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First published online August 31, 2007
Journal of Experimental Biology 210, 3285-3294 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.007542
How fast does a seal swim? Variations in swimming behaviour under differing foraging conditions
1 NERC Sea Mammal Research Unit, University of St Andrews, Fife KY16 8LB,
UK
2 Centre National de la Recherche Scientifique, Institut Pluridisciplinaire
Hubert Curien, Centre d'Ecologie Physiologique et Ethologie, 23 rue Becquerel,
67087 Strasbourg, France
* Author for correspondence (e-mail: slg36{at}st-andrews.ac.uk)
Accepted 9 July 2007
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Summary |
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Diving durations and distances travelled in dives recorded during these experiments were similar to those recorded in the wild. Mean swim speed decreased significantly with increasing distance to the patch, indicating that seals adjusted their speed in response to travel distance, consistent with optimality model predictions. There was, however, no significant relationship between the transit swim speeds and prey density at the patch. Interestingly, all seals swam 10–20% faster on their way to the prey patch compared to the return to the breathing box, despite the fact that any effect of buoyancy on swimming speed should be the same in both directions. These results suggest that the swimming behaviour exhibited by foraging grey seals might be a combination of having to overcome the forces of buoyancy during vertical swimming and also of behavioural choices made by the seals.
Key words: swimming speed, foraging behaviour, minimum cost of transport, grey seals
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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).
Most optimal foraging models assume that the number of prey encountered during
a feeding event is a linear function of the time spent foraging, i.e. foragers
should maximise time spent feeding in the prey patch. To do this, they should
reduce both their transit time to the feeding area and their recovery time
between two successive feeding events (e.g.
Ydenberg and Clark, 1989).
When marine divers feed at greater depths, the time spent travelling to depth
and hence the energy cost increase. Consequently, the expected net foraging
gain of a dive decreases with increasing depth, and the time–energy
trade-off becomes more constrained, thereby limiting the behavioural options
available to the diver (Charnov,
1976; Kramer,
1988; Houston and Carbone,
1992; Beauchamp et al.,
1992).
The rate of oxygen consumption during a dive is directly proportional to
the rate of energy expenditure, which is in turn a function of the swimming
speed (Davis et al., 1985;
Feldkamp, 1987;
Fedak et al., 1988;
Thompson et al., 1993;
Stelle et al., 2000;
Rosen and Trites, 2002). The
swimming speeds employed during a dive will have a major impact on the rate of
depletion of limited oxygen reserves (Davis
et al., 1985; Williams et al.,
1991; Thompson et al.,
1993; Wilson et al.,
2002). Effectively, in order to maximise prey ingestion while
minimising the cost of transport, breath-holding divers are expected to
modulate their swim speed, body angle and swimming pattern
(Dunstone and O'Connor, 1979;
Sato et al., 2003). Thompson
et al. modelled how the optimal foraging tactics of seals may change as a
function of the interactions between physiological constraints (cost of
swimming) and constraints of prey availability
(Thompson et al., 1993). Their
optimality model suggested that seals should swim at the minimum cost of
transport (MCT) speed in deep dives, but in shallower dives they could
increase the proportion of time spent at the foraging area by swimming faster
between the surface and the prey patch.
Animals swimming in the water column are affected by buoyancy, which has
been reported to significantly affect the diving behaviour of seabirds and
marine mammals (Lovvorn and Jones,
1991; Webb et al.,
1998; Skrovan et al.,
1999; Beck et al.,
2000; Williams et al.,
2000; Biuw et al.,
2003; Watanuki et al.,
2003; Sato et al.,
2003; Miller et al.,
2004; Sato et al.,
2007). In diving air-breathing animals that do not trap air in
thick fur or feathers, or that regularly dive below the depth at which lung
collapse occurs, the net buoyancy is largely determined by the relative
amounts of low-density lipid and high-density lean tissues in the body.
Individuals that are positively or negatively buoyant expend more energy to
maintain a position in the water column than individuals of the same species
that are neutrally buoyant (Lovvorn and
Jones, 1991), and net buoyancy might be expected to directly
influence the speeds and swimming modes during descent and ascent swimming
(e.g. Webb et al., 1998;
Beck et al., 2000). However, in
studies on free-diving animals, it is often difficult to distinguish between
the effect of buoyancy and effects of drag and motivational state, and results
from these studies are therefore often inconclusive on this point.
This paper describes the swimming behaviour of grey seals in relation to
food resource availability (prey density and patch distance) in an
experimental set-up that provided us with a unique opportunity to remove the
effect of buoyancy on dive behaviour. We describe the swimming patterns
employed during foraging dives. We use observed behaviours to examine whether
divers decrease their swim speed as patch distance increases, as predicted by
the swimming behaviour model of Thompson et al.
(Thompson et al., 1993). As
all dives are horizontal rather than vertical and therefore not affected by
buoyancy, we hypothesise that the descent and ascent swimming mode and speed
will be similar in any particular dive.
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Materials and methods |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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;
Sparling et al., 2006). Seals
were released back into the wild after a maximum of 10 months in captivity.
All captures and handling procedures occurred between 2002 and 2004 and
conformed to the Animals (Scientific Procedures) Act 1986 under project
licence number 60/2589.
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The foraging patch consisted of a purpose built device that delivered food
on a conveyor belt to a feeding window 2 m below the surface (see
Sparling et al., 2007). An
important aspect of the design was that seals were free to dive at will and
select their own foraging behaviour.
The experimenter controlled the prey encounter rate (PER) by varying the
spacing of prey items on the conveyor belt. PER was held constant within a
given dive, but changed randomly between dives. PER varied between 0 and 13.8
fish min–1. The upper limit of PER corresponds to the highest
PER recorded in the wild with video cameras attached to freely diving harbour
seals Phoca vitulina feeding on sandeel
(Bowen et al., 2002).
Measurement of foraging behaviour
Foraging behaviour was investigated in relation to both prey density and
the distance between the prey patch and the breathing box. Trials were
conducted with the feeding station positioned 40 m, 80 m and 120 m from the
breathing box. Five of the seven seals were tested at all three distances
while two of the juvenile seals were only tested at 80 m. Animals were fasted
for >15 h before each feeding trial.
The start and the end time of each dive and times of arrival at and
departure from the feeder were recorded from direct observation at the
breathing box and an underwater video system at the feeder. These recorded
observations represent the visual data. Swim speed was estimated as distance
between breathing box and feeder divided by time between observed departure
and arrival. Time–depth recorders (TDR; MK 8, Wildlife Computers,
Redmond, WA, USA), attached to the seals' heads, provided an independent
measure of swimming speeds and of durations of travelling and surface periods.
The MCT speed was estimated from the metabolic rate–swim speed
relationship for grey seals swimming in a flume tank
(Fedak et al., 1988;
Thompson et al., 1993). The
measured MCT speed was approximately 1.3 m s–1 and unrelated
to body mass.
Linear mixed effect model for swim speeds
Swimming speed in relation to patch distance and prey density was assessed
using linear mixed effect models in R (v2.01). Models were constructed to
predict the `descent' and `ascent' speed during the dives. We could expect a
seal to alter its `ascent' speed in response to the prey encountered during a
dive, but its `descent' speed could also be a response to the prey encountered
on the preceding dive. Therefore we calculated an index of transit swim speeds
from visual data for each dive for each animal. To make the indices easily
comparable between seals we calculated an index as the sum:
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PER and mass as continuous variables and patch distance as a factor (40 m, 80 m, 120 m) were included as fixed effects and seal ID and PER were included as random effects. This allows the model to fit separate slopes and intercepts for the relationship between index and PER for each seals. We used swim speed data obtained visually from five seals travelling to 40, 80 and 120 m to carry out this analysis. Deletion tests were used to assess the significance of each parameter in the models.
TDR data analyses
Swimming behaviour was investigated using Mk8 TDRs that incorporated a
turbine swim speed sensor. The TDR was set to log velocity every second. Each
TDR velocity meter was calibrated for both pups and adults by recording the
times taken to swim fixed distances along lanes in the pool. Swim speed
profiles were plotted and examined visually for each dive for each animal
(Fig. 2).
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Buoyancy calculation
Because seals were swimming horizontally, buoyancy cannot directly explain
potential differences between `descent' and `ascent' speeds in our study.
However, it is possible that seals may employ a different level of swimming
effort during `descent' and `ascent' as a conditioned response to their actual
body condition and buoyancy. To test this hypothesis, we estimated the
buoyancy for each seal through the year where body composition measures were
available.
For five of the experimental seals, body composition was used to calculate
the seal density according to:
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is the density of the component and P the proportion of the
component for lipid (l), protein (p), bone (b) (ash) and body water (bw),
respectively. We used published values for the density of body components in
humans (Moore et al., 1963)
(l=0.9007 g cm–3, p=1.340 g
cm–3, b=2.300 g cm–3 and
bw = 0.994 g cm–3).
The proportions of body water, lipid, protein and bone mineral were
estimated using published equations for grey seals
(Reilly and Fedak, 1990):
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seawater=1.028 g
cm–3, seal is the density of the seal (g
cm–3), V is the volume of the seal in cm3
and g is the gravity constant.
Buoyancy at the surface and at 1 m and 2 m depths was calculated by adding
the density of the diving lung volume (DLV) for these different depths to the
density of the seal. DLV is about 50–60% of total lung capacity (TLC) in
phocid seals. TLC was estimated from the scaling relationship
(Kooyman, 1989):
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Results |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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).
A summary of the swim speed characteristics is shown in
Table 1. Overall mean `descent'
and `ascent' speeds were 1.71±0.41 m s–1 and
1.37±0.36 m s–1, respectively. Mean `descent' swim
speeds were faster than the estimated MCT speed (1.3 m s–1)
(Thompson et al., 1993) for
all seals except for the adult Q. Mean `ascent' swimming speeds were faster
than the calculated MCT speed for all pups (range 1.53–2.00 m
s–1), while for adults, ascent swimming speeds were close to
or less than MCT speed (Table
1).
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Of the 3220 dives for which swim speed was calculated from visual data, independent swim speeds were also obtained from the TDR data for 1289 dives. There was a strong positive correlation between the visual and TDR data (correlation= 0.786, Z=53.51, P<0.001), but the speed estimates obtained from visual data were significantly higher than those from TDR records (paired t-test, T=25.17, P<0.001) (Fig. 3). The variance in the relationship is due to measurement error in recording the start and end of active swimming and seals slowing or stopping when out of view during transit to and from the surface. Visually recorded swim speed therefore provides a noisier index of true swim speed, while TDR-derived data may be less noisy but may slightly underestimate the true swim speed. While this will reduce the statistical power of any comparisons and increase the probability of type II error it does ensure that any observed relationships are likely to be real, i.e. there is little chance of type I error.
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Swimming behaviour
A total of 1672 dives obtained from visual data for five female grey seals
(L, Q, K, N and R) were used to create linear mixed effects models. X and W
only swam to 80 m, therefore their data were not use in this analysis. A
summary of the parameters obtained is presented in
Table 2.
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The full mixed effects model including data from all five seals suggested that body mass had a significant negative effect on swim speed. However, it was clear that this was being driven mainly by the mass change of one large pregnant adult female `Q', whose swim speed index decreased throughout the study (Fig. 4). This was also apparent when comparing swim speeds to different distances. During the early pregnancy of seal Q, swim speed index to 80 m was 0.5 and in late pregnancy the index fell to –0.6 (Fig. 5). Seal Q was therefore considered separately and removed from the overall analysis.
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There was significant variation between individual seals, but the model indicated that seals did not adjust their transit swim speeds in response to changes in either PER (Fig. 6) or body mass (Fig. 4). However, they did appear to adjust their swim speed in response to patch distance (Table 2, Fig. 7). Transit swim speeds decreased significantly with increasing patch distance. This decrease in swim speed was most pronounced between 40 m and 80 m, with little change between 80 m and 120 m.
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Swimming pattern
In order to describe the swimming patterns of our animals, swim speed
profiles obtained from TDR records of 1289 dives were examined from all seven
seals (L, Q, K, N at 40 m and 80 m and R, W, X at 80 m).
The mean `ascent' speed was always significantly slower than the mean `descent' speed for all seals, at all distances (paired t-test, P<0.01). Interestingly, all seals swam between 10% and 20% slower on their way back to the breathing box despite not having to work against buoyancy in either direction (Fig. 8). We calculated the buoyancy through the year for each seal where body composition was available. The mean body mass of the adult grey seal was 129.7 kg and 34 kg for the pups, with adipose tissue accounting for between 7.1% and 40.8% of body mass (Table 3). There was no relationship between estimated buoyancy and the relative swim speed during `descent' and `ascent'. `Ascent' swimming was always slower than `descent' swimming whereas buoyancy varied widely from +16 N to –20 N.
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All seals used burst and glide swimming during both `descent' and `ascent'. Repeated acceleration (stroke) and deceleration (glide) phases were apparent in swim speed records in all dives (Fig. 2). The number of burst and glide phases was generally significantly higher on the `ascent', except for N and R at 80 m (paired t-test, P<0.05, Fig. 9).
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Discussion |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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Swimming speed
The observed mean swimming speeds for both adults and pups were similar to
those reported for adult grey seals foraging in UK waters
(Thompson et al., 1993) but
were higher than those of adult grey seals foraging around Sable Island
(Beck et al., 2000). However,
Beck et al. reported `descent' and `ascent' rates that are only analogous to
swim speed if the seals were diving vertically to the bottom
(Beck et al., 2000). At any
other angle, such rates would underestimate swim speed.
Marine mammals and penguins should swim at or near the MCT speed when
swimming to and from the surface in order to maximise the amount of oxygen
available during the foraging phases of dives
(Davis et al., 1985;
Feldkamp, 1987;
Ponganis et al., 1990;
Ponganis et al., 1992;
Fish, 1993;
Thompson et al., 1993;
Williams et al., 1993;
Ropert-Coudert et al., 2002).
The MCT speed for grey seals swimming in a flume tank was approximately 1.3 m
s–1 (Fedak et al.,
1988; Thompson et al.,
1993), which was similar to those recorded for harbour seals in
similar conditions (between 0.85 and 1.4 m s–1)
(Davis et al., 1985). On
average, our seals swam approximately 20% faster than the estimated MCT speed
when returning to the surface (Table
1), and over 40% faster than the expected MCT speed when going to
the feeder. Estimates of MCT speed based on animals swimming in flume tanks
may be underestimates. Seals had to swim actively against the flow while
breathing at the surface. Drag is greatly enhanced at or close to the surface
(Hertel, 1966) so seals in
flume tanks would experience higher drag than during submerged swimming at
similar speeds in our set-up or in the wild. At higher speeds this effect is
exacerbated by seals spending proportionately longer breathing
(Fedak et al., 1988). Our
seals may therefore have been swimming at or close to MCT speeds during
`ascent'. If so, however, they must have been swimming faster than MCT speed
when travelling to the feeder.
There was no clear relationship between body mass and swimming speed in our
study. Because drag scales to surface area while available power scales to
body mass, larger animals should be capable of higher sustained swim speeds
(Feldkamp, 1987;
Videler and Nolet, 1990;
Stelle et al., 2000). However,
animals would be expected to swim at or close to their MCT speed during ascent
and descent and the relationship between mass Mb and MCT
speed is not obvious. MCT speed scales to M
0.27b over a wide range of body masses
(Videler and Nolet, 1990).
While this is apparent over the size range investigated by Videler and Nolet
(0.027–11.5 m), it is not clear that such a relationship holds within
the range of sizes and swim speeds observed in marine mammals, and it does not
appear to fit the observed patterns of swim speeds in marine mammals
(Sato et al., 2007;
Hassrick et al., 2007). The
broad-scale allometric relationship, which was determined across swimmers from
many taxa, is due to the fact that drag coefficient CD
decreases as Reynolds number (Re) increases over a wide range of
Re values, and Re scales linearly to body length. However,
at the high Re (>200 000) experienced by swimming pinnipeds, the
simple relationship breaks down and CD remains relatively
constant (Vogel, 1981). If we
can assume that CD is constant over the observed range of
sizes and swim speeds for pinnipeds, we can approximate the metabolic costs of
swimming (SMR) in terms of body mass (Mb) and swim speed
(U), with an equation of the form,
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Cost of transport (J m–1) is simply the metabolic rate
divided by the speed:
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Our largest animal was approximately six times heavier than our smallest so
we would expect its MCT to be only around 5% higher. This is consistent with
previous studies on animals in the wild showing that swim speed is relatively
constant at around 1–2 m s–1and not correlated to body
mass over a wide range, from 30 tonne sperm whales to 0.5 kg seabirds
(Ponganis et al., 1990;
Sato et al., 2007).
Foraging behaviour in relation to food resource availability
Although dive duration is ultimately limited by oxygen stores, it has been
suggested that seals may alter their diving behaviour in response to their
perception of both the quality and depth of a prey patch
(Thompson and Fedak, 2001).
Grey seals do alter their dive durations in response to changes in patch
quality, by ending their dives early at low prey densities
(Sparling et al., 2007). The
results presented here suggest that transit swim speeds are not simply related
to prey density. This is consistent with the assumption that seals will
maximise prey acquisition by maximising the rate of delivery of oxygen to the
foraging patch irrespective of patch quality.
Swim speeds in deep dives should approach, but never go below, MCT speed,
whereas in shallow dives higher swim speeds would allow animals to maximise
the proportion of time spent foraging at the bottom
(Thompson et al., 1993). All
seals tested in this study did alter their swimming behaviour in response to
changes in patch distance, swimming faster to 40 m compared to 80 m or 120
m.
Sparling et al. found that dives to 40 m were generally much shorter than
the estimated aerobic dive limit (ADL; equivalent to the oxygen stores divided
by the metabolic rate) but in dives to 120 m, seals were approaching ADL at
the highest prey densities (Sparling et
al., 2007). Reducing swim speeds and therefore metabolic rates
during transit in deep dives would allow seals to spend longer at the feeding
patch without exceeding their estimated ADL. In contrast, studies on Northern
fur seals, New Zealand sea lions and Brunnich's guillemots found that swimming
speed during descent increased significantly in deeper dives
(Ponganis et al., 1992;
Crocker et al., 2001;
Lovvorn et al., 2004). Unlike
phocid seals, these species store air in their lungs, fur or plumage and
therefore have to work hard against buoyancy at the start of the dive. In
shallow dives a higher proportion of the descent is spent working against
buoyancy so that the apparent drag forces experienced by the animal will be
greater in shallow compared to deep dives.
Swimming mode in absence of pressure effect
Differences between `descent' and `ascent' swim speeds are usually
explained in terms of changes in buoyancy forces
(Webb et al., 1998;
Williams et al., 2000;
Beck et al., 2000;
Sato et al., 2003). However,
despite the fact that all swimming between the surface and the foraging site
was horizontal in our study, seals nevertheless swam slower on `ascent' than
on `descent' despite not having to work against negative buoyancy
(Table 1 and
Fig. 7). This difference
between `descent' and `ascent' speeds was maintained with increasing patch
distance and there was no indication that the slower `ascent' swimming was a
conditioned response to perceived buoyancy. Seals were choosing to swim faster
to their feeding patch. Buoyancy cannot therefore completely explain why
negatively buoyant seals swim more slowly during `ascent'. Motivational state
may have a direct effect on swim speeds; seals may swim faster to the feeding
patch in anticipation of finding food while they might save energy for the
next dive by swimming slower on their way back.
All our seals used burst and glide swimming during both `descent' and
`ascent' (Fig. 2). This is
possibly an energy-efficient way of travelling for marine mammals
(Lovvorn et al., 1999;
Williams et al., 2000;
Lovvorn et al., 2001). Data
from TDRs in this study indicate that there were fewer acceleration and
deceleration phases during the faster `descent' compared to the slower
`ascent' (Fig. 8). Seals might
have increased the frequency and/or the amplitude of their strokes to increase
their speed on the `descent', but our TDR records were not sensitive enough to
detect individual swimming strokes. Several studies have suggested that speed
and acceleration are mediated via changes in stroke amplitude rather
than stroke frequency (Lovvorn et al.,
1999; Wilson and Liebsch,
2003; Lovvorn et al.,
2004). In order to determine in detail the swimming tactics used
by the seals in our set-up, however, we would need to use more precise
accelerometer devices.
In summary, the results of the present study indicate that swim speeds in
grey seals are closely related to resource accessibility, i.e. distance, but
not to the patch quality. Seals adjusted their swim speeds in relation to dive
distance allowing them to increase their time spent foraging underwater. In
addition, our unexpected discovery that seals swim slower on their way back to
the surface in the absence of buoyancy effects suggests that the swimming
behaviour exhibited by foraging grey seals during vertical swimming is
primarily dependent on behavioural choices rather than a result of buoyancy
effects (Fedak and Thompson,
1993; Thompson and Fedak,
2001; Sparling et al.,
2007).
List of abbreviations
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Acknowledgments |
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number of
strokes significantly higher on the `descent'; NS, not significant (paired
t-tests, P<0.05). The bottom and top of each box marks
the 25th and 75th percentile respectively. The black and grey lines within the
box represent the median and mean respectively. Whiskers above and below the
box indicate the 90th and 10th percentiles.