Blubber and buoyancy: monitoring the body condition of free-ranging seals using simple dive characteristics
1 Sea Mammal Research Unit, Gatty Marine Laboratory, University of St
Andrews, St Andrews, Fife, KY16 8LB, Scotland
2 Antarctic Wildlife Research Unit, School of Zoology, University of
Tasmania, GPO Box 252-05, Hobart, Tasmania 7001, Australia
3 Australian Antarctic Division, Channel Highway, Kingston, Tasmania 7050,
Australia
* Author for correspondence (e-mail: emb7{at}st-and.ac.uk)
Accepted 2 July 2003
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Summary |
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Key words: buoyancy, marine mammal, elephant seal, body composition, foraging ecology, satellite telemetry
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Introduction |
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Prey ingestion has been estimated by measuring changes in stomach or
oesophageal temperature in seabirds
(Garthe et al., 1999;
Weimerskirch and Wilson, 1992
;
Wilson, 1992
), sharks
(Klimley et al., 2001
),
penguins (Charrassin et al.,
2001
; Putz and Bost,
1994
; Putz et al.,
1998
), turtles (Tanaka et al.,
1995
) and seals (Bekkby and
Bjorge, 1998
; Gales and
Renouf, 1993
; Hedd et al.,
1996
). Although these techniques are useful for recording the
timing of feeding events, estimates of meal size have generally been less
reliable (Wilson et al.,
1995
), and the technique is further hampered by short retention
times of stomach tags (Wilson et al.,
1998
). More recently, video and/or image recording instruments
have been attached to seals and cetaceans in order to record the timing and
rate of prey encounter (Davis et al.,
1999
; Hooker et al.,
2002
; Sato et al.,
2002a
), but the size of the resulting image data sets are often
too large to be practical for deployments over more than a few days. Moreover,
this technique only reveals what an animal encountered and not what it
ingested, and because the body condition of an animal represents a balance
between energy assimilation and expenditure, information about the timing of
feeding and meal size is not sufficient for estimating the energy balance of
an animal.
An alternative strategy is to use aspects of an animal's diving behaviour,
as measured by existing data recorders, to indirectly estimate changes in body
composition. One such approach is to monitor changes in the buoyancy of an
animal through changes in measured dive characteristics
(Beck et al., 2000;
Crocker et al., 1997
). In
deep-diving phocids such as the elephant seal (Mirounga sp.), the
buoyancy of an individual is determined primarily by its body composition and
particularly by the ratio of lipid to lean tissue
(Crocker et al., 1997
;
Webb et al., 1998
). While lean
tissue is denser than seawater, lipid tissue is less dense, and animals with a
large proportion of lipid will therefore be more buoyant
(Beck et al., 2000
; Lovvorn and
Jones,
1991a
,b
;
Nowacek et al., 2001
;
Webb et al., 1998
). Phocids
that have seasonal cycles of terrestrial fasting and at-sea foraging
demonstrate large fluctuations in body composition, and this should be
reflected by changes in buoyancy (Beck et
al., 2000
; Crocker et al.,
1997
; Webb et al.,
1998
). Buoyancy can be estimated while an animal is drifting
passively through the water column, and this behaviour is regularly observed
in dive records from elephant seals
(Crocker et al., 1997
;
Hindell et al., 1999
;
Le Boeuf et al., 1992
). Drift
dives were first defined by Crocker et al.
(1997
) and are broadly
characterized by a rapid descent phase during which seals swim actively
followed by a prolonged phase of slower descent or, less commonly, ascent
during which seals are assumed to drift passively through the water column.
The drift phase is typically followed by a period of active swimming and
fairly rapid ascent to the surface. Although the function of drift dives is
not clear, Crocker et al.
(1997
) suggested that they may
play a role in food processing, predator avoidance and/or resting. We expect
the rate of vertical descent or ascent during the drift phase to vary
monotonically with the buoyancy of an animal, and this drift rate may
therefore be used to track changes in an animal's body condition while at sea.
Although this was demonstrated for female northern elephant seals
(Mirounga angustirostris) by Crocker et al.
(1997
), they did not model
these relationships to estimate the body condition of seals while foraging at
sea using changes in drift rate.
Although the drift rate of a phocid seal will be determined largely by the
proportions of lipid and lean tissue, it will also be affected by a variety of
external and internal factors. By definition, the constant rate of vertical
displacement (i.e. the terminal velocity) of an object moving through a medium
occurs when all forces acting upon the object (i.e. gravity, buoyant force and
drag resistance) are in equilibrium. The buoyant force is determined by the
difference in density between the object and the surrounding medium, while the
drag resistance is affected by the surface area and shape of the object, the
density of the surrounding medium and the speed at which the object moves
through this medium (Vogel,
1981). In terms of a seal drifting passively through water, the
terminal velocity will be influenced by external characteristics, such as
seawater density, that vary with salinity and temperature. It will also be
influenced by physiological and behavioural changes such as residual air in
the lungs and the orientation of the seal's body in the water. The
interactions of all these variables will determine the accuracy with which the
body composition of a seal can be estimated from observed drift rate.
In this study, we used ARGOS satellite telemetry
(Argos, 1989) to relay dive and
location data from recently weaned southern elephant seals (Mirounga
leonina) from Macquarie Island during their first trips to sea. These
data were used to describe the change in drift rate of individuals over time.
To assess the accuracy of estimating buoyancy and body composition of
individual seals from observed drift rates, we developed a theoretical
mechanistic model of a typical seal pup and examined the relative
contributions of various potential error sources. Finally, we assessed the
accuracy of our model by comparing the body composition of a seal predicted
from drift rates just after departure from Macquarie Island with the body
composition measured just before departure. Throughout this paper, we have
adopted the operational definition of body composition as referring to percent
body lipid.
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Materials and methods |
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Seals were captured and weighed according to McMahon et al.
(2000a). The axial girth
(AG) was measured immediately caudal to the base of the pectoral
flippers, and the length was measured as the straight line between the tip of
the nose and the tip of the tail (referred to as standard length, or
Lst). Seals were then lightly sedated with an intravenous
dose of tiletamine/zolasepam [Zoletil® 100 (Virbac, Peakhurst, NSW,
Australia) or Telazol® (Wildlife Pharmaceuticals Inc., Fort Collins, CO,
USA)] at an approximate dose rate of 0.2-0.4 mg kg-1
(Field et al., 2002
).
Satellite Relayed Data Loggers (SRDLs; Sea Mammal Research Unit, St Andrews,
UK) were glued to the fur on the head or upper neck region using two-component
industrial epoxy glue (Hilti; Silverwater, NSW, Australia) according to the
methods described in Fedak et al.
(1983
), Hindell et al.
(1999
) and McConnell et al.
(2002
). We monitored the
breeding beaches on a daily basis, and if seals were still present at the
colony more than one week after deployment they were captured again and
re-weighed.
Body composition
In 1999 and 2000, we used isotopically labelled water (e.g.
Nagy and Costa, 1980;
Reilly and Fedak, 1990
) to
measure the body composition of pups at weaning, prior to departure and upon
return to the island. Immediately after sedation, a blood sample was collected
from the extradural vein to measure the background isotope level. A weighed
dose of approximately 7.4x104 Bq tritiated water
(3HHO) in 4 ml of distilled water was then injected using a 5 ml
plastic syringe. The syringe was flushed with blood three times to ensure
complete delivery of the 3HHO. A second blood sample was taken 2-3
h after initial injection for determination of dilution space and calculation
of body composition. Blood samples were centrifuged and the plasma transferred
to 2 ml cryotubes, which were stored at -20°C until further analysis.
We analysed plasma samples for 3H specific activity by liquid
scintillation counting. Weighed plasma samples of approximately 100 µl were
added to 10 ml of PicoFlour scintillation cocktail (Packard Instruments) and
counted in triplicate for 10 min on a Packard Tri-Carb® 2000 liquid
scintillation counter. Correction for quenching was made by automatic external
standardization. We prepared standard dilutions by gravimetric dilution of an
aliquot part of the injectate to the approximate dilution expected in the
seals, and these were also counted in triplicate for 10 min. The exact water
content of each plasma sample was determined by transferring 100 µl of
the sample to a weighed glass slide. The slide was weighed again and then
dried on a hot plate at a temperature of
50°C until complete
evaporation of the plasma water had been achieved, after which the slide was
weighed a third time. In this way, we could correct the specific activity of
3H for the exact plasma water content.
We calculated total body water (TBW), according to the empirically
derived equation for grey seals (Halichoerus grypus;
Reilly and Fedak, 1990), as:
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The total amount of lipid (TBL) and protein (TBP),
expressed as percent of body mass (Mb), total body ash
(TBA) and total body gross energy (TBGE; expressed in MJ)
were also calculated according to Reilly and Fedak
(1990):
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![]() | (3) |
![]() | (4) |
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On-board data interpretation and compression
SRDLs collect and store dive parameters using various sensors and later
relay dive records in compressed form via the ARGOS system. Depth was
measured by the pressure transducer and circuitry on board the SRDL (Keller
PA-7; Keller, Winterhur, Switzerland). The output from the depth transducer
was sampled with 16-bit A/D such that, after calibration and offset
correction, it provided an accuracy of 0.20 m. Swim speed was recorded by a
turbine odometer (Logtron; Flasch Electronik GmbH, Munich, Germany; stall
speed 0.25 m s-1) housed in a polyurethane dome on top of the
SRDL housing. Because the SRDL was mounted on the dorsal surface of the head,
it would only be expected to respond to movement through the water if the seal
was swimming nose first. The SRDLs recorded data every four seconds throughout
a dive, and at the end of each dive these data were processed and compressed
by the on-board microprocessor and stored in the short-term memory before
being relayed via the ARGOS satellite system. The details of the
compression algorithm are described in Fedak et al.
(2001
), and the general
telemetry system is presented in Fedak et al.
(2002
). The dive data stored
and transmitted back from the instrument make up two categories: (1) `summary
dives', for which only maximum dive depth and duration are stored and
transmitted, and (2) `detailed dives', for which all inflection points and
speed data are calculated and transmitted. The transmitted parameters
describing a detailed dive are (1) total dive time, (2) the four most
important inflection points (d1-d4;
Fig. 1) with the most rapid
change in dive trajectory, and (3) three mean swim speeds
(U1-U3;
Fig. 1). The first mean swim
speed (U1) is calculated from all recorded swim speeds
between the start of the dive and the first inflection point
(d1), U2 is calculated between
d1 and d4, and U3
between d4 and the end of the dive
(Fig. 1).
|
Selection of drift dives
We used a combination of methods for selecting drift dives for analysis
and, because of differences in the software controlling the SRDLs' data
collection and compression used in different years, the combination of
selection criteria varied between instruments.
The first method for the selection of drift dives was that U2 is equal to zero (i.e. less than the stall speed), while U1 and/or U3 are less than 0. There are two problems with this method: (1) the speed sensor can sometimes get blocked by debris, resulting in erroneous recordings of swim speeds of 0, and (2) the putative drift phase of a dive does not always correspond to the total time between d1 and d4 for which U2 is measured (see also the description for the second method below), and actual drift dives may therefore occasionally have a U2 not equal to 0. We therefore used a second method based on the dive profile rather than swim speed. The shape of the putative drift segment was examined by fitting different regression lines through; (1) all inflection points or excluding (2) the first or (3) the last inflection point. We used the line of best fit by selecting the model with the lowest mean squared residual (MSR), and the corresponding interval was selected as the putative drift phase. The drift rate (in cm s-1) was defined by the slope coefficient for the regression line of the selected segment. The MSR of the best-fitting line also provided an `index of linearity' of the selected phase. After initial visual examination of 1000 dive shapes and their corresponding MSR values, we subsequently rejected all dives with an MSR of >5 m2.
The final method was based on maximum dive depth and the duration of the putative drift phase. We only selected dives where the depth of the shallowest inflection point was at least 10 m and where the drift phase represented more than 40% of the total duration. Although these criteria may have excluded some shallow drift dives of short duration, the drift phase for these dives would be too short to allow a meaningful estimate of drift rate to be calculated. It also minimized the risk of including dives for which air left in the airways may have a significant effect on the buoyancy of an animal (see detailed discussion below).
Overview of numerical and statistical analyses
Once drift dives had been selected, we went through a series of analyses to
test their usefulness for predicting body condition. First, we used a
smoothing algorithm to fit a function to the drift rate records for each
individual seal, allowing us to define a representative daily value of drift
rate. Second, daily changes in drift rate were calculated from these fitted
values. The predictive functions were then inspected to define phases of the
trip with contrasting characteristics, and these definitions were compared
with one commonly used criterion for defining putative travelling and foraging
phases: daily travel rate. Third, we constructed a mechanistic model of drift
dives in relation to body composition, morphometrics (i.e. surface area and
volume), residual lung volume, drag coefficient (CD) and
seawater density. We tested the sensitivity of this model to variations in the
parameter values for these variables using bootstrap resampling. Finally, we
tested the model by comparing the predicted body composition immediately after
departure with the body composition measured using labelled water immediately
before departure.
Time series and fitted daily values
We used a non-parametric smooth spline technique [smooth.spline
(Venables and Ripley, 1994),
as implemented in the R package (Ihaka and
Gentleman, 1996
)] to fit a predictive function to the time series
of drift rates for each individual seal. The fitted values from these curves
were taken to represent the expected drift rate for an individual seal on a
given day. Spline functions divide the range of observed values by an ordered
set of points (knots) along the x (time)-axis. Within each interval,
the fitted curve is a cubic polynomial, and over the whole range the fit is
further constrained to have continuous first and second derivatives at the
knot locations. The curve is normally fitted by generalised cross-validation
(GCV) (Gu and Wahba, 1991
;
Venables and Ripley, 1994
),
resulting in a curve that represents the best compromise between
goodness-of-fit and degree of smoothness. We constrained the algorithm further
by setting the initial intervals between knots to 14 days. The final interval
length was then fine-tuned by the GCV algorithm. We chose 14 days as our
interval to reduce the risk of over-fitting due to occasional high sensitivity
to local minima, while also allowing for biologically realistic changes in
drift rate expected to be detectable over a period of 5-6 days (see Methods
and Results).
Prior to fitting the spline curve, we square transformed the drift rate values. This was done because the quadratic relationship between drag and velocity (see equation 8) results in a time record of change in drift rate with marked discontinuities around zero drift rate (i.e. neutral buoyancy). Transforming the drift rates prior to fitting allowed us to obtain a more continuous distribution, thereby aiding the spline-fitting algorithm. The squared drift rate values, along with the fitted daily values, were then back-transformed for further analyses.
Correlation with travel rate
It is common in analyses of animal movements to divide trips into different
periods of putative travelling and foraging, and one way to do this is by
defining some threshold value for daily horizontal displacement (see e.g.
Hindell et al., 1999;
McConnell et al., 2002
). In
order to examine how daily horizontal displacement (hereafter referred to as
`travel rate') corresponds to drift rate, we also defined three phases based
on daily changes in observed drift rate. Following Hindell et al.
(1999
) and McConnell et al.
(2002
), we determined the
transitions between Phases 1 and 2 and between Phases 2 and 3, respectively,
as the first and last days where the five-day running average of daily travel
rate was less than 20 km. This horizontal displacement is the net displacement
over a 24-h period and may or may not correspond to the total distance
travelled over the same period, depending on the directionality of the
movements taken by the animal. In terms of drift rates, we defined these
transitions as the first and last days when an individual seal showed a
positive daily change in the smoothed drift rate. In order to define the
maximum change in drift rate likely to be observed for a given daily travel
rate, we performed quantile regressions
(Koenker and Bassett, 1978
) as
implemented by the rq function in the R package quantreg. This function fits
conditional regressions through a specified quantile (in this case, the 90th
quantile) of a response variable.
Drift rate and body composition
To test the accuracy of body composition estimated from drift rates, we
developed a theoretical mechanistic model of drift rate for a hypothetical
average elephant seal pup at Macquarie Island. Typical values for standard
nose-to-tail length (Lst) and axial girth (AG)
were taken from all pups captured as part of the larger study in 1998, 1999
and 2000 investigating body composition of pups. For simplicity, we kept the
volume of the pup constant at 100 litres and varied the lipid content between
10% and 60%. The proportions of other body components were then derived from
equations 2-5above. The total density of the seal over the range of body
compositions was calculated according to:
![]() | (6) |
![]() | (7) |
![]() | (8) |
![]() | (9) |
Using this equation, we could then quantify the relative importance of
variations in seawater salinity and temperature, seal surface area and
CD on the predicted drift rate. The maximum and minimum
seawater densities were calculated based on the maximum salinity, temperature
and depth ranges likely to be encountered by elephant seals from Macquarie
Island. The temperature and salinity ranges were taken from Gordon
(1988) and were 0-6°C and
33.9-34.7/##/ respectively. The depth range over which seawater densities were
calculated was set to 0-500 m. The surface area of the seal was initially
modelled as two opposing cones with a common base, the circumference of which
corresponded to the AG of a typical seal pup (135 cm). The anterior
cone was assumed to have a height corresponding to one-third of the mean
standard Lst of a typical seal pup (135 cm), while the
posterior cone had a height of two-thirds of the mean Lst.
Williams and Kooyman (1985) reported a CD of 0.09 for a
harbour seal (Phoca vitulina). However, this was for an actively
swimming animal, travelling headfirst and thereby minimising drag. It is
unlikely that a passively drifting seal will move in the same streamlined
orientation, and we therefore tested a range of coefficients. Here, we assumed
a Reynolds number of a seal in
10°C water to be
60 000 at a
speed of 0.25 m s-1 and
120 000 at a speed of 0.5 m
s-1. This gives CD values of
0.47 for a
sphere and 1.17 for a cylinder travelling crosswise. In our initial analyses,
we therefore used these three values (0.09 for the harbour seal, 0.47 for a
sphere and 1.17 for a cylinder). These values were slightly modified in
subsequent analyses (see below).
The total buoyancy of an air-breathing aquatic animal is not constant but
will change with depth due to residual air in the lungs, which is compressed
at greater depths. We used published equations for the relationship between
body size and residual lung volume to estimate the likely bias caused by this
residual air. Kooyman (1989)
estimated the residual lung volume of a marine mammal at the onset of a dive
to be about 50% of the total lung capacity (TLC), where TLC
(in litres) is estimated as:
![]() | (10) |
While this equation assumes that TLC is approximately proportional
to the total body mass, we instead assumed TLC to be proportional to
the lean body mass of the seal. However, while equation 10 estimates the lungs
to be roughly 10% of total body mass (i.e. a constant of 0.10), we compensated
for the use of lean mass instead of total mass for our calculations and
assumed that the lungs represent 12.5% of total lean mass (i.e. using a
constant of 0.125). This correction was based on the assumption that the mean
lean mass of the seals studied by Kooyman
(1989
) was
70% of total
body mass and that a lung volume of
10% of total body mass is roughly
equivalent to a lung volume of
12.5% of lean mass. To simplify our
calculations, we also substituted lean mass with lean volume, since
our model seal had a total body volume set to 100 litres. This gave us the
following relationship between seal lean volume (Vl),
depth (d) and air volume at a given depth (Va,d):
![]() | (11) |
The estimated density for a seal with a given body composition at a given
depth (seala,d) could then be calculated as:
![]() | (12) |
![]() | (13) |
Because seals change depth during drift dives, the residual lung volume will also change, as will the total body density. To simplify our model, we have defined depth as the midpoint between the start and end depth of the drift phase, and thus the predicted drift rate would correspond to the rate halfway through the drift phase.
Model evaluation
1. Simulation test
We tested the sensitivity of the model to the various parameters using a
randomised bootstrap procedure. One hundred drift dives were randomly selected
from the total sample of observed dives. These dives were selected with
replacement (i.e. a dive could be selected more than once), and the drift rate
and the depth halfway between the start and end depths of the drift segment
(mid-depth) for each of these dives were extracted. For each dive, values of
AG, Lst, CD and sw
were drawn at random from populations of 1000 values, following realistic
distributions for these variables; AG and Lst
were drawn from normal distributions with means ± S.D. of
1.3±0.1 m and 1.5±0.1 m, respectively (based on all measurements
on pups in the larger body composition study before and after the first trip).
Drag coefficients were drawn from a uniform distribution with minimum and
maximum values of 0.47 (sphere) and 1.17 (cylinder), and seawater densities
were drawn from a uniform distribution with minimum and maximum values of
1.027 g cm-3 and 1.030 g cm-3, respectively. The body
surface area and volume were calculated from the AG and
Lst values as previously described, and the volume of
residual air in the lungs was calculated based on this body volume for the
specific mid-depth value for each of the 100 selected dives.
We then calculated the predicted body density by rearranging equation 13:
![]() | (14) |
We compared this set of 100 predicted lipid contents to a set of `actual' lipid contents that were calculated assuming an Lst and AG of 1.5 m and 1.3 m, respectively, a seawater density of 1.028 g cm-3 and a CD of 0.82, i.e. the average of the CD values for a sphere (0.47) and a cylinder (1.17). We calculated the kernel density distribution of all 100 residuals (predicted to actual lipid contents) and defined the range of errors corresponding to a probability density of >0.025 to obtain 95% confidence intervals for our prediction accuracies. We repeated the whole procedure 1000 times and thus obtained mean and variance estimates for the confidence intervals of the prediction errors. We first ran this procedure allowing values for all parameters to be randomly selected and then repeated the procedure keeping all but one of the parameters fixed. This allowed us to estimate how uncertainty in each parameter estimate contributed to the overall error. We also ran two separate series of analyses, one using uncorrected body density values and the other using values corrected for the assumed volume of residual air at the mid-depth of the drift segment. This allowed us to estimate both the likely bias due to residual air in a real data set and also to determine if the error range was similar for uncorrected and corrected values. In the first case, uncorrected predicted values obtained from the simulation procedure were compared with uncorrected actual values obtained by keeping all parameters fixed (as described above), while in the second case both the predicted and actual values were first corrected for the residual air volume at the mid-depth.
2. Correlation between predicted and measured lipid content
The final test of our model was to compare the lipid contents predicted
from drift rates with lipid contents measured using labelled water immediately
prior to departure (hereafter referred to as `measured' lipid contents), as
described above. Because many SRDL records ended well before animals returned
to land, we did not include comparisons of predicted and measured lipid
contents when seals returned after an extended foraging trip.
In this analysis, the volume (V) and surface area (S) of
each individual was calculated from the AG and
Lst measurements made just prior to departure, assuming
the seal was shaped like a prolate spheroid. The functions for calculating
volume and surface area were thus:
![]() | (15) |
![]() | (16) |
![]() | (17) |
For each dive, we then calculated the residual lung volume as a function of
seal volume as described above, using the depth halfway between the start and
end depth of the drift segment. Finally, we were then able to determine a
predicted lipid content for each dive corresponding to the drift rate of that
dive. This gave us an individual time series of predicted lipid contents for
each individual over the duration of the SRDL record, and representative daily
values were again calculated by fitting spline functions (knot intervals = 14
days) to these time series according to the methods described above. The
predicted lipid content for each seal at the day of departure was then
compared with the lipid content measured for that seal using labelled water.
Since the labelled water method gives values for lipid content in mass
percent, all lipid contents predicted from drift rates were multiplied by the
previously published value of 0.9007 for lipid density
(Moore et al., 1963) to
convert them from percent by volume to percent by mass.
In this analysis, we included CD as a parameter to be estimated by the model and tested the fit across the entire range of CD values, from the harbour seal (0.09) to the cylinder (1.17). We used two different methods for assessing the fit. The first method selected the CD that gave the smallest sum of squared residuals between predicted and measured lipid contents. The second method was based on the slope of the regression line between predicted and measured lipid contents and selected a CD that gave a slope coefficient of 1. The rationale of using these two methods was that the CD value giving the smallest absolute errors between predicted and measured lipid contents may have a slope that is significantly different from 1, i.e. that the errors are dependent on the absolute values of lipid content. For instance, a slope of >1 would suggest that for fatter animals the model produces values of lipid content that are underestimated compared with measured lipid contents, while it gives overestimated lipid contents for leaner seals, and vice versa if the slope coefficient is <1.
Temporal resolution of fitted time series
Our model also allowed us to obtain a rough estimate of the period over
which we can detect a true trend in drift rate given the amount of random
noise in the data. The daily lipid gain is likely to be relatively small in
proportion to the lipid already present in the body. This will affect the time
scale over which we can detect a significant change in drift rate. We also
expect some variations in drift rate observed within a given day due to
changes in body orientation within and between dives and variations in
residual lung volume. To estimate the likely effects of these variations, and
to determine the likely temporal resolution of our method, we compared the
estimated daily change in drift rate for a feeding seal with the mean daily
residuals of drift rate (i.e. the difference between each observed drift rate
for a given day and the mean drift rate for that day obtained from the fitted
spline function). We modified our mechanistic model to estimate the mean
observed change in drift rate over the foraging period and to estimate the
likely daily change. We again assumed a total body volume of 100 litres at
departure, of which 38% is lipid, and 150 litres at return 175 days later, of
which 32% is lipid (Sea Mammal Research Unit, unpublished data). We estimated
a mean foraging period of 100 days, starting 30 days after departure. We also
estimated the volume and body composition at the start of feeding based on the
values at departure, assuming that the metabolic rate of a seal while in
transit is similar to that on land. Similarly, we estimated the volume and
body composition at the end of feeding by back-calculating from values at
return to the island 45 days later, again assuming a metabolic rate while in
transit similar to that on land. From these values, we estimated a mean daily
change in lipid content and calculated the expected mean daily change in drift
rate. We could then compare this daily change to the daily residuals. These
calculations were done for each seal separately, and the probability
distributions of the residuals were modelled as kernel density functions. We
selected the residual value corresponding to the maximum kernel density value
as our best estimate of within-day variation in drift rate for a given seal.
By dividing this value by the estimated mean daily change in drift rate we
could then determine the minimum time interval over which any changes in drift
rate would be likely to represent a real change in body composition.
All descriptive statistics are presented as means ± 1 S.D., unless stated otherwise.
![]() |
Results |
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|
|
The frequency distribution of drift rates showed a clear bimodal pattern, and very few dives had a drift rate around zero (Fig. 3). Negative drift rates were on average -19.9±8.2 cm s-1, while positive rates were 16.0±11.9 cm s-1. The 95% probability density range of observed drift rates was -33.4 cm s-1 to 23.1 cm s-1.
|
Time series, fitted values and resolution
Time series with fitted spline curves are presented in
Fig. 3. Although there were
distinct individual variations, a clear general pattern of change in drift
rate over time could also be seen. Trips could be broadly defined by three
distinct phases similar to those obtained using daily travel rates (see e.g.
Hindell et al., 1999;
Le Boeuf et al., 2000
;
McConnell et al., 2002
and
Results and Discussion below). The first phase lasted about 30-50 days, and
most seals showed a gradual decrease in drift rate. Some seals initially had
positive drift rates (i.e. they drifted upwards during the passive phase) but
they all showed exclusively negative drift rates (i.e. sinking) by the end of
this first phase. The second phase was characterised by an initial levelling
out followed by a gradual increase in drift rate. Except for cases where the
record stopped prematurely due to tag failure, loss or death of the animal,
this second phase lasted roughly 100 days. Most of the seals showed positive
drift rates at the end of this phase. In cases where the record lasted longer
than the duration of phase 1 and 2, the drift rate either remained constant or
decreased slightly throughout the third phase, until the end of the
record.
There were large individual variations in drift rate changes over the
course of the trip (Fig. 4).
While some seals (e.g. 26629_99 and 28497_99) appeared to have a continuous
period of negative change followed by a continuous period of positive change,
others (most notably Billie_00 and Cleo_00) switched repeatedly between
positive and negative change. The transitions between phases based on daily
change in drift rate corresponded reasonably well with previous criteria using
daily travel rate. For instance, the switch from negative to positive change
in drift rate after the initial `transit' period was generally associated with
a sudden decrease in travel rate (Fig.
4). In general, the 90th quantile regressions of daily change in
drift rate against daily travel rate indicated an upper `edge' in the change
in drift rate for any given daily travel rate. Although there were large
individual variations, seals generally showed positive changes in drift rate
only during days when their travel rate was less than 50-90 km
day-1 (median = 75.1 km day-1;
S.D. = 31.3 km day-1), while negative changes
in drift rate occurred at all travel rates
(Fig. 5).
|
|
Temporal resolution of time series
The range of residuals of all drift rates for a given day over the fitted
value for that day from the spline function varied between individuals from
about -0.59 cm s-1 to 0.39 cm s-1. The theoretical
estimate of the total change in drift rate over the course of the assumed
100-day feeding period was 20 cm s-1, corresponding to a daily
change of 0.2 cm s-1. The residual corresponding to the maximum
kernel probability density was 1.31±0.43 cm s-1 (all seals
combined). When we divided the residual values for each individual seal by the
estimated mean daily change (e.g. 1.31/0.2), we obtained time intervals of
6.45±2.10 days (median = 6 days), over which any changes in drift rate
are likely to reflect a biological trend as opposed to random variation.
Theoretical model of drift rate and body composition
Our mechanistic model of the predicted drift rates of an average-sized seal
across the range of lipid contents is illustrated in
Fig. 6. The three curves
illustrate the variation in the relationship between lipid content and drift
rates using various CD values, while the vertical
histogram represents the frequency distribution of all observed drift rates.
The relationship between body composition and predicted drift rate shows a
clear sigmoid relationship with a high rate of change in drift rate for seals
close to neutral buoyancy. This shape is a result of the quadratic
relationship between velocity and drag (see equation 8). Using the
CD of a cylinder (1.17), the predicted drift rates ranged
from -34.2 cm s-1 for a seal with 10% lipid to 27.5 cm
s-1 for a seal with 60% lipid. The corresponding values using the
CD of a sphere (0.47) are -52.9 cm s-1 and 42.5
cm s-1, respectively, while the CD of a harbour
seal swimming headfirst (0.09) results in extreme values of -123.5 cm
s-1 and 99.2 cm s-1, respectively.
|
1. Bias due to residual air in the lungs
Our model suggests that the buoyant force caused by residual air in the
lungs can result in a significant positive bias in drift rate
(Fig. 7A). At shallow dive
depths, this extra buoyant force can be of similar magnitude to that caused by
body lipid. For instance, at a depth of 10 m, the positive buoyant force
attributed to lipid ranges from 22 N to 120 N over the range of lipid
contents while the corresponding value for residual air is
28-14 N over
the same range. This means that at 10 m, residual air can provide more than
half the total positive buoyant force for a lean seal (
64% at 10% lipid),
while for a fat seal (60% lipid) residual air provides just over 10% of the
buoyant force. This bias is substantially reduced at greater depths because of
the exponential decrease in volume with increasing depth. For instance, at 50
m, residual air provides
32% of the buoyant force for a seal with 10%
lipid, while for a seal with 60% lipid, air provides less than 4% of the
buoyant force. At 100 m, these figures are further reduced (
20% at 10%
lipid and
2% at 60% lipid).
|
Fig. 7B shows the relationship between lipid content and the expected drift rate for the model seal at the same depths as in Fig. 7A.
2. Error due to variations in body surface area and volume
Fig. 7C illustrates the
relationship between lipid content and theoretical drift rate, allowing for
variations in body surface area from 0.5 m3 to 1.5 m3.
These errors are more pronounced towards the extremes of lipid contents and
drift rates, while they approach zero for seals near neutral buoyancy.
3. Error due to variations in seawater density
The range of seawater temperature and salinities likely to be encountered
by an elephant seal from Macquarie Island corresponds to a seawater density
range of 1.027-1.030 g cm-3. This represents about 2.5% of the
range of densities for seals over the range of lipid contents (0.982-1.100 g
cm-3) and would result in a maximum error in predicted lipid
content of 1.3% (Fig. 7D).
Model evaluation
1. Simulation test
Our sensitivity analysis indicated that the prediction accuracy was highly
dependent on which parameters were kept constant and which were randomly
selected from the pre-defined probability distributions. When all parameter
values were randomly selected, 95% of the residuals of predicted vs
actual lipid contents were between -7.3% and 6.4% lipid
(Table 2). When seawater
density was kept constant at 1.028 g cm-3 while allowing all other
parameters to vary, this error range was reduced by only about 0.5%. The error
reduction was slightly larger when we kept body surface area and volume
constant while allowing all other parameters to vary (error reduction
5.8%). The most dramatic reduction in prediction errors was achieved when
the CD was kept constant at 0.83, resulting in errors
ranging from approximately -2.6% to 1.8% lipid. Our results indicate that
while uncertainties in seawater density and body surface area and volume
together are not likely to result in prediction errors of more than about
±2.5%, uncertainties in CD can cause errors in
lipid content of over 10%. Furthermore, the errors were highly dependent on
the magnitude of positive or negative drift rate. The smallest errors were
calculated for drift rates around zero while errors increased towards the
extremes of the range of our data.
|
|
|
Minimum SSR criterion. The SSR was obtained using a CD of 0.69 (SSR=19.03; Fig. 8). Using this CD, the mean absolute difference between predicted and measured lipid contents was 1.39% (Table 3). The slope of the linear regression of measured against predicted drift rates was less than 1 [%TBLm=0.71(%TBLp)+12.37; F1,10=29.69, r2=0.72], but the deviation from a slope of unity was not quite significant at the 0.05 level (t-test: t=-2.20, P=0.052). Lipid contents predicted from drift dives were underestimated for leaner seals compared with the measured lipid contents, while they were overestimated for fatter seals (Fig. 9).
|
Slope=1 criterion. We obtained a slope coefficient of 1 using a CD of 0.49 (%TBLm=%TBLp+1.80; F1,10=29.69, r2=0.72; Fig. 8). This CD resulted in predicted lipid contents that were on average 1.86% lower than those measured using labelled water (Table 3), and, because the slope was one, this bias was constant throughout the range of lipid contents (Fig. 9).
Using the CD values obtained using the two criteria, we
estimated the lipid content of each seal when this had reached its minimum
value, i.e. at the transition between Phases 1 and 2
(Table 3). Using the minimum
SSR criterion (CD=0.69), the predicted lipid contents
ranged from 23.3% to 32.7% (mean=27.6%), while the slope=1 criterion gave a
higher mean (30.1%) but a smaller range (27.1-33.8%). However, the lowest
predicted values generally occurred within 30-100 days after departure or
80-150 days after weaning but varied substantially between individuals
(Table 3).
![]() |
Discussion |
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---|
General temporal changes in drift rate
The general division of the trips into three phases based on the first and
last days of positive change in drift rate (Figs
3,
4) agreed well with the
transition days based on the travel rate threshold used by, for example, Le
Boeuf et al. (2000) and
McConnell et al. (2002
). Our
data suggest that the initial 30-50-day period is characterised by relatively
high travel rates and a gradual decrease in buoyancy, presumably as a result
of depletion of onboard lipid stores. The switch from a decrease to an
increase in buoyancy is not abrupt and is followed by a longer period
(
100 days) of overall increase in drift rate and buoyancy caused by
increasing lipid stores. While some individuals show a gradual increase in
drift rate and buoyancy, others appear to go through cycles of energy gain and
energy loss (Fig. 4). These
cycles of increase and decrease in drift rate are sometimes correlated with
travel rate, possibly reflecting seals moving between profitable prey patches.
At other times, seals appear to be in negative energy balance despite slow
travel rates (Figs 4,
5), possibly reflecting
foraging in less profitable patches. During the last phase, there was again a
slight decrease in drift rate and buoyancy while seals were in transit back to
the island.
The correspondence between travel rate and change in drift rate is not
perfect, and there often appears to be a time lag between changes in travel
rate and changes in drift rate. For instance, the ingestion of a prey item may
not necessarily be followed by an immediate positive change in drift rate.
Although lipid from prey may be assimilated into the blubber tissue relatively
rapidly, the excretion of residual material as faeces may take longer. These
residual materials are likely to be of similar or higher density to that of
seawater and may result in a decreased overall buoyancy of the animal.
However, captive studies on juvenile southern elephant seals indicate that the
passage rate of faecal matter is in the order of 10-20 h
(Krockenberger and Bryden,
1994), and the delay between prey assimilation and increased
buoyancy caused by this factor is therefore unlikely to be significant over
greater time scales.
The time lags between changing daily travel rates and daily change in drift
rate may occasionally be the result of sampling errors. For instance, the
daily travel rates have been calculated from ARGOS locations, which are
subject to error (e.g. Vincent et al.,
2002). However, we expect these errors to be relatively
insignificant in relation to the distances covered by southern elephant seals,
especially since we have used daily locations based on averaging over several
location fixes.
Mechanistic model and sources of error
Drag, buoyancy and drift rate
The sigmoid shape of the relationship between lipid content and predicted
drift rate has important implications for using drift rate to predict body
composition. For animals close to neutral buoyancy, a small change in body
composition (and thus buoyant force) will cause a large change in drift rate,
while an equivalent change will result in a smaller change in drift rate for
fatter and leaner seals. We may therefore expect smaller prediction errors in
body composition around a drift rate of zero, while these errors should
increase towards both extremes of drift rate and lipid content.
Surface area and volume
For animals like elephant seals that perform extended foraging trips
lasting several months and that have a life history characterized by dramatic
cycles of fasting and feeding, surface area and body volume are likely to
change significantly over the course of the trip. This is particularly true in
the case of juvenile animals that grow continuously during the time spent at
sea. These changes will influence the drag resistance as well as the buoyancy
and may lead to errors in our predictions of body composition from the
observed drift rates. Because drag is directly proportional to the surface
area, errors in our estimates of this area will have greater influence on our
lipid content predictions towards the extremes of drift rate and lipid
content, whereas these errors should approach zero when the buoyancy
approaches neutral. However, based on all seals measured before and after this
first foraging trip (Sea Mammal Research Unit, unpublished data), surface area
for an average seal is estimated to change from 0.8 m3 at
departure to
1.1 m3 at return. For a seal with an average body
composition (
25-35% lipid), this would correspond to an error in
predicted lipid content of no more than
2%. This is further supported by
our simulation analyses, which suggest that errors in estimated surface area
and volume contribute only slightly to the overall prediction errors
(Table 2).
Residual air
As illustrated in Fig. 7A,B,
residual air in the lungs can have a significant influence on our predictions
of body composition, and this bias will depend on the depth over which the
drift segment occurs. Again, for an observed drift rate of -23 cm
s-1, we would predict a lipid content of 27% if residual air
is not accounted for, while the actual lipid content would be about 14%, 23%
or 25% if the drift rate was measured at 10 m, 50 m or 100 m depth,
respectively. This prediction bias can either be reduced, by excluding dives
shallower than a specified depth (e.g.
4% and 2% bias when excluding all
dives of <50 m and <100 m, respectively), or the bias can be controlled
for by estimating the residual lung volume for a given individual at a
representative depth of each dive (e.g. a depth halfway between the start and
end depths of the drift segment). We should also point out that seals in this
study showed a greater variability in drift rate during shallow dives (<100
m) compared with deeper dives. This may be a result of seals voluntarily
adjusting the volume of residual air to optimise buoyancy during shallow
dives. This effect would obviously disappear at greater depths due to the
exponential decrease in air volume by depth, reducing the variability in drift
rate. Minamikawa et al. (2000
)
showed that loggerhead turtles (Caretta caretta) perform dives during
which they remain at a particular depth without actively swimming and that
they select the depth in order to maintain neutral buoyancy. Although this
so-called `residence depth' is affected by the amount of air in the lungs,
turtles do not appear to actively determine this depth by adjusting the amount
of air. However, Sato et al.
(2002b
) argue that king
(Aptenodytes patagonicus) and adelie (Pygoscelis adeliae)
penguins voluntarily regulate the air volume at the start of a dive depending
on the expected duration of the dive in order to optimise the costs and
benefits of buoyancy.
Seawater density
Although elephant seals perform long migrations and deep dives, crossing
many sharp temperature and salinity gradients, the maximum range in seawater
density encountered by seals in this study is very small in relation to the
variations in body density, and our simulation tests showed that errors in
seawater density account for only a small proportion (<1%) of the overall
errors (Table 2;
Fig. 7D).
Drag coefficient
Our simulation tests indicate that CD was the most
important factor contributing to the overall uncertainty in predicted lipid
content (Table 2), even though
the range of CD values in this analysis was limited to
values between 0.47 (sphere) and 1.17 (cylinder). Our previous analyses (see
Fig. 6) suggest that
CD values of the magnitude observed for various
streamlined objects (including fish and marine mammals) at similar Reynolds
numbers [CD 10-4-10-2
(Vogel, 1981
)] are
unrealistic, and we consequently excluded CD values
smaller than that of a sphere (0.47) from this analysis. Randomly selecting
the CD from values between that of a sphere and that of a
cylinder accounted for over 90% of the total error attributed by surface area
and volume, seawater density and CD combined.
Predicted and actual lipid content
There was a strong correlation between lipid content predicted using drift
rates and the lipid content estimated using labelled water
(Fig. 9). When volume, surface
area and residual air volume were estimated from morphometric measurements
prior to departure, we were able to estimate lipid content at the start of the
trip to within a few percent (Table
3). We were also able to obtain a good estimate of the most likely
range of drag coefficients by allowing this to be fitted as a parameter by the
model. Our results suggest that, while drifting passively through the water
column, the CD of our seals was in the range of 0.49
(using the slope=1 criterion) to 0.69 (using the minimum SSR criterion). At
Reynolds numbers between 104 and 106, the
CD of a sphere is
0.47 (see above), while a prolate
spheroid travelling crosswise has a CD of
0.59. It is
important to emphasize that we should not expect CD to be
constant. The drag force acting on animals is likely to vary as a result of
postural changes in response to perceived conditions, and this could have a
confounding effect on the interpretation of buoyancy data. However, if drift
dives are indeed resting dives, as was proposed by Crocker et al.
(1997
), it would make sense
for a seal to minimise its vertical movement while remaining passive. This
could be accomplished by using the flippers as brakes or trim tabs. Such
behavioural adjustments would further increase the drag force and may explain
the discrepancy between the CD value for a prolate
spheroid (
0.59) and our upper estimate (0.69).
Although our analyses have provided a good estimate of
CD for a phocid drifting passively in a direction
different from that of normal travel (headfirst), further experiments in the
laboratory and in the field may improve our understanding of this behaviour.
For instance, the CD of a seal or seal-like body moving at
different angles relative to the surrounding fluid could be measured in the
lab, where these angles, as well as the speed, can be controlled. Another
promising development is that of accelerometer loggers that can be attached to
animals to measure swimming activity and body orientation. These instruments
have already been used to identify fine-scale movements such as so-called
`burst-and-glide' swimming in free ranging cetaceans, seals and penguins (e.g.
Nowacek et al., 2001;
Sato et al., 2002b
). Sato et
al. (2003
) have also measured
the swimming activity and body orientation of free-ranging Weddell seals
(Leptonychotes weddellii) and found that, while fatter seals
predominantly showed burst-and-glide swimming on descent, leaner seals were
able to glide throughout most of this descent phase. These findings suggest
that it may be possible to monitor changes in body composition of seals that
do not regularly perform drift dives. Other indices may be used such as the
depth at which seals stop flipper beating on descent, the ratio of
burst-to-glide periods or the rate of deceleration of a seal after each
burst.
Although such experiments would improve the technique, our simple model
still allowed us to make some inferences about changes in body composition of
seals while at sea. At the start of their first trip, after the 5-8 week
post-weaning fast on land (Sea Mammal Research Unit, unpublished data), the
fattest individuals had a measured lipid content almost twice that of the
leanest seals (range:
23-46%; Table
3). Our model estimated that seals reduced their relative lipid
content by between
7% and
20% during the first
50 days of the
trip and that the lipid content at the transition between phases 1 and 2 was
23-33% (Table 3).
McConnell et al. (2002
)
estimated the time to protein and fat starvation for southern elephant seal
pups using measurements of mass loss and changes in body composition during
the post-weaning fast on land (Arnbom et
al., 1993
; Carlini et al.,
2001
), assuming that the metabolic rate was similar at sea and on
land. They estimated the mean time to protein and fat starvation,
respectively, as 70.2 days and 77.9 days for small (light) seals and 81.1 days
and 113.8 days for large (heavy) seals and suggested that, while light seals
would have reached critical levels at the end of Phase 1 (defined using the
travel rate criterion), heavy seals would still be in relatively good
condition at this transition. These estimates are generally lower than the
80-150 days between weaning and the occurrence of the minimum lipid
content estimated from drift rates in this study
(Table 3). However, the changes
in drift rate indicate that the change from negative to positive energy
balance was not abrupt. Instead, the rate of negative change started declining
around 25-30 days after departure (roughly 60-100 days after weaning; Figs
4,
6;Table
1), and, even at the time of minimum drift rate (and lipid
content), all our seals still appeared to have sufficient energy reserves
[i.e. a lipid content of
23-33% compared with the estimated critical
level of 10% (Cahill et al.,
1979
; McConnell et al.,
2002
)]. This may indicate that most pups start feeding gradually
while still in transit, thereby reducing their rates of energy depletion and
avoiding reaching critical levels. It is also possible that seals have a
suppressed metabolic rate while at sea compared with when resting on land
(Hindell and Lea, 1998
) and
that the times to fat and protein starvation reported by McConnell et al.
(2002
) are slightly
underestimated.
Conclusion
To our knowledge, this is the first time that changes in body composition
of free-ranging phocids have been estimated at sea. We have demonstrated that
a thorough analysis of drift rate is a valuable tool for monitoring the
changes in body composition of free-ranging marine animals and can be used to
predict body composition to within a few percent. Our model also allowed us to
estimate the minimum body composition of seals at the end of the transit from
the island to their foraging grounds. Our estimates suggest that the energy
stores of these naïve seal pups are unlikely to approach critically low
levels before the onset of feeding. We believe that this approach is a
valuable extension to the current use of data recorders and telemetry and that
it has the potential of providing more-accurate and finer resolution data on
important feeding areas and seasons for large marine predators and may
therefore be used as a basis for more informed management decisions for
species exploiting distant areas of the oceans over long time periods.
![]() |
Acknowledgments |
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