Not to be cited without prior reference to the author

ICES ASC Nantes 2010 ICES CM 2010/E: 01 Haematological Stress Parameters in Longline Captured Sharks Heather Marshall, Lyndsay Field, Achankeng Afiadata, Chugey Sepulveda, Greg Skomal, and Diego Bernal Assessments of worldwide longline fisheries reveal that sharks constitute a large portion of bycatch for this gear type. Recently enacted fishing regulations, along with the low economic value of these catches, results in a large percentage of incidentally captured sharks being released. To date, little information exists on the rates of post-release survival for many shark species, and thus the impact of longline fisheries on shark populations cannot be fully estimated. Recent studies have addressed the possibility of using biochemical profiles of secondary haematological stress parameters to predict post-release survivorship, yet little is known about interspecific differences in these indicators. This study sought to compare electrolytes (sodium, chloride, magnesium, calcium, and potassium), metabolites (glucose and lactate), hematocrit, and heat shock protein 70 parameters between twelve species of longline captured sharks (n = 162). Statistical comparison of parameters was conducted according to species, family, and ecological classification. Data reveal species-specific parameter differences in response to longline capture, as well as differences by family (i.e., Lamnidae versus Carcharhinidae) and ecological (i.e., pelagic versus coastal) classification. Results suggest that differences in locomotive and respiratory adaptations between study species bring about differences in stress-response by these sharks to longline capture. This study is the first to report a haematological secondary stress response assessment for such a large number of pelagic shark species, and lays the groundwork for developing species-specific indices for predicting post-release survivorship of longline caught sharks. Keywords: elasmobranch, biochemistry, molecular, fishery Contact author: Heather Marshall

University of Massachusetts Dartmouth Biology Department

285 Old Westport Road North Dartmouth, MA 02740 United States hmarshall@umassd.edu

Introduction

In recent decades, sharks have been increasingly exploited by targeted fisheries and

account for a large percentage of incidental bycatch in pelagic commercial gear

worldwide (Beerkircher et al., 2002; Gilman et al., 2008; Skomal and Bernal, 2010). It is

estimated that in the U.S. commercial longline industry, elasmobranchs, although rarely

targeted, constitute 25% of the overall catch (Mandelman et al., 2008) and that sharks

make up to 94% of total bycatch in commercial longline operations (McKinnell and Seki,

1998). Because both the commercial value of captured sharks is often low and certain

species (e.g., dusky shark, Carcharhinus obscurus) are mandated to be released in an

effort to control fishing pressures, the post-capture release of sharks is now a common

practice (Skomal, 2007; Mandelman et al., 2008; NMFS 2008; Skomal and Bernal,

2010).

One of the principal management assumptions of releasing non-targeted species (i.e.,

sharks) is that caught individuals are being returned to their stocks to contribute to the

overall biomass and growth of the population. Thus, the inherent expectation is that all

of released sharks survive the physical and physiological trauma of capture and handling

from the fishing gear. However, little is known about the post-release survival of large

sharks captured on commercial fishing gear and there is a meager understanding of the

physiological consequences associated with the capture event and how it may affect

short-term (i.e., hours-days) and long-term (i.e., weeks-months) survival (Davis et al.,

2001; Skomal 2007). In short, there is no clear picture on the fate of released sharks, but

it is known that the exercise stress associated with struggling on a fishing line may result

in higher rates of post-release mortality (Wood, 1983). Research assessing the post-

release survival and possible delayed mortality of released sharks can be approached

from various angles, such as the assessment of physical trauma (e.g., hook damage), the

quantification of at-vessel mortality, or the physiological/behavioral effects associated

with both short and long-term mortality/recovery (Cooke et al., 2002). This study has

chosen to approach the problem of post-release mortality by trying to investigate the

physiological indicators of stress in longline-captured sharks.

In order to understand and ultimately minimize the effects of fishing stress on post-

release mortality, the stress response pathways need to be understood, indicators of

physiological stress response need to be identified and quantified, and ultimately the

levels of stress need to be related to the magnitude of the fishing event (Mazeaud et al.,

1977). Fish react to capture and handling with a more exaggerated physiological

response than any other higher vertebrate (reviewed by Skomal and Bernal, 2010) and the

stress response has been classified into three levels: primary, secondary, and tertiary

(Mazeaud et al., 1977). In fish, the primary stress response is neuroendocrine related and

commonly associated with the release of catecholamine and corticosteroids. The

secondary stress response affects metabolic and osmotic physiological processes, which

ultimately result in shifts to equilibrium (e.g., declines in pH). The tertiary stress

response is related to how the effects of stress within in individual carry over to the

population level (Mazeaud et al., 1977).

Within the primary response, catecholamine (e.g., adrenalin and noradrenalin) and

corticosteroids (e.g., cortisol) work to activate the “fight-or-flight” response, as well as

mobilize energetic substrates and oxygen stores to deal with the stress response and

recovery (Mazeaud et al., 1977). These hormones activate the secondary stress response

in fish, which typically: 1) increase the bio-availability of glucose for use by the

swimming muscles, bringing about hyperglycemia, 2) increase cardiac output by

augmenting stroke volume and heart beat frequency, therefore increasing blood pressure

and the delivery of oxygen and substrates to metabolically active tissues, and 3) increase

buccal pumping rates, which increases oxygen uptake at the gills (Mazeaud et al., 1977;

reviewed by Skomal and Bernal, 2010).

If a stress event (such as capture in fishing gear) is of a large magnitude or lasts for a long

period of time, the effects of the secondary stress response can have deleterious

physiological consequences. Because all sharks are not able to buccally pump water over

their gills (i.e., they are obligate ram ventilators) any alteration to normal swimming will

affect ventilation rates and impede adequate gas exchange (i.e., oxygen/carbon dioxide)

at the gills. The decline in oxygen levels will ultimately increase anaerobic metabolism,

which in turn affects carbon dioxide dynamics within the muscles and blood, therefore

exposing these tissues to respiratory acidosis (lowering pH). In addition, the buildup of

lactic acid and protons resulting from anaerobic glycolysis will bring about metabolic

acidosis (lowering pH). Even a moderate change of acid-base balance can affect cellular

function (Abelow, 1998; reviewed by Skomal and Bernal, 2010).

Recent research has focused on ascertaining how fish are affected by stress and

identifying what secondary stress parameters may be utilized as good indicators of the

physiological disruptions that can result from the stress event (Currie et al., 2000; Moyes

et al., 2006; Mandelman and Farrington, 2007; Skomal, 2007; Mandelman and Skomal,

2009). Because the blood in fish (3-6% of body mass) both delivers oxygen (and

energetic substrates) to and removes metabolic end products from the large amount of

swimming muscles (50-60% of body mass), the physiological condition of the blood

should be representative of the condition of the fish as a whole (Skomal and Bernal,

2010). Blood samples can serve as a non-lethal means to assess the short and long-term

effects of stressed fish (Currie and Tufts, 1997). The typical physiological responses to

capture-related stress seen within the blood include: an increase in glucose levels and

depletion of glycogen stores due to glycogenolysis; the depletion of adenosin

triphosphate (ATP) and creatine phosphate stores; the accumulation of lactate resulting

from anaerobic glycolysis; the decrease in blood pH (acidemia) due to metabolic acidosis

(increase in protons) and respiratory acidosis (CO2 elevation); and an overall disruption

of ion, osmotic, and fluid concentrations (reviewed by Skomal and Bernal, 2010).

Although hematocrit levels (the percent of total red blood cells, RBC, in total blood

volume) have also been assessed after stress events, the exact response of hematocrit to a

capture event is a challenging endeavor to pinpoint, as hematocrit is thought to be

influenced by a suite of responses, including: splenic releases of RBC (which would

increase the hematocrit reading); changes in vasodilation (which would physically affect

the percentage of RBCs within blood plasma); RBC swelling due to changes in ion

control (which would increase hematocrit values); or cell lysis due to such changes in ion

concentration (which would decrease the hematocrit reading) (Wells and Davie, 1985).

In sharks, the response of hematocrit to stress is not well understood at this point, but

clearly should be the focus of future investigation as hematocrit has the potential to affect

the oxygen carrying capacity of the blood.

This project also investigates the use of heat shock proteins (HSPs) as potential indicators

of the stress response. Heat shock proteins exist in constitutive form as chaperone

proteins that aid in protein folding after translation, but also can be induced in facultative

form during times of perceived stress to help prevent protein, hemoglobin, and DNA

degradation (Currie, 1997). Previous work on fish indicates that HSP concentrations

increase with the stress response (Currie and Tufts, 1997; Moyes et al., 2006) and that

induction is rapid (Skomal and Bernal, 2010).

The complex system of stress response lends itself to the development of species-specific

genetic variation as a response to various pressures (e.g., environmental or predator)

(Mazeaud et al., 1977). Skomal and Bernal (2010) postulated that stress responses are

linked to: 1) a fish’s metabolic scope and its cruise/burst swimming capacity, 2) the

ability to physiologically respond to stress, and 3) the capacity to recover from the

stressor. Indeed, both fishery catch data from the commercial fleets and research data

show that different species will respond differently to capture by the same fishing gear.

Published at-vessel mortality rates for different species of sharks caught on longline gear

are summarized in Table 1, and clearly show how the effects of longline gear can range

from minimal (e.g., tiger shark, Galeocerdo cuvier) to near fatal (e.g., dusky shark,

Carcharhinus obscurus) and are thus species-specific. In addition, Mandelman and

Skomal (2009) compared 5 species of closely related carcharhinid species and found that

the tiger and sandbar (Carcharhinus plumbeus) sharks showed the least response to

longline stress, but the blacktip (Carcharhinus limbatus) and dusky sharks were highly

disturbed after capture with low pH, high lactate values, and high CO2 loading.

In light of the increasing numbers of post-capture released sharks from longline gear, as

well as reported variation in how different species respond to the same gear, this study

sought to investigate the physiological response to longline capture in various species,

families, and ecological classifications of sharks. This study reports the haematological

secondary stress responses (i.e., metabolites, ions, HSP, hematocrit) for a large number

of shark species, and lays the groundwork for developing species-specific indices for

predicting post-release survivorship of longline caught sharks.

Methods

Sampling

Blood samples were collected onboard shark research longline cruises between the years

of 2006-2009. Research cruises took place in both the Atlantic and Pacific Ocean

(Central Pacific: R/V Oscar Elton Sette, 2008; Southern California Bight: R/V David

Starr Jordan, 2007; George’s Bank: F/V Eagle Eye II, 2006; Southwest Atlantic: R/V

Delaware II, 2007, 2009). The longline gear was a combination of demersal and pelagic

sets, but the hooks were all suspended within the upper 150 meters of the water column.

A combination of circle and J-hooks were used depending on the fishing trip and longline

set. Overall the gear was set in temperatures ranging from 17-22°C. When the fish were

brought on board, blood was collected via caudal venipuncture into a heparinized syringe

with 18 gauge needles. Samples were kept at 4°C until processing, which occurred

mostly 2-4 min after capture, but in some cases were up to an hour after capture.

Hematocrit

Whole blood was drawn into 3-4 microcapillary tubes and spun (5 min 2000xG) in a

hematocrit centrifuge. Hematocrits were calculated as the percentage of red blood cell

volume out of the total blood volume. Multiple samples (n=3-5) were averaged to derive

one value per shark sample.

Plasma samples

Whole blood was transferred into 2mL centrifuge tube and spun (5 min 2000xG) to

separate the red blood cells from the plasma. The plasma from each blood sample was

separated and frozen immediately at -80°C until later analysis. Once in the laboratory,

plasma samples were thawed and diluted 3x with HPLC-grade water and approximately

55 µl were injected into a Critical Care Xpress (CCX, Nova Biomedical, MA) for

analysis. Blood parameters assessed from the CCX were: glucose, lactate, sodium (Na+),

chloride (Cl-), magnesium (Mg2+), calcium (Ca2+), and potassium (K+). Reported values

were multiplied by the dilution factor to obtain actual values for data analysis.

Heat shock proteins

Currie and Tufts (1997) report that, within teleost fish, the 70kDa heat shock protein

(hsp70) is most consistently expressed in response to stress, and so concentrations of this

HSP were assessed. Concentrations of hsp70 were determined using western blot

procedures following the protocol of Currie and Tufts (1997). Equal amounts (20 µL) of

RBC lysate in a protease inhibitor cocktail (Roche) were added to pre-made Bio-Rad

SDS-polyacrylamide (8%) gel, along with a Bio-Rad protein ladder to determine the

presence of both hsp70 in the shark RBC sample and a hsp70 standard. Primary

antibodies against hsp70 (Agrisera) and fluorescent secondary antibodies (LiCor) were

visualized using a Li-Core Odyssey Infrared Imaging System scanner and accompanying

software (version 3.0.16) and used to quantify hsp70 levels in the RBC samples. Relative

pixel intensity was determined for each band and either correlated to the relative amount

of hsp70 between the samples. Thus, by loading equal amounts of total protein of each

sample, it was possible to determine the changes in relative levels of hsp70 between

shark RBC samples.

Statistical Analysis

A General Linear Model Univariate Analysis of Variance (ANOVA) was used to

determine how the blood parameters varied by species, family, and ecological

classification. Assumptions were satisfied by using Levene’s Test of homogeneity of

variances, and by observing the normality of the data. Tukey Post-hoc tests were

performed to determine which species were significantly different from each other when

the sample size was greater than 2 individuals. The alpha level for all statistics was set to

0.05.

Results

The individuals in this study (n=162) were comprised of 12 species, 3 families, and 2

ecological classifications (Table 2) based on the U.S. Fisheries Management Plan

(NMFS, 1999).

Mean (± SD) values for each hematological blood parameter and species are provided in

Table 3. The ANOVA analysis reveal that the following blood parameters has a species

effect (GLM, p<0.05): glucose (porbeagle higher than the sandbar sharks , Figure 1);

lactate (mako and porbeagle higher than the sandbar, dusky, whitetip, blue, and tiger

sharks, Figure 2); and hematocrit (porbeagle, mako, common thresher, and blacktip

higher than the tiger, sandbar, sharpnose, blue, dusky, and silky sharks, Figure 3). There

was no species effect on concentrations of the ions and hsp70 (p>0.05) (Figure 9).

Mean (± SD) values for each hematological blood parameter by family and ecology type

are provided in Table 4. The ANOVA analysis reveals that there is a family effect only

on lactate and hematocrit (Lamnidae higher than Carcharhinidae, p<0.05; Figure 4 and

Figure 5, respectively), and no effect on the ions, glucose, and hsp70 (p>0.05).

Hematocrit values were significantly affected by ecological classification (pelagic higher

than coastal, p<0.05; Figure 6), whereas the ions, metabolites, and hsp70 were not

(p>0.05).

Discussion

Overall, this study found interspecific variation in several secondary stress response

parameters (i.e., glucose, lactate, and hematocrit) of sharks caught using longline gear.

However, our data for all ions and hsp70 showed no significant differences among the

shark species studied (Figure 9, Table 3), a finding that agrees with previously work by

Frick et al. (2009), disagrees with work by Skomal and Bernal (2010), and suggests that

longline capture did not result in interspecific differences in ion concentration or water

gain/loss due to osmotic imbalance, as well as HSP expression. Clearly, this is an area of

research that needs more thorough investigation.

This study found significant species-specific differences in the metabolite values and, in

general, it appears that mako and porbeagle sharks have higher glucose and lactate values

than the other species. These differences were also present when sharks were grouped by

family type (Lamnidae vs Carcharhinidae) and these results agree with previous findings,

where lamnids (e.g., mako sharks) appear to have a stronger stress response relative to

carcharhinids (e.g., blue sharks) (Skomal and Bernal, 2010). This may be due to the

relatively higher aerobic and anaerobic capacities of lamnid sharks (Dickson et al., 1993;

Bernal et al., 2003) which would result in a stress-related faster mobilization of glycogen

stores (i.e., hyperglycemia) and an increase in the levels of lactate. The question,

therefore, is if lamnid sharks are able to recover faster from extreme exercise (i.e.,

capture-stress), or if the lactate levels (and subsequent increase in protons, contributing to

a decline in pH) are high enough to have deleterious physiological effects. When the

longline at-vessel mortality rates for mako and porbeagle sharks (35-40%) (two species

with very high levels of lactate) are compared to that of tiger and blue sharks (8-12%)

(two species with very low levels of lactate) it appears that lamnids do have higher

immediate mortalities than charcharhinids, and that this may be related to the degree of

capture related stress that lamnids encounter when hooked on a longline. However,

lamnids are not the only sharks to have high at-vessel mortalities (e.g., blacktip sharks

have a surprising 88% rate of mortality) (Beerkircher et al., 2002; Morgan and Burgess,

2007; Skomal and Bernal, 2010). Moreover, a comparison of the mortality rates and

levels of lactate in the blood plasma between the sandbar and dusky sharks shows that

even though longline capture results in similar lactate levels among the two species there

is a marked difference in their at-vessel mortality rate (sandbar: 36.1%, dusky: 81.1%)

(Morgan and Burgess, 2007), which is suggestive that perhaps these two congeneric

species may have developed different mechanisms to deal with and recover from stress

and therefore have different tolerances to large physiological disturbances.

The results suggest that the porbeagle, shortfin mako, blacktip, and common thresher

sharks have higher hematocrit values than the remaining carcharhinids. Though the

blacktip is a carcharhinid, it is also an active shark (Brunnschweiler, 2005), and so the

data suggest that more aerobically active species have higher hematocrit values after

capture. The hematocrit values also correlate to family type (with lamnids having higher

values) and ecological classification (with pelagic fish having higher values). Again, the

trend is established that those more open water, active, and aerobically demanding fish

have higher hematocrit values after longline capture. One possible explanation for this is

that the hematocrit is increased due to the stress event. This may be a result of splenic

release of RBCs, therefore increasing the hematocrit value, or a result of red blood cell

swelling, due to osmotic upset from the capture event. However, we did not find any

species-specific differences in plasma ion levels, and thus it is not likely that the elevated

species-specific hematocrits are due to osmotic upset. These data may simply be the

result of more active species having inherently higher hematocrit values at unstressed

conditions. These higher levels would correspond to greater concentrations of

hemoglobin within the blood, therefore increasing oxygen carrying capacity for the

aerobically demanding fish. However, without baseline hematocrit values for each

species, this theory cannot be confirmed. Baseline (i.e., unstressed) values are quite

difficult to obtain for sharks that are large and are not kept well in captivity, and so are

not readily available. It is also interesting to note that the blacktip shark appears to have

similar hematocrit values to the lamnid sharks after capture, and that the blacktip shark

actually has the highest at-vessel mortality rate (88%) (Morgan and Burgess, 2007). It is

possible that this species responds to capture with very high aerobic and anaerobic

demands, resulting in a physiological condition that it cannot recover from.

This study did not find any evidence of species-specific differences in longline capture

induction of heat shock protein (70 kDa) (Figure 9). This result would suggest that

longline capture affects the study species in a similar manner with regards to heat shock

protein induction. However, upon examination of the data, it is evident that intraspecific

variation is quite high, therefore making the data inconclusive. A possible reason for this

is that the collected blood samples have experienced protein denaturation, therefore

introducing variability into the data. However, as this data is preliminary for future

research, it is possible that there is a methods problem that needs to be identified and

corrected for. As previous research suggests, the use of hsp70 for identifying

intracellular changes due to stress is a reliable method (Currie and Tufts, 1997; Currie et

al., 2000; Moyes et al., 2006; Mladineo and Block, 2009; Skomal and Bernal, 2010).

Future work in this area is warranted, as the assessment of HSP concentration and

expression allows for the investigation of stress-response on a molecular level.

Thus far this study has established that there is evidence for species-, family-, and

ecology-specific responses to longline capture for certain blood parameters, which

corroborates previous research (Frick et al., 2009; Mandelman and Skomal, 2009;

Skomal and Bernal, 2010). Such research argues the point that species-specific

management for commercial longline industries would be the most effective technique

for sharks as a whole. As evidence builds that different species show variability in their

physiological response to longlining, it becomes important to consider augmenting

fishing practices (e.g., shorter handling time and soak times) to minimize post-release

mortality for those species that are more sensitive. Also, when comparing species

discussed in this paper to published baseline samples available for sharks, the effect of

longlining on all species is quite evident (Figure 7). Though this paper argues for

species-specific consideration of stress events, the baseline samples available within the

literature contribute some information as to how longline capture events are

physiologically affecting sharks as a whole. Such postulating is supported when capture

stress parameter values are actually compared to species-specific baseline levels that are

available, such as for the sandbar shark (Figure 8). Figure 7 and 8 drive home the idea

that longline capture significantly impacts the physiology of sharks, in which the spike in

lactate in stressed sharks relative to collected baseline values are quite evident. Clearly,

the physiological impact of longlining on all sharks is a dramatic one, and depending on

the species, may drive individuals to an internal chemical state that cannot be recovered

from.

Overall, this study demonstrates the importance of understanding the physiological

implications of longline capture, and assessing which indicators may help to elucidate

post-release mortality. Just as importantly, such data cannot be applied to sharks as a

whole, but must be collected in a species-specific manner, as this study provides further

evidence of the species-specific physiological response to longline capture. More

research is needed in this area to further identify appropriate indicator parameters of

stress response, understand how different species respond to and recover from longline

capture, and ultimately, which management methods should be implemented to help

minimize the damaging effects of fishing practices on elasmobranch species.

Works Cited

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Mandelman, J. and G. Skomal. 2009. Differential sensitivity to capture stress assessed by blood acid-base status in five carcharhinid sharks. Journal of Comparative Physiology B, 179: 267-277. Mazeaud, M., Mazeaud, F. and E. Donaldson. 1977. Primary and secondary effects of stress in fish: Some new data with a general review. Transactions of the American Fisheries Society, 106: 201-212. McKinnell, S. and M. Seki. 1998. Shark bycatch in the Japanese high seas squid driftnet fishery in the North Pacific Ocean. Fisheries Research, 39: 127-138. Mladineo, I. and B. Block. 2009. Expression of Hsp70, Na+/K+ ATP-ase, HIF-1α, IL-1β and TNF-α in captive Pacific bluefin tuna (Thunnus orientalis) after chronic warm and cold exposure. Journal of Experimental Marine Biology and Ecology, 374: 51-57. Morgan, A. and G. Burgess. 2007. At-vessel fishing mortality for six species f sharks caught in the northwest Atlantic and Gulf of Mexico. Proceedings of the 59th Annual Conference of the Gulf and Caribbean Fisheries Institute, 19: 123-130. Moyes, C., Fragoso, N., Musyl, M. and R. Brill. 2006. Predicting post-release survival in large pelagic fish. Transactions of the American Fisheries Society, 135: 1389-1397. National Marine Fisheries Service (NMFS). 1999. Final fishery management plan for Atlantic tuna, swordfish, and sharks, US Department of Congress. National Marine Fisheries Service (NMFS). 2008. Final amendment 2 to consolidated Atlantic highly migratory species fishery management plan, US Department of Congress. Skomal, g. 2007. Evaluating the physiological and physical consequences of capture and post-release survivorship in large pelagic fishes. Fisheries Management and Ecology, 14: 81-89. Skomal, G., and D. Bernal. 2010. Physiological Responses to Stress in Sharks. In J. Carrier, J. Musick, and M. Heithaus (Eds.) Sharks and Their Relatives II: Biodiversity, Adaptive Physiology, and Conservation (pp. 459-490). Boca Raton: CRC Press. Spargo, A. 2001. The physiological effects of catch and release angling on the post-release survivorship of juvenile sandbar sharks (Carcharhinus plumbeus). Master of Science thesis, University of Rhode Island. Wells, R. and P. Davie. 1985. Oxygen binding by the blood and hematological effects of capture stress in two big gamefish: mako shark and striped marlin. Comparative Biochemistry and Physiology, 81A: 643-646. Wood, C., Turner, J., and M. Graham. 1983. Why do fish die after severe exercise? Journal of Fish Biology, 22: 189-201.

Table 1. Published at-vessel mortality rates for 9 species of sharks caught on longline gear, illustrating how different species have varying levels of vulnerability to the same fishing gear.

Species Common Name Mortality Estimate

Carcharhinus limbatus Blacktip 88% (Morgan and Burgess, 2007)

Carcharhinus obscurus Dusky 81.1% (Morgan and Burgess, 2007)

Carcharhinus falciformis Silky 66.3% (Beerkircher et al., 2002)

Lamna nasus Porbeagle 40%

(Skomal, Natanson, and Bernal, unpublished)

Carcharhinus plumbeus Sandbar 36.1% (Morgan and Burgess, 2007)

Isurus oxyrinchus Shortfin Mako 35% (Beerkircher et al., 2002)

Rhizoprionodon terraenovae Atlantic Sharpnose 14.3% (Beerkircher et al., 2002)

Prionace glauca Blue 12.2% (Beerkircher et al., 2002)

Galeocerdo cuvier Tiger 8.5% (Morgan and Burgess, 2007)

Table 2. Fishing efforts allowed this project to analyze blood samples from 162 individuals, falling into the categories of 12 species, 3 family types, and 2 ecological distributions. Individuals were sampled from the years 2006-2009 on 5 different research cruises.

Species Common Name Family Ecological Classification n

Lamna nasus Porbeagle Lamnidae Pelagic 11

Isurus oxyrinchus Shortfin Mako Lamnidae Pelagic 47

Alopius vulpinus Common Thresher Alopiidae Pelagic 4

Prionace glauca Blue Carcharhinidae Pelagic 38

Carcharhinus longimanus Oceanic Whitetip Carcharhinidae Pelagic 3

Carcharhinus falciformis Silky Carcharhinidae Pelagic 2

Galeocerdo cuvier Tiger Carcharhinidae Coastal 9

Carcharhinus plumbeus Sandbar Carcharhinidae Coastal 25

Carcharhinus obscurus Dusky Carcharhinidae Coastal 14

Rhizoprionodon terraenovae Atlantic Sharpnose Carcharhinidae Coastal 3

Carcharhinus brevippina Spinner Carcharhinidae Coastal 1

Carcharhinus limbatus Blacktip Carcharhinidae Coastal 5

12 species 3 families 2 distributions 162 individuals

Table 3. Mean values (± Standard Deviation) of haematological secondary stress parameters for each species within this study. Sample sizes are provided in parenthesis below species name, and units are shown in parenthesis below each blood parameter.

Species Sodium (mmol/L)

Potassium (mmol/L)

Chloride (mmol/L)

Calcium (mmol/L)

Magnesium (mmol/L)

Glucose (dg/ml)

Lactate (mmol/L)

Hematocrit (%)

Porbeagle (11) 272.6±7 5.4±1.5 250.9±7 2.8±0.1 1.2±0.1 124.0±22 22.6±6 36.4±7

Shortfin Mako (47) 273.2±16 4.5±0.8 265.3±16 2.4±0.2 1.1±0.3 100.3±49 18.6±13 27.0±9

Common Thresher (4) - - - - - - - 28.8±6

Blue (38) 259.6±22 4.5±1.4 264.3±18 2.2±0.3 1.0±0.8 103.0±26 4.8±4 18.6±8

Oceanic Whitetip (3) 276.7±21 4.8±0.6 266.9±24 2.6±0.2 1.0±0.1 83.3±25 5.0±8 -

Silky (2) 286.7±16 4.7±0.5 277.5±2 2.6±0.3 1.0±0.1 66.3±94 6.9±7 4.2

Tiger (9) 252.2±16 4.8±0.4 249.9±12 2.3±0.2 1.0±0.1 111.1±10 2.6±2 23.1±14

Sandbar (25) 281.9±14 4.9±0.8 269.6±22 2.3±0.2 1.1±0.1 77.1±13 11.9±7 19.8±8

Dusky (14) 275.6±20 5.2±2.0 249.2±74 2.1±0.7 0.9±0.3 101.7±36 11.1±8 14.4±8

Atlantic Sharpnose (3) 257.0±35 4.8±0.9 243.9±30 2.1±0.3 1.2±0.2 106.5±5 9.6±7 19.1±7

Spinner (1) 228.8 4.7 227.1 1.8 0.8 128.0 17.2 -

Blacktip (5) - - - - - - - 27.6±4

Table 4. Mean values (± Standard Deviation) of haematological secondary stress parameters for each family and ecological classification within this study. Sample sizes are provided in parenthesis below variable name, and units are shown in parenthesis below each blood parameter.

Classification Sodium (mmol/L)

Potassium (mmol/L)

Chloride (mmol/L)

Calcium (mmol/L)

Magnesium (mmol/L)

Glucose (dg/ml)

Lactate (mmol/L)

Hematocrit (%)

Lamnidae (58) 2723.1±15 4.7±1 262.6±16 2.5±0.3 1.1±0.3 104.8±46 19.4±12 28.8±9

Carcharhinidae (104) 268.2±22 4.7±1 262.0±35 2.2±0.4 1.0±0.6 95.4±29 7.9±7 18.8±7

Alopiidae (4) - - - - - - - 28.8±6

Pelagic (62) 268.0±19 4.6±0.9 263.4±17 2.4±0.3 1.1±0.5 103.5±39 13.4±12 24.4±9

Coastal (89) 275.1±20 5.0±1 259.8±45 2.2±0.4 1.0±0.2 89.7±30 10.6±7 19.1±9

Figure 1. Bar chart of mean (± Standard Deviation) glucose concentrations (dg/ml) for the species in this study. Sample sizes are provided within parenthesis for each species. Letter “a” denotes that the porbeagle shark is significantly higher than the sandbar shark (p=0.014).

a

a

Figure 2. Bar chart of mean (± Standard Deviation) lactate concentrations (mmol/L) for the species in this study. Sample sizes are provided within parenthesis for each species. Letter “a” denotes that the porbeagle shark is significantly higher than the sandbar, dusky, oceanic whitetip, blue, and tiger shark (p<0.05), and “b” denotes that the mako shark is significantly higher than the blue and tiger sharks (p<0.05). The porbeagle and mako sharks are not different from each other (p=0.48).

a b

a a

a

a, b

a, b

Figure 3. Bar chart of mean (± Standard Deviation) hematocrit concentrations (%) for the species in this study. Sample sizes are provided within parenthesis for each species. Yellow boxes specify that the porbeagle, common thresher, blacktip, and shortfin mako sharks are significantly higher than the tiger, blue, sandbar, atlantic sharpnose, dusky, and silky sharks (p<0.05).

Figure 4. Bar chart of mean (± Standard Deviation) lactate concentrations (mmol/L) for the two family types in this study. Sample sizes are provided within parenthesis for each family. The asterisk denotes significant difference between Lamnidae and Carcharhinidae (p=0.000).

Figure 5. Bar chart of mean (± Standard Deviation) hematocrit values (%) for the three family types in this study. Sample sizes are provided within parenthesis for each family. Letter “a” denotes that Lamnidae is significantly higher than Carcharhinidae (p=0.000), and “b” denotes that Alopiidae is significantly higher than Carcharhinidae (p=0.000).

a

a,b

b

Figure 6. Bar chart of mean (± Standard Deviation) hematocrit values (%) for the two ecological classifications in this study. Sample sizes are provided within parenthesis for each classification. The asterisk denotes significant difference between coastal and pelagic (p=0.000).

Figure 7. Bar chart of mean (± Standard Deviation) glucose (dg/ml) and lactate (mmol/L) concentrations for the three species in this study as compared to published baseline samples (spiny/smooth dogfish, swell shark, and Port Jackson shark) (Mandelman and Farrington, 2007; Frick et al., 2009). Figure illustrates the effect of longlining on these blood parameters for all species in this study. Sample sizes are provided within parenthesis for each family.

Figure 8. Bar chart of mean (± Standard Deviation) lactate (mmol/L) concentrations for sandbars in this study as compared to published baseline samples (Spargo, 2001). Figure illustrates the effect of longlining on these blood parameters for sandbar sharks in this study. Sample sizes are provided within parenthesis for each study.

Figure 9. Bar chart of mean (± Standard Deviation) heat shock protein concentrations (µg/µl) for the species in this study. Sample sizes are provided within parenthesis for each species.