Accelerating estimates of activity-specific metabolic ...

Journal of Experimental Marine Biology and Ecology 385 (2010) 85–91

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Journal of Experimental Marine Biology and Ecology j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j e m b e

Accelerating estimates of activity-specific metabolic rate in fishes: Testing the applicability of acceleration data-loggers Adrian C. Gleiss a,⁎, Jonathan J. Dale b,c, Kim N. Holland b,c, Rory P. Wilson a a b c

Department of Pure and Applied Ecology, Institute of Environmental Sustainability, Swansea University, SA2 8PP Swansea, UK Hawaii Institute of Marine Biology, University of Hawaii at Manoa, P.O. Box 1346, Coconut Island, Kaneohe, HI 96744, USA Department of Zoology, Edmondson Hall, University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA

a r t i c l e

i n f o

Article history: Received 30 September 2009 Received in revised form 22 January 2010 Accepted 25 January 2010 Keywords: Acceleration Activity Data-logger Metabolic rate Overall Dynamic Body Acceleration (ODBA) Power Shark

a b s t r a c t We report the results of a series of experiments to test the utility of acceleration data-loggers for determining the energy expenditure of juvenile hammerhead sharks (Sphyrna lewini). In one experiment, three sharks were instrumented with miniature acceleration data-loggers and swum in a Brett-style respirometer at a range of speeds. For all three sharks, significant linear relationships were obtained between mean oxygen consumption · (Mo2) and Partial Dynamic Body Acceleration in the lateral and dorso-ventral axes (PDBAy,z) with high predictive power (r2 N 0.71). In a second experiment, PDBA was measured for sharks swimming freely in circular tanks. The free-swimming sharks exhibited wide ranges of PDBAy,z; routine swimming was characterised by low PDBAy,z (0.01–0.12 g) whereas unsteady swimming, (especially fast-start swimming of N 1 g) was characterised by high PDBAy,z. Despite initial evidence of linearity in the oxygen consumption vs. PDBA relationship, incorporating previous estimates of standard metabolic rate of hammerhead sharks suggests a non-linear fit. Further work is needed to establish the exact shape of the relationship beyond the narrow range of speeds that hammerhead pups could be exercised in this study, particularly the low swimming speeds which are frequently observed in free-swimming animals. © 2010 Elsevier B.V. All rights reserved.

1. Introduction The rate at which animals expend energy is a key parameter in understanding their ecology (Brown et al., 2004) from the individual to population level (Sims, 1999; Lowe, 2002). Animal fitness is closely tied to successfully maximising available resources through appropriate management of time and energy (Stephens and Krebs, 1986; Shepard et al., 2009), which ultimately determine survival and reproductive output (Lemon, 1991). Therefore, the ability to quantify key currencies (such as time and energy) provides detailed insight into how animals modulate behaviour according to circumstance and is instrumental in understanding ecology and behaviour (e.g. Sims, 2003). However, estimating energy expenditure in field-settings has proven to be difficult, particularly in fishes. Commonly used methods for avian, reptilian and mammalian subjects such as doubly labelled water and the heart-rate method (Butler et al., 2004) have limited applicability to fishes. Doubly labelled water incurs very large errors because the water flux of aquatic animals is larger than in terrestrial animals (Nagy and Costa, 1980). Despite heart-rate assays having proven successful in a number of teleost species (Rogers and Weatherley, 1983; Armstrong, 1986; Sureau and Lagardere, 1991;

⁎ Corresponding author. Tel.: + 44 1792 295376. E-mail address: [email protected] (A.C. Gleiss). 0022-0981/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jembe.2010.01.012

Lucas, 1994), general applicability appears to be limited (Thorarensen et al., 1996). This seems mainly due to the variability in the relationship between oxygen consumption (M o· 2) and heart-rate according to environmental circumstance, physiological state and large variability in cardiac output (Scharold and Gruber, 1991; Sureau and Lagardere, 1991; Thorarensen et al., 1996; Webber et al., 2001). A number of techniques have been employed to study activity and associated energetics of actively swimming fish in the field including determination of tail-beat frequency (TBF), (Lowe et al., 1998; Kawabe et al., 2003) and measurements of swimming speed (Block et al., 1992; Sundström and Gruber, 1998). However, swimming speed, and to a lesser extent TBF, are strongly tied to forced swimming in a flume as part of the calibration procedure and highly complex swimming patterns can be underrepresented. Fish in the wild often exhibit rapid changes in speed and trajectory with associated variability in tail-beat kinematics (Boisclair and Tang, 1993; Trudel and Boisclair, 1996; Tang et al., 2000; Webber et al., 2001). Other methods that have been used to measure the activity and energetics of free-swimming fish include video-systems to track the swimming paths of fish, which allow calculation of the cost of performing these activities based on hydrodynamic theory (Krohn and Boisclair, 1994; Trudel and Boisclair, 1996; Aubin-Horth et al., 1999). Although this technique accurately reflects the complexity of swimming manoeuvres, investigations are limited to fish in captivity or those which only move over a very limited and predictable range.

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A.C. Gleiss et al. / Journal of Experimental Marine Biology and Ecology 385 (2010) 85–91

Due to the primary limitations discussed, none of these techniques have been commonly adopted. The most common approach of studying the energetics of active fishes in the field is monitoring electromyograms (EMG) of the epaxial musculature. EMG telemetry has most frequently been employed in freshwater ecosystems (Cooke et al., 2004), although increased commercial availability of acoustic EMG transmitters will likely expand this method to marine systems. While this technique has substantially advanced the field of behavioural energetics (of salmonids in particular), the requirements of significant animal handling and associated surgery limit its applicability to robust species. Furthermore, EMG electrodes are usually only implanted into either red or white muscle fiber and therefore fail to monitor all movements of the subject. The capacity to monitor the locomotory activity of subjects using acceleration data-loggers has expanded rapidly in recent years (Tanaka et al., 2001; Whitney et al., 2007; Shepard et al., 2008; Rutz and Hays, 2009), and has led to the development of analytical tools to estimate energy expenditure such as deriving Overall Dynamic Body Acceleration from tri-axial acceleration recorded at high frequency (ODBA; Wilson et al., 2006). Here the two components of total acceleration; gravitational acceleration (which gives information regarding the orientation of the logger with regard to the earth's gravitational field; cf. Tsuda et al., 2006) and dynamic acceleration (which is a function of the animal's movement; Wilson et al., 2006) are separated and the resultant dynamic component used as a proxy for energy expenditure. ODBA is based on the concept that by removing all gravitational acceleration experienced by the animal, all remaining (dynamic) acceleration is a function of the animal's movement and is therefore proportional to the amount of ATP consumed in muscular contraction (Gleiss et al. submitted, Wilson et al., 2006 ). This technique has been validated using gas-respirometry for a number of vertebrates and has shown promise for the determination of energy expenditure in the field (Green et al., 2009; Halsey et al., 2009). However, this approach has not been tested in fishes. The tail-beat of a fish can simply be characterised by its frequency and amplitude (Bainbridge, 1958) and the combination of both should determine the cost of any one tailbeat. ODBA, which incorporates both the frequency and the amplitude of any tail-beat, should consequently be well suited in determining metabolic cost of swimming of different intensities (Gleiss et al., 2009). In this paper we assess the power of the accelerometry method to study the energetics of fish, using juvenile hammerhead sharks (Sphyrna lewini) as a model species. We report a combination of experiments conducted in a Brett-style respirometer and on captive sharks swimming freely in a circular tank and an outdoor pond.

section of 100 cm × 40 cm × 60 cm and a vertical standing loop of 30.5 cm diameter pvc pipe containing a variable speed of 12 V impellor responsible for the circulation of water through the system. Oxygen concentration was measured with a fiber optic oxygen sensor (Golden Scientific, DO400) interfaced with a notebook computer. Temperature was continually monitored with a temperature probe (Golden Scientific, TProbe-403) connected to the oxygen sensor and salinity (YSI 85) was measured at the beginning of each experiment. All equipment was calibrated to the manufacturer's specifications. 2.3. Data-loggers Animals were equipped with tri-axial acceleration data-loggers powered by 2 3 V coin cells in series (Panasonic BR2032), waterproofed using heat-shrink tubing and sealed using hot melt general purpose adhesive (Tec-Bond 232/12, Power Adhesives Ltd., Essex, UK). The complete package measured 25 mm length, 25 mm height and 5 mm depth with a mass of 18 g, representing a minor increase in body mass of the animals (range of 510–710 g). The system was designed to fit around the first-dorsal fin, with the logger located on the right-side and the battery on the left. The connecting cable was contained in the heat-shrink tubing, which ran posterior to the trailing edge of the fin. Devices measured acceleration in 3-orthogonal planes (cf. Fig. 1); heave (dorso-ventral acceleration), sway (lateral acceleration) and surge (anterior–posterior acceleration — not shown in figure) with a range of 0–6 g. Device output was calibrated by rotating the device through known angles to real g (9.8 m s−2). Devices were set to record all acceleration channels at a frequency of 20 Hz during respirometry and between 10 and 32 Hz during freeswimming trials. 2.4. Experimental protocol Animals were removed from holding tanks using dip-nets and manually restrained while data-loggers were fixed to the animal by a stainless steel pin and two small disks (diameter 4 mm) that passed through the anterior portion to the dorsal fin and held the recording device to both sides of the fin (Fig. 1). Total time for device attachment was less than 1 min. After sharks had been equipped with dataloggers, they were immediately placed in the swimming section of the flume at the lowest speed intended for that particular trial. The swimming section was sealed once the shark adopted a steady, horizontal swimming position in the flume. Experimental trials consisted of sharks being swum at a range of arbitrary speeds for at

2. Method 2.1. Collection and maintenance of sharks Scalloped hammerhead (S. lewini) pups were collected from Kaneohe Bay (Oahu, Hawaii; 21° 26.1′ N 157° 46.8′ W) using handline fishing gear with barbless hooks and transported back to recirculating seawater tanks (4 m diameter and 1 m depth) at the Hawaii Institute of Marine Biology (HIMB) during September 2008. Transport time was less than 5 min, during which sharks were kept in small seawater tanks (approximately 50 l). At HIMB animals were maintained and fed a diet of fish and squid. Sharks were not used in any experiments before they were willing to take food in captivity, which was usually 1 day post-capture. 2.2. Respirometry equipment Trials were conducted on site at HIMB in a custom-built flume, previously described in Lowe (1996). In brief, the flume was a 635 l Brett-style respirometer, consisting of an observation chamber

Fig. 1. Schematic diagram of a hammerhead shark pup (Sphyrna lewini) and the location of the dorsal fin mounted acceleration data-logger. Arrows indicate the directional sensitivity of the acceleration axes which were used in the analysis, namely swaying (lateral) and heaving (dorso-ventral) acceleration. Surge is not shown, as it was not used in any analysis (see Results).


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Fig. 2. Representative traces of shark #6 swimming in the flume at three different activity levels. Shown are dynamic acceleration traces which were calculated according to the method of Shepard et al. (2008). PDBAy,z represents the sum of the absolute values of dynamic heave and surge. Oxygen consumption was calculated from the regression obtained from shark #6 (Table 1). Note: the three sections of data have no temporal relationship.

least 30 min at each individual speed. Speeds were progressively increased, unless sharks would have trouble adjusting to the increased speed (evident by frequent turns in the flume), in which case the speed was decreased again. Trials were terminated, either when sharks stopped swimming or the oxygen concentration dropped below 70% saturation. After trials were terminated, the respirometer was run without animals to check for background respiration, which was subsequently subtracted from estimates of M. o2 (Lowe, 2001). In addition to respirometry trials, loggers were attached to 3 sharks in circular holding tanks and 1 shark in an outdoor pond. The pond enclosure was 10 × 20 m and consisted of a sand channel (max depth 3 m) surrounded by coral and a shallow rubble flat (max depth 1 m). All trials were conducted over 24 h while animals were undisturbed. We analysed both the entire data-set, as well as individual arbitrarily selected periods of non-steady-swimming patterns performed by the sharks (e.g., fast-starts and swimming patterns consisting of frequent changes of direction and speed). 2.5. Data analysis Oxygen consumption of sharks was determined by calculating the decline of dissolved oxygen over 15 minute periods and standardized by mass. Concomitant acceleration signatures were extracted and analysed using custom-written software; Snoop (Department of Computer Science, Swansea University) and Origin Pro 8 (Origin Lab Corp., Mass., USA). Statistical tests were performed using SPSS 16 (SPSS Inc., Il., USA). After determining the ideal interval to smooth acceleration data and yield static acceleration (measured acceleration due to earth's gravity) by weighted smoothing (Shepard et al., 2008), static acceleration was subtracted from total acceleration in each individual channel to yield dynamic acceleration. Heaving and swaying dynamic accelerations were converted to absolute values and added to yield Partial Dynamic Body Acceleration of the y (Heave) and z (Sway) planes (PDBAy,z; Halsey et al., 2009). Only PDBAy,z/M o· 2 pairs where animals were swimming horizontally for the entire time (and without losing position in the flume), were used for analysis. All such viable data were subsequently fitted with standard linear regressions. A General Linear Model was used to

test for individual shark effects on the relationship between PDBA and · M o2 as well as the interaction between shark and PDBAy,z. Tukeys pairwise comparisons with a family alpha of 0.05 were used to compare any significant differences between sharks. Acceleration data obtained from sharks swimming in tanks, as well as the single shark swimming in the pond were treated in the same fashion regarding the derivation of gravitational acceleration and the resultant PDBAy,z. Mean PDBAy,z was calculated for periods of 2 s, to smooth the distinct peak and trough of the tail-beat cycle (cf. Gleiss et al., 2009) and separated into bins of 0.005 g. Frequency counts of PDBAy,z were subsequently compared using chi-squared goodness of fit tests to determine if there were differences in the observed frequencies. The level of significance was set at p b 0.05 in all cases. We used the method detailed in Shepard et al. (2008) to determine the optimal smoothing interval to remove gravitational acceleration from our data. We found PDBAy,z and gravitational acceleration to stabilize at a weighted average of approximately 1 s, which was subsequently used to determine gravitational acceleration. Due to large amounts of device related “noise” in the surging acceleration channel, we decided to exclude this channel in any analysis, to ensure that dynamic acceleration values would not be artificially raised (this “noise” was evident in the acceleration channel even while loggers were at rest and is therefore not due to animal movement). It was hence deemed necessary to use PDBAy,z rather than ODBA. In addition, the majority of acceleration present in dorsal-fin mounted data-loggers is measured in the swaying and heaving dimensions rather than in the surging dimension ( Shepard et al., 2008; Gleiss et al., 2009). 3. Results Respirometry trials were attempted with a total of 13 sharks, yet only 3 sharks (mean mass = 613 ± 116 g) were able to swim for at least 150 min (over a range of speeds) and provided sufficient data for analysis. Downloaded acceleration data showed distinct peaks and troughs corresponding to tail-beat activity and associated acceleration amplitude in the swaying acceleration and to a lesser extent in the heaving acceleration (Tanaka et al., 2001; Kawabe et al., 2004) and these were represented in our calculated activity metric PDBAy,z (Gleiss et al., 2009).

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significantly higher than shark #2 (p = 0.02) and #6 (p = 0.01). There was no significant difference in intercepts for sharks #2 and #6. Free-swimming sharks exhibited a wide range of PDBAy,z values (Table 2), the majority b0.15 g (98.7% ± 1.1 SD). The frequency distributions differed significantly between individuals (χ2 = 106, df = 78, p b 0.001). Large PDBAy,z values were generally associated with non-steady swimming, such as fast-starts and rapid turns (Fig. 5), which were evident from acceleration traces (Gleiss et al., 2009). 4. Discussion

Fig. 3. Best-fit linear relationships obtained for shark 12 (■), shark 2 (○) and shark 6 (●).

Mean temperature was 26.1 ± 0.3 °C and salinity was 36‰ during all trials. Measured oxygen consumption in the flume ranged from a minimum of 329 mg O 2 kg −1 h −1 to a maximum of 701 mg O2 kg−1 h−1. Sharks #2 (329–596 mg O2 kg−1 h−1) and #12 (435–701 mgO2 kg−1 h−1) exhibited wide ranges of M o· 2 whereas shark #6 exhibited a much smaller range (478–602 mgO2 kg−1 h−1). We were able to extract between 6 and 9 pairs of Mo· 2 and PDBAy,z for each shark for periods where animals would maintain position in the flume (Table 1). Unfortunately, we were not able to determine the flow speed accurately, due to the control system of the flume (control of the impeller was continuous rather than graded) as well as fluctuations in the battery voltage, which resulted in speeds being somewhat variable within and between trials and prevented accurate calibrations being performed. We subsequently treated data independent of speed. All 3 sharks yielded significant positive linear relationships of M o· 2 and PDBAy,z (Fig. 3) with individual r2 N 0.71 (Table 1). The best-fit for the combined data was M o· 2 = −149 + 6521 × PDBAy,z (r2 = 0.60, p b 0.0001). The General Linear Model found no significant differences in slopes between individual sharks for the relationship between PDBAy,z and M o· 2. However, there were significant differences in intercepts (F = 6.88, p = 0.006). The intercept for shark #12 was

Table 1 · Details of linear relationships of mean oxygen consumption (Mo2) against Partial Dynamic Body Acceleration XY (PDBAx,y) for individual sharks, as well as the combined · data-set, according to Mo2 = c + B × PDBAy,z. Shark ID

n

Coefficient B

Intercept c

r2

F-value

p

#2 #6 #12 Combined

9 6 8 23

11,387 6206 5662 6521

−640 −149 −1 −144

0.81 0.71 0.78 0.60

30.63 9.82 20.70 32.01

b0.001 b0.05 b0.01 b0.0001

Table 2 Descriptive statistics of PDBAy,z values recorded of sharks swimming freely in a large tank and outdoor pond. Shark ID

Mean PDBA [g] (± SD)

Minimum PDBA [g]

Median PDBA [g]

Maximum PDBA [g]

Pondshark #1 Tankshark#1 Tankshark #2 Tankshark #3

0.055 0.045 0.072 0.062

0.020 0.012 0.027 0.013

0.04834 0.04103 0.06447 0.05441

2.72291 2.37906 2.50281 2.04208

(± 0.034) (± 0.027) (± 0.031) (± 0.041)

Our results demonstrate that measures of body acceleration, such as PDBA,y,z provide new activity metrics and as such open new avenues for estimating activity-specific metabolic rate in fishes (cf. Wilson et al., 2006). Our results add further evidence to the general notion that acceleration can be employed as a proxy for energy expended due to movement and can be used to estimate activity associated costs in the field (Wilson et al., 2006; Green et al., 2009). Although predictive power was generally high for individual animals, it was lower than expected, especially compared to recent experiments conducted on terrestrial animals during locomotion, where r2 routinely exceeded 0.9 (Halsey et al., 2009). We cannot see any reason why predictive power should be intrinsically lower in fishes than for terrestrial locomotion and therefore suspect that our results were due to experimental error associated with the respirometry flume. We believe that variability in swimming speed (even during a supposed selected speed) and potential “wall’ effects may have resulted in variable locomotive effort and metabolic requirements. This however, is difficult to assess in closed respirometry systems. In addition, the hammerhead pups are a difficult model organism due to the relatively narrow range of activity levels they are willing to adopt while swimming in the flume (see later in Discussion) and their tendency to not adapt well to the confines of the respirometer. There were significant differences in the intercepts between shark #12 and those of #2 and #6. This could be a function of different physiological states of the animals (i.e. differences in basal metabolism affecting the intercept in our relationship), or slight variations in the positioning of the acceleration sensor in relation to the degree of amplitude perceived by the logger. Amplitude of the tailbeat increases from the head to the tail, yet the largest relative change in amplitude with regard to axial position occurs around 0.4–0.6 total length (Webb and Keyes, 1982), near the dorsal fin in our case. Small variations in position of the data-loggers (with regard to axial position) in this area will thus have the largest effects and may be responsible for varying degrees of perceived acceleration for a given activity level between individuals. This will have to be considered in future studies. Attaching devices to a region of the body where changes in amplitude are minimal with regard to axial position would minimise this error, yet the relative ease of dorsal-fin mounts might outweigh any added benefit. Even more so, the problems of placement are also likely exacerbated in smaller fish, such as the hammerhead pups in this study, where small variations in placement might not be directly obvious. Although we found significant linear relationships for all three sharks, sharks in the pond and tank exhibited significantly lower PDBAy,z than sharks exercised in the respirometer (Fig. 4e). If the regressions obtained from sharks in the flume are applied to these data, estimates of metabolic rate are erroneous as they provide estimates below the calculated SMR of 189 mg O2 kg−1 h−1 for hammerhead pups by Lowe (2001). The requirement of hammerhead sharks to ram ventilate imparts a limit to the lowest speed at which they could be swum. Because of this limit, only a relatively narrow range of speeds could be utilized and resting metabolic rate cannot be directly quantified and subsequently relies on regression techniques. In our case, this was particularly evident, as the majority of activity levels exhibited by free-swimming animals were even lower than


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Fig. 4. Frequency distributions of PDBAy,z [g] for free-swimming sharks in the holding tank and pond (a–d). Note that the majority of recorded acceleration was smaller than recorded during forced-swimming in the flume (e), being a result of the sharks not being willing to swim at their slowest speeds in the flume. Only few higher acceleration values were recorded, which are not shown in this figure as they comprise b 1.3%.

those in the flume, thus only giving a very limited calibration range (Fig. 4e). Future studies need to ensure that all activity levels that sharks are likely to exhibit in the wild can also be calibrated in

validation experiments. This small calibration range has repercussions for determining the appropriate curve to fit the data. In terrestrial locomotion, ODBA (and PDBA y,z ) scale linearly with energy

Fig. 5. Dynamic acceleration traces during 3 distinct unsteady swimming manoeuvres of a shark swimming in a large tank. A) Swimming pattern following the tagging procedure, consisting of a rapid sprint; B) Excited swimming pattern, consisting of rapid turns, and irregular tail-beat activity and C) of a fast-start performed (mean PDBAy,z is given for the duration of the fast-start only). Note the changes in PDBAy,z according to measured acceleration (particularly in relation to steady-swimming, cf. C just before the fast-start or cf. Fig. 2). Note: the three sections of data have no temporal relationship.

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·

Fig. 6. Linear regression of log transformed Mo2 data and PDBAy,z for all three sharks. Note the extrapolated intercept (Δ) in comparison to that of Lowe (2001; ).

expenditure ( Wilson et al., 2006; Halsey et al., 2008) as a result of the linear increase of work performed by the centre of mass with increasing speed in relation to oxygen consumption (Heglund et al., 1982). In swimming animals, work (and oxygen consumption) scales to the power of 2 with speed or above and PDBAy,z (or ODBA) may do the same (Boisclair and Tang, 1993). Yet, if work performed by the muscle scales linearly with oxygen consumption, then the relationship between oxygen consumption and PDBAy,z should be linear, regardless of the medium the animal is travelling in. The intercept of the PDBAy,z/M o· 2 relationship should be near the theoretical resting metabolic rate (Graham et al., 1990; Lowe, 2001) of the animal being exercised (although this includes some postural costs; see Halsey et al. (2009)). This is not the case in our data as the intercepts are negative (Table 1), which is clearly not biologically accurate. Log transformed data on the other hand, provide the following · 2 equation: Log Mo 2 = 5.711 × PDBAy,z + 2.1255 (F1,22 = 30.8, r = 0.59, p b 0.0001). Extrapolating to zero experienced acceleration provides a theoretical SMR of 133 (±38) mg O2 kg−1 h−1, therefore providing results closer to those calculated by Lowe (2001) (see Fig. 6). Ultimately, the exact shape of the curve will only be resolved when data are available for fish swimming over a wide range of speeds in respirometers, which rarely occurs in ram ventilating sharks (Lowe, 2001). In addition, it seems that animal's in our study did not swim near their lowest potential swimming speed (as indicated by low values from free-swimming sharks in comparison to sharks in the flume, Fig. 4e). Despite attempts to swim the sharks at lower speeds in the flume, they would not swim at those speeds and tended to move erratically back and forth, which would ultimately result in their exhaustion and termination of the trial. Using the log transformed data, the lowest PDBAy,z value recorded of 0.012 (Table 2) would subsequently result in an oxygen consumption of only 156 mg O2 kg−1 h−1, which is the lowest measured activity cost in our study, possibly achieved by gliding for short periods. The lowest values that are commonly recorded were around ∼ 0.035 g (Tankshark #1, Fig. 4) corresponding to an estimated M o· 2 of 211 mg O2 kg−1 h−1, which is lower than minimum activity cost of 275 mg O2 kg−1 h−1 calculated by Lowe (2001). The generally low energy expenditure of instrumented animals is in accordance with our observations of freshly instrumented animal's showing depressed activity after tags were attached. This might be an artefact of the increased drag experienced by the sharks and handling stress. Although measurements of acceleration have previously been employed to study the energetics of swimming fishes (Kawabe et al., 2003), calculating TBF from peaks and troughs in the swaying

acceleration trace may severely underestimate the cost of nonsteady-swimming activities. These activities are associated with large changes of the acceleration (and hence force) amplitude. We cannot conclusively comment on the accuracy of the acceleration method to estimate non-steady-swimming patterns, because such manoeuvres cannot be performed in respirometers and hence do not allow for direct comparison. However, we note that non-steady swimming produces higher PDBAy,z than we observed in the respirometer or during steady swimming in tanks (Figs. 2 and 5), which is in agreement with empirical data showing that routine swimming in trout can be 8–20 times as energetically expensive as forced swimming in a respirometer (Tang et al., 2000). Yet, ram ventilating hammerheads appear to be spending relatively little energy while free swimming, only occasionally incurring higher energetic costs due to non-steady-swimming events (Figs. 2 and 4). This confirms that mostly straight trajectories with minimal acceleration and decelerations are energetically inexpensive, whereas changes in speed and/or trajectory appear to increase metabolic requirements substantially, as shown by the long tails in the frequency distribution of recorded PDBAy,z in free-swimming animals and the high values during non-steady swimming (cf. Figs. 2 and 5). A similar pattern was also observed in lemon sharks (Negaprion brevirostris), where fast starts had significantly higher ODBA compared to cruising swimming (Gleiss et al., 2009). The same study also showed a significant relationship between ODBA and TBF, although there was large variability in the relationship. The authors concluded that this variability was due to changes in swimming characteristics and concurrent variation in power requirements. In humans, different gaits can be associated with differing ODBA/M o· 2 relationships (Halsey et al., 2008) and a similar effect could be present in fish. Different behaviours may be powered by either red muscle (cruising) or white muscle (e.g. fast-start swimming). These muscle groups differ in their muscular efficiency; i.e. the amount of ATP consumed in relation to mechanical work conducted (Rome et al., 1988). Because of this difference, the relationship between energy expenditure and PDBAy,z (or ODBA) may vary according to behaviour. This also applies to fish using different gaits according to speed, i.e. pectoral propulsion followed by caudal propulsion in parrotfish (Korsmeyer et al., 2002). This paper provides the first direct evidence that acceleration may be used to quantify the costs of steady and, potentially, non-steady swimming of a fish in a field environment. The quantification of the cost of non-steady swimming is a missing piece of the puzzle in our understanding of the behavioural ecology of fishes and this technique may help fill a large gap in our understanding of the most enigmatic species. Whether or not ODBA can be calibrated using oxygen consumption (due to size limitation of respirometry chambers), it is still able to provide an estimate of relative power output, which may be sufficient for many questions that require attention. Even more so, the identification and quantification of the cost of ecologically meaningful activities will greatly enhance our ability to construct cost–benefit analyses and increase our understanding of decisions fish make in their natural environment. The high resolution of accelerometers allows the construction of detailed time-budgets (Tsuda et al., 2006), enabling researchers to assess key currencies in the ecology of fishes (namely, time and energy) which is not possible using any other technique. Acknowledgments ACG is supported by Swansea University and Wingate Foundation Scholarships. Funding was provided by a Rolex Award for Enterprise awarded to RPW. Wildlife Computers continual support in developing the Software “Snoop” is greatly appreciated. We would also like to acknowledge Chris Lowe for constructing the flume before this study was conceived and Roland Kano for logistical support in getting the flume operational. All procedures were approved by the University of Hawaii's animal care committee (08-488-2). [SS]


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