used. If sway acceleration was used with a
threshold amplitude >1.5g, the statistical fit was greater than when
the other two axes were used. Surge acceleration was used for
subsequent analyses with a threshold amplitude of 1.0g. Analysis of
temporal concordance between images with prey and head movement
indicated that penguins moved their heads within ±2.5s of an image
with prey being taken for a mean of 89.1±16.1% (N7 trips) of cases.
We tentatively defined the number of the head movements per
diving bottom duration as the prey encounter rate of the dive, and
its typical time series during the trips is shown in Fig.6. For
comparison, the number of the depth wiggles per diving bottom
duration is also shown (Fig.6). The indicator of head movement
rate varied largely among dive bouts, and did not always correspond
with the occurrence of dives or the number of depth wiggles per
N. Kokubun and others
diving bottom duration (Fig.6). The coefficient of variation (CV)
during trips was greater using head movement rate as an indicator
of prey encounter rate than the number of depth wiggles per diving
Fig.4. Selected still images taken by camera
loggers obtained from birds C043 (A,B), G038
(C), G044 (D) and G037 (E,F) where ‘C’
represents chinstrap and ‘G’ represents gentoo
penguins. (A,B)Krill swarms at 20.7 and 19.8m
depth, respectively; (C,D) krill swarms at 92.8
and 73.2m depth, respectively; and (E,F)
penguins attempting to capture the krill at 43.7
and 35.4m depth, respectively. The black shapes
in front of the krill are the head of the penguin
with an accelerometer attached.
log likelihood
–40
–50
–60
–70
–80
Threshold amplitude (g)
0.5 1.0 1.5 2.0
Surge
Heave
Sway
*
Number of depth wiggles
Fig.5. Log likelihood values for models with different threshold acceleration
amplitudes ranging from 0.5 to 2.0g, obtained from the generalized linear
mixed model (see Materials and methods) for three axes (heave, surge
and sway) head acceleration and number of depth wiggles.
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3765Penguin head movement and prey encounter
bottom duration (Fig.6). Dives >5m mostly had low values (<0.5s–1)
of head movement rate for both species (Fig.7A). This skewed
distribution was not observed if the number of the depth wiggles
per diving bottom duration was used (Fig.7B).
Overall, in the present study, head movement rate did not differ
between species (for dives >5m, chinstrap: 0.41±0.13s–1; gentoo:
0.42±0.13s–1; GLMM with quasi-Poisson error distribution,
P0.97). Gentoo penguins dived a little deeper than chinstrap
penguins, but the difference was not significant (for dives >5m,
chinstrap: N12 trips, 44.5±13.9m; gentoo: N13 trips, 52.7±16.0m;
GLMM with gamma error distribution, P0.17).
Stomach contents
The main prey item for both chinstrap and gentoo penguins was
Antarctic krill (chinstrap: 99.8±0.2% in wet mass, gentoo:
99.4±1.1%). In addition, three and two Pleuragramma antarcticum
were found in the stomach contents of three chinstrap and two gentoo
penguins, respectively. The fishes were partially digested.
DISCUSSION
In this study we present a case for detecting prey encounter rates
of predators by measuring their active head movements. The
concept will be widely applicable for not only diving birds but also
a variety of predators including terrestrial species, as long as prey
encounter or feeding events associate with head movement.
Therefore, together with other metrics, the present method is likely
to enable a wide range of ecologists to investigate the foraging
strategies of animals at an individual level, in relation to
physiological and environmental constraints (Perry and Pianka,
1997). Here we presented results from the simultaneous recording
Number of head
movements per diving
bottom duration (s–1)
Number of depth
wiggles per diving
bottom duration (s–1)Dive depth (m)
0
50
100
150
0
0.6
1.2
1.8
0
0.2
0.4
0.6
A
CV=121%
1/19 16:4819:12 21:36 1/20 00:00 02:24 04:48
CV=48%
1/18 12:00 14:24 16:4819:12 21:361/19 00:00
B
0
50
100
150
0
0.6
1.2
1.8
0
0.2
0.4
0.6
Time (h)
CV=104%
CV=54%
Time (h)
Number of head
movements per diving
bottom duration (s–1)
Number of depth
wiggles per diving
bottom duration (s–1)Dive depth (m)
Fig.6. Time series of (top) diving depth,
(middle) number of head movements
per diving bottom duration as a proxy of
prey encounter rate and (bottom)
number of depth wiggles per diving
bottom duration for (A) chinstrap and
(B) gentoo penguins. CV, coefficient of
variation.
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3766
of underwater active head movement and prey encounter from free-
ranging Antarctic penguins. Underwater head movement
corresponded well with prey encounter rates detected by bird-borne
cameras (Fig.5), suggesting that head movement (especially in the
surge axis) may be related to prey capture attempts in response to
prey encounter opportunities.
A key challenge for analyzing top predator foraging strategies
is how to increase the recording period as well as how to best
detect signal motion relating to foraging (Halsey et al., 2009).
The recording duration presented here (33.9h) was not much
longer than that of the beak opening sensor used in previous
studies [>21h (Wilson, 2003), 7.8h (Takahashi et al., 2004) and
42 and 52h (Hanuise et al., 2010)]. This is partly because three-
axis head movement (>5Hz) was recorded by a sampling rate of
32Hz in the present study, although the movement will be
detectable using a lower sampling rate of 16Hz. Reduction of the
sampling rate allows flexibility for total recording duration. For
example, if only surge acceleration was recorded and the sampling
rate of 32Hz was reduced to 16Hz, the amount of data recorded
and hence the recording duration could be extended by at least
three times. Another advantage of our method is that it is ‘cable-
free’. Although oesophageal temperature sensors (Hanuise et al.,
2010) or beak opening sensors (Wilson, 2003) can detect foraging
success accurately, these methods require cables that can cause
problems with the operation of these devices in situ. The frequent
regurgitation or dropout of devices and breakage of cables causes
failure and/or reduced records (Hanuise et al., 2010). Thus, the
precise detection of head movement presented here is an effective
N. Kokubun and others
tool for monitoring prey encounter and capture attempts
continuously over long time periods (e.g. covering several
foraging trips), especially for small diving predators.
Nonetheless, there should be caution in the use of head movement
as an indicator of feeding success, for several reasons. First, unlike
beak opening or oesophaegeal temperature drop events (Hanuise et
al., 2010), every single head movement event does not necessarily
indicate foraging success. However, active head movements along
the surge axis (Fig.2) were linearly correlated to and coincident
with the number of prey encounters (Fig.5). In other words, head
movement in the surge axis reflects the sum of prey capture attempts
in response to encounter with krill swarms. Second, the value of
the filtering frequency or threshold amplitude significantly affects
the number of head movements detected, consistent with other
techniques to determine animal behaviors using acceleration data
(Yoda et al., 2001; Sato et al., 2008). The optimum values of the
filtering frequency or threshold amplitude will vary among target
species, as well as prey species or foraging strategies. The high-
pass filtering frequency of 5Hz and the threshold amplitude 1.0g
might be optimum for krill-feeding medium-sized penguins, but
might this not be the case for other predators such as piscivorous
penguins (Takahashi et al., 2004). Hence the method used here will
need to be adapted and validated for other species.
Still images with prey are used as evidence for prey encounter
events (Hooker et al., 2002; Takahashi et al., 2008), and we
demonstrated here the utility of this method for the analysis of
chinstrap and gentoo penguin foraging. Here we found that most
foraging occurs in krill swarms (Fig.4). Nonetheless, this method
has the disadvantage that its success relies on the light level being
high enough for prey to be visible. We found that 23% of the dives
in the present study could not be used to determine prey presence
or absence because of low light levels. Thus foraging behavior during
overnight trips or at depth (more than 70% of dives occur at depths
>70m) could not be monitored continuously using the camera
logger. Equipping camera loggers with flash facilities could result
in biases because of the potential for attracting prey with the flash
(Heaslip and Hooker, 2008). This highlights another advantage for
using head movement data, as in the present study, because it allows
prey encounters to be monitored continuously regardless of light
level.
The pattern of prey encounter events such as those presented here
(Fig.6) offer the opportunity for exploring ecological context of prey
distribution with the obvious step of further analyses of the temporal
and spatial structure of foraging. In the present case, the large
temporal fluctuations in head movement rate suggest temporal
discontinuity of prey encounter rates, which may imply spatial and
temporal patchiness in the distribution of the krill swarms (Fig.6).
Dives with high head movement rate sometimes occurred in clusters
(Fig.6). There was a better fit between head movement and prey
encounter than the number of depth wiggles and prey encounter
(Figs5, 6) because depth wiggles can indicate diving activities
without necessarily encountering prey. Combinations of head
accelerometers with GPS depth loggers will add value to our
approach by allowing precise three-dimensional monitoring of the
prey encounter. The index for prey encounter rate was similar for
both species, which suggests that they both feed on krill swarms in
similar environments at this location.
In conclusion, we propose that recording the head movement of
predators using small accelerometers would be useful for monitoring
temporal variations in prey encounter rates over relatively long
periods, covering foraging trips. Together with other information
such as location (e.g. from GPS depth loggers), this method will
Chinstrap
Gentoo
80
0
40
01.00.5 1.5
Number of head movements per diving bottom duration (s–1)
Dives (%)
80
0
40
00.20.1 0.3
Number of depth wiggles per diving bottom duration (s–1)
0.4
A
B
Fig.7. Distribution of dives in relation to (A) the number of head
movements per diving bottom duration and (B) the number of depth
wiggles per diving bottom time. Data are means ± s.d. (
N
12 and 13 trips
for chinstrap and gentoo penguins, respectively).
THE JOURNAL OF EXPERIMENTAL BIOLOGY

3767Penguin head movement and prey encounter
help advance our understanding of the relationship between the
environment and the foraging behavior of small or medium-sized
predators.
ACKNOWLEDGEMENTS
We would like to thank M. Kurita and I. Uchida of the Port of Nagoya Aquarium for
helping us to test the devices. We are also grateful to the members of the King
Sejong Station, Korea Polar Research Institute (KOPRI), especially Dr S.-H.
Kang, for logistic support in the field. Drs. I.-Y. Ahn and E. J. Choy also helped us
conduct the fieldwork. Dr L. Emmerson kindly reviewed the manuscript and
provided helpful comments.
FUNDING
This work was supported by the Japan Society for the Promotion of Science
(JSPS) Research Fellowship for Young Scientists to N.K.; a JSPS research grant
[grant number 20310016] to A.T.; and the program ‘Bio-logging Science, The
University of Tokyo (UT-BLS)’ led by Drs N. Miyazaki and K. Sato. A research
grant [grant number PE11030: Studies on biodiversity and changing ecosystems
in King George Island, Antarctica] funded by the KOPRI provided support for this
work. The Ministry of the Environment, Japan, issued the permits required to
conduct this work.
REFERENCES
Ainley, D. G., Ballard, G., Blight, L. K., Ackley, S., Emslie, S. D., Lescroel, A.,
Olmastroni, S., Townsend, S. E., Tynan, C. T., Wilson, P. et al. (2010). Impacts
of cetaceans on the structure of Southern Ocean food webs.
Mar. Mamm. Sci.
26,
482-498.
Balance, L. T., Pitman, R. L., Hewitt, R. P., Sniff, D. B., Trivelpiece, W. Z.,
Clapham, P. J. and Brownell, R. L. (2006). The removal of large whales from the
Southern Ocean: evidence for long-term ecosystem effects? In
Whales, Whaling,
and Ocean Ecosystems
(ed. J. A. Estes, D. P. Demaster, D. F. Doak, T. M. Williams
and R. L. Brownell), pp. 215-230. Berkeley, CA: University of California Press.
Bost, C. A., Handrich, Y., Bulter, P. J., Fahlman, A., Halsey, L. G., Woakes, A. J.
and Ropert-Coudert, Y. (2007). Changes in dive profiles as an indicator of feeding
success in king and Adélie penguins.
Deep Sea Res. Part II
54, 248-255.
Boyd, I. L. (2009). Antarctic marine mammals. In
Encyclopedia of Marine Mammals
,
2nd edn (ed. W. F. Perrin, B. Würsig and J. G. M. Thewissen), pp. 42-46. Burlington,
MA: Academic Press.
Boyd, I. L., Wanless, S. and Camphuysen, C. J. (2006).
Top Predators in Marine
Ecosystems: Their Role in Monitoring and Management
. Cambridge, UK: Cambridge
University Press.
Brooke, M. L. (2004). The food consumption of the world’s seabirds.
Biol. Lett.
271,
S246-S248.
CCAMLR (1997).
CCAMLR Ecosystem Monitoring Program: Standard Methods for
Monitoring Studies
. Hobart, Australia: Commission for the Conservation of Antarctic
Marine Living Resources (CCAMLR).
Forcada, J., Trathan, P. N., Reid, K., Murphy, E. J. and Croxall, J. P. (2006).
Contrasting population changes in sympatric species of penguins in association with
climate warming.
Glob. Change Biol.
12, 1-13.
Fraser, W. R. and Hoffmann, E. E. (2003). A predator’s perspective on causal links
between climate change, physical forcing and ecosystem response.
Mar. Ecol. Prog.
Ser.
265, 1-15.
Gon, O. and Heemstra, P. C. (1990).
Fishes of the Southern Ocean
. Grahamstown,
South Africa: J. L. B. Smith Institute of Ichthyology.
Halsey, L. G., Green, J. A., Wilson, R. P. and Frappel, P. B. (2009). Accelerometry
to estimate energy expenditure during activity: best practice with data loggers.
Physiol. Biochem. Zool.
82, 396-404.
Hanuise, N., Bost, C.-A., Huin, W., Auber, A., Halsey, L. G. and Handrich, Y.
(2010). Measuring foraging activity in a deep-diving bird: comparing wiggles,
oesophageal temperature and beak-opening angles as proxies of feeding.
J. Exp.
Biol.
213, 3874-3880.
Heaslip, S. G. and Hooker, S. K. (2008). Effect of animal-borne camera and flash on
the diving behavior of the female Antarctic fur seal (
Arctocephalus gazelle
).
Deep
Sea Res. Part I
55, 1179-1192.
Hindell, M. A., Crocker, D., Mori, Y. and Tyack, P. (2010). Foraging behavior. In
Marine mammal Ecology and Conservation: a Handbook of Techniques
(ed. I. L.
Boyd, W. D. Bowen and S. J. Iverson), pp. 243-262. Oxford, UK: Oxford University
Press.
Hooker, S. K., Boyd, I. L. and Jessopp, M. (2002). Monitoring the prey-field of
marine predators: combining digital imaging with datalogging tags.
Mar. Mamm. Sci.
18, 680-697.
Iwata, T., Sakamoto, K. Q., Takahashi, A., Edwards, E. W. J., Staniland, I. J.,
Trathan, P. N. and Naito, Y. (2011). Using a mandible accelerometer to study fine-
scale foraging behavior of free-ranging Antarctic fur seals.
Mar. Mamm. Sci.
(Epub
ahead of print) doi: 10.1111/j.1748-7692.2011.00482.x.
Kokubun, N., Takahashi, A., Mori, Y., Watanabe, S. and Shin, H. C. (2010).
Comparison of diving behavior and foraging habitat use between chinstrap and
gentoo penguins breeding in the South Shetland Islands, Antarctica.
Mar. Biol.
157,
811-825.
Naito, Y. (2007). How can we observe the underwater feeding behavior of
endotherms?
Polar Sci.
1, 101-111.
Naito, Y., Bornemann, H., Takahashi, A., McIntyre, T. and Plötz, J. (2010). Fine-
scale feeding behavior of Weddell seals revealed by a mandible accelerometer.
Polar Sci.
4, 309-316.
Perry, G. and Pianka, E. R. (1997). Animal foraging: past, present and future.
Trends
Ecol. Evol.
12, 360-364.
Ponganis, P. J., Van Dam, R. P., Marshall, G., Knower, T. and Levenson, D. H.
(2000). Sub-ice foraging behavior of emperor penguins.
J. Exp. Biol.
203, 3275-
3278.
R Development Core Team. (2009).
R: a language and environment for statistical
computing
. Vienna: R Foundation for Statistical Computing.
Ropert-Coudert, Y., Baudat, J., Kurita, M., Bost, C. A., Kato, A., Le Maho, Y. and
Naito, Y. (2000). Validation of oesophagus temperature recording for detection of
prey ingestion on captive Adélie penguins (
Pygoscelis adeliae
).
Mar. Biol.
137, 1105-
1110.
Sakamoto, K. Q., Sato, K., Ishizuka, M., Watanuki, Y., Takahashi, A., Daunt, F.
and Wanless, S. (2009). Can ethograms be automatically generated using body
acceleration data from free-ranging birds?
Plos ONE
4, e5379.
Sato, K., Charrassin, J. B., Bost, C. A. and Naito, Y. (2004). Why do macaroni
penguins choose shallow body angles that result in longer descent and ascent
durations?
J. Exp. Biol.
207, 4057-4065.
Sato, K., Daunt, F., Watanuki, Y., Takahashi, A. and Wanless, S. (2008). A new
method to quantify prey acquisition in diving seabirds using wing stroke frequency.
J.
Exp. Biol.
211, 58-65.
Simeone, A. and Wilson, R. P. (2003). In-depth studies of Magellanic penguin
(
Spheniscus magellanicus
) foraging: can we estimate prey consumption by
perturbations in the dive profile?
Mar. Biol.
143, 825-831.
Skinner, J. P., Norberg, S. E. and Andrews, R. D. (2009). Head striking during fish
capture attempts by Steller sea lions and the potential for using head surge
acceleration to predict feeding behavior.
Endanger. Species Res.
10, 61-69.
Spiller, D. A. and Schoener, T. W. (1994). Effects of top predators in a terrestrial food
web.
Ecology
75, 182-196.
Stephens, D. W. and Krebs, J. R. (1986).
Foraging Theory
. Princeton, NJ: Princeton
University Press.
Suzuki, I., Naito, Y., Folklow, L. P., Miyazaki, N. and Blix, A. S. (2009). Validation of
a device for accurate timing of feeding events in marine animals.
Polar Biol.
32, 667-
671.
Takahashi, A., Dunn, N. J., Trarhan, P. N., Sato, K., Naito, Y. and Croxall, J. P.
(2003). Foraging strategies of chinstrap penguins at Signy Island, Antarctica:
importance of benthic feeding on Antarctic krill.
Mar. Ecol. Prog. Ser.
250, 279-289.
Takahashi, A., Dunn, N. J., Trathan, P. N., Croxall, J. P., Wilson, R. P., Sato, K.
and Naito, Y. (2004). Krill-feeding behavior in a chinstrap penguin (
Pygoscelis
antarctica
) compared with fish-eating in Magellanic penguins (
Spheniscus
magellanicus
): a pilot study.
Mar. Ornithol.
32, 47-54.
Takahashi, A., Kokubun, N., Mori, Y. and Shin, H. C. (2008). Krill-feeding behavior
of gentoo penguins as shown by animal-borne camera loggers.
Polar Biol.
31, 1291-
1294.
Viviant, M., Trites, A. W., Rosen, D. A. S., Monestiez, P. and Guinet, C. (2010).
Prey capture attempts can be detected in Steller sea lions and other marine
predators using accelerometers.
Polar Biol.
33, 713-719.
Wilson, R. P. (2003). Penguins predict their performance.
Mar. Ecol. Prog. Ser.
249,
305-310.
Wilson, R. P., Pütz, K., Gremillet, D., Culik, B. M., Kierspel, M., Regel, J., Bost, C.
A., Lage, J. and Cooper, J. (1995). Reliability of stomach temperature changes in
determining feeding characteristics of seabirds.
J. Exp. Biol.
198, 1115-1135.
Wilson, R. P., Steinfurth, A., Ropert-Coudert, Y., Kato, A. and Kurita, M. (2002).
Lip-reading in remote subjects: an attempt to quantify and separate ingestion,
breathing and vocalization in free-living animals using penguins as a model.
Mar.
Biol.
140, 17-27.
Wilson, R. P., White, C. R., Quintana, F., Halsey, L. G., Liebsch, N., Martin, G. R.
and Butler, P. J. (2006). Moving towards acceleration for estimates of activity-
specific metabolic rate in free-living animals: the case of the cormorant.
J. Anim.
Ecol.
75, 1081-1090.
Yoda, K., Naito, Y., Sato, K., Takahashi, A., Nishiwaki, J., Ropert-Coudert, Y.,
Kurita, M. and Le Maho, Y. (2001). A new technique for monitoring the behavior of
free-ranging Adélie penguins.
J. Exp. Biol.
204, 685-690.
THE JOURNAL OF EXPERIMENTAL BIOLOGY