Florida State University Libraries

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Florida State University Libraries

2016

Post-Release Mortality of Deep Sea Bycatch Species

Brendan Suneel Talwar

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FLORIDA STATE UNIVERSITY COLLEGE OF ARTS & SCIENCES

POST-RELEASE MORTALITY OF DEEP SEA BYCATCH SPECIES

By

BRENDAN SUNEEL TALWAR

A Thesis submitted to the Department of Biological Science

in partial fulfillment of the requirements for the degree of

Master of Science

2016

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Brendan Suneel Talwar defended this thesis on March 31, 2016.

The members of the supervisory committee were:

R. Dean Grubbs

Professor Directing Thesis

Edward J. Brooks Committee Member

Don Levitan

Committee Member

Joseph Travis Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the thesis has been approved in accordance with university requirements.

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This thesis is dedicated to Sydney and Sara.

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ACKNOWLEDGMENTS

This thesis represents the hard work, generosity, and support of countless friends, family members, advisors, and students. Let me begin by acknowledging everyone that helped see it through, from heavily invested volunteers to strangers who donated the funds to get this work off the ground- this would not have been possible without you.

I offer my sincerest thanks to my advisor, Dean Grubbs, for his guidance, support, and incredible expertise. The independence I was afforded within the Grubbs Lab pushed me to become heavily invested in my work and ultimately gain more from my Master’s degree than I thought possible, a testament to his mentorship. I must also thank Edd Brooks and Travis Perry for the opportunities that they have given me since the beginning of my career. My limited success so far is largely attributable to their hard work. I also greatly appreciate Don Levitan, John Mandelman, and Joseph Travis, who improved the quality of this work and act as exceptional role models to many early career scientists including me.

For contributing to my field work, lab work, and general well-being, thank you to numerous hard working volunteers and friends including I Boyoucos, K Durglo, E Van Eepoel, R Fry, A Gokgoz, C Grossi, K Magnenat, J Mitchell, K Ontiveros, O O’Shea, Team EP, C Raguse, C Seslar, M Violich, C Ward, and many others at the Cape Eleuthera Institute. I am also forever grateful to the students of The Island School Fall ’14 and Spring ’15 semesters, including M Abouhamad, K Addams-Pilgrim, C Close, M Edie, S Gallagher, A Heher, N Henderson, A Hoffman, H Lavelle, O Rask, M Rogers, and L Zachau for their field support and contagious enthusiasm. Coiling rope into a bucket wouldn’t have been the same with anyone else.

For financial and logistical support, I thank Experiment.com crowdfunding donors, the PADI Foundation, the Cape Eleuthera Foundation, The Island School, the Guy Harvey Ocean

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Foundation, the Sigma Xi Scientific Research Society, the New England Aquarium, and the Florida State University Coastal & Marine Laboratory.

Lastly, I thank my Mom for throwing me into the deep end before I could walk and for sparking my interest in the natural world. I also thank my Dad for his constant encouragement and interest in my work.

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LIST OF TABLES

1 The reflexes identified for assessing condition of trap-caught Bathynomus giganteus. The test for each reflex is the action required to elicit a given response (i.e. positive or

negative), listed in the same order as they were conducted in the field. ...12 2 Summary of negatively scored reflexes for emersion (n=50; 15 minutes) and control

(n=50) groups of Bathynomus giganteus prior to caging. Proportions of negative responses for each reflex are shown as percentage of total negative responses for each group ...13 3 Full model and minimal adequate model from backwards stepwise GLM analysis to

describe 5-day mortality in Bathynomus giganteus based on seven initial model

parameters and two interaction terms. ...13 4 Akaike Information Criterion (AIC) values for each GLM used to describe the 5-day

mortality of Bathynomus giganteus resulting from stepwise backwards elimination of nonsignificant terms. ...14 5 Description of vitality scores assigned to sharks placed in the post-release cage before

being lowered to the sea floor. ...35 6 Correlation structure of primary stress physiology metrics, length measurements, and

capture characteristics for S. cubensis. ...35 7 Capture composition and characteristics of sharks caught on deep-sea longlines

throughout this study...35 8 At-vessel and 24 h post-release mortality rates for species caught in this study, calculated

using Eqs. (1) and (2) ...36 9 Blood chemistry parameters and corresponding sample sizes for Squalus cubensis and

Centrophorus sp. captured during this study ...36

10 Full model and minimal adequate model from backwards stepwise analysis to describe at- vessel blood pH in Squalus cubensis based on four initial model parameters and one interaction term ...36 11 Akaike Information Criterion (AIC) values for each model used to describe the at-vessel

blood pH of Squalus cubensis resulting from stepwise backwards elimination of

nonsignificant terms and evaluation of changes in AIC and deviance ...37 12 Full model and minimal adequate model from backwards stepwise analysis to describe at-

vessel blood lactate in Squalus cubensis based on four initial model parameters and one interaction term ...37

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13 Akaike Information Criterion (AIC) values for each model used to describe the at-vessel blood lactate of Squalus cubensis resulting from stepwise backwards elimination of nonsignificant terms and evaluation of changes in AIC and deviance. ...38 14 Full model and minimal adequate model from backwards stepwise GLM analysis to

describe 24 h mortality in Squalus cubensis based on five initial model parameters and five interaction terms ...38 15 Akaike Information Criterion (AIC) values for each GLM used to describe the 24 h

mortality of Squalus cubensis resulting from stepwise backwards elimination of

nonsignificant terms and evaluation of changes in AIC and deviance ...39 16 Significance levels for single variable fits to 24 h S. cubensis mortality data from GLMs

with binomial distributions and a logit link function ...39

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LIST OF FIGURES

1 Mortality of giant isopods as a function of reflex impairment score (sum of negative responses from RAMP assessments). Each point represents the calculated 5-day mortality rate for individuals with a given impairment score, pooled across treatment groups. The solid line shows the curved fit from logistic regression which represents the likelihood of mortality for an individual with a given reflex impairment score ...14 2 Number of Bathynomus giganteus with empty and non-empty stomachs grouped by cage

result after five days in experimental enclosures at the seafloor ...15 3 Sum of negative responses for selected reflexes (mouth closure, leg retraction, telson

flexion, and pereopod movement) before and after five days of caging at depth for

Bathynomus giganteus that survived experimental trials (n=29) ...15

4 Relationship between pH values taken from a pH meter and the iStat point of care device, used to convert values from the iStat into their pH meter equivalents (R²=0.7039,

p<0.05) ...40 5 Relationship between sea floor temperature (ºC) and depth (m) at our study site in

Northeastern Exuma Sound, The Bahamas from July 2014 to July 2015 ...40 6 Time of death of Squalus cubensis and Centrophorus sp. that died within the 24 h video

monitoring period in post-release cages at depth ...41 7 Squalus cubensis at-vessel blood pH as a function of capture depth (m) and total length

(cm). Observed blood pH levels are shown with black dots, while the cross-hatched surface represents the values generated from the slope and intercept outputs from the most parsimonious linear model examined...41 8 Squalus cubensis at-vessel blood lactate as a function of capture depth (m) and maximum

capture duration (min). Observed blood lactate levels are shown with black dots, while the cross-hatched surface represents the values generated from the slope and intercept outputs from the most parsimonious linear model examined ...42 9 Squalus cubensis survival probability as a result of total length (cm) and blood lactate

(mmol/L). Predicted values were calculated according to the slope and intercept outputs from a GLM fit to mortality with significant predictors only. Lactate and total length inputs were chosen based on the minimum and maximum values reported in this study .42 10 Survival probability curve using at-vessel blood pH measurements for S. cubensis that

either survived ( ) or died ( ) after 24 h post-capture. The solid line represents the probability of survival calculated using Eq. 4 and the dashed line represents the 50%

chance of survival, calculated at a blood pH of 7.17 ...43

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11 Post-release mortality of S. cubensis placed in 24 h post-release cages by vitality score (n caged). The distribution of sharks that went on to survive or die after 24 h post-release was significantly different between groups of sharks assigned excellent, fair, and poor vitality scores (Pearson’s x²=11.78, p=0.001, df=2) ...43 12 At-vessel S. cubensis blood pH by vitality score. Means that do not share a letter above

each box are significantly different as determined by an ANOVA and Tukey’s test

(p<0.05) ...44 13 Percent of all S. cubensis exhibiting swimming behavior during the 24 h video

monitoring period in post-release cages at depth ...44 14 Squalus cubensis mean time swimming (calculated for the first minute of each video

deployment only) for those that survived or died during the first 900 minutes following cage deployment. Mean time swimming of survivors increased significantly with

duration in the cage (r²=.37, p<0.01) ...45 15 Squalus cubensis mean time swimming (calculated for the first minute of each video

deployment only) for those that survived in either shallow (<625 m) or deep (>625 m) cages during the first 900 minutes following deployment. Mean time swimming increased significantly over time for both shallow and deep groups of survivors (shallow- r²=0.30, p<0.05; deep- r²=0.12, p=0.05), and the rate of increase was marginally higher for the sharks in shallow cages (ANCOVA interaction term, p=0.06) ...45 16 Squalus cubensis time of first swimming was significantly earlier for sharks in shallow

post-release cages compared to those in deep cages (Mann Whitney U Test, p= <0.05)..46 17 At-vessel Squalus cubensis blood glucose levels were significantly greater for sharks that

had a time of first swimming (TOFS) after 120 min post-capture (‘late’) compared to those that had a TOFS earlier than 120 min post-capture (‘early’; t=2.19, p<0.05) ...46 18 At-vessel S. cubensis blood lactate levels were significantly lower for active sharks

compared to inactive sharks observed during the 24 h post-release caging period as

calculated by percent time swimming (<20%- inactive, >20%-active; t=2.56, p<0.05) ...47 19 Squalus cubensis total length was significantly lower for sharks that had a time of death

before 120 min post-caging compared to those that had a time of death after 120 min post-caging (Mann-Whitney U Test, p<0.05) ...47

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ABSTRACT

Deep-sea organisms are increasingly subject to bycatch interactions worldwide. Recent studies have shown that discard mortality can lead to significant declines in deep sea fish stocks, and highlight the inherent vulnerability of deep sea organisms to overexploitation due to their shared suite of conservative life history characteristics. Estimating the post-release mortality (PRM) rates of these deep-sea organisms is a necessary step towards responsible fisheries

management, particularly as PRM represents a substantial source of uncertainty when estimating total fishery mortality.

The deep-sea giant isopod Bathynomus giganteus and its relatives are captured as bycatch in numerous fisheries, although knowledge is limited regarding their population trends or

response to capture and release. In order to assess and predict PRM in B. giganteus, we used reflex action mortality predictors (RAMP) whereby the presence or absence of target reflexes was used to create a delayed mortality model, and considered factors affecting mortality.

Mortality rates five days post-capture ranged from 50-100% and both RAMP scores and time at the surface were significant predictors of mortality, although our conclusions regarding the effect of surface time are limited. In-cage video documented little movement within the 24 h

monitoring period following cage deployment, and it appeared that surviving individuals often fed within the holding period after cage deployment. Our results suggest that PRM in B.

giganteus is common and that this unaccounted source of mortality should be quantified and investigated for other deep-sea crustaceans as well.

Similarly, bycatch interactions with deep-sea elasmobranchs can lead to dramatic declines in abundance over short time scales. Sharks hooked in the deep sea could face a higher likelihood of severe physiological disturbance, at-vessel mortality, and PRM than their shallower counterparts. Unfortunately, robust PRM rates have not yet been estimated for deep-sea

elasmobranchs and as such are not currently incorporated into total fishery mortality estimates or bycatch assessments, limiting the effectiveness of conservation or management initiatives. We empirically estimated PRM for two focal species of deep-sea shark, the Cuban dogfish Squalus cubensis and the gulper shark Centrophorus sp. using post-release cages deployed at-depth. We calculated 24 h PRM rates of 49.7% (± 8.5 SE) for S. cubensis and 83% (± 16 SE) for

Centrophorus sp. and identified shark size (total length), blood lactate, blood pH, and vitality

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scores as predictors of PRM in Squalus cubensis. We also observed all PRM within 11 h post- capture and demonstrated the effects of capture and recovery depth on stress and behavior. Our results suggest that PRM rates of deep-sea sharks are higher than previously assumed, and

highlight the need for filling in this gap in fishery mortality estimates for other common deep-sea discards in the future.

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CHAPTER ONE

AN ASSESSMENT OF POST-RELEASE MORTALITY FOR A COMMONLY DISCARDED DEEP-SEA ISOPOD

(BATHYNOMUS GIGANTEUS) USING REFLEX IMPAIRMENT

This article has been accepted for publication in ICES Journal of Marine Science published by Oxford University Press.

1.1 Introduction

Bycatch, defined as non-target species that are discarded or unmanaged, makes up an estimated 40.4% of total catches for global marine fisheries (Davies et al., 2009), and represents a threat to the sustainable management of marine ecosystems (Crowder and Murawski 1998, Harrington et al., 2005, Kelleher, 2005). As a result of low economic value or harvest

prohibitions (Harrington et al., 2005), many individuals caught as bycatch are discarded alive with unknown post-release mortality (PRM) rates. In the few instances where it has been estimated, PRM rates vary drastically depending on the fishery, gear type, and taxa in question (Davis, 2002). In general there is a lack of data regarding the contribution of discard mortality to total fishery mortality, which poses a serious challenge to marine fisheries management (Hall et al., 2000; Davis, 2002).

Increasingly, commercial fisheries are expanding to the deep-sea (Morato et al., 2006, Watson and Morato, 2013) where species are generally highly susceptible to overexploitation due to their very conservative life histories (Large et al., 2003; Simpfendorfer and Kyne, 2009;

Norse et al., 2012). Individuals captured at depth undergo a forced ascent from hundreds of meters deep to the surface facing extreme thermal, barometric, and photic stress (Brooks et al., 2015) and, if discarded alive, must return to depth after experiencing a suite of sub-lethal impairments that may increase the chance of post-release mortality or predation (Wilson et al., 2014). These sub-lethal effects can include physiological disturbance and/or physical injury, which can affect behavior, growth, reproduction and immune function, ultimately reducing fitness in the long term (reviewed by Wilson et al., 2014). In invertebrates, the magnitude of these sub-lethal effects often depends on gear type, capture duration, emersion time, and

temperature (Giomi et al., 2005; Ridgeway et al., 2006; Haupt et al., 2006; Wilson et al., 2014).

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In deep-water fisheries, the combined effects of high magnitude stressors experienced during capture, ascent, and descent is likely to result in higher PRM rates in these taxa than in their shallower counterparts.

In order to predict the PRM rates of crustaceans, recent field studies have been successful in using reflex action mortality predictors (RAMP) (Stoner et al., 2008; Stoner, 2009; Stoner, 2012b; Urban, 2015) whereby the presence or absence of reflexes at-vessel can be used to quickly gauge an individual’s probability of survival. Reflex impairment scores (the sum of negative reflexes for a single assessment) are robust in that they reflect the cumulative effects of various types of stressors to multiple bodily systems (Stoner, 2012a). After calculating delayed mortality curves, which describe the relationship between reflex impairment scores and PRM rates (reviewed by Stoner, 2012a), RAMP can be employed to predict mortality without containment, tagging, or tracking.

The deep dwelling giant isopod Bathynomus giganteus is the primary bycatch species in the trap fishery for golden crabs (Chaceon fenneri) (Perry et al., 1995; Harper et al., 2000) and common bycatch in the deep trawl fishery for rock shrimp (Sicyonia brevirostris) and royal red shrimp (Hymenopenaeus robustus) in the northern Gulf of Mexico (R.D. Grubbs, personal observation) as well as the monkfish (Lophius gastrophysus) gillnet fishery in southern Brazil (Perez and Wahrlich, 2005). It has also been documented as bycatch in Brazilian deep water shrimp trawl fisheries (Perez et al., 2013) while a close relative, Bathynomus doederleini, is common bycatch of Taiwanese hagfish (Eptatretus spp. / Paramyxine spp.) trap fisheries (Soong and Mok, 1994). Bathynomus giganteus has also been recently targeted in Japanese fisheries to fuel a new demand for isopod-infused rice crackers. The species inhabits depths from 359 to 1050 m off of the Yucatán Peninsula, Mexico (Barradas-Ortiz et al., 2003), 349 to 733 m in the southern Gulf of Mexico (Briones-Fourzán and Lozano-Alvarez, 1991), 594-1415m in

northeastern Exuma Sound, The Bahamas (M. Violich, unpublished data) and to at least 1,735 m in the northern Gulf of Mexico (Grubbs, unpublished data). Whereas B. giganteus primarily scavenges on fish and squid remains (Barradas-Ortiz et al., 2003), active predation of a small squaloid shark has been observed in an experimental enclosure (B. Talwar, unpublished data) and stomach content analysis has suggested a rather wide diet for a strict scavenger (Briones- Fourzán and Lozano-Álvarez, 1991, Barradas-Ortiz et al., 2003).

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Here, we assess the PRM of B. giganteus and used RAMP techniques (Stoner et al., 2008) to create a framework for rapidly predicting discard mortality for this common bycatch species in deep-sea fisheries. We also identify factors contributing to PRM and provide insight into the post-release behavior of individuals in experimental enclosures.

1.2 Materials and Methods 1.2.1 Study Area

Field work was conducted May-June 2015 in northeastern Exuma Sound, approximately 2.5 km west of Powell Point in Eleuthera, The Bahamas (24.541°N, 76.121°W), where water in excess of 1000 m deep is accessible within 4 km of shore (Brooks et al., 2015).

1.2.2 Evaluating Reflexes

Bathynomus giganteus were routinely captured in ongoing trap surveys and examined to identify reflexes that were lost over time while submerged in deck tanks. Target reflexes were chosen based on being 1) quickly and easily tested in the field, 2) consistent and stereotypic, 3) able to be scored as binary positive or negative responses, and 4) unambiguous across experimenters. Based on these criteria, six reflexes were selected for field trials (Table 1).

1.2.3. Field Trials

Individuals were captured in a circular 2.5 m diameter trap made of 3.8 cm x 3.8 cm polyvinyl chloride (PVC) coated wire mesh. A rectangular bait cage filled with 1.4 kg of

miscellaneous fish parts and/or little tunny (Euthynnus alletteratus) was suspended in the center of the trap and multiple layers of fine wire mesh prevented captured individuals from accessing the bait. A single trap per trial was attached to 1000 m of line and set for approximately 24 hours before being hauled to the surface at 0.3 m/s.

Once on the boat, isopods were transferred to coolers filled with water at ambient sea surface temperature (26-28°C) in full sunlight. They were then randomly sorted into two

treatment groups: those exposed to 15 minutes of air at 27-28°C and those retained in the coolers with water changes every five minutes (hereafter termed ‘controls’). During this period, we placed unique combinations of multi-colored 15cm zip ties on the pereopods of each individual

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for identification. Zip ties were affixed with the minimal tightness required to avoid tag loss and tag ends were removed to allow for freedom of movement and pereopod extension.

RAMP assessments were then conducted on haphazardly selected individuals, alternating between an animal exposed to air followed by a control individual. Once the six reflexes (Table 1) were assessed, each animal was assigned a carapace redness score from 0 (white) to 2 (red), which was previously hypothesized to vary with condition, and examined for physical injury.

While tagging procedures and RAMP assessments were conducted, the trap was converted into a cage by sealing the two entrances with 1x1cm wire mesh. The bait cage was also opened to allow for access to the fish remains. Lastly, two programmable white LED lights (“Lanternfish”, Blue Turtle Engineering, Florida, USA) and a GoPro Hero 3 White Edition camera programmed with a Time Lapse Intervalometer (Cam-Do, USA) in a Scout Pro H3 deep- sea housing (Group B Distribution Inc, Florida, United States) were synced to record for four minutes every half hour for 24 hours and attached to the inside of the cage before deployment.

Three 5 lb floats were attached via stainless steel longline snaps at 20, 60, and 80 m above the cage and an archival temperature and depth recorder (Lotek LAT1400, Newfoundland, Canada) inside of a PVC housing was attached just above the cage bridle prior to cage

deployment at the capture location. Floats prevented the line from tangling with the cage or getting stuck on the bottom while the TDR recorded temperature and depth every 4 seconds for roughly 30 h allowing for the calculation of descent and ascent rates.

After five days at the seafloor, cages were hauled to the surface and isopods were

assessed for mortality and condition using the same RAMP assessments as described previously before being euthanized. Lastly, animals were inspected for physical injuries caused by the caging process and dissected to assess feeding.

1.2.4 Data Analysis

Contingency analysis was used to model reflex presence/absence against mortality for animals pooled from both control and air exposed groups. Pearson’s Chi Square tests were then used to test the null hypothesis that the distribution of positive or negative reflexes was equal for groups of survivors and mortalities. Those reflexes that were distributed significantly differently than expected (using an alpha value of α<0.1) were used in all other analyses for predicting mortality.

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A generalized linear model with a binomial probability distribution and a logit link function was fitted to the data using Firth adjusted maximum likelihood estimation (Firth, 1993) with total 5-day mortality as the binary response variable (either dead or alive) and nine possible explanatory variables included as predictors. Categorical variables included emersion, sex, carapace redness score, and physical injury. Continuous variables included surface time (the time interval between a trap reaching the boat and the associated cage being lowered to depth from the surface), reflex impairment score (i.e. sum of negative reflexes that were identified using contingency analysis), total length, and the interactions between both surface time and reflex impairment score and total length and reflex impairment score. The order of RAMP assessments across individuals was not included in the model as animals were chosen

haphazardly and assessments were brief (<1min). A maximal model including all explanatory variables was fitted and nonsignificant factors were removed in stepwise fashion while

evaluating the increases in deviance and Akaike Information Criterion (AIC) with each removal.

The model was reduced until the minimal adequate model remained, which included only significant terms or terms that, once removed, caused a significant increase in AIC or deviance (see Crawley, 2007). Lastly, a Student’s t-test was used to look for differences between reflex impairment scores before and after caging. All of the analyses were performed using JMP 7.0.1 (SAS Institute, Cary, NC, USA) and R Programming Language (R Development Core Team, 2008) and the level of significance for the aforementioned tests was α<0.05.

1.3 Results

1.3.1. Capture Characteristics

A total of 100 isopods of a mean total length of 28 cm (± 4.67 SD) were captured during four trials, including 77 males and 23 females. Traps were set at a mean depth and temperature of 845 m (±27.5 SE) and 7.49°C (± 3.74 SE) on a muddy bottom with no benthic structure.

Physical injury was uncommon, occurring in only 13% of individuals prior to caging, and did not appear significant. Of these injuries, nine were broken pereopod or antennae tips, three were slight carapace breaks along the margins, and one was a small tear to a single pleopod. No at-vessel mortality was observed prior to caging.

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6 1.3.2 Reflex Action Mortality Predictors

Presence or absence of the antennae extension and pleopod movement reflexes could not differentiate between survivors and mortalities based on Contingency Analysis (x²= 2.336, p>0.1 for antennae extension; x²=1.597, p>0.1 for pleopod movement), and were thus not included in the reflexes used to predict mortality or in comparisons between control and treatment groups.

Animals exposed to 15 minutes of air had a greater number of negative responses for target reflexes than those not exposed (Table 2), however there was no significant difference in 5-day mortality rates between these groups. Across subgroups, the most commonly lost (i.e.

negatively scored) reflexes were pereopod movement and mouth closure, followed by telson flexion and leg retraction. Leg retraction was therefore the least sensitive reflex to capture stress and the combination of capture and emersion stress.

Presence or absence of mouth closure (x²=2.908, p=0.08), leg retraction (x²=7.059, p=0.0079), telson flexion (x²=8.554, p=0.003), and pereopod movement (x²=10.005, p=0.001) could differentiate between survivors and mortalities and were thus used to create a mortality curve based on the sum (0-4) of lost reflexes (Figure 1).

1.3.3 Factors Affecting Mortality

Logistic regression suggested a model including only surface time and reflex impairment score provided the best fit to binomial mortality data (AICfull model = 118.6, AICminimal model

=107.7), while sex, physical injury, total length (TL), carapace redness score, and emersion were poor predictors of 5-day mortality (Table 3, 4). Mean surface time was 56 minutes, and ranged from 30-81 minutes across trials. While reflex impairment scores and surface time were

correlated (r²=0.32, d.f. = 99, P<0.01) and total length and reflex impairment scores were weakly correlated (r²=0.05, d.f. = 99, P=0.01), the interactions between these terms were not significant and subsequently removed from the model.

1.3.4 Post-Capture Behavior

Across the four cage trials, only seven individuals were observed moving during the 24 h video monitoring periods. The first movements were always either pleopod or pereopod

twitching. In one trial, for instance, five animals started to move within two hours of reaching the sea floor, and one individual even began scavenging on a dead conspecific and on fish remains

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shortly thereafter. The only other two isopods that exhibited any life were observed moving after 8.5 and 10 hours had elapsed during two other trials. Swimming behaviors were irregular, if they occurred at all, and were characterized by an inability to orient effectively before landing

inverted on the cage floor after a short burst of activity. Furthermore, recovery from capture appeared very slow, with only 24% of surviving isopods exhibiting any sign of movement within the first 24 h following cage deployment.

In all trials, small (<5 cm) isopods entered the cage through the mesh walls within the first hour and swam haphazardly throughout the cage, settling on both the bait cage and less frequently on unmoving B. giganteus. Live, non-caged B. giganteus also appeared on the outside of the cage within three hours and appeared highly active, regularly swimming or crawling on the cage walls with constant antennae movement and normal, highly oriented swimming

behavior. Many were also observed excavating in the muddy benthos in an attempt to access fish remains or dead isopods from beneath the cage floor, at times concentrating around clusters of unmoving isopods within the cage. None showed signs of aversion to the lights. These high levels of activity are in contrast to caged individuals, which moved very little, if at all, during the 24 h post-capture observation period.

Stomachs were present in 71% (n=71) of individuals after the 5-day period, while 29%

(n=29) were completely devoid of stomachs or internal organs, suggesting that they were

scavenged or preyed upon. Of the 71 isopods with stomachs, 42% (n=29) survived the trials and 64% (n=45) had stomach contents (Figure 2). Furthermore, of the 29 that survived, 93% (n=27) had non-empty stomachs.

1.3.5 Cumulative Effects of Capture and Cage Stress

Reflex impairment scores were significantly higher after caging (mean = 1.9) than they were prior to caging (mean = 1.3) in those individuals that survived experimental trials (t = 2.39, d.f.= 28, p<0.05; Figure 3). After caging, the telson flexion reflex was lost most often followed by mouth closure, pereopod movement, and leg retraction. Pereopod movement was the only reflex that was present more often after the trials than before (sum of negative responses= 15 before, 9 after).

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8 1.4 Discussion

This study found that reflex impairment scores were successful in predicting mortality for the deep-sea giant isopod Bathynomus giganteus after five days post-capture, with observed PRM rates ranging between 50-100%. While reflex impairment scores generated from RAMP assessments are a common tool used to predict mortality across a wide range of marine species such as decapod crustaceans (Bergmann and Moore, 2001, Stoner et al., 2008, Stoner,

2011,Urban, 2015,), teleosts (Davis and Ottmar, 2006, Davis, 2007, Raby et al., 2012 ), and elasmobranchs (Danylchuk et al., 2013, Gallagher et al., 2014b), no methods to predict mortality had been previously tested for deep sea taxa or for marine isopods despite their prevalence as bycatch in emerging deep-sea fisheries (e.g. Perez and Wahrlich, 2005).

1.4.1 Reflex Action Mortality Predictors

Only four of six reflexes that were identified in pre-trial assessments were able to differentiate between survivors and mortalities. While all six were stereotypic and repeatable, rough weather conditions during field trials likely contributed to the inaccurate assessment of responses for both pleopod movement and antennae extension. Of the four remaining reflexes, pereopod movement was the most sensitive to capture stress, followed by mouth closure, telson flexion, and leg retraction. Telson flexion and leg retraction were maintained most frequently, and are possible anti-predator responses for an impaired isopod attempting to protect its vulnerable ventral surface.

1.4.2 Factors Affecting Mortality

Surface time was the only variable other than reflex impairment score that could predict mortality. Its effect was largely driven by the extremely high 5-day mortality rate (90%) for individuals that remained at the surface for 81 minutes, and it should be investigated more thoroughly using set increments in the future. Here, its inclusion in the sampling design was largely a result of field constraints on gear and personnel, and conclusions regarding its importance are limited.

Surprisingly, emersion (air exposure) had no effect on mortality, although previous studies have shown that emersion stress, which increases with air temperature and duration, can

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severely disrupt physiological function in decapod crustaceans (Ridgway et al., 2006). Ridgway et al. (2006) and Spicer et al. (1990) reported that acidemia due to an increase in L-lactate and haemolymph CO2 during emersion may suggest that the Norway lobster (Nephrops norvegicus) cannot maintain an adequate oxygen supply when exposed to air. Bathynomus giganteus,

however, is particularly adapted to the low-oxygen environment characteristic of the deep ocean between 400-1000m (Childress and Seibel, 1998), and could thus be less sensitive to a limited oxygen supply.

Perhaps, then, temperature is a more important factor to consider than air exposure or desiccation for B. giganteus. Indeed, the thermal gap experienced by an organism caught in the deep-sea in the summer subtropics is exceptionally high (19°C in the present study), and studies on other crustaceans have shown that thermal stress does affect survival. For a trawl-caught portunid crab Liocarcinus depurator, a thermal gap of 12-14°C resulted in 96% mortality whereas a thermal gap of 0-3°C resulted in 2% mortality (Giomi et al., 2008). Similarly, in the trawl-caught shrimp Crangon crangon, holding temperatures over 20°C resulted in high mortality rates whereas temperatures between 10-20°C resulted in no mortality (Gamito and Cabral, 2003). Further research on the effect of thermal stress is certainly warranted for deep-sea crustaceans.

Along with thermal stress, deep-sea organisms undergo drastic changes in pressure and light levels during gear retrieval. Barometric stress is not typically considered relevant for crustaceans (Stoner 2012a), although it has not been examined for those residing in deep water.

Basti et al. (2010), however, did show that increased hauling speed and increased capture depth negatively affected the survival of the American lobster (Homarus americanus) retrieved from

<200 m. Photic stress can also have deleterious effects in crustaceans (Stoner 2012a). Gaten (1988), for instance, showed that deep-dwelling decapods can experience damage to the photoreceptor layer and are susceptible to morphological changes (e.g. cone shape) within the eye after excessive exposure to sunlight. Similar results have been reported by Loew (1976) for the Norway lobster (Nephrops norvegicus) and by Meyer-Rochow (1981) for deep-sea Antarctic amphipods. Furthermore, Herring et al. (1999) suggested that exposure to submersibles’

floodlights could blind shrimp associated with deep-sea vent communities and Nilsson and Lindstrom (1983) reported that exposure to light of 1,250 lux (approximately that of an overcast day) led to complete breakdown of the visual structures of the deep-sea isopod Natatolana

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(Cirolana) borealis. Chamberlain et al. (1986) described the morphology of the compound eye of the giant isopod examined here (B. giganteus) and unsurprisingly suggested any exposure to daylight causes severe and irreversible damage to their photoreceptors. Given the low-light levels where giant isopod occur, one may predict that vision may not be an important sensory system for such deep-sea species, however the complexity of the compound eyes and their forward orientation suggests they are used not only to detect light but also to estimate distance (Chamberlain et al. 1986). Thus, damage to the visual systems of deep sea crustaceans and the resultant loss of orientation could be a major source of delayed post-release mortality.

Due to the relatively benign traps used in this study, physical injury was rare after the initial capture event and did not affect mortality. Negative gear interactions would likely be more common in other capture gears (e.g. trawl or gillnet; Rose, 1999) and could result in elevated PRM rates compared to those reported here.

1.4.3 Post-Capture Behavior and Cage Effects

While recovery was quite slow, a high percentage of surviving individuals had stomach contents, suggesting that many individuals recovered to the point of feeding after video

monitoring stopped. Still, numerous individuals had non-empty stomachs and did not survive the caging process, suggesting that they had failed to evacuate food eaten prior to their initial capture or that they had recovered and eaten before they perished. Mortality, therefore, could have taken place late in the 5-day period as a result of confinement stress, delayed effects of capture, or predation from other small isopod species that could fit through the cage material.

The caging process introduced a number of confounding factors in our estimation of PRM for each reflex impairment score. Caging could have increased the stress on ‘released’

individuals and artificially inflated the documented PRM rates as suggested by the increase in negative reflex scores after the second haul for surviving isopods. Alternatively, the PRM rate could have been deflated as the cages prevented predation from animals larger than the cage mesh. Given that the isopods exhibit extremely limited movement immediately after capture, and that isopods released at-vessel show little sign of swimming behavior, descending over 800m could take multiple hours. As such, numerous predators in the water column attracted by gear retrieval could easily prey on discarded B. giganteus. Although natural predators of B. giganteus are not well documented, a tiger shark has been found with a giant isopod in its gut (Briones-

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Fourzán and Lozano-Álvarez, 1991) and presumably other large bodied elasmobranchs and teleosts would prey on impaired isopods in mid-water.

1.4.4 Conclusions

Most importantly, this study showed that reflex impairment scores developed using a RAMP methodology can predict post-release mortality for discarded B. giganteus, which was estimated at greater than 50%. Furthermore, it suggests that a short emersion period may be of little consequence to the post-release survival of this species. It also highlights the need for additional research into the effect of surface time on PRM, including a better understanding of the relative contributions of thermal gap, pressure, and light changes on the mortality of

discarded deep-sea crustaceans, particularly as fisheries continue to expand into the deep-sea and bycatch interactions increase. These results further underscore the prevalence of cryptic discard mortality and emphasize the need to better account for it when estimating bycatch mortality.

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Table 1: The reflexes identified for assessing condition of trap-caught Bathynomus giganteus. The test for each reflex is the action required to elicit a given response (i.e. positive or negative), listed in the same order as they were conducted in the field.

Reflex Test Orientation Positive Response Negative Response

Leg retraction

Extend first paired pereopods

In water, ventral side up

Pereopods resist extension and/or retract strongly to the start

position

Pereopods fall limply back into the start position and

present no resistance to extension

Mouth closure

Open mandibles with a blunt probe, then remove the probe

Mandibles resist opening and, upon removal of probe, close quickly and tightly or open and

close rapidly

Mandibles fall limply into the start position and show no resistance to opening

Pleopod movement

Brush the pleopods with a blunt probe towards

the mouth

Pleopods undulate or, if contracted inward towards the midline of the carapace, resist

stimulation

Pleopods fall limply to rest flat against the body and

show no movement

Pereopod movement

Manually stimulate the pereopods with a blunt

probe

Out of water, ventral side up

Pereopods move spontaneously when stimulated

Pereopods are motionless after stimulation

Antennae extension

Manually extend both antennae straight out in

front of the body

Antennae move after stimulation or rapidly return to the start

position

Antennae hang limply and exhibit no response to

stimulation

Telson flexion

Pull the telson downward until it is at a

>180° angle to the body

Telson resists the flattening motion and/or curls upward past

the original start postion

Telson exhibits no resistance or motion

12

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Table 2. Summary of negatively scored reflexes for emersion (n=50; 15 minutes) and control (n=50) groups of Bathynomus giganteus prior to caging. Proportions of negative responses for each reflex are shown as percentage of total negative responses for each group.

Treatment type Air Emersion No Air Emersion

Reflex

No. of negative responses

% of negative responses

No. of negative responses

% of negative responses

Leg retraction 15 13.6 9 9.7

Telson flexion 26 23.6 23 24.7

Mouth closure 28 25.5 25 26.9

Pereopod movement 41 37.3 36 38.7

Totals 110 100 93 100.0

Table 3: Full model and minimal adequate model from backwards stepwise GLM analysis to describe 5-day mortality in Bathynomus giganteus based on seven initial model parameters and two interaction terms.

Parameter Estimate S.E. z-value Pr(>|z|)

Full model

Intercept -0.846 2.315 -0.365 0.715

Sex -0.372 0.657 -0.567 0.571

Physical Injury -0.550 0.853 -0.645 0.519

Emersion -0.391 0.560 -0.699 0.485

TL -0.039 0.107 -0.359 0.719

TL*Reflex impairment score -0.001 0.054 -0.019 0.985 Surface Time*Reflex impairment score 0.004 0.013 0.301 0.764

Carapace redness score 0.630 0.503 1.253 0.21

Surface Time 0.018 0.033 0.552 0.581

Reflex impairment score 0.446 1.092 0.409 0.683 Most parsimonious model

Intercept -1.53 0.631 -2.426 **

Surface Time 0.028 0.013 2.15 *

Reflex impairment score 0.505 2.42 2.089 *

Estimates and standard errors (S.E.) are on a logit scale.

*p<0.05, **p<0.02

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Table 4. Akaike Information Criterion (AIC) values for each GLM used to describe the 5-day mortality of Bathynomus giganteus resulting from stepwise backwards elimination of

nonsignificant terms.

Model AIC

Full Model 118.56

- TL* Reflex impairment score 116.560 - Surface time * Reflex impairment score 114.660

- Sex 112.970

- Emersion 111.450

- Physical injury 109.670

- TL 108.720

- Carapace redness score 107.770

Most parsimonious model 107.770

- Reflex impairment score 110.490

- Surface time 110.660

Figure 1. Mortality of giant isopods as a function of reflex impairment score (sum of negative responses from RAMP assessments). Each point represents the calculated 5-day mortality rate for individuals with a given impairment score, pooled across treatment groups. The solid line shows the curved fit from logistic regression which represents the likelihood of mortality for an individual with a given reflex impairment score.

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Figure 2. Number of Bathynomus giganteus with empty and non-empty stomachs grouped by cage result after five days in experimental enclosures at the seafloor.

Figure 3. Sum of negative responses for selected reflexes (mouth closure, leg retraction, telson flexion, and pereopod movement) before and after five days of caging at depth for Bathynomus giganteus that survived experimental trials (n=29).

0 5 10 15 20 25 30

Survivors Mortalities

Number of individuals

Non-empty stomach Empty stomach

0 2 4 6 8 10 12 14

0 1 2 3 4

Number of individuals

Reflex impairment score

Before caging After caging

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CHAPTER TWO

STRESS, POST-RELEASE MORTALITY, AND RECOVERY OF COMMONLY DISCARDED DEEP SEA SHARKS CAUGHT ON

LONGLINES

2.1 Introduction

In recent decades, commercial fisheries have expanded into the deep-sea (below 200 m) (Morato et al., 2006) due to advancements in fishing technology and declines in some coastal stocks (Cotton and Grubbs, 2015). Unfortunately, deep-sea fishes are highly susceptible to overexploitation due to their very conservative life histories (Large et al., 2003; Simpfendorfer &

Kyne, 2009; Norse et al. 2012). Garcia et al. (2008) suggest that the average fishing pressure that it would take to drive a deep-sea species extinct is only 58% of that for a continental shelf

species, and, as would be expected, rapid depletion and abandonment of deep-sea fish stocks has been documented repeatedly (Koslow et al., 2000, Graham et al., 2001, Jones et al., 2005, Devine et al., 2006, Norse et al., 2012).

Deep-sea elasmobranchs are perhaps the least resilient fishes to exploitation as their maximum rates of population growth are at the lower end for chondrichthyans, making them among the lowest observed for any species (Kyne and Simpfendorfer, 2007; Norse et al., 2012).

Furthermore, the greater a species’ capture depth, the more vulnerable it is to capture-induced stress as a result of decreased metabolic capacity, lowered energy stores (McClain et al., 2012;

Koslow, 1996), and greater changes in temperature, pressure, and light levels experienced during the forced and rapid ascent to the surface. These and other factors can interact to increase the likelihood of at-vessel mortality or cryptic post-release mortality (PRM) after a capture event (Skomal and Mandelman, 2012; Brooks et al., 2015).

At-vessel and PRM rates in elasmobranchs are species-specific and highly variable (Morgan and Burgess, 2007, Enever et al., 2009, Hale and Carlson, 2010, Braccini et al., 2012, Coelho et al., 2012, Gallagher et al., 2014a) depending on factors such as the gear type, capture

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duration, respiratory mode, and metabolic capacity for the species in question (Davis, 2002, Mandelman and Skomal, 2009, Dapp et al., 2015). Similarly, the degree of physiological disturbance and/or physical injury experienced by a released individual can vary greatly, and may result in sub-lethal effects such as impaired behavior, growth, or immune function that ultimately lead to post-release predation or reduced fitness (Davis, 2002, Raby et al., 2014, Wilson et al., 2014).

This variability in the sub-lethal and lethal effects of capture is currently not incorporated into fishery mortality assessments despite data that suggest that PRM of longline-caught deep- sea elasmobranch discards can be common (Brooks et al., 2015). Overall there remains a lack of empirically estimated PRM rates for discarded deep-sea sharks, many of which are thrown back due to low economic value or due to harvest prohibitions requiring their release. As deep-sea elasmobranchs are commonly caught as bycatch in fisheries targeting teleosts and crustaceans worldwide (Cotton & Grubbs, 2015), there is the potential that total fishery mortality estimates for these species are underestimated as a result of not accounting for discard mortality, or

conversely overestimated by ignoring the potential for survivors, likely limiting the effectiveness of management efforts (Coggins et al., 2007; Molina and Cooke, 2012).

Our goals for this study were to empirically estimate the 24 h PRM rates of deep-sea sharks, predict PRM using blood chemistry parameters and vitality scores, identify capture characteristics contributing to at-vessel stress, and shed light on the post-release behavior of individuals held in cages at the seafloor. Our primary species of interest was the Cuban dogfish (Squalus cubensis), the most commonly encountered squalid in the deep reef fish and tilefish fisheries of the northern Gulf of Mexico (National Marine Fisheries Service, unpublished data;

Jones et al. 2013), where over 95% are discarded alive (Hale and Carlson, 2010, Gulak et al., 2012). Our secondary species of interest was the gulper shark (Centrophorus sp.), which is part of one of the most highly exploited species complexes of deep sea sharks to date (Kyne and Simpfendorfer, 2007; Kyne et al., 2012).

2.2 Materials and Methods 2.2.1 Longline Sampling

We conducted our field work from July 2014 to June 2015 in northeastern Exuma Sound, approximately 2.5 km west of Powell Point on Eleuthera, The Bahamas (24.541°N, 76.121°W).

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Standard demersal longlines were set in 450-900 m of water during daylight hours only.

Mainline length was a minimum of 1.5 times the water depth to ensure that hooks were located on the bottom, resulting in lines of 1500-2000 m long. Longlines consisted of a grapnel anchor or weight to attach the mainline to the seafloor, twenty to thirty baited circle hooks (10/0 or 12/0) spaced 5-10 m apart, and an archival temperature and depth recorder (TDR) (Lotek LAT-1400, Newfoundland, Canada) placed 5 m from the last hook. The TDR recorded depth and

temperature every four seconds, allowing for the calculation of longline descent and ascent rates, and longline depths and temperatures were recorded as the deepest and coldest points measured for a given dataset. Furthermore, the TDR allowed for the calculation of maximum capture duration for each individual, defined as the time between the line reaching its maximum depth and the time when an animal was unhooked.

Hooks were baited with miscellaneous fish scraps and/or little tunny (Euthynnus alletteratus) and soak times were roughly 3.5 h. After the desired set duration, longlines were hauled using a commercially available electric pot hauler (Waterman Industries of Florida, Inc., Odessa, FL) at a rate of 0.3 m/s. Once at the boat, sharks were sequentially unhooked and placed in a large cooler to minimize air exposure for the remaining workup, during which sharks were measured for pre-caudal, fork, and total lengths and assessed for maturity based on external morphology and/or published size-at-maturity data. A small fin clip was then removed for genetic analyses. Fin clips were taken from a unique location to distinguish individuals while in the post-release cage during the subsequent 24 h of monitoring.

2.2.2 Blood Sampling

Sharks were placed into tonic immobility and blood (~3 mL) was drawn by caudal venipuncture using a 25.4 mm, 22 gauge needle and either a 3 mL or 5 mL heparinized syringe.

Roughly 95 μl of blood was then inserted into an iStat CG4+ cartridge, which was analyzed by an iStat blood gas analyzer (Heska Corporation, Fort Collins, CO, USA) thermoset to 37 ºC to determine blood lactate and pH levels (Mandelman and Skomal, 2009, Gallagher et al., 2010, Harter et al., 2015). Simultaneously, 1 mL of blood was transferred to a 1.5 mL eppendorf tube and analyzed by a waterproof pH meter (Hanna Instruments, Woonsocket, RI, USA) to

determine blood temperature and pH. Immediately following these analyses, one drop of blood was placed directly onto an Accu-Chek glucose meter strip (Roche Diagnostics, Basel,

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Switzerland) to determine blood glucose levels (validated by Cooke et al., 2008 for fish) and one drop was placed directly on a Lactate Plus Meter test strip (Nova Biomedical, Waltham, MA, USA) to determine blood lactate levels in the event of an iStat cartridge error (see Awruch et al., 2011 for validation of a similar unit for elasmobranchs). Blood chemistry analysis typically occurred within one minute following caudal venipuncture.

The remaining blood sample was injected into a 10 mL vacutainer coated with lithium heparin (Becton, Dickinson and Co., Franklin Lakes, NJ, USA) and placed on ice until we reached the dock. In the lab, a micro-hematocrit tube (Drummond Scientific, Broomall, PA, USA) was filled with a small sample of whole blood and sealed with Critoseal (McCormick Scientific, St. Louis, MO, USA) before it was spun in a micro-hematocrit centrifuge (LW Scientific Zippocrit, Atlanta, GA, USA) at 4400 g for 4.5 min. Hematocrit was calculated as the percentage of total blood volume made up of red blood cells.

2.2.3. Caging

Immediately after taking a fin clip, animals were placed into a circular post-release cage attached to the side of the boat and assigned a vitality score (Table 5). The cage was constructed of 3.8 cm x 3.8 cm polyvinyl chloride (PVC) coated wire mesh reinforced with PVC struts and measured roughly 2.5 m in diameter. After all individuals from a given longline set were added, the cage door was tied shut with a galvanic timed release (Neptune Marine Products, Port

Townsend, WA, USA), allowing the cage door to fall open after 20-22 h so that surviving sharks could swim out. The cage was then lowered to the seafloor at a rate of 0.49 m/s as close to the capture location as possible. A TDR was attached 5 m above the cage bridle and two floats were attached to the mainline with stainless longline snaps at 50 m and 100 m from the cage to prevent the mainline from getting tangled with the cage material. Cage depth, temperature, ascent, and descent rates were calculated as discussed previously. Upon reaching the sea floor, the cage was pulled onto its side by the drag of the boat and buoys at the surface. It then remained untouched for 24 h before being hauled to the surface. Any surviving animals still in the cage either swam out during ascent or were released at the boat, while dead individuals were retained for

dissection.

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Two programmable white LED lights (“Lanternfish”, Blue Turtle Engineering, Florida, USA) and a GoPro Hero 3 White Edition camera programmed with a Time Lapse Intervalometer (Cam-Do, USA) in a Scout Pro H3 deep-sea housing (Group B Distribution Inc, Florida, United States) were synced to record for four minutes every half hour for 24 hours and attached to the inside of the cage before deployment. Videos were later analyzed for time of first swimming (‘TOFS’, defined as time of first sustained forward movement) and time of death (defined as the last time an animal was observed ventilating) for each individual, and total seconds swimming was recorded for the first minute of each four minute video segment for each animal. Percent time swimming was then calculated for each animal by dividing its total time swimming across the first minute of all video segments in a given cage drop by the total time during which that animal was alive and observed. This metric was then binned into active (>20% swimming) and inactive (<20% swimming) categories for analysis. Similarly, time of death and TOFS were binned into early (<120 min post-capture) and late (>120 min post-capture) categories.

2.2.5. Data Analysis

At-vessel mortality rates for all shark species were calculated as the percentage of the total catch of a species found to be dead upon first handling. Twenty-four hour PRM rates and standard errors (SE) were calculated using Eqs. (1) and (2) outlined in Pollock and Pine (2007), where M is the species-specific PRM rate and r is the number of cages.

(Eq. 1)

(Eq. 2)

All further statistical analyses were only conducted for Squalus cubensis due to low sample sizes for other species. Blood chemistry parameters were evaluated using the Shapiro- Wilk test for normality and outliers identified and removed using diagnostic plots in R

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Programming Language (R Development Core Team, 2008). Blood chemistry parameters and total lengths were then re-scaled into measurements of deviation from the mean for use in generalized linear models (GLM). Blood pH values were taken directly from the waterproof pH meter, which measures pH as a function of blood temperature, or, if measurements were taken only from the iStat point of care device, converted from raw iStat values to their pH meter equivalents using an equation from linear regression relating values from both instruments when run simultaneously (Fig. 4).

To predict PRM, a GLM with a binomial probability distribution and a logit link function was fitted to the data using maximum likelihood estimation. The model describes the relationship between 24 h mortality as a binary response variable and five potential explanatory variables as well as interaction terms. A random effect to account for cage deployment was initially included, but as it accounted for <1% of the variance in the full model it was removed. The possible main effects that were included were the continuous variables of blood pH, blood lactate, blood glucose, hematocrit, and total length as well as the interaction terms for lactate and total length, lactate and glucose, pH and lactate, pH and total length, and total length and glucose (see Table 6 for correlation structure). Nonsignificant factors were removed in stepwise fashion while

evaluating the increases in deviance and Akaike Information Criterion (AIC) with each removal.

The model was reduced until the minimal adequate model remained, which included only significant terms or terms that, once removed, caused an increase in AIC or deviance (see Crawley, 2007). Significant terms from the minimal model were then used to create a GLM predicting mortality as described above. Further, GLMs with only one explanatory variable were explored to identify a single blood chemistry parameter that provides the best fit to mortality.

Linear models were then used to determine what capture characteristics best describe blood lactate and blood pH among maximum hooking duration, sea surface temperature, total length, capture depth, and the interaction between capture depth and total length. As capture temperature and depth were tightly correlated (R² = 0.9566, p<0.001; Fig. 5), only depth was included in these models, but biologically represents the cumulative effects of all abiotic variables associated with increased depth. Full models were fit to the response variable and terms removed as

described previously.

Pearson’s Chi Square tests were then used to test the null hypothesis that the distribution of survivors and mortalities was equal across vitality scores. Vitality scores were then examined

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to identify blood chemistry parameters that differed between groups using one-way ANOVAs and Tukey’s tests. Further, the relationship between time post-caging and mean time swimming (pooled across all animals for each video segment) was examined with linear regressions and compared between groups of survivors and mortalities as well as within survivors for those at shallow (<625 m) and deep (>625 m) cage depths. The rate of increase in mean time swimming was compared among these groups using an ANCOVA. Lastly, binned swimming behaviors and times of death were compared with T-tests and/or Mann-Whitney U tests, as were blood

chemistry parameters between species, depending on whether or not data were normally

distributed. All analyses were performed using JMP 7.0.1 (SAS Institute, Cary, NC, USA) and R Programming Language (R Development Core Team, 2008) and the level of significance for all tests was α<0.05. Graphs were created using SigmaPlot (Sigmaplot, vers. 11.0, Systat Software Inc., Richmond, CA, USA).

2.3 Results 2.3.1 Capture Characteristics

A total of 108 sharks from six species were captured over 72 longline sets fishing at a mean depth of 628 m and a mean temperature of 11.95 °C (Table 7). Seventy-seven of these individuals were caged over 37 trials, with post-release cages resting at a mean depth of 641 m and a mean temperature of 11.73 °C after descending at a mean rate of 0.49 m/s (range 0.28-0.59 m/s). The maximum number of sharks placed in a single cage was six, but was more commonly 1-3 animals/cage. The sea surface temperature over the study period ranged from 24 to 30 °C.

Sharks were hooked in the jaw or soft palate except for one instance where an animal was hooked through the right spiracle. Physical injury at-vessel was documented in only one S.

cubensis (broken jaw) and one M. canis insularis (secondary hooking in the pectoral fin). These individuals were not included in post-release caging trials. We saw no evidence of barotrauma.

2.3.2 Mortality and Blood Chemistry

At-vessel mortality rates ranged from 8.33-100% across species while 24 h PRM rates for S. cubensis, Centrophorus sp., and Mustelus canis insularis ranged from 49.7 – 83%, although sample sizes were limited for Centrophorus sp. and M. canis insularis (Table 8). No individuals

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were recaptured from this study; however, one S. cubensis was recaptured from a study by Brooks et al. (2015) after it was initially tagged in 2010.

The mean time of death was 190 min (± 43.8 S.E.) post-capture for S. cubensis and 260 min (± 65.6 S.E.) post-capture for Centrophorus sp. that died within the 24 h caging period (Fig.

6). All mortalities were observed within 690 min post-caging.

Blood glucose levels were significantly lower in Centrophorus sp. compared to those in S. cubensis (Student’s T-Test, p<0.05). There were no significant interspecific differences in blood pH, lactate or hematocrit levels (Table 9).

2.3.3 Predicting At-Vessel Blood Chemistry

Analysis of linear models determined that a model including only capture depth and total length provided the best fit to at-vessel blood pH levels (AICFull Model = -54.08, AICReduced Model = -58.43). Both capture depth and total length were significant predictors of at-vessel blood pH (Table 10,11), which decreased with decreasing capture depth and total length values (Fig. 7).

Analysis of linear models determined that a model including only capture depth and maximum hooking duration provided the best fit to at-vessel blood lactate levels (AICFull Model = 103.2, AICReduced Model =99.56). Only capture depth was a significant predictor of at-vessel blood lactate (Table 12, 13) in the reduced model, which increased with longer maximum hooking durations and shallower capture depths (Fig. 8).

2.3.4 Predicting Post-Release Mortality

The GLM analysis determined that a model including glucose, hematocrit, lactate, total length, and the interaction between glucose and total length provided the best fit to binary 24 h mortality data (AICFull Model = 34.04, AICReduced Model = 26.80). Only lactate and total length, however, were significant predictors of mortality (Table 14, 15).

A logistic model including only these significant terms was used to predict 24 h mortality for practical use in a fishery context. To obtain the probability of survival for an individual with known total length and blood lactate level, the maximum likelihood estimates (b0 = -1.84815, b1= -0.29978, b2= 0.08717) for the survival curve were substituted into the response function in Eq.

(3):

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�� =

_{1+ exp (�}^{exp (�}⁰^+�¹^�¹^+�²^�²⁾

0+�₁�₁+�₂�₂) (Eq. 3) Based on this model, a probability of survival of 0.5 was found at a lactate value of 10.7 mmol/L for a shark of average total length (58 cm). The probability of survival then decreased with higher blood lactate levels and smaller total lengths (Fig. 9).

The best standalone predictor of mortality was then identified (again for ease of practical application) as blood pH (Table 16), which was modeled to create a logistic survival curve using the maximum likelihood estimates (b0 = -51.873, b1=7.234) substituted into the response function (Eq. 4):

�� =

_{1+ exp (�}^{exp (�}⁰^+�¹^�¹⁾

0+�₁�₁)

^{(Eq. 4)}

Based on this model, a probability of survival of 0.5 was found at an at-vessel blood pH value of 7.17. The probability of survival then decreased as pH declined (Fig. 10).

Vitality scores were distributed differently than expected (Pearson’s x²=11.78, p=0.001, df=2), suggesting that these scores are good predictors of mortality. Of those individuals

assigned a score of ‘excellent’, only 21% died whereas a score of ‘fair’ resulted in 42% mortality and a score of ‘poor’ resulted in 100% mortality (Fig. 11).

A one-way ANOVA revealed differences in at-vessel blood pH between S. cubensis assigned vitality scores from poor to excellent (F2, 59= 8.42, p<0.01). Those assigned a vitality score of poor (M=7.01 ± 0.05) had a significantly lower at-vessel blood pH than those assigned a vitality score of excellent (M= 7.27 ± 0.04) or fair (M= 7.23 ± 0.03) according to Tukey’s test (Fig. 12).

2.3.5 Post-Release Behavior of S. cubensis

Squalus cubensis swimming behaviors were normal (e.g. correct orientation, resting on the bottom, exploring the cage) and survivors often swam in circles around the cage perimeter.

The mean time of first swimming (TOFS) was 113 min (± 17.8 S.E.) post-caging for S. cubensis that survived the 24 h caging period, while for those that died it was 172.5 min (± 74.8 S.E.) post-capture. All surviving sharks were documented swimming by the 420 min post-capture video segment while all sharks that died, yet still swam, did so by 400 min post-capture (Fig.

13). Only 19% of sharks that died swam.

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