ArticlePDF Available

A new technique for monitoring the behaviour of free-ranging Ad??lie Penguins


Abstract and Figures

Measurement of the time allocation of penguins at sea has been a major goal of researchers in recent years. Until now, however, no equipment has been available that would allow measurement of the aquatic and terrestrial behaviour of an Antarctic penguin while it is commuting between the colony and the foraging grounds. A new motion detector, based on the measurement of acceleration, has been used here in addition to current methods of inferring behaviour using data loggers that monitor depth and speed. We present data on the time allocation of Adélie penguins (Pygoscelis adeliae) according to the different types of behaviours they display during their foraging trips: walking, tobogganing, standing on land, lying on land, resting at the water surface, porpoising and diving. To illustrate the potential of this new technique, we compared the behaviour of Adélie penguins during the chick-rearing period in a fast sea-ice region and an ice-free region. The proportion of time spent standing, lying on land and walking during foraging trips was greater for penguins in the sea-ice region (37.6+/-13.3% standing, 21.6+/-15.6% lying and 5.9+/-6.3% walking) than for those in the ice-free region (12.0+/-15.8 % standing, 0.38+/-0.60% lying and 0 % walking), whereas the proportion of time spent resting at the water surface and porpoising was greater for birds in the ice-free region (38.1+/-6.4% resting and 1.1+/-1.1% porpoising) than for those in the sea-ice region (3.0+/-2.3% resting and 0% porpoising; means +/- s.d., N=7 for the sea-ice region, N=4 for the ice-free region). Using this new approach, further studies combining the monitoring of marine resources in different Antarctic sites and the measurement of the energy expenditure of foraging penguins, e.g. using heart rates, will constitute a powerful tool for investigating the effects of environmental conditions on their foraging strategy. This technique will expand our ability to monitor many animals in the field.
Content may be subject to copyright.
During the last decade, the study of the foraging behaviour of
marine animals has been made possible by the development of
new technologies resulting from the miniaturization of electronic
devices (Naito et al., 1990; Kooyman et al., 1992; Williams et
al., 1992; Croxall et al., 1993; Wilson and Wilson, 1995).
Logging of dive depth and swimming speed has provided useful
data about the swimming behaviour of penguins. Such results
have revealed that penguins are marvellous divers: king
penguins (Aptenodytes patagonicus) forage at depths of over 300
m (Kooyman et al., 1992) and Adélie penguins forage at a
maximum depth of 180m (Watanuki et al., 1997).
Until now, however, no equipment has been available to
monitor the many possible behaviours of a penguin while it is
commuting between the colony and the foraging grounds. To
determine in detail their time allocation has been one of the three
major goals of researchers studying penguins in recent years
(others are the precise measurement of marine resources and of
the energy expenditure of foraging penguins). Inter-dive
behaviour is important for the elucidation of the time budgets of
penguins during foraging trips, because the time spent at the
surface accounts for a large part of the foraging trip (e.g.
65–70%, Chappell et al., 1993; 52–73%, Watanuki et al., 1997).
We have used a new motion detector, which measures
acceleration, in addition to previously available methods of
inferring behaviour using data loggers, i.e. monitoring depth
and speed. Unlike the previous data, acceleration data permit
The Journal of Experimental Biology 204, 685–690 (2001)
Printed in Great Britain © The Company of Biologists Limited 2001
Measurement of the time allocation of penguins at sea has
been a major goal of researchers in recent years. Until now,
however, no equipment has been available that would allow
measurement of the aquatic and terrestrial behaviour of an
Antarctic penguin while it is commuting between the colony
and the foraging grounds. A new motion detector, based on
the measurement of acceleration, has been used here in
addition to current methods of inferring behaviour using
data loggers that monitor depth and speed. We present data
on the time allocation of Adélie penguins (Pygoscelis adeliae)
according to the different types of behaviours they display
during their foraging trips: walking, tobogganing, standing
on land, lying on land, resting at the water surface,
porpoising and diving. To illustrate the potential of this new
technique, we compared the behaviour of Adélie penguins
during the chick-rearing period in a fast sea-ice region and
an ice-free region. The proportion of time spent standing,
lying on land and walking during foraging trips was greater
for penguins in the sea-ice region (37.6±13.3% standing,
21.6±15.6% lying and 5.9±6.3% walking) than for those in
the ice-free region (12.0±15.8% standing, 0.38±0.60% lying
and 0% walking), whereas the proportion of time spent
resting at the water surface and porpoising was greater for
birds in the ice-free region (38.1±6.4% resting and
1.1±1.1% porpoising) than for those in the sea-ice region
(3.0±2.3% resting and 0% porpoising; means ± S.D., N=7
for the sea-ice region, N=4 for the ice-free region). Using this
new approach, further studies combining the monitoring
of marine resources in different Antarctic sites and the
measurement of the energy expenditure of foraging
penguins, e.g. using heart rates, will constitute a powerful
tool for investigating the effects of environmental conditions
on their foraging strategy. This technique will expand our
ability to monitor many animals in the field.
Key words: Pygoscelis adeliae, Adélie penguin, acceleration data
logger, remote monitoring, behaviour, time budget, allocation,
foraging strategy.
Department of Zoology, Graduate School of Science, Kyoto University, Kitashirakawa, Sakyo, Kyoto 606-8502,
National Institute of Polar Research, 1-9-10 Kaga, Itabashi, Tokyo 173-8515, Japan,
Graduate University
of Advanced Studies, Department of Polar Sciences, National Institute of Polar Research, 1-9-10 Kaga, Itabashi,
Tokyo 173-8515, Japan,
Ocean Research Institute, University of Tokyo, Minamidai, Nakano, Tokyo 146-8639,
Port of Nagoya Public Aquarium, 1–3 Minato-machi, Minato, Nagoya 455-0033, Japan and
Centre d’Ecologie et Physiologie Energétiques, Centre National de la Recherche Scientifique, 23 rue
Becquerel, 67087 Strasbourg Cédex, France
Accepted 4 December 2000; published on WWW 1 February 2001
behaviour to be recorded directly. We present data on the time
allocation of Adélie penguins, categorized into the different
types of behaviours they display during their foraging trips:
walking, tobogganing, standing on land, lying on land, resting
at the water surface, porpoising and diving. To illustrate the
potential of this new technique, we compared the time
allocation of Adélie penguins, during the chick-rearing period,
in a fast sea-ice region and an ice-free region.
Materials and methods
Field experiments
This study was conducted at Hukuro Cove (69°00S,
39°39E) south of Syowa station and at Adélie Land (66°07S,
140°00E) near Dumont d’Urville station in Antarctica from
December 1998 to January 1999. At Hukuro Cove, the bay was
covered with fast sea-ice approximately 1m thick throughout
the study period; the fast sea-ice had completely disappeared
before the beginning of the study at Adélie Land.
The behaviour of breeding Adélie penguins was monitored
using a 12-bit resolution, 16Mbyte memory, four-channel
UWE-PD2G logger (weighing 60g, 20mm in diameter,
122mm in length; Little Leonardo, Tokyo, Japan) that
recorded depth, speed (from the number of rotations of a
propeller) and acceleration. Depth and swimming speed data
were recorded at a frequency of 1Hz. Acceleration data were
recorded at a frequency of 16Hz at Hukuro Cove and at 3.3Hz
at Adélie Land, respectively, using two piezo-resistive
accelerometers (model 3031, IC Sensors). The logger was
attached to the back of the penguin, where it recorded
acceleration in two axes of three directions; surging
acceleration measured along the longitudinal body axis of the
penguin, heaving acceleration measured dorso-vertically and
swaying acceleration measured transversely crossing the
penguin’s body from right to left (Fig. 1).
Adélie penguins captured at their nests were rapidly
equipped with the loggers at Hukuro Cove (N=17) and Adélie
Land (N=8). The data loggers were attached caudally on the
bird’s back to minimize drag (Bannasch et al., 1994; Culik et
al., 1994) using tesa tape (Wilson et al., 1997) at Hukuro Cove
and epoxy adhesive at Dumont d’Urville. The penguins were
recaptured after their foraging trip, and the data loggers were
retrieved. The exact position of the logger on the back of a bird
varied slightly from one individual to another in relation to the
curvature of its back. To reduce the effects of differences in
attachment among individuals as much as possible, at the start
of the analysis of data, surging acceleration was individually
calibrated as being equal to 9.8ms
when the bird was
standing still after attachment of the logger.
Categorization of behaviour from acceleration
The activities of penguins during a foraging trip is divided
into the following seven major categories; walking,
tobogganing, standing on land, lying on land, resting at the
water surface, porpoising and diving; tobogganing penguins lie
on their belly and push themselves forward with alternating
foot movements (Wilson et al., 1991), and porpoising penguins
jump briefly out of the water (Yoda et al., 1999). We defined
diving behaviour as swimming at a depth of more than 1m.
The acceleration profiles for specific types of behaviour were
categorized during a calibration experiment conducted on two
captive Adélie penguins in the Port of Nagoya Public
Aquarium and on each wild penguin equipped with a data
logger during movements from their nest to the sea, where we
could observe the birds directly. Penguins were recorded (at
) using a video camera, and the acceleration
profiles were compared by visual analysis of the videotapes.
The relationships between the behaviour and acceleration
profiles of penguins walking, tobogganing, standing on land,
lying on land and resting on the water surface were confirmed
near their colony in field experiments, and the relationship for
porpoising penguins was calibrated in the aquarium.
The acceleration sensors measure both accelerations related to
changes in the movements of birds and gravitational acceleration
). Thus, the amplitude of surging acceleration when the
penguin is not moving represents the component of gravitational
acceleration that changes in response to the posture of the bird.
This enabled us to determine the posture of the penguins, i.e.
whether they were standing, lying on land or resting at the water
surface. To remove the acceleration of the movement, the surging
acceleration data were smoothed using a moving average over
111 points, which was appropriate for discriminating the dynamic
activities in order to determine posture. The threshold at which
the three postures would best be distinguished from each other
was identified. Below this threshold, the acceleration spectra
were analyzed automatically.
Dynamic behaviour, i.e. walking, tobogganing and
porpoising, were categorized by examining the acceleration
Surging acceleration
Heaving acceleration
Swaying acceleration
Fig. 1. Schematic diagram showing the direction
of surging, heaving and swaying accelerations
recorded by a data logger on the back of an Adélie
687Monitoring the behaviour of free-ranging penguins
profiles by eye. The periodic properties of the acceleration
signal obtained from walking and tobogganing behaviour
allowed a Fourier analysis to be applied, enabling us to
determine the frequency of walking and tobogganing.
Results are presented as means ± S.D. Comparisons were
evaluated using a Mann–Whitney U-test. Differences were
accepted as significant when P<0.05.
Penguins had a mean mass of 4.4±0.43kg (mean ± S.D.,
N=11). In the sea-ice region, all the recorders were recovered,
but seven loggers were missing after one foraging trip, one
acceleration sensor was broken and two penguins did not dive
at all. In the ice-free region, all the loggers were also recovered,
but two penguins did not dive and two recorders was not
attached appropriately. Therefore, seven of the PD2G loggers
delivered reliable data for one foraging trip in the sea-ice
region and four of the loggers for one foraging trip in the ice-
free region. Fig. 2 gives examples of the raw data. The duration
of a foraging trip in the sea-ice region (10.4±6.8h, mean ± S.D.,
range 3.0–21.5h, N=7 birds) was shorter than that in the ice-
free region (27.3±10.5h, mean ± S.D., range 13.0–38.1h, N=4
birds; P<0.05, Mann–Whitney U-test).
The distribution of surging accelerations in relation to the
posture of penguin was bimodal, with higher values (5.0ms
corresponding to upright and lower values (<5.0ms
) to
horizontal positions (Fig. 3A,B). Values 1.5ms
corresponded to lying on land and values <1.5ms
corresponded to resting at the water surface (Fig. 3A,B), although
this value did not always allow a definitive separation between
lying on land and resting at the water surface. Therefore, changes
in heaving acceleration due to sea waves when the bird is at sea
were used in combination with the inclination angles of the
loggers for an accurate discrimination (Fig. 3B).
The large lateral swings of walking penguins (Pinshow et
al., 1977) and the dash-and-stop movements of tobogganing
penguins affected the surging and swaying acceleration pattern
12:00 18:00 00:00 06:00
(m s
(m s
Depth (m)
00:00 06:00 12:00 18:00 00:00
Time of day (h)
(m s
(m s
Depth (m)
Fig. 2. Examples of acceleration and diving profiles of Adélie penguins foraging in an area covered by sea-ice (A) and in an ice-free area (B).
0 1 2 3 4 5
Lying on land Standing on land
Resting at the
water surface Diving
(m s
(m s
Depth (m)
(m s
(m s
(m s
(m s
Depth (m)
(m s
(m s
Elapsed time (min)
Elapsed time (min)
Elapsed time (s)
0 10 20 30
Walking Tobogganing
0 30
Elapsed time (s)
Fig. 3. Profiles of surging, heaving and swaying acceleration and depth for Adélie penguins (A) lying and standing on land, (B) diving and resting at the water surface, (C) walking and
tobogganing and (D) porpoising. Surging acceleration in A and B was smoothed using the procedure described in the text to differentiate the three types of posture. Leaps during
porpoising are indicated by vertical broken lines in D.
689Monitoring the behaviour of free-ranging penguins
recorded by the loggers (Fig. 3C). These two behaviours could
be distinguished using the posture of the birds indicated by the
surging acceleration profile: walking penguins stand up,
whereas tobogganing penguins lie down (Fig. 3C).
When penguins leapt into the air during porpoising, the
amplitude of surging acceleration increased briefly up to
almost 10ms
before decreasing to 10ms
(Fig. 3D). This
might be caused by the change of posture in relation to jumping
into the air and then plunging into the water and to the impact
of the movement.
The difference in sampling frequency between the penguins
at the sea-ice region and the ice-free region did not affect these
categorizations of behaviour. The differences between
individual penguins were barely detectable when these seven
types of behaviour were distinguished.
The frequency of the different behaviour types was
calculated for the two study sites (Fig. 4). There was no
significant difference between the two colonies in the
proportion of time spent diving during foraging trips (P=0.06,
Mann–Whitney U-test). The proportion of time spent standing,
lying on land and walking was greater for birds in the sea-ice
region (37.6±13.3% standing, 21.6±15.6% lying and
5.9±6.3% walking, means ± S.D.; P<0.05, Mann–Whitney U-
test) than for those in the ice-free region (12.0±15.8%
standing, 0.38±0.60% lying and 0% walking, means ± S.D.;
P<0.05, Mann–Whitney U-test) and the proportion of time
spent resting at the water surface and porpoising was greater
for birds in the ice-free region (38.1±6.4% resting and
1.1±1.1% porpoising, means ± S.D.) than for those in the sea-
ice region (3.0±2.3% resting and 0% porpoising, means ± S.D.;
P<0.05, Mann–Whitney U-test).
The stride frequencies of walking and tobogganing penguins
in the sea-ice region were calculated as 1.7±0.3Hz (mean ±
S.D., range 1.0–2.8Hz, N=233) and 1.7±0.2Hz (mean ± S.D.,
range 1.4–1.9Hz, N=5), respectively.
Motion detectors and accelerometers have been used to
monitor objectively the behaviour of ambulatory humans
(Bussmann et al., 1998). Moreover, the use of accelerometers to
monitor the swimming activities of free-ranging animals has
recently been reported for penguins (Yoda et al., 1999; Arai et
al., 2000) and for seals (Davis et al., 1999; Williams et al., 2000).
In the present study, accelerometers were used to monitor the
behaviour of a free-ranging animal both at sea and on land.
In this study, seven behaviour patterns of penguins during
foraging trips were distinguished. It may also be possible to
obtain information to sub-divide these behaviours further, e.g.
jumping on rocks, which is a common activity of rockhopper
penguins (Eudyptes chrysocome), and which may be special
interest for specific research, although the present study
categorized general behaviour during foraging trips. Three
postures (standing on land, lying on land and resting on water)
could be automatically distinguished by using appropriate
thresholds, although changes in acceleration caused by sea
waves were also used to discriminate between two lying
positions. A method by which walking, tobogganing and
porpoising activities can be automatically distinguished from
other behaviours is still under investigation. However, these
dynamic behaviours seemed to be clearly distinguishable by
eye (Fig. 3). Once the behaviour/acceleration profiles had been
Lying on land 0.4%
Tobogganing 0.04%
Porpoising 0%
Walking 0%
Tobogganing 0%
Standing on land
Lying on land
Resting at the
water surface
Resting at the
water surface
Standing on land
Fig. 4. Behavioural time budgets of Adélie penguins foraging in an area covered by sea-ice (Hukuro Cove; N=7 birds) (A) and in a ice-free area
(Adélie Land; N=4 birds) (B).
calibrated at an aquarium or in the field, where the behaviour
can be recorded by video camera, this system proved to be
highly reliable for continuous automatic monitoring.
The short sampling interval enabled us not only to monitor
high-speed behaviour, such as porpoising, but also to calculate
the frequency of walking and tobogganing, using mathematical
methods such as Fourier analysis. These analyses will enable
us to study the energetic efficiency of walking and tobogganing
(Wilson et al., 1991) during foraging trips in the natural
Using this new approach, further studies combining the
monitoring of marine resources in different Antarctic sites and
the measurement of the energy expenditure of foraging
penguins, e.g. by measuring heart rates (Bevan et al., 1995),
will constitute a powerful tool for examining the foraging
strategy chosen in the different environmental conditions, such
as sea-ice conditions. In the present study, the behaviour of
penguins in the sea-ice region and the ice-free region was quite
different because of the difference in resting site between
dives, i.e. on the sea-ice in the sea-ice region and at the water
surface in the ice-free region, and the type of locomotion, i.e.
walking and tobogganing in the sea-ice region and swimming
and porpoising in the ice-free region. These differences in
behaviour require different energy expenditures, which may
lead to different strategies of time allocation during foraging
trips. The possible flexibility of the behavioural strategy of
penguins will be revealed by a combination of measurements
of behaviour and energy expenditure.
This method could be used to monitor not only the behaviour
of aquatic animals, such as penguins, but also the activities of
terrestrial animals. For example, the behaviour of flying slaty-
backed gulls (Larus schistisagus) has been monitored as a
periodical profile using an acceleration data logger (K. Yoda,
Y. Watanuki and Y. Naito, unpublished data). The overall
design of this system offers high flexibility for the study of
many different animals and enables the behaviour of the animal
to be analysed precisely in its natural environment.
This work was supported by the fortieth JARE and Grant-
in-aid for International Scientific Research. We thank J.
Baudat and all the 1998/1999 team in Dumont d’Urville
French station and Syowa station for their assistance in the
field, T. Akamatsu, National Research Institute of Fisheries
Engineering, M. Kuroki, Graduate University of Advanced
Studies, for calibration experiments of the propeller data
logger, M. Fukuda, Tokyo Sea Life Park, and I. Uchida, Port
of Nagoya Public Aquarium, for their assistance in the
aquarium investigations and M. Imafuku, A. Mori, Y. Mori,
Kyoto University, R. P. Wilson, Institut für Meereskunde, and
two anonymous reviewers for comments on the manuscript.
Arai, N., Kuroki, M., Sakamoto, W. and Naito, Y. (2000). Analysis
of diving behavior of Adélie penguins using acceleration data
logger. Polar Biosci. 13, 95–100.
Bannasch, R., Wilson, R. P. and Culik, B. (1994). Hydrodynamic
aspects of design and attachment of a back-mounted device in
penguins. J. Exp. Biol. 194, 83–96.
Bevan, R. M., Woakes, A. J., Butler, P. J. and Croxall, J. P. (1995).
Heart rate and oxygen consumption of exercising gentoo penguins.
Physiol. Zool. 68, 855–877.
Bussmann, J. B. J., van de Laar, Y. M., Neeleman, M. P. and
Stann, H. J. (1998). Ambulatory accelerometry to quantify motor
behaviour in patients after failed back surgery: a validation study.
Pain 74, 153–161.
Chappell, M. A., Shoemaker, V. H., Janes, D. N., Bucher, T. L.
and Maloney, S. K. (1993). Diving behaviour during foraging in
breeding Adélie penguins. Ecology 74, 1204–1215.
Croxall, J. P., Briggs, D. R., Kato, A., Naito, Y., Watanuki, Y. and
Williams, Y. D. (1993). Diving pattern and performance in the
macaroni penguin Eudyptes chrysolophus. J. Zool., Lond. 230,
Culik, B. M., Bannasch, R. and Wilson, R. P. (1994). External
devices on penguins: how important is shape? Mar. Biol. 118,
Davis, R. W., Fuiman, L. A., Williams, T. M., Collier, S. O.,
Hagey, W. P., Kanatous, S. B., Kohin, S. and Horning, M.
(1999). Hunting behavior of a marine mammal beneath the
Antarctic fast ice. Science 283, 993–996.
Kooyman, G. L., Cherel, Y., Le Maho, Y., Croxall, J. P., Thorson,
P. H., Ridoux, V. and Kooyman, C. A. (1992). Diving behavior
and energetics during foraging cycles in king penguins. Ecol.
Monogr. 62, 143–163.
Naito, Y., Asaga, T. and Ohyama, Y. (1990). Diving behavior of
Adélie penguins determined by time–depth recorder. Condor 92,
Pinshow, B., Fedak, M. A. and Schmidt-Nielsen, K. (1977).
Terrestrial locomotion in penguins: it costs more to waddle. Science
195, 592–594.
Watanuki, Y., Kato, A., Naito, Y., Robertson, G. and Robinson,
S. (1997). Diving and foraging behaviour of Adélie penguins in
areas with and without fast sea-ice. Polar Biol. 17, 296–304.
Williams, T. D., Davis, R. W., Fuiman, L. A., Francis, J., Le boeuf,
B. J., Horning, M., Calambokidis, J. and Croll, D. A. (2000).
Sink or swim: strategies for cost-efficient diving by marine
mammals. Science 288, 133–136.
Williams, T. D., Kato, A., Croxall, J. P., Naito, Y., Briggs, D. R.,
Rodwell, S. and Barton, T. R. (1992). Diving pattern and
performance in nonbreeding gentoo penguins (Pygoscelis papua)
during winter. Auk 109, 223–234.
Wilson, R. P., Culik, B., Adelung, D., Coria, N. R. and Spairani,
H. J. (1991). To slide or stride: when should Adélie penguins
(Pygoscelis adeliae) toboggan? Can. J. Zool. 69, 221–225.
Wilson, R. P., Püts, K., Peters, G., Culik, B., Scolaro, J. A.,
Charrassin, J.-B. and Ropert-Coudert, Y. (1997). Long-term
attachment of transmitting and recording devices to penguins and
other seabirds. Wildlife Soc. Bull. 25, 101–106.
Wilson, R. P. and Wilson, M.-P. T. (1995). The foraging
behaviour of the African Penguin Spheniscus demersus. In The
Penguins: Ecology and Management (ed. P. Dann, I. Norman and
P. Reilly), pp. 244–265. Australia: Surrey Beatty & Sons Pty
Yoda, K., Sato, K., Niizuma, Y., Kurita, M., Bost, C. A., Le maho,
Y. and Naito, Y. (1999). Precise monitoring of porpoising
behaviour of Adélie penguins determined using acceleration data
loggers. J. Exp. Biol. 202, 3121–3126.
... Over the past two decades, there has been widespread deployment of animalborne accelerometer data loggers (Brown et al. 2013;Vacquié-Garcia et al. 2015;Yoda et al. 2001). This high resolution data can be used to infer the behavioural states and fine-scale activity budgets of free-ranging individuals Collins et al. 2016). ...
... This high resolution data can be used to infer the behavioural states and fine-scale activity budgets of free-ranging individuals Collins et al. 2016). These devices provide whole body acceleration and, with increasing battery life, can provide information over various spatial and temporal scales (Brown et al. 2013;Vacquié-Garcia et al. 2015;Yoda et al. 2001). With increasing miniaturisation of accelerometer data loggers, it is now possible to obtain this information for relatively small animals (i.e. ...
Quantifying predator-prey interactions can be logistically difficult, especially in marine environments. However, it is essential to predict how individuals respond to changes in prey availability, an important factor in assessing the impact of climate change. In comparison to flying seabirds, penguins (Family: Spheniscidae) experience greater constraints when breeding due to restrictions in foraging range. As such, this group of seabirds are considered good indicators of local ecosystem health. Animal-borne video cameras have made it possible to observe behaviour in response to prey field. In the present study, a combination of animal-borne video cameras, accelerometers, dive recorders and GPS were used to determine the factors influencing foraging effort and efficiency in penguins. These were investigated in 3 species: 1) little penguin, Eudyptula minor; 2) African penguin, Spheniscus demersus, 3) Macaroni penguin, Eudyptes chrysolophus. In each species, the immediate prey field dictated the 3-dimensional movement in the water column. Foraging effort in little penguins was influenced by the abundance of prey, not prey type. The mean body acceleration of little penguins was examined as an index of effort and was found to be highly correlated to energy expenditure rates determined from doubly-labelled water. Machine learning was used to detect prey captures which were validated using video cameras in African and Macaroni penguins. It was found that African penguins exhibited pelagic dives and a large proportion of successful benthic dives. Benthic dives were costlier but more successful than pelagic ones, indicating a trade-off between effort and success. Macaroni penguins displayed prey-specific behaviour, diving deep when foraging on subantarctic krill (Euphausia vallentini) and completing shallow dives when targeting juvenile fish.This body of work highlights the effect of prey field and the drivers of variability in foraging behaviour.
... However, for many behaviors high sampling frequencies are required for detailed resolution of signals. While these signals can be distinguished visually, loggers often produce tremendous quantities of data requiring automated techniques such as decision trees, Random Forests (RFs), and Artificial Neural Networks (ANN; Halsey et al., 2009;Nathan et al., 2012;Wang and Xu, 2015;Yoda et al., 2001) to identify the behaviors associated with a given signal. Much accelerometer research has applied machine learning techniques in a supervised manner which requires a priori labeling of training signals with known behaviors so that the trained model can classify similar signals throughout the full data (Chakravarty et al., 2020;Leos-Barajas et al., 2017;Nathan et al., 2012). ...
... There is a large body of literature on the deployment and use of accelerometers and magnetometers (Brown et al., 2013;Williams et al., 2020;Wilson et al., 2008) and on classifying animal behaviors from direct observation (Chakravarty et al., 2020;Nathan et al., 2012;Shepard et al., 2008;Wilson et al., 2020;Yoda et al., 2001). As such we do not discuss these here and presume that readers have already collected the relevant data ( Fig. 1). ...
Many studies of animal distributions use habitat and climactic variables to explain patterns of observed space use. However, without behavioral information, we can only speculate as to why and how these characteristics are important to species persistence. Animal-borne accelerometer and magnetometer data loggers can be used to detect behaviors and when coupled with telemetry improve our understanding of animal space use and habitat requirements. However, these loggers collect tremendous quantities of data requiring automated machine learning techniques to identify patterns in the data. Supervised machine learning requires a set of training signals with known behaviors to train the model to identify the unique signal characteristics associated with each behavior. In contrast, unsupervised approaches aggregate unlabeled signals into groups based purely on signal similarity but, without additional information, do not identify specific behaviors. In this paper, we propose a probabilistic framework for interpreting uncertainty in machine learning techniques—the probability profile—and demonstrate how to post hoc identify behaviors within signal groups. We assess model performance using a matrix-based measure of dissimilarity. We used a Random Forest (RF) and a clustered self-organizing map (CSOM) for comparison and demonstrate the use of a behavioral profile for each using a data set of high-frequency accelerometer and magnetometer data collected from 7 captive wild pigs (Sus scrofa) moving in a 1 ha outdoor enclosure. We found that the RF had more discrimination than the CSOM which had fewer clusters associated with high probabilities of a single behavior (>50%). The leave-p-out cross validation statistic of the probability matrix (L1¯) indicated that there was an average maximum dissimilarity of 20% and 65% between the training and test data sets for the RF and CSOM methods, respectively. Using a probability profile to describe groups predicted from machine learning allows the variation and error inherent in behavioral prediction to be incorporated directly into the model to better reflect the nuances of behavior derived from accelerometer and/or magnetometer signals. We discuss the data requirements of this framework, demonstrate its application to field data, highlight critical assumptions and caveats, and examine how it may be used to generate new ecological inference.
... The acceleration (in m/s 2 or G-forces (g)) measured by a sensor in three dimensions (X, Y and Z) [Reviewed by : 21], may be separated into both static and dynamic acceleration [22,23]. Animal body orientation may be registered using the static acceleration caused by gravitational force acting on the accelerometers [24,25]. Removing the gravitational component, the dynamic acceleration is revealed. ...
Full-text available
Animal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events.
... Individual study questions will dictate types of analysis to be used, but there are common processes that can be applied to most high-resolution sensor studies. The time series data that results from high-resolution sensor data, such as accelerometry, can be separated into frequency and amplitude by means of signal processing techniques like Fourier transform or wavelet analysis (Yoda et al. 2001;Sakamoto 2009; Figure 16.5c). Specialized software exists for viewing, processing, and analyzing these data (e.g., R statistical software, R Core Team 2013; MATLAB, MATLAB 2010; Igor pro, Wavemetrics, Lake Oswego, Oregon; Framework 4, Walker et al. 2015;Ethographer, Sakamoto et al. 2009;Ac-celeRater, Resheff et al. 2014). ...
Methods for Fish Biology, 2nd edition Chapter 16: Behavior Julianna P. Kadar, Catarina Vila Pouca, Robert Perryman, Joni Pini-Fitzsimmons, Sherrie Chambers, Connor Gervais, and Culum Brown doi: Kadar, J. P., C. V. Pouca, R. Perryman, J. Pini-Fitzsimmons, S. Chambers, C. Gervais, and C. Brown. 2022. Pages 593–642 in S. Midway, C. Hasler, and P. Chakrabarty, editors. Methods for fish biology, 2nd edition. American Fisheries Society, Bethesda, Maryland. Humans interact with fish in a wide variety of contexts. Fish are rapidly becoming the go-to model for medical research because of the conservative nature of vertebrate physiology. We catch and grow fish in captivity for human consumption and frequently rear fish for release into the wild either to supplement wild populations to enhance fisheries or as a conservation measure. In all cases, understanding fish behavior is vital whether you are interested in stock management, conservation biology, or animal welfare (Brown 2015). Gone are the days when fish were viewed as mindless automata. We now know that fish behavior is highly flexible, providing the plasticity to allow individuals to adjust to prevailing conditions or contexts (Bshary and Brown 2014). Their level of cognitive and behavioral sophistication is on par with the rest of the vertebrates (Bshary and Schäffer 2002; Vila Pouca and Brown 2018a; 2018b). Unsurprisingly, a change in behavior is often the first sign that something has shifted in the environment; thus, behavioral studies are at the forefront of environmental and ecotoxicological research (Brown 2012; Oulton et al. 2014). The massive diversity of fishes (currently more than 32,000 described species), and the range of niches they occupy, means that generalization is nearly impossible. Thankfully, the approaches for studying fish behavior are also many and varied and rapidly developing with changes in technology. Here we provide a brief overview of some of the emerging methods for studying fish behavior. We will not be reviewing fish behavior in general since this is the topic of multiple books (e.g., Magnhagen et al. 2008; Brown et al. 2011), nor will we be providing a general overview of how to study animal behavior. Such details can readily be found in any of the many excellent texts on animal behavior or behavioral ecology (Davies 1991; Dugatkin and Earley 2004; Alcock 2005; Goodenough et al. 2009). Many people study fish behavior under captive conditions where it is possible to control the environment and observe behaviors that can be attributed to specific cognitive processes. In most instances, it is simply a matter of refining the standard methods to suit the aquatic environment and the species of interest. The main difficulties of studying fish behavior arise when trying to observe them in their natural environment. The underwater world is not a place with which most people are comfortable or familiar. Humans can stay only so long in the watery world of fishes, so many of the methods we describe here attempt to overcome these problems by studying fish behavior remotely.
... Advanced Global Positioning System (GPS) technology, tri-axial accelerometer data, and high frequency rates of location fixes can help distinguish between animal behaviors that were once only possible through direct observation . Behaviors such as walking, resting, foraging, swimming, and grooming have been revealed using GPS and accelerometer data in a variety of species (e.g., Yoda et al. 2001, Graf et al. 2015, Wang et al. 2015, including birds (Nathan et al. 2012). ...
A female's reproductive status influences her behavior which affects habitat selection and range size; however, reproduction and behavior are generally unaccounted for in habitat selection studies. Range size, daily activity, and habitat selection between reproductive states have rarely been investigated in a connected manner. We focused on brood‐rearing and broodless (i.e., females without young) greater sage‐grouse (Centrocercus urophasianus). Our objectives were as follows: 1) identify differences between reproductive state (females with broods 0–2 weeks, broods 3–5 weeks, and broodless females) and behavioral state (foraging, day roosting, and night roosting) in microhabitat selection, 2) evaluate daily activity for brood‐rearing and broodless females, and 3) contrast daily and seasonal range sizes for each reproductive state. We collected Global Positioning System location and accelerometer data every 5 min from female sage‐grouse in Carbon County, Montana, and Park County, Wyoming, USA, in 2018–2019. We sampled microhabitat for 36 females at 276 bird‐use and random plots, estimated ranges for 38 females, and measured activity for 43 females. Females with broods 0–2 weeks selected against visual obstruction and for perennial grasses at night roosts, females with broods 3–5 weeks selected for visual obstruction when foraging and against visual obstruction and annual grasses but for sagebrush cover at night roosts; however, broodless females showed no selection. Patterns of daily activity differed between females with broods 0–2 weeks and broodless females; females with broods 3–5 weeks showed an intermediate pattern. Females with broods 0–2 weeks had the smallest daily (0.027 km2) and seasonal (0.21 km2) ranges compared with females with broods 3–5 weeks (daily = 0.038 km2, seasonal = 0.36 km2) and broodless females (daily = 0.035 km2, seasonal = 0.44 km2). Our results indicated the importance of considering reproductive and behavioral state and accounting for habitats and space required by all individuals in conservation and management decisions. We investigated and found differences between behavioral states in habitat selection and differences between reproductive states in habitat selection, daily and seasonal range size, and daily activity for greater sage‐grouse (Centrocercus urophasianus). Our results indicate the importance of considering reproductive and behavioral state in habitat selection studies and accounting for habitats and space required by all individuals in conservation and management decisions.
... Based on the acceleration data, we determined whether the animal was 'active' or 'inactive' by the minute. Acceleration signals were reflected by accelerations related to changes in the movements of animals, that is, dynamic accelerations and gravitational acceleration, resulting from changes in body posture (Yoda et al., 2001). The resolution of the acceleration sensor was 0.025 g (0.245 m s −2 ), which was sufficient to determine whether an animal was active or inactive. ...
In Inderasabah (southern Sabah), tri-spine horseshoe crab Tachypleus tridentatus was observed for their locomotion activity using data loggers from September to November in 2015. A female with acceleration and depth-temperature loggers and five males with acceleration loggers were recaptured between 10 and 49 days after their release. From the record of 194 activity days that involve all six T. tridentatus, four horseshoe crabs, including the female, were active throughout the 24h cycle. whereas the activity of the remaining two males was consistent with the 12.4h cycle. Using the 40-days recording, three horseshoe crabs, including the female, were primarily active around the new moon and full moon, but they were dormant around the first and third moon days. The female spent much time in shallow shores (depth <0.3m) during the new moon and full moon. This result indicated that the female attempted to spawn in a minimum of three spring tide periods while lingering in the vicinity. Meanwhile, after spawning, the female spent time foraging in shallow water (depth 0.3-18m). As for the two male individuals, their activity was consistent with semi-lunar periodicity. Therefore, both of them were in amplexus. In addition, a solitary male individual was active only during the first and third quarter moon days. Through activity recording, all the T. tridentatus in Inderasabah was active during daytime and nighttime. This result was contrary to T. tridentatus activity cycles in western Japan, where the species was found to be primarily nocturnal. Perhaps, the regional in activity cycles for T. tridentatus were related to their population adaptation toward water temperature, depth, and pre-searching periods.
... Dynamic body acceleration is measured via tri-axial accelerometers, which can be incorporated into existing biologging devices that are typically externally attached and thus require no additional effort or invasive protocols to deploy. DBA is the sum of the dynamic acceleration along three axes of the body (Wilson et al., 2006;Yoda et al., 2001). Acceleration is achieved through mechanical work performed by muscles, which should be proportional to the amount of energy being used to move Halsey, Shepard, et al., 2011). ...
Full-text available
In animal ecology, energy expenditure is used for assessing the consequences of different behavioural strategies, life‐history events, or environments. Animals can also influence energy expenditure through instantaneous behavioural responses to their external environment. It is therefore of interest to measure energy expenditure of free‐ranging animals across seasons and at high temporal resolutions. Heart rate has historically been used for this, but requires invasive surgery for long‐term use. Dynamic body acceleration (DBA) is an alternative proxy for energy expenditure that is simpler to deploy, yet few studies have examined how it performs over extended time periods, or for species using different locomotory modes, especially passive modes like soaring flight. We measured DBA alongside heart rate in free‐ranging lesser black‐backed gulls, a seabird that moves using flapping flight, soaring, and walking, and rests on both land and water. Our objectives were to compare the relative changes in DBA and heart rate among and within behaviours and to examine how accelerometers can be used to estimate daily energy expenditure by comparing DBA to time‐energy budgets (TEBs). DBA and heart rate were sampled concurrently at 2.5 and 5 min intervals throughout the breeding season, though measurements were not exactly synchronised. Behaviour was identified from accelerometer measurements, and DBA and heart rate were averaged over bouts of consistent behaviour. Heart rate was converted to metabolic rate using an allometric calibration, after confirming its fit using metabolic measurements taken in captivity and values from existing literature. Both proxies showed similar changes among behaviours, though DBA overestimated costs of floating, likely due to waves. However, relationships between DBA and heart rate were weak within a behaviour mode, possibly due to lack of synchrony between proxy measurements. On daily scales, DBA and TEBs perform comparably for estimating daily energy expenditure. Accelerometery methods deviated from a 1:1 relationship with heart rate because acceleration could not measure variation in resting metabolic costs. We conclude DBA functions well for detecting energy expenditure arising from activity costs, including during soaring flight. We discuss scenarios where one method (DBA versus TEBs) may be preferred over the other.
... In fact, over the past two decades, these loggers have become an indispensable component of the research toolkit available for studying wild animals. Amongst other applications, they are being used to chart activity profiles, to estimate energy expenditure, and to detect difficult-to-observe behaviours (Yoda et al., 2001;Wilson et al., 2006;Watanabe and Takahashi, 2013). Yet, despite the success of a first wave of pioneering studies, the potential of accelerometers as 'sleep detectors' remains to be fully exploited (e.g., Miller et al., 2008;Samson et al., 2018; for additional references, see Loftus et al., 2022). ...
Full-text available
Body-motion sensors can be used to study non-invasively how animals sleep in the wild, opening up exciting opportunities for comparative analyses across species.
... To perform such validation, the method consists of synchronising a video recording with the corresponding signals from the accelerometer. This can be realised either in captivity (Yoda et al. 2001) or in the wild with the concomitant deployment of an accelerometer and externally attached camera (Van Dam et al. 2002, Watanabe and Takahashi 2013, Thiebot et al. 2016). ...
Iconic species used to raise public awareness, the Emperor penguin is first and foremost a top predator and umbrella species playing a pivotal role in Antarctic ecosystems. Standing at the forefront of climate upheavals, much remains to be learned about the ecology, distribution, and activities at sea of the species. Biologging allows to refine our understanding of the interactions between a species and the different components (biotic and abiotic) of its environment, in particular with a view of management, conservation, and assessment of the adaptive capacity of populations to face global change.In this study, we develop and share new equipment methods that increase equipment and data collection duration, while reducing the disturbance of the equipped individuals. By carrying out a spatio-temporal analysis of the data collected on individuals of different life-history stages, reproductive status, and from different colonies spanning around Antarctica, we investigate the species’ foraging behaviours and strategies and assess the influence of environmental conditions and habitat on these parameters. Such knowledge acquisition allows us to assess the degree of protection of the species at the scale of the Southern Ocean and to discuss strategic plans for conservation and management, such as the establishment of networks of Marine Protected Areas around the Antarctic continent.
Organisms living in intertidal zones adapt to complex environmental changes with both diurnal (24-h) and tidal (12.4-h) cycles. Activity rhythm of the tri-spine horseshoe crab, Tachypleus tridentatus was examined to understand the mechanism of biological clocks and the ecological functions in its environments. An acceleration data-logger was attached on T. tridentatus to record whether the animal was active or inactive at 1-min interval up to consecutive 45 days in unrestrained condition. The locomotion activity of 15 adult males and 11 females were monitored in two experimental conditions exposed to light/dark (LD) cycles or both LD and tidal cycles. Rhythmicity of activity (24-h: circadian or 12.4-h: circatidal) and chronotype (diurnality or nocturnality) was examined in each trial. The animals exposed to only LD cycles exhibited circadian rhythms, out of synchronization with LD cycles, in all of 14 trials. It is, thus, more likely that T. tridentatus possess an endogenous circadian clock, but LD cycles do not act solely as an external synchronizing cue (Zeitgeber) in its activity. The animals exposed to both LD and tidal cycles exhibited circadian rhythms and nocturnality in 16 of 28 (57%) trials. Their activity rhythms synchronized to high tide at night. These results suggest that tidal cues are the primarily important Zeitgeber modulating the endogenous circadian clock of T. tridentatus, and secondary LD cycles combined with tidal cycles may play a role to regulate activity rhythms in T. tridentatus. However, some individuals expressed both circadian and circatidal rhythms in different trials. Additional experiments using our monitoring techniques are needed to identify entraining agents of activity rhythms of T. tridentatus and to understand the mechanisms of its biological clock.
Full-text available
King Penguins are the second largest of all diving birds and share with their congener, Emperor Penguins, breeding habits striking different from other penguins. Our purpose was to determine the feeding behavior, energetics of foraging and the prey species, and compare these to other sympatric species of subantarctic divers. We determined: (1) general features of foraging behavior using time-depth recorders, velocity meters, and radio transmitters, (2) energetics by doubly labeled water, (3) food habits and energy content from stomach lavage samples, and (4) resting and swimming metabolic rate by oxygen consumption measurements. The average foraging cycle was almost-equal-to 6 d, during which the mass gain of 30 birds was almost-equal-to 2 kg. When at sea, the birds exhibit a marked pattern of shallow dives during the night, whereas deep dives of > 100 m only occurred during the day. Maximum depth measured from 34 birds and 18 537 dives was 304 m, and maximum dive duration from 23 birds and 11 87
The foraging behaviour of African penguins was studied in 10 birds at Dassen Island and 22 birds at Marcus Island, South Africa. Three foraging dive types were identified: 1) 'Search' dives where a constant heading was maintained and the birds descended the water column to a particular depth before returning immediately to the surface. Mean swim speed during the course of the dive was independent of maximum depth reached. 2) 'Feeding' dives where prey were encountered. Here dives were initiated in the same way as 'search' dives but changed when bird swim direction suddenly became erratic and speed varied between 0-3.5 m/sec. 3) 'Post-feeding' dives immediately followed 'feeding' dives and were characterized both by steep dive angles directed to the depth where food was previously ingested and a much more variable heading than birds executing 'search' dives.
The diving behavior of Adélie Penguins, Pygoscelis adeliae, was investigated near Syowa Station during December 1986, with time-depth recorders attached to nesting birds caring for 2- to 3-week-old chicks. Three of four recorders were recovered 150-334 hr after attachment. Most (98%) of the 587 dives recorded were less than 20 m in depth and 40% occurred between 16:00 and 20:00. Mean depths ranged from 6.1-10.9 m and maximum depth was 16.9-26.8 m. Mean and maximum dive durations were 1.4-1.9 min and 2.7-4.0 min. Ninety-seven percent of dives occurred in 44 diving bouts that averaged 25.3 min and 12.9 dives per bout. Descent and ascent rates during dives were similar in 88% of dives, meaning that the penguins dived at low angles, averaging 5°.
We studied diving patterns and performance (dive depth, duration, frequency and organization during the foraging trip) in relation to diet in nonbreeding Gentoo Penguins (Pygoscelis papua) over 59 days (involving 5,469 dives) in winter. We estimated foraging ranges and prey capture rates, and compared foraging behavior with that of breeding (chick-rearing) birds. Foraging was highly diurnal with 98% of foraging trips completed during the same day. Foraging-trip frequency was 0.8/day, trip duration was 6-8 h, and birds spent 51-62% of the foraging trip diving. Dive depth and duration were bimodal. Shallow dives (<21 m; 42% of total number and 16% of total dive time) averaged 5-7 m and 0.5-1.3 min. Deep dives (>30 m; 55% of total number and 81% of dive time) averaged 74-105 m and 2.7-3.5 min, respectively. Deep-dive duration exceeded the subsequent surface interval, but shallow dives were followed by surface intervals two to three times dive duration. Deep dives showed clear diel patterns, averaging 10-20 m at dawn and dusk and 70-90 m at midday. These results are consistent with the patchy vertical and horizontal distribution and diel movements of Antarctic krill, the main winter prey of Gentoo Penguins (including study birds). We suggest that shallow dives are mainly searching dives, and deep dives mainly for feeding. Foraging activity of nonbreeding Gentoo Penguins in winter is similar to that of chick-rearing birds. The only major differences are that foraging-trip frequency is 20% less and stomach-content mass on return ashore 30% less in winter. We conclude that foraging activity in Gentoo Penguins is changed by varying the frequency and duration of foraging trips, rather than by changing the pattern and rate of diving.
We used electronic time depth recorders to examine diving patterns of Adelie Penguins (Pygoscelis adeliae) breeding near Palmer Station, Antarctica. Most hunting dives consisted of a rapid descent to depth, a period of bottom time at near-constant depth, and a rapid ascent to the surface. Most hunting activity occurred in bouts of consecutive dives to similar depths. Adelies foraged at depths between 3 and 98 m, with a mean of 26 m. descent and ascent rates averaged 1.2 and 1.1 m/s, respectively. Foraging was primarily diurnal, but there was relatively little circadian change in foraging depth. The birds' overall hunting effort (cumulative bottom time) was concentrated between 0500 and 2100 at depths between 10 and 40 m. Bottom time decreased slightly with increasing depth but the correlation was weak. Dive duration was positively correlated with dive depth. Maximum dive duration was 160 s; most hunting dives lasted 60-90 s with a mean of 73 s. Post-dive surface intervals averaged @?50% of dive duration. Time use efficiency during dive bouts (bottom time/[dive duration+ surface interval]) decreased with increasing dive depth. Estimates of oxygen stores and diving metabolic rates indicate that the aerobic dive limit of Adelies is 46-68 s and that most hunting dives require some anaerobic metabolism. Use of anaerobiosis engenders and energy penalty and probably affects both the behavior and energetics of foraging.
We noted whether Adelie penguins (Pygoscelis adeliae), when travelling over snow, walked or tobogganed according to gradient, snow friction, or snow penetrability. Both walking and tobogganing penguins reduced stride length and stride frequency, and thus speed, with increasing uphill gradient although tobogganing birds travelled faster and with fewer leg movements. The incidence of tobogganing increased with decreasing friction between penguin and snow. The percentage of penguins tobogganing was also highly positively correlated with increasing snow penetrability. Penguins walking on soft snow must expend additional energy to pull their feet through the snow, whereas tobogganing birds do not sink. It is to be expected that Adelie penguins would utilize the most energetically favourable form of travel which, under almost all conditions, appeared to be tobogganing. Although tobogganing appears to be energetically more efficient than walking, rubbing the feathers over snow increases the coefficient of friction in unpreeened plumage. We propose that a high incidence of tobogganing necessitates increased feather care and that the decision whether to walk or toboggan probably represents a balance between immediate energy expenditure and subsequent energy and time expended maintaining plumage condition.
Acceleration data loggers were attached to five adult Adelie penguins at Hukuro Cove, Lutzow-Holm Bay in austral summer 1997/1998. The loggers recorded time series data of speed, depth, surging acceleration and swaying accelera- tion in flush memories inside. From time series analyses, the frequency of 2- to 3-Hz was found in the surging acceleration during descent in a straight line. The cycle seemed to correspond to wingbeat frequency of the Adelie penguin. The relation between wingbeat frequency and diving depth was that the frequency ranged from 1.5 -Hz to 3.0-Hz when the penguins dive in water shallower than 30-m and was over 2.5 -Hz in water deeper than 50-m. The acceleration data logger is a powerful tool to estimate kinematic parameters of free-range marine animals.
Many researchers use external recording or transmitting devices to elucidate the marine ecology of fish, mammals and birds. Deleterious effects of these instruments on the parameters researchers wish to measure are hardly ever discussed in the literature. Research has shown that, in penguins, volume and cross-sectional area of instruments negatively correlate with swimming speed. dive depth and breeding success, and that device colour affects bird behaviour. Here, a large (200 g, cross-sectional area 2100 mm2) streamlined device was attached to the lower back of Adlie penguins (Pygoscelis adeliae on Ardley Island, South Shetland Island in 1992) and its effects on bird swimming speed and energetics were measured in a water canal in Antarctica. Although the device was 10.5% of penguin cross-sectional area, swimming speed was reduced by only 8.3% and mean power input increased by only 5.6% while swimming. Although our streamlined device was five times more voluminous than one of our older units, the effect on swimming energetics could be reduced by 87%.
The diving and foraging behaviours of Adélie penguins, Pygoscelis adeliae, rearing chiks at Hukuro Cove, Lützow-Holm Bay, where the fast sea-ice remained throughout summer, were compared to those of penguins at Magnetic Island, Prydz Bay, where the fast sea-ice disappeared in early January. Parent penguins at Hukuro Cove made shallower (7.1–11.3 m) but longer (90–111 s) dives than those at Magnetic Island (22.9 m and 62 s). Dive duration correlated with dive depth at both colonies (r 2 = 0.001 ∼ 0.90), but the penguins atg Hukuro Cove made longer dives for a given depth. Parents at Hukuro Cove made shorter foraging trips (8.1–14.4 h) with proportionally longer walking/swimming (diving < 1 m) travel time (27–40% of trip duration) and returned with smaller meals (253–293 g) than those at Magnetic Island, which foraged on average for 57.2 h, spent 2% of time walking/swimming ( < 1 m) travel, and with meals averaging 525 g. Trip duration at both colonies correlated to the total time spent diving. Trip duration at Hukuro Cove, but not at Magnetic Island, increased as walking/swimming ( < 1 m) travel time increased. These differences in foraging behaviour between colonies probably reflected differences in sea-ice cover and the availability of foraging sites.