Journal of Comparative Physiology B

, Volume 186, Issue 2, pp 193–204 | Cite as

The adenylate energy charge as a new and useful indicator of capture stress in chondrichthyans

Original Paper

Abstract

Quantifying the physiological stress response of chondrichthyans to capture has assisted the development of fishing practices conducive to their survival. However, currently used indicators of stress show significant interspecific and intraspecific variation in species’ physiological responses and tolerances to capture. To improve our understanding of chondrichthyan stress physiology and potentially reduce variation when quantifying the stress response, we investigated the use of the adenylate energy charge (AEC); a measure of available metabolic energy. To determine tissues sensitive to metabolic stress, we extracted samples of the brain, heart, liver, white muscle and blood from gummy sharks (Mustelus antarcticus) immediately following gillnet capture and after 3 h recovery under laboratory conditions. Capture caused significant declines in liver, white muscle and blood AEC, whereas no decline was detected in the heart and brain AEC. Following 3 h of recovery from capture, the AEC of the liver and blood returned to “unstressed” levels (control values) whereas white muscle AEC was not significantly different to that immediately after capture. Our results show that the liver is most sensitive to metabolic stress and white muscle offers a practical method to sample animals non-lethally for determination of the AEC. The AEC is a highly informative indicator of stress and unlike current indicators, it can directly measure the change in available energy and thus the metabolic stress experienced by a given tissue. Cellular metabolism is highly conserved across organisms and, therefore, we think the AEC can also provide a standardised form of measuring capture stress in many chondrichthyan species.

Keywords

Elasmobranch Fisheries Gillnet Shark Metabolism 

Introduction

Chondrichthyans (sharks, rays and holocephalans) are vulnerable to fishing pressures and approximately 25 % of all chondrichthyan species appear on the IUCN Red List of Threatened Species (Dulvy et al. 2014). Reducing mortality of bycatch species is a critical issue facing the fisheries management and conservation efforts (Frick et al. 2010a). The stress of fisheries capture can induce significant physiological disturbance, causing death or alterations to behaviour, growth and immunological function (Van Rijn and Reina 2010; Renshaw et al. 2012). Most studies measuring capture stress in chondrichthyans use stress indicators derived from blood biochemistry which show considerable interspecific and intraspecific variation, contributing to uncertainty when relating capture stress measurements to mortality (Skomal and Mandelman 2012). Consequently, there is a need for additional stress indicators to create a more comprehensive physiological profile of captured individuals to increase the accuracy of predicted lethal and sub-lethal outcomes (Renshaw et al. 2012). A recent global meta-analysis of immediate and post-release mortality of shark species has shown that gear type and respiratory mode are important factors influencing survival and that the sensitivity of species varies widely (Dapp et al. 2015). Determining the stress caused to different species in separate fisheries is therefore necessary for understanding the effects of fishing on fish population.

The adenylate energy charge (AEC) concept proposed by Atkinson (1968) offers an additional tool to measure capture stress and can also provide information on the stress response at the cellular level which is poorly understood in chondrichthyans (Skomal and Mandelman 2012). The AEC is a measure of metabolic energy available within the cell, determined from the proportions of ATP, ADP and AMP, expressed as a value between 0 and 1 (Eq. 1).
$${\text{Adenylate energy charge }}\left( {\text{AEC}} \right) = ({\text{ATP}} + [0.5 \times {\text{ADP}}])/({\text{ATP}} + {\text{ADP}} + {\text{AMP}})$$
(1)

The depletion of energy has a value of 0, with the energy store being entirely comprised of AMP. Conversely, at 1 the cell is fully ‘charged’ and the energy store is entirely comprised of ATP (Atkinson 1968). Baseline or ‘unstressed’ AEC values in tissues across a variety of organisms are remarkably consistent around 0.8–0.9, with values below 0.5 indicative of physiological collapse and cell death (Giesy 1988). In many chondrichthyans, the effort to escape from capture is powered by anaerobic metabolism in the white muscle, which provides rapid bursts of speed and power that often characterises struggling during capture. This subsequently causes functional hypoxia due to oxygen debt and the restriction of respiration caused by restraint (Frick et al. 2010a, 2012; Dapp et al. 2015). Several studies have successfully used the AEC to measure hypoxic stress in fish (Jorgensen and Mustafa 1980; Vetter and Hodson 1982; Van der Boon et al. 1992; Caldwell and Hinshaw 1994; Renshaw et al. 2002), suggesting that the AEC may be a similarly useful indicator of capture stress in chondrichthyans. The potential for the AEC to measure capture stress in chondrichthyans has shown promise, with the brain AEC in the epaulette shark (Hemiscyllium ocellatum) being reduced following exposure to environmental hypoxia (Renshaw et al. 2002). To date, the AEC response of other tissues in chondrichthyans subjected to hypoxia has not been examined.

Although tissue-specific responses (predominantly the heart and brain) to environmental hypoxia have been examined in chondrichthyans (Butler and Taylor 1971, 1975; Davie and Farrell 1991; Renshaw et al. 2002, 2010; Speers-Roesch et al. 2012a, 2013), the effects of capture stress have not yet been examined. It is crucial to identify which tissues are susceptible to capture stress from both a physiological and practical standpoint, because the AEC may not only permit identification of physiologically compromised tissues but may also identify appropriate tissues which can be non-lethally sampled at the time of capture to gain a more comprehensive assessment of capture stress.

The primary aim of our study was to determine the viability of the AEC as an indicator of capture stress in chondrichthyans. To achieve this aim, we investigated the metabolic stress response of a range of tissues and determined the practicality of applying the AEC non-lethally at the time of capture. Gummy sharks (Mustelus antarcticus) were used as the model species to examine the effects of capture stress on their AEC in controlled gillnet captures. Mustelus antarcticus is an abundant species that is frequently caught deliberately (target catch) and incidentally (byproduct catch) in gillnet fisheries in the southern waters of Australia. We hypothesised that the AEC would decline from baseline/‘unstressed’ values in response to the stress of capture and would vary with the tissue examined. We also hypothesised that like the teleosts examined, the liver would exhibit the greatest sensitivity to capture stress and that white muscle tissue would offer a practical method for non-lethal sampling at the time of capture.

Methods

Experimental design

The experimental design involved six response variables, the AEC and analytically determined concentrations of the total adenylate pool (TAP), ATP, ADP, AMP and inosine monophosphate load (IMP-L), which were obtained from each of five tissue types (liver, muscle, heart, brain and blood) collected from each of 40 animals. Each animal was randomly assigned to one of five groups where each group was subjected to one of five treatments (control, 30-min capture, recovery from 30-min capture, 60-min capture, and recovery from 60-min capture). Three sets of statistical comparison of results between pairs of these treatments are referred to as Experiment 1 (EXP 1; 30-min capture with control and 30-min capture with 3-h recovery from 30-min capture), Experiment 2 (EXP 2; 60-min capture with control and 60-min capture with 3-h recovery from 60-min capture), and Experiment 3 (EXP 3; 60-min capture with 30-min capture).

Collection of animals

Mustelus antarcticus (n = 40, mean (henceforth all means are reported with ± SE) total length (TL) = 801 ± 19 mm, range TL = 555–1090 mm) were caught using a demersal longline during November and December 2013 in Western Port (Victoria, Australia) and transported to aquaria in Queenscliff (Victoria, Australia) in a circular 4000-L tank containing aerated seawater. All individuals were housed in a 19,000-L tank using a flow-through seawater system continually pumping ambient seawater (17–19 °C). Husbandry of M. antarcticus followed the protocols of Frick et al. (2009) with the exception that M. antarcticus were held for a minimum of 14 days prior to experimentation. All sharks appeared to be in good condition immediately prior to experimentation; they had ample space to rest and swim, exhibited regular, coordinated, swimming and displayed vigorous resistance when removed from the tank using a dip net.

Experimental procedures

For the 30-min capture and 60-min capture treatments, the gillnet webbing of 150-mm mesh-size was draped across a 5,000-L tank with the headline weighted externally on either side to support the net in the water whilst the animal struggled. To ensure entanglement, the net’s leadline was draped over the side of the tank forming a pocket into which the animal could be placed directly. All animals were retrieved from the holding tank by dip net and either immediately euthanased and sampled for baseline values or placed in a gillnet tank and entangled in the net for 30 or 60 min. Animals assigned to 3 h of recovery were removed from the gillnet and placed in a 10,000-L tank ventral side up and the time taken to correct their orientation was recorded to enable comparison of righting response time between the recovery groups. Immediately, prior to tissue sampling, all animals had their overall condition scored from 1 to 4 based on the vitality index in Benoît et al. (2012). Scores indicated the following; (1) excellent condition, vigorous response to stimuli; (2) fair/moderate condition, slow and weakened response to stimuli; (3) poor condition, little response to stimuli and some stiffening of body; (4) moribund/dead, no response to stimuli, stiffened body. All tanks used contained a flow-through seawater system as described above.

Tissue sampling procedure

Immediately prior to removal of the animal from either the holding, gillnet or recovery tanks, 3 ml of whole blood was collected via caudal venipuncture. Approximately 1 ml of blood was transferred to a lithium heparin coated vacutainer (BD Vacutainer®, BD, USA) containing 1 ml of 0.6 M perchloric acid (HClO4) and immediately frozen in liquid nitrogen. Following blood sampling, the animal was immediately decapitated and the brain, epaxial muscle (below the first dorsal fin), heart and liver were removed, freeze-clamped and frozen in liquid nitrogen. The excised heart was rinsed free of blood in elasmobranch saline (described in Speers-Roesch et al. 2012b) prior to clamping. Although sampling under anaesthesia is preferable to minimise any handling stress (Vetter and Hodson 1982), our previous experience is that sedation influences the stress response (Frick et al. 2009). The mean time taken from blood sampling to the clamping of the final tissue was 164 ± 6 s. Frozen tissues and blood were stored at −80 °C for preparation at a later date.

HPLC sample preparation

Each frozen tissue sample was ground to powder using a liquid nitrogen-cooled mortar and pestle, weighed and homogenised in 2 ml of 0.6 M chilled HClO4 by sonication using a Branson Sonifier 450 (Branson Ultrasonics, Danbury, CT, USA) set at position 3 for 2 min. All samples were then centrifuged at 4000 rpm for 10 min at 4 °C (Hereaus Multifuge 3SR+, ThermoScientific, USA). The supernatant was extracted and then the remaining pellet was resuspended in 1 ml 0.6 M HClO4 and centrifuged as described. The supernatant was again collected and the wash procedure was repeated once more. The combined supernatant was then neutralised to a pH 6.0–6.5 with the addition of 5 M KOH and 0.1 M of phosphate buffer (0.1 M Na2HPO4:0.1 M KH2PO4). The perchlorate salt was allowed to settle in solution on ice before being centrifuged (4 °C, 4000 rpm, 10 min). The supernatant was extracted, passed through 0.2 µm filter and stored at −80 °C until HPLC analysis.

HPLC conditions and analysis

The analysis was performed on an Agilent 1220 Infinity LC system (Agilent, California, USA) using a Gemini C18 column (5 µm; 110 Å; 150 × 4.60 mm; Phenomenex, California, USA) at a controlled temperature of 25 °C. Injections of 20 µl were passed through a sample loop. The mobile phase was prepared daily and contained 0.1 M KH2PO4, 5 mM tetrabutylammonium hydrogen sulphate, 10 % acetonitrile and 1 M KOH to achieve a pH of 6.0. The flow rate was 1 ml/min with the wavelength set at 260 nm.

External standards of ATP, AMP, ADP and IMP at 100, 50, 25, 12.5, 6.25 µM were passed through daily for identification of peaks and quantification of concentrations. Recovery of the nucleotides was determined by spiking known amounts of the nucleotides into three of six separate preparations of the same tissue prior to homogenisation and comparing it with unspiked samples. The mean per cent recovery ranged from 89 ± 3 to 99 ± 3 %. Accuracy and precision error of reported concentrations were respectively >99.5 % and 0.25–0.37 %.

The AEC was calculated as (ATP + 0.5ADP)/(ATP + ADP + AMP). TAP was calculated as ATP + ADP + AMP. IMP-L was calculated as IMP/TAP.

Statistical analysis

Changes in tissue AEC, TAP, individual adenylate concentrations and IMP-L as a result of treatment were analysed using a split-plot ANOVA with planned comparisons to reduce the likelihood of type I errors. As described previously, the comparisons were between the three experiments (EXP1-3) whereby, treatment was the main factor, individual M. antarcticus were the blocking factor and tissue type was the subfactor. Split-plot ANOVAs were also used in a similar fashion as described above to compare changes in tissue AEC with whole-animal condition as the main factor instead of treatment.

Violations of normality and variance in all split-plot ANOVA models required a non-parametric approach using an f1-ld-f1 function in the software package ‘nparLD’ (Noguchi et al. 2012) in the statistical program ‘R 3.0.2’ (R Development Core Team 2013). Following any significant interactions, contrast effects were determined using the software package ‘nparcomp’ (Konietschke et al. 2014) to determine tissue AEC, TAP, individual adenylate concentrations and IMP-L with respect to treatment or whole-animal condition.

Righting response times were compared between recovery groups using a Wilcoxon rank sum non-parametric test due to violations of normality ‘R 3.0.2’ (R Development Core Team 2013). Noting the effect on brain AEC on the righting response function in H. ocellatum (Renshaw et al. 2002), righting response times were also correlated with brain AEC using Spearman’s rank correlation.

Results

All statistical outputs of ANOVA models relating EXP 1–3 to AEC, TAP, individual adenylate concentrations and IMP-L are l isted in Table 1. For all statistical outputs of post hoc analyses, see Online Resources (OR) 1-7. All reported means per tissue type with respect to treatments are listed in Table 2.
Table 1

Comparison of results from selected treatments for EXP 1–3 using full non-parametric split-plot ANOVA models for each of the response variables of AEC, TAP, ATP, ADP, AMP and IMP-L for each tissue type, treatment and their interaction

Response variable

EXP comparison

Tissue type

Treatment

Tissue type x Treatment

F statistic

dfa

p

F statistic

dfa

p

F statistic

dfa

p

AEC

1

61.389

2.993

<0.001*

20.947

1.782

<0.001*

3.915

4.758

0.002*

 

2

75.646

3.034

<0.001*

3.833

1.774

0.026*

4.080

4.905

<0.001*

 

3

40.691

3.000

0.255

15.558

1.000

<0.001*

1.355

3.000

0.255

TAP

1

140.266

2.932

<0.001*

8.764

1.968

<0.001*

2.194

4.887

0.054

2

154.521

2.945

<0.001*

7.526

1.721

0.001*

4.981

4.746

<0.001*

3

73.972

3.238

<0.001*

3.318

1.000

0.069

2.196

3.238

0.081

ATP

1

66.915

2.999

<0.001*

13.004

1.842

<0.001*

4.028

4.579

0.002*

2

99.655

2.780

<0.001*

7.783

1.994

<0.001*

4.029

4.753

0.001*

3

37.060

2.873

<0.001*

6.947

1.000

0.008*

2.836

2.873

0.039*

ADP

1

88.504

2.895

<0.001*

2.178

1.698

0.122

1.431

4.942

0.210

2

65.268

2.784

<0.001*

5.216

1.793

0.007*

0.894

5.072

0.485

3

43.678

2.940

<0.001*

3.296

1.000

0.069

0.188

2.940

0.902

AMP

1

31.956

3.022

<0.001*

10.283

1.855

<0.001*

2.082

5.044

0.064

2

30.601

3.130

<0.001*

0.551

1.795

0.558

1.715

5.634

0.118

3

1.264

2.837

<0.001*

12.788

1.000

<0.001*

1.264

2.837

0.285

IMP-L

1

62.905

2.806

<0.001*

4.010

1.911

0.020*

3.832

4.599

0.002*

2

77.610

2.903

<0.001*

3.839

1.974

0.022*

3.542

3.798

0.008*

3

54.892

3.023

<0.001*

1.853

1.000

0.173

4.511

3.023

0.004*

* Significantly different (p ≤ 0.05)

aThe denominator of all df values is ∞; e.g. 2.933, ∞

Table 2

Reported values of the AEC and mean (±standard error) concentrations (µmol g‒1 wet mass) of TAP, individual adenylates (ATP, ADP, AMP) and IMP-L as response variables for each tissue type and of each treatment. Treatments consist of a control (C), captures of 30 and 60 min (C30, C60) and 3-h recovery from respective captures (R30, R60)

Treatment

Tissue

C

C30

R30

C60

R60

Liver

 AEC

0.710 (±0.026)

0.440 (±0.032)#$*

0.651 (±0.045)

0.611 (±0.063)

0.654 (±0.027)

 TAP

2.362 (±0.292)

1.775 (±0.108)#

1.734 (±0.129)#

1.913 (±0.089)

1.835 (±0.142)

 ATP

1.306 (±0.165)

0.453 (±0.058)#$*

0.827 (±0.077)

0.880 (±0.140)

0.915 (±0.105)

 ADP

0.711 (±0.089)

0.642 (±0.053)

0.561 (±0.064)

0.578 (±0.040)#

0.595 (±0.045)#

 AMP

0.344 (±0.076)

0.664 (±0.056)#$*

0.346 (±0.081)

0.455 (±0.110)

0.326 (±0.030)

 IMP-L

0.144 (±0.025)

0.252 (±0.041)#

0.261 (±0.018)#

0.327 (±0.039)#$

0.121 (±0.020)

Muscle

 AEC

0.894 (±0.011)

0.708 (±0.056)#*

0.691 (±0.051)#

0.737 (±0.035)#

0.776 (±0.045)#

 TAP

7.977 (±0.728)

3.480 (±0.502)#

3.054 (±0.384)#

2.823 (±0.207)#

3.141 (±0.351)#

 ATP

6.594 (±0.688)

2.153 (±0.535)#

1.768 (±0.429)#

1.688 (±0.209)#

2.153 (±0.387)#

 ADP

1.187 (±0.098)

0.923 (±0.066)

0.870 (±0.060)

0.851 (±0.050)#

0.731 (±0.035)#

 AMP

0.196 (±0.024)

0.405 (±0.117)#$*

0.416 (±0.102)

0.284 (±0.055)

0.258 (±0.062)

 IMP-L

0.392 (±0.071)

1.789 (±0.323)#

2.022 (±0.358)#

2.443 (±0.218)#

1.972 (±0.290)#

Heart

 AEC

0.810 (±0.016)

0.760 (±0.050)*

0.834 (±0.009)

0.869 (±0.008)#$

0.816 (±0.016)

 TAP

4.580 (±0.416)

3.618 (±0.446)#

4.391 (±0.185)#

4.483 (±0.316)

3.968 (±0.299)

 ATP

3.164 (±0.269)

2.343 (±0.380)*

3.138 (±0.158)

3.490 (±0.267)

2.778 (±0.166)

 ADP

1.075 (±0.148)

0.949 (±0.124)

1.057 (±0.055)

0.828 (±0.061)#

0.874 (±0.087)#

 AMP

0.341 (±0.064)

0.326 (±0.084)#$*

0.197 (±0.018)

0.165 (±0.013)

0.315 (±0.089)

 IMP-L

0.142 (±0.036)

0.394 (±0.220)*

0.105 (±0.018)

0.076 (±0.016)

0.075 (±0.013)

Brain

 AEC

0.717 (±0.022)

0.668 (±0.013)*

0.709 (±0.022)

0.695 (±0.031)

0.707 (±0.029)

 TAP

1.981 (±0.073)

1.886 (±0.099)#

1.761 (±0.167)#

2.168 (±0.082)

2.072 (±0.065)

 ATP

1.121 (±0.055)

0.960 (±0.057)#*

1.005 (±0.113)

1.204 (±0.064)

1.168 (±0.048)

 ADP

0.590 (±0.042)

0.596 (±0.030)

0.514 (±0.044)

0.580 (±0.051)#

0.569 (±0.056)#

 AMP

0.270 (±0.034)

0.330 (±0.031)#$*

0.242 (±0.036)

0.384 (±0.059)

0.335 (±0.068)

 IMP-L

0.148 (±0.026)

0.215 (±0.038)*

0.152 (±0.038)

0.092 (±0.017)

0.100 (±0.041)#

Blood

 AEC

0.940 (±0.003)

0.870 (±0.020)#$*

0.955 (±0.005)#

0.926 (±0.010)

0.919 (±0.018)

 TAP

0.766 (±0.059)

0.822 (±0.080)#

0.561 (±0.063)#

0.590 (±0.049)

0.781 (±0.041)

 ATP

0.702 (±0.052)

0.662 (±0.066)

0.521 (±0.059)

0.531 (±0.041)#$

0.697 (±0.038)

 ADP

0.035 (±0.005)

0.105 (±0.026)

0.030 (±0.005)

0.028 (±0.005)#

0.041 (±0.012)#

 AMP

0.029 (±0.003)

0.055 (±0.010)#$*

0.010 (±0.001)

0.031 (±0.005)

0.043 (±0.011)

 IMP-L

0.014 (±0.002)

0.038 (±0.012)$*

0.158 (±0.056)#

0.051 (±0.030)

0.019 (±0.009)

Mean TL (mm) of M.antarcticus with respect to treatment groups are as described in Fig. 1

#Significantly different (p ≤ 0.05) to C

$Significantly different between capture (C30, C60) and respective recovery (R30, R60)

* Significantly different between capture durations

Liver

In 30-min captures, only the AEC, TAP, ATP, AMP and IMP-L were significantly affected; the AEC, TAP and ATP decreased, whereas, AMP and IMP-L increased from their respective baselines (Fig. 1; Tables 1, 2; OR1-3, 5-6). Following recovery, only TAP and IMP-L were significantly affected and remained at capture levels (Fig. 1; Tables 1, 2; OR1–3, 5–6).
Fig. 1

Tissue-specific AEC values in M. antarcticus from treatments of control (C), captures of 30 and 60 min (C30, C60) and 3-h recovery from respective captures (R30, R60). Numbers above bars show sample size. Mean TL (mm) of M.antarcticus with respect to treatments: 797 ± 4 (C), 801 ± 5 (C30), 753 ± 5 (R30), 852 ± 3 (C60) and 794 ± 3 (R60). #Significantly different (p ≤ 0.05) from control value. $Significant difference between capture duration (C30, C60) and respective recovery (R30, R60). * Significant difference between capture durations (C30 vs. C60)

In 60-min captures, only ADP and IMP-L were significantly affected; ADP decreased and IMP-L increased from their respective baselines (Fig. 1; Tables 1, 2; OR1–4, 6). Following recovery, only ADP was significantly affected and remained at capture levels (Fig. 1; Tables 1, 2; OR1–4, 6).

Only the AEC, ATP and AMP were significantly affected by capture duration; after 30-min captures both the AEC and ATP were lower, whereas, AMP was higher than in 60-min captures (Fig. 1; Tables 1, 2; OR3, 6).

White muscle

In 30-min captures, only the AEC, TAP, ATP, AMP and IMP-L were significantly affected; the AEC, TAP, ATP decreased, whereas, AMP and IMP-L increased from their respective baselines (Fig. 1; Tables 1, 2; OR1–3, 5–6). Following recovery, only the AEC, TAP, ATP and IMP-L were significantly affected and all remained at capture levels (Fig. 1; Tables 1, 2; OR1–3, 5–6).

In 60-min captures, only the AEC, TAP, ATP, ADP and IMP-L were significantly affected; the AEC, TAP, ATP, ADP decreased, whereas, IMP-L increased from their respective baselines (Fig. 1; Tables 1, 2; OR1–4, 6). Following recovery, all of the said variables were significantly affected and remained at capture levels (Fig. 1; Tables 1, 2; OR1–4, 6).

Only the AEC and AMP were significantly affected by capture duration; the AEC was lower, whereas, AMP was higher in shorter captures (Fig. 1; Tables 1, 2; OR3, 6).

Heart

In 30-min captures, only the TAP and AMP were significantly affected; both TAP and AMP decreased from their respective baselines (Fig. 1; Tables 1, 2; OR1–3, 5–6). Following recovery, only the TAP was significantly affected and remained at capture levels (Fig. 1; Tables 1, 2; OR1–3, 5–6).

In 60-min captures, only the AEC and ADP were significantly affected; the AEC increased, whereas, ADP decreased from their respective baselines (Fig. 1; Tables 1, 2; OR1–4, 6). Following recovery, only ADP was significantly affected and remained at capture levels (Fig. 1; Tables 1, 2; OR1–4, 6).

Only the AEC, ATP, AMP and IMP-L were significantly affected by capture duration; the AEC, ATP were lower, whereas, both AMP and IMP-L were higher in 30-min captures compared to 60-min captures (Fig. 1; Tables 1, 2; OR3, 6).

Brain

In 30-min captures, only the TAP, ATP and AMP were significantly affected; TAP and ATP had decreased, whereas, AMP had increased from their respective baselines (Fig. 1; Tables 1, 2; OR1–3, 5–6). Following recovery, only the TAP was significantly affected and remained at the capture level (Fig. 1; Tables 1, 2; OR1–3, 5–6).

In 60-min capture, only ADP was significantly affected and had declined from the baseline (Fig. 1; Tables 1, 2; OR1–4, 6). Following recovery, only ADP and IMP-L were significantly affected both of which remained at capture levels (Fig. 1; Tables 1, 2; OR1–4, 6).

Only the AEC, ATP, AMP and IMP-L were significantly affected by capture duration; after 30-min captures the AEC, ATP and AMP were lower, whereas, IMP-L was higher than in 60-min captures (Fig. 1; Tables 1, 2; OR3, 6).

Whole blood

In 30-min captures, only the AEC, TAP, AMP and IMP-L were significantly affected; the AEC decreased, whereas, the TAP, AMP and IMP-L increased from their respective baselines (Fig. 1; Tables 1, 2; OR1–3, 5–6). Following recovery, only the AEC, TAP and IMP-L were significantly affected; the AEC and IMP-L increased above both their respective baseline and capture levels, whereas, the TAP remained at capture levels (Fig. 1; Tables 1, 2; OR1–3, 5–6).

In 60-min captures, only ATP and ADP were significantly affected and had declined from their respective baselines. Following recovery, only ADP was significantly affected and increased above the baseline yet was similar to capture levels (Fig. 1; Tables 1, 2; OR1–4, 6).

Only the AEC, AMP and IMP-L were significantly affected by capture duration; the AEC and IMP-L were lower, whereas, AMP was higher in shorter captures (Fig. 1; Tables 1, 2; OR3, 6).

Whole-animal condition

There was a significant interactive effect (F5.32, ∞ = 4.333, p ≤ 0.001) between tissue AEC and whole-animal condition (Fig. 2; OR7). Significant declines in AEC were observed in liver (Fig. 2; OR7) and muscle (Fig. 2; OR7) with worsening whole-animal condition. However, there were no significant changes in the heart (Fig. 2; OR7), brain (Fig. 2; OR7) and blood (Fig. 2; OR7) with worsening whole-animal condition.
Fig. 2

Tissue-specific AEC values in M. antarcticus with respect to whole-animal condition scores (1 good condition, vigorous response to restraint; 2 moderate condition, slow and sluggish response to stimuli; 3 poor condition, little or no response to stimuli and stiffening of body). Numbers above bars show sample size, letters denote significant differences

Righting response time following capture

Righting response time was significantly longer for M. antarcticus from 30-min captures (W14 = 50.5, p = 0.011; Fig. 3). Righting response times were not significantly correlated with brain AEC (S15 = 542.680, p = 0.912).
Fig. 3

The righting response time of M. antarcticus immediately following release from 30 and 60 min of capture (R30, R60). Numbers above error bars show sample size. *Significantly different (p ≤ 0.05)

Discussion

General findings

The baseline AEC values of M. antarcticus with respect to tissue type are comparable to those in other fish (Jorgensen and Mustafa 1980; Vetter and Hodson 1982; Heath 1984; Haya et al. 1985; Giesy 1988; Caldwell and Hinshaw 1994). In general, M. antarcticus liver and white muscle were the most sensitive tissues to capture-induced metabolic stress, experiencing the greatest proportional declines in AEC. In contrast, the heart and brain were resilient, with no decline in the AEC detected. Whole blood provided inconclusive results but the analysis of isolated erythrocytes may prove more informative (Yoshino et al. 1992; Renshaw et al. 2012). Although each tissue varied in its sensitivity to capture-induced metabolic stress, the AEC of all tissues was significantly lower in shorter captures, suggesting that the initial stages of capture are most stressful and that physiological recovery can occur as time progresses during capture.

Tissue specific responses to capture stress and implications

White muscle and liver tissue were the most sensitive tissues to metabolic stress, making them the most appropriate tissues to measure the AEC response. Despite the decline in AEC, white muscle was still able to maintain an AEC sufficiently high to support cellular work due to large intracellular ATP stores and the accumulation of IMP at the cost of the TAP (Driedzic and Hochachka 1976; van den Thillart et al. 1980; Van der Boon et al. 1992). Liver showed the greatest degree of metabolic stress during capture relative to other tissues. In several fish species examined, liver has consistently been shown to exhibit significant losses in ATP (Dalla Via et al. 1994; Jibb and Richards 2008; Speers-Roesch et al. 2012a, 2013) and declines in AEC (Jorgensen and Mustafa 1980; van den Thillart et al. 1980; Caldwell and Hinshaw 1994). Unlike white muscle, liver contains less creatine phosphate to buffer against ATP consumption (Richards 2009; Speers-Roesch et al. 2012a) and a limited capacity to sustain an elevated AEC via IMP (Caldwell and Hinshaw 1994).

The liver was able to recover during prolonged capture and subsequent release, whereas, muscle tissue exhibited no recovery within the 3-h recovery period. Unlike white muscle, the liver is comparatively well perfused which probably aided recovery despite reaching an AEC below 0.5. Similarly, Caldwell and Hinshaw (1994) noted that the liver of rainbow trout (Oncorhyncus mykiss) recovered to baseline AEC levels following a decline to levels as low as 0.39. We think that physiological collapse of M. antarcticus liver may occur at a lower AEC than found in our study. White muscle may have exhibited greater recovery beyond the 3-h recovery period. Following exhaustive exercise, white muscle ATP in the spiny dogfish (Squalus acanthias) was not restored during 4-h recovery period (Richards et al. 2003). Prolonged muscle recovery was also evident in Frick et al. (2012) whereby intramuscular lactate in M. antarcticus did not return to control levels until 3–24 h following release from gillnet capture. Intramuscular acidosis may have also affected enzymatic activity and impeded ATP regeneration in M. antarcticus (Haya et al. 1985).

The decline in white muscle AEC and lack of short-term recovery suggests that muscle function was compromised. After immediate removal from the gillnet, muscle tetany was observed in M. antarcticus and throughout the post-capture recovery period, swimming appeared sluggish, unbalanced and would often intermittently cease in mid-water, causing the animal to sink. The degree of muscle tetany in chondrichthyans is often used a visual and rapid indication of worsening whole-animal condition (Frick et al. 2010a; Benoît et al. 2012; Braccini et al. 2012). White muscle AEC was correlated with declining whole-animal condition in M. antarcticus and thus the AEC can also provide a physiological basis for the use visual assessments of condition. Large depletions of ATP can elevate extracellular potassium levels and reduce muscle contractility (Storey and Storey 2005; Richards 2009; Sugaya et al. 2011). Increased plasma potassium concentrations resulting from capture stress have been attributed to muscle tetany and are often associated with increased mortality risk (Cliff and Thurman 1984; Manire et al. 2001; Frick et al. 2010b). However, whether the plasma potassium levels measured are a result of intramuscular ATP depletion in white muscle needs to be determined. Nonetheless, impaired muscle function in the short term, although, not necessarily fatal, may increase mortality risks as it can result in poorer swimming ability and negatively impact predator evasion (Cosgrove et al. 2015) and the respiratory potential of ram-ventilating species in particular (Dapp et al. 2015).

Interestingly, M. antarcticus of the poorest whole-animal condition also had the lowest liver AEC values, highlighting the physiological vulnerability of the liver relative to other tissues. Compromised liver function can have a range of lethal and sub-lethal effects. Acute liver failure can lead to ammonia toxicity (Ip and Chew 2010) and also initiate a cascade in multiple organ failures, of which tissue hypoxia and ischaemia are major causes (Singer 1998). Sub-lethal effects may relate to reduction in delivery and metabolism of energetic substrates (Speers-Roesch and Treberg 2010), thereby limiting ATP regeneration in extrahepatic tissues (Richards 2009). Thus, impaired liver function may have also contributed to the impaired recovery and function of muscle observed in recovering M. antarcticus. The reduced physiological function of liver as result of a lowered AEC and how this relates to worsening whole-animal condition requires further investigation.

The resilience of the heart and brain is aided by extensive perfusion and is related to relatively high endogenous glucose (Soengas and Aldegunde 2002; Gamperl and Driedzic 2009; Richards 2009) and the potential for metabolic depression in the respective tissues. Although, outside the scope of our study, hypoxia-induced bradycardia (Piiper et al. 1970; Butler and Taylor 1971; Davie and Farrell 1991; Stenslokken et al. 2004) and neuronal suppression (Renshaw et al. 2002) may have limited ATP consumption. The capacity for M.antarcticus to enter metabolic depression during capture requires further investigation. Interestingly though, M.antarcticus indicated suppressed neuronal activity by increased righting response times in shorter captures where brain AEC was significantly lower. Suppression of neuronal activity and consequently the righting responses enabled the brain of H. ocellatum to maintain a relatively high AEC when exposed to 50 min of anoxia (Renshaw et al. 2002). Increased righting response times may compromise survival by increasing vulnerability to predation and reducing the respiratory potential of ram-ventilating sharks in particular. The effect of increased righting response times may be further compounded by impaired muscle function as previously mentioned. Righting response times were not correlated with declining AEC and instead may correlate with the release of adenosine (Renshaw et al. 2002) which was not measured in this study.

The adenylate contribution of various blood cell types make whole blood a poor indicator of metabolic stress during capture, but the examination of isolated erythrocytes may prove to be a more useful indictor (Yoshino et al. 1992; Renshaw et al. 2012). Nonetheless, we examined whole blood as a potential indicator considering the practical limitations faced with extraction and analysis of blood on fishing vessels at the time of animal capture. Working space on fishing vessels is often very limited and the instability of the vessel at sea seldom allows the use of a cooled centrifuge. Furthermore, since sampling time is of critical importance, both for the animals’ well-being and for the accurate quantification of adenylates, immersion of whole blood into perchloric acid and subsequent freezing is most practical, allowing for samples to be obtained and stored in less than 1 min from extraction. Despite this limitation, we suggest future work to compare the AEC and adenylate responses in both whole blood and erythrocytes to determine the most practical and informative approach.

According to our results, M. antarcticus shows the greatest level of exertion within the first 30 min of gillnet capture. Frick et al. (2010a) noted that initial struggle bouts of M. antarcticus were visibly intense and despite captures lasting 30, 120 and 180 min, the per centage of time spent struggling was highest in 30-min captures. Therefore, longer captures (to an extent) may promote greater recovery aided by the energetic savings produced by lowered activity levels and possible metabolic depression. Physiological recovery during prolonged capture has been documented in line-caught sharks (Brooks et al. 2012; Gallagher et al. 2014), presumably due to modified behaviour and adequate respiratory potential. In our study, M. antarcticus exhibited buccal and spiracular pumping to assist respiration despite being fully restrained during capture. Unlike M. antarcticus and other species capable of stationary respiration, obligate ram-ventilating species are unlikely to recover during prolonged gillnet capture (Dapp et al. 2015).

Although no deaths from capture were recorded in our study, increased mortality may have been observed following longer recovery periods. Frick et al. (2010a) found that post-capture mortality of M. antarcticus occurred ~12 h following release from gillnet capture and were higher in those following 30-min captures compared to 120- and 180-min captures. Although Frick et al. (2010a) could not account for higher mortalities following shorter captures, we hypothesise that M. antarcticus may have been vulnerable to reperfusion injury following shorter captures. The timing of reperfusion can influence the severity of tissue injury (Coles et al. 2009) which is potentially pronounced given the lowered cellular energy status of tissues, particularly in the liver, following 30-min captures. Additionally, although we did not measure the production of hypoxanthine, greater ATP degradation seen in tissues from shorter captures may have increased hypoxanthine which can form toxic reactive oxygen species and increase reperfusion injury (Eltzschig and Collard 2004). We therefore suggest that future studies incorporate recovery periods of at least 12 h to determine if lowered AECs from shorter captures are associated with increased risk of delayed mortality.

Practicality and potential of the AEC as an indicator of capture stress

Our results show that a sample of white muscle affords the most practical approach by which measurement of the AEC can be used as an additional, non-lethal tool to determine fisheries capture stress. Muscle biopsies impose relatively minor stress on chondrichthyans (Robbins 2006) and because the amount of tissue required for adenylate quantification is small (as low as ~97 mg in our study), smaller individuals (<1 m TL) can also be readily sampled. Obtaining muscle tissue from free-swimming chondrichthyans, particularly larger bodied sharks, via biopsy spears may also allow an accurate measurement of the baseline AEC since there is no confounding factor of handling stress (Marshall et al. 2011).

Use of the AEC may alleviate the need to establish species-specific control/baseline values, which is a problem when attempting to determine mortality thresholds for a range of currently used physiological indicators (Frick et al. 2010b, 2012; Hyatt et al. 2012; Gallagher et al. 2014). With respect to tissue type, the AECs of M. antarcticus are comparable to a range of teleosts (Jorgensen and Mustafa 1980; Vetter and Hodson 1982; Heath 1984; Haya et al. 1985; Giesy 1988; Caldwell and Hinshaw 1994) and one elasmobranch (Renshaw et al. 2002). Although, the consistency of the AEC across a divergent range of chondrichthyan species requires empirical testing, the baseline AEC of white muscle in teleosts has been empirically shown to be remarkably consistent across teleosts spanning a range of morphologies and natural activity levels (Vetter and Hodson 1982).

Assessing the functional viability of white muscle using the AEC in conjunction with current physiological stress indicators can provide a more comprehensive assessment of capture stress and thus, improve the current predictive capacity of post-capture fate. Our data on the AEC strengthens the suggestion that incorporating analysis of muscle biochemistry sampled at the time of capture may be more accurate compared to the sole use of currently used haematological indicators given the delay in changes to blood biochemistry that occur post-capture (Frick et al. 2012). As muscle function is crucial to respiration and predator evasion, AEC values obtained at the time of capture could be tested for their predictive capacity of delayed mortality by corroborating them with laboratory derived thresholds (for physiological collapse) in conjunction with telemetry-derived data from released animals as in Moyes et al. (2006). Sub-lethal effects of capture stress such as changes to movement behaviour could also be inferred from AEC and absolute ATP concentrations and corroborated with telemetry data. Based on our results, post-capture behaviour such as sudden descents and abnormal depth profiles within the first several hours of release (Holts and Bedford 1993; Cartamil et al. 2011; Afonso and Hazin 2014) could relate to increased righting response time (causing ‘sinking’) and muscular exhaustion, factors which can be quantified by the AEC and absolute ATP loss. However, it should also be taken into account that such post-capture swimming behaviours may also relate to a recovery phase involving thermoregulation and improved oxygenation (Holts and Bedford 1993). By using white muscle AEC in conjunction with currently used haematological indicators, we can obtain a more detailed and comprehensive assessment of capture stress in chondrichthyans.

Although liver can be sampled non-lethally using endoscopic forceps (Divers et al. 2013) or needle cores (Tresise et al. 2014), these methods are likely to pose significant logistical difficulties in field studies due to the requirement of surgery under anaesthesia. However, such an approach is feasible under laboratory conditions and should be explored to further investigate liver function and its possible role in capture-related mortality.

Conclusion

The AEC and composition of individual adenylates provides a promising new tool to add to the existing suite of physiological stress indicators in chondrichthyans. Our measurements of the AEC not only identified tissue-specific stress responses to gillnet capture, but also permitted a physiological and behavioural understanding of possible lethal and sub-lethal effects of capture. Liver tissue appears to be particularly sensitive to metabolic stress and its compromised function may contribute to capture-related mortality. Reduced muscle function and lowered brain metabolism may also compromise survival immediately following release by limiting movement, consequently reducing respiratory potential and increasing vulnerability to predation.

The capability for non-lethal sampling and logistical considerations identify white muscle as the suitable method to assess the AEC. Importantly, empirical evidence from teleosts (Vetter and Hodson 1982) suggests that the AEC measured in muscle may alleviate the issue of interspecific and intraspecific variation and provide a standardised form of stress measurement in chondrichthyans. It may then be possible to assess species’ tolerances to capture with greater confidence and also with less reliance on sample size. This is particularly useful for assessing elusive and rarer species with little or no data on their tolerance to capture.

Combining the AEC with current stress indicators will provide a more comprehensive and accurate assessment of capture stress in chondrichthyans. The potential for a standardised form of measurement of stress may not only improve but also expand the current knowledge of species-specific tolerances to capture and their delayed mortality estimates (Dapp et al. 2015). Consequently, more sustainable fishing practises can be implemented based on an improved assessment of fisheries pressures on chondrichthyan population.

Notes

Acknowledgments

We thank Carolina and Thomas Weller, Derek Dapp, Lauren Hall and Ricky Tate for fieldwork and manuscript assistance. We thank Roderick Watson and Elizabeth McGrath from the Victorian Marine Science Consortium (VMSC) and Phillip Holt from Monash University for logistical assistance. Funding for this study was provided by the Australian Research Council (ARC) Linkage Grant LP110200572, the Department of Economic Development, Jobs, Transport and Resources Victoria, Australian Fisheries Management Authority (AFMA) and Melbourne Aquarium. This study was conducted in accordance with Monash University Animal Ethics approval number BSCI/2012/16 and DELWP Fisheries permit number RP1115.

Supplementary material

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References

  1. Afonso AS, Hazin FHV (2014) Post-release survival and behavior and exposure to fisheries in juvenile tiger sharks, Galeocerdo cuvier, from the South Atlantic. J Exp Mar Biol Ecol 454:55–62CrossRefGoogle Scholar
  2. Atkinson DE (1968) Energy charge of adenylate pool as a regulatory parameter. Interaction with feedback modifiers. Biochemistry 7:4030–4034PubMedCrossRefGoogle Scholar
  3. Benoît HP, Hurlbut T, Chassé J, Jonsen ID (2012) Estimating fishery-scale rates of discard mortality using conditional reasoning. Fish Res 125:318–330CrossRefGoogle Scholar
  4. Braccini, M, Rijn, JV, Frick, L (2012) High post-capture survival for sharks, rays and chimaeras discarded in the main shark fishery of Australia? PLoS One 7:e32547PubMedPubMedCentralCrossRefGoogle Scholar
  5. Brooks EJ, Mandelman JW, Sloman KA, Liss S, Danylchuk AJ, Cooke SJ, Skomal GB, Philipp DP, Sims DW, Suski CD (2012) The physiological response of the Caribbean reef shark (Carcharhinus perezi) to longline capture. Comp Biochem Physiol A Mol Integr Physiol 162:94–100PubMedCrossRefGoogle Scholar
  6. Butler PJ, Taylor EW (1971) Response of the dogfish (Scyliorhinus canicula L.) to slowly induced and rapidly induced hypoxia. Comp Biochem Physiol A Physiol 39:307–323CrossRefGoogle Scholar
  7. Butler P, Taylor E (1975) The effect of progressive hypoxia on respiration in the dogfish (Scyliorhinus canicula) at different seasonal temperatures. J Exp Biol 63:117–130PubMedGoogle Scholar
  8. Caldwell CA, Hinshaw JM (1994) Nucleotides and the adenylate energy charge as indicators of stress in rainbow trout (Oncorhyncus mykiss) subjected to a range of dissolved oxygen concentrations. Comp Biochem Physiol B Biochem Mol Biol 109:313–323CrossRefGoogle Scholar
  9. Cartamil DP, Sepulveda CA, Wegner NC, Aalbers SA, Baquero A, Graham JB (2011) Archival tagging of subadult and adult common thresher sharks (Alopias vulpinus) off the coast of southern California. Mar Biol 158:935–944PubMedPubMedCentralCrossRefGoogle Scholar
  10. Cliff G, Thurman G (1984) Pathological and physiological effects of stress during capture and transport in the juvenile dusky shark, Carcharhinus obscurus. Comp Biochem Physiol A Physiol 78:167–173CrossRefGoogle Scholar
  11. Coles, JA, Sigg, DC, Iaizzo, PA (2009) Reversible and irreversible damage of the myocardium: new ischemic syndromes, ischemia/reperfusion injury, and cardioprotection. In: Iaizzo PA (ed) Handbook of cardiac anatomy, physiology, and devices (2nd edn), pp 219–229. (Springer Science + Business Media, LLC)Google Scholar
  12. Cosgrove R, Arregui I, Arrizabalaga H, Goni N, Neilson JD (2015) Predation of pop-up satellite archival tagged albacore (Thunnus alalunga). Fish Res 162:48–52CrossRefGoogle Scholar
  13. Dalla Via J, van den Thillart G, Cattani O, Dezwaan A (1994) Influence of long-term hypoxia exposure on the energy metabolism of Solea solea. II. Intermediary metabolism in blood, liver and muscle. Mar Ecol Prog Ser 111:17–27CrossRefGoogle Scholar
  14. Dapp DR, Walker TI, Huveneers C, Reina RD (2015) Respiratory mode and gear type are important determinants of elasmobranch immediate and post-release mortality. Fish Fish. doi:10.1111/faf.12124 Google Scholar
  15. Davie PS, Farrell AP (1991) Cardiac performance of an isolated heart preparation from the dogfish (Squalus acanthias)—the effects of hypoxia and coronary-artery perfusion. Can J Zool Revue Canadienne de Zoologie 69:1822–1828CrossRefGoogle Scholar
  16. Development Core Team R (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  17. Divers SJ, Boone SS, Berliner A, Kurimo EA, Boysen KA, Johnson DR, Killgore KJ, George SG, Hoover JJ (2013) Nonlethal acquisition of large liver samples from free-ranging river sturgeon (Scaphirynchus) using single-entry endoscopic biopsy forceps. J Wildl Dis 49:321–331PubMedCrossRefGoogle Scholar
  18. Driedzic WR, Hochachka PW (1976) Control of energy metabolism in fish white muscle. Am J Physiol 230:579–582PubMedGoogle Scholar
  19. Dulvy NK, Fowler SL, Musick JA, Cavanagh RD, Kyne PM, Harrison LR, Carlson JK, Davidson LNK, Fordham SV, Francis MP, Pollock CM, Simpfendorfer CA, Burgess GH, Carpenter KE, Compagno LJV, Ebert DA, Gibson C, Heupel MR, Livingstone SR, Sanciangco JC, Stevens JD, Valenti S, White WT (2014) Extinction risk and conservation of the world’s sharks and rays. Elife 3:1–34CrossRefGoogle Scholar
  20. Eltzschig HK, Collard CD (2004) Vascular ischaemia and reperfusion injury. Br Med Bull 70:71–86PubMedCrossRefGoogle Scholar
  21. Frick LH, Reina RD, Walker TI (2009) The physiological response of Port Jackson sharks and Australian swellsharks to sedation, gill-net capture, and repeated sampling in captivity. North Am J Fish Manag 29:127–139CrossRefGoogle Scholar
  22. Frick LH, Reina RD, Walker TI (2010a) Stress related physiological changes and post-release survival of Port Jackson sharks (Heterodontus portusjacksoni) and gummy sharks (Mustelus antarcticus) following gill-net and longline capture in captivity. J Exp Mar Biol Ecol 385:29–37CrossRefGoogle Scholar
  23. Frick LH, Walker TI, Reina RD (2010b) Trawl capture of Port Jackson sharks, Heterodontus portusjacksoni, and gummy sharks, Mustelus antarcticus, in a controlled setting: effects of tow duration, air exposure and crowding. Fish Res 106:344–350CrossRefGoogle Scholar
  24. Frick LH, Walker TI, Reina RD (2012) Immediate and delayed effects of gill-net capture on acid-base balance and intramuscular lactate concentration of gummy sharks, Mustelus antarcticus. Comp Biochem Physiol A Mol Integr Physiol 162:88–93PubMedCrossRefGoogle Scholar
  25. Gallagher AJ, Serafy JE, Cooke SJ, Hammerschlag N (2014) Physiological stress response, reflex impairment, and survival of five sympatric shark species following experimental capture and release. Mar Ecol Prog Ser 496:207–218CrossRefGoogle Scholar
  26. Gamperl, AK, Driedzic, WR (2009) Cardiovascular function and cardiac metabolism. In: Richards JG, Farrell AP, Brauner CJ (eds) Hypoxia, vol 27. Elsevier Academic Press, USA, pp 301–360Google Scholar
  27. Giesy JP (1988) Phosphoadenylate concentrations and adenylate energy-charge of largemouth bass (Micropterus salmoides): relationship with condition factor and blood cortisol. Comp Biochem Physiol A Physiol 90:367–377CrossRefGoogle Scholar
  28. Haya K, Waiwood BA, Vaneeckhaute L (1985) Disruption of energy metabolism and smoltification during exposure of juvenile Atlantic salmon (Salmo salar) to low pH. Comp Biochem Physiol C Pharmacol Toxicol Endocrinol 82:323–329CrossRefGoogle Scholar
  29. Heath AG (1984) Changes in tissue adenylates and water content of bluegill, Lepomis macrochirus, exposed to copper. J Fish Biol 24:299–309CrossRefGoogle Scholar
  30. Holts DB, Bedford DW (1993) Horizontal and vertical movements of the shortfin mako shark, Isurus oxyrhincus, in the Southern California Bight. Aust J Mar Freshw Res 44:901–909CrossRefGoogle Scholar
  31. Hyatt MW, Anderson PA, O’Donnell PM, Berzins IK (2012) Assessment of acid-base derangements among bonnethead (Sphyrna tiburo), bull (Carcharhinus leucas), and lemon (Negaprion brevirostris) sharks from gillnet and longline capture and handling methods. Comp Biochem Physiol A Mol Integr Physiol 162:113–120PubMedCrossRefGoogle Scholar
  32. Ip YK, Chew SF (2010) Ammonia production, excretion, toxicity, and defense in fish: a review. Frontiers in Physiology 1:134PubMedPubMedCentralCrossRefGoogle Scholar
  33. Jibb LA, Richards JG (2008) AMP-activated protein kinase activity during metabolic rate depression in the hypoxic goldfish, Carassius auratus. J Exp Biol 211:3111–3122PubMedCrossRefGoogle Scholar
  34. Jorgensen JB, Mustafa T (1980) The effect of hypoxia on carbohydrate metabolism in flounder (Platichthys flesus L.)—II. High energy phosphate compounds and the role of glycolytic and gluconeogenetic enzymes. Comp Biochem Physiol B Biochem Mol Biol 67:249–256CrossRefGoogle Scholar
  35. Konietschke F, Placzek M, Schaarschmidt F, Hothorn LA (2014) nparcomp: an R software package for nonparametric multiple comparisons and simultaneous confidence intervals. J Stat Softw 61:1–17Google Scholar
  36. Manire C, Hueter R, Hull E, Spieler R (2001) Serological changes associated with gill-net capture and restraint in three species of sharks. Trans Am Fish Soc 130:1038–1048CrossRefGoogle Scholar
  37. Marshall H, Field L, Afiadata A, Sepulveda C, Skomal G, Bernal D (2011) Hematological indicators of stress in longline-captured sharks. Comp Biochem Physiol A Mol Integr Physiol 162:121–129CrossRefGoogle Scholar
  38. Moyes C, Fragoso N, Musyll M, Brill R (2006) Predicting postrelease survival in large pelagic fish. Trans Am Fish Soc 135:1389–1397CrossRefGoogle Scholar
  39. Noguchi K, Gel YR, Brunner E, Konietschke F (2012) nparLD: an R software package for the nonparametric analysis of longitudinal data in factorial experiments. J Stat Softw 50:1–23CrossRefGoogle Scholar
  40. Piiper J, Baumgarten D, Meyer M (1970) Effects of hypoxia upon respiration and circulation in the dogfish Scyliorhinus stellaris. Comp Biochem Physiol 36:513–520PubMedCrossRefGoogle Scholar
  41. Renshaw GMC, Kerrisk CB, Nilsson GE (2002) The role of adenosine in the anoxic survival of the epaulette shark, Hemiscyllium ocellatum. Comp Biochem Physiol B Biochem Mol Biol 131:133–141PubMedCrossRefGoogle Scholar
  42. Renshaw GMC, Wise G, Dodd PR (2010) Ecophysiology of neuronal metabolism in transiently oxygen-depleted environments: evidence that GABA is accumulated pre-synaptically in the cerebellum. Comp Biochem Physiol A Mol Integr Physiol 155:486–492PubMedCrossRefGoogle Scholar
  43. Renshaw GMC, Kutek AK, Grant GD, Anoopkumar-Dukie S (2012) Forecasting elasmobranch survival following exposure to severe stressors. Comp Biochem Physiol A Mol Integr Physiol 162:101–112PubMedCrossRefGoogle Scholar
  44. Richards JG (2009) Metabolic and molecular responses of fish to hypoxia. In: Richards JG, Farrell AP, Brauner CJ (eds) Hypoxia, vol 27. Elsevier Academic Press, USA, pp 443–485Google Scholar
  45. Richards JG, Heigenhauser GJF, Wood CM (2003) Exercise and recovery metabolism in the pacific spiny dogfish (Squalus acanthias). J Comp Physiol B 173:463–474PubMedCrossRefGoogle Scholar
  46. Robbins WD (2006) Evaluation of two underwater biopsy probes for in situ collection of shark tissue samples. Mar Ecol Prog Ser 310:213–217CrossRefGoogle Scholar
  47. Singer M (1998) Management of multiple organ failure: guidelines but no hard-and-fast rules. J Antimicrob Chemother 41:103–112PubMedCrossRefGoogle Scholar
  48. Skomal GB, Mandelman JW (2012) The physiological response to anthropogenic stressors in marine elasmobranch fishes: a review with a focus on the secondary response. Comp Biochem Physiol A Mol Integr Physiol 162:146–155PubMedCrossRefGoogle Scholar
  49. Soengas JL, Aldegunde M (2002) Energy metabolism of fish brain. Comp Biochem Physiol B Biochem Mol Biol 131:271–296PubMedCrossRefGoogle Scholar
  50. Speers-Roesch B, Treberg JR (2010) The unusual energy metabolism of elasmobranch fishes. Comp Biochem Physiol A Mol Integr Physiol 155:417–434PubMedCrossRefGoogle Scholar
  51. Speers-Roesch B, Brauner CJ, Farrell AP, Hickey AJR, Renshaw GMC, Wang YS, Richards JG (2012a) Hypoxia tolerance in elasmobranchs. II. Cardiovascular function and tissue metabolic responses during progressive and relative hypoxia exposures. J Exp Biol 215:103–114PubMedCrossRefGoogle Scholar
  52. Speers-Roesch B, Richards JG, Brauner CJ, Farrell AP, Hickey AJ, Wang YS, Renshaw GM (2012b) Hypoxia tolerance in elasmobranchs. I. Critical oxygen tension as a measure of blood oxygen transport during hypoxia exposure. J Exp Biol 215:93–102PubMedCrossRefGoogle Scholar
  53. Speers-Roesch B, Mandic M, Groom DJE, Richards JG (2013) Critical oxygen tensions as predictors of hypoxia tolerance and tissue metabolic responses during hypoxia exposure in fishes. J Exp Mar Biol Ecol 449:239–249CrossRefGoogle Scholar
  54. Stenslokken KO, Sundin L, Renshaw GMC, Nilsson GE (2004) Adenosinergic and cholinergic control mechanisms during hypoxia in the epaulette shark (Hemiscyllium ocellatum), with emphasis on branchial circulation. J Exp Biol 207:4451–4461PubMedCrossRefGoogle Scholar
  55. Storey KB, Storey JM (2005) Oxygen limitation and metabolic rate depression. In: KB Storey (ed) Functional metabolism. Wiley, USA, pp 415–442Google Scholar
  56. Sugaya M, Yasuda T, Suga T, Okita K, Abe T (2011) Change in intramuscular inorganic phosphate during multiple sets of blood flow-restricted low-intensity exercise. Clin Physiol Funct Imaging 31:411–413PubMedCrossRefGoogle Scholar
  57. Tresise MM, Mokae MLL, Wagenaar GM, Van Dyk JC (2014) A proposed liver needle core biopsy technique for the sharptooth catfish Clarias gariepinus (Burchell) for use in fish health research. J Fish Dis 37:931–934PubMedCrossRefGoogle Scholar
  58. van den Thillart G, Kesbeke F, Waarde AV (1980) Anaerobic energy-metabolism of goldfish, Crassius auratus (L.)—influence of hypoxia and anoxia on phosphorylated compounds and gycogen. J Comp Physiol 136:45–52CrossRefGoogle Scholar
  59. Van der Boon J, de Jong RL, Van den Thillart G, Addink ADF (1992) Reversed-phase ion-paired HPLC of purine nucleotides from skeletal muscle, heart and brain of the goldfish, Crassius auratus L.-II. Influence of environmental anoxia on metabolite levels. Comp Biochem Physiol B Biochem Mol Biol 101:583–586CrossRefGoogle Scholar
  60. Van Rijn JA, Reina RD (2010) Distribution of leukocytes as indicators of stress in the Australian swellshark, Cephaloscyllium laticeps. Fish Shellfish Immunol 29:534–538PubMedCrossRefGoogle Scholar
  61. Vetter RD, Hodson RE (1982) Use of adenylate concentrations and andenylate energy-charge as indicators of hypoxic stress in estuarine fish. Can J Fish Aquat Sci 39:535–541CrossRefGoogle Scholar
  62. Yoshino M, Yamamoto C, Murakami K, Katsumata Y, Mori S (1992) Stabilization of the adenylate energy charge—charge in erythrocytes of rats and humans at high-altitude hypoxia. Comp Biochem Physiol A Physiol 101:65–68CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.School of Biological SciencesMonash UniversityClaytonAustralia

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