Introduction

Many fish in natural water bodies are subjected to large diurnal or seasonal changes in temperature. Consequently, these organisms have developed a wide range of regulatory mechanisms aimed at preserving overall performance at a wide range of body temperatures.

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.

Materials and methods

Results

Metabolic rates and maximum swimming speed

Sea bass maximum swimming speed (U_max), active metabolic rate (AMR) and standard metabolic rate (SMR) increased significantly with acclimisation temperature (ANOVA, P<0.0001; Table 1). Over the whole temperature range tested, the calculated Q₁₀ values were 1.25, 1.63 and 1.71 for U_max, AMR and SMR, respectively. When the seven temperature trials were compared, a strong positive relationship between active metabolic rate and maximum swimming speed was observed (Fig. 1).

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Table 1.

Influence of acclimatisation temperature on sea bass standard metabolic rate, active metabolic rate and maximum swimming speed

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Fig. 1.

Relationship between active metabolic rate (AMR) and maximal swimming speed (U_max) at various temperatures. Solid line: AMR=0.033U_max+0.5051 (r² =0.97).

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) This model corresponds to the solid lines in Fig. 3. The second equation models the influence of swimming speed on the net cost of transport: (12)

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Fig. 2.

Influence of acclimatisation temperature on the relationship between oxygen consumption () and swimming speed (U). The solid line represents the best-fitting regression: (Eqn 4). The corresponding values for a, b, c and r² are given in Table 2.

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Fig. 3.

Relationships between acclimatisation temperature, cost of transport (COT) and swimming speed (U) established using Eqn 6 and the corresponding parameter values from Table 2. The solid lines are a plot of the 3-D model that combines the influence of the temperature and swimming speed on the cost of transport in sea bass, i.e. Eqn 11: COT=5.27[1-exp(-0.007T^1.65)]/U+(11.44U^2.44-1).

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 U_max at the considered temperature. Closed triangles are the metabolic rates at U_opt for the considered temperature (Table 2).

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Fig. 4.

Relationship between acclimatisation temperature, net cost of transports (COT_net) and swimming speed (U). Symbols are calculated COT_net using Eqn 7. Color-coding matches that in Figs 3 and 4. The solid line is a plot of Eqn 12: COT_net=11.44U^2.44-1.

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Fig. 5.

Summary of the interactions between temperature, metabolism and swimming performance. Open symbols are AMR and SMR ±95% confidence interval (Table 1). Solid bold lines represent consolidated models AMR=fl(T) and SMR=fl(T), which were fitted using the equations reported in Claireaux and Lagardère (Claireaux and Lagardère, 1999) and after combining the two data sets. Upper line: AMR=0.00013T^{(-0.033T+4.42)}+3.25; lower line: SMR=5.27[1-exp(-0.0077T^1.65)]. The dotted lines are `isospeed' curves plotted using Eqn 10. The point where one given `isospeed' curve meets the AMR versus T relationship is an indicator of U_max at the considered temperature (see Table 1). Closed symbols are at U_opt for the considered temperature (Table 2).

Discussion

To our knowledge the present study is one of the few that have investigated the effect of thermal acclimatisation upon metabolism and swimming performance in fish (e.g. Butler et al., 1992; 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 (U_max; see Materials and methods) rather than the critical swimming speed (U_crit). U_crit 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 U_crit 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 U_max 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 U_crit. 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 U_crit 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 U_crit 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 (U_crit) (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 COT_net, together with the relatively tight relationship between AMR and U_max 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 U_opt 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 U_crit 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).

List of symbols

AMR

active metabolic rate

A_o

cross-sectional area of the fish

A_t

cross-sectional area of the swim chamber

COT

cost of transport

E

energy expenditure

M_b

body mass

oxygen consumption

MS

metabolic scope

Slope

decrease in water oxygen content per unit time

SMR

standard metabolic rate

U_max

maximum swimming speed

U_opt

optimal swimming speed

V_F

effective water velocity

VOL_resp

volume of the respirometer minus the volume of the fish

V_T

average water velocity

ϵs

fractional error in V_T due to blockage

λ

shape factor for the fish [0.53(length/thickness)]

τ

dimensionless factor depending on the tunnel cross-sectional shape