investigating the drivers of vertical movement patterns in ...€¦ · patterns in predatory...

Investigating the drivers of vertical movement

patterns in predatory pelagic fishes

by

Samantha Andrzejaczek

BMSc James Cook University, BSc (Hons) University of Western Australia

This thesis is presented for the degree of Doctor of Philosophy of The University of Western

Australia

Faculty of Engineering and Mathematical Sciences

Oceans Graduate School

2018

iii

Thesis Declaration

I, Samantha Andrzejaczek, certify that:

This thesis has been substantially accomplished during enrolment in the degree.

This thesis does not contain material which has been submitted for the award of any other degree

or diploma in my name, in any university or other tertiary institution.

No part of this work will, in the future, be used in a submission in my name, for any other degree

or diploma in any university or other tertiary institution without the prior approval of The

University of Western Australia and where applicable, any partner institution responsible for the

joint-award of this degree.

This thesis does not contain any material previously published or written by another person,

except where due reference has been made in the text and, where relevant, in the Declaration that

follows.

The work(s) are not in any way a violation or infringement of any copyright, trademark, patent,

or other rights whatsoever of any person.

The research involving animal data reported in this thesis was assessed and approved by The

University of Western Australia Animal Ethics Committee. Approval #RA/3/100/1437

The following approvals were obtained prior to commencing the relevant work described in this

thesis: Permit numbers 2881 (WA Department of Fisheries) and 08-000322-3 (WA Department

of Parks and Wildlife.

This research is supported by an Australian Government Research Training Program (RTP)

Scholarship.

This thesis contains published work and/or work prepared for publication, some of which has

been co-authored.

Signature:

v

Abstract

Large fishes (>30 kg adult size) of the epipelagic zone of the oceans and coastal seas display a

variety of patterns of vertical movement. Although advances in the affordability and

sophistication of electronic tags now allows researchers to routinely document these patterns, we

lack a standardised approach to classify these behaviours and to investigate their physical and

biological drivers. Here, we review the existing knowledge of the vertical movements of large,

epipelagic fishes and the evidence for the underlying factors that structure this behaviour. Using

data from both recovered satellite and biologging tags, we further investigate the drivers of these

patterns on large- (days to months) and fine- (minutes to hours) scales. Depth and temperature

archives recorded over deployment periods of up to 333 days by satellite tags allowed us to

examine how the external thermal environment influenced vertical movement behaviours in

oceanic whitetip sharks (Carcharhinus longimanus). Warming sea surface temperatures and the

stratification of the water column during summer months marked the onset of reverse

thermoregulatory strategies in this species, where changes in depth distribution and vertical

behaviours reduced their exposure to the warmest external temperatures. Biologging tags

deployed on tiger (Galeocerdo cuvier) and sandbar (Carcharhinus plumbeus) sharks for periods

of up to 48 hours recorded physical parameters including depth and temperature, and through the

use of tri-axial sensors, in-situ measurements of animal trajectory and locomotion. This enabled

calculation of dive geometry, swimming energetics and the cost of transport for these species at

fine spatial scales (m-km). Both species oscillated continually through the water column and data

from tri-axial sensors revealed shallow dive angles and gliding behaviour. Oscillations at these

fine temporal scales appear to be driven by the need to reduce the cost of transport while searching

for food, with observed inter-specific differences likely to be a function of foraging strategy.

Collectively, the drivers responsible for patterns of vertical movement depended on the temporal

scale of the sampling. I argue that characteristic patterns of vertical movement, particularly

oscillatory descents and ascents, result from the need for ram-ventilating animals to move

continuously in a three dimensional environment while optimising food encounter rates, energy

expenditure, and remaining within the limits imposed by the physical environment on their

physiology (notably water temperature).

vi

Acknowledgements

First and foremost a huge thank you goes to my supervisors Dr. Mark Meekan, Dr. Adrian Gleiss

and Prof. Charitha Pattiaratchi for their support and guidance. Mark, I cannot begin to describe

how much I have learnt from you over the last 4+ years. My writing abilities and presentation

skills would not be half of what they are today without your supervision and direction. Thank you

for opening your door to all of my problems large and small, no matter how busy you were. Also

a big thank you for all of the amazing fieldwork and travel opportunities, including my first acting

job on Shark Week (I’m still waiting on that Oscar though); some of the stories from that trip in

particular will always stay with me. Adrian, thank you for being such a brilliant supervisor,

mentor, friend and fellow ocean nerd. You showed me it is possible to be a successful marine

scientist and parent while being able to enjoy weekend adventures at the same time. I’m also so

grateful for the field opportunities you provided me with, especially the chance to fulfil a lifelong

dream and work on Blue Planet II. Chari, thank you for always being excited about my results,

and for providing a completely different perspective to my ideas. To all of my supervisors, despite

you telling me over and over that after the next trip I would be ‘chained to the desk’ until my

thesis was done, you always allowed me to go on ‘one last trip’. I still firmly believe all of the

extra trips kept me sane and productive enough to complete my thesis in the end. I’m also very

grateful for the continued support through the many ups and down, including broken bones, car

accidents and gear malfunctions.

Many collaborators and co-authors have contributed to this thesis. Karissa Lear, you have been

an amazing collaborator, labmate, travel buddy, mentor and friend. You were the best volunteer

I’ve ever had on a trip. Thank you so much for taking over my field trip responsibilities when I

broke my leg. Dr. Luciana Ferreira, you were another amazing mentor and shark nerd, thank you

for making the transition into PhD life so smooth. Thanks to Dr. Nikolai Liebsch for the

invaluable assistance with tag functioning, and always being available and patient. Thanks to Dr.

Lance Jordan, Lucy Howey and Dr. Edd Brooks for letting me have access to such an incredible

dataset for Chapter 3. Thanks to Dr. Taylor Chapple for generously letting me use your tags and

tagging pole, and getting me started on my tagging adventures. Thanks to Dr. Mauvis Gore and

Prof. Rupert Ormond for having me for an incredible month attempting to tag basking sharks. We

may not have tagged any, but it was an invaluable experience learning about academic life and

basking sharks, and giving me the opportunity to swim with my first ever basking shark. To

Kathryn Jeffs, Leon Deschamps, Shayne Thomson, Dan Paris and Nick Pedrocchi, Shark Bay

was an experience I will never forget. Kathryn – you are a force to be reckoned with.

To the amazing volunteers who gave up their time to help me either collect or analyse data -

Frazer McGregor, Abraham Sianipar, Adam Jolly, Blair Bentley, Evan Byrnes, Garry Teesdale,

vii

Michael Tropiano and Olivia Seeger. Abam, Evan, Jolly, Garry, Tropi and Karissa – that month

at Coral Bay stands out as the best month of my PhD. Thank you for making it so easy to lead my

first fieldtrip. Abam, Evan and Jolly – afternoons at ‘Grovy’ post-tagging will forever stay with

me. To all of the sharks I tagged and had the opportunity to study so closely – you are awesome.

To Lauren, Em, Steph, Dani, and Brigit, thanks for proof-reading and helping me retain some

level of sanity in those hectic last two weeks. To Lauren in particular – you are an absolute saint

and superstar.

I would not have been able to produce this thesis without the generous support of the institutions

that have provided funding and field assistance in obtaining data for this research. My gratitude

goes to the Australian Government for my PhD Scholarship, and the University of Western

Australia for the Top-Up scholarship. Thanks to BigWave Productions for having me on the

amazing trip to Cobourg Marine Park, and for providing me with my camera tags that made my

subsequent tagging trips so successful. Thanks to the Holsworth Wildlife Research Endowment

for allowing me to purchase my own diary tags, and helping me have such a long and productive

field trip to Coral Bay. Thanks to the Experiment platform for allowing me to crowdfund for my

field trips, and to the 68 kind souls who donated money to my cause. Thanks to the UWA

Postgraduate Student Association fieldwork award for also making this possible. I’m also grateful

to the UWA Graduate Research School, the Convocation of UWA Graduates, Save Our Seas, the

Australian Society of Fish Biology and KAUST for travel support allowing me to get to

conferences and workshops all around the world.

To my fellow marine nerds at UWA: Lauren, Emily, Luciana, Steph, Conrad, Phillipa, Blair,

Milly, Matty Rees, Becks, Tarryn, Claire, Dani, Jon, Brigit, Anna, Michele, Paul, Sahira,

Yannick, Chenae, Anita, Charli, Todd, Lucy, Jess, Matt H, Fernanada and Mike, thank you for

such a supportive and fun office environment. To my Murdoch family Karissa, Evan, Olly, Lauran

and Jenna – thanks for the family dinners and fun times outside of the ‘fancy’ UWA world. Brigit,

thanks for being a breath of fresh air to the office, and for being such an excellent editor. Michele

and Paul, thanks for all of the advice, morning teas, pizza nights and carpooling. Bec Wellard,

thanks for making my first PhD fieldtrip such a fun one. You opened my eyes to a world of marine

megafauna outside of sharks, and taught me so much about running my own field trips and

asserting myself. Thanks for being such a kind and genuine friend. Lauren and Emily – I don’t

think I could’ve done this without you both. Thanks for all of the coffees, wines, chocolate runs,

laughs, surfs, dives and nerd chats.

A huge thankyou to the support of my friends outside of the marine science world – the

Scarborough girls, the heaps cool stuff group, and my Flamily housemates. Katrina, Kate, Chen

and Claire - your good vibes, food and garage workouts never failed to eliminate the stress of

PhD life. Your generosity also helped me to survive off more than just canned tuna and baked

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beans. Katrines – forever my diving buddy. Thank you for reading through so much of my work

and always being there for me. Mother Kate – I will never be able to thank you enough for what

a kind, generous and thoughtful friend you are.

A big thanks to everyone in the Indian Ocean Marine Research Centre who helped everything run

smoothly. Thanks to Olwyn and Louise for your support. To James Gilmour, Miles Parsons and

Kimbo, thanks for always checking up on me on all of those early mornings. Thanks to Ruth and

Lara for helping with the logistics of travel and getting forms signed in time.

To everyone I met at conferences, thank you for supplying me with endless inspiration and

motivation.

An infinite thankyou to my oldest friends and family. Julia, Elysha and Ashleigh, thanks for your

unconditional friendship, life advice and support. To my mum Tina and dad Chris, thank you for

the weekly dinners, endless support, and for letting me be whatever I wanted to be. I have no

doubt that your love of the beach and Rottnest Island got me to where I am today. To my brother

Dean, thank you for being my stable rock and bank throughout my student life, and supporting

my adventures and choice of lifestyle, no questions asked. To Sal and Tones, thanks for the life

advice and the amazing dinners throughout the last three months.

Lastly, to my high school careers councillor who told me that as a marine biologist I’d end up in

the lab studying microscopic organisms, this one’s for you.

ix

Table of Contents

Thesis Declaration ....................................................................................................................... iii

Abstract ........................................................................................................................................ v

Acknowledgements ..................................................................................................................... vi

Table of Contents ........................................................................................................................ ix

Publications from this thesis ..................................................................................................... xiii

Statement of candidate contributions ........................................................................................ xiv

Co-author authorisation .............................................................................................................. xv

List of Figures ......................................................................................................................... xviii

List of Tables ............................................................................................................................ xxii

Chapter 1 General Introduction ........................................................................................... 25

1.1 Biological drivers ................................................................................................ 26

1.2 Physical drivers ................................................................................................... 27

1.3 Large, epipelagic fishes ....................................................................................... 28

1.4 Thesis aims .......................................................................................................... 28

1.5 Thesis outline ...................................................................................................... 29

Chapter 2 Patterns and drivers of vertical movements of the large fishes of the

epipelagic ............................................................................................................ 31

2.1 Abstract ............................................................................................................... 31

2.2 Introduction ......................................................................................................... 31

2.3 Methods ............................................................................................................... 33

2.4 Definitions and complexities in defining patterns of vertical movement ........... 34

2.4.1 Complexities in documenting vertical movement patterns ................................. 34

2.5 Patterns and drivers of vertical movements ........................................................ 37

2.5.1 Fine-scales (sub-second to hours) ....................................................................... 37

2.5.2 Daily scales ......................................................................................................... 40

2.5.3 Monthly scales .................................................................................................... 43

2.5.4 Seasonal scales .................................................................................................... 44

2.5.4.1 Water column structure ............................................................................... 44

2.5.4.2 Reproductive mode ...................................................................................... 45

2.5.5 Annual scale ........................................................................................................ 46

2.6 Future directions ................................................................................................. 46

2.6.1 Multi-tagging approaches.................................................................................... 47

2.7 Conclusion .......................................................................................................... 50

2.8 Acknowledgements ............................................................................................. 50

2.9 Supporting information ....................................................................................... 51

x

Chapter 3 Temperature and the vertical movements of oceanic whitetip sharks

Carcharhinus longimanus ................................................................................... 57

3.1 Abstract ............................................................................................................... 57

3.2 Introduction ......................................................................................................... 57

3.3 Materials and methods ........................................................................................ 59

3.3.1 Data collection .................................................................................................... 59

3.3.2 Data analysis ....................................................................................................... 60

3.3.2.1 Environmental parameters ........................................................................... 60

3.3.2.2 Vertical movement behaviours .................................................................... 60

3.3.2.3 Generalised linear mixed models ................................................................ 60

3.3.2.4 Breakpoint analysis ..................................................................................... 61

3.3.2.5 Multivariate analysis – hierarchical clustering ............................................ 61

3.4 Results ................................................................................................................. 62

3.4.1 Track summary ................................................................................................... 62

3.4.2 Oscillatory diving behaviours and environmental conditions ............................. 62

3.4.3 Vertical behavioural breakpoints ........................................................................ 65

3.4.4 Cluster analysis ................................................................................................... 66

3.4.5 Inter-individual variability .................................................................................. 69

3.5 Discussion ........................................................................................................... 70


3.7 Supporting information ....................................................................................... 74

Chapter 4 Biologging tags reveal the fine-scale movement behaviours of tiger sharks ...... 79

4.1 Abstract ............................................................................................................... 79

4.2 Introduction ......................................................................................................... 79

4.3 Methods ............................................................................................................... 81

4.3.1 Ethics ................................................................................................................... 81

4.3.2 Data collection .................................................................................................... 82

4.3.3 Data processing and analysis .............................................................................. 83

4.3.3.1 Depth record ................................................................................................ 83

4.3.3.2 Tri-axial sensor data .................................................................................... 86

4.3.3.3 Recovery period .......................................................................................... 86

4.3.3.4 Shark heading and pseudo-track calculation ............................................... 86

4.3.3.5 Window size and statistics .......................................................................... 87

4.3.3.6 Video analysis ............................................................................................. 87

4.3.3.7 Movement classification .............................................................................. 88

4.3.3.8 Generalised linear mixed models (GLMMs) ............................................... 88

4.4 Results ................................................................................................................. 88

4.4.1 Vertical movements ............................................................................................ 89

4.4.2 Horizontal path tortuosity ................................................................................... 89

4.4.3 Oscillations and tortuosity .................................................................................. 93

xi

4.5 Discussion ........................................................................................................... 94

4.5.1 Foraging behaviour and tortuosity ...................................................................... 94

4.5.2 Three-dimensional movements ........................................................................... 95

4.5.3 Challenges and future considerations .................................................................. 95

4.5.4 Conclusion .......................................................................................................... 96


4.7 Supporting Information ....................................................................................... 98

Chapter 5 Depth dependent dive kinematics suggest cost-efficient foraging strategies

by tiger sharks ................................................................................................... 106

5.1 Abstract ............................................................................................................. 106

5.2 Introduction ....................................................................................................... 106

5.3 Methods ............................................................................................................. 108

5.3.1 Ethics ................................................................................................................. 108

5.3.2 Data collection .................................................................................................. 108

5.3.3 Data processing ................................................................................................. 110

5.3.3.1 Gliding behaviour ...................................................................................... 110

5.3.3.2 Ascent and descent speed .......................................................................... 110

5.3.3.3 Window size and statistics ........................................................................ 111

5.3.4 Data analysis ..................................................................................................... 111

5.3.4.1 Generalised linear mixed models (GLMMs) ............................................. 111

5.3.4.2 Cost of transport models ............................................................................ 112

5.4 Results ............................................................................................................... 114

5.4.1 Track summary ................................................................................................. 114

5.4.2 Gliding behaviour ............................................................................................. 115

5.4.3 Relationship between seabed depth and vertical movement behaviours .......... 115

5.4.4 Cost of transport models ................................................................................... 117

5.5 Discussion ......................................................................................................... 120

5.5.1 Cost of vertical movements ............................................................................... 120

5.5.2 Cost-efficient foraging ...................................................................................... 121

5.5.3 Model limitations .............................................................................................. 122

5.5.4 Seascape effects on energy expenditure and gain ............................................. 122

5.5.5 Conclusion ........................................................................................................ 123

5.6 Acknowledgements ........................................................................................... 123

5.7 Supplementary information ............................................................................... 125

Chapter 6 First insight into the fine-scale movement behaviours of sandbar sharks

Carcharhinus plumbeus .................................................................................... 127

6.1 Abstract ............................................................................................................. 127

6.2 Introduction ....................................................................................................... 127

6.3 Materials and methods ...................................................................................... 128

xii

6.3.1 Data collection .................................................................................................. 128

6.3.2 Data processing ................................................................................................. 130

6.3.2.1 Depth Record ............................................................................................. 130

6.3.2.2 Tri-axial sensor data .................................................................................. 130

6.3.2.3 Shark heading and pseudo-track calculation ............................................. 131

6.3.2.4 Window size and statistics ........................................................................ 131

6.3.2.5 Video analysis ........................................................................................... 132

6.3.3 Statistical analysis ............................................................................................. 132

6.4 Results ............................................................................................................... 133

6.4.1 Vertical movements .......................................................................................... 133

6.4.2 Tortuosity and three-dimensional paths ............................................................ 137

6.4.3 Video ................................................................................................................. 139

6.5 Discussion ......................................................................................................... 140

6.5.1 Tortuous circling behaviour .............................................................................. 140

6.5.2 Vertical movements .......................................................................................... 141

6.5.3 Species comparisons ......................................................................................... 141

6.5.4 Limitations and future considerations ............................................................... 142

6.5.5 Conclusions ....................................................................................................... 143

6.6 Acknowledgements ........................................................................................... 143

6.7 Supporting Information ..................................................................................... 144

Chapter 7 General Discussion ........................................................................................... 147

7.1 Seasonal scales of vertical movement ............................................................... 148

7.2 Biologging and fine-scale patterns of vertical movement ................................. 149

7.3 Temporal scale and vertical movements ........................................................... 151

7.4 Limitations and future directions ...................................................................... 151

7.5 Implications ....................................................................................................... 153

7.6 Concluding remarks .......................................................................................... 155

Bibliography ............................................................................................................................. 156

Appendix A .............................................................................................................................. 172

xiii

Publications from this thesis

Chapter 2

Andrzejaczek S, Gleiss AC, Pattiaratchi CB, Meekan MG. In Review. Patterns and drivers of

vertical movements of the large fishes of the epipelagic. Reviews in Fish Biology and Fisheries.

Chapter 3

Andrzejaczek S, Gleiss AC, Jordan LKB, Pattiaratchi CB, Howey LA, Brooks EJ, Meekan MG

(2018) Temperature and the vertical movements of oceanic whitetip sharks, Carcharhinus

longimanus. Scientific Reports 8:8351

Chapter 4

Andrzejaczek S, Gleiss AC, Lear KO, Pattiaratchi CB, Chapple T, Meekan MG (In preparation).

Biologging tags reveal the fine-scale movement behaviours of tiger sharks.

Chapter 5

Andrzejaczek S, Gleiss AC, Lear KO, Pattiaratchi CB, Chapple T, Meekan MG (In preparation).

Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks.

Chapter 6

Andrzejaczek S, Gleiss AC, Pattiaratchi CB, Meekan MG (2018b) First insight into the fine-scale

movement behaviours of sandbar sharks Carcharhinus plumbeus. Frontiers in Marine Science 5

xiv

Statement of candidate contributions

This thesis is presented as a series of five manuscripts in journal formats, as well as the general

introduction and general discussion sections. These papers were developed by my own ideas and

hypotheses with inputs from my supervisors.

Dr. Lance Jordan (Microwave Telemetry), Lucy Howey (Microwave Telemetry) and Dr Edd

Brooks (Cape Eleuthera Institute) contributed recovered tag data for Chapter 3. I deployed tags

at Ningaloo Reef for Chapters 4, 5 and 6.

Fieldwork for Chapter 4, 5 and 6 was supported by a Holsworth Wildlife Research Endowment,

crowdfunding on the Experiment platform (DOI: 10.18258/7190), the University of Western

Australia, the UWA Graduate Research School, the Australian Institute of Marine Science, Coral

Bay Research Station and BigWave Productions.

The analyses described in all data chapters were carried out by myself and all chapters were

written by me with feedback from Dr Mark Meekan (all chapters), Dr Adrian Gleiss (all chapters),

Prof Charitha Pattiaratchi (all chapters), Dr Lance Jordan (Chapter 3), Lucy Howey (Chapter 3),

Dr Edward Brooks (Chapter 3), Karissa Lear (Chapter 4 and 5) and Dr Taylor Chapple (Chapter

4 and 5).

xv

Co-author authorisation

This thesis contains work that has been published and prepared for publication. By signing below,

co-authors agree to the listed publication being included in the candidate’s thesis and

acknowledge that the candidate is the primary author, i.e. contributed greater than 50% of the

content and was primarily responsible for the planning, execution and preparation of the work for

publication.

Details of the work:

Andrzejaczek S, Gleiss AC, Pattiaratchi CB, Meekan MG. In Review. Patterns and drivers

of vertical movements of the large fishes of the epipelagic. Reviews in Fish Biology and

Fisheries.

Location in thesis:

Chapter 2: Patterns and drivers of vertical movements of the large fishes of the epipelagic

Student contribution to work:

I conducted the review of the literature, created all plots, and wrote the manuscript with

criticial input from co-authors.

Co-author signatures and dates:

Dr. Mark Meekan Dr. Adrian Gleiss Prof. Charitha Pattiaratchi

26.11.2018 26.11.18 26.11.18


Andrzejaczek S, Gleiss AC, Jordan LKB, Pattiaratchi CB, Howey LA, Brooks EJ, Meekan

MG (2018) Temperature and the vertical movements of oceanic whitetip sharks,

Carcharhinus longimanus. Scientific Reports 8:8351

Location in thesis:

Chapter 3: Temperature and the vertical movements of oceanic whitetip sharks,

Carcharhinus longimanus.


I analysed the data and wrote the manuscript with critical input from co-authors.


26.11.18 26.11.18 26.11.18

Dr. Lance Jordan Dr. Edward Brooks Miss Lucy Howey

3/10/2018 3/10/2018 2/10/2018

xvi


Andrzejaczek S, Gleiss AC, Lear KO, Pattiaratchi CB, Chapple T, Meekan MG (In

preparation). Biologging tags reveal the fine-scale movement behaviours of tiger sharks.

Target journal: Marine Biology

Location in thesis:

Chapter 4: Biologging tags reveal the fine-scale movement behaviours of tiger sharks.


I collected and analysed the data and wrote the manuscript with critical input from co-

authors.



26.11.18 26.11.18 26.11.18

Dr. Taylor Chapple Miss Karissa Lear

27.11.18 26.11.18


Andrzejaczek S, Gleiss AC, Lear KO, Pattiaratchi CB, Chapple T, Meekan MG (In

preparation). Depth dependent dive kinematics suggest cost-efficient foraging strategies by

tiger sharks.

Location in thesis:

Chapter 5: Depth dependent dive kinematics suggest cost-efficient foraging strategies by

tiger sharks



authors.



26.11.18 26.11.18 26.11.18

Dr. Taylor Chapple Miss Karissa Lear

27.11.18 26.11.18

xvii


Andrzejaczek S, Gleiss AC, Pattiaratchi CB, Meekan MG (2018) First insight into the

fine-scale movement behaviours of sandbar sharks Carcharhinus plumbeus. Frontiers in

Marine Science 5

Location in thesis:

Chapter 6: First insight into the fine-scale movement behaviours of sandbar sharks

Carcharhinus plumbeus



authors.



26.11.18 26.11.18 26.11.18

Student signature

Date: 26.11.18

I, Professor Charitha Pattiaratchi certify that the student’s statements regarding their

contribution to each of the works listed above are correct.

Coordinating supervisor signature:

Date: 26.11.18

xviii

List of Figures

Figure 2.1. Schematic diagrams of the major patterns of vertical movements found in

large epipelagic fishes: (a) Swimming at a constant depth; (b) Surface

oriented single deep dives; (c) bottom oriented single deep dives; (d)

oscillatory swimming; (e) normal diel vertical migration; (f) reverse diel

vertical migration; and (g) diel changes in oscillatory swimming ...................... 35

Figure 2.2. Relationship between the reported maximum duration of tag attachment

(days) and the maximum recorded depth (meters) in a tagging study. Each

dot in the scatterplot corresponds to a peer-reviewed study on a pelagic

predatory fish. The trendline depicts the logarithmic relationship, with the

shaded area displaying error bars. N=120, R2=0.24. ........................................... 36

Figure 2.3. Swim and glide movements of whale sharks at Ningaloo Reef. Ascents show

significant locomotory activity (lateral acceleration) while descents were

largely passive (i.e. gliding). Figure reproduced from Gleiss et al. (2011a). ...... 40

Figure 2.4. Schematic diagram of positive and negative thermotaxis in an epipelagic fish. ..... 43

Figure 2.5. Two tiger sharks (i & ii) with very similar oscillatory depth tracks but

different horizontal tracks in offshore environments over a 2.5 hour period.

(a) Depth track. (b) Pseudo-track. C) 3D track. See Chapter 4 for detail on

how these tracks were derived. ........................................................................... 48

Figure 3.1. Schematic diagrams of (a) hypothetical performance curve of an ectotherm

adapted from Huey and Stevenson (1979). The curve describes the

temperature for which performance is optimised (Topt) and the critical

minimum and maximum temperatures (Tcritmin and Tcritmax) at which an

organism can survive; and (b) the cyclic characteristics of oscillatory

movements; mean cycle length and cycle amplitude were extracted from a

continuous wavelet transformation on depth data. .............................................. 59

Figure 3.2. (a) Vertical movements of a tagged oceanic whitetips over the course of 11

months. (b) The monthly mean temperature range between the surface and

100 meters depth for tagged oceanic whitetips. The red and blue shaded

bars indicate timing of northern hemisphere summer and winter

respectively. ........................................................................................................ 63

Figure 3.3. Daily oscillatory behaviour in a (a) winter and (b) summer water column for

OWT8, and (c) winter and (d) summer for OWT7. Temperatures are

averaged for the months of February and September respectively for 10 m

depth bins. Depth trace is a 24-hour track sampled at a two minute

frequency. Note that temperature scale-bars are not consistent between

individual sharks. ................................................................................................ 63

Figure 3.4. Relationship between SST and (a) daily % of time spent in the top 50 m and

(b) daily mean cycle length (s). Red and blue lines display results of

piecewise regression with the blue line indicating the breakpoint, and the

red lines the linear relationship either side of this breakpoint. ............................ 65

Figure 3.5. Results from multivariate analysis of daily data where SST >28°C. Input

variables were daily values of proportion of time spent in the top 50 m,

mean depth, mean cycle length and mean amplitude. (a) Dendrogram of

vertical movement behaviour determined from hierarchical cluster analysis.

(b) The two principal components scores are plotted for daily data. Points

are coloured by their cluster group. ..................................................................... 67

xix

Figure 3.6. 24-hour depth profiles representative of each group derived from the

hierarchical cluster analysis. a) Medium % top 50, medium cycles, low

amplitude, b) low % top 50, long cycles, low amplitude, c) medium % top

50, long cycles, medium amplitude, d) low % top 50, short cycles, low

amplitude, e) high % top 50, short cycles, low amplitude, f) medium % top

50, long cycles, high amplitude. Note that the scale on the y-axis differs for

Cluster 6. ............................................................................................................. 68

Figure 3.7. The percentage of time each individual OWT spent in each calculated cluster.

The x-axis is ordered based on similarity of cluster composition between

OWTs. OWT11, OWT13 and OWT16 were excluded from this analysis

due to insufficient sample size in days with SST>28°C...................................... 69

Figure 4.1. Tagged tiger sharks at Ningaloo Reef. (a) Tiger shark post-release. Photo

courtesy of Alex Kydd. (b) CATS Cam tag clamped to the dorsal fin. (c)

CATS Diary tag clamped to the dorsal fin. (d) Location of tag

deployments. ....................................................................................................... 82

Figure 4.2. Comparison of dominant vertical movement clusters (V-groups) and path

tortuosity clusters (T-groups) from hierarchical cluster analysis. (a) 15

minute depth profiles representative of V-groups 1, 3 and 6, demonstrating

the increase in oscillatory movements with V-group. (b) 15 minute pseudo-

tracks representative of each T-group, demonstrating the increase in

tortuosity with T-group. Polar plot on bottom right of each track displays

example of heading variance for each group. (c) The % windows found in

each cluster. (d) The % T-group composition of each dominant V-group.

Colours are T-groups from (b). ........................................................................... 90

Figure 4.3. Tiger shark and loggerhead turtle interaction. (a) One-hour long pseudo-track

from TS15. Red square denotes area of prey interaction displayed in (b)

and (c). (b) Depth track. (c) 3D track. (d) Screenshots from video of the

interaction. 1 and 2 refer to where interaction takes place in depth and 3D

tracks. See Supplementary Video 1 for full video of interaction. ....................... 91

Figure 4.4. One minute pseudo-tracks of tiger shark and Chinamanfish (Symphorus

nematophorus) interactions. Track line is coloured by tailbeat frequency

(Hz). Red dashed square indicates where the fish was observed in the video

field of view (displayed in screenshots to the right of the pseudo-track). X

and Y-axes represent arbitrary units of latitude and longitude created by

magnetometer and accelerometer data whilst a constant speed is assumed.

(a) Example of an interaction where the tailbeat of the shark slows upon

encountering the fish. (b) Example of an interaction where the tailbeat of

the shark quickens upon encountering the fish. .................................................. 92

Figure 4.5. Two tiger sharks (i & ii) with very similar oscillatory depth tracks but

different horizontal tracks in offshore environments over a 2.5 hour period.

(a) Depth track. (b) Pseudo-track. (c) 3D track. .................................................. 93

Figure 4.6. 15 minute pseudo-track throughout a tortuous window in TS20. Track is

coloured by bursts (ODBA>0.2) and takes place at approximately 21:30 in

water approximately 2 m in depth. ...................................................................... 93

Figure 5.1. Location of tagging and tag recoveries. Note that eight tags are outside of the

map area – two approximately 10 km further north, and six offshore

(ranging from 15-27 km offshore). ................................................................... 109

Figure 5.2. Schematic representation of an oscillatory movement, and the distance

parameters used in calculating the cost of transport. ........................................ 113

xx

Figure 5.3. Mean instantaneous ODBA for all 21 tiger sharks at a range of absolute (a)

ascent and (b) descent angles (placed in 5° bins). All sharks displayed a

positive slope for ODBA ~ ascent angle. No consistent relationship was

found between ODBA and descent angle. Unique symbols and/or colours

represent individual tiger sharks. ...................................................................... 114

Figure 5.4. Gliding in tiger sharks over the course of A) an entire track (10 hours); B) 45

minutes; and C) 6 minutes. Glides are marked in red on the x axis.

Dynamic angular velocity was taken from the lateral (sway) axes recorded

by the gyroscope. D) The proportion of descent time spent gliding by each

individual tiger shark. ....................................................................................... 116

Figure 5.5. Relationship between dive angle and habitat depth. (a & b) Boxplots

displaying differences in ascent and descent pitch angle between habitat

zones. Inshore denotes areas where maximum depth within a window was

<25 m, and offshore where depths were >25 m. Shark silhouettes angles

are not to scale. (c & d) Relationship between maximum depth in a

sampling window and descent and ascent angle in inshore zones (<25 m). ..... 118

Figure 5.6. (a) Relationship between seabed depth (m) and diving ratio measured in 15

minute windows. Maximum depth is a proxy for seabed depth. The red line

displays diving ratio calculated if time level swimming at the surface and

seabed for oscillations remained the same at all maximum depths. (b, c)

Schematic diagrams representing how diving ratio may differ as a result of

seabed depth and sampling window. Depth traces have the same vertical

velocity, and same time spent level swimming at the surface and seabed in

between vertical movements. (c) Represents a habitat of twice the depth of

habitat (b). Red dashed square represents the fixed time window within

which diving ratio is measured. ........................................................................ 119

Figure 5.7. The cost of transport of a single oscillatory movement of a tiger shark on (a) a

horizontal plane and (b) a vertical plane. Arrows indicate where the cost of

transport is minimised in both cases. Lines represent the cost of a given

descent pitch angle (label on figure) in relation to varying ascent pitch (x-

axis). .................................................................................................................. 120

Figure 6.1. Tagged sandbar sharks at Ningaloo Reef. (a) Location of tag deployments (b)

CATS Cam tag clamped to the dorsal fin. (c) CATS Diary tag clamped to

the dorsal fin...................................................................................................... 129

Figure 6.2. Gliding behaviour in a tagged sandbar shark (Shark 3) over the course of

approximately 30 minutes. Dynamic angular velocity was taken from the

lateral (sway) axes recorded by the gyroscope. Glides are marked where

the mask analysis determined gliding was occurring. ....................................... 134

Figure 6.3. Entire depth record and gliding behaviour for each tagged sandbar shark.

Glides are marked where the mask analysis determined gliding was

occurring. Dashed blue squares denote extended periods of U-shaped

diving, and associated looping behaviour. (a) Shark 1; (b) Shark 2; (c)

Shark 3; and (d) Shark 4. Note that both x- and y-axes scales differ

between sharks. ................................................................................................. 136

Figure 6.4. U-shaped dives and looping behaviour in Shark 1. (a) Depth record displaying

U-shaped dives in the late afternoon and dusk. Red square denotes 15

minute period of looping behaviour displayed in (c) and (d). (b) Pseudo-

track. (c) 3D track. (d) Screenshot of Shark 1 swimming at the seabed on

the bottom phase of a U-shaped dive. Shark silhouette indicates direction

of travel by shark. Note that the x and y-axes in (b) and (c) represent

arbitrary units of latitude and longitude as they were created by dead-

reckoning using magnetometer and accelerometer data assuming a constant

speed. ................................................................................................................ 138

xxi

Figure 6.5. 15 minute pseudo-tracks of looping behaviour from each sandbar individual

around dusk. (A) Shark 1 at 17:30 at depth 36 m; (B) Shark 2 at 18:08 at

depth 67 m; (C) Shark 3 at 17:47 at depth 67 m; and (D) Shark 4 at 17:42

at depth 15 m. .................................................................................................... 138

Figure 6.6. Five minute 3D tracks and pseudo-tracks of turning behaviour throughout the

ascents and descents of U-shaped dives and V-shaped dives. X and Y axes

represent arbitrary units of latitude (P-lat) and longitude (P-long) as they

were created by dead-reckoning using magnetometer and accelerometer

data assuming a constant speed. The Z axes in the 3D plots represents

depth (m). .......................................................................................................... 139

Figure 7.1. Schematic of the timeline of different processes that may influence patterns of

vertical movement in large, epipelagic fishes. Dashed squares denote the

time periods for which biologging tags and recovered satellite tags will be

most effective. ................................................................................................... 147

Figure S 3-1. The relationship between daily averaged vertical movement behaviours and

SST for each individual OWT. (a) Mean daily depth; (b) percentage time in

the top 50 m; (c) mean cycle length (seconds); and (d) mean amplitude. ........... 78

Figure S 4-1. Recovery period in tiger sharks after being caught and released. Black dots

and error bars represent mean tailbeat cycles (the inverse of tailbeat

frequency) during descent for individual tiger sharks over 15 minute

windows. The solid blue line is the combined regression for all individuals,

and the red dashed line indicates the point at which the approximate 80%

threshold was calculated, and individuals were considered to be recovered.

Methods follow that of Whitney et al. (2016b) ................................................... 98

Figure S 4-2. Video validation of seabed. A) Entire depth track from TS24. B) & C)

Excerpts of TS24 track displaying where seabed was observed on video. ......... 99

Figure S 4-3. Interaction between a tiger shark and turtle in the sandflats around Coral

Bay. Photos courtesy of Daniel Thomas-Browne. ............................................ 100

Figure S 5-1. Relationships between dive angles and maximum depth; (a) Maximum

depth and ascent angle for all data. (b) Maximum depth and descent angle

for all data only. ................................................................................................ 125

Figure S 6-1. Mean and sd of tailbeat cycles (inverse of tailbeat frequency) during descent

for individual sandbar sharks over 15 minute windows. (a) Shark 1; (b)

Shark 2; (c) Shark 3; and (d) Shark 4. ............................................................... 144

Figure S 6-2. Relationship between diving ratio and sum of turning angles for each

individual sandbar shark. .................................................................................. 145

Figure S 6-3. Confounding movements into deeper water and increasing tailbeat cycle in

Shark 2. (A) Entire depth trace in Shark 2; (B) Mean and sd of tailbeat

cycle (seconds) during descent for Shark 2. Red line outlines time of

movement into deeper water and increasing tailbeat cycle length. ................... 145

xxii

List of Tables

Table 2-1. Performance of commonly used tag types. Satellite refers to data transmitted to

user through satellites. Data-logger refers to short deployment biologging

tags. Poor Average Good *Will depend on if setting up a new

receiver array, or working within an existing network. ...................................... 35

Table 3-1. Subset of model comparisons using Akaike's Information Criterion (AIC),

Bayesian Information Criterion (BIC) and conditional (R2c) and marginal

(R2m) R2 values. ΔAIC displays deviance in AIC scores from top-ranked

models. All models are generalised linear mixed models and were ran

using the nlme package in R with shark identity as a random variable.

Proportion of time spent in the top 50 m (Prop50) was logit transformed

prior to analysis. All null models include the random effect. To see all

models involved in the model selection process, see Table S 3.2. ...................... 64

Table 3-2. Mean (± standard deviation) of daily vertical movement behaviours above and

below 28°C. ......................................................................................................... 66

Table 3-3. Summarised characteristics of each cluster derived from the hierarchical

cluster analysis. Values are mean ± standard deviation. ..................................... 67

Table 4-1. Terms and definitions used repetitively throughout chapter ..................................... 81

Table 4-2. Summary details of tagged tiger sharks. *Tag malfunction, no data

downloaded **Tag shut off before detachment nResight – same as TS17.......... 84

Table 4-3. Results of generalised linear mixed models testing the relationship between

diving ratio and indicators of horizontal path tortuosity. Diving ratio was

logit transformed prior to analysis. All models were compared with null

models using Akaike’s Information Criterion (AIC) and conditional (R2c)

and marginal (R2m) R2 values. All models were run using the nlme

package in R with shark identity as a random variable. All null models

include the random effect .................................................................................... 92

Table 5-1. Results of generalised linear mixed models testing the relationship between

seabed depth (MaxD) and vertical movement behaviours. All models were

compared with null models using Akaike’s Information Criterion (AIC)

and conditional (R2c) and marginal (R2m) R2 values. All models were run

using the nlme package in R with shark identity as a random variable. All

null models include the random effect. Inshore indicates windows where

maximum depth was <25 m, and offshore where maximum depth was >25

m. ...................................................................................................................... 117

Table 6-1. Summary details of tagged sandbar sharks. CC: CATS Camera tag, CD: CATS

Diary tag ............................................................................................................ 134

Table 6-2. Summary statistics calculated for the entire dataset, and each tagged

individual. Statistics were firstly calculated for 15 minute windows, then

summarised for the entire tagging duration. TBF: tailbeat frequency, VV:

Vertical velocity, 𝑹: mean circular length. ....................................................... 135

Table 6-3. Linear model comparisons using Akaike’s Information Criterion (AIC), R2,

and p-values. ΔAIC displays deviance in AIC scores from null model.

Diving ratio is modelled as a function of turning, where turning is the sum

of the turning angles for a sampling window. Model results where turning

is standardised by vertical phase of movement are shown in Supplementary

Table 1............................................................................................................... 137

xxiii

Table S 2.1. Reported biological characteristics and vertical movement behaviours (as

defined in section 1) in large predatory epipelagic fishes. indicates that

the behaviour was observed in at least one study for the species,

indicates that the behaviour was not observed. *Species does not meet

criteria for large, predatory epipelagic fish, but retained in table for

comparison. **Maximum number of long-term archived datasets reported

to be recovered in a single study. ........................................................................ 51

Table S 3.1. Summary of tagging information and biological information of the tagged

individuals. .......................................................................................................... 74

Table S 3.2. All models involved in the model selection process. Model comparisons

were made using Akaike's Information Criterion (AIC), Bayesian

Information Criterion (BIC) and conditional (R2c) and marginal (R2m) R2

values. ΔAIC displays deviance in AIC scores from top-ranked models. All

models are generalised linear mixed models and were ran using the nlme

package in R with shark identity as a random variable. Proportion of time

spent in the top 50 m (Prop50) was logit transformed prior to analysis. All

null models include the random effect. ............................................................... 75

Table S 3.3. Results from proportion time top 50 m ~ mean SST breakpoint analysis.

Breakpoints were assessed for the full dataset and each individual shark.

Model comparisons were made using Akaike's Information Criterion (AIC)

between models with and without the breakpoint term. ΔAIC displays

deviance in AIC scores between these two models. *Calculated breakpoint

not robust............................................................................................................. 76

Table S 3.4. Results from mean cycle length ~ mean SST breakpoint analysis.

Breakpoints were assessed for the full dataset and each individual shark.

Model comparisons were made using Akaike's Information Criterion (AIC)

between models with and without the breakpoint term. ΔAIC displays

deviance in AIC scores between these two models. *Calculated breakpoint

not robust. **Breakpoint term significantly improves model. ............................ 76

Table S 4.1. Ethogram used in the video analysis in BORIS (Behavioural Observation

Research Interactive Software; Friard and Gamba 2016). ................................ 101

Table S 4.2. Summarised characteristics of each V-group derived from the hierarchical

cluster analysis. Values are mean ± standard deviation. ................................... 102

Table S 4.3. Summarised characteristics of each T-group derived from the hierarchical

cluster analysis. Values are mean ± standard deviation. ................................... 103

Table S 4.4. All predator-prey interactions recorded on CATS video tags .............................. 103

Table S 5.1. Estimated ascent and descent speeds calculated by pitch and vertical velocity

for absolute dive angles >20° for each individual tiger shark, and their

relationship with pitch. Individuals with <20 points were excluded (TS10,

TS16, TS20, TS25). Note that where significant relationships exist, slopes

do not exceed an absolute value of 0.02. .......................................................... 126

Table S 6.1. Linear model comparisons using Akaike’s Information Criterion (AIC), R2,

and p-values. ΔAIC displays deviance in AIC scores from null model.

Diving ratio (DR) is modelled as a function of turning. Turning has been

subset for each vertical phase of movement (level swimming, ascending

and descending), and standardised by the time spent in that phase within a

sampling window. ............................................................................................. 146

25

Chapter 1 General Introduction

Movement, at the scale of an organism, is defined as a change in the spatial location of the whole

individual in time (Nathan et al. 2008). A holistic understanding of this process necessitates

research into the patterns, drivers, physiology and consequences of animal movement;

collectively encompassing the field of movement ecology (Nathan et al. 2008; Hays et al. 2016).

Throughout the lifetime of an organism, movement patterns will be driven by a number of

processes that interact across multiple spatial and temporal scales. Drivers can be either physical

(e.g. temperature, oxygen availability) or biological (e.g. prey density, predator avoidance)

(Schlaff et al. 2014). By understanding how these factors shape movements, we will have a better

capacity to predict how environmental change may alter the distribution of organisms in space

and time, and make more informed management decisions (Hussey et al. 2015).

In recent decades, rapid advances in tagging technologies and our increasing ability to analyse

large datasets has resulted in an exponential increase in the description of patterns of animal

movement, particularly those of megafauna (Hussey et al. 2015; Kays et al. 2015). A focus on

megafauna stems from both the logistical and ethical constraints of attaching tags to individuals

of a minimum size, as well as the importance associated with understanding the movements of

these animals (Kays et al. 2015; Estes et al. 2016). Megafauna, defined by Estes et al. (2016) as

species reaching >45 kg maximum reported mass, are typically wide-ranging, triggering large

energy transfers between spatially distinct habitats, and need to consume large quantities of

biomass. As a result, they influence trophic dynamics and potentially structurally modify the

ecosystems they inhabit through the processes of foraging and locomotion (McCauley et al. 2012;

Roman et al. 2014; Estes et al. 2016). In addition, as a result of their charismatic nature,

megafauna often have high social and economic values (O'Connor et al. 2009; Gallagher and

Hammerschlag 2011; Vianna et al. 2012; Estes et al. 2016). Large body size, however, is

correlated with a number of life history traits, such as slow growth and low reproductive rate, that

increase extinction risk in these animals (Fisher and Owens 2004; Cardillo et al. 2005). Human

exploitation of megafauna has resulted in many of these species undergoing large declines in

abundance so that the status of their populations is often classified as Endangered or Vulnerable

by the International Union for the Conservation of Nature (IUCN) Red List (IUCN 2018). Given

their trophic role, and the extent of anthropogenic threats (harvest, climate change, ocean

acidification, habitat loss etc.) that now confront them, it is imperative that we gain a better

understanding of their movement ecology if we are to effectively manage and conserve these taxa.

In marine habitats, megafauna have been tracked at a diverse range of temporal and horizontal

spatial scales (Block et al. 2011; Hussey et al. 2015). The addition of the vertical dimension of

water depth, however, complicates our understanding of movements in these environments in

26

several ways. Firstly, as physical variables have a much steeper gradient of change vertically than

horizontally (e.g. temperature, light and oxygen), disentangling the multiple interacting physical

and biological drivers of movements in this plane can be very challenging. Secondly, the vastness

of marine systems and the difficulties involved in transmitting data through water has impeded

the progress of the field of movement ecology compared to terrestrial ecosystems. Lastly, the

motivations for movements throughout three-dimensional environments can be non-intuitive to

humans, as the majority of our movements occur on a horizontal plane.

Despite these problems, we have a general understanding of the principal biological and physical

drivers that are likely to act collectively to produce the observed movement patterns of marine

megafauna. The following sections outline these drivers and how they may act individually and

synergistically to influence movements of marine megafauna.

1.1 Biological drivers

Inter- and intra-specific interactions with other individuals are a fundamental driver of movements

through the processes of foraging, predation, competition and reproduction (Hays et al. 2016).

Throughout the lifespan of an animal, it will need to move to search for sufficient prey and mates,

while minimizing detection by potential predators. Although reproductive drivers may only

influence mature individuals at seasonal scales, and species at the apex of food chains may not

need to avoid other predators (at least at adult sizes), all individuals will be driven by the need to

find food. The distribution, quality and density of prey will vary in space and time and trade-offs

may exist to optimize net energy intake across different temporal and spatial scales (Stephens and

Krebs 1986). For example, Womble et al. (2014) tracked juvenile female harbor seals (Phoca

vitulina) using time-depth recorders, real-time vessel tracking, and hydroacoustic surveys, to

investigate how prey availability in contrasting habitats influenced harbor seal diving and

foraging behaviour. The study found that individuals that made use of terrestrial haul-out sites

had shorter horizontal distances to travel to forage on high prey densities at shallower depths,

whereas seals using tidewater glacial ice sites as haul-outs had to travel further and dive deeper

to access prey that occurred at a lower density. Haul-out on glacial ice, however, was found to

offer seals a more stable resting platform and therefore greater refuge from predators. In addition,

the prey accessed by seals from these glacial sites was predicted to be of higher lipid content and

energy density (Womble et al. 2014). Thus, seals appeared to be balancing the costs and benefits

of utilising these distinct habitats, which had impacts on their movement patterns.

Despite prey distribution and density likely acting as a fundamental driver of movements of

marine megafauna, studies that provide direct and simultaneous measurements of the prey field

associated with predator movement, as described above, are rare (Hays et al. 2016). Typically,

movements are indirectly linked with foraging behaviours (e.g. Sims et al. 2006; Aguilar Soto et

27

al. 2008; Friedlaender et al. 2013). For example, the daily migration of the deep scattering layer

(DSL) into deeper and darker waters has been associated with the movements of almost all major

groups of marine megafauna, including sharks, tuna and marine mammals (Hays 2003; Musyl et

al. 2003; Sims et al. 2005; Friedlaender et al. 2013). Despite the volume of this evidence, we lack

studies that have simultaneously measured movement of both predator and prey, or of the

variation in the physical environment that either predator or prey encounter as they descend or

ascend through the water column.

Variation in biological characteristics, such as morphometry and body composition between

individuals, will influence the degree to which hydrodynamic drag affects the movements of

marine megafauna. Water is a dense and viscous medium, and forward motions are resisted by

backward-acting drag forces. As a result, hydrodynamic drag will influence the movements of

marine animals at their finest scale (Schmidt-Nielsen 1972). The energetically expensive cost of

swimming has resulted in a number of morphological and behavioural solutions to reduce the

negative effects of drag (Fish 1994; Williams et al. 2000; Gleiss et al. 2011c). To move more

efficiently through the water, marine mammals, reptiles and fish have been found to display

patterns of intermittent locomotion, whereby individuals intersperse periods of powered

locomotion with passive gliding (Williams et al. 2000; Fossette et al. 2010; Gleiss et al. 2011c).

When and where an individual glide will depend on a number of factors, including its buoyancy,

depth and instantaneous motivation for movement i.e. travel, search or hunting.

1.2 Physical drivers

Physical drivers may affect animal movements indirectly, by altering patterns of prey density and

distribution, or directly, by affecting their physiology (Schlaff et al. 2014). These drivers can

include dissolved oxygen, temperature, tide, pressure and light. For air-breathers, the most

obvious driver of movements is the need to return to the surface between dives to obtain oxygen.

Dive profile, duration and surface interval will all influence how an individual stores and

consumes oxygen while at depth (Boyd 1997; Ponganis and Williams 2015). Although gill-

breathers are not constrained by a need to return to the surface, vertical movements of individuals

may still be limited by concentrations of dissolved oxygen, which generally decline with

increasing depth. Reduced availability of oxygen may act as a barrier to movement or reduce time

available at depths where minimum levels occur (Kramer 1987; Prince and Goodyear 2006).

Environmental temperature plays a fundamental role in structuring the distribution, diversity and

movements across marine taxa (Tittensor et al. 2010). It is arguably the most influential driver

governing the movements of marine animals, particularly for ectotherms that cannot internally

regulate their body temperature (Angilletta et al. 2002). As the rate of many major physiological

functions are determined by core body temperature, the performance of ectotherms is directly

28

related to the temperature of the surrounding environment (Angilletta et al. 2002; Bernal et al.

2012). In order to maximise performance, these animals must use behavioural strategies to

thermoregulate by moving through their habitat in a manner that, on average, maintains a

preferred thermal range (Neill 1979).

Collectively, biological and physical drivers will act synergistically to produce documented

patterns of movement. Determining the influence of each driver will be complex, and dependant

on the spatial and temporal scale in question.

1.3 Large, epipelagic fishes

The relative ease in deploying tags on air-breathing and coastal marine megafauna, such as seals,

means that our understanding of the movement ecology of these animals has progressed faster

and further than that of gill-breathing, pelagic species (Wilson and Vandenabeele 2012; Schlaff

et al. 2014). In addition, fishes are not obliged to return to the surface to obtain oxygen, which

firstly complicates the classification of “dive” types and the comparison of patterns of vertical

movement both within and among species, and secondly impedes satellite tags from transmitting

data back to the end-user. To overcome these issues, studies of epipelagic fishes have traditionally

focused on horizontal, rather than vertical, patterns of movement. This has generated a critical

gap in our understanding of the movement ecology of these species. Although advances in the

affordability and sophistication of electronic tags now allows researchers to routinely document

vertical movement patterns, we lack a standardised approach to classify these behaviours and to

investigate their physical and biological drivers.

1.4 Thesis aims

The overall goal of my thesis is to understand patterns and drivers of vertical movements in large

epipelagic fishes (>30 kg adult size), with a focus on sharks. I use new analytical techniques and

advanced tagging technologies to improve our understanding of the patterns and drivers of vertical

movements of these species across a range of temporal scales. Specifically I:

1) Synthesize our current knowledge of patterns and drivers of vertical movements, and outline

future directions and research priorities.

2) Investigate how temperature drives the vertical movements of sharks on a seasonal time scale.

3) Explore the role of cost-efficient foraging in driving fine-scale vertical movements of sharks

In line with these aims, my thesis is organized into a meta-analysis chapter and four data chapters,

and the findings are drawn together in a final discussion chapter.

29

1.5 Thesis outline

In Chapter 2, I review the database of published tracking studies documenting the vertical

movement patterns of large epipelagic fishes, and discuss the available evidence to explain how

biological and physical drivers may structure these behaviours. I classify vertical patterns, and

identify the complexities in studying these movements that need to be acknowledged and

addressed in future studies. I argue that characteristic patterns of vertical movement result from

the need for ram-ventilating animals to move continuously in a three dimensional environment

while optimising food availability, energy expenditure, and remaining within the limits imposed

by the physical environment (notably water temperature and oxygen) on their physiology. I also

show that the drivers of patterns of vertical movement are dependent on the temporal scale of

observation.

At seasonal scales, epipelagic fishes may encounter changes in the physical structure of the water

column. In Chapter 3, I use depth and temperature archives from satellite tags deployed on oceanic

whitetip sharks (Carcharhinus longimanus) to investigate the role of water temperature in driving

vertical movements in this species. Spectral analysis, linear mixed modelling, segmented

regression and multivariate techniques were used to examine the effect of mean sea surface

temperature (SST) and mixed layer depth on vertical movements at seasonal scales. I discuss the

results of this study in the context of a warming ocean.

At finer scales from seconds to hours, vertical movements of epipelagic fishes are likely to be

driven by the need to optimize the energetic costs of foraging and locomotion. In Chapters 4, 5

and 6, I used biologging tags to examine the extent to which the movements of tiger (Galeocerdo

cuvier) and sandbar (Carcharhinus plumbeus) sharks were consistent with cost-efficient foraging

strategies. Tags recorded both physical parameters, and in situ measurements of animal trajectory

and locomotion, which enabled the calculation of dive geometry, swimming energetics and path

tortuosity at fine spatial scales in both species. In Chapter 4, I explore how biologging data can

be used to link vertical movements to prey-searching behaviours in tiger sharks. In Chapter 5, I

investigate the extent to which patterns of oscillatory diving by tiger sharks conform to a strategy

of cost-efficient foraging by modelling the cost of transport, and describe how habitat depth

influences the movement strategies of these animals. In Chapter 6, I integrate the methods used

in Chapters 4 and 5 to provide an initial insight into the vertical movements of sandbar sharks,

and discuss how recorded patterns conformed to cost-efficient foraging strategies.

In Chapter 7, I synthesize and integrate the findings of my thesis. I discuss how insights into

vertical movements of epipelagic fishes at multiple temporal scales can broaden our

understanding of the primary ecological and physiological drivers of these behaviours. I describe

limitations in our current understanding of vertical movements in large epipelagic fishes given

30

available technologies, and highlight future directions for research in this field. Finally, I discuss

the conservation and management implications of my results.

31

Chapter 2 Patterns and drivers of vertical

movements of the large fishes of the

epipelagic

2.1 Abstract

Large fishes (> 30 kg adult size) of the epipelagic zone of the oceans and coastal seas display a

variety of patterns of vertical movement. Although advances in the affordability and

sophistication of electronic tags now allows researchers to routinely document these patterns, we

lack a standardised approach to classify these behaviours and to investigate their physical and

biological drivers. Here, we provide a review of existing knowledge of the vertical movements of

large, epipelagic fishes and the evidence for the underlying factors that structure this behaviour.

We focus on behaviours occurring at a range of temporal scales, from seconds to days, months

and years. We argue that characteristic patterns of vertical movement result from the need for gill-

breathing animals to move continuously in a three dimensional environment while optimising

food encounter, energy expenditure and remaining within the limits imposed by the physical

environment on their physiology (notably water temperature and oxygen). Biologging

technologies that record both the internal (body temperature, heart rate, blood chemistry) and

external physical environment coupled with direct recording of behaviour from animal-borne

cameras offer a new, synthetic approach to the analysis of the interrelationships among drivers of

patterns of vertical movement. Ultimately, this can provide insights into the evolution of the

behaviour and morphology of these animals.

2.2 Introduction

Understanding the vertical movements of large, epipelagic fishes is important for a number of

reasons. Because they are highly mobile, these fishes create trophic flows through food webs by

consumption of prey and the subsequent excretion of organic material, carbon, nitrogen and other

nutrients (Bird et al. 2018; Williams et al. 2018). This process, entitled active subsidies, can link

spatially distinct habitats (McCauley et al. 2012) and is particularly important on vertical axes

because many species show regular patterns of transit between the surface and deeper waters. The

potential for nutrient and energy transfers among depths by epipelagic fishes is substantial, given

that blue (Balaenoptera musculus) and sperm (Physeter macrocephalus) whales, which consume

prey at depth and subsequently excrete faeces into the photic zone, transfer 3000 and 50 tonnes

of iron respectively into the surface waters of the iron-limited Southern Ocean each year (Lavery

et al. 2010; Lavery et al. 2014), creating a carbon sink that for sperm whales results in the export

of 4 x 105 tonnes of carbon annually to the deep sea (Lavery et al. 2010). The extent to which the

32

movement patterns of epipelagic fishes contribute to similar processes of nutrient enrichment and

carbon transfer is unknown, but could be many times greater, given the higher relative abundance

of these animals (Estes et al. 2016).

Description of vertical patterns in movements is also essential for the management of fisheries

where large epipelagic fishes are caught as targets or by-catch. Tunas and billfishes form the basis

of important commercial and recreational fisheries worldwide, whereas sharks are a large part of

incidental bycatch. More recently, targeted catches of sharks and rays have provided an additional

source of income for fishers due to the high value of their fins, meat and/or gill rakers (Clarke et

al. 2006; Majkowski 2007; Braun et al. 2015; Dent and Clarke 2015; Williams and Terawasi

2015). In 2014, tuna fisheries in the western and central Pacific Ocean reported their highest-ever

catch with a record value of US$ 5.8 billion (Williams and Terawasi 2015). In 2011, the most

recent year for which full global data sets are available, the value of total world imports of shark

meat was conservatively declared at US$ 379.8 million by the FAO, well above the average

annual value for the previous decade (US$ 239.9 million) (Dent and Clarke 2015). Worldwide,

overexploitation by fishing threatens the continued existence of many stocks of epipelagic fishes

(Davidson Lindsay et al. 2015; Punt et al. 2015; Pons et al. 2016).

Determining the environmental drivers influencing vertical movements will assist in predicting

the depth distribution of epipelagic fishes, and as a result, their vulnerability to different fishing

gears. Knowledge of these factors may contribute to the sustainability of catches and to minimise

impacts on non-target species (Bigelow and Maunder 2007). It is also critical in a world facing

global climate change (Koopman et al. 2014; Schlaff et al. 2014). A warming ocean may shift the

distributions of epipelagic species to deeper waters, although the magnitude of this effect will

vary among species and habitats (Schlaff et al. 2014; Andrzejaczek et al. 2018). Conversely,

oxygen minimum zones are expected to expand and shoal with climate change, causing the

hypoxic habitat boundaries of fishes to move upwards towards the surface and to compress

available habitats (Bograd et al. 2008; Stramma et al. 2008; Prince et al. 2010).

The decline in abundance of large, predatory epipelagic fishes due to overfishing not only has

economic consequences, but also impacts the function of oceanic ecosystems through the loss of

both top-down structuring processes and risk-driven effects in food chains (Creel and

Christianson 2008; Heithaus et al. 2008). Direct predation by these fishes and their wide-ranging

nature means their removal is likely to result in trophic cascades that have complex effects in

ecosystems on large spatial and temporal scales (Stevens et al. 2000; Baum and Worm 2009;

Ferretti et al. 2010; Estes et al. 2011) (but see Grubbs et al. 2016).

In recent decades there has been a rapid increase in movement studies of large, epipelagic

predatory fishes that has been driven by technological advances in acoustic and satellite telemetry

(Hammerschlag et al. 2011; Papastamatiou and Lowe 2012; Whitney et al. 2012; Braun et al.

33

2015). New generations of electronic tags now allow researchers to record and observe the

movements of these animals at a wide range of temporal and spatial scales and in many cases,

also provide estimates of environmental parameters such as depth, temperature, light and

dissolved oxygen encountered by the tagged individual (Block et al. 2011; Hammerschlag et al.

2011). However, the majority of early tagging studies focussed on horizontal, rather than vertical,

patterns of movement (Speed et al. 2010; Thums et al. 2013), despite these animals occupying a

three-dimensional environment. This reflected the fact that for reasons of cost, studies often

deployed tags that only reported surface positions, rather than the depths of animals. The

introduction of low-cost acoustic and satellite tags that combine a variety of sensors including

depth and temperature have now made the documentation of vertical patterns in movement of

epipelagic species commonplace.

The aim of this review is to provide a synthesis of existing knowledge of the vertical movements

of large, epipelagic fishes and to provide a framework that can be used to investigate the drivers

of these patterns in future studies. This requires a number of key steps. Unlike marine mammals,

whose vertical movements depart from and ultimately return to the surface in order for animals

to breathe, fishes are not necessarily constrained to visit the surface between excursions to deeper

waters. Nonetheless, most species show patterns of oscillatory descents and ascents through the

water column at various temporal and spatial scales. In Section 1 we define movement patterns

and review some of the complexities involved in drawing general patterns from time-depth

records of epipelagic fishes. We then review the literature for examples of these patterns and

examine the evidence for the proposed biological and environmental drivers of these vertical

movements (Section 2). The final part of the review outlines a framework for use in future studies

that aim to understand the mechanisms behind these movements (Section 3). In this review, we

define large, epipelagic fishes as species that attain maximum sizes > 30 kg and spend a majority

of their lives traversing the top 0-200 m of the pelagic environment (the epipelagic). We focus

primarily on sharks, rays, tunas and billfishes, many of which are considered to be apex or top-

order predators in this environment. In association with their large size, these animals generally

have patterns of relatively low abundance, slow growth rates and late ages at maturity, increasing

their vulnerability to anthropogenic threats (Table S 2.1; Hutchings et al. 2012; Dulvy et al. 2014;

Juan-Jordá et al. 2015).

2.3 Methods

This review is based on key word and title searches of library and electronic databases using the

words ‘shark(s)’, ‘tuna(s)’, ‘vertical movement’, ‘diving behaviour’, ‘telemetry’ and ‘tagging’.

Key words cited in papers found through database searches were also incorporated. For each study

the following information was recorded: (1) study species and location; (2) telemetry technology

(or technologies); (3) length (hours-days) of track(s); (4) vertical movement behaviours

34

identified; and (5) hypothesised drivers of vertical movements. Where possible, maximum

recorded depth (meters) and maximum length of tag attachment (days) were also noted (Table S

2.1).

2.4 Definitions and complexities in defining patterns of

vertical movement

For air-breathing marine vertebrates, the departure from and return to the surface of an animal to

breathe (a “dive”) form the standard unit of analysis of patterns of vertical movement. As sharks

and other predatory fishes breathe using a gill and are not obliged to return to the surface, “dives”

are more difficult to define. However, standardisation of definitions is vital to allow for

comparisons among studies. Time-depth records can instead be used to provide data on central or

baseline depths (Womble et al. 2013), and variation around this baseline depth can be used to

classify the vertical movement patterns of large epipelagic fishes. In this review, ‘dives’ simply

refer to a steep descent from a baseline depth. Our review of the literature found four major types

of vertical movements that occurred recurrently among taxa (Figure 2.1, Table S 2.1): swimming

at a constant depth (Figure 2.1a); single deep dives (Figure 2.1b, Figure 2.1c); oscillatory

swimming (Figure 2.1d, Figure 2.1g); and diel vertical movements (Figure 2.1e, Figure 2.1f,

Figure 2.1g). However, the degree to which these patterns can be resolved depends on the tag

type and its sampling duration and frequency of recording data.

2.4.1 Complexities in documenting vertical movement

patterns

The choice of tag for deployment by a researcher will depend on the question being addressed,

and is often an attempt to achieve a balance between duration of retention on the target species,

resolution of recovered data and cost of the tag (Table 2-1). Analysis of different types of vertical

movements requires data collected at sufficient resolution by pressure sensors in tags. In many

types of satellite tags (e.g. Pop-off archival tags and SPLASH tags) data is transmitted in

summarised form, typically as frequency histograms in depth bins predetermined by the

researcher. This lowers cost and increases efficiency of transmissions due to the limited

bandwidth available for the upload of data by satellites. However, such data sets lack the

resolution required to identify detailed patterns of vertical movements. Complete time series of

depth are only available when tags are recovered after deployments and archives held within the

tag can be downloaded. Unfortunately, for most epipelagic fishes, tag recoveries remain relatively

rare events (on average less than 10% of tags deployed), unless animals are targeted in

commercial or recreational fisheries (Table S 2.1).

35

Table 2-1. Performance of commonly used tag types for recording vertical movement. Satellite refers to

data transmitted to user through satellites. Data-logger refers to short deployment biologging tags. Poor

Average Good *Will depend on if setting up a new receiver array, or working within an existing

network.

Satellite Archival Acoustic Data-logger

Deployment duration

Resolution

Probability of recovering data

Price *

Measurement of additional parameters

Figure 2.1. Schematic diagrams of the major patterns of vertical movements found in large epipelagic

fishes: (a) Swimming at a constant depth; (b) Surface oriented single deep dives; (c) bottom oriented single

deep dives; (d) oscillatory swimming; (e) normal diel vertical migration; (f) reverse diel vertical migration;

and (g) diel changes in oscillatory swimming

36

The duration, timing and location of tag attachment may affect the type and variation of vertical

movements that are identified in a data set. Tracks of longer duration provide greater opportunity

to both record infrequent events such as very deep dives (mesopelagic depths) and to document

changes in vertical movements occurring over larger temporal or spatial scales. Our analysis of

120 studies shows that an asymptotic relationship exists between maximum depth and length of

tag deployment for predatory epipelagic fishes (Figure 2.2). This relationship suggests that tags

needed to be attached to fishes for a minimum of approximately 100 days in order for very deep

dives to be documented, an attachment duration attained for 36 of the 41 species reported here.

The analysis also indicated that although dives to depths greater than 500 m may occur

infrequently, they are a ubiquitous characteristic of the vertical movements of the majority of

species included in our analysis. Due to the rarity of these deep dives, mean or median depth may

be a more informative metric for comparisons among and within taxa. Nevertheless, maximum

depths provide data on performance limits for species and may reveal new insights into

physiology and behavioural ecology (Houghton et al. 2008). However, records of maximum

depths of dives can be limited by tag technology. Depths recorded by acoustic tags will be

restricted by receiver range and the habitats where receivers are deployed and most satellite-linked

tags (e.g. PAT tags) have pressure sensor resolution and design constraints that limit records of

descents to around 2000 m depths (e.g. Wildlife Computers, USA). At present, the maximum

depths recorded for epipelagic predators are descents to 1,994 m by a swordfish (Xiphias gladius)

(Evans et al. 2014) and to 1,928 m by a whale shark (Rhincodon typus) (Tyminski et al. 2015).

As in both cases these were the depth limits of pressure sensors, it remains to be determined if

these maxima simply represent a design constraint of the tag.

Figure 2.2. Relationship between the reported maximum duration of tag attachment (days) and the

maximum recorded depth (meters) in a tagging study. Each dot in the scatterplot corresponds to a peer-

reviewed study on a pelagic predatory fish. The trendline depicts the logarithmic relationship, with the

shaded area displaying error bars. N=120, R2=0.24.

37

A lack of clear boundaries between dives, or the superimposition of different types of dives during

the same bout adds a further layer of complication to any analysis of diving. High-resolution data

sets reveal that some vertical movement behaviours can co-occur within the same dive or can alter

in frequency and amplitude over time (Figure 2.1g). For example, oscillatory dives of blue sharks

(Prionace glauca) were repeated every few hours, but were deeper in the daytime and shallower

at night (Carey et al. 1990) implying both hourly and diurnal cycles in vertical movement

behaviours. Archival tags recovered from deployments on skipjack tuna (Katsuwonus pelamis)

have revealed similar patterns (Schaefer and Fuller 2007), with regular oscillations of this species

recorded between depths of 50-300 m during the day, whereas vertical movements at night were

restricted to waters above the thermocline (44 m deep). Similarly, the amplitude of single deep

dives may change on a diel scale, or deep dives may increase in frequency to an extent that they

become classified as oscillatory movements. Diel patterns in vertical movement of a species may

also vary spatially. For example, albacore tuna (Thunnus alalunga) tagged in tropical latitudes

were found to exhibit a distinct transition from occupation of shallow, warm waters above the

depth of the mixed layer at night to deeper, cooler waters below the mixed layer during the day.

In contrast, there was no evidence of diel patterns for the same species tagged in temperate

latitudes (Williams et al. 2015a).

2.5 Patterns and drivers of vertical movements

The drivers of vertical movement patterns of large epipelagic fishes may be both physiological

and ecological, constituting the drivers setting the maximum limits to vertical distributions, and

influencing how animals may move within these limits. These may act synergistically, with the

relative importance of each dependent on temporal scale and context (Gleiss et al. 2013).

Foraging, thermoregulation, navigation and energy-saving are likely to be the key drivers of

patterns of vertical movements of many epipelagic fishes (Carey et al. 1990). Here, we examine

the evidence for consistent patterns in vertical movement and their suggested drivers among

species, studies and localities. Although the temporal scale of discrete vertical movement patterns

typically operate at hourly-daily scales, the factors driving these movements operate at scales

ranging from seconds to seasons to years. To simplify the discussion of the role of these drivers,

this section follows a temporal construct, beginning with drivers operating at fine timescales (sub-

second – hours) and concluding with those operating at annual timescales.

2.5.1 Fine-scales (sub-second to hours)

Repetitive cycles of oscillations through the water column from the surface to depth (also called

bounce or yo-yo dives; Figure 2.1d) that occur over a few minutes or hours within a diel period

are a characteristic feature of nearly all epipelagic fishes (Table S 2.1) and can occur both in

38

shallow and deep water. For example, 40 tiger sharks (Galeocerdo cuvier) tagged with animal

attached video systems (CritterCam) and acoustic tags in Shark Bay, Western Australia exhibited

oscillatory vertical movements in waters of only 6-15 m depth (Heithaus et al. 2002b). On

average, cycles of these movements occurred 14 times per hour. Whale sharks tracked at Ningaloo

Reef, Western Australia by Gleiss et al. (2011a) displayed three types of oscillatory dives, the

first of which were ‘bounce dives’, where oscillations were greater than 10 m depth in range and

began and ended near the surface, without an extended surface interval between dives. The second

was a series of ‘V-shaped dives’ where the surface interval between consecutive dives was less

than 30 minutes, and the third type was ‘small scale bottom-bounce dives’ with oscillations of

less than 10 m that occurred between the descent and ascent phases of a U-shaped dive. All these

dives occurred in waters 60-100 m deep. In deeper waters, blue sharks were documented

undergoing oscillations from the surface to 200-400 meters at 1-3.5 hour intervals (Carey et al.

1990).

Such oscillatory movements are thought to be a search strategy to detect prey, with the descent

and ascent of fishes through the water column increasing the probability of detecting cues

(olfactory, visual, electrosensory) that may reveal the presence of food (Carey et al. 1990; Klimley

et al. 2002). Despite the prevalence of this argument, very few studies have simultaneously

measured prey abundance and diving behaviours of epipelagic predatory fishes. Josse et al (1998)

tracked yellowfin (Thunnus albacares) and bigeye tuna (Thunnus obesus) using acoustic tags and

concurrently mapped deep scattering layers in the water column to in order to explore the overlap

of tuna movements with their prey. At the small spatial and temporal scales at which these were

measured (up to 24 hours), tuna appeared to display movements in association with the vertical

and horizontal distributions of prey (Josse et al. 1998).

More recently, the development of multi-sensor data-logger (biologging) tags that include

pressure sensors and accelerometers, and accompanying software and data analysis techniques

(Sakamoto et al. 2009; Walker et al. 2015) has enabled researchers to explore the extent to which

these patterns of oscillatory diving can also confer energy savings for epipelagic fishes. The

intermittent swimming hypothesis proposed by Weihs (1973), suggests that oscillatory swimming

may be utilised by negatively-buoyant animals as an energy saving tactic. Theoretically, by

gliding on descent and actively swimming on ascent, fish could generate savings of up to 50% of

locomotor energy expenditure when compared with swimming at a constant depth between two

horizontal points. The hypothesis also calculates the angles of ascent and descent at which energy

savings would be maximised when undergoing these swim-glide movements. Post-processing of

acceleration data collected from a number of different species shows results consistent with this

hypothesis. For example, Gleiss et al. (2011a) used accelerometers to assess the geometry and

power requirements of five dive types employed by whale sharks. The tagged whale sharks

predominately glided on descent and altered between shallower or steeper angles of ascent in

39

order to reduce the cost of transport on horizontal and vertical scales respectively (Figure 2.3).

Gliding behaviour appears to be a common feature of the oscillatory movements of most

epipelagic predatory fishes for which detailed data records from accelerometer tags are available

(Furukawa et al. 2011; Gleiss et al. 2011b; Gleiss et al. 2011c). This implies that both efficient

prey searching and energy saving outcomes can be combined in oscillatory vertical movements,

so that this type of diving can be a strategy for cost-effective foraging (Meekan et al. 2015;

Papastamatiou et al. 2018a). However, in most instances, the evidence for foraging behaviour

during oscillatory dives is largely inferential. For example, in the north eastern China Sea, eight

dolphinfish (Coryphaena hippurus) tagged with acceleration data-loggers glided for 83.4 ± 20.0%

of the descent phase of vertical movements (Furukawa et al. 2011). These fish remained

predominately at the surface (0-5 m depths), but made deeper dives during periods when the

thermocline was deeper in the water column, which Furukawa et al. (2011) suggested was due to

changes in the distribution of their prey, although no direct measures of food resources were made.

The recent integration of cameras into biologging tags now allows the direct observation of

interactions between epipelagic predators and their prey. Although there have been relatively few

published studies that have used this approach, research on tiger and oceanic whitetip sharks

(Carcharhinus longimanus) provides some direct evidence for a link between oscillatory

movements and prey searching. Heithaus et al. (2002b) used Crittercams to record interactions

between tiger sharks and prey in coastal waters and suggested that oscillations allowed tiger

sharks to search the seabed for prey on descent and the surface for silhouettes of prey on ascent.

Shark-borne video footage from tagged oceanic whitetip sharks showed that potential prey items

were most commonly observed during oscillatory dives and at the apex of dives (Papastamatiou

et al. 2018a). In addition, accelerometers and speed sensors showed that dives conformed to

predictions for minimising the cost of transport throughout oscillatory movements.

40

Figure 2.3. Swim and glide movements of whale sharks at Ningaloo Reef. Ascents show significant

locomotory activity (lateral acceleration) while descents were largely passive (i.e. gliding). Figure

reproduced from Gleiss et al. (2011a).

2.5.2 Daily scales

Over a cycle of 24 hours, the most common pattern in vertical movements of epipelagic fishes is

diel vertical migration (DVM), where fishes are distributed deeper or dive deeper during the day

and are distributed shallower or dive shallower at night, with shifts in depth distributions

occurring at dawn and dusk (Figure 2.1e). This pattern of behaviour is largely thought to be a

response to the DVM of the deep scattering layer (or sound scattering layer), which consists of

small fishes (mostly myctophids) and invertebrates that migrate to depth at sunrise and rise to

surface layers near the thermocline at sunset to feed. The descent of the deep scattering layer into

deeper, cooler and darker waters during the day reduces the probability of detection and

consumption by visual predators, and epipelagic fishes are thought to modify their vertical

movements to exploit these migrations (Hays 2003).

Diel patterns in vertical movement were evident in almost all large epipelagic species investigated

in this review (see Supplementary Table 1). Carey (1990) was one of the first studies to directly

link the vertical movements of an epipelagic fish with the DVM of the deep scattering layer.

Echograms revealed the descent of the deep scattering layer just before dawn, and a concurrent

track from an acoustically tagged swordfish found it to follow this migration down 10 minutes

later (Carey 1990). Given that the DVM of many epipelagic fishes mimic those of plankton, it is

perhaps not surprising that some of the clearest patterns of DVM are shown by filter-feeding

elasmobranchs. A megamouth shark (Megachasma pelagios) tracked for 50.5 hours ascended and

descended in the water column on a regular crepuscular schedule, from depths of 12-25 m at night

41

to 120-166 m in the day. (Nelson et al. 1997). Basking sharks (Cetorhinus maximus) tracked in

the English Channel displayed similar patterns of DVM, although these could reverse (shallower

during the day, deeper at night, Figure 2.1f) depending on the distribution of planktonic prey

(Sims et al. 2005).

In the open ocean and highly stratified coastal systems, this occupation of the deep scattering

layer (200-500 m) poses some important physiological challenges for epipelagic fishes as

temperatures are on average 10-20°C cooler than at the surface. As gills are very efficient heat

exchangers, blood passing through the respiratory system can quickly lose its heat to the external

environment, and body temperature can cool below optimal temperatures (Angilletta et al. 2002;

Carey et al. 1971). To alleviate this problem and maintain temperatures within a preferred range

while foraging in very cool water at depth, many epipelagic fishes have been found to perform

physiological and/or behavioural thermoregulation. Ectothermic whale sharks regularly diving to

depths of 200-500 m during the day were found to exhibit a negative relationship between mean

surface duration and the mean minimum temperature of deep dives, suggesting that these sharks

rewarm and therefore behaviourally thermoregulate at the surface. The high thermal inertia of this

very large fish may also be key in slowing cooling rates when foraging at depth (Meekan et al.

2015). Bigeye tuna occupy depths of around 250 m during the day and make episodic, rapid

vertical excursions into warm waters near the surface, and remain in these shallower waters at

night (Evans et al. 2008; Musyl et al. 2003; Schaefer and Fuller 2002). Telemetered data shows

that these endothermic fishes disengage vascular counter-current heat exchangers on ascent

during the day to allow rapid warming at the surface, and reactivate them on return to depth in

order to conserve heat (Holland et al. 1992).

The ability to conserve heat internally and maintain body temperature above ambient

(endothermy) may influence the extent to which epipelagic fishes vertically move to rewarm

while feeding at depth. Various levels of endothermy have evolved in a number of species

examined in this review, namely the tuna, billfishes, lamnid sharks, devil rays and opah (Dickson

and Graham 2004; Thorrold et al. 2014; Wegner et al. 2015). Internal thermistors provide a means

to assess the capacity of these animals to physiologically rewarm (e.g. Carey 1990; Musyl et al.

2003; Wegner et al. 2015). For example, in cooler and deeper waters, thermistors placed in the

muscle and cranial cavity of swordfish exposed the ability to slow cooling rates in these areas,

while blue shark muscle temperature cooled at a more rapid rate (Carey 1990). Swordfish were

consequently able to remain at depth in temperatures approximately 10°C below that of the

surface for up to 8 hours throughout the day, while blue sharks oscillated back to the surface every

few hours, presumably to rewarm (Carey et al. 1990; Carey 1990). Ideally, such evidence would

continue to be gathered by tags that measure internal temperatures and depth, however

deployment of such technology presents a major logistic and ethical challenge in the case of many

large predators.

42

Although many studies of vertical movements of epipelagic fishes, notably those on billfishes,

have suggested that these animals display patterns of behavioural thermoregulation such as

basking after deep diving during the day (Dewar et al. 2010; Chiang et al. 2011; Abecassis et al.

2012; Chiang et al. 2015), field evidence for such behaviours is very limited. Ideally, such

evidence would be gathered by tags that measure internal temperatures and depth (e.g. Musyl et

al. 2003; Nakamura et al. 2015a), however deployment of such technology presents a major

logistical and ethical challenge in the case of many large predators. Indirect evidence of basking

may still be available in the analysis of archived data of time-depth records using the approach of

Thums et al (2013). Alternatively, these vertical movement patterns may be a form of reverse

thermoregulation, whereby fishes close to upper thermal limits either due to very warm surface

water and/or high energy output descend in long glides into cool, deep waters to lower body

temperatures (negative thermotaxis, Figure 2.4). Dives may also serve to cool body temperatures

allowing fishes to be more active for foraging in warm surface waters. In effect, this would be the

opposite of the behaviour of basking prior to diving in order to prolong the ability to forage in

cold water at depth as occurs in species such as whale sharks (positive thermotaxis, Figure 2.4).

Given the very similar shape of these two profiles, a biologging approach may be required to test

these hypotheses as archival depth and temperature data alone may not allow for the distinction

to be made between these two strategies (Figure 2.4).

Although vertical movements at the scale of hours-days of many species can maintain body

temperatures within a preferred range during foraging, there is some evidence that these may also

aid navigation through the use of magnetic, chemical, topographic, electric and light cues that

vary on a gradient throughout the water column. At the surface, fish may be using light and

celestial cues for setting a bearing, particularly for movements in a relatively straight line (Carey

et al. 1990; Lohmann et al. 2008). These cues may aid white sharks (Carcharodon carcharias)

throughout their large-scale ocean migrations, when white sharks predominately swim in surface

waters (Weng et al. 2007b; Domeier and Nasby-Lucas 2008; Bonfil et al. 2010). For example,

throughout this travelling phase, Bonfil et al. (2010) found that white sharks spent an average

61.6% of the time within the 0-1 m depth band. Vertical gradients in magnetic and electric fields

may also assist maintenance of a steady compass heading and orientation to water flows of ocean

currents. Klimley (1993) argued that scalloped hammerhead sharks (Sphyrna lewini) used

geomagnetic topotaxis to home in on seamounts. Highly directional movements and oscillatory

swimming were observed in these animals, with such behaviours explained by navigation through

increases in the intensity of local magnetic gradients with depth and the topography of the seabed

(Klimley 1993). Willis et al. (2009) recorded identical and precisely timed dives at dawn and dusk

in southern bluefin tuna (Thunnus maccoyii) tagged in the Great Australian Bight. They suggested

that these dives, which were characterised by sharp descents and ascents that interrupted a DVM

cycle, were best explained by their navigational role that utilised cues that were light-based (for

orientation) and light-independent (for navigation) (Willis et al. 2009).

43

Figure 2.4. Schematic diagram of positive and negative thermotaxis in an epipelagic fish.

2.5.3 Monthly scales

Both catch data and tracking studies show that the vertical movements of many epipelagic fishes

follow lunar cycles (Lowry et al. 2007). The time around full moons correlates with deeper

nighttime distributions of species such as bigeye and southern bluefin tunas, blue, grey reef

(Carcharhinus amblyrhynchos), whale and basking sharks, and swordfish (Musyl et al. 2003;

Shepard et al. 2006; Bestley et al. 2009; Abascal et al. 2010; Musyl et al. 2011; Vianna et al.

2013). At this time, fish may be taking advantage of increased light at depth for foraging or

navigation, or increasing the depth of their movements to avoid predators (Vianna et al. 2013).

For whale sharks, lunar patterns in vertical movements have been linked to the timing of snapper

spawning, presumably to feed on spawned eggs, which in turn is affected by the lunar phase

(Graham et al. 2006). Outside of the spawning season, whale sharks were not found to be

influenced by lunar phase.

Over the time scales of months, many tagging studies have revealed extraordinarily deep dives

(defined here as dives to depths in the top one percentile of all vertical movements for a given

track) that depart from the normal vertical movements of epipelagic fishes (Figure 2.1b, Figure

2.1c). Archival data from recovered satellite tags deployed on white sharks in the eastern Pacific

Ocean revealed individuals diving to depths > 500 m during open ocean migrations, with sharks

returning to and remaining at the surface for long periods (>24 hours) after these dives (Weng et

al. 2007b). Similar patterns have been found in tagging studies of the same species in other

locations (Weng et al. 2007b; Domeier and Nasby-Lucas 2008; Bonfil et al. 2010). More extreme

deep dives have been recorded for filter-feeding elasmobranchs such as Chilean devil rays

44

(Mobula tarapacana), which were found to dive to depths greater than 1,500 m with total dive

times of 60 to 90 minutes (Thorrold et al. 2014). Generally these dives were only made once

throughout a 24 hour period and involved a descent to maximum depth followed by a slower,

stepwise return to the surface. Similarly, whale sharks have been recorded diving for very short

periods (<1 hour) from the surface to depths of more than 1200 m in the western Indian Ocean

(Brunnschweiler et al. 2009) and eastern Pacific (Ramírez-Macías et al. 2017). Less extreme

examples of deep diving are present in virtually all tagging studies of epipelagic fishes. Howey et

al. (2016) examined the deep-diving behaviour of oceanic whitetip sharks from 16 recovered pop-

up satellite archival tags. These sharks were found to spend a majority of their time in the top 100

m of the water column with occasional dives to depths of greater than 200 m occurring 0.34 ±

0.23% of the time. These excursions occurred throughout the entire migratory circuit (movements

up to 2000 km from the tagging location) of these sharks (Howey et al. 2016). Time-depth records

obtained from 92 tagged bigeye tuna contained a total of 1,618 deep-diving events to maximum

depths between 500 to 1,902 m (mean 853 m) with dive durations of 3 to 392 minutes (mean 54

minutes) (Schaefer and Fuller 2010). These events occurred at very low frequencies, on average

3.5 times per month. Collectively, extreme deep dives occur in diverse habitats and appear to be

rare and irregularly timed.

A number of drivers have been proposed for single deep dives including foraging, navigation,

thermoregulation and predator avoidance. For many species, profiles of these dives are

characterized by rapid descents and slower, step ascents, a pattern that has been argued to be prey-

searching behaviour (Carey et al. 1990; Thorrold et al. 2014; Howey et al. 2016). Alternatively,

deep dives may function to obtain bathymetric and/or geomagnetic at depth to aid navigation

(Klimley 1993; Howey et al. 2016). Although the need to dissipate excess heat may also be

driving these movements (negative thermotaxis), this form of behavioural thermoregulation is

unlikely to be an efficient means of keeping the body cool in cases where dives are highly

irregular. Additionally, the argument that these dives are part of foraging behaviour ignores the

fact that many species descend well below the deep scattering layer into mesopelagic waters that

are unlikely to be rich in prey resources. Instead, where deep dives are irregular and into

mesopelagic depths, epipelagic fish may be escaping visual-based predators (Andrews et al.

2009). Although this has been stated to be an unlikely driver for apex predators (Campana et al.

2011), even the largest epipelagic predatory fishes may be pursued by ‘super-predators’ such as

killer whales (Orcinus orca) (Engelbrecht et al. 2019). A biologging approach is required in order

to test and disentangle these hypotheses.

2.5.4 Seasonal scales

2.5.4.1 Water column structure

At a seasonal scale, epipelagic fishes encounter changing environmental conditions as the

45

physical structure of the water column alters and/or they undergo horizontal migrations,

potentially driving changes in their vertical movement patterns. As the water column warms and

stratifies in summer months, oceanic whitetip sharks in the North Atlantic Ocean reduce the time

spent in the upper 50 m of the water column and increase the amplitude of their oscillatory

movements, behaviours that are thought to be a strategy to maintain body temperature at an

optimal level (Andrzejaczek et al. 2018). For this species, average sea surface temperatures

exceeding 28oC marked a distinct breakpoint in vertical movement behaviours and the potential

onset of reverse thermoregulatory strategies. In the eastern North Pacific, salmon sharks (Lamna

ditropis) displayed consistent changes in depth and temperature use as they migrated between

regions (Coffey et al. 2017). Shifts in vertical use of thermal habitats reflected either horizontal

migration or the seasonal variation in water column structure, and sharks were likely influenced

by temperatures both above and below their optimal range. Salmon sharks were also found to be

able to tolerate low dissolved oxygen levels for extended periods of time, however, the use of

deeper waters tended to decline when a combination of cool temperatures and low dissolved

oxygen concentrations was encountered, indicating a possible physiological limit to the vertical

distribution of these sharks (Coffey et al. 2017).

A combined approach of stomach content analysis and satellite tagging indicated that albacore

tuna modified their vertical and foraging behaviour as they migrated between temperate and

tropical latitudes (Williams et al. 2015a). In tropical latitudes, tuna underwent a distinct DVM,

accessing deeper and cooler waters below the mixed layer during the day and foraging on a

significantly greater range of prey species, including fishes in deeper water. In contrast, in

temperate latitudes there was little difference in diel depth distribution, with descents by tuna

rarely exceeding the depths of the mixed layer. Stomach samples of tunas in these localities

consisted of mostly crustacean prey. This example illustrates the difficulty in assessing the

relative contributions of environmental preferences, physiological limits and prey availability to

determining causes of seasonal changes in vertical movements of fishes.

2.5.4.2 Reproductive mode

Some oscillatory movements have been hypothesised to be associated with mating behaviours,

for example the rapid oscillatory movements observed in white sharks in the “white shark café”

in the Pacific Ocean (Weng et al. 2007b; Jorgensen et al. 2012). In this offshore focal area, vertical

activity has been reported to increase linearly with proximity to the region’s center and to differ

between males and females (Jorgensen et al. 2012). The consistent presence of white sharks here

and the lack of any other obvious unique resources have resulted in the inferred connection

between the area and the associated vertical movement behaviours and mating strategies. Rapid

oscillatory diving may be exhibited when searching for, encountering or selecting mates, and

appears to be similar to courtship activity reported in Atlantic bluefin tuna (Thunnus thynnus)

46

(Teo et al. 2007). Atlantic bluefin tuna were electronically tracked during their putative breeding

migration in the Gulf of Mexico and were found to significantly alter diving behaviour within

phases of this migration. Throughout the assumed breeding phase, tuna displayed extensive

patterns of shallow oscillatory diving and maximum depths of dives were significantly shallower

than at other times. Although there was no direct visual confirmation of spawning during the

study, examination of gonads of tuna caught on longlines at this time within the same area found

fish to be in spawning condition (Teo et al. 2007).

2.5.5 Annual scale

Across years, changes in life history stages of a fish will affect its patterns of vertical movement

through altered habitat and prey preferences and increased thermal inertia as it grows. Expansion

in vertical movements with increasing size has been recorded across many tagging studies when

individuals of a wide range of body sizes have been sampled (Weng et al. 2007a; Afonso and

Hazin 2015; Sousa et al. 2016; Williams et al. 2017b). For example, Afonso and Hazin (2015)

found that tiger sharks were likely to display a four-fold increase in maximum diving depth as

they grew from 150 to 300 cm in total size. Similarly, three year old white sharks underwent

deeper and cooler vertical excursions than young-of-the-year sharks (226 ± 81 m and 9.2 ± 0.9°C

and 100 ± 59 m; 11.2 ± 1.4°C respectively), despite there being no change in the frequency and

duration of these excursions between age classes (Weng et al. 2007a). Williams et al. (2017)

examined the temperature-depth profiles of black marlin (Istiompax indica) from 102 satellite

tags and found that larger size classes occupied a greater thermal range and spent a higher

proportion of time below 150 m depth than smaller size classes. The authors suggested increasing

thermal inertia with larger body size was likely to be a primary driver of these patterns, with larger

size classes losing heat at a slower rate than smaller counterparts, allowing access to cooler depths

for longer periods. Intra-specific differences in body size and therefore thermal inertia will likely

be a driver of variation among individuals at a number of different temporal scales.

As a result of size-dependent rates of heat loss and therefore physiological performance, patterns

of DVM may become more apparent in larger size classes, particularly in endothermic fishes

(Williams et al. 2017b). Although smaller fish may be able to access greater depths to forage on

the deep scattering layer during the day, the decline in performance (i.e. metabolic rate, swimming

speed, prey capture rate) associated with cooler temperatures may outweigh the benefits of higher

prey availability at such depths (Thygesen et al. 2016). Once a critical length is reached, the

energetic cost of vertically migrating in the day may become inferior to remaining at the surface,

and a DVM pattern may develop (Thygesen et al. 2016).

2.6 Future directions

47

Ideally, future studies should incorporate sensors that measure both the external physical and

biological environment, and the locomotion and internal state of the tagged individual. This would

allow researchers to examine how fishes optimise the competing drivers that determine patterns

of vertical movements. At smaller scales, ranging from seconds to days, multi-sensor tagging

approaches are likely to be the most useful for this task, although these are generally large and

require substantial memory capacity, thus limiting tag deployments to short durations and big

fish. At larger scales, archived datasets downloaded from recovered tags in which seasonal

changes in the structure of the water column are recorded at relatively fine scales will be the most

valuable. In both cases, an optimal sampling design will involve tagging of a representative cross-

section of the population, particularly individuals of both sexes, and a range of reproductive states,

sizes and body conditions. The collection of associated and detailed metadata may allow us to

better understand the high levels of individual variation in patterns of movement that typically

occur in most studies.

2.6.1 Multi-tagging approaches

The current and continued expansion of studies using multi-sensor data-loggers and camera tags

are vital to the developing understanding of the drivers of vertical movements of fishes at fine

temporal scales.

The addition of magnetometers, gyroscope sensors and GPS transmitters into biologging tags

provides a new insight that can identify the drivers of oscillatory diving in epipelagic fishes. These

enable the reconstruction of tracks of animals in the three-dimensional environment of the water

column using dead-reckoning and estimation of path tortuosity in both horizontal and vertical

dimensions (Wilson et al., 2007). Tortuosity is often used as a proxy for foraging behaviour,

because evidence across a very wide range of terrestrial and aquatic taxa suggests that animals try

to remain in areas where food densities are high (de Knegt et al. 2007; Weimerskirch et al. 2007;

Byrne and Chamberlain 2012; Papastamatiou et al. 2012; Adachi et al. 2017). As a result, turning

rates tend to increase in more productive areas, a pattern that is termed area-restricted searching

behaviour (Austin et al. 2006). This results in movement paths that are more tortuous in areas of

high food densities. Such changes in movement within a dive can be used to identify potential

foraging behaviour, ground-truthed through the use of sensors measuring prey field and/or prey

capture, such as animal borne cameras. These complex movement pathways are invisible in

simple time-depth records of oscillatory diving behaviour (Figure 2.5).

48

Figure 2.5. Two tiger sharks (i & ii) with very similar oscillatory depth tracks but different horizontal tracks

in offshore environments over a 2.5 hour period. (a) Depth track. (b) Pseudo-track. C) 3D track. See Chapter

4 for detail on how these tracks were derived.

As noted above, a key element of evidence missing from most studies that attribute oscillatory

diving to prey searching behaviour is the simultaneous description of the vertical distributions of

epipelagic predators and their prey. The development of sonar tags (Lawson et al. 2015) that allow

target species to map the prey field that they encounter during a dive could greatly simplify the

logistics of this task, and validate the hypothesis of oscillatory diving as a strategy for cost-

effective foraging at this temporal scale. Animal borne cameras are another tool that could directly

record interactions with prey. These have proven successful in recording prey capture events and

classifying foraging dives in marine mammals (Davis et al. 2003; Davis et al. 2013; Goldbogen

et al. 2013b), however, there has been limited success in recording successful predation events by

sharks tagged with similar devices (Heithaus et al. 2002b; Nakamura et al. 2011; Papastamatiou

et al. 2018a). In addition, sonar and camera tags are typically large in size, precluding ethical

attachment to juvenile individuals and smaller species. Extended recording durations, tag

miniaturization, larger fields of view and cameras triggered by burst movement may improve the

utility of these tags in the future.

At present, analysis of the energetics of oscillatory diving and three-dimensional track

reconstruction is limited by the relatively short durations of deployment of biologging tags.

Because these tags must be recovered in order to download data archives (Whitney et al. 2019),

they are rarely deployed for more than a few days. To attach tags, fishes are usually captured by

hook and line or by nets and require a period of recovery from this process before they return to

baseline behaviours. For example, Whitney et al. (2016b) found tailbeat cycle and amplitude to

49

have 10-14 hour post-capture recovery periods in blacktip sharks (Carcharhinus limbatus) caught

by rod and reel, with individuals exhibiting faster tailbeats immediately following tag attachment.

Similarly, tiger sharks tagged at Ningaloo Reef, Australia, began to glide for a much greater

proportion of their descents approximately four hours post-release (S. Andrzejaczek, unpublished

data). This initial recovery phase should be excluded from analysis of energetic baselines, and

may explain why tiger sharks tagged with accelerometers for a maximum duration of six hours

displayed largely active descents, only gliding for 0-18 % of the total descent time (Nakamura et

al. 2011). Future deployments of these tags need to be of sufficient duration to allow individuals

to recover from capture before data can be used for analysis.

Recently, sensors recording dissolved oxygen levels and internal body temperature have been

used in biologging studies in fishes (Coffey and Holland 2015; Nakamura et al. 2015a). However,

to better understand vertical limits in epipelagic fishes, we need more information detailing

physiological responses and adaptations to changes in depth, particularly in relation to changes in

temperature and oxygen concentration. Despite the obvious need for air-breathing marine

vertebrates to return to the surface, analogous patterns of vertical movement to those of epipelagic

fishes are common in time-at-depth records (e.g. Boeuf et al. 1989; Goldbogen et al. 2013b).

Because of the greater ease in deploying and recovering tags, advances in biologging technologies

that measure physiological parameters have moved forward fastest for species such as colonial

seals and seabirds that return to predictable locations where they can be reliably accessed by

researchers (Wilson and Vandenabeele 2012). Consequently, these studies can be used as models

in the design and deployment of similar tags on epipelagic fishes. For example, heart rate has

been a focus of many physiological studies of air-breathing marine vertebrates (Ponganis 2007).

Diving marine mammals conserve oxygen throughout dives by slowing their heart rate and

peripheral blood flow (Ponganis and Williams 2015). A recent biologging study combining

electrocardiograms, accelerometers, video recorders and pressure sensors found that heart rate

was negatively correlated with depth in bottlenose dolphins (Tursiops truncatus) and Weddell

seals (Leptonychotes weddelli) (Williams et al. 2015b). Although fish are not limited by a need

to return to the surface to re-oxygenate, they are nevertheless likely to be limited by low oxygen

concentrations at depth, and may have evolved physiological adaptations to cope with reduced

oxygen when hunting in deeper waters. Furthermore, the incorporation of internal temperature

sensors into these same tags will allow us to obtain more accurate estimates of the physiological

costs of vertical movements, and how these may change with projected environmental conditions

in the future. For individuals diving to extreme depths (e.g. >2000 m), however, the distinction

of these physiological limits may be limited by the depth restrictions of pressure sensors.

Although modern multi-sensor biologging tags currently appear to be the best way forward in

untangling the drivers of vertical movements, battery and storage limitations mean they can only

be deployed for short durations. In addition, the large size and high cost of these tags preclude

50

population level estimates of vertical behaviours, and deployments are currently restricted to

small sample sizes of adult individuals. Until we can overcome these technological barriers,

recovered archival tags provide the most practical means to gain insights into the drivers of

vertical movements on larger temporal scales for representative cross-sections of the population.

2.7 Conclusion

This is the first comprehensive review examining the scales and drivers of vertical movements in

large epipelagic fishes. Developing this understanding will potentially have important

applications in fisheries and conservation management. A number of descriptive studies have

documented recurring patterns of vertical movements in these fishes that are consistent among

species. Our review defines and classifies these patterns allowing for effective comparison both

within and among taxa. We identified standard types of vertical movements, but emphasise that

behaviours are not mutually exclusive and interpretation of patterns may change depending on

the temporal and spatial resolution at which they are measured. Collectively, patterns of vertical

movement in large, predatory epipelagic fishes are the product of having to move continuously,

while simultaneously optimising the energetic costs of locomotion and foraging, within

physiological limits of abiotic factors, notably temperature and oxygen. A comprehensive

understanding of the vertical movements of a particular species will require tracking of a

representative sample of the population at fine scales and over an extended period of time, in

order to document the range of vertical movements of which different individuals are capable and

the circumstances under which these occur. At present, our understanding is limited by a lack of

technology capable of deploying and recovering data from high frequency, multi-sensor tags over

time scales of more than a few days.

2.8 Acknowledgements

SA was supported by the Australian Federal Government and The University of Western Australia

in the form of an Australian Postgraduate Award and UWA Top-Up Scholarship. Further support

was provided by the Australian Institute of Marine Science.

51

2.9 Supporting information

Table S 2.1. Reported biological characteristics and vertical movement behaviours (as defined in section 1) in large predatory epipelagic fishes. indicates that the behaviour was

observed in at least one study for the species, indicates that the behaviour was not observed. *Species does not meet criteria for large, predatory epipelagic fish, but retained in table

for comparison. **Maximum number of long-term archived datasets reported to be recovered in a single study.

Species Age at

maturity

(years)

Maximum

length

Weight

(kg)

Diel

Effect

Oscillatory

Diving

Deep dives

(>200 m)

Maximum

Recorded

Depth

(meters)

Longest

track

(days)

IUCN

listing

Max archived

datasets

recovered**

Vertical movement

References

Billfishes

Black marlin

Istiompax

indica

- >4.5 m 750 600 m 360 d DD 14 (Chiang et al. 2015;

Williams et al. 2017b)

Blue marlin

Makaira nigricans

2 >4.5 m 636 804 m 180 d VU 6 (Block et al. 1992; Kraus

and Rooker 2007;

Goodyear et al. 2008)

Sailfish

Istiophorus

platypterus

2.5 >3 m 100.2 464 m 135 d LC 4 (Hoolihan 2005;

Hoolihan and Luo 2007;

Chiang et al. 2011;

Hoolihan et al. 2011;

Kerstetter et al. 2011)

Striped marlin

Kajikia audax

5.3 >4 m 224 532 m 259 d VU 9 (Brill et al. 1993; Sippel

et al. 2011; Lam et al.

2015)

Swordfish

Xiphias gladius

5 >4 m 650 1944 m 362 d LC 5 (Abascal et al. 2010;

Abecassis et al. 2012;

Evans et al. 2014)

White marlin

Kajikia albida

2.5-4 >2.5 m 82.5 387 m 181 d VU 1 (Horodysky et al. 2007;

Hoolihan et al. 2015)

52

Rays

Chilean devil ray

Mobula tarapacana

5-6 >3 m DW 350 1896 m 148 d VU (Thorrold et al. 2014)

Common skate*

Dipturus batis

11 >2 m TL 97.1 128 m 20 d CR 6 (Wearmouth and Sims

2009)

Giant devil ray

Mobula mobular

- >5 m DW - 650 m 120 d EN (Canese et al. 2011)

Reef manta ray

Manta alfredi

F: 8-10

M: 6

>5 m DW - 432 m 188 d VU 6 (Braun et al. 2014; Jaine

et al. 2014)

Sharks

Basking Cetorhinus

maximus

F: 16-20

M: UN

(5-7 m)

>8 m TL 4000 1264 m 423 d VU 6 (Skomal et al. 2004;

Sims et al. 2005; Shepard

et al. 2006; Gore et al.

2008; Skomal et al.

2009; Curtis et al. 2014)

Bigeye thresher

Alopias

superciliosus

F: 12-13

M: 9-10

>4 m TL 363.8 955 m 182 d VU 2 (Nakano et al. 2003;

Weng and Block 2004;

Stevens et al. 2010;

Musyl et al. 2011;

Carlson and Gulak 2012;

Coelho et al. 2015)

Blue shark

Prionace glauca

F: 5-7

M: 4-6

>3 m TL 205.9 1160 m 210 d NT 3 (Carey et al. 1990;

Queiroz et al. 2010;


Campana et al. 2011;

Musyl et al. 2011;

Queiroz et al. 2012)

Bull* Carcharhinus

leucas

F: 18

M: 14-15

>3 m TL 316.5 204 m 151 d NT 1 (Brunnschweiler et al.

2010; Carlson et al.

2010; Lea et al. 2015)

Caribbean reef

Carcharhinus

perezi

- >2.5 m TL 69.9 436 m 243 d NT 5 (Chapman et al. 2007;

Shipley et al. 2017)

53

Common thresher

Alopias vulpinus

F: 3-9

M: 3-7

>4 m TL 348 640 m 177 d VU 4 (Cartamil et al. 2010;


Cartamil et al. 2011)

Dusky shark

Carcharhinus

obscurus

F: 20-21

M: 19

>3 m TL 346.5 573 m 182 d VU 2 (Carlson and Gulak

2012; Rogers et al. 2013;

Hoffmayer et al. 2014)

Galapagos

Carcharhinus

galapagensis

F: 6.5-9

M: 6-8

>3 m TL 85.5 680 m 413 d NT (Meyer et al. 2010;

Papastamatiou et al.

2015a)

Greenland*

Somniosus

microcephalus

- >6 m 775 1560 m 196 d NT (Stokesbury et al. 2005;

Fisk et al. 2012;

Watanabe et al. 2012)

Megamouth

Megachasma

pelagios

- >5.5 m 1327.7 166 m 2 d LC (Nelson et al. 1997)

Oceanic whitetip

Carcharhinus

longimanus

4-5 >3.5 m 167.4 1190 m 335 d VU 16 (Musyl et al. 2011;

Carlson and Gulak 2012;

Howey-Jordan et al.

2013; Howey et al. 2016;

Andrzejaczek et al.

2018)

Pacific sleeper*

Somniosus pacificus

- >4 m 701.3 724 m 336 d DD 3 (Hulbert et al. 2006)

Porbeagle

Lamna nasus

F: 13

M: 8

>3.5 m 230 1600 m 365 d VU 3 (Pade et al. 2009;

Saunders et al. 2011;

Francis et al. 2015; Biais

et al. 2017)

Salmon

Lamna ditropis

F: 6-10

M: 3-5

>3 m 175 1864 m 1168 d LC 11 (Weng et al. 2005;

Carlisle et al. 2011;

Coffey et al. 2017)

Sandbar

Carcharhinus

plumbeus

F: 10-16

M: 8-14

>2 m 117.9 298 m 60 d VU 2 (Barnes et al. 2016)

Scalloped

hammerhead

Sphyrna lewini

F: 4.1-15

M: 3.8-

10

>3 m 152.4 1000m 182 d EN (Klimley 1993;

Jorgensen et al. 2009a;

Bessudo et al. 2011;

54

Ketchum et al. 2014;

Spaet et al. 2017)

Shortfin mako

Isurus

oxyrinchus

F: 18-21

M: 7-9

>3.5 m 505.8 888 m 181 d VU 1 (Holts and Bedford 1993;

Klimley et al. 2002;

Sepulveda et al. 2004;


Abascal et al. 2011;

Musyl et al. 2011; Vaudo

et al. 2016)

Silvertip

Carcharhinus

albimarginatus

- >2.5 m 162.2 382 m 11 d VU (Bond et al. 2015)

Sixgill*

Hexanchus griseus

- >4.5 m 590 <800 m 97 d NT 4 (Andrews et al. 2009;

Comfort and Weng 2015;

Nakamura et al. 2015b)

Tiger

Galeocerdo cuvier

F: 3-11

M: 3-8

>5 m 807.4 1136 m 210 d NT 3 (Holland et al. 1999;

Heithaus et al. 2002b;

Meyer et al. 2010;

Nakamura et al. 2011;

Vaudo et al. 2014; Werry

et al. 2014; Afonso and

Hazin 2015)

Whale

Rhincodon typus

F: 19-22

M: 17

>18 m 34,000 1928 m 190 d EN 9 (Gunn et al. 1999;

Graham et al. 2006;

Rowat and Gore 2007;

Brunnschweiler et al.

2009; Gleiss et al. 2013;

Thums et al. 2013;

Meekan et al. 2015;

Tyminski et al. 2015)

White

Carcharodon

carcharias

10-12 >5.5 m 2114 1128 m 386 d VU 14 (Boustany et al. 2002;

Bonfil et al. 2005; Bruce

et al. 2006; Weng et al.

2007b; Weng et al.

2007a; Domeier and

Nasby-Lucas 2008;

55

Jorgensen et al. 2009b;

Bonfil et al. 2010;

Jorgensen et al. 2012;

Skomal et al. 2017)

Tuna

Albacore tuna

Thunnus alalunga

5-7 >1 m 60.3 1150 m 753 d NT 20 (Childers et al. 2011;

Cosgrove et al. 2014;

Williams et al. 2015a)

Atlantic bluefin

tuna Thunnus

thynnus

8-12 >4.5 m 684 1200 m 1817 d EN 49 (Block et al. 2001;

Wilson et al. 2005; Walli

et al. 2009; Galuardi and

Lutcavage 2012;

Cermeño et al. 2015)

Bigeye tuna

Thunnus obesus

2-3 >2 m 210 1901 m 1508 d VU 96 (Dagorn et al. 2000;

Schaefer and Fuller

2002; Musyl et al. 2003;

Evans et al. 2008;

Schaefer et al. 2009;

Schaefer and Fuller

2010; Lam et al. 2014;

Fuller et al. 2015)

Pacific bluefin tuna

Thunnus orientalis

3-5 >2.5 m 450 >250 m 375 d VU 24 (Itoh et al. 2003;

Kitagawa et al. 2004;

Kitagawa et al. 2007;

Furukawa et al. 2014)

Skipjack tuna

Katsuwonus

pelamis

1.5 >1 m 34.5 596 m 10 d LC 5 (Schaefer and Fuller

2007)

Southern bluefin

tuna Thunnus

maccoyii

8-10 >2 m 260 600 m 206 d CR 2 (Patterson et al. 2008;

Bestley et al. 2009)

56

Yellowfin tuna

Thunnus albacares

1-2 >2 m 200 1602 m 655 d NT 52 (Block et al. 1997;

Schaefer et al. 2009;

Hoolihan et al. 2014;

Schaefer et al. 2014)

Other large

epipelagic

predatory fishes

Dolphinfish

Coryphaena

hippurus

<1 >1.5 m 40 256 m 30 d LC (Furukawa et al. 2011;

Merten et al. 2014)

Oceanic Sunfish

Mola mola

5-7** >3 m 2300 704 m 279 d VU 1 (Cartamil and Lowe

2004; Hays et al. 2009;

Sims et al. 2009; Dewar

et al. 2010; Nakamura et

al. 2015a; Sousa et al.

2016)

Opah Lampris

guttatus

- >2 m 270 736 m 168 d LC (Polovina et al. 2008)

Wahoo

Acanthocybium

solandri

1-2 >2 m 83 296 m 111 d LC 22 (Sepulveda et al. 2011;

Theisen and Baldwin

2012)

57

Chapter 3 Temperature and the vertical

movements of oceanic whitetip

sharks Carcharhinus longimanus

3.1 Abstract

Large-bodied pelagic ectotherms such as sharks need to maintain internal temperatures within a

favourable range in order to maximise performance and be cost-efficient foragers. This implies

that behavioural thermoregulation should be a key feature of the movements of these animals,

although field evidence is limited. We used depth and temperature archives from pop-up satellite

tags to investigate the role of temperature in driving vertical movements of 16 oceanic whitetip

sharks (Carcharhinus longimanus; OWTs). Spectral analysis, linear mixed modelling, segmented

regression and multivariate techniques were used to examine the effect of mean sea surface

temperature (SST) and mixed layer depth on vertical movements. OWTs continually oscillated

throughout the upper 200 m of the water column. In summer, when the water column was

stratified with high SSTs, oscillations increased in amplitude and cycle length and sharks reduced

the time spent in the upper 50 m. In winter, when the water column was cooler and well-mixed,

oscillations decreased in amplitude and cycle length and sharks frequently occupied the upper 50

m. SSTs of 28°C marked a distinct change in vertical movements and the onset of

thermoregulation strategies. Our results have implications for the ecology of these animals in a

warming ocean.

3.2 Introduction

Knowledge of how animals respond to the physical environment is vital for management

strategies and prediction of how climate change will impact their ecology (Schlaff et al. 2014).

Water temperature is arguably the most influential physical driver governing the movements of

marine animals, particularly for ectotherms that cannot internally regulate their body temperature

(Angilletta et al. 2002). As all major physiological processes are sensitive to changes in body

temperature, the physiological performance of ectotherms is directly related to the temperature of

the external environment (Angilletta et al. 2002). Thus, in order to maximize performance, these

animals must use behavioural strategies to thermoregulate by moving through their habitat in a

manner as to maintain optimal body temperatures whenever possible (Neill 1979).

Thermal performance curves (Huey and Stevenson 1979) provide a theoretical basis for an

understanding of this behaviour. These curves describe how body temperature influences

physiological performance and are bounded by the minimum and maximum critical temperatures

58

at which an organism can survive, and the temperatures at which performance is optimized

(Figure 3.1a). The curve is typically skewed so that performance gradually rises from a critical

minimum temperature up to an optimum, before sharply dropping to a critical maximum. As a

result, physiological performance is impacted more by an increase in temperature above optimum

than by an equivalent decrease in temperature below optimum (Martin and Huey 2008). For

terrestrial ectotherms, a global analysis has shown that the primary thermal challenge is to avoid

overheating, especially in tropical and desert areas (Kearney et al. 2009). For this reason,

description of behavioural strategies used as a response to temperature is particularly important

in the context of warming global climate (Kearney et al. 2009).

Sharks and other pelagic fishes that inhabit epipelagic tropical environments could be considered

to be the marine counterparts of these terrestrial ectotherms. Similar to deserts, tropical waters are

warm and have low rates of primary production, so that these marine predators have high

metabolic rates that must be maintained within an oligotrophic ecosystem where prey is sparsely

distributed. In order to survive in these environments, ectotherms need to be cost-efficient

foragers (Gleiss et al. 2011b; Meekan et al. 2015) and must keep their body temperature within

an optimal range. Large marine ectotherms are therefore likely to utilise movements throughout

the water column in order to maintain a thermal range within the bounds required for survival

(Figure 3.1a). Given the typical shape of thermal performance curves, we predict that these

movements will be most critical near the upper temperature limits, where sharp breakpoints in

vertical movement behaviours may become evident as animals seek to maintain body

temperatures below critical values.

Satellite tagging offers a methodology to investigate vertical behaviours by simultaneously

recording depth and ambient temperature. However, transmissions via satellite are typically

restricted to summarised forms. The detailed patterns of vertical movement necessary to

reconstruct relationships between temperature and depth generally require physical recovery of

the tag, in order to access the high-frequency data stored in the tag’s archive. Although this is a

rare event (Stevens et al. 2010; Jorgensen et al. 2012) due to the unpredictability of movements

post-tagging and the open ocean environments inhabited by pelagic sharks, such tags are

occasionally recovered and provide access to data that is sampled at sufficient temporal scales to

investigate hypotheses concerning the drivers of movement patterns (Howey et al. 2016).

Oceanic whitetip sharks (Carcharhinus longimanus; OWTs) are large, epipelagic, predatory

ectotherms (maximum body mass >150 kg; Froese and Pauly 2017)) that are globally distributed

in warm-temperate and tropical oceans (Bonfil et al. 2008). Here, we analyse high-resolution data

archived from 16 recovered tags deployed on OWTs in the western North Atlantic Ocean for a

mean of 183 ± 91 days. These data are used to test the hypothesis that, in a seasonally changing

water column, OWTs will change their vertical movement patterns to avoid prolonged exposure

59

to the highest SSTs. Given the shape of the typical thermal performance curve, we predict a

distinct upper temperature breakpoint in vertical behaviour. Further, we characterise the

behaviours that these sharks use to maintain body temperature, and discuss the likely drivers of

individual variation in these behaviours.

Figure 3.1. Schematic diagrams of (a) hypothetical performance curve of an ectotherm adapted from Huey

and Stevenson (1979). The curve describes the temperature for which performance is optimised (Topt) and

the critical minimum and maximum temperatures (Tcritmin and Tcritmax) at which an organism can survive;

and (b) the cyclic characteristics of oscillatory movements; mean cycle length and cycle amplitude were

extracted from a continuous wavelet transformation on depth data.

3.3 Materials and methods

3.3.1 Data collection

Oceanic whitetip sharks were captured and tagged with Standard Rate (SR) X-Tags (Microwave

Telemetry, Inc., Columbia, MD) near Cat Island, The Bahamas (24.12°N, 75.28°W) during May

2011-2013. All equipment, tagging procedures and analysis of horizontal data are described in

detail elsewhere (Howey-Jordan et al. 2013; Howey et al. 2016). In brief, a handheld tagging

applicator was used to insert X-Tags into the dorsal musculature of captured sharks. These tags

recorded depth (0.34 m resolution), temperature (0.16-0.23°C resolution), and light levels at two-

minute intervals. Physical recovery of the tags allowed for whole archival datasets to be

downloaded. This sampling frequency was assessed to adequately capture the vertical movements

of these sharks by comparing profiles obtained at one-second intervals with a two-minute

subsample recorded by an individual tagged with a PD3GT data logger (1 Hz, ± 0.25 m resolution)

(Little Leonardo, Tokyo, Japan; Watanabe et al. 2015; Howey et al. 2016). Prior to analysis, the

first and last day of the tag deployment was discarded from each dataset.

Research was conducted under the Cape Eleuthera Institute (CEI) research permit (MAF/FIS/17

& MAF/FIS/ 34) issued by the Bahamian Department of Marine Resources in accordance with

60

CEI animal care protocols developed within the guidelines of the Association for the Study of

Animal Behaviour and the Animal Behaviour Society.

3.3.2 Data analysis

We used R 3.4.0 for all data analysis (R Core Team 2017), except where stated otherwise.

3.3.2.1 Environmental parameters

Daily mean sea surface temperatures (SST) and mixed layer depths (MLD) were calculated using

depth and temperature data collected by tagged individuals. SST was calculated as the mean

temperature in the uppermost 5 m of the water column each day. MLD was calculated using the

threshold method (de Boyer Montégut et al. 2004). We chose this approach as the tags did not

record salinity, preventing us from using density criteria, and the sampling interval prevented the

sharp gradient-resolved profiles required for gradient criterion (de Boyer Montégut et al. 2004).

The threshold method involves determining the depth at which there was a change in temperature

from a near-surface reference depth by more than a chosen threshold value. The daily mean

temperature from the 7.5-12.5 m depth bin was used as a reference value to avoid the diurnal

variation in the surface layer (de Boyer Montégut et al. 2004) and a classical threshold value of

0.5°C (Levitus 1982) was selected based on visual inspection of daily temperature-depth profiles

recorded by the tags.

3.3.2.2 Vertical movement behaviours

The cyclic nature and amplitude of vertical movements were analysed using the software Igor Pro

(WaveMetrics Inc., Lake Oswego, OR, USA) with the package Ethographer (Sakamoto et al.

2009). Continuous wavelet transformation was applied to the entire depth dataset of each shark.

Based on visual inspection of the depth profiles, minimum and maximum cycle length in

Ethographer were set to 10 minutes and 90 minutes, respectively. The mean cycle length

(seconds) and amplitude (meters) (see Figure 3.1b) of these cycles were extracted and a daily

mean was calculated.

For each day, mean depth and temperature, the mean time spent outside of the mixed layer and

the proportion of time spent within distinct depth ranges were also calculated. Preliminary

analysis revealed that the proportion of time spent in the top 50 m displayed the strongest

relationship with environmental parameters. For this reason, this depth range was used for further

analyses.

3.3.2.3 Generalised linear mixed models

We built three generalised linear mixed models (GLMMs) using the nlme (Pinheiro et al. 2017)

package in R to investigate relationships among daily means of environmental parameters,

61

vertical distribution and oscillatory behaviour. The explanatory variables for each model were

initially set as SST, MLD and total length of the shark (hereafter length), and shark identity as a

random variable. Mean depth, proportion of time in the upper 50 m and mean cycle length were

all sequentially modelled as response variables. The proportion of time spent in the top 50 m was

logit transformed prior to analysis. We used the corAR1 function to account for temporal auto-

correlation in our datasets (Zuur et al. 2009). All possible combinations of the explanatory

variables were modelled, and resulting models were compared and ranked using weights of

Akaike’s information criterion (AIC).

3.3.2.4 Breakpoint analysis

Potential breakpoints in the relationships between SST and vertical movement behaviours were

initially identified through visual inspection. Breakpoints occur where there is a sudden and sharp

change in the directionality of a linear relationship. Piecewise regression was used to statistically

estimate these breakpoints by fitting separate slopes for data above and below modelled

breakpoints until the best fit was found. This method has traditionally been used to successfully

estimate breakpoint positions and ecological thresholds in species-habitat relationships in the

terrestrial realm (Francesco Ficetola and Denoël 2009), and has more recently been applied in

predicting optimal temperatures for the activity of ectothermic fishes such as Arctic char,

Salvelinus alpinus (Hansen et al. 2017). We used an iterative searching method with SST set as

the explanatory variable, and mean cycle length and proportion of time spent in the upper 50 m

each set in turn as the dependent variable. Each unique SST was used as a breakpoint and a linear

regression was run for all possible breakpoints. The model with the lowest residual mean squared

error (MSE) was used to select the breakpoint. The AIC of models with and without the breakpoint

term were compared for the full dataset as well as for each individual shark.

3.3.2.5 Multivariate analysis – hierarchical clustering

Where distinct breakpoints in vertical movement behaviours in response to SST were found, we

applied a hierarchical cluster analysis to categorise vertical behaviours employed in the upper

thermal range. This method has been used by earlier studies to successfully distinguish among

behavioural modes of vertical movement in marine megafauna (Jorgensen et al. 2012; Thums et

al. 2013). The proportion of time spent in the top 50 m, mean depth, mean cycle length and mean

amplitude were summarised to daily values and scaled using the scale function in R. Cluster

analysis was applied on these variables using the hclust function in R based on a dissimilarity

matrix produced from Euclidean distances and complete linkage as the agglomeration method.

We then used principal components analysis (PCA) on these clustered data to determine if vertical

movement behaviours differed among the resulting groups. Vertical movement behaviours were

then examined by clusters and were subjectively broken into groups based on the distance

between clusters and the cluster characteristics. Sharks with less than 50 days of data within the

62

given SST range were not included in this analysis. To determine which individuals were most

similarly grouped, the same clustering procedure was used on the proportion of time spent in each

cluster.

3.4 Results

3.4.1 Track summary

Sixteen X-Tags were recovered from mature sharks providing access to 183 ± 91 days (mean ±

standard deviation) of tracking data with a range of 20-333 days (see Table S 3.1 for more detailed

deployment information). Individuals spent 99.64 ± 1.15% time in the upper 200 m of the water

column, with a principal part of this time in the upper 100 m (90.10 ± 13.08%, Figure 3.2a).

Mesopelagic Excursions (>200 m depths) occurred throughout the year (see Howey et al. 2011

for detailed analyses of these movements). Over the year the mixed layer depth (MLD) increased

from a depth of 28.07 ± 10.11 m in summer (21st June – 22nd September) to 64.85 ± 24.93 m in

winter (21st December – 20th March) as the water column became increasingly well-mixed. Daily

mean sea surface temperature (SST) decreased from 28.55 ± 0.81°C during the summer months

to 25.59 ± 1.07°C during winter months. The mean temperature range from the sea surface to 100

m depth also decreased from 3.88 ± 0.72°C in summer to 0.99 ± 0.39°C in winter (Figure 3.2).

3.4.2 Oscillatory diving behaviours and environmental

conditions

Tagged OWTs continually descended and ascended through the water column. Continuous

wavelet transformation analysis was used to estimate daily mean cycle length (time to complete

an oscillation) and daily mean amplitude (depth of oscillation) (see Figure 3.1b) of these vertical

movements. Vertical distribution and cyclic characteristics changed with environmental

conditions (Figure 3.1b, Figure 3.3). An initial visual inspection of the depth profile indicated that

most time was spent in the top 50 m of the water column, especially in winter months when it was

cooler with larger vertical mixing (Figure 3.3a, Figure 3.3c). As the SST increased in summer

months, depth profiles displayed avoidance of the surface 50 m where waters were warmest

(Figure 3.3b) or larger and longer cycle oscillations (Figure 3.3d).

63

Figure 3.2. (a) Vertical movements of a tagged oceanic whitetips over the course of 11 months. (b) The

monthly mean temperature range between the surface and 100 meters depth for tagged oceanic whitetips.

The red and blue shaded bars indicate timing of northern hemisphere summer and winter respectively.

Figure 3.3. Daily oscillatory behaviour in a (a) winter and (b) summer water column for OWT8, and (c)

winter and (d) summer for OWT7. Temperatures are averaged for the months of February and September

respectively for 10 m depth bins. Depth trace is a 24-hour track sampled at a two minute frequency. Note

that temperature scale-bars are not consistent between individual sharks.

64

Three generalised linear mixed models were built to investigate relationships among daily means

of environmental parameters, vertical distribution and oscillatory behaviour. All three models

retained SST and MLD as explanatory variables and removed animal length in the model selection

process (Table 3-1). A total of 35% of the variation in mean daily depth was explained by our

first model with 21% attributed to variation in SST. Mean daily depth increased with SST,

attaining a mean of 33.68 ± 15.83 m in winter and 56.50 ± 20.02 m in summer. Our second model

explained 47% of the variation in the daily proportion of time spent in the top 50 m, with SST

accounting for 31% of this variation (Table 3-1). The proportion of time spent in the top 50 m

displayed a negative relationship with SST (Figure 3.4a). Although model selection retained SST

and MLD as explanatory variables of mean cycle length, these parameters did not explain a

substantial proportion of the variation (Table 3-1). For all three models, conditional R2 values and

individual OWT plots suggest high inter-individual variability in vertical movement behaviours

(see Figure S 3-1).

Table 3-1. Subset of model comparisons using Akaike's Information Criterion (AIC), Bayesian Information

Criterion (BIC) and conditional (R2c) and marginal (R2m) R2 values. ΔAIC displays deviance in AIC scores

from top-ranked models. All models are generalised linear mixed models and were ran using the nlme

package in R with shark identity as a random variable. Proportion of time spent in the top 50 m (Prop50)

was logit transformed prior to analysis. All null models include the random effect. To see all models

involved in the model selection process, see Table S 3.2.

Model DF AIC BIC ΔAIC wAIC R2c R2m

1. Mean depth ~ SST + MLD 2839 21634 21682

0.645 0.35 0.23

Mean depth ~ SST + MLD + Length 2839 21636 21689 2 0.355 0.35 0.24

Mean depth ~ SST 2840 21671 21713 37 0.00 0.33 0.21

Mean depth ~ 1 2841 21693 21729 59 0.00 0.32 0

2. Prop50 ~ SST + MLD 2839 7104 7152

0.664 0.46 0.32

Prop50 ~ SST + MLD+ Length 2839 7106 7159 2 0.336 0.46 0.33

Prop50~ SST 2840 7140 7182 36 0.00 0.46 0.31

Prop50~ 1 2841 7170 7206 47 0.00 0.62 0

3. Mean cycle length ~ SST + MLD 2839 43765 43812

0.668 0.43 0.05

Mean cycle length ~ SST + MLD + Length 2839 43767 43820 2 0.246 0.43 0.05

Mean cycle length ~ SST 2840 43814 43856 49 0.00 0.40 0.03

Mean cycle length ~ 1 2841 43818 43854 53 0.00 0.41 0

65

Figure 3.4. Relationship between SST and (a) daily % of time spent in the top 50 m and (b) daily mean

cycle length (s). Red and blue lines display results of piecewise regression with the blue line indicating the

breakpoint, and the red lines the linear relationship either side of this breakpoint.

3.4.3 Vertical behavioural breakpoints

A breakpoint of approximately 28°C occurred in the relationships of both daily proportion of time

spent in the top 50 m and mean cycle length with SST (Figure 3.4). Above 27.8°C, the weak

negative relationship between mean SST and proportion of time spent in the top 50 m steepened

considerably from a slope of -2.86 to -13.99 (Figure 3.4a). The mean proportion of time spent in

the top 50 m of the water column at SSTs above and below 27.8°C was 38.21 ± 19.44% and 71.15

± 20.24% respectively. Beyond this breakpoint, individuals rarely spent more than 80% of their

time in the top 50 m. Below a 28.2°C breakpoint, there was no apparent relationship between

mean cycle length and SST (Figure 3.4b). For days exceeding this breakpoint, a positive

relationship with a slope of approximately 329 s was found. Mean cycle length increased from

2570 ± 793 s when SST was below 28.2°C to 3060 ± 677 s when SST was above 28.2°C. Mean

values for other vertical movement parameters above and below an estimated 28°C breakpoint

are listed in Table 3-2. Notably, mean SST increased by more than two degrees between these

two groups, however the overall mean temperature experienced by tagged sharks increased by

less than one degree. The inclusion of the breakpoint term minimised the AIC for both full models,

however, the result was more variable when assessed on an individual basis (Table S 3.3, Table

S 3.4). The AIC suggested that in 11 of 13 individuals the inclusion of a breakpoint between 27-

29°C had greater support than a single linear relationship (ΔAIC = 6-122) for the daily proportion

of time spent in the top 50 m, and nine individuals the inclusion of the breakpoint for mean cycle

length had greater support than the single linear relationship (ΔAIC = 6-60).

66

Table 3-2. Mean (± standard deviation) of daily vertical movement behaviours above and below 28°C.

Parameter Below 28oC Above 28oC

Mean SST 26.56 ± 0.96oC 28.91 ± 0.48oC

Mean temperature 25.70 ± 0.83oC 26.66 ± 0.79oC

Mean depth 38.63 ± 17.0 m 63.06 ± 18.20 m

% time spent in top 50 m 70.61 ± 20.41% 37.57 ± 19.17%

Mean cycle length of oscillations 2576 ± 788 s 3013 ± 711 s

Mean amplitude of oscillations 14.76 ± 6.25 m 20.74 ± 8.0 m

Mean MLD 39.20 ± 23.36 m 32.01 ± 10.65 m

3.4.4 Cluster analysis

To further classify vertical movements when surface waters warmed, metrics of vertical

movement including mean depth, proportion of time spent in the top 50 m, mean cycle length and

mean amplitude were used for a hierarchical cluster analysis for individual days when the SST

exceeded 28°C. Three individual OWTs were excluded due to an insufficient sample size of days

meeting this criteria (<50 days). Six clusters emerged from this analysis and the PCA indicated

that 77.6% of the variation in these vertical movement behaviour could be explained by the first

two components (Figure 3.5). The proportion of time spent at depths <50 m and mean daily depth

each contributed to more than 40% of the variation in PC1, and mean daily cycle length and mean

daily amplitude each contributed to more than 35% of the variation in PC2. Characteristics of

each cluster are summarised in Table 3-3, and a representative example of each are displayed in

Figure 3.6. Cluster 4 was the most common (37.21% of days) and was characterised by a low

proportion of time spent in the top 50 m and short cycle (approximately 44 minutes) and low

amplitude oscillations (Table 3-3). Cluster 1 was the second most common (27.83%) and showed

medium levels of both time spent in the top 50 m and cycle length. Clusters 2 and 3 both showed

long cycles of approximately one hour with cluster 2 being distinguished by very low use of the

top 50 m of the water column. Cluster 5 was characterised by higher use of the top 50 m and

oscillations with relatively small amplitude and short cycles. Although the proportion of the time

spent within the top 50 m for sharks within this cluster was high, the mean MLD was relatively

shallow and the mean time spent outside the ML was comparable with other clusters (Table 3-1).

Cluster 6 only comprised 0.93% of the data, and was characterised by oscillations with the longest

cycle and highest amplitude. Days associated with this cluster were found to have repeated deep

descents to depths greater than 200 m (Figure 3.6f). In summary, the clusters were composed of

different combinations of vertical behaviours that collectively avoided prolonged exposure to the

warmest temperatures found in the surface layers. In addition, with the exception of Cluster 1, the

mean daily temperature was maintained within a standard deviation of the overall mean daily

temperature recorded by the tags (26.14 ± 0.94°C).

67

Figure 3.5. Results from multivariate analysis of daily data where SST >28°C. Input variables were daily

values of proportion of time spent in the top 50 m, mean depth, mean cycle length and mean amplitude. (a)

Dendrogram of vertical movement behaviour determined from hierarchical cluster analysis. (b) The two

principal components scores are plotted for daily data. Points are coloured by their cluster group.

Table 3-3. Summarised characteristics of each cluster derived from the hierarchical cluster analysis. Values

are mean ± standard deviation.

Cluster ID 1 2 3 4 5 6

% days 27.83% 15.12% 12.17% 37.21% 6.74% 0.93%

Mean % time

top 50

51.72 ±

12.44%

17.18 ±

7.69%

45.13 ±

13.17%

25.98 ±

9.07%

67.78 ±

14.22%

40.68 ±

10.64%

Mean depth 50.42 ± 11.99

m

82.43 ± 13.04

m

64.41 ± 17.85

m

68.90 ± 10.60

m

38.41 ± 10.95

m

95.32 ± 25.22

m

Mean cycle

length

3168 ± 509 s 3651 ± 390 s 3620 ± 410 s 2637 ± 528 s 1914 ± 642 s 3995 ± 357 s

Mean

amplitude

19.82 ± 4.72

m

17.51 ± 4.59

m

33.02 ± 6.81

m

19.01 ± 4.81

m

14.43 ± 3.64

m

60.91 ± 8.50

m

Mean

temperature

27.13 ±

0.64oC

26.53 ±

0.65oC

26.57 ±

0.82oC

26.41 ±

0.70oC

26.72 ±

1.11oC

25.73 ±

0.81oC

Mean MLD 31.77 ± 11.37

m

39.68 ± 10.28

m

30.39 ± 9.62

m

32.15 ± 8.55

m

20.84 ± 7.11

m

33.88 ± 8.34

m

Mean % time

outside ML

63.48 ±

15.81%

86.98 ±

7.22%

64.30 ±

14.44%

84.66 ±

8.81%

67.54 ±

22.44%

63.26 ±

10.36%

Mean SST 28.84 ±

0.49oC

29.21 ±

0.43oC

28.91 ±

0.42oC

28.93 ±

0.44oC

28.58 ±

0.46oC

29.28 ±

0.22oC

Behaviour Medium %

top 50,

medium

cycles, low

amplitude

Low % top

50, long

cycles, low

amplitude

Medium %

top 50, long

cycles,

medium

amplitude

Low % top

50, short

cycles, low

amplitude

High % top

50, short

cycles, low

amplitude

Medium %

top 50, long

cycles, high

amplitude

68

Figure 3.6. 24-hour depth profiles representative of each group derived from the hierarchical cluster

analysis. a) Medium % top 50, medium cycles, low amplitude, b) low % top 50, long cycles, low amplitude,

c) medium % top 50, long cycles, medium amplitude, d) low % top 50, short cycles, low amplitude, e) high

% top 50, short cycles, low amplitude, f) medium % top 50, long cycles, high amplitude. Note that the scale

on the y-axis differs for Cluster 6.

69

3.4.5 Inter-individual variability

The clusters were further examined to investigate which individuals were most similarly grouped.

The proportion of time spent in each cluster varied among individuals, and an additional cluster

analysis that focused on this parameter revealed distinct groupings (Figure 3.7a, Figure 3.7b).

Some clear patterns emerged from this analysis. Firstly, the only male shark tagged by the study

(OWT9) displayed a pattern of vertical movement that was distinct from all other tagged sharks.

Secondly, the majority of the female sharks were pooled into two clusters. The first (OWT2,

OWT3, OWT4, OWT5, OWT12 and OWT14) was characterised by individuals that spent very

little time in surface waters and underwent shorter cycle oscillations (Figure 3.7a). The second

group (OWT1, OWT6, OWT8, OWT10 and OWT15) tended to avoid surface waters and

underwent long cycle oscillations.

Figure 3.7. The percentage of time each individual OWT spent in each calculated cluster. The x-axis is

ordered based on similarity of cluster composition between OWTs. OWT11, OWT13 and OWT16 were

excluded from this analysis due to insufficient sample size in days with SST>28°C.

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3.5 Discussion

Theories of foraging ecology and thermal physiology suggest that large animals found in tropical

environments should maintain an optimal and stable body temperature range (Huey and

Stevenson 1979; Sims 2003; Bernal et al. 2012). This stability is required because a number of

physiological processes, such as growth, have a tendency to increase with temperature. Thus, an

individual will have optimal performance if temperatures are maintained within the upper limits

of this range, assuming there is sufficient prey available to support the relatively high metabolic

rates that this entails (Hight and Lowe 2007; Bernal et al. 2012; Whitney et al. 2016a). Due to the

asymmetry of the thermal performance curve, slight increases in temperature above optimum can

result in rapid declines in performance (Martin and Huey 2008). This will require aquatic

ectotherms to vacate environments when temperatures become unfavourable for maintaining

performance. We predicted that OWTs would modify their diving behaviour as the thermal

structure of the water column changed seasonally and that sharp behavioural breakpoints would

be evident at higher temperatures that might indicate the upper limits of thermal performance

curves. In cooler months, when SST was lower than 28°C, OWTs spent a high proportion of their

time in the top 50 m of the water column. Beyond this limit, OWTs avoided the top 50 m where

waters were the warmest, and/or performed longer cycle and higher amplitude oscillations from

the surface to deeper, cooler waters. As a result, the mean daily temperature experienced by the

shark shifted by only a degree (25.70 ± 0.83°C below 28°C SST, 26.66 ± 0.79°C above 28°C),

despite much larger changes in SST in the top 50 m of the water column across the season.

Evidence for a behavioural breakpoint with changes in temperature are consistent with the

hypothesis that OWT sharks have behavioural strategies for thermoregulation. Our data suggests

that OWT sharks in the western North Atlantic Ocean face metabolic challenges similar to some

terrestrial ectotherms (Kearney et al. 2009) in having to cope with habitats close to upper thermal

limits and potential overheating. To date, most studies of strategies for thermoregulation in marine

ectotherms have focused on behaviours that might function to regain heat lost after descents into

deeper and cooler waters to feed on the deep scattering layer (Thums et al. 2013; Nakamura et al.

2015a). There is, however, evidence that endothermic fishes that inhabit cooler waters might face

similar physiological challenges when migrating through warmer latitudes. For example, Teo et

al. (2007) hypothesised that Atlantic bluefin tuna (Thunnus thynnus) descended to deeper waters

on entry and exit to the Gulf of Mexico in order to avoid warm surface waters. Similarly, female

porbeagle sharks (Lamna nasus) are thought to avoid warm surface waters (22-29°C) in the Gulf

Stream and Sargasso Sea, with no individual recorded in waters with a six-hour mean greater than

21.9°C (Campana et al. 2011). In the northern Pacific, salmon sharks (L. ditropis) have been found

to descend to deeper, cooler waters as they migrate south towards lower latitudes (Weng et al.

2005; Coffey et al. 2017). Tagged individuals of this species generally positioned themselves at

71

deeper and cooler depths when surface temperatures exceeded 23°C in a multi-year satellite tag

dataset (Coffey et al. 2017). These observations suggest that breakpoints in vertical movement

behaviours at high SSTs might also exist in other species, both endothermic and ectothermic.

Although OWTs displayed a variety of vertical movement behaviours that we suggest avoided

prolonged exposure to surface waters when SST warmed above 28°C, these behaviours were not

necessarily consistent among tagged individuals. Such inter-individual variation has been

reported by many studies tracking both the horizontal and vertical movements of sharks (Musyl

et al. 2011; Vaudo et al. 2014; Ferreira et al. 2015). In our study, the most important differences

in vertical behaviours among OWT sharks occurred in the proportion of time individuals resided

in the top 50 m of the water column and in the length and amplitude of their vertical oscillations,

although it is important to note that despite this variation in strategies, mean water temperatures

experienced by the sharks were always maintained between 25-27°C. Whereas at present we

cannot be certain of the drivers of these individual variations in movement patterns, differences

could reflect the influence of prey availability, ontogenetic and reproductive stage, intraspecific

interactions and interaction with predators, and/or differences in thermal physiology.

Regardless of the use of horizontal space by an individual shark, all OWTs underwent

mesopelagic excursions to depths of up to 1190 m, a behaviour that may represent prey searching

(Howey et al. 2016). Around the tagging locality of Cat Island, OWTs are thought to feed

predominately on large pelagic teleosts and after movement away from this region, their diet alters

to include a significantly higher proportion of squid (Madigan et al. 2015). Such changes in diet

and the consequent distribution of potential prey may drive differences in vertical behaviours

among individuals, by determining whether an individual needs to access warmer surface waters.

If prey are available at depth, an OWT will not need to enter the top 50 m, whereas if prey are

only available in surface waters, an individual may undergo longer oscillations to cooler depths

in order to dissipate excess heat gained while foraging in surface layers. Similarly, individual

dietary specialisation may drive differences in movements if preferred dietary items differ in their

vertical distributions. Comparing the isotopic composition of tagged individuals may help to

assess differences in vertical behaviours.

We tagged sharks at an aggregation site off Cat Island, in the Bahamas. The sex ratio of the

aggregation is dominated by females that either remain in the region year round or undergo long-

distance migrations that commence around July (Howey-Jordan et al. 2013), coinciding with

seasonal warming of surface waters. The onset of changes in vertical habitat use we observed –

higher-amplitude and longer cycle oscillations and avoidance of waters >28°C – were not

confined to those individuals that migrated, since one female shark (OWT3) that remained near

the Bahamas also exhibited the same pattern of avoidance of the upper 50 m depths as surface

waters warmed. In contrast, the only male tagged by our study remained near the Bahamas (except

72

for a three-week period from late August through early September), but did not have any obvious

strategy in vertical movements that avoided the warmest surface waters. While bearing in mind

the limitations of generalisations based on the results from a single tag, it might be possible that

this difference between males and females occurs because females have a greater need to maintain

a narrower thermal range due to higher energetic demands (Schlaff et al. 2014). A larger number

of males need to be sampled to investigate this possibility.

The total length of the tagged individuals did not appear to drive any of the variation in movement

patterns among individuals and, as a majority were mature females, we could not explore the

effect of sex or life history stage in detail. As OWTs are thought to have biennial reproductive

cycles (Tambourgi et al. 2013), it is possible that differences in patterns of vertical movement we

documented among females may arise from reproductive status, as developing pups may make

greater metabolic demands and thus require higher rates of food intake and/or maintenance of

body temperatures within a smaller temperature range.

Our results were consistent with the hypothesis that OWT sharks display behavioural strategies

that maintain stable body temperatures in a warm and oligotrophic tropical environment. An

understanding of relationships between vertical movement behaviours and ambient temperature

is essential if we are to document the capacity for ectothermic sharks to buffer the impact of

changing environmental conditions, particularly warming oceans (Kearney et al. 2009).

Irrespective of SST, sharks experienced very similar mean daily temperatures throughout the time

they were tagged. This was achieved in a variety of ways when waters warmed above 28°C. At

temperatures below this breakpoint, OWTs continually displayed oscillatory diving behaviour,

even when the water column was well-mixed down to 100 m, suggesting that thermoregulation

was not the primary driver of these movements (Figure 3.3a, Figure 3.3c). We also acknowledge

that some other unmeasured environmental variable, such as light or water quality, prompted the

observed changes in vertical movement behaviours. Nevertheless, if this was the case, OWTs

would likely continue to benefit from the maintenance of a stable body temperature.

Although these behaviours demonstrate the potential of OWTs to adapt to a warming ocean, there

are several implications of these results. Given variability in species thermal optima, potential

habitat mismatches between OWT and their prey are possible in the future if warming increases

thermal habitat close to upper thermal limits, reducing the zone of overlap in which OWTs can

feed (Hazen et al. 2013). This avoidance of surface waters will reduce the vulnerability of these

sharks to fishing gears targeting this zone, but may increase their vulnerability to deeper-set

longlines by compressing the zone of available habitat for OWTs (Dulvy et al. 2014), magnifying

the spatial overlap of OWT distributions with pelagic longline fisheries that already occurs on a

horizontal scale (Tolotti et al. 2015a). In winter months, when the top 100 m becomes well-mixed,

these problems may be compounded if winter SSTs begin to attain upper thermal limits and sharks

73

have to descend to greater depths to reach cooler temperatures. Fortunately, in recent years, SSTs

in regions around the Bahamas have followed a cooling trend in winter months (Chollett et al.

2012), however, this may be a problem confronting the species in other regions. Additionally,

other impacts of climate change may also influence the response of OWTs to a warming ocean.

Lower oxygen content of warming waters may reduce the ability of sharks to chase prey (Pistevos

et al. 2016), escalating the difficulties facing OWTs in an oligotrophic pelagic environment.

Several studies have reported declines in the abundance and size of the OWT (Baum and Myers

2004; Cortés et al. 2007; Clarke et al. 2012). Although the retention of OWTs by fisheries has

generally been banned, high spatial overlap with targeted species means capture rates are unlikely

to decline and other strategies are needed to conserve populations (Tolotti et al. 2015b).

Anticipating future seasonal and overall shifts in vertical distribution will have important

implications for developing and implementing effective mitigation measures such as spatial

and/or temporal closures.

In order to measure the impacts of warming on the physiological performance of OWTs, we need

to quantify the upper limits of the thermal performance curve for the species. Traditionally,

respirometry approaches have provided the data on metabolic rate and cost of transport of fishes

under different temperature regimes (Whitney et al. 2016a) , although this usually requires captive

trials in laboratories and are unlikely to be a logistically feasible option. However, accelerometry

now offers a viable technique for calculating the metabolic rate of these animals (Lear et al. 2016)

and deployment of tags containing accelerometers in different seasons to compare energy

expenditure at SSTs above and below 28°C will be crucial for predicting the future consequences

of a warming ocean. It is also vital that we continue to explore how temperature structures the

vertical movements of sharks and incorporate our findings into projections of future distribution

shifts of these animals. As the effect of temperature may vary between regions for a given species,

it will also be useful to assess vertical movements on a broader geographical range.


Michele Thums, Luciana Ferreira and two anonymous reviewers for critical comments that

greatly enhanced the quality of the manuscript.

74

3.7 Supporting information

Table S 3.1. Summary of tagging information and biological information of the tagged individuals.

Shark ID Deployment

date

Days at

liberty

Fork length

(cm)

Sex Pop-off

latitude (oN)

Pop-off

longitude

(oS)

Mean (± SD)

depth (m)

Mean (± SD)

temperature

(oC)

Maximum

depth (m)

Minimum

temperature

(oC)

OWT1 5/1/2011 184 218 F 22.89 74.68 59.92 ± 45.02 26.35 ± 1.68 1081.9 7.75

OWT2 5/4/2011 153 189 F 23.20 74.91 46.02 ± 38.52 26.16 ± 1.87 1008.7 8.54

OWT3 5/4/2011 245 214 F 23.73 74.93 50.03 ± 37.29 26.59 ± 1.46 751.8 10.89

OWT4 5/1/2011 153 233 F 24.36 75.37 41.66 ± 37.42 25.98 ± 1.66 720.2 7.90

OWT5 5/7/2012 245 212 F 24.60 75.50 43.32 ± 33.9 26.18 ± 1.56 577.3 13.53

OWT6 5/7/2012 150 179 F 22.69 73.38 50.8 ± 39.9 27.09 ± 1.65 635.1 11.04

OWT7 5/11/2012 278 228 F 23.94 74.93 45.37 ± 48.83 26.34 ± 1.74 1190.2 7.43

OWT8 5/12/2012 335 212 F 24.75 75.52 45.82 ± 40.55 25.29 ± 1.54 1081.9 6.79

OWT9 5/12/2012 325 195 M 24.01 74.46 57.65 ± 41.11 25.33 ± 1.56 875.2 9.48

OWT10 5/9/2012 112 216 F 24.22 75.32 74.94 ± 44.19 26.58 ± 1.70 845.6 9.32

OWT11 5/10/2012 60 168 F 24.67 76.05 37.28 ± 29.26 26.04 ± 1.26 420.6 17.63

OWT12 5/9/2012 245 210 F 21.77 74.20 48.64 ± 43.02 26.31 ± 1.65 895.3 9.48

OWT13 5/8/2013 83 198 M 23.11 75.02 42.04 ± 41.29 25.95 ± 1.32 855.3 9.63

OWT14 5/9/2013 174 183 F 25.30 76.34 46.63 ± 33.04 26.36 ± 1.56 442.1 17.94

OWT15 5/12/2013 202 203 F 24.63 75.83 45.04 ± 35.54 26.56 ± 1.53 838.5 9.32

OWT16 5/8/2013 22 203 F 24.83 75.70 39.42 ± 30.49 25.33 ± 0.94 232.0 20.21

75

Table S 3.2. All models involved in the model selection process. Model comparisons were made using

Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC) and conditional (R2c) and

marginal (R2m) R2 values. ΔAIC displays deviance in AIC scores from top-ranked models. All models are

generalised linear mixed models and were ran using the nlme package in R with shark identity as a random

variable. Proportion of time spent in the top 50 m (Prop50) was logit transformed prior to analysis. All null

models include the random effect.


Mean depth ~ SST + MLD + Length 2839 21636 21689 2 0.355 0.35 0.24

Mean depth ~ SST + MLD 2839 21634 21682 0.645 0.35 0.23

Mean depth ~ SST + Length 2840 21672 21720 38 0.00 0.33 0.22

Mean depth ~ MLD + Length 2840 21734 21781 100 0.00 0.06 0.006

Mean depth ~ MLD 2840 21659 21701 25 0.00 0.35 0.01

Mean depth ~ Length 2841 21694 21736 60 0.00 0.32 0.005

Mean depth ~ SST 2840 21671 21713 37 0.00 0.33 0.21

Mean depth ~ 1 2841 21693 21729 59 0.00 0.32 0


Prop50 ~ SST + MLD + Length 2839 7106 7159 2 0.336 0.46 0.33

Prop50 ~ SST + MLD 2839 7104 7152 0.664 0.46 0.32

Prop50 ~ SST + Length 2840 7142 7189 38 0.00 0.45 0.31

Prop50 ~ MLD + Length 2840 7135 7183 31 0.00 0.65 0.01

Prop50 ~ MLD 2840 7134 7176 30 0.00 0.67 0.007

Prop50 h ~ Length 2841 7168 7210 64 0.00 0.59 0.005

Prop50 ~ SST 2840 7140 7182 36 0.00 0.31 0.46

Prop50 h ~ 1 2841 7167 7203 63 0.00 0.62 0


Mean cycle length ~ SST + MLD +

Length

2839 43767 43820 2 0.246 0.43 0.05

Mean cycle length ~ SST + MLD 2839 43765 43812 0.668 0.43 0.05

Mean cycle length ~ SST + Length 2840 43815 43863 50 0.00 0.40 0.03

Mean cycle length ~ MLD + Length 2840 43771 43819 6 0.023 0.45 0.02

Mean cycle length ~ MLD 2840 43769 43811 4 0.063 0.34 0.009

Mean cycle length ~ Length 2841 43887 43917 122 0.00 0.41 0.005

Mean cycle length ~ SST 2840 43814 43856 49 0.00 0.40 0.03

Mean cycle length h ~ 1 2841 43818 43854 53 0.00 0.41 0

76

Table S 3.3. Results from proportion time top 50 m ~ mean SST breakpoint analysis. Breakpoints were

assessed for the full dataset and each individual shark. Model comparisons were made using Akaike's

Information Criterion (AIC) between models with and without the breakpoint term. ΔAIC displays deviance

in AIC scores between these two models. *Calculated breakpoint not robust.

**Breakpoint term significantly improves model.

OWT ID Estimated breakpoint AIC breakpoint AIC no breakpoint

(ΔAIC)

All 27.8 25793 26111 (318)**

OWT1 27.2 1584 1611 (27)**

OWT2 28.4 1279 1306 (27)**

OWT3 27.4 1896 1940 (44)**

OWT4 28.1 1267 1353 (86)**

OWT5 27.6 2084 2163 (115) **

OWT6 28.9 1219 1253 (34) **

OWT7* 26.1 2390 2430 (40)

OWT8 27.5 2805 2927 (122) **

OWT9* 25.2 2812 2849 (37)

OWT10 28.2 939 945 (6) **

OWT12 28.3 1987 2029 (42) **

OWT14 28.6 1370 1379 (9) **

OWT15 27.5 1631 1683 (52) **

Table S 3.4. Results from mean cycle length ~ mean SST breakpoint analysis. Breakpoints were assessed

for the full dataset and each individual shark. Model comparisons were made using Akaike's Information

Criterion (AIC) between models with and without the breakpoint term. ΔAIC displays deviance in AIC

scores between these two models. *Calculated breakpoint not robust. **Breakpoint term significantly

improves model.

OWT ID Estimated breakpoint AIC breakpoint AIC no breakpoint

(ΔAIC)

All 28.2 47351 47457 (106)**

OWT1 28.6 2826 2826 (0)

OWT2 28.4 2416 2428 (12)**

OWT3 27.4 3793 3806 (13)**

OWT4 27.5 2402 2430 (28)**

OWT5* 25.6 3735 3757 (22)

OWT6* 29.3 2290 2306 (16)

OWT7 28 4271 4295 (24)**

OWT8 27 5256 5271 (15)**

OWT9 27.4 5180 5240 (60)**

OWT10 28.4 1724 1730 (6)**

OWT12 27.4 3731 3744 (13)**

OWT14* 29.3 2648 2653 (5)

OWT15* 26.5 3099 3100 (1)

77

(a)

(b)

78

(c)

(d)

Figure S 3-1. The relationship between daily averaged vertical movement behaviours and SST for each

individual OWT. (a) Mean daily depth; (b) percentage time in the top 50 m; (c) mean cycle length (seconds);

and (d) mean amplitude.

79

Chapter 4 Biologging tags reveal the fine-

scale movement behaviours of

tiger sharks

4.1 Abstract

An understanding of the role that large marine predators play in structuring trophic flow and

nutrient cycling in marine ecosystems requires knowledge of their fine-scale (m-km) movement

behaviours. In this study, we explore the extent to which biologging tags reveal new insights into

the fine-scale movement ecology of tiger sharks (Galeocerdo cuvier) at Ningaloo Reef, Western

Australia. Tags deployed on 21 sharks for durations of 5-48 hours recorded both physical

parameters such as depth and temperature, and, through the use of accelerometers, gyroscopes

and compasses, in-situ measurements of animal trajectory and locomotion. Animal-borne-video

enabled the validation of behavioural signatures, mapping of habitat, and interactions with prey

to be recorded. Collectively, we used this data to examine the link between vertical (oscillations)

and horizontal (tortuosity) movements, and link sensor data to prey interactions recorded by the

video. Our biologging approach revealed complex movements that would otherwise be invisible

within the time-depth records provided by traditional tagging techniques. We found no

relationship between path tortuosity and vertical movements, suggesting that vertical movements

occur independently of search behaviours. Tiger sharks displayed tortuous movements possibly

associated with prey searching for 27% of their tracks, and interactions with prey elicited varied

responses including highly tortuous paths and burst movements. Accurate speed measurements

and GPS anchor points will considerably enhance the value of magnetometer data in future studies

by facilitating more accurate dead-reckoning and geo-referencing of area-restricted search

behaviours.

4.2 Introduction

The movement patterns of large marine predators such as tiger sharks (Galeocerdo cuvier) have

typically been sampled using acoustic telemetry and satellite tagging approaches (Andrews et al.

2009; Papastamatiou et al. 2009; Barnett et al. 2010; Brunnschweiler et al. 2010; Vaudo et al.

2014; Comfort and Weng 2015; Heupel and Simpfendorfer 2015; Papastamatiou et al. 2015a).

These studies generate either presence/absence data sets (acoustic telemetry), or movement

patterns of animals over large horizontal spatial scales (satellite tagging, 10s – 1000s km).

Although such studies continue to transform our understanding of the ecology and biology of

these animals (Chapman et al. 2015), they have two major shortcomings. Firstly, they provide

limited opportunities to identify and categorize behavioural modes at the fine spatial scales (m –

80

km) relevant to predator-prey and other inter and intraspecific interactions (competition, social

behaviours, cannibalism etc.) (Gleiss et al. 2009a). Secondly, the time-depth records that are

usually recorded by these techniques offer only coarse resolution of the patterns of vertical

movements of these predators through the water column (Ryan et al. 2004). Given that the

behaviour of large marine predators is thought to have a major role in structuring trophic flows

and nutrient cycling within marine ecosystems (Heithaus et al. 2008; Lavery et al. 2010), it is

imperative that we link their large-scale movement patterns with their day-to-day behaviours

within the environment.

Biologging approaches offer a means to achieve this aim. Tags such as the daily diary (Wilson et

al. 2008) incorporate a range of sensors including accelerometers and physical sensors, and can

be used to characterize fundamental aspects of the behaviour of individuals through quantitative

measurement of body kinematics. The combination of these sensors can therefore record detailed

movements of a target species as well as the environmental context in which they occur. First

used on marine animals in the late 1990s (Yoda et al. 1999), tri-axial accelerometers are often

incorporated into biologging devices, and have allowed researchers to categorise the behaviours

of sharks, including swimming, bursting, resting, and mating, and have also been used to quantify

activity patterns and the swimming energetics of vertical movements (Whitney et al. 2010; Gleiss

et al. 2011b; Whitney et al. 2012; Meekan et al. 2015). However, to link these fine-scale

behaviours to larger-scale movements, we need to understand how they relate to an animal’s path

through the environment. Accelerometers do not provide information on animal heading, limiting

most analysis of behaviours to a two-dimensional plane. Furthermore, the direct observation and

consequent validation of behaviours recorded by these sensors is almost impossible for a number

of large species of shark due to their high mobility and cryptic nature. The recent addition of

magnetometers and animal-borne cameras to biologging tags overcomes these issues. When used

in tandem with accelerometers, tri-axial magnetometers enable the reconstruction of movements

in three dimensions through the process of dead-reckoning (Wilson et al. 2008; Walker et al.

2015; Williams et al. 2017a). Animal-borne cameras add the ability to validate classifications of

behavioural signatures recorded by tri-axial sensors (Davis et al. 1999; Heithaus et al. 2001;

Narazaki et al. 2013) and enable interactions with prey to be recorded (Heithaus et al. 2002b;

Nakamura et al. 2015a; Papastamatiou et al. 2019).

Here, we use a biologging approach to examine the fine-scale movement and behaviour of the

tiger shark at Ningaloo Reef, Western Australia. Tiger sharks are a partial migrator, where some

individuals remain resident in coastal areas for long periods of time and others undertake long

distance movements (Meyer et al. 2010; Ferreira et al. 2015; Acuña-Marrero et al. 2017). Recent

studies have revealed that this species continuously oscillates through the water column, driven

by the need to either conserve energy, or search for benthic prey on descent, and silhouetted air-

breathing prey on ascent (Heithaus et al. 2002b; Nakamura et al. 2011).

81

In this study, we explore the extent to which multiple sensors in biologging tags reveal new

insights into the fine-scale movement ecology of tiger sharks. Our tags combined video,

environmental sensors, tri-axial accelerometers, magnetometers, and gyroscopes, allowing us to

classify behavioural signatures in vertical movements, validated by the video, and reconstruct

three-dimensional reconstructed paths of these animals while concurrently recording the

environmental context in which they occurred. This allowed us to (1) examine the relationship

between vertical (oscillations) and horizontal (tortuosity) movements, and (2) link sensor data,

including tailbeats, burst acceleration and tortuosity, to prey interactions recorded on video,

collectively identifying likely prey-searching behaviours.

4.3 Methods

4.3.1 Ethics

All methods were used in accordance with approved guidelines by the University of Western

Australia Animal Ethics Committee (RA/3/100/1437), and under permit numbers 2881 (WA

Department of Fisheries) and 08-000322-3 (WA Department of Parks and Wildlife).

Table 4-1. Terms and definitions used repetitively throughout chapter

Term Definition

Diving Ratio The proportion of time spent moving vertically (ascending or descending) within a

15 minute window

𝑅 Mean resultant length. A measure of the concentration of the heading data. When 𝑅

is equal to or close to one, points are closely clustered around the mean direction.

When 𝑅 approaches zero, points are spread more evenly in a circle.

Pseudo-track An approximation of a tiger shark’s horizontal track.

Inshore Inside the reef lagoon in depths of <25 m.

Offshore Outside the reef lagoon in depths of >25 m.

VV Vertical velocity (m/s)

82


Tiger sharks were captured using baited drumlines inside the reef lagoon at Ningaloo Reef,

Western Australia (22.99°S, 113.8°E, Figure 1a) in April-May 2017. Drumlines were equipped

with a single 20/0 circle hook baited with fish scraps. Three drumlines were deployed

approximately 100 m apart between 07:00 and 16:00, with lines checked every hour for captures.

Once a shark was caught, it was secured alongside a 5.8 m vessel with the leader and a tail rope.

Each shark was measured (pre-caudal length, fork length, total length and maximum girth) and

its sex recorded, before a biologging tag was clamped to the base of its dorsal fin (Figure 4.1).

The dorsal fin of each shark was also photographed before and after tagging for identification

purposes and to assess any potential tag effects.

Figure 4.1. Tagged tiger sharks at Ningaloo Reef. (a) Tiger shark post-release. Photo courtesy of Alex

Kydd. (b) CATS Cam tag clamped to the dorsal fin. (c) CATS Diary tag clamped to the dorsal fin. (d)

Location of tag deployments.

83

A combination of CATS Diary tags (Customised Animal Tracking Solutions, Australia) and

CATS Cam tags were deployed on tiger sharks (Figure 4.1). Both were equipped with tri-axial

accelerometers, magnetometers, and gyroscopes, and depth, temperature and light sensors. In

addition, the Cam tag housed a HD video camera. The sensors continuously recorded all

parameters at 20 Hz, and video was recorded at pre-programmed hours of the day for a maximum

of six hours per deployment, due to storage limitations. In order to attach the tags to the dorsal

fins of the sharks, CATS tags were joined to a stainless steel spring clamp (CATS, Australia) via

a docking pin and a corrodible galvanic timed release (GTR, Ocean Appliances, Australia).

Previous work has shown that these clamps allow tags to remain rigidly attached to dorsal fins of

large sharks for up to 93 hours (Gleiss et al. 2009b; Chapple et al. 2015), and the GTR models

used were designed to dissolve in seawater after 7-48 hours (Table 4-2). Once the GTR dissolved,

the tag released from the clamp, allowing the tag to float to the surface. Floating tag packages

were tracked down using a hand-held VHF receiver operated from a vessel (Lear and Whitney

2016). A magnesium sleeve on the clamp itself also dissolved after approximately seven days, so

that the clamp detached, leaving no tagging equipment attached to the shark.

4.3.3 Data processing and analysis

Data recorded by the tags were processed to obtain a number of parameters classifying shark

movements and behaviours. The depth record was firstly split into vertical phases of ascent,

descent and constant depth. Tri-axial sensor data were then used to calculate the pitch angles of

vertical movements, overall dynamic body acceleration (ODBA), the acceleration signal

amplitude and frequency of tailbeats, and gliding behaviour. Tailbeat data were used to calculate

the recovery from capture following methods outlined by Whitney et al. (2016b). Tri-axial data

were also used to calculate shark heading, turning angles and other measures of track tortuosity.

Lastly, video data were used to record interactions with prey, determine habitat types, and validate

behaviours recorded by the sensors (e.g. tailbeats and bursting behaviour). The following

describes these methods in greater detail.

4.3.3.1 Depth record

Zero offset corrections were applied to the depth trace based on periods where sharks were being

tagged and known to be at the surface. The depth record was then split into vertical swimming

phases (‘ascending’, ’descending’ and ‘level swimming’) using vertical velocity (VV). To do this,

the depth trace was firstly smoothed using a ten second running mean and the average VV was

calculated by taking the difference of this smoothed depth between successive points at one

second intervals. Ascents and descents were defined where VV exceeded an absolute value of

0.05 m/s for more than ten seconds, and level where this value was not exceeded (Whitney et al.

2016b).

84

Table 4-2. Summary details of tagged tiger sharks. *Tag malfunction, no data downloaded **Tag shut off before detachment nResight – same as TS17

Shark

ID

Tag

ID

Deployment

date

Galvanic timed

release deployed

Attachment

duration

Pre-caudal

length

(cm)

Fork

length

(cm)

Total

length

(cm)

Girth

(cm)

Sex Recovery

latitude

(oS)

Recovery

longitude

(oE)

Mean (± SD)

depth (m)

Maximum

depth (m)

TS1 CC1 23/4/2017 11:33 A4 – 15 hours 9h 51min 254 286 347 169 F 22.91 113.81 6.61 ± 4.39 17.49

TS2 CC2 23/4/2017 13:47 A4 – 15 hours 13h 14min** 272 311 345 NA F 22.93 113.57 9.37 ± 5.89 34.45

TS3 CD1 23/4/2017 14:49 A4 – 15 hours 4h 41min 250 224 266 122 F 23.06 113.79 9.08 ± 3.59 18.61

TS4 CC1 26/4/2017 10:14 A4 – 15 hours 11h 20min 264.5 289 331 169 F 22.99 113.79 9.01 ± 3.41 17.21

TS5 CD2 26/4/2017 11:07 A4 – 15 hours 11h 37min NA NA ~350 NA F 23.06 113.74 6.77 ± 4.23 19.17

TS6 CD1 26/4/2017 12:50 A4 – 15 hours 10h 24 min 240 253 300 150 F 23.04 113.78 8.01 ± 3.74 16.07


TS9 CC1 28/4/2017 12:40 A4 – 15 hours 9h 10min 293 314 345 NA F 22.98 113.62 21.10 ± 24.99 74.33

TS10 CD2 28/4/2017 14:12 A4 – 15 hours 9h 52min 283 312 362 142 UN 23.05 113.80 8.09 ± 4.35 17.90

TS11* CD2 30/4/2017 13:05 A6 – 25 hours NA 257 284 336 127 M 23.05 113.60 NA



TS14 CC1 30/4/2017 15:13 A6 – 25 hours 17h 32min 267 299 351 167 M 22.96 113.81 4.09 ± 3.92 17.75

TS15 CC1 2/5/2017 12:19 C5 – 40 hours 48h 44min 270 298 329 161 F 22.76 113.70 7.08 ± 5.41 32.83



85




TS24n CC2 14/5/2017 12:08 C5 – 40 hours 23h 43min 300 330 373 171 F 22.73 113.73 2.77 ± 3.46 17.78

TS25 CC2 18/5/2017 11:31 B5 – 32 hours 5h 7min** 201 223 265 104 F 22.86 113.65 7.06 ± 3.19 14.95

TS27 CC1 18/5/2017 14:31 A6 – 25 hours 13h 54min NA 322 370 133 F 22.91 113.76 23.62 ± 21.39 72.79

86

4.3.3.2 Tri-axial sensor data

Data recorded by the accelerometer (acceleration) and gyroscope (angular velocity) were analysed

using Igor Pro ver. 7.0.4.1 (Wavemetrics, Inc. Lake Oswego, USA) and Ethographer (Sakamoto

et al. 2009). The gravitational component of acceleration (static acceleration) was determined

using a three second box smoothing window on the raw acceleration data (Shepard et al. 2008).

Pitch angles were derived by calculating the arcsine of the static acceleration in the surging

(posterior-anterior) axis. To correct for the tag attachment angle on each individual shark, we

determined the pitch when the shark was swimming at a constant depth (when VV was equal to

zero), and subtracted this value from all pitch estimates (Kawatsu et al. 2009). ODBA (overall

dynamic body acceleration) was calculated by summing the absolute value of dynamic

acceleration from all three axes (Wilson et al. 2006). Comparing ODBA with video recorded burst

events allowed us to classify bursts as events where ODBA >0.2. The dynamic component of

acceleration was calculated by subtracting the gravitational component from the raw acceleration

for each axis. We used a continuous wavelet transformation on the dynamic component of sway

(lateral) axis to calculate the acceleration signal amplitude and frequency of tailbeats (Sakamoto

et al. 2009). Using these same methods, angular velocity signal amplitude and frequency were

calculated using the angular velocity data, and the resulting signals were compared with those

derived from the acceleration data to determine the best measure of tailbeats.

4.3.3.3 Recovery period

We used metrics quantifying tailbeat activity calculated from tri-axial sensor data to estimate the

duration of recovery from stress of capture following the methods of Whitney et al. (2016b). This

study found that immediately after release, the tailbeats of blacktip sharks Carcharhinus limbatus

were elevated, and would slowly decline in frequency over the course of an individual’s recovery.

Briefly, to calculate this period for tiger sharks, tailbeat cycle (the inverse of tailbeat frequency)

throughout descent was summarized for 15 minute windows, and plotted against time post-

release. A recovery period was defined as the time it took for this metric to reach 80% of its

asymptote (Whitney et al. 2016b). This recovery period was eliminated from the data prior to

further analyses to remove potential sublethal and unnatural behaviours resulting from stress of

capture by drumlines.

4.3.3.4 Shark heading and pseudo-track calculation

Acceleration and magnetometer data were used to calculate head yaw angle (hereafter referred to

as ‘heading’) and pseudo-tracks in the software Framework 4 (Walker et al. 2015). Heading

calculations required input of orientation of the sensors in the tags, and magnetometer and

acceleration data. The orientation of the device was corrected using orientation specifications

provided by CATS (N. Liebsch pers. comm). The coordinate systems of the accelerometer and

87

magnetometer were perfectly aligned and consequently no adjustment was required to align these

sensors. Pitch and roll calculated from the accelerometer data were used to correct for tilt (Walker

et al. 2015). In addition to the calculations applied by Framework 4, we applied a three second

box smoothing window to the heading data to filter out the dynamic movements of the sharks

caused by tailbeats.

Heading was used to calculate pseudo-tracks for each shark. These tracks were used only for

plotting short-term movements of the sharks in three-dimensional space. They could not be used

to estimate accurate locations of the tagged sharks due to the accumulation of errors from the

estimates of heading and speed over the course of the dead-reckoned track (Wilson et al. 2007;

Walker et al. 2015). Heading data were also used to calculate the turning angles of individuals on

a between-second basis. Data were resampled to a one second frequency and converted to a 0-

360o scale. The minimum difference in angle between consecutive observations was used to

determine turning angle.

4.3.3.5 Window size and statistics

The time window used for analysis was selected by determining the window for which the highest

variance in turning angle was observed, while being of sufficient width to capture the longest

dives in their entirety. This time window was estimated to be 15 minutes, and within this period,

a number of vertical movement parameters were summarized including mean (± standard

deviation) and maximum depth, ascent pitch, descent pitch, ascent VV and descent VV. The

percent of time spent moving vertically (ascending and descending), termed the ‘diving ratio’,

was also calculated for each window. In addition, heading was used to calculate measurements of

path tortuosity and direction. Firstly, turning angles were summed for each window to obtain an

estimate of tortuosity. Secondly, the Circular package in R was used to calculate the mean

resultant length, or 𝑅, of the heading, a measure of the concentration of unimodal circular data

(Pewsey et al. 2013). When 𝑅 is close to or equal to one, points are closely clustered around the

mean direction and are highly directional, and as 𝑅 approaches zero, points spread more evenly

in a circle indicative of a tortuous path.

4.3.3.6 Video analysis

Video recorded by the tags was analysed in BORIS (Behavioural Observation Research

Interactive Software; Friard and Gamba 2016). This open-source software allows the user to set

an ethogram and record the timing of behavioural events. We set an ethogram to mark the

occurrence of prey interactions, shark responses to the presence of prey, burst swimming events,

presence of seabed in the field of view, habitat type, and other notable behaviours (Table S 4.1).

Video was also used to validate parameters recorded and calculated from the diary data, such as

tailbeat frequency, bursts and glides.

88

4.3.3.7 Movement classification

Hierarchical cluster analysis was used to categorize both vertical movements and path tortuosity

for each analysis window in the software R 3.4.0 (R Core Team 2017). Vertical movements were

classified using mean ascent and descent pitch, and diving ratio, and the path tortuosity using the

sum of the turning angles and 𝑅. These variables were scaled using the scale function in R and a

cluster analysis was applied using the hclust function, based on a dissimilarity matrix produced

from Euclidean distances and complete linkage as the agglomeration method. We chose the

number of clusters by looking at the gradient of reduction in within groups sum of squares with

additional number of clusters added. Groups resulting from the clustering of vertical movements

were labelled as ‘V-groups’ and from the clustering of path tortuosity as ‘T-groups’. The resulting

V-groups were compared through qualitative analysis and correlation coefficients with T-groups

to investigate if more tortuous movements were related to more oscillatory movements. Where

movements were identified to be tortuous, available behavioural records from concurrent videos

were examined.

4.3.3.8 Generalised linear mixed models (GLMMs)

To further investigate relationships between oscillatory and tortuous movements, generalised

linear mixed models (GLMMs) were built in R using the nlme package (Pinheiro et al. 2017).

Diving ratio was set as the response variable, 𝑅 and sum of turning angles were sequentially set

as explanatory variables (due to correlation >0.6 between these variables), and tiger shark identity

was set as a random variable. Diving ratio was logit transformed prior to analysis. We used the

corAR1 function to account for temporal auto-correlation in our datasets (Zuur et al. 2009). The

resulting models were compared with null models using Akaike’s information criterion (AIC).

4.4 Results

A total of 22 tiger sharks ranging in length from 2.65 – 3.8 m TL were caught and tagged (14

camera tag and 8 daily diary tag deployments) (Table 4-2). Tags were attached for a mean duration

of 15 hours (range 4.5-48 hours) and recorded approximately 410 hours of diary data and 50 hours

of video footage. One diary tag failed to record any data. One shark was recaptured after 11 days

and was re-tagged (TS17 and TS24 in Table 4-2). Based on an analysis of tailbeats, we assumed

a recovery period from capture and tagging by the sharks of four hours and for this reason, the

first four hours of each dataset were excluded from further analysis (Figure S 4-1). Evidence for

recovery after this time was also provided by the video records, which showed investigations of

potential prey and consumption of a discarded fish head by sharks within two hours of tagging

and release (Supplementary Video 1). The angular velocity data produced the clearest tailbeat

signal and was therefore used for further data exploration.

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4.4.1 Vertical movements

The seabed was observed in videos at least every 15 minutes in all but 14 (~2%) sampling

windows where the water visibility was very poor, or in one case, during a period of extended

surface swimming by a shark while offshore. As a result, we assumed that vertical oscillations

were depth-limited and could be used to map the approximate depth of the seabed throughout the

tracks (Figure S 4-2). Tagged tiger sharks swam at a mean depth of 11.6 ± 17.5 m, predominately

remaining in inshore habitats, with four tiger sharks moving into offshore habitats and diving to

a maximum depth of 94 m. Video analysis showed tiger sharks transiting a variety of habitats,

including sand, macroalgal reefs, coral reef, bare reef, pelagic, and edge habitats (Bancroft 2003)

(Supplementary Video 1).

The cluster analysis revealed six vertical-groupings (V-groups) as the dominant vertical

movement modes, with an additional six V-groups describing <8% of the data (Figure 4.2,

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Table S 4.2). Diving ratio increased from 0.5 ± 0.8% in V-group 1 to 80 ± 6% in Group 8. Groups

4, 5 and 7 were characterized by mean diving ratio and/or mean pitch that were greater than overall

mean values, and examination of tracks associated with these groups demonstrated that they were

associated with a higher frequency of oscillations throughout the water column. Offshore periods

were only clustered into these three V-groups, whereas all modes of vertical movement were

displayed by inshore sharks.

4.4.2 Horizontal path tortuosity

The classification of path tortuosity generated seven tortuosity-groups (T-groups) (Figure 4.2,

Table S 4.3). Movements increased in tortuosity from T-groups 1-7, ranging from highly

directional with low turning to almost uniform distributions of direction with high levels of

turning. A total of 72.6% of windows were classified in T-groups 1-3 where movements were

categorized as directional and the remaining 27.4% in T-groups 4-7 were categorized as tortuous.

All T-groups were present in both inshore and offshore habitats.

Video cameras were recording for 21 of the windows assessed to have tortuous paths. For almost

half of these windows (43%), tiger sharks were located on the shallow sandflats (<3 m in depth).

Six tortuous windows were associated with investigations of turtles. For example, TS15 was

tracking through coral reef habitat when a loggerhead turtle (Caretta caretta) was sighted on the

far right of the field of view (Figure 4.3, Supplementary Video 1). After this first observation, the

shark turned sharply and the turtle was again spotted in mid-water, tightly turning away from the

shark with its shell perpendicular to the shark. The shark then approached the surface and

descended before circling the same area of coral reef for 25 minutes, remaining on the seabed and

following the structure of the reef (Figure 4.3, Supplementary Video 1). The turtle was not

observed again in the field of view for the remainder of this 25 minute period.

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Figure 4.2. Comparison of dominant vertical movement clusters (V-groups) and path tortuosity clusters

(T-groups) from hierarchical cluster analysis. (a) 15 minute depth profiles representative of V-groups 1, 3

and 6, demonstrating the increase in oscillatory movements with V-group. (b) 15 minute pseudo-tracks

representative of each T-group, demonstrating the increase in tortuosity with T-group. Polar plot on bottom

right of each track displays example of heading variance for each group. (c) The % windows found in each

cluster. (d) The % T-group composition of each dominant V-group. Colours are T-groups from (b).

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Figure 4.3. Tiger shark and loggerhead turtle interaction. (a) One-hour long pseudo-track from TS15. Red

square denotes area of prey interaction displayed in (b) and (c). (b) Depth track. (c) 3D track. (d)

Screenshots from video of the interaction. 1 and 2 refer to where interaction takes place in depth and 3D

tracks. See Supplementary Video 1 for full video of interaction.

A number of other interactions with prey were recorded on the cameras (Table S 4.4), however

none of these involved successful foraging on live prey. Most interactions (92%) occurred when

sharks were level swimming with the seabed in the field of view of the camera and included

investigations and possible stalking of several lutjanid fish (Symphorus nematophorus), in at least

one case prompting a rapid colour change, 17 interactions with turtles, in at least four cases

eliciting tight turning and shell up responses from turtles, and potential investigations of a tawny

nurse shark (Nebrius ferrugineus) and another tiger shark (Table S 4.4 & Supplementary Video

1). Several investigations of prey were immediately preceded by burst, stalking and/or turning

behaviours (Figure 4.4). Sharks did not pursue vigilant or fleeing prey, and no bursts were

observed in the direction of prey when prey were in the field of view.

No extended periods of bursting that may be indicative of headshaking behaviour (bursts greater

than two seconds in length; Brewster et al. 2018) were observed in any part of the dataset. Burst

behaviour was highly variable with a mean 3 ± 15 bursts occurring in a 15 minute window. Highly

tortuous windows (T-groups 6 and 7) were associated with higher than average bursts (e.g. Figure

4.6), however further analysis of bursting was confounded by artificially high ODBA levels when

swimming at the surface due to the effects of chop.

93

Table 4-3. Results of generalised linear mixed models testing the relationship between diving ratio and

indicators of horizontal path tortuosity. Diving ratio was logit transformed prior to analysis. All models

were compared with null models using Akaike’s Information Criterion (AIC) and conditional (R2c) and

marginal (R2m) R2 values. All models were run using the nlme package in R with shark identity as a random

variable. All null models include the random effect

Inshore Model DF AIC R2m R2c

Diving Ratio ~ 𝑅 865 2340 0.001 0.51

Diving Ratio ~ 1 866 2339 0 0.51

Diving Ratio ~ Sum of turning angles 865 2361 0.0002 0.51

Diving Ratio ~ 1 866 2339 0 0.51

Figure 4.4. One minute pseudo-tracks of tiger shark and Chinamanfish (Symphorus nematophorus)

interactions. Track line is coloured by tailbeat frequency (Hz). Red dashed square indicates where the fish

was observed in the video field of view (displayed in screenshots to the right of the pseudo-track). X and

Y-axes represent arbitrary units of latitude and longitude created by magnetometer and accelerometer data

whilst a constant speed is assumed. (a) Example of an interaction where the tailbeat of the shark slows upon

encountering the fish. (b) Example of an interaction where the tailbeat of the shark quickens upon

encountering the fish.

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4.4.3 Oscillations and tortuosity

There were no strong correlations between vertical movement and tortuosity variables (all r <0.3)

and almost all vertical movements displayed all levels of tortuosity. For example, highly

oscillatory movements classified in V-group 8 were classified in both T-group 1 with directional

swimming, and in T-group 6 where movements were highly tortuous (Figure 4.5). Conversely,

Figure 4.6. 15 minute pseudo-track throughout a tortuous window in TS20. Track is coloured by bursts

(ODBA>0.2) and takes place at approximately 21:30 in water approximately 2 m in depth.

Figure 4.5. Two tiger sharks (i & ii) with very similar oscillatory depth tracks but different horizontal tracks

in offshore environments over a 2.5 hour period. (a) Depth track. (b) Pseudo-track. (c) 3D track.

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windows containing entirely level swimming in V-group 1 were also classified in both T-groups

1 and 6. In addition, generalized linear mixed models revealed no relationships between diving

ratio and variables describing tortuosity (𝑅 and sum of turning angles, Table 4-3).

4.5 Discussion

Our study shows how a multi-sensor biologging approach may be used to investigate foraging

behaviour of sharks as large, top-order predators. This information is essential if we are to

understand the keystone role that these animals are thought to play in marine ecosystems.

4.5.1 Foraging behaviour and tortuosity

We found evidence of foraging behaviour based on path tortuosity and video recorded encounters

with potential prey. Tiger sharks displayed tortuous horizontal paths for approximately 27% of

their tracks. As straight-line directional movement has been calculated to be the most

energetically efficient form of travel (Wilson et al. 2013), the initiation of tortuous movements

should provide some form of benefit to offset the energetic costs of turning. Tortuous movements

have been linked with searching behaviour and increased foraging success in many marine

animals, and are thought to indicate area-restricted searching (Kareiva and Odell 1987; Austin et

al. 2006; Papastamatiou et al. 2012; Adachi et al. 2017). For this reason, we argue that the tortuous

tracks we recorded represented prey searching, despite the fact that we did not witness any

consumption of prey. It should also be noted that the direct observation of natural predation events

by marine predators tends to be very rare (Pitman et al. 2014; Papastamatiou et al. 2019),

particularly for ectothermic shark species that are likely to feed less frequently compared to

marine mammals and seabirds (Papastamatiou et al. 2019). Given that video recordings were

limited to a maximum of six daylight hours per tag due to data storage and lighting constraints, it

is perhaps not surprising that we did not record any predation events involving live prey.

In addition, previous observations of foraging behaviour suggest that tiger sharks have a

preference for weakened and/or injured prey and rarely predate on vigilant animals (Heithaus et

al. 2002b; Hammerschlag et al. 2016). In several cases prey investigations were followed by either

escape maneuvers performed by turtles, or colour displays from demersal fish, all of which

culminated with the shark continuing on its course without attempting to predate on the alert

individual. It is also possible that some of these foraging behaviours may not have been focused

on the potential prey species that were seen in the video, particularly in the case of benthic fishes.

Facultative scavenging is an important feature of the behaviour of tiger sharks (Clua et al. 2013;

Hammerschlag et al. 2016) and in some cases sharks may have been drawn to approach

mesopredatory benthic fishes simply by the potential opportunity to scavenge food, i.e.

96

kleptoparasitism. This might explain why burst acceleration occurred during only sporadically in

prey investigations (Figure 4.4).

Video data showed that tortuous movements were often associated with shallow sandflat habitats,

a location where tiger sharks at Ningaloo Reef have frequently been observed hunting prey by

ecotourism operators (pers. comm Frazer McGregor, Figure S 4-3). It is possible that tiger sharks

may be showing a pattern of area-restricted search after detecting potential prey, either to

investigate this prey source or due to the high probability of encountering other prey items nearby,

or when entering areas linked to high foraging success based on past learning experiences (Adachi

et al. 2017). Similar patterns in exploratory foraging have been found in the movements of lions

(Panthera leo), where GPS-collared individuals exhibited more tortuous and slower movements

when in the vicinity of waterholes, a pattern that suggested area-restricted searching in an area

with potentially higher prey density (Valeix et al. 2010). In our study, sandflats may be preferred

habitats for foraging by tiger sharks because prey here have less room to perform evasive

maneuvers and to escape (Heithaus et al. 2002a), increasing the efficiency of hunting.

4.5.2 Three-dimensional movements

Fine-scale movement behaviours were invisible in the time-depth record. A comparison of

vertical and horizontal tracks of tagged tiger sharks found no relationship between vertical

movements and path tortuosity, and some tracks with the same time-depth record displayed very

different horizontal paths. Level or oscillatory swimming occurred while the shark displayed

either directional or tortuous swimming, suggesting that vertical movements may not change

significantly when undergoing area-restricted search. The majority of investigations (92%) of

potential prey occurred when tiger sharks were level swimming, with several of these taking place

during tortuous paths. Oscillatory behaviour was often observed prior to prey investigation events.

This behaviour suggests that sharks may have been searching the water column for visual or

olfactory cues (Carey et al. 1990; Klimley et al. 2002), and once triggered, began tortuous paths

in order to locate prey and to remain in a potentially profitable area. For example, one shark

encountered a loggerhead turtle on a small coral reef and then circled the same area for 25 minutes,

although the prey was not re-encountered. The variance in behaviours observed in similar depth

profiles highlights the need for a combination of high resolution tri-axial movement and depth

sensors to gain a full picture of the behaviour of these animals, as time-depth records alone would

be insufficient to distinguish prey searching and fine-scale habitat use.

4.5.3 Challenges and future considerations

Although biologging approaches provide a wealth of new information, there are many challenges

in processing, analyzing, visualizing and interpreting the large amount of data recorded by these

tags (Whitney et al. 2019). Manipulation of the datasets can require access to large computers and

97

proficiency in multiple types of specialist software and challenges also arise in visualizing the

data to display biologically relevant patterns within very complex data sets. Here, many of the

different data streams were shown to be important in understanding the fine-scale movement

behaviour of tiger sharks. Pseudo-tracks and three-dimensional plots presented a relatively simple

means to visualize the data, and both heading and tailbeat kinematic data were demonstrated to

be useful in describing interactions with prey. Such an approach is likely to be of use for

investigating and comparing these behaviours among other shark species, where differences in

foraging ecology may drive differences in prey searching behaviours and therefore three-

dimensional movement patterns.

Despite our biologging approach revealing detailed insights into movements and behaviours of

tiger sharks, interpretation of data sets was hampered by several issues. Firstly, the field of view

of the camera often limited our interpretation of behaviours, and was not likely to record all

interactions of sharks with prey. A wider field of view, or optimally cameras capable of filming

across 360° may enable further behavioural insights. Secondly, storage and battery constraints

and the need to recover the tags in order to download archived data sets constrained tag

deployments to relatively short periods (a few days at most). Delaying video activation until after

predicted recovery periods for sharks could assist in extending tag recording time. However, there

is no current solution to the need to recover the tags, which makes it difficult to place the fine-

scale movement patterns we described into a longer term context. Lastly, accurate speed

measurements and GPS anchor points would considerably increase the value of the magnetometer

data in allowing us to calculate more accurate fine-scale locational and habitat preferences, and

the incidence of area-restricted search (Walker et al. 2015).

4.5.4 Conclusion

Our study demonstrates the utility of multi-sensor biologging tags in classifying the fine-scale

movements and behaviours of a keystone marine predator. Our results showed that recording

movement in two-dimensions alone, as is the case with traditional time-depth recorders, will not

be sufficient in distinguishing among fine-scale behavioural modes. Data obtained from the

combination of magnetometers, accelerometers and video effectively described predator-prey

interactions and habitat use, providing important information about how these sharks interact with

their ecosystem. Such information will enable a greater understanding of the role these predators

play in coral reef ecosystems.


Fieldwork and tags were funded by crowdfunding on the Experiment platform (DOI:

10.18258/7190), a Holsworth Wildlife Research Endowment, a UWA Graduate Research School

98

fieldwork award and BigWave Productions. We thank Murdoch University and Coral Bay

Research Station for accommodation and vessel use while conducting fieldwork. This work

would not have been possible without the generous support of numerous volunteers, particularly:

Frazer McGregor, Abraham Sianipar, Adam Jolly, Blair Bentley, Evan Byrnes, Garry Teesdale

and Michael Tropiano providing valuable field support; Olivia Seeger and Abraham Sianipar who

helped with the video analysis. Nikolai Liebsch provided invaluable assistance with tag

functioning. Alex Kydd, David Palfrey and Daniel Thomas-Browne generously contributed

photos and video. Ryan Daly supplied helpful assistance for the analysis.

99

4.7 Supporting Information

Supplementary Video 1: https://vimeo.com/303181061

Password: shark

Figure S 4-1. Recovery period in tiger sharks after being caught and released. Black dots and error bars

represent mean tailbeat cycles (the inverse of tailbeat frequency) during descent for individual tiger sharks

over 15 minute windows. The solid blue line is the combined regression for all individuals, and the red

dashed line indicates the point at which the approximate 80% threshold was calculated, and individuals

were considered to be recovered. Methods follow that of Whitney et al. (2016b)

100

Figure S 4-2. Video validation of seabed. A) Entire depth track from TS24. B) & C) Excerpts of TS24

track displaying where seabed was observed on video.

.

101

Figure S 4-3. Interaction between a tiger shark and turtle in the sandflats around Coral Bay. Photos courtesy

of Daniel Thomas-Browne.

102

Table S 4.1. Ethogram used in the video analysis in BORIS (Behavioural Observation Research Interactive

Software; Friard and Gamba 2016).

Behaviour Code Description Modifiers

Release Tiger shark swims off from boat

Seabed Tiger shark descended to seabed, and

habitat classification

(1) Sandy (2) Weedy (3) Weedy reef

(4) Patchy reef and sand (5) Coral

reef (6) Clumpy weed and sand (7)

Rubble reef

Burst Burst movement

Lunge Tiger shark lunges (1) Left (2) Right (3) Down (4) Up

(5) Forward

Left turn Tiger shark turns left

Right turn Tiger shark turns right

Speeds up Increase in speed

Slows down Decrease in speed

Glide Tailbeats cease

Prey item Prey animal in field of view, prey

type and reaction by shark

(1) Fish (2) Turtle (3) Ray (4)

Cephalopod

(A) No reaction (B) Investigate (C)

Chase (D) Attack (E) Chew

Other animals Non-prey animals in field of view (1) Bait fish school (2) Other shark

(3) Remora activity (4) Reef fish

Visibility Change in water visibility 1 (low), 2, 3, 4, 5 (high)

Weird behaviour Strange unclassifiable behaviour

Trevally Change in accompanying trevally

Gulp Shark appears to take a gulp of

seawater

Other Other

103

Table S 4.2. Summarised characteristics of each V-group derived from the hierarchical cluster analysis.

Values are mean ± standard deviation.

Cluster

ID

No. % Vertical

ratio

Descent

Pitch

Ascent

Pitch

Description Example

G1 66 7.1 0.005 ±

0.008

-6.63 ±

2.02

4.07 ± 0.64 Very low vertical, low-med descent

angle, low ascent angle

TS24

G2 190 20.4 0.12 ± 0.09 -7.38 ±

1.83

7.24 ± 1.53 Low vertical, low-med descent

angle, med ascent angle

TS16, TS24

G3 15 1.6 0.32 ± 0.13 -19.73 ±

2.85

12.0 ± 2.20 Low-med vertical, high descent


TS15

G4 52 5.6 0.37 ± 0.14 -13.25 ±

1.63

13.22 ±

2.38

Low-med vertical, med-high

descent angle, med-high ascent

angle

TS15

G5 287 30.8 0.52 ± 0.13 -10.71 ±

1.83

8.88 ± 1.45 Med-high vertical, med descent

angle, low-med ascent angle

Most, TS2,

TS12

G6 40 4.3 0.65 ± 0.15 -17.91 ±

2.74

8.59 ± 1.78 Med-high vertical, med-high

descent angle, low-med ascent

angle

TS13

G7 143 15.3 0.24 ± 0.10 -10.90 ±

1.56

10.04 ±

1.69

Low-med vertical, med descent


TS15

G8 120 12.9 0.80 ± 0.06 -12.55 ±

2.37

11.18 ± 2.1 High vertical, med-high descent

angle, med-high ascent angle

TS27, TS13

G9 9 0.9 0.26 ± 0.12 -13.57 ±

1.40

4.87 ± 1.04 Low-med vertical, med descent

angle, low ascent angle

TS15

G10 7 0.8 0.35 ± 0.08 -27.32 ±

4.13

19.25 ±

1.77

Med vertical, high-very high

descent angle, high ascent angle

TS9

G11 1 0.1 0.82 -19.57 22.23 High vertical, high descent angle,

very high ascent angle

TS13

G12 3 0.3 0.18 ± 0.24 -5.80 15.31 ±

1.24

Low-med vertical, low descent

angle, med-high ascent angle

TS15, TS27

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Table S 4.3. Summarised characteristics of each T-group derived from the hierarchical cluster analysis.

Values are mean ± standard deviation.

Cluster

ID

No. % 𝑹 Turning Description Example

1 351 37.6 0.93 ±

0.04

1489 ±

501 High 𝑅, low turning TS14, TS15, TS18

2 108 11.6 0.80 ±

0.08

4273 ±

820 Med-high 𝑅, med

turning

TS13, TS14, TS15

3 218 23.4 0.70 ±

0.1

2611 ±

987 Med-high 𝑅, low-med

turning

TS15, TS24, TS8

4 73 7.8 0.64 ±

0.10

7623 ±

1544 Med-high 𝑅, med-

high turning

TS13, TS19

5 93 10 0.33 ±

0.13

3603 ±

1193 Low-med 𝑅, low-med

turning

TS15, TS24

6 78 8.4 0.34 ±

0.13

6086 ±

1269 Low-med 𝑅, med-

high turning

TS13, TS15, TS5

7 12 1.3 0.35 ±

0.16

13031 ±

1431 Low-med 𝑅, high

turning

TS20

Table S 4.4. All predator-prey interactions recorded on CATS video tags

Shark

ID

Prey Shark

behaviour

Depth &

vertical phase

Habitat Observational notes

TS1 Turtle Investigate 14 m – level Sandy seabed Bad vis, veers towards it then

away

TS1 Trevally – F.

Carangidae

Investigate 13 m – level Sandy seabed Turn right towards it quickly

TS1 Chinamanfish -

Symphorus

nematophorus

Investigate 4 m – level Sandy seabed Slows down, stalks then veers

away

TS1 Tuskfish –

Choerodon sp.

Investigate 4.8 m – level Sandy and

weedy seabed

Slows down, investigates and

moves on

TS1 Spangled emperor

- Lethrinus

nebulosus

Investigate 4 m – level Sandy seabed Increases speed and turns left

towards fish, swims past

TS1 Chinamanfish -

Symphorus

nematophorus

Investigate 4 m – level Sandy with

weed clumps

Turn left, burst, slows down,

inspects fish and swims by

TS1 Chinamanfish -

Symphorus

nematophorus

Investigate 4 m – level Sandy seabed Turn left, speed up and inspect

from distance

TS1 Batfish - Platax

teira

Investigate 3 m – level Sandy seabed Burst, slowdown, slight turn

right as investigates

Tortuosity

105

TS1 Sweetlips -

Plectorhinchus

sp.

Investigate 3 m – level Sandy seabed Slows down and turns lightly left

to inspect. Continues swimming.

TS2 Unidentified fish

sp.

Investigate 15 m – Level Sandy seabed Turns slowly towards then

swims by

TS4 Chinamanfish -

Symphorus

nematophorus

No reaction 14 m – Level Sand and weed

clumps

Fish swims from right to left in

front of shark

TS4 Queenfish –

Scomberoides sp.

Slight

investigation?

9 m –

Descending

Sand and weed

clumps

May turn towards fish, fish

swims from left to right in front

of shark

TS4 Dead fish head Consume 8 m – Level Sandy seabed Picks head off ground and chews

down

TS4 Chinamanfish -

Symphorus

nematophorus

Investigate 9.5 m - Level Sandy seabed Slows down and turns slightly

towards as fish swims past

TS9 Turtle Investigate Level – 1.5 m Weedy reef Shark about to swim over turtle,

turtle quickly flees and shark

moves away from turtle

TS9 Turtle Investigate Level – 1.8 m Weedy reef Swims straight for turtle, turtle

spins around so shell up, shark

continues on

TS9 Tawny nurse

shark - Nebrius

ferrugineus

Investigate Level – 1.3 m Weedy reef Swims behind then alongside

tawny before turning away

TS9 Turtle No reaction Level – 0.5 m Weedy reef Turtle lifts head, shark turns

slightly away

TS9 Turtle No reaction Level – 0.7 m Weedy reef Turtle not moving, shell up

towards shark

TS9 Turtle No reaction Level – 6 m Weedy reef Turtle facing shark and remains

vigilant

TS9 Turtle Investigate Level – 8 m Rubble reef Turtle swims shell up on angle

from left to right in front of

shark. Shark turns away from

turtle

TS9 Turtle No reaction Descending –

5 m

Weedy reef Turtle swimming shell up on

right side of shark

TS9 Turtle Investigate Level – 7 m Weedy reef Turtle resting on reef, facing

away from shark

TS9 Turtle Investigate Level – 8 m Weedy reef Turtle swimming mid-water

from right to left in front of

shark. Tightens angle of turn

with shell up as shark gets closer

106

TS9 Turtle No reaction Level – 9 m Weedy reef Turtle resting on reef. Buries self

in weed/turns shell on higher

angle towards shark.

TS14 Turtle Investigate 1 m – Level Sandy seabed Shark swimming fast and turning

lots until turtle observed. Turtle

shell up and turning tightly.

TS15 Turtle Investigate 12.5 m –Level Rubble reef Approaches turtle slowly. Turtle

rests on reef and then slowly

reverses, whilst remaining

vigilant. Shark may bump into

turtle.

TS15 Turtle No reaction 12.5 – Level Coral reef and

sand

Ahead on shark’s right, shark

doesn’t appear to see turtle, and

suddenly bursts left

TS15 Turtle Investigate 14 m – Level Coral reef and

sand

Likely to be same turtle as (2).

Big right turn back to where

turtle was, turtle mid-water

moving from shark’s right-left.

Shell up and tightly turning.

TS15 Tiger shark Investigate 5.5 m – Level Coral reef Tiger shark swimming in front of

this tiger shark. Appears to

follow it for a little while.

TS15 Turtle No reaction 4.7 m – Level Coral reef On reef slope to shark’s right

(other tiger shark in front of

shark to left). Unsure if observed

by TS.

TS24 Turtle May

investigate

1.7 m – Level Sandy Turtle swimming slowly to

shark’s left. TS may or may not

inspect it.

TS27 Chinamanfish -

Symphorus

nematophorus

Investigates 2.8 m –

Descending

Sandy Shark descends and turns

towards fish, slows and

approaches from behind. Slight

turn towards fish then continues

on.

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Chapter 5 Depth dependent dive kinematics

suggest cost-efficient foraging

strategies by tiger sharks

5.1 Abstract

Tiger sharks (Galeocerdo cuvier) are a keystone, top-order predator that are assumed to engage

in cost-efficient movement and foraging patterns. To investigate the extent to which patterns of

oscillatory diving by these animals conform to a strategy of cost-efficient foraging, we used a

biologging approach to model their cost of transport. High-resolution biologging tags were

deployed on 21 tiger sharks at Ningaloo Reef for durations of 5-48 hours. Tags recorded animal

depth, and through the use of accelerometers, in situ measurements of animal trajectory and

locomotion, which enabled calculation of dive geometry and swimming energetics. Using overall

dynamic body acceleration (ODBA) as a proxy for energy expenditure, we modelled the cost of

transport of oscillatory movements of varying geometries in both horizontal and vertical planes

for tiger sharks. The cost of horizontal transport was minimized at absolute dive angles greater

than 0°, so that oscillations conserved energy relative to swimming at a level depth. Optimization

of vertical travel occurred at steeper angles. The absolute dive angles of tiger sharks increased

between inshore and offshore zones, presumably to reduce energy costs while continuously

hunting for prey in both benthic and surface habitats. On shallow (<3 m depth) sandflats at

Ningaloo Reef, tiger sharks were able to hunt continuously while exploiting the most cost-

efficient dive angles. In deeper environments, steeper dive angles were found to partially

compensate for the added energy costs of transiting a longer distance through the water column

between the surface and the seafloor. Oscillatory movements of tiger sharks conform to strategies

of cost-efficient foraging, and shallow sandflats appear to be an important habitat for both hunting

prey and saving energy.

5.2 Introduction

The tiger shark is a top-order predator that has wide-ranging impacts on the ecology of the tropical

and warm-temperate marine ecosystems they inhabit (Heithaus et al. 2002b; Heithaus et al. 2007).

These sharks are generalist feeders with anatomical specializations for feeding on large prey

(Lowe et al. 1996; Heithaus 2001; Simpfendorfer et al. 2001; Hammerschlag et al. 2016; Ferreira

et al. 2017). Although they actively hunt prey, they are also facultative scavengers

(Hammerschlag et al. 2016), which avoids or reduces many of the energetic costs involved in the

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process of predation (Clua et al. 2013; Hammerschlag et al. 2016). However, injured or dead prey

are windfalls that only occur sporadically and for the most part, tiger sharks must actively locate,

hunt and capture their food. Like all top-order predators, they are assumed to do so in a cost-

efficient manner (Ruxton and Houston 2004).

Tagging studies have revealed that the fine-scale movements of tiger sharks are characterised by

patterns of oscillatory ascents and descents (“dives”) throughout the water column (Heithaus et

al. 2002b; Nakamura et al. 2011; Chapter 4), a behaviour that is shared by many species of large

epipelagic fishes (see Chapter 2). For tiger sharks, these dives have been recorded in a number of

habitats, ranging from very shallow (<4 m) seagrass beds in Shark Bay (Heithaus et al. 2002b),

to deeper (>100 m), offshore waters in Hawaii and Brazil (Holland et al. 1999; Nakamura et al.

2011; Afonso and Hazin 2014). It has been argued that these oscillatory movements are not likely

to be primarily driven by behavioural thermoregulation, because they often occur in well-mixed

and/or shallow environments (Heithaus et al. 2002b; Nakamura et al. 2011) where there is little

gradient in temperature between the surface and the seafloor. Instead, previous studies have

suggested that oscillatory diving provides an effective search strategy for an animal that feeds on

prey both at the surface and near the seabed (Heithaus et al. 2002b; Nakamura et al. 2011). More

recently, tiger sharks tagged at Ningaloo Reef have been found to make oscillatory dives during

the tortuous movements associated with area-restricted search (see Chapter 4). These oscillatory

dives also occurred during directional swimming, suggesting that there may be drivers other than

foraging contributing to these patterns.

In addition to allowing sharks to search for prey, oscillatory dives may represent a strategy to

reduce the cost of transport. Weihs (1973) proposed that a two-stage mode of swimming may

allow negatively-buoyant animals to reduce energy expenditure relative to swimming at a level

depth between two horizontal points, whereby individuals oscillate through the water column,

gliding on descent and actively swimming on ascent. Recent advances in the use of tri-axial

sensors in biologging tags provide a means to test this hypothesis in a natural setting. Tri-axial

accelerometers can be used to calculate dive angles in addition to overall dynamic body

acceleration (ODBA), a parameter that can act as a proxy for energy consumption (Gleiss et al.

2011a), and can therefore be used to quantify and compare the energy costs of different movement

behaviours. Gleiss et al. (2011a) used a biologging approach to explore how differences in the

dive geometry in whale sharks (Rhincodon typus) influenced the cost of transport with respect to

both horizontal and vertical distance, using dynamic body acceleration as a proxy for power.

Empirical optimality models suggested that some dive profiles reduced the cost of horizontal

transport, while other steeper-angled dives minimized the cost of vertical transport; indicating

that the choice of dive undertaken by an individual may be dependent on the ecological context

i.e. travel, search or foraging (Gleiss et al. 2013). Collectively, whale sharks were found to use

oscillatory movements to conserve energy while both travelling and searching for prey,

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culminating in the observed patterns of cost-efficient foraging.

As tiger sharks are negatively buoyant (Nakamura et al. 2011), we hypothesise that they may also

exploit their weight in water and use oscillatory movements to reduce the cost of transport while

travelling or searching for prey. However, given that dead prey will sink to the bottom whereas

active prey may be most easily ambushed at the surface where they can be approached from below

(Martin and Hammerschlag 2012) movement strategies may be habitat dependent. Tiger sharks

tagged at Ningaloo Reef were found to undertake oscillatory movements in both inshore and

offshore habitats (see Chapter 4). In very shallow habitats (<3 m), tiger sharks would have no

need to undergo vertical search patterns given the short distance between the surface of the water

and the seabed. In deeper habitats, however, individuals would need to actively transit through a

larger water column between the surface and seabed in their search for prey. We therefore predict

that the diving energetics and kinematics of these animals will be dependent on the depth of the

habitat being occupied, with deeper waters featuring steeper dive angles to allow for faster transit

times.

Here, we use a biologging approach to examine the energetics of oscillatory diving behaviour in

tiger sharks at Ningaloo Reef, Western Australia. We examine the incidence of gliding behaviour

in tiger sharks and model the cost of transport of oscillatory movements of varying movement

geometries using ODBA as a proxy for energy expenditure. We document the range of dive angles

utilised by tagged sharks, particularly in relation to habitat depth, discuss the role of oscillatory

movements in both efficient foraging and energy conservation, and re-evaluate the drivers of

oscillatory diving in this species.

5.3 Methods

5.3.1 Ethics

All methods were used in accordance with approved guidelines by the University of Western

Australia Animal Ethics Committee (RA/3/100/1437), and under permit numbers 2881 (WA

Department of Fisheries) and 08-000322-3 (WA Department of Parks and Wildlife).


Tiger sharks were captured and tagged at Ningaloo Reef, Western Australia (Figure 6.1) in April

and May 2017 following the methods described in Chapter 5. In brief, biologging tags were

clamped to the dorsal fins of 22 captured tiger sharks for periods of 7-48 hours (see Table 4-2).

Tags were equipped with tri-axial accelerometers, magnetometers and gyroscopes, and sensors

for depth, temperature and light. All sensors recorded continuously at 20 Hz. In addition, 14 of

the 22 deployments recorded video at pre-programmed hours of the day for a maximum of six

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hours per deployment. The tags detached from the clamp in the days following tagging, and were

recovered using a handheld VHF receiver operated from a vessel.

Figure 5.1. Location of tagging and tag recoveries. Note that eight tags are outside of the map area – two

approximately 10 km further north, and six offshore (ranging from 15-27 km offshore).

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5.3.3 Data processing

Data recorded by the tags were processed to obtain a number of parameters classifying shark

movements, behaviour and the external environment. Vertical phases of movement, dive angles,

overall dynamic body acceleration (ODBA), and tailbeat kinematics were calculated as described

in Chapter 4. In brief, the depth record was split into vertical phases of ascent, descent and

constant depth. Tri-axial sensor data were then used to calculate the pitch angles of vertical

movements, ODBA, and the signal amplitude and frequency of tailbeats in Igor Pro ver. 7.0.4.1

(Wavemetrics, Inc. Lake Oswego, USA). Tailbeat data were used to calculate the recovery period

from capture following methods outlined by Whitney et al. (2016b). Video data were also

processed as per Chapter 4, and were used to record interactions with prey, determine habitat

types, and validate behaviours recorded by the sensors (e.g. tailbeats and gliding behaviour). In

addition, sensor data were used to compute gliding behaviour, and ascent and descent speed, as

described below.

5.3.3.1 Gliding behaviour

We used a continuous wavelet transformation on the dynamic component of the sway (i.e. lateral)

axis to calculate the signal amplitude and frequency of shark tailbeats using both the acceleration

and angular velocity data (Sakamoto et al. 2009). The resulting signals were compared to

determine the best measure of tailbeat activity. The angular velocity data produced the clearest

tailbeat signal, and were consequently used to quantify the incidence of gliding behaviour –

defined here as a cessation of tailbeats for more than one second – through a two-step process.

Gliding behaviour was firstly computed using the ‘k-means cluster’ function in the Ethographer

for Igor Pro (Sakamoto et al. 2009). This function clusters the spectra computed by the wavelet

transformation based on similarity of shape. The behavioural spectrum with the lowest peaks in

angular velocity signal amplitude was assumed to represent gliding behaviour (Nakamura et al.

2011), and the incidence of the resulting cluster was then inspected against the dynamic sway

data. As this cluster did not match with gliding behaviour in some individuals (i.e. tailbeats

evident in sway data were classified to be gliding, and vice versa), a mask was created using the

characteristics of the angular velocity signal amplitude and tailbeat frequency where the cluster

was judged to correctly classify gliding behaviour (from visual inspection of the dynamic sway

data and concurrent videos). This mask was then used to extract glides from all sharks. These

were confirmed by a visual scan of the data.

5.3.3.2 Ascent and descent speed

Vertical velocity (VV) and pitch were used to estimate the mean speed of ascents and descents

through trigonometry, however, this could only be calculated when pitch exceeded 20° due to

the large errors associated with estimating speed at low pitch angles (Gleiss et al. 2011b).

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The sampling window used for analysis was determined in Chapter 4 by calculating the time

period for which the highest variance in turning angles was observed, while being of sufficient

size to capture the longest recorded dives in their entirety. This time window was estimated to be

15 minutes (900 seconds), and within this period, a number of vertical movement parameters were

summarized including mean (± standard deviation) and maximum depth, ascent pitch, descent

pitch, ascent VV and descent VV. The percent of time spent moving vertically (ascending and

descending), termed the ‘diving ratio’, was also calculated within each window for each

individual as per:

𝐷𝑖𝑣𝑖𝑛𝑔 𝑟𝑎𝑡𝑖𝑜 = 𝑇𝑖𝑚𝑒 𝑣𝑒𝑟𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑚𝑜𝑣𝑖𝑛𝑔 𝑖𝑛 𝑤𝑖𝑛𝑑𝑜𝑤 (𝑠𝑒𝑐𝑜𝑛𝑑𝑠)

𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑖𝑛 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑤𝑖𝑛𝑑𝑜𝑤 (900 𝑠𝑒𝑐𝑜𝑛𝑑𝑠)

5.3.4 Data analysis

5.3.4.1 Generalised linear mixed models (GLMMs)

Generalised linear mixed models (GLMMs) were built using the nlme package in R 3.4.0

(Pinheiro et al. 2017; R Core Team 2017) to investigate possible relationships between seabed

depth and vertical movement behaviours in tiger sharks. The maximum depth (m) recorded within

each time window was used as a proxy for seabed depth (based on video analysis; see Chapter 4

Results and Figure S 4-2) and was set as the explanatory variable, and tiger shark identity set as

a random variable for all models. Ascent pitch, descent pitch, ascent VV, descent VV and diving

ratio were all set sequentially as response variables. We used the corAR1 function to account for

temporal auto-correlation in our datasets (Zuur et al. 2009). Together with nautical charts from

Ningaloo Reef, maximum depth was used to classify windows as either ‘inshore’ (<25 m, inside

the reef) or ‘offshore’ (>25 m, outside the reef). GLMMs were analysed separately for inshore

and offshore periods due to heterogeneity in residuals and an unbalanced design. The resulting

models were compared against the null models and ranked using Akaike’s information criterion

(AIC).

To investigate if observed changes in diving ratio with depth were an artifact of our selected

sampling window, we calculated diving ratio for oscillations occurring in increasingly deeper

water given a fixed interval of level swimming at the surface and seabed. One-hour long depth

traces were simulated for a hypothetical shark oscillating in depths of 5, 10, 20, 30, 40, 50 and 60

m. The ascent VV and descent VV for each depth zone were determined following relationships

calculated between VV and seabed depth (see above). The fixed interval spent at the surface and

on the seabed between vertical movements was set at two minutes following exploration of the

depth traces. Diving ratio was calculated for each of the four 15 minute windows, and averaged

for the hour so that one value of diving ratio was calculated for each depth.

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5.3.4.2 Cost of transport models

We modelled the cost of transport of oscillatory movements of varying geometries in relation to

optimization of either horizontal or vertical distance travelled following methods similar to those

described by Gleiss et al. (2011a) (Figure 5.2). Firstly we calculated the total cost of an oscillation

(ascent (a) and descent (d) combined; TC) based on pitch angle (φ) and time taken (T) using the

equation:

TC = Ta(φa) × ODBA (φa) + Td (φd) × ODBA (φd) + k × (Td (φd) + Ta (φa))

ODBA was used to calculate energetic costs of a given dive angle. For ascents, this was calculated

from the relationship between ODBA and ascent angle (Figure 5.3a), and for descents a single

mean value (0.012) was used as no relationship was found between descent angle and ODBA

(Figure 5.3b). The proxy for basal cost in our model (k) was estimated from published figures to

be approximately 0.6 × routine metabolic rate, as estimated from a range of species of shark

irrespective of size (see Dowd et al. 2006 and references therein). As mean ODBA recorded for

all sharks was ~0.026 g we estimated k at 0.0156 g. The time spent ascending (Ta) and descending

(Td) are a function of ascent and descent pitch respectively, and the mean speed:

Ta =

𝐷𝑒𝑝𝑡ℎsin (φa)

mean ascent speed

Td =

𝐷𝑒𝑝𝑡ℎsin (φd)

mean descent speed

We used a fixed mean estimate of speed for both ascents (0.87 m/s) and descents (0.85 m/s) as no

relationship was found between speed and pitch angle. TC was calculated for fixed ascent angles

from 5° to 45°, binned in 5° increments. For each ascent angle, TC was calculated sequentially

for decent angles of 5° to 20°, binned in 5° increments.

We constructed two different models based on the cost of transport for tiger sharks when moving

horizontally (COTHD) and vertically (COTVD). These models calculated the cost of moving a unit

of horizontal (HD) and vertical distance (VD) respectively, and were used to determine the angles

that optimized the efficiency of transport on each of these scales for this species. The COTHD was

modelled by:

𝐶𝑂𝑇𝐻𝐷

= 𝑇𝐶

𝐻𝐷

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Where horizontal distance was calculated as a function of trigonometry from ascent and descent

pitch using the equation:

𝐻𝐷 = 𝐷𝑒𝑝𝑡ℎ

tan(𝜑𝑑) +

𝐷𝑒𝑝𝑡ℎ

tan(𝜑𝑎)

The COTVD was modelled by:

𝐶𝑂𝑇𝑉𝐷

= 𝑇𝐶

2 × 𝐷𝑒𝑝𝑡ℎ

All model calculations used oscillations of 10 m depth, however, the resulting COT for horizontal

and vertical distance was the same regardless of depth.

Figure 5.2. Schematic representation of an oscillatory movement, and the distance parameters used in

calculating the cost of transport.

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Figure 5.3. Mean instantaneous ODBA for all 21 tiger sharks at a range of absolute (a) ascent and (b)

descent angles (placed in 5° bins). All sharks displayed a positive slope for ODBA ~ ascent angle. No

consistent relationship was found between ODBA and descent angle. Unique symbols and/or colours

represent individual tiger sharks.

5.4 Results

5.4.1 Track summary

Tag data was recovered from a total of 21 tiger sharks (ranging 2.65 – 3.8 m total length) tagged

at Ningaloo Reef during this study (see Table 4-2 for full summary of tag deployments). We

assumed a recovery period from capture and tagging by the sharks of four hours based on an

analysis of tailbeats, and for this reason the first four hours of each dataset were excluded from

further analysis (see Chapter 4, Figure S 4-1). Evidence for recovery after four hours was also

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provided by the video records, which showed investigation of prey and consumption of a

discarded fish head by sharks within two hours of release after tagging (Supplementary Video 1).

Tagged tiger sharks swam at a mean depth of 11.6 ± 17.5 m, predominately residing in inshore

habitats, with four tiger sharks moving into offshore habitats and one individual diving to a

maximum depth of 94 m. Sharks moved vertically for a mean of 38.4 ± 26% of their track and

utilised significantly steeper descent (-11.11 ± 3.6o) than ascent (9.38 ± 2.6o) angles (Wilcoxon

signed-ranks test, P < 0.001).

5.4.2 Gliding behaviour

There was a high level of individual variation in the proportion of gliding descents by tiger sharks

(range 0-35%, Figure 5.4). Six sharks glided for more than 19% of their total descent time, of

which three glided for more than 30% of their descents. A maximum uninterrupted glide time of

two minutes was recorded for one individual. Gliding was rarely recorded in the first four hours

of each tag deployment (designated as the recovery period) and no gliding behaviour was recorded

for two sharks (Figure 5.4). Gliding was exhibited by tiger sharks in both inshore and offshore

zones and occurred at a minimum descent angle of -8° and at a mean angle of -12.2 ± 2.7°.

5.4.3 Relationship between seabed depth and vertical

movement behaviours

GLMMs revealed significant relationships between seabed depth and vertical movements of tiger

sharks (Table 5-1). For inshore GLMMs, all models retained their predictor variables of vertical

movement, whereas for offshore models, only the random effect of tiger shark identity was

retained. Up to depths of 25 m (inshore) diving ratio, pitch and VV all increased with seabed

depth, after which point relationships plateaued and displayed high levels of variation (Figure S

5-1). Inshore, maximum/seabed depth explained 35% (marginal R2) of the variation in both diving

ratio and descent pitch. For all models, high conditional R2 values (up to 55%) suggested high

inter-individual variability in vertical movements (Table 5-1). Mean absolute pitch angles

increased between inshore and offshore habitats from 10.3 ± 2.8° to 14.1 ± 4.7° for descent, and

9 ± 2.3 to 10.7 ± 3.2° for ascent (Figure 5.5).

Between inshore and offshore habitats, the mean diving ratio increased from 31 ± 23% to 69 ±

17% (Figure 5.6a). From simulated depth data generated using VV from GLMM relationships

and fixed intervals of level swimming between vertical movements, diving ratios were found to

exhibit a similar pattern to that observed in the raw data (Figure 5.6). Despite VV also increasing

with depth, diving ratio increased quickly between 5 and 20 m depths, after which the slope

between subsequent points rapidly decreased.

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Figure 5.4. Gliding in tiger sharks over the course of A) an entire track (10 hours); B) 45 minutes; and C)

6 minutes. Glides are marked in red on the x axis. Dynamic angular velocity was taken from the lateral

(sway) axes recorded by the gyroscope. D) The proportion of descent time spent gliding by each individual

tiger shark.

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Table 5-1. Results of generalised linear mixed models testing the relationship between seabed depth

(MaxD) and vertical movement behaviours. All models were compared with null models using Akaike’s

Information Criterion (AIC) and conditional (R2c) and marginal (R2m) R2 values. All models were run

using the nlme package in R with shark identity as a random variable. All null models include the random

effect. Inshore indicates windows where maximum depth was <25 m, and offshore where maximum depth

was >25 m.

Inshore Model DF AIC R2m R

2c

Diving Ratio ~Maximum Depth 629 -831 0.35 0.55

Diving Ratio ~ 1 629 -629 0 0.42

Descent Pitch ~Maximum Depth 629 2724 0.35 0.48

Descent Pitch ~ 1 629 2903 0 0.31

Ascent Pitch ~Maximum Depth 629 2672 0.18 0.24

Ascent Pitch ~ 1 629 2743 0 0.14

Descent Vertical Velocity ~Maximum Depth 629 -2695 0.26 0.53

Descent Vertical Velocity ~ 1 629 -2555 0 0.30

Ascent Vertical Velocity ~Maximum Depth 629 2748 0.2 0.36

Ascent Vertical Velocity ~ 1 629 2668 0 0.18

Offshore Model DF AIC R2m R2c

Diving Ratio ~Maximum Depth 173 -165 0.09 0.39

Diving Ratio ~ 1 173 -168 0 0.37

Descent Pitch ~Maximum Depth 173 1062 0.002 0.26

Descent Pitch ~ 1 173 1054 0 0.27

Ascent Pitch ~Maximum Depth 173 919 0.007 0.15

Ascent Pitch ~ 1 173 912 0 0.11

Descent Vertical Velocity ~Maximum Depth 173 -348 0.03 0.26

Descent Vertical Velocity ~ 1 173 -360 0 0.35

Ascent Vertical Velocity ~Maximum Depth 173 -466 0.004 0.21

Ascent Vertical Velocity ~ 1 173 -482 0 0.23

5.4.4 Cost of transport models

The mechanical cost of ascending was greater than that for descending (one-way ANOVA,

F1,240002 = 1635, p < 0.0001). The cost of level swimming by tiger sharks could not be calculated

as the effects of wave action at the surface frequently created superficially high ODBA levels that

could not be removed reliably from the data. These effects of surface waves were confirmed by

the video data.

For all sharks, a positive quadratic relationship was found between mean binned ascent angle and

ODBA (Figure 5.3a, r2 = 0.96). This relationship had positive slopes for all individual sharks with

a mean r2 of 0.8 ± 0.23 (range 0.12-0.99). No consistent relationship was found between mean

binned descent angle and ODBA (Figure 5.3b). We regressed swimming speed and body pitch

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where pitch was greater than 20° for each individual shark, and did not detect quantifiable changes

in speed with pitch angle (Table S 5.1). Where the relationship was found to be significant, slope

did not exceed 0.02, and we therefore concluded that increased locomotory activity at increasing

pitch was due to counteracting gravity, rather than increasing drag due to greater speed (Gleiss et

al. 2011b).

Figure 5.5. Relationship between dive angle and habitat depth. (a & b) Boxplots displaying differences in

ascent and descent pitch angle between habitat zones. Inshore denotes areas where maximum depth within

a window was <25 m, and offshore where depths were >25 m. Shark silhouettes angles are not to scale. (c

& d) Relationship between maximum depth in a sampling window and descent and ascent angle in inshore

zones (<25 m).

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Figure 5.6. (a) Relationship between seabed depth (m) and diving ratio measured in 15 minute windows.

Maximum depth is a proxy for seabed depth. The red line displays diving ratio calculated if time level

swimming at the surface and seabed for oscillations remained the same at all maximum depths. (b, c)

Schematic diagrams representing how diving ratio may differ as a result of seabed depth and sampling

window. Depth traces have the same vertical velocity, and same time spent level swimming at the surface

and seabed in between vertical movements. (c) Represents a habitat of twice the depth of habitat (b). Red

dashed square represents the fixed time window within which diving ratio is measured.

We used the relationship between ascent pitch angle and ODBA to calculate ODBA as a proxy

for mechanical cost. This was done at 5° intervals for COT models, and a single mean value of

ODBA was used for descents. As gliding behaviour was highly variable and we found no evidence

of accelerating VV towards the maximum depth of dives, we did not calculate the terminal

velocity as per Gleiss et al. (2011). The total cost of an oscillation of given ascent and descent

angles was then standardized by the corresponding vertical and horizontal distances travelled in

order to calculate the cost of transport on each respective plane. We found that the cost of travel

was optimized on both horizontal and vertical planes at absolute ascent and descent angles greater

than 0° (Figure 5.7a). The cost of transport for tiger sharks in the horizontal plane was minimized

by descending at the lowest possible angle, and ascending at an angle of 5-14°; decreasing with a

shallower descent angle. The cost of transport in the vertical plane was optimized at steeper ascent

angles of 35°, and decreased with steeper descent angles, however, the potential energy savings

at absolute ascent and descent angles steeper than 20° and 15° respectively were minimal (Figure

5.7b).

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5.5 Discussion

Our study provides evidence that oscillatory movements through the water column allowed tiger

sharks to conserve energy while travelling and searching for prey and resulted in a strategy of

cost-efficient foraging. Our empirical models of the cost of transport showed that, while in transit,

tiger sharks required less energy to move in oscillatory dives at shallow angles through the water

column than they did while swimming at a constant depth. Shallow angles of ascent and descent

allowed tiger sharks to reduce their COT in the horizontal plane, whereas steeper angles reduced

their energetic costs in the vertical plane. The absolute dive angles of tiger sharks increased

between inshore and offshore zones, presumably to reduce energy costs while continuously

hunting for prey in both benthic and surface habitats.

5.5.1 Cost of vertical movements

Patterns of gliding varied among tagged sharks. Gliding occurred through 0-35% of the overall

descent phase of oscillations for individuals at Ningaloo, a finding comparable to that of

Nakamura et al. (2011). Although the majority of sharks displayed increasing levels of gliding

behaviour post-release, as previously observed in black-tip sharks (Carcharhinus limbatus;

Whitney et al. 2016b), some sharks – including an individual tagged for >15 hours – recorded no

gliding behaviour throughout tag deployment. This variability may be driven by minor individual

differences in body composition (Adachi et al. 2014; Gleiss et al. 2017a), and/or behavioural

differences in their response to catch and release. At all spatial scales movements of tiger sharks

have been characterized by high levels of individual variation (Vaudo et al. 2014; Ferreira et al.

Figure 5.7. The cost of transport of a single oscillatory movement of a tiger shark on (a) a horizontal plane

and (b) a vertical plane. Arrows indicate where the cost of transport is minimised in both cases. Lines

represent the cost of a given descent pitch angle (label on figure) in relation to varying ascent pitch (x-axis).

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2015).

The energetic costs involved in descents were significantly lower than those of ascent in tiger

sharks, despite the majority of the descent phases of oscillations involving active swimming. This

result is a factor of the negative buoyancy of these animals (Nakamura et al. 2011), as it would

enable their descent phase to be partially powered by gravity. When ascents and descents were

considered together, the cost of horizontal transport of this two-stage mode of swimming was

reduced at absolute angles greater than 0°, allowing tagged individuals to conserve energy relative

to the cost of swimming an equivalent horizontal distance at a level depth. The modelled cost of

horizontal transport for tiger sharks was minimized at a descent angle of -5° and ascent angle of

14°. This closely matches modelled dive angles of whale sharks, where horizontal transport was

optimized at -10° and 11° for descents and ascents, respectively (Gleiss et al. 2011b). For whale

sharks, costs were minimized at the smallest angle at which whale sharks could glide. Tiger sharks

displayed a minimal glide angle of -8°, and therefore we suspect that steeper optimal descent

angles may exist for individuals that display a greater degree of gliding behaviour. Optimization

of vertical travel for tagged tiger sharks also exhibited a similar patterns to whale sharks, whereby

steeper angles on ascent than descent reduced the cost of vertical transport. Ascent angles of 35°

were found to reduce the cost of vertical transport for tiger sharks at Ningaloo – a marked increase

from the 23° reported for whale sharks – although energy savings beyond 20° appear to be

minimal for tiger sharks (Gleiss et al. 2011b). The costs of vertical transport decreased with

increasingly steep descent angles, but again, energy savings beyond -15° appeared to be minimal.

5.5.2 Cost-efficient foraging

Oscillatory movements of tiger sharks have previously been thought to be a strategy to allow them

to alternate between searching the surface and seabed for prey (Heithaus et al. 2002b; Nakamura

et al. 2011). Heithaus et al. (2002b) found that tiger sharks often spend a short period of time

swimming at the surface before descending to swim along the seabed. After another short period

they ascend, presumably searching for prey at the surface during the time they reside there. These

patterns were consistent with those observed in our study. The diving ratio of tiger sharks tagged

at Ningaloo Reef increased as seabed depth increased i.e. sharks spent more time within a

sampling window ascending and descending relative to level swimming in deeper water. Upon

further examination of our data, however, we believed this pattern to be an artefact of our 15

minute sampling window. Larger distances needed to be traversed between the surface and seabed

in deeper water, meaning a greater proportion of time is spent vertically moving in these areas.

We therefore hypothesise that tiger sharks were in fact spending a similar length of time searching

the surface and benthos for prey in between oscillations, regardless of water depth (Figure 5.6).

In addition, ascent angles were shallower than descent angles and remained relatively constrained

between habitats. This may be a function of retinal specialization in the visual system of tiger

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sharks (Bozzano and Collin 2000). Tiger sharks are able to observe their upper visual field in

greater detail (Bozzano and Collin 2000), and their angle of ascent may optimize the field of view

for searching for unsuspecting prey silhouetted above them.

In the shallowest, inshore waters (< 3 m), tiger sharks would be able to continuously hunt as either

the seabed or the surface would always remain within their field of view. Oscillatory behaviours,

however, continued in these habitats at very shallow dive angles, consistent with movements that

reduced the horizontal cost of transport. In addition, these movements occurred throughout both

directional travel and tortuous movements assumed to be associated with foraging behaviour

(Chapter 4), suggesting that foraging behaviour alone was not primarily driving these movements.

As tiger sharks moved into deeper waters, they would need to transit through a deeper water

column between surface and benthic habitats in their hunt for prey or to scavenge, reducing the

amount of time spent foraging and increasing the energy costs of this activity. Here, we found

that the dive angles used by tiger sharks at Ningaloo increased as these animals moved into deeper

waters, allowing them to compensate for the increased cost of hunting through a larger water

column. Collectively, vertical movements of the tagged tiger sharks culminated in patterns of

cost-efficient foraging.

5.5.3 Model limitations

The cost of transport models constructed here from tiger shark acceleration data incorporated

some parameters that do not allow for the accurate measurement of movement costs in this

species. Despite this, we believe that the overall model is relatively insensitive to errors, allowing

us to test our hypotheses regarding the cost-efficiency of tiger shark movements against the

qualitative predictions produced by the models. Errors are likely to occur in the estimate of basal

cost, as this has not specifically been calculated for tiger sharks, and in the estimates for speed,

as this was only calculated where absolute pitch angles exceeded 20°. Gleiss et al. (2011a) tested

the sensitivity of cost of transport models to predicting qualitative trends for whale sharks and

found that (1) both doubling and halving of basal cost had no appreciable change on optimal

ascent angle, and (2) variations in speed of 0.2 m/s either side of the mean descent speed only

changed optimal ascent angle by 2°. In addition, our speed estimates (0.85 m/s for descent and

0.87 m/s for ascent) fit into the range of those directly quantified by Nakamura et al. (2011)

(overall mean speed 0.54-0.92 m/s among individuals, 0.53-0.82 m/s when gliding). Therefore,

we believe model results accurately reflect the trends in dive pitch angles for optimizing the cost

of vertical and horizontal transport.

5.5.4 Seascape effects on energy expenditure and gain

Our results suggest that very shallow waters (<3 m) are the most cost-efficient place for tiger

sharks to forage, and may therefore represent an important energy landscape for these animals.

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Variation in an energy landscape occurs where the cost of transport is influenced by physical

characteristics of the environment (Wilson et al. 2012). This results in animals modifying

movements among habitats to either exploit opportunities for energy gain, and/or reduce costs

(Shepard et al. 2013). Establishing the variability in movement costs across landscapes can help

us to understand how and why animals distribute themselves in space (Wilson et al. 2012). For

tiger sharks, alternating between the seabed and surface in the search for prey means that the

energetic costs of foraging increase with depth. Very shallow environments (<3 m), such as the

sandflats of Ningaloo Reef, represent a landscape where tiger sharks can move and forage at the

most cost-efficient dive angles. In addition, pseudo-tracks, video data and observations from the

ecotourism industry have highlighted the importance of the shallow sandflat environments for

tiger sharks at Ningaloo Reef (see Chapter 4). In these habitats, tiger sharks often displayed the

tortuous movement patterns thought to denote foraging behaviour (Kareiva and Odell 1987;

Adachi et al. 2017), and predator-prey interactions have frequently been observed here. Tiger

sharks tagged in other regions have also been found to display a preference for shallow habitats,

including seagrass habitats less than four meters in depth in Shark Bay, and lagoon habitats in

Hawaii (Heithaus et al. 2002b; Meyer et al. 2010).

The preferential selection of these shallow water habitats has implications for the distribution of

predation pressure, and therefore the distribution of prey species. A study conducted over 15 years

in the largely seagrass habitat of Shark Bay, Western Australia, found that the presence of tiger

sharks influenced the movements and behaviour of a number of prey species, including dolphins,

turtles and dugongs (Heithaus et al. 2012). Given the prolonged residency of tiger sharks at

Ningaloo Reef (several months) (Ferreira et al. 2015) it seems likely that they will have similar

structuring roles in these coral reef environments.

5.5.5 Conclusion

Collectively, our data showed that the oscillatory movements of coastal tiger sharks are the

product of a combined foraging and energy conservation strategy. Tiger sharks modified their

vertical movement behaviours between inshore and offshore zones, presumably to optimize the

use of energy while continuously hunting for benthic and air-breathing prey. The shallow

sandflats of Ningaloo Reef appear to be an important habitat for hunting prey and saving energy.


Fieldwork and tags were funded by crowdfunding on the Experiment platform (DOI:

10.18258/7190), a Holsworth Wildlife Research Endowment, a UWA Graduate Research School

fieldwork award and BigWave Productions. We thank Murdoch University and Coral Bay

Research Station for accommodation and vessel use while conducting fieldwork. This work

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would not have been possible without the generous support of numerous volunteers, particularly:

Frazer McGregor, Abraham Sianipar, Adam Jolly, Blair Bentley, Evan Byrnes, Garry Teesdale

and Michael Tropiano providing valuable field support; Olivia Seeger and Abraham Sianipar who

helped with the video analysis. Nikolai Liebsch provided invaluable assistance with tag

functioning. Thanks to Lauren Peel for helpful comments on an earlier version of this manuscript.

126

5.7 Supplementary information


Password: shark

Figure S 5-1. Relationships between dive angles and maximum depth; (a) Maximum depth and ascent

angle for all data. (b) Maximum depth and descent angle for all data only.

127

Table S 5.1. Estimated ascent and descent speeds calculated by pitch and vertical velocity for absolute dive

angles >20° for each individual tiger shark, and their relationship with pitch. Individuals with <20 points

were excluded (TS10, TS16, TS20, TS25). Note that where significant relationships exist, slopes do not

exceed an absolute value of 0.02.

Shark

ID

Vertical

phase

N Mean

speed

F-value P r2 Slope Intercept

TS1 Ascent 58 0.78 3.7 0.06 0.06 -0.01 1.04

Descent 540 0.79 0.41 0.52 0.008 <-0.001 -0.8

TS2 Ascent 181 0.87 1.70 0.19 0.009 -0.003 0.96

Descent 281 0.86 59.2 <0.001 0.17 -0.01 0.56

TS3 Ascent 24 0.79 17.1 <0.001 0.43 -0.008 0.99

Descent 88 0.87 3.6 0.06 0.04 -0.006 0.71

TS4 Ascent 215 0.91 8.8 0.003 0.04 0.01 0.59

Descent 315 0.90 10.36 0.001 0.03 -0.018 0.45

TS5 Ascent 504 0.97 5.8 0.02 0.01 0.004 0.87

Descent 979 0.82 21.59 <0.001 0.02 -0.003 0.75

TS6 Ascent 85 0.72 34.6 <0.001 0.3 0.009 0.49

Descent 456 0.58 41.02 <0.001 0.08 -0.007 0.41

TS8 Ascent 294 0.89 6.24 0.01 0.02 0.004 0.77

Descent 716 0.69 72.3 0.001 0.09 -0.01 0.40

TS9 Ascent 858 1.03 38.87 <0.001 0.04 0.004 0.92

Descent 1580 1.0 3157 0.001 0.67 -0.02 0.43

TS12 Ascent 50 0.65 2.1 0.155 0.04 -0.002 0.71

Descent 112 0.70 0.001 0.97 <0.001 -0.0001 0.69

TS13 Ascent 2041 0.92 3.93 0.05 0.002 -0.0007 0.94

Descent 3439 0.96 2455 0.001 0.42 0.02 0.4

TS14 Ascent 216 0.91 0.06 0.81 0.0003 -0.0007 0.92

Descent 301 0.83 0.17 0.68 0.0005 -0.002 0.79

TS15 Ascent 1149 0.74 9.5 0.002 0.008 0.002 0.67

Descent 1362 0.76 181.6 <0.001 0.12 -0.009 0.53

TS17 Ascent 127 0.98 0.52 0.47 0.004 0.002 0.94

Descent 663 0.96 46.2 <0.001 0.07 -0.02 0.56

TS18 Ascent 1059 1.25 11.48 <0.001 0.01 -0.003 1.3

Descent 3115 1.1 373.6 <0.001 0.11 -0.01 0.84

TS19 Ascent 101 0.90 13.02 <0.001 0.12 0.01 0.62

Descent 78 0.73 5.57 0.02 0.07 -0.005 0.60

TS24 Ascent 63 0.98 1.8 0.18 0.03 0.01 0.67

Descent 263 0.89 15.4 <0.001 0.06 -0.005 0.75

TS27 Ascent 958 0.76 26.3 <0.001 0.03 0.004 0.66

Descent 118 1.03 49.4 <0.001 0.31 -0.02 0.21

128

Chapter 6 First insight into the fine-scale

movement behaviours of sandbar

sharks Carcharhinus plumbeus

6.1 Abstract

The expanding use of biologging tags in studies of shark movement provides an opportunity to

compare fine-scale movement behaviours among species. In May 2017, we deployed high-

resolution biologging tags on four mature female sandbar sharks Carcharhinus plumbeus at

Ningaloo Reef for durations ranging between 13 and 25.5 hours. Pressure and tri-axial motion

sensors within these tags enabled the calculation of dive geometry, swimming kinematics and

path tortuosity at fine spatial scales (m-km) and concurrent validation of these behaviours from

video recordings. Sandbar sharks oscillated through the water column at shallow dive angles, with

gliding behaviour observed in the descent phase for all sharks. Continual V-shaped oscillatory

movements were occasionally interspersed by U-shaped dives that predominately occurred

around dusk. The bottom phase of these U-shaped dives likely occurred on the seabed, with dead-

reckoning revealing a highly tortuous, circling track. By combining these fine-scale behavioural

observations with existing ecological knowledge of sandbar habitat and diet, we argue that this

circling behaviour is likely to be a strategy for hunting benthic prey. Comparing dive geometry

and energetics with those of other sharks reveals common patterns in energetically efficient

oscillatory swimming.

6.2 Introduction

An understanding of the movement ecology of marine megafauna requires investigation of

patterns at a range of spatial and temporal scales across an individual’s lifetime (Nathan et al.

2008). Broad-scale movements determined by acoustic and satellite tracking can reveal migratory

pathways, environmental preferences and patterns in habitat use and residency (Braccini et al.

2017; Ferreira et al. 2019). Fine-scale movements recorded by advanced biologging tags allow

the quantification of patterns in activity, feeding and energetics (e.g. Gleiss et al. 2013;

Brownscombe et al. 2014; Brewster et al. 2018), and can provide behavioural and physiological

context to other studies of broad-scale movements (Whitney et al. 2012; 2019).

Recent biologging studies on whale sharks (Rhincodon typus) and oceanic whitetip sharks

(Carcharhinus longimanus) have shown that the foraging, energetics and movement patterns of

sharks are likely linked through behaviours that minimize energy outputs, while maximizing

foraging opportunities (cost-efficient foraging) (Gleiss et al. 2011b; Meekan et al. 2015;

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Papastamatiou et al. 2018a). To date, the applicability of this concept to other sharks is difficult

to determine, as relatively few species have been studied using a biologging approach.

Here, we used biologging tags to investigate the fine-scale movement behaviours of adult sandbar

sharks (Carcharhinus plumbeus) for the first time. This species is globally distributed in tropical

and warm temperate coastal waters, and is commercially important throughout its range

(McAuley et al. 2007; Ebert et al. 2013). K-selected species traits, such as low fecundity and late

age at maturity, targeted fishing and predicted population declines have led to the sandbar shark

being listed as Vulnerable on the IUCN Red List (Musick et al. 2009). Largely because of its

commercial importance, there exists a robust understanding of the broad-scale (10-1000 km)

distribution, movements and diet of the species (Stevens and McLoughlin 1991; McElroy et al.

2006; Ellis and Musick 2007; Grubbs et al. 2007; McAuley et al. 2007; Barnes et al. 2016;

Braccini 2017; Braccini et al. 2017). Tracking studies have found that sandbar sharks are

generally wide-ranging, undergo large migrations (>1000 km) but also display evidence of site-

fidelity (Grubbs et al. 2007; Papastamatiou et al. 2010; Barnes et al. 2016; Braccini et al. 2017).

Diet studies in a number of regions have found a preference for feeding on benthic prey,

predominately demersal fishes, cephalopods and crustaceans (Cliff et al. 1988; Stevens and

McLoughlin 1991; McElroy et al. 2006; Ellis and Musick 2007). Our use of biologging tags

allowed our study to record fine spatial and temporal scale (m and seconds) behaviour that linked

foraging to movement. Our aim was to determine the extent to which these movements conformed

to patterns of cost-efficient foraging seen in other species monitored with this type of technology.

Patterns consistent across species support the contention that this type of foraging is a key

determinant of the vertical movements of sharks and other megafauna in marine environments.

6.3 Materials and methods


In Western Australia, mature sandbar sharks are predominately found in north and north-west

tropical waters (McAuley et al. 2007; Braccini and Taylor 2016), and some degree of site fidelity

have been revealed around Ningaloo Reef (Figure 6.1a) (Braccini et al. 2017). We captured

sandbar sharks at this locality using baited drumlines inside the reef lagoon (22.99°S, 113.8°E) in

May 2017 (Figure 6.1a). Drumlines were equipped with a single 20/0 circle hook baited with fish

scraps. Three drumlines were deployed approximately 100 m apart at depths of 8-12 m between

7:00 and 16:00, and were monitored continuously. Once a shark was caught, it was secured

alongside a 5.8 m vessel by the leader of the fishing line and a tail rope. Each shark was measured

and its sex recorded before a biologging tag was clamped to the base of its dorsal fin.

Either a CATS Diary Tag (Customised Animal Tracking Solutions, Australia) or CATS Cam Tag

130

was deployed on sandbar sharks (Figure 6.1b, Figure 6.1c). Both are equipped with tri-axial

accelerometers, magnetometers and gyroscopes, depth, temperature and light sensors In addition,

the Cam tag housed a HD video camera, and speed sensors were present, but not functional, in

the diary tags. Active sensors recorded all parameters at 20 Hz, and video recorded at pre-

programmed hours of the day. CATS tags were attached to a stainless steel spring clamp [CATS,

Australia, (Gleiss et al. 2009b; Chapple et al. 2015)] via docking pin and a corrodible galvanic

timed release (GTR, Ocean Appliances, Australia) allowing the tags to be rigidly attached to the

dorsal fin. The GTRs were designed to dissolve in seawater after 24 hours. After this time the tag

would release from the clamp and float to the surface. Floating tag packages were tracked down

in the days following tagging using a hand-held VHF receiver operated from the vessel (Lear and

Whitney 2016). The magnesium sleeve on the clamp also dissolved after seven days, detaching

the clamp from the shark.

Figure 6.1. Tagged sandbar sharks at Ningaloo Reef. (a) Location of tag deployments (b) CATS

Cam tag clamped to the dorsal fin. (c) CATS Diary tag clamped to the dorsal fin.

131

6.3.2 Data processing

6.3.2.1 Depth Record

Zero offset corrections were applied to the depth record based on times when sharks were being

tagged at the surface. The depth record was then split into vertical swimming phases (‘ascending’,

’descending’ and ‘level swimming’) using vertical velocity (VV). To do this the depth record was

firstly smoothed using a ten second running mean and the average VV was calculated by taking

the difference of this smoothed depth between successive points at one second intervals. Ascents

and descents were defined where VV exceeded an absolute value of 0.05 m/s for more than ten

seconds, and level where this value was not exceeded (Whitney et al. 2016b).

6.3.2.2 Tri-axial sensor data

Data recorded by the accelerometer (acceleration) and gyroscope (angular velocity) were analysed

using Igor Pro ver. 7.0.4.1 (Wavemetrics, Inc. Lake Oswego, USA) and Ethographer (Sakamoto

et al. 2009). The gravitational component of acceleration (static acceleration) was determined

using a three second box smoothing window on the raw acceleration data (Shepard et al. 2008).

Shark body pitch angles (orientation of the shark with regard to the horizontal plane) were derived

by calculating the arcsine of the static acceleration in the surging (posterior-anterior) axis. To

correct for the tag attachment angle on each individual shark, we determined the pitch when the

shark was swimming at a constant depth (when VV was equal to zero), and subtracted this value

from all pitch estimates (Kawatsu et al. 2009). The dynamic component of acceleration was

calculated by subtracting the gravitational component from the raw acceleration for each axis. We

then used a continuous wavelet transformation on the dynamic component of the sway (lateral)

axis to calculate the acceleration signal amplitude and frequency of tailbeats. Using these same

methods, amplitude and frequency were calculated using the angular velocity data, and the

resulting signals were compared with those derived from the acceleration data to determine the

best measure of tailbeat kinematics.

The angular velocity data produced the clearest tailbeat signal and consequently was used to

quantify the incidence of gliding behaviour (cessation of tailbeats for more than one second) using

a combination of two methods. Gliding behaviour was firstly computed using the k-means cluster

function in Ethographer. This algorithm clusters the spectra computed by the wavelet

transformation based on similarity of shape. The behavioural spectrum with the lowest peaks in

amplitude was assumed to represent gliding behaviour (Nakamura et al. 2011). The incidence of

the resulting cluster was then inspected against the dynamic sway data. As this cluster did not

match with gliding behaviour in some individuals (i.e. tailbeats evident in sway data classified to

be gliding and vice versa), a mask was created using the characteristics of the tailbeat amplitude

and frequency where the cluster was judged to correctly classify gliding behaviour (from visual

132

inspection of the dynamic sway and concurrent videos). This mask was then used to extract glides

from all sharks, and an additional manual quality control was undergone in the case where the

mask obviously misclassified glides.

Tailbeat signals were also examined for evidence of a recovery period following the stress of

capture following Whitney et al. (2016b).

6.3.2.3 Shark heading and pseudo-track calculation

Acceleration and magnetometer data were used to calculate head yaw angle (hereafter referred to

as ‘heading’) and pseudo-tracks in the software Framework 4 (Walker et al. 2015; Whitney et al.

2019). Heading calculations required input of orientation of the sensors in the tags, and

magnetometer and acceleration data. The orientation of the device was corrected using orientation

specifications provided by CATS (N. Liebsch pers. comm). Pitch and roll calculated from the

accelerometer data were used to correct for tilt (Walker et al. 2015). In addition to the calculations

applied by Framework 4, we applied a three second box smoothing window to the heading data

to filter out the dynamic movements of the sharks caused by tail beating that created significant

yaw.

Heading was then used to calculate pseudo-tracks for each shark using dead-reckoning (Mitani et

al. 2003; Wilson et al. 2007). These calculations operate under the principle that the heading and

speed of an individual at time ‘t’ can be used to calculate the position at time ‘t+1’ (Walker et al.

2015). Because a fixed value of speed is unlikely to be representative of individual sharks, the

dead-reckoning will accumulate errors over the course of the track. Therefore, tracks were used

only for plotting short-term movements of the sharks in three-dimensional space (‘pseudo-tracks’)

and could not be used to estimate geographical positions (Wilson et al. 2007; Walker et al. 2015).

Heading data were also used to calculate the turning angles of individuals on a second-by-second

basis. Data were resampled to a one second frequency and converted to a 0-360° scale. The

minimum difference in angle between consecutive observations was used to determine turning

angle.


The time window used for analysis was selected by determining the window for which the highest

variance in turning angle was observed. This time window was estimated to be 15 minutes (900

seconds), and within this period, a number of vertical movement parameters were summarized

including mean and maximum depth, ascent pitch, descent pitch, ascent VV and descent VV. The

proportion of time spent moving vertically (ascending and descending), termed the ‘diving ratio’,

was also calculated for each window as per:

133

𝐷𝑖𝑣𝑖𝑛𝑔 𝑟𝑎𝑡𝑖𝑜 =𝑇𝑖𝑚𝑒 𝑣𝑒𝑟𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝑚𝑜𝑣𝑖𝑛𝑔 𝑖𝑛 𝑤𝑖𝑛𝑑𝑜𝑤 (𝑠𝑒𝑐𝑜𝑛𝑑𝑠)

𝑇𝑜𝑡𝑎𝑙 𝑡𝑖𝑚𝑒 𝑖𝑛 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 𝑤𝑖𝑛𝑑𝑜𝑤

This parameter allowed us to discriminate between sampling windows where vertical movements

consisted of continuous oscillations through the water column or extended periods of level

swimming between dives. In addition, heading data were used to calculate measurements of path

tortuosity and direction. Firstly, turning angles were summed for each window to obtain an

estimate of tortuosity. This was also separately calculated for each vertical phase (level

swimming, ascending and descending), and standardized by the time spent in the relative phase

within a sampling window (i.e. the mean). Secondly, the Circular package in R was used to

calculate the mean resultant length, or 𝑅 of the heading, a measure of the concentration of

unimodal circular data (Pewsey et al. 2013). When 𝑅 is close to or equal to one, points are closely

clustered around the mean direction and are highly directional, and as 𝑅 approaches zero, points

spread more evenly in a circle and become tortuous.

6.3.2.5 Video analysis

Video recorded by the tags was analysed in BORIS (Behavioural Observation Research

Interactive Software; Friard and Gamba 2016). This open-source software allows the user to set

an ethogram and record the timing of these events. We set an ethogram to mark burst swimming

events, presence of seabed in the field of view, habitat type, and other notable behaviours. Video

was also used to validate parameters recorded and calculated from the tri-axial sensor data, such

as tailbeat frequency, bursts and glides.

6.3.3 Statistical analysis

To investigate if changes in vertical movements were related to changes in horizontal movements,

we constructed a series of generalized linear models (GLMs) for each individual sandbar shark

(N=4). Diving ratio within a sampling window (see 2.2.4) was set as the response variable in all

models, and turning (a proxy for tortuosity) as the explanatory variable. The first set of models

used summed turning angles to explore the general relationship between tortuosity and diving

behaviour in tagged sharks. For the remaining models, mean turning angle for each phase of

vertical movement (level swimming, ascent and descent, see 2.2.4) was sequentially set as the

explanatory variable. Inspection for correlation at lag = 1 found no serial correlation in the data.

Statistical analysis was performed in R 3.4.0 (R Core Team 2017).

134

6.4 Results

Four mature female sandbar sharks ranging in length from 1.76 – 1.89 m TL were caught and

tagged at Ningaloo Reef in May 2017 (two camera tag and two diary tag deployments, Table 6-1).

Time from capture to release did not exceed 25 minutes for any of the tagged sharks. All deployed

tags were recovered after a mean attachment duration of 18 hours (range 13-25.5 hours). All

sensors recorded continuously for all four tag deployments, excluding the video camera, which

only recorded a total 5.5 hours and 30 minutes in two deployments.

The quantification of the post-release recovery period for sandbar sharks using tailbeat data was

confounded by the immediate movement into deeper water by three of the tagged individuals

(Figure S 6-1). For these sharks, increases in tailbeat frequency were associated with deeper dives.

Shark 4 remained inshore and no recovery period was observed i.e. there was no apparent effect

of capture on activity (Figure S 6-1). As a result, the entire recorded track from shark release to

tag detachment for each individual was used in the analysis.


Tagged sandbar sharks swam at a mean depth of 23.4 ± 17.5 m, reaching a maximum depth of

96.6 m (Table 6-2). Sharks moved vertically in an oscillating pattern for a mean 57 ± 17.6% of

their track at a mean ascent angle of 11.4 ± 2.2° and descent angle of -11.5 ± 3.5° (Table 6-2).

Sharks displayed a mean tailbeat frequency of 0.65 ± 0.19 Hz, averaging 0.72 ± 0.12 Hz on ascent

and 0.57 ± 0.13 Hz on descent. Gliding behaviour was observed throughout 13.2 ± 14.3% of the

descent phase (range 0-75%), with a maximum continuous glide time of 210 seconds (Figure 6.2,

Table 6-2). Sparse tailbeats often interrupted glides during descents (Figure 6.2). For other

summary statistics of vertical movements, see Table 6-2.

The depth record for each shark consisted of continual V-shaped oscillatory vertical movements,

interspersed by occasional U-shaped dives with extended bottom periods (Figure 6.3). These U-

shaped dives occurred predominately around dusk for all individuals. Through a combination of

seabed observations in the video, dead-reckoning and the gradual increase in window maximum

depth as three sharks moved offshore, we assumed that the oscillations were limited vertically by

the seabed. Comparing these maximum depths with bathymetry of the study site, three sharks

moved offshore (maximum depths >70 m), and one shark appeared to remain inside the reef

(Shark 4, maximum depth <20 m).

135

Table 6-1. Summary details of tagged sandbar sharks. CC: CATS Camera tag, CD: CATS Diary tag

Shark ID Tag

ID

Deployment

Date

Deployment

duration

Pre-

caudal

length

(cm)

Fork

length

(cm)

Total

length

(cm)

Girth

(cm)

Sex

Shark 1 CC2 11/5/2017 8:54 25h 33min 141 156 185 82.5 F

Shark 2 CD1 11/5/2017 10:27 13h 5min 137 152 189 79 F

Shark 3 CC1 11/5/2017 14:10 17h 46min 138 149 180 84 F

Shark 4 CD1 18/5/2017 12:29 17h 15 min 125 140 176 75 F

Figure 6.2. Gliding behaviour in a tagged sandbar shark (Shark 3) over the course of approximately 30

minutes. Dynamic angular velocity was taken from the lateral (sway) axes recorded by the gyroscope.

Glides are marked where the mask analysis determined gliding was occurring.

136

Table 6-2. Summary statistics calculated for the entire dataset, and each tagged individual. Statistics were

firstly calculated for 15 minute windows, then summarised for the entire tagging duration. TBF: tailbeat

frequency, VV: Vertical velocity, 𝑹: mean circular length.

Overall Shark 1 Shark 2 Shark 3 Shark 4

Mean depth (m) 23.4 ± 17.5 21.9 ± 18.5 34.6 ± 19.7 29.7 ± 13.8 10.7 ± 4.4

Maximum depth (m) 96.6 96.6 73.3 73.7 19.9

Mean temperature (°C) 25.9 ± 0.8 26.2 ± 0.5 27 ± 0.4 25.6 ± 0.3 24.9 ± 0.5

Temperature range 20 – 27.5 20 – 27.4 22.4 – 27.5 24.9 – 26.3 24 - 26

Mean TBF (Hz) 0.65 ± 0.19 0.67 ± 0.13 0.74 ± 0.22 0.60 ± 0.16 0.61 ± 0.22

Mean ascent TBF (Hz) 0.72 ± 0.12 0.73 ± 0.06 0.82 ± 0.14 0.68 ± 0.12 0.69 ± 0.13

Mean descent TBF 0.57 ± 0.13 0.60 ± 0.08 0.66 ± 0.19 0.53 ± 0.15 0.52 ±0.09

Mean level TBF 0.66 ± 0.13 0.68 ± 0.07 0.77 ± 0.17 0.59 ± 0.13 0.62 ± 0.12

Mean ascent angle (°) 11.4 ± 3.5 10 ± 4.7 11.7 ± 2.1 13.2 ± 2.5 11.4 ± 1.8

Mean descent angle (°) 11.5 ± 2.2 11.8 ± 2.5 11.6 ± 2.3 11.1 ± 2.2 11.3 ± 1.4

Mean ascent VV (m/s) 0.15 ± 0.04 0.14 ± 0.05 0.17 ± 0.04 0.16 ± 0.04 0.15 ± 0.02

Mean descent VV (m/s) 0.15 ± 0.04 0.16 ± 0.04 0.17 ± 0.05 0.14 ± 0.04 0.13 ±0 .03

Vertical ratio 0.57 ± 0.18 0.58 ± 0.15 0.62 ± 0.17 0.55 ± 0.17 0.53 ± 0.21

% descent gliding 13.2 ± 14.3 13.2 ± 13.6 8.4 ± 13.9 17.5 ± 16.7 12.4 ± 11.6

Max glide time (sec) 210 196 210 179 131

Mean 𝑅 0.51 ± 0.22 0.54 ± 0.19 0.44 ± 0.18 0.71 ± 0.12 0.30 ± 0.16

𝑅 range 0.007 – 0.96 0.13 - 0.96 0.08 – 0.88 0.39 – 0.95 0.007 – 0.76

Mean sum of turning

angles

7001 ± 4715 5024 ± 3156 12290 ± 5297 4339 ± 2115 8643 ± 4214

Mean turning angle

level swim

8.1 ± 5.9 5.6 ± 3.9 14.2 ± 7.6 5.3 ± 2.9 10.1 ± 5

Mean turning angle

ascending

6.5 ± 5.7 4.4 ± 3.1 12.5 ± 8.8 3.5 ± 1.5 8.3 ± 4.1

Mean turning angle

descending

7.7 ± 5.4 6.0 ± 3.7 14.1 ± 7.1 4.8 ± 2.4 8.6 ± 3.8

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Figure 6.3. Entire depth record and gliding behaviour for each tagged sandbar shark. Glides are marked where the

mask analysis determined gliding was occurring. Dashed blue squares denote extended periods of U-shaped diving,

and associated looping behaviour. (a) Shark 1; (b) Shark 2; (c) Shark 3; and (d) Shark 4. Note that both x- and y-

axes scales differ between sharks.

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6.4.2 Tortuosity and three-dimensional paths

All sharks displayed periods of both directional and tortuous travel, with an overall mean 𝑅 of

0.51 ± 0.22 (range among individuals 0.007 – 0.96, Table 6-3). The GLMs revealed a significant

negative relationship between tortuosity and diving ratio for three of the four individual sharks,

with turning explaining 53-66% of the variation in diving ratio (Table 6-3, Figure S 6-2). Shark

2 showed no significant relationship between tortuosity and diving ratio, with this individual

observed to display relatively high levels of tortuosity throughout the track in comparison to the

other sharks (Table 6-1). Further investigation of the windows with the highest tortuosity (high

sum of turning angles and low 𝑅), found tortuous windows to be associated with U-shaped dives

(Figure 6.4). Due to extended periods of level swimming, the diving ratio was relatively low in

sampling windows containing U-shaped dives. For all tagged sharks, pseudo-tracks displayed

repeated circling on the bottom phase of these dives, irrespective of depth (Figure 6.5). For

example, U-shaped dives recorded throughout dusk periods had circling phases at depths of 36.5,

67, 67 and 14.6 m for each individual shark respectively (Figure 6.5). These bottom phases lasted

a maximum of 55 minutes.

As sampling windows of low or high diving ratio represented U- or V-shaped dives respectively,

models were taken a step further to investigate changes in turning angle for each phase of vertical

movement. Significant negative relationships were revealed between diving ratio and turning

angles for both level and ascent phases in Sharks 1, 3 and 4 (Table S 6.1). The relationship was

stronger for level swimming phases (R2 = 0.32-0.42) than ascent phases (R2 = 0.12-0.19). More

tortuous ascents were evident in U-shaped dives, and directional ascents in V-shaped dives

(Figure 6.6). No relationship was found between descent turning angles and diving ratio, and

similar patterns of descent were found in both V- and U-shaped dives (e.g. Figure 6.6).

Table 6-3. Linear model comparisons using Akaike’s Information Criterion (AIC), R2, and p-values. ΔAIC

displays deviance in AIC scores from null model. Diving ratio is modelled as a function of turning, where

turning is the sum of the turning angles for a sampling window. Model results where turning is standardised

by vertical phase of movement are shown in Supplementary Table 1.

Shark ID Model df Slope AIC ΔAIC R2 p

Shark 1 Diving ratio ~ turning

Diving ratio ~ 1

101

102

-0.03 -169

-91

-66 0.54 <0.001

Shark 2 Diving ratio ~ turning

Diving ratio ~ 1

51

52

-0.006 -37

-36

-1 0.04 0.15

Shark 3 Diving Ratio ~ turning

Diving ratio ~ 1

70

71

-0.05 -97

-44

-53 0.53 <0.001

Shark 4 Diving Ratio ~ turning

Diving ratio ~ 1

68

69

-0.03 -93

-18

-75 0.66 <0.001

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Figure 6.4. U-shaped dives and looping behaviour in Shark 1. (a) Depth record displaying U-shaped dives in the

late afternoon and dusk. Red square denotes 15 minute period of looping behaviour displayed in (c) and (d). (b)

Pseudo-track. (c) 3D track. (d) Screenshot of Shark 1 swimming at the seabed on the bottom phase of a U-shaped

dive. Shark silhouette indicates direction of travel by shark. Note that the x and y-axes in (b) and (c) represent

arbitrary units of latitude and longitude as they were created by dead-reckoning using magnetometer and

accelerometer data assuming a constant speed.

Figure 6.5. 15 minute pseudo-tracks of looping behaviour from each sandbar individual around dusk. (A) Shark 1 at

17:30 at depth 36 m; (B) Shark 2 at 18:08 at depth 67 m; (C) Shark 3 at 17:47 at depth 67 m; and (D) Shark 4 at 17:42

at depth 15 m.

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Figure 6.6. Five minute 3D tracks and pseudo-tracks of turning behaviour throughout the ascents and

descents of U-shaped dives and V-shaped dives. X and Y axes represent arbitrary units of latitude (P-lat)

and longitude (P-long) as they were created by dead-reckoning using magnetometer and accelerometer data

assuming a constant speed. The Z axes in the 3D plots represents depth (m).

6.4.3 Video

The six hours of video validated gliding and turning behaviour, and identified seabed habitat at

the bottom of dives. No interactions with prey or conspecifics were observed, and the only other

animals observed were trevally accompanying the shark, and one school of baitfish briefly

swimming through the field of view. For Shark 1, a 24 minute video recorded a U-shaped dive at

dusk. The video confirmed the bottom phase of the dive occurred at the seabed in a sandy habitat

with patchy weed and reef, and validated the circling behaviour (Supplementary Video 2).

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6.5 Discussion

Our deployment of biologging tags on adult female sandbar sharks revealed oscillatory and

tortuous movement paths. Tagged sharks underwent continual V- and U-shaped dives through the

water column, diving at shallow angles and displaying tortuous circling behaviours throughout

the bottom phase of U-shaped dives.

6.5.1 Tortuous circling behaviour

As travelling in a straight line is the most energetically efficient form of movement, the higher

energetic costs of turning should be offset by other benefits, such as enhanced foraging success

(Wilson et al. 2013). Because turn costs increase linearly with angle, Wilson et al. (2013)

predicted that more acute turns should be strongly motivated, and would have a tendency to be

clustered in time and space. The tortuous circling behaviour observed in all sandbar sharks

supported these predictions, occurring on the seabed throughout the bottom phase of U-shaped

dives, and predominately occurring around dusk. Possible explanations for this behaviour include

intra-specific interactions, the presence of favourable oceanographic conditions, or foraging

behaviour. Given that no conspecifics were observed in any of the recorded video, and circling

behaviour was observed in both well-mixed offshore and protected inshore environments, it is

unlikely that intra-specific interactions or oceanographic conditions are acting as the primary

drivers of the recorded patterns. The most likely explanation for such behaviour is that it aids in

foraging in this species.

Tortuous movement paths across a range of both aquatic and terrestrial taxa have been used as a

proxy to identify foraging and may be associated with prey pursuit, or search for prey either upon

encountering a cue or based on past learning experiences (Austin et al. 2006; Wilson et al. 2007;

Byrne and Chamberlain 2012; Papastamatiou et al. 2012; Adachi et al. 2017). Sandbar shark diets

are dominated by demersal fauna, such as bentho-pelagic fishes, cephalopods and crustaceans

(McElroy et al. 2006; Ellis and Musick 2007), and at dusk many nocturnal benthic species emerge

from their diurnal refuges (Helfman 1986). The circling behaviour that sandbar sharks were

recorded to display on the seabed may therefore represent a strategy to increase the chance of

capturing prey at this time. In addition to undertaking circling behaviour on the bottom phase of

U-shaped dives, relatively higher turning angles were recorded on the ascent phase of U-shaped

dives in comparison to V-shaped dives. It is possible that sandbar sharks may also be searching

for prey throughout the ascent phase of U-shaped dives while moving between benthic foraging

patches. On ascent, sharks may be able to search for prey that are backlit and silhouetted against

the surface, and potentially ambush them from below (Martin and Hammerschlag 2012). No

changes in turning angle were observed between dive types during descent, indicating that

individuals may be less motivated to search for prey in this phase.

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Previous studies reporting vertical movements of sandbar sharks have found these animals to

reach maximum depths of over 250 m and display diel patterns in movement (Papastamatiou et

al. 2006; Barnes et al. 2016). Barnes et al. (2016) found that sandbar sharks tagged off eastern

Australia exhibited a preference for deeper waters throughout daylight hours. Our fine-scale

movement data showed that sandbar sharks displayed continual patterns of oscillatory vertical

movements in both inshore and offshore habitats that were likely bathymetrically limited,

reaching a maximum depth of 96.6 m. Shallow dive angles occurred throughout the tracks, with

relatively symmetrical patterns in ascent and descent. Due to the limited duration of tag

attachment, we were not able to explore diel patterns in vertical movement, particularly as patterns

during the day were biased to the shallow inshore waters in which the sharks were tagged. U-

shaped dives, however, occurred in both inshore and offshore habitats, predominately around

dusk. Several other species of elasmobranch have been found to display diel patterns in foraging-

related behaviours, with crepuscular periods commonly being associated with enhanced activity

rates (Gleiss et al. 2013; Papastamatiou et al. 2015b; Gleiss et al. 2017b; Papastamatiou et al.

2018b). Despite a potential preference for foraging at this time, sandbar sharks may still feed

opportunistically throughout other diel periods, as occurs in grey reef sharks (Carcharhinus

amblyrhynchos) (Papastamatiou et al. 2018b). This may account for occasional U-shaped dives

observed throughout the night in sandbar shark depth traces (Figure 6.3). However, we again

highlight the limited duration of tag attachment in our study and longer tag deployments are

required to determine if these patterns occur on a diel cycle.

6.5.3 Species comparisons

Repetitive oscillatory movements through the water column are a common feature of the tracks

of nearly all epipelagic fishes for which fine-scale depth traces have been recorded (Carey et al.

1990; Klimley et al. 2002; Schaefer et al. 2009). Recent studies using biologging tags on tiger

sharks (Galeocerdo cuvier) and oceanic whitetip sharks (Carcharhinus longimanus) suggested

that these dives likely function as a means of searching for prey (Nakamura et al. 2011;

Papastamatiou et al. 2018a). However, if sandbar sharks are predominately searching for prey on

the seabed, continual hunting forays through the water column are unlikely to be the primary

motivator of these movements. Alternatively, these animals may be indirectly searching for

benthic prey by oscillating through vertically stratified layers for olfactory cues in between

patches (Carey et al. 1990; Klimley et al. 2002). In addition, data from tri-axial sensors suggest

that they are doing so in an energetically efficient manner. Shallow dive angles and gliding

behaviour appear to be common feature of the vertical movements of all large epipelagic fishes

for which detailed data records are available (Furukawa et al. 2011; Gleiss et al. 2011b; Gleiss et

al. 2011c; Meekan et al. 2015; Papastamatiou et al. 2018a). Given the need to oscillate through

143

the water column to search for cues, shallow dive angles and intermittent gliding behaviour have

been calculated to minimize the cost of transport, culminating in patterns of cost-efficient foraging

(Gleiss et al. 2011b; Papastamatiou et al. 2018a). Sandbar sharks descended at very shallow dive

angles (mean 11.4 ± 2.2° and -11.5 ± 3.5° for ascent and descent respectively), with uninterrupted

glides of up to 210 seconds in duration. These angles are comparable with mean dive angles for

both tiger sharks and whale sharks (Rhincodon typus). Mean ascent and descent angles for tiger

sharks were recorded at 9.4° and -11° respectively (see Chapter 5), and for whale sharks mean

descent angles of 12-13° were documented, with highly variable ascent angles varying 10-25°

depending on dive type (Gleiss et al. 2011b). The shallower dive angles were estimated to

minimize the cost of horizontal travel for whale sharks, and may have been near the minimum

angle required for gliding to be possible. However, in contrast to sandbar sharks, whale sharks

would continuously glide on a majority of descents. A number of factors may be responsible for

these inter-specific differences in gliding behaviour, including body composition (i.e. ratio lipids

to lean tissue) (Gleiss et al. 2017a), morphometrics influencing lift and drag, and the increased

drag incurred by smaller sharks carrying relatively large biologging tags.

6.5.4 Limitations and future considerations

Despite our limited sample size and tag attachment durations, consistent patterns emerged among

tagged individuals. Populations of sandbar sharks at Ningaloo mostly consist of adult females

(Braccini and Taylor 2016; Braccini et al. 2017), therefore tagging in other locations will likely

be required to obtain a more representative sample of both sexes and all size classes. Durations

of attachment (>24 hours) in our study were limited by our need to recover biologging tags to

download the data. Offshore swimming behaviour of the tagged individuals meant that the

distances involved in tag recovery following 24 hours of attachment were at the limit of our VHF

radio range and offshore boat license in two cases (25-30 km). Longer durations of tag attachment

will provide a greater understanding of diel cycles of movement in this species, and an increased

capacity to record and store video will aid in revealing more about the drivers of movements,

particularly in relation to foraging behaviours. We also acknowledge that the recovery rate of

sharks from capture processes can be difficult to define, and that we may not be recording natural

behaviours in our current study. Although continuous glides of up to 210 seconds were recorded

by sandbar sharks, sparse tailbeats frequently interrupted gliding descents. The possibility that

drag due to the tag (Whitney et al. 2019) may be playing a role in inhibiting natural gliding

behaviours in this and other studies that use relatively large biologging devices requires future

investigation. Lastly, accurate speed measurements and GPS anchor points would considerably

enhance dead-reckoning analyses, allowing further insight into fine-scale locational and habitat

preferences (Walker et al. 2015).

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6.5.5 Conclusions

Evaluating the results of our study in the context of broad scale ecological knowledge for the

species provided new ecological insights into the fine spatial and temporal scale movement

behaviours of the sandbar shark. Collectively, patterns recorded by biologging tags on sandbar

sharks suggested energetically efficient oscillatory swimming behaviours whilst moving between

benthic hunting patches, consistent with biologging studies emphasizing the role of cost-efficient

foraging. Larger sample sizes with extended tag recording durations will enable greater ecological

insight into these behaviours.


We thank Murdoch University and Coral Bay Research Station for accommodation and vessel

use while conducting fieldwork. This work would not have been possible without the generous

support of numerous field volunteers, particularly: Karissa Lear, Frazer McGregor, Abraham

Sianipar, Adam Jolly, Evan Byrnes, Garry Teesdale and Michael Tropiano. Nikolai Liebsch

provided invaluable assistance with tag functioning. We are grateful to Lauren Peel and two

reviewers, whose comments greatly improved this manuscript.

145

6.7 Supporting Information


Password: shark

Figure S 6-1. Mean and sd of tailbeat cycles (inverse of tailbeat frequency) during descent for individual

sandbar sharks over 15 minute windows. (a) Shark 1; (b) Shark 2; (c) Shark 3; and (d) Shark 4.

146

Figure S 6-3. Confounding movements into deeper water and increasing tailbeat cycle in Shark 2. (A) Entire depth

trace in Shark 2; (B) Mean and sd of tailbeat cycle (seconds) during descent for Shark 2. Red line outlines time of

movement into deeper water and increasing tailbeat cycle length.

Figure S 6-2. Relationship between diving ratio and sum of turning angles for each individual

sandbar shark.

147

Table S 6.1. Linear model comparisons using Akaike’s Information Criterion (AIC), R2, and p-values.

ΔAIC displays deviance in AIC scores from null model. Diving ratio (DR) is modelled as a function of

turning. Turning has been subset for each vertical phase of movement (level swimming, ascending and

descending), and standardised by the time spent in that phase within a sampling window.

Shark ID Model Slope AIC ΔAIC R2 p

Shark 1 DR ~ level turning

DR ~ 1

-0.02 -176

-138

38 0.32 <0.001


DR ~ 1

-0.001 -83

-84

-1 0.01 0.5


DR ~ 1

-0.03 -98

-64

34 0.40 <0.001


DR ~ 1

-0.02 -81

-47

34 0.42 <0.001


Shark 1 DR ~ ascent turning

DR ~ 1

-0.02 -153

-138

15 0.15 <0.001


DR ~ 1

-0.002 -84

-84

0 0.04 0.003


DR ~ 1

-0.03 -71

-64

7 0.12 0.15


DR ~ 1

-0.02 -59

-47

12 0.19 <0.001


Shark 1 DR ~ descent turning

DR ~ 1

-0.007 -141

-138

3 0.05 0.03


DR ~ 1

-0.001 -83

-81

-1 0.009 0.52


DR ~ 1

-0.02 -67

-64

3 0.07 0.02


DR ~ 1

-0.009 -48

-47

1 0.05 0.08

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Chapter 7 General Discussion

Advances in the affordability and sophistication of electronic tags now allow researchers to

routinely document the vertical movements of large, epipelagic fishes (>30 kg adult size). Despite

the increasing research focus, there has been no standardised approach to classify these

behaviours and investigate their physical and biological drivers. This thesis aimed to review the

existing knowledge of the vertical movements of large, epipelagic fishes, and improve our

understanding of the underlying factors structuring these behaviours through several case studies.

Through a synthesis of the existing knowledge of vertical movements, I was able to provide a

framework to investigate the drivers of these patterns in future studies (Chapter 2). I then used

this framework to explore some of the key drivers highlighted in the synthesis, using advanced

tagging technologies and new analytical techniques at seasonal (Chapter 3) and fine scales

(Chapters 4, 5 and 6, Figure 7.1). There were two common themes through all of my chapters.

Firstly, cost-efficient foraging appears to play a significant role in the vertical movements of large,

epipelagic fishes. Secondly, our overall understanding of these patterns is limited by tagging

technologies, and as a result, I highlight the important trade-offs in data resolution and

deployment duration that must be considered before commencing studies of this kind. I argue that

patterns of vertical movement result from the need for ram ventilating animals to move

continuously in a 3D environment while simultaneously optimizing prey encounter, energy

expenditure and remaining within physical limits imposed by the environment on their

physiology, and that an understanding of such patterns is beneficial to conservation efforts in the

context of a warming ocean.

Figure 7.1. Schematic of the timeline of different processes that may influence patterns of vertical

movement in large, epipelagic fishes. Dashed squares denote the time periods for which biologging tags

and recovered satellite tags will be most effective.

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7.1 Seasonal scales of vertical movement

Due to limited band width of satellite communications, data from satellite-linked tags are typically

transmitted in a summarized form (histograms in pre-defined bins), preventing the detailed

reconstruction of relationships between depth and temperature (Chapter 2). However, the rare

recovery of these tags and subsequent download of detailed data sets archived within the tag

provides us with the opportunity to explore these relationships in detail at seasonal time scales

(Chapter 2). Depth and temperature archives from 16 recovered satellite tags enabled me to

investigate how seasonal changes in the thermal environment influenced the vertical movement

patterns of a large, epipelagic, ectothermic shark (Chapter 3). As the water column warmed and

stratified in summer months, oceanic whitetip sharks in the North Atlantic Ocean were observed

to reduce the proportion of time spent in the upper 50 m of the water column and to increase the

amplitude of their oscillatory movements. These patterns of vertical movement allowed

individuals to maintain a relatively stable mean daily temperature year-round, despite much larger

annual changes in mean sea surface temperatures. Such behaviours likely represent behavioural

thermoregulation, a strategy to maintain a favourable body temperature (Chapter 3).

Neill et al. (1979) proposed that a fish that successfully thermoregulates will move through its

habitat in a way that allows it to maximize time spent within a preferred temperature range where

physiological processes are optimised. However, there may be discrepancies between this range

and the distribution of prey, and for this reason, these animals may need to perform physiological

and/or behavioural thermoregulation to avoid reaching critical temperature limits when foraging

(Chapter 2). Thermoregulation has typically been described by tagging studies as a process

whereby large, epipelagic fishes utilize surface waters to warm up between periods of foraging in

cooler and deeper habitats (positive thermotaxis, Chapter 2) (Carey et al. 1990; Klimley et al.

2002; Abecassis et al. 2012; Thums et al. 2013; Nakamura et al. 2015a). However, at a time where

the consequences of a warming climate are a major concern for marine ecosystems, and given the

asymmetric shape of the thermal performance curve (Martin and Huey 2008), it is somewhat

surprising that the reverse pattern has not been described to a greater extent.

Ectothermic fishes in tropical epipelagic systems, where waters are warm and prey sparsely

distributed, pose an interesting case when considering thermal tolerance. Large ectothermic fish

in these environments will have high metabolic rates, and in order to survive, they need to be cost-

efficient foragers and maintain their body temperatures within an optimal range. Oceanic whitetip

sharks were able to do this by shifting their vertical behaviours and distribution on a seasonal

scale (Chapter 3). In this case, temperature appeared to be a physiological driver of vertical

movements (Chapter 2), setting an upper limit to vertical distributions. An interesting finding was

that there was individual variability in the vertical movement behaviours used to thermoregulate.

Shark size and migratory phase did not appear to be responsible for these patterns. Beyond this,

150

the data gathered by the tag did not allow me to determine the processes driving these differences.

In addition, oscillatory movements continued in winter months when the water-column was well-

mixed, indicating that an ecological driver (Chapter 2) may be influencing how these animals

moved within their physiological limits. Insight into these patterns of vertical movement on a

seasonal scale was made possible through the long-term deployment and retrieval of archival

satellite tags, however, a biologging approach may enable the ecological driver(s) and individual

differences to be more clearly determined.

7.2 Biologging and fine-scale patterns of vertical

movement

Recent advances in biologging technologies have fundamentally enhanced our understanding of

the fine-scale (seconds to days, m to km) movements of marine megafauna (Bograd et al. 2010;

Goldbogen et al. 2013a). The additional sensors and high resolution of data recorded by these tags

have documented detail well beyond that of the traditional depth and temperature logs produced

by conventional tagging technologies. At fine-scales, oscillatory movement patterns are a

common feature of the tracks of nearly all epipelagic fishes for which high-resolution depth traces

are available (Chapter 2). By using advanced biologging tags, I was able to investigate the role of

cost-efficient foraging in driving these patterns in tiger sharks and sandbar sharks. Data from these

tags revealed how vertical oscillations were linked to energetics, kinematics, prey interactions,

and fine-scale habitat use.

Initially, I demonstrated the utility of these tags to document complex movement behaviours in

tiger sharks that would otherwise be invisible within time-depth records (Chapter 4). I found that

oscillatory movements occurred not only during directional travel, but continued throughout the

tortuous movements that are likely to be associated with foraging behaviour. This suggests that

the vertical movements of tiger sharks were not primarily driven by prey search alone as

hypothesized in previous studies (Chapter 4; Heithaus et al. 2002b; Nakamura et al. 2011). To

investigate if oscillatory movements were also a strategy for conservation of energy, I modelled

the cost of transport of varying movement geometries using overall dynamic body acceleration

(ODBA) as a proxy for energy expenditure (Chapter 5; Gleiss et al. 2011b). I found that tiger

sharks could reduce their cost of horizontal transport relative to level swimming by swimming at

shallow dive angles, and this supported my hypothesis that conservation of energy was a primary

factor in the oscillatory movement patterns I observed (Chapter 5). Energy savings were likely

optimized in very shallow environments (<3 m), where tiger sharks were able to continuously

search for prey at the surface and seabed while exploiting the most cost-efficient dive angles. In

deeper environments, however, tiger sharks encountered added energy costs from transiting a

longer distance through the water column between the surface and the seabed, and to compensate,

they displayed steeper angles that reduced the cost of vertical transport (Chapter 5).

151

Tiger sharks are a well-studied and charismatic species, and a number of previous studies have

explored their fine-scale vertical movements (E.g. Holland et al. 1999; Heithaus et al. 2002b;

Nakamura et al. 2011; Afonso and Hazin 2015). In contrast, sandbar sharks are less charismatic,

and their commercial importance has led previous studies to focus on their broad-scale (10-1000

km) horizontal movement patterns, with only brief descriptions of vertical movements

(Papastamatiou et al. 2006; Barnes et al. 2016). By using a biologging approach, I was able

examine these vertical movements at a fine-scale for the first time, and to reveal energetically

efficient oscillatory diving behaviours in sandbar sharks (Chapter 6). As with the tiger sharks, the

dive angles used by this species were very shallow, and gliding behaviour was observed in all

four tagged individuals. In addition, the fine-scale depth record exposed two different vertical

movement patterns in this species; U-shaped dives and V-shaped dives. U-shape dives occurred

most frequently around dusk and dead-reckoning using accelerometer and magnetometer data

recorded highly tortuous, circling tracks on their bottom phase. Given the largely benthic diet of

the sandbar shark, I believe this vertical movement behavior is a strategy for hunting emergent

benthic prey (Chapter 6).

The biologging approach described above revealed disparities and similarities between the

vertical paths of tiger and sandbar sharks. Tiger sharks exhibited an affinity for shallower waters

where they could reduce the cost of transport while searching the surface and seabed, and

displayed no relationship between oscillatory and tortuous horizontal movements (Chapter 4, 5).

Sandbar sharks displayed V- and U-shaped dives in both inshore and offshore habitats, with the

tortuosity of movements increasing with bottom time (Chapter 6). I argue that these contrasting

patterns are a function of inter-specific differences in foraging ecology. Tiger sharks are likely

transiting continuously through the water column to search for prey and scavenging opportunities

at the surface and near the seabed (Simpfendorfer et al. 2001), while sandbar sharks are

predominately searching for demersal prey at a level depth along the seabed (McElroy et al. 2006)

before using vertical movements to travel between benthic hunting patches. Despite foraging in a

different manner within distinct regions of the water column, however, both species were found

to display patterns of energetically efficient oscillatory swimming.

The expanding use of biologging tags on sharks and other large epipelagic fishes is revealing

common patterns in the energetics and kinematics of oscillatory movement behaviours of these

animals. For example, shallow dive angles and/or gliding behaviour have now been recorded for

oceanic whitetip (Papastamatiou et al. 2018a), whale (Rhincodon typus; Gleiss et al. 2011a), white

(Carcharodon carcharias; Gleiss et al. 2011c), tiger (Chapter 5) and sandbar (Chapter 6) sharks.

It has also been recorded in dolphinfish (Coryphaena hippurus) and Pacific bluefin tuna (Thunnus

orientalis) (Furukawa et al. 2011; Noda et al. 2016). The diversity of habitats, distribution and

feeding ecologies among these species suggests that shallow, energetically efficient dive angles

and gliding behaviour may be commonplace in large epipelagic fishes, and as a result, cost-

152

efficient foraging a primary driver of vertical movements at fine-scales. The approaches used in

this thesis are likely to be of use for studying the movements of other animals that move in three-

dimensions, notably flying animals.

7.3 Temporal scale and vertical movements

All case studies in this thesis utilized data from tags that had been physically recovered,

highlighting the applicability of these data sets to investigations of the patterns and drivers of

vertical movements in large epipelagic fishes. It remains significant to future studies of vertical

movements to emphasise the importance of determining the temporal scale of interest before

forming specific hypotheses and selecting a tagging technique, given the short deployment

periods of biologging tags in contrast to the durations that are feasible for satellite tags and the

recovery period associated with most tagging procedures.

Biologging tags enabled fine-scale patterns to be recorded that could then be compared to habitat

use, diel phase and environmental temperature. In the case of tiger and sandbar sharks at Ningaloo

Reef, no changes in environmental temperature were observed in this well-mixed, shelf

environment, precluding examination of the role of this driver on the vertical movement patterns

of these species. Absence of a temperature gradient was beneficial as it eliminated the possibility

of this variable playing a synergistic role with other drivers, however, well-mixed water columns

are a feature mostly limited to shallow coastal shelves. Because they encompassed a much longer

period of deployment (up to a year) where animals moved across coastal and oceanic habitats,

recovered satellite tag data from oceanic whitetip sharks recorded seasonal changes in the water

column and allowed me to investigate the role of environmental temperature on vertical

movements. The disparity between the environmental temperature datasets collected in these

studies highlight how these two tagging methods can currently be used to separately address

drivers at different scales (Figure 7.1). Delayed tag activation, onboard processing and cameras

triggered by predetermined movement thresholds (e.g. Naito et al. 2017) may help in extending

biologging tag recording duration in the future. Until the deployment duration of biologging tags

can be extended to that currently available from satellite tags, however, a comprehensive

understanding of the collective patterns and drivers of vertical movements of epipelagic fishes

will require these two discrete tag types.

7.4 Limitations and future directions

The choice of tag type for a study is currently a tradeoff between deployment duration, data

resolution, tag cost and sensor capabilities. In an ideal world, biologging tags with a wide range

of high-resolution sensors would be deployed on a representative cross-section of a population of

large epipelagic fishes for seasonal to annual scales, enabling thorough investigation of

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energetics, kinematics, foraging, and physiological limits at all temporal scales of movement.

Given current battery and storage limitations, high costs, the need to recover these tags to

download archived data, and the likely influence of physical variables that require sensors (e.g.

oxygen concentration) not typically available in tags, however, this remains a goal for future tag

development.

Alongside advancing the battery life and storage capabilities of biologging tags, additional

sensors are being developed and trialed, and will soon enable more hypotheses driven approach

to vertical movement studies. For example, dissolved oxygen sensors in satellite tags (Coffey and

Holland 2015) will allow us to test the influence of this physical factor as a physiological driver

on the vertical movement patterns of epipelagic fishes over seasonal time scales. For oceanic

whitetip sharks, these tags would allow us to determine if dissolved oxygen concentration is acting

synergistically with environmental temperature to produce the observed seasonal scale patterns

in Chapter 3, as was found to be the case in salmon sharks (Lamna ditropis) (Coffey et al. 2017).

These will also provide insight into the physiology of epipelagic fishes and their capacity to

tolerate low oxygen concentrations. Sonar tags are also being trialed, and will allow focal species

to map the prey field that they encounter during a dive (Lawson et al. 2015). These tags would

allow testing of hypotheses attributing oscillatory diving to prey search (see Chapter 2), and

reduce a reliance on video for this task, which is both battery hungry and requires large data

storage capacity.

There is currently no solution to extend the sampling duration of biologging tags, largely because

of the need to physically recover the devices. Where accelerometers have been incorporated into

acoustic tags to enable longer-term studies of activity and metabolic rate (Papastamatiou et al.

2015b; Barnett et al. 2016), transmissions are typically summarized into lower resolution time

bins, and patterns with vertical habitat use are masked (Whitney et al. 2019). Until we are able to

provide a means to both extend the deployment duration of high-resolution tags and ensure

retrieval of the full, high-resolution data sets, recovered archival tags provide the most practical

means to gain insights into the drivers of epipelagic fish vertical movements on larger temporal

scales.

Whereas advances towards extending the temporal scales over which the vertical movement

patterns of an individual can be monitored are ongoing, it is also interesting to consider how such

patterns might change throughout the lifetime of the individual. Acoustic tags currently provide

the longest view (multiple years) into the movement patterns of epipelagic fishes and are an

affordable means to obtain population-scale estimates of movement behaviour, however,

sampling periods are restricted to when tagged individuals enter the boundary of a receiver array.

Satellite tags overcome the spatial restrictions imposed by acoustic tags and receivers, but are

rarely attached to individuals for over one year (Chapter 2). In cases where tag attachment exceeds

154

an annual cycle, collected data is still of a relatively low resolution, preventing a detailed

assessment of how drivers vary in their impact on ontogenetic scales (Figure 1). For these reasons,

previous studies investigating how vertical movements change with increasing body size have

been limited to comparisons among individuals of different sizes (E.g. Afonso and Hazin 2015;

Williams et al. 2017b), rather than observation of changes as an individual grows. Although not

ideal, these studies have enabled insights into how ontogenetic differences in foraging behaviour

and/or physiology may drive changes in vertical movements, highlighting the benefits of tagging

representative cross-sections of a population (Chapter 2), and providing added motivation for the

continued development and improvement of biologging techniques.

The challenges of assessing patterns of vertical movement in large epipelagic fishes are not only

restricted to technology. The logistics, ethics and often opportunistic nature of field sampling,

generate difficulties in obtaining the large and representative sample sizes of study species that

are necessary to understand movement drivers for a population as a whole. Tag deployments are

also ethically constrained to individuals of a minimum size, precluding attachment to juveniles

until this size is attained. The expense of field operations, coupled with the need to retrieve tags

to download collected data means that tag deployments are often limited to coastal areas and

restricted to a non-migrating portion of the population. As many species of shark also tend to

segregate by sex and size (Sims 2005; Speed et al. 2010), sampling will often be biased by the

regional composition of the population. In this thesis, adult females comprised the majority of the

tagging cohort (93%), likely reflecting the composition of the respective study region (Chapters

3, 4 & 6). The one male oceanic whitetip shark tagged in Chapter 3 was the only individual of

this species to not show a thermoregulatory strategy. Further sampling of males is therefore

required to explore the possibility that male oceanic whitetips have a wider thermal range

compared to females. The continuing advance of tagging technologies will enable us to address

these issues in future studies. Miniaturization and extended recording durations will enhance our

capacity to tag a more representative portion of the population, as well as to investigate vertical

movements throughout both resident and migratory phases of a species.

7.5 Implications

The combination of high commercial importance and k-selected life history traits in many large,

epipelagic fishes (Chapter 2) means that the odds of continued survival are already stacked against

this group of animals. Given the importance of remaining within an optimal thermal range, global

climate change could further lower these odds by expanding regions of the water column that are

unsuitable for these taxa to inhabit. Epipelagic species in a warming ocean are more likely to

encounter upper thermal limits in surface waters, and could use vertical behaviours to avoid

prolonged exposure to these temperatures as a result (Chapter 3). This behavioural shift would

have repercussions for available foraging time and space, particularly as there may be mismatches

155

with how prey distributions respond to warming temperatures (Edwards and Richardson 2004;

Hazen et al. 2013). Fishes with higher energy demands, such as tuna and billfishes (Wegner et al.

2010), are hypothesised to pay a high energetic price to find prey, maximize energy surplus per

unit time, and together, lead a life of high energy turnover (Papastamatiou et al. 2018a). Warming

waters may then increase the susceptibility of these fishes to overheating (Gilman et al. 2016),

resulting in the generation of higher metabolic rates that require supplementary energy intake,

and amplifying the consequences of trophic mismatch. Conversely, large epipelagic fishes with

lower metabolic rates, such as ectothermic sharks (Dowd et al. 2006), likely utilize cost-efficient

strategies that enable them to expend less energy to find prey (Chapter 5), lowering the rate of

energy turnover and surplus in these species (Papastamatiou et al. 2018a). These animals,

however, have a lower probability of food acquisition per unit time and are more liable to

starvation and breeding failure (Wilson et al. 2018). A reduction in available foraging time and

space, coupled with prey mismatch, could then further thin this energetic knife-edge on which

they currently live, increasing the probability of mortality through exhausted energy reserves in

these taxa (Wilson et al. 2018).

The ability of large epipelagic fishes to adapt to these environmental impacts is questionable given

the accelerated rates of climate change (Intergovernmental Panel on Climate Change 2013), in

addition to the synergistic effect of other anthropogenic threats. Conservation and management

strategies therefore need to be adaptive, and focus on ensuring that populations remain resilient

to give them the best chances of survival (Fuentes et al. 2016). Adaptive management takes

advantage of new information that will improve management outcomes. Examples of such

information that would improve management strategies were found in this thesis.

Firstly, given that different fishing gears target different depths, an understanding of how

temperature influences vertical movement behaviour of oceanic whitetip sharks (Chapter 3) will

enable adaptive management by both target and non-target fisheries. The avoidance of surface

waters by this species in summer months will reduce the susceptibility of individuals to surface-

set fishing gears, but may increase their vulnerability to deeper-set longlines (Chapter 3). Fisheries

management could take advantage of this information by introducing temporal changes in

management strategies and fishing practices. Secondly, at finer-scales, biologging tags enabled

better understanding of habitat use in two species of coastal shark. Tiger and sandbar sharks

display patterns of residency in coastal and well-mixed regions (Ferreira et al. 2015; Braccini et

al. 2017), and warming temperatures may instead elicit shifts to higher latitudes. Understanding

the patterns of fine-scale movement and habitat use will enable us to foresee the viability of these

range shifts. Shallow habitats, for example, were found to be an important area for tiger sharks to

reduce energy expenditure while hunting (Chapter 5). Identifying these key foraging areas will

assist in optimizing strategies for spatial protection such as marine protected areas (MPAs), the

efficacy of which will be maximized by allowing them to be adaptive and compensate for future

156

range shifts.

7.6 Concluding remarks

My thesis demonstrated how tagging datasets can enable us to improve our understanding of the

patterns and drivers of vertical movements in large, epipelagic fishes. I examined and

acknowledged the trade-offs between biologging and satellite tagging technologies and their

ability to target specific temporal resolutions of vertical movement. Through both my methods

and results, I provide a framework for future studies to continue to explore and understand vertical

movements in these animals using both tag types. I argue that patterns of vertical movement result

from the need for gill-breathing animals to move continuously in a 3D environment while

optimising food encounter, energy expenditure and remaining within limits imposed by the

physical environment on their physiology. The expanding use of biologging tags on large fishes

will continue to uncover cryptic behaviours, and provide revelations about movements in

environments where they would otherwise be very difficult to observe. Representative sampling

of populations and extended deployment durations of high resolution biologging tags will enable

us to continue improving our understanding of vertical movements in these charismatic taxa and

in so doing, generate the knowledge base essential for determining the impacts of a warming

ocean on these species.

157

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