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First published online August 17, 2006
Journal of Experimental Biology 209, 3420-3428 (2006)
Published by The Company of Biologists 2006
doi: 10.1242/jeb.02346
Effect of temperature on maximum swimming speed and cost of transport in juvenile European sea bass (Dicentrarchus labrax)
1 Station Méditerranéenne de l'Environnement Littoral, 1 Quai
de la Daurade, Sète, 34200 France
2 Centre de Recherche sur les Ecosystèmes Littoraux
Anthropisés, Place du Séminaire, L'Houmeau, 17137 France
and
3 University of Bergen, Biology Department, Thormøhlensgt. 55, Bergen
5020, Norway
* Author for correspondence (e-mail: guy.claireaux{at}univ-montp2.fr)
Accepted 23 May 2006
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Summary |
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Key words: swimming, cost of transport, metabolism, temperature, sea bass, modelling
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of symbolsReferences |
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The ability to move is a fundamental property of animal life. Many factors,
such as predator-prey interaction, reproductive behaviour and habitat
selection, are of profound ecological importance and depend heavily on an
animal's capacity for movement (Baker,
1978
). Although rarely proven, intuitive logic would dictate that
the ability to swim factors into Darwinian fitness
(Arnold, 1983;
Nelson, 1989). Despite this
intuitive link to fitness, and despite the large number of studies conducted
to date, the impact of water temperature on locomotor performance in fish
still remains poorly described. This lack of understanding results mainly from
the diversity of physical, biochemical, physiological and behavioural
processes that are involved, and which interact at various organisational
levels. In two reviews (Johnston and Ball,
1996; Taylor et al.,
1997), a series of missing or incomplete pieces of information
that prevent us from making adequate predictions regarding the effects of
temperature on swimming performance were identified. These include the linkage
between muscle work and swimming speed, the nature of the functional limits to
swimming (see also Kieffer,
2000), the factors controlling swimming performance, the effect of
acclimation versus acclimatisation
(Butler et al., 1992), and
phenotypic plasticity (see also Johnston
and Temple, 2002). Moreover, although generally recognised, the
multifaceted environmental influence on fish swimming ability remains
insufficiently understood. For instance, Day and Butler compared the swimming
ability of brown trout (Salmo trutta) acclimatised to seasonal
temperature to that of individuals acclimated to reversed seasonal temperature
(Day and Butler, 2005). They
demonstrated that in the latter case full thermal compensation does not occur.
They suggested that swimming abilities are influenced by seasonal factors
other than temperature, possibly photoperiod (see also
Kolok, 1991).
Sea bass are active pelagic fish that occupy estuarine marine waters as
adults. They capture prey by pursuit, usually as part of a shoal when they are
juveniles. In addition, adult sea bass undergo substantial annual migrations
between coastal and offshore marine waters. Both juveniles and adults have
been observed holding station in strong tidal currents and are subject to
predation by marine mammals, pelagic fishes and several types of birds
(Pickett and Pawson, 1994).
Some degree of swimming performance is undoubtedly critical to the success of
individual sea bass (Dicentrarchus labrax). Therefore, understanding
the factors that control swimming performance should be important in
predicting a sea bass's chances of survival in its natural environment.
Management of the sea bass fishery in Europe is currently based on a package
of technical measures designed to protect juveniles and to improve recruitment
into the adult spawning stock. These measures are based on knowledge of the
distribution of the various life history stages, their growth, and
interactions with capture fisheries around the coasts of Europe
(Pickett and Pawson, 1994).
This information is important for understanding the relationship between adult
biomass and ensuing recruitment. However, knowledge of biological factors that
contribute to the recruitment into the reproducing population could vastly
improve management models and should be an important element of any ecosystem
approach to fisheries management. In the case of sea bass, the deficiency in
basic information is striking, particularly when it comes to defining
environmental tolerances and physiological optima. In recent years a
progressive northward spread of sea bass has been recorded along the western
coasts of Europe; this expansion is believed to be related to the warming of
the European Atlantic waters
(Quéro, 1998;
Quéro et al., 1998;
O'Brien et al., 2000;
Stebbing et al., 2002). As the
northern limits to the ranges of the species extend further, the effect on the
fisheries is likely to be important but remains very difficult to predict.
With this deficit of information as a background, the present experiment was designed to investigate the effect of temperature on the relationship between aerobic metabolism and swimming performance in acclimatised European sea bass. In order to improve our ability to predict the effect of water temperature on that relationship, locomotion and associated metabolic performance were examined in seasonally acclimatised individuals. Our objectives were: (1) to establish the link between active metabolic rate and maximum swimming speed, (2) to model the controlling effect of temperature on the cost of swimming and (3) to propose an integrated description of the interactions between temperature, metabolism and swimming performance in sea bass.
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Materials and methods |
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Respirometer
A Brett-type swimming tunnel respirometer (39 litre)
(Brett, 1964) was used to
measure fish oxygen consumption rate
(
) as a
function of swimming speed (U). The working section of the tunnel was
60 cm in length and 11.5 cm in diameter. A screen darkening the upstream
section of the swimming chamber motivated the fish to occupy an upstream
position. Water temperature in the respirometer was controlled within
±0.2°C with a Hubber (Offenburg, Germany) chilling unit connected
to a titanium heat exchanger. A variable-speed pump propelled the water at a
constant velocity. Plastic honeycomb and deflectors were inserted in the
recirculation loop to promote rectilinear flow and a uniform velocity profile.
Flow characteristics were visually assessed with dye flow patterns. A flow
meter (Marsh-McBirney 200, Frederick, MA, USA) mounted in the centre of the
swimming chamber was used to calibrate water velocity to voltage output from
the pump controller. Swimming speeds were corrected for solid blocking effects
using a procedure derived from Bell and Terhune
(Bell and Terhune, 1970):
 | (1) |
where VF is the effective water velocity,
VT is the average water velocity in the empty swimming
section and 
s is the fractional error in
VT due to blockage. s was calculated as
follows:
 | (2) |
with 
a dimensionless factor depending on the tunnel cross-sectional
shape (0.8), 
a shape factor for the fish [0.53(length/thickness)],
Ao the cross-sectional area of the fish and
At the cross-sectional area of the swim chamber.
Water oxygen concentration was recorded continuously using an Orbisphere
Laboratories (Neuchâtel, Switzerland) oxygen meter (2610) and probe
(27141) interfaced to a computer. In open mode, the respirometer was supplied
with fully aerated and thermoregulated seawater that circulated to a reservoir
tank at a flow rate of 4-5 l min-1. In closed mode, fish
was
calculated from the rate of oxygen depletion according to the equation:
 | (3) |
where
(mmol
kg-1 h-1) is oxygen consumption, slope (mmol
h-1) is the decrease in water oxygen content per unit time,
VOLresp (l) is the volume of the respirometer minus the
volume of the fish, and Mb (kg) is the mass of the
fish.
The level of oxygen saturation in the respirometer was never allowed to
fall below 85%.
values
resulting from a regression with a r2<0.95 were not
considered in the analysis. Oxygen consumption measurements were adjusted to a
standard body mass of 100 g using a mass exponent of 0.77
(Herskin and Steffensen,
1998). Prior to each experimental run, the oxygen probe was
calibrated in air-saturated water.
Experimental protocol
Experiments took place from December 1999 to June 2001, on fish seasonally
acclimatised to the environmental temperature prevailing in their rearing
facility. At each experimental temperature (7, 11, 14, 18, 22, 26 and
30°C) 7-10 fish were transferred to a 500 litre tank situated in a
thermoregulated room, with a photoperiod matched to the natural seasonal hours
of daylight and darkness. The temperature difference between the two rearing
facilities never exceeded 0.5°C. After 12-18 days (experimental
temperature, salinity 25%-28
), during which they were fed regularly,
animals were sequentially anaesthetised (2-phenoxyethanol; dilution 0.3 ml
l-1), weighed, and their length, width and depth measured. Fish
were then placed in the respirometer and left undisturbed overnight at a water
velocity of 10 cm s-1.
Each experimental run consisted of a 30 min period at each of the
designated speeds up to the maximum speed. The speed increment was 10 cm
s-1, with the speed raised at a rate of 4 cm s-1
min-1. At each speed, fish
was
calculated using the last 15 min of a 30 min O2 monitoring period.
After each run the fish was removed from the swimming chamber and the
respirometer was re-sealed for determination of background
by
micro-organisms, which was then subtracted from fish
measurements. Between experimental runs the entire apparatus was cleaned using
a chlorine bleach solution, flushed repeatedly and refilled with seawater.
Maximum swimming speed
The maximum swimming speed (Umax) that fish were able
to attain was determined by direct observation. The criteria that we used to
define Umax were similar to those used by Sepulveda and
Dickson (Sepulveda and Dickson,
2000), i.e. fish generally displayed burst-and-glide swimming mode
and
usually levelled off or decreased slightly
(Tang et al., 1994;
Drucker, 1996;
Sepulveda and Dickson, 2000;
Peake and Farrell, 2004).
Standard metabolic rate and active metabolic rate
For each temperature trial, an exponential regression equation was fitted
by least squares to the
versus swimming speed (U) relationships:
 | (4) |
where a, b and c are constants.
Standard metabolic rate (SMR) was determined by extrapolating the
corresponding
versus swimming speed relationship to zero swimming speed. The
metabolic rate associated with Umax was defined as the
active metabolic rate (AMR). After transforming Eqn 4, the optimal swimming
speed (Uopt) was calculated according to the following
equation (Videler, 1993):
 | (5) |
where the optimal swimming speed is the speed that minimises that the energy expenditure per unit of travel distance.
Swimming efficiency
The cost of transport (COT, in J km-1 kg-1) provides
an index of overall swimming efficiency; the lower the COT value, the more
efficient the locomotion. At each experimental temperature,
values
were converted to J km-1 kg-1 using an oxycalorific
equivalent [3.24 cal mg O2
(Beamish, 1978)] and converting
cal to J (4.18 J cal-1). The obtained values were divided by the
corresponding speed to obtain cost of transport. The relationship between COT
and swimming speed was plotted and an exponential regression was fitted using
the following equation (Videler,
1993):
 | (6) |
When plotted in relation to U, COT is a J-shaped function. As
swimming speed increases, the initial decline of COT is due to a decrease in
the relative contribution of SMR to the overall metabolic demand. At
Uopt, COT reaches a minimum and thereafter increases as
rises
with U. In order to avoid the temperature-mediated interaction
between Uopt and SMR, we calculated the net metabolic
demand
(
)
by subtracting the corresponding SMR from all
measurements. The net cost of transport (COTnet, in J
km-1 kg-1) was calculated according to the following
equation (Videler, 1993):
 | (7) |
where Enet, in J h-1 kg-1, is the
energy expenditure corresponding to
and U is in km h-1. Values of COTnet were then
plotted in relation to U and a regression was fitted using the
following equation:
 | (8) |
Statistical analysis and modelling procedure
Umax, SMR and AMR measures were examined for normality
and homogeneity of variance using Kolmogorov-Smirnov and
Fmax tests, respectively. No transformation was applied
prior to analysis. Umax, SMR and AMR data were analysed
via an ANOVA with water temperature being used as main factor. A
posteriori t-tests for comparison of means with 
=0.05 were applied
following ANOVA (Sokal and Rohlf,
1981). In order to combine the effects of temperature and swimming
speed on metabolic rate the following modelling procedure was applied. The
temperature-dependence of the parameters a, b and c (Eqn 4) were examined via
regression analysis. The resulting functions a=fl(T),
b=fl'(T) and c=fl''(T) were
then merged in Eqn 4 in order to draft a three-dimensional model:
 | (9) |
Throughout the manuscript, data are presented as mean ± 1 s.e.m. In all statistical analysis, P<0.05 was taken as the fiducial level for significance.
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Results |
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Energetics
At each experimental temperature a relationship between oxygen consumption
(mmol h-1 kg-1) and swimming speed (cm s-1)
was fitted by the least squares using Eqn 4
(Fig. 2). The values of the
corresponding constants and r2 are reported in
Table 2. The effect of
acclimatisation temperature on the relationship between swimming speed
(U) and cost of transport (COT) was examined using Eqn 6
(Fig. 3) using the appropriate
values for a, b and c given in Table
2. The optimal swimming speed (Uopt),
at
Uopt and COT at Uopt were also
calculated (Table 2). Values of
at
Uopt increased with temperature
(r2=0.93; P<0.01) but always represented
44.5±2.45% of AMR at the temperature under consideration. The
relationship between swimming speed and net cost of transport
(COTnet) showed that the cost of swimming at a given speed was not
influenced by acclimatisation temperature
(Fig. 4).
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Modelling
In order to investigate the effect of seasonal temperature changes on the
relationship between aerobic metabolism and swimming performance we designed a
model on the basis of Eqn 6. We first examined the influence of temperature on
the values of the constants a, b and c reported in
Table 2. Since b and c followed
no clear trend with temperature, average values were calculated (11.44 and
2.44, respectively). The value for parameter a was strongly
temperature-dependent. Since a is closely related to standard metabolic rate,
the value of parameter a was modelled as a function of temperature by fitting
the equation SMR=fl(T) proposed for sea bass
(Claireaux and Lagardère,
1999). Eqn 4 then became:
 | (10) |
The level of fit between observed
and
modelled
was
examined using a linear regression (y=0.9307x+24.27;
r2=0.82; F=1709; P<0.0001). From Eqn
10 we derived two equations (see Materials and methods for details). The first
one summarises the combined influence of temperature and swimming speed on
cost of transport.
 | (11) |
 | (12) |
This model is represented as a solid line on Fig. 4.
The various models and experimental data that describe the interactions
between temperature, metabolism and swimming performance are summarised in
Fig. 5. Open squares are AMR
and SMR ± s.e.m. (Table
1). Thick solid lines represent the consolidated models
AMR=fl(T) and SMR=fl(T), which were fitted
using the equations reported in Claireaux and Lagardère after combining
with the current data set (Claireaux and
Lagardère, 1999). Th dotted lines are `isospeed' curves
that were drawn using Eqn 10. Isospeeds depict the influence of water
temperature on the cost of swimming at a given speed. The point where one
given `isospeed' meets the AMR=fl(T) relationship indicates
Umax at the considered temperature. Closed triangles are
the metabolic rates at Uopt for the considered temperature
(Table 2).
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Discussion |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of symbolsReferences |
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; Taylor et al.,
1996; Day and Butler,
2005). While investigating the influence of seasonal changes in
water temperature we specifically examined the link between active metabolic
rate and maximum swimming speed. We then modelled the controlling effect of
temperature on the cost of swimming. Finally, we conducted an integrative
interpretation of the relationships between temperature, metabolism and
swimming performance in sea bass.
There are few published measurements of metabolic rate in European sea
bass. Oxygen consumption at increasing swimming speed was measured in 250 g,
20°C-acclimated individuals (Herskin
and Steffensen, 1998). The relationship established superimposes
nicely onto our own results. For instance at U=0.67 m s-1,
Herskin and Steffensen report
mmol h-1 kg-1. After correcting for the difference in
body mass, Eqn 10 predicts
mmol h-1 kg-1. Claireaux and Lagardère modelled
the effect of ambient temperature on bass' active and standard metabolic rates
(Claireaux and Lagardère,
1999). We compared both data sets and found a close match between
SMR values (Fig. 5). Values for
AMR were also very similar, although Claireaux and Lagardère's model
tended to underestimate AMR at both ends of the thermal range tested. Apart
from differences in the experimental approach we have no satisfying
explanation for this discrepancy. We can only emphasise the fact that in the
earlier study, fish were larger (500-600 g) and that only four temperatures
were tested (10, 15, 20 and 25°C), in contrast to the seven temperatures
in the present study. Nevertheless, both data sets were combined, and equation
2 [AMR=fl(T)] and equation 4 [SMR=fl(T)] from
Claireaux and Lagardère (Claireaux
and Lagardère, 1999) were fitted to the consolidated series
(thick solid lines in Fig.
5).
Because we were interested in linking aerobic swimming and metabolic
performances, we chose to examine the maximum swimming speed
(Umax; see Materials and methods) rather than the critical
swimming speed (Ucrit). Ucrit is
generally defined as the speed at which fish are unable to remove themselves
from the posterior grid of the swim chamber
(Brett, 1964). Consequently,
values for swimming performance reported in
Table 1 are slightly lower than
the critical swimming speeds found using a true Ucrit
protocol (Chatelier et al.,
2005). Nevertheless they are within the range of values reported
for most active teleosts (Beamish,
1978; Videler,
1993).
It has long been recognised that the swimming performance of fish is
influenced by water temperature (Beamish,
1978). In many fish species, maximum swimming speed or endurance
are reduced at low temperature, increase to a peak at an optimum temperature
and then decrease as temperature approaches the upper thermal limit
(Randall and Brauner, 1991;
Myrick and Cech, 2000;
Ojanguren and Braña,
2000; Lee et al.,
2003). This bell-shaped curve outlines the integrated temperature
optima for physiological processes related to swimming and it also prevails in
sea bass. Fig. 5 shows that at
temperatures above 22-24°C the increase in Umax with
temperature tends to level off, suggesting that the optimal temperature for
sea bass is being attained. Reasons for the drop-off as temperature departs
from optimal are many and are not fully elucidated. There is good evidence to
suggest that fish reach their maximum aerobic capacities (AMR) at swimming
speeds near Ucrit. This implies that as fish approach
maximal swimming, arterial blood is being pumped at the maximum rate by the
heart (Farrell, 2002), that
arterial blood is fully saturated with oxygen at the gills
(Randall and Daxboeck, 1982;
Thorarensen et al., 1993;
Gallaugher et al., 2001) and
that venous blood is maximally depleted of oxygen by the tissues
(Farrell and Clutterham,
2003). The present investigation does not provide direct evidence
to validate this view but the strong relationship that we found between AMR
and Ucrit as acclimatisation temperature increased
(Fig. 1) suggests that the
temperature-related changes in cardioresrespiratory performance support the
seasonal changes in maximum swimming performance. Along the same lines,
aerobic metabolic scope and swimming performance in rainbow trout were
positively related to in vivo (forced swimming test) or in
vitro (in situ perfused heart preparations) measurements of
maximum cardiac pumping capacity
(Claireaux et al., 2005).
Conversely, oxygen supply to the red muscle was not a limiting factor at
exhaustion in trout (McKenzie et al.,
2004) and Ucrit in chinook salmon is virtually
independent of functional haemoglobin concentration until [Hb] is less than
50% of control level (Brauner et al.,
1993). Moreover, Day and Butler convincingly demonstrated that
brown trout acclimated to reversed seasonal temperature displayed reduced
swimming ability (Ucrit)
(Day and Butler, 2005),
suggesting that more factors than just temperature determined the ability of
fish to swim maximally.
It has been shown that temperature has a significant effect on the maximum
velocity of shortening of muscle as well as on the maximum power production,
while having relatively little effect on sarcomere length excursions and tail
beat frequency (Rome, 1990;
Rome et al., 1992). On that
basis, questions have been raised concerning the mechanisms that allow fish to
produce the same movement at different temperatures despite the marked
susceptibility of muscle power-generating capacity to thermal conditions.
Electromyography studies have shown that during a standardised swim test, only
red muscle is recruited at low speed and that anaerobically fuelled, white
muscle fibers are recruited at high speed. These experiments also revealed
that the recruitment order of the different fibers does not change with
temperature but that white muscle fibers are recruited at much lower speed in
cold water (Rome et al.,
1984). Consequently, it is the compression of the speed range over
which motor units are recruited, and the early recruitment of anaerobic
fibers, that are believed to explain the reduction in maximal sustainable
swimming speed in cold conditions. Our own experimental results clearly
support this view. For instance Fig.
4 shows that while maximum swimming speed increases with
acclimatisation temperature, the cost of swimming at a given submaximal
swimming speed does not change. The lack of thermal effect on
COTnet, together with the relatively tight relationship between AMR
and Umax discussed above, suggest that changes in swimming
capacity largely result from variations in energy fluxes and support
metabolism, hence in scope for activity, rather than in properties of muscle
function per se (Rome,
1990; Taylor et al.,
1996). However, there are some examples in the literature where
swimming cost was shown to be influenced by water temperature (reviewed by
Johnston and Temple, 2002).
For instance, the energetic costs of swimming at a given speed increased with
temperature in the chub mackerel (Scomber japonicus)
(Dickson et al., 2002). Many
reasons can be put forward to explain these inter-species differences, among
which species-specific temperature acclimation responses or diversity in
molecular mechanisms (Johnston and Temple,
2002). These aspects have not been properly addressed in the
literature but their elucidation is a promising avenue for future
research.
The current work provides an integrated view of the influence of water
temperature on sea bass aerobic swimming performance. However, to convincingly
transfer this knowledge into an ecological context, three issues require
further investigation. The first concerns the relationship between optimal and
spontaneous swimming speeds. Throughout the literature the use of
Uopt during routine swimming by wild fish is often assumed
but this has not been satisfactorily demonstrated, essentially due to
technical limitations (Webber et al.,
2001a; Webber et al.,
2001b). This missing piece of information remains an obstacle to
establishing a reliable field energy budget in fish.
The second issue relates to the fact that although the study of locomotion
in fish has a long history, little effort has been allocated into
investigating the ecological relevance of inter-individual diversity in
swimming performance (Kolok,
1999; Plaut, 2001;
Nelson and Claireaux, 2005).
The basic patterns revealed by Fig.
5 are based on an `average fish' and therefore do not consider
inter-individual variability nor phenotypic plasticity. Identifying the
potential sources and determinants of such variation is beyond the scope of
this paper, but it clearly is the next step in linking locomotor performance
to distribution, abundance or fitness of sea bass. In this regard, dietary
studies have shown interesting features, including the influence of tissue
fatty acid profile on a number of performance traits including
cardiorespiratory and swimming performance
(McKenzie et al., 1998;
McKenzie et al., 2000;
McKenzie, 2001;
Chatelier et al., 2006).
Finally, swim tests and particularly Ucrit tests have
been used to investigate the relationships between swimming speed and
cardiorespiratory variables such as oxygen demand, cardiac and respiratory
frequencies, blood gas tensions and cardiac output. However, various authors
have questioned the adequacy of a swim-tunnel tests in replicating natural
swimming conditions and their energetic implications
(Tang and Boisclair, 1995;
Plaut, 2001;
Nelson et al., 2002). In this
regard, Peake and Farrell recently obtained clear evidence for the
interference imposed by the size of swim tunnel devices on gait transition
(Peake and Farrell, 2004).
Energetically speaking, the consequences of this interference remain to be
established, but linking the ecological function of swimming and its measure
in the laboratory certainly requires that this question be resolved
(Swanson et al., 1998;
Irschick and Garland,
2001).
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Acknowledgments |
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References |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionList of symbolsReferences |
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Johnston, I. A. and Temple, G. K. (2002).
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(Eqn 4). The corresponding values for a, b, c and r2 are
given in 
