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Empirical testing of optimal foraging models on diving air-breathing animals is limited due to difficulties in quantifying the prey field through direct observations. Here we used accelerometers to detect rapid head movements during prey encounter events (PEE) of free-ranging benthic-divers, Australian fur seals, Arctocephalus pusillus doriferus. PEE signals from accelerometer data were validated by simultaneous video data. We then used PEEs as a measure of patch quality to test several optimal foraging model predictions. Seals had longer bottom durations in unfruitful dives (no PEE) than those with some foraging success (PEE. ≥. 1). However, when examined in greater detail, seals had longer bottom durations in dives with more PEEs, but shorter bottom durations in bouts (sequences of dives) with more PEEs. Our results suggest that seals were generally maximizing bottom durations in all foraging dives, characteristic of benthic divers. However, successful foraging dives might be more energetically costly (e.g. digestive costs), thus resulting in shorter bottom durations at the larger scale of bouts. Our study provides a case study of how the foraging behaviour of a central place forager foraging in a fairly homogeneous environment, with relatively high travel costs, may deviate from current foraging models under different situations. Future foraging models should aim to integrate other aspects (e.g. diet) of the foraging process for more accurate predictions.
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Testing optimal foraging theory models on benthic divers
Dahlia Foo
, Jayson M. Semmens
, John P. Y. Arnould
, Nicole Dorville
Andrew J. Hoskins
, Kyler Abernathy
, Greg J. Marshall
, Mark A. Hindell
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia
Remote Imaging Department, National Geographic Television, Washington, D.C., U.S.A.
article info
Article history:
Received 10 April 2015
Initial acceptance 7 May 2015
Final acceptance 22 October 2015
Available online
MS. number: 15-00297R
Arctocephalus pusillus doriferus
benthic foragers
marine predators
Empirical testing of optimal foraging models on diving air-breathing animals is limited due to difculties
in quantifying the prey eld through direct observations. Here we used accelerometers to detect rapid
head movements during prey encounter events (PEE) of free-ranging benthic-divers, Australian fur seals,
Arctocephalus pusillus doriferus. PEE signals from accelerometer data were validated by simultaneous
video data. We then used PEEs as a measure of patch quality to test several optimal foraging model
predictions. Seals had longer bottom durations in unfruitful dives (no PEE) than those with some foraging
success (PEE 1). However, when examined in greater detail, seals had longer bottom durations in dives
with more PEEs, but shorter bottom durations in bouts (sequences of dives) with more PEEs. Our results
suggest that seals were generally maximizing bottom durations in all foraging dives, characteristic of
benthic divers. However, successful foraging dives might be more energetically costly (e.g. digestive
costs), thus resulting in shorter bottom durations at the larger scale of bouts. Our study provides a case
study of how the foraging behaviour of a central place forager foraging in a fairly homogeneous envi-
ronment, with relatively high travel costs, may deviate from current foraging models under different
situations. Future foraging models should aim to integrate other aspects (e.g. diet) of the foraging process
for more accurate predictions.
© 2015 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
The ability to acquire resources is crucial for the survival and
tness of animals. Optimal foraging theory (OFT) is a widely used
conceptual framework for explaining and predicting foraging be-
haviours of animals. It attempts to predict how an animal makes
foraging decisions to maximize the net rate of energy intake (also
known as the currency that is being optimized) by minimizing
energy costs while maximizing energy gain under relevant con-
straints in a particular situation (Pyke, Pulliam, & Charnov, 1977;
Stephens & Krebs, 1986). Thus, OFT provides testable predictions
that can improve our understanding of how animals make foraging
decisions to cope in heterogeneous environments where food
availability uctuates spatially and temporally.
For air-breathing diving aquatic animals (hereafter divers),
including turtles, marine mammals and seabirds that forage in a
three-dimensional environment, OFT is also known as optimal
diving theory. Optimal diving theory attempts to model how divers
modify their time allocation within a dive. A dive is typically broken
into four phases: descent, bottom (time assumed to be spent
foraging), ascent and a postdive surface interval (SI), when the
animal stays on the surface to replenish its oxygen stores before its
next dive (Heerah, Hindell, Guinet, & Charrassin, 2014). Bestley,
Jonsen, Hindell, Harcourt, and Gales (2014) broadly classied
optimal diving models as either physiological or ecological models.
Although this dichotomy has limitations, as the optimal diving
models already integrate physiological and ecological constraints to
some extent, this categorization is useful as it simply considers one
type of constraint to be more dominant than the other. We there-
fore used this dichotomy in a very general sense, while recognizing
that it does not affect the fundamental notion of foraging currency
in OFT, which in this case is energy for all foraging models
mentioned in this paper.
Physiological models place emphasis on oxygen depletion of
divers (Houston & Carbone, 1992; Kramer, 1988) because, unlike
terrestrial animals, divers are ultimately limited by oxygen when
they dive. Thus, physiological models assume that within a dive
cycle, divers should maximize their bottom duration (i.e. when
divers can gain net energy), while minimizing travel duration (i.e.
when divers incur a net cost; predictions 1, 2 in Table 1) and/or
* Correspondence: D. Foo, Institute for Marine and Antarctic Studies, University
of Tasmania, Private Bag 1, Hobart 7001, Tasmania, Australia.
E-mail address: (D. Foo).
Contents lists available at ScienceDirect
Animal Behaviour
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0003-3472/© 2015 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
Animal Behaviour 112 (2016) 127e138
extend their dive duration when travel duration increases (pre-
diction 3 in Table 1). Therefore, patch quality should be less
important to divers primarily constrained by their physiology
(Thompson & Fedak, 2001).
Simple physiological diving models assume that divers
encounter prey at a constant rate in the prey patch, so the number
of prey encounters in a dive should increase linearly with bottom
duration (prediction 4 in Table 1; Kramer, 1988). Consequently,
longer dive durations, longer bottom durations and/or higher dive
rates have been used as proxies for increased foraging success and
energy gain (Austin, Bowen, McMillan, & Iverson, 2006) even
though this may not necessarily be true (Thums, Bradshaw,
Sumner, Horsburgh, & Hindell, 2013; Watanabe, Ito, & Takahashi,
2014). For many species, longer dive durations require a longer
time on the surface to reoxygenate (prediction 5 in Table 1; Zimmer
et al., 2010) reducing the proportion of time spent diving relative to
overall time spent at sea (Elliott, Davoren, & Gaston, 2008a).
Ecological models consider ecological factors such as prey
density, quality and distribution, which are attributed to patch
quality (Charnov, 1976; Mori, 1998; Mori & Boyd, 2004; Thompson
& Fedak, 2001), as primary constraints in foraging. The marginal
value theorem (MVT), a classic and inuential concept in OFT, is
often used to model how an optimal forager allocates its time
within a hierarchical patchy environment (however, see Shepard,
Lambertucci, Vallmitjana, & Wilson, 2011 who used it to model
physiological currencies), whereby smaller-scale, short-term
patches of varying patch quality are nested within larger-scale,
long-term habitats. The MVT assumes that an animal foraging in
small-scale patches will experience patch depletion effects and
therefore predicts that a forager should leave all patches, regardless
of their protability, when the instantaneous extraction rate (i.e.
marginal value) reaches the average overall extraction rate for the
habitat as a whole (Charnov, 1976). This leads to two opposing
predictions: the patch residence time of a forager should be longer
in a higher productivity, small-scale patch, but shorter in a higher
productivity, large-scale habitat (prediction 6 in Table 1; see
Figure 1 in Watanabe et al., 2014).
The MVT can be applied to divers, for which individual dives can
be considered a small-scale patch, and a series of dives with
relatively short surface intervals between them (bouts) can be
considered large-scale habitat. Most studies have shown support
for either the short-term (Austin et al., 2006; Sparling, Georges,
Gallon, Fedak, & Thompson, 2007) or long-term (Mori
& Boyd,
2004; Thums et al., 2013) predictions of the MVT on captive and
wild marine predators, while one has recently shown support for
both small- and large-scale predictions for Ad
elie penguins, Pygo-
scelis adeliae (Watanabe et al., 2014).
The model developed by Thompson and Fedak (2001) uses a
simple give-up rule based on the diver's ability to assess patch
quality, while still emphasizing the importance of maximizing
bottom duration in a high-quality patch; their model predicts that
shallow divers should terminate a dive early in a poor-quality
patch, as travel costs are relatively inexpensive. Conversely, deep
divers should maximize bottom duration, regardless of patch
quality (prediction 7 in Table 1). In addition, Thompson and Fedak
(2001) also predicted that divers should increase ascent and
descent rates as patch quality increases (prediction 8 in Table 1).
Empirical tests of these model predictions are rare due to the
lack of data on prey elds, and therefore patch and habitat quality.
Measuring the foraging success of free-ranging divers has largely
been limited to using proxies such as dive or bottom duration, body
condition (Thums et al., 2013) or animal track-based methods
(Dragon, Bar-Hen, Monestiez, & Guinet, 2012a, 2012b), in the
absence of evidence of actual prey feeding events.
Animal-borne video cameras are one of the few practical
methods for directly measuring the prey eld. However, they are
costly, can be difcult to deploy and have limited recording capacity
(Biuw, McConnell, Bradshaw, Burton, & Fedak, 2003; Thums,
Bradshaw, & Hindell, 2011). Studies that used them have rela-
tively small sample sizes and short-term records (Heaslip, Bowen,
& Iverson, 2014) preventing the testing of foraging theory pre-
dictions at larger timescales. Alternatively, accelerometers can
measure characteristic head and jaw movements of an animal
during prey encounter or captures, and also provide longer data
records (Hochscheid, Maffucci, Bentivegna, & Wilson, 2005). When
used in combination, short-term video evidence of a diver's
foraging behaviour can be used to quantitatively validate the prey
encounter events (PEE) of free-ranging predators detected by
Table 1
Predictions of optimal diving models and optimal foraging models that were tested on Australian fur seals, including the response variable and covariates used for statistical
analysis (for each prediction or model)
Prediction Type Response variable Covariates(s) Source
1 For relatively long travel durations, foraging time
decreases with travel duration
Physiological Bottom duration Travel duration Houston and Carbone 1992
2 Proportion of time spent in the foraging area
decreases with travel duration
Physiological Percentage bottom
duration (¼bottom
duration/dive duration)
Travel duration Houston and Carbone 1992
3 Dive duration increases with dive depth and/or
travel duration
Physiological Dive duration Dive depth and travel duration Kramer, 1988, Houston and
Carbone 1992,
Mori et al. 2002
4 Resource gain (no. of prey encountered) increases
linearly with search time spent at depth
Physiological Prey encountered Bottom duration Kramer, 1988
5 Postdive surface interval increases as dive duration
Ecological Postdive surface interval Dive duration Thompson and Fedak 2001
6 Optimal stay-time should be greater in more productive
patches than in less productive patches (dive scale patch
quality); however, optimal stay time should be shorter
where the environment (bout-scale habitat quality) as a
whole is more protable
Ecological Bottom duration Dive scale patch quality, bout
scale patch quality and dive depth
Charnov, 1976
7 For deep dives, bottom duration should be largely
invariant, no matter the prey density/patch quality
Ecological Bottom duration Prey presence or absence (controlled
for travel duration and depth), and
the dive scale prey encounter or
prey encounter rate
Thompson and Fedak 2001
8 Ascent and descent rates should increase with patch
quality if seals are reducing transit time
Ecological Ascent and descent rates Patch quality represented by prey
encounters or prey encounter rate
Thompson and Fedak 2001
D. Foo et al. / Animal Behaviour 112 (2016) 127e138128
simultaneous long-term accelerometry data (Guinet et al., 2014;
Watanabe & Takahashi, 2013).
Australian fur seals (hereafter seals), Arctocephalus pusillus
doriferus, inhabit an environment (Bass Strait, Australia) with a
relatively uniform bathymetry (Arnould & Kirkwood, 2007). Within
the Bass Strait, seals are generalist benthic foragers that feed on a
variety of prey types, including bony sh, cephalopods and elas-
mobranchs at depths of 60e80 m (Deagle, Kirkwood, & Jarman,
2009; Kirkwood, Hume, & Hindell, 2008). Benthic environments
are generally more stable and less heterogeneous than pelagic
environments. This provides a good opportunity to test foraging
theory predictions on seals, as their maximum dive depth is pre-
determined to relatively similar depths, and their typical dive
proles are consistently simple and U-shaped, making identica-
tion of the four dive phases straightforward. Our study therefore
aimed to test the model predictions given in Table 1 on seals by
using a measure of the prey eld obtained from PEEs detected from
animal-borne head accelerometers, which were validated by
simultaneous video evidence from animal-borne video cameras.
Ethical Note
The study was carried out with the approval of the Deakin
University Animal Ethics committee under Permit No. A14-2011
and in accordance with the Department of Sustainability and
Environment (Victoria, Australia) Wildlife Research Permit No.
10005848. Kanowna Island is part of the Wilsons Promontory
Marine National Park and was accessed under permit from Parks
The study was conducted between May and September (winter)
in 2011 and 2012 on Kanowna Island, central northern Bass Strait,
southeastern Australia (39.1547S, 146.3108E). The island has
approximately 10 700 seals, with an annual pup production of
approximately 3000 (Kirkwood & Arnould, 2011). The Australian
fur seal is a protected species in Australia and is listed as least
concern by the International Union for Conservation of Nature.
During pup rearing, females are central place foragers, returning to
the colony regularly. Thus, they were used in this study as they can
be recaptured to retrieve deployed data loggers. As the most
important demographic component of the population, it is impor-
tant to understand the ecology of females to understand the effect
of environmental changes on the population.
Eight adult female seals provisioning pups were randomly
selected in the colony. Individuals were approached stealthily so as
to not disturb surrounding animals and captured by placing a
modied hoop net (Fuhrman Diversied, Seabrook, TX, U.S.A.) over
it so that it faced the closed tapered end of the net. This procedure
does not harm the animals. Upon capture, individuals were
manually restrained and immediately administered isourane
anaesthesia delivered via a portable gas vaporizer (StingerTM,
Advanced Anaesthesia Specialists, Gladesville, NSW, Australia;
Gales & Mattlin, 1998). Sedation was generally attained within
5 min, upon which the animal was removed from the net for easier
Accelerometers were glued to the seals' heads while the GPS
data loggers, video cameras and time-depth recorders (TDRs) were
glued in series to the dorsal fur along the mid-line posterior to the
scapula using a quick-setting epoxy. Seals were equipped with
triaxial accelerometers that measured accelerations in the surge (x),
sway (y) and heave (z ) axes (ca. 3 g, G6A, 40 28 16.3 mm, Cefas
Technology Limited, Suffolk, U.K.), GPS data loggers
(63 24 22 mm, 31 g, Fastloc 2 GPS data-logger; Sirtrack, Ham-
ilton, New Zealand), TDRs (68 17 17 mm, 30 g, MK9-TDR,
Wildlife Computers, Redmond, WA, U.S.A.) and video cameras
(Crittercam, 25 cm length 5.7 cm diameter, 1.1 kg, National
Geographic Society, Washington, DC, U.S.A.).
The accelerometers, TDRs and GPS data loggers sampled at
20 Hz, 1 Hz and every 5 min, respectively. For all animals, the
Crittercams were programmed to start and stop recording when
seals descended, and ascended past 40 m, on a 1 h on:3 h off duty
cycle to maximize battery life while minimizing the potential for
missing foraging dives. In total, all devices attached to the seals
represented <2% body mass and <1% cross-sectional surface area
and probably had negligible additional hydrodynamic drag (Casper,
2009). Data from the GPS data loggers were not included in this
study. Instrumentation procedures were usually completed within
45 min of capture. Following the procedures, individuals were
allowed to recover from the anaesthetic and resume normal
behaviour, and were regularly observed to recommence suckling
soon after. There were no observed effects on the behaviour of the
mothers or their pups from this procedure. After one or more
foraging trips (i.e. 4e8 days) to sea, the animals were recaptured as
previously described, anaesthetized briey(<10 min) and the de-
vices were removed by cutting the fur beneath them. Full sets of
overlapping useable data were successfully recovered from ve
Video Analyses
The ultimate objective for quantifying the prey eld was to
obtain a measure of prey abundance and prey density (and there-
fore patch quality); therefore we quantied the number of PEEs and
PEE per unit time (i.e. prey encounter rate; PER), respectively. Since
seals usually only make a rapid head movement during a prey
capture attempt or while ingesting it, a PEE was only considered if
there was a prey capture attempt, irrespective of whether the prey
was successfully caught or not. A PEE typically consisted of prey
detection, chase and prey capture attempt, where the seal moved
its head rapidly in a darting motion with its jaw open to capture
that prey item. Videos were rst synchronized with the TDR data.
Each video le (representing a single dive) was then examined for
PEEs. The timing, duration and capture outcome of all PEEs were
Dive Analyses
The data from the TDRs were extracted from the Wildlife
Computers proprietary format using Instrument Helper (Wildlife
Computers). Zero offset drift in the depth values for each TDR tag
was corrected and subsequent summary dive statistics were
calculated using a custom-developed R script (R Development Core
Team, 2012). Individual dives were dened as any depth exceeding
3 m from the surface (Gentry & Kooyman, 1986).
Sequential dives were assigned to bouts when the surface in-
terval was <10 min. This was determined by survival analysis of all
the data pooled (Gentry & Kooyman, 1986). Only bouts with at least
three dives were included in the analyses to exclude solitary iso-
lated dives. The last dive before the end of a bout was also excluded
from the analyses, as the diving behaviour (especially SI) for that
dive would be inuenced by behaviours other than foraging.
Accelerometer Analysis
Identifying prey capture attempt signals
Acceleration on all three axes (surge, sway and heave) were
processed and analysed using the Ethographer package (Sakamoto
et al., 2009) with Igor Pro (6.30 J; WaveMetrics, Portland, OR,
U.S.A.). The acceleration record was rst synchronized with the
D. Foo et al. / Animal Behaviour 112 (2016) 127e138 129
depth record from TDRs (see Appendix 1 for details). The process of
identifying rapid head movements (hereafter PEE signals) during
PEEs on each accelerometer axes involved (1) applying a 3 Hz high-
pass lter (Iwata et al., 2012) to the accelerometry data to remove
low-frequency head acceleration due to swimming movement
(Fig. 1aec). This highlighted the peaks due to dynamic head ac-
celerations. (2) The standard deviation (SD) was then calculated
along a moving window of 1.5 s (Viviant, Trites, Rosen, Monestiez,
& Guinet, 2010), which smoothed the time series and highlighted
extreme accelerations (peaks; Fig. 1c and d). (3) Subsequently, only
the SD values that occurred below 40 m (where the video cameras
were set to start recording) were retained (Fig. 1d). At this point, the
heave (z) axis was dropped from further analyses, as it did not
highlight the dynamic accelerations as well as the other two axes
visually. (4) PEE signals were extracted by using the mask function
in Ethographer to lter out SD values above a threshold. The
thresholds ranged from 0.1 to 0.4. (5) PEE signals often occurred in
fairly distinguishable clumps ranging from 1.5 to 3.5 s apart, as
smaller peaks often occurred on either side of a larger peak. Hence,
PEE signals within 3.5 s of each other were combined into a single
signal (Fig. 1e).
Signal validation
PEE signals were then clas sied into true positives (i.e. signal
corresponded to an actual PEE) or false positives (i.e. signal
detected, but there was no actual PEE based on video evi dence).
PEE signals detected on the sway axis at the SD threshold of 0.35 g
had the best trade-off between hi t rate and false discovery rate; i.e.
high proportion of true positives but low propo rtion of fa lse pos-
itives (see Appendix 2 for more details of how this was deter-
mined). The PEE s ignals detected from the sway axis at the optima l
SD threshold (0.35 g) were then quantitatively evaluated and
validated in more detail. From the videos, periods b etween PEEs
were counted as a non-P EE (Fig. 1e); thus each recorded event was
assign ed to one of four categories: true positive (TP; PEE signal
detected with a PEE); true negatives (TN; non-PEE and no PEE
signal detected); false positives (FP; non-PEE but PEE signal
detected); and false negatives (FN ; PEE but no PEE signal detected)
(Fig. 1e). Subsequently, the efciency of the PEE signals in identi-
fying actual PEEs from changes in acceleration was assessed from
two metrics:
(1) accuracy (¼[TP þ TN]/[TP þ TN þ FP þ FN]);
(2) weighted accuracy (¼[TP þ TN [TP þ FN]/[TN þ FP]]/
þ FN] þ [TN þ FP][TP þ FN]/[TN þ FP]]).
After the acceleration signal for PEEs was validated, the accel-
eration signal analysis was extended to the complete acceleration
record to identify all possible PEE signals. PEE signals could in re-
ality be part of the same PEE since there can be multiple prey
Depth (m)
Y axis -
acceleration (g)
acceleration (g)
SD of
acceleration (g)
30 60
0.35 threshold
90 120
Time (s)
150 180 210
if < 3.5 s a
standard deviation Hi
ass filtered
Original signal
combined signal
Figure 1. An example of prey encounter events (PEEs ) within a 210 s dive by an Australian fur seal showing (a) the depth prole with corresponding raw swaying head accelerations
(y axis) recorded on (b). Data were ltered (c) using a high-pass 3 Hz lter to remove noise associated with swimming and the SD (d) was calculated along a moving window of 1.5 s
to highlight the dynamic head movement; (e) the timing of PEEs was inferred from SD peaks above the threshold of 0.35 g (Original signal); original signals were combined if they
were less than 3.5 s apart (Combined signal); the combined signals were compared to the answers, which were actual PEEs, and nonprey events (black solid lines) in the videos,
resulting in a true positive, true negative, false negative or false positive (denoted by TP, TN, FN and FP, respectively, at the bottom). Vertical dotted lines represent the time when the
seal is above 40 m, when acceleration values were unlikely to reect feeding events.
D. Foo et al. / Animal Behaviour 112 (2016) 127e138130
capture attempts during a single PEE; hence PEE signals that were
separated by less than 17 s were aggregated into one PEE. This value
was selected from the second inection point in a survival analysis
of all the PEE data pooled.
Model Fitting
Prey encounter events (a proxy of prey abundance) derived from
the complete acceleration record, and the derived prey encounter
rates (a proxy of prey density; PEE divided by bottom duration)
were used as measures of patch/habitat quality. These were used
together with simultaneous diving behaviour parameters to test
the predictions given in Table 1. We used linear mixed-effect
models (LME) and generalized linear mixed models (GLMM) to
model the relationships among the variables listed in Table 1 ac-
cording to the various model predictions. Only benthic dives (88%
of all dives) that were part of a bout were used in model tting.
Count (i.e. PEEs) response variables were tted using a GLMM with
a Poisson error distribution while continuous response variables
were tted using LMEs. GLMMs and LMEs were developed with the
R version 3.1.0, using the lme4 and nlme package, respectively. All
models included seal ID as a random term. All continuous pre-
dictors were scaled and centred before tting to facilitate model
convergence and to be able to compare the respective contribution
of the predictors (Zuur, Leno, & Smith, 2007). Continuous response
variables were also checked for normality and transformed where
The signicance of parameters included in the models was
examined by adding the parameter of interest to a null model
(Table 1) and assessing the effect of the addition on the tofthe
model using likelihood ratio tests and the change in Akaik e in-
formation criterion (AIC). For predi ction 6 (Table 1), this was
instead done by removing the parameter of intere st from the full
prediction model. Signicance levels for the likelihood ratio tests
were set at
¼ 0.05. Model selection was not conducted a s we
were in terested only in the specic effect of the predictor
(explanatory ) variables of interest on the response variables to
test mode l predictions. For prediction 6 (Table 1), both measures
of patch quality, i.e. PEE and PER, were use d in separate models.
Additionally, we examined the effect of PEEs on the p ostdive
surface interval (SI), and whether the seals were terminating their
dives immediately after capturing a prey. We tested the latter by
examining the effect of the bot tom du ration before and after the
rst PEE. Data are pre sented as mean ± SD unless otherwise
Video Observations
An average of 3.45 ± 0.96 h of video footage, containing 61 ± 32
individual dives and 118 ± 86 prey encounter events (range
20e252) was obtained per seal. The video samples of dives had a
mean duration of 2.05 ± 1.02 min (Appendix Table A1). Overall,
77.8% (individual range 50e90.4%) and 64.3% (individual range
50e71.3%) of the PEEs were conrmed PEEs (prey item was in
camera's view) and successful captures, respectively. Almost all of
the prey captures were on the sea oor. The mean durations of a
prey chase and handling were 13.5 ± 17.5 s and 4.02 ± 5.32 s,
respectively. Upon catching a prey item, the seals either consumed
the prey at the sea oor, indicated by head jerks while swallowing
the prey (69%), or ascended to the surface with it (31%). The iden-
tiable prey species were cephalopods, crustaceans, elasmobranchs
or teleost shes (Kernal
eguen et al., 2015).
Acceleration Signal Validation
Using prey encounter event signals obtained from the sway axis
at the optimal threshold (0.35 g), 589 PEEs and 836 non-PEE (from
video evidence) were classied into 409 true positives, 54 false
positives, 782 true negatives and 180 false negatives, resulting in an
accuracy and weighted accuracy of 83.6% and 81.5%, respectively.
The accelerometer data, therefore, provided a robust index of
relative prey eld attributes, which was required to test model
Prey Encounter Events During the Foraging Trip
A total of 2320 benthic dives (depth 70 m) were extracted
from the entire simultaneous acceleration and TDR data set across
the ve seals. Of the benthic dives, 5.3% were isolated dives (i.e. not
part of a foraging bout) and were excluded from analysis. Summary
statistics of dive parameters are given in Table 2. Overall, 5526 PEE
signals were detected in 76.4% of the benthic dives across the ve
individuals. Of that number, 3391 PEEs remained after aggregating
PEE signals that were within 17 s of each other (
Appendix Table A2).
Test of Model Predictions
Physiological model predictions
The dive data supported physiological predictions 1 and 2: seals
shortened their bottom duration, both absolutely (Fig. 2a) and
proportionally (Fig. 2b), with increasing travel duration. Seals also
extended their dive duration when their travel duration increased
(Fig. 3a), but not when their maximum dive depth increased, thus
partially supporting prediction 3.
The data did not fully support the prediction that PEEs should
increase linearly with increasing bottom duration (prediction 4);
while PEE and bottom duration did have a positive relationship,
there was a relatively poor t(Fig. 3b). Seals did, however, support
the prediction that SI increases with dive duration (prediction 5,
Table 3, Fig. 3c).
Ecological model predictions
Support for the MVT varied according to the parameter used to
represent patch quality. Using PEEs as a measure of patch quality,
seals showed support for the MVT, with their bottom duration
(when controlled for dive depth) in the prey patch increasing with
dive (small-) scale patch quality, but decreasing with bout (large-)
scale habitat quality (prediction 6a, Fig. 4). Conversely, if prey
encounter rate was used as the measure of patch quality, the MVT
was not supported as bottom duration decreased with dive scale
PER, and bout scale PER had little effect on bottom duration (pre-
diction 6b).
When maximum depth and travel duration were xed, bottom
duration was slightly longer when no suitable prey were present
(Fig. 5), partially supporting the prediction for deep divers (pre-
diction 7). As predicted, seals increased their ascent rate and
descent rate for the subsequent dive when they encountered more
prey in the dive (prediction 8, Table 3, Fig. 6). Additionally, their SI
decreased when they encountered more prey in the dive (Table 3,
Fig. 3d), and their bottom duration after the rst PEE increased
when their rst PEE occurred earlier in the dive (Appendix 3,
Fig. A2).
Empirical testing of foraging theory on all free-ranging animals
is challenging. However, when achieved, it provided substantial
insights into animal ecology. New and emerging technologies are
D. Foo et al. / Animal Behaviour 112 (2016) 127e138 131
allowing us better access to data needed to test foraging theory
empirically, and this is particularly true for divers.
Longer Bottom Durations Do not Mean Greater Foraging Success
There was weak support for the common prediction that
resource gain increases linearly with time spent searching at depth
(bottom duration; Kramer, 1988; Thompson & Fedak, 2001). While
the number of prey encounters increased slightly with bottom
duration, the tted model did not explain a lot of the variation. This
result was similar to that found in harbour seals, Phoca vitulina
concolor, using video evidence (Heaslip et al., 2014). This suggests
that the functional response of seals might be nonlinear, where
prey availability decreases with foraging time due to either prey
escaping or consumption (Charnov, 1976; Mori & Boyd, 2004;
Viviant, Monestiez, & Guinet, 2014; Watanabe et al., 2014). Thus,
the common interpretation that increased bottom duration is an
indication of increased foraging success may not always be
The data supported predictions for a deep diver (Thompson &
Fedak, 2001) with seals' bottom duration being relatively longer
in dives with no PEEs than in dives with PEEs. This indicates that
the seals tended to continue searching at the bottom for prey
instead of terminating the dive prematurely based on some initial
assessment of the patch quality. Similar behaviour has been
observed in deep-diving mesopelagic short-nned pilot whales,
Globicephala macrorhynchus, which also did not shorten their bot-
tom duration in unfruitful dives (Aguilar Soto et al., 2008). In
particular, benthic divers have limited foraging habitat, especially
in the Bass Strait where productivity is very homogeneous and
relatively depauperate (Arnould & Warneke, 2002); hence they
should maximize every opportunity to forage when in the benthic
zone. Furthermore, dives with no PEEs would be less energetically
costly than dives with PEEs, as they require less swimming effort to
chase and capture prey, and hence seals can have longer bottom
durations in unsuccessful dives. Consistent with previous studies
on Australian fur seals (Hoskins, Costa, & Arnould, 2015), our re-
sults suggest that seals were maximizing their bottom duration
within the benthic foraging zone. Similarly, other benthic divers,
including other species of pinnipeds (e.g. Australian sea lions,
Neophoca cinerea: Costa & Gales, 2003; Galapagos sea lions, Zalo-
phus wollebaeki: Villegas-Amtmann, Costa, Tremblay, Salazar, &
Aurioles-Gamboa, 2008) and seabirds (e.g. emperor penguins,
Aptenodytes forsteri: Rodary, Bonneau, Le Maho, & Bost, 2000;
Brunnich's guillemots, Uria lomvia: Elliott, Davoren, & Gaston,
2008b) generally indicate that they are maximizing their bottom
Table 2
Description, mean and SD of parameters used in testing model predictions across ve Australian fur seals
Parameter Description Mean SD
Ascent rate (m/s) Depth of ascent phase/duration of ascent phase 1.62 0.20
Bottom duration (s) Duration of bottom phase 118.19 40.40
Bottom duration after rst prey encounter (s) Bottom duration remaining after rst prey encounter 72.41 46.39
Bout scale prey encounter rate Total prey encounters in the bout/bout duration 0.004 0.002
Bout scale prey encounters Total prey encounters in the bout 106 89
Descent rate (Nþ1) (m/s) Depth of descent phase/duration of descent phase 1.61 0.17
Dive duration (s) Total duration of dive 212.53 39.27
Dive scale prey encounter rate Prey encounters in the dive/bottom duration of the dive 0.013 0.011
Dive scale prey encounters Prey encounters in the dive 1 1
% Bottom duration Bottom duration/dive duration 0.54 0.10
Postdive surface interval (s) Duration at the surface between feeding dives (see Methods) 118.50 72.86
Time to rst prey encounter Time from the start of the bottom phase to the rst prey encounter 35.97 35.23
Travel duration (s) Ascent durationþdescent duration 95.34 10.22
Bottom duration (s)% Bottom duration (s)
(56,66] (76,86] (106,116]
Travel duration (s)
Figure 2. Box plots of tted values from the model showing the effect of travel
duration (binned into 10 s intervals) on (a) absolute bottom duration (prediction 1,
Table 3) and (b) squared-transformed percentage bottom duration (¼bottom duration/
dive duration; prediction 2, Table 3)byve Australian fur seals. Ends of whiskers
represent the greatest and lowest values, excluding outliers (i.e. values outside 1.5
times above and below the interquartile range). Upper and lower boundaries of the
box represent the upper quartile and lower quartile of values. The solid horizontal line
within the box represents the median.
D. Foo et al. / Animal Behaviour 112 (2016) 127e138132
Seals Adjusted Diving Behaviour According to Patch Quality
Support for the small-scale patch predictions of the marginal
value theorem varied according to the measure of patch quality
used. For a given depth, bottom duration increased with dive
(patch) scale PEEs but decreased with dive scale PER. In other
studies that used PER as a measure of patch quality, mesopelagic
southern elephant seals, Mirounga leonina (Guinet et al., 2014) and
elie penguins, Pygoscelis adeliae (Watanabe et al., 2014) had
longer bottom durations with increasing PER. This difference may
be attributed to the type of habitat used: benthic prey occur in
relatively low densities within a habitat, whereas mesopelagic prey
tend to occur in higher-density patches, providing a richer food
source once located (Chilvers & Wilkinson, 2009). The seals in our
study did not terminate a dive immediately when they encountered
a prey item early in the dive and were capable of capturing multiple
prey within a dive. However, many of their dives resulted in only
one or two PEEs, meaning that the search time between PEEs would
have been relatively high, and would have thus inuenced the
calculation of PER during bottom duration. In contrast, Ad
penguins, which primarily feed on mesopelagic Antarctic krill that
occur in swarms, would have had relatively little search time be-
tween prey encounters within a dive, and thus have a wider range
of PEEs (range 0e61). One assumption of the MVT is that foragers
deplete the exploited patch at a continuous rate (Wajnberg,
Bernhard, Hamelin, & Boivin, 2006). However, patches might
have stochastic characteristics; for example, when patches contain
discrete resource items, such as those in a benthic environment, the
time and energy spent searching and sampling the environment
have to be taken into account. Thus, by averaging the resource gain
over some time interval (e.g. calculating PER), we assumed that the
forager is omniscient when it is unlikely to be, resulting in con-
clusions that differ from the original predictions of the MVT
(Wajnberg et al., 2006). Therefore, prey encounter rate might not be
a suitable proxy for prey density or patch quality in this case for
There was support for the bout (habitat) scale predictions of the
MVT where bottom duration decreased with increasing bout scale
PEEs. Similarly, southern elephant seals (Bestley et al., 2014; Thums
et al., 2013) and Ad
elie penguins (Watanabe et al., 2014)have
shorter bottom durations in high-quality habitats. Therefore, the
foraging behaviour of seals showed support for the small-scale
(6,36] (66,96] (126,156] (216,246]
Bottom duration (s)
(103,133] (193,223] (283,313]
SI (s)
SI (s)
Dive duration (s)
Travel duration (s)
Dive duration (s)
Figure 3. Box plots of tted values from the model showing the effect of (a) travel duration (controlled for dive depth) on dive duration (prediction 3, Table 3); (b) bottom duration
of the dive on number of prey encounter events (PEE) (prediction 4, Table 3); (c) dive duration (prediction 5, Table 3) and (d) number of prey encounter events on log-transformed
postdive surface interval (SI) by ve Australian fur seals. Continuous variables on the x axes (travel, bottom and dive duration) were put into bins for data representation. Ends of
whiskers represent the greatest and lowest values, excluding outliers (i.e. values outside 1.5 times above and below the interquartile range). Upper and lower boundaries of the box
represent the upper quartile and lower quartile of values. The solid horizontal line within the box represents the median.
D. Foo et al. / Animal Behaviour 112 (2016) 127e138 133
patch and large-scale habitat predictions of the MVT, whereby their
foraging behaviour changed in opposite directions according to the
spatial scale of the foraging patch/habitat quality. Similar changes
in foraging behaviour with increasing spatiotemporal scales were
also observed in Antarctic fur seals, Arctocephalus gazella (Viviant
et al., 2014). Successful foraging can lead to an increase in recov-
ery oxygen consumption as compared to unsuccessful foraging as
energy is required for the digestion and warming of prey (Williams,
Fuiman, Horning, & Davis, 2004). This suggests that long and suc-
cessful foraging dives at the short-term dive cycle might be more
energetically costly than unsuccessful ones, resulting in seals hav-
ing to adapt their diving behaviour by decreasing their overall
bottom duration at bout level (Viviant et al., 2014). Interestingly,
Hoskins and Arnould (2013) reported that Australian fur seals
reduced foraging effort across the day and proposed that it might
be due to physiological constraints (digestive costs) or prey avail-
ability as the day progresses. Our study did not consider different
timescales; hence we are unable to conrm whether diurnal
changes in prey availability affected foraging behaviour.
It seems contradictory that the seals made shorter dives when
they encountered less prey within a dive, but also displayed be-
haviours that suggested they were maximizing bottom duration
even in poor-quality patches (i.e. longer bottom duration when no
preferred prey was present). Jackson (2001) reported that while
Brants's whistling rats, Parotomys brantsii, might follow simple
central place foraging strategies, factors such as time of day and
food plant species also inuence their foraging behaviour (Jackson,
2001). If benthic divers should generally prioritize maximizing
bottom duration regardless of patch quality, and increased prey
availability generally results in increased foraging effort (e.g.
increased time in patch due to increased handling times; McAleer &
Giraldeau, 2006), then bottom duration may be directly inuenced
by physiological and ecological constraints in different situations.
For example, we observed the seals processing larger prey such as
cephalopods at the surface whereas they consumed smaller prey at
the foraging zone. Such behaviour has been observed in other free-
ranging pinnipeds as well (Cornick & Horning, 2003); although this
strategy is uncommon, the effect of prey type can inuence
Table 3
Ranked generalized linear mixed-effects models (GLMM) and linear mixed-effects models (LME) used to test foraging theory predictions
Prediction Follows? Response Parameter AIC
1 Yes Bottom NULL 22128.08 0 11061.04
Travel duration 22030.99 97.09 11011.5 *** 7.770 0.777
2 Yes % Bottom NULL 4289.16 0 2147.58
Travel duration 4881.045 591.885 2444.522 *** 0.047 0.002
Dive duration NULL 22040.23 0 11017.12
3a Yes Travel duration 22030.99 9.24 11011.5 ** 2.449 0.777
3b No Depth 22038 2.23 11015 * 1.673 1.150
4 Yes SI NULL 2314.827 0 1154.414
Dive duration 2207.319 107.508 1099.659 *** 0.1038 0.0095
5 Somewhat PEE NULL 6155.4 0 3075.7
Bottom 6111.3 44.1 3052.6 *** 0.136 0.020
6a Yes Bottom Depth (NULL) 22122.19 0 11057.09 2.671 1.192
Full model 21997.99 124.2 10993 ***
Term dropped:
Bout PEE 22044.76 77.43 11017.38 *** 0.085 0.011
Dive PEE 22087.47 34.72 11038.73 *** 7.131 0.743
6b No Bottom Depth (NULL) 22122.19 0 11057.09 0.985 1.219
Full model 21917.36 204.83 10952.68 ***
Term dropped:
Bout PER 21917.5 204.69 10953.75 *** 0.208 1.144
Dive PER 22115.52 6.67 11052.76 ** 11.695 0.811
7 Yes Bottom Depth (NULL) 22027.99 0 11008.99 1.350 1.165
Travel duration (NULL) 7.980 0.784
Prey present 22016.78 11.21 11002.39 ** 5.965 1.873
8a Yes Descent rate (Nþ1) NULL 1766.787 0 886.3935
PEE 2033.919 267.132 1020.9597 *** 0.056 0.003
8b Yes Ascent rate NULL 1698.399 0 852.1993
PEE 1869.277 170.878 938.6383 *** 0.046 0.003
Extra SI NULL 2314.827 0 1154.414
PEE 2237.251 77.576 1114.625 *** 0.079 0.008
Extra Bottom duration after rst PEE Descent duration (NULL) 15926.97 0 7959.483
Time to rst prey 15488.59 438.38 7739.293 *** 22.633 1.005
Models were tted according to the foraging model predictions in Table 1 or were extra tests. Parameters of interest are in bold, while those not in bold are part of the null
model. Also shown are the maximum log-likelihood (LL), Akaike's information criterion (AIC), the difference in AIC for each model from the null model (
AIC). Log-likelihood
ratio tests and its P values were also used to test the signicance of a parameter by removing it from the full model (prediction 6) or adding it to the null model. Slope and SE
values of the parameters are from the lowest ranking model. Type of transformation on response variables is also indicated.
*P < 0.05; **P < 0.01; ***P < 0.001.
D. Foo et al. / Animal Behaviour 112 (2016) 127e138134
foraging behaviour in unexpected ways. Likewise, guillemots tend
to dive sequentially to the same depth when high-quality prey
patches are discovered, lending support to the hypothesis that
sequential dives are inuenced by patch quality rather than inter-
nal physiology (Elliott et al., 2008b). Thus, the dominant constraints
on the foraging behaviour of benthic divers at the dive scale might
be too complex to tease apart. Nevertheless, dive scale foraging
behaviours still manifested into bout scale foraging behaviours that
supported the predictions of the MVT.
Seals Swam Faster During Transit when they Encountered Prey
Dive duration increased with travel duration, which also resul-
ted in shorter absolute and proportional bottom duration, concur-
ring with previous ndings on Australian fur seals (Hoskins &
Arnould, 2013). Since 70% of dives were to consistent depths, a
change in travel duration represents a change in travel rate rather
than a change in dive depth (Hoskins & Arnould, 2013); thus seals
compensated for longer bottom durations by swimming faster
during vertical transit, suggesting that seals were maximizing their
bottom duration not only for physiological reasons contrary to
some optimal diving theory but also for ecological reasons such as
patch quality. Similarly, when controlled for dive depth, the dive
duration of harbour seals increased with their travel duration
(Heaslip et al., 2014).
As predicted by Thompson and Fedak (2001), increased prey
encounters per dive resulted in increased ascent rate for the cur-
rent dive and increased descent rate for the subsequent dive. This
is evidence that the sea ls were reducing transit time when in a
good prey patch (Thompson & Fedak, 2001), consistent with the
ndings in other marine diver studies (Gallon et al., 2013; Hanuise,
Bost, & Handrich, 2013; Hoskins & Arnould, 2013; Viviant et al.,
Shorter SI was associated with more PEEs but shorter dive
duration within a dive. Seals may facilitate the continued exploi-
tation of a good prey patch after a successful foraging dive by
having a shorter SI. However, because divers are ultimately con-
strained by their oxygen balance (oxygen gained on the surface is
related to the oxygen used in the subsequent dive), a shorter SI
would confer a shorter subsequent dive, which would also be
facilitated by shorter transit times to good-quality patches. In
guillemots, SI may be considered anticipatory in short dives or
reactive in long dives where birds replenish oxygen according to
what they need for the following dive, or what they have used from
the previous dive, respectively (Elliott et al., 2008b). Southern
elephant seals also reduce their SI after dives with prey encounters
as opposed to none (Gallon et al., 2013). However, Bestley et al.
(2014) found that increased horizontal foraging movement of
multiple seal species was associated with longer SI. Successful
foraging dives may have higher energy expenditure and thus
require longer SI needed to replenish oxygen stores (Bestley et al.,
2014 ). Therefore, the relative duration of SI after a successful
foraging dive may simply reect different foraging strategies of
Bottom duration (s)
–200 –100 50 1000
–2 –1 0 1 2 3
Figure 4. Partial regression plots (i.e. the relationship between the response variable
and a predictor, with the other predictors held constant) from a linear mixed model
(bottom duration ¼ dive depth þ dive scale prey encounter events (Dive-PEE) þ bout
scale prey encounter events (Bout-PEE)) with seal ID as a random factor (prediction 6,
Table 3). Dive depth was added as a control. For example, the x axis is the residuals
from a linear mixed model of (a) dive-PEE and (b) bout-PEE against the other pre-
dictors. The y axis in (a) and (b) is the residuals from a linear mixed model of bottom
duration against all predictors excluding dive depth. Black solid lines represent least-
squares regression lines.
Absent Present
Bottom duration (s)
Figure 5. Mean bottom duration of ve foraging Australian fur seals in response to the
effect of suitable prey presence (controlled for dive depth and descent duration;
prediction 7, Table 3). Error bars represent the 95% pointwise condence interval.
D. Foo et al. / Animal Behaviour 112 (2016) 127e138 135
Validation of Prey Encounter Detection with Accelerometry Data
Prey encounter event signals could detect PEEs with a relatively
high level of accuracy. Although relatively low, the occurrence of
false negatives was none the less greater than that of false positives,
which indicated that the detection of PEEs using accelerometers
was relatively conservative. False negative PEEs (i.e. PEEs in the
videos that the accelerometer failed to detect) were mostly un-
successful foraging events, and occasionally occurred when seals
captured large prey in a swift and smooth process with no rapid
head movement. In contrast, head accelerometers attached to
elie penguins produced many false positive PEEs when they
were foraging for benthic prey as opposed to pelagic prey, owing to
the penguins' head movements when searching the seabed
(Watanabe & Takahashi, 2013). Therefore, it is important to validate
the acceleration signals for different species, especially under
natural conditions, as nuances in foraging behaviour differ between
species depending on what and where they forage in the water
column (Meynier, Morel, Chilvers, Mackenzie, & Duignan, 2014;
Naito, Bornemann, Takahashi, McIntyre, & Pl
otz, 2010). The PEEs
extracted from the accelerometry data proved to be a valid measure
of patch quality to test foraging theory predictions.
Lactating female seals showed that their foraging behaviour is
complex and may be inuenced by physiological and/or various
ecological factors. Their foraging behaviour also varied according to
the spatial scale of prey patch quality. Overall, the foraging
behaviour of lactating female Australian fur seals supports the
predictions of some optimal foraging models. Our study provides a
case study of how the foraging behaviour of a central place forager
foraging in a fairly homogeneous environment, with relatively high
travel costs may deviate from current optimal foraging models
under different situations. Future optimal foraging models should
aim to integrate other aspects (e.g. diet) of the foraging process for
more accurate predictions.
We thank the numerous volunteers, in particular Kathryn
Wheatley and Beth Volpov, who assisted in the eld work
throughout the study. Logistical support was provided by Parks
Victoria and Geoff Boyd (Prom Adventurer Boat Charters). Data
from the accelerometer, TDR and timings of actual prey encounter
events will be accessible under the Dryad database. Original video
les are unavailable due to copyright.
Aguilar Soto, N., Johnson, M. P., Madsen, P. T., Díaz, F., Domínguez, I., Brito, A., et al.
(20 08). Cheetahs of the deep sea: deep foraging sprints in short-nned pilot
whales off Tenerife (Canary Islands). Journal of Animal Ecology, 77(5), 936e 947.
Arnould, J. P. Y., & Kirkwood, R. (2007). Habitat selection by female Australian fur
seals (Arctocephalus pusillus doriferus). Aquatic Conservation-Marine and Fresh-
water Ecosystems, 17, S53eS67.
Arnould, J. P. Y., & Warneke, R. M. (2002). Growth and condition in Australian fur
seals (Arctocephalus pusillus doriferus) (Carnivora: Pinnipedia). Australian Jour-
nal of Zoology, 50(1), 53e66.
Austin, D., Bowen, W. D., McMillan, J. I., & Iverson, S. J. (2006). Linking movement,
diving, and habitat to foraging success in a large marine predator. Ecology,
87(12), 3095e3108.
Bestley, S., Jonsen, I. D., Hindell, M. A., Harcourt, R. G., & Gales, N. J. (2014). Taking
animal tracking to new depths: synthesizing horizontal-vertical movement
relationships for four marine predators. Ecology, 96,417e427.
Biuw, M., McConnell, B., Bradshaw, C. J. A., Burton, H., & Fedak, M. (2003). Blubber
and buoyancy: monitoring the body condition of free-ranging seals using
simple dive characteristics. Journal of Experimental Biology, 206(19), 3405e3423.
Casper, R. M. (2009). Guidelines for the instrumentation of wild birds and mam-
mals. Animal Behaviour, 78(6), 1477e1483.
Charnov, E. L. (1976). Optimal foraging, marginal value theorem. Theoretical Popu-
lation Biology, 9(2), 129e136.
Chilvers, B. L., & Wilkinson, I. S. (2009). Diverse foraging strategies in lactating New
Zealand sea lions. Marine Ecology Progress Series, 378, 299e308.
Cornick, L. A ., & Horning, M. (2003). A test of hypotheses based on optimal foraging
considerations for a diving mammal using a novel experimental approach.
Canadian Journal of Zoology, 81(11), 1799e1807.
Costa, D. P., & Gales, N. J. (2003). Energetics of a benthic diver: Seasonal foraging
ecology of the Australian sea lion, Neophoca cinerea. Ecological Monographs,
73(1), 27e43.
Deagle, B. E., Kirkwood, R., & Jarman, S. N. (2009). Analysis of Australian fur seal diet
by pyrosequencing prey DNA in faeces. Molecular Ecology, 18(9), 2022e2038.
Dragon, A. C., Bar-Hen, A., Monestiez, P., & Guinet, C. (2012a). Comparative analysis
of methods for inferring successful foraging areas from Argos and GPS tracking
data. Marine Ecology Progress Series, 452, 253e267.
Dragon, A. C., Bar-Hen, A., Monestiez, P., & Guinet, C. (2012b). Horizontal and ver-
tical movements as predictors of foraging success in a marine predator. Marine
Ecology Progress Series, 447, 243e257.
encounter events
Ascent rate (m/s)
Descent rate (N+1) (m/s)
Figure 6. Box plots of tted values from the model showing the effect of prey
encounter events on (a) ascent rate and (b) descent dive duration (prediction 8,
Table 3) within a foraging dive of ve Australian fur seals. Ends of whiskers represent
the greatest and lowest values, excluding outliers (i.e. values outside 1.5 times above
and below the interquartile range). Upper and lower boundaries of the box represent
the upper quartile and lower quartile of values. The solid horizontal line within the box
represents the median.
D. Foo et al. / Animal Behaviour 112 (2016) 127e138136
Elliott, K. H., Davoren, G. K., & Gaston, A. J. (2008a). Increasing energy expenditure
for a deep-diving bird alters time allocation during the dive cycle. Animal
Behaviour, 75,1311e1317 .
Elliott, K. H., Davoren, G. K., & Ga ston, A. J. (2008b). Time allocation by a deep-
diving bird reects prey type and energy gain. Animal Behaviour, 75,
Gales, N. J., & Mattlin, R. H. (1998). Fast, safe, eld-portable gas anesthesia for
otariids. Marine Mammal Science, 14(2), 355e361.
Gallon, S., Bailleul, F., Charrassin, J. B., Guinet, C., Bost, C. A., Handrich, Y., et al.
(2013). Identifying foraging events in deep diving southern elephant seals,
Mirounga leonina, using acceleration data loggers. Deep-Sea Research Part Ii-
Topical Studies in Oceanography, 88e89,14e22.
Gentry, R. L., & Kooyman, G. L. (1986). Fur seals: Maternal strategies on land and at
sea. Princeton, NJ: Princeton University Press.
Guinet, C., Vacquie-Garcia, J., Picard, B., Bessigneul, G., Lebras, Y., Dragon, A. C., et al.
(2014). Southern elephant seal foraging success in relation to temperature and
light conditions: insight into prey distribution. Marine Ecology Progress Series,
499, 285e301.
Hanuise, N., Bost, C. A., & Handrich, Y. (2013). Optimization of transit stra-
tegies while diving in foraging king penguins. Journal of Zoology, 290 (3),
Heaslip, S. G., Bowen, W. D., & Iverson, S. J. (2014). Testing predictions of optimal
diving theory using animal-borne video from harbour seals (Phoca vitulina
concolor). Canadian Journal of Zoology, 92(4), 309e318.
Heerah, K., Hindell, M., Guinet, C., & Charrassin, J.-B. (2014). A new method to
quantify within dive foraging behaviour in marine predators. PLoS One, 9(6),
Hochscheid, S., Maffucci, F., Bentivegna, F., & Wilson, R. P. (2005). Gulps, wheezes,
and sniffs: how measurement of beak movement in sea turtles can elucidate
their behaviour and ecology. Journal of Experimental Marine Biology and Ecology,
316(1), 45e53.
Hoskins, A. J., & Arnould, J. P. Y. (2013). Temporal allocation of foraging effort in
female Australian fur seals (Arctocephalus pusillus doriferus). PLoS One, 8(11),
Hoskins, A. J., Costa, D. P., & Arnould, J. P. Y. (2015). Utilisation of intensive foraging
zones by female Australian fur seals. PLoS One, 10(2), e0117997.
Houston, A. I., & Carbone, C. (1992). The optimal allocation of time during the diving
Behavioral Ecology, 3(3), 255e265.
Iwata, T., Sakamoto, K. Q., Takahashi, A ., Edwards, E. W. J., Staniland, I. J.,
Trathan, P. N., et al. (2012). Using a mandible accelerometer to study ne-scale
foraging behavior of free-ranging Antarctic fur seals. Marine Mammal Science,
28(2), 345e357.
Jackson, T. P. (2001). Factors inuencing food collection behaviour of Brants'
whistling rat (Parotomys brantsii): a central place forager. Journal of Zoology, 255,
eguen, L., Dorville, N., Ierodiaconou, D., Hoskins, A. J., Baylis, A. M. M.,
Hindell, M. A., et al. (2015). From video recordings to whisker stable isotopes: a
critical evaluation of timescale in assessing individual foraging specialisation in
Australian fur seals. Oecologia,1e14.
Kirkwood, R., & Arnould, J. P. Y. (2011). Foraging trip strategies and habitat use
during late pup rearing by lactating Australian fur seals. Australian Journal of
Zoology, 59(4), 216e226.
Kirkwood, R., Hume, F., & Hindell, M. (2008). Sea temperature variations mediate
annual changes in the diet of Australian fur seals in Bass Strait. Marine Ecology
Progress Series, 369,297e309.
Kramer, D. L. (1988). The behavioral ecology of air breathing by aquatic animals.
Canadian Journal of Zoology, 66(1), 89e94.
McAleer, K., & Giraldeau, L. A. (2006). Testing central place foraging in eastern
chipmunks, Tamias striatus, by altering loading functions. Animal Behaviour, 71,
Meynier, L., Morel, P. C. H., Chilvers, B. L., Mackenzie, D. D. S., & Duignan, P. J. (2014).
Foraging diversity in lactating New Zealand sea lions: insights from qualitative
and quantitative fatty acid analysis. Canadian Journal of Fisheries and Aquatic
Sciences, 71(7), 984e991.
Mori, Y. (1998). The optimal patch use in divers: optimal time budget and the
number of dive cycles during bout. Journal of Theoretical Biology, 190(2),
Mori, Y., & Boyd, I. L. (2004 ). The behavioral basis for nonlinear functional
responses an d optimal foraging in Antarctic fur seals . Ecology, 85(2),
Mori, Y., Takahashi, A., Mehlum, F., & Watanuki, Y. (2002). An application of optimal
diving models to diving behaviour of Brünnich's guillemots. Animal Behaviour,
64(5), 739e
Naito, Y., Bornemann, H., Takahashi, A., McIntyre, T., & Pl
otz, J. (2010). Fine-scale
feeding behavior of Weddell seals revealed by a mandible accelerometer. Polar
Science, 4(2), 309e316.
Pyke , G. H., Pulliam, H. R., & Charnov, E. L. (1977). Optimal foraging e ase-
lective review of theory and tests. Quarterly Review of Biology, 52(2),
R Development Core Team. (2012). R: A language and environment for statistical
computing. Vienna, Austria: R Foundation for Statistical Computing.
Rodary, D., Bonneau, W., Le Maho, Y., & Bost, C. A. (200 0). Benthic diving in male
emperor penguins Aptenodytes forsteri foraging in winter. Marine Ecology
Progress Series, 207,171e181.
Sakamoto, K. Q., Sato, K., Ishizuka, M., Watanuki, Y., Takahashi, A., Daunt, F., et al.
(2009). Can ethograms be automatically generated using body acceleration data
from free-ranging birds? PLoS One, 4(4), e5379.
Sato, K., Mitani, Y., Cameron, M. F., Siniff, D. B., & Naito, Y. (2003). Factors affecting
stroking patterns and body angle in diving Weddell seals under natural con-
ditions. Journal of Experimental Biology, 206(9), 1461e1470.
Shepard, E. L., Lambertucci, S. A., Vallmitjana, D., & Wilson, R. P. (2011). Energy
beyond food: foraging theory informs time spent in thermals by a large soaring
bird. PLoS One, 6(11), e27375.
Sparling, C. E., Georges, J. Y., Gallon, S. L., Fedak, M., & Thompson, D. (2007). How
long does a dive last? Foraging decisions by breath-hold divers in a patchy
environment: a test of a simple model. Animal Behaviour, 74, 207e218.
Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton, NJ: Princeton
University Press.
Thompson, D., & Fedak, M. A. (2001). How long should a dive last? A simple model
of foraging decisions by breath-hold divers in a patchy environment. Animal
Behaviour, 61(2), 287e296.
Thums, M., Bradshaw, C. J. A., & Hindell, M. A. (2011). In situ measures of foraging
success and prey encounter reveal marine habitat-dependent search strategies.
Ecology, 92(6), 1258e1270.
Thums, M., Bradshaw, C. J. A., Sumner, M. D., Horsburgh, J. M., & Hindell, M. A.
(2013). Depletion of deep marine food patches forces divers to give up early.
Journal of Animal Ecology, 82(1), 72e83.
Villegas-Amtmann, S., Costa, D. P., Tremblay, Y., Salazar, S., & Aurioles-Gamboa, D.
(2008). Multiple foraging strategies in a marine apex predator, the Galapagos
sea lion Zalophus wollebaeki. Marine Ecology Progress Series, 363, 299e309
Viviant, M., Monestiez, P., & Guinet, C. (2014). Can we predict foraging success in a
marine predator from dive patterns only? Validation with prey capture attempt
data. PLoS ONE, 9(3), e88503.
Viviant, M., Trites, A. W., Rosen, D. A. S., Monestiez, P., & Guinet, C. (2010). Prey
capture attempts can be detected in Steller sea lions and other marine preda-
tors using accelerometers. Polar Biology, 33(5), 713e71 9.
Wajnberg, E., Bernhard, P., Hamelin, F., & Boivin, G. (2006). Optimal patch time
allocation for time-limited foragers. Behavioral Ecology and Sociobiology, 60(1),
Watanabe, Y. Y., Ito, M., & Takahashi, A. (2014). Testing optimal foraging theory in a
penguin-krill system. Proceedings of the Royal Society B: Biological Sciences,
281(1779), 20132376.
Watanabe, Y. Y., & Takahashi, A. (2013). Linking animal-borne video to accelerom-
eters reveals prey capture variability. Proceedings of the National Academy of
Sciences of the United States of America, 110(6), 2199e2204.
Williams, T. M., Fuiman, L. A., Horning, M., & Davis, R. W. (2004). The cost of
foraging by a marine predator, the Weddell seal Leptonychotes weddellii: pricing
by the stroke. Journal of Experimental Biology, 207(6), 973e982.
Zimmer, I., Wilson, R. P., Beaulieu, M., Ropert-Coudert, Y., Kato, A., Ancel, A., et al.
(2010). Dive efciency versus depth in foraging emperor penguins. Aquatic
Biology, 8(3), 269e277.
Zuur, A. F., Leno, E. N., & Smith, G. M. (2007). Analysing ecological data. Springer.
Appendix 1. Synchronization of acceleration and depth
For each seal, the acceleration record was synchronized with the
depth record (from TDRs) by calculating pitch (body orientation)
and matching it to the dive depth proles. Pitch was derived by
isolating the static component (low-frequency readings due to
gravity) in the surge (x) acceleration axis from the dynamic
component (high-frequency readings due to animal movement)
using a 0.1 Hz low-pass lter (Sato, Mitani, Cameron, Siniff, & Naito,
2003). The pitch reected the device angle of the logger itself and
thereby reected the animal's lateral posture. Descents were rep-
resented as negative pitch and ascents by positive pitch values
(Sato et al., 2003).
Appendix 2. Determination of optimal acceleration axis and
threshold for detecting prey capture attempts
A TP was assigned if at least 50% of a combined PEE signal
overlapped with the duration plus 4.5 s to the start and end of an
answer (Fig. 1e); otherwise, it was considered an FP. Next, for each
D. Foo et al. / Animal Behaviour 112 (2016) 127e138 137
axis and threshold combination, the hit rate (¼number of answers
that corresponded with at least 1 TP/total number of answers) and
false discovery rate (¼FP/(FP þ TP)) were determined. The optimal
axis and threshold combination was the one that maximized the
sum of the hit rate and precision ( ¼1 false discovery rate), which
was the sway (y) axis acceleration at the threshold of 0.35 g
(Fig. A1); only its associated PEE signals were used for further
Appendix 3. Testing whether seals terminate a dive
immediately after a single prey encounter event
Appendix 4. Summary of deployment information and
predicted prey encounter events from accelerometry data
0.1 0.2 0.3 0.4
Hit rate + precision
Figure A1. The sum of hit rate and precision (¼1 false discovery rate), potentially
ranging from 1 (a random guess) to 2 (ideal signal), plotted against the threshold in the
head-only acceleration (g) on the surge (x) and sway ( y) axes for the signal of prey
encounters (with prey capture attempt). Open circles represent the surge (x) axis; solid
circles represent the sway (y) axis. The arrow points to the optimal threshold (0.35 g)
determined on the sway (y) acceleration axis.
(0,10] (60,70] (130,140]
Time to first
Bottom duration after first prey (s)
Figure A2. Box plots of tted values from the model showing the effect of the time to the
rst prey encounter event (controlled for descent duration) on the bottom duration after
the rst prey of ve Australian fur seals. Ends of whiskers represent the greatest and lowest
values, excluding outliers (i.e. values outside 1.5 times above and below the interquartile
range). Upper and lower boundaries of the box represent the upper quartile and lower
quartile of values. The solid horizontal line within the box represents the median.
Table A1
Female Australian fur seal identity, deployment and recovery dates, mass, total video, acceleration and TDR record durations, number of video recorded dives, total number of
prey encounter events in the videos and number of successful prey captures from all types of prey encounter events in the videos
Seal ID Deployment Recovery Mass (kg) Total video
duration (h)
Video recorded
dives (N)
Recorded prey
encounter events
Recorded successful
prey captures
Total acceleration
duration (h)
W1855 15 May 2011 21 May 2011 50.5 2.24 57 84 42 144
W1859 16 May 2011 20 May 2011 54.5 4.67 89 94 67 83
W1873 26 May 2011 1 June 2011 88 3.99 93 252 173 148
W1881 15 June 2011 25 July 2011 55.5 2.79 53 139 84 192
W1905 17 May 2012 7 June 2012 78 3.56 13 20 13 104
Mean±SD 65.3±16.7 3.45±0.96 61±32 118±86 76±61 134±42.3
Total 17.3 305 589 379 671
Table A2
Summary of the number of prey encounter events retrieved from the accelerometry data
Total Per day Per hour Per dive
Seal Mean Mean SD Mean SD Mean SD
W1855 289 58 57 7 5 1 1
W1859 617 206 63 11 5 1 1
W1873 744 149 102 12 9 2 1
W1881 1543 257 80 20 13 3 1
W1905 198 50 53 6 4 2 1
Total 3391 719 55 9
Overall mean 678 144 11 2
Overall SD 533 91 6 0
D. Foo et al. / Animal Behaviour 112 (2016) 127e138138
... Although several track-and dive-based metrics have been validated in (semi-)controlled experimental setups [52][53][54], they have rarely been tested on free-ranging species in natural conditions, which raises questions on their reliability as general proxies for feeding activity [45,[55][56][57]. The theoretical models developed from the optimal foraging and diving theory do not account for many ecological and physiological factors that may modulate predator movements. ...
... Among all metrics tested, both transit rate metrics (i.e., ascent rate and descent rate) were the best metrics in predicting the variance of nPEE, regardless of the temporal scale, the resolution of the dive profiles, and the model type used. This important contribution of transit rates in the seal behavioral response to prey encounter was also found in other studies on SES [13,45,58,62] and other diving species [8,40,51,54,56]. This result is consistent with optimal diving theoretical models predicting that F ; grey bar), by the individual-specific means via the random intercept variance ( R 2 I ; yellow bar), and by the predictors via the random slope variance/ covariance ( R 2 S ; blue bar). ...
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Background Studying animal movement in the context of the optimal foraging theory has led to the development of simple movement metrics for inferring feeding activity. Yet, the predictive capacity of these metrics in natural environments has been given little attention, raising serious questions of the validity of these metrics. The aim of this study is to test whether simple continuous movement metrics predict feeding intensity in a marine predator, the southern elephant seal (SES; Mirounga leonine ), and investigate potential factors influencing the predictive capacity of these metrics. Methods We equipped 21 female SES from the Kerguelen Archipelago with loggers and recorded their movements during post-breeding foraging trips at sea. From accelerometry, we estimated the number of prey encounter events (nPEE) and used it as a reference for feeding intensity. We also extracted several track- and dive-based movement metrics and evaluated how well they explain and predict the variance in nPEE. We conducted our analysis at two temporal scales (dive and day), with two dive profile resolutions (high at 1 Hz and low with five dive segments), and two types of models (linear models and regression trees). Results We found that none of the movement metrics predict nPEE with satisfactory power. The vertical transit rates (primarily the ascent rate) during dives had the best predictive performance among all metrics. Dive metrics performed better than track metrics and all metrics performed on average better at the scale of days than the scale of dives. However, the performance of the models at the scale of days showed higher variability among individuals suggesting distinct foraging tactics. Dive-based metrics performed better when computed from high-resolution dive profiles than low-resolution dive profiles. Finally, regression trees produced more accurate predictions than linear models. Conclusions Our study reveals that simple movement metrics do not predict feeding activity in free-ranging marine predators. This could emerge from differences between individuals, temporal scales, and the data resolution used, among many other factors. We conclude that these simple metrics should be avoided or carefully tested a priori with the studied species and the ecological context to account for significant influencing factors.
... Es así que, a diferencia del estudio a gran escala (bout de buceo), la calidad del parche afecta de manera positiva el tiempo de residencia dentro de un parche de alimento a pequeña escala (buceo).Ésto no sólo se ha evidenciado en aves, sino también en mamíferos marinos. En el caso de lobos marinos, tienden a ajustar el tiempo de permanencia dentro de un parche de alimento en función a la calidad del parche (Jouma'a, 2013;Foo et al., 2016). ...
... para el guanay, se observa una ligera tendencia positiva entre ambas variables indicándo que el guanay emplea mayor tiempo dentro de un bout cuando las presas observadas dentro de un bout fueron más abundantes, acorde con el Teorema del Valor Marginal (Charnov, 1976 Estudio realizado sobre el pingüino Adélie (Pygoscelis adeliae), evidencia que el tiempo dentro de un bout disminuye cuando incrementa la profundidad del buceo como respuesta fisiológica.Ésto afecta tanto el comportamiento como la energía requerida para el forrajeo (Chappell et al., 1993). La relación entre variables como el tiempo de permanencia dentro de un parche de alimento y la calidad de un parche ha sido evidenciado por Foo et al. (2016) y Jouma'a (2013) en lobos marinos. Por otra parte, Chimello de Oliveira (2009) ha observado este comportamiento en pescadores en el momento de la búsqueda de parches de alimento. ...
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In the marine environment, pelagic forage fish are not randomly distributed and form patches. According to the Marginal Value Theorem, a predator in a heterogeneous environment should spend more time in more profitable patches (i.e. patches that bring more energy per unit of time). The objectives of this study were to (i) describe the time of residence in patches as well as the time between them, and (ii) assess if these durations reflect optimization strategies in relation to prey availability, in seabirds. Birdborne miniaturized equipment (GPS and Dive recorder or GPS recorder and video camera) were used in three species of seabirds (Cape gannet Morus capensis, Peruvian booby Sula variegata and Guanay cormorant Phalacrocorax boungainvillii) during the breeding season from 2008 to 2013 in Bird Island (South Africa) and Pescadores Island (Ancon, Peru). The results showed no relationship between the time spend inside a patch and between patches. Additionally, the time spend between patches can not be considered an indicator of the time spend inside the patch probably because it depends on other factors (closeness and quality of patches, social information, etc.). The time of residence inside the patch was related to the presence of prey in Cape gannets. In the Guanay cormorant this time was related to the presence and the abundance of prey in each patch. This study will serve as the basis for future research in which seabird are used as indicators to understand the relationship between the foraging effort and other factors, such as the fishing season or climate variation.
... In addition, individuals could estimate the contribution of nutritional resources to fitness from their physiological state (e.g., hunger) (Nakashima et al., 2002). Handling times, prey encounters, resources of prey, and traveling times between patches could also provide foragers with additional information about the quality of patches and habitats (Williams and Flaxman, 2012;Foo et al., 2016). On the other hand, animals are sensitive to prey quality as a function of net energy intake, that is, energy intake per unit of foraging effort (Charnov, 1976). ...
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The ability to locate suitable food resources affects fitness in animals. Therefore, movements are necessary to optimize foraging in habitats where food is distributed in patches of different qualities. The aim of this work was to investigate the dispersal and distribution of females and males of the omnivorous mirid D. hesperus in mesocosms composed by food patches of different values in terms of fitness. In agreement with the Marginal Value Theorem (MVT) and the Ideal Free Distribution (IFD), individuals were expected to aggregate in the highest quality patches. Besides, the proportion of individuals in patches was predicted to be proportional to fitness, and interference among individuals was expected to rise as the density of individuals increased. Emigration rates were predicted to be higher for low- than for high-quality patches, while the opposite was predicted for immigration. Three types of habitats each with different combinations of food resources were tested: (1) habitat including patches of tomato plants with no-prey, and patches infested with either mite or whitefly; (2) with no-prey and whitefly; (3) with no-prey and mites. Each type of habitat was set up in a tomato greenhouse compartment and replicated four times. Individuals were tracked by mark-recapture methods using luminous paintings. The number of females and males in whitefly patches was significantly higher than in mite and no-prey patches, but a significant interaction sex∗ habitat and sex∗ patch was found. In habitats with only one type of prey, D. hesperus adults fitted the IFD, while in mixed prey habitats their distribution diverged from IFD. Interference was found to be significant, with female fitness decreasing as their density increased. Emigration rates were significantly lower for whitefly patches with a significant interaction patch∗sex; the opposite was found for immigration. This research shows that it is unlikely that D. hesperus forage according to the omniscient principle of IFD and MVT; in contrast, it strongly suggests that it uses some simple rules to make decisions about inter-patch movement, and emigration from habitats and patches.
... Indeed, the majority of MVT studies have focussed on terrestrial environments where prey field is immobile or more easily predictable (Krebs et al. 1974;MacArthur & Pianka 1966;McNickle & Cahill 2009;Nonacs 2001). Previous studies in marine environments have focussed on the benthic environment or on a krill feeding species, where prey is more uniformly distributed (Foo et al. 2016;Watanabe et al. 2014) but still differ somewhat from the expected assumptions. The deviations observed in these marine MVT studies, and the present study, likely reflect the unpredictability of the marine environment and the limitations of the theory itself to incorporate the complexity of predator-prey interactions. ...
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.
... Underlying this theory is the assumption that an animal will make foraging decisions that maximise the amount of energy ingested, while minimising the energy used during feeding (Pyke et al. 1977). Although sometimes criticised for being too simple and not representative of the natural environment (Pierce and Ollason 1987), this theory continues to provide a useful framework for exploring foraging behaviour and has been used recently to make and test predictions about foraging for a range of taxa, including marine mammals (Foo et al. 2016;Tyson et al. 2016), birds (Hernández-Pliego et al. 2017), lions (Barnardo et al. 2020) and fish (Thygesen et al. 2016). ...
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Context Zooplanktivorous fish are a key link between abundant zooplankton and higher trophic levels but the foraging behaviour of zooplanktivorous fish is not fully understood. Selective feeding behaviours have been observed, with many species of planktivorous fish targeting certain species and sizes of zooplankton for prey. However, why certain size classes of zooplankton are preferred remains unclear. Aim This study investigated prey selection by three zooplanktivorous fish species through the lens of optimal foraging theory. Methods We assessed the size structure of zooplankton in the environment and compared this with the size distribution of zooplankton in gut contents from three zooplanktivorous fish. Key results The targeted prey size of Atypichthys strigatus and Scorpis lineolata aligns with the prey size classes in the environment that contain the highest overall biomass. Trachurus novaezelandiae showed little evidence of targeting these size classes. Conclusions These prey sizes therefore represent the most efficient prey to target because the return on foraging effort is greatest. By contrast, T. novaezelandiae showed only an underselection of large and small prey. Implications By incorporating this information on this key trophic link between zooplankton and fish, ecosystem models could better resolve the size dependant predation, particularly in size-based models.
... Predicting the optimal foraging dive versus surface durations (see Houston & Carbone, 1992;Kramer, 1988) has therefore been the topic of many studies (e.g. Foo et al., 2016;Stephens et al., 2007;Walton, Ruxton, & Monaghan, 1998). Beyond simple pulmonary exchange, these MVT predictions are crucially based on the expectation of an exponential increase in surface duration costs. ...
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Keywords: body mass dive behaviour dive preparation dive recovery European shag foraging behaviour Phalacrocorax aristotelis sex differences TDR telemetry Foraging dives in birds and mammals involve complex physiological and behavioural adaptations to cope with the breaks in normal respiration. Optimal dive strategies should maximize the proportion of time spent under water actively foraging versus the time spent on the surface. Oxygen loading and carbon dioxide dumping carried out on the surface could involve recovery from the consequences of the last dive and/or preparation in anticipation of the next dive depth and duration. However, few studies have properly explored the causal pattern of effects within such dive cycles, which is crucial prior to any assessment of optimal dive strategies. Using time depth recorders and global positioning system loggers, we recorded over 42 000 dives by 39 pairs of male and female European shags, Phalacrocorax aristotelis. Dives either involved a straight descent and ascent, presumably reflecting an unsuccessful search for prey, or a descent followed by horizontal movement followed by an ascent, presumably reflecting active hunting pursuit of pelagic prey. Males were larger than females, but we were unable to distinguish between sex effects and the nonlinear effects of body mass on dive behaviour. Path analysis showed that within-individual dive-to-dive variation in surface times can best be explained as recovery from the previous dive. As expected in a pelagic hunter with unpredictable dive durations, there was no evidence of anticipatory preparation of oxygen stores in predive surface durations. Among-individual variation in dives showed that body mass directly affected descent durations, but individual variation in all other dive and surface durations was driven by variation in descent duration, suggesting a critical role for dive depth in overcoming body mass-dependent effects of hydrodynamic/wave drag and buoyancy. Our analyses test for the first time certain critical assumptions for studies assessing optimal dive strategies in birds and mammals, thereby revealing new details and avenues for research concerning adaptive diving behaviour.
... 495 Antarctic fur seal females increased their foraging effort by diving more and 496 spending more time searching for prey at the bottom of dives in poor-quality patches 497 (Mori and Boyd 2004; Viviant et al. 2014). Similarly, Australian fur seals decreased 498 bottom duration with increasing prey encounter rate at the scale of a dive, possibly to 499 come back to the surface to consume larger prey items, but not at the scale of a bout 500 (Foo et al. 2016). It is however interesting to note that Antarctic fur seals did tend to 501 dive and forage at a depth shallower than the depth with the highest rate of prey 502 capture attempts (Viviant et al. 2016) in accordance with Mori's model (1998) that 503 postulates that if species are physically capable of reaching depths of highest prey 504 densities, they will tend to dive at depths slightly shallower than the maximum prey 505 density as a trade-off with physiological constraints of diving. ...
Fur seals, sea lions and the walrus (Odobenus rosmarus) are breath-hold divers that rely on swimming at depth to feed at sea. As their diving capacities are more limited than phocids, otariids and odobenids are geographically constrained to highly productive environments and relatively shallow dive depths. They are also mostly coastal species, central place foragers with relatively limited foraging ranges. Diving patterns and strategies are diverse among the otariid group—although fur seals tend to be more pelagic and sea lions more benthic divers—, and driven by extrinsic factors such as the type of habitat they occupy, environmental factors, intra- or inter-specific density-dependent competition, predation risk and the behavior of the prey they feed on; as well as intrinsic factors such as age, sex, reproduction status, size and experience. There are usually several foraging strategies present within a species, and individuals tend to specialize to one of these strategies, with a degree of adaptability to changing conditions possible. Diving behaviors and strategies define the feeding success and foraging efficiency of individuals, and as such their capacities to successfully survive and reproduce in their environment. The diversity of these behaviors within otariid and odobenid populations are likely evolutionary stable strategies that provide a buffer under changing environmental conditions.
... Once the acceleration signal associated to a particular behaviour (e.g., capture event) has been validated, the behaviour can be identified from acceleration signals without the need of a camera. Using video-cameras in the wild to determine prey capture event signals has a great potential and during the last years has allowed researchers to test Optimal Foraging Theory predictions in the wild (Watanabe et al. 2014;Foo et al. 2016;Chimienti et al. 2017), to determine the distribution of prey encounter events in relation to oceanographic features such as water temperature, salinity and light level , and to examine relationships between prey distribution and spatially explicit patterns of prey capture (Carroll et al. 2017). Moreover, since dynamic acceleration can predict the costs of movement for terrestrial, aquatic and even aerial locomotion (by means of overall dynamic body acceleration (ODBA) or vectorial dynamic body acceleration (VeDBA), see Halsey et al. 2011;Qasem et al. 2012;Wilson et al. 2019), it offers the opportunity to estimate the energy expenditure associated to both pursuit and capture behaviours (Wilson et al. 2013;Tennessen et al. 2019). ...
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The identification of when, how and where animals feed is essential to estimate the amount of energy they obtain and to study the processes associated with prey search and consumption. We combined the use of animal-borne video cameras and accelerometers to characterise the body and head movements associated to four types of prey capture behaviours in the Magellanic Penguin (Spheniscus magellanicus). In addition, we evaluated how the K-Nearest Neighbour (K-NN) algorithm recognized these behaviours from acceleration data. Finally, we compared the total capture and the capture per unit time (CPUT) derived by identifying prey capture events using the K-NN algorithm to that derived by counting undulations in the dive profile (“wiggles”). During captures, body and head movements were highly variable in the tridimensional space. Energy expenditure (i.e., VeDBA values) during diving periods with prey captures was from three to four times higher than during controls diving periods (i.e., with no capture events). The K-NN classification resulted effective and showed accuracy scores above 90% when considering both head and body related features. In addition, when captures were estimated using the K-NN method, the CPUT was similar or higher to that estimated by counting wiggles. Our study contributes to the knowledge of the trophic ecology of this species and provides an alternative method for estimating prey consumption in the Magellanic Penguin and other diving seabirds.
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Understanding foraging strategies and decision-making processes of predators provide crucial insights into how they might respond to changes in prey availability and in their environment to maximize their net energy input. In this work, foraging strategies of Antarctic fur seals (Arctocephalus gazella, AFS) and Northern fur seals (Callorhinus ursinus, NFS) were studied to determine how they adjust their foraging behavior according to their past prey capture experiences. AFS on Kerguelen Islands are exclusively oceanic divers, while NFS population of St Paul Island shows both oceanic and neritic divers. We thus hypothesized that the two species would respond differently to a change in prey capture success depending on their foraging strategy. To test this, 40 females were equipped with tags that measured tri-axial acceleration, dive depth, and GPS coordinates, from which we derived prey capture attempts and behavioral metrics. Influence of prey capture success on horizontal and vertical movements of seals was investigated at different time scales: multi-dive, night, and trip. Both AFS and NFS traveled further during the day if they encountered low prey capture periods during the previous night. However, at the multi-dive scale, neritic NFS differed from oceanic NFS and AFS in terms of decision-making processes, e.g., both AFS and oceanic NFS dived deeper in response to low prey capture rate periods, while neritic NFS did not. Similarities in decision-making processes between NFS and AFS foraging on pelagic prey suggest that pelagic vs. neritic prey type is a key factor in defining foraging decisions of diving marine predators.
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Overall foraging success and ultimate fitness of an individual animal is highly dependent on their food-searching strategies, which are the focus of foraging theory. Considering the consistent inter-individual behavioural differences, personality may have a fundamental impact on animal food-scratching behaviour, which remains largely unknown. In this study, we aimed to investigate how personality traits (i.e., boldness and exploration) affect the food-scratching behaviour and food intake of the domestic Japanese quail Coturnix japonica during the foraging process. The quails exhibited significant repeatability in boldness and exploration, which also constituted a behavioural syndrome. More proactive, that is, bolder and more explorative, individuals scratched the ground more frequently for food and began scratching earlier in a patch. Individuals that scratched more frequently had a longer foraging time and a higher food intake. The correlation between personality traits and temporary food intake during every 2 min varied over time and was sex dependent, with females exhibiting a positive correlation during the first half of the foraging stage and males after the initial stage. These findings suggest that personality traits affect the food-scratching behaviour and, thus, the food intake of quails. Our study provides insights into the impact of personality traits on animal’s foraging behaviour by influencing their food-searching strategies.
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Estimating the degree of individual specialisation is likely to be sensitive to the methods used, as they record individuals’ resource use over different time-periods. We combined animal-borne video cameras, GPS/TDR loggers and stable isotope values of plasma, red cells and sub-sampled whiskers to investigate individual foraging specialisation in female Australian fur seals (Arctocephalus pusillus doriferus) over various timescales. Combining these methods enabled us to (1) provide quantitative information on individuals’ diet, allowing the identification of prey, (2) infer the temporal consistency of individual specialisation, and (3) assess how different methods and timescales affect our estimation of the degree of specialisation. Short-term inter-individual variation in diet was observed in the video data (mean pairwise overlap = 0.60), with the sampled population being composed of both generalist and specialist individuals (nested network). However, the brevity of the temporal window is likely to artificially increase the level of specialisation by not recording the entire diet of seals. Indeed, the correlation in isotopic values was tighter between the red cells and whiskers (mid- to long term foraging ecology) than between plasma and red cells (short- to mid-term) (R2 = 0.93–0.73 vs. 0.55–0.41). δ13C and δ15N values of whiskers confirmed the temporal consistency of individual specialisation. Variation in isotopic niche was consistent across seasons and years, indicating long-term habitat (WIC/TNW = 0.28) and dietary (WIC/TNW = 0.39) specialisation. The results also highlight time-averaging issues (under-estimation of the degree of specialisation) when calculating individual specialisation indices over long time-periods, so that no single timescale may provide a complete and accurate picture, emphasising the benefits of using complementary methods.