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Testing optimal foraging theory models on benthic divers
Dahlia Foo
a
,
*
, Jayson M. Semmens
a
, John P. Y. Arnould
b
, Nicole Dorville
b
,
Andrew J. Hoskins
b
, Kyler Abernathy
c
, Greg J. Marshall
c
, Mark A. Hindell
a
a
Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia
b
School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia
c
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
Keywords:
accelerometry
Arctocephalus pusillus doriferus
benthic foragers
biologging
marine predators
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 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
fitness 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 fluctuates 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 classified
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: xhdfoo@utas.edu.au (D. Foo).
Contents lists available at ScienceDirect
Animal Behaviour
journal homepage: www.elsevier.com/locate/anbehav
http://dx.doi.org/10.1016/j.anbehav.2015.11.028
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 influential 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 profitability, 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 fi 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 field. However, they are
costly, can be difficult 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
increases
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 profitable
Ecological Bottom duration Dive scale patch quality, bout
scale patch quality and dive depth
(control)
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 fish, 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
profiles are consistently simple and U-shaped, making identifica-
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 field obtained from PEEs detected from
animal-borne head accelerometers, which were validated by
simultaneous video evidence from animal-borne video cameras.
METHODS
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
Victoria.
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
modified hoop net (Fuhrman Diversified, 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 isoflurane
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
access.
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 briefly(<10 min) and the de-
vices were removed by cutting the fur beneath them. Full sets of
overlapping useable data were successfully recovered from five
animals.
Video Analyses
The ultimate objective for quantifying the prey field was to
obtain a measure of prey abundance and prey density (and there-
fore patch quality); therefore we quantified 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 first synchronized with the TDR data.
Each video file (representing a single dive) was then examined for
PEEs. The timing, duration and capture outcome of all PEEs were
recorded.
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 defined 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 influenced 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 first 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 filter (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 filter 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 sified 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 efficiency 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]]/
[[TP
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)
Filtered
acceleration (g)
SD of
acceleration (g)
0
–40
–80
(a)
(b)
(c)
(d)
(e)
2
0
–2
2
–2
0.8
0.4
0
30 60
TN TP FP FN TN TN TN
0.35 threshold
TP TP
90 120
Time (s)
150 180 210
Combine
if < 3.5 s a
p
art
Mobile
standard deviation Hi
g
h-
p
ass filtered
0
Original signal
combined signal
answer
Figure 1. An example of prey encounter events (PEEs ) within a 210 s dive by an Australian fur seal showing (a) the depth profile with corresponding raw swaying head accelerations
(y axis) recorded on (b). Data were filtered (c) using a high-pass 3 Hz filter 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 reflect 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 inflection 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 fitting.
Count (i.e. PEEs) response variables were fitted using a GLMM with
a Poisson error distribution while continuous response variables
were fitted 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 fitting 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
appropriate.
The significance 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 fitofthe
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. Signifi cance levels for the likelihood ratio tests
were set at
a
¼ 0.05. Model selection was not conducted a s we
were in terested only in the specific 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
first PEE. Data are pre sented as mean ± SD unless otherwise
stated.
RESULTS
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 confirmed PEEs (prey item was in
camera's view) and successful captures, respectively. Almost all of
the prey captures were on the sea floor. 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 floor, indicated by head jerks while swallowing
the prey (69%), or ascended to the surface with it (31%). The iden-
tifiable prey species were cephalopods, crustaceans, elasmobranchs
or teleost fishes (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 classified 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 field attributes, which was required to test model
predictions.
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 five 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 five
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 fit(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 fixed, 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 first PEE increased
when their first PEE occurred earlier in the dive (Appendix 3,
Fig. A2).
DISCUSSION
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 fitted 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
accurate.
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-finned 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
durations.
Table 2
Description, mean and SD of parameters used in testing model predictions across five 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 first prey encounter (s) Bottom duration remaining after first 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 first prey encounter Time from the start of the bottom phase to the first prey encounter 35.97 35.23
Travel duration (s) Ascent durationþdescent duration 95.34 10.22
(56,66]
(76,86]
(106,116]
80
100
120
160
Bottom duration (s)% Bottom duration (s)
(a)
(56,66] (76,86] (106,116]
0.2
0.3
0.4
0.5
Travel duration (s)
(b)
140
Figure 2. Box plots of fitted 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)byfive 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
Ad
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 influenced the
calculation of PER during bottom duration. In contrast, Ad
elie
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
seals.
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
(56,66]
(96,106]
(116,126]
(a)
(6,36] (66,96] (126,156] (216,246]
1
1.5
2.5
3
Bottom duration (s)
(b)
(103,133] (193,223] (283,313]
4.4
4.6
4.8
5
5.2
5.4
SI (s)
SI (s)
(c)
0123456
4.2
4.3
4.4
4.5
4.6
4.7
4.8
PEE
(d)
180
190
200
210
220
230
240
Dive duration (s)
(76,86]
Travel duration (s)
2
PEE
Dive duration (s)
Figure 3. Box plots of fitted 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 five 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 confirm 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 influence 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 influenced
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 influence
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
D
AIC LL P Slope SE
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 first PEE Descent duration (NULL) 15926.97 0 7959.483
Time to first prey 15488.59 438.38 7739.293 *** 22.633 1.005
Models were fitted 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 (
D
AIC). Log-likelihood
ratio tests and its P values were also used to test the significance 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 influenced 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 findings 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
findings in other marine diver studies (Gallon et al., 2013; Hanuise,
Bost, & Handrich, 2013; Hoskins & Arnould, 2013; Viviant et al.,
2014).
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 reflect different foraging strategies of
seals.
–100
–50
0
100
Dive–PEE
Bottom duration (s)
(a)
–200 –100 50 1000
–100
–50
0
50
100
150
Bout–PEE
(b)
–2 –1 0 1 2 3
50
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
0
20
40
60
80
100
120
140
Bottom duration (s)
Figure 5. Mean bottom duration of five 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 confidence 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
Ad
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.
Conclusions
Lactating female seals showed that their foraging behaviour is
complex and may be influenced 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.
Acknowledgments
We thank the numerous volunteers, in particular Kathryn
Wheatley and Beth Volpov, who assisted in the field 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
files are unavailable due to copyright.
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Appendix 1. Synchronization of acceleration and depth
records
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 profiles. 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 filter (Sato, Mitani, Cameron, Siniff, & Naito,
2003). The pitch reflected the device angle of the logger itself and
thereby reflected 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
analysis.
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
0
0.5
1
1.5
2
Hit rate + precision
Threshold
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]
0
20
40
60
80
100
Time to first
p
re
y
(s)
–20
Bottom duration after first prey (s)
Figure A2. Box plots of fitted values from the model showing the effect of the time to the
first prey encounter event (controlled for descent duration) on the bottom duration after
the first prey of five 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
